Vol 14, No 3 (2019) by Chimera Innova Group

J. Technol. Manag. Innov. 2019. Volume 14, Issue

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J. Technol. Manag. Innov. 2019. Volume 14, Issue 3

Perspectives on The Techno-Economic Analysis of Carbon Capture and Storage Simon P Philbin1*, Steve Hsueh-Ming Wang2 Abstract: Carbon capture and storage (CCS) is required in order to reduce the impact of fossil fuel burning on global warming and the resulting climate change. The use of CCS technology offers much promise in regard to the capture of major levels of waste carbon dioxide produced from the burning of fossil fuels for electricity generation and from industrial processes. Crucial to the development of CCS technology is the need for improved decision-making tools to underpin sustainable investment and associated policy initiatives for CCS technology and infrastructure. Consequently, this paper provides the results from the techno-economic analysis of CCS. This includes regression modelling of the levelized cost of electricity for power generation via combined cycle gas turbine both with and without CCS. In order to inform future research in the area, a supporting CCS research agenda has been formulated. Keywords: carbon capture and storage (CCS); techno-economic analysis; sustainable development; policy framework; decision-making Submitted: Jun 9th, 2019 / Approved: Sep 9th, 2019

Introduction The use of carbon capture and storage (CCS) technology offers much promise in regard to the capture of major levels of waste carbon dioxide (CO2) produced from the burning of fossil fuels for electricity generation and from industrial activities (Metz et al., 2005). This is required in order to reduce the impact of fossil fuel burning on global warming and the resulting climate change. Indeed, CCS technology is poised to play a significant part in helping nations to meet the obligations set out in the Paris Climate Conference of December 2015 (Cornwall, 2015), where 195 countries adopted a legally binding agreement and action plan to work towards limiting global warming to well below 2°C. Moreover, the impacts of global warming of 1.5°C above pre-industrial levels have recently been highlighted (IPCC, 2018), which has underlined the need for action on this matter. Although CCS technology has to date not been able to reach a level of industrial development that was envisaged a decade ago and there remain a number of technical and commercial challenges to be addressed for the technology to be successfully deployed on an industrial scale (Bui et al., 2018), it does nevertheless provide a viable route to minimize net CO2 emissions. In the CCS process, carbon dioxide is captured from power plants or industrial facilities, transported to an appropriate storage site and finally the carbon dioxide is deposited in a long-term storage medium, such as a geological formation, so that it will not enter the atmosphere. Although carbon dioxide has been injected into rock formations for many years as part of enhanced oil recovery (EOR), it is still a relatively new approach for storing carbon dioxide produced by power plants in order to reduce carbon dioxide levels in the atmosphere and mitigate the effects of global warming (Benson and Cole, 2008). In regard to the CCS options for natural gas and coal there are primarily three processes available to capture the carbon dioxide generated by combustion of these fossil fuels. These are post-combustion,

pre-combustion and oxy-fuel capture systems (Kunze and Spliethoff, 2012). Implementation of these technologies will depend on a number of technological and process engineering factors that need to be investigated further. The technology to enable capture and storage of carbon dioxide has been under development for several years (Figueroa et al., 2008) and a number of CCS projects are now online with more facilities to be established in the future. In addition to the development of commercial and industrial scale plants (Global CCS Institute, 2017), there are a number of technology demonstration and pilot scale facilities around the world (Global CCS Institute, 2016). There are also supporting feasibility and other studies that have been undertaken to investigate CCS technology applications as well as the commercial case for investment in CCS infrastructure. For an example techno-economic study for CCS technology implementation, see the work of Nakaten et al. (2014) in regard to calculating the cost of electricity, energy demand and CO2 emissions of an integrated UCG (underground coal gasification)–CCS process. Although there are various CCS projects that have been commissioned there remain significant challenges that still need to be overcome, including technological, economic and environmental issues (Pires et al., 2011) as well as the need for effective engagement with societal groups on the benefits of CCS adoption and mitigation of the perceived risks of implementing the technology. Nevertheless, CCS projects offer much potential and there is also the scope for an entire new CCS industry and corresponding industrial supply chain to be created as the projects are delivered globally (Haszeldine, 2009). Consequently, it is appropriate to consider investment decisions for CCS facilities and underpinning technologies from a sustainability perspective, which needs to integrate environmental, social and economic interests to yield effective business strategies (Schwarz, Beloff, and Beaver, 2002).

1) Nathu Puri Institute for Engineering and Enterprise, London South Bank University, United Kingdom. 2) Sichuan University-Pittsburgh Institute (SCUPI), China. *Corresponding author: philbins@lsbu.ac.uk.

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This this paper will provide the results from the techno-economic analysis of carbon capture and storage technologies. This analysis includes consideration of a range of different supporting areas or themes, namely CCS technologies and investment levels; CCS policy determinants (regulatory and environmental, economic and technological aspects); economic analysis of CCS with LCOE (levelized cost of electricity); and the review of data on CCS pilot-scale projects. In order to inform future research studies in the area, a CCS research agenda has also been formulated.

Methodology The methodology adopted in this research study was to consider the technological and economic aspects of carbon capture and storage

according to four main areas or themes, which are summarized in Figure 1. The method is based on techno-economic analysis of available data relating to the adoption of CCS technologies and also the sustainability of the process from an economic perspective. Techno-economic analysis is a recognized method for analyzing complex situations and enabling the resulting synthesis of evidence-based findings. For example, see the work of Zoulias and Lymberopoulos (2007) on the integration of hydrogen energy technologies with renewable energy-based stand-alone power systems, and Yang et al. (2009) on the design of a hybrid solar–wind power generation system. Furthermore, technoeconomic analysis can be considered as being complementary to other technology evaluation approaches, such as technology forecasting (Philbin, 2013).

Figure 1. Schematic view of the research methodology and main themes of the techno-economic analysis of CCS.

Main themes for the techno-economic analysis

•

• • •

(1). CCS Technologies and investment Modelled contributions for CCS and other technologies to meet GHG targets Summary of main capture technologies Pros and cons for capture technologies Government investment levels on CCS technologies

•

(2). CCS Policy determinants Review of selected CCS literature and expert opinion articles CCS policy determinants identified (regulatory and environmental, economic and technological areas) Bibliometric searching to identify frequencies

(3). Economic analysis of CCS with LCOE • Introductory material on levelized cost of electricity (LCOE) • LCOE for different power generation technologies (including CCS for coal and natural gas) • LCOE trend analysis for combined cycle gas turbines (CCGT) with and without CCS

(4). CCS Pilot-scale projects • Review of data on pilot-scale facilities from the carbon capture sequestration project database (MIT, 2016) • Statistical analysis for different capture technologies • Analysis of CCS facilities according to number per year and country of origin

Techno-economic analysis of carbon capture and storage CCS technologies and investment The implementation of CCS technology has the capacity to be an important component in regard to international efforts to limit greenhouse gas (GHG) emissions. Indeed, the International Energy Agency (IEA, 2015) has modelled that CCS could potentially drive 13%

of the cumulative emissions reductions that are required by 2050 in order to limit the global increase in temperature to 2°C (see Figure 2). This would represent the capture and storage of approximately 6 billion tonnes (Bt.) of CO2 emissions per year in 2050.

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Figure 2. Modelled contributions from different technologies and sectors to meet required global cumulative CO2 reductions (source: IEA, 2015).

8% 30% 38% 13% 10% 1%

Renewables CCS Power generation efficiency and fuel switching End-use fuel switching End-use fuel and electricity efficiency Nuclear

burned. The CO2 is captured from the combustion gas through an appropriate method, such as being absorbed in a solvent, membrane separation or cryogenic separation. Once the CO2 has been extracted it is compressed and either transported or stored, as appropriate.

This highlights the role that CCS can play alongside various carbon mitigation strategies, such as an increasing adoption of renewables, nuclear power generation as well as other power generation and fuel usage approaches. There are three core technologies (Kanniche et al., 2010) that are available to support the capture of CO2 and these are as follows: •

Pre-combustion capture: This involves gasification of the fuel (typically coal) to produce a synthesis gas, whereupon after further conversion the CO2 is removed followed by combustion. There is growing interest in IGCC (integrated gasification combined cycle applications) as a pre-combustion CCS technology.

•

Post-combustion capture: This involves capture of CO2 through separating from the combustion gases after the fuel has been

•

Oxy-fuel capture: This involves combustion in oxygen along with recycling of the exhaust gases that are composed principally of CO (carbon monoxide) and water, followed by purification of the CO flow to eliminate incondensable gases.

In order to highlight some of the key differences between these three core capture technologies, the advantages and disadvantages can be considered, which are summarized in Table 1.

Table 1. Advantages and disadvantages for CO2 capture technologies (source: Figueroa et al., 2008). Technology

Pre-combustion capture

Post-combustion capture

Oxy-fuel capture

Advantages

Disadvantages

Synthesis gas is both high pressure and with high CO2 concentrations. Various technology options available to enable separation. Gasification is a recognized process.

Equipment potentially expensive. Supporting systems are needed. Application more towards new build facilities and not existing plants.

Scope to apply to most power stations. Retrofit technology options. High CO2 partial pressures generated.

Flue gas can have lower CO2 concentrations and a resulting lower CO2 partial pressure. Economic impact of low pressure.

Very high concentrations of CO2 in flue gas. Retrofit technology options available.

Less advanced technology base when compared to pre- and postcombustion. Equipment cost base could be high. Process efficiency not optimized.

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As can be ascertained, each capture technology has its own pros and cons, although on balance it is recognized that post-combustion capture technology is currently the most promising technology to reduce CO2 emissions from the conversion of fossil fuels as sources of energy (Anthony and Clough, 2019). Moreover, we can consider the cumulative growth in storage capacity for operational and planned CCS facilities (Global CCS Institute, 2017) in Mtpa (million metric tonnes per annum) and it can be observed that storage capacity has grown considerable since around the year 2000 (see Figure 3). The data shows that since the first CCS facility opened in 1972 (Val Verde Natural Gas

Plant in USA, which is an EOR facility with a capacity of 1.3 Mtpa), capacity had grown to ca. 13 Mtpa in 2000. The global capacity grew further to 31 Mtpa by early 2017, with a further expected increase to 41 Mtpa by the end of 2017 assuming all the scheduled CCS facilities come online that year. This rate of growth in capacity highlights the increasing rate of adoption of CCS technologies along with a rapidly increasing level of global CO2 storage capacity. There is no reason to currently suggest this increase will not continue as CCS technologies are further proven and as more CCS projects are commissioned beyond the 2017-2019 period.

Figure 3. Cumulative increase in storage capacity (Mtpa) for operational and planned CCS facilities - based on data from the Global CCS Institute (2017).

45.0 40.0 35.0 30.0 25.0 20.0 15.0 10.0 5.0 0.0

Cumulative storage capacity (Mtpa): Operational Cumulative storage capacity (Mtpa): Operational & Planned

On the matter of governmental level investment in CCS technologies, a range of projects have been supported by the United States (US) Department of Energy (United States Department of Energy, 2019). This includes investment in post-combustion and pre-combustion CCS technology projects, with a total investment of USD $83.8million across 18 projects. This includes USD $71.5million (85%, N = 15 projects) invested on post-combustion technologies and USD $12.3million (15%, N = 3 projects) invested on pre-combustion technologies, and the current preference to financially support postcombustion technologies can be observed from this data. The post-combustion technologies supported by the US Department of Energy include a range of areas, such as CO2 sorbent capture process, solvent-based technology to extract CO2, hybrid membraneabsorption CO2 capture system as well as various other solvent and membrane separation technologies. The pre-combustion technologies supported include membrane-based CO2 capture processes, and

sorbent-based carbon capture system. Investment into these CCS technology projects highlights the level of interest in certain core technology areas, namely membrane and solvent-based CO2 capture systems and the associated engineering and process aspects. It is envisaged that continued investment is required in these underpinning areas in order to improve engineering efficiencies as well as cost reductions for the technology implementation as part of both postcombustion and pre-combustion large-scale CCS facilities. CCS policy determinants Investment into CCS technologies and projects, including pilot scale as well as larger operational scale plants can be influenced by a range of factors, which includes regulatory and environmental, economic as well as technological factors. Sustainable development should take account of the need for integration across social, economic and ecological perspectives (Gibson, 2006). Indeed, the development of CCS technologies and corresponding power generation systems is a

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complex matter and the supporting policy frameworks for such implementations need to be carefully developed through taking account of different stakeholder perspectives. Furthermore, we can consider these factors as determinants of CCS policy and it is therefore useful to review the literature in a rigorous manner in order to derive the main CCS policy determinants according to these three areas. In a re-

lated approach, dos Santos et al. (2014) reviewed literature sources in order to map the sustainable structural dimensions for managing the biodiesel supply chain in Brazil. Consequently, Table 2 provides the results from the review of selected literature and expert opinion based publications on CCS in order to establish the main policy-based decision factors associated with implementation of CCS.

Table 2. Findings from review and analysis of expert opinion based studies from the literature. Main findings

Reference

Research study identified two barriers to the deployment of CCS technologies, which are as follows: A need for appropriate funding mechanisms that are sufficiently large and long-term; legal and regulatory frameworks designed for the transport and geological storage of carbon dioxide.

Gibbins 2008

Report described six main CCS components, which are as follows: Capture, transportation, geological storage, ocean storage, mineral carbonation, and industrial uses of carbon dioxide.

Metz et al., 2005

This research identified seven key uncertainties for CCS deployment, which are as follows: Variety of pathways; safe storage, scaling up, speed of development & deployment; integration of CCS systems, economic and financial viability; policy, political & regulatory uncertainty; public acceptance. Additionally, inter-linkages between the uncertainties were identified, which are as follows: regulatory uncertainty; public support for policy & regulation for confidence, selective opposition, lock-in versus diversity; risk perception; a top-down push for speed; design consensus; learning by doing; business models & costs of learning to organize; electricity bills; liabilities.

Markusson et al., 2012

Review of research concentrated on opportunities for carbon dioxide capture (electric power generation and industry), carbon dioxide transportation and storage (transportation, geologic storage and ocean storage), and other considerations (direct use, conversion to carbonates, biological conversion to fuels, regulatory issues and leakage, carbon capture and storage cost modeling for electricity generation).

Anderson and Newell, 2004

Survey based research identified a number of potential show stoppers that could prevent implementation of CCS in the united Kingdom, which are as follows: lack of long-term policy framework; costs; international regulatory framework; public opinion; technical and engineering challenges; leakage of stored carbon dioxide; environmental impacts; unsatisfactory verification methods; NGO (non-governmental organizations) responses; ineffectiveness as a mitigation option; inadequate monitoring methods; skills shortage; other (cooperation).

Gough, 2008

Review of carbon capture and storage, which is viewed as a bridging technology to a sustainable energy production and its largescale deployment depends on technological advances and social processes. In this context, public perception is viewed as being of paramount importance to implementation of CCS technologies.

Selma et al., 2014

Review that described how the commercialization of CCS depends on many technological, commercial, and political hurdles to be overcome in regard to carbon capture, transportation of liquefied carbon dioxide and its storage in exploited oil fields or saline formations.

Haszeldine, 2009

Review of key CCS processes, which are as follows: chemical absorption, physical absorption, physical adsorption, membrane separation, compression and pumping, condensation and liquefaction, pipeline transport, ship transport, geological storage, and ocean storage.

Tan et al., 2016

Review of carbon dioxide sequestration in deep sedimentary formations that elucidated the need for rigorous scientific studies on the coupled hydrologic–geochemical–geo-mechanical processes that govern the long-term fate of carbon dioxide in the subsurface. The study also identified the need for methods designed to characterize and select sequestration sites as well as sub-surface engineering to optimize performance and cost, safe operational processes, monitoring technology, remediation methods, regulatory oversight mechanisms, and institutional approaches designed for managing long-term liabilities.

Benson and Cole, 2008

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and

Chalmers,

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Consideration of the findings from the literature allows the CCS policy determinants to be synthesized according to the three main areas and they are as follows: •

Regulatory and environmental factors: Regulatory framework (no. 01), site selection (no. 02), public awareness (no. 03), and environmental assessment (no. 04). Economic factors: Cost reduction (no. 05), government funding (no. 06), investment decision (no. 07), and international collaboration (no. 08). Technological factors: Capture technology (no. 09), storage technology (no. 10), transportation system (no. 11), and monitoring technology (no. 12).

•

Bibliometric analysis has been undertaken in order to derive the relative weightings for these decision factors and the structured literature search was carried out on 13th April 2019 using the ScienceDirect online database, which specializes in scientific, engineering, and medical research. Publications searched include review articles, research articles, book chapters, and conference abstracts. The search was restricted to publications from 2014 onwards, thereby providing a minimum of 5 years of publications’ data that is up-to-date. The results from the literature review according to the key decision factors is provided in Table 3.

Table 3. Results from structured literature review according to the key decisions factors contributing to sustainable policy for CCS investment decisions. ID

Area of policy determinant

CCS decision factor

Bibliometric search term

Regulatory and environmental

Regulatory framework

“Carbon capture and storage” AND “regulatory framework”

307

Regulatory and environmental

Site selection

“Carbon capture and storage” AND “site selection”

228

Regulatory and environmental

Public awareness

“Carbon capture and storage” AND “public awareness”

165

Regulatory and environmental

Environmental assessment

“Carbon capture and storage” AND “environmental assessment”

336

Economic

Cost reduction

“Carbon capture and storage” AND “cost reduction”

711

Economic

Government funding

“Carbon capture and storage” AND “government funding”

Economic

Investment decision

“Carbon capture and storage” AND “investment decision”

421

Economic

International collaboration

“Carbon capture and storage” AND “international collaboration”

Technological

Capture technology

“Carbon capture and storage” AND “capture technology”

1,196

Technological

Storage technology

“Carbon capture and storage” AND “storage technology”

997

Technological

Transportation system

“Carbon capture and storage” AND “transportation system”

171

Technological

Monitoring technology

“Carbon capture and storage” AND “monitoring technology”

We can observe from the results from the structured literature search (Figure 4) that the CCS decision factors with the highest frequency are capture technology (N = 1,196), storage technology (N = 997), and cost reduction (N = 711). Mid-level frequencies include investment decision (N = 421), environmental assessment (N = 336), regulatory framework (N = 307), and site selection (N = 228).

No. of publications

Low-level frequencies are transportation system (N = 171), public awareness (N = 165), government funding (N = 96), monitoring technology (N = 89), and international collaboration (N = 70). These frequencies provide an indication of the relative importance (and weighting) of such factors in regard to policy and investment decisions for CCS technologies.

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Figure 4. Frequency of the CCS decision factors ascertained through structured literature review.

1,400 1,200 1,000 800

1,196 997 711

600 421 336

400

307 228

200

171

165

Economic analysis of CCS with LCOE Levelized cost of electricity (LCOE) is a numerical measure that is calculated in order to assess the commercial case for power generation technologies (Irlam, 2015). The LCOE approach is based on calculating the present value of costs per unit of electricity that is generated over the life of a specific power plant. A comprehensive treatment of LCOE is provided by Short et al. (2005). In high level terms, LCOE can be viewed as a long-term cost measure that takes account of the total life cycle cost and the total lifetime energy production (see Figure 5).

Figure 6. Levelized cost of electricity, LCOE (2014 USD $) for power generation technologies in the United States. Source: Global CCS Institute (Irlam, 2015).

LCOE Data (US$/MWh) 300 250 200 150 100

Figure 5. Levelized cost of electricity calculation.

50 0

LCOE =

Total life cycle cost Total lifetime energy production

LCOE takes account of the number of hours per year that a power generation facility can operate, fuel costs and the corresponding fuel efficiency as well as the power plant life of operation as well as construction factors, such as construction schedule. Data has been assembled by the Global CCS Institute (Irlam, 2015), which provides a comparison of the LCOE for different power generation technologies including data for non-CCS and CCS variants of gas fired and coal fired power generation plants (see Figure 6).

= Natural gas fired plant (ca. 55 USD $/MWh) = Coal fired plant (ca. 80 USD $/MWh) In the case of traditional natural gas fired plants, the LCOE is ca. 55 USD $/MWh, whereas the CCS variant has a LCOE of 82-93 USD $/ MWh, i.e. representing a cost premium of ca. 30 USD $/MWh for CCS adoption to natural gas fired plants. Additionally, in the case of traditional coal fired plants, the LCOE is ca. 80 USD $/MWh, whereas the CCS variant has a LCOE of 115-160 USD $/MWh, i.e. representing a cost premium of ca. 60 USD $/MWh for CCS adoption to coal fired plants. It should be noted the range of LCOE values represents the sensitivity of the data.

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LCOE allows comparison of different forms of power generation and this data shows that while application of CCS technology to existing fossil fuel burning plants does add a cost premium to the LCOE, it does nevertheless compare favorably with some other forms of power generation. For instance, CCS (natural gas) has an estimated LCOE of 82-93 USD $/MWh, whereas various renewable technologies have the following estimated LCOE ranges (allowing for sensitivities): wind offshore (158-224 USD $/MWh), solar PV (158-224 USD $/ MWh), and solar thermal (168-228 USD $/MWh). Consequently, adoption of CCS technology for fossil fuel burning plants does appear to be affordable (and especially for natural gas power generation) when compared to certain renewable energy options. In order to focus on LCOE trend analysis, we can consider the case for Combined Cycle Gas Turbines (CCGT). These forms of power

generation are based on a gas-fired turbine combined with a steam turbine (Horlock, 1992). This technology is based on the use of a gas turbine to generate electricity with the waste heat that is generated used to produce steam, which then drives a further steam turbine thereby increasing the level of power generation that is achieved by the system. Figure 7 provides LCOE trend analysis for CCGT power generation systems both with CCS and without CCS (sources of data: ETI, 2012; Irlam, 2015; Gammer, 2016; EIA, 2018). Based on regression analysis we can see that there is a trend towards both systems having lower costs, with CCGT fitted with CCS (R2 = 0.8862) expected to have a lower LCOE in 2022 (R2 = 0.9098), when compared to conventional CCGT without CCS in 2012. This trend indicates a potentially improving economic position for the adoption of CCS technology for the application of power generation via CCGT.

Figure 7. LCOE trend analysis for CCGT with and without CCS (sources of data: ETI, 2012; Irlam, 2015; Gammer, 2016; EIA, 2018)

CCGT LCOE (with and without CCS) 120.0 100.0

R² = 0.8862

80.0 60.0 40.0

R² = 0.9098

20.0 0.0 2010

2012

2014

2016

2018

2020

2022

2024

CCGT (without CCS). USD/MWh CCGT (with CCS). USD/MWh Expon. (CCGT (without CCS). USD/MWh) Expon. (CCGT (with CCS). USD/MWh)

CCS pilot scale projects Various data is available from the Carbon Capture Sequestration project database provided by the Carbon Capture and Sequestration Technologies Group at Massachusetts Institute of Technology, USA

(MIT, 2016) and this includes data on pilot-scale CCS projects. It is useful to review this data and Table 4 provides a summary of the data for various plants where capacity levels are shown in MW, and Table 5 provides further data on other plants where the capacity data is in Mt/yr.

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Table 4. Summary of pilot–scale CCS projects with capacity data given in MW. Source of data: Carbon Capture and Sequestration Technologies Group at Massachusetts Institute of Technology, USA (MIT, 2016).

Project Name

Leader

Location

Size (MW)

Feedstock

Schwarze Pumpe Vattenfall

Germany

Coal

ECO2 Burger

Powerspan

USA

Coal

Pleasant Prairie

Alstom

USA

Coal

AEP Mountaineer

AEP

USA

Coal

Karlshamn

E.ON

Sweden

Oil

Compostilla

ENDESA

Spain

Coal

ELCOGAS

Spain

Coal

Total

France

Oil

Vattenfall

Netherlands

Coal

Enel &Eni

Italy

Coal

SSE

Coal

RWE

Coal

CS Energy

Australia

Coal

Puertollano Lacq Buggenum Brindisi Ferrybridge CCSPilot100+ Aberthaw Callide-A Oxy Fuel Ordos Wilhelmshaven Plant Barry Boryeong Station

Shenhua group China

Liquefaction 0.1

E.ON

Germany

Coal

3.5

Southern Energy

USA

Coal

KEPCO

South Korea

Coal

Capture Process Oxyfuel PostCombustion PostCombustion PostCombustion PostCombustion Oxyfuel PreCombustion Oxyfuel PreCombustion PostCombustion PostCombustion PostCombustion Oxyfuel PostCombustion PostCombustion PostCombustion PostCombustion

CO2 Fate

Year Operational

Completed or Operating

Depleted Gas

2008

Completed

Vented

2008

Completed

Vented

2008

Completed

Saline

2009

Completed

Vented

2009

Completed

Saline

2009

Completed

Recycled

2010

Completed

Depleted Gas

2010

Completed

Vented

2011

Completed

EOR

2011

Completed

Vented

2012

Completed

N/A

2013

Completed

Saline

2012

Completed

EOR / Saline

2011

Operating

Vented

2012

Operating

Saline

2011

Operating

Vented

2013

Operating

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Table 5. Summary of pilot–scale CCS projects with capacity data given in Mt/yr. Source of data: Carbon Capture and Sequestration Technologies Group at Massachusetts Institute of Technology, USA (MIT, 2016).

Project Name

Leader

Location

Feedstock

Size (Mt/yr)

Capture Process

Year Operational

K12-B

GDF Suez

Netherlands Gas Processing 0.2

Ketzin

GFZ

Germany

H2 Production 0.06

Otway

CO2CRC

Australia

Natural Deposit 0.065

Natural Deposit Depleted Gas 2008

USA

Coal

Pre-Combustion Saline

2014

Canada

Gas Processing 0.026

Gas Production EOR

2006

LNG Processing

PostCombustion PostCombustion PostCombustion PostCombustion PostCombustion N/A Gas Production PostCombustion PostCombustion

Tampa Electric Apache & PCOR

Polk Zama

StatoilHydro Norway

Snohvit

0.7

Coal

0.1

PetroChina

China

Nat. Gas Processing

0.2

Sinopec

China

Coal

0.04

Mongstad

Statoil

Norway

Gas

0.1

Jingbian Lula

Yanchang Petrobas

China Brazil

Chemicals 0.04 Gas Production 0.7

SaskPower

Canada

Coal

0.043

Japan

Hydrogen Production

0.1

Jilin Shengli

Shand Tomakomai

Huaneng

0.3

China

Shidongkou

JCCS

Depleted Gas PostCombustion

CO2 Fate

The pilot scale CCS facilities include use of the full range of capture technologies, including post-combustion, pre-combustion and oxyfuel. Further statistical analysis of this data can be undertaken in regard to the mean (mathematical average) and standard deviation (SD) for the pilot-scale plants with capacity levels according to the

2004

Saline

2008

Saline

2007

Commercial Use

2009

EOR

2009

EOR

2007

Saline

2012

EOR EOR

2012 2013

Vented

2015

Saline

2016

Completed or Operating Completed Completed Completed Completed Operating Operating Operating Operating Operating Operating Operating Operating Operating Operating

categories, which is provided in Table 6. This analysis highlights that there is a broad range of capacity levels adopted by pilot scale CCS facilities deploying post-combustion, whereas facilities adopting precombustion and oxy-fuel technologies have a narrow range of capacity levels.

Table 6. Further analysis of pilot-scale CCS facilities according to type of capture technology implemented. Source of data: Carbon Capture and Sequestration Technologies Group at Massachusetts Institute of Technology, USA (MIT, 2016). Capture technology used on pilot scale CCS facility (data in MW or Mt/yr)

Total capacity

Number of facilities (N)

Mean

Standard deviation (SD)

Post-combustion (MW)

135.60 MW

12.33 MW

14.63 MW

Post-combustion (Mt/yr)

1.34 Mt/yr

0.17 Mt/yr

0.21 Mt/yr

Pre-combustion (MW)

34.00 MW

17.00 MW

3.00 MW

Oxy-fuel (MW)

125.00 MW

31.25 MW

2.17 MW

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Additional analysis can include calculating the number of pilot-scale CCS facilities commissioned per annum as well as the cumulative number of facilities (N = 31). Consequently, Figure 8 provides the number of CCS facilities commissioned per annum alongside the cumulative data

line. It can be observed that the peak years for new facilities to be commissioned were 2008, 2009 and 2012 (N = 5), followed by 2011 (N = 4), and 2013 (N = 3). In more recent years for the data available, which is from 2014 to 2016, the number of facilities was at a lower level (N = 1).

Figure 8. Number of CCS facilities commissioned per annum along with the cumulative data. Source of data: Carbon Capture and Sequestration Technologies Group at Massachusetts Institute of Technology, USA (MIT, 2016).

6 5

15 2

10 1

5 0

1 0

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

No. per annum Further analysis can be carried out in regard to the countries where the pilot-scale facilities were commissioned and this is provided in Figure 9 (data provided as the percentage share of the total, N = 31). As can be observed, the countries with the greatest share of CCS fa-

Cumulative No. cilities with 16.1% (N = 5) are USA and China. Germany has 9.7% (N = 3). Spain, the Netherlands, UK, Australia, Canada, and Norway have 6.5% (N = 2), and Sweden, France, Italy, Brazil, Japan, and South Korea have 3.2% (N = 1).

Figure 9. Geographical location (percentage) of CCS pilot-scale facilities commissioned. Source of data: Carbon Capture and Sequestration Technologies Group at Massachusetts Institute of Technology, USA (MIT, 2016).

18.0 16.1

16.1

16.0 14.0 12.0 10.0 8.0

9.7

6.5

6.0 4.0

3.2

2.0 0.0

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CCS research agenda In order to inform future research directions, the following research agenda has been developed through considering the findings from this research study (see Table 7). The proposed research areas

have been categorized according to being technology, economic, and policy & regulation related as well as broader integrating type areas.

Table 7. CCS Research agenda Category

Proposed research areas •

Technology related research for CCS projects

Economic related research for CCS projects

• • •

Life cycle analysis and system level analysis (e.g. system dynamics models) to consider environmental impact of technology options across capture, transport and storage phases of CCS projects.

•

Further comparative studies on the whole-life costs of CCS plants, building on existing models (such as levelized cost of electricity models). Role of government funding instruments (such as carbon taxes) to promote CCS technology adoption. Cost reduction strategies for core CCS technologies. Industrial supply chain initiatives to support supply side provision of CCS capabilities, including role of SMEs (small and medium enterprises) on CCS projects. Improved decision-making frameworks and cognitive processes for CCS project investments.

• • • • •

Policy & regulation related research for CCS projects

Integrating research approaches for CCS projects

• •

• • •

Effectiveness of public engagement mechanisms to improve awareness of the societal benefits of CCS projects as well as the associated health, safety and environmental considerations. Policy instruments that support joint government and industry investment frameworks for long-term CCS options on electric power generation and industrial process applications. Multi-level frameworks that link international, national and local government level regulatory mechanisms and decisionmaking (e.g. review and approval of CCS plant site selection decisions). Sharing of data and information on CCS cost reduction strategies through establishing CCS networks and partnerships. Stakeholder liaison and public engagement to raise the profile of CCS along with other climate change mitigation strategies. Multidisciplinary research programs drawing on technological and engineering disciplines as well as social and economic areas that enable system level perspectives to be developed on CCS projects.

Conclusions This paper has provided the findings and insights from the technoeconomic analysis of carbon capture and storage, which has focused on the adoption of CCS technologies as well as the sustainability of the process from an economic perspective. Implementation of CCS technologies is required as part of the global attempts to mitigate the deleterious impact that greenhouse gases (GHG) are having on the environment and the resulting climate change. Furthermore, it is recommended that CCS adoption will need to sit alongside other power generation sources such as renewables (e.g. solar, wind, and tidal) and next generation nuclear fission in conjunction with energy savings measures and the use of alternative fuel systems (such as electric vehicles, which is dependent on the electrical power having a low carbon penalty at the point of source generation). This can be viewed in the context of a need for a greater multiplicity of energy sources. The level of investment into new CCS projects has been increasing dramatically over the last several years and this is a resulting in a significant increase in the level of global capacity for CO2 storage and

this includes both EOR and geological storage mechanisms (with the former still be the largest share of capacity). This trend is set to increase along with increasing investment in CCS technologies across post-combustion, pre-combustion and oxy-fuel capture systems. Technology is also being rapidly developed to support specific CCS applications, such as for use in integrated gasification combined cycle (IGCC) applications, which offers significant potential to capture CO2 while having low penalties in terms of plant energy efficiency as well as capital and operational costs. On the matter of policy determinants relating to investment into CCS technologies, it has been found that that the CCS decision factors with the highest impact are capture technology, storage technology, and cost reduction. Other factors having a moderate impact include investment decision, environmental assessment, regulatory framework, and site selection. Factors identified to have a low level impact include transportation system, public awareness, government funding, monitoring technology, and international collaboration. This highlights that CCS is still very much driven by the maturity and capabilities of the capture and storage technologies as well as the need to reduce the

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costs for implementing such technologies. Although other areas have the potential to impact CCS technology adoption, such as environmental and regulatory aspects and site selection.

Benson, S. M., & Cole, D. R. (2008). CO2 sequestration in deep sedimentary formations. Elements, 4(5), 325-331. https://doi.org/10.2113/ gselements.4.5.325

In regard to the implementation of CCS technologies as part of pilot scale facilities, it has been found that post-combustion technology is the most common capture technology adopted when compared to pre-combustion and oxy-fuel capture systems. Furthermore, statistical analysis has highlighted that there is a broad range of capacity levels adopted by pilot scale CCS facilities deploying post-combustion, when compared to facilities adopting pre-combustion and oxy-fuel technologies, which have a narrow range of capacity levels. Nevertheless, and despite the challenges associated with CCS, there has been growth in CCS capacity up to the year 2017. The rate of growth in capacity highlights the increasing rate of adoption of CCS technologies along with a rapidly increasing level of global CO2 storage capacity. CCS pilot scale facilities have been commissioned in countries across the World and the current leaders in the field are USA and China.

Bui, M., Adjiman, C. S., Bardow, A., Anthony, E. J., Boston, A., Brown, S., ... & Hallett, J. P. (2018). Carbon capture and storage (CCS): the way forward. Energy & Environmental Science, 11(5), 1062-1176. https://doi.org/10.1039/C7EE02342A

Levelized cost of electricity (LCOE) is a useful numerical framework for assessing the lifetime costs for various power generation technologies, including assessing the case for CCS adoption. Although the addition of CCS to gas fired and coal fired plants does result in an LCOE cost premium being added, such systems appear to compare favorably to various renewable energy technologies, such as offshore wind and solar photovoltaics (PV power systems). Moreover, results from this research study based on a regression model on the adoption of CCS for combined cycle gas turbines (CCGT) have indicated that there is a trend for CCGT systems (both with and without CCS) to have lower costs. In this case, CCGT fitted with CCS is expected to have a lower LCOE in 2022, when compared to conventional CCGT without CCS in 2012. This trend indicates a potentially improving economic position for the adoption of CCS technology for the application of power generation via CCGT. Future work is suggested to enable further detailed research on existing CCS projects and also projects under development. This includes case study investigation and use of appropriate quantitative methods, such as structural equation modelling, or analytic hierarchy process. Further research is also suggested on the development of innovative business models to support investment into CCS technologies as part of clean energy systems.

References Anderson, S., & Newell, R. (2004). Prospects for carbon capture and storage technologies. Annual Review of Environment and Resources, 29, 109-142. https://www.annualreviews.org/doi/abs/10.1146/annurev.energy.29.082703.145619 Anthony, E. J., & Clough, P. T. (2019). Post-Combustion Carbon Capture and Storage in Industry. In CO2 Separation, Puriﬁcation and Conversion to Chemicals and Fuels (pp. 39-53). Springer, Singapore. https://link.springer.com/chapter/10.1007/978-981-13-3296-8_4

Cornwall, W. (2015). Inside the Paris climate deal. Science, 350(6267), 1451-1451. https://doi.org/10.1126/science.350.6267.1451 Dos Santos, S. F., Borschiver, S., & de Souza, V. (2014). Mapping sustainable structural dimensions for managing the Brazilian biodiesel supply chain. Journal of Technology Management and Innovation, 9(1), 27-43. https://doi.org/10.4067/S0718-27242014000100003 Figueroa, J. D., Fout, T., Plasynski, S., McIlvried, H., & Srivastava, R. D. (2008). Advances in CO 2 capture technology—the US Department of Energy’s Carbon Sequestration Program. International Journal of Greenhouse Gas Control, 2(1), 9-20. https://doi.org/10.1016/S17505836(07)00094-1 Gammer, D. Energy Technologies Institute (2016). Reducing the cost of CCS - Developments in Capture Plant Technology, Energy Technologies Institute, Retrieved: https://d2umxnkyjne36n.cloudfront.net/ insightReports/Capture-Plant-Insight.pdf?mtime=20160908134243 Gibbins, J., & Chalmers, H. (2008). Carbon capture and storage. Energy Policy, 36(12), 4317-4322. https://doi.org/10.1016/j.enpol.2008.09.058 Gibson, R. B. (2006). Beyond the pillars: sustainability assessment as a framework for effective integration of social, economic and ecological considerations in significant decision-making. Journal of Environmental Assessment Policy and Management, 8(03), 259-280. https:// doi.org/10.1142/S1464333206002517 Gough, C. (2008). State of the art in carbon dioxide capture and storage in the UK: An experts’ review. International Journal of Greenhouse Gas Control, 2(1), 155-168. https://doi.org/10.1016/S17505836(07)00073-4 Global CCS Institute (2016). The Global Status of CCS: Summary Report. Retrieved: http://hub.globalccsinstitute.com/sites/default/files/ publications/201158/global-status-ccs-2016-summary-report.pdf Global CCS Institute (2017). Large Scale CCS Projects. Retrieved: https://www.globalccsinstitute.com/projects/large-scale-ccs-projects Haszeldine, R. S. (2009). Carbon capture and storage: how green can black be?. Science, 325(5948), 1647-1652. https://doi.org/10.1126/ science.1172246 Horlock, J. (1992). Combined Power Plants: Including Combined Cycle Gas Turbined CCGT Plants. Elsevier.

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International Energy Agency (2012). Energy Technology Perspectives 2012 - Pathways to a Clean Energy System (p. 345), Retrieved: https:// www.iea.org/publications/freepublications/publication/ETP2012_ free.pdf

Philbin, S. P. (2013). Emerging requirements for technology management: a sector-based scenario planning approach. Journal of Technology Management and Innovation, 8(3), 34-44. http://dx.doi. org/10.4067/S0718-27242013000400004

International Energy Agency (2015). Carbon Capture and Storage: The solution for deep emissions reductions. Retrieved: https://www.iea. org/publications/freepublications/publication/CarbonCaptureandStorageThesolutionfordeepemissionsreductions.pdf

Pires, J. C. M., Martins, F. G., Alvim-Ferraz, M. C. M., & Simões, M. (2011). Recent developments on carbon capture and storage: an overview. Chemical Engineering Research and Design, 89(9), 1446-1460. https://doi.org/10.1016/j.cherd.2011.01.028

Intergovernmental Panel on Climate Change (2018). IPCC Special Report on Global Warming of 1.5 °C. Retrieved: https://unfccc.int/ topics/science/workstreams/cooperation-with-the-ipcc/ipcc-specialreport-on-global-warming-of-15-degc

Selma, L., Seigo, O., Dohle, S., & Siegrist, M. (2014). Public perception of carbon capture and storage (CCS): a review. Renewable and Sustainable Energy Reviews, 38, 848-863. https://doi.org/10.1016/j. rser.2014.07.017

Irlam, L. (2015). The costs of ccs and other low-carbon technologies - 2015 update. Global CCS Institute. Retrieved: https://hub.globalccsinstitute.com/sites/default/files/publications/195008/costs-ccs-otherlow-carbon-technologies-united-states-2015-update.pdf

Schwarz, J., Beloff, B., & Beaver, E. (2002). Use sustainability metrics to guide decision-making. Chemical Engineering Progress, 98(7), 5863. https://pdfs.semanticscholar.org/b29a/e01b85d8ef7c72150a7e938823c02a4ee685.pdf

Kanniche, M., Gros-Bonnivard, R., Jaud, P., Valle-Marcos, J., Amann, J. M., & Bouallou, C. (2010). Pre-combustion, post-combustion and oxy-combustion in thermal power plant for CO2 capture. Applied Thermal Engineering, 30(1), 53-62. https://doi.org/10.1016/j.applthermaleng.2009.05.005

Short, W., Packey, D. J., & Holt, T. (2005). A manual for the economic evaluation of energy efficiency and renewable energy technologies. Hawaii: University Press of the Pacific. https://www.osti.gov/ biblio/35391

Kunze, C., & Spliethoff, H. (2012). Assessment of oxy-fuel, pre-and post-combustion-based carbon capture for future IGCC plants. Applied Energy, 94, 109-116. https://doi.org/10.1016/j.apenergy.2012.01.013 Markusson, N., Kern, F., Watson, J., Arapostathis, S., Chalmers, H., Ghaleigh, N., ... & Russell, S. (2012). A socio-technical framework for assessing the viability of carbon capture and storage technology. Technological Forecasting and Social Change, 79(5), 903-918. https:// doi.org/10.1016/j.techfore.2011.12.001 Massachusetts Institute of Technology (MIT). (2016). Carbon Capture Sequestration Project Database provided by the Carbon Capture and Sequestration Technologies Group at MIT (USA). Retrieved: https://sequestration.mit.edu/tools/projects/index_pilots.html Metz, B., Davidson, O., De Coninck, H. C., Loos, M., & Meyer, L. A. (2005). IPCC, 2005: IPCC special report on carbon dioxide capture and storage. Prepared by Working Group III of the Intergovernmental Panel on Climate Change. Cambridge, United Kingdom and New York, NY, USA, 442. Nakaten, N., Schlüter, R., Azzam, R., & Kempka, T. (2014). Development of a techno-economic model for dynamic calculation of cost of electricity, energy demand and CO2 emissions of an integrated UCG–CCS process. Energy, 66, 779-790. https://doi.org/10.1016/j. energy.2014.01.014

Tan, Y., Nookuea, W., Li, H., Thorin, E., & Yan, J. (2016). Property impacts on Carbon Capture and Storage (CCS) processes: A review. Energy Conversion and Management, 118, 204-222. https://doi. org/10.1016/j.enconman.2016.03.079 United States Department of Energy (2019). US Energy Department Investments in Innovative Carbon Capture Projects, Retrieved: https:// energy.gov/energy-department-investments-innovative-carbon-capture-projects United States Energy Information Administration (2018). Levelized Cost and Levelized Avoided Cost of New Generation Resources in the Annual Energy Outlook 2018 (p. 6), Retrieved: https://www.eia.gov/ outlooks/aeo/pdf/electricity_generation.pdf Yang, H., Wei, Z., & Chengzhi, L. (2009). Optimal design and techno-economic analysis of a hybrid solar–wind power generation system. Applied Energy, 86(2), 163-169. https://doi.org/10.1016/j.apenergy.2008.03.008 Zoulias, E. I., & Lymberopoulos, N. (2007). Techno-economic analysis of the integration of hydrogen energy technologies in renewable energy-based stand-alone power systems. Renewable Energy, 32(4), 680-696. https://doi.org/10.1016/j.renene.2006.02.005

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Industry Platforms as Facilitators of Disruptive IoT Innovations Cristian IonuĹŁ PĂŽrvan1, Ozgur Dedehayir2*, and Hans Le Fever1 Abstract: We undertake an inductive study of four firms developing industry platforms upon which potentially disruptive IoT products and services are developed by external firms. Our results indicate there to be four types of industry platform (generalist, specialist, technology-centric, and industry-centric), each facilitating a unique mode of disruptive change. We propose that technology-centric platforms are more likely to facilitate business model disruptions, while industry-centric platforms are more likely to facilitate technological disruptions. Generalist industry platforms, by contrast, are able to facilitate both business model and technological disruptions, given the freedom they allow IoT firms to build their product and service solutions. Keywords: internet of things; IoT; disruptive innovations; digital platforms; industry platforms Submitted: Aug 28th, 2019 / Approved: Oct 3rd, 2019

1. Introduction By the beginning of 2018, Netflixâ&#x20AC;&#x2122;s market capitalization exceeded $100 billion, Airbnb acquired a valuation of $30 billion after another round of funding, while Spotifyâ&#x20AC;&#x2122;s market valuation reached $26 billion following its initial public offering. What do these firms have in common? Netflix, Airbnb, and Spotify are not traditional players in their respective industries, but are disruptors, having reimagined the way they could better serve their markets to great effect. What is just as interesting and yet often unseen is that the platform businesses of these and other firms have been built on top of another platform, namely, the Amazon Web Services platform. Through this arrangement, arduous tasks such as database replication and scaling, as well as capacity provisioning, whether it is for storage, servers, or networks, have all been reduced through Amazonâ&#x20AC;&#x2122;s platform to a basic API (Application Programming Interface) call, thus allowing the firms to devote resources only to their core businesses. The success of Netflix, Airbnb, and Spotify have thus come about through the facilitating partnership offered by Amazon. We identify these facilitating platforms, such as the Amazon Web Services, as â&#x20AC;&#x2DC;industry platformsâ&#x20AC;&#x2122; (Gawer & Cusumano, 2002; Cusumano, 2010; Gawer 2014), defined as â&#x20AC;&#x153;products, services, or technologies that act as a foundation upon which external innovators, organized as an innovative business ecosystem, can develop their own complementary products, technologies, or servicesâ&#x20AC;? (Gawer & Cusumano, 2013, p.417). Platforms, at industry level, are steadily becoming the pervasive, dominant business model of the 21st century (Hoelck & Ballon, 2015). In the automotive sector, for instance, Audi Connect, BMW ConnectedDrive, and Mercedes Connect Me platforms are already used to boost industry-wide innovation (Mikusz et al., 2015). Scholars have captured these phenomena in studies that incorporate platform thinking, such as in evaluating incumbent performance with a focus on how established companies are able to keep their dominant positions in response to emerging disruptive innovations (Ansari & Krop,

2012; Brown et al., 2007). Other studies have, in turn, centered on the successful strategies employed by firms to disrupt incumbents in different industries through platform-based business models (Kenagy & Christensen, 2002; Sapsed et al., 2007; Soleimani & Zenios, 2011; Walsh, 2004). Notwithstanding these earlier contributions, there is still little known about how industry platforms facilitate disruptive change (Christensen, 1997; Dedehayir et al., 2014; Shea, 2005). This empirical and conceptual gap deserves attention given the technological paradigm shift currently taking place, accelerated by the Internet of Things (IoT) phenomenon, which is likely to impact many industries (Harris et al., 2015; Uckelmann et al., 2011). IoT captures the interaction and cooperation of objects â&#x20AC;&#x201C; such as Radio-Frequency IDentification (RFID) tags, sensors, actuators, mobile phones, etc. â&#x20AC;&#x201C; using unique addressing schemes and modern wireless telecommunication technology, to reach common goals (Atzori et al., 2010; Fleisch, 2010; Gubbi et al., 2013). It is currently one of the most attractive and impactful research areas for future work, especially when converged with other synergistic research streams such as Big Data (e.g. Wang et al., 2018). In July 2015, McKinsey & Company concluded that the IoTâ&#x20AC;&#x2122;s total economic impact could be as high as $3.9 trillion to $11.1 trillion per annum by the year 2025. In January 2016, Gartner argued that by the year 2020, more than half of the major new business processes and systems will include some elements of the IoT. Furthermore, a study conducted by the GSM Association, representing the interests of nearly 800 mobile operators worldwide, revealed that in the coming years, the rate of expansion and evolution of IoT will make it imperative for industry actors to cooperate on interoperability to avoid fragmentation and ensure that different devices and services will be able to communicate seamlessly (Bouverot, 2015). Given these trends and the innate, systemic nature of IoT, we anticipate that the number of platforms and platform-centric business ecosystems centering about IoT products and services will increase noticeably in the coming years. We additionally expect to see a greater abundance of IoTbased businesses that build upon industry platforms, which hold the potential of disrupting existing marketplaces (Ebersold & Hartford, 2015).

1) Leiden Institute of Advanced Computer Science, Leiden University, Niels Bohrweg 1, 2333 CA, Leiden, The Netherlands 2) Queensland University of Technology, School of Management, Level 9, Z Block, 2 George Street, Brisbane, 4000, QLD, Australia. * Corresponding author ozgur.dedehayir@qut.edu.au

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In fact, a number of large organizations are already building solutions that deploy IoT. Together with its ecosystem partners, Intel has defined a system architecture specification (SAS) to connect almost any type of device to the cloud, whether it has native internet connectivity or not. In a similar vein, the IBM Watson technology platform extends the power of cognitive computing to the Internet of Things, while Microsoft’s Azure IoT platform helps to connect devices, analyze previously-untapped data, and integrate business systems. The Google Brillo project meanwhile introduces an Android-based embedded OS that brings the simplicity and speed of mobile software development to IoT hardware, thus making it cost-effective to build a secure, smart device, and to keep it updated over time. Within the scope of this paper and commensurate with the definition provided by recent scholars (Gawer & Cusumano, 2013; Gawer & Henderson, 2007; Gawer & Phillips, 2013), we refer to such IoT systems as industry platforms. Rather than focusing on the strategies of businesses such as Netflix, Airbnb, Spotify, and online 3D printing service providers (Rayna et al. 2015), however, the primary line of inquiry driving this research pertains to how these disruptive business models are facilitated by industry platforms, specifically in the IoT context. Through this study we aim to contribute to the platform and business ecosystem literatures, with our findings anticipated to carry relevance not only for the facilitation of IoT disruptive innovations, but also for disruption in other contexts employing industry platforms.

2. Theoretical Background In the research domain of business economics, the evolution of platform thinking can be traced back to the 1990s when the concept ‘product platform’ was first introduced. Product platforms describe how companies achieve cost savings and benefit from adopting an in-house modular architecture for their product development process (Cusumano, 2010). As a result, the role of a product platform has traditionally been to serve as a foundation around which a company can develop a series of related products by reusing common components. Over time, observing the evolution of technology and rise of the Internet, scholars have proposed the concept of ‘industry platform’ (Gawer & Cusumano, 2002). Similar to an in-house product platform, an industry platform offers a common base (often technological) that an organization can reuse in different product variations (Cusumano, 2010). However, the parts of an industry platform are not exclusively provided by a single organization nor is the usage kept in-house. Instead, due to increased scale and impact, the technological components of industry platforms are likely to be added by different external, autonomous agents referred to as complementors (Gawer & Cusumano, 2013). Recent research streams have added more dimensions to our understanding of industry platforms by considering two different approach angles. Firstly, the economic perspective, focused on platform competition, views platforms as multi-sided markets (Hagiu & Wright, 2015; Hagiu, 2006). This perspective allows the evaluation of network effects, which explain strategic pricing behavior and product

design decisions in two-sided markets (Parker & Van Alstyne 2005; 2012), as well as the identification of challenges and working strategies for multisided platforms (Eisenmann et al., 2006; Hagiu, 2014; Muzellec et al., 2012). Secondly, the engineering design perspective, concerned with platform innovation, views platforms as technological architectures (e.g. Chesbrough, 2003). This research stream established that platforms systems evolve due to a combination of stability and variety made possible by their interfaces (e.g. Baldwin & Woodard, 2008). With the need for a more holistic view on technological platforms, the two theoretical perspectives have been recently integrated into one comprehensive framework that refers to platforms as evolving organizations, and distinguishes between three main categories: internal platforms, supply chain platforms, and industry platforms (Gawer, 2014). This integrative framework states that internal platforms are used exclusively within one firm and governed by internal managerial authority, while a supply chain platform is shared by partners within a supply chain organizational structure having the coordination mechanisms enforced by contractual relationships. According to the same framework, industry platforms are seen as operating at the ecosystem level, and having specific ecosystem governance mechanisms. The latter offers potentially unlimited external innovative capabilities, allowing a myriad of external agents (e.g. complementors) to innovate without restrictions (Gawer, 2014).

3. Research Method We studied the industry platform’s facilitation of disruptive business models through a multiple case study design (Eisenhardt, 1989; Yin, 1994). Our design selection was motivated primarily by the very little that is known about the phenomenon in question and the relative nascence of conceptual frameworks built to study platforms, which guided us towards an exploratory, inductive method (Edmondson & Mcmanus, 2007). The multiple cases allowed us to implement a replication logic (Yin, 1994), through which we could seek repeating patterns among the cases that informed of an underlying theory. Our study focused on four industry platform firms that allowed IoT businesses to build their offerings upon, drawn from a population predefined with respect to two major considerations. The selected population comprises firms that firstly operate according to a platform-based business model, and secondly relate to businesses in one of the identified IoT related areas - including networks for IoT, sensors for collecting data, and infrastructure for assuring the data flow, processing, and analysis (Atzori et al., 2010). We implemented theoretical sampling to select the cases for our investigation, using additional theoretical criteria provided by the literature in defining the concept of industry platforms (Gawer, 2014). According to Gawer (2014), industry platforms share a set of characteristics which set them apart from other types of platforms, such as internal platforms (i.e. a platform that operates within firms, allowing connectivity between sub-units through a closed technological interface) and supply chain platforms (i.e. a platform that operates across supply chains, enabling suppliers to deliver components to an assembler, with a semi-closed technological interface). As this paper focuses exclusively on industry platforms (i.e. platforms that operate

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across industry ecosystems, with an open technological interface), we have selected the case firms as real world representations of

this theoretical notion. Table 1 offers a description of each of the four selected companies 1.

Table 1: Overview of the four industry platform companies studied. Company

Description

Size

Year founded

Company A

Industry platform for capturing, processing, visualizing and controlling enormous 51-200 amounts of IoT data in real-time. employees

2001

Company B

IoT platform for connecting devices with proximity awareness to the cloud.

51-200 employees

2013

Company C

Cloud-based data analytics platform for the IoT.

1-10 employees

2011

Company D

A business unit of a large multinational set up to develop solutions within the IoT 10,001+ context. employees*

2014**

* This is the total number of employees of the organization and not of the IoT dedicated business group. ** This is the year the IoT focused business group was founded within the organization.

Company A’s platform provides new ways for IoT firms to capture, process, visualize and control enormous amounts of data in realtime, which can help businesses in various industries improve what they do, and how they do it. The service is a next generation SaaS (Software as a Service) suite that enables customers to gather system, network and cloud measurement data, and arrange the information in a context that is relevant to businesses and their customers. The offered service displays the data in an easily understandable, concise and relevant way, in real-time as well as historically. A unified view of status and performance can be seen at a glance, not limited only to infrastructure but also to applications and services. Company B’s platform helps IoT firms build technological proximity solutions using beacons. These are fundamentally very simple pieces of hardware - small, generally very short ranged, battery-powered devices that broadcast a unique signal at regular intervals over Bluetooth radio. Because Bluetooth is very short ranged it rarely detects a signal beyond 30-40 meters, however, this is not a flaw but rather a feature. When the signal is detected it means that a person or an object (carrying a beacon reader device) is in proximity of a stationary beacon, and a series of such detections can further identify when the person or object is in motion. While this system may seem similar to GPS, the latter provides physical location data and requires a lot of battery power to operate. Company C provides a cloud-based data analytics platform for IoT firms, offering real-time decision making capabilities to users and devices. It has been built with big data tools to manage large amounts of devices and data streams with very high frequency sample rates. The platform has also been designed to allow IoT entrepreneurs and developers to start with small test projects and scale up to capture

millions of streams of data coming in from sensors, apps, and other fixed and mobile devices across the globe. The firm has patented its data analytics platform component which gives immediate access to stream data, roll-up data, and up to 140 statistics per stream. It is designed as a horizontal platform to be used across all industries. Finally, Company D’s digital platform represents a new era in connected healthcare for both patients and providers, as healthcare continues to move outside hospital walls, and into patients’ homes and everyday lives. The platform, supported by salesforce.com, is open and cloud-based, which collects, compiles and analyzes clinical and other data from multiple devices and sources to be used by IoT firms. Health systems, care providers and individuals can access data on personal health, specific patient conditions and entire populations — so care can be more personalized and people empowered in their own health, wellbeing and lifestyle. 3.1 Research instruments and protocols We employed semi structured-interviews as the predominant tool for data gathering, supplemented by secondary sources such as corporate websites. The interviews comprised two sections: (i) to gain insights about the firms’ industry platforms, and (ii) to gain the respondents’ opinions on the platform’s facilitation of disruptive innovation. Special attention was given to collect an even and balanced amount of data regarding both themes from each interview. Questions were firstly asked about industry platforms, and were followed by questions about disruptive innovation. For the second interview component, we were well aware of the ongoing debate in the literature on the definition of disruptive innovation. Despite the concept’s introduction more than 20 years ago, opinions remain divided as to how disruptive innovations can be observed in the real world, as reflected in a recent

1 The firms selected for our study chose to remain unidentified. Any sensitive information was therefore left out and the study was conducted by assuring the complete anonymity of the participants. Nevertheless, the research process and the results acquired from the study were not affected.

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article entitled “What Is Disruptive Innovation?” appearing in the Harvard Business Review (Christensen et al., 2015). As a result, the risk in our data collection process was the interviewees’ lack of clarity of the concept (e.g. seen to be synonymous with radical innovation), as defined by Christensen and his colleagues and the definition we employ in our research. A special strategy was thus adopted to mitigate this risk, whereby, rather than asking the respondents’ perception of disruption directly, the interview questions were devised to reflect the characteristics of disruptive innovation, thus acquiring insights on this issue indirectly. Due to the strategic nature of the concepts analyzed in our research we interviewed the highest level of management in each of the case firms (see Table 2). The duration of interviews ranged from one hour to one hour and twenty minutes. Table 2: Respondents and the timetable for data collection. Firm

Interviewees level in the company

Date of collection

Company A

C-level

November 2015

Company B

Senior management

December 2015

Company C

VP level

December 2015

Company D

Senior management

December 2015 - January 2016

3.2 Data analysis The process of analyzing data and reaching conclusions from case study research is a highly systematic one. The process involves constant iteration backward and forward between data analysis, shaping hypotheses, and enfolding literature, and reaching closure when

marginal improvements become insignificant (Eisenhardt, 1989). It starts with the step of analyzing data, which is seen as the heart of building theory from case studies, but is, at the same time, the most difficult and least transparent component. The difficulty often lies in the coding and interpreting the transcripts once the interviews have been completed (Burnard, 1991). We took special care to deal with this challenge. Each interview from our four case studies was recorded in full, transcribed in full, and coded using a generic form of open-coding meaning that the categories were freely generated. The interview transcriptions were analyzed using a method of thematic content analysis, a method that is particularly suitable for semi-structured interviews (Burnard, 1991). The aim of this exercise is to produce a detailed and systematic recording of the themes and issues addressed in the interviews and to link the themes and interviews together under a reasonably comprehensive category system. For validation purposes multiple researchers evaluated the coding, and emerging discrepancies were then discussed to reach consensus. A thorough reading of the transcripts allowed us to become immersed in the data. This process of immersion was used to increase our awareness of the “outside world” of the respondent and to enter the other person’s “frame of reference” (Burnard, 1991). As many codes as necessary were generated to label all aspects of the content of each interview. The issues that were not related to the themes of interest, namely, ‘platform thinking’ and ‘disruptive innovation’, were intentionally left out. The categories were freely generated at this stage. As certain categories occurred more than once, the emerging coding labels were ranked based on the frequency of their appearance, such that codes appearing multiple times were considered to be recurring themes, as shown in Fig. 1.

Fig. 1: The five most commonly occurring categories for each case.

Company A

Company B

#amazon_customers_building_their_own_platforms #amazon_IoT_is_expensive #paradigm_change #fault_tolerance_platform #modular_structure_platform

#proximity_relevant_for_many_industries #serve_side_A #proximity_technology_is_disruptive #adding_value_beyond_hardware #proximity_platform

Company C

Company D

#serve_side_A #platform_simple_and_easy_to_use #data_analytics #side_A_can_build_solutions_for_any_industry #software_interface_is_important

#analytics #focus_on_healthcare_market #preventive_medicine #serve_side_B_directly #Iot_platform_for_healthcare

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The emergent list of categories was, in turn, grouped together under higher-order concepts (i.e. in order to reduce the numbers of categories, similar labels were grouped into broader themes). Following a repetitive process, a new category catalogue was developed and overlapping headings removed to refine and produce a final list. Interview transcripts were read through one more

time in light of the final list of categories, to establish the degree with which they covered all aspects of the interviews. Adjustments were made as necessary in line with the stage-by-stage method of analyzing qualitative interview data (Burnard, 1991). The final list of broader themes (containing grouped categories) for each case is provided in Table 3.

Table 3: Final list of broader themes for each case. Company A

Company B

Company C

Company D

modular structure open platform fault tolerant platform linear scalability service level agreement multitenancy platform shift from fire-and-forget no external dependencies serve verticals directly continuously feeding data serve different industries fast deployment platform data repository platform data retrieval platform data visualization platform disruption as a service platform complementors

proximity technology serve verticals directly proximity platform analytics hardware platform software platform open platform platform complementors available across industries proximity technology broadly applicable

advanced backend customers pull solutions analytics fast deployment platform security simplicity easy to use serve different industries platform complementors software platform open platform real time visualization scalability serve verticals directly stay horizontal user experience is critical

analytics focus on healthcare fast deployment platform for healthcare multitenancy platform open platform hardware agnostic preventive medicine serve verticals directly special regulations agile generic capabilities real time flexible complementors platform reliability artificial intelligence modular structure platform security multi layered platform evolved from internal platform part of a bigger organization

Following the completion of the interview coding process, we undertook the highly iterative process of systematically comparing the emergent theoretical constructs from cases with existing literature. The main aim of this exercise is to compare theory and data, iterating towards generating theory which is tightly linked to the data. Linking results to the literature is particularly important in theory building research from case studies because the findings rely on a limited number of cases (Eisenhardt, 1989). We continued by analyzing within-case data in order to become intimately familiar with each case individually as a stand-alone entity. This step allowed the emergence of unique patterns from each case before cross-case patterns were developed. Next, the within-case analysis was coupled with cross-case analysis in search of patterns in the data. To negotiate the danger of reaching premature and even false conclusions as a result of information-processing biases, we looked at the data in divergent ways. To this end, we employed the tactic of selecting categories or dimensions, and then looking for withingroup similarities coupled with intergroup differences.

4. Dimensions that Define Industry Platforms for IoT We observe that the industry platforms of Company A and Company C are designed to allow IoT firms to develop products and services upon these platforms, without major restrictions on the technology used or the industry served. By contrast, Company Bâ&#x20AC;&#x2122;s industry platform is built around one particular technology, namely, the proximity technology. Despite its technological restriction, the platform allows IoT firms to leverage this technological capability to develop solutions that can serve multiple industries. As for Company D, while it does not impose any technological restriction, its industry platform appears to constrain IoT firms in building solutions for only a single industry (the platform has a clear focus on healthcare and will only accept solutions that serve the healthcare market). Two dimensions subsequently emerged from these observations, which help define and classify industry platforms for the Internet of Things context:

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(1) Technological focus This dimension captures the scope of technologies that can be utilized by IoT firms in building their solutions upon a given industry platform. With respect to this dimension, industry platforms range from specific to generic technologies that they sponsor. For instance, Company B’s platform enables solutions using only proximity technology (a specific technological focus), whereas Company D does not impose any constraint on technologies used to develop solutions (a generic technological focus). (2) Industry focus This dimension captures the scope of industries that can be accessed by IoT firms, which build their solutions upon a given platform. With respect to this dimension, industry platforms range from a few to many industries that they support (or restrict) solutions for. For example, Company D has a clear focus on healthcare, and will only accept solutions that serve the healthcare market (serves few industries), whereas Company B does not constrain the markets IoT firms would like to provide solutions for (serves many industries). Used in conjunction, these two dimensions have led us to propose the framework (a taxonomy of industry platforms) depicted in Fig. 2. Fig. 2: Taxonomy of industry platforms facilitating IoT businesses.

Many I. Technology-Centric

No industry focus

Technology focus

II. Generalist

No industry focus

No technology focus

Industries served by platform IV. Specialist

III. Industry-Centric

Industry focus

Technology focus

Industry focus

No technology focus

Few Specific

Technological solutions sponsored by platform

Generic

This framework delineates industry platforms with respect to the breadth of activities they sponsor, or their ‘niche width’. According to population ecology theory (Hannan & Freeman, 1977; Loree, 2008), niche width refers to “a population’s tolerance for changing levels of resources, its ability to resist competitors, and its response to other factors that inhibit growth” (Freeman & Hannan, 1983). Organizational populations that have a broad niche width are said to be ‘generalists’, while those with a narrow niche width referred to as ‘specialists’. An organization’s niche width may be measured with respect to different dimensions. In our framework we employ the industry focus and technology focus dimensions to determine the niche widths

of the four companies, with broad niche width (generalist) platforms located in Quadrant II, and narrow niche width (specialist) platforms in Quadrant IV. Quadrants I and III mark specialism with respect to a particular dimension while generalism with respect to the other. Technology-centric industry platforms offer a specific technology to external innovators (i.e. IoT firms), which can be employed to create solutions to serve many industries. According to this definition, we position Company B in Quadrant I. By contrast, industry-centric platforms offer a generic technology for external innovators to develop solutions to serve only a few industries, subsequently positioning Company D in Quadrant III. Quadrant II comprises generalist platforms offering a generic technology that can be employed to create solutions to serve many industries. This quadrant subsequently comprises Company A as well as Company C. Finally, specialist industry platforms allow IoT businesses to utilize a specific technology to serve only a few industries. None of the case firms displayed these characteristics, leaving Quadrant IV unoccupied within the scope of our exploration. The vacancy of Quadrant IV can be explained by extrapolating the works of population ecology scholars that suggest the generalist strategy to be fitting for uncertain environments in ensuring the survival firms. In other words, specialism can be a risky tactic when the environment is uncertain, with the organization focusing on a limited bandwidth of resources. In the relatively nascent (and therefore uncertain) context of industry platforms for the Internet of Things, we anticipate that a generalist strategy is therefore more likely to be deployed in preference to a specialist one – hence the current vacancy of Quadrant IV. Notwithstanding, we expect specialist industry platforms to successfully enter the fray as the IoT context matures over time. Our proposed framework complements the existing literature on platforms by underscoring the fundamental decisions platform leaders undertake in establishing their ecosystems, and designing industry architectures that determine the ways in which activities along the value chain are divided between industry participants (Hatchuel et al., 2010; Parker & Van Alstyne, 2012; Tee & Gawer, 2009; Thomas et al., 2014). In this regard, we suggest that the four ‘levers’ of successful platform leadership – scope (the activities to be performed by the platform leader as opposed to those performed by external parties), technology design (functionalities included in the platform, degree of modularity, and openness to outside firms), external relationships (managing complementors), and internal organization (assuring external collaborators of ecosystem viability through the platform leader’s internal processes) – can be deployed with varying strength in different quadrants of the framework. For instance, the scope lever may be manipulated by the technology-centric industry platform leader to ensure greater control of technological activities undertaken in its ecosystem that serves multiple industries, while the industry-centric platform leader may focus on the technology design lever to create higher degree of openness for external parties to serve an industry with a multitude of technological solutions.

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5. Facilitation of Disruptive IoT Innovation Since the development of the disruptive innovation theory (Christensen & Bower, 1995; Christensen & Rosenbloom, 1995; Christensen, 1997) its popularity among researchers has increased steadily while its enhancement and refinement has also grown considerably (Markides 2006). Christensen distinguishes disruptive innovations based on the market where they impact as low-end market disruptions and new market disruptions. Scholars have since differentiated disruptive innovation types based on their diverse competitive effects and the markets they create (Charitou & Markides, 2003; Gilbert & Bower,

2002; Markides, 2006; Dedehayir et al., 2014). From the work of prior scholars, we synthesize three generic forms of disruptive innovation: (i) disruption with business model innovation; (ii) disruption with product (new-to-the world) innovation; and (iii) disruption with technological innovation. For the purposes of this exploratory study, however, we have grouped the product and technological modes of disruptive innovation into a single category (technological disruptive innovation), given their commonality as innovations that are customer-centric, as distinct from business model disruptive innovations that are firm-centric. We provide an overview of these two overarching modes of disruption in Table 4.

Table 4: Overview of disruption with business model innovation and technological innovation. Business model disruptive innovation

Definition

Extends the â&#x20AC;&#x153;economic pieâ&#x20AC;? by attracting new customers, and expands the existing market by convincing existing customers to consume more. Aspects

Technological disruptive innovation

Does not imply launch of a new product or service, but the redefinition of what a product or service is and how it is provided to the customer. Requires a different and conflicting value chain from the ones of incumbents.

Perturbs prevailing consumer habits and behaviors in a major way. Results from supply-push processes rather than demand-pull approaches. Early pioneers are very rarely the ones that capture the market, while latecomersâ&#x20AC;&#x2122; products are generally preferred by the average consumer. The new technology changes the traditional attributes with respect to which firms compete. The new technology makes the product cheaper and broadly available.

Impact

It is difficult for incumbents to make the new and established business models coexist.

The new technology undermines the competencies and complementary assets upon which incumbents have built their success.

Strategies

Incumbents may invest in their existing business model to compete more aggressively with the new business model.

Incumbents should create small or start-up firms that are autonomous in governance.

Examples

No-frills airlines; internet banking and internet brokerage; internet bookstores

The automobile; televisions; PCs; mobile phones; hard disk drives; digital cameras; minicomputers.

* Table adapted from existing literature (Charitou & Markides 2003; Danneels 2004; Gilbert & Bower 2002; Markides 2006; Dedehayir et al. 2014).

To uncover how industry platforms facilitate disruptive IoT innovations we map the two modes of disruption defined in Table 4, onto the taxonomy of industry platforms presented in Fig. 2. If the industry platform allows a specific technology to be used by IoT businesses, it implies that these external innovators are likely to have less control of the technology and need to innovate with their business models to disrupt the market. This necessity is further exacerbated by, for instance, the dictation of complementary hardware, in addition to the

software, which the industry platform makes available for IoT firms (as witnessed for Company B), in a sense, locking the latter onto a specific technological path. When, by contrast, the industry platform limits the application context (i.e. the industries) for IoT firms, but with no technological restrictions, these external innovators should have greater propensity to disrupt the market with technological innovations, such as through the low-end disruption mechanism. The outcomes of this mapping process are shown in Fig. 3.

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Fig. 3: Modes of disruptive IoT innovation facilitated by different types of industry platforms.

concurrently attempting to win a niche market segment is a highly difficult task. It may therefore follow that technology-centric and industry-centric platform strategies are likely to be less successful than the generalist or specialist designs in fostering disruptions.

Many I. Technology-Centric Business Model Disruption Industries served by platform

IV. Specialist

II. Generalist Technological Disruption Business Model Disruption

III. Industry-Centric Technological Disruption

Few Specific

Technological solutions sponsored by platform

Generic

We synthesize the outcomes of this mapping exercise through the following three propositions: Proposition 1: Technology-centric industry platforms that have a specific technological focus, but allow the serving of many industries, are more likely to facilitate business model disruptive IoT innovations. Proposition 2: Industry-centric industry platforms that have a generic technological focus, but allow the serving of only a few industries, are more likely to facilitate technological disruptive IoT innovations. Proposition 3: Generalist industry platforms that have a generic technological focus and serve many industries are likely to facilitate business model and technological disruptive IoT innovations. Our propositions complement existing research that welds business model innovation and platform design, addressing issues such as designing a winning business model through platform thinking (Cennamo & Santalo, 2015; Chen et al., 2009; Frery et al., 2015; Markus & Loebbecke, 2013). From the industry platform perspective, a generic platform design invites a greater number of IoT firms, which may themselves vary with respect to the scope of industrial and technological application of their offerings. On the contrary, a specialist platform strategy will limit the number of IoT firms to only those that develop offerings in restricted industrial and technological applications, precluding IoT firms that desire to develop offerings for a wider audience. The degree of generalism-specialism is therefore a strategic design choice of industry platform firms, which can determine their success bestowed by the population of complementary firms that build upon the platforms. On this point we may emphasize one of the platform traps identified by Cennamo and Santalo (2015), namely that, attempting to conquer the mainstream market while

6. Conclusions While much of the attention among academics and practitioners has hitherto centered on disruptors, and the incumbents that suffer the consequences of disruption, our aim in this paper has been to move beyond the disruptor-incumbent dichotomy, and to capture the important backstage role assumed by companies like Amazon, in facilitating disruptive change through their platform designs. More specifically, this paper aimed to unveil insights about facilitating the emergence of disruptive IoT (Internet of Things) firms, inspired by the successes of companies such as Netflix, Airbnb, and Spotify that have fundamentally changed the way their respective markets are served. We focused on industry platforms upon which IoT firms can build their disruptive products and services, in the same manner Netflix, Airbnb, and Spotify have established their businesses upon Amazonâ&#x20AC;&#x2122;s Amazon Web Services platform. Given the very nascence of the topic under consideration, we have implemented an inductive research design, with the objective of building theory from case studies in the IoT realm. Our exploration of four firms providing industry platforms for IoT applications led us to propose a taxonomy of industry platforms based on their degree of specialism along two dimensions â&#x20AC;&#x201C; industry focus (number of industries that can be served), and technology focus (scope of technological solutions allowed). This taxonomy includes our industry platform types: (i) the generalist (many industries and wide scope of technological solutions); (ii) the technology-centric (many industries but narrow scope of technological solutions); (iii) the industry-centric (a few industries but wide scope of technological solutions); and (iv) the specialist (a few industries and narrow scope of technological solutions). In turn, by conceptually mapping two generic types of disruptive innovation identified from our examination of the literature upon this taxonomy, we proposed that technology-centric industry platforms are more likely to facilitate business model disruptions, while industry-centric platforms are more likely facilitate technological disruptions. Generalist industry platforms, by contrast, are able to facilitate both business model and technological disruptions, given the freedom they allow IoT firms to build their product and service solutions. The paper contributes to the industry platform and business ecosystem literatures by underlining the role industry platforms enact in facilitating the emergence of new businesses. Our work firstly has implications for industry platform companies, whose success is reliant on IoT firmsâ&#x20AC;&#x2122; ability to innovate upon their platforms. The proposed framework can assist industry platforms companies strategically position themselves with respect to the dimensions of industry and technology focus, thereby attracting IoT firms with a particular disruptive innovation vision. Our work is secondly relevant for IoT

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firms that aim to disrupt the market with new product or service solutions. Specifically, the proposed model can aid these firms select the industry platform that will best facilitate the mode of disruption (i.e. business model or technological) that they have internal capabilities to execute.

Cennamo, C., & Santalo, J. (2015). How to avoid platform traps. MIT Sloan Management Review, 57(1), 12–15.

There are limitations of this study that need mention however. Firstly, our results are constrained by the exploratory nature of our work and by the number of cases considered. Although the four cases selected in this paper allowed us to propose a theoretical framework from their analyses, we believe that the examination of a larger number of cases in future work would validate and strengthen our propositions. A second limitation is born from the theoretical sampling implemented in selecting cases. While this method helped us focus in on the highly interesting empirical setting of the Internet of Things, the relevance of our results to other settings needs to be concluded with some care. Nevertheless, the scope of industries as well as the geographical diversity covered in the cases provides some confidence of the transferability of our findings to different industry and platform contexts.

Chen, J., Zhang, C., & Xu, Y. (2009). The role of mutual trust in building members’ loyalty to a C2C platform provider. International Journal of Electronic Commerce, 14(1), 147–171.

Our study opens stimulating possibilities for future work. A natural continuation of this study is the employment of the proposed industry platform taxonomy in different empirical examinations and the testing of our propositions. Another fruitful research agenda will be to establish the conditions under which an industry platform should pursue an industry-centric or technology-centric strategy, and the conditions that warrant generalist or specialist tactics. Furthermore, given the evolutionary nature of industry platforms, the analysis of these platforms’ movement within our emergent framework can provide valuable insights with respect to success factors. Finally, our proposed framework can be extended through future work that takes into account the strategic thinking of external innovators (i.e. IoT firms), which aim to develop disruptive innovations.

Charitou, C.D., & Markides, C.C. (2003). Responses to disruptive strategic innovation. MIT Sloan Management Review, 44(2), 55–63.

Chesbrough, H. (2003). Open platform innovation: Creating value from internal and external innovation. Intel Technology Journal, 7(3), 7. Christensen, C.M. (1997). The Innovator’s Dilemma: When New Technologies Cause Great Firms to Fail. Boston: MA: Harvard Business School Press. Christensen, C.M., & Bower, J.L. (1995). Customer power, strategic investment, and the failure of leading firms. Strategic Management Journal, 17, 197-218. Christensen, C.M., & Rosenbloom, R.S. (1995). Explaining the attacker’s advantage: Technological paradigms, organizational dynamics, and the value network. Research Policy, 24(2), 233–257. Cusumano, M. (2010). Technology strategy and management: The evolution of platform thinking. Communications of the ACM, 53(1), 32. Danneels, E. (2004). Disruptive technology reconsidered: A critique and research agenda. Journal of Product Innovation Management, 21(4), 246–258.

References

Dedehayir, O., Nokelainen, T., & Makinen, S.J. (2014). Disruptive innovations in complex product systems industries: A case study. Journal of Engineering and Technology Management, 33, 174-192.

Ansari, S., & Krop, P. (2012). Incumbent performance in the face of a radical innovation: Towards a framework for incumbent challenger dynamics. Research Policy, 41(8),1357–1374.

Ebersold, K., & Hartford, T. (2015). The impact of disruptive technology: The Internet of Things. Issues in Information Systems, 16(4), 194–201.

Atzori, L., Iera, A., & Morabito, G. (2010). The Internet of Things: A survey. Computer Networks, 54(15), 2787–2805.

Edmondson, A.C., & Mcmanus, S.E. (2007). Fit in methodological management. Academy of Management Review, 32(4), 1155–1179.

Baldwin, C.Y., & Woodard, C.J. (2008). The architecture of platforms: A unified view. Working Papers -- Harvard Business School Division of Research, 1–31.

Eisenhardt, K.M. (1989). Building theories from case study research. Academy of Management Review, 14(4), 532–550.

Bouverot, A. (2015). GSMA: The impact of the Internet of Things. The connected home, 20. Brown, J., Hendry, C., & Harborne, P. (2007). Developing radical technology for sustainable energy markets: The role of new small firms. International Small Business Journal, 25(6), 603–629. Burnard, P. (1991). A method of analysing interview transcripts in qualitative research. Nurse Education Today, 11(6), 461–466.

Eisenmann, T., Parker, G., & Alstyne, M.W. Van. (2006). Strategies for two- sided markets. Harvard Business Review, 84(10), 12. Fleisch, E. (2010). What is the Internet of Things? An economic perspective. Economics, Management, and Financial Markets, 5(2), 125–157. Freeman, J., & Hannan, M.T. (1983). Niche width and the dynamics of organizational populations. American Journal of Sociology, 88(6), 1116-1145.

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Frery, F., Lecocq, X., & Warnier, V. (2015). Competing with ordinary resources. MIT Sloan Management Review, Spring, 1–14. Gawer, A. (2014). Bridging differing perspectives on technological platforms: Toward an integrative framework. Research Policy, 43(7), 1239–1249. Gawer, A., & Cusumano, M.A. (2013). Industry platforms and ecosystem innovation. Journal of Product Innovation Management, 31(3), 417–433. Gawer, A., & Cusumano, M.A. (2002). Platform Leadership: How Intel, Microsoft, and Cisco Drive Industry Innovation, Harvard Business School Press: Boston. Gawer, A., & Henderson, R. (2007). Platform owner entry and innovation in complementary markets: Evidence from Intel. Journal of Economics and Management Strategy, 16(1), 1–34. Gawer, A., & Phillips, N. (2013). Institutional work as logics shift: The case of Intel’s transformation to platform leader. Organization Studies, 34(8), 1035–1071.

Loree, D. (2008). Density-dependent strategic action: Outcomes of structural market commitment in the global integrated circuit industry. Journal of Engineering and Technology Management, 25(1-2), 23-57. Markides, C. (2006). Disruptive innovation: In need of better theory. The Journal of Product Innovation Management, 23(1), 19–25. Markus, M.L., & Loebbecke, C. (2013). Commoditized digital processes and business community platforms: New opportunities and challenges for digital business strategies. MIS Quarterly, 37(2), 649–654. Mikusz, M., Jud, C., & Schäfer, T. (2015). Business model patterns for the connected car and the example of data orchestrator. Icsob, 114(April), 261–266. Muzellec, L., Ronteau, S., & Lambkin, M. (2012). Two-sided Internet platforms: A business model lifecycle perspective. Industrial Marketing Management, 45, 139–150.

Gilbert, C., & Bower, J.L. (2002). Disruptive change. Harvard Business Review, 80(5), 94–101.

Parker, G.G., & Van Alstyne, M.W. (2012). A digital postal platform: Definitions and a roadmap. MIT Sloan School of Management, January, 30.

Gubbi, J., Buyya, R., Marusic, S., & Palaniswami, M. (2013). Internet of Things (IoT): A vision, architectural elements, and future directions. Future Generation Computer Systems, 29(7), 1–19.

Parker, G.G., & Van Alstyne, M.W. (2005). Two-sided network effects: A theory of information product design. Management Science, 51(10), 1494–1504.

Hagiu, A. (2014). Strategic decisions for multisided platforms. MIT Sloan Management Review, 55(2), 71–80.

Rayna, T., Striukova, L., & Darlington, J. (2015). Co-creation and user innovation: The role of online 3D printing platforms. Journal of Engineering and Technology Management, 37, 90-102.

Hagiu, A. (2006). Multi-Sided platforms: From microfoundations to design and expansion strategies. Business, 1–25. Hagiu, A., & Wright, J. (2015). Multi-sided platforms. International Journal of Industrial Organization, 43, 162–174.

Rodrigues, M.A.D.S., Chimenti, P., Nogueira, A.R.R., Hupsel, L.F., & Repsold, A. (2014). From print to screen: Changes and challenges facing the Brazilian publishing industry. Revista de Administração, 49(3), 491–505.

Hagiu, A., & Wright, J. (2013). Structuring your business as a marketplace may seem attractive, but it’s often a recipe for failure. Harvard Business Review, 103–108.

Sapsed, J., Grantham, A., & DeFillippi, R. (2007). A bridge over troubled waters: Bridging organisations and entrepreneurial opportunities in emerging sectors. Research Policy, 36(9), 1314–1334.

Hannan, M.T., & Freeman, J. (1977). The population ecology of organizations. American Journal of Sociology, 82(5), 929-964.

Shea, C.M. (2005). Future management research directions in nanotechnology: A case study. Journal of Engineering and Technology Management, 22(3), 185-200.

Harris, I., Wang, Y., & Wang, H. (2015). ICT in multimodal transport and technological trends: Unleashing potential for the future. International Journal of Production Economics, 159, 88–103.

Soleimani, F., & Zenios, S. (2011). Disrupting incrementalism in health care innovation. Annals of surgery, 254(2), 203–208.

Hoelck, K., & Ballon, P. (2015). Competitive dynamics in the ICT sector Strategic decisions in platform ecosystems. Communications & Strategies, 21.

Tee, R., & Gawer, A. (2009). Industry architecture as a determinant of successful platform strategies: A case study of the i-mode mobile Internet service. European Management Review, 6(4), 217–232.

Kenagy, J.W., & Christensen, C.M. (2002). Disruptive innovation: A new diagnosis for health care’s “financial flu”. Healthcare financial management, 56(5), 62–66.

Thomas, L.D.W., Autio, E., & Gann, D.M. (2014). Architectural leverage: Putting platforms in context. Academy of Management Perspectives, 28(2), 198–219.

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Uckelmann, D., Harrison, M., & Michahelles, F. (2011). Architecting the Internet of Things. Architecting the Internet of Things, January, 1–353. Walsh, S.T. (2004). Roadmapping a disruptive technology: A case study the emerging microsystems and top-down nanosystems industry. Technological Forecasting and Social Change, 71(1-2), 161–185.

Wang, Y., Kung, L.-A., & Byrd, T.A. (2018). Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations. Technological Forecasting and Social Change, 126, 3–13. Yin, B.R.K. (1994). Case Study Research. Design and Methods. Sage Publications. p.312.

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University Research Centres: Organizational Structures and Performance Isabel Torres Zapata* Abstract: Currently, there are different types of University Research Centres (URCs) around the world. This research is focused on organizational structure and its influence on better research performance in URCs. In this case, URCs located in Aragon, Spain have been studied. A data set was extracted from their STI (Science, technology and innovation) indicators from 2000 to 2016. Using a self-built data base, constructed from reports, web pages and the university’s data set, this information was analysed using a mixed-method approach, which involves data panel analysis and case studies, as a way of determining how these institutions are organized and how these influences on their performance. As a result, those URCs which showed a complex structure emerged has the best performers. This kind of structure similar to corporate governance at URCs promote better research performance within each URC. Keywords: University research centres; organizational structure; STI performance; Spain Submitted: May 15th, 2019 / Approved: Oct 22nd, 2019

Introduction Currently, there is a wide range of scientific institutions. The proliferation of scientific institutions based on Science, technology and innovation (STI) has been promoted by local, regional and national public policy. In this way, a wide range of new institutions and structures have emerged giving shape and background to each innovation ecosystem: research centres, laboratories, hubs, technology parks, scientific parks, business incubators, etc. (Albahari, Pérez-Canto, Berge-Gil and Modrego, 2017). All of them, according to local policies are focused on economic development (Organisation for Economic Co-operation and Development [OECD], 1999). These sets of institutions are defined according to different National Innovation Systems (Nelson, 1993). One of the key issues in these systems has been the involvement of universities through their URCs. They are located in new buildings, constructed with government funding, in order to help promote their development (Toker and Gray, 2008). How they have been able to organize and how they have developed their capabilities and organize their resources has created a set of different institutions. URCs are well established in the USA, the first being created almost one-hundred years ago (Mowery and Ziedonis, 2002), while in the rest of the world they are relatively new. URCs are very important institutions in every NIS due to their double role of promoting economic development and technology transfer (Bozeman and Boardman, 2013). URCs have been analysed in depth with respect to their relationship with industry (Santoro and Chakrabarti, 2001; Boardman and Corley, 2008; Perkman and Walsh, 2007), their researchers and their relationship with academic activities (Bozeman and Boardman, 2013), human capital (Ponomariov and Boardman, 2010) and research collaboration (Corley, Boardman and Bozeman, 2006) to mention a few. Nevertheless, how organizational structure and research characteristics are influencing their results has seen less attention (Gray, Lindblad and Rudolph, 2001). Every URC has a system of internal management, a defined structure, various resources and interacts differently with society. The sum of these elements affects their scientific performance. In consequence, this research describes a

set of elements involved in structure/design and researcher characteristics in URCs belonging to the University of Zaragoza. Those URCs are located in the Aragon Autonomous Community in Spain. This paper is organized, in the following manner describes the organizational structure and researcher characteristics in the research institutions and defines research performance in the case of URCs, as literature review. Following section there is a short description of the Aragon region’s innovation system. In the last part, the current study is explained as an introduction to the research design. The following sections describe the findings in order to promote discussion and the conclusion of the implications of the empirical findings.

Organizational structures and researchers at URCs Research institutions show a set of conditions which promote scientific excellence. Excellence is based on doing the best you can in order to achieve the best possible performance. This is possible with the best institutions having the best people, doing the best that they can. This way of doing research has been widely analysed under the concept of Research Collaboration (RC). One of the main discoveries claims that RC impacts positively on scientific productivity (Corley, et al., 2006). Boardman and Bozeman (2006) developed a Contingency Model of Research Collaboration Effectiveness (CMRCE). This model is composed of three elements: attributes of collaborating individuals, attributes of institutions and attributes of collaboration and processes. Attributes of collaborating individuals and attributes of collaboration and processes are not analysed because the main goal of this research is to get a better mix of researchers and research institutions, while those aspects refer to the research collaboration activity that occurs inside a research institution. Nevertheless, this model is adopted because it describes the relationship between organization and researchers. This relationship is natural and symbiotic, nevertheless scarcely analysed in terms of defining the characteristics of the best research institutions and the characteristics of the best set of researchers. An adequate mix of them could promote better research performance.

Department of Accounting and Auditing, Faculty of Administration and Economy, Universidad de Santiago de Chile. *E-mail: Isabel.torres@usach.cl

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With respect to the attributes of institutions, the CMRCE definition is composed of resources, structure/design, organizational culture and role clarity. Given that this model, was defined in order to describe research collaboration effectiveness. From CMRCE we have adopted the analysis of structure/design as organizational structure. As Gray et al., 2001, claims the lack of studies on how organizational factors influence URCs is a missing link in the literature. According to the current literature review this gap remains open, hence this research proposes a way of closing this gap. The main resource implicitly involved in this model is human capital: researchers. They at least describe features like: gender, age and level of education. These features have been considered as control variables within model testing aspects such as: relationship with academic activities (Bozeman and Boardman, 2013) and human capital (Toker and Gray, 2008; Ponomariov and Boardman, 2010).

Organizational structures in URCs Currently, due to the diversity of research institutions it is possible to observe several levels of administration according to the organizational structures chosen by their leaders or by their owners. Structure/ design, is a concept defined in terms of a loose and/or informal way of managing the institution as a condition of improving collaboration (Boardman and Bozeman, 2006). Different kinds of structures promote a kind of management which shows different levels of control, communication, participation, roles, incentives, duties, to mention but some of them. The structure or design within an institution describes the form of institutional organization, how it is linked to society, government and several intangible aspects as well as how it affects its environment. Hence, this apparent lack of control or rigid structure, is an illusion, because this organization is being controlled and managed in terms of resources and performance (Bok, 2003). Differences among URCs, after decades of policies promoting these kinds of institutions, come from: resource allocation, human capital availability, research activities, etc. URCs are more focused on research than on development. In consequence, the structure and design of URCs is determined by National Innovation Systems (NIS) (Nelson, 1993). According to the “triple helix”, the relationship of university-industry and government promotes innovation inside each NIS (Etzkowitz and Leydesdorff, 2000). Universities are widely understood to be entities which develop and spread knowledge, as well as, currently, promoting and exploiting it. In this sense, URCs are institutions created by universities so that resources and research goals linked to industry and society can be managed separately from their normal academic activities. Simply, it is possible to describe these URCs as a branch of the university, controlled and structured by them. Many of the resources and the infrastructure come from the university which “own” them, but they grow over time thanks to public grants (local, regional or international) obtained by the researchers based in these institutions. Another emergent factor in this system is the formation of alliances. These alliances between universities and public and private institutions has changed the way that organizational structure in URCs is defined (Magro and Wilson, 2013).

S&T Human capital within URCs S&T Human capital (Boardman and Bozeman, 2006) is a valid and interesting concept, which involves social capital, experience and how a researcher is able to enhance his/her capabilities in order to become a mature researcher. Nevertheless, the literature has not been able to determine a validated set of conditions that describes this complex concept. One of the main causes could be that this concept was coined under research collaboration studies, which promotes how research institutions and its researchers are able to create trust, networking and carry out successful projects. Hence, the concept in itself is collective, while in this research each institution is analysed through a set of people with some specific and measurable characteristics. In this research, the concept is reduced to a set of individual characteristics like: gender, educational level and age. These characteristics describe the people inside each URC and how they influence results, according to the organizational structure in the URCs.

Science and Performance Ben-Davis (1972; in Stigler, 1993) claims that universities compete by prestige. This prestige could be understandable as a set of conditions that allows an institution to be placed first in some international ranking or to be recognized by its peers as the best institution in some specific area, or as an institution as a whole. However, this concept is vague (Stigler, 1993) and also difficult to measure. Nevertheless, this goal seems to be in the line with the mission of many universities around the world. Stigler, claims that reputation more than prestige is a better indicator to measure performance in an intellectual competition among universities. He describes this competition based on ideas. These ideas are spread by papers, lessons, books, conferences, research groups, new school programs, etc. Currently, this prestige or reputation is measured by Higher Education International Rankings, the data base indexation of papers, international quality certification of higher education programs, etc. Thus, if it is necessary to see the current level of prestige of any given university, this information is easily obtainable by visiting the necessary web page. Nevertheless, this kind of information is not available for URCs as yet. In spite of this, prestige and reputation are also valid goals for every URC. Many of them are closely linked with local or regional development, and therefore linked to the improvement of the standard of living of local people. This seems to be normal due to the location and relationship with local industry and local firms, as part of its research activity or as a way to link its research discoveries with society in general (OECD, 1999). On the other hand, they are part of a university. URCs indicators or results are part of the university’s indicators. URCs are financed by public funds, in the form of grants for specific research projects, and/or directly by the university itself. As mentioned Bozeman and Boardman (2013) describe a taxonomy for different kinds of URCs in the USA. This is a country that counts on more than one thousand of this kind of institution. It is possible to describe URCs as State, University among others. In the case of URCs, the relationship between universities and industry has encouraged an intricate,

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visible, influential and heterogeneous relationship between industry and university. (Lin and Bozeman 2006). In practice these institutions are producing research that is published in the relevant journals, obtaining grants and public funds, registering patents for their inventions as result of their research collaboration inside the URCs (Boardman and Bozeman, 2006). According to this discussion it is interesting to see how their performance has been. The definition of a set of indicators for the STIs developed by universities is until now an unsolved issue, at least in Europe (EC, 2010). How scientific advances emerge from universities is also very controversial. The difficulties in measuring this activity stems from: the diversity of research missions, the scope of research, the hierarchy of publication outlets, the differences in publication and citation practices, to mention only a few (EC, 2010). To define indicators for a university’s STI, requires the solving of problems such as how to measure the intangibility of scientific work, its scope, and the resources involved (Wildson, 2015). These aspects emerged from a couple of studies on scientific performance in the EU and the UK respectively, which has opened new doors and posed new questions. Meanwhile, the scientific community as a whole, not only the URCs, continues to carry out its job advancing in science and technological issues. In this context, URCs are also being analysed intensively with respect to their performance. In this research, scientific performance is measured by three aspects: publications, projects and patents. These are taken as relevant outcomes of the work done by each URC. We have amassed a total of these indicators obtained on a yearly basis by each URC under study. All those outcomes correspond to a collective endeavour. Hence, these indicators follow the idea of scientific knowledge value (Bozeman and Rogers, 2002). The knowledge and technology transfer within each URC is a consequence of scientific leadership and knowledge sharing shown by each member of URC, in order to obtain grants or publish in the best journals and share knowledge based on interdisciplinary teams of researchers (König, Diehl, Tscherning and Helming, 2013). Most of the scientific performance indicators are based on individual production (De Rijcke, Wouters, Rushforth, Fransson and Hammarfelt, 2016). Nevertheless, research in most cases, is a collective activity. In this sense to consider the total done by year is the best way to see the results of the URC because it is the consequence of an internal and external synergy not only individual activity.

Regional innovation ecosystem in the region of Aragon In the particular case of the Spanish Innovation System, it is composed of firms, a governmental system of R&D, governmental bureaucracy, innovation supporting institutions and society (Cotec, 2007). A number of Technology and Scientific parks have emerged since the turn of the century and have promoted more intensive innovation and firm links to science, technology and industry (Albahari et al., 2017). On the other hand, (Buesa, Heijs, Martinez and Baumert, 2006) describes, as a critical part of system, the regional and productive environment, the university, the Civil Service and innovating

firms. These authors claim that the regional and productive environment is the factor that has the greatest impact on the generation of technological knowledge, as evidenced by patents. They also describe a great diversity in patterns of innovation as a regional growth policy in each Autonomous Community (AC) in Spain. This issue is very important because each AC promotes and puts emphasis on different aspects within the regional system generating different performance and outcomes. Thus, the country does not show an equalitarian level of capabilities around science and technology. As a country, the government as developed the Spanish Strategy of Science, Technology and Innovation 2013-2020 (MINECON, 2012). This document gives the relevant issues in order to obtain social and economic benefits from firms based on locally created technology. This kind of policy is relevant in a country which only entered the technology era in the 1980s following the end of the Franco dictatorship (Buesa, 1988). Hence, this is an economy that has only recently looked to science, technology and innovation as a motor for economic growth. R+D+i institutions in some ACs are young in comparison to other countries in the European region, while in others like Catalonia or the Basque Country they date back to the beginning of the 20th Century. On the other hand, these institutions emerged from European policy promotion which gave the country financial resources in order to build a scientific infrastructure and improve its human capital (Magro et al., 2013). Aragon is placed 11th in terms of inhabitants in Spain (1.3 million) and placed 4th in terms of size. Aragon produces 3.2% of Spain’s GDP. In this context, the Aragon region does not show relevant innovation indicators in the country (See table 1). Nevertheless, it has developed its own innovation promotion policy and receives grants from the national government and the European Union (Law 9, 2003). This situation has promoted indicator increases and firm competitiveness over the last few decades. Furthermore, it is important to highlight that URCs in Aragon are relatively new and have emerged from EU research and innovation policies. This policy has not been analysed at this level before and it provides an interesting view point to observe how a group of university institutions has promoted research and university-industry productive alliances in a specific region in Spain, a country also relatively new in this arena. In spite of this situation as a country, Spain is in 10th place among global publication with 3.19% (2014). This is the most relevant indicator for Spain as a developed country in reference to this topic. In this context Aragon accounts for 5.4% of this total (See table 2). Other indicators like doctoral dissertations and patents are less important. Aragon is behind other ACs such as Madrid, Catalonian, the Valencian Community, the Basque Country, Andalucia and Galicia (ICONO, 2016). In terms of research project grants, one of the most relevant is the recent Horizon 2020 Program from the EU. In 2015, Spain received 178 million Euros in grants (8th place in the EU region) of which Aragon only received 2.3%. This would indicate that Aragon needs to increase its public policies and financial resources in order to improve its performance In this context it is also important to know the influence of organizational structure on this performance.

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Table 1. Key indicators of Science and Innovation in Aragon versus Spain. Data from ICONO (2016)

Year

R&D Expenses (Euros)

R&D expenses as GDP Expenses per inhabitant (%) (Euros)

Full time employees in R+D

Full time researchers

Aragon

Spain

Aragon

Spain

Aragon

Spain

Aragon

Spain

Aragon

Spain

2005

221,261

8,441,118

0.79

1.12

175

234

5,285

174,773

3,550

109,720

2006

263,428

9,467,323

0.87

1.20

205

266

5,886

188,978

3,924

115,798

2007

296,894

10,423,729

0.90

1.27

227

295

6,522

201,108

4,549

122,624

2008

352,376

11,265,434

1.03

1.35

264

320

6,912

215,676

4,743

130,986

2009

370,945

11,156,600

1.12

1.39

276

315

7,106

220,777

4,884

133,803

2010

374,240

11,077,035

1.13

1.39

279

313

7,102

222,022

4,853

134,653

2011

322,113

10,656,871

0.95

1.33

240

304

6,534

215,079

4,462

130,235

2012

312,795

10,053,758

0.93

1.30

233

286

6,133

208,831

4,094

126,778

2013

298,081

9,724,812

0.90

1.24

223

279

5,534

203,302

3,699

123,225

2014

300,795

9,617,972

0.91

1.23

226

276

5,402

200,233

3,671

122,235

Table 2. STI Productivity in Aragon versus Spain. Data from ICONO (2016)

Papers (2014) % Spain Aragon

77,013 4,191

Doctoral dissertations (2014) Public University

Private University

10,724 5.4

326

Patents (2015) %

592 3

One of the most influential institutions in this performance is University of Zaragoza (UniZar). It was established in 1542 by Emperor Charles V. This institution has had a strong link with science from its origins with scientists such as: Miguel de Servet and Santiago Ramón y Cajal. Nevertheless, the in 20th Century when technology emerged as a motor of economic growth among developed countries, this university maintained its focus on science and research scarcely linked to industry and technology. This University recovered its autonomous status in 1985, after a long period of dictatorship in Spain. In this period, university-industry was not

Application

Concession

2,855 0.1

153

2,426 5.4

112

4.6

a public policy in the country. Hence, the main institutions linked with this activity in Aragon emerged after this time. This is the most important university in the region where 50% of the population lives in Zaragoza. The UniZar has three regional branches in Teruel, Huesca y Jaca. Currently, UniZar is a complex institution that has around 32,000 students from bachelor to doctoral. The university possesses 11 University Research Centres (See table 3) which emerged from the 1980s onwards through its own endeavours (Own) or via alliances with public and private institutions (Mixed).

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Table 3. University Research Centres (URCs) belonging to the UniZar* (Data 2016) Name

Acronym

Establishment

Research groups

Researchers

Instituto de Ciencia de Materiales de Aragón / Aragon Materials Science Institute (ICMA)

ICMA

1985

174

Mixed

http://www.icma.unizar-csic.es/ICMAportal/

Laboratorio de Investigación en Fluidodinámica y Tecnologías de la Combustión / The Laboratory of Research in Fluid Dynamics and Combustion Technologies

LIFTEC

2000

Mixed

http://www.liftec.unizar-csic.es/es/

Instituto de Investigación en Ingeniería de Aragón / Aragon Institute of Engineering Research

I3A

2002

560

Own

http://www.i3a.unizar.es/es

Instituto de Biocomputación y Física de Sistemas Complejos / The Institute for Biocomputation and Physics of Complex Systems

BIFI

2002

153

Own

http://www.bifi.es/es/

Instituto de Nanociencia de Aragón / The Institute of Nanoscience of Aragon

INA

2003

140

Own

http://ina.unizar.es/es/

Instituto de Investigación Sanitaria de Aragón / Aragon Health Research Institute

IIS

2004

553

Mixed

http://www.iisaragon.es

Instituto Universitario de Matemáticas y Aplicaciones / The Institute of Mathematics and Applications

IUMA

2007

Own

https://iuma.unizar.es

Instituto Universitario de Ciencias Ambientales / Environmental Sciences Institute

IUCA

2008

228

Own

http://iuca.unizar.es

Instituto Mixto Circe / Research Centre for Energy Resources and Consumption

CIRCE

2009

102

Mixed

http://www.fcirce.es

Instituto de Síntesis Química y Catálisis Homogénea / Institute of Chemical Synthesis and Homogeneous Catalysis

ISQCH

2011

149

Mixed

http://www.isqch.unizar-csic.es/ISQCHportal/

Instituto Agroalimentario de Aragón / Agro-Food Institute of Aragon

IA2

2014

306

Mixed

https://ia2.unizar.es

Classificahttp://www.icma.unizar-csic.es/ICMAportal/ tion

Source: www.unizar.es (7th March, 2017) and each web page by URC. ‘* Active in March 2017. According to UniZar Report 2015, LIFTEC is also considered a research centre.

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Method In the following sections of this paper, we will report the findings from an inquiry that has attempted to link organizational structure and performance at URCs in the Aragon region. Eleven URCs from University of Zaragoza were studied using a mixed-methods approach including quantitative panel data multivariate analyses along with a multiple case study methodology. The overriding objective of this study was to identify how organizational structure promotes different levels of performance amongst URCs. In order to meet this objective, we attempted to answer the following research questions: 1. What kind of structure does each URC show? a. Which classification emerges from this analysis? 2. How are research characteristics influencing URC performance? 3. According to the URCs classification (point 1), are there any differences in performance amongst them? 4. Which group achieves the best performance? As discussed in Section 3, throughout the study, the term URC performance will be used to refer to the results obtained by each URC on a yearly basis with respect to publications, projects and patents. Research design A mixed-methods approach was used to address our research questions. A data panel analysis was used to address research question 2 (Baltagi, 2005), while a multiple-case study design with pattern matching was used to address research questions 3 and 4 (Yin, 1994). URC was the unit of analysis for the research questions. Simple descriptive analyses were used to address question 1, based on available data from each URC web page (See table 3). Case selection In order to be able to understand the effects of organizational structure in each URC, all of the URCs belonging to University of Zaragoza were analysed. The cases were selected because they were different in their scientific focus and resources but relatively similar in organizational context. The University of Zaragoza was chosen because it is the largest and most influential university in the region. Hence, through observing the URCs belonging to UniZar we were able to describe the situation in the Aragon region. This region only has one private university which is less than 20 years old, so its influence on research, development and innovation within the region is still relatively small. Other measures and analytical tools A variety of analytical tools were used to interpret case relationships, including descriptive statistical analysis, graphics, etc. but these are not presented in this paper.

Results Overview: Organizational structure in URCs Appendix 1, shows a description of the structure in each URC under analysis. They were classified as own/mixed. Own corresponds to those URCs created and managed by UniZar, while mixed refers to

those which are managed by an external actor in the Spanish Innovation System and UniZar. According to this analysis, the presence of Corporate governance has been highlighted (OECD, 1998) in these institutions. This is demonstrated by different levels of management, in most cases a Directory, composed of representatives from UniZar or an external institution. The more complex structures show more than four levels of management. A summary is observed in table 4. According to this description, in both classifications there are complex or simple structures. A complex organizational structure is composed of a URC Governing board, Director, Management commission or management team, research council and research divisions. Some of them have an external commission also. They describe 4 or 5 levels of management. In describing the organizational structure in these institutions, it is necessary to show how they define the course of their URCs, define the director, sub-directors or deputy director, scientific director, new researchers and also supervise the strategic plan, the budget, the annual report, and propose external commissions. These duties are defined by each URC according to their goals and vision, and especially by its condition as mixed or own. A URC Governing board is composed of representatives of UniZar and representatives of an external partner (in the case of Mixed URC). They elect a URC Director every four years. Own URC base their functions on democracy and participation, while Mixed base their functions on mutual control and coordination. Table 4. Summary of the classification of URCs under analysis Classification

Simple

Complex

Total Cases

Own

IUMA

I3A, BIFI, INA, IUCA

Cases

Mixed

LIFTEC, CIRCE, ISQCH

ICMA, IIS, IA2

Cases

Total cases

URCs which have a simple organizational structure have two levels: Management team and research divisions. There is no set trend in this group, they can be mixed or own and in one case (CIRCE) the first level is composed of an URC Governing board. In this case, it has not been possible to discover, according to the web page information available, if the board members have similar duties to those in the URCs with a complex organizational structure. In spite of this, its URC Governing board is similar to others. In summary, these findings indicate a trend of complex organizational structures. This finding suggests an analysis of each group according to its organizational structure (Simple/Complex). In the following analysis, the relationship between URC resources and research and technological performance is shown using the OS (Simple/Complex) as a dummy variable as a way of analysing its influence on performance.

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Scientific performance and URCs: data panel analysis The empirical approach proposes to test the relationship between URCâ&#x20AC;&#x2122;s scientific and technological performance (publications, projects and patents) and the resources involved. Resource and capabilities approach (Barney, 1991) allows for the definition of the main resource needed in order to achieve a distinctive competitive advantage. This means, each URC has infrastructure and financial support in order to achieve its research goals. Nevertheless, these resources are generic in nature, such as: offices, buildings, laboratories. The latter could be special or specific, even unique, but nevertheless they represent tangibles that need a researcher or specialist in order to get the best out of this specific asset. In this way, the main resource in each URC is the research personnel. They are working together, coordinating, obtaining grants, giving orientation to its researches, proposing new topics, mixing ideas, materials, sharing knowledge and networking. In summary, they allow each URC to obtain its performance. In this research, the research personnel are described according to three main characteristics: age, gender and educational level. These aspects are similar to those used by Dietz and Bozeman (2005), in order to determine the influence of experience on technical human capital improvement. In other researches these aspects are treated as control variables (Lin and Bozeman, 2006). However, in this research the research unit is each URC. Hence, the conditions of their members as a whole are relevant and in this case, cannot be a control aspect. The control variable is the resources that the University of Zaragoza gives to each institution yearly. This financial support is described as the amount of money that the university pays, in the form of a salary, to the researchers in each URC. Some researchers belong to the University of Zaragoza. The university divides its academic activity into docent and researchers. UniZar controls the scholars in this condition and calculates the amount involved according to this dual work and scholar category in each case. We have a time series (from 2000 to 20161) for each variable and URC under analysis (11 cases). The time series depends on the establishment date of each URC. Hence, there are differences in terms of the data available for each URC, as well as non-observable individual effects. This situation, suggests the use of econometric technics such as data panel analysis (Arellano-GonzĂĄlez and Bover, 1990). This is a mix of cross-sectional analysis and time series, which means considering specific units under analysis and allowing for the gathering of information for the observation over time, controlling non-observable individual heterogeneity. In this research, each URC is heterogeneous to the research activity carried out according to its endeavour, the resources involved, the date of establishment or the kind of organizational structure. According to this situation, the quantitative methodology of analysis proposed possesses a set of positive aspects such as: the reduction of collinearity among variables, obtaining more freedom grades

and more efficiency, better testing of the dynamic-fit, the identification and measuring effect that time series effect or a cross-sectional test does not detect, to mention some of them (Baltagi, 2005). The time period 2000 to 2016 has been chosen so as to obtain the widest picture possible of the activities carried out by each URC during this period. Nevertheless, the data available from each UCRâ&#x20AC;&#x2122;s yearly report, is not coincident with the establishment year. In this sense, we have an unbalanced panel data, composed of 119 observations, 11 cases and the time-series 2000-2016. Thus, to determine a possible relationship between the research and the technological performance of a URC and the resources involved, the following set of regressions have been tested, considering research and technology performance by a URC as 3 different dependent variables: X1 = Publications (PUBL) X2 = LN_Projects (LN_PROJ) X3 = Patents (PAT) Hence, it is possible to define the following set of regressions: X1t= _1OS1t +_2GEN1t+_3AGE1t+_4EDU_L1t Č&#x2022;1 Ä°1t (1) X2t= _1OS2t +_2GEN2t+_3AGE2t+_4EDU_L2t Č&#x2022;2 Ä°2t (2) X3t= _1OS3t + _2GEN3t+_3AGE3t+_4EDU_L3t Č&#x2022;3 Ä°3t (3) The dependent variable (Xit) is an approximation of the outcomes of each URC i in the time t, in this case it is composed of three elements analysed separately, as a way to observe the differences among them: Publications, projects and patents. The terms bi and eit represents the individual effect and idiosyncratic error respectively. The financial support that the University of Zaragoza gives annually to each URC has been defined as a control variable: Unizar_FS. The data used in this calculus was obtained from each URC annual report during the period under analysis which were available on their web pages and in the SEGEDA2 data base. The datasets generated during and/or analysed during the current study are not publicly available. They are open to administrative and scholars at UniZar members. In this case was available to this research, but are available from person outside UniZar on reasonable request. Once the data base was completed, it was possible to adjust the variables X2(Projects) and LN_UZ_FS. Both are defined in thousands of euros. As a way to be more comparable in each regression with others variables, both were recalculated using a Natural logarithm. In table 5 there is a description of each variable defined.

From establishment date or agreement date by each URC. https://segeda.unizar.es/pentaho/Home. SEGEDA: Service Management Data of University Zaragoza (Servicio de GestiĂłn de Datos de la UniZar) / Data extracted from January to May 2017. 2

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Table 5. Variables description Tag

Description

X1: Publications

PUBL

Total number of publications per URC per year

X2: Projects

LN_PROJ

Natural log of the total amount of grants or funds obtained by each URC per year

X3: Patents

PAT

Total number of patents (applications and concessions) per URC per year

Dummy variable which describes Complex organizational structure (1) or simple (0)

Male

GEN_M

Total number of men within the total number of researchers per URC per year

Female

GEN_F

Total number of women within the total number of researchers per URC per year

Less than 30 years old

AGE_L_30

Total number of researchers less than 30 years old within the total number of researchers per URC per year

More than 30 years old

AGE_M_30

Total number of researchers more than 30 years old within the total number of researchers per URC per year

Hold PhD

PHD

Total number of researchers holding a PhD within the total number of researchers per URC per year

Non-hold PhD

NON_PHD

Total number of researchers not holding a PhD within the total number of researchers per URC per year

LN_UZ_FS

Natural log of the total amount of financial support given by the University of Zaragoza to each URC per year

PROJ

Total amount of grants or funds obtained by each URC per year

Dependent variables

Independent variables Organizational Structure GENDER (GEN)

AGE (AGE)

EDUCATIONAL LEVEL (EDU_L)

Control Variable

University of Zaragoza financial support

Others Projects

We proceeded to calculate the descriptive statistics for each variable under analysis (see table 6). Once this was done, a sequence of econometric models formulated successively was calculated, according to

the Hausman test which defines whether a panel is random or fixed. The results from this procedure are shown in Annex 2, according to the models proposed in this research.

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Table 6. Variable descriptive statistics. Variable

Sub-category

Mean

Std. Dev.

Min

Max

Dependent

PUBL

119

146,983

118,29

445

LN_PROJ

119

14,157

1,355

7,472

16,563

PAT

119

2,270

7,315

Independent

119

0,655

0,477

GEN_M

119

94,420

82,375

400

GEN_F

119

56,344

56,129

277

AGE_L_30

119

26,319

23,126

100

AGE_M_30

119

124,445

114,273

494

PHD

119

97,613

88,018

326

GEN

AGE

EDU_L

NON_PHD

119

53,151

54,921

244

Control

118

14,331

1,123

11,448

16,917

119

2,789,982

3,295,445

1,759

1,56e7

LN_UZ_FS Other PROJECT

PROJ

As a way of solving the second research question - Which variables influence URC performance? and the sub-question - What are the net or multivariate effects of significant performance variables within URCs?. The panel data analysis, is summarised in table 7, Model 1 involves the basic model without a control variable, considering OS, GENDER: GEN_M, AGE: AGE_L_30, EDU_L: PHD. Model 1A uses the same variables plus a control variable: LN_ UZ_FS, Model 1B is composed of OS, GENDER: GEN_F, AGE:

AGE_M_30, EDU_L:NON-PHD and Model 1C uses the same variables as Model 1B but with a control variable. Each model is calculated using a part of the variable, for example: GEN_M or GEN_F, not both, because of collinearity. The same situation occurs for AGE and EDU_L. For the same reason, we test Model 1 with a part of each variable, while Model 1B is composed of the other part. It is not necessary to process all possible combinations among variables.

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Table 7. Summary Data panel results

X1: PUBL

Wald Chi2

X2: LN_PROJ

X3:PAT

Control

Model 1

Model 1A

Model 1B

Model 1C

Model 1

Model 1A

Model 1B

Model 1C

Model 1

Model 1B

Model 1C

239,69***

255,93***

288,54***

251,91***

30,67***

166,42***

44,72***

162,67**

90,93***

134,56***

144,26***

Overall

0,8683

0,872

0,873

0,874

0,4088

0,561

0,263

0,561

0,668

0,694

0,709

3,61***

3,96***

4,23***

4,44***

2,10**

2,09**

2,90***

1,98**

1,04

-1,33

-1,80*

GENDER

GEN_M

1,86**

1,88**

1,09

1,65*

5,41***

GEN_F

-1,27

-1,38

-1,50

-1,48

-6,87***

-6,69***

AGE

AGE_L_30

-0,68

-0,75

-0,54

-1,27

-4,37***

AGE_M_30

7,24***

7,42***

3,04***

1,84**

10,66***

9,65***

EDU_L

PHD

2,20**

2,32**

-0,12

-1,15

-2,84***

NON_PHD

0,57

-0,63

0,66

0,9

2,72***

2,78***

-1,62

-1,52

7,13***

6,98***

1,68*

0,14

1,61

0,45

1,59

42,96***

3,79***

50,09***

3,90***

-1,29

-1,67*

-1,90*

LN_UZ_FS _Cons

* <0,10; ** <0,05; *** <0,01

In the case of Publications (PUBL:X1), all models are significant with a R2 overall over 85%, this shows a high explanation capacity over 10%, the critical figure in this calculus (Falk and Miller, 1992). According to the Hausman test all models are random, this means that there is not a systematic pattern in time in each URC. OS, GEN_M and EDU_L:PHD, in model 1 and 1A, AGE_M_30 in model 1B and 1C, are significant variables. In summary, male researchers, more than 30 years old and holding a PhD are influencing Research performance in terms of Publications. On the other hand, Projects are influenced by OS and the control variable is significant. In this case, the models are significant in all cases. R2 overall is significant in levels from 26% to 56% which is considered adequate for this kind of analysis. GEN_M is significant in Model 1A and AGE_M_30 in models 1B and 1C. In this case, the control variable has a positive and high rate of influence on getting projects. The constants in all models are significant. In summary, men over 30 years old in those URCs with complex OS do have influence in terms of obtaining grants for projects by a URC. Lastly, patent models as a part of the technological performance of each URC show significant models with a high capacity for explanation (R2 from 67% to 71%). In this tested case model OS was not significant or it was low and negative as in the case Model 1C. It was not possible to calculate Model 1A because the Hausman test was not viable. The Models in this case are interesting because they show an influence by the variable as a whole. For example, Gender is significant, both male and female, but males have a positive influence. In terms of Age, researchers over 30 years old have a positive influence. Meanwhile, according to the level of education, those without a PhD

have positive influence on patent development. The control variable does have influence in Model 1C and the constant is less significant and negative. In summary, male researchers over 30 years old and not holding a PhD and UniZar financial support promotes patents in URCs. In conclusion, Complex OS shown in a group of URCs influences both publications and projects. The resources involved in publications are male researchers, over 30 years old and holding a PhD, while projects are also influenced by men over 30 years old, but they do not require a specific level of education. At the same time, the resources involved are influenced by male researchers over 30 years old without a PhD as well as UniZar financial support. These results are very consequent with the institutionalized scientific performance, publications and projects. Complex OS in these institutions promotes a standard performance, while technological performance, such as patents, requires a strong relationship with industry. Some of the URCs under analysis have been able to achieve this kind of linkages over a long period of time and through personal relationships between researchers and company managers, but these are not very well institutionalized. Each technology-transfer model represents an activity that is not very well defined in some URCs, also they have not been defined as the main activity because the URC promotes research more than patent development. This is a distinctive characteristic of the Spanish Innovation System, because technology transfer is done by: technological parks, while science and research is done by URCs. Therefore, to be able to generate patents in an institution with little focus on patenting is a value added that URCs are giving to the Spanish Innovation System. In this system, universities have been able to give maturity and competitiveness to the relationship university-industry (Buesa, 2012).

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Scientific performance and URCs: Best performance In the following analysis performance will be measured. This will effectively allow us to know if organizational structure promotes better or lesser performance in these institutions. This analysis solves question 3 and 4. We have calculated the mean of each group showed in Table 4: Group 1: URCs Complex and Own, Group 2: Complex and Mixed, Group 3: Simple and Mixed. The 4th group Simple and Own is composed of just one URC and was considered inadequate for analysis in isolation from all the other URCs. According to results described in Table 8, it is possible to conclude that there is not one group that is the best in all the performance indicators, instead there is a group of URCs which show a kind of structure which promotes some of the performance indicators (publications, projects and patents. Publications are promoted by Group 2, URCs which show a Complex OS and are composed of the University and a public partner or private institution (Mixed). They on average produced 257.4 publications (mean) during the period under analysis (2000 to 20016). With respect to Projects and Patents, they are promoted by Group 1 (Complex and Own URCs). In both cases, having a Complex Organizational structure leads to greater influence on URCs performance. It is important to note a better performance in Group 1, because it is a set of URCs which have worked without formal alliances within their organizational structure, although some of them have been able to develop linkages with industry and other research institutions.

males over 30 years old, but a specific level of education is not necessary. Patents are influenced by male researchers over 30 years old without a PhD and with UniZar financial support.

Table 8. Mean of Scientific performance by URC according to grouping

PUBL

PROY

PAT

Mean

Group 1

Own_Complex

156.19

4,012,296

4.88

Other

139.84

1,841,321

0.42

119

Group 2

Mixed_Complex

257.42

2,253,678

0.58

Other

116.10

2,939,917

2.87

119

Group 3

Mixed_Simple

21.90

806,883

0.55

Other

191.05

3,488,574

3.01

119

In summary, from the results we have found that URCs with a complex style of organizational structure have influence on their scientific performance. On observing the URCs with a complex structure plus whether they are also own or mixed, it is apparent that the complex and own URCs perform better in projects and patents, while complex and mixed show a greater influence on publications. In terms of research characteristics which allow each URC to achieve their results, the resources involved in publications are male researchers over 30 years old and holding a PhD, while projects are also influenced by

Educational level, as a variable is composed of researchers with or without a PhD. This indicator has been built as a way to show that an important number of people doing research and pushing publications, patents and projects have not, necessarily, been trained to a PhD level. In this research, this status is irrelevant in obtaining grants and projects. This could be due to a project being obtained by a set of people with different skills and experiences. Hence, a project in itself requires people with different levels of education. Nevertheless, publications are promoted by researchers holding a PhD. This a natural consequence of their training as people that spread their knowledge by publishing their discoveries and the results of their research. On the other hand, non-holding PhD researchers show a positive influence on the promotion of patents, while those holding a PhD demonstrate a more negative influence. This is due to the relationship with industry and scarce experience, in most cases, of people holding recently obtained PhDs (Dietz et al., 2005). The building of these linkages is more difficult and artificial for PhD holders than for people who do not attain this kind of educational level. Meanwhile the theoretical models studied at PhD level tend to be unclear to people without this level of education. This causes communication difficulties between people from industry and researchers (NASEM, 2017). Gender in science is a widely analysed issue, especially with respect to woman in science, at least from the 1970s in the USA (Gaughan, 2005). With respect to the results obtained in each proposed model they are related to educational level, with males showing little

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influence on projects, some influence on publication, and a marked influence on patenting. This is due to the scarce number of women holding PhDs, there is a 0.934*** (Pearson correlation) in the data base. However, the number of women is not particularly small, on average 34% of URCs members are women (Dev Std 13%). They add diversity and a different point of view to research results. Nevertheless, if they are not holding a PhD with respect to this research they are not influencing publication and patenting. There is something to analyse within regional public policies on this issue, because at national level (Spain) women make up 39% of all researchers (ICONO, 2016). With respect to the control variable, financial support from UniZar to each URC under analysis, this has a marked influence on projects. This kind of variable has not been analysed before in this kind of study. In this case, it was considered relevant due to the fact that financial support given to URCs by a public university is very important with financial support for these institutions from national and regional government being very limited. This puts a lot of pressure on URCs which must survive using their own financial resources. All institutions require working capital in order to cover all their expenses. Hence, every URC has a set of internal policies which allows it to obtain these financial resources from researchers who have been able to obtain grants and external financial support for their projects. Obtaining financial support for projects is a critical activity for URCs and Universities. In the case that a researcher or research team obtains a grant for a project, a part of this is given to the university as an overhead. This situation produces a virtuous circle between the university and its researchers. This is the case of UniZar which invests these resources in research and not in other activities. This means that the level of commitment from the university to research activity within URCs allows both institutions to improve their indicators and compete in the global scientific context. With respect to publications, as seen in the results of this research, there is a lack of financial support from UniZar. Publishing is an activity done by all scholars within a university and URCs. This double status of publications, from a critical point of view, could be seen as a result of the university more than the URC. Nevertheless, this result shows that the university has more publications and that the URCs represent only a part of it. Publication is an important activity within every URC because it allows the researchers to spread their discoveries and share their knowledge. Thus, this is also a relevant indicator of URC activity (Dietz et al, 2005). In the case of patenting, results show a link between patenting and university financial support. This is similar to projects. Patenting increases university income, which in turn increases university resources for URCs. The symbiotic relationship between the university and the URCs and how they manage their financial resources shows their dependence on public funds as well as requiring increased financial support and involvement in R+D+i from private institutions (foundations and companies). In conclusion, this set of simple characteristics that describe researchers (age, gender and educational level), influence research results in URCs. Hence, the distribution and the management of the characteristics of the researchers can improve URCs performance. Therefore,

this kind of analysis is the seed for the creation of a standard report and its revelations about the research institutions from a global, national and regional point of view. Despite the discussion on indicators still being open, this research proposes a simple set of indicators which allow for the observation of research performance in URCs, institutions where research is done by people coming together and mixing a set of resources and capabilities within their field of research based on social networks and scientific knowledge (Bozeman et al., 2002). This research is in line with those which have analysed organizational and institutional conditions that promote research activity. Hunter, Jansen Perry and Currall (2011) described the positive relationship between an organizational climate characterised by support for commercialization and invention disclosures and patents. With respect to the creativity in scientific research, this is promoted by organizational structures which give researchers access to a variety of complementary technical skills, stable research sponsorship, timely access to extramural skills and resources as well as facilitating leadership (Heinze et al.,2009). In consequence, a researcher needs the kind of structure and processes which help him/her to be excellent. Therefore, this research is also adding elements in research management and research policy. Research performance is strongly linked to the URCs organizational structure. This kind of structure coincides with a loose management style with regards to the organization of their resources and internal capabilities, as a way of promoting research collaboration (Boardman et al., 2006) within each URC. On the other hand, a complex organizational structure promotes better performance. This result is aligned with authors who describe university corporatisation (Parker, 2011). In his paper Parker, describes how university governance is promoting similarities with large companies (corporations). According to this approach, this set of UCRs, as part of a public university are doing this through their organizational structures based on a Directory and a set of internal rules, controls and reports. This is due to the legacy of EU public policies in Aragon, as well as local conditions that promote a scientific culture that emerged from the first URCs in the region (I3A, BIFI and INA) owned by UniZar. Currently, all financial resources obtained from local or international sources, are required to be open to the scrutiny of the local community. This trend promoted by Governance of science (De Rijcke et al., 2016) as well as the pressure of competition among universities trying to increase their international ranking requires attention and the disclosure of the scientific activity within the universities. In relation to this issue it is interesting to observe the set of reports done by URCs under analysis. Their organizational structures and level of commitment within the region and society compel them to do it. Also, these research institutions have been able to go beyond themselves by patenting and, in some cases, promoting spin-offs. This situation shows the possibility of establishing a strong relationship between academia and industry in institutions which were not originally created with this goal in mind. Spain has Technology parks, Technology Centres and Innovation Technology Support Centres as its main institutions focussed on patenting and technology transfer. Hence, URCs are able to give some support to the Innovation ecosystem in this topic which is a highly valuable aspect to the people in charge of URCs in Aragon.

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This research is an example of the recent history in regional STI development. Knowing, in detail, these kinds of results in URCs management, allows for better decision making and the development of adequate public policies, especially in those countries and regions which have recently established these kinds of institutions. All those public policies promoting STI require the practice of good governance in order to adequately use public resources (Prewitt, 1993) as the demand for them intensifies.

Acknowledgment This research has been possible through financial support from VRIDEI and Accounting and Auditing Department at Universidad de Santiago de Chile.

References Albahari, A., Pérez-Canto, S., Barge-Gil, A. & Modrego, A. (2017) Technology Parks Versus Science Parks: Does The University Make The Difference?. Technological Forecasting And Social Change, 116, 13-28. https://doi.org/10.1016/j.techfore.2016.11.012 Arellano – Gonzalez, M. & Bover, O. (1990). La econometría de Datos de Panel [The econometry of data pannel]. Investigaciones Económicas, 14(1), 3-45. Baltagi, B. (2005). Econometric Analysis of Panel Data. Third edition. Wiley, Chichester. Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management. 17(1), 99-120. https://doi. org/10.1177/014920639101700108 Boardman, C. & Bozeman, B. (2006). Implementing A ‘Bottom-Up,’ Multi-Sector Research Collaboration. Economics of Innovation and New Technology.15(1), 51-69. https://doi. org/10.1080/1043859042000332196 Boardman, P. & Corley, E. (2008) University Research Centers and the composition of Research Collaborations. Research Policy, 37(5), 900-913. https://doi.org/10.1016/j.respol.2008.01.012 Bok, D. (2003). Universities in the Marketplace: The Commercialization of Higher Education. Princeton, NJ: Princeton University Press. Bozeman, B. & Boardman, C. (2013) Academic Faculty in University Research Centers: Neither Capitalism’s Slaves Nor Teaching Fugitives. The Journal Of Higher Education, 84(1), 88-120. https://doi. org/10.1353/jhe.2013.0003 Bozeman, B. & Rogers, J. (2002). A Churn Model of Scientific Knowledge Value: Internet researchers as a knowledge value collective. Research Policy, 31(5), 769-794. https://doi.org/10.1016/s00487333(01)00146-9 Buesa, M. (1988). Estructura Industrial de España [Spanish industrial structure]. Fondo de Cultura Económica, Mexico.

Corley, E., Boardman, C. & Bozeman, B. (2006). Design and the management of multi-institutional research collaborations: Theoretical implications from two case studies. Research Policy, 35, 975–993. https://doi.org/10.1016/j.respol.2006.05.003 COTEC (2007). Las relaciones en el sistema español de Innovación [The relationship in the Spanish Innovation System]. Libro Blanco [White book]. Fundación COTEC para la Innovación Tecnológica [COTEC Fundation to the Technology Innovation]. Madrid: Spain. De Rijcke, S., Wouters, P., Rushforth, A., Fransson, T. & Hammarfelt, B. (2016). Evaluation Practices and effects of indicator use - A literature review. Research Evaluation, 25(2), 161-169. https://doi. org/10.1093/reseval/rvv038 Dietz, J. & Bozeman, B. (2005). Academic careers, patents, and productivity: industry experience as scientific and technical human capital. Research Policy 34, 349–367. https://doi.org/10.1016/j.respol.2005.01.008 Etzkowitz, H. & Leydesdorff, L. (2000). The Dynamics of Innovation: From National Systems and “Mode 2” to a Triple Helix of University–Industry–Government relations. Research Policy, 29(2), 109-123. https://doi.org/10.1016/s0048-7333(99)00055-4 EC (2010) European Commission, Assessing Europe’s University-Based Research, Expert Group on Assessment of University-Based Research. Directorate-General for Research. Belgium. Falk, R. & Miller, N. (1992). A Primer for Soft Modelling. Akron OH: University of Akron Press. Gaughan, M. (2005). Introduction to the Symposium: Women in Science. Journal of Technology Transfer, 30,339–342. https://doi. org/10.1007/s10961-005-2579-z Gray, D., Lindblad, M. & Rudolph, J. (2001) Industry-University Research Centers: A multivariate analysis of member retention. Journal of Technology Transfer, 26,247-254. https://doi. org/10.1023/A:1011158123815 Heinze, T., Shapira, P., Rogers, J. D., & Senkerd, J. (2009) Organizational and institutional inﬂuences on creativity in scientiﬁc research. Research Policy 38, 610–623. https://doi.org/10.1016/j.respol.2009.01.014

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Huisman, J., De Weert, E. & Bartelse, J. (2002) Academic Careers from an European Perspective: The declining desirability of the faculty position. The Journal of Higher Education, 73(1), 141-160. https:// doi.org/10.1353/jhe.2002.0007 Hunter, E., Jansen Perry, S. & Currall, S. (2011). Inside multi-disciplinary science and engineering research centers: The impact of organizational climate on invention disclosures and patents. Research Policy, 40, 1226– 1239. https://doi.org/10.1016/j.respol.2011.05.024 ICONO (2016). Indicadores del Sistema Español de Ciencia, Tecnología e Innovación [Indicators of Spanish system of Science, technology and innovation]. ICONO: Observatorio Español de I+D+i [Spanish Observatory of R+D+i]. FECYT : Fundación Española para la Ciencia y la Tecnología [Spanish Fundation for Science and Technology]. Madrid: Spain. König, B., Diehl, K., Tscherning, K., & Helming, K. (2013). A framework for structuring interdisciplinary research management. Research Policy, 42, 261– 272 https://doi.org/10.1016/j.respol.2012.05.006 LAW 9 (2003) Ley de fomento y coordinación de la investigación, el desarrollo y transferencia de conocimientos en Aragón [Law 9/2003, March 12, Foster and research coordination, development and knowledge transfer in Aragon]. Boletin Oficial de Aragon [Aragon Official Bulletin]. Lin, M. & Bozeman, B. (2006). Researchers’ industry experience and productivity in University–Industry Research Centers: A “Scientific and Technical Human Capital” explanation. The Journal of Technology Transfer, 31(2), 269-290. https://doi.org/10.1007/s10961-005-6111-2 Magro, E. & Wilson, J. (2013). Complex Innovation Policy Systems: Towards an evaluation mix. Research Policy, 42(9), 1647-1656. https:// doi.org/10.1016/j.respol.2013.06.005 MINECON(2012). Estrategia española de Ciencia de Tecnologia y de Innovacion 2013-2020 [Spanish Strategy of Science, Technology and Innovation 2013-2020]. Ministerio de Economía, Industria y Competitividad del Gobierno de España [Ministry of Economy, Industry and Competitiveness of Spanish government]. Madrid: Spain. Mowery, D. & Ziedonis, A. (2002) Academic patent quality and quantity before and after the Bayh–Dole act in the United States. Research Policy 31, 399–418. https://doi.org/10.1016/s0048-7333(01)00116-0

Nelson, R. (1993). National Innovation Systems. Oxford Univ. Press, New York U.A. OECD (1998). Corporate Governance: Improving competitiveness and access to capital in Global Markets. Paris: France. https://doi. org/10.1787/9789264162709-en OECD (1999). Managing National Innovation Systems. Paris: France. Parker, L. (2011). University Corporatisation. Critical Perspectives on Accounting, 22(4), 434-450. https://doi.org/10.1016/j. cpa.2010.11.002 Perkmann, M. & Walsh, K. (2007) University-industry relationships and open innovation: towards a research agenda. International Journal of Management Reviews, 9(4), 259-280. https://doi.org/10.1111/ j.1468-2370.2007.00225.x Ponomariov, B. & Boardman, C. (2010) Influencing scientists’ collaboration and productivity patterns through new institutions: University research centers and scientific and technical human capital. Research Policy 39, 613–624. https://doi.org/10.1016/j.respol.2010.02.013 Prewitt, K. (1993). America’s Research Universities under Public Scrutiny. Daedalus, 122(4), 85-99. Retrieved from http://www.jstor.org/stable/20027200 Santoro, M. & Chakrabarti, A. (2001) Corporate Strategic Objectives for establishing relationships with University Research Centers. IEEE Transactions on Engineering Management, 48(2), May, 157-163. https://doi.org/10.1109/17.922475 Stigler, S. (1993). Competition and the Research Universities. Daedalus, 122(4), 157-177. Retrieved from http://www.jstor.org/stable/20027203 Toker, U. & Gray, D. (2008) Innovation spaces: Workspace planning and innovation in U.S. University Research Centers, Research Policy 37, 309–329 https://doi.org/10.1016/j.respol.2007.09.006 Wildson, J. (2015). The Metric Tide: Report of the Independent Review of the Role of Metrics in Research Assessment and Management. Sage: London. http://dx.doi.org/10.4135/9781473978782 Yin, R. (1994). Case Study Research: Design and Methods. London, Thousand Oaks (CA) and New Dehli, Sage.

NASEM (2017) National Academies of Sciences, Engineering, and Medicine. Communicating Science Effectively: A Research Agenda. Washington, DC: The National Academies Press. https://doi. org/10.17226/23674

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When Size Matters: Trends in Innovation and Patents in Latin American Universities Luis Fernando Ramirez-Hernandez¹*, Jairo Guillermo Isaza-Castro² Abstract: This paper characterizes the trends in technological innovation and intellectual property in four Latin American countries (Chile, Colombia, Mexico, and Peru). Toward this aim, we collected a database of patents granted at the national and university levels in combination with information from a variety of sources to construct a set of plausible explanatory variables. Based on panel data at the national level, we verify that the number of patents granted to universities is strongly associated with the share of resources, as a percentage of GDP, invested in science and technology. At the university level, we find that institutions with more scientific publications and larger enrolment size tend to be granted more innovation patents. To some extent, the evidence presented in this paper indicates that both the absolute and relative sizes of resources invested in scientific and technological research at the university level are subject to economies of scale: a greater amount of resources invested in technological research is associated with increasing levels of innovation and patenting activity. Keywords: innovation; patents; R&D policy; universities; Latin America Submitted: Jun 5th, 2019 / Approved: Oct 22nd, 2019

1. Introduction A political concern in the agenda for governments and universities alike has been the relationship between science and technology and the corresponding link between universities and industries. Globally, as a key element of their institutional missions, universities search to find the most efficient way to transfer the outcomes of their research to society and to industries. One important basis of this concern is the 1945 Bush Report called: Science, The Endless Frontier. The basic principle of the report is that discoveries resulting from research through technology transfer must support economic development and social welfare. Technology licensing, patents, and publications in high-impact journals are materialization of such transfer. The linear model of innovation was the first analytical framework to explain the relationship between science and technology (Godin, 2006). This model proposes that innovation begins with basic research, continues with applied research, and ends with production and transference. To support the final stage at the policy level, efforts to diffuse and commercialize the innovation outcomes of scientific research have been supported at the legislative level in many countries (Bradley, Hayter & Link, 2013). In the United States, the Bayh–Dole Act of 1980 allowed universities to retain intellectual property and to appropriate the proceeds of licenses from patents obtained through federal research funding (Fish, Hassel, Sander & Block, 2015). European and East Asian nations have emulated the US by enacting domestic legislation specifying that intellectual property be privileged at the institutional level; this is evident in Finland, Germany, Spain, the UK, Korea, and Singapore, among others (Geuna & Rossi, 2011). Hayter and Rooksby (2016) recently stated that research on technology transfer has now broadened its field of action generating links to the theory of economic development and providing a vision of growth and prosperity related to the creation, diffusion, and

marketing of new knowledge. The impact of this new knowledge depends on its ability to flow within societies, fostering social and economic development. According to Rodeiro, Lopez, Otero and Sandias (2010), there is a wide range of possibilities for interaction between universities’ science and technology output and industries, including entrepreneurship, recruitment of graduates, technology diffusion and transfer, specialized consulting, collaborative projects, the use of patents and licenses, and the creation of spin-off companies. In this regard, the study on university technology transfer elaborated by Bradley et al. (2013) found universities’ interest in obtaining patents has grown rapidly in the last decade; there has been a significant increase in licensing activities and the creation of university spin-off companies, both inside and outside the United States. Much of the literature has emphasized the transfer of innovation and technology from the university sector to the rest of the economy in the industrialized world. This topic has received less attention regarding developing countries, particularly in Latin America. Consequently, the purpose of this paper is to answer two questions. First, to what extent can the amount of resources invested in research and development by the innovation systems at the national level be associated to technology transfer activity as measured by the number of patents granted to universities? Second, what is the relationship between the technology transfer from universities to society in terms of granted patents to both their enrolment size and their scientific publications? We aim to answer these questions with an empirical application based on quantitative data from four Latin American countries: Chile, Colombia, Mexico, and Peru. For this aim, we assembled a database of granted patents at the national and university levels in combination with information from a variety of sources to construct a set of plausible explanatory variables. Based on panel data at the national level, we verify that the number of patents granted to universities is strongly associated with the share of resources as a percentage of GDP invested

1) CENTRUM Graduate Business School, Pontificia Universidad Católica del Perú. Alamos de Monterrico, Lima, Perú. 110231, (51)1-6267100. 2) Facultad de Ciencias Económicas y Sociales, Universidad de La Salle, Colombia. Carrera 5 No. 59-91, Bogotá, Colombia. *Corresponding author: a20165447@pucp.edu.pe

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in science and technology. At the university level, we find that those universities with more scientific publications and higher enrolment size tend to obtain more granted innovation patents. To some extent, the evidence presented in this paper indicates that both the absolute and relative size of the resources invested in scientific and technological research are subject to scale economies whereby a larger size of resources invested in technological research is associated with an increasingly larger innovation and patenting. The rest of this paper is organized as follows. Section II presents a literature review, which includes a theoretical framework for the study, a review of the innovation systems in Latin America, and a review of previous research in the field of patents and innovation. The third section explains the data sources for the data presented in this paper. Section IV displays the statistical and econometric results and discusses them in the light of the existing literature. Finally, the fifth section makes a summary of the findings and puts forward some limitations and considerations for further research.

2. Literature Review 2.1 Theoretical Framework of the Study From a theoretical perspective, Audretsch (2014) presents an interesting review of how and why the role of the university in society has evolved over time, arguing that the forces shaping economic growth have influenced the corresponding role of the university. He stated, “As the economy has evolved from being driven by physical capital to knowledge, and then again, to being driven by entrepreneurship, the role of the university has evolved over time” (p. 313). In this sense, he makes a comparison between the influences of the so-called Solow economy (popularized by Robert Solow) and the Romer economy (introduced by Paul Romer). The Solow model puts “emphasis on physical capital and unskilled labor as the twin factors shaping economic performance. Despite the preeminent contributions to social and political values, the economic contribution of universities [is] modest” (Audretsch, 2014, p. 315). Meanwhile, in the Romer economy, knowledge is considered particularly potent as a driver of economic growth. Audretsch states, “As the Romer economy replaced the Solow economy, a new role for the university emerged, as an important source of economic knowledge” (Audretsch, 2014, p. 316). In a related stream of research, Kesan (2015) examined several theories that explain and justify the role of patents in today’s knowledge-based, technology-intensive economy, stating, “patents reduce transaction costs, help convert inventions into transferable assets, promote disclosure, provide a system of certification and standardization, and allow greater divisibility of technology” (p. 903). In relation to the marketing of innovations, Kesan (2015) assured, “All of these functions make transactions in the marketplace for inventions more efficient, to the benefit of both inventors and consumers” (p. 903). In this context, acquiring patents helps a university bring in revenue, and allows for technology transfer offices and corporate firms interested in commercializing innovations to be connected to the universities through industrial property.

These theoretical approximations indicate that universities’ scientific and technological development is a source of economic growth through offering new technologies to the market and providing basic support to nations’ innovation systems. In sum, the university today has a role that goes beyond teaching and involves the transference of research knowledge to society. 2.2 Innovation Systems in Latin America In the last decade, innovation has gained increasing importance in Latin America. Most of the countries in the region now have national strategies for innovation and have created governing institutions for this purpose. While these countries have accumulated experience in designing innovation policies, they still sometimes struggle to articulate industrial policies and domestic production from the generation of scientific knowledge and technological capabilities (Primi, 2014). When the concept of National Systems of Innovation (NIS) gained importance in the region in the mid-1990s, the main concern was how to articulate cooperation between the public sector and the private sector to boost efforts of science and technology (Edquist & Hommen, 1999; Lundvall, 1992; Nelson, 1993). At the time, most countries suffered from a lack of industrial transformation and limited development of technological capabilities. This was due to the growing specialization that guided nations’ development models according to the comparative advantages they exhibited for international trade. A national innovation system can be described as the flow of technology and information among the actors of the system—companies, universities, and government—that generates processes of innovation at national level (Russo-Spena, Tregua & Bifulco, 2017). In the case of Latin American countries, this concept has been used to design policies and instruments to establish organizational infrastructures to facilitate the connections between the different actors, to promote knowledge networks that generate innovation at the firm level. National innovation systems, therefore, define the basic conditions for this research, like mechanisms for protecting inventions, incentives for promoting scientific research, mechanisms for financing projects, conditions for licensing of patents, and aligning universities and businesses for innovation. However, other innovation scholars have identified different contexts to conceptualize national innovation systems. Specifically, they refer innovation by clusters, regions, and within technological areas, rather than by a national system (Russo-Spena, et al., 2017). Later, toward the middle of the 2000s, along with an increase in the prices of commodities worldwide, new financial opportunities emerged for countries in Latin America, sparking a relaunch in public policies for innovation. At that time, innovation policies redirected emphasis on (i) sectorial differentiation, (ii) the generation of incentives for science and technology, and (iii) the definition of new priorities for social and territorial inclusion and environmental sustainability (Primi, 2014).

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Latin American institutions have different policies in relation to the governance of innovation policies. Developments in the four countries in this study are as follows. In Chile, the Ministry of Science, Technology, Knowledge and Innovation was created in 2018; it reports directly to the Presidency of the Republic. In Colombia, the agency responsible for innovation is Colciencias, which in 2009 was declared an autonomous department and was recently elevated to the Ministry of Science, Technology and Innovation, which begin operation in 2020. In Mexico, the agency in charge is the CONACYT, which reports to the Ministry of Economy. In Peru there are two entities, CONCYTEC, which depends on the Ministry of Education, and the National Council for Competitiveness, which reports to the Ministry of Economy and Finance. Each country differs in the magnitude of resources applied to the promotion of innovation and in the way that the resources are assigned. However, for all Latin American countries, progress has been made in at least three areas: (i) institutional strengthening, with the creation of bodies charged with guiding the innovation policy with sufficient autonomy and capabilities, (ii) new funding sources for innovation programs through the collection of royalties for the production of commodities and through the establishment of sectorial funds for technological development, and (iii) improvements in the legal framework for innovation, through the establishment of clear policies on industrial property, and the simplification of procedures for access to resources and the promotion of technology-based companies (OECD, 2016) Finally, from an analysis of the innovation systems in Latin America, it can be concluded that they have the following features in common: - Almost all of them have an overarching plan for science, technology, and innovation that identifies the challenges and goals, establishes programs, and defines the plans of action. - The programs tend to be similar in terms of priority areas (nanotechnology, biotechnology, alternative energies, health, and agricultural production). - Most countries today have a territorial perspective in their national innovation strategies. In the case of Chile, Colombia, and Peru, this perspective is closely related to the funding structures from taxes associated with the exploitation of natural resources, where territorial authorities have great influence on the allocation of resources for science, technology, and innovation. It is undeniable that the governments of the region have improved policies for innovation, especially in the last decade. Today, institutions are empowered, available budgets have been increased to finance programs for innovation, and regulatory frameworks support industrial property and encourage the creation of companies based on innovation. An adequate alignment of innovation policies with efforts for productive transformation will generate new development opportunities for these countries in the immediate future.

2.3. Previous Research An industrial property is the legal framework that protects the interests of innovators, giving them rights over their creations. This legislation is part of the wider body of law known as intellectual property (IP) (WIPO, 2016). These rights confer to the inventor(s) an exclusive monopoly on exploitation, after completing some formalities. Patents of invention intended to protect innovations of a technical nature fit in this category. In this sense, Savescu (2017) stated, “Industrial property rights are outlined in Article 27 of the Universal Declaration of Human Rights, which states that everyone should enjoy the protection of moral and material interests resulted from any scientific, literary or artistic production of which is the author” (p. 136). An efficient patent system contributes to the stimulation of innovation, because is a condition for economic growth, through the design and implementation of new products. On the 30th anniversary of the enactment of the Bayh–Dole Act in the US, Grimaldi, Kenney, Siegel, and Wright (2011), considered the rationale for academic entrepreneurship and described the evolving role of universities in the commercialization of research. They considered that the Act “was both an outcome of and response to the changing climate, by enhancing incentives for firms and universities to commercialize university-based technologies. Specifically, the legislation instituted a uniform patent policy across federal agencies and removed many restrictions on licensing” (p. 1046). Several European (Wright et al., 2008) and Asian (Kodama, 2008) countries adopted similar legislation (Grimaldi et al., 2011). In a similar vein, Drivas, Economidou, Karamanis, and Zank (2016) conducted a study to determine whether university patents are licensed over their enforceable lifecycle and at what point in time the licensing occurs. Based on an analysis of over 20,000 university patents granted between 1990 and 2000, they stated that since the Bayh–Dole Act was enacted, “most research universities have established their own Offices of Technology Transfer to undertake these commercialization and patent monetization activities. These academic technology transfer entities use a wide range of exclusive and non-exclusive licensing agreements to monetize the IP they own.” (p. 46). Using an external change in German Federal law Czarnitzki, Doherr, Hussinger, Schliessler, and Toole (2016) examined how entrepreneurial support and the ownership of patent rights influence academic entrepreneurship. They carried out a study on the impact of the Federal Government regulations in Germany since 2002, following the objectives of the US Bayh–Dole Act. The German reform called Knowledge Creates Markets generates subsidies, supports technology transfer, and assigns patent rights that result from university inventions from the individual level to the university level. An empirical analysis showed a strong relationship between patents and the creation of university companies. The evidence then suggests the existence of a high dependence on academic entrepreneurship regarding industrial protection granted by patents.

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Fisch, Hassel, Sandner, and Block (2015) conducted a research from an international perspective, examining patents at the top 300 universities worldwide from 32 different countries, indicating a predominance of US universities. They found that “18 of the top 25 universities are located in the US, with the Massachusetts Institute of Technology ranked as first” (p. 318). They concluded that the propensity to apply for patents is very high in universities in the US and Asia; comparatively, it is lower in European universities. Their international comparison shows profound differences between countries that equally affect licensing, the creation of university spin-offs and other technology transfer mechanisms. Additionally, Chang (2017) employed a two-mode network analysis method (using countries and fields of technology) to highlight the pivotal role of various countries in technology networks. He found that “the key technologies in the more recent UIC (University-Industry collaboration) technology network were largely in the fields of measurement and chemistry, which are characterized as basic sciences with cross-disciplinary traits” (p. 107). Chang concluded, “Patents directly reflect innovative output. Therefore, they can serve as an indicator for measuring national technology output. The country-technology network analysis results revealed that Japan and the United Stated played crucial roles in the UIC technology network” (Chang, 2017, p. 107). As demonstrated, the emergence of the Bayh–Dole Act in the U.S marked a milestone in the granting of university patents. This act generates an environment conducive to research and the commercialization of the results. The legal protection offered to the innovations encourages more university research and the transfer of the results to society. For Latin America countries, Sargent and Matthews (2014) examined the efforts of elite universities in Chile, Mexico, and Brazil to transfer faculty inventions to the marketplace. Based on statistical information about patents filing, they found, for this sample, that a “significant percentage of the new knowledge produced by researchers employed at universities has commercial value. Universities can take this knowledge, file for patents or other forms of IP protection, and then license the IP to existing or spinout companies” (p. 169). These authors recognized that there are clearly weaknesses in the Latin American NIS. However, “in cities such as Sao Paulo, Campinas, Santiago, and Monterrey, elite universities have established well designed systems to both create and commercialize knowledge in S&T fields. In general these initiatives have significant financial support from state and federal governments” (Sargent and Matthews, 2014, p. 184). They recommended exploring how legal barriers in Latin America affect the evolution of licensing efforts and university spin-offs, and analyzing the support received by the industry in the success or failure of university commercialization systems. For its part, the recent study prepared by Fischer, Schaeffer, Vonortas, & Queiroz (2018), empirically assesses the extent to which institutional openness in universities toward UIC linkages affect the

generation of knowledge-intensive spin-offs and academic patenting activity in the context of the State of Sao Paulo, Brazil. They concluded that in terms of science and technology policy, it is necessary to promote deeper linkages between companies and universities, saying “a stronger coordination between industrial policy, regulation of the competitive environment and the institutional framework of UIC is needed to build an environment conducive to the deep links we are discussing” (p. 280). In a similar way, a study by Guerrero, and Urbano (2017) tried to provide a better understanding of the influence of Triple Helix agents on the performance of entrepreneurial innovations in emerging economies. They analyzed the effects on innovation performance resulting from the links of enterprises with other enterprises, with universities, and with government. The study concluded that it is necessary in these countries to reinforce both the innovation system and the entrepreneurial ecosystem. On the other hand, Jefferson, Maida, Farkas, Alandete-Saez, and Bennett, (2017) focused on comparing the structure and operation of programs for IP management and technology transfer, and the mechanisms through entrepreneurship is fostered in five high-profile research institutions across the Americas. Their study, based on five universities in three countries found that there were “common goals and core activities, shared and implemented in similar ways among all five institutions. However, some divergent areas within the structure and operation of the technology transfer and entrepreneurial support programs […] represented significant differences between the five institutions” (p. 1307). Finally, in relation to the business models that can be derived from the Intellectual Property of the innovations, a good part of the universities have chosen to establish Technology Transfer Offices (TTO), which are responsible for the orientation of the mechanisms for the commercialization of patents. Some studies suggest (Siegel & Wright, 2015) that different types of business models applied by universities can be associated with the characteristics of their corporate governance and this directly influences the ability of TTOs to achieve their objectives. In addition, the longitudinal study conducted at 60 US universities by Baglieri, Baldi and Tucci (2018) found that “business models that leverage high-quality research (ie, catalyst) and startup creation (ie, orchestrator of local buzz) are associated with higher economic performance” (p. 51). Therefore, the way technology transfer is guided is key for value creation and rent capture, according to the university strategic goals.

3. Data Sources To achieve a better understanding of the dynamics of university patenting in Latin America, we carried out a comparative analysis based on the number of patents granted to universities from four Latin American countries: Chile, Colombia, Mexico, and Peru. These countries are the signatories of the Pacific Alliance (Alianza del Pacífico), a regional integration initiative to promote economic and social development in the region, and are where countries innovation activities have gained importance in recent years (OECD, 2014).

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The information for the present analysis comes from secondary sources through the consultation and systematization of public data that are available in electronic databases held by national agencies in the field of IP. These institutions are as follows: the Instituto Nacional de Propiedad Industrial de Chile (INAPI) (www.inapi.cl); the Superintendencia de Industria y Comercio de Colombia (SIC) (www. sic.gov.co); the Instituto Mexicano de Propiedad Industrial (IMPI) (www.impi.gob.mx); and the Instituto Nacional de Defensa de la Competencia y de la Protección de la Propiedad Intelectual de Peru (INDECOPI) (www.indecopi.gob.pe). For each of them, information was collected regarding invention patents granted to universities from these countries over the period of 2008 to 2017. Given that this work seeks to correlate the conditions of the innovation systems with the evolution of granted patents, we gathered information related to the total amount of resources invested in research and development as a percentage of GDP. For this purpose, we consulted the annual reports of the Global Innovation Index Database (www.globalinnovationindex.org). In addition, we consulted information from UNESCO’s Science, Technology and Innovation database to identify the capacity to mobilize resources for innovation activities in each one of the four selected countries. To control for the overall level of economic development in each country, we gathered information on the national GDP per capita at purchasing power parity (PPP) at constant prices for 2011 expressed in US dollars from the World Bank’s World Development Indicators database. Because one of the two central questions of this study aims to correlate the institutional capabilities of universities with obtaining patents, we collected information for a sample of 165 higher education institutions that have received patents in the period of the study. To have an indicator of the production of knowledge derived from research in each university, we found the number of scientific publications registered on two platforms, Scopus® and Web of Science® -WOS, between 2013 and 2017. To identify the size of each institution as a proxy of its capacity to mobilize resources over the same years, we compiled information about the number of students enrolled by consulting the Statistical Yearbooks in the Ministries of Higher Education of each country. Similarly, in order to control for the research institutional

capacity, we collected the number of researchers with a PhD degree for a subsample of the universities available at QS University Rankings database. This entire battery of information was used to organize the descriptive statistics and perform the econometric analyses, whose results are presented below.

4. Descriptive Statistics and the Results 4.1 Descriptive Statistics Table 1 presents some descriptive statistics on innovation outcomes in the four countries in this study: Chile, Colombia, Mexico, and Peru. Clearly, Mexico reports the highest average number of patents granted per year (69) from 2008 to 2017; this is more than twice the average for Chile and more than three times the average for Colombia. At the other extreme, Peru averages only nine patents per year. These results are somewhat correlated with the average expenditure of R&D as a percentage of GDP. Mexico reports the highest average value (0.52%), which is more than double the average for Colombia and Peru and 1.4 times that observed for Chile. Although GDP per capita in Chile is nearly double that of Colombia and Peru, the size of the Mexican economy and its R&D expenditure might entail some advantages in terms of scale economies that could explain its superior performance in terms of patents granted. The superior performance of Mexico over the other three countries deserves some qualification. In absolute terms, Mexico’s average budget in R&D is 7.6 times that reported in both Chile and Colombia and 34 times that of Peru. Although such a level of expenditure should entail some scale economies in terms of technological research and development for Mexico, it is in Chile where the expenditure in R&D is the most effective in materializing innovation patents between 2008 and 2017. Every registered patent in that country required an average investment of US $1.25 million dollars over this period, a figure that is just 43% the average for Mexico, 46% that of Colombia, and 33% that of Peru. However, variations in the required investments in R&D might be quite diverse across scientific fields or economic sectors and our data lacks the required details to disentangle the nature of such differences.

Table 1. Descriptive statistics on innovation trends in Chile, Colombia, Mexico, and Peru (average values for 2008–2017) Average values

(1)

(2)

(3)

(4)

Colombia

Chile

Mexico

Peru

Number of granted patents to Universities

20.7

29.3

68.6

8.7

(6.4)

(4.7)

(12.3)

(3.8)

GDP per capita at constant prices of 2011

11,977

21,088

16,412

10,905

(333)

(506)

(209)

(345)

0.24

0.37

0.52

0.09

(0.01)

R&D expenditure % of GDP Observations

Source: own estimates based on OECD (2014), Instituto Nacional de Propiedad Industrial de Chile (INAPI) (www.inapi.cl); the Superintendencia de Industria y Comercio de Colombia (SIC) (www.sic.gov.co); the Instituto Mexicano de Propiedad Industrial (IMPI) (www.impi.gob.mx); and the Instituto Nacional de Defensa de la Competencia y de la Protección de la Propiedad Intelectual de Peru (INDECOPI) (www.indecopi.gob.pe).

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In Table 2, we report some additional descriptive statistics based on a database of 165 universities from the four countries selected for this study. The averages displayed in Table 2 show the (arithmetic) annual average of the total number of granted patents, enrolment and publications reported by each university in the sample over the period 2013-2017. For instance, the table indicates that each one of the 39 Chilean universities included in the sample reported an average of 0.94 granted patents per year between 2013-2017. According to these statistics, Mexico not only reports the highest number of universities in the sample but also records the highest average annual number of granted patents per university over 2013 to 2017. The scale effects mentioned above in relation to Mexico could be explained at least in part, by the larger size of the universities in this country, with an

average enrolment of 28.4 thousand students per institution, which is 1.3 times higher that Peru and about 1.8 times higher than Chile and Colombia. The same figures reveal that both Chilean and Mexican universities report a similar average number of scientific publications per institution in Scopus (with 359 and 354 publications, respectively) for 2013 to 2017, while Colombian universities report about half of that average and Peruvian schools, one fourth. With the smallest visible sample, Peruvian universities were able to obtain an average of 0.76 granted patents per institution, not far from their Chilean counterparts (0.94) and above the average for the Colombian ones (0.60) although such differences are not statistically significant.

Table 2. Innovation statistics in universities from Chile, Colombia, Mexico, and Peru (average annual values per university for 2013–2017) VARIABLES patents enrollment Publications in Scopus Publications in WOS Observations

(1)

(2)

(3)

(4)

Chile

Colombia

Mexico

Peru

0.95

0.60

1.70

0.76

(0.19)

(0.10)

(0.33)

(0.25)

15,609

16,124

28,438

22,131

(697)

(589)

(2,012)

(1,585)

359

177

354

(41)

(21)

(51)

(12)

270

110

257

(31)

(13)

(37)

(7)

195

255

290

Source: own estimates based on OECD (2014), Instituto Nacional de Propiedad Industrial de Chile (INAPI) (www.inapi.cl); the Superintendencia de Industria y Comercio de Colombia (SIC) (www.sic.gov.co); the Instituto Mexicano de Propiedad Industrial (IMPI) (www.impi.gob.mx); the Instituto Nacional de Defensa de la Competencia y de la Protección de la Propiedad Intelectual de Peru (INDECOPI) (www.indecopi.gob.pe); Scopus® and, Web of Science® -WOS.

4.2. Econometric Results Table 3 displays the results of a preliminary econometric analysis of panel data for the four countries included in this study over the period 2008 to 2017. Given the limited number of (i × t = 10 × 4=) observations, only 40, for this stage of research, it is necessary to interpret these results with caution. In this analysis, the dependent variable is the natural log of the annual number of registered patents in each one of the four countries. As explanatory variables, we have the natural logarithm of GDP per person at PPP values (lnpibpc) and the overall expenditure of the country in R&D as a percentage of GDP (gerddelpib). Other variables, such as the number of researchers per million people in the country and FDI as a percentage of GDP were not statistically significant and, therefore, were excluded from the results presented here. The results in Table 3 display different estimation techniques: ordinary least squares (OLS), random effects (RE), fixed effects (FE), fi-

xed effects with robust standard errors (FE_robust), and FE with cluster-robust standard errors (FE_cluster_robust). According to the results of a Hausman type test for fixed versus random effects, there is strong evidence to reject the null hypothesis of non-systematic differences between coefficients from these two models. Therefore, we conclude that the appropriate estimator is the fixed effects model. 1 For this reason, we further elaborate on the fixed effects results and display alternative estimates of the standard errors for this model in columns (4) and (5) to control for either general serial autocorrelation or country (cluster) specific autocorrelation of the error term. 2 According to these results, we validate, under all five specifications, a positive relationship between a country’s GDP per capita and its number of registered patents annually. Such a relationship is statistically significant at the 1% level under the FE specification with uncorrected standard errors (see column 3 in Table 9); however, its precision diminishes to 10% significance with robust standard errors (in columns 4 and 5). Given the small

The test yields a Chi-squared statistic = 50.08 with an associated p-value = 0.000. We computed the Hausman test in Stata 13.0 with the Hausman command. The robust standard errors and the cluster-robust standard errors implemented in this application are a generalization of White’s (1980) procedure for the estimation of the robust covariance matrix with panel data. Chapters 8 and 9 on Cameron and Trivedi (2009) provide an overview of procedures to obtain robust standard errors, which are serially correlated in the context of panel data. 2

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number of observations for each combination of year and country, this loss of precision is not a surprising result. We also verify a positive relationship between public expenditure as a percentage

of GDP and the log of annual number of registered patents, with the same loss of precision when adjusted robust standard errors are applied.

Table 3. Regression coefficients from panel data models for the (log) number of granted university patents in Chile, Colombia, Mexico, and Peru (2008–2017) (1)

(2)

(3)

(4)

(5)

OLS

FE_robust

FE_cluster_robust

lnpibpc

0.7461

5.1505***

5.1505*

(0.5396)

(1.8470)

(2.0279)

7.0357***

12.5595***

12.5595*

(1.2849)

(4.1936)

(5.0382)

−6.6985

−51.7167***

−51.7167*

(5.0525)

(16.9170)

(20.5518)

Observations

R-squared

0.5757

0.6334

Number of countries

Variables

gerddelpib

Constant

Given the small number of observations in the models just discussed above, we implemented an alternative approach based on a sample of 165 universities in the four countries. We initially gathered data on the annual number of patents granted, the number of scientific publications in both Scopus and WOS and the enrolment size.3 Table 4 displays the results of panel data coefficients for i = 165 universities and t = 2013 to 2017. All variables in this analysis are

expressed in logs. The results on the top of the table three columns, numbered from 1 to 3, include all regressors for OLS, fixed effects and, random effects. The results in the middle part of the table, numbered from 4 to 6, only control the number of papers using data from WOS in addition to the enrollment size. Lastly, the results in columns 7 to 9 display the number of published papers in Scopus with the enrollment size. All standard errors are robust to serial autocorrelation within universities.

We are grateful for a comment from one of the referees in which it was suggested to include the number of published papers from WOS. It was very satisfying to see that the results obtained from this variable corroborate those derived from the number of papers published in Scopus.

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Table 4. Regression coefficients from panel data models between the annual number of granted university patents in Chile, Colombia, Mexico, and Peru, and their number of publications in Scopus and WOS, and the enrollment size, 2013–2017 Variables (All) ln_enrollment ln_publications ln_wos Constant

Observations R-squared Number of institution Variables (only WOS) ln_enrollment ln_wos Constant

Observations R-squared Number of institution

(1)

(2)

(3)

OLS

Fixed Effects

Random Effects

0.1257**

0.1407

0.1472***

(0.0505)

(0.1333)

(0.0527)

0.1383***

0.0100

0.0792***

(0.0376)

(0.0218)

(0.0276)

0.0181

0.0472*

0.0407**

(0.0178)

(0.0263)

(0.0193)

-1.6840***

-1.3627

-1.7109***

(0.5329)

(1.2789)

(0.5321) 825

825

0.2603

0.0152

165

(4)

(5)

(6)

OLS

Fixed Effects

Random Effects

0.1584***

0.1450

0.1745***

(0.0563)

(0.1367)

(0.0592)

0.1114***

0.0511**

0.0833***

(0.0216)

(0.0224)

(0.0171)

-1.7370***

-1.3733

-1.7814***

(0.5561)

(1.2907)

(0.5637)

825

0.2355

0.0151

165

(7)

(8)

(9)

OLS

Fixed Effects

Random Effects

ln_enrollment

0.1265**

0.1656

0.1528***

(0.0508)

(0.1327)

(0.0536)

ln_publications

0.1573***

0.0509**

0.1194***

(0.0311)

(0.0213)

(0.0242)

Constant

-1.7063***

-1.6021

-1.7875***

(0.5301)

(1.2756)

(0.5364) 825

Variables (only Scopus)

Observations R-squared Number of institution

825

0.2597

0.0110

165

The results in Table 4 point to a positive relationship between the size of the institution, measured by the (log) of total enrolment (including undergraduate and postgraduate students), although the significance of the coefficient for this variable is statistically

insignificant for this regressor under the fixed effects estimator in all cases. On average and ceteris paribus, the elasticity of the number granted patents with respect to the enrollment size ranges from 0,12 to 0,18.

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The same results point towards a positive and statistically significant relationship between the (log) number of registered patents by a university and the (log) number of scientific publications either in Scopus or in WOS. The elasticity coefficients tend to be less statistically significant, particularly in the case of the fixed effects estimator, when they are included jointly. When included separately, these two variables are statistically significant under all specifications with point estimates ranging from 0,5 to 0,15, on average and ceteris paribus. It is worth to mention that fixed effects estimates for this variable tend to be smaller and, comparatively, less significant than those from pooled OLS and random effects. According to the results from a robust Hausman test based on a method developed by Wooldridge (2002) for fixed versus random effects models with cluster-robust standard errors, we find sound evidence in favor of the fixed effects model when the variable for the number of published papers is obtained from Scopus.4 When we use the number of published papers in WOS, the same test does not allow to reject the null hypothesis of differences in coefficients and, therefore, the random effects model could be appropriate.5 The random effects model is attractive from an analytical point of view given the fact that this estimator allows to identify the effect of time-invariant regressors such as country effects and the public/private

nature of university institutions. Based on this intuition, we further advance the analysis to explore the possible effects of time-invariant regressors: (4-1=), three dummies for Chile, Mexico, and Peru (we leave Colombia as the base category) and a control for public/private universities. We also include the (log) number of enrolled students (in thousands) and the (log) number of published papers in WOS. These results are displayed in Table 5 under two specifications, OLS and RE, both with clustered-robust standard errors. According to these results, country-specific effects, as well as the private/ public nature of the universities, are not statistically significant.6 As such, these results also confirm that both the enrolment size and the scientific output (measured by the number of publications in WOS) are positively correlated to the annual number of registered patents by universities in the four selected countries of this study. All of this indicates that the relationship between the specific characteristics of an institution and its innovation activity at the university level is of a complex nature. A specific country environment does not emerge as a differentiating factor in determining the innovation activity of universities in Colombia, Chile, Mexico, and Peru, nor the private/public nature. This also suggests that other institutional, managerial or regional factors play a significant role in universities’ performance of technological innovation and, probably, justify a qualitative approach to further investigate the behavior of university innovation.

Table 5. Relationship between the annual (log) number of granted university patents in universities from Chile, Colombia, Mexico, and Peru and their (log) number of publications in Scopus and WOS, with dummies for country location and public/private origin, 2013–2017 Variables ln_publications ln_enrolment Chile Mexico Peru public_uni Constant Observations R-squared Number of institutions

(1) OLS_ROB 0.1762*** (0.0338) 0.1099** (0.0488) −0.0782 (0.0735) 0.0913

(2) RE_ROB 0.1272*** (0.0256) 0.1455*** (0.0525) −0.0424 (0.0733) 0.0900

(0.0866)

(0.0873)

0.1388 (0.1071) −0.0711 (0.0742) −1.6252*** (0.5164) 825 0.2726

0.0940 (0.1052) −0.0474 (0.0730) −1.7605*** (0.5325) 825 165

Own estimates based on OECD (2014), Instituto Nacional de Propiedad Industrial de Chile (INAPI) (www.inapi.cl); the Superintendencia de Industria y Comercio de Colombia (SIC) (www.sic.gov.co); the Instituto Mexicano de Propiedad Industrial (IMPI) (www.impi.gob.mx); the Instituto Nacional de Defensa de la Competencia y de la Protección de la Propiedad Intelectual de Peru (INDECOPI) (www.indecopi.gob.pe); Scopus® and, Web of Science® -WOS. Standard errors in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1. 4 The conventional Hausman test requires that the random effects estimator is efficient, an invalid assumption under cluster-robust standard errors. To overcome this difficulty, we implemented in Stata 13.0 a robust version of the Hausman test proposed in Cameron and Trivedi (2009: 261-262) based on a Wald test developed by Wooldridge (2002), which is asymptotically equivalent to the conventional test when the random effects model is fully efficient. The test yields an estimated F-statistic (with 2 and 820 degrees of freedom) =3.55 and an associated p-value= 0.0292; this suggest that differences in the coefficients from fixed and random effects models are systematic. The result of this test is conclusive at the 5% level (but not at the 1%) against the random effects model. 5 In this case, the estimated F-statistic (with 2 and 820 degrees of freedom) is 2,47 with a probability value of 0,0855, indicating that the null hypothesis of systematic differences in coefficients cannot be rejected by the data at hand. 6 We obtained a similar result when the log number of published papers in WOS is replaced with the number of papers included in Scopus. However, as explained in the previous footnote, when the log number of papers in Scopus is included in the specification, the random effects model is inappropriate and that is why we prefer not to include it in the table.

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Finally, we expand the analysis by including the (log) number of research staff with a PhD, an additional variable which was only available for a subsample of 93 university institutions in 2016 and 2017 in the QS Universities’ Database.7 With such data, we estimated five di-

fferent comparable models that are displayed in Table 6 where column 1 presents OLS estimates, columns 2 and 3 feature fixed and random effects, respectively, and column 5 shows random effects estimates with dummy variables.

Table 6. Relationship between the annual (log) number of granted university patents in universities from Chile, Colombia, Mexico, and Peru and their (log) number of publications (Scopus and WOS), (log) number of researchers with PhD degrees and with dummies for country location and public/private origin, 2013–2017 VARIABLES ln_enrollment

ln_publications

ln_wos

ln_staff_phd

(1)

(2)

(3)

(4)

OLS_ROB

FE_ROB

RE_ROB

0.2594**

-0.1708

0.2459**

0.2407**

(0.1012)

(0.9110)

(0.0971)

(0.1123)

0.2874***

0.1964

0.2761***

0.2560**

(0.0964)

(0.1489)

(0.0819)

(0.1029)

-0.0355

-0.0898

-0.0351

0.0068

(0.0783)

(0.1319)

(0.0648)

(0.0785)

0.2469*

0.1255

0.2620**

0.2332*

(0.1438)

(0.1825)

(0.1158)

(0.1201)

dummy_chl

-0.0621 (0.2187)

dummy_mx

-0.0591 (0.2019)

dummy_pe

0.1309 (0.2629)

public_uni

-0.1187 (0.1525) Constant

-4.4394***

0.9731

-4.3143***

-4.1615***

(1.0702)

(8.8633)

(1.0172)

(1.1319)

Observations

170

R-squared

0.3789

0.0076

Number of institution

According to these results, the fixed effects estimates (in column 2) perform poorly as all its coefficients are statistically insignificant and some are even negative. Such a result could be explained, at least in part, by the substantial reduction of the sample size. Conversely, results from the RE model corroborate the statistical significance of all continuous regressors, except in the case of the (log) number of papers published in WOS. The elasticity coefficients for the (log) enrollment size are statistically significant at the one percent level ranging from 0,241 to 0,251 while the (log) number of publications fluctuates between 0,256 and 0,287.

The same results suggest a positive relationship between the (log) number of granted patents and the (log) number of research staff with a PhD degree with an elasticity of 0,262 in the case of the random effects model, a result that is statistically significant at the five percent. With the inclusion of time-invariant regressors, this coefficient decreases in terms of both size and statistical significance at the 10 percent level. Again, the coefficients for the time-invariant regressors reflecting both the country-specific effects and the public/private nature of institutions are not statistically different from cero. To some extent, the limited number of observations for the number of PhD

For more information about this database, see: https://www.topuniversities.com -retrieved: 28 October 2019. We are also grateful for the suggestion from one of the referees to include the number of researchers with PhD as an additional regressor.

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entails limitations to present comparable evidence of its effects on the innovation performance in the universities of these four countries. Nonetheless, these results are indicative of the importance of having qualified research staff in the technological innovation performance of universities in the four selected countries of this study.

5. Final Remarks In the regressions at the country level, we verify a positive relationship between a countries’s GDP per capita and its annual number of registered patents. We also verify a positive association between public expenditure as a percentage of GDP and the (log of) the annual number of registered patents. This evidence suggests that the amount of resources invested in research and development at the national level is strongly associated with the performance of innovation systems, measured by the number of patents granted. This evidence is in line with the related literature in this field (see: Ho, Liu, Lu, & Hang, 2014; Hsu, Shen, Yuan, & Chou, 2015; Drivas, et al., 2016). Another related finding is that the level of economic development, measured by the GDP per capita, is an important determinant of the performance of the innovation systems at the national level (Rasmussen, Mosey, & Wright, 2014; Calcagnini, & Favaretto, 2016; Chang, 2017; Guerrero, & Urbano, 2017). Although there are limitations based on the number of observations reported in this four-country study, these results are coherent with the relevant literature in this field. Looking at university-specific data in the four countries for 2013– 2017, we corroborate a relationship of technology transfer from universities to society in terms of granted patents with both enrollment size and scientific publications. We find a positive statistically significant relationship between the (log) number of registered patents at the university level and the (log) number of scientific publications in Scopus. This result was confirmed using WOS as an alternative source of information for the number of scientific papers published annually at the university institutions level. Such a conclusion corroborates the findings in a number of related studies in this field (Hsu, & Ken, 2014; Thompson, Ziedonis, & Mowery, 2016). The same data suggests that larger universities are able to generate larger numbers of registered patents; this suggests the possibility that larger institutions are able to afford certain types of research infrastructure such as specialized laboratories and related facilities that endow them with higher innovation performance (Ho et al., 2014; Moutinho, Au-Yong-Oliveira, Coelho, & Manso, 2016; Cantu-Ortiz, Galeano, Mora-Castro, & Fangmeyer, 2017). The inclusion of the number of research staff with PhD as an additional regressor further confirms that universities with larger research teams tend to produce more granted patents. This line of analysis points to the presence of both scale economies and institutional capacities at play in the generation of technological innovation in the universities of the four countries reviewed in this study. Interestingly, the public/private nature of the university and their country location do not emerge as relevant factors in the determination of innovation performance. The findings reported so far point to the relevance of investing resources at the national level to achieve higher levels of innovation patents.

This coincides with Number Nine of the Sustainable Development Goals set by the United Nations, which seeks to increase the public and private research and development spending (UNDP, 2017). This conclusion is also valid at the university level, where the scientific output of published papers in peer-reviewed journals (measured by publications in both Scopus and WOS) appears to be a significant factor related to the production of scientific innovation. There is also a positive association between both the enrolment size and the number of PhD researchers of a university, on the one hand, and its innovation output, on the other, as measured by the number of registered patents. This again suggests that the size of an institution is a relevant factor in the generation of scientific innovations. Certainly, universities’ infrastructure in terms of laboratories, highly trained scientific human resources and related facilities can be more affordable with a large number of students. This could be a possible limitation for small universities where economies of scale do not allow expensive investments in R&D. A way out in this case could be an association among several smaller universities around common scientific innovation agendas in which the pooling of economic resources and scientific capabilities enable the economies of scale to reach higher levels of scientific innovation. Such association among universities could be highly relevant at the regional level for developing countries where infrastructure and scientific expertise are scarce resources. This present study could be further advanced in several ways. One limitation relates to the number of countries included in the analysis. The collection of data for four countries was certainly a challenging task but we believe that a similar effort with an increase in sample size would certainly enhance the capacity to generalize the conclusions, as well as the recommendations, presented here. Moreover, the measurement of a university’s variables related to its innovation capacity, such as the number of published papers and number of researchers in different areas of knowledge, would enable the elaboration of more refined conclusions for innovation policy in the higher education sector. A similar remark applies to other variables related to the production function of university innovation, such as the resources and infrastructure devoted to R&D. We were unable to differentiate between the numbers of scientific patents in different areas of knowledge in which the production function for each of them could be subject of a high degree of heterogeneity. For instance, the infrastructure requirements in diverse fields of knowledge could be highly differentiated; this is an unaccounted factor in this research that could be addressed in the future in discipline-specific studies of innovation for relevant sectors in emerging-market economies such as biotechnology, medicine, agricultural production, and alternative energies.

References Audretsch, D.B. (2014). From the entrepreneurial university to the university for the entrepreneurial society. Journal of Technology Transfer, 39, 313-321 Baglieri, D., Baldi, F., & Tucci, Ch. (2018). University technology transfer office business models: One size does not fit all. Technovation, 76–77, 51–63

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Bradley, S., Hayter, C. S., & Link, A. N. (2013). Models and methods of university technology transfer. Foundations and trends in Entrepreneurship, 9 (6), 571 - 650. Bush, V. (1945). Science, the Endless Frontier: a Report to the President. Washington, D.C.: U.S. Government Printing Office. Calcagnini, G. & Favaretto, I. (2016). Models of university technology transfer: analyses and policies. Journal of Technology Transfer, 41, 655 – 660 Cameron, C. & Trivedi, P. (2009) Microeconometrics using Stata. College Station, Texas, Stata Press. Cantu-Ortiz, F., Galeano, N., Mora-Castro, P., & Fangmeyer Jr., J. (2017). Spreading academic entrepreneurship: Made in Mexico. Business Horizons, 60, 541-550

Guerrero, M., & Urbano, D. (2017). The impact of Triple Helix agents on entrepreneurial innovations’ performance: An inside look at enterprises located in an emerging economy. Technological Forecasting & Social Change, 119, 294 – 309 Hayter, C., & Rooksby, J. (2016). A legal perspective on university technology transfer. Journal of Technology Transfer, 43(8), 270 - 289. doi:10.1007/s10961-015-9436-5 Ho, M., Liu, J., Lu, W., & Hang, C. (2014). A new perspective to explore the technology transfer efficiencies in US universities. Journal of Technology Transfer, 39, 247 – 275 Hsu, W., & Ken, Y. (2014). License income of technology commercialization: The case of U.S. universities. The International Journal of Organizational Innovation, Vol 6, Num. 3, 21-30

Chang, S. (2017). The technology networks and development trends of university-industry collaborative patents. Technological Forecasting & Social Change, 118, 107-113

Hsu, D., Shen, Y., Yuan, B., & Chou, C.J. (2015). Toward successful commercialization of university technology: Performance drivers of university technology transfer in Taiwan. Technological Forecasting & Social Change, 92, 25-39

Czarnitzki, D., Doherr, T., Hussinger, K., Schliessler, P., & Toole, A. (2016). Knowledge Creates Markets: The influence of entrepreneurial support and patent rights on academic entrepreneurship. European Economic Review, 86, 131 – 146

Jefferson, D.J., Maida, M., Farkas, A., Alandete-Saez, M., & Bennett, A.B. (2017). Technology transfer in the Americas: common and divergent practices among major research universities and public sector institutions. Journal of Technology Transfer, 42, 1307 – 1333

Drivas, K., Economidou, C., Karamanis, D., & Zank, A. (2016). Academic patents and technology transfer. Journal of Engineering and Technology Management, 40, 45-63

Kesan, J. P. (2015). Economic rationales for the Patent System in current context. George Mason Law Review, Vol 22:4, 897-924

Edquist, C., & Hommen, L. (1999). Systems of innovation: theory and policy for the demand side. Technology in Society, 21(1), 63-79. Fish, C., Hassel, T., Sandner, P., & Block, J. (2015). University patenting: a comparison of 300 leading universities worldwide. Journal of Technology Transfer, 40(2), 318 - 345. doi:10.1007/s10961014-9355-x Fischer, B., Schaeffer, P., Vonortas, N., & Queiroz, S. (2018). Quality comes first: university-industry collaboration as a source of academic entrepreneurship in a developing country. Journal of Technology Transfer, 43 (2), 263-284 Geuna, A., & Rossi, F. (2011). Changes to university IPR regulations in Europe and the impact on academic patenting. Research Policy, 40, 1068 - 1076.

Lundvall, B.A. (Ed.). (1992). National innovation systems: Towards a theory of innovation and interactive learning. London, UK: Pinter Moutinho, R., Au-Yong-Oliveira, M., Coelho, A., & Manso, J.P. (2016). Determinants of knowledge-based entrepreneurship: an exploratory approach. International Entrepreneurship Management Journal, 12, 171-197 Nelson, R. (Ed.). (1993). National innovation systems: A comparative analysis. New York, NY: Oxford University Press OECD (2014). Gross domestic spending on R&D (indicator). http:// dx.doi.org/10.1787/d8b068b4 OECD (2016). Start-Up Latin America 2016: building an innovative future. Paris, France: OECD Publishing

Godin, B. (2006). The Linear Model of Innovation: The Historical Construction of an Analytical Framework. Science, Technology & Human Values, 31, 639 - 667

Primi, A. (2014). Promoting Innovation in Latin America: What Countries Have Learned (and What They Have Not) in Designing and Implementing Innovation and Intellectual Property Polices (Doctoral Thesis, Maastricht University, Maastricht, The Netherlands).

Grimaldi, R., Kenney, M., Siegel, D., & Wright, M. (2011). 30 years after Bayh-Dole: Reassessing academic entrepreneurship. Research Policy, 40, 1045 – 1057

Rasmussen, E., Mosey, S., & Wright, M. (2014). The influence of university departments on the evolution of entrepreneurial competencies in spin-off ventures. Research Policy, 43, 92– 106

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Rodeiro, D., López, F., Otero, L., & Sandías, R. (2010). Factores determinantes de la creación de spin-offs universitarias. Revista Europea de Dirección y Economía de la Empresa, 19(1), 47 – 68.

Thompson, N.C., Ziedonis, A.A., & Mowery, D.C. (2016). University Licensing and the Flow of Scientific Knowledge. MIT Sloan School Working Paper, 5189-16, 1-33

Russo-Spena, T., Tregua, M., & Bifulco, F. (2017). Searching through the jungle of innovation conceptualizations: System, network and ecosystem perspectives. Journal of Service Theory and Practice, Vol. 27 (5), p.977-1005

UNDP (2017). The Sustainable Development Goals Report. New York, NY: United Nations Publications

Sargent, J., & Matthews, L. (2014). Latin American Universities and Technology Commercialization. Latin American Business Review, 15, 167–190 Savescu, D. (2017, January). The importance of Intellectual Property Protection in Technological Transfer. Some aspects. Fiability & Durability, No. 1, 135 – 141

White, H. (1980). A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity. Econometrica, 48, 817-838. WIPO (2016). Understanding Industrial Property. Genève, Switzerland: WIPO Publications. Wooldridge, J.M. (2002) Econometric Analysis of Cross Section and Panel Data. Cambridge, MA: MIT Press.

Siegel, D.S., & Wright, M. (2015). Academic Entrepreneurship: Time for a Rethink? British Journal of Management, 2, 582-595

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Transferencia Tecnológica en Universidades Chilenas: El Caso de la Universidad de Concepción Pablo Catalán1*, Eliana Sepúlveda1, Annabella Zapata1 Resumen: El presente estudio busca contribuir a la identificación de los determinantes de transferencia tecnológica en la Universidad de Concepción (UDEC), universidad chilena de larga tradición en investigación aplicada. Con tal fin, se aplicó un modelo probit a una muestra de 190 proyectos de Investigación y Desarrollo (I+D) ejecutados por la universidad entre los años 2005 y 2016. De forma de observar en mayor detalle posibles tendencias temporales, la muestra fue dividida en dos períodos: 2005-2013 y 2014-2016. Para el período 2005-2016, los resultados muestran que el financiamiento público de I+D y el número de proyectos de I+D en los cuales ha participado el investigador principal inciden positivamente en la probabilidad de transferencia tecnológica, mientras que el número de publicaciones científicas asociadas al investigador principal presenta un efecto negativo. En cuanto a los subperíodos propuestos, en ambos casos el financiamiento público de I+D mantiene su efecto positivo. Adicionalmente, el número de patentes en el primer período, y el financiamiento privado de I+D y el número de organizaciones sociales que participan del proyecto en el segundo período se suman como determinantes de efecto positivo. Asimismo, durante el último período, el número de organismos públicos que participan en el proyecto y el número de publicaciones científicas que posee el investigador principal presentan efectos negativos en la probabilidad de transferencia tecnológica. Keywords: transferencia tecnológica; universidades; investigación y desarrollo; capacidades de investigación Title: Technology Transfer in Chilean Universities: The Case of The University of Concepcion Abstract:The present study aims to identify the determinants of technology transfer in the University of Concepción (UDEC), a Chilean university with a long-applied research tradition. To address the research question we applied a probit model to a sample of 190 R&D projects executed by UDEC researchers between 2005 and 2016. To observe in greater detail timing trends we divided the sample into two periods: 2005-2013 and 20142016. During the period 2005-2016, we found that R&D public funding and the number of previous R&D projects per principal investigator have a positive effect on the likelihood of technology transfer, whereas the number of scientific publication per principal investigator shows a negative effect. In terms of the sub periods proposed, the positive effect of R&D public funding remains in both cases. In addition, the number of patents per principal investigator during the first period, and R&D private funding and the number of social organizations per project during the second period positively affect the likelihood of technology transfer. Likewise, during the last period, the number of public organizations that participate in each R&D project and the number of publications per principal investigator has a negative impact on technology transfer. Keywords: technology transfer; universities; research and development; research capacity Submitted: Jan 18th, 2019 / Approved: Aug 26th, 2019

Introducción Tradicionalmente, la justificación de las universidades se ha basado en la provisión de personas capacitadas, la preservación de la herencia cultural y el avance del conocimiento en las distintas ramas. Sin embargo, los temas económicos se han vuelto tan importantes como los culturales, lo cual ha provocado una ampliación del papel de las universidades en la sociedad frente a los distintos individuos, proyectando una nueva imagen de centros de innovación tecnológica y desarrollo económico regional (Peters, 1989). Así, las universidades han asumido un papel como inventor y agente de transferencia de conocimiento y tecnología (Etzkowitz, 2017). De este modo, en la actualidad se extiende la misión de las universidades a la solución de problemas y demandas de mediano y corto plazo del sector empresarial y de la sociedad en general, lo que ha exigido a las universidades

una reconceptualización y reordenamiento organizativo para realizar los procesos de producción, almacenamiento y transferencia del conocimiento (López, Mejía, & Schmal, 2006). En economías emergentes como Chile, las capacidades de Investigación y Desarrollo (I+D) tienden a estar altamente concentradas dentro de universidades e institutos de investigación pública (Fernández, Otero, Rodeiro, & Rodríguez, 2009), siendo necesarias políticas sistemáticas de transferencia tecnológica de modo de facilitar la fluidez de la relación entre el mundo académico y empresarial chileno. En Chile, se destina en promedio un 26% del presupuesto nacional para I+D al financiamiento de investigación básica, mientras que un 41% se destina a investigación aplicada, dejando un 33% para invertir en desarrollo experimental para actividades comerciales, con apenas un 0,4% del PIB nacional destinado a I+D.

(1) Departamento de Ingeniería Industrial, Universidad de Concepción, Chile. *Autor de correspondencia: pacatala@udec.cl

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La presente investigación pretende aportar al desarrollo de políticas públicas que permitan mejorar los indicadores de transferencia tecnológica universitaria en Chile, a través de la exploración de las dinámicas de transferencia tecnológica en una universidad chilena compleja y de orientación pública, tomando como caso particular la Universidad de Concepción. Con tal fin, se realizó una investigación de carácter cuantitativo para la cual se recopilaron datos asociados a los proyectos de I+D desarrollados dentro de la universidad, luego se aplicó un modelo de respuesta binaria probit con el fin de identificar qué factores determinan que un proyecto transfiera efectivamente su conocimiento al mercado. Adicionalmente, mediante el mismo modelo, se realizó un análisis por período de tiempo para identificar si los determinantes de la transferencia tecnológica en la Universidad de Concepción se ven condicionados por el cierre temporal del proyecto. Lo anterior se realizó en base a cinco factores principales: 1) Financiamiento de I+D, 2) Capacidades de Investigación, 3) Estructuras de Soporte Tecnológico, 4) Redes y 5) Estructura de Soporte Académico. Es así como el objetivo del estudio es responder la pregunta ¿Cuáles son los factores que rigen la transferencia tecnológica en una universidad chilena compleja y de orientación pública?, pues la identificación de estos factores puede constituir nuevo conocimiento útil para la formulación de protocolos institucionales y políticas públicas por parte de hacedores de política y autoridades académicas, pudiendo resultar ello en una mejora de la gestión de la ciencia, tecnología e innovación en las universidades chilenas. La presente investigación se estructura de la siguiente manera: luego de la introducción, se presenta el marco teórico de la investigación. Posteriormente, se describe la metodología utilizada en el desarrollo del estudio. A continuación, se muestran los resultados y análisis de la investigación y finalmente, se presentan las conclusiones de la investigación junto con las implicancias que los resultados obtenidos tienen para la formulación de futuras políticas públicas. Marco Teórico La transferencia tecnológica desde las universidades y centros de investigación hacia el sector privado ha cobrado cada vez más importancia dentro del contexto actual, de tal manera que los temas asociados a transferencia tecnológica se han convertido en una prioridad de las agendas políticas y académicas en distintos países del mundo (Rodriguez & Casani, 2007). Tradicionalmente, las universidades han cumplido dos funciones dentro de la sociedad: educar a los estudiantes y realizar investigaciones. En los últimos años, estas instituciones han debido incorporar una función adicional: promover la comercialización de los resultados derivados de su investigación (Fernández et al., 2009). La expansión generada en el rol de la universidad chilena, ha requerido grandes cambios en cuanto a políticas, distribución de recursos y cultura académica, derivando en una significativa reorganización del Sistema Nacional de Ciencia, Tecnología e Innovación (SNCTI). Este sistema está compuesto por entidades tanto públicas como privadas, cuya operación se orienta al desarrollo de investigación básica y aplicada, formación de capital humano, innovación y emprendimiento.

Para contribuir con el buen desempeño del SNCTI, la participación estatal se vuelve esencial. El gobierno chileno ha formulado políticas públicas de forma de apoyar la investigación e innovación que desarrollan las empresas y universidades chilenas a través de diferentes fondos e incentivos de financiamiento. Por un lado, ha invertido mediante las principales agencias del Sistema de Innovación Chileno, CONICYT y CORFO. Por otro lado, se han promovido programas de asociatividad ciencia–empresa mediante la ejecución de proyectos de I+D puntuales que funcionan bajo la modalidad de fondos concursables. En general, la transferencia tecnológica de las universidades en Chile se ve condicionada por la acción del Estado en cuanto a la generación de normas que regulen de buena manera la propiedad intelectual, la promoción de la protección de invenciones y la puesta a disposición de la comunidad, la predominancia del financiamiento público en las actividades asociadas con I+D y transferencia tecnológica, como también en el fomento de la innovación y adopción de nueva tecnologías por parte del sector productivo. El aumento en el fortalecimiento de los incentivos estatales hacia la innovación y el emprendimiento en los últimos años ha generado un incremento significativo del gasto en I+D por parte del gobierno chileno. Sin embargo, este aumento no ha sido suficiente para acercar a Chile a países con economías similares en términos de I+D (Lederman & Maloney, 2014). Transferencia Tecnológica Dentro de la literatura existente, el concepto de transferencia tecnológica se aprecia como un concepto bastante amplio. Según la Association of University Technology Managers (AUTM), para la mayoría de las universidades y centros de investigación, la transferencia tecnológica se define como el proceso de transferir de una organización a otra los descubrimientos científicos, con el fin de promover el desarrollo y la comercialización1. Existen diferentes canales y mecanismos para la transferencia tecnológica, asociados a las relaciones recíprocas entre universidad-industria-gobierno, a procesos de interacción universidad-empresa o a modelos de innovación abierta. Dentro de los canales de transferencia tecnológica universitaria destaca la literatura abierta, las patentes, derechos de autor, licencias, demostraciones personales, intercambios de personal, spin-offs (Bozeman, Rimes, & Youtie, 2015). También son mencionados mecanismos de transferencia tecnológica menos renombrados, como las importaciones de bienes de capital, la inversión extranjera directa y el licenciamiento de tecnologías, estándares de productos y procesos entre productor y proveedor, contratación de nuevos graduados y posgraduados, programas de capacitación, publicaciones científicas, conferencias y redes de interacción informales entre científicos y empresas (Zuñiga & Correa, 2013). Es importante destacar que el éxito de la transferencia tecnológica universitaria mediante los distintos métodos no termina cuando la tecnología es

Association of University Technology Managers, 2017. Recuperado de https://www.autm.net/autm-info/about-tech-transfer

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comercializada y entregada a la industria, sino que requiere la utilización de la tecnología en nuevos productos, procesos o cambios organizativos innovadores (Heinzl, Kor, Orange, & Kaufmann, 2013). Dentro de los medios existentes, se considera que las patentes son el mecanismo más común que facilita protección de la propiedad intelectual y la posterior transferencia tecnológica por parte de las universidades (Van Norman & Eisenkot, 2017). En tal sentido, el éxito de las patentes universitarias se ve sujeto a distintos factores. Azagra (2001) determinó que el financiamiento público es la variable más importante de cara a la generación de patentes, pero que existe sinergia con el financiamiento complementario de las empresas. También obtiene dentro de sus resultados una relación con el tamaño de los departamentos, afirmando que los departamentos universitarios de mayor tamaño, inmersos en la enseñanza de masas, muestran una menor propensión a patentar. En los últimos años, las empresas nacidas dentro de los centros de investigación y universidades, categorizadas como spin-offs, se han convertido en uno de los mecanismos más eficaces de transferencia de resultados de investigación y tecnología, contribuyendo al desarrollo empresarial y potenciando un crecimiento económico que incide sobre la mejora competitiva del sector productivo en su conjunto (Iglesias, Jambrino, & Peñafiel, 2012). El éxito de estas empresas es un tema de estudio recurrente en la literatura. Sternberg (2014) ilustra como el contexto que rodea a las spin-off, específicamente su entorno regional, afecta de manera significativa su éxito, mientras que, Beraza & Rodríguez (2010) recalcan en su estudio que la transferencia tecnológica mediante spin-offs se ve favorecida por determinantes propios del inventor como su espíritu emprendedor, su experiencia profesional previa y su influencia personal en la empresa. En el ámbito financiero, Iglesias et al., (2012) concluye que las fuentes de apoyo económico que favorecen al desarrollo de la actividad de I+D de las spin-offs universitarias no son solo las ayudas con recursos públicos, sino que también adquiere relevancia la participación externa de capital, la modalidad de proyectos en colaboración y el ineludible desafío de dirigirse al mercado internacional. La comercialización por los distintos medios de transferencia tecnológica también se ve determinada por la calidad de la investigación universitaria a nivel de departamentos, por la participación del investigador en el proceso y por la existencia de normas y procedimientos internos de apoyo. El tipo de universidad y su localización también son factores que afectan de manera relevante la comercialización, siendo las universidades privadas más propensas a responder a los cambios del entorno que las universidades estatales y, por ende, tienen mayores indicadores de transferencia tecnológica, mientras que las universidades localizadas en regiones, con concentración empresarial de alta tecnología, se ven potenciadas en su comercialización tecnológica (Perkmann et al., 2013; Thursby, Jensen, & Thursby, 2001). La existencia de redes es uno de los mayores determinantes de la comercialización, siendo una de las relaciones más estudiadas la relación universidad–industria.

Los canales y mecanismos tratados que favorecen la comercialización de know-how tecnológico desde la universidad al mercado, se ven apoyados por instituciones que facilitan el proceso (Bradley, Hayter, & Link, 2013). Estas instituciones pueden ser clasificadas en intermediarios internos, dentro de los cuales están las Oficinas de Transferencia y Licenciamiento (OTLs), Incubadoras y Parques Científicos, como también en intermediarios externos, dentro de los cuales se encuentran empresarios, firmas de capital de riesgo y agencias de desarrollo (Wright, Clarysse, Lockett, & Knockaert, 2008). Las OTLs tienen como objetivo comercializar resultados de investigación a través de patentes, licencias y empresas spin-off, facilitando la transferencia de conocimientos universitarios al mercado mediante distintas formas de propiedad intelectual que resulten de la investigación universitaria (Algieri, Aquino, & Succurro, 2013; Siegel, Waldman, Atwater, & Link, 2004). Para esto, se debe tener en consideración el potencial comercial de la invención, así como también, el interés existente en el sector público y privado (Siegel, Waldman, & Link, 2003). En general, la capacidad de éxito en el apoyo a la comercialización que tienen estas instituciones se ve afectada por factores internos, dentro de los cuales se encuentra la edad de la OTL y el número de trabajadores que se desempeñen dentro de la institución (Foltz, Barham, & Kim, 2000; Thursby et al., 2001). La presente investigación busca identificar cuáles son los factores, tanto internos como externos, que determinan las capacidades de transferencia tecnológica mediante los distintos mecanismos definidos a partir de una visión enfocada en la Universidad de Concepción en Chile.

Metodología Datos La base de la investigación fueron proyectos de Investigación y Desarrollo (I+D) ejecutados por la Universidad de Concepción entre los años 2005 y 2016. Se utilizó información contenida en la base de datos de la Vicerrectoría de Investigación y Desarrollo (VRID) de la universidad, de donde se obtuvieron datos asociados a los proyectos efectivamente realizados y cerrados durante los años de estudio y antecedentes básicos sobre los investigadores responsables del proyecto. A partir de la base de datos obtenida, se procedió a eliminar ciertos proyectos cuyo fin no era la transferencia tecnológica y otros de los cuales no se pudo obtener información precisa sobre los resultados finales del proyecto. Posterior a la corrección, se levantó información referente a campos adicionales necesarios para el testeo del modelo econométrico propuesto, obteniéndose una base de datos compuesta por un total de 190 proyectos de I+D. A continuación, se procede a la descripción detallada de todas las variables incorporadas en la base de datos utilizada, tanto variable dependiente como variables explicativas.

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Tabla 1 Variables modelo de respuesta binaria probit Macro Variable

Nombre de la variable en el modelo

Nombre de la variable en software

Descripción de la variable

Transferencia Tecnológica (dependiente)

Transferencia Tecnológica

transferencia tecnológica

Variable dicotómica. Toma el valor 1 si el proyecto de I+D ha realizado transferencia de conocimiento por al menos uno de los siguientes mecanismos; acuerdo de licencia, consultoría, contratos I+D, creación de empresas spin-off y/o solicitud de patente.; 0 en caso contrario

Financiamiento Público de I+D

financiamiento publico

Variable explicativa continua. Indica el monto de financiamiento (millones de pesos) que aportó la agencia pública asociada al proyecto de I+D analizado para su desarrollo.

Financiamiento Privado de I+D

financiamiento privado

Variable explicativa continua, que indica el monto de financiamiento pecuniario que aportaron las empresas participantes en el proyecto de I+D analizado.

Número de Académicos en Proyecto

total académicos

Variable explicativa discreta. Indica el número de académicos de la universidad que participaron en el proyecto de I+D analizado.

Número de Alumnos en Proyecto

total alumnos

Variable explicativa discreta. Indica el número de alumnos de pregrado y postgrado que participaron en el proyecto de I+D muestreado.

Número de Departamento Participantes en Proyecto

total departamentos

Variable explicativa discreta. Indica el número de departamentos académicos involucrados en el proyecto de I+D, a través de la participación de sus académicos.

Cargo Director del Proyecto

director centro i+d

Variable explicativa dicotómica. Toma el valor de 1 si el director del proyecto de I+D dirige un Centro o Laboratorio I+D dentro de la universidad, 0 en caso contrario.

Número de Patentes Solicitadas

n° patentes solicitadas

Variable explicativa discreta. Indica el número de patentes que tiene solicitadas el director del proyecto de I+D analizado.

Número de Patentes Adjudicadas

n° patentes adjudica- Variable explicativa discreta. Indica el número de patentes que tiene adjudicadas das el director del proyecto de I+D analizado.

Número de Proyectos de I+D

n° proyectos

Variable explicativa discreta. Indica el número de proyectos de I+D en que ha participado el académico.

Número de Publicaciones

n° publicaciones

Variable explicativa discreta. Indica el número de publicaciones ISI y Scielo que tiene el director del proyecto de I+D analizado durante el período de estudio.

Año de Termino Proyecto

año200_

Variable explicativa categórica. Indica el año en el cual termina el proyecto de I+D muestreado. La variable toma valores entre los años 2005 y 2016. Para introducir la variable al modelo, se transformó en n-1 variable dicotómicas, donde n es el número total de años incluidos en el estudio, correspondiente a 12. Se creó una variable dicotómica para cada año entre 2006 y 2016. Estas variables explicativas dicotómicas, toman el valor de 1 si el proyecto de I+D fue cerrado en el año correspondiente a la variable.

Número Personal de la OTL

n° trabajadores otl

Variable explicativa discreta. Indica el número de personas que trabajan en la OTL de la universidad en el año de inicio de cada proyecto. Si la OTL no existía en la fecha de inicio del proyecto, se consideró un valor de cero personas.

Número de Empresas Asociadas

n° empresas

Variable explicativa discreta. Indica el número de empresas asociadas que participaron en el proyecto de I+D analizado.

Número de Instituciones Extranjeras Asociadas

n° extranjeras

Variable explicativa discreta. Indica el número de instituciones extranjeras que participaron en el proyecto de I+D analizado.

Número de Organismos Públicos Asociados

n° org públicos

Variable explicativa discreta. Indica el número de instituciones públicas que participaron en el proyecto de I+D analizado.

Número de Organizaciones Sociales Asociadas

n° org sociales

Variable explicativa discreta. Indica el número de organizaciones sociales que participaron en el proyecto de I+D analizado.

tamaño facultad

Variable explicativa discreta. Indica el tamaño de la facultad a la que pertenece el director del proyecto de I+D analizado, medido por la cantidad académicos contratados a jornada completa de 44 horas que posee la facultad.

Financiamiento de I+D (explicativa)

Capacidades de Investigación (explicativa)

Estructura de Soporte Tecnológico (explicativa)

Redes (explicativa)

Estructura de Soporte AcaTamaño Facultad démica (explicativa) Fuente: Elaboración Propia

ISSN: 0718-2724. (http://jotmi.org) Journal of Technology Management & Innovation © Universidad Alberto Hurtado, Facultad de Economía y Negocios.

J. Technol. Manag. Innov. 2019. Volume 14, Issue 3

Regresión Probit

Resultados

En los modelos de regresión lineal se tiene una o múltiples variables explicativas (X) y una variable dependiente (Y) de naturaleza aleatoria continua. En algunas situaciones, la variable dependiente puede ser categórica, por lo cual puede adoptar un número limitado de categorías, a las cuales se asignan determinados valores. En tal situación, el método de mínimos cuadrados no es apropiado para obtener buenas aproximaciones (Menezes, Liska, Cirillo, & Vivanco, 2017). Una mejor estimación se obtiene por el modelo de regresión probit, ya que permite el uso de un modelo de regresión para calcular la probabilidad específica de ocurrencia de un evento definido como p(x) (Atkinson, 1985). Así, el modelo de regresión planteado se utiliza cuando se desea pronosticar la probabilidad de que ocurra o no un suceso determinado (Rojo, 2007), en donde los eventos toman valores de 1 o 0, en caso de ocurrir o no respectivamente. En la presente investigación, la variable dependiente será del tipo categórica binaria o dicotómica, en donde se obedecerá los siguientes valores:

Y (dicotómica)=

Proyecto ha transferido tecnología { 10 →→ Proyecto no ha transferido tecnología

El objetivo de aplicar la metodología probit fue determinar la relación existente entre las distintas variables de proyectos, definidas como explicativas y que afectan la transferencia tecnológica y el efectivo proceso de transferencia tecnológica de los proyectos, considerada como variable dependiente.

Se realizaron estimaciones en base al modelo probit para evaluar la influencia de las variables explicativas en la variable dependiente, considerando ocho modelos en los cuales las variables fueron introducidas gradualmente. Dado que los proyectos incluidos dentro de la muestra se desarrollaron durante distintos períodos de tiempo, resulta necesario evaluar si el año de término del proyecto influye de manera significativa en la determinación de la probabilidad de transferencia tecnológica. En este ámbito, se observa que existe una concentración, dentro de la muestra, de proyectos terminados en los últimos tres años. Así, el 63% de los proyectos muestreados finalizó su ejecución entre los años 2005-2013, mientras que un 37% lo hicieron dentro del período 2014-2016, mostrando un considerable aumento de los proyectos finalizados en la Universidad de Concepción en el último período. Dado lo anterior, resulta de interés evaluar los determinantes de la transferencia tecnológica en la universidad según el momento del tiempo en el cual se cerró el proyecto de I+D, con el fin de identificar si los determinantes de la transferencia tecnológica de los proyectos cambian según el período de tiempo en que se desarrollaron. Así, en primer lugar, se muestran los resultados obtenidos a partir del modelo aplicado a la muestra total de proyectos (ver Tablas 2 y 3).

ISSN: 0718-2724. (http://jotmi.org) Journal of Technology Management & Innovation © Universidad Alberto Hurtado, Facultad de Economía y Negocios.

J. Technol. Manag. Innov. 2019. Volume 14, Issue 3

Tabla 2 Análisis estadístico 2005-2016 Modelo 1 financiamiento publico

0.00587

Modelo 2 ***

[0.00127] tamaño facultad

-0.00771 [0.00315]

n° publicaciones n° proyectos

0.00541

Modelo 3 ***

[0.00121] *

-0.00775

total académicos total alumnos

***

[0.00123] *

[0.00314] -0.00575 [0.00275] 0.0228 [0.0101]

0.00536

Modelo 4

-0.00795

-0.00591 [0.00287] 0.0229 [0.0101] -0.0193 [0.0136] 0.00918 [0.0118]

***

0.00524

-0.00740

[0.00124] *

[0.00321] *

0.00541

Modelo 5

-0.00723

total departamentos director centro i+d

-0.00651 [0.00300] 0.0238 [0.0101] -0.029 [0.0181] 0.00827 [0.0119] 0.0467 [0.0760]

-0.00646 [0.00283] 0.0233 [0.0100] -0.0305 [0.0227] 0.00512 [0.0121] 0.0498 [0.0796]

***

[0.00127]

[0.00124]

[0.00121]

-0.00675

-0.00671

-0.00542

[0.00361] * *

-0.00577 [0.00272] 0.0211 [0.00983] -0.0319 [0.0235] 0.00629 [0.0121] 0.0514 [0.0826]

0.00500

[0.00362] * *

-0.00571 [0.00272] 0.0210 [0.00976] -0.0312 [0.0238] 0.00654 [0.0120] 0.0495 [0.0824]

-0.266

[0.239]

[0.238] 0.0596 [0.0783]

[0.240] 0.0471 [0.0810]

[0.242] 0.0483 [0.0801]

[0.252] 0.0277 [0.0784]

0 .0113

0.0087

0.00597

0.0233

[0.130]

[0.134]

[0.136]

0.274

0.272

0.292

[0.201]

[0.199]

[0.234]

-0.0895 [0.0876]

-0.0887 [0.0877] -0.0245 [0.0979]

-0.531 [0.822] 0.226 [0.811] 0.585 [0.863] -0.211 [0.804] -0.0526 [0.823] 0.268 [0.848] -0.484 [0.782] -0.0394 [0.772] -0.365 [0.770] 0.118

-0.521 [0.816] 0.144 [0.804] 0.459 [0.864] -0.412 [0.813] -0.0579 [0.820] 0.089 [0.846] -0.512 [0.782] -0.0671 [0.765] -0.459 [0.769] 0.0215

-0.515 [0.811] 0.185 [0.798] 0.539 [0.870] -0.404 [0.811] -0.0794 [0.822] 0.0649 [0.844] -0.478 [0.777] -0.0521 [0.763] -0.408 [0.765] 0.011

-0.531 [0.778] 0.271 [0.768] 0.509 [0.845] -0.424 [0.791] -0.0792 [0.794] 0.118 [0.823] -0.471 [0.748] -0.0196 [0.734] -0.38 [0.733] 0.0589

-0.511 [0.785] 0.256 [0.779] 0.511 [0.854] -0.432 [0.803] -0.0642 [0.805] 0.0215 [0.838] -0.491 [0.759] -0.0136 [0.745] -0.382 [0.745] 0.0679

-0.415 [0.738] 0.538 [0.737] 0.763 [0.836] -0.11 [0.764] 0.141 [0.769] 0.364 [0.797] -0.238 [0.713] 0.274 [0.685] -0.115 [0.691] 0.287

-0.411 [0.740] 0.536 [0.739] 0.766 [0.838] -0.105 [0.766] 0.142 [0.771] 0.389 [0.806] -0.191 [0.755] 0.357 [0.762] -0.0246 [0.788] 0.396

-0.077 [0.0879] -0.00172 [0.100] 0.0936 [0.0582] 0.0186 [0.101] 0.00594 [0.00447] -0.582 [0.740] 0.534 [0.739] 0.697 [0.847] -0.137 [0.763] -0.024 [0.767] 0.296 [0.810] -0.341 [0.753] 0.123 [0.759] -0.314 [0.789] 0.197

[0.815] 0.3400 [0.765] 190

[0.811] 0.2950 [0.774] 190

[0.808] 0.3460 [0.772] 190

[0.782] 0.3060 [0.752] 190

[0.792] 0.3010 [0.768] 190

[0.733] 0.0127 [0.748] 190

[0.859] 0.0127 [0.750] 190

[0.853] 0.0776 [0.762] 190

0.198

0.223

0.248

0.266

n° trabajadores otl n° patentes solicitadas n° patentes adjudicadas financiamiento privado

año 2010 año 2011 año 2012 año 2013 año 2014 año 2015 año 2016 Constante N pseudo R-sq

-0.00545 [0.00294] 0.0109 [0.0134] -0.0318 [0.0241] 0.00688 [0.0114] 0.066 [0.0829]

-0.137

n° org públicos

0.228

0.232

0.235

0.247

***

[0.00389] *

-0.148

n° org sociales

año 2009

0.00421

-0.192

n° extranjeras

año 2008

***

-0.183

n° empresas

año 2007

Modelo 8

0.00508

[0.00351] *

Modelo 7

***

[0.00130]

[0.00346] *

Modelo 6

Fuente: Elaboración propia. Nota: * p<0.05, ** p<0.01, *** p<0.001

ISSN: 0718-2724. (http://jotmi.org) Journal of Technology Management & Innovation © Universidad Alberto Hurtado, Facultad de Economía y Negocios.

J. Technol. Manag. Innov. 2019. Volume 14, Issue 3

Tabla 3 Contribución marginal variables explicativas del 8vo modelo 2005-2016 Variable

dy/dx

Std. Err.

P>z

[ 95%

C.I. ]

financiamiento publico

0.0013722

0.00035

3.92

0.000

0.000685

0.002059

137.4240

tamaño facultad

-0.0017641

0.00131

-1.34

0.179

-0.004337

0.000808

620.8420

n° publicaciones

-0.0017768

0.00094

-1.88

0.060

-0.003628

0.000075

43.70000

n° proyectos

0.0035447

0.00432

0.82

0.412

-0.004919

0.012008

187.3680

total académicos

-0.0103640

0.00786

-1.32

0.187

-0.025774

0.005046

498.4210

total alumnos

0.0022399

0.00374

0.60

0.550

-0.005096

0.009576

585.2630

total departamentos

0.0215001

0.02710

0.79

0.428

-0.031620

0.074620

245.7890

director centro i+d

-0.0869518

0.08240

-1.06

0.291

-0.248454

0.074551

0.473684

n° empresas

0.0090248

0.02576

0.35

0.726

-0.041464

0.059513

104.2110

n° extranjeras

0.0076025

0.04418

0.17

0.863

-0.078994

0.094199

0.689474

n° org sociales

0.0951771

0.07286

1.31

0.191

-0.047621

0.237975

0.378947

n° org publicas

-0.0250901

0.02875

-0.87

0.383

-0.081436

0.031256

0.636842

n° trabajadores otl

-0.0005613

0.03270

-0.02

0.986

-0.064643

0.063520

172.6320

n° patentes solicitadas

0.0304772

0.01936

1.57

0.115

-0.007475

0.068429

210.5260

n° patentes adjudicadas

0.0060682

0.03298

0.18

0.854

-0.058572

0.070708

112.6320

financiamiento privado

0.0019363

0.00140

1.39

0.165

-0.000798

0.004671

210.0660

año2007

-0.2126262

0.29106

-0.73

0.465

-0.783095

0.357843

0.078947

año2008

0.1474601

0.16342

0.90

0.367

-0.172839

0.467760

0.094737

año2009

0.1775499

0.15103

1.18

0.240

-0.118472

0.473572

0.057895

año2010

-0.0462818

0.26593

-0.17

0.862

-0.567490

0.474926

0.089474

año2011

-0.0078683

0.25327

-0.03

0.975

-0.504270

0.488533

0.084211

año2012

0.0883565

0.21562

0.41

0.682

-0.334248

0.510961

0.078947

año2013

-0.1188717

0.27917

-0.43

0.670

-0.666028

0.428285

0.131579

año2014

0.0388916

0.23204

0.17

0.867

-0.415896

0.493679

0.136842

año2015

-0.1083757

0.28689

-0.38

0.706

-0.670662

0.453911

0.168421

año2016

0.0604396

0.24502

0.25

0.805

-0.419800

0.540679

0.068421

Fuente: Elaboración propia.

A partir de las Tablas 2 y 3, no se observa un efecto estadísticamente significativo en la determinación de la probabilidad de transferencia tecnológica por parte de alguna de las variables asociadas al año de término del proyecto. Al mismo tiempo, se observa que el financiamiento público de I+D recibido por el proyecto, tiene un impacto positivo estadísticamente significativo en la probabilidad de transferencia tecnológica en todos los modelos evaluados. Lo anterior indica que, para cada año muestreado, el financiamiento que recibe el proyecto por parte del sector público determina en gran medida el éxito del proyecto en transferir tecnología. Por otro lado, se observa que el número de publicaciones que posee el director del proyecto, muestra un efecto negativo estadísticamente significativo sobre la probabilidad de transferencia tecnológica, mientras que, el número de proyectos de I+D en el cual ha participado el investigador tiene un impacto positivo estadísticamente significativo sobre dicha probabilidad en seis de ocho modelos evaluados.

Adicionalmente, se evaluaron los determinantes de la transferencia tecnológica separando la muestra obtenida en dos períodos. El primero, en base al período 2005-2013, comprende un lapsus de nueve años, en el cual se cerraron 119 proyectos de I+D pertenecientes a la muestra. El segundo, basado en el periodo 2014-2016, abarca un total de tres años, en el cual se cerraron 71 proyectos de I+D pertenecientes a la muestra. Se puede observar, que la cantidad de proyectos cerrados en la muestra aumenta de manera considerable dentro del último período, por lo cual la separación de los periodos permite obtener antecedentes actualizados acerca de los factores que explican la variable dependiente, como también identificar si han existido cambios en los determinantes de la transferencia tecnológica en la Universidad de Concepción dentro de los últimos años. En Tablas 4 y 5 se presentan los resultados obtenidos para los determinantes de la transferencia tecnológica. En relación al período comprendido entre los años 2005 y 2013, en Tablas 4 y 5 se presentan los resultados obtenidos para los determinantes de la transferencia tecnológica.

ISSN: 0718-2724. (http://jotmi.org) Journal of Technology Management & Innovation © Universidad Alberto Hurtado, Facultad de Economía y Negocios.

J. Technol. Manag. Innov. 2019. Volume 14, Issue 3

Tabla 4 Análisis estadístico 2005-2013 Modelo 1 financiamiento publico

0.00474

Modelo 2 ***

[0.00139] tamaño facultad

-0.00918

[0.00443] n° publicaciones n° proyectos

0.00438

Modelo 3 **

0.00432

Modelo 4 **

0.00434

Modelo 5 **

0.00432

Modelo 6 **

0.00402

Modelo 7 **

0.00386

Modelo 8 **

0.00232

[0.00138]

[0.00142]

[0.00147]

[0.00149]

[0.00141]

[0.00140]

[0.00139]

-0.00843

-0.00851

-0.00874

-0.00898

-0.008

-0.00802

-0.00758

[0.00452] -0.00286 [0.00291] 0.0184 [0.0124]

[0.00455] -0.00277 [0.00296] 0.019 [0.0123] -0.0158 [0.0328] 0.0024

[0.00479] -0.00282 [0.00307] 0.0194 [0.0124] -0.0385 [0.0442] 0.0053

[0.00493] -0.00263 [0.00292] 0.0188 [0.0126] -0.0324 [0.0480] 0.00161

[0.00506] -0.00244 [0.00271] 0.0172 [0.0113] -0.0696 [0.0519] -0.00633

[0.00506] -0.0024 [0.00273] 0.0173 [0.0114] -0.0672 [0.0524] -0.00545

[0.00589] 0.00279 [0.00324] -0.00369 [0.0178] -0.0738 [0.0564] 0.00419

[0.0174]

[0.0187]

[0.0180]

[0.0176]

[0.0175]

[0.0215]

0.0648 [0.106]

0.0646 [0.107]

0.114 [0.115]

0.103 [0.117]

0.124 [0.124]

0.081

0.0732

0.138

0.161

-0.207

[0.285]

[0.285] 0.0315 [0.0870] -0.0188 [0.148]

[0.293] 0.0573 [0.0848] 0.0526 [0.158] 0.201 [0.186] 0.355 [0.208]

[0.294] 0.0602 [0.0837] 0.0518 [0.157] 0.194 [0.186] 0.344 [0.210] -0.0429

[0.342] 0.0541 [0.0837] 0.127 [0.175] 0.0847 [0.205] 0.434 [0.232] -0.07

[0.118]

[0.128]

total académicos total alumnos total departamentos director centro i+d n° empresas n° extranjeras n° org sociales n° org publicas n° trabajadores otl n° patentes solicitadas

0.490 [0.151]

n° patentes adjudicadas

-0.318 [0.178]

financiamiento privado año2007 año2008 año2009 año2010 año2011 año2012 año2013 constante

0.00448

-0.51 [0.820] 0.205 [0.815] 0.536 [0.867] -0.211 [0.808] -0.0517 [0.830] 0.311 [0.840] -0.438 [0.777] 0.4900

-0.525 [0.834] 0.146 [0.831] 0.442 [0.887] -0.386 [0.839] 0.000872 [0.848] 0.209 [0.859] -0.463 [0.798] 0.3170

-0.514 [0.836] 0.186 [0.834] 0.52 [0.914] -0.378 [0.843] 0.0311 [0.865] 0.225 [0.866] -0.426 [0.803] 0.3470

-0.467 [0.839] 0.235 [0.844] 0.582 [0.908] -0.329 [0.850] 0.0724 [0.868] 0.299 [0.876] -0.402 [0.808] 0.2090

-0.447 [0.839] 0.217 [0.849] 0.575 [0.909] -0.338 [0.852] 0.077 [0.869] 0.237 [0.893] -0.43 [0.810] 0.2170

-0.335 [0.888] 0.434 [0.910] 1.018 [1.020] 0.0219 [0.911] 0.416 [0.932] 0.545 [0.936] -0.162 [0.863] -0.3020

-0.337 [0.895] 0.419 [0.917] 1.010 [1.024] 0.0171 [0.918] 0.407 [0.938] 0.57 [0.946] -0.0795 [0.909] -0.2790

[0.00532] -1.246 [0.943] 0.12 [0.975] 0.531 [1.072] -0.503 [0.990] -0.111 [0.984] 0.338 [0.988] -0.468 [0.948] -0.0289

[0.798]

[0.844]

[0.849]

[0.882]

[0.886]

[0.958]

[0.965]

[1.035]

119 0.227

119 0.229

119 0.231

119 0.232

119 0.259

119 0.350

N 119 pseudo R2 0.213

Fuente: Elaboración propia. Nota: * p<0.05, ** p<0.01, *** p<0.001.

ISSN: 0718-2724. (http://jotmi.org) Journal of Technology Management & Innovation © Universidad Alberto Hurtado, Facultad de Economía y Negocios.

J. Technol. Manag. Innov. 2019. Volume 14, Issue 3

Tabla 5 Contribución marginal variables explicativas del 8vo modelo 2005-2013 Variable

dy/dx

Std. Err.

P>z

[ 95%

C.I. ]

financiamiento publico

0.0006394

0.00035

1.82

0.069

-0.000049

0.001328

158.4040

tamaño facultad

-0.0020893

0.00166

-1.26

0.208

-0.005340

0.001161

547.3110

n° publicaciones

0.0007681

0.00091

0.84

0.400

-0.001021

0.002558

521.7650

n° proyectos

-0.0010177

0.00495

-0.21

0.837

-0.010718

0.008682

21.04200

total académicos

-0.0203296

0.01590

-1.28

0.201

-0.051501

0.010842

492.4370

total alumnos

0.0011552

0.00595

0.19

0.846

-0.010511

0.012822

571.4290

total departamentos

0.0341966

0.03461

0.99

0.323

-0.033646

0.102040

258.8240

director centro i+d

-0.0576184

0.09634

-0.60

0.550

-0.246450

0.131213

0.436975

n° empresas

0.0148995

0.02341

0.64

0.524

-0.030974

0.060773

109.2440

n° extranjeras

0.0350737

0.04871

0.72

0.472

-0.060398

0.130545

0.806723

n° org sociales

0.0233596

0.05631

0.41

0.678

-0.087013

0.133732

0.478992

n° org publicas

0.1196220

0.06320

1.89

0.058

-0.004253

0.243497

0.579832

n° trabajadores otl

-0.0192898

0.03561

-0.54

0.588

-0.089075

0.050496

0.537815

n° patentes solicitadas

0.1350896

0.04216

3.20

0.001

0.052454

0.217725

210.0840

n° patentes adjudicadas

-0.0875361

0.04591

-1.91

0.057

-0.177525

0.002453

112.6050

financiamiento privado

0.0012346

0.00139

0.89

0.373

-0.001482

0.003952

270.2920

año2007

-0.4358726

0.35041

-1.24

0.214

-1.122670

0.250921

0.126050

año2008

0.0317506

0.24941

0.13

0.899

-0.457093

0.520594

0.151261

año2009

0.1188336

0.18638

0.64

0.524

-0.246456

0.484123

0.092437

año2010

-0.1582492

0.34508

-0.46

0.647

-0.834600

0.518101

0.142857

año2011

-0.0315554

0.29007

-0.11

0.913

-0.600078

0.536967

0.134454

año2012

0.0829778

0.21264

0.39

0.696

-0.333782

0.499738

0.126050

año2013

-0.1431503

0.31616

-0.45

0.651

-0.762818

0.476517

0.210084

Fuente: Elaboración propia.

A partir de las Tablas 4 y 5 se observa que el financiamiento público de I+D impacta de forma positiva y estadísticamente significativa en siete de los ocho modelos evaluados en el periodo 2005-2013. Lo anterior indica que el aporte financiero recibido por parte del sector público se mantiene como un factor que incide en la determinación de la probabilidad de transferencia. Sin embargo, pierde significancia estadística como variable explicativa dentro del último modelo evaluado. Al mismo tiempo, se observa que su contribución marginal a la probabilidad de transferencia tecnológica disminuye en relación al modelo global condicionado por año, dejando de ser estadísticamen-

te significativa. Por otro lado, se obtiene que el número de patentes solicitadas que posee el director del proyecto impacta de forma positiva y estadísticamente significativa la probabilidad de transferencia tecnológica. En cuanto a su contribución marginal, se obtiene que por cada solicitud de patente extra que posee el director de proyecto la probabilidad de transferir tecnología aumenta en un 13,5%. En relación al segundo período, entre 2014 y 2016, los resultados obtenidos para los determinantes de la transferencia tecnológica se ilustran en Tablas 6 y 7.

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J. Technol. Manag. Innov. 2019. Volume 14, Issue 3

Tabla 6 Análisis estadístico 2014-2016 Modelo 1 financiamiento publico

0.00915 [0.00272]

tamaño facultad

Modelo 2 ***

0.00814 [0.00255]

Modelo 3 **

0.00860

Modelo 4 **

[0.00263]

0.00858 [0.00277]

Modelo 5 **

0.00659 [0.00292]

Modelo 6 *

0.00692

Modelo 7 **

[0.00251]

0.00765

Modelo 8 **

[0.00272]

0.00909 [0.00289]

-0.00702

-0.00717

-0.00729

-0.00431

-0.00196

-0.00367

-0.00415

-0.000683

[0.00478]

[0.00466]

[0.00476]

[0.00538]

[0.00565]

[0.00664]

[0.00729]

[0.00805]

n° publicaciones

-0.0142

n° proyectos

** -0.0162

** -0.0189

** -0.0182*

-0.0189

-0.0261

-0.0253

[0.00508]

[0.00521]

[0.00732]

[0.00750]

[0.00929]

[0.0122]

[0.00932]

0.0232

0.0200

0.0378

0.0383

0.042

0.0584

0.0278

[0.0172]

[0.0167]

[0.0233]

[0.0214]

[0.0283]

[0.0330]

[0.0328]

-0.0397

-0.0448

-0.02

-0.0548

-0.0341

[0.0227]

[0.0387]

[0.0519]

[0.0599]

[0.0615]

total académicos

-0.0323 [0.0144]

total alumnos

0.0235

0.0173

0.0166

0.0252

0.0234

0.0208

[0.0133]

[0.0132]

[0.0136]

[0.0137]

[0.0140]

[0.0144]

total departamentos director centro i+d

0.0641

0.0571

0.104

0.131

0.125

[0.134]

[0.144]

[0.134]

[0.145]

[0.157]

-0.684

-0.761

-0.732

-0.971

-1.034

[0.421]

[0.414]

[0.469]

[0.539]

[0.574]

0.247

0.162

0.154

-0.14

[0.188]

[0.196]

[0.205]

[0.201]

0.0247

-0.497

-0.295

-0.463

[0.291]

[0.408]

[0.421]

n° empresas n° extranjeras n° org sociales

1.849

n° org publicas

1.967

[0.654]

[0.755]

-0.772*

-0.973

[0.381]

[0.463]

n° trabajadores otl

[0.461] **

2.437

[0.813] *

-0.862

[0.436]

0.271

0.367

[0.226]

[0.228]

n° patentes solicitadas

0.181 [0.103]

n° patentes adjudicadas

0.100

financiamiento privado

[0.164] 0.0186 [0.00795]

año2014

año2015 constante N pseudo R2

-0.213

-0.0981

-0.0787

-0.186

-0.17

0.184

0.62

0.562

[0.465]

[0.460]

[0.472]

[0.515]

[0.505]

[0.587]

[0.678]

[0.686]

-0.633

-0.533

-0.446

-0.549

-0.525

-0.506

-0.238

-0.162

[0.493] 0.275 [0.546] 71 0.187

[0.512] 0.342 [0.554] 71 0.245

[0.522] 0.431 [0.566] 71 0.274

[0.524] 0.399 [0.613] 71 0.301

[0.521] 0.205 [0.653] 71 0.320

[0.597] 0.364 [0.873] 71 0.422

[0.635] -0.714 [1.141] 71 0.439

[0.635] -1.333 [1.295] 71 0.469

Fuente: Elaboración propia. Nota: * p<0.05, ** p<0.01, *** p<0.001

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J. Technol. Manag. Innov. 2019. Volume 14, Issue 3

Tabla 7 Contribución marginal variables explicativas del 8vo modelo 2014-2016 Variable

dy/dx

Std. Err.

P>z

[ 95%

C.I. ]

financiamiento publico

0.0030111

0.00088

3.44

0.001

0.001296

0.004726

102.2600

tamaño facultad

-0.0002261

0.00266

-0.08

0.932

-0.005446

0.004994

744.0850

n° publicaciones

-0.0083726

0.00279

-3.00

0.003

-0.013841

-0.002904

29.49300

n° proyectos

0.0092058

0.01059

0.87

0.385

-0.011549

0.029961

148.7320

total académicos

-0.0112897

0.02009

-0.56

0.574

-0.050658

0.028079

508.4510

total alumnos

0.0068833

0.00498

1.38

0.167

-0.002882

0.016649

608.4510

total departamentos

0.0413908

0.05190

0.80

0.425

-0.060322

0.143104

223.9440

director centro i+d

-0.3262934

0.16856

-1.94

0.053

-0.656661

0.004074

0.535211

n° empresas

-0.0464240

0.06457

-0.72

0.472

-0.172977

0.080129

0.957746

n° extranjeras

-0.1533355

0.15723

-0.98

0.329

-0.461504

0.154833

0.492958

n° org sociales

0.8071220

0.27041

2.98

0.003

0.277136

133.7110

0.211268

n° org publicas

-0.2856554

0.15963

-1.79

0.074

-0.598528

0.027217

0.732394

n° trabajadores otl

0.1215766

0.07448

1.63

0.103

-0.024401

0.267554

371.8310

n° patentes solicitadas

0.0600045

0.03298

1.82

0.069

-0.004629

0.124638

211.2680

n° patentes adjudicadas

0.0332367

0.05366

0.62

0.536

-0.071927

0.138400

112.6760

financiamiento privado

0.0061668

0.00262

2.35

0.019

0.001023

0.011310

109.1230

año2014

0.1760020

0.20195

0.87

0.383

-0.219816

0.571820

0.366197

año2015

-0.0539835

0.21179

-0.25

0.799

-0.469081

0.361114

0.450704

Fuente: Elaboración propia.

De las Tablas 6 y 7 se obtiene que el financiamiento público de I+D recibido por el proyecto sigue siendo una variable que impacta de forma positiva y estadísticamente significativa la probabilidad de transferencia tecnológica en la Universidad de Concepción en el periodo 2014-2016. Se observa que la contribución marginal de la variable sobre la probabilidad de transferencia tecnológica, aumenta en comparación al modelo global condicionado por años. También se obtiene que el número de publicaciones que posee el director del proyecto de I+D, tiene un impacto negativo estadísticamente significativo sobre la probabilidad de transferencia tecnológica, dentro de todos los modelos estimados. La contribución marginal del número de publicaciones aumentó, indicando que, dentro del último período, la probabilidad de transferir tecnología disminuye un 0,84% por cada publicación científica extra que posee el director del proyecto de I+D. Por otro lado, se observa que otras variables presentan significancia estadística en la determinación de la transferencia tecnológica, entre ellas, el monto de financiamiento privado recibido por el proyecto de I+D ha tomado relevancia en la determinación de la transferencia tecnológica dentro de los últimos tres años, mostrando un impacto positivo y estadísticamente significativo dentro del último modelo estimado. La contribución marginal del financiamiento recibido por parte de

empresas indica que por cada millón de pesos que recibe el proyecto por parte del sector privado, la probabilidad de transferir tecnología aumenta en 0,62%. También es posible observar que el número de organizaciones sociales que participa dentro del proyecto de I+D ha tenido un impacto positivo y estadísticamente significativo en la transferencia tecnológica, dentro de todos los modelos evaluados. La contribución marginal de esta variable indica que por cada organización social que participa dentro del proyecto, la probabilidad de transferir tecnología aumenta en un 80,7%. Finalmente, se observa que el número de organizaciones públicas que participan en el proyecto de I+D, determina de forma negativa y estadísticamente significativa la probabilidad de transferencia tecnológica. Su contribución marginal indica que por cada organismo público que participa en el proyecto, la probabilidad de transferir tecnología disminuye en 28,6%.

Discusión El financiamiento público de I+D ha sido un determinante importante para la transferencia tecnológica de la Universidad de Concepción a lo largo de los años, cobrando una papel aún más importante en el último período. Tal financiamiento permite generar nuevas

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instancias de desarrollo que pudieran tener un impacto significativo en el mercado. La investigación generada en las universidades tiende a ser embrionaria, por lo que es necesario que los resultados obtenidos sean validados técnica y comercialmente en diversas etapas y escalas lo que requiere una gran inversión. Dentro de la literatura, Foltz et al., (2000) y Azagra (2001) afirman que el financiamiento público es un factor importante para fomentar la transferencia de tecnologías desarrolladas en universidades, teniendo un impacto positivo y significativo en cuanto a la transferencia de conocimiento. En relación a los resultados obtenidos para el número de publicaciones del director del proyecto, se observa que cumplen un papel importante en la determinación de la probabilidad de transferencia tecnológica en la Universidad de Concepción, cobrando un efecto negativo estadísticamente significativo dentro de los últimos tres años. Las publicaciones científicas generalmente se asocian a fines de investigación básica. Las capacidades de los investigadores dentro de la universidad se miden principalmente por lo académico, teniendo a las publicaciones como principal indicador de productividad científica, mientras que su desempeño en temas de transferencia tecnológica no se considera de igual forma. Al mismo tiempo, las evaluaciones de las actividades de los grupos de investigación se respaldan en indicadores que se basan en medidas bibliométricas de volumen (número de publicaciones) y calidad (número de citas o factor de impacto), por lo cual las publicaciones científicas son altamente valoradas2. En este ámbito, la gradual mejora presentada por Chile en cuanto a volumen y calidad de sus publicaciones, no se condice aún con una mayor transferencia de tecnologías desarrolladas en el país hacia el sector productivo nacional. Por otro lado, se observa que, en los últimos tres años, el financiamiento privado recibido por los proyectos ha cobrado significancia en la determinación de la transferencia tecnológica. El financiamiento privado que reciben los proyectos de la universidad por parte de empresas es bajo, debido a que el sector privado en Chile realiza poca inversión en I+D por el riesgo que esto conlleva. A pesar de lo anterior, los resultados evidencian que, en los últimos años, tal financiamiento se ha vuelto un determinante de la probabilidad de transferencia tecnológica en la Universidad de Concepción. Al igual que el financiamiento público, el financiamiento privado aporta al nivel de inversión requerido para la validación técnica y comercial de las invenciones universitarias. Por otro lado, la cooperación entre la academia y la industria resulta ser clave para que las invenciones desarrolladas a nivel universitario tengan una orientación directa a los problemas y demandas del sector privado. Fernández et al., (2009), asegura que el financiamiento privado ha estado siempre más orientado a la obtención de resultados que puedan comercializarse en el corto y mediano plazo, contribuyendo a la generación de procesos de transferencia tecnológica efectivos. Dentro del último período, también se observa una influencia relevante en la probabilidad de transferencia tecnológica por parte del 2 3

número de organismos públicos que participan en el proyecto, afectando de forma negativa la probabilidad de transferir tecnología. En Chile, varios de los programas públicos que financian investigación universitaria buscan obtener resultados de aplicación, innovación, emprendimiento y comercialización. Sin embargo, es probable que los resultados obtenidos dentro del período se encuentren condicionados por el número de proyectos muestreados pertenecientes a la Facultad de Educación de la universidad, los que se realizaron en colaboración con diversos organismos públicos y asociados chilenos del área. En general, el concepto de transferencia tecnológica no se relaciona de manera adecuada con el ámbito de la educación, debido a que la principal finalidad al apoyar proyectos en el área de la educación se relaciona con innovar en metodologías de enseñanza o procesos que permitan tener un impacto en la sociedad, lo cual no está incluido dentro de las metodologías de transferencia tecnológica definidas en la presente investigación. El interés de las organizaciones públicas por respaldar proyectos de I+D se basa en los potenciales beneficios de desarrollar resultados de la investigación científica que sirvan de respaldo al mercado y a la sociedad3. También se observa en los últimos años, un efecto positivo y significativo del número de organizaciones sociales que participan dentro del proyecto. Las organizaciones sociales caracterizan a una sociedad en un determinado momento del tiempo. Dentro de los modelos de transferencia tecnológica universitaria, Carayannis & Campbell (2012) destacan la importancia de la incorporación de la sociedad a los procesos de transferencia tecnológica, indicando que las personas y la sociedad aportan al proceso en ámbitos tan importantes como la cultura y la aceptación de las innovaciones. La sociedad es la encargada de legitimar las innovaciones generadas, por lo cual un buen respaldo de organizaciones sociales al proyecto de I+D, permite obtener resultados validados por un entorno social relacionado directamente con la invención. Adicionalmente se obtuvo un efecto positivo estadísticamente significativo asociado al número de patentes solicitadas por el director del proyecto de I+D, dentro del período evaluado entre 2005 y 2013. El número de patentes solicitadas se asocia a procesos de transferencia de conocimiento iniciados en el pasado, por lo cual actúa como indicador de las capacidades de investigación y experiencia del propio director de proyecto en el ámbito de transferencia tecnológica. En el mismo contexto, en relación al número de proyectos en los que ha participado el investigador previo a la realización del proyecto muestreado, se obtuvo un impacto positivo estadísticamente significativo en seis de ocho modelos considerando el total de la muestra, lo cual advierte la existencia de una relación con la variable dependiente que resulta interesante de explorar. Así, el número de proyectos y el número de patentes previas se asocian directamente con la experiencia del investigador, su trabajo previo y dedicación al área de la investigación. En la literatura, Kochenkova, Grimaldi & Munari (2016), destaca la importancia que poseen los ámbitos de comunicación y de conocimiento en el proceso de transferencia tecnológica, indicando

Estudio cualitativo sobre el estado actual de la transferencia tecnológica en Chile. Informe Final. Ministerio de Economía, Fomento y Turismo, 2016. Estudio cualitativo sobre el estado actual de la transferencia tecnológica en Chile. Informe Final. Ministerio de Economía, Fomento y Turismo, 2016.

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que, para poder transferir los resultados de proyectos realizados, se requiere tener experiencia en trabajo con empresas, con académicos de diversas áreas y con estudiantes, lo cual se refuerza con el trabajo reiterado, aumentando la confianza y la credibilidad del mismo investigador.

Conclusiones La presente investigación analizó los determinantes de transferencia tecnológica en universidades chilenas, específicamente el caso de la Universidad de Concepción. Para esto, se utilizó una base de datos construida a partir de la recolección de antecedentes de 190 proyectos de I+D que ha desarrollado la Universidad de Concepción desde el año 2005 hasta el año 2016. Para poder dar respuesta a la pregunta de investigación planteada, se realizó un análisis cuantitativo a través de un modelo de regresión probit. Con esto se analizó la relación existente entre el proceso de transferencia tecnológica y las variables que afectan este proceso, las que se definieron en cinco conjuntos: Financiamiento de I+D, Capacidad de Investigación, Estructuras de Soporte Tecnológico, Redes y Estructura de Soporte Académica. En primer lugar, se calcularon los determinantes de transferencia tecnológica en base a un modelo global con el total de la muestra recopilada de proyectos de I+D, para posteriormente separar el modelo en dos sub-períodos condicionados por el año de término del proyecto, el primero entre 2005-2013 y el segundo entre 2014-2016, con el fin de identificar si los determinantes de la transferencia tecnológica en la Universidad de Concepción se ven condicionados por la localización temporal del proyecto. Los resultados del modelo global indicaron que el financiamiento público de I+D incrementa la probabilidad que un proyecto de I+D de la universidad pueda generar procesos de transferencia tecnológica a través de consultorías, contratos de I+D, licenciamientos, creación de spin-offs o generación de proceso de patentamiento. En relación al período 2005-2013, se obtuvo como determinantes de la transferencia tecnológica el financiamiento público de I+D y el número de patentes solicitadas, ambas variables aumentando la probabilidad de transferencia tecnológica. Para el período 2014 a 2016, se identifican como determinantes de la transferencia tecnológica el financiamiento público de I+D, el financiamiento privado de I+D, el número de organizaciones sociales, el número de organismos públicos y el número de publicaciones del investigador. Las tres primeras variables aumentan la probabilidad de transferencia tecnológica, mientras que las últimas dos variables impactan negativamente la variable estudiada. Así, se considera que es la variable más adecuada para explicar, de forma transversal en el tiempo, la probabilidad de transferencia tecnológica en la Universidad de Concepción. Existen alternativas diversas para acceder a recursos públicos que cofinancian los gastos en tecnología e innovación. Sin embargo, no solo la falta de recursos públicos limita el crecimiento del gasto en I+D por parte del sector privado chileno. Existen barreras adicionales, propias de ámbitos regulatorios y administrativos, en las cuales el Estado cumple un rol fundamental. Para superar tales limitaciones, desde el sector público se debiese perseverar e intensificar las actuales

políticas destinadas a fomentar la competencia, incentivar la creación de empresas y reducir las barreras administrativas existentes al día de hoy. El Estado también debe fomentar en mayor medida las políticas de incentivos tributarios existentes para incentivar la instalación de grandes empresas tecnológicas internacionales en el país. Por otro lado, el networking y la creación de redes también requieren un impulso por parte del sector público, generando conexiones entre mentores, mercados e inversionistas en el área de la ciencia, tecnología e innovación. Así, se sugiere generar mayores y mejores condiciones para fortalecer la I+D en el sector privado, facilitar la iniciación y cierre de empresas, desburocratizar y eliminar regulaciones excesivas, cambiar la percepción negativa del fracaso en emprendimientos previos y eliminar las barreras de entrada en los mercados, lo cual va de la mano con el fortalecimiento de la competencia y la generación de la necesidad de innovar y mejorar la tecnología existente para mantenerse competitivo dentro del mercado. Adicionalmente, se debiese explorar incrementar los subsidios a la I+D y los fondos destinados al fortalecimiento de la base científico-tecnológica del país, la subvención de procesos de comercialización, subsidios para apoyar la creación y crecimiento de empresas innovadoras en etapas tempranas y, aumentar el financiamiento para establecer oficinas de transferencia tecnológica, incubadoras de empresas tecnológicas y parques científicos. Las barreras administrativas propias del Estado, pueden tener estrecha relación con el impacto negativo obtenido por parte del número de organizaciones públicas sobre la transferencia tecnológica en la Universidad de Concepción. Así, los organismos públicos que se involucran en áreas de investigación universitaria pudiesen flexibilizar sus requisitos exigidos a los proyectos de I+D que apoyan, centrando su aporte en un impacto positivo para la industria en colaboración con la sociedad. Por otro lado, el número de proyectos en los cuales ha participado el investigador que desarrolla un proyecto de I+D dentro de la Universidad de Concepción resultó ser determinante para la probabilidad de transferir tecnología al mercado dentro de seis de ocho modelos evaluados en el periodo 2005-2016. De manera similar, las solicitudes de patente del investigador principal del proyecto resultaron ser determinantes en la transferencia tecnológica durante el período 20052013. Ambas variables se relacionan directamente con la experiencia que posee el investigador en el ámbito de participación en proyectos de I+D e iniciación de procesos de transferencia tecnológica. Dentro de cualquier universidad, la participación en proyectos de I+D y la realización de investigaciones que generen procesos de patentamiento, le permite al investigador desarrollar una experiencia valiosa en el ámbito de la transferencia tecnológica. Kochenkova et al., (2016) destaca, entre las principales ineficiencias que impiden alcanzar el óptimo social en materia de transferencia e innovación tecnológica, la falta de comunicación y de conocimiento. En general, existe una brecha de conocimiento entre los investigadores, emprendedores y el personal de las OTLs, lo cual afecta las posibilidades de transferencia tecnológica de los proyectos asociados. Mientras que algunos investigadores poseen habilidades y experiencia que aumentan sus posibilidades de transferencia tecnológica, otros carecen de ellas,

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generando disminución de estas probabilidades. Por lo anterior, se deben potenciar programas internos a las instituciones y políticas públicas a nivel nacional, que creen programas de entrenamiento y desarrollo de competencias para la transferencia tecnológica y comercialización de tecnologías. En cuanto a las publicaciones científicas, resultaron afectar de manera negativa la probabilidad de transferencia tecnológica en la Universidad de Concepción en el último período evaluado. Dentro del ámbito académico, las publicaciones científicas, tanto ISI como SCIELO, tienden a ser relacionadas con estudios de investigación básica. Al mismo tiempo, las capacidades de investigación de los propios académicos universitarios se miden por criterios de productividad científica, más que por sus capacidades de transferencia tecnológica, dando la impresión de que las publicaciones académicas son excesivamente valoradas dentro del prestigio académico. Dentro de la Universidad de Concepción, el enfoque de investigación básica de los académicos pudiese guiar sus proyectos hacia un entorno de menor aplicación e interacción con la industria, disminuyendo las probabilidades de transferencia tecnológica. Así, el desarrollo de más investigación aplicada con impacto en la sociedad resulta clave para vincular el mundo universitario con las necesidades de las empresas y sectores productivos. Para esto, el sector público cumple un rol importante promoviendo mediante financiamiento de I+D la investigación aplicada. Lo anterior, no necesariamente va en deterioro de la investigación científica básica, pero si amerita una mirada particular desde el punto de vista de la formación académica, de la asignación de recursos públicos y el cofinanciamiento del sector privado. Los factores identificados en el presente trabajo como determinantes de la transferencia tecnológica en la Universidad de Concepción, pueden constituir nuevo conocimiento útil para la formulación de protocolos institucionales y políticas públicas por parte de hacedores de política y autoridades académicas, pudiendo resultar ello en una mejora de la gestión de la ciencia, tecnología e innovación.

Referencias Algieri, B., Aquino, A., & Succurro, M. (2013). Technology transfer offices and academic spin-off creation: the case of Italy. The Journal of Technology Transfer, 38(4), 382-400. doi:10.1007/s10961-011-9241-8 Atkinson, A. C. (1985). Plots, Transformations and Regression: An Introduction to Graphical Methods of Diagnostic Regression Analysis. Technical Report. Oxford: Oxford University Press.

Bozeman, B., Rimes, H., & Youtie, J. (2015). The evolving state-ofthe-art in technology transfer research: Revisiting the contingent effectiveness model. Research Policy, 44(1), 34-49. doi:10.1016/j.respol.2014.06.008 Bradley, S., Hayter, C., & Link, A. (2013). Models and Methods of University Technology Transfer. Foundations and Trends® in Entrepreneurship, 9(6), 571-650. doi:10.1561/0300000048 Carayannis, E., & Campbell, D. (2012). Mode 3 Knowledge Production in Quadruple Helix Innovation Systems. Etzkowitz, H. (2017). Innovation Lodestar: The entrepreneurial university in a stellar knowledge firmament. Technological Forecasting and Social Change, 123, 122-129. doi:10.1016/j.techfore.2016.04.026 Fernández, S., Otero, L., Rodeiro, D., & Rodríguez, A. (2009). Determinantes de la capacidad de las universidades para desarrollar patentes. Revista de la educación superior, 38, 7-30. Foltz, J., Barham, B., & Kim, K. (2000). Universities and agricultural biotechnology patent production. Agribusiness, 16(1), 82-95. doi:10.1002/(sici)1520-6297(200024)16:1<82::Aid-agr7>3.0.Co;2-v Heinzl, J., Kor, A.-L., Orange, G., & Kaufmann, H. R. (2013). Technology transfer model for Austrian higher education institutions. The Journal of Technology Transfer, 38(5), 607-640. doi:10.1007/s10961012-9258-7 Iglesias, P., Jambrino, C., & Peñafiel, A. (2012). Caracterización de las Spin-Off universitarias como mecanismo de transferencia de tecnología a través de un análisis clúster. Revista Europea de Dirección y Economía de la Empresa, 21(3), 240-254. doi:10.1016/j.redee.2012.05.004 Kochenkova, A., Grimaldi, R., & Munari, F. (2016). Public policy measures in support of knowledge transfer activities: a review of academic literature. The Journal of Technology Transfer, 41(3), 407-429. doi:10.1007/s10961-015-9416-9 Lederman, D., & Maloney, W. (2014). Innovación en Chile: ¿Dónde Estamos? , (ISSN 0717-9987). López, M., Mejía, J., & Schmal, R. (2006). Un Acercamiento al Concepto de la Transferencia de Tecnología en las Universidades y sus Diferentes Manifestaciones. Panorama Socioeconómico.

Azagra, J. M. (2001). Determinantes de las patentes universitarias: el caso de la Universidad Politécnica de Valencia. Estudio, EC 2001-03, IVIE. Valencia.

Menezes, F., Liska, G., Cirillo, M., & Vivanco, M. (2017). Data classification with binary response through the Boosting algorithm and logistic regression. Expert Systems with Applications, 69, 62-73. doi:10.1016/j.eswa.2016.08.014

Beraza, J. M., & Rodríguez, A. (2010). Factores determinantes de la utilización de las spin-offs como mecanismo de transferencia de conocimiento en las universidades. Investigaciones Europeas de Dirección y Economía de la Empresa, 16(2), 115-135. doi:10.1016/S11352523(12)60115-4

Perkmann, M., Tartari, V., McKelvey, M., Autio, E., Broström, A., D’Este, P., . . . Sobrero, M. (2013). Academic engagement and commercialisation: A review of the literature on university–industry relations. Research Policy, 42(2), 423-442. doi:10.1016/j.respol.2012.09.007

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Peters, M. (1989). Techno-Science, Rationality, and the University: Lyotard on the “Postmodern Condition”1. Educational Theory, 39(2), 93-105. doi:10.1111/j.1741-5446.1989.40000.x

Sternberg, R. (2014). Success factors of university-spin-offs: Regional government support programs versus regional environment. Technovation, 34(3), 137-148. doi:10.1016/j.technovation.2013.11.003

Rodriguez, J., & Casani, F. (2007). La transferencia de tecnología en España: diagnóstico y perspectivas. Economía industrial, ISSN 04222784, Nº 366.

Thursby, J., Jensen, R., & Thursby, M. (2001). Objectives, Characteristics and Outcomes of University Licensing: A Survey of Major U.S. Universities. The Journal of Technology Transfer, 26(1), 59-72. doi:10.1023/A:1007884111883

Rojo, J. M. (2007). Regresión con variable dependiente cualitativa. In: Laboratorio de estadística. Siegel, D., Waldman, D., Atwater, L., & Link, A. (2004). Toward a model of the effective transfer of scientific knowledge from academicians to practitioners: qualitative evidence from the commercialization of university technologies. Journal of Engineering and Technology Management, 21(1), 115-142. doi:10.1016/j.jengtecman.2003.12.006 Siegel, D., Waldman, D., & Link, A. (2003). Assessing the impact of organizational practices on the relative productivity of university technology transfer offices: an exploratory study. Research Policy, 32(1), 27-48. doi:10.1016/S0048-7333(01)00196-2

Van Norman, G., & Eisenkot, R. (2017). Technology Transfer: From the Research Bench to Commercialization: Part 1: Intellectual Property Rights—Basics of Patents and Copyrights. JACC: Basic to Translational Science, 2(1), 85-97. doi:10.1016/j.jacbts.2017.01.003 Wright, M., Clarysse, B., Lockett, A., & Knockaert, M. (2008). Mid-range universities’ linkages with industry: Knowledge types and the role of intermediaries. Research Policy, 37(8), 1205-1223. doi:10.1016/j.respol.2008.04.021 Zuñiga, P., & Correa, P. (2013). Technology Transfer from Public Research Organizations: Concepts, Markets, and Institutional Failures. In: The Innovation Policy Platform.

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E-Commerce C2C en Chile: IncorporaciĂłn de Ma ReputaciĂłn y de la Confianza en Fl TAM Renato Sukno1; Isabel Pascual del Riquelme2* Abstract: E-commerce in Chile has been growing considerably in recent years. However, it is still far from reach its full potential. Considering the benefits that e-commerce offers to small and medium companies to improve their competitiveness on a global scale, it is essential to improve our understanding about the factors that could encourage the use of this digital shopping channel. In this research we adopted the Technology Acceptance Model (TAM) as the basis for analyzing Consumer-to-Consumer (C2C) e-commerce in Chile, one of the most important ways of online shopping in this country. As additional predictors, both web reputation and consumer trust were included in our research model. Data from 468 real Chilean shoppers of this C2C online platforms provide important insights about the antecedents of using this digital online channel, also explaining the interrelationships between these antecedents. Both managerial and theoretical implications are provided. Keywords: E-commerce; C2C; TAM; trust; reputation; purchase behavior; Chile Title: $ $ & DPNNFSDF JO $IJMF *OUFHSBUJPO PG 3FQVUBUJPO BOE 5SVTU JO 5". Resumen: El comercio electrĂłnico en Chile ha ido creciendo de manera considerable en el Ăşltimo tiempo. Sin embargo, aĂşn estĂĄ lejos de alcanzar su potencial. Dadas las ventajas que el comercio electrĂłnico ofrece para mejorar la competitividad de las pequeĂąas y medianas empresas globalmente, resulta vital mejorar el conocimiento de aquellos factores que pueden incrementar su uso. En la presente investigaciĂłn se emplea el Modelo de AceptaciĂłn de la TecnologĂa (TAM) como base para analizar el comercio electrĂłnico Consumidor-a-Consumidor (C2C) en Chile, una de las formas de compra online mĂĄs importantes de ese paĂs. Como antecedentes adicionales, se incluyeron la reputaciĂłn de la plataforma web como la confianza del consumidor. Datos de 468 compradores chilenos reales proporcionaron importantes resultados acerca de los factores explicativos del uso de estas plataformas en Chile, asĂ como tambiĂŠn de las relaciones entre las variables estudiadas. Con esto, hemos proporcionado importantes contribuciones teĂłricas y prĂĄcticas. Keywords: comercio electrĂłnico; C2C; TAM; confianza; reputaciĂłn; comportamiento de compra; Chile Submitted: Mar 21st, 2019 / Approved: Oct 24th, 2019

IntroducciĂłn El comercio electrĂłnico ha ido ganando participaciĂłn en Chile en los Ăşltimos aĂąos, mostrando un crecimiento importante promedio del 28,7% y llegando a ser de un 39,4% real anual el segundo semestre del aĂąo 2018 (CNC, 2019). Una de las razones fundamentales de este crecimiento es la competitividad de estas plataformas online, ya que gestionar las ventas a travĂŠs de Internet cuesta un 5% menos que por vĂas tradicionales (Paredes y Velasco, 2007), lo que se vuelve una alternativa a la reducciĂłn de costos y una mejora para el desempeĂąo del vendedor, aumentando su utilidad (Paredes y Velasco, 2007; Lu, Zhao y Wang, 2010; Chen, Su y Widjaja, 2016). Sin embargo, y a pesar de este notable crecimiento, el comercio electrĂłnico en Chile, a modo general, aĂşn estĂĄ lejos de alcanzar su potencial, mostrando notables posibilidades de crecimiento. Esto puede observarse en la tasa de penetraciĂłn de este canal de compras que, con un 3,1%, aĂşn queda lejos del 4,7% de los paĂses de la OrganizaciĂłn para la CooperaciĂłn y el Desarrollo EconĂłmicos u OCDE (CNC, 2018) (estando estos paĂses aĂşn lejos, igualmente, de cifras mĂĄs notorias de penetraciĂłn), lo que motiva el estudio y anĂĄlisis de los factores que puedan explicar su mayor o menor adopciĂłn.

Diversos estudios han propuesto modelos basados en el Modelo de AceptaciĂłn TecnolĂłgica o TAM (Davis, 1989; Venkatesh y Davis, 2000) para explicar el comportamiento de los compradores en Internet, demostrando que la utilidad percibida y la facilidad de uso percibida son variables que preceden a la actitud del consumidor frente al comercio electrĂłnico y por ende determinan su participaciĂłn del mismo (He, Lu y Zhou, 2008; Lu et al., 2010; Ye y Zhang, 2014; Chang, Shen y Yeh, 2017). Estos estudios coinciden en agregar, para este contexto, la confianza al modelo TAM, puesto que la literatura considera esta variable de especial importancia en condiciones de incertidumbre y riesgo como las que se dan en la compra online. En este sentido, incluso para los compradores mĂĄs expertos, las compras online implican mĂĄs incertidumbre y riesgo que las compras tradicionales (Biswas y Biswas, 2004), dado que Internet es un entorno virtual en el que no se puede experimentar fĂsicamente el producto y no se puede verificar de forma tangible quiĂŠn es el vendedor â&#x20AC;&#x201C; o incluso si este realmente existe â&#x20AC;&#x201C; (Riquelme y RomĂĄn, 2014). AdemĂĄs, los riesgos de que nunca llegue el producto o de robo de informaciĂłn privada o financieramente sensible siempre son mayores en el entorno online que en el tradicional (Biswas y Biswas, 2004). Esto transforma a la confianza en una variable vital para la adopciĂłn del comercio electrĂłnico (Gefen et al., 2003; Wu et al., 2011; Ben Mansour, 2016; Jamshidi y Hussin, 2016).

1) Escuela de IngenierĂa Civil, Universidad CatĂłlica del Norte, Larrondo 1281, Coquimbo, Chile. 2) Departamento de Estudios EconĂłmicos y Financieros, Universidad Miguel HernĂĄndez, Elche, EspaĂąa. *Autor de correspondencia: ip.riquelme@umh.es

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Al integrar la confianza al modelo TAM los autores han llegado a similares conclusiones, demostrando que afecta de forma significativa y positiva a la utilidad percibida de Internet como canal de compras y a la intención de utilizarlo (Koufaris, 2002; Pavlou, 2003; Palvia, 2009; Schilke y Wirtz, 2012; Ballesteros et al., 2014; Ye y Zhang 2014; Ashraf, Thongpapanl y Spyropoulou, 2016; Jamshidi y Hussin, 2016; Chang et al., 2017). En el contexto C2C, la confianza se enfoca principalmente en la plataforma y sus miembros, e implica confiar tanto en un vendedor individual, como en su producto (Lu et al., 2010; San Martín y Camarero 2010; Ye y Zhang, 2014; Joo, 2015; Ben Mansour, 2016; Chang et al., 2017). En la medida en que el usuario es capaz de confiar en dicho vendedor y su producto, así como en la plataforma que intermedia, puede percibir como útil esta intermediación y, por ende, sentirse dispuesto a comprar a través de ella (Xiang et al., 2016). No obstante, incorporar la confianza a cualquier modelo de compra online no ha sido fácil, ya que tradicionalmente ha sido un constructo que ha costado definir tanto conceptualmente como a nivel operativo, lo que ha dificultado el avance en esta línea y la consecución de resultados consistentes (Benamati et al., 2010; Ballesteros, Tavera y Castaño, 2014; Jamshidi y Hussin, 2016). En el presente estudio, tal y como se detallará más adelante, se ha avanzado en esta problemática abordando la confianza en su naturaleza multidimensional y adaptándola al contexto C2C. De este modo, se pretende profundizar en el entendimiento del rol de esta variable a través de evaluar si la distinta naturaleza de las dimensiones consideradas (competencia y benevolencia) juega un papel diferenciador en la intención de uso que pueda mejorar nuestra comprensión del fenómeno. Además de la confianza, en el contexto general de comercio electrónico, otra variable señalada como importante antecedente de la intención de compra online ha sido la reputación del vendedor (Mui y Halberstadt, 2002; Pavlou, 2003; Xiong y Liu, 2003; Wang y Vassileva, 2003). En un contexto de compra C2C, la reputación del vendedor se traslada, al igual que en el caso de la confianza, a la plataforma y sus miembros, lo que sirve como señal en situaciones de insuficiencia informativa, ayudando a diferenciar a los buenos miembros de los malos dentro de las comunidades virtuales. Esto, en última instancia, motiva la confianza y predispone al consumidor a comprar (Wang y Vassileva, 2003; De Obesso et al., 2012).

En el presente trabajo se pretende avanzar en el entendimiento de los factores que afectan al desarrollo del comercio online C2C en Chile a través de integrar la confianza y la reputación como antecedentes de la intención de compra en estas plataformas, utilizando como base el modelo TAM. En esta línea, aunque el comercio C2C implica interacciones entre consumidores, supone también la interacción con una plataforma que actúa como intermediaria (e.d., eBay, mercado libre, etc.), implicando por tanto una oportunidad de negocio en esa línea de intermediación. Aún más, los servicios asociados a cualquier proceso de compra online (entrega a domicilio, sistemas de pago seguro, garantías, etc.) presentan también importantes oportunidades de negocio que aún están por explotarse en Chile. En conjunto, la revisión de la literatura llevada a cabo sustenta la idea de integrar la confianza y la reputación como variables a un modelo de aceptación de tecnologías en el entorno C2C de comercio electrónico. Su incorporación puede ayudar a incrementar la comprensión del rol de las variables del TAM a la hora de explicar el comportamiento de compra en este tipo de plataformas. La Figura 1 presenta el modelo de investigación propuesto en el presente trabajo, donde se detallan las variables objeto de estudio y las relaciones esperadas entre ellas. Específicamente, en dicho modelo se relacionará la reputación de la plataforma web utilizada para la compra-venta online, entendida como la percepción compartida de los usuarios en torno al buen comportamiento de la misma (Walsh y Beatty, 2007; Lu et al., 2010), con la confianza que siente el usuario en la benevolencia – creencia de que la plataforma se preocupa sinceramente por el interés de sus clientes –, y en la competencia – confianza en que la plataforma es capaz y tiene los recursos para cumplir con lo que promete – (Doney y Cannon, 1997; Singh y Sirdeshmukh, 2000). Estas dos dimensiones de la confianza, junto con la norma subjetiva, entendida como la convicción del usuario de que los demás (familiares, conocidos, amigos) piensan que dicho usuario debería usar las citadas plataformas online para hacer sus compras (Fishbein y Ajzen, 1975), se proponen por tanto como antecedentes de las principales variables del TAM: facilidad percibida en el uso de la plataforma, utilidad percibida de la misma para llevar a cabo compras online, e intención de utilizarla para ello (Davis, 1989). A continuación, se detalla la forma y sentido esperado de todas estas relaciones en la Figura 1.

Figura 1. Modelo de investigación propuesto

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Hipótesis

Diseño de la investigación

Asumiendo como objeto de estudio las percepciones, actitudes y comportamientos de los potenciales compradores de plataformas online C2C, la literatura revisada anteriormente sugiere que, en un contexto de venta minorista B2C1, la reputación del vendedor es una variable que antecede a la confianza (competencia y benevolencia) del consumidor, variables que a su vez afectan de forma significativa a la utilidad percibida y a la intención de uso (Wang y Vassileva, 2003; Benamati et al., 2010; De Obesso et al., 2012). Tal y como se anticipó en la introducción, la confianza (competencia y benevolencia) se presenta como un antecedente de la utilidad percibida, puesto que en la medida que un usuario confía en la plataforma podrá confiar en la información que esta provee y posteriormente encontrar utilidad en la plataforma. Además de esto, diversos estudios previos han concluido que existe una relación positiva entre la facilidad de uso percibida y la confianza, de modo que en la medida que se perciba mayor facilidad, aumentará la predisposición a confiar en dicha plataforma (Gefen et al.,2003; Benamati et al., 2010; Wu et al., 2011; Belanche et al., 2012; Ballesteros et al., 2014). En el presente estudio se busca ampliar estos hallazgos previos al entorno de venta online C2C, a través del análisis de las siguientes hipótesis:

Esta investigación adopta un enfoque cuantitativo, de corte transversal y naturaleza predictiva. Para la recolección de información sobre las variables incluidas en el modelo de investigación propuesto se empleó un cuestionario formal estructurado que se distribuyó entre una muestra representativa de compradores online en el territorio chileno.

H1: La reputación de la plataforma web tiene un efecto positivo en: (a) la benevolencia y (b) la competencia. H2: La benevolencia afecta positivamente a: (a) la intención de compra online y (b) la utilidad percibida. H3: La competencia afecta positivamente a: (a) la intención de compra online y (b) la utilidad percibida. H4: La facilidad de uso percibida afecta positivamente a: (a) la benevolencia y (b) la competencia. Por otro lado, las relaciones provenientes del TAM ya se han probado numerosas veces en la literatura previa, demostrando que, en primer lugar, la norma subjetiva influye positivamente en la facilidad de uso percibida y la intención de uso (Yu et al., 2005; Schepers y Wetzels, 2007). Además, cuanto más fácil de utilizar resulta una plataforma, más sencillo es valorar su utilidad para hacer compras online, lo que a su vez redunda positivamente en la intención de utilizarla para ese propósito (Koufaris, 2002; Pavlou, 2003; Schepers y Wetzels, 2007; Benamati et al., 2010; Ye y Zhang, 2014). Esperando de nuevo que estos resultados se repliquen en nuestro contexto de estudio, se proponen las siguientes hipótesis: H5: La norma subjetiva influye positivamente en: (a) la facilidad de uso percibida de la plataforma de venta online, y (b) la intención de compra. H6: La facilidad de uso percibida afecta positivamente a: (a) la utilidad percibida, y (b) la intención de compra. H7: La utilidad percibida influye positivamente en la intención de compra. 1 2

Específicamente, dicho cuestionario fue distribuido vía online, siendo publicado en grupos de Facebook dedicados a la compra-venta entre usuarios. Se utilizó un muestreo estratificado por variables sociodemográficas, de modo que la muestra fuese representativa de la población de compradores online de Chile en términos de género, estudios y edad. Metodologías similares a la aquí descrita pueden observarse en numerosos estudios previos sobre uso de comercio electrónico (Brashear et al., 2009; Riquelme y Román, 2014; Riquelme et al., 2016).

Muestra La población objeto de estudio estuvo compuesta de hombres y mujeres de Chile que hubiesen comprado por lo menos alguna vez algún artículo a través de las plataformas de compra especificadas (mercadolibre, yapo.cl y grupos de Facebook)2. La muestra así obtenida estuvo compuesta por 468 compradores reales de estas plataformas, equilibrada en términos de género (48,4% hombres y 51,6% de mujeres). La edad media de los encuestados se situó en torno a los 27 años. Respecto del nivel de estudios, el 48% cuenta con escolaridad completa (cursando estudios superiores) y el 52% ya completó estudios universitarios o posteriores. En términos de experiencia previa, el 60,1% de los encuestados afirmó realizar compras online en plataformas C2C con una frecuencia moderada (“de vez en cuando”), mientras que el 19,6% lo hace frecuente o muy frecuentemente. El restante 20,3% representan compradores eventuales de estas plataformas (que compran una vez al año o menos). Por otro lado, el 60,1% de los encuestados indicó tener un manejo de sitios web de compra C2C de nivel avanzado o experto, mientras que el 30,7% se situó en un nivel medio. Solo el 9,2% se clasificó en el nivel de principiante. Estos resultados confirman que los individuos encuestados disponían de los conocimientos y experiencia suficientes como para valorar las cuestiones incluidas en el cuestionario.

Variables de medida Para la medición de las variables incluidas en la Figura 1, se emplearon diversas escalas tipo Likert de 5 puntos desarrolladas y validadas en la literatura previa. Estas escalas emplean múltiples reactivos (preguntas o ítems) para evaluar cada constructo (la confianza percibida, la reputación y las variables básicas del TAM). Cada reactivo fue evaluado de forma subjetiva por el encuestado en un escalamiento de 1 a 5 dependiendo de qué tan de acuerdo o desacuerdo esté con la afirmación (1: “muy en desacuerdo” y 5: “muy de acuerdo”). El detalle de cada variable incluida en el cuestionario, así como de los reactivos o ítems que la componen y la fuente o referencia pueden observarse en la Tabla 1.

Business To Consumer, se trata de resultados obtenidos en entornos de venta minorista donde el vendedor es una empresa y el cliente un consumidor final. Plataformas del tipo C2C más utilizadas en Chile (Netrica, 2018).

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Tabla 1. Escalas de medida utilizadas y resultados sobre su validez de medida (validez convergente)

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Tabla 2. Descriptivos, correlaciones, Varianza Media Extraída (VME) y validez discriminante de las escalas empleadas

Procedimiento Para analizar la información reunida, se empleó un análisis estadístico basado en estructuras de covarianza de las variables empleadas, usando como software de análisis el programa Lisrel 8.80. Dicho análisis se llevó a cabo en dos etapas: en primer lugar, se validó el instrumento de medida empleado a través de un Análisis Factorial Confirmatorio (AFC); tras esto, se procedió al test de hipótesis estimando para ello el modelo de Ecuaciones Estructurales (SEM) correspondiente.

procedimientos señalados por Fornell y Larcker (1981), la validez discriminante se testó comparando los valores de la VME de cada constructo con la varianza compartida de dicho constructo y el resto de variables. Tal y como muestra la Tabla 2, para cada comparación la VME excedió a la varianza compartida, confirmando así la validez discriminante.

Resultados

En definitiva, todas las pruebas llevadas a cabo en este punto permiten afirmar que las escalas cumplen con unas buenas propiedades psicométricas, lo que faculta su uso en la posterior comprobación de hipótesis.

Validez del modelo de medida Previo al test de hipótesis, se procedió a verificar la validez convergente y discriminante –a través del AFC– de las escalas de medición empleadas en el cuestionario. En primera instancia, el modelo de medición resultó tener unos excelentes indicadores de ajuste3. (χ2(168) = 373.11, p < .01; GFI = 0.92; AGFI = 0.88; NNFI = 0.99; CFI = 0.99; RMSEA = 0.05). Además, la χ2 normalizada es de 2.22, menor que 3, por lo que también se indica un buen ajuste del modelo (Fornell y Larcker, 1981).

Contraste o test de hipótesis Para llevar a cabo el test de hipótesis, se procedió a la estimación del modelo estructural propuesto en la Figura 1 en Lisrel 8.80. Dicho modelo resultó tener unos buenos indicadores de ajuste globales (χ2(172) = 500.86, p < .01; GFI = 0.89; AGFI = 0.85; NNFI = 0.98; CFI = 0.98; RMSEA = 0.06). De nuevo, la χ2 normada reportó valores por debajo del valor de 3 (2.91) recomendado por Fornell y Larcker (1981), indicando por ende un buen ajuste teniendo en cuenta el tamaño muestral.

Siguiendo los procedimientos sugeridos por Bagozzi y Yi (1988) y Fornell y Larcker (1981), se evaluó la validez convergente verificando la importancia de los valores t asociados con las estimaciones de los parámetros. Como se muestra en la Tabla 1, todas las cargas factoriales estandarizadas fueron positivas y estadísticamente significativas (p<.01) para todos los ítems. Además, la fiabilidad de las escalas de medida también se confirmó a través del índice de fiabilidad compuesta o IFC (superior a 0.60; Bagozzi y Yi, 1988) y de la varianza media extraída o VME (superior a 0.50; Bagozzi y Yi, 1988, p.80) para todos los constructos latentes (ver Tabla 2). Por último, siguiendo los

Los resultados de las relaciones estimadas se muestran en la Figura 2. Ahí podemos ver que la reputación incide positiva y significativamente tanto en la benevolencia (γ = 0.68; p = 12.62) como en la competencia (γ = 0.72; p = 11.85), lo que permite confirmar tanto H1a como H1b. La benevolencia no afecta de forma significativa ni a la intención de compra (β = -0.09; p = -1.20) ni a la utilidad percibida (β = 0.11; p = 1.67), por lo que se rechazan tanto H2a como H2b. La competencia resulta tener un efecto positivo y significativo sobre la intención de compra (β = 0.18; p = 2.26). Sin embargo, esta dimensión de la confianza no influencia significativamente la utilidad percibida

3 Para seleccionar el método de estimación apropiado, primero se testó la normalidad multivariada de toda la muestra. La prueba de Mardia rechazó esta suposición, por lo que procedimos con el método de Máxima Verosimilitud estimando con la corrección de Satorra-Bentler (2010) (basada en la matriz de covarianza asintótica), que proporciona estimaciones robustas de los parámetros incluso para distribuciones no normales.

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(β = 0.09; p = 1.44), por lo que de la tercera hipótesis se confirma H3a y se rechaza H3b. La facilidad de uso percibida tiene un efecto positivo significativo sobre la competencia (β = 0.15; p = 2.42), pero no en la benevolencia (β = 0.09; p = 1.64), lo que confirma H4b y no H4a. Por otro lado, la norma subjetiva afecta de forma positiva y significativa tanto a la facilidad de uso percibida (γ = 0.12; p = 2.11) como a la intención de compra (γ = 0.09; p = 2.32). Por ende, se confirma H5a y H5b. La facilidad de uso percibida incide positiva y significativamente en la utilidad percibida (β = 0.86; p = 10.78), pero no presenta efecto significativo sobre la intención de compra (β = 0.06; p = 0.32), lo que confirma H6a y rechaza H6b. Finalmente la utilidad percibida presenta un efecto positivo y significativo sobre la intención de compra (β = 0.71; p = 3.60), lo que permite confirmar H7. El modelo propuesto consigue explicar, en su conjunto, un 67.3% de la variable intención de compra en la plataforma, lo que indica un excelente potencial explicativo.

Por último, dado que algunas de las relaciones directas entre variables ampliamente validadas dentro del TAM no se hallaron significativas, se procedió a evaluar la posibilidad de que estos efectos pudiesen estar mediados. Para esto, se estimó en Lisrel 8.80 los efectos indirectos estandarizados, cuyos principales resultados se reportan en la Tabla 3. Ahí se puede confirmar que, en el caso del comercio C2C en Chile, la relación entre la facilidad de uso percibida en la plataforma y la intención de usarla para comprar parece estar completamente mediada (coef. indirecto = 0.65; p < 0.01) por los efectos positivos y significativos que este antecedente (e.d., la facilidad de uso) tiene sobre la utilidad percibida en la plataforma y la confianza del usuario en la competencia de la misma (ver Figura 2). Se ha hallado, además, que la reputación afecta de forma indirecta a la intención de usar la plataforma (coef. indirecto = 0.17; p < 0.01), lo que corrobora la importancia de esta variable como antecedente. También, junto al efecto directo significativo hallado para la norma subjetiva, también se ha encontrado un efecto indirecto marginal de esta variable en la intención de uso (coef. indirecto = 0.08; p < 0.05). A continuación, se discutirá con más detalle estos resultados.

Tabla 3. Efectos indirectos

Figura 2. Modelo estimado (cargas estructurales estandarizadas)

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Discusión Los resultados obtenidos validan, por un lado, la mayoría de las hipótesis planteadas en torno a las variables del modelo TAM (H5 a y b, H6a y H7), demostrando que éste resulta ser aplicable a la realidad chilena en un contexto de comercio electrónico C2C. Además, también se ha podido constatar la relevancia de incorporar al modelo las variables de nuestra propuesta inicial, esto es, la confianza y la reputación (H1 a y b, H3a y H4b), variables que presentan una participación significativa en el modelo, demostrando que existe una relación entre la reputación y la confianza del punto de vista de la competencia y posteriormente que esta explica la intención del usuario frente a la participación de las plataformas C2C. Estos resultados confirman lo hallado en estudios previos en el entorno B2C online (De Obesso et al., 2012; Ben Mansour, 2016; Jamshidi y Hussin, 2016), indicando la posibilidad de extrapolar resultados y modelos de un contexto al otro. Respecto de la benevolencia, no se tuvo el impacto que se esperaba en el modelo. Sin embargo, esto nos ayuda a comprender de qué manera percibe la confianza el usuario en estos entornos y cuál de sus componentes resulta ser realmente importante para determinar el uso de la plataforma. Dado que la confianza, para este estudio, se enfocó en la plataforma y no en quienes participan de la venta, tiene sentido que se perciba importancia en la competencia y no en la benevolencia. Futuras líneas de investigación podrían evaluar en qué medida la confianza se manifiesta a través de distintas dimensiones (competencia, benevolencia) según esta se dirija a la plataforma o al vendedor particular que la usa. Por otra parte, el hecho de que la competencia resulte ser significativa en este modelo, es coherente con los estudios previos que integraron la confianza (Gefen et al.,2003; Benamati et al., 2010; Wu et al., 2011; Belanche et al., 2012; Ballesteros et al., 2014) a modo general en el modelo TAM dentro del contexto de comercio electrónico. Además de esto, los resultados en torno a la relación existente entre la facilidad de uso percibida y la competencia concuerdan con los estudios previos (Gefen et al., 2003; Benamati et al., 2010; Wu et al., 2011; Belanche et al., 2012; Ballesteros et al., 2014). Estos estudios muestran que existe una relación positiva entre estas variables, de modo que, en la medida que se perciba mayor facilidad de uso en una determinada plataforma, aumentará la confianza en su competencia, lo que indica que una plataforma fácil de usar ayuda a que el usuario comprenda su funcionamiento, siendo éste la base para confiar en la competencia de la misma. Lo anterior se complementa con el hecho de que, además, existe una fuerte relación indirecta entre la facilidad de uso y la intención de uso por medio de la competencia (junto con la utilidad percibida), lo que no solo confirma la validez de integrar esta dimensión de la confianza al modelo sino que, además, da potentes motivos a las empresas que operan online a que inviertan en la facilidad de uso de sus plataformas webs. Unido a esto, el hecho de que la facilidad de uso percibida afecte a la intención de uso por medio, también, de la utilidad percibida, es coherente con el modelo original de Davis (1989), quien plantea que, en la medida que se percibe facilidad de uso hacia una tecnología, aumenta la percepción de utilidad de la misma y predispone al usuario a utilizarla.

Además, es relevante comentar los efectos indirectos (Tabla 3) que tienen las variables reputación y norma subjetiva sobre la intención de uso. La reputación, por un lado, provee de información necesaria al comprador para que, a través de la confianza como variable mediadora, éste tenga una predisposición a comprar o no comprar en un determinado sitio o plataforma de compra online. Es decir, nuestros resultados sugieren que los efectos positivos de crear y mantener una buena reputación online se capitalizan a través de la confianza que dicha reputación genera en los usuarios ya que, sin dicha confianza, la reputación no afecta a la intención de compra. Este interesante resultado, además de ofrecer ideas de valor a las empresas que operan en Internet sobre cómo gestionar su reputación –e.d., hacia qué conceptos orientarla–, también resulta corroborar lo hallado y sugerido en estudios previos llevados a cabo en diferentes contextos B2C (Campbell, 1999; Xia et al., 2004), lo que también contribuye a avanzar en conocimientos teóricos en este ámbito. Por otro lado, la norma subjetiva, además de tener un efecto directo sobre la intención de compra, afecta de manera indirecta por medio de la facilidad de uso percibida, competencia y utilidad percibida. La norma subjetiva motiva al usuario a enfrentarse a la plataforma con una mayor disposición e interés, lo que genera que este perciba mayor facilidad en la plataforma y por consecuencia encuentre en la misma utilidad y confianza, lo que finalmente lo llevará a utilizar dicha plataforma. De nuevo, el análisis llevado a cabo sobre estos efectos indirectos permite entender mejor de qué manera cada variable del TAM, así como la reputación y la confianza, contribuyen a explicar la intención de uso de las plataformas C2C online. Resumen En resumen, los resultados obtenidos corroboran la importancia de incorporar las variables de reputación y confianza al modelo TAM en el contexto del comercio electrónico C2C en Chile. La reputación de la plataforma es fundamental para que el usuario pueda determinar si confía o no en la plataforma puesto que, en concordancia con estudios previos, es ésta la que provee de información al usuario, lo que genera una predisposición a confiar o no en la plataforma. Si bien solo la competencia resulta ser un componente significativo de la confianza en nuestro estudio, esto también arroja luz sobre el rol de dicha confianza en estos contextos de compra-venta online. Dada la mediación de la plataforma, las percepciones del usuario se centran y emergen de la misma, dando lugar a que variables más centradas en lo humano (benevolencia), esto es, en las percepciones que podrían estar más relacionadas con el vendedor individual (que podría ser otro consumidor en otro momento del tiempo, dado que no es empresa, y que es el que en última instancia debería asegurar un comportamiento íntegro en términos del interés de su comprador) queden relegadas en pos de aquellas que se centran en la plataforma donde opera. Por otro lado, se concluye que las variables del TAM resultan útiles para explicar el comportamiento de los usuarios en relación a las compras C2C online en Chile, ya que se logró validar de forma significativa la relación que existe entre ellas. En el caso de la facilidad de uso percibida, pese a que no se observa una relación directa con la intención de uso, se puede apreciar una relación indirecta muy potente que puede dar cuenta de la particularidad con que el modelo TAM se adapta a la realidad de este tipo de comercio en Chile.

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Para finalizar, se espera que estos resultados permitan mejorar y potenciar el crecimiento del comercio electrónico C2C en Chile, siendo considerados estos factores al momento de diseñar y desarrollar las plataformas de compra online, con el fin de que el país logre alcanzar su potencial en cuanto a crecimiento y desarrollo económico se refiere. Por otro lado, para futuras investigaciones, además de las ya indicadas a lo largo de esta discusión, se sugiere explorar la influencia de variables como la predisposición a aprender por cuenta propia y la participación de foros de ayuda y de fenómenos como el aprendizaje social, que ayudarían a comprender mejor el rol que tiene la facilidad percibida en el contexto cultural de Chile y países similares.

Referencias Ashraf, A. R., Thongpapanl, N. T., & Spyropoulou, S. (2016). The connection and disconnection between e-commerce businesses and their customers: Exploring the role of engagement, perceived usefulness, and perceived ease-of-use. Electronic Commerce Research and Applications, 20, 69-86. https://doi.org/10.1016/j.elerap.2016.10.001 Ballesteros Díaz, B., Tavera Mesías, J. F., & Castaño Serna, D. (2014). Aceptación tecnológica de la publicidad en dispositivos móviles en Colombia. Semestre Económico, 17(36). https://doi.org/10.22395/ seec.v17n36a6 Belanche, D., Casaló, L. V., & Flavián, C. (2012). Integrating trust and personal values into the Technology Acceptance Model: The case of e-government services adoption. Cuadernos de Economía y Dirección de la Empresa, 15(4), 192-204. https://doi.org/10.1016/j. cede.2012.04.004 Ben Mansour, K.(2016). An analysis of business’ acceptance of internet banking: an integration of e-trust to the TAM. Journal of Business & Industrial Marketing, 31(8), 982-994. https://doi.org/10.1108/jbim-10-2016-271 Benamati J, Fuller, M. A., Serva, M. A., & Baroudi, J. (2010). Clarifying the integration of trust and TAM in e-commerce environments: implications for systems design and management. IEEE Transactions on Engineering Management, 57(3), 380-393. https://doi.org/10.1109/ tem.2009.2023111 Biswas, D., & Biswas, A. (2004). The diagnostic role of signals in the context of perceived risks in online shopping: do signals matter more on the web?. Journal of Interactive Marketing, 18(3), 30-45. doi: https://doi.org/10.1002/dir.20010 Brashear, T. G., Kashyap, V., Musante, M. D., & Donthu, D. (2009). A profile of the Internet shopper: Evidence from six countries. Journal of Marketing Theory and Practice, 17(3), 267–281. https://doi. org/10.2753/mtp1069-6679170305 Campbell, M. C. (1999). Perceptions of price unfairness: antecedents and consequences. Journal of Marketing Research, 36(2), 187-199. https://doi.org/10.2307/3152092

Chang, S. E., Shen, W. C., & Yeh, C. H. (2017). A comparative study of user intention to recommend content on mobile social networks. Multimedia Tools and Applications, 76(4), 5399-5417.https://doi. org/10.1007/s11042-016-3966-1 Chen, J. V., Su, B. C., & Widjaja, A. E. (2016). Facebook C2C social commerce: A study of online impulse buying. Decision Support Systems, 83, 57-69. https://doi.org/10.1016/j.dss.2015.12.008 CNC (2019), Informe de ventas online – Segundo semestre 2018, Cámara de Chile, disponible en: https://www.cnc.cl/wp-content/ uploads/2019/01/Informe-Ventas-Online-Segundo-Semestre-2018. pdf (último acceso 14/03/2019) CNC (2018), Informe de ventas online – Segundo semestre 2017, Cámara de Chile, disponible en: https://www.cnc.cl/wp-content/ uploads/2015/10/Informe-Ventas-Online-Segundo-Semestre-2017. pdf (último acceso 14/03/2019) Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 319-340. https://doi.org/10.2307/249008 De Obesso Grijalvo, A. M., Gutiérrez, S. S. M., & Torres, N. H. J. (2012). Investigaciones Europeas de Dirección y Economía de la Empresa, 18(3), 190-199. https://doi.org/10.1016/j.iedee.2012.05.006 Doney, Patricia M. and Joseph P. Cannon (1997), “An Examination of the Nature of Trust in Buyer-Seller Relationships,” Journal of Marketing, 61 (2), 35-51.https://doi.org/10.1177/002224299706100203 Fishbein, M., & Ajzen, I. (1975). Belief. Attitude, Intention and Behavior: An Introduction to Theory and Research Reading, MA: Addison-Wesley, 6. Garbarino, E., & Lee, O. F. (2003). Dynamic pricing in internet retail: effects on consumer trust. Psychology & Marketing, 20(6), 495-513. https://doi.org/10.1002/mar.10084 Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping: An integrated model. MIS quarterly, 27(1), 51-90. https://doi.org/10.2307/30036519 He, D., Lu, Y., & Zhou, D. (2008). Empirical study of consumers’ purchase intentions in C2C electronic commerce. Tsinghua Science & Technology, 13(3), 287-292. https://doi.org/10.1016/s1007-0214(08)70046-4 Jamshidi, D., & Hussin, N. (2016). Forecasting patronage factors of Islamic credit card as a new e-commerce banking service: An integration of TAM with perceived religiosity and trust. Journal of Islamic Marketing, 7(4), 378-404. https://doi.org/10.1108/jima-07-2014-0050 Joo, J. (2015). Roles of the Buyer’s Trust in Seller in Posted-Price Model of Consumer to Consumer E-Commerce. Journal of theoretical and applied electronic commerce research, 10(3), 30-44. https://doi. org/10.4067/s0718-18762015000300004

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Koufaris, M. (2002). Applying the technology acceptance model and flow theory to online consumer behavior. Information systems research, 13(2), 205-223. https://doi.org/10.1287/isre.13.2.205.83 Lu, Y., Zhao, L., & Wang, B. (2010). From virtual community members to C2C e-commerce buyers: Trust in virtual communities and its effect on consumers’ purchase intention. Electronic Commerce Research and Applications, 9(4), 346-360. https://doi.org/10.1016/j.elerap.2009.07.003 Mui, L., Mohtashemi, M., & Halberstadt, A. (2002, January). A computational model of trust and reputation. In System Sciences, 2002. HICSS. Proceedings of the 35th Annual Hawaii International Conference on (pp. 2431-2439). IEEE. https://doi.org/10.1109/ hicss.2002.994181 Netrica (2018), Primer ranking de e-commerce revela quiénes lideran el negocio en Chile, Netrica by Netquest, disponible en: https:// www.netrica.com/2017/10/16/primer-ranking-e-commerce-revelaquienes-lideran-negocio-chile/ (último acceso: 14/03/2019) Palvia, P. (2009). The role of trust in e-commerce relational exchange: A unified model. Information & management, 46(4), 213-220. https:// doi.org/10.1016/j.im.2009.02.003 Paredes, E. y Velasco, M.E. (2007). Comercio electrónico. McGrawHill/Interamericana de España, SAU. Pavlou, P. A. (2003). Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model. International journal of electronic commerce, 7(3), 101-134. https://doi. org/10.1080/10864415.2003.11044275 Rauniar, R., Rawski, G., Yang, J., & Johnson, B. (2014). Technology acceptance model (TAM) and social media usage: an empirical study on Facebook. Journal of Enterprise Information Management, 27(1), 6-30. https://doi.org/10.1108/jeim-04-2012-0011 Riquelme, I. P., & Román, S. (2014). The influence of consumers’ cognitive and psychographic traits on perceived deception: A comparison between online and offline retailing contexts. Journal of Business Ethics, 119(3), 405-422. https://doi.org/10.1007/s10551-013-1628-z Riquelme, I. P., Román, S., & Iacobucci, D. (2016). Consumers’ perceptions of online and offline retailer deception: a moderated mediation analysis. Journal of Interactive Marketing, 35, 16-26. https://doi. org/10.1016/j.intmar.2016.01.002 San Martín Gutiérrez, S., & Camarero Izquierdo, C. (2010). Los determinantes de la confianza del comprador online. Comparación con el caso de subasta. Cuadernos de gestión, 10. https://doi.org/10.5295/ cdg.100187ss Satorra, A., & Bentler, P. M. (2010). Ensuring positiveness of the scaled chi-square test statistic. Psychometrika, 75, 243-248. https://doi. org/10.1007/s11336-009-9135-y

Schepers, J., & Wetzels, M. (2007). A meta-analysis of the technology acceptance model: Investigating subjective norm and moderation effects. Information & management, 44(1), 90-103. https://doi. org/10.1016/j.im.2006.10.007 Schilke, O., & Wirtz, B. W. (2012). Consumer acceptance of service bundles: An empirical investigation in the context of broadband triple play. Information & Management, 49(2), 81-88. https://doi. org/10.1016/j.im.2011.12.003 Singh, J., & Sirdeshmukh, D. (2000). Agency and trust mechanisms in consumer satisfaction and loyalty judgments. Journal of the Academy of marketing Science, 28(1), 150-167. https://doi. org/10.1177/0092070300281014 Taylor, S., & Todd, P. A. (1995). Understanding information technology usage: A test of competing models. Information systems research, 6(2), 144-176. https://doi.org/10.1287/isre.6.2.144 Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management science, 46(2), 186-204. https://doi.org/10.1287/ mnsc.46.2.186.11926 Walsh, G., & Beatty, S. E. (2007). Customer-based corporate reputation of a service firm: scale development and validation. Journal of the academy of marketing science, 35(1), 127-143. https://doi.org/10.1037/ t68604-000 Wang, Y., & Vassileva, J. (2003, September). Trust and reputation model in peer-to-peer networks. In Peer-to-Peer Computing, 2003.(P2P 2003). Proceedings. Third International Conference on (pp. 150-157). IEEE. https://doi.org/10.1109/ptp.2003.1231515 Wu, K., Zhao, Y., Zhu, Q., Tan, X., & Zheng, H. (2011). A meta-analysis of the impact of trust on technology acceptance model: Investigation of moderating influence of subject and context type. International Journal of Information Management, 31(6), 572-581. https://doi. org/10.1016/j.ijinfomgt.2011.03.004 Xia, L., Monroe, K. B., & Cox, J. L. (2004). The price is unfair! A conceptual framework of price fairness perceptions. Journal of Marketing, 68(4), 1-15. https://doi.org/10.1509/ jmkg.68.4.1.42733 Xiang, L., Zheng, X., Lee, M. K., & Zhao, D. (2016). Exploring consumers’ impulse buying behavior on social commerce platform: The role of parasocial interaction. International journal of information management, 36(3), 333-347. https://doi.org/10.1016/j.ijinfomgt.2015.11.002 Xiong, L., & Liu, L. (2003, June). A reputation-based trust model for peer-to-peer e-commerce communities. In E-Commerce, 2003. CEC 2003. IEEE International Conference on (pp. 275-284). IEEE. https:// doi.org/10.1109/coec.2003.1210262

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Ye, L. R., & Zhang, H. H. (2014). Sales promotion and purchasing intention: Applying the technology acceptance model in consumer-to-consumer marketplaces. International Journal of Business, Humanities and Technology, 4(3), 1-5. https://doi. org/10.4135/9781452229669.n797

Renato Sukno es Profesor de Sistemas de Información en la Universidad Católica del Norte (Chile), Ingeniero Civil Industrial con Licenciatura en Ciencias de la Ingeniería y Magister en Gestión de Tecnologías de Información. Su línea de investigación se enfoca en el comercio electrónico y la aceptación tecnológica.

Yu, J., Ha, I., Choi, M., & Rho, J. (2005). Extending the TAM for a tcommerce. Information & management, 42(7), 965-976. https://doi. org/10.1016/j.im.2004.11.001

Isabel P. Riquelme es Profesora de Marketing en la Universidad Miguel Hernández (España). Sus artículos han aparecido en numerosas publicaciones internacionales de reconocido prestigio, tales como Journal of Interactive Marketing, Review of Marketing Research, Journal of Business Ethics, Ethics and Information Technology, Journal of Electronic Commerce Research o Electronic Markets, entre otras. Sus áreas de investigación comprenden el comercio electrónico y la ética en las actividades de venta y consumo.

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J. Technol. Manag. Innov. 2019. Volume 14, Issue 3

Spinning Out of Control? How Academic Spinoff Formation Overlooks Medical Device Regulations Paul Scannell1, Kathryn Cormican1* Abstract: This paper investigates the impact of the medical device regulatory framework on the academic spinoff formation process and contributes to knowledge in the domain by expanding and deepening our understanding of its underlying routines and capabilities. A detailed case study focusing on academic spinoff formation in the Irish medical device industry was conducted and found that the consideration given to the medical device regulatory framework significantly lags behind that given to other commercialisation activities. This trend has potential to both significantly delay spinoff formation and negatively impact its potential success and survival. Findings indicate that incorporating expert regulatory knowledge earlier within the process may enhance the spinoff activities within universities, particularly funding, research and capital investment. Keywords: academic spinoff formation; medical device; regulatory framework; case study Submitted: Jul 9th, 2019 / Approved: Oct 17th, 2019

Introduction The commercialisation of scientific and technological knowledge is crucial to economic growth and development (Fontes, 2005; Ndonzuau et al., 2002). Within a knowledge based economy, the university becomes a component of the innovation system where academic technology transfer can occur through several mechanisms such as licensing, publication, cooperative research and development agreements and spinoff formation (Iacobucci and Micozzi, 2014; Rogers et al., 2001). Fontes (2005) describes technology transfer as a process comprising the development of applications for new scientific concepts and turning these into viable technologies, products or services. Rogers et al. (2001) considers technology transfer to be an information transformation process where information is moved from a research and development organisation to a receptor organisation such as a private company. Siegel et al. (2004) investigate the process within an academic setting and defined it in terms of a linear flow model beginning with a discovery by a university scientist through to its patenting and licensing to an existing firm or start-up. However, Rogers et al. (2001) argues that such a linear model of the process may not fully account for external environmental factors such as market demands and regulatory factors. Spinoffs are identified as a particularly effective means of technology transfer (Rasmussen and Borch, 2010). Researchers have found that they are an important mechanism for the commercialization of research results (Rasmussen and Borch, 2010; Lee, 2001) leading to both job and wealth creation (Rogers et al., 2001; Ndonzuau et al., 2002, Pérez and Sánchez, 2003). Moreover, many researchers have found that spinoffs have a positive effect on the local economy (Iacobucci and Micozzi, 2014; Vincett, 2010; Pérez and Sánchez, 2003). Wennberg et al. (2001) define two discrete spinoff routes: spinoff firms that emerge directly from universities, university spinoffs (USOs), and firms that are spun out by university-educated founders

who pursue careers in private industry and subsequently spinoff from this commercial setting, corporate spinoffs (CSOs). It should be noted, however, that whilst an effective means of technology transfer spin-off creation is also “the most complex way of commercializing academic research” (Iacobucci and Micozzi, 2014). Compared with other technology transfer mechanisms, it is risky and fraught with challenges. Furthermore, there is no guarantee of success. Indeed, research suggests that spinoff ventures emerging from incumbent firms within a specific industry are more likely to commercialise a product than other entrants, such as those emerging from academia (Curran et al., 2011; Wennberg et al., 2001). This is largely attributed to the fact that ventures emerging from incumbent firms inherit sector specific knowledge, something which ventures emerging from academic backgrounds largely lack. While there are many factors that impede technology transfer and market entry (Pérez and Sánchez, 2003; Van Dierdonck and Debackere, 1988), D’este et al. (2012) highlights the importance of nonfinancial factors such as market focus, knowledge management and regulation. Indeed, these factors may be more pertinent to specific technologies or industries. For example, regulatory knowledge has been identified as a key knowledge deficit for academics entering the medical device industry (Curran et al., 2011; Chatterji, 2009; van Egeraat et al., 2009; Regnstrom et al., 2010). Academic research must cross the regulatory ‘chasm’ whilst navigating a multitude of regulatory routes and permutations. This market entry barrier may, potentially, be a causative factor for the low incidence and success of spinoff formation. To investigate this further, this study sought to analyse the academic medical device spinoff formation process through a regulatory lens. We advocate that in order to understand what drives behaviours in specific contexts, distinctive factors (such as processes and practices) pertaining to pertinent issues (such as regulation activities) must

1) College of Engineering & Informatics, National University of Ireland, Galway, University Road, Galway, Ireland *Corresponding author: kathryn.cormican@nuigalway.ie

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A detailed synthesis of the literature reveals that much work has been conducted in the space. The extant literature comprises studies from many different thematic areas ranging from motivation and personality characteristics of the founder and the team, to the spinoff process and the role of support organisations. Table 1 presents an overview of the type of academic studies that have been conducted in the area of University spinoff formation. Table 1. Synthesis of relevant studies

Three groups namely; academic researchers; facilitators of the spinoff process (such as funding agencies, technology transfer offices and investors); and existing spin off companies, working in the medical device industry in Ireland are examined. Our research explores the perceived level of importance of medical device regulations as well as the level of regulatory knowledge in the sample. We then investigate the spin off formation process in more detail and ascertain when regulatory issues are first considered. We asked participants in our study to rate the criticality of activities that support spin off formation; to determine the barriers to spin off formation and to determine the key factors that influence spin off success and survival. Findings from this analysis are reported and discussed. The remainder of the paper proceeds as follows. We begin with a synthesis of the extant literature in the area of academic spinoffs to understand the concepts, issues and themes. Next, we provide a summary of the research methods employed in this study. Thereafter we present the findings of our study and discuss these findings relative to the pertinent literature.

Research theme

Reference

Academic motivation to spinoff

Fini et al., 2008; Henrekson and Rosenberg, 2001; D’Este and Perkmann, 2010; Louis et al., 2001

Characteristics of the spinoff founder

Rosa and Dawson, 2006; Grandi and Grimaldi, 2005; Klofsten and Jones-Evans, 2000

Characteristics of the

Knockaert et al., 2011; Vanaelst et al., 2006; Heirman and Clarysse, 2007; Clarysse and

spinoff team

Characteristics of the spinoff organisation

Iacobucci et al. 2011; Niosi 2006; Vanaelst et al. 2006; Vohora et al. 2004

The spin off process

Harrison and Leitch, 2010; Poon and Liyanage 2004 Ndonzuau et al 2002; Bower 2003

The role of the parent organisation

Rasmussen and Borch, 2010; Harrison and Leitch, 2010; Lockett and Wright, 2005; Franklin, et al 2001; Bray and Lee, 2000, Rappert and Webster, 1997, Rogers et al., 2001

The role of technology transfer offices

Algieri et al. 2011; Mustar et al 2008; Siegel et al 2007; Lockett et al. 2005; Sharif and Baark, 2008

Understanding university spinoffs The knowledge spillover theory of entrepreneurship emphasises the importance of university spinoffs as a mechanism for exploiting knowledge and scientific discoveries created by academic researchers (Carree et al 2014; Audretsch and Lehmann, 2005). According to Uctu and Jafta (2012) there is no universally accepted definition of university spinouts in the literature. They are sometimes referred to as academic spin-offs (Ndonzuau et al., 2002), and spin-outs (Smilor et al., 1990). Link and Scott (2005) contend that university spinoffs are “extraordinarily heterogeneous” and so it is difficult to generalise the research findings. However, there is general consensus regarding two key elements, namely the status of the founder and the nature of the knowledge transferred. Simply put, the founder of an academic spinoff is or was affiliated to a university and the knowledge or invention was originally developed within a university (O’Shea et al., 2008; Link and Scott, 2005; Nicolaou and Birley 2003a; Smilor et al., 1990). Pérez and Sánchez, 2003 assert that spinoffs transfer technology in two ways; (a) they transfer the technology from the parent organization to the new business entity and (b) they transfer the technology to the market. In an attempt to better understand the types of spinoffs Wright et al., (2006) have classified them along three dimensions the ‘Venture Capital backed’ type, the ‘prospector’ type and the ‘lifestyle’ type.

Moray, 2004; Grandi and Grimaldi, 2003

Role of partners and advisors

Walter et al, 2006; Mosey and Wright, 2007; Hoang and Antoncic, 2003; Perez and Sánchez 2003; Nicolaou and Birley, 2003a,b; Phan et al. 2005; Siegel et al., 2003d

While these studies have contributed significantly to advance our understanding of the concept of the academic spinoff there is a dearth of focused empirical data on specific real-world contexts. More specifically, explicit underlying factors that are essential to the spinoff process in particular industries require further attention. For example, regulatory factors have been found to have a significant inﬂuence on the performance of the medical device industry. Blind (2012) argues that regulations increase the hurdles and consequently the compliance costs, which companies must overcome to enter a speciﬁc market. Moreover Curran et al. (2011) identified knowledge about regulatory procedures as a critical competency required in the early stages of a university spinoff. Despite this, little progress has been made to advance our knowledge in this domain, Few, if any, studies have specifically looked at the impact of medical device regulatory requirements on the academic spinoff process. This study attempts to bridge this gap.

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Research Methodology

Findings

Case study analysis was used to determine the relationship between regulatory knowledge and the academic spinoff formation process. The reasons for this are as follows;

Overall, 34 responses were received out of a total sample size of 55 resulting in a relatively high response rate of 61.8% (Sauermann and Roach, 2012). Group I achieved the highest overall response rate of 92.3%, closely followed by Group III at 87.5%. Group II resulted in the lowest response rate of 44.1%. Group I predominately consisted of principal investigators, postdoctoral and postgraduate academics. Most respondents in Group II comprised technology transfer office staff. Other participants included research funding agencies and venture capital ﬁrms. In relation to Group III, all respondents were derived from spin-off medical device organisations. Of the 7 respondents, 4 were previously employed in academia and 3 in a medical device ﬁrm prior to the spinoff formation. A 100% retention rate was achieved for survey Groups I and III with 85% achieved for Group III. As a result, an overall retention rate of 93% was achieved.

• The research undertaken in this study is considered exploratory in nature, as relevant variables have yet to be defined. • The exact subject under investigation is not very well documented in the literature; therefore, the research could not be conducted experimentally. • The study investigates complex issues and processes and hence the researcher anticipated that as the research proceeded the issues were likely to unfold to reveal new dimensions. • A substantial amount of research was concerned with collecting and assessing the views and opinions of participants. Care was taken to ensure rigour and objectivity in the study. Evidence was collected from multiple sources and triangulated. A purposive, non-probability stratified sample was identified. A non-probability sample is effective when, as in this study, the research is exploring what is occurring. Sample selection was dictated by analytical (rather than statistical) generalisation and replication in accordance with best practice. Samples were carefully selected so that they matched the purpose of the study i.e. structural representation (Voss et al., 2002; Yin, 2014). In total 91 organisations were initially contacted. The sample comprised three cohorts. Group I contained academic researchers, Group II included facilitators of the spinoff process and Group III was made up of founders of medical device spinoff companies (see table 2). Each potential participant was sent a personalised pre-notiﬁcation invitation outlining the supporting background information, purpose and use of the study as recommended by Fan and Yan, 2010 and Sanchez-Fernandez et al., 2012. Structured templates were used to help organise and capture the data (Kvale and Brinkmann, 2009). The data collection instrument was pilot tested prior to distribution (Panacek, 2008) and modified to ensure that the correct information was gathered. Data was coded and analysed following best practice protocols. Themes were advanced, and propositions were compared to the extant literature. This helped to strengthen internal validity and reliability. Table 2. Sample size metrics Group

Invited

Questionnaire sent (n)

Questionnaire Response (%) received (n)

Group I

92.3

Group II

44.1

Group III

87.5

Total

61.8

Regulatory knowledge and awareness Less than half of all respondents deemed medical device regulations to be critical while many considered them to be very important. Respondents in Group I stated that the principle reason for attributing a high level of importance to regulatory requirements was to enable academic research to be taken seriously within the medical device industry. Similarly, respondents from Group II considered due diligence, in terms of regulatory requirements and strategies, as key factors. Respondents from Group III also noted that regulatory strategies and intellectual property protection were critical for securing investment. The majority (75%) of Group I rated their regulatory knowledge as between fair and good with less than half indicating that they have had prior experience in medical device regulations. However, only 2 respondents considered their knowledge to be very good. 60% of Group III rated their regulatory knowledge as poor at the time of spinoff formation. No one, from either group, considered their level of regulatory knowledge to be excellent. Interestingly the majority of respondents from Group III initially outsourced regulatory affairs when spinning off their respective organisations. As the organisations have matured, only 1 spinoff remains fully reliant on outsourced regulatory expertise. The remainder have either fully developed in-house regulatory expertise or take a blended approach using both in-house and third party expertise. The latter case appears to be particularly relevant when entering markets with differing regulatory frameworks.

Regulatory considerations in the spin off process Respondents were asked when they incorporate regulatory requirements and strategies in academic research. 62.5% of respondents in Group I incorporate regulatory requirements and strategies as part of their research proposals and grant applications with 68.8% continuing their incorporation when undertaking pre-clinical research activities. These requirements and strategies are predominantly identiﬁed by the researchers themselves with approximately 70% of

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researchers attesting to doing so. Those who do not take regulatory considerations into account indicate that the reasoning for this relates to the type of research being conducted, i.e. Proof-of-Principle (PoP) or Proof-of-Concept (PoC) studies and blue sky research. 82.4% of respondents in Group II review regulatory strategies as part of their assessments with 76% considering them to be either very important or critical. All 7 respondents within Group III incorporated regulatory strategies within their business plans.

Actors in Groups I and III were asked to identify at what stage in the process is (Group I), or was (Group III), commercialisation of their research first considered. Three quarters of actors in Group I state that commercialisation is first considered in the early stages of the process between project proposal and early stage research, with the majority considering it at the project proposal stage. This trend is not, however, mirrored by actors in Group III, with 100% indicating early to late stage research as when commercialisation was first considered, with the majority (57.1%) indicating early stage research as the relevant stage.

Figure 1. Stage at which commercialisation of academic research is ﬁrst considered

Participants across all three actor groups were asked to indicate at what stage regulatory considerations should begin to be considered when academic research is being undertaken. Over half of respondents in Groups II and III believe they should be considered in the ﬁrst stage, funding application, whilst only 25% of Group I have the same opinion. There is, however, an overall trend indicating that regulatory requirements require consideration in the earlier rather than later stages of the process.

The actors within Group III were investigated further to ascertain whether the process stage at which they indicated regulations should be ﬁrst considered matched that at which they were in practice. Whilst 58% of these respondents consider the funding application stage to be the most relevant one at which regulatory requirements should be considered, none however implemented this in practice. 58% of respondents indicated that regulatory requirements were ﬁrst considered in the latter stages of the process; those of seeking investment and spinoff formation.

Figure 2. Actual versus suggested stage at which regulatory requirements are considered during the spinoff formation process

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Supports to spin off formation All three actor groups were asked to rate the criticality of the activities which support spinoff formation e.g. funding, due diligence, commercial assessment, technical assessment and regulatory assessment. Respectively, 62.5% and 68.75% of respondents within Group I deemed funding and due diligence to be critical in supporting spinoff formation. Commercial, technical and regulatory assessments were largely regarded as having the same level of criticality. Within Group II, funding, due diligence and commercial and technical assessments were broadly considered as having equal weight. Overall, commercial assessment was considered as most important with 82.4% of respondents deeming it to be critical. Regulatory assessment was considered as being critical by just 47.1% of respondents in this group. Funding was considered the most important factor in supporting spinoff formation by respondents in Group III with 85.7% viewing

it as being critical. The levels of importance attributed to due diligence, and technical and regulatory assessments were equally distributed at 57.1% critical, 28.6% very important and 14.3% important. Participants were asked to rank four categories of barriers to spinoff formation namely 1. Cost factors (i.e. ﬁnancing) 2. Knowledge factors (i.e. Acquiring the appropriate staff) 3. Market factors, (i.e. Competition and customer demand) 4. Regulatory factors (i.e. Meeting requirements). Four weighted ranking levels were provided: low (1), medium (2), high (3) and highest (4). The average ranking for each barrier is presented in Figure 3.

Figure 3. Average ranking of barriers to spinoff formation

Across all three groups cost factors were considered as being the highest barrier to spinoff formation with average weighted rankings of between 2.82 and 3. Respondents in Group III attributed the highest average weighted ranking (3) to this factor. Regulatory factors were considered as being second to cost factors by Groups I and III followed by knowledge and market factors which were broadly attributed the same average weighted ranking. The opposite trend was observed in responses received from Group II; knowledge and market factors were equally placed second at 2.47 followed by regulatory factors with a value of 2.24. To investigate what inﬂuences a spinoff ’s success and survival all three groups were asked to rate the relative importance of ﬁve

factors: funding, intellectual property (IP) protection, market analysis, research and development, and regulatory strategy. All three groups considered continued funding to be the most critical factor to ensure success and survival. Equally, both IP protection and regulatory strategy were considered second to continued funding by actors in Group I with 32.5% of respondents considering such factors to be critical. At 42.9%, a similar trend is seen in Group III with the addition of market analysis and awareness. Group II, however, places more importance on market awareness with 58.8% considering it to be critical. Continued research and development is considered critical by only 18.75%, 17.6% and 14.3% of respondents in Group I, II and III respectively. It is, however, acknowledged as being very important across all three groups.

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Egeraat et al. 2009). It has been previously identified that spinoffs who emerge from corporate parents are more liable to inherit this knowledge than academic spinoffs (Wennberg et al., 2001); this is also reﬂected in the ﬁndings of the survey which reveal that, whilst approximately half of the actors in Group I indicate having prior regulatory experience; the majority has been gained through academic pursuits such as workshops etc. Of the 7 responses received from academic spinoffs, over 70% relied on outsourced regulatory expertise.

Regulatory considerations in the spin off process There is further strong agreement between the groups with approximately 70% of both groups considering regulatory strategies would enhance business plans when embarking on the spinoff process. Regarding capital investment applications and reviews, and how the incorporation of regulatory strategies could enhance the process, the opinions of both groups mirror each other However, in both cases there is not as strong an agreement as previously seen, with approximately 55% strongly agreeing and 42% agreeing. There is diverging opinion as to whether the early identiﬁcation of regulatory requirements and strategies would reduce spinoff costs and time to market. 71.4% of respondents in Group III strongly believe cost and time to market could be reduced but only 35.3% of Group II has the same opinion. 5.9% of Group II disagree, primarily as they believe that by identifying such requirements both cost and time may be increased to enable such requirements to be met. This, however, is countered by the agreement of those who agree; whilst initially both costs and time may increase it acts to reduce errors which, if realised at a later stage, could signiﬁcantly increase both costs and time, particularly in the case of innovative technologies.

Discussion Regulatory knowledge and awareness Results reveal that 87% of respondents from Groups I and III rate the importance of medical device regulations as being either very important or critical, particularly in the case of applied research. This is reﬂected in the ﬁnding that over 60% of Group I incorporate regulations within both funding applications (25%) and pre-clinical research activities (38%) whilst all responses received from Group III indicated that regulatory requirements were incorporated within their business plans. However, 65% of these respondents rated their regulatory knowledge as between poor and fair. Furthermore, 82% of those who review research for the purposes of funding, patenting or investment (i.e. Group II) also review the associated regulatory strategies with 76% considering them to be a very important or critical component of the research. There is an apparent inequality between regulatory awareness, or perceived importance, and knowledge. This inference is supported by those who found that spinoffs who inherit non-technical complementary knowledge, such as regulatory knowledge, are more likely to successfully commercialise a medical device and that a lack of such knowledge may be an important contributory factor to low incidence of spinoff formations (Curran et al., 2011, Chatterji, 2009 and van

A disparity between the stage at which commercialisation of research and the stage at which regulatory requirements are first considered is evident where just under half of Group I respondents stated that commercialisation decisions are made at the project proposal or funding application stage. Comparatively, only 25% considered that regulatory requirements should be considered at the same stage. This gap is further exacerbated when the responses of Group III are reviewed. Whilst commercialisation decisions were made at a later stage to that indicated by Group I, regulatory considerations were also considered at a much later stages of the spinoff process; 58% of respondents indicated that they were only considered when seeking investment or at the time of actual spinoff. This finding mirrors that found by the European Medicines Agency’s SME Office who found that SMEs also tend to seek their advice at the later stages of the development process. This observation appears to contradict the finding that over 60% of Group I incorporate regulations within both funding applications and pre-clinical research activities. The reason for this contradiction is not immediately evident. Perhaps, the level of initial regulatory consideration is minimal with the burden of regulatory compliance only becoming evident at later stages. Interestingly, 58% of actors in Group III would now first consider the identification of regulatory requirements at the earliest stage. Responses from Group II also indicated that regulations should be considered sooner rather than later with 53% indicating funding application as the most appropriate stage. A strong case can be made for the earlier consideration of regulations for applied research, whereas the commercial viability of, and hence the need to consider regulatory requirements for, exploratory research may only become apparent at a later stage. What we may deduce from these findings is that, although regulations are considered they may only be accurately considered during the later stages of the commercialisation process. A theoretical explanation of this, based on skill complementarities which are required for entrepreneurs, is proposed by Lazear (2004). This theory recognises that entrepreneurs must have knowledge of a wide variety of business areas and skill complementarities. Empirical evidence suggests academics with a balanced skill profile experienced shorter time-lags in spinoff formation than academics with an unbalanced skill profile. An unbalanced skill profile may be considered as a barrier to spinoff formation, more specifically, a revealed barrier which is defined as barriers which emerge due to the direct

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experience in the engagement of innovation activities resulting in the awareness of the associated difficulties (D’este et al., 2012). Regulatory knowledge may be considered as a revealed barrier with potential to delay spinoff formation; this is perhaps reflected in the shift in the opinions of Group III respondents as to when regulatory factors should first be considered. In order to overcome these barriers Muller (2010) suggests that matching spinoff founders with complementary skill profiles should be taken into account when designing policy measures to foster spinoff creation such as supporting and assisting founders.

Supports to spin off formation As expected, funding is largely considered as the most critical aspect across all three survey groups for both enabling spinoff formation and supporting its success and survival. However, regulatory assessment is ranked as one of the least critical factors in supporting spinoff formation. It could be argued that this is due to the formation of a spinoff company not being dependent on complying with medical device regulations. It is this market activity which is ultimately critical to the success and survival of the company, an argument supported, to a certain degree, by the relative increase in the percentage of respondents who rank regulatory assessment as being critical in supporting success and survival; regulatory assessment moves from being one of the least critical factors for spinoff formation to being rated on par with intellectual property/due diligence factors in terms of success and survival by actors in Groups I and III. Safeguarding and marketing the universities intellectual property is the technology transfer office’s primary motive whist commercialising university-based research for ﬁnancial return is that of investors (Siegel et al., 2004).

the required pre-clinical and animal testing and potential necessity to conduct clinical investigations. Incorporating these requirements at this stage not only gives a better estimate of the required funding and anticipated research duration but also allows for both pre-clinical and clinical work to be conducted within the requirements of the legislation. This latter point has been demonstrated by the Investigational Assistance Program (IAP) at the University of Minnesota’s Academic Health Center (AHC) (Arbit and Paller, 2006). Prior to the establishment of the program 24 pre-existing clinical studies were being conducted; only 5 were shown to meet the required regulatory obligations. Subsequent to the establishment of the IAP, 20 new clinical studies commenced bringing the total number of active studies to 44; all of which were shown to be compliant with the regulations and, in some cases, the amount of research time saved amounted to one year. The early incorporation of regulatory strategies within a business plan would enhance the spinoff process. Muller (2010) observes having complementary skills reduces the time-lag in the establishment of an academic spinoff ﬁrm supported by Grimaldi et al. (2011) who notes that one of the primary challenges in the evolution of technology transfer is that of identifying suitable actors to bridge the academic and commercial divide. In the medical device industry, this particularly concerns regulatory requirements which rapidly increase as a medical device approaches market entry. The suggestion of the earlier incorporation of regulatory strategies to support spinoff formation is further supported by Curran et al. (2011) who note that it may be prudent to include people with strong industry knowledge (i.e. regulatory knowledge) in the management team of university spin-offs at the earliest possible stage.

Regulatory requirements have a direct relationship with funding requirements, in terms of both initial and continued funding but differ across the different medical device classiﬁcations which, in turn, heavily inﬂuences the costs associated with ensuring compliance. The weighting attributed to criticality of regulatory factors is perhaps undervalued, and in particular that attributed during spinoff formation. Perhaps if there were more awareness of the regulatory requirements and their implications at an earlier stage, the criticality attributed to them in supporting both the spinoff venture formation and the subsequent success and survival may be higher?

Venture capitalists prefer to invest after the seed stage once ventures have become established and are likely to have already demonstrated regulatory compliance where regulatory considerations may not be a significant contributor to investment decisions (Wright et al., 2006). It appears the decision to invest is largely focused on factors which directly contribute to return on investment such as IP protection and market opportunity. In the case of ventures seeking seed or start-up capital the influence of regulatory considerations on investment decisions are liable to increase. The benefits of addressing regulatory requirement at an early stage can be seen to be dependent on the type of capital being sought: seed, start up, early stage, expansion stage or late stage.

Inﬂuence of early incorporation of regulations

Conclusions

Although regulatory requirements are often intended to be first considered during the early stages of academic research, they are more likely to only be appropriately considered during later stages. Over 70% of respondents from both groups I and II strongly agree that the early incorporation of regulatory strategies within funding applications would enhance the funding process. However, whilst funding agencies are conscious of the regulatory needs it only becomes a critical factor in the case of applied research. The funding required to conduct such applied research can be heavily influenced by the specific regulatory requirements of the medical device, particularly those of

Our findings reveal is that there is an apparent degree of separation between the academic spinoff formation process and the regulatory process, with the regulatory process lagging that of the spinoff process. Whilst the medical device regulatory framework may not prevent a spinoff from forming, it certainly has the potential to delay, perhaps signiﬁcantly, market entry. To temper this, these two processes should be seen to work in parallel from the earliest stage of the commercialisation process. Furthermore, given the nuances of the medical device regulatory framework, expert regulatory input is

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highly recommended to be sought at this early stage. Such an approach can be seen to significantly support the spinoff process across several stages: • Funding: both the duration and resources required to commercialise medical device research are heavily influenced by the specific regulatory requirements of the concerned technology. A better commercial case for the medical device, based on more accurate estimates of duration and cost, resulting from a sound understanding of regulatory requirements would be provided for. • Research Activities: conducting pre-clinical testing in line with relevant standards reduces the burden of demonstrating conformance to the relevant medical device legislation. This is a long term benefit which pre-empts the regulatory requirements which increase substantially as market entry approaches. • Capital Investment: market access is dictated by meeting the regulatory requirements. This is particularly pertinent in the case of highly innovative medical technologies seeking seed or start-up capital. Demonstrating an astute regulatory strategy corroborates market access strategies. A key aspect of this is the establishment of a micro enterprise support structure should be established to support indigenous startups at third level. To foster and support spinoff creation within the medical device sector it is essential that this structure incorporates a regulatory support mechanism. Such a support mechanism may become a necessity should the proposed new medical device regulations come into force as currently proposed as there will be a requirement for manufacturers to have available within their organisation a person responsible for regulatory compliance activities who possesses expert knowledge in the field of medical devices. In the case of micro and small enterprises, whilst they are not required to have such expertise within their organisation they will be required to have such person permanently and continuously at their disposal. Our analysis makes important contributions to technology management research. Our findings provide an insight into the impact of medical device regulations on academic spinoff formation across a wide and diverse range of stakeholders. Prior research recognizes that regulations are essential to commercialisation success and our findings add to this debate. These results allow us to advance the general theoretical development of the field. These findings are useful in furthering our understanding of how to best bridge the gap between theory and practice. Hence, this study is of managerial relevance to entrepreneurs. Certain limitations of this study should be noted. This study focused solely on academic spinoffs operating in the medical technology industry in a small open economy i.e. Ireland. Consequently, the context of this study is quite specific, and the explanatory power of our findings may be limited to this particular industry or country. Future studies could strive to address this deficit.

References Abell, P., Felin, T. & Foss, N. (2008). Building micro-foundations for the routines, capabilities, and performance links. Managerial and Decision Economics, 29, 489–502. Arbit, H. & Paller, M.S. (2006). A program to provide regulatory support for investigator-initiated clinical research. Academic Medicine, 81, 146-15 Algieri, B., Aquino, A. & Succurro, M. (2011). Technology transfer offices and academic spin-off creation: the case of Italy. The Journal of Technology Transfer, 38(4), pp.382–400. Argote, L. & Ren, Y. (2012). Transactive memory systems: a micro foundation of dynamic capabilities. Journal of Management Studies, 49(8), pp.1375–1382. Audretsch, D.B. & Lehmann, E.E. (2005). Mansfield’s Missing Link: The Impact of Knowledge Spillovers on Firm Growth. Essays in Honor of Edwin Mansfield, 271–274. Blind, K. (2012). The inﬂuence of regulations on innovation: A quantitative assessment for OECD countries. Research Policy, 41, 391–400. Bower, D. J. (2003). Business model fashion and the academic spinout firm. R&D Management, 33(2), 97-106. Bray, M.J. & Lee, J.N. (2000). University revenues from technology transfer. Journal of Business Venturing, 15(5-6), 385–392. Carree, M., Malva, A. D., & Santarelli, E. (2014). The contribution of universities to growth: Empirical evidence for Italy. The Journal of Technology Transfer, 39(3), 393–414. Chatterji, A. K. (2009). Spawned with a silver spoon? Entrepreneurial performance and innovation in the medical device industry. Strategic Management Journal, 30, 189–206. Clarysse, B., & Moray, N. (2004). A process study of entrepreneurial team formation: The case of a research-based spin-off. Journal of Business Venturing, 19(1), 55–79. Curran, D., van Egeraat, C., & O’Gorman, C. (2011). New entrants and inherited competence: The evolution of the Irish biotech sector. Working Paper No.66, National Institute for Regional and Spatial Analysis. D’este, P., Iammarino, S., Savona, M., & von Tunzelmann, N. (2012). What hampers innovation? Revealed barriers versus deterring barriers. Research Policy, 21, 482–488. D’Este, P., & Perkmann, M. (2010). Why do academics engage with industry? The entrepreneurial university and individual motivations. The Journal of Technology Transfer, 36(3), 316–339. Fan, W. & Yan, Z. (2010). Factors affecting response rates of the web survey: A systematic review. Computers in Human Behaviour, 26, 132–139.

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Felin, T. & Foss, N. J. (2005). Strategic organization: a field in search of micro-foundations. Strategic Organization, 3, 441–55. Felin, T., Foss, N. J., Heimeriks, K. H. & Madsen, T. L. (2012). Microfoundations of Routines and Capabilities: Individuals, Processes, and Structure. Journal of Management Studies, 49: 1351–1374. Fontes, M. (2005). The process of transformation of scientiﬁc and technological knowledge into economic value conducted by biotechnology spin-offs. Technovation, 25, 239–347. Franklin, S. J., Wright, M., & Lockett, A. (2001). Academic and surrogate entrepreneurs in university spin-out companies. The Journal of Technology Transfer, 26(1-2), 127-141. Grandi, A., & Grimaldi, R. (2003). Exploring the networking characteristics of new venture founding teams. Small Business Economics, 21(4), 329–341. Grandi, A., & Grimaldi, R. (2005). Academics’ organizational characteristics and the generation of successful business ideas. Journal of Business Venturing, 20(6), 821-845. Harrison & Leitch, 2010; Harrison, R. T., & Leitch, C. (2010). Voodoo Institution or Entrepreneurial University? Spin-off companies, the entrepreneurial system and regional development in the UK. Regional Studies, 44(November), 1241–1262.

Kvale, S. & Brinkmann, S. (2009). Interviews: Learning the Craft of Qualitative Research Interviewing (2nd Edition). Thousand Oaks, CA: Sage, 2009. Lazear, E.P. (2004). Balanced Skills and Entrepreneurship. American Economic Review, 94(2), 208–211. Link, A. N., & Scott, J. T. (2005). Opening the ivory tower’s door: An analysis of the determinants of the formation of US university spinoff companies. Research Policy, 34(7), 1106-1112. Lockett, A., & Wright, M. (2005). Resources, capabilities, risk capital and the creation of university spin-out companies. Research Policy, 34(7), 1043–1057. Lockett, A., Siegel, D., Wright, M., & Ensley, M. D. (2005). The creation of spin-off firms at public research institutions: Managerial and policy implications. Research Policy, 34(7), 981–993. Louis, K. S., L. M. Jones, M. S. Anderson, D. Blumenthal & Campbell, E. G. (2001). Entrepreneurship, secrecy, and productivity: a comparison of clinical and non-clinical life sciences faculty, Journal of Technology Transfer, 26(3), 233–245. Mosey, S., & Wright, M. (2007). From Human Capital to Social Capital: A Longitudinal Study of Technology-Based Academic Entrepreneurs. Entrepreneurship Theory & Practice, 31(6), 909–935. Muller, K. (2010). Academic spin-offs transfer speed - analyzing the time from leaving university to venture. Research Policy, 39:189–199.

Heirman, A., & Clarysse, B. (2007). Do intangible assets and prefounding R&D matter for innovation speed in start-ups. Journal of Product Innovation Management, (4), pp.303–315.

Mustar, P., Wright, M., & Clarysse, B. (2008). University spin-off firms: lessons from ten years of experience in Europe. Science and Public Policy, 35(2), 67-80.

Henrekson, M., & Rosenberg, N. (2001). Designing efficient institutions for science-based entrepreneurship: Lesson from the US and Sweden. The Journal of Technology Transfer, 26(3), 207–231.

Ndonzuau, F. N., Pirnay, F., & Surlemont, B. (2002). A stage model of academic spin-off creation. Technovation, 22(5), 281–289.

Hoang, H. & Antoncic, B. (2003). Network-based research in entrepreneurship. Journal of Business Venturing, 18(2), pp.165–187.

Nicolaou, N., & Birley, S. (2003). Academic networks in a trichotomous categorisation of university spinouts. Journal of Business Venturing, 18(3), 333–359.

Iacobucci, D., Iacopini, A., Micozzi, A., & Orsini, S. (2011). Fostering entrepreneurship in academic spin-offs. International Journal of Entrepreneurship and Small Business, 12(4), 513–533.

Nicolaou, N., & Birley, S. (2003). Social Networks in Organizational Emergence: The University Spinout Phenomenon, Management Science, 49(12),1702–1725.

Iacobucci, D., & Micozzi, A. (2014). How to evaluate the impact of academic spin-offs on local development: an empirical analysis of the Italian case. The Journal of Technology Transfer, 1-19.

Niosi, J. (2006). Success factors in Canadian academic spin-offs. The Journal of Technology Transfer, 31(4), 451–457.

Klofsten, M., & Jones-Evans, D. (2000). Comparing Academic Entrepreneurship in Europe – The Case of Sweden and Ireland, Small Business Economics, 14(4), 299–309. Knockaert, M., Ucbasaran, D., Wright, M., & Clarysse, B. (2011). The relationship between knowledge transfer, top management team composition, and performance: The case of science-based entrepreneurial firms. Entrepreneurship: Theory and Practice, 35(4), 777–803.

O’Shea et al. 2008; O’Shea, R. P., Chugh, H., & Allen, T. J. (2008). Determinants and consequences of university spinoff activity: a conceptual framework. The Journal of Technology Transfer, 33(6), 653-666. Panacek, E. A. (2008). Survey based research: Performing the survey. Air Medical Journal, 27(2), 64–66. Pérez, M. P., & Sánchez, A. M. (2003). The development of university spin-offs: early dynamics of technology transfer and networking. Technovation, 23(10), 823-831.

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Phan, P. H., Siegel, D. S., & Wright, M. (2005). Science parks and incubators: observations, synthesis and future research, Journal of Business Venturing, 20(2), 165–182. Poon, P. & Liyanage, S. (2004). Commercialisation of biotechnology in newly industrialised economies. International Journal of Biotechnology, 6(2/3), 243. Rappert, B. & Webster, A. (1997). Regimes of ordering: the commercialization of intellectual property in industrial-academic collaborations. Technology Analysis & Strategic Management, 9(2), 115–130. Rasmussen, E., & Borch, O. J. (2010). University capabilities in facilitating entrepreneurship: A longitudinal study of spin-off ventures at mid-range universities. Research Policy, 39(5), 602–612. Regnstrom, J., Koenig, F., Aronsson, B., Reimer, T., Svendsen, K., Tsigkos, S., Flamion, B., Eichler, H. G., & Vamvakas, S. (2010). Factors associated with success of market authorisation applications for pharmaceutical drugs submitted to the european medicines agency. European Journal of Clinical Pharmacology, 66(1), 39–48. Rogers, E. M., Takegami, S., & Yin, J. (2001). Lessons learned about technology transfer. Technovation, 21, 253–261. Rosa, P., & Dawson, A. (2006). Gender and the commercialization of university science: academic founders of spinout companies. Entrepreneurship and Regional Development, 18(4), 341-366. Sanchez-Fernandez, J., Mun˜oz-Leiva, F., & Montoro-R´ıos, F. J. (2012). Improving retention rates and response quality in web-based surveys. Computers in Human Behaviour, 28, 507–514. Sauermann, H. & Roach, M., 2012. Science PhD Career Preferences: Levels, Changes, and Advisor Encouragement L. A. Nunes Amaral, ed. PLoS ONE, 7(5), 36307. Sharif, N. & Baark, E. (2008). Mobilizing technology transfer from university to industry, Journal of Technology Management China, 3(1), 47–65. Siegel, D. S., Veugelers, R., & Wright, M. (2007). Technology transfer offices and commercialization of university intellectual property: performance and policy implications. Oxford Review of Economic Policy, 23(4), 640-660. Siegel, D. S., Waldman, D.A., Atwater, L. E., & Link, A. N. (2004). Toward a model of the effective transfer of scientiﬁc knowledge from academicians to practitioners: qualitative evidence from the commercialization of university technologies. Journal of Engineering and Technology Management, 21, 115–142.

Siegel, D. S., Westhead, P. & Wright, M. (2003d). Science parks and the performance of new technology-based firms: a review of recent U.K. evidence and an agenda for future research, Small Business Economics, 20, 177–184. Smilor, R. W., Gibson, D. V., & Dietrich, G. B. (1990). University spinout companies: Technology start-ups from UT-Austin. Journal of Business Venturing, 5(1), 63–76. Uctu, R., & Jafta, R. C. (2012). Academic entrepreneurship in South East Asia: an exploratory study of spin-offs in biotechnology from Hong Kong universities. Asian Journal of Technology Innovation, 20(1), 141-154. Van Dierdonck, R., & Debackere, K. (1988). Academic entrepreneurship at Belgian Universities, R&D Management, 18 (4) (1988), pp. 341–354. Van Egeraat, C., Curran, D., & Kerr, A. (2009). Social and spatial structures of collaboration and innovation in the knowledge economy. Deliverable 11.2 for EU FP6 OPAALS research project, National Institute for Regional and Spatial Analysis. Vanaelst, I., Clarysse, B., Wright, M., Lockett, A., Moray, N., & S’Jegers, R. (2006). Entrepreneurial team development in academic spinouts: An examination of team heterogeneity. Entrepreneurship Theory and Practice, 30(2), 249-271. Vincett, P. S. (2010). The economic impacts of academic spin-off companies, and their implications for public policy. Research Policy, 39(6), 736–747. Vohora et al. 2004; Vohora, A., Wright, M., & Lockett, A. (2004). Critical junctures in the development of university high-tech spinout companies. Research Policy, 33(1), 147–175. Voss, C., Tsikriktsis, N. & Frohlich, M. (2002). Case research in operations management, International Journal of Operations & Production Management, 22 (2), 195 - 219 Walter et al 2006; Walter, A., Auer, M., & Ritter, T. (2006). The impact of network capabilities and entrepreneurial orientation on university spin-off performance. Journal of Business Venturing, 21(4), 541-567. Wennberg, K., Wiklund, J. & Wright, M. (2001). The effectiveness of university knowledge spillovers: performance differences between university spinoffs and corporate spinoffs, Research Policy, 40 (2011), 1128–1143. Wright, M., Lockett, A., Clarysse, B., & Binks, M. (2006). University spin-out companies and venture capital. Research Policy, 35, 481–501. Yin, R.K. (2014). Case study research: Design and methods (5th ed.). Thousand Oaks, CA: Sage publications.

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Stakeholders’ Contribution towards Responsible Innovation in Information and Communication Technology Research Projects Tilimbe Jiya1 Abstract: Information and Communication Technology (ICT) research projects engage stakeholders who contribute towards different aspects of research and innovation. One of the aspects that stakeholders contribute towards in ICT research projects is responsibility. There is a need for those engaged in ICT research projects to take into consideration the impacts of their activities on society as part of responsible research and innovation (RRI) through finding solutions to emerging societal problems and developing sustainable processes of carrying out research and innovation. In this paper, the focus is two-fold. Firstly, I focus on understanding how stakeholders are identified to contribute towards responsibility in ICT research projects and secondly, I focus on how stakeholders contribute towards responsibility in such projects. I conducted an interpretive case study in which data were collected by semi-structured interviews with 11 stakeholders from two ICT research projects. Through thematic data analysis, their perceptions and understanding of their contribution towards responsibility were conceived. From the study findings, I established that there are problems in understanding the meaning of term ‘responsibility’ among stakeholders which later affects the identification of roles that deal with it. Despite the ambiguity of the meaning, I found that stakeholders contribute towards responsibility in many ways although there are barriers that affect their contribution. Keywords: Responsibility; stakeholder engagement; stakeholder contribution; responsible innovation; ICT research projects Submitted: Jan 7th, 2019 / Approved: Sep 19th, 2019

Introduction In recent years there has been an upsurge of technological innovation through ICT research projects. These projects engage an array of stakeholders who are assigned roles that deal with different aspects of the project ranging from financial viability to social sustainability. One of such aspects is ‘responsibility’. Responsibility is a fundamental aspect of research and innovation (R&I) (Grinbaum & Groves, 2013; Noorman, 2014; Urbanovič & Tauginienė, 2013). Therefore, it is vital that it is part of the discourse that takes place with regards to the implementation of successful ICT research projects (Sullins, 2012). One way of achieving the successful implementation of the projects is by engaging stakeholders who assimilate roles that could contribute towards the recognition of responsibility within those projects (Chatfield, Iatridis, Stahl, & Paspallis, 2017; Frankel, 2015; Sullins, 2012). For instance, in recent years, this view has been supported by the concept of Responsible Research and Innovation (RRI). Under RRI, stakeholders should have roles that contribute towards the recognition and integration of responsibility in innovation processes within ICT research projects (Bauer, Bogner, Fuchs, Kosow, & Dreyer, 2016; Jirotka, Grimpe, Stahl, Eden, & Hartswood, 2017). The engagement of different stakeholders facilitates an open, inclusive and timely exploration of different aspects of the innovation process (B. C. Stahl, Eden, Jirotka, & Coeckelbergh, 2014). The contribution of stakeholders that are engaged in ICT research projects has the potential to ensure that the outputs and outcomes of R&I that is taking place in such projects are not detrimental to society’s wellbeing (Ayuso, Ángel Rodríguez, García‐Castro, & Ángel Ariño, 2011; Grunwald,

2011). In ICT research projects, it is essential that stakeholders are determined and effectively engaged so that there is a progressive consideration of ethical and social implications of technology innovation and advancement. Responsibility is an important theme in recent European policy discussions about the future of research and innovation, particularly about new and emergent fields in technology (Owen, Bessant, and Heintz 2013a). The main goal is to maximise the positive and minimise the negative impacts of new technologies such as ICTs, by intervening in the process of their development through more awareness and collective consideration of emerging societal impacts (Stahl, 2012). Most of the societal challenges are pervasive and interconnected. Thus, to effectively resolve them there is need to engage a range of stakeholders in contributing towards the resolution. As part of their contribution, stakeholders offer a range of perspectives and expertise which positively influence the perception and integration of responsibility within ICT research projects. In this paper, my focus is on understanding how stakeholders are identified to contribute towards responsibility in ICT research projects and how stakeholders contribute towards the assimilation of responsibility in ICT research projects. There has been considerable research on stakeholder contribution towards different aspects of technology such as impact and financial viability. However, there seem to be a gap in research that looks at contribution towards responsibility specifically in ICT research projects. Also, the range of stakeholders is so wide and therefore leads to problems when identifying relevant stakeholders to contribute towards responsibility or

1) Centre for Computing and Social Responsibility, De Montfort University, The Gateway, Leicester, United Kingdom. *Corresponding author: tilimbe.jiya@dmu.ac.uk

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responsible behaviour in ICT research projects. Responsibility is an essential element when carrying out innovative activities within ICT research projects. Therefore, for stakeholders to effectively contribute towards this important element, their roles should be incontrovertibly defined so that they are certain about what is expected of them about responsibility. To gain such an understanding, I have chosen ICT research projects as the case since they are an important platform for the discovery and exploitation of new technologies that affect society. I used two ICT research projects as case studies. Stakeholders engaged in these projects need to take into consideration the impacts of their activities on society and should find solutions to emerging societal challenges. For that reason, the critical question addressed in this paper is; how do stakeholders that are engaged in ICT research projects contribute towards responsibility in research and innovation? This paper contributes to the discourse on the social and human aspects of ICT research and innovation by taking a step back and reflecting on how human behaviour influences the process and its outcomes.

Responsibility in ICT research and innovation New ICTs that affect individuals and society in many ways are continually being developed through R&I that is taking place in projects. Although these new technologies have benefits to society, they can have negative consequences too. These consequences are responsibility issues that affect society.

Responsibility Responsibility is indispensable and significant at all levels of ICT research and innovation starting from idea generation all the way up to society utilisation of the outputs and outcomes (Auhagen & Bierhoff, 2001; Lenk & Maring, 2001). However, although this may sound obvious, there are issues with how responsibility can be understood and integrated into stakeholders’ actions and therefore R&I processes within ICT research projects. One of the issues could be down to the ambiguity of what ‘responsibility’ means (Pellé & Reber, 2015) which then affects how one makes reference to it and understands it within an ICT research project. For instance, responsibility within an ICT research project could be looked at in terms of moral values that are affected by the outcomes of the innovation being developed in that project. Taking this stance translates into having an understanding of responsibility regarding the moral commitment of stakeholders that are engaged in the realisation of these outcomes (Noorman, 2014; Sand, 2016; Sullins, 2012). Another angle could be looking at responsibility within ICT research projects in connection to the social and ethical desirability of the innovation and research process as mentioned in RRI accounts (Guston 2011; Owen et al. 2013b; Von Schomberg 2013; Simon 2017; Jirotka et al. 2017; Stahl et al. 2017). In these accounts, responsibility is looked at in the light of anticipation, reflexivity, responsiveness, transparency and public participation.

With the former, responsibility in ICT research projects could be understood in terms of using morally adequate standards and principles when executing innovation processes complemented by the stakeholders who sensitively accommodate ‘accepted’ moral values into the processes and outcomes of their project. While with the latter, responsibility in ICT research projects could be looked at regarding an active collective understanding that seeks to prevent harm and identify more positive outcomes for the innovation process (Lenk & Maring, 2001; Pellé & Reber, 2015). This distinction between the meanings of responsibility is imperative because it highlights a profound issue with regards to stakeholders’ contribution towards responsibility or responsible behaviour. It partly shows how responsibility can variably be understood and interpreted which consequently affects the way it would be regarded as an element of the processes within ICT research projects. In terms of conception, the notion of responsibility has various constructions that are linked to assignment, attribution and imputation of one’s actions or their consequences under the judgement of an agent. The imputation of one’s actions is in relation to a ‘set of criteria of attribution and accountability within a specific context of responsibility and action’ (Lenk & Maring, 2001, p. 95). In simple terms, responsibility means that stakeholders may be expected to justify situations, actions and their tasks with respect to their obligations and roles according to set standards, criteria and norms (Auhagen & Bierhoff, 2001). It also covers the capacity and authority of an agent undertaking a certain task and is further understood regarding an agent’s accountability, obligation, liability and their link to the causes and outcomes of a particular endeavour (Pellé & Reber, 2015). These are elements of responsible behaviour in ICT research projects, and stakeholders play a very significant role in influencing such behaviour. This then gives me a departure point to look at who are the stakeholders and how are they engaged to contribute. It is a challenge to integrate responsibility into a project of any type, including an ICT research project if the project fails to understand its stakeholders and their role.

Stakeholder theory Stakeholder theory is an approach to recognising and dealing with relationships among stakeholders of a project. There is more literature on stakeholder theory in business, and environmental studies which can be used in technology studies as well since the underlining principles and theoretical underpinnings are transferable and can, therefore, be adopted. In most of the literature, the stakeholder theory is linked to firms and organisations. However, I see no reason to not apply the same to ICT research projects because the general dynamics within a project resemble those of an organisation or a firm. Stakeholder theory can be traced back to the seminal work of Freeman (1984), who articulated a new conceptual model of the firm [the project] that must address the interests of its stakeholders, both groups and individuals who can affect or are affected by the firm’s purpose (Röbken, 2013, p. 63). There is a variance on what defines a stakeholder and the definition of a stakeholder spans across many disciplines and industries. Taking into consideration the definition adopted for this paper, from an ICT

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research project perspective, stakeholders may include researchers, funders, local authorities, civil society organisations and industry players. These are all critical for the ICT research project’s survival and success in that they significantly affect how the project is directed and controlled (Freeman, 2010; Lozano, 2005). Because of the variations in defining stakeholders, there are issues and ambiguities when defining roles that are assigned to these stakeholders. These ambiguities are related to two viewpoints; one that a stakeholder could be defined with regards to the legitimate interest of the prospective agent (person or a group) in the project. However, legitimate interest is subjective and can cause further problems with regards to stakeholder definition. The other viewpoint is that a stakeholder could be defined in terms of a categorisation of a group of members of the society (Carney, Whitmarsh, Nicholson-Cole, & Shackley, 2009). Despite that these perspectives are fundamental to the ambiguity resulting from the variation in defining stakeholders, they present a reference point in determining who is a stakeholder in an ICT research project. The conceptualisation of a stakeholder is quite broad therefore it can be troublesome when it comes to defining a stakeholder in an ICT research project, leading to confusion and misconception of who is a stakeholder and what role do they assume. To avoid this misconception, in this paper, a stakeholder will among others include researchers that are engaged in ICT research projects. One reason for this proposition is that researchers should arguably be considered as stakeholders since they can affect or be affected by the outcomes of ICT research projects.

Stakeholder identification and engagement Stakeholders may be identified using a stakeholder analysis approach which involves categorising stakeholders in relation to their level of interest, influence and relevance to the project (Leventon, Fleskens, Claringbould, Schwilch, & Hessel, 2016). This approach has the potential to enhance the effectiveness of the stakeholder identification process in ICT research projects. For instance, regarding influence, stakeholders could be identified to contribute based on of their knowledge and expertise (Rahman, Moonira, & Zuhora, 2015). Another approach suggested by Reed et al. (2009) distinguish between different mechanisms of stakeholder identification through a typology specifically focusing on stakeholder engagement in research. The typology highlights the notion that different types of stakeholders may be engaged subject to the perceived technical competence and influence on outcomes at different phases of the ICT research project. Using a typology when inviting stakeholders to contribute to ICT research projects aids the clarification of the level of contribution that is expected from those who are assigned certain roles. If the identification of stakeholders and their roles is wrong, the expected contribution will not be very effective to the project (Durham, Baker, Smith, Moore, & Morgan, 2014). Having looked at what a stakeholder theoretically entails and how their roles can potentially be identified for ICT research projects, I will now shift my focus to the importance they generally have when engaged in ICT research projects.

Importance of stakeholders engaged in ICT research projects From the literature on stakeholder engagement, the following could be deemed as the main reasons why stakeholders engaged in ICT research projects are important with regards to the overall success of the project. The first reason is one of knowledge co-production between stakeholders (internal and external). This co-production of knowledge is often a result of active input from different stakeholders which facilitates mutual learning (Chilvers, 2013). Secondly, different stakeholders contribute in increasing the legitimacy of the technology project. Results from the projects that engaged different stakeholders claim legitimacy compared to one that did not engage relevant stakeholders (Spitzeck & Hansen, 2010). Thirdly, stakeholders facilitate accountability of significant uncertainties that occur in ICT research projects (Taghian, D’Souza, & Polonsky, 2015). The fourth reason is that stakeholders influence the success of the project by bringing a wider input based on their different disciplines and background on different perspectives. Lastly, stakeholders inform policy formulation and assist in maintaining the relevance of the project to a policy (Webler et al., 2001). The importance of engaging stakeholders that is discussed here is more generic, and it relates to a higher-level aspect of project success. However, stakeholders may also contribute to more specific lower-level aspects of the project such as the integration of responsibility.

Challenges of engaging stakeholders in ICT research projects Notwithstanding the benefits of engaging stakeholders in ICT research projects, there are challenges. Engaging multiple stakeholders’ increases costs to the ICT research projects and might make the execution of the projects more complicated, for example, through conflict of interest among the different stakeholders (Carney et al., 2009). In ICT research projects, some internal stakeholders may see the engagement of external stakeholders as a constraint instead of an opportunity (Durham et al., 2014) which then results in a conflict of interest and direction. Another challenge could be that some stakeholders may lack time to engage, or may experience ‘stakeholder fatigue’, that is, they may feel overloaded with engagement activities. This then adversely affect their willingness to participate and therefore lessen the quality of their contribution (Blok et al. 2015). In this section, I have looked at how stakeholders are identified in ICT research projects. Stakeholders are influential in technology, and they contribute towards many aspects of ICT research projects despite challenges that could potentially affect the effectiveness of their contribution. In the next section, I will present the method used in the study that informs this paper.

Method This section presents the method that was used in the conducting the case study that informs this paper. I discuss the design of the research, the cases and their participants that were involved and the procedure that was used during data collection and analysis.

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Design This paper used an interpretive case study to understand how different stakeholders contribute towards the integration of responsibility in ICT research projects. The cases used in this paper were two ICT

research projects that addressed ways of improving environmental sustainability through ICT innovation. These projects were selected based on the criteria provided in Table 1 below.

Table 2: Criterion and indicators for selecting case projects Criterion

Indicator(s)

The project should be an ICT research project that is involved with research and innovation in ICT in the UK.

The research and innovation in ICT should involve; Stakeholder communication processes. Innovation processes that deal with societal challenges. The development of new technology that will tackle social challenges. The development of new methodologies that will deal with social impacts.

Cases Five ICT research projects were identified as potential cases and using the criteria presented in Table 1 above, two ICT research projects were

selected for the interpretive case study. The cases have been anonymised by using naming codes and are described in Table 2 below.

Table 2: Case ICT Research Project Descriptions Case

Description

I-Traq (IT)

The focus of IT was the use of ICT in developing a dynamic traffic management system across ‘city X’. The purpose of IT was to research and develop an innovative way for optimising the use of the road network while meeting growing demands to sustain high standards of air quality in urban environments. The ICT research project involved developing a system concept around an existing operational traffic control system that was already in use in one of the UK cities. The system was augmented with traffic flow and air quality information and near real-time data from space and in situ measurements

Smartspaces (SS)

SS aimed to research the use of ICT in promoting environmental sustainability. SS focused on how carbon dioxide emissions from buildings could be monitored and consequently reduced. The project was involved in energy use optimisation through a comprehensive approach to exploiting the potential of ICT. As part of the optimisation, the research employed the use of smart metering to achieve significant energy saving in public buildings. To accomplish this aim, the research project built on existing services to develop a comprehensive ICT system (sometimes referred to as a ‘dashboard’) that provides feedback on energy consumption and information for organisational energy management. The system used ICT to offer access to energy consumption data and provide the results of the sophisticated data analysis intuitively and engagingly

Participants Emails requesting participation in the study were sent to 26 study participants who were involved in the two ICT research projects described in Table 2 above. The potential study participants included four industry representatives, seven researchers, two project sponsor, two principal investigators, three software designer and

two advisors and six local authority representative. Out of the 26 stakeholder study participants, 11 participated in the interviews. The others either never responded or cancelled their interview arrangements. The study participants were recruited based on the criteria shown in Table 3.

Table 3: Criteria for selecting study participants Criteria

Indicator

A participant should be from a relevant context

Engaged in an ICT research project that is taking place at a recognised research institution or organisation in the UK.

A participant should comprehend the language used in the research

Should understand both written and spoken English since this study is conducted in that language.

A participant should have a stakeholder role in the ICT research project whether as an external or internal stakeholder.

Should be a member of civil society, policy-making organisation, research institutions, academia and funding organisation.

A participant should be accessible during the data collection phase of the research.

Should be accessible in person, by telephone or VOIP (e.g. Skype).

Should consent to be interviewed.

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Procedure

Time of identification

Two key stakeholders from each case ICT research project were purposively sampled. Once the key stakeholders were sampled, using a snowballing sampling technique, these stakeholders suggested others for potential participation in the case study. The study participants took part in semi-structured interviews that lasted between 45 minutes to 1 hour. The interviews were held at places of convenience for the study participants to ensure having as many study participants as possible. The interviews focused on understanding how the stakeholders got engaged in the ICT research projects and how they felt that they contributed towards integrating responsibility in their respective projects. All the interviews were recorded and transcribed before being uploaded to Nvivo qualitative data analysis software for storage and analysis.

The time at which stakeholders are identified and defined is crucial in ICT research projects because it influences the level of the stakeholders’ contribution towards responsibility in a project. From the study, I found that stakeholders are more often identified, and their roles defined in advance at the inception phase of ICT research projects. Identifying stakeholders and their roles pre-project works well at the early stages of the projects because it helps to lay down the foundations of the projects and recognise roles that will put the projects into motion and integrate responsibility from the word go. However, as the projects progress there should be new stakeholders identified to deal with ad-hoc needs. In terms of responsibility, identifying stakeholders ad hoc could be a better approach in dealing with emerging issues that innovative ICTs may pose to the society.

Data analysis

Who is in control for identifying and defining stakeholder roles

The analysis process was based on a thematic analysis (Patton, 2002). First, I developed a coding scheme based on the main themes that were identified from the research question. Using the identified themes, data were then grouped under the main themes which included; understanding responsibility, stakeholder identification and form of stakeholder contribution. While these broad categories were initially based on the research question, sub-categories to populate these categories were inductively identified from the transcription through initial coding in Nvivo software. The first stage of the data analysis was a careful reading of the interview transcripts from which a thematic outline was developed which included code classification and justification for the code. Data were extracted from the transcripts and summarised into a series of nodes that represented the categories and sub-categories identified at the beginning of the data analysis. New nodes emerged during the second and third iteration of coding. The second stage of the data analysis involved grouping the nodes and developing them into theoretical constructs based on their relevance and similarity in meaning until no new thematic meanings were emerging from the text. These constructs were the findings from the study, and they are discussed in the following section.

Another important thing to consider in identifying and defining stakeholders is the question of who should do it. Regarding responsibility, this is crucial because if those designated to identify and define roles are not very familiar with potential responsibility issues relating to the processes, outputs and outcomes of the ICT research projects, they can miss out on engaging appropriate stakeholders. As Durham (2014) suggested, this could result in the projects having negative impacts. From the study, it was learnt that stakeholders are better identified, and their roles defined if the processes are a result of combined thoughts and ideas from the project initiators and other stakeholders rather than an individual. This was highlighted in one of the responses where a stakeholder alleged that;

Findings and Discussion In this section, I discuss the findings from the case study. The findings are categorised into three main areas that consist of stakeholder identification, the contribution of stakeholders towards responsibility and barriers to stakeholder contribution towards responsibility.

Stakeholder identification As discussed earlier, there is ample literature on how stakeholders are identified in different projects (Durham et al., 2014; Reed et al., 2009). However, regarding identifying stakeholders to contribute towards responsibility in ICT research projects, some relevant themes emerged. These themes included the time of stakeholder identification, who identifies the stakeholders and issue with identifying stakeholders with regards to responsibility.

…the identification of stakeholders and their roles was a combination of my ideas together with XXX’s ideas...-IT01 To support the above statement, it was also learnt that there was an agreement by all project parties to include certain stakeholders within the ICT research projects who should look at specific aspects of the projects. This was revealed by these two responses from the interviews where study participants were asked about their involvement in identifying and defining stakeholder roles; The first respondent said that; I was in the process … well, I helped shape the proposal, therefore helped shape stakeholder roles...at the proposal stage, I helped change the roles but then after that I wasn’t involved …-IT02 While the second alleged that; We worked together with people involved in XXX, in XXX University and so we put the proposal [with the proposed roles] together to EMDA…. -SS01 Such a collective approach in identifying stakeholders and defining their roles ensures that there is a consensus among the parties

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involved in determining which roles are ideal for the projects’ aspects such as responsibility in innovation. In addition to taking a collective approach, care should be taken on the level of explicitness in defining the roles so that there is a sustainable buy-in among the stakeholders. One benefit resulting from explicitly defining roles is that it minimises confusion among stakeholders regarding their respective roles in the ICT research projects. This means that to achieve a considerable level of explicitness and therefore reduce confusion and at the same time increase buy-in from the stakeholders on the identified roles, there should be a substantial level of stakeholder involvement in role identification and definition process at the pre-project phase. Contrary to consensual role determination, the study showed that identifying stakeholders and defining roles could sometimes be ‘naturally’ endowed to the people who propose the project and have a high level of influence and interest on the ICT research project and its objectives. In determining who should define roles, a stakeholder analysis based on the stakeholder’s influence gauged against their interest, as suggested by Durham (2014) and Leventon et al. (2016) can be used to identify those to collaborate and involve in identifying new stakeholders and what roles they should take. However, this could only work well if the project is small and involve a small number of stakeholders otherwise there is a risk of conflict and confusion among the stakeholders undertaking the identified roles. It is worth pointing that every ICT research project is unique and is a construct of some issues that need to be considered such as decision-making processes, the culture of the project, context and aims. All these may affect which stakeholders can be and cannot be involved in contributing to certain aspects of the project, for instance, responsibility. Therefore, understanding some of these issues will ensure that the appropriate stakeholders who are relevant to contribute towards responsible behaviour within ICT research projects are identified.

The problem with identifying stakeholders with regards to responsibility In the study, it was acknowledged that the problem of stakeholder identification becomes more complicated and ambiguous when it comes to identifying stakeholders that deal with responsibility due to a misapprehension of the concept. An issue that was apparent from the study was the lack of knowledge or understanding of the terms ‘responsibility’ or ‘responsible innovation’. The apprehension of these terms is a prerequisite to effectively define stakeholders that will directly deal with them as is the case with any other aspects of the ICT research project that stakeholders could potentially be engaged for. However, in agreement with Pellé and Reber (2015), I learnt that stakeholders did not understand ‘responsibility’ per se, therefore, found it difficult to give their opinion and could not articulate what their expectation was regarding contributing towards responsible behaviour in an ICT research project. Hence, from the interviews, two problems that affect stakeholders’ expectation of their contribution towards responsibility were identified. These included ambiguity of what responsibility or responsible behaviour meant and the lack of knowledge about a clear connection between their roles and responsible behaviour.

This highlights a crucial point with regards to contributing towards responsibility, in other words, responsible behaviour in ICT research projects. What is crucial here, is a need for a wider awareness of the concepts of ‘responsibility’ or ‘responsible innovation’ and any other related concepts to stakeholders within ICT research and innovation so that they understand it more and embrace it without uncertainty. A bit of clarity on the use of terms could make it easier for stakeholders in ICT research projects to understand what their expectations are within the project with regards to promoting responsible behaviour or responsibility. To incontrovertibly and effectively identify stakeholders to support responsibility, it is crucial that they understand what responsibility in an ICT research project entails. Otherwise, their contribution becomes inadequate and has implications for stakeholders’ expectations. Concerning this study’s participants, they struggled to talk about their contribution regarding responsibility because they did not clearly understand the term. This correlates with the notion that there are a number of definitions or descriptions of responsibility (Auhagen & Bierhoff, 2001; Lenk & Maring, 2001) therefore it is not surprising to establish that there is confusion among stakeholders in ICT research projects caused by its conception. Evidence of the issue with understanding is shown in one of the responses when study participants were asked about their contribution towards integrating responsibility; Good question. Can you remind me … responsible innovation … responsibility in terms of the outputs of it ...or what? What do you mean by responsibility? - SS03 This could indicate that either the concept of responsibility in terms of responsible innovation was novel and therefore was not known to them or they had more interest in other aspects other than responsibility that was directly linked to the final outputs of the ICT research projects. The responses from the interviews indicated that the use of the term was ambiguous for their comprehension. It also was found that there was a divide in the apprehension of the term, with one interviewee giving an impression that they perhaps knew what responsibility in innovation involves due to their prior experience and knowledge of the term while the other had no clear apprehension. There was clearly a semantic confusion about the word responsibility, and it was looked at in terms of both as an obligation and attribution for the stakeholder (Lenk & Maring, 2001; Pellé & Reber, 2015). Comparatively, responsibility was partly understood as a requirement to carry out tasks as part of a duty that would result in responsible outcomes and therefore contributing towards responsibility in ICT research projects. This was shown in the following response where the interviewee was asked about their contribution towards responsibility within the project; …my responsibility was to implement a demonstrator to prove the feasibility of XXX. I guess the contribution towards responsible innovation is the air quality side of things ….my role was to look at ways how we could come up with a methodology that would combine these different objectives and deliver something that actually gives an output that will change lives... may potentially change lives by reducing air quality and at the same time reducing traffic congestion - IT02.

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A response such as one above indicates that the contribution of stakeholders towards responsibility could also be better understood in terms of the project’s processes and outputs. Therefore, to ascertain an integration of responsibility or responsible behaviour in ICT research projects, one could look at how the process is being carried out and then look at the project’s outcomes for instance, in relation to societal impacts.

How stakeholders contribute towards responsible innovation From the study, some ways through which stakeholders contribute towards responsibility in ICT research projects were brought to light. The contributions were either directly or indirectly and are discussed below.

Provision of expert knowledge The study participants in the interviews mentioned expertise as one way of contributing towards responsibility. Through their expert knowledge, stakeholders suggest innovative ways that can bring forth a public good from their research activities and outcomes. For instance, one of them said that they had to use their expert knowledge to come up with a methodology and design for improving air quality through a traffic management system. The stakeholders’ expert knowledge is one of the crucial elements to consider when contributing to responsibility in ICT research projects as can be evidenced by this response; …am an expert in the area of XXX. I work with optimisation…what was needed for the role was somebody who can design a system that could ensure a sustainable environment…. -SS04.

Sharing experiences Different stakeholders contribute towards responsibility in ICT research projects by sharing their experiences to capitalise on the power of combining perspectives from a spectrum of capabilities. This was substantiated by the following responses from the study participants; We had to combine my knowledge with the knowledge of XXX and the knowledge of XXX to come and make something that will make a difference in the world. -SS07 Engaging various stakeholders gather knowledge and cultivate different perspectives on how certain social problems could be solved and how to behave to mitigate the problems. In ICT research projects where different roles are assigned, stakeholders that have different backgrounds and experiences, therefore, different perspectives could be considered to have solutions to problems relating to certain aspects of the project. These different stakeholders bring with them new and unique perspectives on how certain issues could be resolved. So, with regards to responsibility, having a unique perspective on solutions require stakeholders to collectively think outside the norm and start looking at certain elements of the ICT research project such as objectives and outputs, in light of social impact and responsible innovation.

Provision of advice The study participants stated that they provided advice on carrying out the projects’ processes to attain outputs responsibly. The advice included directing procedures of the innovation process to have a positive social impact. These procedures involved developing methodology designs which could ensure that responsibility is upheld throughout the project’s activities, which then translated to responsible and sustainable outcomes from the ICT research projects. In the study, one of the respondent’s role was to give advice in steering the processes of the project towards achieving outputs that have a greater positive impact on society and at the same time ensuring that there were no deviations from achieving other objectives of the projects.

Provision of resources for promoting responsible behaviour For ICT research projects to integrate responsibility, there should be access to resources for promoting the agenda. These resources range from human resources to financial resources. Talking to the study participants, I found that funding is an important element of ICT research projects and that certain stakeholders are engaged to pool their resources both financial and human to promote responsibility in ICT research projects. As an example, from the study, an industrial stakeholder contributed financially by funding part of the project and its activities. Successful integration of responsibility in ICT research projects depend on the availability of resources that can be directed towards efforts that will ensure prioritisation of responsibility as part of the objectives of the project as said by one respondent; So, the outcome of my contribution could be cost saving […] my contribution towards behavioural change […] for example switching lights at night. -SS03.

Barriers to stakeholders’ contribution towards responsibility Having looked at how stakeholders contribute towards responsibility in ICT research projects, I turn my focus on some of the barriers to stakeholders’ contribution towards responsibility. From the study, I found that they are hindrances to integrating responsibility in ICT research projects as discussed below.

Dissimilarity in the way stakeholders do things The first barrier to stakeholders’ contribution towards responsibility within ICT research projects is the differences in the way engaged stakeholders do things. This affects the consensus on determining responsibility priorities and responsible processes within the project. The dissimilarities could be down to a variation of backgrounds and intentions of the stakeholders, which later affects the attitudes towards responsibility and prioritisation of its implementation. Also, due to the dissimilarities, misunderstandings among stakeholders emerge which then affect the way they would implement and ensure responsibility or at least contribute towards it.

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Stakeholder non-commitment The second barrier is the let downs among stakeholders who are assigned roles. Stakeholders can frustrate each other by not providing the necessary input that is necessary for the integration of responsibility such as data, information and materials that are required by others to fulfil their tasks. As said earlier, stakeholders need to be collaborative to implement responsibility effectively, however, stakeholders that are entrusted with roles can let each other down in many ways. For instance, it was highlighted in the study that some stakeholders were not providing resources such as data, as they promised, therefore, affecting the interim outcomes of the project. In other cases, the resources shared were sometimes not as valuable as expected, rendering them inadequate for purpose as evidenced in this response; …there were a few friends gathering the data [who] would promise… but sometimes the data was not really valuable as we thought. It, therefore, had an impact on the modelling that we developed.- SS04.

Stakeholder scepticism The third barrier in integrating responsibility in ICT research projects is related to fear of change. From this study, it was mentioned that ‘people fear change’ (SS02) and some stakeholders could be sceptical when it comes to contributing towards integrating responsibility because it is a change that alters the stakeholders’ way of doing things when executing project processes as one of the interviewees put it; Responsibility in innovation requires people to look outside of the box or to accept change, and that is probably the biggest problem when it comes to such projects in terms of innovation…- SS03 As a result of the fear of change or what may transpire from the change, stakeholders are comfortable to maintain the status quo which jeopardises the integration of responsibility and ultimately responsible innovation within ICT research projects.

Unforeseen resource constraints Another barrier to contributing towards responsibility within ICT research projects is resource deficits or changes that are unforeseen. These resources could include among other things time and funds for activities that could have an impact on contributing towards integrating responsibility. This was pointed out in the interviews by one of the study participants when they were asked about the barriers encountered during the project that could have affected its execution responsibly. The respondent alleged that; Another hurdle that we went through was that we had to run an extension of the project for two years… the company that we were working with was running through some economic problems...-IT02.

This indicates that the availability of resources could limit the contribution of stakeholders engaged in ICT research projects which then potentially results in cutting corners when executing the processes and therefore overlooking important elements that could impact society.

Lack of stakeholder compulsion Due to the unfamiliarity with the terms ‘responsible innovation’ or ‘responsibility’, stakeholders are inclined not to appreciate the need for integrating responsibility in the first place. Stakeholders will find it difficult to appreciate the need for responsibility or responsible innovation in ICT research projects until the notion is widely seen as a crucial requirement in ICT research projects. However, with the policy push by governments towards policies on responsible innovation such as RI (Murphy, Parry, & Walls, 2016) and RRI (Stahl, Timmermans, & Flick, 2016) a lot of stakeholders will start appreciating the need to implement responsibility in ICT research projects consistently.

Prioritising other aspects The last barrier to integrating responsibility in ICT research projects is stakeholders’ consideration of other aspects of the project to be of more significance than responsibility. The priority could be towards other aspects such as cost reduction and rapid output production (Schenke, van Driel, Geijsel, Sligte, & Volman, 2016). According to one study participant, this is the biggest barrier as they found out in their project; Unfortunately, very often [responsibility] is not enough. What is enough is if there is a cut in cost or if there is a policy that requires it. Possibly it’s important if somebody big enough says that, ok […] this is what we are supporting, but it’s very difficult, and that’s the biggest obstacle I would say – SS05. Integrating responsibility within ICT research projects is likely to be compromised to satisfy other prioritised aspects depending on the level of buy-in to the need for responsibility from both the project sponsors and influential stakeholders. This could culminate in corners being cut to push for final outputs and fulfil the objectives of the prioritised aspects with disregard to the social impact of their outputs. In a nutshell, the above discussion means that contributing towards responsibility in ICT research projects is not smooth sailing, but some obstacles get in the way. However, although this is the case, these hindrances could be circumvented by effective stakeholder engagement.

Conclusion To conclude, in this paper I have discussed how stakeholders that contribute towards responsibility in ICT research projects could be identified. Understanding how they are identified, and their roles defined is very crucial in engaging stakeholders that will perhaps contribute towards responsibility in ICT research projects in an effective manner. When ICT research projects are being implemented, some expectations must be met about specific aspects such as finance or responsibility. Therefore, stakeholders need to be identified particularly regarding how they will contribute towards the expectations. This is

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a problem when stakeholders are identified to contribute towards the integration of responsibility in ICT research projects mainly due to the nature of responsibility and how its meaning can be understood by different stakeholders. This shows that part of the problem with the stakeholders contributing towards responsibility in ICT research projects is down to the level of clarity about the rationale for engaging them in the first place. It was surprising to learn that those engaged in ICT research projects do not easily understand what is meant by ‘responsibility’ in technology innovation when the term is used without further explanation of its meaning. This misapprehension, in turn, affects the stakeholders’ understanding of how they contribute towards this important aspect of ICT research projects. Nevertheless, once the meaning of the term is clear, it is evident that stakeholders contribute towards responsibility in many ways although there are barriers that affect their contribution. Therefore, the overall conclusion here is that stakeholders are integral to the integration of responsibility in ICT research projects although their contribution is implicit due to the nature of responsibility.

References Auhagen, A. E., & Bierhoff, H. W. (2001). Responsibility: The Many Faces of a Social Phenomenon. Routledge. Blok, V. (2014). Look who’s talking: responsible innovation, the paradox of dialogue and the voice of the other in communication and negotiation processes. Journal of Responsible Innovation, 1(2), 171–190. https://doi.org/10.1080/23299460.2014.924239 Blok, V., Hoffmans, L., & Wubben, E. F. M. (2015). Stakeholder engagement for responsible innovation in the private sector: critical issues and management practices. Journal on Chain and Network Science, 15(2), 147–164. https://doi.org/10.3920/JCNS2015.x003 Carney, S., Whitmarsh, L., Nicholson-Cole, S. A., & Shackley, S. (2009). A dynamic typology of stakeholder engagement within climate change research. Tyndall Center for Climate Change Research, Working Paper, 128. Retrieved from http://www.tyndall.ac.uk/sites/ default/files/wp128.pdf Durham, E., Baker, H., Smith, M., Moore, E., & Morgan, V. (2014). The BiodivERsA Stakeholder Engagement Handbook. Paris: BiodivERsA. Retrieved from http://www.biodiversa.org/698/download Freeman, R. E. (2010). Strategic Management: A Stakeholder Approach. Cambridge University Press. Retrieved from https://books. google.co.uk/books?id=NpmA_qEiOpkC Guston, D. H. (2011). Participating Despite Questions: Toward a More Confident Participatory Technology Assessment: Commentary on: “Questioning ‘Participation’: A Critical Appraisal of its Conceptualization in a Flemish Participatory Technology Assessment”. Science and Engineering Ethics, 17(4), 691–697. https://doi.org/10.1007/s11948-011-9314-y Lenk, H., & Maring, M. (2001). Responsibility: The Many Faces of a Social Phenomenon. (A. E. Auhagen & H. W. Bierhoff, Eds.). Routledge.

Lozano, J. M. (2005). Towards the relational corporation: from managing stakeholder relationships to building stakeholder relationships (waiting for Copernicus). Corporate Governance: The International Journal of Business in Society, 5(2), 60–77. https://doi. org/10.1108/14720700510562668 Murphy, J., Parry, S., & Walls, J. (2016). The EPSRC’s Policy of Responsible Innovation from a Trading Zones Perspective. Minerva. https://doi.org/10.1007/s11024-016-9294-9 Nathan, G. (2015). Innovation process and ethics in technology: an approach to ethical (responsible) innovation governance. Journal on Chain and Network Science, 15(2), 119–134. https://doi.org/10.3920/ JCNS2014.x018 Owen, R., Bessant, J. R., & Heintz, M. (Eds.). (2013a). Responsible innovation: managing the responsible emergence of science and innovation in society. Chichester, West Sussex: John Wiley & Sons Inc. Owen, R., Bessant, J. R., & Heintz, M. (Eds.). (2013b). Responsible innovation: managing the responsible emergence of science and innovation in society. Chichester, West Sussex: John Wiley & Sons Inc. Patton, M. Q. (2002). Qualitative research and evaluation methods (3rd ed.). Thousand Oaks, CA. Pellé, S., & Reber, B. (2015). Responsible innovation in the light of moral responsibility. Journal on Chain and Network Science, 15(2), 107–117. https://doi.org/10.3920/JCNS2014.x017 Reed, M. S., Graves, A., Dandy, N., Posthumus, H., Hubacek, K., Morris, J., … Stringer, L. C. (2009). Who’s in and why? A typology of stakeholder analysis methods for natural resource management. Journal of Environmental Management, 90(5), 1933–1949. https://doi. org/10.1016/j.jenvman.2009.01.001 Röbken, H. (2013). Inside the “Knowledge Factory”: Organizational Change in Business Schools in Germany, Sweden and the USA. Deutscher Universitätsverlag. Retrieved from https://books.google.co.uk/ books?id=cUMBCAAAQBAJ Stahl, B. C. (2012). Responsible research and innovation in information systems. European Journal of Information Systems, 21(3), 207–211. Stahl, B. C., Timmermans, J., & Flick, C. (2016). Ethics of Emerging Information and Communication Technologies: On the implementation of responsible research and innovation. Science and Public Policy, scw069. https://doi.org/10.1093/scipol/scw069 Sullins, J. (2012). Information Technology and Moral Values. Retrieved from http://stanford.library.usyd.edu.au/archives/fall2013/entries/it-moral-values/ Von Schomberg, R. (2013). A vision of Responsible Research and Innovation. In R. Owen, M. Heintz, & J. Bessant (Eds.), Responsible Innovation. London: John Wiley.

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Technological Extension Networks and Regional Development: A Case Study In Brazil Heitor Soares Mendes1*, Marta Lucia Azevedo Ferreira1, Lia Hasenclever2, Carlos Alberto Marques Teixeira3 Abstract: This paper outlines the performance of the recent policies to strengthen the Brazilian productive structure and to support the country’s innovation. The method selected for the analysis is the case study. Based on bibliographic, documental, and empirical evidences, the objective of the present study is to investigate the effectiveness of the Brazilian Technology System (SIBRATEC), founded in 2007. The effectiveness of this system will be assessed through an outline of the institutional arrangement of the Technological Extension Network in Rio de Janeiro (SIBRATEC-ET Rede RJ) which supports competitiveness of micro, small and medium industrial enterprises (MPMEs) in the state of Rio de Janeiro. Results indicate that the institutional aspects of the arrangement do not contribute to facilitate the support to enterprises. The levels of efficiency and efficacy achieved with the use of the available technical-administrative state capabilities have failed to correspond to the expectation of providing consistent support to the state’s industrial base. However, the instruments SIBRATEC-ET Rede RJ uses to assist local MPMEs are flexible and designed aiming to adjust to the identified enterprises’ demands, which evidences great adherence in the provision of support to improve the performance and the capacities of enterprises, in order to reach the level of key-technologies. Keywords: Public Policy; Science, Technology and Innovation Policy; Brazilian Technology System; Technological Extension Networks; Regional Development. Submitted: Aug 31st, 2018 / Approved: Oct 25th, 2019

Introduction After a long period without elaborating explicit industrial and technologic policies, the Brazilian government decided to support the development of the national innovation system in the 21st century, following the trend of other developing countries (Salami and Soltanzadeh, 2012; Chaurasia and Bhikajee, 2016). The Growth Acceleration Program (PAC1) was founded in 2007, and was articulated with the Action Plan of Science, Technology and Innovation for National Development during 2007-2010 (PACTI 2007-2010). In this context, in the same year the Brazilian Technology System (SIBRATEC) was implanted. This system aims to support the technological and innovative development of Brazilian enterprises through the installation of three types of network (innovation centers, technological services, and technological extension) that had their own Technical Committee, which articulated with one another and were coordinated by a centralized management committee. The network structure of the SIBRATEC is organized in sectors or regions so as to articulate the Science and Technology Institutions’ (ICTs) infrastructure, which fosters the service provision according to the demands of Micro, Small, and Medium-sized Enterprises (MPMEs) from the industrial and service sectors, with funds granted by the National Fund for Scientific and Technologic Development (FNDCT). In the present paper we intend to investigate, on the supply side, the provision of institutional support and the effectiveness of the new model of technological extension policy (SIBRATEC-ET) in the regional context, that is, in the state of Rio de Janeiro (ERJ), named 1

the Technological Extension Network of Rio de Janeiro (SIBRATECET Rede RJ). On the demand side, we focus on the current demand for technological and organizational capability of the industrial MPMEs located in the West Zone (ZO) of the state’s capital, an important area of industrial activity in the state of Rio de Janeiro (ERJ). One of the core aspects of technological extension – previously named industrial extension – is the support to the modernization of enterprises through the diffusion of existing technologies that are not used in groups of smaller enterprises, because of the characteristics of the latter. Technologies used to upgrade and enhance the development of both domestic products and processes that need to have compliance with international patterns for exportation have come across continuity problems of the existing extension programs (Madeira, 2009). Innovation is a fundamental element of economic dynamism; it is the perspective that guides the analysis of the current technological extension model, considering the regional arrangements and their capacity to provide consistent support to MPMEs in terms of production modernization, namely, improvement of technological and organizational capabilities. Therefore, we intend to answer the following research question: is this model appropriate for providing support to the productive and innovative development of the MPMEs in the ZO? In order to answer this question, the present paper is divided in six sections, besides its introduction. In section two, a brief literature review is presented regarding the aforementioned themes; in section three, the methodology used is displayed; in section four, the

All abbreviations will be translated to English and their corresponding initials will be maintained in Portuguese.

1) Federal Center for Technological Education Celso Suckow da Fonseca (CEFET-RJ), Rio de Janeiro, Brazil. 2) Federal University of Rio de Janeiro, Institute of Economics (IE-UFRJ), Rio de Janeiro, Brazil. 3) National Institute of Technology (INT), Rio de Janeiro, Brazil. *Corresponding author: heitor.mendes@cefet-rj.br

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implementation process of the SIBRATEC-ET Rede RJ is described; the demands of the metal-mechanical sector, with focus in the ZO are presented in section five, followed by the analysis and discussion of empirical evidence in section six; in section seven the conclusion of the paper is presented.

Literature Review Both the State and institutions play a key role in economic development, since such development comprises structural changes, and the market itself is not the only nor the most efficient organization institution of the economic system (Fiani, 2011; Chang, 2002, 2003, 2011; Evans, 1995, 2010, 2011; Mazzucato, 2011). It is to increase coordination of economic activities and to reduce waste that societies create rules or institutions that both restrict and stimulate collective actions. The State’s active intervention through specific policies to stimulate S&T, industrial development, competition and trade is achieved by different means, as the results obtained in the institutional arrangements that involve interaction of the State with the market and the society, that have increased the interest of policy makers. It is worth mentioning that the interdependence among the technological, institutional and ideological dimensions of these changes was pointed out by Kuznets (1973) when referring to the model of economic growth inaugurated in the Industrial Revolution. Gerschenkron (1962) provided seminal contribution to the understanding of the cathing up process, as he highlighted the role of institutions, of the intellectual climate, and of the ideologies in the accelerated industrialization process experienced in Germany and in Russia during the 19th and the beginning of the 20th centuries. Schumpeter (1961, 1976), in turn, emphasized the impact of technological innovations, of entrepreneurship, and of competition on the economy, exposing the unstable nature of the capitalist system. The author also explained the role of incremental changes in economic growth, and how redesigning processes affects development; the latter being a result of a “creative destruction” process. Institutions certainly introduce regularities in the economic environment, in contrast with the inscreasingly rapid pace in which technologies are created and diffused. There is an intense interaction among technological innovation, economic growth and institutions, which indicates that such concepts cannot be fully understood separately (North, 1990). Due to the uncertainty, dynamism and complexity that characterize the current economic environment, the States have been challenged to design development strategies and specific policies that consider international integration, and also consider and meet domestic demands. In this context, it is possible to observe the resuming of the discussions about the theories of development, while the institutional economy has drawn more and more attention. Public policies can be characterized as a construct that derives from various variables, and is oriented by values, ideologies, among other aspects that are inseparable from the policy makers; aspects that stem from the social forces that are involved in the formation and effectiveness of the public policies, and to which the public policies are the

balancing point. Chrispino (2016, p.19) claims that “Public Policies” is a meta-concept since it is characterized as an “intentional government action that aims to meet collective necessities”. In Brazil, the PACTI 2007-2010 comprises various initiatives that meet these collective necessities, here understood as the actions that streghten S&T and innovation, and that foster the national economic development. One of the most recent initiatives articulated in Brazil is the SIBRATEC system. It involves dimensions that will be analyzed under the institutional arrangements spectrum, which are responsible for the sustainability of the implementation of public policies (Gomide and Pires, 2014). We intend to verify if the set of rules and regulations that are present in the arrangement of one of SIBRATEC’s programs – the technological extension program in one of the country’s state – permits the coordination of the operation of economic activities that are carried out with the policy’s targeted public. In this sense, we also consider the relevant role the State and formal rules play in the coordination of the operation of economic activities among independent agents that have divergent interests and heterogeneous knowledge (Chang, 2002, 2003, 2011; Evans, 1995, 2010, 2011; Mazzucato, 2011). Therefore, it is hoped that the SIBRATEC-ET network as an institutional arrangement presents regularity of action over time, with predictable and agreed changes that promote the absence of conflict, since its managing function will be defined and executed in a viable manner (Hodgson, 1988; Langlois, 1986; Rutherford, 1994, 2001). As a public policy, it is hoped that the architecture and the strategies of the implementation of such an arrangement are materialized in a set of intentional actions that correspond satisfactorily to the input – the state capacities made available aiming their successful completion. In this dynamic process, the program may also be evaluated through the analysis of technical-administrative capacities, and through the basic public policy indicators: efficiency, efficacy and effectiveness (Arretche, 1999; Chrispino, 2016). Thus, taking into account the goals set by PACTI as a public policy, it is important to understand the relevance of supporting the MPMEs’ technological and organizational capacities for the national development. The latest United Nation Conference for Trade and Development Report (UNCTAD, 2016) emphasizes the importance of the transformation industry in all countries’ economic growth and development, especially in developing countries. Moreover, it is known that there are barriers to the access to the technological information frontier and even to the access of the already well-established production and business management technologies, which are widely disseminated in big companies operating in Brazil, but not used by MPMEs. Rovere et al. (2014) and Mendes and Hasenclever (2015) explore the barriers that hinder the catching-up process of this type of national enterprise. It is important that MPMEs cease to represent the ‘weak link’ in potential productive chains in the national economic scenario, especially in the industrial base of the ERJ. If the technological and organizational deficiencies these companies present persist, the outcomes will consist in weak economic results in terms of value creation in the industry, in spite of the quantitative

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importance these companies have when it comes to income and employment generation in relative terms2. However, the elevation of the technologic pattern is not a spontaneous, available and attainable process for MPMEs; the provision of support to the capacitation of these enterprises is indispensable. Studies confirm the lack of the much-needed support to MPMEs, which if properly provided could result in the possibility of reaching higher technologic capacity patterns (Nogueira, 2017; OECD, 1997). Indeed, such enterprises need to migrate from lower standards (operation with low or minimum technological capacities), to reach the level of technological capacity currently followed by the competition; a level that is present in their routines, and that will enable them to develop their capabilities to the point they are able to be included in the group of enterprises that have endogenous research and development resources, which, in turn, will enable them to invest on continuous innovation to reach and to stay in the technological frontier.

Methodology This paper presents the results of a research of qualitative and empirical nature with descriptive purpose. The selected method was the case study3 that is compatible with the deep analysis of contemporary and complex phenomena. This method involves the collection of multiple sources of evidence, also involving the chaining of evidence towards convergence, based on previously developed theoretical foundations (Yin, 2014). Therefore, we use bibliographic, documental, and observational sources, besides the collection of information through the application of a semi-structured questionnaire to experts and managers of MPMEs working in the metal-mechanical sector in the ZO of the ERJ. To the evidence are also added semi-structured interviews conducted personally with experts and managers of the SIBRATECET Rede RJ, carried out with the purpose of understanding the opinions and perceptions of interviewees. Based on the technological and organizational demands identified in the studied industries, and on the collected information about the operation of the institutional arrangement, we intend to verify the effectiveness of the support the arrangement provides to the competitiveness of the MPMEs in the region. The method of assessment of the provision of support vis-à-vis demand is the analysis of adherence among the instruments of support provided by the extension program. This analysis was carried out through the elaboration of a correlation matrix that included technological and organizational demands of MPMEs, and the SIBRATEC-ET Rede RJ support supply. The analysis of the institutional arrangement is based on the identification of objectives and results achieved with the implementation

of the arrangement in the ERJ in the period 2009-2016. All activities carried out during this period are considered, as well as the use of State technical-administrative capabilities. The parameters for measuring the arrangement’s performance included: the efficacy of policies, with the use of a degree of alignment with the expected results; the efficiency of the program, including a benefit-cost evaluation; the effectiveness related to citizen degree of satisfaction or related to the assistance and its scope considering the targeted beneficiaries. Other aspects included in the evaluation of the arrangement regard the operation of coordination mechanisms, and to the identified monitoring strategies. The selected empirical research object is the metal-mechanical sector of the ERJ, represented by a subgroup, the agglomeration of enterprises of this industrial area, located in the west zone of the city of Rio de Janeiro (MRJ). The metal-mechanical sector involves an elevated number of microeconomic agents disseminated nationally. As the identification of such agents is not viable considering their elevated number, we selected a sectoral agglomeration that is economically relevant in the regional level. The list of the enterprises that make up the base selected for a field research was elaborated based on the 2007/2008 records of the Federation of Industries of Rio de Janeiro (FIRJAN) - updated in 2014. The MPMEs samples were organized considering two selection criteria: the first was the sectorial criterion of the companies’ operation, using the National Classification of Economic Activities (CNAE) (IBGE, 2014), with enterprises that are in the following CNAE 2.0 classification: section C, divisions 24 (Manufacture of Basic Metals), 25 (Manufacture of Fabricated Metal Products, except Machinery and Equipment), 28 (Manufacture of Machinery and Equipment n.e.c.). The second selection criterion was the size of the enterprises, defined here by the number of employees, considering the methodologic orientation provided by the Brazilian Support Service to Micro and Small Enterprises (SEBRAE), which makes the following classification: microenterprise - up to 19 employees; small enterprise - 20 to 99 employees; medium-sized enterprise - 100 to 499 employees. The intentional sampling includes 59 enterprises of the sector and 23 interviews, which represents 39% of the sampling collected between August of 2015 and January of 2016. Interviews lasted about 40 minutes each.

The Supply Side: SIBRATEC-ET Rede RJ Table 1 presents the aspects concerning the implantation of SIBRATEC, and the characteristics of its technological extension network are displayed. The operation of the arrangement in the ERJ had specificities in each modality of assistance, but, mainly, it followed a pattern.

2 In the industrial sector, the MPMEs represent 98.8% of the total, but generate only about 24% of the gross value of industrial production (VBPI), and 22% of the value added (Rovere et al., 2014). 3 Further details on the questionnaire and methodology used available in Mendes (2016).

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Table 1. SIBRATEC and SIBRATEC-ET.

Origin: Science, Technology and Innovation Action Plan for National Development (PACTI 2007-2010) - MCTI. Launched in 2007. Framework: Structuring axis II. Strategic Priority: the promotion of technological innovation at enterprises. Objective: intensification of the actions to promote innovation and technology at enterprises. SIBRATEC: part of line of action number 5 (Technology for Enterprise Innovation). Legal framework of creation: Decreet number 6.259/2007. Systemic model, structured in three networks: Innovation Centers; Technological Extension; and Technological Services. Governance: each SIBRATEC network has its own managing technical committee; however, their actions are interrelated, and have centralized coordination from the system, performed by a management committee. Objective: the structuring of a national technology system, through the creation of networks, entities and orgains that promote innovation and provide technological services for enterprises, aiming to have nationwide coverage. In Article number 1, subsection II of Decreet number 6.259/2007, the extension of scope is displayed as one of the SIBRATEC goals. SIBRATEC-ET: one of the three networks structured by SIBRATEC. It concerns the technological extension of the system. Technological Extension Program: Structured in regional institutional arrangements; Proactive character, prospection of enterprises to receive direct assistance; the program has a permanent source of financing: the National Fund for Scientific and Technologic Development – FNDCT; state programs were structured by arrangements that originally aimed to join successful extension products, which in turn would join actors in a local logic of development, with local actors, besides FINEP -the institution that manages the FNDCT-, and other entities that would provide corporate support, such as SEBRAE. The implementation of SIBRATEC-ET Rede RJ happened in 2009, and its assistance service started in 2010. Institutional Arrangement: technical coordination performed by the National Technology Institute (INT); The Technology and Innovation Network of Rio de Janeiro (REDETEC), worked with the articulation with the ICTs and as the administrative and financial managing institution; the Brazilian Support Service to Micro and Small Enterprises in Rio de Janeiro (SEBRAE-RJ), worked with the provision of support to the articulation with micro and small enterprises (MPE) in the state, and also financing 10% of the arrangement; the Foundation for Research Support in Rio de Janeiro (FAPERJ), also financed 10% of the arrangement; the Studies and Project Financer (FINEP) provided the biggest amount of funds to the arrangement (70%). The assisted enterprises had 10% of participation in the financing of the arrangement.

Source: Adapted from MCTI (2013a); Mendes (2016); Mendes and Hasenclever (2015); and Mendes et al. (2017)

Firstly, the technological extentionists prospected enterprises for assistance, and presented the modalities of support to the productive development to them, as well as the conditions to take part in the program, emphasizing the low financial participation of the MPMEs. Secondly, diagnosis of each situation was carried out, which could be either be performed by the extentionist, or, in case the extentionist lacked the expertise, the diagnosis was delegated to a group of experts.

Table 2. Amount of Estimated and Completed Assistance of SIBRATEC-ET Rede RJ (Aug 2009/Feb 2016).

Table 2 presents the distribution of services by modalities used by the arrangement in the ERJ, as well as it summarizes the goals set for each modality of assistance. According to the arrangement’s final report (INT, 2016), the MPMEs were selected in order to strengthen the local productive systems in the ERJ, considering both the definition provided by the Secretariat of Economic Development, Energy, Industry and Services of the state of Rio de Janeiro (SEDEIS), as well as the local economic peculiarities.

Goals Set

Total of Answered Calls

Technological compliance via mobile unit - foods and beverages

Technological compliance of products for the external market

Technological compliance of products for the domestic market

Technological compliance via production management

Technological compliance via cleaner technologies

Total

143

144

Modalities of Assistance

Source: Adapted from Mendes et al. (2017); INT (2016).

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The various modalities of the SIBRATEC-ET assistance followed an average duration pattern: Technological compliance via mobile unit – foods and beverages (four months); Technological compliance of products for the domestic market (one year); Technological compliance of products for the external market (one year and six months); Technological compliance via production management (five months); Technological compliance via cleaner Technology (eight months) (INT, 2016). Originally, the agreement made for the arrangement estimated two modalities of assistance and the following targets: ‘Technological compliance of products for the external market’ – the goal set was of 140 calls; and ‘Mobile Unit – Foods and Beverages’ – 60 calls. The original perspective of the arrangement’s scope was expanded throughout the term of the agreement (including renegotiations of terms), and new modalities of assistance were incorporated, which in turn created the need for the reconfiguration of general goals, with the inauguration of a new scope of assistance to be provided by the arrangement in the ERJ. During the term of the agreement, 264 calls were answered and classified in the arrangement as ‘closed’ and ‘suspended’. Classified as

‘closed’ were all of the cases that had their assistance completed with the solicitants. ‘Suspended’ is the term that includes all of the other conditions, according to the status of the assistance by the end of the agreement’s term; for instance: ‘in progress’, ‘negotiations in progress’, ‘pending’, ‘suspended’, ‘expired’, and ‘invalid’. Regarding the size of companies, 45 micro (42%) and 62 small-sized enterprises (58%) were assisted, totalizing 107 enterprises. In terms of modalities of assistance, extension services were provided through all five modalities. The assistance provided can be discriminated by size. Table 3 presents these results, taking into account only the cases considered completed. There seems to be an inconsistency in these data, since SIBRATEC-ET Rede RJ assisted only 107 enterprises in the term of the agreement, and the total of answered calls is of 133 enterprises. The explanation is that some enterprises have had more than one call answered. In addition, it is also noticeable in Table 3 the absence of provision of support to medium-sized enterprises during the term of the agreement in the ERJ.

Table 3. Estratification by Enterprise Size and by Modalities of Assistance of SIBRATEC-ET Rede RJ. Modalities of Assistance Total Amount of Assisted Enterprises of Enterprises 1 1 1 1 1 I G T UA S Micro 13 11 5 1 21 51 Small 21 25 4 13 19 82 Medium 0 0 0 0 0 0 Total 34 36 9 14 40 133 Note 1- Codification: S – External market; I – Internal market; G – Production management; T – Cleaner technologies; UA – Mobile units. Source: Adapted from Mendes et al. (2017); INT (2016). Size

Table 4 presents a qualitative synthesis of the institutional arrangement’s implementation in the ERJ. This one indicates that the results obtained with the implementation of the SIBRATEC-ET Rede RJ extension program in the ERJ were not satisfactory. The program already presented poor results in the ‘Management Report

– SIBRATEC Program 2013’ (MCTI, 2013b), and based on the final observations made in the final report elaborated by the coordination agent (INT, 2016), it is possible to notice the general challenges and difficulties all extension programs came across; notably, governance difficulties.

Table 4. SIBRATEC-ET Rede RJ - Qualitative Results Obtained in the Implementation of the Program. Characterization of the SIBRATEC-ET Rede RJ Arrangement 1. 2. 3. 4. 5. 6. 7. 8. 9.

Demand oriented program in terms capacity of assistance; it had initial coverage of 10 enterprise calls per month. Five modalities of assistance were used in the SIBRATEC-ET Rede ERJ. Modalities with specific characteristics, but with relatively open instruments of assistance, adjustable to the demands of the solicitants. Low efficacy; the assistance was interrupted multiple times during the term of the arrangement in the ERJ. There was the necessity to renegociate the agreement throughout the operation of the program. Adjustments were made even in the goals originally set and agreed on. The support was not provided in the ZO of the MRJ, area selected for the study of the MPMEs’ demand for technological capabilities. The program was not formally publicized; the publicizing was carried out in an informal, non agreed manner, with the support of the INT at one point, through the elaboration of brochures. The program did not achieve the original goals in the period estimated in the agreement. The number of calls answered was very small considering the time of operation of the program in the ERJ. Consequently, the program had low efficiency. Conflicts have been identified in the operation of the extension instruments SIBRATEC-ET Rede RJ and SEBRAETEC1, since there were many similarities in both, and there was potencial competition in the ERJ. A governance agreement was necessary in SIBRATEC-ET for the activities of both instruments to be as complementary as possible in their operation, so as to optimize the use of resources, and to generate less conflict and less competing actions. This agreement was partially effective, but not for long. Nonetheless, the SIBRATEC-ET Rede RJ lost intensity in this process.

Note 1: The SEBRAETEC program is a SEBRAE National initiative reactivated in 2012, with ambicious goals. It is regionally operated by SEBRAE-RJ, an institution that takes part in the governance of the SIBRATEC-ET Rede RJ arrangement. Source: Adapted from Mendes (2016) and Mendes et al. (2017); INT (2016).

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The present research presents the identified following barriers to the implementation of the SIBRATEC-ET Rede RJ: the difficulties that result from the SIBRATEC-ET networks’ governance organization, since it involves two levels of federative entities (federal and state) in the same arrangement; the different administrative/legal federal and regional requirements that hamper the transfer of funds from these two federal levels of government to the programs; the difficulties in the insertion of SIBRATEC-ET networks into other public or private initiatives that support micro, small-sized and medium-sized enterprises; the temporality, since the networks operate during specific previously agreed periods of time, and their management projects do not have prospects nor criteria for continuity; the lack of instruments to conduct periodic performance evaluations of the networks and their institutions. It is possible to identify in the final report of the arrangement in the ERJ the cities4 where the assistance to support enterprises was more intense. Only three cities (out of 92) concentrated more than half of the assistance provided: Rio de Janeiro, that concentrated 24.2% of the answered calls, followed by Nova Friburgo that concentrated 17.9%, and Petrópolis with 11% of the total of calls answered in the ERJ. The initial criterion for the area selection was the export potential the enterprises presented. A total of 190 enterprises have been diagnosed in the ERJ. Diagnoses were performed in 42.4% of the cities in the ERJ during the term of the SIBRATEC-ET Rede RJ agreement (Aug 2009 – Feb 2016). The following sectors have had ‘closed’

calls: Foods and Beverages (49%); Fashion (26%); Transformation Industry (furniture and plastic) (14%); Metal-Mechanical (10%); and Civil Construction (1%). Out of the 190 diagnosed enterprises, 107 have had adjustments completed and ‘closed’ calls in the ERJ, which represents 56.3% of diagnosed MPMEs.

The demand side: The Metal-Mechanical sector of the West Zone of the city of Rio de Janeiro In this section we present a synthesis of the partial results obtained from a survey that identified MPMEs’ capacities and demands for technological and organizational capabilities in the metal-mechanical sector of the ZO of the MRJ, elaborated by Mendes (2016). Considering the number of participant enterprises, in relation to the total of enterprises listed in the ZO separated by main economic activity, we verified that 29% of the enterprises listed under the ‘basic metals’ division (CNAE 2.0 code 24) have taken direct part in the survey; in the ‘metal products’ division, (CNAE 2.0 code 25) 38%; and in the ‘machinery and equipment’ division (CNAE 2.0 code 28), 46% participated directly in the survey. Table 5 summarizes part of the results obtained, describing the situation of the metal-mechanical enterprises located in the ZO. The aspects presented in the chart relate to the identification of the MPMEs’ demands for technological and organizational capabilities.

Table 5. Case Study: Metal-Mechanical MPMEs – ZO of the MRJ – Summary of the Interviews’ Results. 1. 2. 3. 4. 5. 6. 7.

Few actions aimed at seeking improvements externally. Principal access to new technologies: acquisition of machinery and equipment. Fragility in Basic Industrial Technology (TIB). Human resources with low technical qualification in TIB. Organizational demand: under 50% of the MPMEs use organizational management systems. Unfamiliarity with the SIBRATEC system. Where would they ask for support? -FIRJAN: 65% -UFRJ: 52% -SENAI: 65% -SEBRAE: 56% Strategic business partnerships: only one enterprise has confirmed to have strategic business partners.

10.

11.

12.

Financial support: where would they ask for it? Banco do Brasil: 61% The Brazilian Development Bank (BNDES): 48% Demand for qualified professionals: Technicians (high school level): 83% Engineers: 44% Benchmarking: Never procured from suppliers: 73.9% Never procured from current or future clients/markets: 69.6% Managers’ behavioral characteristics: a. Pessimistic when it comes to governament support; b. Unfamiliar with local enterprises; c. Fail to establish local inter-enterprise relationships.

Source: Adapted from Mendes (2016).

Regarding competitiveness and enterprises’ growth, results indicate that, currently, the five main criteria of business competition preservation in decrescent order, according to the interviewed group of metal-mechanical enterprises, are: ‘product quality’, ‘cost control’, ‘customer relationship management’, ‘access to new technologies’, ‘investment in human resources’. 4

Demands are presented under the name ‘challenges’ that enterprises come across. Three of these demands stand out for representing the needs that most of the interviewees have: the first persistent challenge for 82.6% of the interviewed enterprises is ‘continuous market monitoring’; the second challenge pointed out by 73.9% of interviewed MPMEs is the demand for ‘new technologies for the manufacturing

City in this case refer to a municipality.

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of the enterprises’ current products’. The third challenge mentioned by 65.2% of interviewees is the insertion of ‘new management technologies to increase productivity’. Besides the three aforementioned needs, three other relative demands also stood out for 39% to 56% of the interviewed enterprises. These demands are worth highlighting because of their complementary character in relation to bigger demands; namely: ‘new quality management technologies’ (56.5%), ‘design improvement / product development’ (43.5%), and ‘use of new materials’ (39.1%). Regarding the institutional support, the majority of enterprises do not use nor would they use the support provided by all institutions and organizations listed. The SIBRATEC-ET institutional arrangement in the state of Rio de Janeiro was not solicited by any of the local interviewed enterprises. None of the enterprises mentioned the institutions of the SIBRATEC-ET arrangement, and only 13% of enterprises confirmed that they would use the arrangement’s services to benefit from the provided technological and organizational development support. It is noticeable that there is great unfamiliarity with the SIBRATEC-ET network, whose main characteristic is the provision of support to the state’s MPMEs. This scenario is not favorable considering the institutional mission of this program: meeting the technological and organizational demands of the national industry. In addition, this nationwide system has structured support that could perfectly meet the local enterprises’ needs.

Analysis and Discussion Considering the described structural aspects of the SIBRATEC-ET arrangement, it is possible to identify the complex character that inter-institutional interactions involve. Considering such complexity, it is necessary to perform an evaluation of the relevance of the proposals present in the studied public action (Gomide and Pires, 2014). The period of the agreement, including the extension of time, terminated in February of 2016. Based on the presented results regarding both the technological and organizational demands of MPMEs as well as the implementation of the SIBRATEC-ET Rede RJ program, it is possible to, critically evaluate this institutional arrangement. The ‘Management Report –SIBRATEC Program 2013’ (MCTI, 2013b) indicates poor results of the extension network in the ERJ until that year. The results obtained in the state of Rio de Janeiro were the worst among all other programs in operation. Programs operating

in the states of Minas Gerais and Paraná also presented weak results according to the report. By the end of the agreement’s term, the results in the ERJ remained weak, even though a modest evolution was identified. Meanwhile, the other two states have presented significant evolution in their results.5 The scenario described by the responsibles for the arrangement already indicated little advancement and many barriers to the management and to the provision of effective support for the group of enterprises located in the ERJ (Mendes, 2016). Indeed, under the quantitative spectrum, and considering the results of the agreement presented in the present research, it is possible to see that the SIBRATEC-ET Rede RJ underperformed its operation. Results failed to achieve the goals originally set, which projected an average of annual assistance. Other difficulties in the implementation of the SIBRATEC-ET Rede RJ were identified: interruption of operation during one year and seven months; dependence on bids to find the consultation indispensable for its execution; discontinuation of assistance; lack of institutional security; faulty prospection of the enterprises’ demands; lack of publicizing of the program; lack of funds destined for the program’s publicizing. Table 6 shows the arrangement’s capacities comparing the normative character, present in the institutional design, to the effective operation of the arrangement in the state. As to the state capacities made available for the development of the state’s enterprises, the conclusion is that there was weak capacity of providing effective support to the enterprises, even though there was flexibility in the modalities of assistance - due to the demand orientation of enterprises, and not due to rigid and standardized mechanisms. However, as it is possible to note, because enterprises had difficulties in the access of the program, the flexibility differential has mostly lost its relevance. It is necessary to work on the failures identified in the present study, so that the regional arrangement achieves effectiveness. One of the failures identified regards the coordination. Many difficulties hindered the production of a continuous flow of assistance, because of many reasons: sometimes because of failures in the information flow among the arrangement’s partners (in the ‘supply x demand’ relation, and in the back office support), sometimes because of the lack of flow of the funds which support the operation of the arrangement. Nonetheless, as mentioned in this study, coordination failures stem from a SIBRATEC systemic problem (MCTI, 2013b).

5 This becomes evident when visiting the following website: <https://www.dropbox.com/sh/40axojcm5dknebc/AABcVdS3pW274ZMZ6sklTyn5a?dl=0>. Last visited on April 25, 2017. The states of Minas Gerais and Paraná are mentioned as successful cases of the regional technological extension network.

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Table 6. SIBRATEC-ET - Barriers to the implementation in the ERJ (Capacities). Characterization Capacities

Observations Normative

Operational

Agreed and assigned

Underperformance

Many and repeated conflicts were observed

Granted funds

Irregularity

Interruptions paralized the network’s operation

Predictable and controllable

Unpredictable No internal control

The main source of funding – FNDCT – has constantly faced contingencies

Diffused in the network among ICTs (ERJ)

Difficulties to use ICTs infrastructure

The design advocated the use of ICTs’ capabilities in the state, but the legal rule was impediment for its direct operation

Use on demand

Performed as predicted, but at a slow pace

Assistance services subject to bids

Interinstitutional Actions

Between ET networks and government actors

Underperformance

No formal project to promote interaction of the arrangement with the society in general was designed

Field Operation

Demand-pull

Mostly led by the extensionists’ actions

Viable because of flexbility

Intrainstitutional Actions

Distributed and complementary

Difficulty in the integration of partners

Program Evaluation

Active and periodical

Irregular and reactive

Relationship Arrangement - Enterprises

Satisfactory

Deficient

Established by extensionists that were temporary scholarship holders

Monitoring

Continuous

Irregular

There is no intensive process for monitoring activities

Capacities

High

Low

The arrangement was not able to obtain significant results in terms of goals and diffusion in the ERJ

Coordination

Financial Control

Operational Competences

Source: Adapted from Mendes (2016) and Mendes et al. (2017).

Further problems were identified in the implementation of the arrangement, such as: complementarity versus competition between SIBRATEC-ET and SEBRAETEC, which has even caused misunderstanding among solicitants, since the names of the two programs are extremely similar. The agreement, terminated in 2016, does not have a certain future when it comes to continuity. Such uncertainty weakens even more the credibility of the system in the operation of government actions to support the MPMEs in the state of Rio de Janeiro.

of these demands represent challenges for this group of MPMEs that hinder: the acquisition of the necessary capabilities to absorb technologies, the compliance of such technologies to make them proper for use, the diffusion of these new capacities among the technical team and the codifying of new knowledges in the corporate routines in order to better the performance of the business. All of these challenges if overcome would make the enterprises more competitive, considering the current competition conditions (Mendes, 2016).

Table 7 presents the correlation of the MPMEs’ technological demands, and the adherence of the SIBRATEC-ET Rede RJ modalities of assistance made available for enterprises. Results indicate that 91% of the enterprises that took part in the research have at least one technological or organizational demand. All

In Table 7 the adherence of the modalities of assistance to the MPMEs’ demands becomes evident. There are theoretical conditions regarding the metal-mechanical MPMEs’ demands in product technology, process technology and management technology that are to be met by the SIBRATEC extension network.

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Table 7. Correlation - General Demands and the SIBRATEC-ET supply. (%)1

SIBRATEC-ET Supply

Product compliance for the domestic market

40-60

Yes

Design, computer oriented project, product development, prototyping

30-40

Yes

Elements

Product Technology

Process Technology

Management Technology

General Demand

Use of new materials

30-40

Yes

Product compliance for exportation

30-40

Yes

Technological information – new technologies

80-90

Yes

Technological compliance of processes (automation, good practice etc.)

70-80

Yes

Support for the modernization of machinery and equipment

50-80

Yes

Increase of productivity

80-90

Yes

Update in the management of industrial operation (JIT, ToC, Supply Chain management etc.)2

80-90

Yes

Implementation of quality control systems

50-70

Yes

Certifying of industrial management systems

20-40

Yes

Implementation of laboratories – Product compliance

20-30

Yes

Access to external laboratories – inspection and tests

10-20

Yes

Business Intelligence Systems

Technologic information – technology monitoring routines

80-90

Partial

Computerized corporate control

20-40

Partial

Inter-Enterprise Interaction

Improvement of local inter-enterprise interaction

100

Human Resources

Skilled workforce in metalwork technologies

30-40

Intellectual Property Managment

Licensing, trademark, and patent support etc.

20-30

Yes

Marketing and Comunications

Support to the prospection of new markets

10-20

Institutions that support the development of companies

80-90

yes

Institutional Support

Support to Employers Entities (federation of industries, syndicates, industrial associations)

60-80

Partial

Support to the ICTs of the ERJ

30-50

Subject to bidding

TIB

Note: 1- Relative amount estimated based on the demands of the interviewed companies. Note: 2- JIT: Just in Time; ToC: Theory of Constraints. Source: Adapted from Mendes (2016) and Mendes et al. (2017).

However, the implemented arrangement falls short on effectiveness in the state, in view of the multiple implementation obstacles, the regional network has come across in the ERJ. In this state, the assistance was not provided properly to the targeted enterprises in the institutional design: the industrial and technological services MPMEs located in the ERJ. There was poor efficiency and limited efficacy, once the goals met have had significantly poorer results when compared to the annual minimum that was projected for the operation with this kind of intervention.

Conclusions Even though the instruments SIBRATEC-ET uses to operate are flexible and were designed aiming to ajust to the enterprises’ demands, the institutional arrangement is not properly structured, once its weaknesses include coordination problems. The conduction rules were not well received by its members, and internal conflicts in the institutional partnership were identified. The present institutional

aspects represent obstacles to the increase of competitiveness and of the development, instead of facilitating these processes – for example the case of the regularity expected from the institutional arrangement. In addition, the targeted levels of efficiency and efficacy were not achieved, and, even though some goals were achieved by the end of the agreement’s term, this happened because terms were renegotiated, and the new terms agreed expired long after the original ones. Therefore, the results presented indicate the lack of effectiveness of the SIBRATEC - technological extension network in the ERJ. The arrangement did not properly fulfill its institutional mission in the ERJ, which was to provide support to corporate development; the mission was not completed because the arrangement failed to have a satisfactory performance with the use of the technical-administrative capacities made available for its execution. This scenario generated inefficiency and inefficacy during the implementation of the system in the ERJ, which in turn frustrated the expectations of granting consistent industrial support.

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The demands of the metal-mechanical MPMEs located in the ZO indicated that the majority of this kind of enterprise have as a priority the development of products and processes that are more compatible with the market in which they operate, which requires continuous assistance in changes, and also assistance in the compliance of new requirements. In spite of the fact that part of the sector’s enterprises belongs to low or medium-low technological intensity segments, the machinery and equipment segment is one of the most dynamic and innovative segments in the world, a pattern that is not followed in the country, nor in the studied enterprises. Prioritizing the elevation of technological and organizational capabilities of a group of enterprises (CNAE 28), for instance, is indication that the industry’s innovation base is seeking expansion. In this case, the argument is that if a policy focuses on this division of economic activity, an important complement of this initiative (in the case studied the SIBRATEC system) would provide support to the productive chain of the segment through the insertion of technological extension mechanisms like the ones analyzed in the present paper.

Considering the results identified for the technological extension network in the ERJ, the extinction of the network would be premature. Means of achieving its enhancement should be pursued instead, with the correction of the detected problems, especially considering that the program is regional in its arrangement implementation, however it is essentially nationwide. The program represents a potential element for making scale profit in the improvement of MPMEs’ technological and organizational capacities, aiming to reduce the gap the country presents when compared to developed countries.

The results presented must be used as base for the elaboration of a reform in the technological extension policy, for they suggest the redesigning of the composition of arrangements of this nature, which indicates the necessity of insertion of local leaders that will assist the targeted public of the policies in the regional level. Results also point out that the coordination of the arrangement will require, in the national level, reconfiguration of governance strategies.

References

The studied system also has important features that are worth mentioning, for they represent advancements in relation to previous adopted support systems. One of these advancements is the fact that the system’s operation is led by the demand of enterprises for technological capability, and it is also worth highlighting that the system has presented flexibility to meet such demands, by making available for the MPMEs various modalities of assistance. An example of such flexibility is found in the present study, where the adherence of the supply to the support of the demand for key technology is evident in the studied metal-mechanical sector. The SIBRATEC-ET network can also play an important role for one group of enterprises that has not been the focus of development public policies during the past decades: medium-sized industrial enterprises. The support for this group of enterprises has not been provided since the extinction of the Brazilian Center of Support to Small and Medium-sized Enterprises (CEBRAE); when this institution was transformed into SEBRAE, medium-sized enterprises ceased to receive support from the government. Therefore, the enhancement of the studied system would resume the provision of support to this group of industrial enterprises, with the reinsertion of such group in the agenda of public policies that support production development. Micro and small-sized enterprises have the support of SEBRAE, an organization with great penetration in the country. Notwithstanding, medium-sized enterprises do not count on specific institutional support to foster their development. It is time to pay close attention to this group of national enterprises.

It is necessary to analyze of all the implemented technological extension networks, so as to point out the elements that must be the foundation of the arrangement’s design and also the elements that create conflicts inside the arrangement, and, consequently, must be eliminated from the institutional arrangement’s structure. Morevover, it is important to identify the successful results of each regional network that may be indicative of the path to be trailed.6

Arretche, M. T. S. (1999). Tendências no estudo sobre avaliação. In Rico, E. M. (Org.), Avaliação de políticas sociais: uma questão em debate (pp. 29-39). 2. ed. São Paulo: Cortez. Chang, H. J. (2002). Kicking away the ladder: development strategy in historical perspective. London: Anthem Press. Chang, H. J. (2003). Globalisation, economic development and the role of the state. London, New York: Zed Books. Chang, H. J. (2011). Hamlet without the prince of Denmark: how development has disappeared from today’s development discourse. In Khan, S. R. & Christiansen, J. (Eds.), Towards new developmentalism: market as means rather than master (pp. 47-58). Abingdon: Routledge. Chaurasia, R., & Bhikajee, M. (2016). Adding entrepreneurship to India’s science, technology and innovation policy. Journal of Technology Management and Innovation, 11(2), 86-103. http://dx.doi. org/10.4067/S0718-27242016000200009 Chrispino, A. (2016). Introdução ao estudo das políticas públicas: uma visão interdisciplinar e contextualizada. Rio de Janeiro: FGV. Evans, P. B. (1995). Embedded Autonomy: states and industrial transformation. New Jersey: Princeton University Press. Evans, P. B. (2010). Constructing the 21st century developmental state: potentialities and pitfalls. In Edigheji, O. (Ed.), Constructing a democratic developmental state in South Africa: potentials and challenges (pp. 37-58). Cape Town: HSRC Press. Evans, P. B. (2011). The capability enhancing developmental state: concepts and national trajectories. [Discussion Text n. 63]. Centro de Estudos sobre Desigualdade e Desenvolvimento (CEDE). Niterói: UFF.

To see the SIBRATEC-ET cases in Santa Catarina, Paraná and Minas Gerais, Retrieved from https://www.dropbox.com/sh/40axojcm5dknebc/AABcVdS3pW274ZMZ6sklTyn5a?dl=0

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Fiani, R. (2011). Cooperação e conflito: instituições e desenvolvimento econômico. Rio de Janeiro: Elsevier. Gerschenkron, A. (1962). Economic backwardness in historical perspective. In Granovetter, M. & Swedberg, R., The sociology of economic life (pp. 5-30). Westview Press. Gomide, A. A., & Pires, R. R. C. (2014). Capacidades estatais e democracia: a abordagem dos arranjos institucionais para a análise de políticas públicas. In Gomide, A. A. & Pires, R. R. C. (Eds.). Capacidades estatais e democracia: arranjos institucionais e políticas públicas (pp. 15-28). Brasília: IPEA. Hodgson, G. M. (1988). Economics and institutions: a manifesto for modern institutional economics. Philadelphia: University of Pennsylvania Press. Instituto Brasileiro de Geografia e Estatística (IBGE). (2014). Classificação Nacional de Atividades Econômicas (CNAE). Retrieved from https://cnae.ibge.gov.br/?view=estrutura Instituto Nacional de Tecnologia (INT). (2016). Implantação da Rede de Extensão Tecnológica do Rio de Janeiro para apoio às micro, pequenas e médias empresas. [Relatório Técnico-Gerencial Final]. Rio de Janeiro: INT. Kuznets, S. S. (1973). Modern economic growth: findings and reflections. American Economic Review, 63(3), 247-258. Langlois, R. N. (1986). The new institutional economics: an introductory essay. In Langlois, R. N. (Ed.), Economics as a process: essays in the new institutional economics (pp.1-25). Cambridge University Press: Cambridge. Madeira, P. (2009). Extensão industrial em sistemas locais de produção: os efeitos da aglomeração na aquisição e difusão de novas capacitações em PME. Dissertação (Mestrado em Engenharia de Produção) - Escola Politécnica, Universidade de São Paulo (USP), São Paulo, Brasil. Mazzucato, M. (2011). The entrepreneurial state. London: Demos. Mendes, H. S. (2016). Extensão tecnológica no Brasil: uma análise da oferta e da demanda das empresas de metalmecânica da zona oeste do município do Rio de Janeiro. Tese (Doutorado em Políticas Públicas, Estratégias e Desenvolvimento) - Instituto de Economia, Universidade Federal do Rio de Janeiro (UFRJ), Rio de Janeiro, Brasil. Mendes, H. S., & Hasenclever, L. (2015). Development, entrepreneurial activity and industrial extension. In Rovere, R. L. L., Ozório, L. M. & Melo, L. J. (Orgs.), Entrepreneurship in BRICS: policy and research to support entrepreneurs (pp. 239-253). Cham: Springer International Publishing. Mendes, H. S., Ferreira, M. L. A., Hasenclever, L., & Teixeira, C. A. M. (2017). Extensão tecnológica e desenvolvimento regional: o caso Sibratec-ET Rede RJ (2017). Blucher Engineering Proceedings. http:// dx.doi:10.5151/enei2017-44.

Ministério da Ciência, Tecnologia e Inovação (MCTI). (2013a). Ações de C,T&I - Sistema Brasileiro de Tecnologia (SIBRATEC). [Relatório Técnico]. Brasília: MCTI. Ministério da Ciência, Tecnologia e Inovação (MCTI). (2013b). Secretaria de Desenvolvimento Tecnológico e Inovação. Programa SIBRATEC 2013. [Relatório de Gestão]. Brasília: MCTI. Nogueira, M. O. (2017). Um pirilampo no porão: um pouco de luz nos dilemas da produtividade das pequenas empresas e da informalidade no Brasil. Brasília: IPEA. North, D. C. (1990). Institutions, institutional change and economic performance. Cambridge: Cambridge University Press. Organisation for Economic Co-operation and Development (OECD). (1997). Small businesses, job creation and growth: facts, obstacles and best practices [Best Practices]. Paris: OECD. Rovere, R. L. L., Hasenclever, L. & Pinto, J. P. de M. (2014). An introduction to small and medium-sized enterprises (SMEs) in Brazil. In Hernandez, N. & Ramirez, J. (Orgs.), MEs unleashing the potential: opportunities and challenges? The cases of Brazil, Mexico, France, Germany, Italy and Spain (pp. 16-41). New Delhi, Índia: Bloomsbury. Rutherford, M. (1994). Institutions in economics: the old and the new institutionalism. Cambridge University Press, Cambridge. Rutherford, M. (2001). Institutional Economics: then and now. Journal of Economic Perspectives, 15(3), 173-194. http://dx.doi:10.1257/ jep.15.3.173. Salami, R., & Soltanzadeh, J. (2012). Comparative analysis for Science, Technology and Innovation Policy: lessons learned from some selected countries (Brazil, India, China, South Korea and South Africa) for other LDCs like Iran. Journal of Technology Management and Innovation, 7(1), 211-227. http://dx.doi:10.4067/ S0718-27242012000100014. Schumpeter, J. A. [1934] (1961). The theory of economic development: an inquiry into profits, capital, credit, interest and the business cycle. Oxford: Oxford U.P. Schumpeter, J. A. [1943] (1976). Capitalism, socialism e democracy. London: George Allen & Unwin (Publishers). United Nations Conference on Trade and Development (UNCTAD). (2016). Structural transformation for inclusive and sustained growth. [Trade and Development Report]. New York and Geneva: United Nations. Yin, R. K. (2014). Case study research: design and methods. Fifth edition. Thousand Oaks, CA: Sage Publications.

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Research Articles Perspectives on The Techno-Economic Analysis of Carbon Capture and Storage Simon Patrick Philbin, Steve Hsueh-Ming Wang

Industry Platforms as Facilitators of Disruptive IoT Innovations Ozgur Dedehayir, Cristian Ionuţ Pîrvan, Hans Le Feverm

University Research Centres: Organizational Structures and Performance Isabel Edith Torres Zapata

When Size Matters: Trends in Innovation and Patents in Latin American Universities Luis Fernando Ramirez, Jairo Isaza Castro

Transferencia Tecnológica en Universidades Chilenas: El Caso de la Universidad de Concepción Pablo Catalan, Eliana Sepúlveda, Annabella Zapata

E-Commerce C2C en Chile: Incorporación de la Reputación y de la Confianza en el TAM Renato Sukno, María Isabel Pascual del Riquelme

Case Studies Spinning Out of Control? How Academic Spinoff Formation Overlooks Medical Device Regulations

Paul Scannell, Kathryn Cormican

Stakeholders’ Contribution towards Responsible Innovation in Information and Communication Technology Research Projects Tilimbe Jiya

Technological Extension Networks and Regional Development: A Case Study In Brazil Heitor Soares Mendes, Marta Lucia Azevedo Ferreira, Lia Hasenclever, Carlos Alberto Marques Teixeira

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Methodology The methodology adopted in this research study was to consider the technological and economic aspects of carbon capture and storage

Figure 1. Schematic view of the research methodology and main themes of the techno-economic analysis of CCS.

Main themes for the techno-economic analysis

•

• • •

•

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Figure 2. Modelled contributions from different technologies and sectors to meet required global cumulative CO2 reductions (source: IEA, 2015).

8% 30% 38% 13% 10% 1%

Renewables CCS Power generation efficiency and fuel switching End-use fuel switching End-use fuel and electricity efficiency Nuclear burned. The CO2 is captured from the combustion gas through an appropriate method, such as being absorbed in a solvent, membrane separation or cryogenic separation. Once the CO2 has been extracted it is compressed and either transported or stored, as appropriate.

•

Post-combustion capture: This involves capture of CO2 through separating from the combustion gases after the fuel has been

•

In order to highlight some of the key differences between these three core capture technologies, the advantages and disadvantages can be considered, which are summarized in Table 1.

Table 1. Advantages and disadvantages for CO2 capture technologies (source: Figueroa et al., 2008). Technology

Pre-combustion capture

Post-combustion capture

Oxy-fuel capture

Advantages

Disadvantages

Synthesis gas is both high pressure and with high CO2 concentrations. Various technology options available to enable separation. Gasification is a recognized process.

Equipment potentially expensive. Supporting systems are needed. Application more towards new build facilities and not existing plants.

Scope to apply to most power stations. Retrofit technology options. High CO2 partial pressures generated.

Flue gas can have lower CO2 concentrations and a resulting lower CO2 partial pressure. Economic impact of low pressure.

Very high concentrations of CO2 in flue gas.

Retrofit technology options available.

Less advanced technology base when compared to pre- and postcombustion. Equipment cost base could be high. Process efficiency not optimized.

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Figure 3. Cumulative increase in storage capacity (Mtpa) for operational and planned CCS facilities - based on data from the Global CCS Institute (2017).

45.0 40.0 35.0 30.0 25.0 20.0 15.0 10.0 5.0 0.0

Cumulative storage capacity (Mtpa): Operational Cumulative storage capacity (Mtpa): Operational & Planned

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Table 2. Findings from review and analysis of expert opinion based studies from the literature. Main findings

Reference

Gibbins 2008

Report described six main CCS components, which are as follows: Capture, transportation, geological storage, ocean storage, mineral carbonation, and industrial uses of carbon dioxide.

Metz et al., 2005

Markusson et al., 2012

Anderson and Newell, 2004

Gough, 2008

Selma et al., 2014

Haszeldine, 2009

Tan et al., 2016

Benson and Cole, 2008

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and

Chalmers,

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Consideration of the findings from the literature allows the CCS policy determinants to be synthesized according to the three main areas and they are as follows: Regulatory and environmental factors: Regulatory framework (no. 01), site selection (no. 02), public awareness (no. 03), and environmental assessment (no. 04). Economic factors: Cost reduction (no. 05), government funding (no. 06), investment decision (no. 07), and international collaboration (no. 08). Technological factors: Capture technology (no. 09), storage technology (no. 10), transportation system (no. 11), and monitoring technology (no. 12).

•

Table 3. Results from structured literature review according to the key decisions factors contributing to sustainable policy for CCS investment decisions. ID

Area of policy determinant

CCS decision factor

Bibliometric search term

Regulatory and environmental

Regulatory framework

“Carbon capture and storage” AND “regulatory framework”

307

Regulatory and environmental

Site selection

“Carbon capture and storage” AND “site selection”

228

Regulatory and environmental

Public awareness

“Carbon capture and storage” AND “public awareness”

165

Regulatory and environmental

Environmental assessment

“Carbon capture and storage” AND “environmental assessment”

336

Economic

Cost reduction

“Carbon capture and storage” AND “cost reduction”

711

Economic

Government funding

“Carbon capture and storage” AND “government funding”

Economic

Investment decision

“Carbon capture and storage” AND “investment decision”

421

Economic

International collaboration

“Carbon capture and storage” AND “international collaboration”

Technological

Capture technology

“Carbon capture and storage” AND “capture technology”

1,196

Technological

Storage technology

“Carbon capture and storage” AND “storage technology”

997

Technological

Transportation system

“Carbon capture and storage” AND “transportation system”

171

Technological

Monitoring technology

“Carbon capture and storage” AND “monitoring technology”

No. of publications

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Figure 4. Frequency of the CCS decision factors ascertained through structured literature review.

1,400 1,200 1,000 800

1,196 997 711

600 421 336

400

307 228

200

171

165

Figure 6. Levelized cost of electricity, LCOE (2014 USD $) for power generation technologies in the United States. Source: Global CCS Institute (Irlam, 2015).

LCOE Data (US$/MWh) 300 250 200 150 100

Figure 5. Levelized cost of electricity calculation.

50 0

LCOE =

Total life cycle cost Total lifetime energy production

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Figure 7. LCOE trend analysis for CCGT with and without CCS (sources of data: ETI, 2012; Irlam, 2015; Gammer, 2016; EIA, 2018)

CCGT LCOE (with and without CCS) 120.0 100.0

R² = 0.8862

80.0 60.0 40.0

R² = 0.9098

20.0 0.0 2010

2012

2014

2016

2018

2020

2022

2024

CCGT (without CCS). USD/MWh CCGT (with CCS). USD/MWh Expon. (CCGT (without CCS). USD/MWh) Expon. (CCGT (with CCS). USD/MWh)

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Project Name

Leader

Location

Size (MW)

Feedstock

Schwarze Pumpe Vattenfall

Germany

Coal

ECO2 Burger

Powerspan

USA

Coal

Pleasant Prairie

Alstom

USA

Coal

AEP Mountaineer

AEP

USA

Coal

Karlshamn

E.ON

Sweden

Oil

Compostilla

ENDESA

Spain

Coal

ELCOGAS

Spain

Coal

Total

France

Oil

Vattenfall

Netherlands

Coal

Enel &Eni

Italy

Coal

SSE

Coal

RWE

Coal

CS Energy

Australia

Coal

Puertollano Lacq Buggenum Brindisi Ferrybridge CCSPilot100+ Aberthaw Callide-A Oxy Fuel Ordos Wilhelmshaven Plant Barry Boryeong Station

Shenhua group China

Liquefaction 0.1

E.ON

Germany

Coal

3.5

Southern Energy

USA

Coal

KEPCO

South Korea

Coal

CO2 Fate

Year Operational

Completed or Operating

Depleted Gas

2008

Completed

Vented

2008

Completed

Vented

2008

Completed

Saline

2009

Completed

Vented

2009

Completed

Saline

2009

Completed

Recycled

2010

Completed

Depleted Gas

2010

Completed

Vented

2011

Completed

EOR

2011

Completed

Vented

2012

Completed

N/A

2013

Completed

Saline

2012

Completed

EOR / Saline

2011

Operating

Vented

2012

Operating

Saline

2011

Operating

Vented

2013

Operating

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Project Name

Leader

Location

Feedstock

Size (Mt/yr)

Capture Process

Year Operational

K12-B

GDF Suez

Netherlands Gas Processing 0.2

Ketzin

GFZ

Germany

H2 Production 0.06

Otway

CO2CRC

Australia

Natural Deposit 0.065

Natural Deposit Depleted Gas 2008

USA

Coal

Pre-Combustion Saline

2014

Canada

Gas Processing 0.026

Gas Production EOR

2006

LNG Processing

PostCombustion PostCombustion PostCombustion PostCombustion PostCombustion N/A Gas Production PostCombustion PostCombustion

Tampa Electric Apache & PCOR

Polk Zama

StatoilHydro Norway

Snohvit

0.7

Coal

0.1

PetroChina

China

Nat. Gas Processing

0.2

Sinopec

China

Coal

0.04

Mongstad

Statoil

Norway

Gas

0.1

Jingbian Lula

Yanchang Petrobas

China Brazil

Chemicals 0.04 Gas Production 0.7

SaskPower

Canada

Coal

0.043

Japan

Hydrogen Production

0.1

Jilin Shengli

Shand Tomakomai

Huaneng

0.3

China

Shidongkou

JCCS

Depleted Gas PostCombustion

CO2 Fate

2004

Saline

2008

Saline

2007

Commercial Use

2009

EOR

2009

EOR

2007

Saline

2012

EOR EOR

2012 2013

Vented

2015

Saline

2016

Completed or Operating Completed Completed Completed Completed Operating Operating Operating Operating Operating Operating Operating Operating Operating Operating

Total capacity

Number of facilities (N)

Mean

Standard deviation (SD)

Post-combustion (MW)

135.60 MW

12.33 MW

14.63 MW

Post-combustion (Mt/yr)

1.34 Mt/yr

0.17 Mt/yr

0.21 Mt/yr

Pre-combustion (MW)

34.00 MW

17.00 MW

3.00 MW

Oxy-fuel (MW)

125.00 MW

31.25 MW

2.17 MW

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6 5

15 2

10 1

5 0

1 0

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

18.0 16.1

16.1

16.0 14.0 12.0 10.0 8.0

9.7

6.5

6.0 4.0

3.2

2.0 0.0

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have been categorized according to being technology, economic, and policy & regulation related as well as broader integrating type areas.

Table 7. CCS Research agenda Category

Proposed research areas •

Technology related research for CCS projects

• • •

• Economic related research for CCS projects

Policy & regulation related research for CCS projects

Integrating research approaches for CCS projects

Enhancement of membrane and solvent-based CO2 capture systems along with the associated engineering and process improvements. Optimization of underpinning technologies to support post-combustion, pre-combustion and oxy-fuel capture systems. Simulation models to improve the understanding of long-term storage of CO2 in geological formations. Life cycle analysis and system level analysis (e.g. system dynamics models) to consider environmental impact of technology options across capture, transport and storage phases of CCS projects.

• •

•

medium enterprises) on CCS projects. Improved decision-making frameworks and cognitive processes for CCS project investments.

•

Effectiveness of public engagement mechanisms to improve awareness of the societal benefits of CCS projects as well as the

•

• • • • •

associated health, safety and environmental considerations. Policy instruments that support joint government and industry investment frameworks for long-term CCS options on electric power generation and industrial process applications. Multi-level frameworks that link international, national and local government level regulatory mechanisms and decisionmaking (e.g. review and approval of CCS plant site selection decisions). Sharing of data and information on CCS cost reduction strategies through establishing CCS networks and partnerships. Stakeholder liaison and public engagement to raise the profile of CCS along with other climate change mitigation strategies. Multidisciplinary research programs drawing on technological and engineering disciplines as well as social and economic areas that enable system level perspectives to be developed on CCS projects.

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costs for implementing such technologies. Although other areas have the potential to impact CCS technology adoption, such as environmental and regulatory aspects and site selection.

Benson, S. M., & Cole, D. R. (2008). CO2 sequestration in deep sedimentary formations. Elements, 4(5), 325-331. https://doi.org/10.2113/ gselements.4.5.325

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J. Technol. Manag. Innov. 2019. Volume 14, Issue 3

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J. Technol. Manag. Innov. 2019. Volume 14, Issue 3

ISSN: 0718-2724. (http://jotmi.org) Journal of Technology Management & Innovation ÂŠ Universidad Alberto Hurtado, Facultad de EconomĂa y Negocios.

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Industry Platforms as Facilitators of Disruptive IoT Innovations Cristian Ionuţ Pîrvan1, Ozgur Dedehayir2*, and Hans Le Fever1 Abstract: We undertake an inductive study of four firms developing industry platforms upon which potentially disruptive IoT products and services are developed by external firms. Our results indicate there to be four types of industry platform (generalist, specialist, technology-centric, and industry-centric), each facilitating a unique mode of disruptive change. We propose that technology-centric platforms are more likely to facilitate business model disruptions, while industry-centric platforms are more likely to facilitate technological disruptions. Generalist industry platforms, by contrast, are able to facilitate both business model and technological disruptions, given the freedom they allow IoT firms to build their product and service solutions. Keywords: internet of things; IoT; disruptive innovations; digital platforms; industry platforms Submitted: Aug 28th, 2019 / Approved: Oct 3rd, 2019

1. Introduction By the beginning of 2018, Netflix’s market capitalization exceeded $100 billion, Airbnb acquired a valuation of $30 billion after another round of funding, while Spotify’s market valuation reached $26 billion following its initial public offering. What do these firms have in common? Netflix, Airbnb, and Spotify are not traditional players in their respective industries, but are disruptors, having reimagined the way they could better serve their markets to great effect. What is just as interesting and yet often unseen is that the platform businesses of these and other firms have been built on top of another platform, namely, the Amazon Web Services platform. Through this arrangement, arduous tasks such as database replication and scaling, as well as capacity provisioning, whether it is for storage, servers, or networks, have all been reduced through Amazon’s platform to a basic API (Application Programming Interface) call, thus allowing the firms to devote resources only to their core businesses. The success of Netflix, Airbnb, and Spotify have thus come about through the facilitating partnership offered by Amazon. We identify these facilitating platforms, such as the Amazon Web Services, as ‘industry platforms’ (Gawer & Cusumano, 2002; Cusumano, 2010; Gawer 2014), defined as “products, services, or technologies that act as a foundation upon which external innovators, organized as an innovative business ecosystem, can develop their own complementary products, technologies, or services” (Gawer & Cusumano, 2013, p.417). Platforms, at industry level, are steadily becoming the pervasive, dominant business model of the 21st century (Hoelck & Ballon, 2015). In the automotive sector, for instance, Audi Connect, BMW ConnectedDrive, and Mercedes Connect Me platforms are already used to boost industry-wide innovation (Mikusz et al., 2015). Scholars have captured these phenomena in studies that incorporate platform thinking, such as in evaluating incumbent performance with a focus on how established companies are able to keep their dominant positions in response to emerging disruptive innovations (Ansari & Krop,

2012; Brown et al., 2007). Other studies have, in turn, centered on the successful strategies employed by firms to disrupt incumbents in different industries through platform-based business models (Kenagy & Christensen, 2002; Sapsed et al., 2007; Soleimani & Zenios, 2011; Walsh, 2004). Notwithstanding these earlier contributions, there is still little known about how industry platforms facilitate disruptive change (Christensen, 1997; Dedehayir et al., 2014; Shea, 2005). This empirical and conceptual gap deserves attention given the technological paradigm shift currently taking place, accelerated by the Internet of Things (IoT) phenomenon, which is likely to impact many industries (Harris et al., 2015; Uckelmann et al., 2011). IoT captures the interaction and cooperation of objects – such as Radio-Frequency IDentification (RFID) tags, sensors, actuators, mobile phones, etc. – using unique addressing schemes and modern wireless telecommunication technology, to reach common goals (Atzori et al., 2010; Fleisch, 2010; Gubbi et al., 2013). It is currently one of the most attractive and impactful research areas for future work, especially when converged with other synergistic research streams such as Big Data (e.g. Wang et al., 2018). In July 2015, McKinsey & Company concluded that the IoT’s total economic impact could be as high as $3.9 trillion to $11.1 trillion per annum by the year 2025. In January 2016, Gartner argued that by the year 2020, more than half of the major new business processes and systems will include some elements of the IoT. Furthermore, a study conducted by the GSM Association, representing the interests of nearly 800 mobile operators worldwide, revealed that in the coming years, the rate of expansion and evolution of IoT will make it imperative for industry actors to cooperate on interoperability to avoid fragmentation and ensure that different devices and services will be able to communicate seamlessly (Bouverot, 2015). Given these trends and the innate, systemic nature of IoT, we anticipate that the number of platforms and platform-centric business ecosystems centering about IoT products and services will increase noticeably in the coming years. We additionally expect to see a greater abundance of IoTbased businesses that build upon industry platforms, which hold the potential of disrupting existing marketplaces (Ebersold & Hartford, 2015).

ISSN: 0718-2724. (http://jotmi.org) Journal of Technology Management & Innovation © Universidad Alberto Hurtado, Facultad de Economía y Negocios.

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across industry ecosystems, with an open technological interface), we have selected the case firms as real world representations of

this theoretical notion. Table 1 offers a description of each of the four selected companies1.

Table 1: Overview of the four industry platform companies studied. Company

Description

Size

Year founded

Company A

Industry platform for capturing, processing, visualizing and controlling enormous 51-200 amounts of IoT data in real-time. employees

2001

Company B

IoT platform for connecting devices with proximity awareness to the cloud.

51-200 employees

2013

Company C

Cloud-based data analytics platform for the IoT.

1-10 employees

2011

Company D

A business unit of a large multinational set up to develop solutions within the IoT 10,001+ context. employees*

2014**

* This is the total number of employees of the organization and not of the IoT dedicated business group. ** This is the year the IoT focused business group was founded within the organization.

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Interviewees level in the company

Date of collection

Company A

C-level

November 2015

Company B

Senior management

December 2015

Company C

VP level

December 2015

Company D

Senior management

December 2015 - January 2016

Fig. 1: The five most commonly occurring categories for each case.

Company A

Company B

#amazon_customers_building_their_own_platforms #amazon_IoT_is_expensive #paradigm_change #fault_tolerance_platform #modular_structure_platform

#proximity_relevant_for_many_industries #serve_side_A #proximity_technology_is_disruptive #adding_value_beyond_hardware #proximity_platform

Company C

Company D

#serve_side_A #platform_simple_and_easy_to_use #data_analytics #side_A_can_build_solutions_for_any_industry #software_interface_is_important

#analytics #focus_on_healthcare_market #preventive_medicine #serve_side_B_directly #Iot_platform_for_healthcare

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Table 3: Final list of broader themes for each case. Company A

Company B

proximity technology advanced backend serve verticals directly customers pull solutions proximity platform analytics analytics fast deployment hardware platform platform security software platform simplicity open platform easy to use platform complementors serve different industries available across industries platform complementors proximity technology broadly appli- software platform cable open platform real time visualization scalability serve verticals directly stay horizontal user experience is critical

Company C

Company D analytics focus on healthcare fast deployment platform for healthcare multitenancy platform open platform hardware agnostic preventive medicine serve verticals directly special regulations agile generic capabilities real time flexible complementors platform reliability artificial intelligence modular structure platform security multi layered platform evolved from internal platform part of a bigger organization

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Many I. Technology-Centric �

No industry focus

�

Technology focus

II. Generalist �

�

No industry focus

No technology focus

Industries served by platform IV. Specialist �

�

III. Industry-Centric

Industry focus

Technology focus

�

Industry focus

No technology focus

Few Specific

Technological solutions sponsored by platform

Generic

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Table 4: Overview of disruption with business model innovation and technological innovation. Business model disruptive innovation

Definition

Technological disruptive innovation

Innovations the disruptive tendencies of which stem from advancements Discovery of a fundamentally different business model in an in technological components, resulting in new-to-the-world products or existing business. services. Perturbs prevailing consumer habits and behaviors in a major way. Extends the â&#x20AC;&#x153;economic pieâ&#x20AC;? by attracting new customers, and expands the existing market by convincing existing customers Results from supply-push processes rather than demand-pull approaches. to consume more.

Aspects

Early pioneers are very rarely the ones that capture the market, while lateDoes not imply launch of a new product or service, but the recomersâ&#x20AC;&#x2122; products are generally preferred by the average consumer. definition of what a product or service is and how it is provided to the customer. The new technology changes the traditional attributes with respect to which firms compete. Requires a different and conflicting value chain from the ones of incumbents. The new technology makes the product cheaper and broadly available.

Impact

It is difficult for incumbents to make the new and established business models coexist.

The new technology undermines the competencies and complementary assets upon which incumbents have built their success.

Strategies

Incumbents may invest in their existing business model to compete more aggressively with the new business model.

Incumbents should create small or start-up firms that are autonomous in governance.

Examples

No-frills airlines; internet banking and internet brokerage; internet bookstores

The automobile; televisions; PCs; mobile phones; hard disk drives; digital cameras; minicomputers.

* Table adapted from existing literature (Charitou & Markides 2003; Danneels 2004; Gilbert & Bower 2002; Markides 2006; Dedehayir et al. 2014).

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Fig. 3: Modes of disruptive IoT innovation facilitated by different types of industry platforms.

Many I. Technology-Centric Business Model Disruption Industries served by platform

IV. Specialist

II. Generalist

6. Conclusions

Technological Disruption Business Model Disruption

While much of the attention among academics and practitioners has hitherto centered on disruptors, and the incumbents that suffer the consequences of disruption, our aim in this paper has been to move beyond the disruptor-incumbent dichotomy, and to capture the important backstage role assumed by companies like Amazon, in facilitating disruptive change through their platform designs. More specifically, this paper aimed to unveil insights about facilitating the emergence of disruptive IoT (Internet of Things) firms, inspired by the successes of companies such as Netflix, Airbnb, and Spotify that have fundamentally changed the way their respective markets are served. We focused on industry platforms upon which IoT firms can build their disruptive products and services, in the same manner Netflix, Airbnb, and Spotify have established their businesses upon Amazonâ&#x20AC;&#x2122;s Amazon Web Services platform.

III. Industry-Centric Technological Disruption

Few Specific

Technological solutions sponsored by platform

Generic

Given the very nascence of the topic under consideration, we have implemented an inductive research design, with the objective of building theory from case studies in the IoT realm. Our exploration of four firms providing industry platforms for IoT applications led us to propose a taxonomy of industry platforms based on their degree of specialism along two dimensions â&#x20AC;&#x201C; industry focus (number of industries that can be served), and technology focus (scope of technological solutions allowed). This taxonomy includes our industry platform types: (i) the generalist (many industries and wide scope of technological solutions); (ii) the technology-centric (many industries but narrow scope of technological solutions); (iii) the industry-centric (a few industries but wide scope of technological solutions); and (iv) the specialist (a few industries and narrow scope of technological solutions). In turn, by conceptually mapping two generic types of disruptive innovation identified from our examination of the literature upon this taxonomy, we proposed that technology-centric industry platforms are more likely to facilitate business model disruptions, while industry-centric platforms are more likely facilitate technological disruptions. Generalist industry platforms, by contrast, are able to facilitate both business model and technological disruptions, given the freedom they allow IoT firms to build their product and service solutions. The paper contributes to the industry platform and business ecosystem literatures by underlining the role industry platforms enact in facilitating the emergence of new businesses. Our work firstly has implications for industry platform companies, whose success is reliant on IoT firmsâ&#x20AC;&#x2122; ability to innovate upon their platforms. The proposed framework can assist industry platforms companies strategically position themselves with respect to the dimensions of industry and technology focus, thereby attracting IoT firms with a particular disruptive innovation vision. Our work is secondly relevant for IoT