D10.6 - Exploitation and Sustainability Report and Planning (b)

SmartSociety Hybrid and Diversity-Aware Collective Adaptive Systems When People Meet Machines to Build a Smarter Society Grant Agreement No. 600854

Deliverable D10.6 Work package WP10

Exploitation and Sustainability Report and Planning (b)

Dissemination level (Confidentiality)1:

PU

Delivery date in Annex I:

31st December 2016

Actual delivery date:

5th January 2017

2

1

Status :

F

Total number of pages:

20 (without references and appendix)

Keywords:

Exploitation, Planning, Reporting

PU: Public; RE: Restricted to Group; PP: Restricted to Programme; CO: Consortium Confidential as specified in the Grant Agreement 2 F: Final; D: Draft; RD: Revised Draft

Deliverable D10.6

Š SmartSociety Consortium 2013 - 2017

This document contains material, which is the copyright of SmartSociety Consortium parties, and no copying or distributing, in any form or by any means, is allowed without the prior written agreement of the owner of the property rights. The commercial use of any information contained in this document may require a license from the proprietor of that information.

Neither the SmartSociety Consortium as a whole, nor a certain party of the SmartSociety Consortium warrant that the information contained in this document is suitable for use, nor that the use of the information is free from risk, and accepts no liability for loss or damage suffered by any person using this information.

This document reflects only the authors’ view. The European Community is not liable for any use that may be made of the information contained herein.

Full project title:

SmartSociety - Hybrid and Diversity-Aware Collective Adaptive Systems: When People Meet Machines to Build a Smarter Society

Project Acronym:

SmartSociety

Grant Agreement Number:

600854

Number and title of work package:

WP10 - Dissemination, Collaboration and Exploitation

Document title:

Exploitation and Sustainability Report and Planning (a)

Work-package leader:

Stuart Anderson, UEDIN

Deliverable owner:

Daniele Miorandi, UH

Quality Assessor:

Ronald Chenu, DISI

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List of contributors

Partner Acronym UH

Contributor Daniele Miorandi, Iacopo Carreras, Diego Taglioni

UEDIN

Michael Rovatsos, Stuart Anderson

UNITN

Ronald Chenu

BGU

Kobi Gal

SOTON

Luc Moreau

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Deliverable D10.6

Executive summary This deliverable reports on the planning and implementation of the exploitation activities undertaken by the SmartSociety project during its lifecycle, together with an outlook of what the partners will do in the future to fully exploit the knowledge generated during the course of the project.

The deliverable is structured in three main parts.

The first part describes the overall strategy and methodology implemented by the Consortium for exploitation of knowledge and intellectual property and for ensuring the sustainability of the project results.

The second part describes, classifies and analyses the exploitable knowledge generated by the Consortium.

Exploitable knowledge items have been ranked and the four with the expected highest potential for exploitation have been selected.

Finally, for said four selected exploitable knowledge items a business model is presented, together with an analysis of the next steps required for successfully implementing a go-to-market plan.

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Deliverable D10.6

Table of Contents Table of Contents ............................................................................................................................................... 5 1 Introduction ................................................................................................................................................ 6 2 Methodology and process ........................................................................................................................... 7 2.1 Knowledge management in SmartSociety .......................................................................................... 7 2.2 Use of knowledge in SmartSociety ..................................................................................................... 7 2.3 Approach ............................................................................................................................................. 8 2.4 Step 1: Identification of exploitable knowledge.................................................................................. 9 2.5 Step 2: Business analysis ..................................................................................................................... 9 2.6 Step 3: Business modelling ................................................................................................................. 9 3 Exploitable knowledge generated by the project ...................................................................................... 10 3.1 Clustering and preliminary analysis .................................................................................................. 10 3.2 Mapping to quadrants ........................................................................................................................ 11 3.3 Analysis and qualification of exploitable knowledge items .............................................................. 12 4 Business models for selected exploitable knowledge items ..................................................................... 15 4.1 WhiteRabbit....................................................................................................................................... 15 4.2 SmartOrch ......................................................................................................................................... 17 4.3 Incentive server ................................................................................................................................. 18 4.4 The SmartCollectives Toolkit ........................................................................................................... 20 5 Conclusion ................................................................................................................................................ 21 References ........................................................................................................................................................ 22 Appendix A: Exploitable knowledge detailed description .............................................................................. 23

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Deliverable D10.6

1 Introduction This report summarizes the outcomes of the activities carried out within the framework of Task T10.5 “Sustainability and exploitation planning”. The goal of the task is to maximise the impacts of the project R&D&I activities by designing and executing a strategy for exploiting the knowledge and Intellectual Property (IP) generated by the Consortium members.

The SmartSociety Consortium acknowledges the importance that the use and dissemination of projects results (‘foreground’) plays in FP7, and has always been committed to take the necessary actions to maximise the impacts on science, technology and society of the project results, in line with the mission of the FET programme.

Generally speaking, the SmartSociety project outcomes present a great potential for innovation. Even if the project is of foundational nature and has always had a strong focus on novel science paradigms (as customary for a FET-funded project), the project devoted significant efforts to advancing technology able to support the full-fledged project vision. The ability of merging forward-looking research with implementation and field trials is rooted in the focus of the project itself; which deals with systems deeply embedded in society, [1] which in turn cannot be fully understood and designed in vitro but require to intertwine theory-oriented activities with empirical ones following a ‘lean research’ approach [2].

The outstanding innovation potential of the SmartSociety project has been also externally recognized, in particular by the FET2RIN consortium3, which selected SmartSociety within the first batch of FET projects to support in the design and execution of their exploitation strategy. SmartSociety project Consortium members took part in the three-steps training program promoted by FET2RIN, culminating in a pitch of one of the project innovative outcomes in front of investors, business developers and industry representatives.

From a project-level perspective, the main innovation highlight is the release of an open source toolkit, called SmartCollectives4, for prototyping and building applications for the sharing economy. The toolkit, which includes a number of embodiments of the research results of the project, was officially launched on Dec. 5th, 2016, during the event “Governing Smart Platforms: Policy Directions in the Collaborative Economy”. The toolkit is released under an Apache 2.0 license and can be freely accessed at https://gitlab.com/smartsociety.

The main innovation results of the project will be presented in the “Innovation in the Sharing Economy” event that the Consortium will hold in Berlin on Feb. 15th, 2017.

The deliverable is structured in three main parts.

The first part (Section 2) describes the overall strategy and methodology implemented by the Consortium for exploitation of knowledge and intellectual property and for sustainability of the project results. The methodology used is based on a three-steps funnel approach, where the knowledge developed by Consortium partners is collected and organised, to be then classified and analysed resulting in a ranking of the exploitation potential. The knowledge items with the highest expected innovation potential are then analysed more in detail, resulting in the delivery of a business model and in the definition of experiments for validating the underpinning assumptions.

In the second part of the deliverable (Section 3) we report a description of the exploitable knowledge generated by the Consortium. Exploitable knowledge items are clustered, mapped to exploitation quadrants and finally ranked according to a set of Consortium-defined criteria.

Finally in Section 4, for four selected exploitable knowledge items a business model is presented, in the form of Osterwalder’s canvas [3], together with an analysis of the next steps required for successfully implementing a go-to-market strategy.

3 4

http://www.fet2rin.com/ http://smartcollectives.com/

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Deliverable D10.6

2 Methodology and process This section describes the methodological steps followed in order to create and continuously update the SmartSociety exploitation plan during the course of the project. This includes a description of the activities and actions put in place by the SmartSociety Consortium to maximise the impacts (on science, technology, society and economy) of the knowledge and IP generated by the Consortium within the SmartSociety project.

The plan includes both actions aimed at supporting the partners in the exploitation of their own knowledge as well as coordination of partner-level strategy towards joint exploitation of knowledge and IP. 2.1 Knowledge management in SmartSociety Knowledge management procedures and processes for the SmartSociety project are specified in the SmartSociety Consortium Agreement (CA), which has been signed by all Consortium partners.

In setting and executing the exploitation plan, the Consortium will operate within the boundaries defined by the Consortium Agreement. In particular Sec. 9 (Foreground) regulates the ownership and exploitation of knowledge and IP generated by Consortium partners.

IPR ownership and protection of foreground is based on articles II.26-II.29 of the EC-Grant Agreement (GA), with special provisioning for: • Joint ownership; • Transfer of foreground.

Each partner in SmartSociety is the sole owner of any knowledge developed by that partner. The joint ownership of the intellectual property is ruled by articles in the EC-GA, with additional provisions reported in the CA for the case in which no joint ownership agreement can be reached among the interested parties.

2.2 Use of knowledge in SmartSociety In general terms, knowledge and IP generated in an R&D&I project can be used in two basic ways [4,5]. They can be used in further research activities (i.e., development or improvement of the generated results, either in a different setting or in a more applied scenario) or in commercial activities (i.e., production and marketing of new products and services).

Also, ‘use’ can take the form of direct utilisation, when the foreground owners intend to industrially or commercially exploit the results, or indirect utilisation, when other project partners or third parties exploit the project results, for example, through licensing.

This gives rise to a ‘use of knowledge quadrant’, as depicted below in Figure 1.

Accordingly, exploitation strategies can be classified along two dimensions, related to: • Where exploitation will lead: commercial activities or (other) research activities; • Who will execute the exploitation: Consortium members (direct) or third parties (indirect).

Examples of possible strategies include: • Research activities/Direct use: satellite projects, spin-off R&D&I initiatives • Research activities/Indirect use: release of open datasets for further research and open source code • Commercial activities/Indirect use: patent and licensing • Commercial activities/Direct use: new products and services Exploitation in SmartSociety builds upon a coherent and appropriate mix of strategies. Different exploitable knowledge items will follow different strategies, depending on the IP owner’s plan as well as on the specific market conditions.

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Figure 1. The exploitation quadrant for the SmartSociety project.

2.3 Approach In terms of drafting of the overall exploitation plan, a hybrid approach has been used, based upon a combination of: • Bottom-up activities: collection of partner-level exploitation plans and search for opportunities and synergies.

• Top-down activities: development and continuous evolution of a global Consortium-wide exploitation plan, building upon the joint contribution of a plurality of Consortium members.

Our hybrid approach has started with a bottom-up phase (‘radar phase’), then complemented with a top-down one (‘tracking phase’), which has then converged in the final version of the exploitation plan at hand.

In terms of the process followed, in particular for the commercial exploitation of knowledge and IP generated within the project, a three-staged funnel approach outlined in Figure 2 has been used. The first stage leads to the identification of the exploitable knowledge generated by the Consortium. In the second phase the business potential of each exploitable knowledge is assessed in the relevant context(s), and exploitable knowledge items are prioritized according to their business potential. It is worth remarking that, as SmartSociety solutions are naturally embedded in a given social context (which provides the resources for computational tasks), the specific exploitation context (i.e., market, application domain) plays a key role in terms of exploitation strategies and should therefore be explicitly considered in this phase.

For the most promising exploitable knowledge (selected according to Consortium-specified criteria) viable business models (BMs) are developed, together with a plan including a number of experiments aimed at validating the underlying business assumptions.

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Deliverable D10.6

Š SmartSociety Consortium 2013 - 2017 Exploita5on)) Context)

Exploitable) knowledge) with)BM,) technology) matura5on) roadmap)&) G2M)strategy)

Knowledge) generated)by) SmartSociety) Consor5um)

Exploitable)) knowledge)

Exploitable)) knowledge)with) clear)business) poten5al)

Figure 2. The SmartSociety exploitation funnel.

2.4 Step 1: Identification of exploitable knowledge A key step in the definition of the exploitation plan is the identification of exploitable knowledge. This activity has been coordinated by UH and carried out in tight coordination with the WP leaders and partner representatives. A form, described in Deliverable D10.5, has been purposefully designed by UH to collect relevant data. The collection has been done in a series of one-to-one virtual meetings between UH and the WP leader/partner representative. The list of exploitable knowledge has been continuously updated throughout the project lifetime (responsible: UH). The complete identification of exploitable knowledge is provided within the document at hand.

2.5 Step 2: Business analysis For each exploitable knowledge element identified in the previous step, its business potential has been analysed along the following seven dimensions: 1. Identification of potential targets/target market and segmentation (whenever appropriate); 2. Direct vs Indirect exploitation; 3. Description of innovative aspects/competitive advantage, level of innovation; 4. Competition; 5. Assessment of the maturity level of the solution (technology readiness level) – whenever appropriate; 6. Market potential (market size, CAGR); 7. Feasibility and risk analysis: time-to-market, required funding.

Exploitable knowledge items have been scored and ranked according to such criteria, resulting in the selection of four exploitable knowledge items with clear business potentials, which have been moved to the third stage.

2.6 Step 3: Business modelling Based on the outcomes of the second step, a business modelling exercise for the most promising exploitable knowledge elements has been carried out.

First, knowledge owners were asked to fill a CPS (Customer-Problem-Solution) canvas, which was used as a basis for streamlining the identification of target customers and the definition of a suitable value propositions.

Starting from the CPS, a business model for each selected exploitable knowledge item has been drafted using the canvas model [3]. Together with the business model canvas several experiments were defined in order to

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© SmartSociety Consortium 2013 - 2017

validate the underpinning assumptions and pave the way to a successful market entry, using the lean, iterative Lean Startup Machine5 approach.

3 Exploitable knowledge generated by the project The collection of knowledge generated by the Consortium has started in Y-2 with a series of one-to-one virtual meetings between the Exploitation Manager (D. Miorandi, UH) and all Consortium partners. This resulted in the creation of a catalogue of the exploitable knowledge generated by the Consortium, which was subsequently maintained and updated throughout the project lifecycle. The final list included twenty-two (22) exploitable knowledge items (in brackets the responsible partner/owner): 1. Privacy-protected peer spaces – WhiteRabbit (UNITN) 2. A methodology for R&D&I projects on socio-technical systems and a Social Charter for Smart Platforms (OXF) 3. Interventions to foster participation in socio-technical systems (OXF) 4. Methods for user engagement (IMA) 5. Virtual gamified environment (IMA) 6. Reputation service (SOTON) 7. Explanation service (SOTON) 8. Software toolkit for building social computation apps (UEDIN) 9. Fair group task recommendation algorithms and their adaptation for specific application scenarios, specifically for sharing economy style applications (UEDIN) 10. Middleware for communication with collectives of human/software peers (TUW) 11. Programming model and algorithms for managing collective teams and tasks (TUW) 12. SmartShare (BGU) 13. Incentive server (BGU) 14. Algorithms for incentives and interventions (BGU) 15. Incentives design for e-learning systems (BGU) 16. Algorithms for privacy-preserving provenance (KAU) 17. Methods for privacy-preserving incentives and/or reputation (protect privacy while providing incentives or reputation feedback) (KAU) 18. SmartSociety Platform (UH) 19. Ask SmartSociety! (UH) 20. Context recognition algorithms (DFKI) 21. Human interaction based semantic annotation (DFKI) 22. Simulations of collective, self-organizing behaviour of machines and people (DFKI) A detailed description of such knowledge items is reported in Appendix A. 3.1 Clustering and preliminary analysis In general, we can cluster the exploitable knowledge items identified so far in the following six (6) categories: • Applications/services: applications that leverage on hybrid and diversity-aware CASs to provide a well-defined functionality, with an inherent value for the user(s). Examples are SmartShare (#12) or AskSmartSociety! (#19).

• Platform: an integrated set of software components propelling the aforementioned applications/services (#18).

• Technological Enablers: components, integrated in the platform, which provide a given, wellspecified functionality. Examples include #1, #6, #7, #9, #10 etc.

• Algorithms: specifications of innovative methods for performing a given function relevant to HDACAS. May be embedded in one of the aforementioned technological enablers. Examples include #14, #16 and #20.

• Datasets: data from empirical activities, which may result useful for benchmarking/comparison or for usage in further applications/services.

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https://www.leanstartupmachine.com/

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•

Methods and Theories: refers to skills and capabilities built up during the course of the project which can find application in other fields of investigations or other R&D&I endeavour. Examples include #2.

The items in the previous categories may show dependency patterns (e.g., the platform includes a number of technological enablers, which may embed specific algorithms etc.), so that exploitation plans for the different exploitable knowledge items should be carried out in a coordinated way.

Further, it is worth remarking that the items in the first two categories are typically characterised by joint ownership of knowledge and IP, which may make exploitation more complex, although opportunities may be wider.

3.2 Mapping to quadrants The exploitable knowledge items identified in the first step and listed in the previous section have been mapped to the exploitation quadrants depicted in Figure 1. The result is shown in Figure 3. Each pentagon corresponds to one single exploitable knowledge item (EKI). The number within the pentagon corresponds to the EKI identifier as described in the previous section. The ones highlighted in orange are the ones that have been selected for further analysis due to their high innovation potential (as detailed in Sec. 3.3). EKI number 7 (‘Explanation service’ by SOTON) has not been classified as its exploitation strategy could follow different avenues and the owner has not decided yet on a preferred one.

As it can be seen there is a strong prevalence of the direct exploitation approach. The classification into research or commercial activities is rather balanced, reflecting the equilibrium between research and innovation activities in the project.

Figure 3. Mapping exploitable knowledge items to quadrants.

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Deliverable D10.6

3.3 Analysis and qualification of exploitable knowledge items In terms of deciding which exploitable knowledge items shall be moved to the next step (as described in D10.5) we preliminarily decided to focus only on those that may have exploitation in commercial activities.

These are (ID – owner - title: short description): • 1 - UNITN - Privacy-protected peer spaces: A framework for building privacy-aware backend servers; share something while keeping control of it (retract/change/see what they have been used for etc.).

Users in full control of their own data and their usage by a plurality of services.

• 5 - IMA - Virtual gamified environment: A set of functionalities and services (interoperating with existing ones) for citizens and tourists, who shall actively participate to the system growth • 6 - SOTON - Reputation service: A service by which we can collect feedback reports about a given subject, and a reputation rating is computed. Reputation can be used in various aspects (e.g., for performing matching or to present matches etc.)

• 8 - UEDIN - Software toolkit for building social computation apps: A software framework allowing developers to quickly build collaborative web applications • 9 - UEDIN - Recommender systems and methods: A generic, not very domain-dependent algorithm for building recommendations • 10 - TUW - Middleware for communication with collectives of human/software peers: A framework that can be deployed in the cloud and integrated with different software products that can support the communication/routing/message transformation functionalities among software and humans • 11 - TUW - Programming model and algorithms for managing collective teams and tasks: A programming model, language APIs, and a set of algorithms for collectives and tasks lifecycle management.

• 12 - BGU - SmartShare: a service that arranges shared rides. The service relies on an advanced ICT system built with other Consortium partners. Offered to students in the CS department at BGU as project topic. And get people to deal with challenges around smart applications.

• 13 - BGU - Incentive server: An open source software package supporting people to actually execute interventions in community-based applications and services. Reusable, can be customized/adapted to different applications.

• 15 - BGU - Incentives design for e-learning systems: The expertise developed on incentives and interventions is being applied in other areas (in particular: e-learning/ education) • 18 - UH - SmartSociety Platform: An ICT platform supporting the deployment of services/applications with a social computation focus. Supports different computational patterns, ranging from collective intelligence to tasking/crowdsourcing.

• 19 - UH - AskSmartSociety!: A tasking/crowdsourcing service based on a Q&A pattern, able to compose both machine-provided services as well as individuals and collectives.

• 20 - DFKI - Context recognition algorithms: A solution by which using smart phone sensors and dedicated external sensors if available, the user's context can be identified, described, tagged and forwarded to a database server.

These 13 exploitable knowledge items have been ranked according to the following Consortium-identified criteria: • Clear market identification: is the target market clearly identified and well specified? Do the target customers represent a homogeneous group? Is customers’ problem clearly identified?

• Direct exploitation: is the partner planning to exploit the knowledge directly?

• Innovation level/type: how much does the knowledge represent an innovation over state-of-the-art? Is it disrupting the target market? How much does it clearly differentiate from existing solutions?

• Competition: Does competition exist for the proposed innovation? Is competition fragmented? Are competitors clearly identified?

• Maturity: Is there a prototype available? Has it been tested in the lab and in operational environment?

What is the TRL? • Market potential: How big is the target market? How fast is it growing? Has data on market been researched by the knowledge owner?

• Feasibility: What is the potential time-to-market? How many resources need to be mobilised to enter the market?

For each of them scores in the range 0 to 5 were assigned, 0 being the worst and 5 the best. The scores were assigned by the project Exploitation Manager (D. Miorandi, UH) based on the data provided by the partners.

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Deliverable D10.6

The results can be found in the following table. Based on the ranking we selected the following four exploitable knowledge items to move forward with the last step of the methodology (business modelling): •

• •

•

Privacy-protected peer spaces (WhiteRabbit): A framework for building privacy-aware backend servers; share something while keeping control of it (retract/change/see what they have been used for etc.). Users in full control of their own data and their usage by a plurality of services.

Software toolkit for building social computation apps (SmartOrch): A software framework allowing developers to quickly build collaborative web applications Incentive server: An open source software package supporting people to actually execute interventions in community-based applications and services. Reusable, can be customized/adapted to different applications.

SmartSociety Platform (SmartCollectives): An ICT platform supporting the deployment of services/applications with a social computation focus. Supports different computational patterns, ranging from collective intelligence to tasking/crowdsourcing.

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Table 1: Ranking of exploitable knowledge items

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Deliverable D10.6

4 Business models for selected exploitable knowledge items For the four aforementioned exploitable knowledge items with clear business potential a business modelling exercise has been carried out. During such exercise, which benefitted from the training received by the FET2RIN action, virtual and face-to-face meetings between the Exploitation Manager and the knowledge items owner were organised.

The first step was to fill a CPS (Customers, Problems, Solutions) board. The aim of the CPS exercise was to make sure EKIs owners ask themselves the fundamental questions: • Who are my customers?

• What problems do they have?

• How do I solve their problems?

The rationale behind the CPS structure is that, in order to make business, you should solve a problem for somebody who cares about it, so that she is willing to pay (in some way) for a solution. This is key for avoiding an unfortunately common problem in moving research outcomes to market, which can be informally summarized as “Beautiful solutions to problems nobody really cares about”. The CPS (Customers, Problems, Solutions) board is a simple tool for putting order in the process of understanding how your exploitable knowledge may produce value Once the CPS is filled and stabilized, the exploitable knowledge owner was asked to reason on the assumptions underpinning the CPS content and to design suitable experiments for validating said assumptions. Each experiment is specified as: • Assumption: a concise statement of the assumption to be validated; • Experiment design: how the experiment will be carried out; • Validation criteria: quantitative criteria for deciding on whether the assumption was validated or not.

(This part of the business modelling process was freely inspired by the LeanStartupMachine Validation Board6 and by the more recent Javelin Board7).

Last, in order to get a more comprehensive picture, a Business Model Canvas (BMC, [3]) was developed. Also in this case the canvas was used as both a self-reflection tool (forcing EKI owners to ask themselves questions relevant for turning their innovation into a sustainable business) as well as for identifying weaknesses and potential improvements. What is presented in the deliverable is the final result of such process.

4.1 WhiteRabbit The CPS for WhiteRabbit is reported in Figure 4.

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https://www.leanstartupmachine.com/validationboard/ http://vip.javelin.com/

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Figure 4: CPS for WhiteRabbit Three experiments have been identified as necessary to validate the assumptions underpinning the WhiteRabbit business model: • GDPR compliance costs o Assumption: companies are going to incur in significant costs to become compliant with and operate under the regulations from the GDPR. o Experiment design: we will interview up to 10 online service companies and ask them about their preparation and plans for complying with the GDPR and how much they are prepared to spend. o Validation criteria: finding that more of 70% of them are not prepared or not sufficient have plans/budget for complying with the GDPR. • Transparency & Engagement o Assumption: more transparency and trust from the users leads to higher engagement. o Experiment design: we will interview up to 20 users of online and mobile services and ask about their participation and trust in different commonly known online services (Facebook, Gmail, etc) and also about common “mainly offline” services. o Validation criteria: establish If a significant relation (higher than 60%) exists between how much information or participation they have in the given service and their trust in that service.

• Cost Saving o Assumption: providing a “tax accountant” for privacy middleware that can integrate with current working systems will save costs and time for currently running online service companies. o Experiment design: we will interview up to 5 online service companies and review their deployed infrastructure. We would then compare the cost of the conventional approach (privacy consultancy firm + integrator) with our projected costs for deploying our solution in that company. o Validation criteria: If our solution is at least 50% less costly than the conventional approach in at least 90% of the cases.

The business model canvas (BMC) for WhiteRabbit is reported in Figure 5.

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Figure 5: BMC for WhiteRabbit 4.2 SmartOrch The CPS for SmartOrch is reported in Figure 6.

Figure 6: CPS for SmartOrch Three experiments have been identified as necessary to validate the assumptions underpinning the SmartOrch business model: • Cost of developing sharing apps o Assumption: The time-to-market and cost of implementing sharing apps really too high. o Experiment design: Ask 10 sharing economy companies what developing their “social orchestration” components cost, relative to their overall budget, and how timely this was completed.

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Validation criteria: If 7 out of ten confirm it took longer than expected, and cost more, or was delivered later than expected, this would confirm our assumption.

• Optimisation is a need o Assumption: The addition of optimisation features is necessary but too costly o Experiment design: Ask the same 10 companies whether they are experiencing usability problems and feel user decision making should be assisted further, if they are aware that choices made are not rational or optimal for the platform. Offer to run optimisation on sample data sets provided by them. o Validation: If 7 out of them confirm the potential or confirm they would be interested in a product that would perform this optimisation rather than building it in-house we confirm our assumption.

• Costs saving o Assumption: SmartOrch reduces implementation cost and provides effective optimisation. o Experiment design: Ask students to emulate 10 common sharing economy apps in a weeklong Hackathon, ideally with some datasets from companies. o Validation: If 7 of them were built within a week successfully and reduce churn rate by 20% (in simulation) our assumption is confirmed.

The business model canvas (BMC) for SmartOrch is reported in Figure 7. o

Figure 7: BMC for SmartOrch

4.3 Incentive server The CPS for the Incentive Server is reported in Figure 8.

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Figure 8: CPS for the Incentive Server Two experiments have been identified as necessary to validate the assumptions underpinning the Incentive Server business model: • Dropout o Assumption: high dropout rate in collaborative task systems. o Experiment design: Find several companies who rely on self-motivated users as a client base.

Examples: E-learning software, Zooniverse. o Validation criteria: 70% of company execs confirm that high dropout rates pose a real problem to company profits.

• Engagement o Assumption: Using incentive server API alleviates dropout in the system without affecting the quality of user input. o Experiment design: Conduct a between-subject experiment with two groups. One group receives motivation notification using the incentive server, and one does not. o Validation criteria: At least 25% increase in the amount of contribution for each group; no significant difference in the quality of contribution between groups.

The business model canvas (BMC) for the Incentive Server is reported in Figure 9.

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Figure 9: BMC for the Incentive Server 4.4 The SmartCollectives Toolkit The CPS for SmartCollectives is reported in Figure 10.

Figure 10: CPS for SmartCollectives Two experiments have been identified as necessary to validate the assumptions underpinning the SmartCollectives business model: • Cost issues o Assumption: Developers of collective-based applications spend a lot of time/$ to implement platforms due to the lack of reusable stacks. o Experiment design: Interview 20 developers of collective-based applications.

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Validation criteria: 70% of them confirm that time and money for the initial platform implementation is a real issue.

Time saving o Assumption: SmartCollective is able to cut the development time of collective-based applications by 40%. o Experiment design: Two groups of CS students (MS level) asked to program a simple airbnblike application, one who has to do things from scracth and one using the SmartCollectives toolkit. o Validation criteria: Fraction of time saved. o

•

The business model canvas (BMC) for SmartCollective is reported in Figure 11.

Figure 11: BMC for SmartCollectives

5 Conclusion The SmartSociety project has developed a number of tools and technology enablers that have a very high potential for exploitation in the general field of the sharing/collaborative economy.

This deliverable reports on the exploitation planning activities carried out by Consortium partners, and presents concrete outcomes in terms of business models for four selected exploitable knowledge items.

One special mention is worth for SmartCollectives, which, by bringing together the software enablers developed by most Consortium partners in a coherent and cohesive form, represents probably the main heritage – from an innovation perspective – of the project as a whole. In this respect the exploitation path adopted, which is based on an open source model (whereby a liberal license, Apache v.2 has been unanimously adopted for all components) for the toolkit, presents as main challenge the ability of fostering and nurturing the arising of a community of researchers, innovators and entrepreneurs using the toolkit and sustaining its growth.

On top of such framework it is expected that some of the Consortium partners will develop sharing/collaborative economy applications, tailored to specific vertical domains (transportation, healthcare, tourism, labour among the most prominent ones) and able to fully exploit the disruptive potential of the toolkit, which embodies most of the research results developed within the scope of the project.

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References [1] Giunchiglia, F., Maltese, V., Anderson, S., & Miorandi, D. (2013). Towards hybrid and diversity-aware collective adaptive systems [2] Miorandi, D., Carreras, I., & Chlamtac, I. (2014). The Lean Research: How to Design and Execute Social Collective Intelligence Research and Innovation Projects. In Social Collective Intelligence (pp. 267-276).

Springer International Publishing.

[3] Osterwalder, A., & Pigneur, Y. (2010). Business model generation: a handbook for visionaries, game changers, and challengers. John Wiley & Sons.

[4] “Introduction to IP Rules in FP7 Projects”: http://www.iprhelpdesk.eu/sites/default/files/relateddocuments/Factsheet%20IP%20rules%20FP7%20June% 202011.pdf [5] “Guide to Intellectual Property Rules for FP7 Projects”: ftp://ftp.cordis.europa.eu/pub/fp7/docs/ipr_en.pdf

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Appendix A: Exploitable knowledge detailed description This appendix includes the detailed description of the exploitable knowledge summarised in Sec. 3.

Information about each exploitable knowledge element is organised in a form, and what is reported is the final version as delivered by Consortium partners in December 2016.

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