Visualization tools for intuitive decision making by Amir Mosavi

VISUALIZATION TOOLS FOR INTUITIVE DECISION-MAKING

Atiyeh Vaezipour

MASTER THESIS 2014 INFORMATICS

Sammanfattning

VISUALIZATION TOOLS FOR INTUITIVE DECISION-MAKING

Atiyeh Vaezipour Detta examensarbete är utfört vid Tekniska Högskolan i Jönköping inom ämnesområdet informatik. Arbetet är ett led i masterutbildningen med inriktning informationsteknik och management. Författarna svarar själva för framförda åsikter, slutsatser och resultat. Handledare: Ulf Seigerroth Examinator: Vladimir Tarasov Omfattning: 30 hp (D-nivå) Datum: Spring 2014 Arkiveringsnummer:

Sammanfattning

There is no logical way to the discovery of elemental laws. There is only the way of intuition, which is helped by a feeling for the order lying behind the appearance... The intuitive mind is a sacred gift and the rational mind is a faithful servant. We have created a society that honours the servant and has forgotten the gift. Albert Einstein

Abstract Information systems (IS) have been recognized as major tools to be utilized in supporting decision-makers in enterprises worldwide. However, despite of all the recent advancements in developing rational tools of information and communication technologies (ICT) for decision-making, e.g. decision support system (DSS) and business intelligence (BI), intuition still plays major role in solving problems. Thus, the purpose of this study is to expand knowledge and understanding on the concept of intuition and its systematic use, combined with visualization tools in todayâ&#x20AC;&#x2122;s globally competitive, uncertain and dynamic business environment. The focus is therefore to investigate decision-making problems related to engineering design, where decision makers face uncertainties in complex circumstances and the additional constraint of time. Furthermore, as the topic of intuition may be investigated from different perspectives, there has been a demand for multidisciplinary research on the topic. In response to this demand, a qualitative research method is selected to collect data and literature review on the latest research in psychology and creativity proves that intuition cannot always be trusted to lead to the optimal decision. A case study in engineering design is then given to validate the assumption which is, using human intuition and creativity in a balance with visualization tools is effective and beneficial in enterprise decision making. The results show that the permanent solution to creative decision-making would ideally be an integration of intuition and rational tools, to be studied in the framework of visualization tools. Furthermore, the visualization tool for decision-making was accordingly presented as a powerful Information Technology (IT) tool in dealing with decision-making problems.

Acknowledgements

Acknowledgements This thesis could not be completed without support of many people. I am gratefully acknowledging their inspiration. My deepest appreciation goes to my family for their valuable support and encouragement throughout my life. With the continued advices of my supervisor, Ulf Seigerroth, my research is further shaped, formulated, and documented in the current form. I am truly grateful to him for his time through the preparation of this work, and also I am thankful to him for guiding me to achieve my earlier research careerâ&#x20AC;&#x2122;s goals. Furthermore, I would strongly acknowledge supports and great organization of Vladimir Tarasov during progressing of my master program. In addition, I would like to thank Rob Day for valuable guidance and organization in the last steps of my thesis. Also, I am grateful to the interviewees for taking the time to accomplish interviews. And last but not least, I would also like to thank Amir Mosavi, for giving me the great chance of research collaboration.

Atiyeh Vaezipour Spring 2014

Key words

Key words Decision-Making, Creativity, Intuition, Data Visualization, Rationality Multidimensional Visualization Tool.

iii

Contents

Contents Introduction ............................................................................... 1

PURPOSE AND RESEARCH QUESTION ....................................................................................... 3 LIMITATIONS............................................................................................................................ 4 THESIS OUTLINE ...................................................................................................................... 5

1.1 1.2 1.3

Research Method ........................................................................ 6

RESEARCH DESIGN .................................................................................................................. 6 RESEARCH APPROACH.............................................................................................................. 7 MULTIDISCIPLINARY RESEARCH METHOD ............................................................................... 8 LITERATURE REVIEW ............................................................................................................... 9 DATA COLLECTION................................................................................................................ 10 2.5.1 Case Study ....................................................................................................................... 10 2.6 DATA ANALYSIS ..................................................................................................................... 11 2.7 RESEARCH VALIDATION AND GENERALIZATION ................................................................... 13

2.1 2.2 2.3 2.4 2.5

Decision-Making Under Uncertainty ........................................ 14

DECISION-MAKING AND INFORMATION TECHNOLOGY ........................................................ 14 Brief History of Decision-Making ......................................................................................... 15 Concept of Intuition and Creativity in Decision-Making ............................................................ 19 Information Technology in Decision-Making ........................................................................... 21 Intuitive Decision-Making ................................................................................................... 25 Problem with Intuition ........................................................................................................ 27 Human Mind in Decision-Making ....................................................................................... 30 Summary of Section ........................................................................................................... 32 3.2 HUMAN BRAIN AND INFORMATION TECHNOLOGY ................................................................ 34 3.2.1 Intuitive Mind Vs. Rational Mind ....................................................................................... 35 3.2.2 Human Brain in Decision-Making ....................................................................................... 37 3.2.3 When Intuition and Creativity Happen ................................................................................. 39 3.2.4 Summary of Section ........................................................................................................... 43 3.3 CONCLUDING REMARKS ........................................................................................................ 44 3.1

3.1.1 3.1.2 3.1.3 3.1.4 3.1.5 3.1.6 3.1.7

Data Visualization Tools Supporting Decision Making ............. 47

4 4.1

4.1.1 4.1.2 4.1.3 4.1.4 4.2

MULTIDIMENSIONAL DATA VISUALIZATION .......................................................................... 47 Considering Example in Multidimensional Data Visualization ................................................. 50 Engineering Design ............................................................................................................ 51 Role of Intuition and Creativity in Engineering Design .............................................................. 54 Intuitive Decision-Making ................................................................................................... 56 CONCLUDING REMARKS ........................................................................................................ 58

Empirical Findings and Analysis .............................................. 60

EMPIRICAL FINDINGS............................................................................................................. 60 1st Interview ...................................................................................................................... 60 2nd Interview ..................................................................................................................... 62 3rd Interview ...................................................................................................................... 63 5.2 ANALYSIS ............................................................................................................................... 65 5.2.1 Role of Visualization Tools in Decision-Making ..................................................................... 65 5.2.2 Role of Intuition and Creativity in Decision-Making ................................................................ 67 5.3 CONCLUDING REMARKS ........................................................................................................ 68

5.1

5.1.1 5.1.2 5.1.3

Conclusion ............................................................................... 70

6 6.1

FUTURE RESEARCH................................................................................................................. 71

Contents

References ................................................................................ 72

Appendix .................................................................................. 88 INTERVIEW QUESTIONS ...................................................................................................................... 88

List of Figures

List of Figures FIGURE 1. RESEARCH DESIGN & CORRESPONDING CHAPTERS .............. 7 FIGURE 2. STEPS TOWARD LITERATURE REVIEW (ADOPTED FROM DAWSON, C., 2002) ............................................................................................... 9 FIGURE 3. THE HERBERT SIMONâ&#x20AC;&#x2122;S GRAPH OF DECISION-MAKING (SIMON, 1976) ....................................................................................................... 17 FIGURE 4. THE TABLE INCLUDES THE DATASET (VAEZIPOUR & MOSAVI, 2012A). .................................................................................................. 24 FIGURE 5. DATA VISUALIZATION IN BI; THE ORGANIZATIONAL CHART OF AIESEC (VAEZIPOUR & MOSAVI, 2012A). ........................................... 24 FIGURE 6. THE VALUE FUNCTION THAT PASSES THROUGH THE REFERENCE POINT; DESCRIPTION OF VALUE OF LOSSES AND GAINS IN A DECISION (KAHNEMAN AND TVERSKY, 1979) ................. 29 FIGURE 7. THE ICEBERG OF UNCONSCIOUSNESS; A VISUAL REPRESENTATION OF FREUD'S THEORY INDICATING THAT MOST OF THE HUMAN MIND OPERATES UNCONSCIOUSLY; THE YET TO BE KNOWN CAPACITY (KENDEL, 2000)...................................................... 31 FIGURE 8. BRAIN AS A WHOLE WITH BOTH RATIONAL AND INTUITION INTERACTIONS (MCGILCHRIST, 2009) ....................................................... 38 FIGURE 9. MULTIDIMENSIONAL VISUALIZATION, CONSIDERING FIVE DESIGN CRITERIA SIMULTANEOUSLY....................................................... 49 FIGURE 10. SIMULATION OF DRAPING PROCESS INCLUDING A COMBINED MECHANICAL MODELING OF COMPRESSION, BEND, STRETCH, AND SHEAR, (VAEZIPOUR & MOSAVI, 2013C) ............... 52

List of Figures

FIGURE 11. SIMULATION OF THE DRAPING PROCESS; CONSIDERING DIFFERENT MATERIALS (MOSAVI, HOFFMANN & VAEZIPOUR, 2012) ........................................................................................................................ 53 FIGURE 12. SIMULATION OF THE DRAPING PROCESS; CONSIDERING A DIFFERENT PRODUCT (VAEZIPOUR & MOSAVI, 2013C) ............. 53 FIGURE 13. THE GROWTH OF COMPUTATIONAL AND MATHEMATICAL OPTIMIZATION RESEARCH VS. SITUATION OF USAGE OF THESE TOOLS IN INDUSTRY SINCE 1994 (MOSAVI, 2013C). .............................. 55 FIGURE 14. MULTIDIMENSIONAL VISUALIZATION GRAPH USED FOR CONSIDERING DIFFERENT PRODUCTS, MATERIALS AND DRAPING CHARACTERISTICS SIMULTANEOUSLY. HERE THE COST, WEIGHT, ENVIRONMENTAL, ELECTRICAL, AND MECHANICAL FACTORS ARE SIMULTANEOUSLY CONSIDERED (MOSAVI, HOFFMANN & VAEZIPOUR, 2012). ....................................... 56 FIGURE 15. A MULTIDIMENSIONAL VISUALIZATION GRAPH USED FOR CONSIDERING DIFFERENT PRODUCTS, MATERIALS AND DRAPING CHARACTERISTICS SIMULTANEOUSLY. (VAEZIPOUR & MOSAVI, 2013C) ..................................................................................................................... 57

vii

List of Abbreviations

List of Abbreviations AI

Artificial Intelligence

Business Intelligence

CEO

Chief Executive Officer

DSS

Decision Support System

EDM

Enterprise Decision Management

EIS

Executive Information System

HCI

Humanâ&#x20AC;&#x201C;Computer Interaction

Information System

Information Technologies

Intelligence Quotient

MCDM

Multiple Criteria Decision-Making

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Introduction

1 Introduction This chapter presents an overview of our research topic and description of the problem domain. Furthermore, the purpose along with research question is formed. In addition, this chapter covers the limitation and scope of the study and ends with thesis outline which aim to provide an overview of remaining chapters. Decision-making is a general term associated with the choices made in everyday life (Janis & Leon, 1977; Edwards, 1954; Kahneman, 2011). Bianchi (2009) explains that when a decision-making task is mathematically described, such as an optimization problem, computer science and mathematical optimization can contribute significantly to finding the optimal solution. In fact, with the convenient support of computers today, even with inadequate or imperfect information and restricted computation capability metaheuristics, one can still find an acceptable solution to such problems (Blum & Roli, 2003). According to Jessup et al. (2003), with the involvement of computer applications in every aspect of life today, IS (Information System) have been broadly recognized as the primary means to be utilized in supporting decision-making in enterprises worldwide. In this realm, the decisionsupport systems (DSS) and Business intelligence (BI) tools have been recognized to be the specific IS which deal with decision-making tasks. However, according to Proctor (2011), IT as the study, design, development, application, implementation, support and management of computer-based IS has been the main contributor to the development of rational tools. As Gigerenzer (2008) states, the increasing level of uncertainties in life, combined with limited time and inadequate computational power when dealing with practical, real-life problems has made decision-making challenging. According to Dhar and Stein (1997), in order to deal with the above challenges, BI tools (Negash, 2004), as the convenient data-driven DSS (Power, 2007; Turban, 2007), have been increasingly contributing to business decision-making. In this context, the rational and analytical tools of BI would support decision-makers by providing meaningful information and insight from historical data. However, the conventional BI tools used in dealing with uncertainties are limited in recording, mapping and visualizing the historical data (Negash, 2004), which cannot easily be handled by the limited information processing capacity of the human mind (March, 1978; Battiti & Brunato, 2011). According to Rud (2009), this is one of the major 1

Introduction

reasons why the general usage of BI tools has not been fully promising or reliable. As Joseph Stiglitz, the recipient of the Nobel Memorial Prize in Economic Sciences (2001) noted in his Nobel lecture, An early insight in my work on the economics of information concerned the problem of appropriability, the difficulty that those who pay for information have in getting returns. As Turban et al. (2007) state enterprises today are using snapshots of databases to understand and react to future trends. Therefore, when using BI tools, as described by researchers such as Andersson et al. (2008), Turban et al. (2007), and Negash (2004), enterprises require personnel who are highly trained in statistics, analysis, optimization, post-processing, and databases, in a system where experts design data extraction strategies and relay them to programmers for the actual execution. However, Battiti and Brunato (2013) reported this process to be slow, complicated, and expensive in the uncertain and dynamic environments that most businesses encounter. As Gigerenzer (2008) and Rud (2009) also explain, relying exclusively on the rational approaches of conventional business DSS such as BI, does not solve the problem of complexity. In this situation, as Rud (2009) clearly states, there must be both an economic motivation and a human initiative to move beyond the rational, logical, linear, and reductionist view to a more intuitive and inventive approach, in order to achieve creative decisions. Westall (2007) provides evidence that an enterprise must benefit from creative decision-making in order to remain profitable and competitive. According to Rud (2009), innovation is derived from enterprises that nurture creative decision-making. Creativity in enterprises is very important, because without it, products and services become increasingly similar, while progress may become linear and begin to flatten into a limited-growth line (Rud, 2009). Nevertheless, as Moore (2005) states, creative decision-making and the production of innovative ideas in problem-solving would allow enterprises to differentiate themselves from their competitors and experience exponential advancement. This advancement would consequently enable smart pricing, thereby leading to higher value, increased innovation, and greater overall success (Rud, 2009). Fiore and Schooler (1998) and later Gigerenzer et al. (1999) confirm that the concept of creativity in the realm of decision-making is strongly associated with intuition. In addition, Gigerenzer and Selten (2002) and Gigerenzer (2007) provide evidence that the success of enterprises in 2

Introduction

todayâ&#x20AC;&#x2122;s globally competitive and dynamic business environment has been more dependent upon intuition than on the rational tools of DSS. Gigerenzer and Gaissmaier (2011) conclude that due to the presence of increasingly complicated problems in this highly uncertain world, understanding the concept of intuition is considered more vital than ever in order to fuel creativity and innovation. Therefore, our research has been inspired by scholars such as Pascal, Rud (2009), Gigerenzer (1999), Battiti and Brunato (2011 & 2013), and Einstein, all of whom seek answer to complex problemsolving, involving intuition and simple or creative methods rather than rational approaches. Obviously, as Kahneman (2011) also clarifies, intuitive-based approaches to decision-making may have their own marvels and flaws and might easily go wrong in todayâ&#x20AC;&#x2122;s world of complexity and manipulative media (Ariely, 2009; Bargh et al., 1996).

Consequently, understanding the concept of human creativity and most importantly, intuition, is effective in order to create a reliable decisionmaking structure in enterprises. Dane et al. (2007) & (2011) further discuss the vital role of intuition in industrial decision-making, while also demonstrating that relying merely on intuition and ignoring rational tools may also be harmful. In this context, therefore, understanding the concept of intuition and the mechanism of human creativity on one hand, and on the other identifying (and/or developing) the proper DSS tools which can effectively nurture and empower creativity, are of great importance in order to create a reliable and effective decision-making system in enterprises. To conclude this section, it is worth quoting a line from the IBM Global CEO Study (2010), which states: More than rigor, management discipline, integrity or even vision â&#x20AC;&#x201C; successfully navigating an increasing complex world will require creativity.

1.1 Purpose and Research Question This study aims to expand knowledge, insight and understanding on the subject of intuition, human creativity, and their potential applications in enterprise decision-making tasks considering a number of real-life examples where uncertainty and limited time are the major challenges. Furthermore, the hope is to allow decision-makers to benefit from the great potential of intuition while minimizing its drawbacks. For this reason, the objective of our research has been set to reach a balance between intuition and IS/IT in enterprise decision making. In this realm, visualization tools have been recognized to be specific IS/IT that deal with decision making tasks (Jessup et al., 2003). This objective is find out through interactive 3

Introduction

visualization tools by providing the means to transform data into valuable and actionable knowledge. It is thus expected that intuition will be better understood as one of the main sources of creativity and insight in enterprises, instead of being constantly ignored. In respect to the above, the main question of our research is formulated as the following: ď&#x201A;ˇ

How can intuition and creativity, along with visualization tools, be of a help in enterprise decision making?

1.2 Limitations This report is concerned with business decision-making problems in situations where conventional DSS are not reliable tools. In such cases, creative approaches and human intuition have seen to be the best potential alternatives. Furthermore, we limit our study on decision-making tasks to engineering design and manufacturing-related enterprises. In applying this limitation, the definition of creativity would be highly connected to achieving the optimal configuration of designs. This definition of creativity would therefore differ greatly from the situation of organizational decisionmaking, where creativity is associated more with satisfying options rather than optimal solutions (Simon, 1976; Simon, 1987; March & Simon, 1958; Simon, 1956). According to the background provided above, this would further limit our research, primarily in regard to the attempt to establish a balance between intuition and visualization tools specifically multidimensional visualization tools in looking for novel designs. Worth mentioning that in this report we donâ&#x20AC;&#x2122;t distinguish nor aim to survey different BI tools. Instead we would consider the BI tools as a whole where BI is mainly referred as a rational tool built upon analytics. However we limit our usage from the BI tools to the common applications of data analytics and data visualization which are common in almost all BI software packages as the basic tools for processing and reporting.

Introduction

1.3 Thesis Outline The remainder of this report is organized into five chapters. Chapter two describes the research method toward answering our research question. Chapter three provides literature review on the subjects of decision-making, intuition, rationality, and creativity in enterprise decision-making problems. This chapter, which is divided into two subsections, provides multidisciplinary research on the topics of creativity and intuition. The first subsection makes a contribution by extending an understanding on the topic of intuitive decision-making approaching the fast and creative choices that are carried out from a psychological point of view. The second subsection challenges and considers the drawbacks of intuitive decisionmaking, providing an investigation from the alternative and rather young perspective of Neuroscience. A deep understanding of the human-computer interaction in the particular realm of decision-making is therefore delivered. Based on the literature review in chapter three, data visualization as a tool of IT for enterprise decision-making will be applied for solving engineering design problem in chapter four. Furthermore, the assumption which is communicated in chapter three and four which is, using human intuition and creativity in a balance with visualization tools is effective and beneficial in enterprise decision making, is validated in the chapter five based on empirical findings which are collected through semi-structured interviews and further analysis of empirical finding. The last chapter draws a conclusion to the overall report and points out direction to future research.

Research Method

2 Research Method This chapter presents research methods and steps used toward answering research question. In this context, research design and techniques for data collection and data analysis are discussed.

2.1 Research Design Ghauri (2005) describes research design as a plan or framework for collecting, analysing data, defines the type of research and main concerns of researcher. The overall strategic choice of research design assist researcher to answer research question in the best manner considering constraints i.e. time and skill. Basically our research adopted scientific method (Figure 1), towards conducting our research and answering research question and make sure the results are accurate. Scientific method implicates analysis of theories in which the human behaviour involves (Hugh et al., 2012), in our case use of intuition in decision making in real-life situations, based on empirical evidence. Furthermore, scientific method includes series of steps that recognized over eras of investigation by scholars (Hugh et al., 2012) consist of following: 1. First step of scientific method is to ask question, and determine a problem of interest to solve and formulating the research question. The formulation of the problem is the result of reviewing papers on decision making and role of intuition, creativity, IT tools, etc. in order to gain a better understanding of the study area. 2. Reviewing and conducting research on existing literatures and related studies, in two different disciplines of psychology and neuroscience of intuitive decision making and creativity in making decisions along with the use of visualization tools. 3. Based on initial conclusions from state of the art literatures, assumption shaped as, using human intuition and creativity in a balance with visualization tools is effective and beneficial in enterprise decision making, which will later evaluated. 4. Validation, conduct research and test the assumption based on data collected from case study evidence. 5. Analyse results, evaluate findings and conclusions.

Research Method

Step one Determine & Define Research Question (Ch1)

 

Step two Literature Review (Ch3) Decision-making and Information Technology. Intuition and Creativity.

 From psychological perspective.  From neuroscientific perspective.

Step three Formulate Assumption (Ch4)



Step four Validation (Ch5) Apply and evaluate the assumption based on data collected from case study evidence. Summarizing the results.



Step five Conclusions of the report (Ch6)

Figure 1. Research Design & Corresponding Chapters (Adopted from Hugh et al., 2012)

2.2 Research Approach Based on our research question the method should be able to answer how question. According to Stebbins (2001), to doing so an integrated exploratory and evaluative research method can utilized, due to the fact that we have built our approach on the basis of the existing knowledge (Ghauri, 2005). At this point existing knowledge mean all information collected from literature review, which help us to narrow down to specific part of the thesis. Furthermore, usage of case study would empower the evaluative part of our research. 7

Research Method

In our study we start with theory and present an extended analysis of literature review on the topic of intuitive decision-making under uncertainties and time constraint. However, our approach builds its basis on empirical data and findings achieved by surveys of available past studies relevant to the field e.g. Andersson et al. (2008). In our case along with considering the solution to decision-making problems, the intuitive decision-making is investigated from psychological discipline as well the neuroscientifical discipline. In other words, the research question has been investigated from two perspective. The result of literature review will then explored and proposed utilizing a visualization tools, that may facilitate decision making, which later evaluated by case study. In this sense the research method may have involved more complexity in realization comparing to the single discipline research projects e.g. Chaudhuri and Deb (2010), Dane et al. (2007), Edwards (1954), and Gigerenzer (2008).

2.3 Multidisciplinary Research Method Numerous empirical research including Andersson et al. (2008) and also IBM global study (2010) suggests that human factors, in particular intuition characteristics, are highly involved in decision-making. Consequently, there has been an urge for conducting a multidisciplinary research. For this reason, along with considering DSS and BI tools which are often studied from only an IT perspective, the other research disciplines i.e. psychology have been traditionally dragged into the study (Gigerenzer, 2008; Kahneman, 2003). According to Herbst (1974) multidisciplinary research, as a broader approach, is often developed to answer complex questions and model complicated research projects where a single discipline is unable to handle them. Rud (2009) as one of our main references to our research suggests that, the task of decision-making is considered as a complex and multidisciplinary research. Beside Rud (2009), the research works of Gigerenzer (2008), Kahneman & Tversky (1979), and prior to them Simon (1956) strongly suggests that research on the topics related to decision-making requires a multidisciplinary research method. The above mentioned scholars along with considering the research on executive information systems (EIS) (Watson & Walls, 1993) have actively included the research on psychology as the major contributor discipline to the research on human decisionmaking. Nevertheless our research takes the research further by considering the discipline of neuroscience.

Research Method

According to Younglove-Webb (1999) in multidisciplinary research each discipline may follow a different research methodology and in some points the results may be required to integrate. Alternatively in our research method, different research disciplines have been conducted in parallel and the results of each discipline have been highly contributed to our goal and further understanding the situation and the concepts involved.

2.4 Literature Review The aim of literature review is to ground and motivate our work. Attention here has been to get a general and broader understanding of decisionmaking under uncertainty. The literature review which is provided in chapter three would contribute in identifying the major disciplines of the research for taking the proper action. After identifying the researchâ&#x20AC;&#x2122;s major disciplines the literature review in each discipline come to order. In the first stage of our research which is concern with developing the knowledge, our researchâ&#x20AC;&#x2122;s major disciplines on intuition are identified to be psychology and neuroscience. Figure 2, better presents the steps toward our literature review which is adopted from (Dawson, 2002). In step one, the main literature sources gained from research books, scientific journal papers and conference papers related to decision making, concept of modern decisionmaking in the world of business, and theory of creativity and intuition. In step two, after identifying the relevant sources in the domain area, we tried to read them critically and organize the issues that are associated with our study which then used as a basis for developing theoretical framework in step three. To doing so, we start with writing general information in area of decision making and then progressively narrow down and emphasise on decision making problems under uncertainty and limited time and further restrict the scope of our literature to the engineering applications.

Step one Search for Existing literature in area of study

Step two Review the selected literature

Step three Develop theoretical framework (literature review)

Figure 2. Steps toward literature review (Adopted from Dawson, C., 2002)

Research Method

Furthermore, we compare our finding from literature review and identify issues with respect to our research question, in order to further develop theoretical framework and show how our findings contradict, approve or add to them. Briefly, we starts with a brief history and literature review to decision-making where decision-making under uncertainty and challenges to rational methods are described. Furthermore, intuition as the products of the creative and intuitive mind are seen to be faster and more reliable in solving demanding decision-making tasks. However, they are associated with a number of drawbacks. Yet it is assumed that the usage of intuition in a balance with rational IT tools in a controlled manner can lead to better decisions suitable for todayâ&#x20AC;&#x2122;s challenging, complicated and dynamic market. Information which are reviewed from existing literature helps us to setting assumptions, in this case, suitable for the decision-making problems in engineering design.

2.5 Data Collection Our research used qualitative method to collect data since findings are not achieved through statistical and quantitative procedures but rather gathered through case study. According to Yin (1998), the collection of data is depend on the data which is need to collect for specific research objectives. The goal is to create a deep and overall understanding of the study subject which is one important feature of qualitative method (Ghauri, 2005). The objective is to investigate the important of both intuitive and creative decision making under uncertainties and combination with visualization tools on decision making. According to Ghauri (2004), research problems which are unstructured in nature and investigating people experience which is known a little about it demand qualitative method. Based on this, the qualitative approach seems to be more appropriate for this research. Furthermore, we used case study method as one of the qualitative methods along with interview as qualitative technique. Section 2.5.1 presents details of how we select our cases based on the scope and characteristic of our study. 2.5.1

Case Study

Case study is a suitable research method when research phenomenon is difficult to study outside its natural background and there are too many variables to be considered which are difficult to measure. Furthermore, case study is a preferred choice when researcher has little control over the research area in real-life context (Yin, 1998). According to Ghauri (2005), 10

Research Method

once the research question under investigation involves how and why , case study is a convenient approach. In our research the aim is to understand how intuition and creativity along with visualization tools can be of a help in decision-making problems. Therefore, case study as a qualitative research method has been selected. Case study is usually involves collecting data from various sources such as, personal interviews, verbal reports, observations, etc. Therefore, case method is usually beneficial for theory development and testing (Ghauri, 2005). The main purpose of case study in our research is to test and validate the assumption shaped from literature review. Moreover, we used cases to identify decision maker behaviours involved in making organizational decisions. Duo to the limited time we had, our target population is aimed toward, ď&#x201A;ˇ Individuals who are active in engineering design industry and material selection because they are involved in making choices between various design criteria and materials.

ď&#x201A;ˇ Having experience in making decisions with visualization tools specifically multidimensional visualization feature. Based on the qualitative nature of our study, semi-structured Interview seems to be more appropriate because we are investigating decisions made in real life situations and it is done by asking questions from the people who have been involved in the scope of our study. The main purpose of interview was to gain understanding of decision making along with using visualization tools. Moreover, the data collected from literature reviews was later considered in designing the questionnaire. Duo to our study target we could arrange two Skype interviews and one face to face interview. The interview questions chosen as open-ended questions in a way that aligns with our research problem and can obtain the right viewpoint of decision makers in engineering design field (Interview questions included in Appendix). Note that interviewees requested to stay anonyms therefore it is expected from them to answer the questions truthfully from their past and present experiences regarding the decision making and using visualization tools for designing the products. These data are then classified in a way that conclusions can be drawn from them.

2.6 Data Analysis The main purpose of data analysis is to get insight from the data collected (Ghauri, 2005). Analysing the case study is challenging because there is no 11

Research Method

clear techniques defined in the past. Yet our research considered analytic strategy for analysing the collected data. According to Yin (1988), analytic strategy rely on theoretical proposition along with pattern matching technique. In our case, data collected through interviews and has been analysed based on theoretical proposition, which is using intuition in a balance and control manner with rational tools can lead to better decisions suitable for todayâ&#x20AC;&#x2122;s challenging, complicated and dynamic market . Thus, we categorized and structured information gathered from interview which explained creativity and intuition in dealing with decision making. Yin (1988) argue that, analysis should follow cross experiment than within experiment design so we choose cross case analysis which required us to look at data from different perspectives. So we compare empirical findings with the predicted results and looking if some of the theoretically salient explaining conditions that might be observe in interview findings. To be specific, from a psychological perspective, it is discussed that intuition as the source of human creativity has marvel and flaws. Therefore, it cannot always lead to a logical choice and beneficial decisions (kahneman, 2011). In fact, Prospect theory of Kahneman and Tversky (1979) well describes that using the intuition can sometimes lead to failure and irrationality. Moreover, studying the brain as the source of all mindâ&#x20AC;&#x2122;s functions is suggested as a key answer to creativity and innovation (Kandel et al. 2000). Therefore, a conclusive research on neuroscience discipline is suggested to be further conducted. Accordingly study continues by investigating the creativity and intuition from inside the brain with the aid of recent advancement of neuroscience. At this point, we try to find intuitive decision-making in the neuroscience of brain. With this we aim to expand knowledge, insight and understanding on the subject of intuition and human creativity. However, there exist different theories of brain functioning developed by devoted scientists, and the pieces of the puzzle of creativity, as Jung et al. (2013) would say, are not quite gathered to present a clear picture. Further, we compared the preliminary conclusions from literature review with the result of case study, if we found a match between the effective use of visualization tools for intuitive decision making in more than two interview findings, then we conclude our theoretical findings are valid and can produce conclusion to answer the how question.

Research Method

2.7 Research Validation and Generalization According to Lee and Baskerville (2003) the credibility, including the generalizability and validity, are the big concerns for any research. By its very nature it refers to the external validity of the research and therefore whether or not the work can be applied to other research settings as well. Here it is worth mentioning that the validation part of our research has been widely built its bases on the usage of case study. In this regard, the literature of Stake (1995) on the art of using case studies has been very influential and constructive for the purpose of developing our approach where the case study are used for the purpose of credibility. To increase the credibility of our research in the chapter four which is devoted to implementation aims to accommodate a decision-making problem in engineering design application i.e. (Mosavi, Hoffmann & Vaezipour, 2012; Mosavi & Vaezipour, 2012) where uncertainty and limited time are highly involved. Consequently the proposed approach can be well validated and also well generalized to the similar class of problems in the engineering design applications. Via implementation of case study the validation is done, in this step it is discussed that where problem is involved in uncertain situations, the usage of intuition will be expected. By providing case study, our approach toward our research is developed wiser as the problem is seen clearer.

Decision-Making Under Uncertainty

3 Decision-Making Under Uncertainty This chapter presents the theoretical framework of our research and is divided into two main subsections to fully satisfy the need for a multidisciplinary research. We also focus on different aspects of intuition and creativity in decision making.

3.1 Decision-Making and Information Technology Life is the sum of all your choices, Albert Camus

We are facing with numerous decision-making tasks every day. Some of our decisions may carry only minor significance, and some can impact greatly on our lives. March (1958) believes that in behaving on the heat of the moment in the uncertain world even though we try our best, we may make wrong decisions. In addition, Ariely (2009) found out that our brains has limited data processing ability and also can be subliminally manipulated and easily distracted from making a rational choices. Even when the choices seem to be well though decisions, Bargh (1996) states that generally we are often wrong. The reason, as Kahneman (2011) would say, is that people tend to frame things very narrowly. They take a narrow view of decision-making at the time but not its consequences in the future. Therefore, from the angle of that narrow view they consider the problem in an isolated manner. In the other worlds people would deal with the problems as if it is the only problem. However, it is desirable to deal with problems as they may effect throughout the life. Kahneman (2011) suggests that developing a systematic approach that could be adopted for a class of problems is essential. In this case people would be able to take a broader view resulting to make better decisions. According to Duncan (1973) the decision-making problems whether in business, industry or engineering can be mathematically formulated to find the optimal value of x in order to optimize a measure f(x) where x may be described as a collection of decision variables; x = (x1; . . . ; xn). However, for a well-thought decision in real-life decision-making a problem has to be analysed from very different perspectives. In fact in the humanâ&#x20AC;&#x2122;s daily organizational problem-solving conflicting and nonlinear criteria evaluated in making decisions

life including his individual and/or duties, there are typically multiple as well as uncertainties that need to be (Duncan, 1973). Furthermore, today 14

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availability and access to data has been more than ever convenient to enterprises. Scholars like McGilchrist (2009) and Rud (2009) believe that due to the low cost availability of data storages, high-tech data warehouses, advanced data acquisition technologies, and most importantly expansion of social networks, there won’t be any lack of data issues anymore but the lack of IT tools for getting insight into the decision-making problems to be able to react speedy, creatively and wisely to the dynamic market. The availability of today’s databases as such has even made the decision-making even more complicated. 3.1.1

Brief History of Decision-Making

According to Turskis and Zavadskas (2011) in real-life decision-making a problem has to be considered from very different perspectives. Piero et al. (2009) and Robles et al. (2011) suggest that the scientific solution into such problems has been the approach what today we call it multiple criteria decision making (MCDM) where the problem’s multiple criteria are analysed simultaneously. In fact, in the human’s daily life and problemsolving duties there are typically multiple conflicting and nonlinear criteria as well as uncertainties that need to be evaluated in making decisions. Consequently, a vast number of MCDM methods, surveyed by e.g. Henderson et al. (1993), Gandibleux et al. (2002), Marler et al. (2004), Pohekar et al. (2004), and Figueira et al. (2005), have been around since 1654. Turskis & Zavadskas (2011) show how MCDM methods are widely utilized to model a wide range of the decision-making problems for instance in economics, managements, engineering, design, energy, business, etc. The French mathematician Blaise Pascal in 1654, in order to deal with uncertainties in real-life decision-making problems proposed the initial form of expected value theory. The theory of expected value could simultaneously consider the probabilities as well as values and consequences. The methods on the basis of the Pascal’s theory have been used ever since in different problem solving realm as a rational approach. In expected value theory; the consequences’ values and probabilities are multiplied and summed, and then the different decisions’ utilities are compared for an optimal decision It has been documented by Hanna (1964) and other historians that politician, Benjamin Franklin, the founding father of the United States widely used and promoted basically the same method, yet he called it as moral algebra. This method is well described, in details, by Gigerenzer 15

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(1999 & 2007 & 2008). He simplifies and explains the method further as; for coming up with a rational decision, you should make a list of all that might happen as a result of choosing a particular option, and then decide how good or bad each of these possible outcomes would be (probability). According to Gigerenzer (1999), this has been one of the earliest ways of rational and scientific decision-making in real-life applications. However, he further argues in (Gigerenzer, 2008), whether the inclusion of probability theories and complicated mathematical modelling in calculating the decision values actually worth implementing in real-life applications where uncertainties are way challenging. The theory of decision-making had been progressed over the years from the further advances in expected value theory, and later, expected utility theory (Mongin, 1997) starting by the works of e.g. Swiss mathematicians, Bernoulliâ&#x20AC;&#x2122;s family; Nicholas and Daniel from the years 1713-1740 until now. According to Mongin (1997) in short, expected utility theory is the theory of utility that uncertain outcomes are defined by the function probabilities of occurrence, risks and utilities of probabilities of occurrence. In fact, the idea of weighting and adding scheme in the expected value theory, and later calculating the weighted average of all possible values in expected utility theory had been highly influencing the rational and logical thinking in modelling the decision-making problems over the years in numerous areas e.g. moral behaviour, motivational behaviour, managements, engineering design, health and life sciences. Subsequently sometimes around and after world war II other theories and disciplines e.g. game theory, graph theory, operational research and other analytical methods as well as probability theory have become more popular and further well contributed to the progressing of the rational and logical decision-making Buchanan & Oâ&#x20AC;&#x2122;Connell, .

In the modern days, the major advancements in decision-making theory have been accomplished by the genius works of Herbert Simon, from about 1950 up until 2001. Herbert Simon worked on artificial intelligence (AI), decision-making and its interactions with psychology, sociology, economics, human behaviour in organization, and also intuition (Frantz, 2003).

Considering a definition to organizational decision, Simon (1976) states that any decision involves a choice selected from a number of alternatives, directed toward an organizational goal or sub goal . Following figure describes the (erbert Simonâ&#x20AC;&#x2122;s graph of decision-making; the three steps, 16

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pointing out the important role of AI in organizational decision-making tasks.

Figure 3. The Herbert Simonâ&#x20AC;&#x2122;s graph of decision-making (Simon, 1976)

According to Simon (1976), the task of rational decision-making is to select the alternative that results in the more preferred set of all the possible consequences. This task is divided into three required steps: firstly the identification of all the alternatives; secondly the determination of all the consequences resulting from each of the alternatives; and finally the comparison of the accuracy and efficiency of each of these sets of consequences. Barnard (1968) introduced the concept of modern decision-making to the world of business. Later Simon (1976) discussed the topic further and particularly argued that with the dynamic nature of the modern-dayâ&#x20AC;&#x2122;s industries and businesses at the presence of uncertainties, limited time, and inadequate mental computational power the task of decision-making is rather considered as bounded rationality. Further, Simon (1976) suggests that people would make economically rational decisions if only they could gather enough information. However, Simon (1976) explains that this is often not the case. According to Etzioni (2001), the conventional rational approaches to decision-making does not meet the needs of a world with too much information and limited time. He further clarifies that rational decision-making requires comprehensive knowledge of different angel of a problem, which is clearly impossible due to today large data bases. On the other hand Kahneman and Tversky (1979) identify factors that cause people to make decision against their own economic interest even at the presence of adequate information in solving even simple problems. In this 17

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context theorists such as Gigerenzer (1999) aimed to propose ways to achieve acceptable decisions instead of optimal ones in solving complex problems. Gigerenzer and Selten (2002) encourage decision-makers to make a virtue of the limited time, information and knowledge by following an approach that they call it fast and frugal reasoning which is the approach of mastering simple heuristics. Their approach for solving real-life decisionmaking problems highly relays on intuition. Alternatively Etzioni (2001) proposes the approach of humble decision-making which is a mixture of reliable tactics that include uncertainties, delay, hedging and most importantly intuition. This has been mainly the reason why the concept of intuition has become an important topic of research in todayâ&#x20AC;&#x2122;s decisionmaking tasks. However, Treffinger (2004) describes that subject of creative decisionmaking and intuition has been considered as a tabu subject to be investigated for centuries. Although during past three decades it has become a topic of considerable interest. Nevertheless, the research on creativity and relative investigation on rational and intuitive approaches to creative decision-making can be pursued from very different perspective and scenarios in different decision-making applications e.g., business, industry, engineering, production, management, politics, leadership, organization, administration and policy making.

Here it is worth mentioning that our research primarily considered the topic of creativity by reviewing numerous theories of creativity and intuition from different perspectives e.g. psychology (Sternberg, 1999; Simon et al. 1987), sociology (Amabile, 1983), organizational (Amabile, 1996; Woodman et al., 1993; Simon, 1976), cognitive behaviour (Gustafsson, 2004), and so on. Therefore the literature of our logbook has become highly exhaustive partly reported in e.g. (Vaezipour, 2012 & 2013a; Mosavi & Vaezipour, 2013). This made a meaningful furthering and managing the research very complicated. In fact because of the complexity of the subject and also because of the variety of the manifestations and definitions of creativity and intuition in real-life, agreement on a single theory, and carrying out the research accordingly made impossible. Therefore, narrowing the focus of the study on a single application would be inevitable. As the result, in this report the application of intuition is mainly focused in the field to engineering design and the role of intuition in uncertain cases.

Decision-Making Under Uncertainty

3.1.2

Concept of Intuition and Creativity in Decision-Making As these trends continue and global pressures increase, the next phase for business is one that competes on innovation. Innovation emerges from organizations that nurture creativity. So how is that done? The first step is to understand creativity. Rud, Olivia Parr (2009); Business intelligence success factors

According to Christoff (2009) in studying the creativity the concept of intuition, depending on the task at hand and the theory of interest, may be initiated, sourced and named after different operational mechanism of mind e.g. ability to create heuristics (Gigerenzer, 1999), insight (Limb et al., , divergent thinking Gilhooly et al., , aha moment (Bowden, 1997) and/or precognition (Radin, 1997; Radin, 1988). Although here we will touch these definitions, for the sake of simplicity of the thesis we would consider them as a single mechanism to be referred as intuition. Psychologist refer to intuition as Immediate and a priori knowledge or experiential belief . The word intuition itself means, The ability of having direct perception of truth and fact, independent of any reasoning process . It is a sort of knowledge that difficult to measure and transmit to another person which is known as tacit knowledge. Herbert Simon in one of his interviews in by Doug Stewart states, How can you tell when people have had an intuition? You give them a problem, and all of a sudden, maybe after a pause, they get the answer. And they can't tell you how they get it. He further mentions, people cannot have accurate intuitions in the areas that they do not have experience. According to Gigerenzer and Selten, (2002), Gigerenzer and Gaissmaier (2011), and Gigerenzer et al. (1999) the concept of creativity in the realm of decision-making is highly associated with intuitively producing the simple alternative solutions, so called heuristics. Gigerenzer (2007) and his colleagues, as the pioneer researchers in intuitive decision-making believe that we can associate the creativity with the ability to intuitively build simple solutions to the tough decisions. Prior to them, March & Simon (1958) however described that human as a creature of emotion in most of his individual and organizational decision-making, plenty of feelings and conflicting psychological factors as well as sociological factors are involved

Decision-Making Under Uncertainty

to be well studied. Clearly this fact by far has made the investigation on creativity complicated and rather multidisciplinary. Furthermore, number of surveys conducted by e.g. Mansfield et al. (1978), Isaksen et al. (1985), Albert (1990), and recently Treffinger (2004), concluded that there is still no unified theory of creativity and intuition accepted by the majority of researchers. In fact a number of well-known scholars in this realm like Herbert Simon (1956), Daniel Kahneman (2003), Gigerenzer (2007), Iain McGilchrist (2009), and also Albert Einstein (Finetti, 1978) have different ideas and theories on intuition, rationality and human creativity in problem solving and decision-making. Consequently until today there has not been a unified theory on creativity and intuition proposed yet. For instance, Albert Einstein (Finetti, 1978) believes that the society honours rationality more than intuition. However, Simon (1991) describes that organizations often in complex situations duo to their inability to process and compute the expected utility of every alternative action use intuition and simple alternatives to make decisions rather than a rational/analytical process. Gigerenzer (2011) has shown such simple alternatives produced intuitively i.e. simple heuristics, frequently lead to better decisions comparing to a fully rational analysis as a mechanism for decision making e.g. BI. Battiti and Brunato (2011) also agree with Gigerenzer (2007) in the sense that often the communicated information via BI tools would still make problem which cannot be easily handled by the limited information processing capacity of human brain; the complex entity of mind (March, 1978). While Gigerenzer (2008) insists on the potential and effectiveness of intuition, Kahneman (2003) however believes that intuitive decisions and most of the associated heuristics accordingly produced, on the basis of the Prospect theory, cannot always be reliable. Considering the two major conflicting theories on intuition; the prospect theory of Kahneman (Kahneman & Tversky, 1979; Kahneman & Klein, 2009; Kahneman, 2011) in one hand, and the short cuts to better decision-making of Gigerenzer and his colleagues, on the other hand (Gigerenzer 2008; Gigerenzer & Selten, 2002; Gigerenzer & Gaissmaier, 2011; Gigerenzer, 1999; Gigerenzer, 2007), would obviously give the idea that the mechanism of intuition and its failure-to-success ratio over rational tools is still under debate and yet unclear. In other words it is true that, intuition is fast and can often lead to creativity however the wrong assumption and following the 20

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gut feelings may lead enterprises to collapse and failure. Therefore conducting research on finding a balance of rational-intuition in the particular applications to business and industry, and in our case, decisionmaking in engineering design would be vital. This fact by far would justify the need for conducting this research. To sum up, according to above, there exist a number of conflicting theories on creativity and beneficial usage of intuition in decision-making. Nevertheless, in surviving from the tough decision-making situations intuition had been long identified as a fast method of decision-making (Albert, 1990). Yet it has its marvels and flaws, as Kahneman (2011) would describe so. Kahneman (2011) believes that the intuitive mind is associated with creativity with an insight which makes it very valuable in todayâ&#x20AC;&#x2122;s most complicated business decision-making problems. In this context, understanding the mechanism of intuition and creativity associated with it in problem solving and decision-making is of importance. On the other hand, identifying the IT tools in which can suite, empower and nurture the intuition is also important. 3.1.3

Information Technology in Decision-Making

The above description of Herbert Simon on rational decision-making as logical as it may sound cannot be practical in real-life problems (March 1987). Simon (1976 & 1956) clarifies that due to uncertainties involved in real-life situations, any organization attempting to implement such model would be unable to fully satisfy the three requirements. Although still a group of scientists e.g. (Russell, 1997 & 2003), has a strong belief that Simonâ&#x20AC;&#x2122;s three steps toward a rational decision can be accomplished along with the progressing of IT.

However, Shafer (2013 & 1987) and Horvitz (1998) argues that it is highly improbable that one could study all the alternatives, and all the consequences relying only on IT. They conclude that IT actually cannot be adequate and, one should therefore carry out the law of probability e.g. Bayesâ&#x20AC;&#x2122; theorem to analyse the total uncertainties involved, along with benefiting from the IT convenient tools. However, doing so clearly makes solving the task even more complicated involving complicated mathematical modelling which expensive to compute. According to Battiti and Brunato (2011), even though the optimization problem can be mathematically implemented, it is still impossible in most real-world cases to calculate the optimal value of f(x). In fact, due to the 21

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dimension of problems and uncertainties in most real-world business contexts, it is extremely difficult and costly to build a function and calculate its optimal value. Gigerenzer (2007 & 2008 & 2011) argues that despite of all the advancements that IT has brought to the mathematical modelling, implementation of decision-making models with the ever increasing complexity of todayâ&#x20AC;&#x2122;s decision-making problems at the presence of huge uncertainties, multicriteria problems, the conventional procedures to rational decision-making simply cannot be the answer. With this, Gigerenzer strongly criticizes the efficiency of the most logical and analytical-based decision-making tools ever been produced for rationally making better choices. Simon et al. (1987) therefore suggests that decision-making should be considered as bounded rationality. Simon (1991) later offered a model in which utility maximization was replaced by satisficing. According to the bounded rationality the task of decision-making due to the complexities, limited amount of time and the cognitive limitations of mind would rather be seeking a satisfactory solution rather than the optimal one. Both Kahneman (2003) and Gigerenzer (2007) proposed that the bounded rationality as a practical model of decision-making overcomes the limitations of the rational models including mathematical models and all analytical approaches to decision-making. In this sense the creativity and human intuition in building the heuristics plays the major role (Gigerenzer 2008). Concerning the EDM (Enterprise Decision Management), Battiti and Burnato (2011) describe that CEOs are not necessarily aware of the mathematical formula that their business is optimizing. In other words a manager may have some ideas about objectives and trade-offs, however these objectives are not specified as a mathematical model. In fact, the business objectives are highly dynamic, changing in time, fuzzy and subjected to estimation errors and human learning processes. According to Battiti and Burnato (2011) this would clarify the importance of managerial gut feelings and intuition in quantitative and data-driven decision processes. In particular, in the research on EDM the definition of satisficing is highly involved where approaching the optimal decisions is often not the case. In the article Enterprise decision management with the aid of advanced business intelligence and interactive visualization tools Vaezipour & 22

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Mosavi, 2012a) and later in (Vaezipour & Mosavi, 2012b) the concept of decision-making in satisficing tasks has been practiced where the manager has to make fast decision as a speedy reaction to the dynamic situations. In the above cases the data analysis and information visualization tools of conventional DSSs (Power, 2007; Turban, 2007), in this case BI tools (Negash, 2004) can well contribute in modelling the problem and describing the dimension of the problem even though the answer cannot be an optimal one. Following example aims to better describes, how data visualization can empower the satisficing decision-making in EDM-related tasks. 3.1.3.1

Considering Example in using IT for Satisficing Decision

In this section for better understanding the concept of satisficing decision an example is given. In this case visualization and reporting tools of BI contribute in providing insight into a problem (Vaezipour & Mosavi, 2012a). The problem is considered as a demanding problem with some missing parts of the dataset. Furthermore, due to the uncertainties, analytical models cannot model the problem and find the optimal solution. Instead the BI tool provides insight to the problem to facilitate a satisficing solution. Here one of the usages of BI within the field of enterprise management is visualizing the structure of an organization. It gives an overview of an organization’s relationships and data involved. )t also allows focusing on various levels in hierarchy organizational data and navigating through layers to find out the full potential within each department. International Association of Students in Economic and Commercial Sciences A)ESEC is the world’s largest student organization providing opportunities for members to develop leadership capabilities through their internal leadership and internship programs for profit and non-profit organizations around the world. The focus of AIESEC is increasing the quality of opportunities given to its members and expanding their network. Clearly making any decision in such a huge organization would be involved with uncertainty. Here we are dealing with following data;  Region: Location of AIESEC offices.  Department name: Different departments in AIESEC.  People: Number of people involves in each department.  Growth in the region: Annual growth in each region.

Decision-Making Under Uncertainty

Figure 4. The table includes the dataset (Vaezipour & Mosavi, 2012a).

Figure 5. Data visualization in BI; The organizational chart of AIESEC (Vaezipour & Mosavi, 2012a).

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Concerning decision-making in this example; it is highly improbable that one could study all the alternatives, and all the consequences. Here the task of decision-making due to the complexities, limited amount of time and the cognitive limitations of mind would rather be seeking a satisfactory solution rather than the optimal one. Therefore, the bounded rationality as a practical model of decision-making overcomes the limitations of the rational models including mathematical models and all analytical approaches to decision-making. In this sense the creativity and human intuition in building the heuristics plays a major role. 3.1.4

Intuitive Decision-Making

Gigerenzer (2011) and Simon (1976), believe that using AI, analytics or laws of probability e.g. Bhayes theorem can be useful for rational decisionmaking but only in considering simple problems at the presence of adequate amount of data which can well describe the problem and uncertainties. However, this is often not the case in todayâ&#x20AC;&#x2122;s most enterprise decisionmaking tasks in the uncertain world. At first sight, it seems to be a huge obstacle and concrete limitation to the rational decision-making. On the other hand, human being striving for rationality and yet with limited knowledge and shortage in data processing abilities, which have been studied in e.g. (March, 1978), has been appeared to have a certain ability to develop some simple working procedures, so called Intuition (Gigerenzer & Gaissmaier, 2011; Gigerenzer et. al., 1999). Intuition basically as the product of creative minds can overcome the difficulties and complexities that often face in rational decision-making. According to Kahneman (2011), there are two systems of decision-making. Intuitive (fast) and rational (slow). Depends on what method of thinking selected, it would affect our judgment and decision-making. Intuitive thinking, sometimes also known as associative thinking, the one leading to heuristics, works automatically and we donâ&#x20AC;&#x2122;t need to decide it. )n the other words it is effortless. While rational thinking is done by mind under selfsupervision, control and investment of efforts and perhaps utilizing analytics. Most of the time, human makes decisions based on intuition and follows simple heuristics. Often relying on intuition works just fine. This has been due to the result of practice in which makes us good at what we do. Practice in fact, makes us over confident to do the tasks intuitively. Gigerenzer in gut feelings: the intelligence of the unconscious later in gut feelings: short cuts to better decision making 25

, and , shows

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that, heuristics are often created based on gut feelings, and accuracy of the method and its success rate depend on the structure of the organization environment and experience of decision-maker. According to the literature of (erbertâ&#x20AC;&#x2122;s administrative behaviour (1976), and later Gigerenzerâ&#x20AC;&#x2122;s simple heuristics that make us smart (1999), the heuristic is what a person or organization uses to achieve approximately the best result in a speedy and seamless manner, and often more accurately comparing to the complex optimization models. The research of Gigerenzer and his colleagues describes, heuristics overall can be more accurate than more complex strategies even though they process less information. In fact, decision-making typically involves heuristics because the conditions for rational models utilizing logical, statistical and/or analytical rules cannot effectively deal with an uncertain and dynamic world. However, developing a systematic theory of building the effective heuristics is proposed by Gigerenzer (2011) as the major challenge for the future research. He further clarifies that for now we know something for certain that with sufficient experience, human can learn to select proper heuristics from his adaptive decision-making toolbox e.g. BI tools. Simon (1976), believes that creativity in building heuristics is a worthy occurrence of human mind which one can bring to enterprise. Yet it is a nonlinear, unexpected and intuitive approach and hard to actually be planned in IT projects for instance in the business/IT alignment. Furthermore, it clearly cannot be produced by increasing the IT usage. However, as it is discussed later in this report, it can be further directed and empowered in a balanced and informed form with BI applications. The procedures of producing heuristics as the efficient cognitive processes consist in assuming that, decision-making task can be isolated from the rest of the world including a limited number of variables and a limited range of consequences therefore, uncertainties by ignoring some parts of information (Gigerenzer, 1999). Creating heuristics is considered as a valuable approach and a creative accomplishment in any organization. It is worth mentioning that indeed in an organization, experience of employees whether consciously or unconsciously plays an important role in being creative to produce heuristics methods (Simon, 1976). Gigerenzer and Gaissmaier (2011), reviewed studies on decisions by individuals and institutions, including business, medical, and legal decisionmaking, showing that heuristics have been often reported to be more accurate and reliable than complex rational strategies utilizing AI, 26

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probability theory and/or analytics. In this sense, they confidently announce heuristics as a rational method which is a creative product of human mind. Gigerenzer (2007), includes heuristics as one of the major approaches to modelling decision-making problems along with logic and analytics. Although each of these approaches is suited to a particular kind of problem, heuristics have not been treated equally. In fact in rational problem solving, heuristics have been often associated with errors, while logical and analytical rules are understood to define rational thinking in the major situations (Gigerenzer, 2008). This would clearly contradict the fact that huge amount of decision-making tasks in today enterprises are often done using heuristics, intuition and on the gut feeling, whether consciously or unconsciously (Andersson et al., 2008). 3.1.5

Problem with Intuition

Kahneman and Tversky (1979) mention that, human mind has both capacities for sequential and simultaneous functioning of thoughts. Simultaneous functioning provides the ability to interpret information simultaneously which enables people to make sense of very complex situations. Consequently, in uncertain world where complexity is involved, the intuitive human mind can come up with simple solutions of heuristics in a speedy manner (Gigerenzer & Selten, 2002). This is why the magic has been often associated with intuition when everything is worked out according to the plan. However, Kahneman (2011) very strongly states that intuition is the result of regularity, there is no magic involved, and intuition is not always the best solution even though it may have its benefits. As Gigerenzer (2007 & 2011) clarifies with sufficient experience, human can learn to create proper heuristics from his adaptive decision-making toolbox e.g. BI tools. This has been due the result of practice in which makes human good at what he does. Practice in fact makes us over confident to do the tasks intuitively. In the situations that there are regularities with minimum uncertainty practice can work wonder and the intuitive decisions which are made that way can be highly reliable (Kahneman & Tversky, 1979). According to Kahneman (2003) people are good in intuition and can use it in various situations. Considering an examples by Kahneman (2011); British male upper class says, I have large tattoos all over my back. In third of a second the brain reacts with the surprise. In fact, a huge amount of information has to be processed in third of the second to come up with 27

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surprise. A chess player recognizes a chess situation so fast. And you can easily find out your partner’s mood first word on the phone. To be master in such situation it would need a lot of practice within an environment of regularity. Such abilities of mind are extraordinary and hard to explain. However, for now we know that learning to read, playing chess and diagnosing the patients by medical experts are the tasks that duo to regularity the situations are recognizable and then people can intuitively work upon them to come up with fast decision upon creative heuristics (Kahneman & Klein, 2009). However, intuition cannot be always accurate. Here are a number of reasons why:  Irregularity and high uncertainty; Kahneman (2003) believes that decision-making in highly uncertain situations e.g., picking up a particular stock in the stock market, where there is no regularity to learn and practice, intuition has no place to count on. He further suggests, in the situations that there is no regularity, instead of intuition the analytical algorithms and computation tools for prediction, calculation of probability and decisionmaking would be more reliable. In this sense, working upon information and using metaherustics algorithms would be the better rational approach (Kahneman, 2011). As the result of Kahneman and his colleagues’ study (ibid) it is concluded that dealing with complicated problems where lots of uncertainty are involved with no regularity intuition cannot work properly.  Media and environmental manipulation; the subliminal stimuli in this case might be visual stimuli, emotion eliciting stimuli, and auditory stimuli. Ariely (2009), Iyengar (2010), Iyengar and Lepper (1999) and Johansson et al. (2008), argue on the importance, misleading and manipulating process of choosing and decision-making in respect to self-satisfaction. In their experiments the cultural background of individuals has reported to be highly influential in the decision-making ability.

 Prospect theory; in study of intuition to show that intuition is not always accurate and therefore unreliable, Kahneman and Tversky (1979) invented some problems that they knew the answers, yet intuitively people had in fact opposite answers. To doing so people were asked to predict the probability of some events say hitting the floods or earthquakes. For instance, they asked two different groups to predict the probability of one of the following events; 1: hitting a flood in a city in US that would accrue and kill 1000 people within next 10 years, 2: hitting an earthquake in a 28

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particular city of US, say California, what would cause a flood and could kill 1000 people within next 10 years. Obviously the second event is less probable, yet people intuitively found the second event more probable. In another case, people were asked that how much they would pay for their travel insurance policy in two different cases; 1: in the case of death for any reason, 2: in the case of death in a terrorist attack. Obviously the first scenario would cover the second one. However, people were willing to pay way more in the case of death in a terrorist attack. This means that people are more afraid of dying in terrorist attack than dying. They conclude that, intuition comes from the fear. This is why the decisions upon intuition cannot be fully reliable. Intuitive decisions would follow the Prospect theory (Kahneman & Tversky, 1979) which can explain many of our irrational decisions (Roiser et al., 2009). Prospect theory is considered as a behavioural economic theory that describes the way people make decision among probabilistic alternatives that involve risk and uncertainty. The prospect theory describes the decision processes in two stages: editing and evaluation. In fact heuristics are considered to be the outcomes the decision in editing stags. Further people decide which outcomes they consider equivalent, set a reference point and then consider lesser outcomes as losses and greater ones as gains. Following graph better describes the losses and gains in a decision. According to the graph, the value function that passes through the reference point has an S-shape and is asymmetrical. Losses hurt more than gains feel good. This would differ greatly from expected utility theory, in which a rational agent is indifferent to the reference point.

Figure 6. The value function that passes through the reference point; description of value of losses and gains in a decision (Kahneman and Tversky, 1979) 29

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According to Prospect theory because of fear associated with the perception of lost or gain, people simply may choose to risk or play safe (De Martino et al., 2006). In fact people make decision more rationally in the situation of gain and may make decision more aggressively in the situation of fear and lost. According to Prospect theory, even making the simplest choices could be in fact a tricky process. As the result, for the sake of avoiding and minimizing the mistakes in decision-making the usage of intuition and heuristics should be done in a balanced form with rational tool to benefit from the both. However, finding a balance between intuition and rationality is still hard and very complicated to accomplish. Although, today a few number of firms exists that have established a systematic way of optimizing decisions via a proper mix of intuition and rationality with in fact promising results (Kahneman, 2011). However, we should note that although there have been always lots of interests in improving the quality of rationality in enterprise decisionmaking to better implement rationality in organizations, it would certainly need fundamental changes in architecture of the organizations. There is actually massive resistance in organizations to implement programs that can improve the rationality of their decisions. This resistance has been mainly due to the difficulties that such programs may bring to the leadership positions. In fact, leaders like to be in charge of their decisions and yet replacing people with a structured system of decision-making would be something for them to strongly hesitate. In this sense, it is believed that the tool should be at the service of the leaders and work interactively with them. 3.1.6

Human Mind in Decision-Making

According to Rud (2009) companies are so keen on benefiting from the creative minds of their employees who generate heuristics. Potential employees may be encouraged to walk in the woods, listening to their favourite music, having flexible working hours and comforting themselves in their workplaces in order to get more inspiration. This is called mind wandering and as it is discussed in the next section, neuroscientists such as Kandel (2007), believes that it is highly connected to creativity. However as Kandel (2007) argues we are at a very early stage in understanding the creativity and other higher mental processes, certainly due to the technological advancements of this era, one can get a very good insights in 30

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situations that may lead to increased creativity. Figuring out the origins of creativity in organizations or creativity as an individual occurrence, has long been the topic considered both from a social, psychological and very recently from a neuroscientific point of view. Considering the belief of Kandel (2000) and Freud (1931) that human makes a lot of decisions by unconscious evaluations makes the situation even more interesting to explore further. Kandel (2000) provides concrete reasons e.g., Libet â&#x20AC;&#x2122;s experiments on free will and unconscious decision, to prove that human is not consciously aware of most of his decisions. Further, evidence suggests that unconscious phenomena may include repressed feelings, visual memories, automatic skills, subliminal perceptions, thoughts, habits, and automatic reactions (Westen, 1999). The unconscious mind consists of the processes in the mind that occurs automatically without introspection. )n fact in every dayâ&#x20AC;&#x2122;s life, there are lots of decisions that are made unconsciously than consciously (Freud, 1931). As shown in figure 7, now we know that human makes a lot of decisions by unconscious evaluations (Kandel, 2000).

Figure 7. The iceberg of unconsciousness; a visual representation of Freud's theory indicating that most of the human mind operates unconsciously; the yet to be known capacity (Kendel, 2000)

On the other hand conscious decision-making can function well when one is dealing only with a very limited number of fixed alternatives as it would be possible to focus consciously very effectively on one thing at a time using some rational approaches e.g. moral algebra (explained in Gigerenzer, 1999). Yet at the presence of multiple options, relying on unconscious mind is very likely to be creative and effective (Kandel, 2000). 31

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Simon (1960, 1958, 1972, 1987, 1955), had well studied the concept of unconsciousness and creativity in human organizational behavior and decision-making from the psychological and sociological point of view. According to Simon and the earlier works of Barnard (1938), the creativity of an individual in organization could be highly affected by the goals and environments of that organization. They further argue that, personal choices may be determined whether an individual joins a particular organization. As a member of organization, an individual makes decisions not in relationship to personal needs, but in an impersonal sense as part of the organizational goals. And oneâ&#x20AC;&#x2122;s experience in an organization using a proper tool can bring him a learning and creativity ability to create heuristics (Gigerenzer, 2001). Along with psychological and sociological factors involved in human creativity in organizations, on the other hand, the anatomical structure and functioning aspects of the brain are also identified as one of the major effective success factors to implementing any IT alignment project (Rud, 2009). To draw attention to the importance of study on the function of the brain, worth mentioning that Kandel (2000) in the book principles of neural science argue that all mental functions, including conscious and unconscious decision, whether a creative heuristic or a logical approach, come from the brain. In this sense studying the structure, function, ability and processing quality of the brain plays an important role in investigation of the creative thinking and problem solving. Recently, due to the advancements of the neuroscience and availability of the tools to actually study the function and the structure of brain, the concept of creativity has been also become subjected to investigation from the neuroscientific perspective. This would provide the ability to investigate the creativity from inside of the brain; the entity that originate all mental functions including creativity in decision-making (Kandel, 2000). 3.1.7

Summary of Section

Considering the history of decision-making provided for instance by Buchanan and Oâ&#x20AC;&#x2122;Connell would strongly support the idea that relying only on rational approaches of conventional DSS (Power, 2007; Turban, 2007) and BI (Negash, 2004) had no answer to complexity involved. Yet there has been always simple solution around associated with intuition (Rud, 2009).

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We discussed in this chapter that the intuitive mind is associated with creativity with an insight which makes it very valuable in todays most complicated business problems. In fact, in business and industry, whether production, life sciences, energy, engineering, design, or fashion industry, there are tough and rather complicated decisions which are highly dependent on managerial intuition. However, the research of Gigerenzer and his colleagues (2008 & 2011) supports the idea that intuition in uncertain cases can be effective and efficient. On the other hand, Kahneman (2011) points out the drawbacks of intuition which are associated in intuitive decision-making. In fact, when the human emotions e.g. fear of gain and lost, are highly involved in the decision-making task the result might not be desirable or close to optimal. However, regularity and practice on utilizing a data analysis tool box e.g. BI while the gain and the loss of the decision-maker is not involved would result in better decisions. With having the above facts in mind, in order to benefit from the intuition in solving problems under uncertainty, it would be essential yet adequate taking into consideration the enemies of intuition which are irregularity, absence of proactive and emotional decision-making. In this sense, practicing and mastering one of the conventional DSS tools and regularity in dealing with similar cases would lead to better decision. Here we can conclude that, the permanent solution to creative problem-solving is neither intuition nor rationality but a fine balance and combination of these two. This section tried to contribute by extending an understanding on the topic of intuitive decision-making for approaching fast and creative choices in decision-making under uncertainty. To doing so, the challenges with the usage of intuition, towards creativity in decision-making, are investigated from a psychological point of view. The preliminary results which are concluded from this subsection would contribute in identifying the success factors in utilizing IT tools for decision-making. In the next section, we will provide a research on creative and intuitive decision making based on this literature review, yet this time from neuroscience point of view. With this we aim to better understand the concept of creativity. Overall, an independent investigation on creativity from the neuroscientific point of view would contribute in confidently choosing the proper psychological theory of creativity. It would further

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increase the understanding and knowledge on the true potentials and drawbacks of the intuitive decision-making.

3.2 Human Brain and Information Technology Watch the functioning of your own mind in a calm and detached manner so you can gain insight into your own behaviour. Henepola Gunaratana

As we discussed in the previous chapter, in decision-making realm the concept of creativity is highly associated with producing the heuristics. Yet the subject of creativity for centuries has been considered as a tabu subject to be investigated. However, during past three decades it has become a topic of considerable interest (Treffinger, 2004). Psychological researchers have been in fact pioneers in examining the creative thinking and creative problem solving programs and methodologies as an external and behavioural occurrence. Such researches have been extensively reviewed for instance by Mansfield (1978). However, up until today there is still no unified theory of creativity accepted by all researchers. In fact, because of the variety of the manifestations of creativity in real-life, agreement on a single theory would have been impossible. Isaksen et al. (1985), Runco and Albert (1990), and Runco (2010), have well reviewed the theories on creative problem solving and decision-making from a psychological point of view. In this section, alternatively we aim to investigate the creativity and intuition from inside the brain with the aid of recent advancement of neuroscience. Here we try to find the truth of intuitive decision-making in the neuroscience of the brain. However, there exist different theories of brain functioning developed by devoted scientists, and the pieces of the puzzle of creativity, as Jung et al. (2013) would say, are not quite gathered to present a clear picture. Nevertheless, the current state of the research in this realm presents information that can contribute in developing an understanding about the intuitive problem solving and creative decision-making. This understanding would lead to better managing of a balance between rationality and intuition in todayâ&#x20AC;&#x2122;s tough business decision-making tasks. 34

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Kandel (2000), in the book principles of neural science argue that all mental functions, including decision-making tasks, whether a creative heuristic or a logical approach, come from the brain. In this sense studying the structure, functions, neuroscience, ability and processing quality of the brain plays an important role in investigation of the creative thinking, problem solving and decision-making. Being aware of characteristics of brain along with other psychological and sociological aspects of human creativity, would be one of the major effective success factors to be considered in implementing any IT tools (Rud, 2009). The recent research proves that the structure and the quality of data processing of the human brain play an important role in creativity and innovation. In the other words, considering the human brain structure and its innerouter interactions would be essential in a successful aligning of the business-intelligence (Watson et al., 2007), and also business/IT alignment (Murer 2011, Seigerroth 2011, Kaidalova and Seigerroth 2012) in general. In other words, in aligning any software application into business in order to be able to achieve objectives the human factors, and before of all the quality of implemented brain-computer-interaction should be well considered. In this sense understanding human brain, its limitations, functions and potentials would give a concrete idea on what kind of tools can better satisfy the need of todaysâ&#x20AC;&#x2122; businesses decision-making. The suitable tools would be able to empower the information processing capability of human brain and can well interact with the intuitive mind and decision-makerâ&#x20AC;&#x2122;s gut feeling.

3.2.1

Intuitive Mind Vs. Rational Mind

After the first split-brain operation in 1969 by Roger W. Sperry, who was awarded Nobel Prize in 1981, theory of left-brain/right-brain has become very popular. His operation was performed by cutting the corpus collosum, the structure that connects the two hemispheres of the brain. After the communication pathway between the two sides of the brain was cut the patients found themselves unable to name objects that were processed by the right side of the brain, but were able to name objects that were processed by the left-side of the brain. Based on this experiment, Sperry (1969) made a number of suggestions on the functions of the brain. In short his theory is based on what is known as the lateralization of brain function. It basically says that one side of the brain very specifically controls a particular function(s), and people either are left-brained or right-brained 35

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(Cherry, 2010). Sperry (1969) further unveiled that the right brain is the superior cerebral member when it came to performing certain kinds of mental tasks. According to Sperry (1969), left brain functions sequentially and excels at analysis (rational functions) while right brain operates holistically, reads emotions, and recognizes patterns (intutive functions). According to the lateralization theory, the right side of the brain is best at expressive and creative tasks. Some of the abilities that are popularly associated with the right side of the brain include: recognizing faces, expressing emotions, music, reading, emotions, color, images, intuition and creativity. Yet the left-side of the brain is considered to be devoted to at tasks that involve logic, language and analytical thinking, critical thinking, numbers, reasoning and rational thought in general. On the basis of the theory of lateralization (Sperry, 1969) it would be possible to map a number of common business functions to a particular quadrant of the brain (Herrmann, 1991 & 1981). For instance Left Cerebral Cortex gathers facts, analyzes issues, solves problems logically, argues rationally, measures precisely, understands technical elements and considers financial aspects. The right cerebral cortex, on the other side, reads signs of coming change, sees the big picture, recognizes new possibilities, tolerates ambiguity, integrates ideas and concepts, bends or challenges established policies, and does problem solving in intuitive ways. Left limbic system finds overlooked flaws, approaches problems practically, stands firm on issues, maintains a standard of consistency, provides stable leadership and supervision, organizes and keep track of essential data, develops detailed plans and procedures, implements projects in a timely manner, articulates plans in an orderly way and keeps financial records straight (Rud, 2009). However, very differently right limbic system recognizes interpersonal difficulties, intuitively understands how others feel, picks up nonverbal cues of interpersonal stress, relates to others in empathetic ways, engenders enthusiasm, teaches, conciliates, understands emotional elements and consider values (Herrmann, 1991).

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3.2.2

Human Brain in Decision-Making

Based on the recent researches e.g. (Benes et al., 2000), although the right brain might be what allows us to access more expansive thinking, the effective use of both hemispheres (both cerebral cortexes) is necessary to survive in our rapidly evolving business landscapes. Later research (Singh & O'Boyle, 2004) has shown that the brain is not nearly as divided as once Sperry had thought. For example, recent research by Singh and O'Boyle (2004) has shown that, abilities in subjects such as math are actually strongest when both sides of the brain work together. They found out that mathematically gifted teens did better than averageability teens and college students on tests that required the two sides of the brain to cooperate. Those who were precocious in math were equally good at processing global and local elements with either hemisphere, suggesting more interactive, cooperative left and right brains. In addition, whereas average-ability boys and college students were slower on cooperative trials, the math-gifted showed the opposite pattern. They were slower on one-sided trials, but when a task required both sides of the brain to work together, they were considerably faster than the other boys (Singh and O'Boyle, 2004). Their study supports the growing notion that the gifted problem-solvers are better at integrating information between the cerebral hemispheres. They conclude, it is not that you have a special module somewhere in your brain (whether right or left), but rather the brain's particular connection with right hemisphere would deliver the creativity. It has been seen that interactive connection of right-left brain would be the source of creativity in problem solving. In the other words creativity is about shifting between rationality and intuition (Dane, 2007 & 2011). Further research of neuroscientists on creativity e.g. (Schooler & Fiore, 1998; Kounios et al., 2008; Jung et al., 2013; Limb, 2008; Gilhooly et al., 2007; Zabelina et al., 2012) investigate what actually happens in the brain during the creative process. The latest findings from the real neuroscience of creativity suggest that the right brain/left brain distinction is not the right one when it comes to understanding how creativity is implemented in the brain (Jung et al., 2013). Creativity does not involve a single brain region or single side of the brain. Instead, the entire creative process consists of many interacting cognitive processes and emotions. Depending on the stage of the

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creative process, and what youâ&#x20AC;&#x2122;re actually attempting to create, different brain regions are recruited to handle the task.

However, interactive connection of the left part of brain to the intuition source of right brain can provide outstanding performance in decisionmaking and problem solving. Recent researches (Santhanam, 2006; Brynielsson, 2009) even suggest that, the human intuition may also be integrated with computers where in fact computers would take part in activities of the left part of the brain. McGilchrist (2009) further explains the updated theory of lateralization as; although the right hemisphere gives sustained, broad, open, vigilant, alertness, and the left hemisphere gives narrow, sharply focused attention to details, it is not true that one part of the brain does reason and the other does emotion in dealing with a decisionmaking task. In fact, both parts of brain are profoundly involved in both rationality and intuition. This is not limited to decision-making and problem solving tasks as further functions such as language, visual imagery would be the result of interaction of both hemispheres. He believes that for creativity both hemispheres should be involved. According to (McGilchrist, 2009) the right hemispheres manipulate the world as we need to be able to creatively use, interact with the world and use it for our benefit in a novel way. On other hand, with left hemispheres we make tools. In fact when brain already knows something is important and we want to be precise about it, we use our left hemispheres in that way. To doing so human has been using a simplified version of reality such as a simulation model or a reporting graph of visualization software.

Figure 8. Brain as a whole with both rational and intuition interactions (McGilchrist, 2009)

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In this case using a simplified version of reality is considered as the knowledge that is immediate by the left hemisphere. Even though it has the advantage of perfection but lacks the creativity. In this sense, the world of the left hemisphere is dependent on abstraction, and it yields clarity and power to manipulate things that are known, fixed, static, isolated, explicit, and general in nature. However, novelty of the right hemisphere is always looking out for things that might be different from our expectations in order to discover things in context, and understands individuals, not just categories. In fact, the right hemisphere yields a world of individual, changing, evolving, interconnected, implicit, and living beings in the context of the dynamic world, and in the nature of the unknown (McGilchrist, 2009). The role of the right brain in todayâ&#x20AC;&#x2122;s global economy in dealing with uncertain problems is vital. With computers becoming increasingly used at handling the linear processes, the competitive advantage for humans is in the ability to access the power of the right hemisphere. Furthermore, the skills needed to participate in an adaptive organization are also governed dominantly by right hemisphere in an intuitive manner (Herrmann, 1981). )n fact, research suggests that brainâ&#x20AC;&#x2122;s right hemisphere is the only area that deals effectively with change and the dynamic nature on business (Rud, 2009). But not to forget that in general, the two hemispheres of brain work together to orchestrate every human activity (Herrmann, 1991). Yet neuroscientistsâ&#x20AC;&#x2122; research suggest that, the two hemispheres approach every situation slightly differently. Understanding and enhancing the use of one side or the other can enhance creative endeavours (Rud, 2009). 3.2.3

When Intuition and Creativity Happen

Advancement of humanity is fundamentally dependent upon creativity and innovation (Schooler & Fiore, 1998). As it was discussed in last section human decision-making can be slow and deliberate followed by logic, or rapid followed by intuition (Kahneman, 2011). Along with the general solutions to problem-solving strategies which are methodical, conscious, and logical, people can solve problems intuitively, way apart from logical thinking, with the strike of a novel idea which emerges into consciousness (Kounios et al., 2008). In the other words, without thinking logically we sometimes come up with the creative solution. Today the tools of neuroscience facilitate scientists to see the intuitive function of the brain to be able to uncover the concepts of intuition as it strikes. 39

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The long term idea to study the neuroscience of creativity in brain is, if we were able to define the circuits of the creativity functions we will be able to enhance them in many ways so at the end human might be better at being creative. There are in fact parts in the brain that intuitively correspond in the moments of creativity (Kounios et al., 2008). With the aid of neuroscience we have the tools to see what creativity is, and what goes on when people have moment of creativity. As the result the source of such intuitive functions has been identified to be divergent thinking, insight, or precognition (Limb, 2008). Divergent thinking. Creativity and intelligence have been long known to psychologists to be different processes in terms of human behaviour. In fact, the basic neural mechanism of intelligence, which is about the fast and efficient wiring of neurons in the grey matter, has been known to scientists for almost a century now (Lashley, 1929). Psychologists have found that a high intelligence quotient (IQ) alone does not guarantee creativity. Consequently, intelligent people are not necessarily creative people. Instead, personality traits which promote divergent thinking are considered to be more important in creativity. Divergent thinking as one of the creative mechanism of thinking is identified as a thought process used to generate creative ideas by exploring many possible solutions. It is also identified as the essential capacity for creativity to explore possible answers to a question. Divergent thinking is found among people with personality traits such as nonconformity, curiosity, willingness to take risks, and persistence (Gilhooly et al., 2007). Therefore, the ability to explore more options and solutions to a problem is considered to be more valuable for creativity. Nevertheless, the process of creativity, as a complicated phenomenon inside the brain, has not been totally clear to scientists until now (Jung et al., 2010). Further research by neuroscientist Jung et al. (2010) has investigate this realm with an exclusive focus on inter-structure of brain aiming to find out what makes creativity. In their research the white matter of the brain and the connective networks within have been reported to be highly connected to the creativity. The white matter of the brain actually accommodates the connective wirings that connect the regions of the brain which creates a very complicated neural network. The structure of such neural network has in fact major differences in highly creative persons as if the creative person has way efficiently wired different parts of the brain. Jung et al. â&#x20AC;&#x2122;s study connects the creativity more with the white matter of brain where the 40

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simple, shortened and efficient structure of the connective neural networks would allow the different creative ideas fellow speedy into the awareness empowering the divergent thinking. Insight. Insight is the overcoming some particular assumption when it is suddenly realized that there is other way of doing things. This accuracy is in fact the critical element of creativity (Schooler et al., 1993) as it targets the problems that rational resources cannot solve. To doing so, brain functions in certain way to allow us to be creative. Accordingly insight has been identified as one of the most important mental functions to be well researched (Limb et al., 2008). Due to the advancements of the neuroscience and availability of the tools to actually study the functions and structure of brain the concept of creativity has been recently become subjected to investigation from the neuroscientific perspective providing the ability to investigate the creativity from inside of the brain. This has been complementing the former researches from psychological perspective. Zabelina et al. (2012) tried to capture the insight moments and novel ideas utilizing an electroencephalography (EEG) cap over the head of volunteers as they solve the problems. An EEG cap is the recording of electrical activity along the scalp. EEG measures voltage fluctuations resulting from ionic current flows within the neurons of the brain and can identify the location of the brain activity at the time of insight (Niedermeyer & da Silva, 2004). They identified that at the time of insight the superior temporal gyrus of the right side of the brain is the parts that highly react and is involved in the process of creativity. As the result they found out that neurons in the right and left side of brain are very differently wired. In the right hemisphere neurons gather information from broader source of inputs comparing to the left hemisphere and this would allow them to connect to other parts of the brain. In the other words, the brain cells in the left hemisphere of brain have short dendrites just useful to get information from the nearby parts. However the cells of the right hemisphere branch way further to be able to get unrelated ideas from other parts of the brain. In fact having such broad connections has been the main reason that why in this particular part of the brain the novel connections of concepts are made in the instant of an insight (Zabelina et al., 2012). Further research (Kounios et al., 2008; Wegbreit et al., 2012) show that a very short moment before insight happens a burst of alpha waves at the back side of right hemisphere. The backside of the hemisphere 41

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accomplishes the visual processing and yet the occurrence of alpha waves shuts that aria down and would stop processing the visual information. This occurrence right before the insight would stop the brain distraction and it would be very likely to allow the novel idea to come up to consciousness. This study suggests that cutting off the distraction of outside world can help to increase the creativity. The process of cutting off the distraction of outside world is called mind wandering (Schooler et al., 2011). Mind wandering has been found to be highly associated with creativity (Smallwood et al., 2006). It means that taking a break from the problem and doing something undemanding for some minutes and getting back to the problem could highly increase the quality of insight leading to creativity (Schooler et al., 2011). Furthermore mind wandering activities such as taking long walks, meditation and bath, have been also investigated to be highly effective in divergent thinking (Christoff, 2009). Studying the brain while doing the mind wandering activities shows that doing physical activities as such would affect the frontal lobes of the brain to force the brain to go into a sleep mode (Jung et al., 2010). In this case down regulating the brain in this sense would be a motivation for ideas to show up from subconscious to conscious and awareness. In fact less activities in the frontal lobes has been highly associated with creativity. This has been previously reported in improvisation and creativity (Limb, 2008) as people with lower frontal lobe activities are more creative. Consequently frontal lobes are now considered as the major parts of the brain involved in creativity to be researched further. Along with mind wandering it has been proven that visual hints also highly contribute in insight (Bowden, 1997; Schooler 1999). In fact the evidence for a meaningful relation between vision and creativity comes from the striking parallels between creative discoveries and the perceptual identification of degraded images (Schooler et al., 1995). Schooler and his colleagues found out by presenting the hints to the left vision field of human which communicates with the right side of the brain the moments of insight is highly enhanced. In fact, the right side of the brain has found out to be more sensitive to the hints leading to flash of insight. Precognition. In the conventional perspective we make decision based on our memory and our expectation which are all past stuff processed in a way that allows us to make decision. Although this is often true but sometime we make decision upon what is about to occur. In fact sometimes people without knowing it, sense the future and accordingly can intuitively come to 42

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a creative solution. One of the implications of such ability would be in decision-making. Knowing the future in decision-making could be very handy. Yet knowing the future could be conscious or unconscious (Radin, 1997). Radin (1997) believes that people can sense the future and predict the situations. Developing this ability in fact make some people exceptional managers/leaders. He provides the evidence that such prediction ability could be in fact precognition if they occur systematically instead of following a guessing pattern (Radin, 1988). 3.2.4

Summary of Section

This section provided understanding on the neuroscience concept of creativity. Creativity has been identified as a product of the whole brain, and a process that consists of many interacting cognitive processes and emotions. In this sense, the interactive connection of the rational and intuitive parts of the brain can provide outstanding performance in decision-making and problem-solving. As all the mental functions, including decision-making, come from the brain, studying the brain functions has been the particular concern. Consequently in this chapter, with the aid of recent advancements of neuroscience, the creativity and intuition were investigated. In fact, being aware of characteristics, limitations, functions and potentials of brain is considered to be one of the effective factors in implementing any DSS tool. This section on neuroscience by providing an understanding about the human brain give a concrete idea on what kind of tools may better satisfy the need of todaysâ&#x20AC;&#x2122; businesses decision-making. Moreover, it was reviewed that some particular parts of the brain are involved more than other parts in decisionmaking. In fact the frontal lobe highly contributes in rational decisionmaking and well contributes in creating seamless heuristics. This would strongly suggest that intuition cannot be often effective. This finding would fundamentally justify the Prospect Theory. Nevertheless creativity has been seen as a product of the whole brain, and a process that consists of many interacting cognitive processes and emotions. In this sense the interactive connection of the rational part of the brain and intuitive part can provide outstanding performance in decision-making and problem solving. However, the role of the right brain in todayâ&#x20AC;&#x2122;s global economy in dealing with uncertain problems found to be vital. In fact the brainâ&#x20AC;&#x2122;s right hemisphere is the only area that deals effectively with change and the dynamic nature on business. 43

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Today with computers becoming increasingly powerful at handling the linear processes and data analysis, the competitive advantage for humans is in the ability to access the power of the right hemisphere to deal with uncertainty. In the situation that human has to rely on certain rational approaches to manipulate the world, for a broad understanding of todayâ&#x20AC;&#x2122;s problems we should be utilizing knowledge that comes from the right hemisphere. This can be happening via a well-designed human-computerinteraction which presents a well-balanced of intuition and rationality. Further evidence for a meaningful relation between vision and creativity suggested that information from rational tools can be visually communicated with the human to reach the creative discoveries. One of the other factors which should come to consideration for increasing the creativity is to enhance the divergent thinking. Divergent thinking as one of the creative mechanism of thinking is identified as a thought process used to generate creative ideas by exploring many possible solutions. Our study suggests that cutting off the distraction of outside world can help to increase the creativity by enhancing the divergent thinking. The process of cutting off the distraction of outside world is called mind wandering. It means that taking a break from the problem and doing something undemanding for some minutes and getting back to the problem could highly increase the quality of insight leading to creativity. Furthermore mind wandering activities such as taking long walks, meditation and bath, have been also investigated to be highly effective in divergent thinking. In this subsection the challenges and rather drawbacks to intuitive decisionmaking are investigated from neuroscience perspective. Our literature review on neuroscience of creativity and intuitive decision-making are used to examine the findings in the first subsection. Neuroscience of creativity and intuitive decision-making would deliver an extensive understanding on human factors from inside of the brain. Therefore a deep understanding on the human-computer interaction in the particular realm of decision-making is delivered.

3.3 Concluding Remarks The end point of rationality is to demonstrate the limits to rationality. Pascal Today most of enterprises are overwhelmed with the huge amount of information. Nowadays with availability of the TVs, social networks and huge data storages we are dealing with huge information but we are not able 44

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to properly process them and use them with the rational approaches such as B). Today’s managers in knowledge-based organizations, especially those working in analytical and decision-making positions, assume that rightbrain creative processes are irrelevant to their line of work (Rud, 2009). They only rely on rational approaches of data analytics where rationality, simply, has no answer to the complexity involved. There is no doubt that the logic and reasoning associated with left hemisphere of the brain is desirable. For today’s needs however increasing the usage of the knowledge which comes from the right hemisphere and the need to benefit from a broader context and novelty which is associated with the right hemisphere would be vital (McGilchrist, 2009). Nowadays decision-making tools are entirely made on rationality which indeed limits the freedom of thoughts and creativity. As McGilchrist (2009) would say in our modern world we develop something that looks awfully like the left hemisphere’s world: we priorities the virtual over the real, the technical becomes important . )n this situation individuals tend to favour more left-brain, linear, hierarchical thinking processes. However, evidence shows that the best way to solve complex analytical problems is to access the whole brain Rud, 2009). Consequently there is an economic motivation and a human pull to move beyond the logical, linear, reductionist view to a more compassionate, inventive, holistic and intuitive approach. Although Pascal, Einstein, Gigerenzer (2007 & 2008) would respect rationality they would favour more right side of the brain and go for intuition in problem solving. On the other hand as we mentioned in last section according to Kahneman & Tversky (1979) and their Prospect theory intuition has failure moments in the situation of lost. In this situation clearly dealing with the real-life problems with either intuition or rationality would lead to a critical problem. They represent two different versions of the worlds and sometimes we may combine them in different ways. However, human need to rely on certain rational approaches to manipulate the world. Nevertheless for a broad understanding of today’s problems we should be utilizing knowledge that comes from the right hemisphere. This can be happening via a well-designed human-computer-interaction which presents a balance of intuition and rationality. This would be an alternative view that takes humanity through stages based on value systems described in e.g. (Pink, 2005) which would be a fascinating concept with broad implications for human development.

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The theoretical framework of this chapter provided an extended research on the topic of creativity and intuition in decision-making. In the light of that, in the next chapter, a creative solution to the decision-making problems is shown accordingly in real life situation.

Data Visualization Tools Supporting Decision Making

4 Data Visualization Tools Supporting Decision Making In this section, considering our theoretical framework, multidimensional visualization as a tool of BI applied for solving decision making problems under uncertainty and time constraints particularly in the process of engineering design. The powerful IT tool should be designed in the way to be, an effective decision-making tool at the hand of professional designer. In this case their creativity will be enhanced and their intuition would be used in a more confident manner. This would be by far contribute in creating intuitive decisions which in this context of our particular importance. Furthermore, it must be able to present an effective integration of computer data analysis power and human creative mind. As a result, decision-maker can get an insight into the data. According to Jahan and Edwards (2013), one of the important issues in which should come to consideration is that often in the process of engineering design and manufacturing the whole process is dependent on the professionals and expert engineering designer which typically are not familiar with decision-making tools. Or the on other hand, the task of decision-making might be at the hand of professional decision-makers whom are not a trained engineer. For this reason, the IT tool should have been designed as simple as possible to be simply adopted by engineers. As it was learned mastering the utilization of a data analysis toolbox would be one of the important and essential parts of a creative decision-making to be at the hand of todayâ&#x20AC;&#x2122;s decision-maker. Here in order to make the usage and mastering process easier for the common users simple data visualization graphs are introduced to create a reliable computer-humaninteraction.

4.1 Multidimensional Data Visualization There are many problems of pure theory, which no one who has once learned to use diagrams will willingly handle in any other way. (Marshall, 1890) Battiti and Bruanto (2013) describe that a big portion of our brain is devoted to processing visual information. Our ancestors needed to be very 47

Data Visualization Tools Supporting Decision Making

fast to identify predators and to react accordingly. Todays, we need to be very fast to transform huge amounts of information into insight, knowledge, engineering designs and, decisions. According to Larkin and Simon (1987), decision makers in different domains such as physics and engineering make extensive use of visualization and for this matter diagrams are of particular importance. They suggested that a diagrammatic representation in an informationprocessing system is beneficial. Data visualization by Battiti and Brunato (2011) was introduced as a great data representation, with computational efficiency which makes it a very valuable tool in dealing with decisionmaking problems. According to Geoffion (1976), visualization is an effective approach for decision-making as it can summarize the information into an insight, instead of numbers. Mosavi and Vaezipour (2012) described the importance of visualization and multidimensional graphs in decisionmaking. However Jones (1994) noted that due to poor visualization techniques, the nature of decision conflicts may not completely come to consideration. Therefore, the decision-makers may not be able to confidently make decisions. However, according to Piero (2009), and later Battiti and Brunato (2013) during past few years, due to the huge development in combinational optimization, machine learning and intelligent optimization, there has been a huge advancement in visualization tools. Multidimensional graphs are the common tools available in the most IT tools. In this case the engineer can consider plenty of design criteria simultaneously on a multidimensional graph. When by practice and regularity in using such data analysis tool the procedure is mastered the creativity in making fast decision is highly expected. In the other worlds the intuitive decision-making in this case would be highly accountable. The technology of multidimensional visualization can be implemented using the common IT tools available in the market. It presents effective and flexible software architecture for integrating problem-solving and decisionmaking schemes into the integrated engineering design processes and optimal design. The workflow in this case implements a strong and seamless interface between the analytics and decision-maker. While multidimensional visualization systems produce different solutions, the decision-maker will be pursuing conflicting goals and tradeoffs which are represented on the multi-dimensional graphs. Multidimensional 48

Data Visualization Tools Supporting Decision Making

visualization tools can be learned and mastered easily and with practice on the different problems the regularity in using such system can be achieved.

Figure 9. Multidimensional visualization, considering five design criteria simultaneously

Figure 9, presents a sample of multidimensional data visualization which may be effectively used in providing solution for decision makers, as a number of design criteria can be considered simultaneously. The visualization of data of engineering simulation would bring instant insight into the designer. In the situation that the designer is well familiar with such system, when his/her emotions are not involved in decision-making, the creative results are highly expected. In order to make a decision in uncertain situations utilizing a multidimensional data visualization graph, the decision-maker would get an insight into the problem with the aid of simultaneously considering multiple decision criteria. This would facilitate the intuitive decisionmaking. It is worth mentioning that during considering a solution to the problem however the mind wandering activity, as we studied earlier, would even further enhance the creative decisions. Note that there are various IT tools which have similar visualization capacity. Here we use Graphuer as an example of BI tools with multidimensional data visualization graphs.

Data Visualization Tools Supporting Decision Making

4.1.1

Considering Example in Multidimensional Data Visualization

In this section, we aim to narrow the attention of our study on intuition and creativity to the potential applications of engineering design. Therefore, here in particular the decision-making tasks related to design would be centre of our attention prior to the other applications. In order to further the study on the role of intuition in engineering design, here we briefly describe the concept, importance and status of the field of engineering design. According to Sen and Jian-Bo (1998), engineering design is considered as a decision-making process. Accordingly, it overlaps with a number of disciplines e.g. decision sciences, economics and management. This fact would demand that design decisions would need a productâ&#x20AC;&#x2122;s integrated development process. In such process, the real-life industrial problems typically need to be considered from very different perspectives. This leads to the need for optimizing several conflicting objectives, and decision-making on several conflicting criteria. Marler and Jasbir (2004) put it in this way that in any task of design at least two conflicting objectives are involved i.e. Cost vs. Quality. Further it would be the complicated task of designer to find an optimal balance between the conflicting objectives. According to Krish (2011), with the aid of advancement in DSS, interdisciplinary and data analysis tools, a series of criteria including mechanical, electrical, chemical, cost, life cycle assessment and environmental aspects are now able to be simultaneously considered. As one of the most efficient approach, the multicriteria decision-making (MCDM) applications (Piero et al., 2009) can provide the ability to formulate and systematically compare different alternatives against sets of design criteria. In this context, the benefits of utilizing MCDM include the conflicting design objectives that are taken into account simultaneously leading to an overall insight of the problems which would deliver a significant and competitive advantage to the engineering design community. Deb (1999) describes that the task of solving an engineering design, utilizing MCDM, is considered as a combined task of optimization and decision-making. Yet as the process of MCDM is much expanded, most optimization problems in different disciplines can be classified on the basis of it. It is very important that before the actual decision about the final solution takes place the decision-Maker should gain a good understanding 50

Data Visualization Tools Supporting Decision Making

about the trade-offs between the solution alternatives. Then the final decision can be firmly taken. Here it is worth mentioning that, implementing the MCDM task for solving engineering problems is considered as a very important yet complicated approach for engineers to pursue. According to Miettinen (1999) the problems of this type are mostly nonconvex, nonlinear and computationally expensive, including numerous variables and several conflicting objectives. Yet according to Jones et al. (1998) solving the engineering design problems as such, which are mostly referred to black-box optimization problems can be formulated as a MCDM task. Huang et al. (2006) and Piero et al. (2009) survey the potential approaches of MCDM in engineering design. Note that often these approaches aim to design products with the main objectives such as low prices, high quality and minimum manufacturing time. Yet often the problem is solved in an isolated manner. 4.1.2

Engineering Design

According to Sall (2001) engineering design and product development are not isolated processes. In fact, engineering designers and manufacturers no longer aim to, for instance, only reach low prices, high quality and on-time delivery. These attributes, which were advantages a decade ago, are now the minimum requirements to stay in the market. In addition, in the dynamic world of business the rules are constantly changing. Now enterprises face globalization, more competition than ever and customers whose demands reflect their own knowledge and expectations of a global market. Today, a successful enterprise must track and move extensive inventories, generate a greater number of products, negotiate with numerous suppliers, and maintain a multitude of quality standards. They also have an ever-increasing need to acquire, satisfy and retain additional customers to remain profitable. Because of these complex pressures, it is imperative that all links in the supply chain managed successfully. The above issues would demand for the new tools in addition to conventional approaches of MCDM in which can well deal with the uncertainty of the dynamic market. In this context with integrating DSS e.g. BI with the design and optimization processes dealing with uncertain problems has become more convenient in dealing with real-life decision tasks. BI further provides designers with a reporting, monitoring and alerting, and root-cause analysis solution where it is possible to gain

Data Visualization Tools Supporting Decision Making

visibility into quality processes. Moreover, BI is widely used for pulling data together, analyzing it and then making it available to decision-makers. Nevertheless, the convenient usage of BI, according to Battiti & Brunato (2011), does not provide any good for the uncertainty involved. In this context the creativity and innovation have been proposed by Battiti and Brunato to be the right thing in todayâ&#x20AC;&#x2122;s situation in order to make the most of decision-making resource for an optimal design. Furthermore Rud (2009) argues that conventional DSS e.g. BI as the rational approach to problem solving in an enterprise can only be successful and lead to creativity and innovation when the human factors are well considered, implemented, and interacted within the solution procedures. This would in fact lead to a systematic balance of rational-intuition strategy to creativity which is highly desirable. There are numerous BI software packages available in the market and they provide a very diverse list of tools including but not limited to; reporting, online analytical, data/information processing and visualization, analytics, metaheuristics optimization, data mining, visual analytics and very recently predictive analytics. Thomsen and Pedersen (2005 & 2011) and Negash (2004), have well surveyed the available BI tools in the market for further reading. 4.1.2.1

Description of the problem in Engineering Design

This example is concerned with designing the composite parts (Barbero, 2010) and selecting the optimal materials for a particular design. In this problem designer must make decision on the optimal materials and also on the optimal degree of draping. To doing so the criteria of mechanical behavior of the woven textile during the draping and further involved simulations and analysis are all included in the process of the design and decision-making.

Figure 10. Simulation of draping process including a combined mechanical modeling of compression, bend, stretch, and shear, (Vaezipour & Mosavi, 2013c)

Data Visualization Tools Supporting Decision Making

The manufacturing of woven reinforced composites requires a forming stage so called draping in which the preforms take the required shapes. The main deformation mechanisms during forming of woven reinforced composites are compression, bend, stretch, and shear which cause changes in orientation of the fibers. Since fiber reorientation influences the overall performance it would be an important factor that along with the other criteria e.g. mechanical, electrical, chemical, thermal, environmental, life cycle and costs should take into account.

Figure 11. Simulation of the draping process; considering different materials (Mosavi, Hoffmann & Vaezipour, 2012)

Figure 12. Simulation of the draping process; considering a different product (Vaezipour & Mosavi, 2013c)

According to Jahan and Edwards (2013), yet the materials selection for the composite can determine the durability, cost, manufacturability of final products as well as customer satisfaction. For this reason, a number of materials should be simulated for a particular application and accordingly pros and cons to be considered. Therefore, the mechanical behavior of woven textiles during the draping processes should be fully integrated to the MCDM algorithms. According to Edwards (2002), when multiple criteria from different disciplines are to be satisfied in a materials selection problem, often because of the criteria conflicts the complexities are increased. Furthermore, due to the numerous candidate materials, their detailed properties and the results of draping simulations designer is facing a problem. In addition, the mechanical modeling of the draping for different candidate materials would increase the uncertainty of the design. 53

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4.1.3

Role of Intuition and Creativity in Engineering Design

The recent investigation of Andersson et al. (2008) on the usage of expensive DSS implementations in engineering design and manufacturing industries located in the Jรถnkรถping region in Sweden confirms that despite of the availability of engineering, statistical and analytical software packages, the intuition has a major impact in the fast and creative decisionmaking tasks of the leaders. In addition, the result of an IBM global study in 2010, which surveyed 1,500 chief executive officers (CEO) from 60 countries and 33 industries worldwide, reveals that decision-making in complex situations is highly dependent on creativity and managerial intuition. This has been often the case when the speedy decisions are required to be made in solving complicated problems in the situation that the DSS lacks communicating the insight in dealing with problems. Nevertheless, Andersson et al. (2008) clearly states the major and ignorable role of intuitive decision-making in the industry and engineering. As Kahneman (2011) would say this phenomenon is quite known to psychology where managers and designers would often hesitate to give away their power of decision-making to rational tools. Yet very surprisingly the role of intuition, as Battiti and Brunato (2011) describe, is not limited to the managerial decision-making and business related tasks. In fact, as Roy et al. (2008) also describe, despite of all advancement in computational design and mathematical optimization still trial-and-error, and expertbased approaches to engineering design which are indeed highly associated with the intuition and creativity, take part in decision-making. This phenomenon has been primarily studied by e.g. Gott (1988). Following graph describes the growth of computational and mathematical optimization research vs. the situation of actual usage of these tools in industry during past two decades. Obviously the rational methods have been dramatically increased while there have not been reported any major growth in the usage of the rational tools in the industry of design. As a matter of fact decision-makers in design and industry have been rather relying on simple methods of trial-and-error and intuition. This fact would strongly justify, engineering designer would often rely on his own intuition and hesitate to give away his power of decision-making to rational tools (Kahneman, 2011).

Data Visualization Tools Supporting Decision Making

Figure 13. The growth of computational and mathematical optimization research vs. situation of usage of these tools in industry since 1994 (Mosavi, 2013c).

According to Mosavi (2010a, 2010b, 2013a), although since past two decades the complexity of design, due to the increasing of design criteria, has been continuously increased, yet the continuous advancements in analytical tools have been found to be not the permanent answer to the most of the complexity involved. Here we can conclude that by increasing the decision-making complexity in industry, which has been mainly due to uncertainty, the designers, as the response, tend to rely more on the power of their intuition. Although one may argue that; decision-making on the optimal configuration of an engineering design is a pure rational process which has to be conducted with only relying on mathematical and computational tools where the human interaction is minimized. To explain this claim here we should distinguish the two major groups of problems in engineering design. One group of the problems are those which decision-making task is rather an isolated problem. This is why it can be mathematically described and also can be in a reasonable manner computationally implemented. In the problems as such, either the uncertainty or large databases are not involved or they are in a manageable level. In the second group of problems, we are facing a huge deal of uncertainty in decision-making task. In the problems as such, due to uncertainly the mathematical model cannot be created and also due to the nature of problem cannot be computed. In the latter group the designer with the aid of intuition aims to overcome the complexity. Yet the success/failure ratios of these cases are not clear.

Data Visualization Tools Supporting Decision Making

4.1.4

Intuitive Decision-Making

Considering the textile composite materials selection and design in the multidimensional graph provided in the following figure 14, six different design criteria is considered simultaneously. In this case cost, weight, electrical, environmental, mechanical factors and most importantly draping simulation results are considered. The draping simulation for different materials and different draping angles which are stored in the database here are available for the consideration of decision-maker.

Figure 14. Multidimensional visualization graph used for considering different products, materials and draping characteristics simultaneously. Here the cost, weight, environmental, electrical, and mechanical factors are simultaneously considered (Mosavi, Hoffmann & Vaezipour, 2012).

Here modeling, visualization and learning tools via a handy procedure stretches beyond a decision-making task and attempts to discover new optimal designs relating to decision criteria, so that an insight of deeper understanding of the underlying problem can be obtained. The applicability of multidimensional visualization can be easily customized for different problems and usage contexts. This example in the concrete context of designing textile composite products has shown the effectiveness of the 56

Data Visualization Tools Supporting Decision Making

approach in rapidly reaching a design preferred by the decision-maker at the present of the large databases and dynamic market. In order to make an optimal decision, information from a multidimensional visualization graph is communicated with the decision-maker and the final decision can be intuitively taken. The proposed explanation was earlier published in the magazine of simulation based engineering & science (Mosavi, Hoffmann, and Vaezipour, 2012) where the use of multidimensional visualization graph has been approved and recommended to the industry by the Europeâ&#x20AC;&#x2122;s leader and key partner in design process innovation; ENGINSOFT. In addition Vaezipour and Mosavi (2013c) further evaluate different cases in materials selection of composites. Vaezipour, Mosavi and Seigerroth (2013b) further point out the potential of this approach in life science industries. Following figure presents a different snapshot of considering different materials, product and draping simulation in order to come up with an intuitive decision.

Figure 15. A multidimensional visualization graph used for considering different products, materials and draping characteristics simultaneously. (Vaezipour & Mosavi, 2013c)

In order to make the final decision in uncertain decision problem utilizing a multidimensional data visualization graph, the decision-maker would get an insight into the problem with the aid of simultaneously considering multiple decision criteria. This would facilitate a proper strategy toward an

Data Visualization Tools Supporting Decision Making

intuitive decision-making as a number of design criteria as well as design configurations are visually presented to the decision-maker. Here modeling, visualization and learning tools via a handy procedure stretches beyond a decision-making task and attempts to discover new optimal designs relating to decision criteria, so that an insight of deeper understanding of the underlying problem can be obtained. The applicability of multidimensional visualization can be easily customized for different problems and usage contexts. This example in the concrete context of designing textile composite products have shown the effectiveness of the approach in rapidly reaching a design preferred by the decision-maker at the presence of dynamic market.

4.2 Concluding Remarks The materials selection problem for textile composites creates an uncertain problem where the designer should consider the results of draping for a number of materials for a number of products. To doing so, different design criteria should also come to consideration for making the final decision. In this problem the designer must make decision on the optimal materials and also on the optimal degree of draping after considering a huge deal of possibilities. Many applications and algorithms of MCDM e.g., Jones (1994) have been presented to deal with decision conflicts often seen among design criteria in materials selection. However, many drawbacks and challenges are identified associated with the applicability on most of DSS in this problem as described for instance by Piero et al. (2009). The description of the example above demonstrates a problem in engineering design where conventional DSS and decision-making tools cannot provide the reliable solutions. In the problem of this kind, the human creativity and intuition would be indeed the potential alternatives. In this regards, an extended understanding about the concept of the intuition and its mechanism would contribute in developing novel approaches to scale design problems as such. According to the findings in this section, the multidimensional visualization tool for decision-making in particular was shown as a BI tool for dealing with decision-making tasks. The multidimensional visualization graphs present a well-designed human-computer-interaction. In this example a meaningful relation between vision and creativity was shown where the 58

Data Visualization Tools Supporting Decision Making

information from the BI tool can be visually communicated with human to reach the creative discoveries. To doing so the intuitive role of the right brain in dealing with decision problems found to be vital. In this sense the interactive connection of data analysis tool of BI and intuitive part of human brain can provide outstanding performance in decision-making and problem solving. In the next chapter the case study is given to evaluate and validate the effectiveness of our initial conclusions from the state of the art literatures which is; using human intuition and creativity in a balance with visualization tools is effective and beneficial in enterprise decision making.

Empirical Findings & Analysis

5 Empirical Findings and Analysis This chapter presents the findings of interviews conducted with the purpose of providing information for the readers about the impact on the practical views of decision makers of using data visualization tools. Therefore, the main purpose of this chapter is to analyse interview questions in a way that will connect to our research questions, the purpose of our study, and our critical analysis.

5.1 Empirical Findings In this section, we present the empirical findings of our study. Based on the preliminary conclusions from state-of-the-art literature, visualization implements for decision-making are proposed as powerful IT tools which are positively associated with producing intuitive and creative decisions under conditions of uncertainty and time constraints. In order to evaluate and validate the proposed assumption, that using human intuition and creativity in a balance with visualization tools is effective and beneficial in enterprise decision-making, we conducted interviews with respect to our research question and the purpose of our study. In this section the main focus is to present the results of the interview questions. Three interviews were conducted with professionals who are using visualization and other IT tools for making real-life decisions in the field of engineering design. One important issue that was considered when selecting the interviewees was whether or not they had experience in designing products. The ultimate purpose of the interviews was to capture the opinion of the participants and collect their knowledge about use of visualization tools for making decisions in the field of engineering design. 5.1.1

1st Interview

The interviewee is currently working as a PhD candidate and researcher in the department of mechanical engineering at JĂśnkĂśping University, Sweden. He had more than ten years of experience in the field of engineering design before beginning to use visualization tools and six years of experience in the field of engineering design after implementing the use of visualization tools. The interviewee stated that he relies mostly on IT tools for decisions regarding, parametric design, where a set of rules are defined for a geometry (the designed product)â&#x20AC;Śby changing a parameter (constrain) the geometry will be updated according to the defined rules. He believes that the IT tools he has used in his work thus far are user-friendly because they are designed specifically to solve problems that are related to his field of expertise. When asked if there were any circumstances in which he would hesitate to relinquish his power of decision-making to IT tools, he replied: sometimes a visualized model for a customized product is a perfect solution for the 60

Empirical Findings & Analysis

problem in hand, but production and manufacturing of that product might be a big issue. In these circumstances, it is expected that other factors not provided by IT tools, such as those related to manufacturing and market, will also be considered. 5.1.1.1

Role of Visualization Tools in Decision-Making

In order to clarify the role of visualization tools in the decision-making process, the interviewee was asked a question about how he made decisions in his field before using visualization tools. He stated that, usually some designs and simulations are done by hand to be able to make a decision. Nowadays, however, the use of IT tools is inevitable. Visualization tools are widely used in the field of engineering design; indeed, the interviewee reported that he always uses these kinds of tools when making decisions. However, the interviewee stated that visualization tools have both positive and negative effects on the results of decision-making. There might be some limitations in visualizing a model, on the other hand a model could be visualized but not possible to produce and manufacture it, he declared (1st Interviewee, 2014-05-06). In response to a question about whether or not he believed that visualization tools could improve his decision-making, the interviewee answered: Yes. It is a good support for making decisions but it is not sufficient. Another support for making decision could be referring to the existing information from previous project activities. As a final point, he stated that he believes he makes better decisions now as compared to the time before the use of visualization tools became common practice, but also that it is not sufficient to make decisions based solely on visualization tools. 5.1.1.2

Role of Intuition and Creativity in Decision-Making

In response to a question regarding the use of intuition in the decisionmaking process, the interviewee indicated that for the majority of situations he uses his intuition, due to the fact that the experience of previous projects is very important in the field of engineering design. He stated that in general, when dealing with real-life situations, the decision-making is based on a combination of intuition, visualization tools and of course experience. The interviewee stressed the fact that visualization tools can change intuitive decision-making in such a way that, small changes in a new customized product might bring new results which are then possible to consider during visualization. Furthermore, when the interviewee was questioned about the existence of any circumstances in which he would hesitate to give up his power of decision-making to intuition or a process of trial-and-error, and if he trusted current visualization tools, he replied: Trial-and-error is a routine task in my field in order to come up with an optimal solution for developing a product. 61

Empirical Findings & Analysis

I believe in visualization tools but the manufacturing of a product should be also considered in parallel. 5.1.2

2nd Interview

The interviewee is currently working as a senior heat exchanger designer in KBR Inc., in Houston, USA. He is responsible for designing heat exchangers and coordinating project teams. He has more than ten years of experience in working with visualization tools in the field of engineering design, and relies on IT tools for designing the heat exchanger which is kind of equipment in his field. In addition, he compares data to industry standards promoted by TEAM (Tubular Exchanger Manufacturers Association). When asked about the ease of using IT tools in his decision-making, he stated: Super easy, as ) am working with it for many years now! nd interviewee, 2014-05-08). However, he mentioned that despite the many advantages of IT tools, they do not analyse the maintenance side of his job, thus he generally does not relinquish all his decision-making power to IT tools. As he explained, I need to refer to my experience to see how people did it before in similar cases. On a question regarding the potential problems and limitations the interviewee faces in using )T tools, he stated that, the program that I am working with is in need of a lot of entry and sometimes we just need to estimate the price so we cannot use this program because it takes too long. 5.1.2.1

Role of Visualization Tools in Decision-Making

The interviewee stated that he always uses visualization tools for making decisions, except for those situations where he needs to consider the maintenance part of his job. In response to a question about how his decision-making has changed as a result of using visualization tools, he reported that he believes that they have definitely had a positive effect on the process, reporting it to be faster and more accurate. From the intervieweeâ&#x20AC;&#x2122;s perspective, although visualization tools are accurate and beneficial, they do not always provide enough information for making real-life decisions. For instance: For those equipment that are not included in TEAM standards we have to refer to our handbooks and calculate and estimate the various criteria by hand. (2nd interviewee, 2014-05-08). 5.1.2.2

Role of Intuition and Creativity in Decision-Making

When the interviewee was asked how often he used intuition in his decision-making process, he replied: Most of the time, especially when I am in meetings or persons who are not expert in the field ask a question. In these circumstances, I mostly use the power of my intuition to manage the situation. Furthermore, the interviewee explained that intuition plays an 62

Empirical Findings & Analysis

important role in his decision-making. His responsibilities require him to refer to his experiences from previous projects and also to be creative in situations where he is facing a lack of information and there are not specific guidelines available to follow. In response to a question of how his intuitive decision-making has changed as a result of using visualization tools, he stated: With visualization tools I got more accurate answers . He believes that visualization tools are a necessary part of his job. On the other hand, he stresses circumstances where he hesitates to give up his power of decision-making to his intuition, stating: Based on the lessons learned (bad experiences) from other projects that use the same program as mine, in those circumstances I cannot trust visualization tools. (2nd interviewee, 2014-05-08). 5.1.3

3rd Interview

The interviewee is currently working as a quality engineer in a company in GĂśteborg, Sweden; he is responsible for quality control, documentation, and reporting. He had around eight years of experience in the field of engineering design before beginning to use visualization tools, and has two additional years of experience in the field of engineering design while using visualization tools. The interviewee stated that he uses IT tools in his current position for importing data into the software, which is later used for documentation and reporting for customers and the organization. These imported data are later checked with the previous records and history of customers. He reported that he considers the IT tools that he is currently working with to be userfriendly, but at the same time, he is facing difficulties. For example, he is required to enter all data into the system twice, once for placing the order and the second time for invoicing. In response to the question about the existence of any circumstances in which he would hesitate to give away his power of decision-making to IT tools, the interviewee replied, Yes. Sometimes I need to check the product based on the paper records cause the information that the IT tool provides for me is not sufficient (3rd interviewee, 2014-05-09). 5.1.3.1

Role of Visualization Tools in Decision-Making

The interviewee stated that before using visualization tools, he was forced to record all the data by hand, on paper, piece-by-piece, which was very time consuming. Nowadays I always use visualization tools for my decisionmaking tasks, he declared. (e believes that visualization tools have positive effects on his work in a variety of different ways. For example, it speeds up the decision-making process and makes it easier, as well as providing the possibility to search for specific purpose. As he reported, The only negative point is that we canâ&#x20AC;&#x2122;t say who entered the specific data in the system. 63

Empirical Findings & Analysis

In reply to the question about whether or not there are any circumstances in which he faces insufficient information when using visualization tools, he said, Yes, there are circumstances where we need to consider the cost of the product which is not provided for us through visualization tools. In general, he believes that visualization tools can improve his decision-making. The tools can visualize various parameters simultaneously, and thus provide the possibility to compare available data and subsequently produce convenient decisions in a timely manner. 5.1.3.2

Role of Intuition and Creativity in Decision Making

The interviewee stated that he uses intuition most of the times in his decision-making tasks. He defines intuition as prior experience in his field, and believes that experience plays an important role in his job. In regard to the question of how his intuitive decision-making has changed as a result of using visualization tools, the interviewee said, I always compare the history of records which is derived from visualization tool and make decisions based on the result of data comparison. If they are not available in the database I have to use my own experience and intuition to make a decision and hope that it will be an optimal one. He further explained, We usually trust the result of visualized data and the information which is provided by the tool, but this will only works if we entered the correct data from the beginning into the database. (3rd interviewee, 2014-05-09) One important issue that the interviewee identified is that regular use of visualization tools can decrease creativity in his job. He reported that there are specific guidelines and routines which he needs to follow every day, which makes it almost impossible to consistently involve human creativity in his role. To sum up this section, following table summarizes the opinion of interviewees regarding the use of visualization tools and intuition in their decision making process. Questions

1st

2nd

3rd

Interviewee

How often do you use visualization tools in your decision making process?

Always

How often do you use intuition in your decision making process?

Most of the times

Empirical Findings & Analysis

5.2 Analysis Our research aims to address problems associated with decision-making, particularly the problems related to engineering design. Therefore, our research method sought particularly to answer the following research question: How can intuition and creativity, along with visualization tools, be of a help in enterprise decision making? For this reason, we formulated the assumption (based on the findings from literature review) that using human intuition and creativity in a balance with visualization tools is effective and beneficial in enterprise decision making. In the end, the analysis are combined to either approve or reject our assumption. In this section, data collected through interviews has been analyzed based on the cross case analysis. Therefore, preliminary conclusions of literature reviews and the opinion of the interviewees are compared in an iterative process in order to find similarities and differences. For data analysis, first we study our empirical findings and later use literature review to either approve or reject our assumption. During this process, we attempt to better understand the empirical findings and add our own opinion and judgments. We compared the result of case study with preliminary conclusions from literature review, if we found a match between the effective use of visualization tools for intuitive decision making in real life context, then we conclude our theoretical findings are valid. To make sense of the data, we divided information gathered from interviews into two categories, based on our research question and the purpose of our research. (5.2.1 Role of Visualization Tools in Decision-Making and 5.2.2 Role of Intuition and Creativity in Decision-Making) 5.2.1

Role of Visualization Tools in Decision-Making

Based on the responses taken from the interviewees, it appears that the use of IT tools is inevitable in this modern age. Visualization tools are widely used in the field of engineering design; indeed, the interviewees always use these kinds of tools in their decision-making tasks. Moreover, the interviewees argued that visualization tools have positive effects on their work in several different ways. For example, they allow for faster, easier, and more accurate decision-making. When utilizing data visualization in order to make decisions in uncertain situations, the decision-maker is able to gain insight into the problem with the aid of tools that simultaneously consider multiple decision criteria. Visualization tools also provide the possibility to compare the available data and consequently produce a 65

Empirical Findings & Analysis

convenient decision in a timely manner, thereby facilitating intuitive decision-making. Similarly, Larkin and Simon (1987) report that decision makers in different domains such as physics and engineering make extensive use of visualization, with diagrams playing a role of particular importance. In addition, they suggest that diagrammatic representation in an informationprocessing system is beneficial. Marler and Jasbir (2004) describe the situation as such: in any task of design at least two conflicting objectives are involved, such as Cost vs. Quality. It is therefore a complicated task for the designer to find an optimal balance between the conflicting objectives. Data visualization, as depicted by Battiti and Brunato (2011), was introduced as a great method of data representation, involving computational efficiency that makes it a very valuable tool in dealing with decision-making problems. However, Shafer (2013 & 1987) and Horvitz (1988) argue that it is highly improbable that one could identify all alternative solutions and all potential consequences by relying solely on IT. They conclude that IT actually cannot be adequate, and that one should therefore carry out the law of probability (e.g. Bayesâ&#x20AC;&#x2122; theorem) to analyse the total uncertainties involved and to supplement the benefits derived from convenient IT tools. However, doing so clearly makes solving the task even more complicated, as it requires complicated mathematical modelling which is expensive to compute. We can clearly observe this in our empirical findings when one of the interviewees mentions, There are circumstances where we need to consider the cost of the product which is not provided for us through visualization tool, therefore we need to do the calculation by hand. According to Mosavi (2010a, 2010b, 2013a), over the past two decades the complexity of design has been continuously increased, due to the increasing quality of design criteria, yet the continuous advancements in analytical tools have not been found to be the permanent answer to most of the complex issues involved. We can thus conclude that the increasing complexity of decision-making in industry, which has been due primarily to factors of uncertainty, has resulted in designers tending to rely more on the power of their intuition. In this sense, the results of empirical findings have many features in common with the literature review. As a final point, it should be noted that the interviewees reported that they believe they make better decisions now, as compared to before the use of visualization tools, but it is not enough to make decisions based only on visualization tools. They stressed circumstances in which these tools cannot provide enough information for 66

Empirical Findings & Analysis

making real-life decisions. For example, the third interviewee states, there are circumstances where we need to consider the cost of the product which is not provided for us through visualization tools. 5.2.2

Role of Intuition and Creativity in Decision-Making

The empirical findings demonstrate that in order to solve decision-making problems, due to the complexity involved, the usage of intuition is inevitable. The interviewees believe that intuition plays an important role in their decision-making. Their responsibilities require them to refer to their experiences from different projects and also be creative in situations where they are facing a lack of information and there are not specific guidelines to follow. Accordingly, Simon (1991) describes that organizations facing complex situations often use intuition and simple alternatives to make decisions rather than a rational and analytical process, due to their inability to process and compute the expected utility of every alternative action. Business objectives are highly dynamic, changing over time, unclear, and subject to estimation errors and human learning processes. According to Battiti and Burnato (2011), this clarifies the importance of managerial gut feelings and intuition in quantitative and data-driven decision processes. Roy et al. (2008) also report that, despite of all advancement in computational design and mathematical optimization, trial-and-error and expert-based approaches to engineering design that are highly associated with intuition and creativity, still influence decision-making. This conclusion corroborates an observation of our empirical findings, in which one interviewee states, trial-and-error is a routine task in my field in order to come up with an optimal solution for developing a product. I believe in visualization models, but then manufacturing of a product should be also considered. One important issue raised by an interviewee was that the regular use of visualization tools can decrease creativity in his job. He believes that there are specific guidelines and routines which he needs to follow every day. Based on the literature review, Gigerenzer (2007 & 2011) clarifies, with sufficient experience that humans can learn to create intuitive decisions from his adaptive decision-making toolbox, e.g. visualization tools. This lesson is the result of practice which makes humans good at what they do. Practice, in fact, has the tendency to provide excessive confidence about doing tasks intuitively. In situations where there are regularities and minimum uncertainty, practice can work wonders and the intuitive decisions that are made in this manner can be highly reliable (Tversky & Kahneman, 1979). However, Kahneman (2011) states very strongly that 67

Empirical Findings & Analysis

intuition is the result of regularity, there is no magic involved, and that although it may have its benefits, intuition is not always the best solution. This fact would strongly justify the reasons for engineering designers to often rely on their intuition and hesitate to give away the power of decisionmaking to rational tools (Kahneman, 2011). To conclude, we can state that the results found in our empirical study are aligned with our findings from the literature review.

5.3 Concluding Remarks In the following section, the contribution of our research is provided, according to the cross case analysis of our empirical findings and also the concluding remarks from literature review. ď&#x201A;ˇ

ď&#x201A;ˇ

This report primarily tries to expand knowledge, insight and understanding on the subject of intuition, creativity and their potential applications in enterprise decision-making tasks where uncertainties constitute the major challenge. Furthermore, to answer our research question, it was found that it is optimal to benefit from the great potential of intuition while simultaneously minimizing its drawbacks. For this reason, we must establish a balance between intuition and visualization tools in solving decision-making problems. In this case, the aim is to better understand intuition as one of the main sources of creativity and insight in enterprises, rather than constantly ignoring it. To doing so creates knowledge on the mechanism of intuition, human creativity, and also considers practically a number of real-life decision-making problems that have been solved previously. In order to benefit from the use of intuition in decision-making in situations of uncertainty, it is essential to take into consideration the enemies of intuition, which are irregularity and the absence of proactive and emotional decision-making. In this sense, practicing and mastering the use of one of the conventional IT tools under conditions of regularity while dealing with similar cases would lead to better decisions. It is therefore concluded that the permanent solution to creative problemsolving is neither intuition nor rationality but a balance and combination of these two.

Empirical Findings & Analysis ď&#x201A;ˇ

ď&#x201A;ˇ

In respect to the findings presented above, our research results in a solution that is reliable for dealing effectively with decision-making tasks under circumstances of uncertainty. The proposed assumption includes the usage of visualization tools in dealing with decision making problems, where by intuition and creativity can be well nurtured and empowered. Therefore, based on our findings, the assumption that using human intuition and creativity in a balance with visualization tools is effective and beneficial in enterprise decision-making, is aligned with our empirical findings. To sum up, it is worth noting that visualization tools for decision-making were proposed as powerful IT tools for dealing with decision-making tasks. The multidimensional visualization graphs present a welldesigned human-computer interaction, which presents an ideal balance of intuition and rationality. In this case, a meaningful relation between vision and creativity was implemented, whereby information from the IT tool could be visually communicated with humans to inspire creative discoveries. In doing so, the intuitive role of the right brain in dealing with problems was found to be vital. In this sense, the interactive connection of data analysis tools and the intuitive part of human brain can provide outstanding performance in decision-making and problemsolving. Furthermore, the case study that was provided validated our claims and the theoretical works.

Conclusion and future research

6 Conclusion The purpose of this research is to answer the research question of this thesis through aligning the conclusions from state of the art literatures and results of conducted interviews with three professionals in the field of engineering design. In this research, we have discussed that todayâ&#x20AC;&#x2122;s enterprises due to globalization and more competition in the dynamic business market, face way more complicated decision-making tasks ever. In particular, application of engineering design and product development cannot be seen as an isolated process anymore. In fact, the task of decision-making, in the dynamic nature of the modern-dayâ&#x20AC;&#x2122;s industries and businesses at the presence of complex circumstances, uncertainties, limited time, and inadequate computational power is considered as complicated problem. We have discussed that rational tools and in particular, visualization tools due to todayâ&#x20AC;&#x2122;s dynamic market cannot be effective. Further as an alternative, using the human intuition and creativity have been proposed to be very effective and beneficial. In fact, decision-makers are encouraged to make a virtue of time constraints, information and knowledge by following the creative approaches by utilizing intuition. Consequently, this report aimed to answer the following research question. Research Question: How can intuition and creativity, along with visualization tools, be of a help in enterprise decision making? It was found that we should benefit from the great potential of the intuition while minimizing the drawbacks of it. For this reason, we have to set a balance between intuition and visualization tools in solving decision making problems. Further our research revealed that in order to effectively benefit from the potential of intuition the influence of human emotions e.g. fear of gain and lost, should be excluded from the process of decision-making. Moreover, regularity and practice on utilizing a data analysis tool box e.g. visualization tools, would enhance the intuitive decision-making. By considering the above finding we can well benefit from the intuitive decision-making and make confident decisions in an uncertain situations. Yet a deep understanding on the human-computer interaction in the particular realm of decision-making, intuition and human creativity is delivered.

Conclusion and future research

6.1 Future research Once the potential and drawbacks of intuition in creative decision-making are understood, enterprises will consequently be able to confidently govern their IT investments in such a way as to employ more suitable BI software. As was discussed, analytics software packages for decision-making, including BI, often lack the proper tools to communicate the true insight provided by data. Moreover, many data processing strategies are limited to mining historic data. Battiti and Brunato (2013) would describe it as such: Like driving a car by looking into the rear view mirror, itâ&#x20AC;&#x2122;s highly likely that youâ&#x20AC;&#x2122;re going to hit something. Yet todayâ&#x20AC;&#x2122;s enterprises are struggling to replace advanced technologies with those that do not only make sense of the current data, but that can also provide guidance for the future through capabilities to model and evaluate the what-if-scenarios . Consequently, the BI market is currently witnessing a shift from traditional analytics to predictive analytics. Predictive analytics has the potential to provide better insight about the problems and effect(s) of a number of possible decisions that can be evaluated, thereby empowering divergent thinking. Furthermore, clear insight about problems and all dimensions of the decision-making task can enhance the creation of heuristics and informed decisions, which are, in fact, optimized choices. Therefore, it would be a transmission from the data directly to the best improving plan, from actionable insight to actions! (Battiti & Brunato, 2013). One way of doing so would be to implement machine learning integrated with optimization. With the above description, the research on predictive analytics sounds very tempting for further developments as tools that can determine the probable future outcome of an event, the likelihood of a situation occurring, and a short cut to completing complicated optimization tasks. This has been the main reason why we have already focused our further research on the applications of predictive analytics in e.g., informed decisions (Mosavi & Vaezipour, 2013; Vaezipour et al., 2012a), health and life sciences (Vaezipour et al., 2013b; Vaezipour et. al, 2013c). Today visual analytics is a very fast-growing science and independent field of BI, in which analytical reasoning provides advanced data visualization tools facilitated by visual interactive interfaces. Although in this report we only touch upon the potential importance and convenience of using data visualization tools in a limited scope, the concept of visual analytics is so valuable as to be worth a separate research. 71

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Appendix

8 Appendix Interview Questions Respondent Background 1. What is your current position? 2. What is your tasks and responsibilities? 3. How long do you have experience in the field (engineering design)? (Before using visualization tools) 4. How long do you have experience in the field (engineering design)? (After using visualization tools) 5. For what kind of decision making do you rely on IT tools and what kind of information these tools provide for you? 6. How easy is for you to use IT tools in your decision making? Do you consider it as user friendly? Please comment your answer. 7. Is there any circumstances that you hesitate to give away your power of decision-making to IT tools? Please comment your answer. 8. What kind of problems/limitation are you facing in using IT tools? 9. What kind of tools are you using for Decision-making?

Role of Visualization Tool in Decision-Making 10. How did you make decisions in your field before using the Visualization tool? 11. How often do you use Visualization tools in your decision making process? Always

Most of the times

Frequently

Occasionally

Never

Please comment your answer. 12. How has your decision making changed as a result of using Visualization tools? (Positive/Negative). Please comment your answer. 13. Is there any circumstances that you are facing an insufficient information, provided by visualization tools? If yes how do you make your decisions in this kind of situations? 14. Do you believe Visualization tools can improve your decisions? (E.g. give insight to your existing data/ support you for making effective decisions in a timely manner?). Please comment your answer. 15. Do you believe you make better decisions now, compare to before Visualization tools used? Please comment your answer. 88

Appendix

Role of Intuition and Creativity in Decision Making 16. How often do you use intuition in your decision making process? Always

Most of the times

Frequently

Occasionally

Never

Please comment your answer. 17. How do you make decision based on your intuition? What is the role of intuition in your decision making process? 18. How has your intuitive decision making changed as a result of using visualization tool? Please comment your answer. 19. Is there any circumstances that you hesitate to give away you power of decision-making to your intuition/gut feeling/trial and error? How much do you trust visualization tool? Please comment your answer. 20. Is there any concluding remarks or clarification you would like to mention?