The potential of a Multi Criteria Decision Analysis (MCDA): does it improve the measurability and accuracy of the human capital evaluation in the venture capital decision process

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1 The potential of a Multi Criteria Decision Analysis

(MCDA): does it improve the measurability and

accuracy of the human capital evaluation in the

venture capital decision process

THESIS RESEARCH

Researcher: Stefan van Duin (S1014785), Master student Business Analysis and Modelling, Radboud University, Nijmegen

Supervisor: Vincent Marchau, Radboud University, Nijmegen Second examiner: Etiënne Rouwette, Radboud University, Nijmegen

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Abstract

The current state of literature has provided lots of insights on the evaluation of ventures in the decision process of potential investments by venture capitalist, yet the measurability and accuracy of human capital evaluations remain low and in-depth knowledge is lacking. Disappointed by this absence, this study seeks to gain an understanding of the measurability and the low accuracy of human capital evaluations, to offer insights on the improvement. The researcher does this via the development and application of a human capital

evaluation model, using the Simple Multi Attributed Rating technique. A technique which falls under the theory of Multi Criteria Decision Analysis, which has the potential to improve the measurability and accuracy of human capital evaluations. The result of the case study shows a small improvement on the accuracy of the human capital evaluation, through the use of the developed model, whereas it describes the

recommendations for further improvements. Additionally, the research concludes upon the improvements of the measurability of human capital evaluations compared to the current state of literature, using qualitative measures.

Keywords: Venture capital, Multi Criteria Decision Analysis, Human capital, Selection process, Accuracy of

human capital evaluation.

Acknowledgements

I would like to thank my supervisor Vincent Marchau for the guidance and support throughout the research and writing process. He steered me in the right direction whenever needed and helped to connect me to the right persons for the support of the theoretical parts of the research. Additionally, I would like to thank my supervisors Rogier de Groot and Karel Asselbergs at Startgreen Capital. They were of great help to be able to collect my data for the research and were always available to discuss relevant issues or to think along in the research approach. Apart from my supervisors, I would like to thank Startgreen Capital for the opportunity to perform the data collection of my research. Whereas I would like to especially thank its employees who were part of the research, by either attending or contributing to the questionnaire, the workshop, the case studies and/or the presentation of the results. Finally, I would like to thank Etiënne Rouwette, as the second examiner, for its help during the design of my workshop and his flexibility during the research. But also as the coordinator of the master Business Analysis & Modelling, as he was always open for additional opportunities within the master program, whereas he was there to support in the realization.

Stefan van Duin, September 2019

Disclaimer: This document is written by Stefan van Duin who declares to take full responsibility for the content

of this document. Everything written in this research is solely the findings and opinions of the author, it does not represent the public opinion of Startgreen capital nor its employees unless explicitly stated. The faculty of business administration and the supervisor for this study were solely responsible for the supervision during the study, not its contents.

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1. Introduction

This chapter gives an introduction to the broader perspective of the research, which explains the context of human capital evaluation by venture capitalist and the potential of a Multi Criteria Decision Analysis to improve the measurability and accuracy. Where after it guides the reader to the objective of the research, and how this research contributes to the current state of literature and practice. Concluding with the structure of the report. 1.1 Background

The world economy has entered the 21st century and changes rapidly, technical developments and an increasing amount of information offers unbelievable opportunities (Andersson & Napier, 2007). Young innovative firms play a key role in this modern economy because they are an important source of new jobs, radical innovations as well as productivity growth. Unfortunately, these firms often suffer from financial constraints and have difficulty to access money through traditional banks, which limit their growth and threaten their survival (Block et al, 2017). A wide literature of entrepreneurial finance addresses these problems and investigates how young innovative firms can access capital for financial growth, innovation, and internationalization.

The domain of entrepreneurial finance is characterized by the investments in firms that are typically small and young, which have little performance history and therefore consist of lots of uncertainty. In literature a distinction is made between different sources of capital for entrepreneurial finance; venture capital, angel investors, private equity, project finance, and crowdfunding. Venture capital is directed to investments in the early stages of a company, whereas managing partners invest on behalf of limited partners. An Angel investor is described as a high net worth individual who invests his/her money in the early stage of a company, mostly prior to venture capital (Denis, 2004). Private equity is comparable to venture capital, but its directed to later stage investments in companies which are more mature and where risks are lower. Project finance relates to the financing of projects of mature companies. Lastly, in crowdfunding the investment in the early stage of a company is financed by a large amount of individuals which invest a small amounts of capital. This research is directed to the most important source of funding for entrepreneurs, start-ups and fast-growing ventures within entrepreneurial finance, which is venture capital (Breuer & Pinkwart, 2018).

Due to the information technology and technical progress more and more start-ups are established, thence venture capitalists receive over thousands of requests for financing each year (Cumming et al, 2017). Wherewith the investment in a specific venture contributes as one of the main factors to the success of a venture capitalist. Therefore a significant amount of their time and effort goes to the selection and evaluation of investment opportunities (Kaplan & Stromberg, 2001). This resulted in frequent calls from literature for a theoretically grounded evaluation tool, to help venture capitalist select ventures successfully (Tyebjee & Bruno, 1984; Franke et al 2008; Kollman & Kuckertz 2009, Wallmeroth et al, 2017). A theoretically grounded evaluation tool can be defined as the “grand challenge” in the literature of venture capitalists (Colquitt & George, 2011). A recent literature study by Wallmeroth et al 2017 amplifies the request for additional research to the selection process of potential ventures. The research sheds light on the developments of venture capital research and concludes that the decision process of the venture capitalist is still under-researched and should receive more attention in future research.

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Venture capitalist select potential ventures going through a decision process, in which they evaluate the venture on certain investment criteria, to decide whether or not to invest in a venture. The evaluation of a venture is portioned in four categories, which is commonly agreed upon in literature (Franke et al, 2008; Smart, 2010). Section 2.1 goes into more detail about the categories, the decision process and current debates in the literature. This research focusses on one of the categories; the evaluation of the skills, abilities, and knowledge of the entrepreneur(s) and management team (human capital) of a venture, due to the following reasons: I) It is determined as one of the most important categories in the evaluation by venture capitalist (Kaplan et al, 2009), II) The current state of literature lacks in depth knowledge on the criteria within this category (Franke et al, 2008), III) 57% of venture capitalist fail to accurately evaluate the human capital of a venture prior to their investment (Smart, 2010). This signifies that venture capitalists are by a large extent surprised by the performance of the entrepreneur and the management compared to the evaluation before their investment. The causes of the low accuracy are discussed in section 2.2.

One of the fields in literature which approaches complex decisions is Multi Criteria Decision Analysis (MCDA), it makes use of techniques which support decision-makers to increase the decision quality (accuracy of evaluations) in complex environments. There are several reasons why this technique aligns with the problem of this research, whereas it has the potential to improve the measurability and accuracy of human capital evaluations. At first, this technique helps decision-makers to identify, structure, and formally assess important aspects of a decision (Mustajoki & Hamalainen, 2007), as venture capitalist find it difficult to evaluate human capital (Hsu, 2007; Kollman & Kuckertz 2009). Secondly, the information derived from identifying structuring and assessing the important parts of the evaluations contributes to the current lack of in-depth knowledge on human capital evaluations. And lastly, MCDA has the potential to overcome the causes of the low accuracies in human capital evaluations, which help to increase the measurability and accuracy. Section 2.3 further elaborates on the potential of MCDA and the specific technique which aligns the best with the research approach.

1.2 Research objective and question

The aim of this study is to contribute to a theoretically grounded evaluation tool for venture capitalist to select potential ventures, with particular focus to gain an understanding of the low accuracy of human capital evaluation in the decision process, with a view to offer insights on the improvement of the measurability and the accuracy of human capital evaluations. The following objectives have been identified to be of paramount importance in helping to achieve the aforementioned aim;

1. Identify and explore the causes for the low accuracy of human capital evaluations to improve upon the measurability and accuracy of human capital evaluations

2. Explore and evaluate the development and application of a Multi Criteria Decision Analysis to improve the measurability and accuracy of human capital evaluations

The following research question is derived from the objectives of this research;

To what extent does a Multi Criteria Decision Analysis improve the measurability and accuracy of the human capital evaluation in the venture capital decision process?

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This research question leads to two sub-questions, which form the structure of the theoretical background in chapter 2. The sub-questions are outlined below including a description of the information it seeks to explore and how they contribute to the empirical study of this research.

1. What is known about the venture capital decision process and its causes for the low accuracy of human capital evaluations?

This question explores the current state of literature and ongoing debates about the decision process of venture capitalists in the selection of potential ventures. It elaborates on the definitions of human capital, the measurability and accuracy of human capital evaluations, thereby it explores the causes for the low accuracy of human capital evaluations by venture capitalist. The discovered causes will be further explored in the study of this research, directed by the empirical sub-questions which are formed at the end of chapter two.

2. Which Multi Criteria Decision Analysis technique has the greatest potential and how does it contribute to improve the measurability and accuracy of human capital evaluation?

This question discusses the different MCDA techniques in literature and determines the technique which aligns best with the focus of the research through a set of criteria. Additional, it explores the approach of the technique to improve the measurability and accuracy of human capital evaluations. Like the results of the first sub-question, the chosen technique and its approach will be further explored in the study due to the empirical sub-questions.

1.3 Theoretical and practical relevance

The theoretical relevance of this study is twofold. Firstly the human capital of a venture plays a major role in venture capital evaluations, but knowledge of the evaluation remains on a fairly general level, accordingly this research contributes to more in depth knowledge on the evaluation of human capital. A substantial amount of research highlights the human capital of a venture as an important evaluation factor for venture capitalist (Kaplan et al, 2009; Gladstone 2003; Franke et al, 2008), other sources go a step beyond and state that the entrepreneur has a huge influence on the economic value and the success of the venture (Gustafsson & Snogren, 2017). Whereas Perez & Pablos 2003 highlight the increasing importance of a shift to intangible assets for ventures to retain a sustainable advantage in the current economy, which depends on “people embodied know-how”. The lack of in depth knowledge on human capital evaluation emerged as most prior studies researched the complete evaluation of ventures (Franke et al, 2008). Whereas results on complete evaluation of ventures are important to obtain an overall understanding. They are limited in depth of insights they offer on human capital evaluations. This research, therefore, contributes to the current literature by the development and application of a Multi Criteria Decision Analysis.

Secondly, a research to human capital evaluation techniques by Smart 2010 recommends that “future studies could contribute by focusing a microscope on an individual level”, to increase more in depth knowledge on the accuracy of human capital evaluations. Additionally, Smart 2010 recommends to “develop methods for human capital evaluation that achieve the most accurate evaluation possible while consuming the fewest resources possible”. This research will follow up on those recommendations, in which it focusses on the human capital evaluation of one particular venture capitalist, determined as a scope on an individual level. At which it seeks to

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develop and apply a MCDA. A technique which has the potential to improve the measurability and accuracy and is not earlier applied in such a context.

This study also contributes in a practical sense, as venture capitalist will be able to make more efficient and effective human capital evaluations. The use of the MCDA allows the venture capitalist to better understand and substantiate their human capital evaluations for potential ventures, which result in more accurate evaluations. 1.4 Thesis structure

This thesis dissertation is structured among five chapters, whereas the following chapter outlines the theoretical background of venture capital and literature related to theoretical sub-questions, guiding the reader through the current discussions in the literature that are relevant for this research. Chapter three represents the methodology of the research, which describes the research strategy aligned with the objectives of the study. Among others, it describes the data collection techniques including the data analysis procedure. Thereafter the results of the empirical research are analysed and compared to the literature in chapter four. And finally, the conclusions, limitations, and recommendations for further research are outlined in chapter five.

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2. The theoretical background

To acquire a better understanding of the research focus, this chapter starts with the explanation of the venture capital mechanism and guide you to the scope of the research (Denscombe, 2012). All literature to substantiate the research is cited within the text, to give the reader a proper guidance and to increase quality and incredibility of the research.

2.1 The venture capital mechanism and decision process

Venture capitalists are particularly successful at solving important problems in market economies, connecting entrepreneurs with investors. They identify themselves as active investors who take part in the strategic development of the venture as well as occasionally partaking in operations (Berger & Udell, 1998). Warne 1988, for instance, describes venture capitalists as a cross between capital providers and consultants. In which they especially focus on the earlier stage equity investments in ventures which are in the phase of launch, development or expansion of their business model. These investments are typical of durations of five or more years and are risky and uncertain, therefore venture capitalists often focus on a certain industry in which they become knowledgeable and understand the characteristics of the market. With the high-risk investments, venture capitalists aim to achieve a high return on investment (Wallmeroth et al, 2017).

Venture capitalists determine their decision to invest in a venture on an evaluation, which is developed through an interplay between the venture, the venture capitalist and the external environment (Kohn, 2017). Faced with high uncertainty and limited information in assessing ventures, venture capitalists rely on those characteristics of ventures that are observable. They asses the inherent value of these observable characteristics (information cues), and will likely take them for concluding unobservable characteristics of the venture (Hoenig & Henkel, 2015). Given the fact that venture backed ventures have a higher survival rate than non-venture capital-backed ventures, numerous academics started identifying the information criteria (cues) on which venture capitalist evaluate and select a potential investment, which has resulted in a wide variety of investment criteria (Tyebjee & Bruno, 1984; Macmillan et al, 1998; Mason & Stark, 2004). Literature commonly agrees that the investment criteria can be summated in four categories (Franke et al, 2008; Smart, 2010);

1. Product and/or service 2. Market characteristics 3. Financial characteristics

4. Entrepreneur(s) and management team

Regardless of categorization, it is important to point out that the importance of the categories and underpinned criteria are different for a venture capitalist (Gompers & Lerner, 2004). An on-going debate among venture capitalists concerns the importance of a venture product/service and management team to the company’s success. While venture capitalists try to invest in ventures which have both a strong business idea and entrepreneur and management team, venture capitalists apply different importance to the characteristics. Some venture capitalists believe that the company’s business and market are the most important characteristics of success while others believe the key characteristic is the company’s management (Kaplan et al, 2009). Gladstone 2003 takes the perspective of the entrepreneur and management team, quoting: “You can have a good idea and poor management and lose every time. You can have a poor idea and good management and win every time”.

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Franke et al 2009 confirms that good management is important but questions the quote above, since their results indicate that firms that go public rarely change or make a huge leap form their initial business idea. At the same time, firms commonly replace their initial management, suggesting that management is more likely to be replaced than the business idea. This research harmonizes with the first perspective and evaluates the perspective of Franke et al 2008, whereas the researcher understands that management teams in corporate companies are replaced but do not see such an easy replacement of the management in small ventures, with is determined as the focus of this research.

In practice, the relative importance of any given criteria is often specific for a venture capitalists (Franke et al, 2008), a recent research by Kohn 2017 complements this reasoning and concludes that the following characteristics are of influence for the evaluation by a venture capitalist; I) The type of venture capitalist, II) The reputation and added value of the venture capitalist, and III) The evaluation methodologies used by the venture capitalist. As a result of this reasoning and to increase the replicability of this research, the context of the venture capitalist at which this research is performed is outlined according to these criteria in section 3.2.

2.2 The causes of a low accuracy of human capital evaluation

Literature commonly agrees upon the fact that venture capitalists find it hard to evaluate the human capital of a venture (Hsu, 2007; Kollman & Kuckertz 2009). A research by smart 2010 complements this and concludes that the accuracy by venture capitalist is only 57%. According to literature by the researcher, the following reason occur as an explanation for the low accuracy of human capital evaluation: I) Signaling, II) The conceptualization of human capital, and III) Heuristics and biases by decision-makers. This chapter goes into more detail on these causes, after the description of the definition of the accuracy of human capital evaluation for this research. This definition of the accuracy of human capital evaluation, derives from earlier research by Smart 2010, in which different techniques are compared to the accuracy of human evaluation. An accurate human capital evaluation means that the predictions by the venture capitalist of the behaviours of the people in a venture before the investment match the actual performance of those behaviours (Smart, 2010). The assumption is that very accurate human capital evaluations would lead to no surprises by the performance of the management, that the management team of one of the team members were not removed for incompetence’s, and that the evaluation before the investments was identical to the post evaluation of the performance of human capital. For ‘very inaccurate’ human capital evaluations, the opposite is true. How the accuracy will be measured in this research is explained in section 3.3.4. The measurability of human capital evaluations is described as the quality to measure the human capital in the evaluation, whereas the term “improve” in the context of the research is defined as follows. To improve, the quality needs to “better”, which is explained as better educated, more transparent, more thoughtful decisions and easier to evaluate (Beim, 2004).

2.2.1 Signaling theory

When an entrepreneur is attracting investments for his venture he/she communicates and signals the skills, abilities, and knowledge of him/her and the team in the most positive form, even though he/she may not be aware of what a venture capitalist is searching for. Due to this, venture capitalist encounter information asymmetry problems in the evaluation of investment opportunities (Mason & Stark, 2004). This is better known as the economics-based signaling theory and will be explained according to the timeline in figure 1. The primary

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elements of the theory consist of four steps over time, including two primary actors, the signaler (the entrepreneur) and the receiver (the venture capitalist) (Connelly et al, 2011).

Figure 1: signaling timeline

In the context of this research, the entrepreneur is to a certain extent aware of the skills, abilities, and knowledge of him-/herself and the management team, which is t0. Subsequently, the entrepreneur determines which information he/she communicates to the venture capitalist to receive the funding in step t1. In step t2 the actor switches from signaler to receiver, which describes how the venture capitalist observes and interprets the information received from the entrepreneur. As explained in section 2.1, venture capitalists use indirect measures (information criteria) to evaluate the human capital of a venture, examples are a ‘complete management team’ and ‘relevant industry experience’ (Kohn, 2017). In the final step, t3, the venture capitalist provides feedback to the entrepreneur, which comes in the form of an investment or reasoning why the venture capitalist decided not to invest in the venture.

Reflecting on the accuracy of human capital evaluation, venture capitalists have an incomplete and/or indirect representation of the real performance of the human capital of the venture. Whereas signally theory tries to minimize the information asymmetry using techniques to obtain a complete as possible set of information. However, this research will only focus on the parts of signaling theory starting from the venture capitalists perspective (t2 in the timeline), due to a limited time frame for this study and its contribution to the objective of the study. This contributes to the first objective of this research, such that it focusses on the investment criteria venture capitalists use to evaluate potential ventures.

2.2.2 Conceptualization of human capital

As described in the previous section venture capitalists are looking for clues in the available information that offer insight on the quality of the entrepreneur(s) and the management team (Kohn, 2017). These qualities can be summarized under a brother term in literature, human capital. Which is generally defined as the skills, abilities, and knowledge that individuals acquire through investments in schooling and experience (Becker, 1964).

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The definition of human capital for this research is derived from the definition of Becker 1964 and adjusted to the purpose of this research, formulated as the skills, abilities and/or knowledge of the entrepreneur(s) and management team of a venture which from the behaviour valuable for the success of the venture.

For several decades entrepreneurship researchers have been interested in the relationship between human capital and the success of a venture. To date, the interest in human capital continues but there is disagreement about the relative importance of human capital in entrepreneurship research. While some authors argue that the relationship between human capital and entrepreneurial success is commonly overestimated (Baum & Saunders, 2004), most of the authors determine it as one of the core factors for the success (Kaplan et al, 2009; Gladstone 2003; Franke et al, 2008).

This research is performed under the perspective that human capital is one of the core factors of entrepreneurial success, based on the fact that Unger et al 2011 found the first significant relationship between human capital and success. But also the underlying thoughts that human capital rests on the foundation that knowledge is specific and not easily attained, which makes it an important source of innovation, strategy and economic growth for a venture (Gustafsson & Snogren, 2017). Examples of arguments in literature which supports this relation to success are; human capital increases the entrepreneurs capabilities of discovering and exploiting new business opportunities (Shane & Venkatraman, 2000), human capital helps the entrepreneur to acquire other utilitarian resources such as financial and physical capital (Brush et al, 2001), and it is a precursor for further learning and the accumulation of new knowledge and skills (Baum & Locke, 2004). Although the majority of literature declare a positive relationship between human capital variables and success, there remains to be an uncertainty over the magnitude of the relationship along with the context in which human capital is accurately assessed and relates to success. This research will, therefore, contribute to identify a complete set of investment criteria to asses human capital and evaluate the importance of contribution for the accuracy of human capital evaluation.

According to Unger et al 2011, the success is determined by the conceptualization of human capital in which he concludes that criteria related to the outcomes of human capital investments and/or are task-related give a better estimate for the success of a venture. This research will evaluate these findings by comparison with inclusion and exclusion of these particular criteria, according to the two categories as described below.

Human capital investments versus outcomes of human capital investments

Human capital investments include experiences such as education and work experience that may or may not lead to skills, abilities, and knowledge. The outcomes of human capital investments are results of the investments, which are defined as the acquired skills, abilities an knowledge (Becker, 1964). In literature, there are different views upon the relationship between these variables, whereas the main factor exemplifying whether or not investments in human capital turn into skills, abilities or knowledge depends on the characteristics of a person (Reuber & Fischer, 1994). Unger et al 2011 concludes that human capital investments can be perceived as indirect measures of human capital and the outcomes as direct measures of human capital which declare a higher successful relationship.

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Task relatedness of human capital

The degree of task relatedness of human capital devotes itself to whether or not the human capital investment or outcome is related to specific tasks, for example managing a group of people or managing a business (Becker, 1964). In the case of an entrepreneur, task-related to human capital is explained as; management/leadership skills, attention to detail and the ability to represent the business. On the other end non-task an example of a human capital item, general education. The task relatedness is perceived as high if human capital is related to processes of running a business and specific to the industry of the venture (Shane & Venkatraman, 2000). Unger et al 2011 concludes that human capital with high entrepreneurial task relatedness is better measurements than factors that are not entrepreneurial task-related.

2.2.3 Heuristics and biases

Current literature has detected a gap between “espoused” criteria and criteria “in-use” (Levie & Gimmon, 2008), espoused criteria are those criteria venture capitalist say they use to evaluate a venture and in-use criteria are the criteria they use in practice. This means venture capitalists use fewer criteria to evaluate human capital than they say they do, whereas shepherd 1999 has suggested that venture capitalists rely more on gut feeling rather than objective criteria. Complemented by Wu 2016, which states this is especially the case for the evaluation of the human capital of a venture.

According to research on the psychology behind decision making is determined that decision-makers who use their intuition to make decisions use a mental toolbox of available strategies that they adapt to the specific decision to be made (Kahneman & Tversky, 1982). Simon 1982 used the term bounded rationality to refer to the fact that the limitations of the human mind mean that people have to use ‘approximate methods’ to deal with most decision problems, as a result, they seek to identify satisfactory rather than optimal solutions. These approximate methods are often referred to as heuristics and can lead to efficient and effective decision making under uncertain and complex environmental conditions, especially when decisions need to be made quickly and deep thought is an unaffordable luxury (Goodwin & Wright, 2014). However there are positive effects of using heuristics in a certain situation, it also brings many negative effects. Biases cause decision-makers to process information incorrectly, which leads to inaccurate decisions and judgments. Decision making in highly uncertain environments limits decision-makers on information processing capabilities, high levels of emotion and time constraints, which lead to cognitive errors (Goodwin & Wright, 2014). Venture capitalist find themselves in a comparable complex environment (Moesel et al, 2001), which determines the relevance of studying heuristics and biases within this research.

It must be noted that the magnitude of biases differs in each situation and that even the consequences of bias likely depends on the decision task and the amount of experience with the tasks. In current literature there are several cognitive factors that may affect venture capital decision making, venture capitalists may perceive the same information differently based on their past beliefs, experience, and biasness. Thereby decision-makers may match proposals to past successful or failed investments, demonstrating an ‘availability bias’ which leads to overconfidence in evaluating new ventures and may lead to wrong investment decisions (Shepherd & Zacharakis, 2001). Several studies emphasize that more experienced decision-makers, make better decisions and find different investment criteria more important than less experienced decision makers (Shepherd et al, 2003; Franke et al, 2008). But even highly experienced venture capitalists may be susceptible to various forms of bias

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and error due to their intuitive drive and heuristic processing (Shepherd et al, 2003). Biases and cognitive illusions in intuitive judgment such as overconfidence, optimism, hindsight, and overreaction to random events could cause venture capitalists to disregard certain types of human capital and pay attention to other types that might have no value in the context (Shepherd & Zacharakis, 2001).

Research has demonstrated that judgments and decisions of decision-makers are subject to numerous biases (Montibeller & Winterfeldt, 2015). In current literature there is limited understanding of how to overcome these biases, to be able to make optimal decisions (Milkman, 2009). The following issues explain why it is important to overcome biases in the context of decision making for venture capitalist:

- Errors are costly, biases in judgments lead to wrong investments by venture capitalist. In the context of venture capitalist, wrong investments are equal to losses and written off investments. Therefore venture capitalist must focus on strategies that can lead to better decisions.

- Errors will get costlier, the knowledge-based economy we currently live in requires more decisions to be made due to the increasing amount of start-ups which are established. Having to select from a larger group of start-ups, it will be harder for venture capitalist to select the successful ventures, whereas strategies that can lead to better decisions become even more valuable.

- Academic insights await, currently the field primary offers descriptions of biases that decision-makers perceive, but there is still a shortcoming on techniques for improving decision making (Milkman, 2009). Testing of different techniques contributes to improving decision making, where this research will contribute to decision making in the context of venture capitalist.

Several studies suggest using decision models to overcome heuristics and biases and improve decision making (Zacharakis & Meyer, 2000). Goodwin & Wright 2014 explain that Multi Criteria Decision Analysis can improve decision quality, overcoming biases and heuristics. MCDA allows decision-makers to use time effectively and enable them to structure and clarify their thinking. It encourages decision-makers to explore trade-offs between important criteria and it clarifies and challenges perceptions of risk and uncertainty.

2.3 Multi Criteria Decision Analysis (MCDA) techniques

Multi Criteria Decision Analysis is designed to support decision-makers in complex environments, using multi criteria techniques to come to a recommended course of action. These methods help decision-maker to identify, structure, and formally assess important aspects of a problem (Mustajoki & Hamalainen, 2007). By requiring a commitment of time and effort, analysis encourages the decision-maker to think deeply about the problem enabling a rationale, which is explicit and defensible. As a result, the decision-maker can explain and justify why a particular option is favoured instead of grounding a decision on gut feeling. MCDA allows decision-maker to track-back why a certain decision was made and which parts received a certain evaluation.

In most cases, the information which is used in MCDA is determined in cooperation with the expert in the field of the decision context, the so-called decision-maker. Which means that multi criteria methods use the information decision-makers determines as important. Despite the fact that decision makers determines the importance and the information used in a multi criteria method, past research finds that the use of a MCDA often outperform experts that make the decisions without the use of the technique, due to the following reasons:

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- The consistency of the method

- Optimally weighting (averaging out error in) individual criteria

- Decision analysis techniques rest on important coherence assumptions

- Sensitivity analysis helps to determine whether conclusions are robust to different estimates of the components (Koehler & Harvey, 2004)

2.3.1 Comparison of MCDA techniques

MCDA has evolved to suit various types of applications, tons of techniques have been developed with small variations to suit specific purposes where each method has its pros and cons. Popular techniques that are often used are; the Multi-attributed Utility Theory (MAUT), the Analytical Hierarchy Process and Case-based Reasoning (Velasquez & Hester, 2013). To find the best technique for this research the top eleven techniques are weighted according to five criteria in table 1. The criteria are determined based on the analysis of Velasquez & Hester 2013 and the objectives of the research, each criteria is further described below.

1. Ease of use: As the research is performed in a limited time frame the technique should be easy to apply in the context of the research without a substantial amount of experience

2. Ability to weight intangible criteria: The context of the research requires to measure intangible criteria, therefore the technique should have the ability to weight intangible criteria

3. Required effort of the decision maker: As the time of decision makers is valuable and limited, the effort should be minimized

4. Calculation without additional software: the researcher does not have any budget to pursue software for this research, therefore it is not possible to use techniques which require the use of software 5. Easy to combine with other techniques: As every technique has its drawbacks, it increase the

performance of the technique when it allows to combine with other techniques Table 1: Comparison of MCDA techniques

Method 1. Ease of use (High) 2. Ability to weight intangible criteria (High) 3. Required effort of the decision maker (low) 4. Calculation without additional software (yes) 5. Easy to combine with other techniques (yes)

1. Multi-Attributed utility theory (MAUT) Low Medium High No Yes

2. Analytic Hierarchy Process (AHP) Medium High Medium Yes Yes

3. Case-based reasoning (CBR) Low Medium Low Yes No

4. Data Envelopment analysis (DEA) Low Medium Low No Yes

5. Fuzzy set theory Low Medium Medium No No

6. Simple Multi-attributed Rating

Technique (SMART) High Medium Medium Yes Yes 7. Goal Programming (GP) Low Low Low No Yes

8. ELECTRE Medium Low Low Yes No

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10. SIMPLE Additive Weighting (SAW) High Low Medium Yes Yes

11. Technique For Order Preferences By

Similarity to ideal Solutions (TOPSIS) High Low Medium Yes No

The review in table 1 is based on a set of research articles (Velasquez & Hester, 2013; Liu & Pedrycz, 2009; Figueira et al, 2005), which are interpreted and evaluated on the criteria by the researcher. Of these different techniques, the Simple Multi Attribute Rating Technique (SMART) aligns the best with the research approach, as it receives the best scores on the criteria. SMART receives the highest score on all the criteria except the “ability to measure intangible criteria” and the “required effort of the decision maker”. As SMART allows to combine with other techniques, it can use the AHP technique to measure the intangible criteria, which compensates for the lower score on the “ability to measure intangible criteria” (Rahim, 2016). The required effort of decision maker for the SMART is higher when compared to other techniques, as it requires more input from the decision maker’s. For the comparison of the techniques this is seen as a disadvantage, on the other hand, it can be seen as an advantage for the validity of the research, since the accuracy of the decision maker’s preferences increases due to the extra effort to acquire a realistic representation.

2.3.2 Simple Multi Attributed Rating Technique (SMART)

SMART is a decision support system that intends to assist the decision-maker in the decision-making process, it allows to structure a decision process in components that interact with each other through different weights, and makes it possible to unite them to one measurement (Rahim, 2016).

Given that the SMART is determined as the best technique for this research, it will be used as a framework to develop a human capital evaluation model for the decision process of selecting potential ventures for venture capitalists. This technique provides a specific order of steps to develop a model, outlined in the first column of table 2 (Goodwin & Wright, 2014). The standard order of steps is presented in table 2, followed by a description of how these steps will be followed up and are adjusted for this research. This technique makes use of attributes to split up a decision in smaller pieces, in the context of this research they are defined as “criteria” to overcome confusion.

Table 2: Research steps of the SMART framework

Steps according to SMART Empirical steps in this research

1. Identify the decision maker

Defined in the scope and context of the research 2. Identify the alternative courses of action

3. Identify the attributes which are relevant to the decision makers

1. Operationalization of criteria to evaluate human capital

4. Assign values to measure the attributes 2. Assign values to measure the human capital criteria 5. Determine weights for each attribute 3. Determine the weights for the human capital criteria 6. Calculate the score for the alternative(s)

4. Determine the accuracy of the human capital evaluation

7. Make a decision

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Based on table 2 can be observed that the first two steps of SMART are not part of the empirical research in the study. Having said that it does not mean these steps are not taken into account, since these steps are quite small they are described in the scope and context of this research. Another noteworthy point is that steps 6, 7 and 8 will be combined in empirical research step 4. Lastly, in step five of the SMART steps and step three in the empirical research the weights of the criteria will be determined, as mentioned 2.3.1 the SMART is not so good in weighting intangible criteria. However a strength of SMART is that it allows to combine other techniques, therefore this step is performed using the Analytical Hierarchical Process (AHP).

2.3.3 Analytical Hierarchical process (AHP)

The main advantage of the AHP technique is that allows decision-makers to easily compare the importance of intangible criteria, which is described as criteria which are hard to measure quantitatively (Saaty, 2008). This harmonizes impeccably within the context of this research, considering that the research attempts to determine the relative importance of human capital criteria, which exemplify intangibility. The AHP technique employs this using a pairwise comparison with a verbal scale, wherein the decision-maker displays the importance of a variable against the other.

Equally important (1)

Marginally more important (3) Strongly more important (5) Very strongly more important (7) Extremely more important (9)

The scale simplifies the decision maker’s judgmental task, as far that the criteria do not need to be quantified and only two variables are compared at the time (Goodwin & Wright, 2014). As soon as all the criteria are compared, the researcher can convert the scales and quantify the result to the relative importance of the criteria in weights.

The drawbacks of the AHP technique are the number of comparisons and the 1-to-9-scale. The number of pairwise comparison is determined by the number of criteria, and can be calculated by;

n(n-1) 2

As a result, the number of comparison increase significantly with additional criteria, which requires extra work for the decision-maker. Regarding the scale, decision-makers may find it difficult to distinguish whether an alternative is for example 5 or 7 times more important. Besides the scale does not make it possible to assign a value of an attribute to be 20 times more important than the other, due to the 1-to-9-scale.

2.4 Emerging issues and the need for empirical research

Through the exploration of the theoretical sub-questions in the sections of the theoretical background, several literature gaps appear. These literature gaps are formed into three empirical sub-questions, which are outlined below including a summary of the related theoretical background. The empirical sub-questions function as the basis for the empirical research of this study.

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3. How does the development of a Simple Multi Attributed Rating Technique improve the measurability of human capital?

As explained in the theoretical background, the Simple Multi Attributed Rating Technique aligns the best with this research approach. The SMART will be used to develop a human capital model, with the purpose to increase the measurability of human capital evaluations and thereby contribute to the lack of in-depth knowledge on human capital evaluations (Franke et al, 2008).

4. To what extent do the causes of low accuracies influence the accuracy of human capital evaluations? As described in the theoretical background there are three causes which influence the accuracy of human capital evaluations, which are the signalling theory, the conceptualization and biases and heuristics. Within these broader causes there are three factors of which current literature suggests it has an influence on the accuracy of the evaluation, which are: I) The conceptualization, II) The espoused theory and III) The experience of the investors, which will be further explored in the empirical study.

Conceptualization

According to Unger et al 2011, the success of a venture is determined by the conceptualization of human capital in which he concludes that criteria related to the outcomes of human capital investments and/or are task-related give a better estimate for the success of a venture. This research will evaluate these findings by comparing the results with inclusion and exclusion of these particular criteria to the accuracy of human capital evaluation.

Espoused theory

Current literature has detected a gap between “espoused” criteria and criteria “in-use” (Levie & Gimmon, 2008), espoused criteria are those criteria venture capitalist say they use to evaluate a venture and in-use criteria are the criteria they use in practice. Because this research will focus on a specific venture capitalist, it allows exploring the relation between espoused and in-use criteria and the accuracy of human capital evaluations in more depth.

The experience of investors

Several studies emphasize that more experienced investors, make better decisions and find different investment criteria more important than less experienced investors (Shepherd et al, 2003; Franke et al, 2008). This research will explore and compare the difference between the weights attached by experienced investors and compare it to the accuracy of human capital evaluation.

5. What is the influence of the application of a Simple Multi Attributed Rating Technique on the accuracy of human capital evaluations?

Given the foregoing reasoning in the theoretical background, this research suggests that a human capital evaluation with the use of a SMART (one based upon the information that a specific venture capitalist deems most important in the decision process) is more accurate than without. This because a SMART allows experts to determines the criteria which are of importance according to the signalling theory, conceptualize these to measure the human capital, and to optimally weight the criteria in a consistent way to overcome biases and heuristics.

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3. Methodology

This chapter illustrates the research design and its substantiation, with the purpose to enrich the current state of literature and achieve the objectives of this research. The research design outlines the empirical research which is performed to create insights and knowledge on the empirical sub-questions, as explained in section 2.4. The design of the research consist of a research strategy, followed by a description for the context of the research, the data collection techniques, and its data analysis procedure, thereafter the acceptability and trust of the research are outlined in a section about the reliability and validity and lastly, the research ethics are described.

3.1 Research strategy

The overall strategy of this research is an exploratory case study, in which the measurability and the accuracy of human capital evaluation will be explored by the development, application, and evaluation of a venture capitalist specific Multi Criteria Decision Analysis, henceforth be known as “the human capital model”.

A case study is used in many situations wherein it tries to illuminate a decision or a set of decisions in a real-world context, and acquire in-depth knowledge about the how, why and the results of the decisions (Yin, 2013). This aligns with this research since it seeks to explore in-depth knowledge about the measurability and accuracy of human capital evaluation in a specific venture capitalist, which represent the real-world context and the results on which the study will evaluate. Whereas the “why” of this study is explained in section 1.3 and the “set of decisions” is represented by the application of a MCDA in the process to evaluate potential investments. As last, the “how” is explained in section 3.3, through the different techniques which will be used to develop and evaluate the human capital model.

As described in section 2.3.1, the Simple Multi Attributed Rating Technique (SMART) aligns the best with the purpose of this research. Therefore the SMART is determined as the framework of the research design, which directs the research through the steps as outlined in table 3. The majority of the steps require their own approach, therefore each step has a specific data collection technique, outlined in table 3.

Table 3: Research steps including techniques

Steps in this research Techniques used in step

1. Operationalization of criteria to valuate human

capital Systematic review

2. Assign values to measure the human capital

criteria Facilitated modelling – direct rating technique

3. Determine the weights for the human capital criteria

Analytical hierarchical process (AHP) - Questionnaire

4. Determine the accuracy of the human capital

evaluation Evaluating the model – case studies

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3.2 Context of the research

This research is performed in cooperation with a Dutch venture capitalist, named Startgreen Capital. Startgreen capital fits the purpose of this research because it has experience in venture capital funding since 2006. Thereby it is recently titled with a second place for the most active venture capital investors in the Dutch start-up environment (Hodgsen, 2019), which indicates that it is still highly active in venture capital funding in the Netherlands.

As concluded by Kohn 2019, there are three characteristics of a venture capitalist that influence the evaluation of potential ventures: I) The type of venture capitalist, II) The reputation and added value, and III) The evaluation methodologies. These characteristics are shortly described below for Startgreen capital, to increase the reliability and replicability of the research.

The mission of Startgreen Capital is to contribute to an economy that does not exhaust itself and where everybody can participate to its fullest, supporting entrepreneurs who contribute to sustainable value creation for people, the environment and society. With this in mind it focusses on the following themes; the energy transition, circular economy, inclusive society, healthcare innovation, nature and environmental conservation, fair economy, sharing economy and sustainable food and agriculture. These focus areas are spread over five funds and count up to a current of 300 million euro under management, the spread of investments and loans is displayed in figure 2. The five funds all have a different area of focus, varying between seed capital to later-stage capital in the start-up financing cycle, the position of each fund is presented in figure 3 (Cotei & Farhat, 2017).

Figure 2: Startgreen's capital under management Figure 3: Startgreen's funds positioned in the start-up financing cycle

Startgreen capital works with a team of 22 professionals, whereas 18 of them are actively involved in the evaluation of potential ventures. As these 18 professionals take part in the evaluations, they are defined as the decision-makers in the context of this research. Since the development of a MCDA is created by judgment and information from the decision-makers in the context wherein the model is developed (Goodwin & Wright, 2014), the 18 professionals are asked to contribute in the empirical research in this study, and further described as the

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experts. The courses of action in this research is described as the potential ventures which are evaluated on whether or not they will receive an investment.

Startgreen capital follows a standardized procedure for the evaluation of potential ventures. At first a venture has to pass a quick scan, wherein Startgreen capital determines if the venture aligns with the focus of the fund, if the business plan, management and the market of the venture have the potential to pursue an investment and if there are mitigating actions for the risks/dislikes of the venture. When the venture has passes the quick scan, the experts go through a process in which they write an investment proposal. This investment proposal goes in depth about the product, the market, the human capital and the financials of the venture. As Kohn 2019 especially mentions the financial evaluation to be of influence for the evaluation by a venture capitalist, the methodologies are outlined. Startgreen Capital makes use of multiple evaluation methodologies for the evaluation, although there are two standard methods. Which are the venture capital method and the DCF (Discounted Cash Flow) method in combination with an objectifiable discount rate in line with the WACC (Weighted Average Cost of Capital). Whenever an investment proposal is determined as complete, the proposal is internally reviewed by an investment committee which often consists of more experienced investors, whereas they have a fresh perspective on the deal. When the corrections derived from the investment committee are processed, the proposal is presented to an external Advisory Committee, under the so-called name ‘pre-advice’. The Advisory Committee consists of professionals who are experienced in investing and are knowledgeable of the focus area of the fund. In a special set up session, the advisory committee and the experts go through the investment proposal and determine if it is further pursued (under certain circumstance) or that the process is stopped. Whenever the advisory committee is positive, the experts follow up on the potential investment by performing due diligence and additional requests from the advisory committee. Due diligence is described as, a detailed examination of a venture and its financial records, completed before becoming involved in the ventures business. After completing these steps it is brought back to the advisory committee for the final decision to invest in the venture or not.

3.3 Data collection techniques and research sample

As described in section 3.1, this research uses different techniques, following the SMART framework, to collect the empirical data and achieve the objective of the research. The following paragraph explains the contribution of each technique in the specific research step, including the reason why this technique is chosen and how it contributes to the overall objective of the research.

3.3.1 Systematic review – Identifying the criteria

The first step in this research is to identify the criteria which are of importance for the experts in the context of the research, namely the criteria to evaluate the human capital of a potential venture. This is carried out in a systematic review, because numerous studies have already explored the criteria which venture capitalist find important to evaluate a potential venture (Tyebjee & Bruno, 1984; Macmillan et al, 1998; Mason & Stark, 2004), however there is no study which determines a complete list of human capital criteria. A systematic review thereby allows to identify a reliable and complete list of criteria, based on a broad set of literature. This research step contributes to the development of the Multi Criteria Decision Analysis model, as outlined in the second objective of this study.

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Several names are used to describe the process of systematically reviewing and integrating research data, this research uses the term “systematic review”. Which is explained as, ‘a review that has been prepared using a systematic approach to minimalize biases and random errors which are documented in a materials and methods section’ (Egger et al, 2001). Further elaboration on how the systematic review will be performed is outlined in section 3.4.1.

3.3.2 Facilitated modelling – Assign values to measure the criteria

The second step in the research is to determine the values, which the experts of Startgreen Capital declare to be of importance to measure the criteria. As stated before, current research to venture capital has not provided in-depth knowledge of human capital criteria. In particular, the knowledge on the measurements of the criteria is limited, hence this research contributes to provide more in-depth knowledge.

To come to these measures the researcher will use the direct rating technique in an intervention with the 18 experts of Startgreen capital. Within SMART, there are traditionally two methods of eliciting value functions (measurements), the bisection method, and direct rating technique. As the criteria in the context of this research are qualitative, the direct rating technique will be used, as this the one method that has the possibility to measure qualitative criteria (O’Brien & Dyson, 2007). The intervention will be carried out using a facilitated modelling approach, as this allows the researcher to guide the experts in the development of the measures, which helps to create the most accurate representation of the values of the experts and thereby improve the validity of the research. Figure 4 gives a graphical illustration of the components of a facilitated modelling session.

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The model (outcome) will, in this case, be represented by the values the experts believe are important to evaluate the human capital criteria (Franco & Montibeller, 2010). The researcher will have the role of facilitator and analyst during the intervention and the OR (Operational Research) methodology is represented by the direct rating technique.

3.3.3 Questionnaire – Determine weights for each criterium

The third step in this research is to determine the weights of the criteria using the Analytical Hierarchy Process (AHP), which represents the relative importance of the criteria in the overall evaluation of human capital. Since the importance of the criteria are company-specific (Franke et al, 2008; Kohn 2017), the weights of the criteria are determined by the 18 experts of Startgreen capital. The AHP technique will be performed through a questionnaire because this will require the least amount of time of the experts.

The AHP technique will be used because of the following reasons. Firstly, it is the best MCDA method to assign weights for intangible criteria according to section 2.3.1, as it is designed to make pairwise comparisons on a verbal level. Secondly, the AHP technique can easily be combined with the SMART framework. Thirdly, it allows to compare the relative importance for different groups of people, which contributes to evaluate the influence of experienced investors on the accuracy of human capital evaluations in sub-question four. And lastly, it gives the opportunity to compare the consensus among experts on the importance of the criteria, which provides new in-depth knowledge on how the experts agree upon the importance of human capital criteria. 3.3.4 Case studies – Evaluate the human capital model

The first three steps in this empirical research will establish the foundation for the human capital model to evaluate potential ventures. As follows, this research step will evaluate the human capital model using two forms of case studies. The results of the case studies help to reflect upon the last two empirical sub-questions, as formed in section 2.4.

The recent investment case

The first form of case study is the recent investment case, wherein the human capital model will be applied to three recent investment cases by Startgreen Capital. The purpose of this case study is to evaluate the applicability of the model and acquire information about the measurability of human capital using the model.

The recent investment cases will be selected on the following requirements: I) The investment in the venture should not be more than three months ago, II) At least one of the experts who evaluated the venture should be available in the time frame of the research. The selection will be performed in cooperation with two experts of Startgreen capital, as they have more knowledge about the cases and can thereby help to find the right cases for this research step.

The researcher chose to perform three of these case studies because this would give enough information to evaluate the applicability of the human capital model and it would fit according to the time frame of the research. The individual case studies will be performed by one expert, as this gives enough indication for the applicability and measurability of the model.

The historical investment case

The second form of case study is a historical investment case. This form of case study helps to acquire in-depth explanations of the causal effect of one or more independent values on the dependent value (George & Bennett,

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2005). In the context of this research, it allows to directly compare the results on the accuracy of human capital evaluation, for the human capital model and the causes for the low accuracy of human capital evaluations. To the knowledge of the researcher, this is not possible with other methods like experiments or surveys, due to the fact that the accuracy of human capital evaluation can only be determined by a comparison between an actual evaluation and the real performance of the human capital.

The historical investment case(s), will be selected with the help of two experts, on the following requirements: (I) The documented information about the human capital of the ventures prior to the investment is sufficient, to be able to apply the model (II) The investment in the venture is at least one year ago, to be able to determine the real performance of the entrepreneur and management team, (III) At least two of the experts who were involved in the evaluation of the potential venture are still working for Startgreen capital, to easily retrieve information.

The historical case study will consist of two subparts, which will be performed by at least two experts, which helps to increase the reliability of the results. The number of historical cases will be determined among the time schedule and available information on prior investments by Startgreen capital.

3.4 Data analysis procedure

This section describes how the data will be collected and analysed using different data collection techniques. To contribute to the reliability of the study, it will be described in such a way that the research could be repeated using this procedure as a guideline.

3.4.1 Literature review – Identifying the criteria

The systematic review will be performed using google scholar as a search engine. To guarantee the actuality and reliability of the review, papers that are either; published before 2000 or had less than five citations will be excluded from the search. Additionally, to only retrieve relevant research papers, the terminology in venture capital research will be used in the search. Specified to the focus of the research, the first filter will be set on the articles which specifically mention “criteria” in the title of the article. Considering the fact that the term “criteria” is used in all forms of research, the search will be delimited by the inclusion of one of the following words; “evaluation”, “investment”, “decision”, “screening” or “selection”. Furthermore, researchers use different forms to describe venture capitalist, which will be dissolved by a search on “venture” in the title of the article including one of the following combinations; “capitalist”, “capitalists”, “capital”, “capitalism”.

The researcher expects to retrieve a manageable number of studies of the aforementioned search criteria, which will be carefully analysed by the researcher. During the analysis, the researcher will apply the last filter to exclude the grey literature and end up with a remainder of papers which include human capital criteria. Grey literature covers working papers, institutional documents and conference papers (Biggam, 2011).

After the systematic review, all the relevant articles will be analysed using a qualitative meta-synthesis, to conclude on the most relevant criteria to evaluate human capital. To contribute to the reliability of the synthesis, only those criteria which are at least mentioned by two of the final research papers will be included in the total list of criteria. A qualitative meta-synthesis is described as a critical evaluation of the evidence in the selected research papers, to combine the data to retrieve one outcome (Biggam, 2011). One of the limitations of this

The potential of a Multi Criteria Decision Analysis (MCDA): does it improve the measurability and accuracy of the human capital evaluation in the venture capital decision process

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