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Decision-making process of start-ups during their incubator choice in Switzerland

Master's Thesis 2015 185 Pages

Business economics - Company formation, Business Plans

Excerpt

Content

List of illustrations

List of tables

List of appendices

1 Introduction
1.1 Research aim
1.2 Structure
1.3 Scope
1.4 Benefits to Management

2 Literature review
2.1 Introduction
2.2 Incubation
2.2.1 Incubation in Switzerland
2.2.2 Incubation in Zurich
2.3 Start-ups
2.3.1 Start-ups in Switzerland
2.4 Conclusion
2.5 Decision-making
2.5.1 Introduction
2.5.2 Judgments
2.5.3 Decisions
2.5.3.1 Normative theory
2.5.3.2 Descriptive theory
2.5.3.3 Neoclassical theory
2.5.4 Decision-making process
2.5.5 Decision-making in organisations
2.5.6 Decision-making by entrepreneurs
2.6 Research gap

3 Research methodology
3.1 Introduction
3.1.1 Research purpose
3.1.2 Research philosophy
3.1.3 Research approach
3.1.4 Research strategy
3.1.5 Choices
3.1.6 Time horizons
3.1.7 Data collection
3.1.7.1 Construction of research sample
3.1.8 Data analysis
3.2 Limitations
3.3 Conclusion

4 Results and analysis of data collection
4.1 Introduction
4.2 Overview of results
4.3 Presentation of results
4.3.1 Knowledge about incubators
4.3.2 Feeling about incubators
4.3.3 Time of action
4.3.4 Trigger for action
4.3.5 Information channels
4.3.6 Selection process
4.3.7 Criteria of choice
4.3.8 Time horizon of application process
4.3.9 Satisfaction with chosen incubator
4.3.10 Use of coaching
4.3.11 Incubation Improvements
4.3.12 Suggestions to start-ups
4.4 Conclusion

5 Discussion
5.1 Introduction
5.2 Implications for literature review
5.2.1 Success criteria of incubators
5.2.2 Needs of start-ups
5.2.3 Decision-making
5.2.3.1 Organisational decision-making
5.2.3.2 Entrepreneurial decision-making
5.3 Interpretation of results
5.3.1 Knowledge and feeling about incubators
5.3.2 Time of action
5.3.3 Trigger for action
5.3.4 Information channels
5.3.5 Selection process
5.3.6 Criteria of choice
5.3.7 Time horizon of application process
5.3.8 Satisfaction with chosen incubator
5.3.9 Use of coaching
5.3.10 Incubation Improvements
5.3.11 Suggestions to start-ups
5.3.12 Conclusion
5.4 Introduction of framework
5.5 Limitations
5.6 Further research

6 Conclusion

7 Reflection

8 Reference list

9 Appendices

Abstract

This following report investigates the decision-making process of Swiss start-ups during their selection of an incubator in Zurich. It explores the ways start-ups decide on an incubator, which allows introducing a model for this matter. Thus, this study is motivated by the following research question: ‘What is the decision-making process of start-ups when selecting an incubator in Switzerland’? Previous literature failed to address the combination of incubation, start-ups and decision-making. Existing literature has focused predominantly on incubators’ performance, success factors of start-ups and decision-making reasons for creating a new venture. In other words, the gap in the literature is that primary research regarding incubator selection by start-ups has not been conducted. Hence, the aim of this research is to fill this gap by identifying the trigger for a start-up’s incubator search, the information channels it uses and the criteria for decision-making, which makes it possible to develop a framework for future start-ups, and existing as well as future incubators. The outcomes were based on semi-structured interviews with 10 start-ups in Zurich. Results showed that the trigger for incubator search is infrastructure. Furthermore, entrepreneurs mostly use search engines such as Google or obtain information through close social circles. Criteria for decision-making for experienced entrepreneurs are tangible services such as infrastructure and location whereas for inexperienced entrepreneurs, intangible services such as networking and coaching. The findings allow introducing a model that is useful for both existing and future incubators with suggestions to service offerings and future start-ups, providing suggestions about the decision-making process.

Acknowledgments

I would like to thank my supervisor Noreen O’Shea for her useful advice on major parts of this research. Furthermore, a ‘thank you’ to the 10 founders of start-ups that took part in the interviews and made this research possible. Lastly, my special thanks to Beway Bakir who had a major stimulus on my work. He was always there for me when I had difficulties in proceeding further. Thank you for your support throughout and the motivation to keep going. I love you.

“Failure will never overtake me

if my determination to succeed is strong enough.”

List of illustrations

Illustration 1: Incubation levels in Switzerland

Illustration 2: Overview of incubators in Zurich

Illustration 3: The lens model by Egon Brunswick

Illustration 4: General decision-making process

Illustration 5: Strategic decision process

Illustration 6: Conditions favouring different decision-making processes

Illustration 7: Levels of analysis in the decision-making process

Illustration 8: The research onion

Illustration 9: Research choices

Illustration 10: Overview over methodology

Illustration 11: Framework about decision-making process of start-ups

List of tables

Table 1: Company structure in Switzerland in

Table 2: Start-ups creation in Switzerland

Table 3: Number of start-ups considering region in

Table 4: Start-ups survival in Switzerland in

Table 5: Gender in Start-ups in Switzerland

Table 6: Start-ups in tertiary sector

Table 7: The four research philosophies

Table 8: Types of interviews compared to the research purposes

Table 9: Sample description

Table 10: Overview of results

List of appendices

Appendix 1: Thesis proposal

Appendix 2: Interview guide for the researcher

Appendix 3: Interview key words for the interviewees

Appendix 4: Interview Transcriptions

Appendix 5: Codes and allocated findings

1 Introduction

According to the Cambridge University Press (2014) a start-up is defined as “a small business that has just been started”. Mostly they lack different resources – funding, networks and knowledge amongst others – that are essential in order to grow and become competitive in the chosen industry (Aernoudt, 2002; Carter & Jones-Evans, 2012; Hansen, Chesbrough, & Nohria, 2000). Innovation start-ups are especially in need of funding. Kaufmann and Tödtling (2002) found that such start-ups not only require strong funding but also support in the commercialisation of such innovations. Furthermore, Chen (2009, p. 93) stated that technology start-ups face more issues than other start-ups gaining “adequate knowledge of their environments, new product development experience as well as financial resources”.

This need has made incubators an important tool for growing start-ups (Aernoudt, 2002; Becker & Gassmann, 2006; Bergek & Norrman, 2008; Charry, Pérez, & Barahona, 2013; Chen, 2001; Grimaldi & Grandi, 2005; Hackett & Dilts, 2004; Phan, Siegelb, & Wright, 2005; Thierstein & Wilhelm, 2001). In the 1980s incubators emerged in order to support and coach start-ups in their first steps into the business world (Tamasy, 2007). Incubators are defined as “organizations designed to accelerate the growth and success of entrepreneurial companies through an array of business support resources and services [...]” (Entrepreneur, 2015).

Switzerland is among the most innovative countries worldwide (KOF, 2013; OECD, 2013). GEM (2013) positioned Switzerland in 2012 as n°7 in the Global Entrepreneurship and Development Index (GEDI) meaning that Switzerland is one of the top countries in entrepreneurship. This demonstrates that Switzerland has a significant number of activities in forming start-ups. However, as start-ups may not be able to develop themselves as mentioned above, they rely on support. Incubators provide stimulation of entrepreneurship (Grimaldi & Grandi, 2005) and thus are able to support start-ups. Therefore, it seems that start-ups as well as incubators have an important economic responsibility in Switzerland. Research on the activities carried out by start-ups, along with an exploration of their decision-making process should uncover the necessary information and knowledge for incubators to improve their services and positioning which will enhance innovation growth and in turn, the Swiss economy.

1.1 Research aim

The above-mentioned discussion about entrepreneurship in Switzerland and the relevance of incubators form the basis of the aim of this research. Thus, its purpose is to identify the decision-making process of start-ups that leads to an incubator choice. More detailed objectives are the following:

1. Identify the trigger for start-ups to decide to look for an incubator
2. Identify how start-ups inform themselves about incubators
3. Identify the criteria that led to the decision to choose a particular incubator
4. Analyse the decision-making process and give useful interpretations for management
5. Develop a framework that argues what new start-ups should pay attention to and what existing as well as new incubators require to meet the needs of start-ups.

The information about start-ups’ initial activities and plans will give institutions such as incubators and the canton and city of Zurich a better understanding of what the needs of start-ups are and how they can improve their offerings and services. In particular it will provide information on how to make certain services more attractive for start-ups in order to be able to address start-ups in incubation centres and support them appropriately. This will then strengthen the marketplace in Switzerland and make it even more competitive – start-ups may grow into big companies or get sold to a big company and thereby generate new jobs – as well as innovative in the future if institutions are able to adapt their offerings to start-ups’ needs accordingly (Aerts, Matthyssens, & Vandenbempt, 2007; Rogova, 2014).

1.2 Structure

This research consists of four major chapters. Firstly, the literature review discusses relevant existing literature, in the process identifying a research gap. This gap allows for the conducting of fieldwork to identify a novel relevant matter. Methodology is the subsequent chapter discussing how this was conducted, which is followed by a presentation of results of the data collection and analysis, under the heading of ‘Discussion’. A conclusion and personal reflection complete this research.

1.3 Scope

The scope of this research is Zurich, Switzerland, as start-ups of that region were interviewed. Nevertheless, the findings may still be relevant to other cities or countries. Furthermore, the time horizon of this research was three and a half months and limited to around 20,000 words.

1.4 Benefits to Management

The benefit of this research is especially aimed at management. More precisely, general institutions such as the canton and city of Zurich, existing and future incubators as well as future start-ups should profit from the findings and their interpretation. A developed framework, which is the major objective of this research, demonstrates a major guideline for incubators and start-ups equally when opening a new centre and going through the decision-making of selecting an incubator.

2 Literature review

2.1 Introduction

This chapter contains several different topics that eventually illustrate a gap in the existing literature. First of all, the literature about incubation is summarised, followed by the literature about start-ups. After the general summary of each theme, the literature regarding the situation about incubators and start-ups in Switzerland is explained. The data about start-ups in Switzerland illustrates their importance in the Swiss economy and thus the relevance as to why this matter is addressed further in a research study. Furthermore, the other main theme is about the decision-making of human beings. This chapter is split into three sub-chapters – decision-making, organisational decision-making and entrepreneurial decision-making – to demonstrate the literature as well as explain how an individual makes decisions so that when conducting this research interpretations can be made based on the theory identified in the literature.

2.2 Incubation

Literature about incubation is comprehensive. Phan et al. (2005) stated that the literature about incubators and science parks can be put into four foci: companies located on these facilities; those that attempt to provide an assessment of the science parks and incubators themselves; those that focus on the systemic level of the university, region or country; and those that examine the individual entrepreneur or teams of entrepreneurs in these facilities.

Similarly, Charry et al. (2013) reviewed 50 articles from 2000 to 2012 about business incubators in journals of entrepreneurship, technology and innovation management. Only seven out of these 50 articles were theoretical studies, while 43 were empirical studies. They stated that publications about business incubators have increased from 2000 to 2012, showing a higher interest in business incubators. The results of that study showed a predominance of qualitative studies about business incubators that focused mainly on the analysis of the creation of business incubators and their effects. However, research is still scarce regarding the incubatee, “there is little existing evidence on the search processes adopted by firms concerning their decisions to locate to a particular business incubator” (Charry et al., 2013, p. 60).

Furthermore, Hackett and Dilts (2004) analysed 38 studies about business incubators and identified four levels of analysis in publications about incubators: the incubatee, incubation processes, incubators and community. They found that although there is much focus about the description of incubator facilities, there is a scarcity in terms of the incubatee (Hackett & Dilts, 2004).

The literature includes many different incubation types mentioned that exist (Aernoudt, 2002; Becker & Gassmann, 2006a; Becker & Gassmann, 2006b; Grimaldi & Grandi, 2005; Hansen et al., 2000; Thierstein & Wilhelm, 2001). Becker and Gassmann (2006b) stated that although there are so many different types of incubation, there are two fundamental ones: profit and non-profit incubators. Profit incubators, or in other words corporate incubators, have as a main focus financial success and are therefore interested in a start-up that has a value-creating product or service that may give a high return. On the other hand, non-profit incubators have a large number of stakeholders to satisfy on the level of community development. Typical non-profit incubators are science parks and university incubators. Scquicciarini (2007) stated that science parks have a positive impact on companies that spend the first years in these parks. Furthermore, Akçomak (2009) found that contemporary successful incubators seek profits, offer many services with a focus on intangible services such as networking, support with making a business plan and marketing; they also possess skilled employees. Similarly, Töttermann and Sten (2005) argued that start-ups, which receive support from the incubator are more satisfied with the incubator than such ones that do not receive any support. However, Chen and Lau (2005) stated that the merits incubatees need from the incubator are controversial and thus the services should be customised according to the development of the incubatee.

2.2.1 Incubation in Switzerland

Thierstein and Wilhelm (2001) analysed incubator, technology and innovation (ITI) centres in Switzerland. They stated that there are both private and public ITI centres that are concerned with developing and supporting start-ups. However, there is little awareness and knowledge among the wider community about these ITI centres. Likewise, Swiss federal policy does not possess an in-depth account of the various local and regional activities. The reason for that is because Switzerland is in a federalist system, where cantons and cities are responsible for their undertakings. Thus, it is a bottom-up and self-organising process driven by private institutions and a proactive public authority. Furthermore, Thierstein and Wilhelm (2001) stated that economic development does not depend on the number of technology start-ups but the diversity of start-ups. Thus, public authority is forced to intervene in order to balance the lobby situation that technology start-ups possess in terms of receiving venture capital. Indeed, there is no literature about business incubators in Switzerland nor is there general data about it. Nevertheless, the supply of such incubators and start-up activities is very well spread over Switzerland. There are three levels of incubation support.

Illustration 1: Incubation levels in Switzerland

illustration not visible in this excerpt

Source: Own creation (2015).

On the level of federal incubation there is the Commission for Technology and Innovation (CTI) that focuses on innovative start-ups that have a scientific or research element. CTI consists of two support programmes – CTI entrepreneurship and CTI start-up coaching. CTI entrepreneurship comprises four courses in which basic information such as business ideas or knowledge about start-up creation gets conciliated. CTI start-up coaching cares about individual start-ups and supports them in developing their business plans. Once the start-up masters the four processes from application to coaching, it gets titled with the CTI start-up label and is then able to receive funding from investors through the programme CTI invest. Every year 120 start-ups undergo that process and 25-30 of them eventually receive that label. Subsequently, start-ups are able to present their business plans to a wide array of venture capital companies (CTI, 2015). Furthermore, there are 163 foundations that support start-ups in different fields with various awards such as prices, PR recognition and funding (Sabeti & Schumacher, 2013). Besides that, there is the Institut für Jungunternehmen (IFJ) that supports start-ups from all over Switzerland with creating a company. Together with Venturelab and Venture Kick, IFJ pursues to stimulate potential entrepreneurs. Venturelab organises start-up events and the best start-up talents. On the other hand, Venture Kick is a consortium of different foundations and provides start-ups with pre-seed capital (IFJ, 2015). On the level of canton, there are incubators and programmes differentiating from region to region. Each city in Switzerland is responsible for the central region of a particular canton. Therefore, the subject matter will be addressed in the context of one region only, namely Zurich, which is the cluster region for start-ups, being the region with the second highest amount of start-up creation (STATENT, 2014).

2.2.2 Incubation in Zurich

The canton Zurich plays only a secondary role regarding incubation. It provides a homepage that gives all major information about preparation and creating a start-up. Individuals are able to write questions to the partners of the canton that consist of the social insurance institution, cantonal bank Zurich, the office for economy and work and the commercial register office of Zurich (Gruenden.ch, 2015). Furthermore, the canton as well as other public bodies and private companies support financially and work together with the association Startzentrum in Zurich that organises events and coaches entrepreneurs regarding start-up creation and acts as a knowledge provider (Startzentrum, 2015). Blue Lion is the sister organisation of Startzentrum that was introduced in May 2012 with a focus on ICT and clean tech start-ups. Furthermore, there are other incubators present in Zurich; Swiss Start-up Factory is one of the top incubators in Switzerland and has been founded in 2014 focusing on digital service start-ups (TallyFox, 2015). Impact Hub is an international incubator that works in several countries and cities. It may be rather defined as a co-working space because start-ups need to win a competition in order to receive a coaching. Impact Hub employs international start-ups that have a social background. Moreover, there are several techno parks such as the Technopark in Zurich and the Technopark in Winterthur, the Rocket park in Zurich focused on internet start-ups, the Bio Technopark in Schlieren focused on life science start-ups and glaTech, which is a technology centre at the research institute EMPA of the ETH Zurich – ETH Zurich is one of the leading universities in natural sciences and technology. Lastly, there is the start-up centre grow in Wädenswil that focuses on life science, facility management and computer science. Those techno parks as well as the incubation centres employ not only start-ups but also established companies. An overview of Swiss incubators in Zurich is shown below.

Illustration 2: Overview of incubators in Zurich

illustration not visible in this excerpt

Source: Own creation (2015).

2.3 Start-ups

Literature about start-ups and their initial procedure in growing is limited by a few factors. Different authors analysed the factors that are essential for a start-up to succeed. Thus, research is focused on the outcome of various inputs given by intangible and tangible assets such as institutions, funding, knowledge, experience, networks, etc. Major impacts for start-ups growth discussed are networking (Aernoudt, 2001; Hansen et al., 2000; Jenssen & Koenig, 2002; Witt, 2004), human capital (Colombo & Grilli, 2010) and funding (Bertoni, Croce, & D’Adda, 2010; Cassar, 2004; Heirman and Clarysse, 2007; Hellmann & Puri, 2002; Minola & Giorgino, 2008).

Allen and Rahman (1985) found in their study that consulting is a major service that has medium to high demand from start-ups. However, some incubators lack in providing such a service, which may be amongst others, tax and advertising and marketing services. Similarly, Arlotto, Sahut and Teulon (2013) argued that incubators have room for improvement. Start-ups are especially in need of management services and support in obtaining funding, which is not as developed as entrepreneurs wish for. However, Hansen et al. (2000) stated that 86% of incubators they interviewed offer funding but only 26% offer an organised network. Indeed, social ties influence the success of start-ups due to their access to information or funding (Jenssen & Koenig, 2002). Likewise, Aernoudt (2002) stated that incubators ought to have a close link to business angels to provide funding networks. In contrast, Heirman and Clarysse (2007) found that collaborations with universities slow down start-up development. Nevertheless, the literature agrees that venture capital is contributing to technology start-ups’ success and affects the human capital within the company positively (Colombo & Grilli, 2010; Minola & Giorgino, 2008).

2.3.1 Start-ups in Switzerland

Switzerland was for the fourth time in a row the most innovative country worldwide in 2014 on the basis of the Global Innovation Index (GII) that includes 84 criteria (Schneider, 2014). Especially in terms of innovation output, which includes the amount of high-tech companies as well as the number of venture creation and patent registration, is where Switzerland is at the top. Furthermore, the high investments in research and development of companies as well as the close connection between universities and the economy are factors that add to this high innovative position (Tagesanzeiger, 2013).

In Switzerland start-ups are categorised as small and medium enterprises (SME) (Portal SME, 2015). Although there is no official definition of SME in Switzerland, the State Secretariat for Economic Affairs (SECO) takes the number of employees as a basis. Therefore a SME is defined to be between one and 249 employees (Portal SME, 2015). SME play a crucial role in the Swiss economy, more concrete on the Swiss labour market they account for 99% of all companies and secure two thirds of employment. The position of especially micro companies is very dominant as illustrated in table 1. Companies with only one employee account for almost half of all company structures. Furthermore, micro companies with one to nine employees account for 89.6%, which equals a quarter of Swiss employment. This major position demonstrates the importance of micro companies in Switzerland (Portal SME, 2015).

Table 1: Company structure in Switzerland in 2012

illustration not visible in this excerpt

Source: Portal SME (2015).

In 2014 there were 41,588 new entries in the commercial register (Startups.ch, 2015). At the same time, 11,853 declared bankruptcy (STATENT, 2014). However, these figures include all kinds of company forms. Regarding start-ups, as illustrated below (see table 2), the latest figures go back to 2012 where 11,891 start-ups were founded, which is 3.1% higher than in 2011 but lower than the all-time-high in 2010 with 12,093 start-ups (STATENT, 2014).

Table 2: Start-ups creation in Switzerland

illustration not visible in this excerpt

Source: Swiss Federal Statistical Office (2014).

Start-ups have been mainly formed in the region of Geneva as well as Zurich, as illustrated below in table 3.

Table 3: Number of start-ups considering region in 2012

illustration not visible in this excerpt

Source: SME portal (2015).

The average survival rate of start-ups in Switzerland has changed only slightly in the previous years; after starting up, meaning within the first year, the survival rate is approximately 80% and after five years is less than 50%, according to the Swiss Federal Statistical Office (2014). Considering the sectors, the survival rate in the secondary sector (industry sector) is slightly higher. After one year the rate is 83.3% compared to 79.9% in the tertiary sector (service sector) and after five years is 57.4%, whereas the rate is 48.6% in the tertiary sector.

Table 4: Start-ups survival in Switzerland in 2008

illustration not visible in this excerpt

Source: SME portal (2015).

Regarding gender in start-ups, women are still subordinated by men. Start-ups contain mainly men whereas start-ups with dominantly women accounts for only around a sixth of all start-ups in 2012 (see table 5). Similarly, a study of the University of Applied Sciences and Arts Northwestern Switzerland FHNW in 2009 interviewed 4,698 new entrepreneurs in Switzerland and their profile. Results were that the average age is 45 years old and 80% are men. Furthermore, 38% have a university degree whereas only 15% had one in 1999. Start-ups involve merely one to two entrepreneurs and they generally remain small. Out of these interviewed start-ups every third is an innovative company. Regarding funding, two thirds are able to cope with less than Fr. 50,000 seed capital. Additionally, 90% did not take a bank loan or did not get any other funding.

Table 5: Gender in Start-ups in Switzerland

illustration not visible in this excerpt

Source: Swiss Federal Statistical Office (2014).

In the following table there is an overview shown over the industry of start-ups in the tertiary sector. It is dominated by start-ups in freelance, scientific and technical services, followed by trade and repairs and real estate, economic services.

Table 6: Start-ups in tertiary sector

illustration not visible in this excerpt

Source: Swiss Federal Statistical Office (2014).

2.4 Conclusion

Incubation in Switzerland is a bottom-up and self-organising process driven by private institutions, which demonstrates that individual services are offered depending on the kind of incubatees they seek to attract. The literature is discordant about the kind of services that need to be offered for incubatees that match best start-ups needs. The discussion earlier demonstrates that controversial findings exist and thus enables the researcher to study this matter further by questioning the decision-making of start-ups. In other words, the way entrepreneurs proceed in finding a suitable incubator allows the researcher to judge what they are looking for and need in terms of services of an incubator. Furthermore, authors have mainly focused on the outcome of incubation and their effects rather than on the incubatee. In particular, the search process of start-ups is little known as well as the criteria used by start-ups when considering incubators. Therefore, the next chapter considers the topic around decision-making in order to give an understanding towards organisational and entrepreneurial decision-making, to be able to comprehend start-ups’ process when searching and selecting an incubator, which has not been taken into account by the literature so far.

2.5 Decision-making

2.5.1 Introduction

Individuals make decisions constantly throughout each day. Some decisions we make unconsciously, without thinking, such as itching a mosquito bite or taking a sip of water, others are important decisions that have a big impact on our lives and we thus think actively about them. This could be whether to invest in a company or at what university to study. A decision is also called an action, which is defined as “one of the possibilities for acting which depends on the decision maker and on him alone” (Pomerol, 2012, p. 2). On the other side, the term event symbolises nature, in which the decision maker has no control. In other words, the decision maker is able to take actions by deciding within an event, meaning in a certain environment, in which he has no control (Pomerol, 2012).

An important factor of what decision we make is the way information is presented and processed. Any change in the presentation of percentages, numbers and options influence our decision unconsciously and thus other decisions may be made when changing that aspect (Newell, Lagnado, & Shanks, 2007). Whether a decision is good or bad has several impacts such as the outcome, the probability of that outcome and the value that the decision gives one (Hastie & Dawes, 2001).

The study of decisions can be split into normative and descriptive theory of choice (Newell et al., 2007). The normative theory addresses issues in the nature of rationality, thus the logic of decision-making whereas the descriptive theory addresses issues concerned with the beliefs and preferences of human beings, the decisions humans actually make (Kahneman & Tversky, 2000). Furthermore, recent research about decision-making can be put into two main fields such as decisions and judgments. Psychologists interested in decisions seek answers to how do individuals choose a certain course of action? How do they decide when being in ambiguity about consequences or having uncertain goals? Psychologists interested in judgments seek clarity about the availability of information in a certain given situation. Thus, questions involve how individuals process information to understand a situation (Eysenck & Keane, 2005; Goldstein & Hogarth, 1997). Therefore, the next chapters include first the topic about judgments followed by the decision-making with its different theories such as normative, descriptive and neo-classical theory. Afterwards decision-making in organisations as well as entrepreneurial decision-making are addressed.

2.5.2 Judgments

When judging, as for example when estimating the price of a car on display in a garage, individuals go through different key processes to be able to make a judgment (Newell et al., 2007). These include:

1. Discovering information: The first process for judging can be explained by looking at the lens model by Brunswick:

Illustration 3: The lens model by Egon Brunswick

illustration not visible in this excerpt

Source: Newell, Lagnado, & Shanks (2007).

On the left hand side there is the illustration of the real world and on the right hand side the mind of the judger, which attempts to see the real world through various cues that exist. The criterion is the object that an individual attempts to judge such as the price of a car that is on display in a garage. Features, brand, etc. of that car are then the cues of that criterion and enable the individual to judge. Identifying valid cues is a long trial. However, there is more research needed in that area (Newell et al., 2007).

2. Acquiring and searching through information: Before individuals make a decision, they seek to gather as much information as possible in order to avoid making wrong decisions. This behaviour is called pre-decisional acquisition of information, which is, in other words, a strategy to reduce the risk of making a wrong or unsatisfactory decision for the decision-maker. Usually information costs are obvious whereas the outcome and the profitability of that information is unsure, which leads to risk-seeking behaviour and thus deciding with an imperfect amount of information instead of scanning all available information (Connolly & Thorn, 1987; March, 1987; Simon, 1956). When searching for information there are two strategies – alternative-based strategy and the ‘good enough’ strategy as this researcher calls it. According to Simon (1956), human beings are faced with bounded rationality and thus accept ‘good enough’ alternatives (more details in the following chapter). Similarly, the alternative-based strategy works; the decision-maker defines an individual set requirement, e.g. when searching for an apartment to rent, setting the requirement to not pay more than $1,000, and choosing the alternative that matches these requirements best (Newell et al., 2007).

3. Combining information: Once the individual has discovered and searched for relevant information, the most difficult process begins when deciding what to do with the information found (Newell et al., 2007). Different authors found that individuals seek out information that reflect their beliefs rather than information, which is able to be falsified and stand against these present beliefs. Therefore, once a belief exists, there is a bias that the decision becomes reinforced through denying the relevance of the information (Klayman & Ha, 1987; Wason, 1960). Consequently, statistical models are thought to be more accurate than human beings’ predictions due to the fact that human beings make predictions with changing behaviours and moods, which can affect the prediction. Statistical models instead arrive constantly at the same result and are thus more representative (Dawes, Faust, & Meehl, 1989). Similarly, Eysenck and Keane (2005) stated that when confronted with new information, individuals tend to change their initial belief and make a different judgment considering the new piece of information and thus change their estimates of the probability. Nevertheless, human beings are able to identify components that are essential in order to make an accurate judgment. On the other side, they lack the capacity to combine these and weigh the components in an optimal way due to their own beliefs and false probabilities (Dawes et al., 1989; Einhorn, 1972; Eysenck & Keane, 2005). However, as Simon (1956) stated, individuals do not make such an effort to question all kinds of information. Therefore, individuals often use representative heuristics, which is defined as “events that are representative or typical of a class are assigned a high probability of occurrence. If an event is highly similar to most of the others in a population or class of events, then it is considered representative” (Kellogg, qtd. in Eysenck & Keane, 2005). Kahneman and Tversky (2000) found in a study that individuals tend to make judgments by thinking of the probabilities in terms of stereotypes, e.g. when asking what profession the following attributes match – shy and withdrawn, helpful but little interest in people – and giving options such as farmer, doctor, pilot or librarian, individuals mostly chose librarian as it fits to the stereotypes of this profession. In other words, individuals use stereotypes to judge about probabilities.

4. Feedback: Feedback is important for learning in problems relating to decisions. However, for this study that process is not relevant and thus will not be explained further.

2.5.3 Decisions

2.5.3.1 Normative theory

In contemporary economic theories of individual choice, utility maximisation is at the core (Glimcher & Fehr, 2013) and will therefore be explained further. Ricardo (as cited in Glimcher & Fehr, 2013) was the most important economist in the early nintheenth century when he was leading the Marginalist Revolution with his early price theory. During that revolution it was recognised that the exchange of goods depends on both the intrinsic value of the good and the amount an individual already possesses of it. The more one possesses, the less value that good has for the person. Thus, the more one person possesses the less he is willing to pay, whereas one who has only little is willing to pay a higher price. Economists then assumed that this price behaviour means that individuals strive for utility maximisation meaning that they act in order to maximise their happiness. Later on, several other authors developed economic theories. Most notably, von Neumann and Morgenstern (as cited in Glimcher & Fehr, 2013) belong to the contemporary economists in utility theory. They recognised that in order to achieve maximised utility, the calculation of the expected utility of every action is needed. Thus, every decision depends on the balance between the value of each option and the probability of it occurring (Newell et al., 2007).

In decision theory the satisfaction of the decision maker can be expressed in a formula u (utility of the decision maker) of A x E in R.

Here, A stands for the ensemble of alternatives, E for the ensemble of events and R for the ensemble of real facts (Pomerol, 2012). However, that formula is not complete because it neglects a representation of the future, which is called probability. The probability of an event is between zero and one. Zero indicates that the event will not happen whereas one means that the event is certain to happen. In situations where events occur frequently, probabilities can be stated as personal estimations. Such estimations belong to objective probabilities where individuals are faced with risky decisions, e.g. a die has 6 different sides and thus the probability is one out of six. On the other hand, subjective probability reflects uncertainty – an individual makes an intuitive estimation of the probability based on a view of the future. However, those estimations are different from person to person and indicate the differences in the perception of the future (Pomerol, 2012). As we live in a world filled with uncertainty, decision-making is difficult and thus is often dealt with subjective probability (Eysenck & Keane, 2005).

2.5.3.2 Descriptive theory

In modern decision theory, researchers found that human behaviour contradicts the expected utility theory (Gigerenzer & Selten, 2001; Kahneman & Tversky, 2000; March, 1978; Simon, 1955, 1956). In particular Simon (1955, 1956), being the Nobel Prize winner in economics in the 1950s, made a crucial impact with his study. He argued that human beings are unable to process information in a cognitive way and thus do not make decisions that are rational. There is rarely ever the possibility that all information is available and is correctly used by an individual. Therefore, Simon (1955, 1956) used the term ‘bounded rationality’ to express that human beings satisfy themselves with only a limited amount of information and make decisions without knowing all possible options or alternatives. Instead they accept ‘good enough’ results, unlike seeking to obtain an optimal choice, which uses a high amount of resources and time.

Similar to Simon (1955, 1956), several authors stated there is not only utility but also other factors that influence decision-making such as emotions and self-confidence, past experiences, stereotyping, information availability and risk perception (Altman, 2012; Eysenck & Keane, 2005; Kahneman & Tversky, 1979a; Kahneman & Tversky, 1984, as cited in Eysenck & Keane, 2006; Newell et al., 2007). This change in the perception of decision-making theory has led researchers to study in the behavioural perspective, thus not in the rational area but how human beings really behave and act (Douglas, 2005).

2.5.3.3 Neoclassical theory

Kahneman and Tversky were the turning point in the contemporary decision research (Douglas, 2005). They proved with their research that human beings are not rational as utility theory suggests. Kahneman and Tversky (as cited in Eysenck & Keane, 2006) introduced the prospect theory that explains that individuals simplify their decision of choice by disregarding components that occur in all alternatives. This simplification leads to inconsistent preferences when presenting the choice possibilities in different forms. Furthermore, individuals are risk-averse, going for certain outcomes rather than taking risks and thus having an uncertain outcome. Kahneman and Tversky (as cited in Eysenck & Keane, 2006) found in their study that individuals rather reject the chance of winning when there is a 50% probability to lose the same amount. Initially, they asked participants to toss a coin and win $10 when it came up heads and on the other hand losing the same amount when it came up tails. Afterwards they raised the amount to win $20, while the loss stayed the same. Nevertheless, the participants mostly denied the offer. According to the utility theory, individuals should have been willing to play the game, as there is a $10 gain for winning each time. Additionally, there is evidence that decision-makers frame their decisions (Tversky & Kahneman, as cited in Newell et al., 2007). This means that our decisions are influenced by irrelevant information that is given to us. “It simultaneously highlights people’s susceptibility to reference frames, and their risk-aversion in the domain of gains but risk-seeking in the domain of losses” (Newell et al., 2007, p. 124).

2.5.4 Decision-making process

In general Pomerol (2012) suggested the decision-making process begins by recognising the state of nature, which means neglecting governmental, political or lawful influences that we are exposed with every day. Furthermore, an important aspect is the scenario building of the potential future, which is called the projection phase. In that phase the decision-maker anticipates potential outcomes and probabilities. For each action one expects a certain result. According to one’s preferences an action will be chosen.

Illustration 4: General decision-making process

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Source: Pomerol (2012).

On the other hand, there are decisions that are made through recognising past experiences and happenings. Such decisions are called case-based reasoning. Pomerol (2012) stated that once a decision has been made leading to a bad experience, any following similar situation will automatically make us decide the same way as we experienced in the first situation. Case-based reasoning is especially found in the area of medicine where doctors use symptoms as predictions for the identification of diseases and treatments. However, it is also common in other professional areas, where individuals adapt certain behaviour through learning. However, the risk with that decision-making behaviour is that individuals neglect the environment around them, which may have changed in the meantime. Thus, adapting decisions from former experiences are no longer good decisions and are likely to harm the decision-maker with the result.

2.5.5 Decision-making in organisations

Simon (as cited in Miller, Hickson, & Wilson, 1996, in Clegg, Hardy, & Nord, 1996) stated that managing is a synonym to decision-making, meaning that decision-making belongs to managers. Also Mintzberg, Raisinghani, and Theoret (1976) pointed out that decision-making “is at the top levels of organisations where better decision-making methods are most needed” (Mintzberg et al., 1976, p. 246). With the help of students they made a comprehensive study for three to six months, analysing 25 strategic decision-making processes in organisations. ‘Strategic’ means in their case an important decision that is made in the upper level of an organisation. Based on the results made in their study, they developed a framework of the strategic decision process, which will be explained further.

Illustration 5: Strategic decision process

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Source: Mintzberg, Raisinghani, & Theoret as cited in Forlani & Mullins (2000).

The strategic decision process contains three phases, namely the identification, development and selection phases. Within these phases, top-level managers have different routines they go through. In the first routine, the recognition phase, there are three stimuli recognised: problem, opportunity and solution. An interesting phenomenon in that phase is that managers tend to match a problem with a solution or opportunity. As long as there is no problem to an idea or any opportunity or solution in sight to a problem, managers do not take action. Mintzberg et al. (1976) stated that reaction to a problem is more prevalent than looking for opportunities. Once the stimulus is found and recognition occurred, the diagnosis of the problem is made through collecting information and clarifying the issue. In the development phase, managers begin to search for either ready-made solutions or they seek to design new solutions. By screening, managers screen opportunities or solutions and weigh all possible alternatives with each other. In order to make a choice, Mintzberg et al. (1976) identified three routines that managers apply: judging, bargaining and analysing, of which judging is the most used when faced with making a choice. Lastly, the phase authorisation is used when there is another party needed to approve the choice made.

However, Miller et al. (as cited in Clegg et al., 1996) stated that a step-by-step process is not easy, as managers are possibly faced with new problems when recognising an opportunity and thus cutting the sequential process of decision-making. Furthermore, as mentioned by Mintzberg et al. (1976), authorisation of third parties was predominant in their study when approving choices that are made by managers. Miller et al. (as cited in Clegg et al., 1996) stated that decision-making could be seen as a game of power in an organisation where competing parties strive to win control over the organisation’s resources. This political impact hinders managers from making rational decisions. Due to high uncertainty with which managers are faced, political actions are easier to take up by individuals (Huczynski & Bachanan, 2013). This power game is due to the way organisations function – as soon as there are different groups with unequal tasks, they each develop individual targets and interests that they seek to obtain. Managers make decisions that favour their own interest. They choose alternatives that add to their own value instead of those of the organisation. Furthermore, they gather only certain items of information while they neglect others and manipulate them in reaching their own targets (Miller et al., as cited in Clegg et al., 1996). As stated in the former chapter, Cyert and March (1963), March (1978) and Simon (1955, 1956) were major opponents of rational theory of choice. Cyert and March (1963) were engaged in organisational behaviour and introduced politics in the decision-making process (Huczynski & Bachanan, 2013). In organisations the rational model is not possible due to ambiguity, having an agreement over what problem to solve and uncertainty over the outcome of the decision made. These unequal conditions are summarised in the following illustration, which lead to different decision-making models.

Illustration 6: Conditions favouring different decision-making processes

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Source: Huczynski & Bachanan (2013).

When following a rational model, managers agree with each other and are certain of the outcome of their action. As Huczynski and Bachanan (2013) stated, an ice cream company introduces an extra shift due to the rise in demand of ice cream in the summer. As they know the costs of the manning the machine, the capabilities of it as well as the sales from that extra shift, they are able to calculate the outcome via a computational strategy.

When having agreement, albeit with uncertainty, the incremental model is used. This model was introduced by Lindblom (1959), which he called the science of ‘muddling through’. He was a supporter of the bounded rationality stated by Simon (1955, 1956) and argued that the limited information and alternatives that are available to individuals differ only slightly due to the reason that we base our decisions on former decisions made. Therefore, judgements are made incrementally and opportunities can be overseen as managers only focus on the past, “they [the policies] are not suggested by the chain of successive policy steps leading up to the present” (Lindblom, 1959, p. 88). Thus, policy formulation does not happen out of one decision but many small, disconnected decisions that are made by many individuals or groups in the past.

In case of certainty but disagreement, Huczynski and Bachanan (2013) stated that this always becomes resolved through judging, reasoning, influence and politics. As mentioned earlier, politics have a predominant position in organisations. Individuals have unequal targets and manipulate available information in order to obtain their interests and ‘cut a deal’. Thus, teams that fall into place that follow the same interests, negotiate and compromise with others to reach agreement, follow what is called the compromise strategy.

The fourth model is called the garbage can model according to the introducers Cohen, March and Olsen (1972). This model is mostly found in complex organisations, where processes are complicated and little understood. Cohen et al. (1972) stated that in such organisations the processes of decision-making become uncoupled due to the loss of linkage between problem identified and solution proposed. Thus, decisions happen before a solution or a problem has been identified (Miller et al., as cited in Clegg et al., 1996). Cohen et al. (1972) argued that decisions are made randomly due to a mix of different components such as problems, solutions, participants and choice opportunities/situations. The processes from one choice to the other “all depend on a relatively complicated intermeshing of elements” (Cohen et al., 1972, p. 16). The choice opportunity here is the garbage can that mixes the other components with each other. Similarly, March and Olsen (as cited in Huczynski & Bachanan, 2013) illustrated this issue by arguing that decisions are not made rationally, instead individuals make decisions in order to seek to gain power and prestige or harm and penalise others.

2.5.6 Decision-making by entrepreneurs

Literature about entrepreneurship is centred on decision-making when creating a start-up; as Elissaveta and Gibcus (2003, p. 31) called it the “start-up decision-making”, which is also named opportunity identification (Gustaffson, 2006). Mainly authors focused their research on factors that influence decision-making and led entrepreneurs to decide to create a new venture (Dew, Read, Sarasvathy, & Wiltbank, 2009; Gustaffson, 2006; Mullins & Forlani, 2003). Furthermore, risk-taking is a popular topic that is covered when establishing start-ups due to the fact that start-ups are faced with uncertain environments and thus need to make decisions in uncertainty, which raises the risk aspect (Forlani & Mullins, 2000; Janney & Dess, 2006). A number of authors focused on how and why entrepreneurs take more risks than managers in other organisations (D’Amboise & Muldowney, 1988; Hebert & Link, 1988). However, the literature also states that although entrepreneurs are constantly faced with risks, there is little support that they tend to be risk-takers (Brockhaus, 1980; Low & MacMillan, 1988). Busenitz (1999) argued that there is no empirical evidence that entrepreneurs are higher risk-takers. Instead he stated that what differentiates entrepreneurs from managers in other organisations is that entrepreneurs use biases and heuristics more. As stated in chapter 2.5.3.3 Neo-classical theory introduced by Kahneman and Tversky (as cited in Eysenck & Keane, 2006) suggested that individuals make decisions with cognitive mechanisms and subjective opinions that individuals perceive as giving effective and efficient results. Similarly, Palich and Bagby (1995) argued that entrepreneurs do not differ in the perception of taking risks. Instead, they are more optimistic; they “categorized equivocal business scenarios significantly more positively than did other subjects” (Palich & Bagby, 1995, p. 426). Thus, whereas managers in other organisations view a scenario or situation as a risk, entrepreneurs view it as an opportunity, although entrepreneurs do not actively seek to take risks.

In order to be able to analyse the decision-making process in SME, Elissaveta and Gibcus (2003) illustrated a scheme that exemplify the interrelation between the entrepreneur, the environment and the strategic decision process.

Illustration 7: Levels of analysis in the decision-making process

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Source: Elissaveta & Gibcus (2003).

The relationship between the entrepreneur and the decision process indicates that the entrepreneur is able to influence the decision depending on what approach he chooses (rational, emotional or intuitive). As the environment is constantly changing, the entrepreneur is faced with new opportunities and threats, thus providing different stimuli for the entrepreneur to take actions. On the other hand, the entrepreneur changes the environment itself by creating a new venture. Between environment and decision process the correlation is that the decision process allows for new innovation and product introductions whereas the environment possesses uncertainty for the decision made (Elissaveta & Gibcus, 2003).

2.6 Research gap

Having discussed the literature about the three matters within the research topic, it can be stated that, although there is comprehensive literature about all three issues – incubators, start-ups and decision-making – there is no research combining all these topics with each other. Literature about incubation has missed the issue of the selection process by start-ups while research on start-ups is only focused about the outcome of venture creation and its performance. Lastly, the decision-making process by entrepreneurs is centred on opportunity identification and risk-taking at the time when the venture becomes created.

However, there is a lack of literature about the process that happens after creation has occurred and start-ups are faced with further decision-making such as the question whether to choose an incubator. “Unfortunately, there are not many existing surveys conducted on entrepreneurial decision-making in a later stage of the business” (Elissaveta & Gibcus, 2003, p. 31). Also Mullins and Forlani (2003) suggested further research to elaborate how start-ups evolve. In other words, there is no study made elaborating the decision-making of start-ups when looking for an incubator. Furthermore, start-ups in Switzerland demonstrate a crucial sector for its economy (Portal SME, 2015). However, data and written literature about start-ups or incubators does not exist. Therefore, incorporating Swiss start-ups and their decision-making process may give Swiss incubators relevant knowledge about how start-ups proceed leading to the decision to apply for an incubator. Thus, incubators are able to gain relevant understanding regarding start-ups proceeding after venture creation and thus are able to adapt and improve their services and positioning. Therefore, the above discussion leads to the following research question: ‘What is the decision-making process of start-ups when selecting an incubator in Switzerland?’

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Details

Pages
185
Year
2015
ISBN (eBook)
9783668057043
ISBN (Book)
9783668057050
File size
1.6 MB
Language
English
Catalog Number
v307361
Institution / College
Novancia Business School Paris
Grade
A
Tags
Decision-making Incubator Start-up Switzerland Choice Selection Process

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Title: Decision-making process of start-ups during their incubator choice in Switzerland