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1. i
WHICH MATTER MORE: A CONJOINT
ANALYSIS ON THE BIOGRAPHICAL
CHARACTERISTICS OF ENTREPRENEURS
A dissertation submitted to the University of Manchester for the degree of Master of Science in the Faculty of
Humanities
BY
QIUHAO XU
2014
MANCHESTER BUSINESS SCHOOL

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UNIVERSITY OF MANCHESTER
ABSTRACT
WHICH MATTER MORE: A CONJOINT ANALYSIS ON THE BIOGRAPHICAL
CHARACTERISTICS OF ENTREPRENEURS
by Qiuhao Xu
MSc Innovation Management & Entrepreneurship
Supervised by Dr. Ronnie Ramlogan
Manchester Business School
The quality of entrepreneurs has been demonstrated to be crucial in affecting investor’s
decision-making, where biographical characteristics i.e. the traits and features that could
be obtained from personal records directly, are considered to be important criteria.
However, most previous studies used post hoc methods such as traditional questionnaire
and interviews to study the decision-making process, which may lead to biased results
and revisionism (Lohrke, Holloway, & Woolley, 2010; Shepherd & Zacharakis, 1999).
Conjoint analysis is suggested to be one of the methods that can avoid such bias.
Applying both traditional questionnaire and the method of conjoint analysis, this paper
quantitatively studied the significance of different biographical characteristics of
entrepreneurs in affecting investors’ decision-making. The results show that in terms of
getting venture investment, with/without startup experience is the most significant
feature of entrepreneurs. The occupational expertise is also heavily weighted, among
which technology specialists and jack-of-all-trades are most preferred. The research also
demonstrated the applicability of conjoint method in entrepreneurship studies.

4. iv
DECLARATION
No portion of the work referred to in the dissertation has been submitted in support of
an application for another degree or qualification of this or any other university or other
institute of learning.

5. v
COPYRIGHT STATEMENT
i. Copyright in text of this dissertation rests with the author. Copies (by any
process) either in full, or of extracts, may be made only in accordance with
instructions given by the author. Details may be obtained from the appropriate
Programme Administrator. This page must form part of any such copies made.
Further copies (by any process) of copies made in accordance with such
instructions may not be made without the permission (in writing) of the author.
ii. The ownership of any intellectual property rights which may be described in
this dissertation is vested in the University of Manchester, subject to any prior
agreement to the contrary, and may not be made available for use by third
parties without the written permission of the University, which will prescribe
the terms and conditions of any such agreement.
iii. Further information on the conditions under which disclosures and
exploitation may take place is available from the Academic Dean of Manchester
Business School.

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ACKNOWLEDGEMENT
I am using this opportunity to give my appreciation to everyone who supported me
throughout this dissertation and the master programme. A special thank is given to my
supervisor Dr. Ronnie Ramlogan for his comments and guidance through the research.
Also, I would like to thank all the respondents who took time to participate in my survey.
Thank all the friends and classmates who helped me to distribute my questionnaires.
My deepest gratitude should be given to my parents and family who supported me and
gave me this chance to persuade this degree here in Manchester. I would also sincerely
thank my partner Sally Kuok for her warm love and for kindly taking care of my daily life
during the writing up.

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TABLE OF CONTENTS
ABSTRACT ...................................................................................................................................iii
DECLARATION.........................................................................................................................iv
COPYRIGHT STATEMENT.....................................................................................................v
ACKNOWLEDGEMENT.........................................................................................................vi
Chapter 1. Introduction ................................................................................................................1
1.1 Background......................................................................................................................1
1.2 Why Conjoint Analysis?.................................................................................................3
1.3 Aims and Objectives......................................................................................................4
1.4 Dissertation Structure....................................................................................................6
Chapter 2. Background & Literature Review.............................................................................7
2.1 Context in Entrepreneurship Studies..........................................................................8
2.1.1 The Investors’ Role and Their Judging Criteria.............................................8
2.1.2 Discussions on Biographical Characteristics of Entrepreneurs..................9
2.2 Background of Conjoint Analysis..............................................................................12
2.2.1 Overview............................................................................................................12
2.2.2 General Procedure ...........................................................................................15
2.2.3 Advantages and Disadvantages......................................................................17
2.3 Application of Conjoint Analysis in Entrepreneurship Studies............................19
2.4 Summary ........................................................................................................................23
Chapter 3 Methodology ..............................................................................................................25
3.1 Overview........................................................................................................................26
3.2 Research Design............................................................................................................28
Survey 1: Traditional Questionnaire .......................................................................28
Survey 2: Conjoint Analysis Survey ........................................................................29
3.3 Sample Structure of Survey 1 & 2 .............................................................................33
3.3.1 Survey 1: Traditional Questionnaire..............................................................33
3.3.2 Survey 2: Conjoint Analysis............................................................................36
3.3 Summary ........................................................................................................................37
Chapter 4. Data Analysis & Key Findings ...............................................................................38
4.1 Survey 1 Findings .........................................................................................................38

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4.1.1 Overview............................................................................................................38
4.1.2 Comparison Between Clusters....................................................................40
4.1.3 Summary.........................................................................................................54
4.2 Survey 2 Conjoint Analysis Findings.........................................................................55
4.2.1 Overview............................................................................................................55
4.2.2 Comparison Between Clusters.......................................................................61
4.2.3 Summary............................................................................................................78
4.3 Summary of Chapter....................................................................................................79
Chapter 5 Discussion...................................................................................................................81
5.1 Overview of findings...................................................................................................81
5.2 Method Validation........................................................................................................82
5.3 A discussion...................................................................................................................83
5.3.3 Why is startup experience regarded as the most important characteristic?
......................................................................................................................................83
5.3.4 Why jack-of-all-trades outmatched management expertise? .....................84
5.3.5 Why investors care education background less?..........................................85
5.3.6 Why do we claim to behave different from we actually behaved?...........86
5.4 Limitations.....................................................................................................................88
Chapter 6. Conclusion and Recommendations.......................................................................90
6.1 Summary of the Dissertation......................................................................................91
6.2 Suggestions for the Entrepreneurs ...........................................................................92
6.3 Recommendations for Further Studies ....................................................................92
Bibliography..................................................................................................................................93
Appendix.......................................................................................................................................97
Appendix I. Conjoint Analysis Usage in Entrepreneurship Research, 1999-2008...98
Appendix II. Survey 1 Questionnaire............................................................................100
Appendix III. Survey 2 (Conjoint Analysis) Questionnaire.......................................102
word count: 18,454

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TABLE OF FIGURES
Figure 1 Overview of CA procedure, revised from Christofor and Kollmann (2008).........16
Figure 2 Stages of the Research Process ......................................................................................27
Figure 3 Sample profile cards used in Survey 2...........................................................................31
Figure 4 Respondents by age and gender in Survey 1................................................................34
Figure 5 Percentage of respondents by nationality in Survey 1................................................34
Figure 6 Number of male and female respondents by Background in Survey 1...................35
Figure 7: Number of Male and Female Respondents by Age Group in Survey 2.................36
Figure 8: Percentage of Respondents by Occupation in Survey 2 ...........................................37
Figure 9 Frequency of All Attributes in Survey 1.......................................................................39
Figure 10 Percentages of Attributes Chosen by Entrepreneurial Persons in Survey 1 ........42
Figure 11 Percentages of Attributes Chosen by Non-entrepreneurship Related
Respondents in Survey 1........................................................................................................42
Figure 12: Percentages of Attributes by Male Entrepreneurial Persons in Survey 1 ............43
Figure 13: Percentages of Attributes by Female Business Professionals in Survey 1 ...........44
Figure 14: Comparison between Male and Female Entrepreneurial Persons in Survey 1 (1)
....................................................................................................................................................44
Figure 15: Comparison between Male and Female Entrepreneurial Persons in Survey 1 (2)
....................................................................................................................................................45
Figure 16: Comparison between Male and Female Entrepreneurial Persons in Survey 1 (3)
....................................................................................................................................................45
Figure 17 Age Groups of Entrepreneurial persons in Survey 1...............................................46
Figure 18 Percentage of Significant Attributes by Entrepreneurial Persons in different age
groups in Survey 1...................................................................................................................47
Figure 19: Percentage of Attributes by Entrepreneurial People in Greater China Region in
Survey 1 ....................................................................................................................................49
Figure 20: Percentage of Attributes by Entrepreneurial People in English-speaking
Countries in Survey 1 .............................................................................................................49
Figure 21: Comparison between the Great China Region and Main English-speaking
Countries in Survey 1 (1)........................................................................................................50
Figure 22: Comparison between the Great China Region and Main English-speaking
Countries in Survey 1 (2)........................................................................................................50

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Figure 23: Choices by Chinese Entrepreneurs (narrow sense) in Survey 1............................51
Figure 24: Choices by British Entrepreneurs in Survey 1..........................................................52
Figure 25: Comparison between Chinese (narrow sense) and British Entrepreneurs in
Survey 1 (1) ..............................................................................................................................52
Figure 26 Comparison between Chinese (narrow sense) and British Entrepreneurs in
Survey 1 (2) ..............................................................................................................................53
Figure 27 Ranking Tendencies in Survey 2..................................................................................56
Figure 28: Part Worth of All Attribute Level in Survey 2..........................................................58
Figure 29: Relative Importance of Attribute in Survey 2...........................................................60
Figure 30 Part worth of Attribute between Male and Female Respondents in Survey 2 .....62
Figure 31 Relative Importance of Attribute between Male and Female Respondents in
Survey 2 ....................................................................................................................................63
Figure 32: Part-worth of Attribute between Respondents from Greater China Region and
Main English-speaking Countries.........................................................................................66
Figure 33 Relative Importance of Attribute between Respondents from Greater China
Region and Main English-speaking Countries ...................................................................67
Figure 34: Part worth of attribute between investors and entrepreneurs ...............................70
Figure 35 Relative Importance of Attributes between Investors and Entrepreneurs...........71
Figure 36 Part worth of Attribute between large and small business owners ........................73
Figure 37 Comparison between large and small business owners............................................74
Figure 38 Part-worth of Attributes from Respondents between different Age Group .......76
Figure 39: Relative Importance of Attributes between different Age Group ........................77

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TABLE OF TABLES
Table 1 Attributes and levels in Riquelme and Rickards (1992)................................................................20
Table 2 Attributes (criteria) involved in (Muzyka et al., 1996)...................................................................21
Table 3 Characteristics listed in Survey 1......................................................................................................28
Table 4 Attributes and levels applied in Survey 2 conjoint analysis study...............................................29
Table 5 Orthogonal plans in Survey 2...........................................................................................................30
Table 3: Frequency Table of All Attributes in Survey 1 .............................................................................38
Table 7 Frequency Table of Attributes Selected by Entrepreneurial Persons and the Rest
Respondents in Survey 1 ........................................................................................................................41
Table 5: Frequency Table of Attribute chosen by Male and Female Entrepreneurial Respondents in
Survey 1.....................................................................................................................................................43
Table 6: Percentage of Significant Attributes Selected by Entrepreneurial Person in Different Age
Groups in Survey 1..................................................................................................................................46
Table 7：Frequency Table of Attributes between entrepreneurial persons in Greater China Region
and English speaking countries in Survey 1 ........................................................................................48
Table 8: Frequency Table of Attributes between Chinese and British Entrepreneurs in Survey 1.....51
Table 9 Frequency Table of Profile Ranking in Survey 2 (Conjoint) .......................................................55
Table 10: Part-worth of all Attribute Level in Survey 2..............................................................................57
Table 11: Relative Importance of Attributes in Survey 2...........................................................................59
Table 12: Part-worth of Attribute between Male and Female Respondents in Survey 2 ......................61
Table 13: Relative Importance of Attribute between Male and Female Respondents in Survey 2 .....64
Table 14：Part-worth of Attribute between Respondents from Greater China Region and Main
English-speaking Countries....................................................................................................................65
Table 15 Relative Importance of Attributes of Greater China Region and Main English-speaking
Countries...................................................................................................................................................65
Table 16: Part worth of Attribute between Investors and Entrepreneurs in Survey 2..........................69
Table 17: Relative Importance of Attributes between Investors and Entrepreneurs............................71
Table 18 Part-worth of large and small business owners ...........................................................................72
Table 19: Part Worth of Attributes from Respondents between different Age Group........................75
Table 20: Relative Importance of Attributes between different Age Group...........................................77

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Chapter 1. Introduction
1.1 Background
In recent years, mobile technologies and consumer Internet have globally revolutionised
people’s lives. Owing to the technological development, the technical threshold has been
distinctly lowered, so that starting a business (particularly in the Internet domain) seems
easier than ever. As likened by TechCrunch1
as the “Cambrian Explosion”, hundreds of
startups mushroom every month, not only in industrialised countries but also in some
developing countries such as China (Chang, 2014; Schonfeld, 2011).
In this startup boom, although bootstrapping is possible, more entrepreneurs will need
to secure capital from investors to implement their ideas. The importance of early-stage
investment (including angel investment, venture capital and also crowd funding) is
becoming more remarkable, which has led to a significant expansion in the venture
investing industry. According to Dow Jones VentureSource, in the first quarter of 2014,
budding firms of all types in the United States raised almost $10 billion from venture
capital while in China also more than $1 billion was raised, which was a 35% increase
compared to the same period last year and let China go ahead of Europe in terms of the
volume of venture investment (Chang, 2014).
However, the fact is that dreamers are always more than successes. Due to the high risk
of financing early-stage startups, venture capitalists (VCs) are still highly fastidious. One
example given by Prive (2012) is Charles Rivers Ventures. This veteran firm, which was
founded in 1970, claims to receive thousands of inquiries, and reviews and meet ups with
hundreds of teams, but would only invest in one or two of them. Many startups would
1 TechCrunch is a famous news website focusing on IT industry, especially on startups and technology
entrepreneurship.

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have been rejected before they had a chance to meet the investors. There could be
countless reasons in regard to ideas, products or market potential etc., but the most
important assessment consideration has been reported as the quality of the entrepreneur
or the team (Shepherd & Zacharakis, 1999). This makes it a meaningful question as to
how investors screen out qualified entrepreneurs without any in-depth communication
with them.
In practice, some qualities of entrepreneurs could be easily captured through application
materials (e.g. founder’s resume) and, in some cases, short meetings. These qualities
(including age, gender, nationality, education background, speciality, working experience,
presentation skill etc.), which can be categorised as biographical characteristics, have
been demonstrated to be highly influential in predicting the success of one’s job search
and interview (Tay, Ang, & Van Dyne, 2006). Thus, accordingly, the author decided to
explore whether biographical characteristics of entrepreneurs also play fundamental roles
affecting the startups, particularly during the early-stage fundraising. The main research
question is:
• Which ones of the entrepreneurs’ biographical characteristics (i.e. age,
gender, education level, occupational expertise/background etc. some
more are selected during the study) could significantly affect the
decision-making during early-stage fundraising and how?
• Which of these characteristics are more significant?
• What are the variances of the judging criteria of people with different
background?

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1.2 Why Conjoint Analysis?
Before the recent startup explosion, the earlier vitality of high technology startups and
investment industry, as well as the achievements of regional entrepreneurship clusters
such as the Silicon Valley since 1980s have already created new zones of scholarships in
the field of entrepreneurship (Jones & Wadhwani, 2006). Plentiful research studies have
emerged, studying entrepreneur characteristics, investors’ decision-making and other
related topics (Begley & Boyd, 1988; Brandstätter, 1997; Buttner & Rosen, 1989; Delmar
& Davidsson, 2000; Dubini, 1989; Ehrlich, De Noble, Moore, & Weaver, 1994; Fischer,
Reuber, & Dyke, 1993; Forbes, 2005; Khan, 1986; McClelland, 1987; Donald L. Sexton
& Bowman-Upton, 1990; Thompson, 2004).
In order to examine the decision behaviour of entrepreneurs and investors, these studies
often apply methods such as survey or interview. However, since these methods are
based on post hoc information collection that asks respondents to recall or explain the
decisions they have already made, it is difficult to avoid attribution bias, hindsight or
revisionism (Golden, 1992, cited in Lohrke et al., 2010; Shepherd & Zacharakis, 1999).
An appropriate approach that may overcome the above limitations should be designed.
Conjoint analysis (CA) is considered to be one such method. Initially known as conjoint
measurement, CA is a method that can quantitatively measure people’s preferences. It is
theoretically based on an assumption in which a product or service is composed of
different attributes (e.g. colour and price as for the attributes of a car product), and each
attribute consists of different levels (e.g. white, red or blue, and £10,000 or £15000 could
be the levels of colour and price attributes). Therefore, different products or services can
be described as various profiles, i.e. combinations of attributes and levels (e.g. a £15,000
blue car). The value (consumer’s evaluation) of each profile can be decomposed into the
values of its attributes.

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CA is designed to assess people’s theory in use when making decisions. In a CA test,
respondents are usually required to make judgements on profiles, from which their
decision processes can be decomposed into its underlying structure (i.e. the attributes’
significance in the judgement, how these attributes affect the judgement and the relative
importance of each attribute in the decision process) (Shepherd & Zacharakis, 1999). As
the decision-making scenarios are simulated, CA can prevent the bias that often exists in
post hoc research. Another benefit of the CA method is that it can directly obtain the
importance of each level of attribute of the testing object. For this dissertation, as one
research question is to discover which of the biographical characteristics are more
significant, CA is very applicative.
Currently, the CA method is relatively rarely used in entrepreneurship studies. Several
scholars have suggested more CA usage in this field (Lohrke et al., 2010; Shepherd, 1997).
This research could possibly provide a new practical case. However, considering the
relative higher complexity of participating in a CA survey, as well as the narrow range of
targeted population (entrepreneurship related people), the sample size is predictably
small. Thus, in this dissertation, the author also conducts a more traditional survey
collecting feedback about the research question from the general public. This may help
obtain more meaningful data. Also, it makes a comparison between traditional survey
and CA test possible, which could bring more interesting results.
1.3 Aims and Objectives
The aim of this research is to evaluate how entrepreneurs’ biographical characteristics
create impact on investors’ decision-making process, to what extent these characteristics
affect investing judgment, and which of them are more effective, by applying both
traditional questionnaire and conjoint analysis.

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The research also attempts to provide guidance for entrepreneurs, investors and scholars
as well as to introduce CA as a productive methodology for future entrepreneurial
studies.
Several objectives have been scheduled in order to achieve the above aims:
1. To review the history of related entrepreneurial studies and select key
biographical characteristics for investigation;
2. To conduct surveys applying both traditional questionnaire and the CA
approach, involving both general public and entrepreneurial groups;
3. To explore the relative importance of different biographical characteristics,
and draw significant conclusions from the findings, which can be helpful
to entrepreneurs, investors as well as scholars; and
4. To compare between traditional and CA methods, and verify the
applicability and feasibility of the CA method in entrepreneurial studies.

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1.4 Dissertation Structure
This dissertation consists of 6 chapters.
The present chapter (Chapter 1) elaborates on the background and the purpose of this
study with a preliminary overview to conjoint analysis. In Chapter 2 the author reviews
related literature including previous studies on entrepreneurs’ characteristics, theoretical
background of the CA method and applications of CA in entrepreneurship studies.
Chapter 3 explains the methodology of this research, including experimental design, data
collection and processing. Chapter 4 describes the results and key findings of both
surveys, followed by a discussion in Chapter 5. Finally, Chapter 6 concludes with a
summary of the paper, evaluates the experiment and makes recommendations for further
research.

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Chapter 2. Background & Literature Review
As a recently emerging research field, entrepreneurship study has been under rapid
development in the past decades, among which the prediction of investor
decision-making and the evaluation of entrepreneurs as an individual or a team have
become key research domains. Meanwhile, although the method of conjoint analysis (CA)
is relatively unrecognised in the research of entrepreneurship, it has been marginally
conducted when examining venture financing and decision-making.
In this chapter, the author will firstly introduce the related context in entrepreneurship
studies, followed by a detailed overview of the theoretical background of CA method.
Then, previous discussions about the CA method in entrepreneurship research will be
examined. In the end, the author will conclude this literature review with the experience
gained from, as well as the deficiency and potential improvements found in, the previous
studies.

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2.1 Context in Entrepreneurship Studies
The emergence of the venture investment industry in the past decades has redefined the
relationship between entrepreneurs and investors, and re-established a series of criteria
and examination processes for new ventures, making it a key research field in
entrepreneurial studies. A number of research studies on VC investment
decision-making have been conducted previously, and findings indicate that the human
factor is one of the most important judging criteria, among which the biographical
records of entrepreneurs are considered as crucial.
2.1.1 The Investors’ Role and Their Judging Criteria
New ventures always carry very high levels of risk. Thus, entrepreneurs are less able to
fund the business by themselves, leading to the need for external investors, typically the
VCs (Muzyka, Birley, & Leleux, 1996). Those investors are frequently the crucial factor
enabling entrepreneurial activities to thrive. Their relationship with entrepreneurs is
highly comprehensive, particularly in high-technology industry.
Coopey (2005) highlighted that the action of investment is more than a symbolic
function of funding in new firms. He (ibid) indicated that investors restore the
connection between the bank and the entrepreneurs. On the one hand, investors
themselves seek investments and strive to enhance the success of those investments,
while on the other hand they are the main suppliers of funds to ventures. At the same
time, investors hold equity, which puts themselves in direct ownership, sometimes in the
management of the startups along with the entrepreneur. Beyond this, investors could
bring in technical knowledge and publicity, which were often of great importance to an
early start up.

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Apparently, external resources from investors are crucial to startups. However, investors
could be highly picky. Their deciding of investment has always been complex and
cautious due to the high risk. In general, they will go through a series of screening
processes and criteria before reaching an investment deal with startups. Key
considerations may include the market sizes, product features, projected return on equity
capital, and the management teams etc. (Sandberg & Hofer, 1988).
Among these aspects, the nature and quality of the entrepreneur and management teams
is considered to be crucial. Tyebjee and Bruno (1984) listed the founders’ managerial
capabilities as one of the most important criteria after they studied 41 VCs. MacMillan,
Siegel and Narasimha (1986) also studied the evaluation criteria ratings given by 100
venture capitalists, and concluded that although the product, market and financial factors
are considered, it is the entrepreneur who fundamentally determines whether the VC will
invest. Sandberg and Hofer (1988) argued that disregarding the interactive effects, the
industry structure had a greater influence than the characteristics of the entrepreneurs.
Nevertheless, they also admitted that the entrepreneur is of the top three factors (with
industry structure and strategy) that have the greatest impact on new venture
performance.
To summarise, although there is great diversity in the evaluation criteria (e.g. quality of
business idea, market potential etc.), the “factor of people” (e.g. entrepreneur’s capability)
is considered to be generally predominant (Zopounidis, 1994, cited in Muzyaka et al.,
1996).
2.1.2 Discussions on Biographical Characteristics of Entrepreneurs
Biographical characteristics, i.e. information and traits that can be obtained easily from
one’s personal records and introduction, have been used as important assessment criteria

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in job interviews (Tay et al., 2006). Similarly, when assessing the capacity of
entrepreneurial individuals and teams, it is inevitable to examine their biographical
characteristics. Previous literature has provided diverse discussions on the validity and
impact of biographical characteristics.
Age, Gender and Nationality
Braguinsky, Klepper, and Ohyama (2009) reported that the percentage of entrepreneurs
would increase with age until around the age of 45 and forming a plateau till age 60, then
followed with a sudden surge in the age group of 60-65 (Evans & Leighton, 1989b).
With reference to cognitive and experience difference, young entrepreneurs were notable
for their high novelty, but at the same time susceptible to overconfidence and
overestimation of knowledge in decision-making (Forbes, 2005). In contrast, older
entrepreneurs would benefit from the accumulation of human, financial and social
capital in the creation of new ventures, resulting in a higher survival rate to their younger
counterparts, although they might rely on an uncertainty-reducing cognitive framework
that hindered innovation (Cressy, Storey, & Sweeting, 1995; Kautonen, 2008; Singh &
DeNoble, 2003).
Regarding gender, a sizable body of research has shown that that female entrepreneurs
might receive unequal treatment from venture capitalists, loan officers and business
partners (Buttner & Rosen, 1989; Fischer et al., 1993; Donald L. Sexton &
Bowman-Upton, 1990; Donald L Sexton & Kent, 1981).
In regard to nationality, Muzyka et al. (1996) found that a number of venture capitalists
only concern themselves with entrepreneurs of the same nationality and with ventures
inside their own country. However, there has been an uncommon biographical factor in
studies targeting respondents of the same nationality or country of residence.
Educational Level and Occupational Expertise

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Blanchflower (2000) discovered that a higher percentage of entrepreneurs seemed to be
among the least and most educated. The most educated often benefited from more
wealth to support their venture whereas the least educated demonstrated a greatest
willingness for risk-taking (Evans & Jovanovic, 1989). However, in particular industry
such as technology and the Internet, around 80% of the founders in Inc 500 had acquired
at least a college degree (Bhide, 2000; Donald L. Sexton & Bowman, 1986).
Regarding occupational expertise, technology, business management or design
background are normally desired, while Lazear (Backes-Gellner & Lazear, 2003; Lazear,
2004, 2005) proposed a series of theories that an entrepreneur should be a “jack of all
trades”, suggesting that they should hire specialists to work for them while they
themselves remained a generalist with balanced skills.
Personality and Language Skills
The personality (extroversion & introversion) of entrepreneurs is considered to be
controversial. Van de Ven, Hudson, and Schroeder (1984) suggested that an extroverted
entrepreneur might maintain a broad personal network and more sources of information,
leading to their success in entrepreneurship, whereas Lee and Tsang (2001) examined
168 Singaporean entrepreneurs and found that extroversion had little impact on venture
success.
Communication or language ability, such as pitching skill, has received strong evidence
showing its positive correlation with venture success, particularly in the past 20 years
when business angels, venture capitalists and investors started inviting entrepreneurs to
deliver business pitches for all kinds of occasions. These presentations vary in length
from 1-minute elevator pitch to 30-minute demonstrations and are crucial factors in the
early stages of the investor’s decision-making process. However, some business angels
seemed to be unaware or reluctant to acknowledge that presentation factors have a key

24. 12
influence on their judgment (Clark, 2008). However, it has been questioned as a pitch
does not merely demonstrate presentation skill but is also backed by the entrepreneur’s
passion and substantiality of the business plan (Chen, Yao, & Kotha, 2009).
Startup Experience
Gompers, Kovner, Lerner, and Scharfstein (2006) have proven that performance
persistence may favour entrepreneurs with successful start-up experience, particularly in
choosing subsequent industry and market timing; thus, venture capitalists were inclined
to support them in return for higher return of investment (Gompers et al., 2006; Wright,
Robbie, & Ennew, 1997). On the other hand, investors seem not to only rely on start-up
experience but would also analyse entrepreneurs’ backgrounds, liability and motivation to
take the next venture. Some venture capitalists reported that serial entrepreneurs were
less able to recognise their own weaknesses than novice entrepreneurs. Some investors
might even feel unease with second-time entrepreneurs as they may be more inclined to
have relatively higher bargaining power and demanding requirements (Forbes, 2005;
Westhead & Wright, 1998).
2.2 Background of Conjoint Analysis
Although it has been frequently applied in product development, marketing analysis and
many academic fields, conjoint analysis (CA) is relatively less cognised in the domain of
entrepreneurship study. Having CA as the main research approach in this dissertation,
within this section the author will introduce the CA method in detail.
2.2.1 Overview
CA is a quantitative, multivariate approach for examining and assessing people’s
preference structures. American mathematical psychologist, Luce, and statistician, Tukey
(1964) first proposed this method as conjoint measurement in psychological research.

25. 13
Paul Green, from the Wharton School, who has been called “the father of conjoint
analysis”, firstly recognised the possibility of using conjoint measurement in the field of
marketing to study how individuals make buying decisions, and to predict potential
consumer behaviours (Green & Rao, 1971; B. K. Orme, 2010). Nowadays, this method is
still widely applied in marketing research and practice (Christofor & Kollmann, 2008).
The term “preference” is defined as evaluative judgments in the sense of liking or
disliking an object over other objects (Scherer, 2005). It is conceivable that to
quantitatively measure people’s preferences is very difficult, especially the exact degree of
their preferences for different stimulus. By using the metaphor quoted below, Huber
(2005, pp. 1-2) made it easier to understand this situation:
“We know what it means to say that we like potatoes better than rutabagas, but generally not
what it means to say that our liking for potatoes over rutabagas is greater than our liking for
artichokes over eggplant.”
The basic concept of CA is to solve this problem by converting non-metric observations
of human preferences into metric values and to discovering the inner relationship
between them (Christofor & Kollmann, 2008). It was designed as a decompositional
method based on the assumption that an object constitutes a series of attributes (e.g.
colour, price) at different levels (e.g. red or green, and £10, £20 or £30), and people’s
preferences are decided upon rational consideration and trade-offs of these attributes
and levels. Accordingly, the total utility of an object equals to the sum of the utility of
each attribute at the corresponding level (Green, Krieger, & Agarwal, 1993; B. K. Orme,
2010). The combinations of attributes and levels are usually referred to as a “profiles” or
“stimuli”. The specific utility of each attribute level, which represents people’s preference
for this attribute level when making a trade-off, is referred to as “part-worth”.

26. 14
There are several major types of data collection in CA, in which the full profile approach
is most frequently applied (Green, Krieger, & Wind, 2001). In a full profile CA test,
researchers will give respondents complete combinations of attributes and levels and ask
them to provide their preferences by ranking, rating or selecting. Then, by measuring the
frequency of being preferred, researchers can uncover the part-worth of each level and
the relative importance of each attribute (Lohrke et al., 2010). Higher part-worth denotes
higher preference, and the attribute with largest part-worth range among its levels has the
greatest relative importance weight.
The basic equation of the full profile model is as follows:
�(�) = �!"
!!
!!!
!
!!!
�!"
where �(�) = utility of profile �
� = 1 … � = number of attributes
� = 1 … �! = number of levels of attribute �
�!" = part-worth of level � �f attribute �
�!" is a dummy variable.
When level � of attribute � exists, �!" = 1, otherwise �!" = 0
Meanwhile, the importance I of attribute i equals to the range between its maximum and
minimum utilities:
�! = max �!" − min �!"
Thus, the relative importance W can be weighted by:
�! = �! �!
!
!!!
To summarise, CA estimates the utility of each attribute by establishing equations
between each level of attributes and respondents’ ratings. The mathematical difficulty

27. 15
was a barrier of wider CA usage. Fortunately, since the development of microcomputer
and related software in the 1980s, the application of CA has become easier and less costly
(Green & Srinivasan, 1990).
2.2.2 General Procedure
A CA research includes several typical steps. First of all is to define the attributes, and
designate them into numerical or categorical levels. Then, stimuli (i.e. profiles) are
generated through combining these attributes and levels. In most cases, the number of
possible profiles can be too large to be fully valued by the respondents. Normally, a
fractional factorial design will be adopted, i.e. to generate a representative subset of
profiles by using an orthogonal method (Hair et al., 2006, cited in Christofor and
Kollmann, 2008). With generated stimuli set, the next step is to appropriately present the
profiles to the participants and ask them to provide preferences by rating, ranking etc.
Traditional ways include profile cards and verbal descriptions, while nowadays computer
based survey is also popular. Lastly, data collected will be processed and analysed, usually
with the aid of computer software (e.g. SPSS). The indicators of CA, such as part-worths
and utilities are estimated at this step.
An overview of CA procedure is shown in Figure 1.

28. 16
Figure 1 Overview of CA procedure, revised from Christofor and Kollmann (2008).
Profile 1
A1 B2
C4 D5
Profile 2
A2 B1
C3 D2
Profile …
…
Profile 3
A4 B3
C1 D3
Testing Object
Attribute A B C D …
Levels A1 B1 C1 D1 …
A2 B2 C2 D2 …
A3 B3 C3 D3 …
A… B… C… D… …
Preference Data Collection
Processing with Conjoint Analysis
Object
A B C …
A1
A2
…
B1
B2
…
C1
C2
…
…
Overall utility of object (profile)
Relative importance of each attribute
Part-worth of each level

29. 17
2.2.3 Advantages and Disadvantages
The most important advantage of CA is that it is able to measure respondents’ “theory in
use” (how people actually behave) rather than “espoused theory” (how people claim to
behave), which is often different (Argyris, 1976). Different to other compositional and
retrospective methods, in CA the decision-making scenario is simulated and the
respondents are asked to make real-time decisions. This makes CA an excellent technique
for investigating relationships between a number of evaluating criteria and a particular
judgement as it is able to avoid the post hoc bias results, which is regularly seen in many
studies (Lohrke et al., 2010; Shepherd & Zacharakis, 1999).
CA is also unique in its data collection process. In some compositional approaches,
researchers are required to collect both independent and dependent variables to compose
the predicting model; while in a CA test, independent variables (attributes and levels) are
specified beforehand, so only the dependent variable (respondents’ preference data) is
collected (Hair et al., 2006; Lohrke et al., 2010). It seems to collect less data but actually it
could generate abundant results. Moreover, CA offers the ability of conducting statistical
tests at the individual level, which means that a sample of one can be enough to obtain
statistical power to test for importance (Shepherd & Zacharakis, 1999). This is very
meaningful for studies targeting relatively small populations such as entrepreneurs and
investors.
However, despite the long-standing recognition of its effectiveness, it has been rarely
used in entrepreneurship studies. In fact, Dean, Shook, and Payne (2007, cited in Lohrke
et al, 2010) found that the CA method had only been used to test 2% of hypotheses
between 1976 and 2004 in two leading entrepreneurship journals. A possible reason
could be the lack of knowingness and the difficulty of application to most
entrepreneurship scholars (especially in profile presenting). Compared to traditional
questionnaires, sometimes CA research is more difficult for the survey participants to

30. 18
understand. If face-to-face data collection is not possible, the participation rates of CA
research might be low.
Also, in CA tests, unrealistic profiles sometimes might be generated when the attributes
are correlated. For example, a profile of very cheap luxury cars (as the two attributes,
price and luxury, are positively correlated) can be generated in a CA research on
automobile products. In order to keep the orthogonality, it is usually not suggested to
simply delete the unrealistic profiles. Although it has been demonstrated that such
situations will hardly affect the validity of the results, unrealistic profiles may cause
confusion to the respondents and may influence their decision-making. As the author
also met this issue in this research, this problem will be discussed more in the
methodology chapter.
Nevertheless, seeing its exclusive advantages, the CA is a valued tool to study people’s
preferences and decision-making strategies. It is suggested that space exists for more CA
usage in entrepreneurship studies (Lohrke et al., 2010).

31. 19
2.3 Application of Conjoint Analysis in
Entrepreneurship Studies
As discussed before, despite its recognised advantages in studying decision-making, the
CA method has not been frequently used in entrepreneurial research. Consequently, the
author experienced difficulty in searching for related previous studies. To the author’s
knowledge, there was no exact study particularly on entrepreneurs’ biographical
characteristics that applied CA as its main methodology. Nevertheless, the method has
been marginally used in some field of entrepreneurship study, for example, some studies
of general VC’s decision-making. These existing studies still provide the author with
good reference value.
A study conducted by Riquelme and Rickards (1992) from Manchester Business
School was one of the earliest attempts to apply CA method in the field of
entrepreneurship. The main purpose of their study was to demonstrate the potential of
CA as a practical research method in entrepreneurship study as well as to test the utility
of different CA models (self-explicated, traditional – which is applied in the present
research, and the hybrid which comprises the previous two models). Therefore, it was a
relatively small-scale research that only involved several respondents in their test. Their
research question was to test if the characteristics of entrepreneurs, the product or the
market could predict the VC decision. The attributes and levels applied in their research
are given in the table below. By using an orthogonal design, the number of profiles was
reduced to a minimum of 27 profiles from 864 (26
×33
).
Attributes Levels
1. Entrepreneur’s Knowledge of Production and Technological process A. Unacceptable
B. Acceptable
2. Entrepreneur’s Managerial Experience A. Unacceptable
B. Acceptable
3. Unique Features of the Product A. Unacceptable
B. Acceptable
4. Patent to Protect the Product A. Unacceptable
B. Acceptable
5. Functioning Product Prototype A. Unacceptable
B. Acceptable
6. Market Growth A. Less than 10%

32. 20
Attributes Levels
B. 10%-19%
C. Over 20%
7.Level of Competition in the Industry A. Low
B. Medium
C. High
8. Expected Product Gross Margin A. Below 40%
B. 40%-49%
C. Over 50%
Table 1 Attributes and levels in Riquelme and Rickards (1992).
They found that during the screening stage, VCs use a non-compensatory method (i.e. a
low performance on one major criterion results in rejection) where entrepreneurs’
experience and the existence of product prototype are considered as crucial criteria.
While in the later stage, VCs use a compensatory method (i.e. low score of certain criteria
can be offset by high scores in other criteria) where entrepreneurs’ experience, patents
and product margin are important. Their results confirmed the significance of
entrepreneurs’ experience in the VC’s decision process, which verified results of some
other scholars (Khan, 1986; MacMillan et al., 1986; Tyebjee & Bruno, 1984). More
importantly, Riquelme and Rickards (1992) confirmed that VC decision-making can be
modelled with both traditional CA (full-profile method) and hybrid CA at a good
reliability. Also, it has served as a guide for the later CA usage in entrepreneurship
research.
Muzyka, Birley and Leleux (1996) applied CA in examining the key criteria used in the
investment decisions of 73 European VCs. A total of 35 investment criteria were
identified from VCs self-reporting in a preliminary interview. Each criterion was
designated with three trade-off options (e.g. high, medium and low for market size).
Pair-comparison method was used to collect the data, which asked VCs to make 53 pairs
of trade-offs (See Table 2 for details of investment criteria). Its complexity and multiple
levels required each VC to take up to one hour to complete the survey.
Financial Management team
• Time to break even • Leadership potential of management

33. 21
• Time to pay back
• Expected rate of return
• Ability to cash out
team
• Leadership potential of lead
entrepreneur
• Recognized industry expertise in team
• Track record of lead entrepreneur
• Track record of management team
Product-Market Strategic
• Degree market already established
• Market size
• Seasonality of product-market
• Sensitivity to economic cycles
• Market growth and attractiveness
• Uniqueness of product and technology
• National location of business
• Degree of product market
understanding
• Ease of market entry
• Ability to create post-entry barriers
• Sustained share competitive position
• Nature and degree of competition
• Strength of suppliers and distributors
Deal
• Stage of investment required
• Number and nature of co-investors in
deal
• Ability to syndicate deal
• Scale and chance of later funding
rounds
Management competence
• Marketing/Sales capabilities of team
• Process/Production capabilities of team
• Organizational Administrative
capabilities of team
• Financial/Accounting capabilities of
team
Fund
• Business meets fun constraints
• Business and product fit with fund
portfolio
• Ability of investors to influence nature of
business
• Location of business relative to the fund
Table 2 Attributes (criteria) involved in (Muzyka et al., 1996)
Their results showed that all the five management competence attributes were ranked as
the most important ones, in which the leadership ability and the management team were
ranked as first and second most important. Product-market was only moderately
significant, and fund and deal criteria were at the bottom of the rankings.

34. 22
There were several more studies in entrepreneurship that applied similar CA methods.
Lohrke, Holloway and Woolley proposed a research agenda about CA in
entrepreneurship research in 2010. They thoroughly searched leading entrepreneurship
journals from 1999 to 2008, looking for empirical studies that applied CA and examined
entrepreneurship process issues, and ultimately located 16 studies (see Appendix I).
Although they recorded an upward movement in CA usage, they suggested that 16
studies in 10 years from 25 journals is still a very low usage rate. They pointed out that
the fact that entrepreneurship scholars lack training in CA methods could be a major
reason for limited CA usage in this field. Nevertheless, they again emphasised the CA’s
advantage of allowing researchers to assess the “theory in use” and encouraged more CA
application in future entrepreneurship research.

35. 23
2.4 Summary
In this chapter, the author reviewed the context of entrepreneurship research, introduced
the methodological background of CA, and examined the previous application of CA in
entrepreneurship studies.
The literature study shows that entrepreneurs’ biographical characteristics can be crucial
in effecting investors’ decision-making. To sum up, age, gender, nationality,
educational level, occupational expertise, personality (extroversion & introversion),
language skill (pitching & communication ability), and startup experience are claimed
to be typical influential factors. The later survey of this research will use these as main
attributes to study.
CA as a research method can help assess respondents’ “theory in use”. Using CA in
entrepreneurship studies, especially in evaluating the judging criteria, can help avoid post
hoc bias and revisionism. However, due to some limitations of both the method and the
cognition of scholars, CA usage is relatively low in entrepreneurship studies. More
application of CA is suggested, yet some problems should be noticed and solved when
using this method, such as the problem of unrealistic profiles.
In the next chapter, the author will explain the research design of this study, which
includes both a traditional questionnaire and a CA test. He will also describe the sample
involved in two surveys.

36. 24

37. 25
Chapter 3 Methodology
As discussed in the previous chapters, this dissertation purposes to study that how can
biographical characteristics of entrepreneurs affect the investment judgment of investors.
Through the literature review, a list of worth-studying biographical characteristics was
extracted.
Since inspired and suggested by several previous studies, conjoint analysis (CA) was
selected to be the main method in this research for its capacity of assessing respondents’
“theory in use” and estimating the relative importance of each attribute. Therefore, this
research would also verify the usability of CA in similar research field. In advance of the
CA survey, a traditional questionnaire was conducted independently to cover the
potential shortage of CA (e.g. low participating rate), as well as to allow the validation of
the later CA results.
In this chapter, all the research steps and survey designs will be familiarised in detail. The
author will explain why two surveys are necessary, define the variables involved, describe
the design and implementation of both surveys, and also demonstrate the credibility of
this research by introducing the composition of data samples.

38. 26
3.1 Overview
The author would like to reiterate the main research question of this research. That is to
study how and to what extent could the entrepreneurs’ biographical characteristics affect
the investing decision-making, which of those characteristics are considered as more
important, and are there any variances between clusters with different background (age,
gender or nationality etc.).
From related researches done by the other scholars, the author gained much experience.
However, most of those studies used normal questionnaire and interviews as main
approach, which are demonstrated to possibly cause biased results due to post hoc data
collection and revisionism (Lohrke et al., 2010; Shepherd & Zacharakis, 1999). As a tool
measuring “real time” decision-making, CA was suggested by some scholars due to its
ability to cover the above-mentioned problem. Thus, for this research, CA was
nominated as the main research approach.
The author believes that the most appropriate respondents to answer the research
questions would be experienced venture investors. However, as restricted by the rareness
of venture investing professionals as well as the author’s limited business network, it
would be difficult to get enough respondents from only investors. Considering that this
is an experimental research, the author decided to also involve entrepreneurs (and
sometimes nascent entrepreneurs) who are familiar with entrepreneurship topics. Even
so, the targeting population is still relatively small. Besides, understanding the CA test
could be comparatively difficult. Given this situation, the author decided to conduct an
independent pilot survey facing a wider range of population in the form of traditional
questionnaire, which is easier to participate in. By doing so, the research could obtain
more data for analysis. The pilot study could also provide guidance and reference to the
design of the later conjoint survey. Moreover, the results of two surveys could possibly
validate each other.
Therefore, the whole research process could be divided into several stages: Preliminary
Research, Survey 1, Survey 2 and ultimately Data Analysis (shown below).

39. 27
Figure 2 Stages of the Research Process
The major purpose of the preliminary research was to learn the approach and gain
experience from previous studies, as well as to designate key attributes for the present
study. From the literature context, the author extracted 8 key biographical characteristics,
which were considered potentially affecting the investing decision-making : age, gender,
nationality, educational level, occupational expertise, personality (extroversion &
introversion), language skill (pitching & communication ability), and startup
experience. These characteristics will be the studying attributes in the present research.
As the preliminary literature studies have been introduced in detail in Chapter 2, in this
section the author will mainly focus on the design and implementation of two surveys.
Preliminary
Research
•Litearature Study
•Designating
Attributes
Survey 1
•Traditional
Questionnaire
•Designed for anyone
who is interested in
entrepreneurship
topics
•Distributed on social
networks
•Large sample
Survey 2
•Conjoint Analysis
•Designed for
entrepreneurial
professionals
•Invited only through
emails and private
messages
•Smaller sample
Data
Analysis
•SPSS and Excel
used
•Comparison
between clusters
•Comparison
between two
Surveys

40. 28
3.2 Research Design
In this section, the author will introduce the design and implementation of both surveys.
Survey 1: Traditional Questionnaire
Survey 1 was designed in the form of a traditional online questionnaire. The research
used the university’s online survey system (powered by Qualtrics) which provides great
convenience from survey design, distribution to the final data processing.
Respondents were asked to assume that they are venture investors that are going to
investing on a technology startup in IT industry. They are given a list of 12 biographical
characteristics of the potential investees. The task was to consider the importance of
those characteristics, and pick out four most significant characteristics as well as four
least significant ones based on their understanding and preference. The list used in the
survey is given below (full questionnaire available in Appendix II.).
Items Most significant
• Age
• Gender
• Nationality
• Education level
• Technology background/experience
• Art & Design background/experience
• Management, marketing or finance background/experience
• Start-up or fundraising experience
• Presentation skill (language level)
• Employment record
• Extroversion/Introversion (personality)
1
2.
3.
4.
Least significant
1.
2.
3.
4.
Table 3 Characteristics listed in Survey 1
In Survey 1, the previously discussed attribute “occupational expertise/background” was
divided into technology background, art & design background and management,
marketing or finance background. The aim is to help identify the importance of each of
these skills. Except these factors, all other attributes (e.g. product, market etc.) were
controlled variables. Respondents were asked to ignore all the controlled variables when
making decisions.
The questionnaire also record some personal information of the respondents, including
age, gender, nationality, country of residence, occupation etc. This is for verifying the

41. 29
representativeness of the sample i.e. the credibility of the data. This information also
helped conducting the later comparison between different clusters.
As the author’s personal network is largely based on Chinese population, both English
and Chinese versions of the questionnaire were created. The questionnaire was then
spread through social networks (e.g. Facebook, LinkedIn etc.).
Survey 2: Conjoint Analysis Survey
The Survey 1 was first conducted as the pilot study. Survey 2 was designed after a
primary analysis on Survey 1 data (for detail please refer to the data & findings chapter).
As it has been discussed in the literature review, full-profile method is proved to be the
most feasible and stable model of CA. Thus the CA survey is based on the full-profile
model.
CA survey design
The first step is to designate the attributes and levels involved in the CA test. Below is
the list of the attributes and levels of this research.
Attributes Levels
Age
21 years
26 years
36 years
47 years
Gender
M
F
Expertise/ Technology
Background Business/Management
Design/Creativity
Jack-of-all-trades
Education
Secondary level/no degree
Bachelor's level
Master's level
Doctoral level
Personality
Extroversion
Introversion
Language Skill
Good (native)
Good (non-native)
Average
Below average
Startup Experience
Yes
No
Table 4 Attributes and levels applied in Survey 2 conjoint analysis study

42. 30
In Survey 2, technology, management and design background are classified under the
attribute “occupational expertise/background”. Meanwhile, a level of “jack-of-all-trades”,
which was defined to the respondents as having capacity in 2 or more fields, was added
in Survey 2. The number of all the possible combinations of these attributes and levels is
2048 (44
×23
), which means it is not possible to be displayed all to the participants. A
fractional factorial design was necessary. By using the orthogonal module of the SPSS
software, the author generated a representative group of 16 profiles (given in the table
below).
Name Age Gender Occupational Expertise/
Background
Education Level Personality Language Skill St
Exp
1 James 26 M Technology Bachelor's level Introversion Average No
2 Mary 26 F Jack-of-all-trades Master's level Introversion Good (native) Ye
3 Linda 36 F Design/Creativity Secondary level/no degree Introversion Average Ye
4 Robert 21 M Technology Secondary level/no degree Extroversion Good (native) Ye
5 Susan 47 F Technology Doctoral level Introversion Good (non-native) Ye
6 Michael 36 M Jack-of-all-trades Bachelor's level Extroversion Good (non-native) Ye
7 Nancy 36 F Technology Master's level Extroversion Below average No
8 David 47 M Jack-of-all-trades Secondary level/no degree Introversion Below average No
9 Lisa 21 F Jack-of-all-trades Doctoral level Extroversion Average No
10 William 21 M Design/Creativity Master's level Introversion Good (non-native) No
11 Helen 21 F Business/Management Bachelor's level Introversion Below average Ye
12 Laura 26 F Business/Management Secondary level/no degree Extroversion Good (non-native) No
13 Paul 26 M Design/Creativity Doctoral level Extroversion Below average Ye
14 Mark 36 M Business/Management Doctoral level Introversion Good (native) No
15 John 47 M Business/Management Master's level Extroversion Average Ye
16 Amy 47 F Design/Creativity Bachelor's level Extroversion Good (native) No
Table 5 Orthogonal plans in Survey 2
In the survey, profiles were presented to the respondents as “entrepreneur cards” (see
samples below, full set available in Appendix III.). Respondents were put under the same
situation as Survey 1, and were asked to rank these cards from 1 to 16 according to there
intend of investing.

43. 31
Figure 3 Sample profile cards used in Survey 2

44. 32
The problem of unrealistic profile
As a common issue in many CA studies, the present research also met the problem of
unrealistic profiles. This is usually due to the underlying correlations between attributes.
In this research, the attribute age and education level have certain correlation i.e. see
profile No.9 Lisa, regularly 21 years old is not able to achieve doctoral level education
(although there are a number of exceptions).
A number of scholars have discussed such problem. Green and Srinivasan (1989) made
suggestion of making up “super-attributes” to deal with this problem. Which means, in
this case, crossover age and education level to make a new attribute, and exclude the
combination of “21 years PhD”. However, this solution could cause a significant growth
in the number of orthogonal plans. When super-attribute is not feasible, it is not unusual
to simply delete the totally unrealistic profiles, allow some correlations between attributes,
and depart from the fully orthogonal design (Green & Srinivasan, 1989). According to
Gleser (1972), such correlation between attributes cannot be more negative than -1/(t-1),
where t represents the number of attributes. Therefore, in this case (t = 7), the average
inter-correlation cannot be more negative than -0.167. This is not too different from the
full orthogonal case with no correlation (Green & Srinivasan, 1989). Therefore, Wiley
(1978), Krieger and Green (1988) put forward the concept of “Pareto-optimal” stimuli
sets which means no attribute dominates any other attribute in a CA project.
However, Moore and Holbrook (1990) conducted several experiments and concluded
that such correlation between attributes are not as effective as they are theoretically, and
the realism of profiles may not be as important as scholars feared. Because, while
participants found several less realistic profiles (especially when being asked by the
researcher), these realism difference hardly affect judgments (Moore & Holbrook, 1990).
Overall, they indicated that using less extreme combinations in CA may slightly improve
the authenticity for the respondents, but will not cause huge difference in the predicting
power compared with full orthogonal plans. On the contrary, this would largely affect
the experiment efficiency. B. Orme (2002) also pointed out that it is often harmful, and
sometimes fatal, to simply delete unrealistic profiles, although this could bring more
realistic scenarios. Non-necessary or excessive exclusion of profiles is commonly seen
mistakes in CA study. He suggested that prohibition of profiles should be used sparingly,

45. 33
or not at all (B. Orme, 2002). The better solution could be urge respondents to answer as
if these less realistic profiles were actually exist.
Return to the present study, there is no absolute correlation between age and education
level (only very young age may be considered as less possible to obtain high level degree).
Also, there are indeed a number of genius people who can make such achievement.
Therefore, the author decided not to damage the orthogonality of the research design.
Distribution and data collection
Survey 2 targets on entrepreneurial people (entrepreneurs, nascent entrepreneurs,
investors etc.). Within the survey, information such as length of running current business,
number of staff (for entrepreneurs) and length of investing experience (for investing
professionals) were collected. This was not an invited only survey. Respondents are
invited through email and private messages on LinkedIn.
A Chinese annotated version was generated for the convenience of Chinese respondents.
Seeing the complicity of ranking 16 profiles (which may take 10 to 15 minutes to finish),
respondents are allowed to quit from the study by selecting option “No” in Q8 (see
Appendix III.).
3.3 Sample Structure of Survey 1 & 2
The survey distribution and data collection have lasted for about three weeks, in which
268 respondents provided valid data for the two surveys: 231 responses for survey 1 and
37 responses for survey experiment 2 respectively. In this section the composition of the
sample will be demonstrated.
3.3.1 Survey 1: Traditional Questionnaire
Our sample has received a diverse body of responses in the online survey. Among the
231 respondents female participants are slightly more in proportion representing a 6:4
gender ratio in the self-explicated survey. Due to the personal network of the author,

46. 34
about 70% of the sample respondent between the age group of 18 to 25, followed by 16%
of participants aged between the age group of 26 to 35. For the age group of 36-45 and
45 above there is 7% and 8% respectively. Despite of the majority of young adults, the
sample still presents a certain representativeness of the overall population.
Figure 4 Respondents by age and gender in Survey 1
Respondents of Survey 1 come from over 34 countries, in which about 61% come from
the Greater China Region (including Mainland China, Hong Kong, Macau and Taiwan),
10% from the United Kingdom and 29% come from other countries.
Figure 5 Percentage of respondents by nationality in Survey 1
0 50 100 150 200
18-25
26-35
36-45
45 above
Male Female
Greater China
Region
61%
United Kingdom
10%
Other Countries
29%
Australia
Bulgaria
Canada
Chile
Croatia
Cyprus
Egypt
Finland
France
Georgia
Germany
Greece
Hungary
India
Italy
Kazakhstan
Latvia
Malaysia
Mexico
Netherlands
Nigeria
Philippines
Portugal
Romania
Russia
South Africa
South Korea
Sweden
Thailand
U.S.A.

47. 35
Figure 6 Number of male and female respondents by Background in Survey 1
Respondents in Survey 1 come from various backgrounds. Again, due to the limited
personal network of the author, about 38% (88) of respondents are business students.
However, there are still a number of entrepreneurship related respondents were involved.
33 entrepreneurs and 4 investing professionals participated, plus 12 startup employees
and 12 from other entrepreneurship related industries (e.g. incubators, consultancy etc.).
Besides, data shows that 52% of the students and respondents from the other industries
claimed that they would start their own business in the foreseeable future. The diversity
of the sample may allow comparisons of different cluster in the next chapter.
0 10 20 30 40 50 60 70 80 90 100
Business Student
Others
Entrepreneur
Other entrepreneurship related industry
Startup employee
Investor/investing industry
Business Student Others Entrepreneur
Other
entrepreneurship
related industry
Startup
employee
Investor/investing
industry
Male 31 22 25 7 5 2
Female 57 59 8 6 7 2
Total 88 82 33 12 12 4
Number of Male and Female Respondents
by Background in Survey 1

48. 36
3.3.2 Survey 2: Conjoint Analysis
Survey experiment 2 has, to a certain extend, study the opinions received in survey
1 as pilot study. It is designed specially for business professional that preferably
have entrepreneurial or investing experience. Since the target population does not
seem to overlap with the author’s personal background, the respondents of survey 2
are in general a different group of people from survey 1.
By sending formal email invitations and personal messages, about 50 responses
have been collected in the initial stage. However, due to the complexity of the
experiment, about 10 respondents have reported that they were uncertain of their
preference and chose to abstain from the survey. 3 other responses are ruled out for
various reasons such as very fast completion time and unchanged profile order. As a
result survey 2 has been left with 37 valid responses. Among these respondents,
about 25% age between 21 and 25, followed by about 40% who age between 26 and
35. Then, about 16% age between 36 to 45 and about 18% age 45 or above. It can
be seen that a high majority of 86% of respondents are male, compared with only
14% who are female respondents.
Figure 7: Number of Male and Female Respondents by Age Group in Survey 2
Differentiated from Survey 1, a majority of 74% of respondent in Survey 2 originate
from countries outside Greater China Region. However, half of these respondents
are currently living in the United Kingdom. About 32% of participants live in
0 2 4 6 8 10 12 14
21 to 25
26 to 35
36 to 45
46 to 55
56 and above
21 to 25 26 to 35 36 to 45 46 to 55 56 and above
Male 8 13 5 4 2
Female 2 1 1 1 0
Total 10 14 6 5 2

49. 37
Greater China Region and the remaining 20% live in various countries such as the
United States of America, Romania, The Netherlands, Germany and Oman.
Respondents in Survey 2 mainly consist of entrepreneur, investor and business
students. Some of them are both entrepreneur and investor at the same time,
whereas some are student entrepreneurs. A majority of the respondents are
entrepreneurs and in general, about 67% of respondents have start-up experience
and about 16% have investment experience.
Figure 8: Percentage of Respondents by Occupation in Survey 2
3.3 Summary
In this chapter, the author introduced the design and implementation of the two
surveys conducted for this research. Specifically, he discussed the problem of
unrealistic profile when generating the stimuli set for Survey 2 and demonstrated
that the present design is robust.
There were over nearly 270 respondents participated in the two surveys. They are
from various backgrounds. Although in Survey 1 a large proportion of the
respondents are business or management students, this sample produced
meaningful results and findings, which will be introduced in next chapter.
49%
3%
22%
13%
5%
8%
Entrepreneur
Investing industry
Business Student
Both Entrepreneur and
Investor
Entrepreneur and Student

50. 38
Chapter 4. Data Analysis & Key Findings
As demonstrated in the last chapter, the sample of this research involved a diverse
body of participants involving different nationalities, age groups and occupations.
In the following chapter the author attempts to draw findings from both the
surveys conducted, as well as compare the results between different sample clusters.
4.1 Survey 1 Findings
4.1.1 Overview
Frequency Table of All Attributes in Survey 1
Characteristics Significant Unimportant Not Selected
Age 25 163 43
Art & Design Background 75 59 97
Education Level 77 86 68
Employment Record 75 63 93
Gender 8 188 35
Language Skill 113 38 80
Management Background 154 25 52
Nationality 10 192 29
Personality (Extraversion/Introversion) 102 70 59
Startup Experience 136 26 69
Technology Background 149 14 68
Table 6: Frequency Table of All Attributes in Survey 1
In Survey 1, 231 respondents from 34 countries and regions pretended they are
investors and were asked to choose the four most significant and four less
significant characteristics of entrepreneurs from the given list. The above table
provides an overview of the overall preference of each attribute. A diverse result is
shown in the significance column in which management background received the
highest number of votes (154). Technology background follows very closely (149).
Startup experience (136), language skill (113), and personality (102) are also largely
claimed to be significant. On the contrary, in regards to the least significant option,
nationality (192), gender (188) and (age) are claimed to be absolutely insignificant

51. 39
and finally the education level (86). It is controversial that there were also 70
respondents who consider personality as less significant. The following graph will
better illustrate the pattern drawn from this table.
Figure 9 Frequency of All Attributes in Survey 1
The blue bars show the frequency of being selected as important. The orange bars
on the right represent a strong view of participants who regard the attributes as
unimportant whereas the grey bars in the middle denote the proportion that
selected the attribute as neither significant nor unimportant. Among the preference
of 11 attributes, gender, nationality and age are very rarely selected as important,
while they are clearly marked as not important. Then, about 30% of respondents
believe that employment record, education level, and art & design background are
significant, but also a certain proportion of the respondents considered these
characteristics as unimportant. Around half of the respondents regard personality
traits and language skill to be important, and over 60% have rated technology
background and management background as essential to venture success. The gaps
between each attribute are greater in terms of unimportant characteristics. Over 80%
of the respondents considered nationality and gender as less important and over 65%
selected age, whereas very few people regard management and technology
background as unimportant.
0% 20% 40% 60% 80% 100%
Age
Art & Design Background
Education Level
Employment Record
Gender
Language Skill
Management Background
Nationality
Personality
Startup Experience
Technology Background
Significant Not Selected Unimportant

52. 40
4.1.2 Comparison Between Clusters
In the following section, the author will make comparisons between:
1. Entrepreneurship related respondents (entrepreneurs, investors and others
working in entrepreneurship related industries) and the others (students and
non-business-related respondents);
2. Male and female entrepreneurial persons (students and non-business
respondents are excluded);
3. Entrepreneurship related respondents in different age groups;
4. Entrepreneurs in main English speaking countries (UK, US, Australia and
Canada) and Greater China Region (Mainland China, Hong Kong, Macau and
Taiwan);
5. Chinese entrepreneurial people in the narrow sense (Chinese nationality and
resident in China) and British ones in the UK (British citizen and resident in the
UK).

53. 41
Comparison 1: Entrepreneurial people and the others
The group of entrepreneurial people include 33 entrepreneurs who have already
founded their business and 1 investor as well as professionals in entrepreneurship
related industries, on average aged 35.36 years. Nearly 80% of those who are not
entrepreneurs or investors claimed that they plan to start a business in the
foreseeable future. The remaining respondents were considered as not closely
related to entrepreneurship topics, although many of them are business students
(52%, 88 out of 170). They are significantly younger at an average age of 24.76, and
51% stated that they plan to start a business in the future. The table below shows
the results of two clusters.
Frequency Table of Attributes between Entrepreneurial Persons and the Rest
Entrepreneurial persons
(61)
The others (170)
Attributes Significant Unimportant Not
Selected
Significant Unimportant Not
Selected
Age 4 47 10 21 116 33
Art & Design
Background
23 13 25 52 46 72
Education Level 15 27 19 62 59 49
Employment Record 25 9 27 50 54 66
Gender 0 54 7 8 134 28
Language Skill 27 11 23 86 27 57
Management
Background
46 0 15 133 0 37
Nationality 2 50 9 6 108 15
Personality 30 17 14 72 53 45
Startup Experience 38 5 18 98 21 51
Technology Background 42 3 16 107 11 52
Table 7 Frequency Table of Attributes Selected by Entrepreneurial Persons and the Rest Respondents in Survey 1

54. 42
Figure 10 Percentages of Attributes Chosen by Entrepreneurial Persons in Survey 1
Figure 11 Percentages of Attributes Chosen by Non-entrepreneurship Related Respondents in Survey 1
It can be seen that the two figures are generally similar. A major variance is that a
group of entrepreneurial persons show a lower counting on education level but
higher on employment record, i.e. the entrepreneurial people think degree is
relatively less important than real working experience, and the rest of the younger
group hold the opposite opinion.
0% 20% 40% 60% 80% 100%
Age
Art & Design Background
Education Level
Employment Record
Gender
Language Skill
Management Background
Nationality
Personality
Startup Experience
Technology Background
Entrepreneurial respondents
Significant Not Selected Unimportant
0% 20% 40% 60% 80% 100%
Age
Art & Design Background
Education Level
Employment Record
Gender
Language Skill
Management Background
Nationality
Personality
Startup Experience
Technology Background
Business students and the other respondents
Significant Not Selected Unimportant

55. 43
Comparison 2: Male and Female Entrepreneurial Persons
The above-mentioned 61 entrepreneurship-related persons could be further divided
into 39 males and 22 females.
Frequency Table of Attributes between Male and Female Entrepreneurial Persons
Male (39) Female (22)
Attributes Significant Unimportan
t
Not
Select
ed
Significa
nt
Unimporta
nt
Not
Select
ed
Age 2 32 5 2 16 6
Art & Design
Background
13 7 19 10 7 7
Education Level 6 19 14 10 9 5
Employment Record 18 3 18 8 6 10
Gender 0 36 3 0 19 5
Language Skill 18 5 16 10 6 8
Management
Background
26 5 8 14 3 7
Nationality 1 33 5 1 19 4
Personality 18 11 10 13 7 4
Startup Experience 26 2 11 13 4 7
Technology Background 28 3 8 15 0 9
Table 8: Frequency Table of Attribute chosen by Male and Female Entrepreneurial Respondents in Survey 1
Figure 12: Percentages of Attributes by Male Entrepreneurial Persons in Survey 1
0% 20% 40% 60% 80% 100%
Age
Art & Design Background
Education Level
Employment Recordment Record
Gender
Language Skill
Management Background
Nationality
Personality
Startup Experience
Technology Background
Male Entreprenurial Persons
Significant Not selected Unimportant

56. 44
Figure 13: Percentages of Attributes by Female Business Professionals in Survey 1
In general, both male and female respondents have unanimously chosen technology
background, start-up experience, management background, and language skill as the
four most significant attributes, whereas strongly believe that nationality, age and
gender as unimportant.
Figure 14: Comparison between Male and Female Entrepreneurial Persons in Survey 1 (1)
0% 20% 40% 60% 80% 100%
Age
Art & Design Background
Education Level
Employment Record
Gender
Language Skill
Management Background
Nationality
Personality
Startup Experience
Technology Background
Female Entrepreneurial Persons
Significant Not selected Unimportant
72%
67% 67%
46%
68%
59%
64%
45%
Technology Background Start-up Experience Management
Background
Language Skill
Male Female

57. 45
Figure 15: Comparison between Male and Female Entrepreneurial Persons in Survey 1 (2)
Figure 16: Comparison between Male and Female Entrepreneurial Persons in Survey 1 (3)
However, male businesspersons have presented slightly higher significance than
females in all four attributes mentioned above, whereas more female business
professionals favour personality and art & design background, by about 10 % more
significance than males.
The most interesting contrast between the two groups is on an education level: over
40% of females believe it is significant while only 15% of male respondents agree.
At the same time, over 40% of males regard education as unimportant compared to
only 20% of the female counterpart.
46%
33%
54%
42%
Personality Art & Design
Background
Male Female
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Male
Female
Significance Not Selected Unimportant

58. 46
Comparison 3: Entrepreneurial persons in different age groups
Figure 17 Age Groups of Entrepreneurial persons in Survey 1
The author makes a comparison between the preferences of 61 entrepreneurial
persons in different age groups. As mentioned earlier, the age group of 18 to 25 has
occupied a majority of the respondent body. In general, female entrepreneurial
persons seem to be less represented than male, but a growing tendency is spotted in
younger age groups. Due to the considerable number of attributes, the author
attempts to draw up trends in association with age in different factors.
Percentage of Significant Attributes Selected by Entrepreneurial Person in Different Age
Groups
Attributes 18 to 25 26 to 35 36 to 45 45 or above
Age 8.0% 0.0% 10.0% 6.7%
Art & Design Background 44.0% 36.4% 30.0% 33.3%
Education Level 32.0% 18.2% 30.0% 13.3%
Employment Record 40.0% 45.5% 40.0% 40.0%
Gender 0.0% 0.0% 0.0% 0.0%
Language Skill 40.0% 54.5% 30.0% 53.3%
Management Background 76.0% 54.5% 80.0% 86.7%
Nationality 8.0% 0.0% 0.0% 0.0%
Personality 52.0% 54.5% 40.0% 46.7%
Startup Experience 56.0% 54.5% 70.0% 73.3%
Technology Background 60.0% 81.8% 90.0% 60.0%
Table 9: Percentage of Significant Attributes Selected by Entrepreneurial Person in Different Age Groups in Survey 1
0 5 10 15 20 25 30
18 to 25
26 to 35
36 to 45
46 and above
M F

59. 47
Figure 18 Percentage of Significant Attributes by Entrepreneurial Persons in different age groups in Survey 1
As the above figure shows, there is no clear pattern found between age groups.
Both technology and management background are often regarded as the most two
important attributes. Startup experience is also considered as significant. However,
the author notices that the selection rate of startup experience as a crucial factor
grows stronger with age and ultimately reaches over 75% in the age group of 46 and
above. On the other side, art and design background shows an opposite trend that
its significance seems to decrease with age.
0.0% 20.0% 40.0% 60.0% 80.0% 100.0%
Age
Art & Design Background
Education Level
Employment Record
Gender
Language Skill
Management
Background
Nationality
Personality
Startup Experience
Technology Background
18 to 25 26 to 35 36 to 45 46 and above

60. 48
Comparison 4-1: Entrepreneurs in English speaking countries and the
Greater China Region
Respondents from Mainland China, Hong Kong, Macau, and Taiwan have a very
similar cultural background. These regions consist of the Greater China Region,
from where the respondents comprise a major proportion of the sample.
Correspondingly, respondents from four main English-speaking countries (which
are the United Kingdom, the United States, Canada and Australia) are also
considered to have similar cultural backgrounds. There are a number of
entrepreneurs (including entrepreneurs and employees of startups, not included
investors) in both clusters. Therefore, the author also conducted a comparison
between them.
Frequency Table of Attributes between Entrepreneurial Persons in Greater China Region and
English-speaking Countries
Greater China Region (21) English-speaking country (18)
Attributes Significant Unimportant
Not
Selected
Significant Unimportant
Not
Selected
Age 2 14 5 1 14 3
Art & Design
Background
13 3 5 0 4 14
Education 3 10 8 4 6 8
Employment
Record
8 4 9 8 4 6
Gender 0 18 3 0 17 1
Language Skill 7 4 10 11 2 5
Management
Background
15 0 6 16 0 2
Nationality 0 17 4 0 16 2
Personality 6 10 5 10 3 5
Start-up
Experience
14 1 6 14 2 2
Technology
Background
18 1 2 12 0 6
Table 10：Frequency Table of Attributes between entrepreneurial persons in Greater China Region and English
speaking countries in Survey 1

61. 49
Figure 19: Percentage of Attributes by Entrepreneurial People in Greater China Region in Survey 1
Figure 20: Percentage of Attributes by Entrepreneurial People in English-speaking Countries in Survey 1
Among the 39 respondents, 21 of them come from the Greater China Region and
18 from English-speaking countries. Similar to the observation made between
British and Chinese businesspersons, 86% of the Chinese entrepreneurs rated
technology background as the top-most important attribute, compared to 67% of
the counterpart. For entrepreneurs in English-speaking countries, business
management expertise (almost 90%) is considered as the essential. A remarkable
difference between the two clusters is that entrepreneurs from the Great China
Region express great emphasis on art & design background (62%), whereas no one
in English-speaking countries considers it to be crucial.
0% 20% 40% 60% 80% 100%
Age
Art & Design Background
Education Level
Employment Record
Gender
Language Skill
Management Background
Nationality
Personality
Startup Experience
Technology Background
Greater China Region
Significant Not Selected NO
0% 20% 40% 60% 80% 100%
Age
Art & Design Background
Education Level
Employment Record
Gender
Language Skill
Management Background
Nationality
Personality
Startup Experience
Technology Background
English-speaking Countries
Significant Not selected Unimportant

62. 50
Figure 21: Comparison between the Great China Region and Main English-speaking Countries in Survey 1 (1)
The five most chosen attributes (shown in the figure below) by the businesspersons
in English-speaking countries are shown in the figure above. As shown in the figure,
management background is considered as the most significant by almost 90% of
businesspersons in English speaking countries, which is nearly 20% more than
Chinese ones. Start up experience is also rated as significant by the majority of both
of the two groups, while Chinese entrepreneurs again have a slightly smaller
percentage. Both personality and language skill are only supported by around 30%
of Chinese entrepreneurs, but the majority of the other group regards them as
essential. Once again age, gender and nationality seem to be considered as
unimportant by the two groups with no hesitation.
Figure 22: Comparison between the Great China Region and Main English-speaking Countries in Survey 1 (2)
86%
62%
67%
0%
Technology Background Art & Design Background
Greater China Region English-speaking countries
71%
67%
33%
29%
38%
89%
78%
61%
56%
44%
Management
Background
Start-up
Experience
Language Skill Personality Employment
Record
Greater China Region English-speaking countries

63. 51
Comparison 4-2: Chinese business professionals in China and British
business professionals in the UK
Bearing in mind the impact of residential countries, the author made a further
comparison between entrepreneurial people whose nationality and current country
of residence are China (narrow sense, only the mainland) and the United Kingdom
respectively. Interesting comparisons have been found in this section.
Frequency Table of Attributes between Chinese and British Entrepreneurs
Chinese business professionals
(20)
British business professionals
(10)
Attributes Significa
nt
Unimporta
nt
Not
Select
ed
Significant Unimpor
tant
Not
Select
ed
Age 1 14 5 0 8 2
Art & Design Background 13 4 3 0 4 6
Education Level 6 9 5 2 4 4
Employment Record 8 3 9 4 2 4
Gender 0 18 2 0 9 1
Language Skill 5 4 11 7 1 2
Management Background 13 0 7 9 0 1
Nationality 0 17 3 0 9 1
Personality 7 9 4 6 0 4
Startup Experience 10 1 9 8 1 1
Technology Background 18 0 2 6 0 4
Table 11: Frequency Table of Attributes between Chinese and British Entrepreneurs in Survey 1
Figure 23: Choices by Chinese Entrepreneurs (narrow sense) in Survey 1
0% 20% 40% 60% 80% 100%
Age
Art & Design Background
Education Level
Employment Record
Personality
Management Background
Gender
Nationality
Language Skill
Startup Experience
Technology Background
Significant Not selected Unimportant

64. 52
Figure 24: Choices by British Entrepreneurs in Survey 1
Similar to the last comparison, it can be observed that 90% of Chinese business
professionals favour technology background at a considerable 30% more than
British professional. In regard to art & design skills, 65% of Chinese respondents
rate it as important while no one from the British counterpart agrees with its
significance. Likewise, the Chinese seem to favour education level slightly more
than the British.
Figure 25: Comparison between Chinese (narrow sense) and British Entrepreneurs in Survey 1 (1)
On the other side, 90% British business professionals vote management
background as an essential factor and largely exceed the Chinese by 25%. Also,
British professionals rate start up experience and language skill as the second and
0% 20% 40% 60% 80% 100%
Age
Art & Design Background
Education Level
Employment Record
Personality
Management Background
Gender
Nationality
Language Skill
Startup Experience
Technology Background
Significant Not selected Unimportant
90%
65%
30%
60%
0%
20%
Technology Background Art & Design Background Education Level
Chinese British

65. 53
third most significant, namely 80% and 70%; whereas these two attributes are 40%
less preferred by Chinese entrepreneurs, namely 50% and 25% respectively. 60% of
the British believe personality to be a significant factor in becoming a successful
entrepreneur, but only 35% of Chinese respondents agree and about 45% regard it
as ‘not significant’ at all. Finally, for demographic attributes such as age, gender and
nationality, both groups display the same tendency with 80% regarding it as
unimportant.
Figure 26 Comparison between Chinese (narrow sense) and British Entrepreneurs in Survey 1 (2)
65%
50%
25%
35%
90%
80%
70%
60%
Management
Background
Start-up Experience Language Skill Personality
Chinese British

66. 54
4.1.3 Summary
A considerable number of the respondents in Survey 1 are closely related to
entrepreneurial activities. They provided valuable data for this research. From
Survey 1, the author has found distinct differences among respondents of different
gender, nationality, occupation and age group. Overall, technology background,
management background and startup experience seem to be the most significant
biographical characteristics of entrepreneurs. Chinese entrepreneurs highly
preferred the background of art and design but no other groups echo their
preference. Meanwhile, personality and language skill are given more weight by
entrepreneurs from English-speaking countries. Nationality, age and gender seem
to be of little to no significance to the success of an entrepreneur.
It is interesting to draw patterns from Survey 1 as it serves as both a pilot study for
CA and a source for cross-sample validation. The author studies the result of
Survey 1 and decides to adjust the attribute levels in order to obtain a maximum
extent of validity. Since both technology and management backgrounds are highly
favoured, it sparks the author’s interest to know their respective relative importance
in the trade-off process of conjoint analysis. Also, based on study of previous
literature, the author assumes that a generalist of two or more backgrounds may be
favoured and adds another attribute level in background - the jack-of-all-trades level.
For the most unimportant factors, nationality, gender and age seem to have little to
no influence on the decision-making; thus, the nationality attribute will be
imbedded as one of the levels in language skill, i.e. native English speaker and
non-native speaker.

67. 55
4.2 Survey 2 Conjoint Analysis Findings
4.2.1 Overview
With referencing to the results of Survey 1, a conjoint analysis (CA) test was run on
another group of participants that is independent from the previous group. 37
participated, who are mostly entrepreneurs, nascent entrepreneurs and investors
from multiple countries.
The raw data is summarised as below.
Frequency Table of Profile Ranking
Profile Rank 1st 2nd 3rd 4th 5th 6th 7th 8th 9th 10th 11th 12th 13th 14th 15th 16th
1 0 1 1 2 1 2 2 8 5 3 2 2 2 1 2 3
2 5 9 8 2 3 7 2 0 1 0 0 0 0 0 0 0
3 0 2 3 2 5 1 12 2 1 3 2 2 0 1 1 0
4 7 6 2 4 4 2 1 4 2 3 1 0 1 0 0 0
5 3 5 5 6 3 6 2 1 0 1 2 1 1 1 0 0
6 11 6 5 9 2 1 0 1 1 1 0 0 0 0 0 0
7 0 0 3 1 2 2 0 4 5 3 4 3 5 3 2 0
8 0 0 1 1 0 0 1 0 0 3 1 2 2 1 8 17
9 1 0 2 3 3 1 2 1 3 5 4 2 5 0 3 2
10 0 0 0 1 0 0 1 1 7 1 6 5 2 9 3 1
11 1 0 1 0 1 2 3 4 4 1 1 3 4 7 4 1
12 0 0 0 0 1 2 2 1 0 6 5 4 4 5 4 3
13 2 0 0 3 4 3 5 5 2 1 4 4 0 1 2 1
14 1 0 0 1 1 2 2 3 3 1 1 4 6 3 4 5
15 6 8 5 2 7 4 1 1 0 0 0 1 1 0 1 0
16 0 0 1 0 0 2 1 1 3 5 4 4 4 5 3 4
Table 12 Frequency Table of Profile Ranking in Survey 2 (Conjoint)
The following chart may make the choosing tendency easier to read.

68. 56
Figure 27 Ranking Tendencies in Survey 2
The blue colour represents that the profile is relatively more preferred and the
orange colour vice versa. It is clear that profile 2, 6 and 16 received distinct higher
preference while profile 8 is clearly the least preferred (for details of profiles, please
refer to Chapter 3 or the appendix). However, the raw data is not able to
demonstrate any detailed conclusion about the effectiveness of each biographical
characteristic designated in the research.
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
Profiles
16th
15th
14th
13th
12th
11th
10th
9th
8th
7th
6th
5th
4th
3rd
2nd
1st

69. 57
By using the conjoint module of SPSS software, the author could effectively
compute and estimate the part-worth score of each attribute level and the relative
importance of each attribute (both shown below), as well as concluding interesting
findings.
Attribute Level Part-worth
Age 21 years -0.669
26 years 0.25
36 years 0.676
47 years -0.257
Gender M 0.149
F -0.149
Expertise/
Background
Technology 1.162
Business/Management -0.959
Design/Creativity -1.189
Jack-of-all-trades 0.986
Education Secondary level/no degree -0.912
Bachelor's level -0.196
Master's level 1.176
Doctoral level -0.068
Personality Extroversion 0.605
Introversion -0.605
Language Skill Good (native) 0.628
Good (non-native) 0.554
Average 0.926
Below average -2.108
Startup Experience Yes 2.564
No -2.564
Table 13: Part-worth of all Attribute Level in Survey 2