From Displacement to Successful Entrepreneurship? The Effect of Displaced Employees’ Human Capital on the Success of Their Startups

MASTER’S THESIS in International Business & Politics Copenhagen Business School 2016 Silja Kulvik-Laine

Page count: 64 STU: 107.270

SUPERVISOR: Wolfgang Sofka HAND-IN DATE: 23.05.2016

1

Abstract

This thesis focuses on the effects of displaced employees’ human capital on their

startups. The approach of this thesis is motivated by the growing literature on the

impact of human capital which, however, fails to isolate the effects of human capital

developed specifically during employment at a single employer. At the same time,

subsidiary closures are nowadays quite common (Berry, 2013; Mata & Freitas,

2012), and they can create an abundance of skilled workforce in a specific sector

in the local labor market resulting in significantly increased unemployment

(Neittaanmäki & Kinnunen, 2016). Furthermore, losing a job due to a subsidiary

closure has been empirically found to have long-term, negative effects on an

employee’s future earnings (Couch & Placzek, 2010). Entrepreneurship can be a

valid way of continuing to utilize the human capital that displaced employees

accumulated at an MNC, when the job market does not provide good opportunities

for exploiting the human capital. These factors together prompted the following

research question:

How does the human capital that a displaced employee has developed at an

MNC contribute to the success of the employee’s startup?

To answer this question, a theoretical framework is developed based on existing

literature on human capital and entrepreneurship. Even though the theoretical

predictions that are made based on the framework are not supported by the

empirical results, they can guide future research in the area.

2

Table of Contents

PART ONE: INTRODUCTION ...................................................................................................... 4

1.1. Setting the Stage .................................................................................................................... 5

1.2. Problem Statement ............................................................................................................... 9

1.3. Scope of the Thesis .............................................................................................................. 10

1.4. Reading Guide – Structure of the Thesis ............................................................................ 10

PART TWO: THEORY ................................................................................................................. 12

2.1. Indicators of Startup Success .............................................................................................. 13

2.2. Human Capital as a Determinant of Startup Success ......................................................... 14

2.3. MNC Human Capital Contributing to Startup Success ....................................................... 15

2.4. Creating Valuable Human Capital at MNCs ........................................................................ 18

2.4.1. Entrepreneurial Human Capital..................................................................................... 19

2.4.2. Managerial Human Capital ............................................................................................ 20

2.5. Summary of the Theoretical Framework ............................................................................ 21

PART THREE: METHODOLOGY AND METHOD .................................................................. 23

3.1. Introduction to Methodology and Method ........................................................................ 24

3.2. Methodology ....................................................................................................................... 25

3.2.1. Research Philosophy ..................................................................................................... 26

3.2.2. Approaches .................................................................................................................... 27

3.2.3. Summary of the Methodology Section ......................................................................... 28

3.3. Methods ............................................................................................................................... 28

3.3.1. Research Approach and Strategy .................................................................................. 29

3.3.2. Time Horizons ................................................................................................................ 30

3.3.3. The Research Context ................................................................................................... 31

3.3.4. Nokia Bridge .................................................................................................................. 33

3.3.5. Data Collection .............................................................................................................. 36

3.3.6. Variables ........................................................................................................................ 42

3.3.7. Data Analysis ................................................................................................................. 46

3.4. Methodological Quality ....................................................................................................... 48

3.4.1. Validity ........................................................................................................................... 48

3.4.2. Reliability and External Validity ..................................................................................... 50

3

PART FOUR: RESULTS ............................................................................................................. 52

4.1. Results .................................................................................................................................. 53

PART FIVE: DISCUSSION AND CONCLUSION .................................................................... 59

5.1. Discussion and Conclusion .................................................................................................. 60

REFERENCES .................................................................................................................................... 64

4

PART ONE: INTRODUCTION

SETTING THE STAGE

PROBLEM STATEMENT

SCOPE OF THE THESIS

ROADMAP FOR THESIS

This chapter will introduce the research field to the reader. I will begin the chapter

by describing the relevance of the research topic and the research setting, after

which the I will introduce the problem statement and the scope of the thesis. Finally,

a roadmap for the thesis will be provided to guide the reader through the document.

5

1.1. Setting the Stage

International business research has studied extensively the knowledge flows

between multinational corporations’ (MNC) subsidiaries and their host countries,

focusing specifically on how an MNC opening up a subsidiary contributes to the

host-country pool of human capital (for example Meyer & Sinani, 2009). However,

MNCs typically try to actively counteract knowledge flows to domestic firms in the

host country, while at the same time domestic firms may not have the capabilities

required to absorb all knowledge transferred from MNC subsidiaries. Therefore, it

is claimed that foreign direct investment (FDI) usually does not improve the

productivity of domestic firms through enhanced human capital (Alcacer & Chung,

2007; Feinberg & Majumdar, 2001; Zhao, 2006).

What the discussion in international business research to a large extent fails to take

into consideration is the human capital acquired and possessed by employees that

are let go from MNCs due to subsidiary closures (Sofka et al., 2014). This albeit

that it is nowadays quite common that MNCs close subsidiaries or move their

operations to countries with cheaper costs (Berry, 2013; Mata & Freitas, 2012). A

typical situation is that an MNC reconfigures its international assets, investing in

some locations and divesting in others in order to maximize performance

(Chakrabarti et al., 2011). What is important to note is that even though poor

performance is a significant predictor of divestment, divestment decisions can also

be based on reasons unrelated to the subsidiary, such as increased focus on core

product lines or new market opportunities in foreign countries (Berry 2009). Hence,

a subsidiary’s closure does not necessarily reflect the performance of that

subsidiary.

The consequences that firm closures have on employees have been extensively

studied in labor economics. The displacement model of labor economics defines

job seekers that are in the job market due to subsidiary closures as “displaced”,

since their dismissal was neither caused by poor performance nor misconduct, but

rather the employees were “involuntarily separated from their jobs by mass layoff

or plant closure […], who have little chance of being recalled to jobs with their old

employer” (Kletzer, 1998:116).

6

Displaced employees are not representative of all unemployed. They report notably

longer periods of unemployment than workers that have been laid off do, and they

often face difficulties in finding new full-time jobs (Kletzer 1998). Hence, there is a

need to study displaced employees as a separate group of unemployed.

Sofka et al (2014) introduce subsidiary closures and employee displacement as a

“channel for flows of human capital between MNC subsidiaries and the host

country” (p.741), suggesting that when MNCs close subsidiaries, they, in effect,

create a valued pool of human capital for firms in the host country.

During their tenures in the MNC subsidiaries, employees have gained valuable

human capital which, unlike the human capital inherent in the subsidiaries, now

becomes available to firms in the host country. This creates knowledge flows into

the host country that have previously been unaccounted for. Hence, the effects for

the host country are not necessarily merely negative: The human capital that

becomes available through the displaced employees can be a valuable resource

pool for domestic firms (Sofka et al., 2014).

Nevertheless, losing a job due to a subsidiary closure has been empirically found

to have long-term, negative effects on an employee’s future earnings (Couch &

Placzek, 2010). Even though Sofka et al. (2014) have found that new employers

will pay higher wages for employees who seem to have valuable, foreign human

capital which they have acquired at a multinational corporation (MNC), they also

contend that employers will pay lower wages if that human capital seems to be

highly specific to the MNC. Finding a new job within the same industry (or sector)

may mitigate this effect by making skills more transferable – several papers find

that individuals who change industry earn consistently less after displacement than

individuals who remain in the same industry (Podgursky and Swaim, 1987; Addison

and Portugal, 1989; Kletzer, 1998; Ong and Mar, 1992; Carrington, 1993).

However, when a key player in the market decides to close its subsidiaries and

release its employees to the job market, it can create an abundance of skilled

workforce in a specific industry (e.g. Ong et Mar, 1992; Carrington, 1993). This was,

for instance, the case in Finland, where Nokia’s subsidiary closures lead to an

7

increase of 63% in unemployment in the ICT sector from 2006 to 2015

(Neittaanmäki & Kinnunen, 2016). In general, theory on human capital assumes

that people try to maximize the benefits they can get for their human capital

investments (Unger et al., 2011), leading to people with a lot of human capital

choosing not to pursue a career in entrepreneurship, as other employment

opportunities may lead to better income (Cassar, 2006; Evans & Leighton, 1989)

The same mechanism of people maximizing the return on their human capital

investments may drive displaced employees with high human capital in challenging

job markets to establish their own companies.

In this thesis, I use Ployhart & Moliterno’s definition of human capital that describes

it as the knowledge, abilities, skills, and other characteristics of an employee that

bring value to an organization (2011). The success of startups is essentially shaped

by the human capital of their founders (e.g. Baptista et al., 2014; Bates, 1990;

Brüderl et al., 1992; Gimeno et al., 1997; Unger et al., 2011). New ventures benefit

from the skills and knowledge that their founders have accumulated throughout their

careers, and human capital has been empirically found to correlate positively with

success across different measures of success and different attributes of human

capital (e.g. Agarwal et al., 2004; Unger et al., 2011; Florin et al., 2003; Sexton &

Bowman, 1985). Human capital improves the entrepreneurs’ capabilities in

discovering and exploiting business opportunities (Chandler & Hanks, 1994; Shane,

2000), increases the entrepreneurs’ capabilities in strategy planning (Baum et al.,

2001; Frese et al., 2007), facilitates gaining access to other resources, such as

physical and financial capital (Brush, Greene & Hart, 2001), and human capitalis

essential for further learning, supporting the accumulation of new skills and

knowledge (e.g., Ackerman & Humphreys, 1990; Hunter, 1986; Cohen & Levinthal,

1990). These mechanisms together make entrepreneurs with higher human capital

more effective in running their business than entrepreneurs with lower human

capital (Unger et al., 2011).

This thesis will study the extent to which the human capital that displaced

employees have built up during their tenure at a multinational corporation (MNC)

contributes to the success of their startups. Displaced employees that establish

8

their own businesses are a distinct group of entrepreneurs in that they are

dismissed from their previous jobs due to reasons unrelated to their performance

(Kletzer, 1998), and they are in possession of valuable human capital (Sofka et al.,

2014). Furthermore, they would not necessarily have chosen entrepreneurship

without this disruption in their careers (Kiuru et al., 2014). On average,

entrepreneurs who chose entrepreneurship in order to avoid unemployment are not

as successful as the ones who became entrepreneurs without such a push-factor

(e.g. Rocha et al., 2015; Hintz & Jungbauer-Gans, 1999; Caliendo & Kritikos, 2009).

However, displaced employees that turn to entrepreneurship may have

contemplated becoming entrepreneurs for some time already, without simply

having had the final push to go for it (Kiuru et al., 2014).

I will study this phenomenon in a particularly relevant context, that is, former Nokia

employees who chose to become entrepreneurs after Nokia closed its subsidiaries

in Finland. It is a group of 500 people who, in the face of losing their jobs at Nokia

due to Nokia’s change of strategy, chose to set up their own companies and

become entrepreneurs (Bosworth, 2014).

When Nokia closed its factory in Bochum, Germany in 2008, making approximately

4,300 employees redundant, a heated discussion arose in Europe about MNCs’

responsibilities when they close subsidiaries and downsize personnel. When Nokia

in early 2011 was again faced with wide scale workforce reductions due to the

launch of its Microsoft strategy, it decided to take a proactive role and try to minimize

the negative impacts that the workforce reductions would have on its employees.

To make the best of the difficult situation, Nokia established the Bridge program: a

program that aimed to support the re-employment of the Nokia employees that were

laid off due to the workforce reductions. The Bridge comprised of five different paths

that were designed to accommodate the different approaches of displaced

employees to finding meaningful employment opportunities after Nokia: Job Inside

Nokia; Job Outside Nokia; Start a New Business; Learn Something New; and

Create your Own Path. (Rönnqvist et al., 2015)

9

The Nokia Bridge scheme was available to around 18 000 employees across 13

different countries. In Finland, altogether 5000 people participated in Nokia Bridge,

500 of which in the entrepreneur track (Bosworth, 2014). This thesis is based on

two surveys on Nokia Entrepreneurs in Finland, the first of which took place in 2013,

and the second of which was sent out in relation to this thesis in the spring of 2016.

This focus group allows examining the success of new startups of displaced

employees in a market that was characterized by rising employment in the ICT

sector (Neittaanmäki & Kinnunen, 2016).

To my knowledge, the effect of displaced employees’ human capital on the success

of startups that they established has not been studied in the context of the ICT

sector. The ICT sector is a particularly interesting setting, as it is an industry

characterized by rapid change, strong fluctuations, and high knowledge intensity.

According to OECD statistics, employment within the ICT sector grew steadily from

1995 until 2007, after which it dropped significantly and continued to fall until 2010

in most countries, just to peak at employment growth (OECD). At the same time,

the industry is dominated by small businesses employing less than 100 employees

(CompTIA, 2016), and is renowned for its numerous high-tech startups (Compass,

2015). Furthermore, the ICT industry is characterized by virtual teams and working

from distance – jobs within the ICT sector are often not strictly location-specific

(CompTIA, 2016). Hence, employees displaced from the ICT sector can well

escape the wage loss and maximize the economic returns for their human capital

investments by establishing their own companies.

1.2. Problem Statement

Nokia Bridge startups have been argued to be especially successful among

startups in general (e.g. Klemettilä, 2015; Kiuru et al., 2014), and these startups are

estimated to create a notable number of jobs in Finland when compared with the

workforce reductions at Nokia (Lappalainen, 2013). However, at the time when the

startups established through the Nokia Bridge program were evaluated, they had

been operating for two years at most: hence, it was too soon to draw conclusions

on the success of these companies (Kiuru et al., 2014). Using these startups as an

empirical setting, this thesis will look into the following problem statement:

10

How does the human capital that a displaced employee has developed at an MNC

contribute to the success of the employee’s startup?

In order to answer this research question, I will look into the following sub-questions:

1. How does human capital contribute to start-up success?

2. What kind of human capital can be applied by the displaced MNC employees

in their own startup and thereby be transferred from the MNC to the startup?

1.3. Scope of the Thesis

This thesis will study employees displaced from the ICT sector that chose to

establish their own companies after displacement. More specifically, it will look into

the types of human capital that these displaced employees can transfer from the

MNC to the startup, and the effects that this transferred human capital has on the

success of the startup.

This thesis will delimit itself from studying in more detail the mechanisms of human

capital transfer, as well as from exploring in more detail the different ways of

accumulating human capital at an MNC. Furthermore, the human capital that the

employees have accumulated in similar roles in other companies before their

displacement is outside the scope of this thesis.

1.4. Reading Guide – Structure of the Thesis

Overall, this thesis is divided into 5 parts, where each of the parts consists of 1-5

sections. The report structure is depicted in Figure 1.

11

Figure 1: Structure of the Thesis

Part One: Introduction

•SETTING THE STAGE

•PROBLEM STATEMENT

•SCOPE OF THE THESIS

•ROADMAP FOR THESIS

Part Two: Theory

•INDICATORS OF STARTUP SUCCESS

•HUMAN CAPITAL AS A DETERMINANT OF STARTUP SUCCESS

•MNC HUMAN CAPITAL CONTRIBUTING TO STARTUP SUCCESS

•CREATING VALUABLE HUMAN CAPITAL AT MNCS

•SUMMARY OF THEORETICAL FRAMEWORK

Part Three: Methodology

& Method

•INTRODUCTION TO METHODOLOGY AND METHOD

•METHODOLOGY

•METHOD

•METHODOLOGICAL QUALITY

Part Four: Results

Part Five: DIscussion and

Conclusion

12

PART TWO: THEORY

INDICATORS OF STARTUP SUCCESS

HUMAN CAPITAL AS A DETERMINANT OF STARTUP SUCCESS

MNC HUMAN CAPITAL CONTRIBUTING TO STARTUP SUCCESS

CREATING VALUABLE HUMAN CAPITAL AT MNCS

SUMMARY OF THEORETICAL FRAMEWORK

In this chapter, I will first define startup success, after which I will take a look at

human capital as a determinant of startup success. Finally, I will explore in more

detail the types of human capital that are needed in order for a startup to be

successful, and conclude with the theoretical framework used in this thesis.

13

2.1. Indicators of Startup Success

Measuring performance of ventures presents a notable challenge for scholars

(Chandler & Hanks, 1993). There is an abundance of indicators used to measure

venture performance, and scholars agree to some level that it is appropriate to use

different measures of venture performance for different fields of study and different

research questions (Venkatraman and Ramanujam 1986; Chandler & Hanks,

1993). When studying the success of small, emerging firms, the choice of

performance measures becomes particularly significant. For instance, conventional

measures on return (e.g. ROI, ROE, and ROA) show huge variance based on the

initial investment requirements for the startup, resulting in inaccurate indications of

performance: a marginal company with a minor investment base can show

remarkably higher returns than a relatively strong company with a more extensive

investment base (Chandler & Hanks, 1993).

Most studies in economics and management find that probabilities of exit are higher

for younger firms than they are for older firms (Santarelli & Vivarelli, 2007). The

three first years after startup are considered critical for the new venture’s survival,

and they also indicate the opportunities for success going forward (Littunen et al.,

1998). Hence, startup survival is widely accepted as a success measure for startups

(Arribas & Vila, 2007; Brüderl et al., 1992; Cooper et al., 1994). During the crucial

early years, the firm has not had the time to develop new capabilities or to learn

how to navigate in the market, and yet survival depends on the speed with which

the startup learns to adapt its resources and capabilities to market requirements

(Baptista et al., 2014). Thus, pre-entry human capital is a key contributor in the early

years of a new venture’s existence.

Another measure frequently used for startup success in entrepreneurship research

is employment growth (Rauch et al., 2005). It has been empirically proven to be

strongly related to sales growth – another measure commonly used as an indicator

for startup growth (Delmar, 1997 in Rauch et al., 2005; Weinzimmer et al., 1998).

However, employment and sales growth can be argued to indicate different types

of growth: even though a startup can increase sales by, for example, using

14

subcontractor and yet simultaneously decreasing the number of employees, it is

unlikely for a firm to employ more people if they are not increasing sales (Rauch et

al., 2005). Employment growth is also a more stable meter than changes in sales,

it is linked to business success (Rauch et al., 2005), and it also entails startup

survival: if the startup has ended its operations, it will have reduced its number of

employees, which will be noted in this metric. Furthermore, employment growth is

a performance measure that is relevant on a national economy level and not only

on an individual firm-level (Bosma et al., 2004). As subsidiary closures have a direct

impact on the employment in the host country, it seems suitable to use employment

growth as an indicator for business growth and, hence startup success.

Profit is also often used as a variable to measure startup success (e.g. Bosma et

al., 2004; Haber & Reichel, 2005; Stuart & Abetti, 1990), especially after three years

of operations. However, profit can be a somewhat misleading measure in such an

early phase due to the initial costs of setting up a business: profits are reduced in

the first years since the startup needs to gain back the initial costs (Bosma et al.,

2004). Furthermore, profit can be reduced by strong growth or, for instance,

research and development, which can be essential parts of the early strategy of the

startup. Therefore, it will not be used as a success measure here.

2.2. Human Capital as a Determinant of Startup Success

The success of startups is essentially shaped by the human capital of their founders

(e.g. Baptista et al., 2014; Bates, 1990; Brüderl et al., 1992; Gimeno et al., 1997).

New ventures benefit from the skills and knowledge that the founders have

accumulated throughout their careers, and human capital has been empirically

found to correlate positively with different measures of success and different

attributes of human capital (e.g. Agarwal et al., 2004; Unger et al., 2011; Florin et

al., 2003; Sexton & Bowman, 1985). Furthermore, the profound role of founders’

human capital is reflected in investors’ selection criteria, where the experiences of

the entrepreneurs play a key role in evaluating a startup’s potential (Stuart & Abetti,

1990). Finally, the relationship between human capital and success is stronger for

15

young businesses than older businesses (Unger et al., 2011), further highlighting

the link between especially startups and human capital.

This relationship between human capital and startup success has been explained

through different mechanisms. First, human capital improves the entrepreneurs’

capabilities in first discovering and then exploiting business opportunities (Shane,

2000). Prior knowledge increases the founders’ “entrepreneurial alertness”

(Westhead et al., 2005: 396), allowing them to discover the types of business

opportunities that other people do not detect (Shane, 2000; Venkatraman, 1997).

In addition, human capital affects the entrepreneurs’ approaches to exploiting the

opportunities (Chandler & Hanks, 1994; Shane, 2000).

Second. human capital increases the entrepreneurs’ capabilities in strategy

planning, which contributes to startup success (Baum et al., 2001; Frese et al.,

2007). Human capital also facilitates gaining access to other resources, such as

physical and financial capital (Brush, Greene & Hart, 2001), and it is essential for

further learning, supporting the accumulation of new skills and knowledge (e.g.,

Ackerman & Humphreys, 1990; Hunter, 1986; Cohen & Levinthal, 1990). These

mechanisms together make entrepreneurs with higher human capital more effective

in running their business than entrepreneurs with lower human capital (Unger et al.,

2011). Furthermore, characteristics of the entrepreneur have been found to have a

larger impact on startup success than factors such as location (Duchesneau &

Gartner, 1990).

2.3. MNC Human Capital Contributing to Startup Success

The resource-based view of the firm maintains that the key to firm performance and

sustainability lies in valuable resources that are both rare and difficult to imitate

(Barney, 1991). As established in the previous chapter, in the case of startups, one

of the key resources that contribute to the company’s success is the human capital

that especially their founders have accumulated throughout their careers (Agarwal

et al., 2004). The type of human capital that is to provide a source of competitive

advantage for a startup should not be widely available in the local labor market or

duplicated easily by competing firms (Lepak & Snell, 2002).

16

Employees that are working for an MNC subsidiary are often exposed to the type

of knowledge that is not available for employees in host-country firms. The

underlying reason is that knowledge in general flows less easily across borders

than within country-borders (Feldman & Audretsch, 1996). The knowledge pools in

different countries are separated by differences in language, culture, or institutions

at the national level (Kogut, 1991). Furthermore, knowledge carriers have limited

willingness to overcome these barriers by moving across country boundaries

(Almeida & Kogut, 1999).

MNC subsidiaries are embedded in two knowledge contexts at the same time: the

internal organization of the MNC and the external local environment in the host

country (Almeida & Phene, 2004). This allows subsidiaries of MNCs to access

resources in both knowledge contexts and develop knowledge and capabilities that

are not readily available in the host country (ibid.).

MNCs have been found to be particularly efficient in facilitating international

knowledge transfer within the organization. They can create social communities

with shared understanding of knowledge and shared routines across their

subsidiaries no matter the geographical location. This creates enhanced

opportunities for MNC employees to acquire intra-MNC knowledge as well as

develop valuable skills, such as the capability to coordinate and lead international

teams (Kogut & Zander, 1993; Brown & Duguid, 1991).

However, the human capital developed by employees in MNC subsidiaries can be

highly specific to the setting in which it was acquired, meaning that it will be difficult

to transfer the knowledge to a new employer or, alternatively, the new employer

cannot benefit from this knowledge to the same extent as the previous employer

(Dokko et al., 2009; Huckman & Pisano, 2006). There are two primary reasons why

employees cannot transfer human capital efficiently from one firm to another. First,

the firm where the employee has developed the human capital controls the type of

complementary assets that are required in order for the employee to be productive

– examples include brands and patented technologies (Helfat, 1994). Unless the

new employer possesses similar complementary assets, the employee’s human

17

capital cannot be fully utilized. Second, the type of social mechanisms can be in

place that make human capital specific to the firm in which it was created even if

the new firm possesses equivalent complementary assets. An individual’s human

capital is often embedded in the processes of a team or an organization as a whole

so that it cannot be fully capitalized in a new setting or another team (Groysberg et

al., 2008).

Human-capital theory tends to assume that the longer an employee has worked for

a specific organization, the more valuable human capital he or she has developed

(Sofka et al, 2014). Employees with longer tenures have had more opportunities to

accumulate human capital through work experience and formal education than

employees with shorter tenures. Hence, tenure is often used as a signal of an

employee’s knowledge and skills, leading to potential productivity (Brown, 1989; Ng

& Feldman, 2013; Sicherman & Galor, 1990). However, as employees with longer

tenures have been exposed to more firm-level knowledge and information, they are

more likely to have tacit and explicit knowledge that is specific to the firm and cannot

be utilized outside the context in which it was acquired (Gilson et al., 2013; Nonaka,

1994; Sturman, 2003). Hence, longer tenure at an MNC should contribute to the

accumulated human capital of a displaced employee, but this human capital does

not necessarily contribute towards the success of the displaced employee’s startup.

Theory on human capital does not explain how human capital can be transferred: it

simply notes that investments in human capital improve skills and knowledge, thus

improving performance (Unger et al., 2011). However, in order to contribute to

business success, human capital needs to be transferred successfully to the

business’ context (ibid.).

Personnel mobility literature typically acknowledges that employees can take

strategic assets with them to a new employer, including skills (Wang, He, &

Mahoney, 2009) and tacit knowledge (Almeida & Kogut, 1999; Song, Almeida, &

Wu, 2003). Tacit knowledge refers to the type of knowledge that “cannot be

communicated precisely using words, numbers, or pictures, and which therefore is

difficult to codify” (Helfat 1994: 174). The general rule of thumb is that no matter

18

how firm-specific knowledge is, as long as it can be easily codified, it can also be

relatively easily transferred (Brown & Duguid, 1991; Lecuona & Reitzig, 2014). The

transferability of tacit knowledge is a more complicated issue.

Organizations often aim to codify the tacit knowledge related to their processes into

manuals, organizational charts, training programs, and job descriptions. Yet, people

usually work in a way that differs fundamentally from the codified descriptions,

meaning that if the person carrying the knowledge moves to another organization,

he or she will take the knowledge with him or her (Brown & Duguid, 1991). Research

on knowledge management has shown that the mobility of skilled knowledge

workers is a significant means of transferring or buying knowledge assets (Song et

Almeida, 2003; Zander and Kogut 1995; Almeida & Kogut 1999). For example, if an

inventor moves to another organization, the invention itself loses much of its value

since the initial organization does not have access to some of the key knowledge

that is vital for commercializing the invention (Agrawal 2006). Meanwhile, the

organization in possession of the inventor’s knowledge may find a way to gain value

from this knowledge.

When MNCs close subsidiaries, they voluntarily let go of employees that are

carrying valuable human capital that was developed in the MNC (Sofka et al., 2014).

This human capital can be a valuable source of competitive advantage for new

startups due to its rareness, especially to the extent that it is transferable. Hence, I

hypothesize the following:

HYPOTHESIS 1: The human capital that a displaced employee has

transferred to his or her startup from the MNC contributes positively to

the employment growth of the startup.

2.4. Creating Valuable Human Capital at MNCs

In Hypotheses 1 I have predicted a linkage between the human capital that a

founder has transferred to his or her startup and the employment growth of this

startup. However, that linkage tells us little about how this human capital is created,

or what type of human capital can be transferred so that it contributes to the success

of the startup. In this chapter, I will look into more detail in what types of tasks or

19

roles at an MNC contribute to the creation of valuable human capital that a

displaced employee can transfer to his or her own startup.

2.4.1. Entrepreneurial Human Capital

When entrepreneurs found businesses, they inevitably need to perform tasks such

as scanning the environment, selecting opportunities that they wish to pursue, and

formulating strategies that allow them to exploit these opportunities (Mintzberg &

Waters, 1982; Thompson & Strickland, 2003). In order to succeed in these tasks,

the entrepreneur needs the ability to identify opportunities and envision how to take

advantage of them (Chandler & Jansen, 1992).

Selecting a business opportunity of a high quality is essential for venture

performance (Hofer & Sandberg, 1987), and this competence depends strongly on

the entrepreneur’s familiarity with the market (MacMillan et al., 1985; Jansen et al.,

2013). Entrepreneurs with strong industry experience have better knowledge of

suppliers, products, services, and customers in relation to their business context

(Gimeno et al., 1997). Hence, the entrepreneur’s prior work experience in the same

industry in which the new startup operates should contribute to the success of the

startup. This relation between industry experience and startup success has been

empirically supported in several studies (e.g. Baptista et al., 2014; Bosma et al.,

2004; Davidsson & Honig, 2003; Stuart & Abetti, 1990). Additionally, human capital

related to the industry of the startup facilitates the accumulation of new knowledge

and learning in the same context (Cohen & Levinthal, 1990). However, industry

knowledge and relationships get old over time, meaning that the industry-specific

human capital depreciates over time (Baptista et al., 2014). Hence, I expect that

startups in the same industry where the founder was employed immediately before

establishing the company will benefit more strongly from the founders’ industry-

specific human capital.

HYPOTHESIS 2: With all else equal, startups established in the same

industry in which the displaced employee worked prior to displacement

have more employment growth than their counterparts.

20

2.4.2. Managerial Human Capital

When an entrepreneur has identified a business opportunity and established a

strategy for exploiting this opportunity, he or she must take on a managerial role

(Chandler & Jansen, 1992). The entrepreneur needs to work with budgets and

programs, evaluate performance, create procedures, and perform other similar

tasks that are essential to actually implementing the strategy (Wheelen & Hunger,

2006). An effective business owner must have human capital in three distinctive

areas: (1) Conceptual competence; (2) Human competence; and (3) Political

competence (Chandler & Jansen, 1992).

(1) Conceptual competence refers to a person’s ability to coordinate the interest

and activities of the organization (Pavett & Lau, 1983). This refers to

performing the actual managerial duties that arise in an organization, such

as e.g. financial management and marketing, and it is an important factor in

venture success (Ibrahim & Goodwin, 1987; Hood & Young, 1993).

(2) Human competence describes the ability to motivate, understand and work

with people in teams and individually (Pavett & Lau, 1983). The

entrepreneur’s leadership skills are considered an important factor in startup

success (MacMillan et al., 1985). Furthermore, human competencies not

only refer to managing employee relations and motivation, but they also

cover managing customer relations and, for instance, the ability to delegate

(Chandler & Jansen, 1992).

(3) Political competence describes the ability to establish useful connections,

build a power base, and enhance one’s own position (Pavett & Lau, 1983).

This is considered particularly significant for startups, since the entrepreneur

must create a well-functioning support network and establish a relationship

with people in control of essential resources, skills and abilities in order for

the startup to succeed (Chandler & Jansen, 1992).

It is widely established that managerial skills contribute strongly to business

performance both in the short term and in the long term (e.g. Haber & Reichel,

2007; Cooper & Gimeno-Gascon, 1992). Management skills and management

21

experience are also the selection criteria that are most frequently used by venture

capitalists (Zacharakis & Meyer, 2000). Managers face a broad set of

responsibilities from decision-making tasks to managing employees, customers,

suppliers, and government authorities. This requires knowledge and skills that are

more than simply a step-by-step guidance on how to tackle different challenges,

and allows managers to develop valuable human capital in conceptual, human and

political competence (Lecuona & Reitzig, 2014).

This effect is especially strong for managers in MNC subsidiaries, since a significant

part of their work is to consolidate intra-MNC procedures and practices with those

of the host country (Almeida & Phene, 2004). This responsibility allows the MNC

subsidiary managers to absorb knowledge from the MNC and utilize that knowledge

in the host-country setting (Sofka et al., 2014), indicating that they have built up

human capital by combining the best of both worlds. Hence, I hypothesize the

following:

HYPOTHESIS 3: With all else equal, startups of entrepreneurs with

managerial experience from the MNC have more growth in

employment than their counterparts.

2.5. Summary of the Theoretical Framework

Figure 2 below summarizes the theoretical framework used in this thesis.

22

Figure 2: The Theoretical Framework

23

PART THREE: METHODOLOGY AND METHOD

INTRODUCTION TO METHODOLOGY AND METHOD

METHODOLOGY

METHOD

METHODOLOGICAL QUALITY

The following sections outline the methodology and methods that are used in this

thesis. The purpose of this chapter is to describe and clarify the philosophies that

are guiding the research approach chosen for this thesis as well as walk the reader

through the process of data gathering and analyzing in this thesis.

24

3.1. Introduction to Methodology and Method

The following sections outline the methodology and methods that are used in this

thesis. Methodology refers to the philosophical assumptions that guide research –

a theoretical approach to how research should be carried out (Saunders et al.,

2016). On the contrary, methods describe the procedures and techniques that are

used to gather and analyze data (ibid). The purpose of this chapter is to describe

and clarify the philosophies that are guiding the research approach chosen for this

thesis as well as walk the reader through the process of data gathering and

analyzing in this thesis.

In order to take a structured approach to methodology and the choice of method, I

approach the methodology and method using the ‘Research Onion’ (Figure 3)

(Saunders et al., 2016: 124). The Research Onion consists of six layers that should

be covered and explained in all research: (1) philosophy, (2) approach to theory

development, (3) methodological choice, (4) strategies, (5) time horizon, and (6)

techniques and procedures. Using this framework should assist in structuring,

investigating and supporting the choice of data. Furthermore, it should provide a

roadmap for analysis (ibid). The aim of the research onion is to help researchers to

increase their understanding of and associations related to research design. It also

helps researchers to set boundaries for their research and increase its consistency

while at the same time setting the study into a context (Saunders & Tosey, 2012).

25

Figure 3: The Research Onion (Source: Saunders et al., 2016: 124)

In this thesis, the Research Onion is divided into two larger concepts: Methodology

and Method. Consisting of the two outer layers – Philosophies and Approaches -

Methodology will describe the philosophical background of the research. Method

will contain the core layers of the Research Onion – Strategies, Choices, Time

Horizons, and Techniques and Procedures – and its objective is to describe the

methods that are used to gather and analyze the data in this thesis.

3.2. Methodology

Before determining which methods are appropriate for the research, it is essential

to understand the fundamental principles guiding methodology. There is no one

best philosophy or one correct answer, but the different philosophies complement

each other and supplement different kinds of research objectives. They also affect

26

how researchers understand the field that is studied and how they see the world

(Saunders et al., 2016).

3.2.1. Research Philosophy

Research philosophy describes the “system of beliefs and assumptions about the

development of knowledge” (Saunders et al., 2016: 124). Conducting research

requires making a number of different kinds of assumptions (Burrell & Morgan,

1982), such as assumptions on ontology, epistemology and axiology.

Ontology refers to the underlying assumptions that the research has about reality.

It determines how the researcher views the world and, consequently, guides the

choice of research topics (Saunders et al., 2016). Epistemology denotes the

assumptions that the researcher has about knowledge: what are the determinants

of acceptable and valid knowledge, and how can knowledge be communicated to

other people? (Burrell & Morgan, 1982) Finally, axiology describes the part that

values and ethics played in the research process, referring both to the researcher’s

and the research participants’ values.

The assumptions made during the research process affect the way that the

research question should be interpreted, the methods used for the study and the

interpretation of the findings (Crotty, 1998). A coherent research philosophy will put

together these assumptions in a manner that allows for a logical research project

where the different elements of research fit well together (Saunders et al., 2016).

This thesis will follow the positivist research philosophy, which assumes that the

social reality is observable and can be generalized (Saunders et al., 2016).

Organizations and social objects are viewed as real entities that can be studied in

a scientific, orderly manner. The epistemological assumptions rely on scientific

method which creates generalizable theory based on a large sample that will

explain causalities (Gill & Johnson, 2002). The starting point for this thesis will be

in existing theory, which will be used to formulate hypotheses. With regard to

axiology, the researcher is viewed as a detached actor which is neutral towards the

topic that is researched (ibid).

27

3.2.2. Approaches

Saunders et al. (2016) present three distinctive approaches to acquiring knowledge:

deductive, inductive, and abductive reasoning:

Deductive reasoning means that the study uses a set of premises to reach a logical

conclusion which is true if all the premises hold true (Ketokivi & Mantere, 2010). In

the deductive approach, you will usually use existing literature to identify theories

and then design your research strategy with the aim of testing this theory using

empirical data (Saunders et al., 2016).

On the contrary, inductive reasoning identifies a logic gap between the premises

that are observed and the conclusion, but the observations are judged to support

the conclusion (Ketokivi & Mantere, 2010). Inductive approaches usually explore

phenomena using empirical data, generate theory based on the data and then

relate it to existing literature in the consequent discussion (Saunders et al., 2016).

Finally, abductive reasoning refers to an approach where research is based on a

‘surprising fact’ (Ketokivi & Mantere, 2010: 330) which functions as a conclusion.

This conclusion is then used to determine a set of premises that should be sufficient

to explain the surprising fact. In the abductive approach, data is collected to explore

a defined phenomenon, to identify patterns and themes, and to generate new

theories or modifications to existing theories which can be tested using additional

data (Saunders et al., 2016).

This thesis is seeking to explain the causalities in the relationships between

variables and identified concepts (Saunders et al., 2016). Furthermore, it is using

existing literature to identify a theory which will then be tested using quantitative

data. Hence, this thesis is following the deductive approach to research design.

It is important to acknowledge that no research approach is superior to others – the

right choice of approach depends on the focus of the research and the

characteristics of the research topic (Saunders et al., 2016). There are several

arguments to support the choice of approach in this thesis: The research topic calls

for an examination of a large sample and the research is intended to identify

28

variables that can be used in relation to Nokia Bridge entrepreneurship track as a

whole. Furthermore, Human capital is a field that has been studied extensively,

meaning that there is a “wealth of literature” for defining a theoretical framework

and the hypotheses (Saunders et al., 2016: 149). Finally, the timeframe available

for conducting the research is limited, supporting one-take data collection and an

approach to data analysis where schedules can be predicted fairly accurately.

3.2.3. Summary of the Methodology Section

To conclude the methodology outlined in the previous chapters, this thesis will be

guided by the positivist research philosophy, assuming that social reality can be

observed and generalized. Epistemological assumptions build on the scientific

method, aiming to create theory that explains causalities using a large sample. The

axiological approach adopted views the researcher as a detached actor which is

neutral towards the researched topic. Finally, knowledge will be acquired using

deductive reasoning, highlighting the role of existing literature to identify theories

that will then be tested with quantitative data.

3.3. Methods

By peeling off the first two layers of the Research Onion, the methodology section

has described the way that the researcher views the world and outlined the

approach to this thesis. The following section on methods will follow the structure

from the remaining layers of the Research Onion, namely Methodological Choice,

Research Strategies, Time Horizons, and Techniques and Procedures (Saunders

et al., 2016).

I used the article by Sofka et al. (2014) on human capital during subsidiary closures

as a starting point for this thesis process, familiarizing myself with the concepts of

human capital and displaced employees, and exploring opportunities for developing

a research question around these topics. Before settling on the specific research

setting, I conducted a thorough literature review using existing literature on human

capital, employee displacement, and determinants of startup success. Studying the

topic in academia help to develop a relevant research topic and problem

formulation. After deciding on the research topic, I contacted Matti Vänskä from

29

Nokia, who referred me to Jari Handelberg from Aalto University Small Business

Center. Jari Handelberg had conducted a survey in 2013 together with Pertti Kiuru

and Heikki Rannikko to assess the entrepreneurship track of the Nokia Bridge

Program (Kiuru et al., 2013). This was the starting point for a collaboration on

conducting a follow-up study on the Nokia Bridge entrepreneurship track.

Yin (2009) suggests that in order to increase the quality of the research,

researchers should rely on previous research on the subject. In line with the

recommendation from Yin (2009), the theoretical framework in this thesis is

constructed based on a review of existing literature. Supported by existing theory,

XX hypotheses are formulated to predict the relationship between human capital

and startup success, measured as growth in employment.

3.3.1. Research Approach and Strategy

In building a research design, several methodological choices need to be made

which will affect the type of knowledge that will be created through the research.

In order to be able to choose a suitable research strategy, it is essential to decide

upon research objectives. Saunders et al. (2016) outline four types of research

objectives: (1) exploratory; (2) descriptive; (3) explanatory; and (4) Evaluative. An

exploratory study looks to gain insights and find out what is happening with regard

to a topic of interest, whereas a descriptive study aims at building an accurate

profile of persons, events, or situations. The intention of explanatory research is to

demonstrate causal relationships between different variables, while evaluative

studies seek to determine how well something is working (Saunders et al., 2016).

A study can be designed to fulfil either one of the four abovementioned research

purposes or some combination of them.

As this thesis seeks to demonstrate a causal relationship between a set of variables

exhibiting human capital that was built up during employment at an MNC and the

success of a displaced employee’s startup, the research objective of this study is

clearly explanatory.

30

A research strategy is the plan that a researcher makes of how to answer his or her

research question. It links the research philosophy with the methods that were

chosen for data collection and analysis (Denzin and Lincoln, 2011).

Even though specific research objectives cannot be directly linked with specific

research strategies, questionnaires are often suggested to for explanatory research

(Saunders et al., 2016). Saunders et al. (2016) outline that questionnaires are a

suited means of collecting data when the study seeks to examine and explain

causal relationships using standardized questions. Given that this thesis aims to

test hypotheses that predict the effect of different human capital indicators on the

employment growth of startups, where most of the variables used can be surveyed

with standardized questions, it fulfils the criteria listed above. Hence, the choice of

a questionnaire conforms to the criteria outlined by Saunders et al. (2016) and

should be preferable, since it allows the study to test hypotheses on cause-and-

effect relationships (ibid.).

A drawback with using a questionnaire is that the data is unlikely to be as

comprehensive and thorough as data collected using alternative research

strategies (Saunders et al., 2016). However, as a more exploratory approach to

studying the different determinants of human capital and mechanisms of human

capital transfer are outside the scope of this thesis, a questionnaire should be able

to collect the data comprehensively enough in order to be able to test the

hypotheses.

3.3.2. Time Horizons

Saunders et al. (2016) outline two types of time horizons: cross-sectional and

longitudinal. A cross-sectional study refers to a ‘snapshot’ (p. 200) in time, where

the phenomenon is studied at a particular time, whereas a longitudinal study is

described as the ‘diary’ perspective (p. 200). Due to the time constraints in place,

this study will employ a time-series cross-sectional time horizon. The cross-

sectional time horizon is considered suitable when the study is aiming to describe

a relationship between different factors at a single point of time (ibid). However, the

first snapshot used in this study, meaning the first survey sent out in 2013, did not

31

allow to measure the success factors of the startups accurately, since the startups

had not been in operation for three years yet. Hence, another snapshot was taken

three years later, in 2016, by sending a simplified version of the survey out again

with the aim of measuring especially the success factors of the startups.

3.3.3. The Research Context

I test the hypotheses using data collected from employees that were displaced from

Nokia in the ICT sector in Finland between 2011 and 2013, and who chose to

pursue a career in entrepreneurship through the Nokia Bridge program.

It is nowadays quite common that MNCs close subsidiaries or move their operations

to countries with cheaper costs (Berry, 2013; Mata & Freitas, 2012). A typical

situation is that an MNC reconfigures its international assets, investing in some

locations and divesting in others in order to maximize performance (Chakrabarti et

al., 2011). Even though poor performance is a significant predictor of divestment,

divestment decisions can also be based on reasons unrelated to the subsidiary,

such as increased focus on core product lines or new market opportunities in

foreign countries (Berry 2009). Hence, a subsidiary’s closure does not necessarily

reflect the performance of that subsidiary.

The decision of Nokia to close down a large number of its operations in Finland was

a typical example of a situation, where the subsidiaries were not performing poorly,

but the closures were the result of a change in strategy: as Nokia decided to adopt

Windows operating system as the primary platform on its smartphones, phasing out

both Symbian and MeeGo, the production and research sites related to the older

platforms became redundant and were closed (Rönnqvist et al., 2015).

Empirical evidence suggests that local labor market conditions play a significant

role in a displaced employee’s post-displacement wage (e.g. Carrington, 1993).

Finland was one of the countries hit particularly hard by Nokia’s strategic change:

until 2012, Nokia was the largest employer in Finland, providing work directly for

over 24 000 employees in the country at its peak in 2000 (Bosworth, 2014). Nokia

alone accounted for 4% of Finland’s GDP. By the end of year 2013, the number of

employees that Nokia employed had fallen to 10 600, meaning a cut down of over

32

half of its employees. (Bosworth, 2014) Nokia’s subsidiary closures lead to an

increase of 63% in unemployment in the ICT sector from 2006 to 2015, and the

unemployment focused strongly on the areas where Nokia had closed down its

operations (Neittaanmäki & Kinnunen, 2016). It had created an abundance of skilled

workforce in the ICT industry in Finland, which is typical for a situation where a key

player in a market decides to close its subsidiaries and release its employees to the

job market (e.g. Ong et Mar, 1992; Carrington, 1993). Hence, Finland provides an

interesting empirical setting to study displacement after subsidiary closures in a

challenging labor market.

The ICT sector is an exciting setting to study the human capital of displaced

employees who turn to entrepreneurship, as it is an industry characterized by rapid

change, strong fluctuations, and high knowledge intensity. According to OECD

statistics, employment within the ICT sector grew steadily from 1995 until 2007,

after which it dropped significantly and continued to fall until 2010 in most countries,

just to peak at employment growth (OECD). At the same time, the industry is

dominated by small and medium-sized businesses employing less than 100

employees (CompTIA, 2016), and is renowned for its numerous high-tech startups

(Compass, 2015). Furthermore, the ICT industry is characterized by virtual teams

and working from distance, meaning that jobs within the ICT sector are often not

location-specific (CompTIA, 2016). Hence, entrepreneurship should be a viable

option for employees displaced from the ICT sector even in a situation where the

local job market is highly competitive.

When Nokia in early 2011 started its wide scale workforce reductions due to the

launch of its Microsoft strategy, it launched the Bridge program to support the re-

employment of the displaced employees. The Nokia Bridge scheme was available

to around 18 000 employees across 13 different countries. In Finland, altogether

5000 people participated in Nokia Bridge, 500 of which chose to follow a track that

entailed becoming an entrepreneur (Bosworth, 2014).

The startups established through the Bridge program have been suggested to be a

particularly successful group of new ventures (e.g. Klemettilä, 2015; Kiuru et al.,

33

2014), and they are estimated to create a notable number of jobs in Finland when

compared with the workforce reductions at Nokia (Lappalainen, 2013). However,

the success of these startups in terms of employment growth has not been studied,

nor have the startups been studied from a founder human capital perspective.

Hence, this thesis could shed insights on the effects of the valuable human capital

that MNCs release when closing subsidiaries in creating further employment in a

market characterized by high unemployment.

The unit of observation in this thesis is the individual displaced employee. What

also makes the Nokia Bridge entrepreneurship track a particularly relevant context

for the research objectives of this thesis – studying the impact that the human

capital that displaced employees had built up during their employment at an MNC

on the success of their startups – is the opportunity to not only get data on the job

titles, tenures and other similar indicators of accumulated human capital, but also

to ask displaced employees themselves about the human capital that they have

transferred from the MNC to their startups. To my knowledge, there exist few, if any,

empirical studies addressing specifically the human capital that a displaced

employee has accumulated during tenure at an MNC and transferred to a startup

after displacement. Hence, there is a need to focus explicitly on the human capital

transferred from an MNC to the startup and bridge this gap in human capital

literature, which this research setting allows to do.

3.3.4. Nokia Bridge

Nokia Bridge is a corporate social responsibility program that Nokia launched in the

spring 2011 to support the re-employment of the employees affected by the

subsidiary closures related to the launch of the Microsoft strategy (Rönnqvist et al.,

2015). The Bridge comprised of five different paths that were designed to

accommodate the different approaches of displaced employees to finding

meaningful employment opportunities after Nokia. The five paths are:

Job inside Nokia – The Bridge offers career counseling and identifies

available positions to assist employees to acquire jobs within Nokia.

34

Job outside Nokia – The Bridge offers career counseling and interacts with

its contact network to help job seekers to find and identify opportunities

outside of Nokia.

Start a New Business – The Bridge offers training and financial support for

employees interested in starting their own businesses, and it also helps them

to identify partnerships and business opportunities.

Learn Something New – The Bridge offers training that supports rapid re-

employment. The Bridge also provides information and counseling on further

education opportunities.

Create your own path – The Bridge supports unique re-employment or

career opportunities that the employee identifies. This path was created to

support employees that cannot find meaningful solutions through the first

four paths, but it was rarely used.

(Source: Nokia Oyj, 2011 in Rönnqvist et al., 2015)

The Nokia Bridge Entrepreneurship track – the “Start a New Business” path -

offered the participants information on business services available in the region,

individual startup coaching, a Bridge grant of up to EUR 25 000 for the startup’s

operation, and a possibility of receiving Nokia’s guarantee for startup bank loans.

In addition, Nokia actively supported technology licensing agreements and idea

release agreements with the Bridge entrepreneurs, resulting in approximately one

out of five Bridge entrepreneurs concluding such an agreement with Nokia.

However, only startups operating within the ICT sector were able to conclude these

agreements, as Nokia’s license portfolio and operations were strongly centered on

ICT. (Kiuru et al., 2014)

The Nokia Bridge scheme was available to around 18 000 employees across 13

different countries. In Finland, altogether 5000 people participated in Nokia Bridge,

500 of which in the entrepreneur track (Bosworth, 2014).

35

The support that Nokia gave to the Bridge entrepreneurs meant that employees

who had long had a dream of becoming an entrepreneur, but who never got the

final push or the financial support to do so, now got a chance to pursue this dream.

The Aalto Small Business Center conducted surveys on all Nokia Bridge

Entrepreneur track participants in Finland, Canada and the US. Out of 168 Bridge

entrepreneurs that responded to the survey in Finland in 2013, 43% stated that the

primary reason why they chose to become entrepreneurs was that they had wanted

to be entrepreneurs for a long time and now got the chance. 18% became

entrepreneurs primarily due to that they were able to start a business around an

innovation or technology that they had been working with at Nokia, or Nokia’s

technology that it didn’t leverage, and only 13% chose entrepreneurship as an only

means to avoid unemployment. Other reasons included having found an attractive

opportunity through the Bridge program and not wanting to continue in paid

employment (Kiuru et al., 2014).

The majority of employees choosing the entrepreneurship track did not have any

previous entrepreneurial experience (10% of respondents in Finland had been

entrepreneurs at a startup before), and only approximately 10% of respondents had

a company set up already when they joined the Bridge entrepreneurship track,

meaning that the majority of respondents set up their companies while taking part

in the Bridge program (Kiuru et al., 2014). This suggests that displacement was a

key driver for the Bridge entrepreneurs to set up their own companies.

However, what is noteworthy is that even though Nokia provided the Bridge

entrepreneurs with additional startup funding, displaced employees often receive

severance packages that provide them with economic stability for some time. In

addition, they are able to apply for startup funding through traditional channels, and

get the same entrepreneurship coaching and support than the Bridge entrepreneurs

did – the Bridge program collaborated with local startup centers and pushed the

Bridge entrepreneurs to use public startup support services (Kiuru et al., 2014).

Furthermore, broad empirical evidence suggests that startup founders tend to

pursue opportunities that relate closely to their previous employment (Hvide, 2009).

Hence, even though the Bridge program facilitated the displaced employees’

36

journey towards entrepreneurship, it did not provide them with exceptional support

that other displaced employees could not reach. However, the Bridge employees

themselves did consider the Bridge program a fairly significant factor in their

decision to become an entrepreneur (an average of 5,1 on a 7-point rating scale,

where 1 is Not at all important and 7 is Very important), meaning that the support

provided by the program should be taken into consideration when generalizing

results from this study.

3.3.5. Data Collection

As mentioned in chapter 3.2.1. on Research Approach and Strategy, the empirical

section of this thesis is primarily based on data collected using questionnaires.

However, several data sources are utilized to support the empirical analysis,

including academic literature, the company database of the Finnish Patent and

Registration Office, and industry reports.

When using a questionnaire as a means of collecting data, the design of the

questionnaire is essential to the validity and reliability of the data (Bryman & Bell,

2015; Saunders et al., 2016). First, the questions, explanations, and headings

should be formatted consistently, using a similar style (Bryman & Bell, 2015).

Second, the choice of question category – open or closed questions – should be

taken into consideration: Open questions allow the respondents to elaborate on

their responses, while closed questions are easy to answer for the respondents and

allow for easier comparisons between respondents (ibid.). As the quantitative data

will be used to build statistics, this study will mostly use close-end questions in the

questionnaire. However, open questions will be added to the questionnaire as a

means for the respondents to elaborate on their responses, if they feel that the

categories available do not fully capture their responses, or if there is any ambiguity

with regard to the interpretation of the questions.

The questionnaire was executed as a self-completed questionnaire, also known as

a survey, distributed to the participants using a hyperlink sent out either to the email

addresses. Web questionnaires provide an effective means of gathering responses,

37

as they allow for the respondents to answer the questions at a time slot suitable for

themselves, using their own computers or smartphones (Saunders et al., 2016).

3.3.5.1. Sample

Kiuru et al. (2014) assessed the entrepreneurship track of the Nokia Bridge program

in Finland in April 2013 using a survey that was sent to the employees who went

through the Bridge entrepreneurship track. Exactly three years later, in April 2016,

a shortened version of the same survey was sent out to the same group of Nokia

Bridge entrepreneurs in Finland in relation to this thesis. The alterations made to

the survey were simply cutting out the types of questions that relate to the

establishment of the company and would remain the same throughout the time-

period (e.g. How many co-founders did the company have). The results from the

two surveys were then unified using the name of the startup to identify responses

from the same respondents. This allowed measuring financial success and survival

in the longer run while at the same time utilizing the information gathered in the first

wave of surveys to avoid asking the same questions over again.

The sample used in this thesis comprises of those Nokia Bridge entrepreneurs that

voluntarily responded to both rounds of surveys. The first survey in 2013 was

distributed by Nokia to all 427 Bridge entrepreneurs, 413 of whom were reached.

Altogether 196 Bridge entrepreneurs responded to the survey, representing 187

different startups. The next survey in 2016 was sent via email only to those Bridge

entrepreneurs that had responded to the first survey in 2013. A part of the

respondents had filled in their email addresses in relation to the first round of

surveys. The remaining email addresses were retrieved from either Finnish Patent

and Registration Office database directly or from company web pages or founder

LinkedIn page based on a combination of information from the Finnish Patent and

Registration Office database and respondent information from the first round of

surveys. Out of a total of 153 respondents that gave their permission to be

contacted for a follow-up survey during the first survey round, 145 personal email

addresses were identified.

38

At first, a hyperlink to the Webropol survey was sent to all identified email addresses

with an explanation of the purpose of the study, an assurance of confidentiality of

the responses, and contact information for both representatives from the Aalto

University Small Business Center and me. Two weeks later, a reminder email was

sent out to the same email addresses to improve response rate. In the end, 33

Nokia Bridge entrepreneurs responded to the survey, one of which, however, did

not fill in background information on him/herself or the company, meaning that the

response could not be used (it could not be combined with the previous responses).

This represents a response rate of 22%.

Delivering the questionnaire as a hyperlink on an email offers some control over

whether the intended recipient is the actual respondent of the survey, as most

people read their emails personally. This increases the reliability of responses

(Saunders et al., 2016). In addition, the cover letter in the email clearly stated who

was intended to respond to the survey. However, a setback with email distribution

is that it does not allow for collecting details about non-respondents, as there is no

contact between the researcher and the non-respondent (ibid.). Hence, selection

bias due to non-respondents needs to be assessed using other means.

3.3.5.2. Sample Representativeness

Even though Saunders et al. (2016) note that the response rate of a study does not

automatically reflect how biased the responses are, it is important to take into

consideration the potential sample selection bias of the sample. The response rate

is quite low considering the small size of the target population, and the respondents

self-selected to participate in the study, meaning that they did not have external

incentives to do so. These factors may together lead to bias in the sample selection.

Sample selection bias refers to a systematic error, where the sample is not

representative of the population (Saunders et al., 2016). This effects the internal

validity of the study, meaning that the results of the study can over represent e.g. a

subgroup of the analyzed population, compromising the conclusions drawn from

these results (ibid.).

39

Saunders et al. (2016) suggest that one way of accounting for sample selection

bias is to check that the sample in the study is representative of the population

analyzed. I will do this by checking first, if the respondents of the second round of

survey are representative of the entire Nokia Bridge entrepreneur population on

geographical location, and second, if the second round survey respondents are

representative of the first round survey respondents on demographical and firm

characteristics. For a discussion on the representativeness of the sample of the first

survey rounds, see Kiuru et al. (2014).

First, the sample representativeness on the Nokia Bridge entrepreneurs was

estimated based on geographical division and industry division of the startups. As

can be seen from Table 1 below, the Capital Region was overrepresented in the

sample in the expense of Salo region. Oulu region was also moderately

underrepresented. As the Capital region is a very different environment for startups

than for example Salo, which is characterized by a particularly difficult employment

situation after Nokia closed its operations in the region and an overrepresentation

of skilled ICT employees (Neittaanmäki & Kinnunen, 2016), the bias in geographical

distribution may in itself affect the results in this study.

Region Target Population Respondents

Units % Units %

Capital Region

Tampere Region

Oulu Region

Salo Region

128

72

87

61

37

21

25

17

16

7

7

2

50

22

22

6

Total 348 100 32 100

Table 1: The Geographical Division of Respondents

40

Table 2 presents the division of the respondents based on the industry of the startup

in the surveys from 2013 and 2016. As can be seen from the table, the percentage

of startups in the ICT industry remained somewhat constant, while especially Expert

services were overrepresented in 2016 compared with 2013. In 2016, all industries

for the respondents’ startups were accounted for.

Industry Respondents 2013 Respondents 2016

Units % Units %

Industry etc.

Commercial etc.

ICT

Expert Services

Other Services

Unknown

11

24

64

50

13

34

6

12

33

26

7

17

1

2

10

15

4

0

3

6

31

47

13

0

Total 196 100 32 100

Table 2: The Industry Division of Respondent Startups

When categorized by sector, roughly 40% of the entire population of Nokia Bridge

entrepreneurs established companies in the ICT sector or similar; around 30%

founded their company within expert services and similar, and 30% of the

companies operated in other sectors. Hence, in the survey responses from 2016,

Expert Services were overall overrepresented, while ICT sector and other sectors

were moderately underrepresented.

An assessment of the non-respondent companies was conducted using the Finnish

Patent and Registration Office Business Information System to see if a systematic

reason not to respond to the survey is that the startup has already stopped its

operations. 19 of the non-respondents’ startups had officially stopped operations or

were filed in liquidation, representing 17% of the 113 non-respondents. Similarly, 6

respondents accounting for 16% of the survey respondents reported discontinued

41

operations for their startup. This shows that discontinuation of operations should

not lead to selection bias with regard to the non-respondents.

The positions that the respondents held at Nokia were also well in line with the

2013 survey respondents (Table 3).

% of respondents in 2013

% of respondents in 2016

Operator or supervisor in production/distribution center

1 3

Technical position without staff (e.g. researcher, engineer, architect, designer project/program manager, scrum master, product owner)

43 41

Non-technical position without staff (e.g. analyst, quality specialist, HR consultant, legal councel)

18 16

Manager with staff responsibilities (other than below)

33 37

Senior manager (e.g. vice president, country manager)

3 3

Table 3: Respondent positions at Nokia

The average survey respondent is a middle-aged male (between 35 and 54 in age),

worked for Nokia for 14 years, held a technical position without staff position, and

started a company in the “Other Management Consulting” industry after

displacement from Nokia. On average, respondents are highly educated: 91% of

respondents have a university-level degree. The majority of survey respondents did

not have previous entrepreneurship experience. Only 16% of the respondents

42

reported having been a startup entrepreneur before. These measures are very well

in line with the respondent demographics from the 2013 survey, with only previous

startup experience being slightly overrepresented (16% in comparison with 10% in

2013).

3.3.6. Variables

In this chapter, I will present the dependent variable, the independent variables,

and the control variables used in this study, as well as elaborate on the literature

that these variables are based on.

Bourque and Clark (1994) outline three options available for researchers when

designing questions for surveys: (1) adopting questions from other questionnaires;

(2) adapting questions from other questionnaires; or (3) developing own questions.

In order for results to be comparable with other studies, it may be necessary to

adopt or adapt questions already used in literature. Whenever possible, Saunders

et al. (2016) suggest using existing questions from the literature in order to ensure

the quality of the questions and avoid extensive pilot testing. Hence, most of the

questions used in the questionnaire for this thesis were adopted from the previous

questionnaire used by Kiuru et al. (2014)

3.3.6.1. Dependent Variable

My dependent variable is employment growth, which is measured as the difference

between number of full-time employees in the first and second round of surveys.

Based on the development in employment, companies are divided into two

categories: growing or stable (employment growth is positive or 0), and shrinking

or closed down (employment growth is negative or operations are discontinued).

These categorizations will allow me to look into the factors contributing to startup

success in the form of employment growth.

There are conflicting opinions about whether employment growth should be

measured by absolute measures (t2-t1) or relative measures (t2-t1/t1) (Rauch et

al., 2005; Davidsson & Wiklund, 2000), but as the abovementioned categorization

only takes into account whether growth is positive, nonexistent, or negative, both

measures will give the same results. Hence, in line with Rauch et al (2005), I use

43

absolute measures in this study. If a startup has stopped its operations, employee

number is set to 0.

3.3.6.2. Independent Variables

Hypothesis 1 predicts that the success of the startup is dependent of the human

capital that a displaced employee has transferred to the startup from the MNC.

Theoretically, human capital, such as knowledge or skills, is a result of investments

in human capital in the form of e.g. education and work experience (Becker, 1993).

Therefore, most studies use metrics such as years of experience in certain roles or

years of education to measure the human capital of an entrepreneur (Unger et al.,

2011). This is a valid measure as long as investments in human capital lead to

human capital outcomes, as seems to be the case based on current research (ibid.).

However, what this approach fails to take into account is the transferability of human

capital – as a part of human capital accumulated at a company tends to be firm-

specific and cannot be transferred outside the particular context in which it was

developed (Huckman & Pisano, 2006; Groysberg et al, 2008), this measure is not

particularly fitting for the purpose of this study. Furthermore, using general

measures such as years of education or years of work experience does not allow

to account for the particular value of the human capital accumulated at an MNC in

specific.

In order to isolate the human capital developed during tenure at Nokia and capture

the parts of it that the displaced employees were able to transfer to their startups,

a scale of 13 different scale items aimed at capturing different aspects of human

capital that an employee may have built up during their employment at Nokia was

developed.

The respondents were asked to evaluate on a 7-point scale how much they have

used the following resources that they obtained either during their employment at

Nokia or through the Bridge Program:

Technological knowledge (i.e. knowledge required to create product or

service)

44

Functional knowledge

Knowledge of customer needs

Knowledge of industry conditions

Knowledge of country or regional conditions

Marketing and sales

Research and development

Operations (e.g. logistics)

Strategy

Human resources

Relationships with buyers, suppliers

Brand

Patents and trademarks

The ranks from each scale item were then added together to gain a single measure

for the human capital that the displaced employees had transferred to their startups.

To test Hypothesis 2, a dummy variable was created to measure whether or not the

startup is operating within the same industry as Nokia (Baptista et al., 2014). The

respondents’ startups were divided into different industries based on the industry

codes that the business identity code of the startup was registered on. In line with

the division in Kiuru et al., 2014, startups with industry codes within the ICT sector

(61200, 62010, 62020, and 63110 – for more details see Appendix A) were

considered to be within the same industry as Nokia.

In Hypothesis 3, I predict that entrepreneurs with managerial experience from the

MNC will have transferred more valuable human capital than other entrepreneurs.

In line with Sofka et al. (2014), I include management experience at Nokia as a

dummy variable. Two roles at Nokia were included in this variable: “Manager with

staff responsibilities” and “Senior manager (e.g. vice president, country manager)”.

Consequently, the control group includes employees with roles in support functions

45

and employees with technical roles without managerial responsibility (e.g.

programmer or researcher).

3.3.6.3. Control Variables

I also include several control variables on the firm and entrepreneur based on

characteristics that have been identified to influence startup success in the

literature.

On an individual level, (1) age; (2) education; (3) gender; (4) previous

entrepreneurship experience; (5) motivation to become an entrepreneur have been

reported to affect the success of a startup (Bosma et al., 2004; Baptista et al., 2014).

(1 & 2) Dummy variables on age and whether or not the respondent has received

higher education (university or applied sciences) are included as a measure of

investment in general human capital (Bosma et al., 2004). In line with Baptista et

al. (2014), I control for founder age with a dummy variable for three different age

groups: less than 35; and 35-44; and 45 years and older. I group all the founders

who are 45 years or older into one age class, since empirical evidence suggests

that around this age, the negative effect that age has on startup success begins to

dominate over the positive effects (Rees and Shah 1986; Carrasco 1999). (3)

Empirical findings on the effects of the entrepreneur’s gender on the success of the

startup are mixed, but for example Cooper et al. (1994) find that startups of male

entrepreneurs have greater probabilities of high growth. Hence, I include gender as

a dummy variable to the control variables. (4) I also control for previous

entrepreneurship experience in the form of a dummy variable, since previous

experience as an entrepreneur is often found to be the one of the most significant

factor influencing startup performance (e.g. Bosma et al., 2004; Baptista et al.,

2014; Arribas & Vila, 2007). (5) The motivation of an entrepreneur is reported to

affect the success of a startup notably (Baptista et al., 2014). Hence, I include

dummy variables for opportunity-based entrepreneurship (the entrepreneur

reported his or her motivation for becoming an entrepreneur to be based on an

identified opportunity or a long wish to become an entrepreneur) and necessity-

based entrepreneurship (the entrepreneur reported his or her motivation for

46

becoming and entrepreneur to be based on that it was the only way to avoid

unemployment or that he or she did not want to continue as an employee).

At the firm level, I will control for (1) licensing or idea release agreement; (2) the

age of the startup; (3) the total number of founders; and (4) the number of

employees in t1. (1) Nokia was supporting the Bridge entrepreneurs by actively

concluding licensing or idea release agreements with them, which certainly puts the

startups in different positions in the market. Hence, I include a dummy variable to

control for a licensing or idea release agreement. (2) Furthermore, as the first three

years of a startup are particularly crucial for the survival and success of the new

venture (Littunen et al., 1998; Santarelli & Vivarelli, 2007), I include age as the

difference between 2016 and the year of establishment as a control variable. (3-4)

Human capital has been found to be accumulative in the sense that the more people

are founding a startup together, the better its chances of survival (e.g. Arribas &

Vila, 2007; Baptista et al., 2014). However, when it comes to employment growth,

studies across various countries and industries have found that small startups grow

significantly faster than larger ones (Colombo et al., 2004). Furthermore, it seems

logical that it would be easier for a startup with a significantly larger number of

employees to grow by one employee than for a startup with only one full-time

employee. Hence, I control for both having more than one founder, i.e. founding the

startup in a team (Baptista et al., 2014) and the number of full-time employees in

t1, as both may impact the survival and employment growth of the startup.

3.3.7. Data Analysis

Data form survey responses was extracted to Excel, where the two datasets were

combined using the Finnish business identity code to identify same startups.

First, I checked the values for any missing data. As mentioned in chapter 3.2.5.1.

on Sample, one respondent had only filled in approximately a half of the

questionnaire and did not leave contact information or information on the startup.

Hence, all the responses from this specific respondent had to be removed, since it

was not possible to combine the responses of the two survey rounds. In addition,

there was one missing value in the data: one of the respondents had failed to

47

answer the question on whether or not the startup is still active. As the startup had

stopped operations already before the previous survey in 2013, the response was

retrieved from the previous data and filled in. Considering that nearly all of the

respondents had filled in all items in the questionnaire, I concluded that there is no

form or pattern in the missing data, but these were merely single, random cases

(Saunders et al., 2016).

Next, data was checked for errors on two different aspects in line with Saunders et

al. (2016):

Looking for illegitimate codes. As the coding came straight from Webropol

where most of the questions were multiple choice without an opportunity to

modify the answer, no illegitimate codes were found.

Looking for illogical relationships. This was mostly done concerning the

number of founders in total, and number of Nokia & External founders by

checking that the numbers match. Furthermore, it was checked that

companies whose operations were reported discontinued did not report full-

time employees. Furthermore, it was checked that answers did not follow a

regular pattern, for instance all answers being the first available alternative.

No errors were found.

There was no need to check that rules in filter questions were followed, since no

filter questions were used in the analysis.

Then, data was re-coded to create new variables for employment growth, sum of

transferred human capital, startup operating in the same industry as Nokia,

founder’s managerial experience, age categories, founder being highly educated,

and opportunity- and necessity-based entrepreneurship. For a more detailed

description on how these variables were created, see chapter 3.2.6. on Variables.

Finally, the data was divided into two samples: successful startups, meaning those

that had an employment growth larger than 0, the control group, meaning those

startups that had an employment growth of 0 or below. The control variables were

48

checked in relation to each group to identify any bias in the results caused by control

variables rather than the independent variables.

To test hypothesis 1, I conducted a means comparison to see if the founders of

successful startups had transferred statistically significantly more human capital to

their startups than the founders of less successful startups. First, I checked that the

two samples are roughly normally distributed taken into account the small sample

size. In this case, the data did not contain any outliers and the distribution was

reasonably similar to a normal distribution, so a t-test should be an appropriate

means for testing the hypothesis. Furthermore, the standard deviations of the two

samples were checked to be approximately equal (1.67 for successful startups vs.

1,31 for unsuccessful startups). Finally, an unpaired t-test was conducted to test

the hypothesis.

Hypotheses 2 and 3 were tested using the f-test for two proportions to see if the

proportion of companies operating in the same industry as Nokia is higher for

successful startups than the control group, and if the proportion of founders with

managerial experience was higher for successful startups than it was for the control

group.

3.4. Methodological Quality

According to Saunders et al. (2016), the quality of an empirical study can be

evaluated based on three criteria, namely internal validity, external validity, and

reliability. The following chapter will evaluate the methodological quality of this

thesis in relation to these criteria.

3.4.1. Validity

When using questionnaires, internal validity – also known as measurement validity

– estimates how well the questionnaire measures what it is intended to measure

(Saunders et al., 2016). Furthermore, the validity of a questionnaire can be divided

into three aspects: content validity (do the questionnaire questions provide

adequate coverage of the research questions), criterion-related validity (do the

questions allow for making accurate predictions), and construct validity (does the

set of questions really measure what they are intended to measure) (ibid.).

49

Saunders et al. (2016) suggest several ways of improving the internal validity of a

questionnaire. First, in order to ensure content validity in a questionnaire, two

alternative methods are suggested: the scope of questions in the questionnaire

need to be based on a careful review of the literature and, where possible, a panel

of several individuals to assess the necessity of each question (ibid.). In this

research, a thorough review of existing literature was followed by a discussion on

the content of the questionnaire with the two researchers from Aalto University

Small Business Center, where the necessity of the different questions was carefully

evaluated.

Second, the predictive validity (criterion-related validity) can be assessed using e.g.

correlation tests (Saunders et al., 2016). In this thesis, criterion-related validity is

assessed using means comparison and the f-test for two proportions. A drawback

with these methods is the inability to hold other factors constant, meaning that the

utilized methods do not allow disentangling particular effects. A better measure to

assess the criterion-related validity would be to use regression analysis in order to

disentangle the effects of the independent variables on the dependent variable, but

such an analysis is outside the scope of this thesis.

Finally, construct validity discusses how well the scale items in the questionnaire

actually measure the construct that they were intended to measure (Saunders et

al., 2016). In order to improve the construct validity of the questionnaire used in this

thesis, the scale items were based on a careful review of the literature and adapted

from the previous survey.

Saunders et al. (2016) suggest pilot testing the questionnaire with respondents

similar to the actual target group before the questionnaire is really sent out. This

should allow to refine the questions and improve the validity and reliability of the

questionnaire. In this thesis, pilot testing on the questions was conducted by Aalto

Small Business Center researchers before the first round of surveys was sent out,

and questions for the second survey round in 2016 were adapted from the previous

survey. Furthermore, both questionnaires included an open answer field where

respondents had the chance to comment on the questionnaire or their answers.

50

In the end, the validity of a study is always a trade-off, where decisions need to be

made based on access to information, available time and resources, the scale and

scope of the study and the validity required from the study. Considering the scope,

scale, and constraints for this thesis as well as the precautions taken to improve

validity, I consider the validity and of this thesis to be satisfactory.

3.4.2. Reliability and External Validity

According to Saunders et al. (2016), reliability refers to the consistency and

replicability of the research. If a research design can be replicated by another

researcher and it results in the same findings, the research would be found reliable.

Reliability is often divided into internal and external reliability, where internal

reliability looks into consistency within a research project, and external reliability,

which refers to the quality of data collection techniques and analytic processes:

would the findings remain consistent, if the research setting was replicated on

another occasion or by a different researcher? (Saunders et al., 2016)

Saunders et al. (2016) outline four factors that threat the reliability of a study. (1)

Participant error refers to any factor which harms a participant’s performance. This

was counteracted by collecting data from voluntary respondents and by allowing

them to respond to the survey at a time slot suitable for themselves. (2) Participant

bias refers to factors that encourage participants to respond falsely. This is usually

not a problem with online questionnaires, but participants were also assured of

complete anonymity throughout the research process to avoid respondent bias. (3)

Researcher error refers to factors that influence the researchers’ interpretation,

such as tiredness or insufficient preparation. In order to ensure internal reliability,

it is suggested to use more than one researcher to collect and analyze data to verify

the accuracy of the analysis (Saunders et al., 2016). Unfortunately, due to time and

resource constraints, it was only possible to use one researcher for the data

analysis, which reduces the internal reliability of this study. However, data collection

was done in collaboration with two researchers from Aalto University Small

Business Center, and the thesis supervisor was able to comment on the

interpretation of the results. Finally, (4) researcher bias refers to factors that

51

influence the researcher’s interpretations or measurements. Researcher influence

on the results was minimized by using standard methods and measurements.

The focus throughout this thesis has been on documenting and clearly outlining all

steps of the research process. Key concepts have been defined, constraints to the

study have been discussed, and the theoretical predictions have been made based

on existing literature. Hence, I believe that readers should be able to build a

comprehensive understanding of the research process.

The external validity of research refers to the generalizability of the research

findings to other settings or groups (Saunders et al., 2016). Bryman and Bell (2015)

suggest that increasing the sample size will also increase the accuracy – and,

hence, generalizability of the data. However, as the objective of this thesis is to

study the human capital that displaced employees have built up during their tenure

at an MNC, it calls for an individual estimation of the accumulated human capital

from the MNC specifically. As a consequence, it will not be possible to use e.g.

centrally-collected datasets with longitudinal data on employment, as is done in

existing research studying the effects of human capital on career paths of displaced

employees (e.g. Sofka et al., 2014; Baptista et al., 2014) - this type of datasets do

not allow to isolate specifically the human capital accumulated at one single

employer and transferred to the next position, but rather support the use of

measures such as experience at a certain role or education to estimate the

accumulated human capital. Hence, it is essential for the purposes of this study to

reach the displaced employees personally through a questionnaire to allow for a

more accurate estimate of transferred human capital. At the same time, this

requirement places constraints on the sample size for the study, as access to

displaced employees that have become entrepreneurs is limited.

Due to the small sample size in this study, the hypotheses would need to be tested

using a notably larger sample and analyzed with regression analysis before they

could be generalized to other settings or groups with confidence.

52

PART FOUR: RESULTS

RESULTS

This chapter will present the results of the data analysis. It will begin by going

through the results from testing the hypotheses and then discuss the impact of

control variables on the results.

53

4.1. Results

To test Hypotheses 1, predict that transferred human capital contributes to the

success of a startup. In order to test if the founders of successful startups had

transferred statistically significantly more human capital to their startups than the

founders of less successful startups, I conducted a means comparison. First, I

checked that the two samples are roughly normally distributed taken into account

the small sample size. In this case, the data did not contain any outliers and the

distribution was reasonably similar to a normal distribution. Furthermore, the

standard deviations of the two samples were checked to be approximately equal

(1.67 for successful startups vs. 1,31 for unsuccessful startups). Hence, a t-test is

an appropriate means for testing the hypothesis.

The results of the two-tail t-test can be seen in Table 4. The null hypothesis is that

the difference in the means of transferred human resources between successful

startups and the control group is statistically insignificant. The mean of transferred

human resources is higher for successful startups than the control group, but since

the t Stat (1,01) < t Critical two-tail (2,04), I cannot reject the null hypothesis at a

5% level of significance. Hence, I cannot confirm Hypothesis 1.

Table 4: Results of the t-test on Hypothesis 1.

54

In Hypothesis 2, I predict that startups operating in the same industry as Nokia are

more successful than startups operating in other industries. Already when

comparing the proportions of companies operating in the same industry as Nokia

between the group of successful startups and the control group, it is evident that

Hypothesis 2 is not supported by the data (Table 5). To check this results, I conduct

a 2-sample test for equality of proportions (Table 6) which confirms the null

hypothesis the proportions are not different. Hence, I reject Hypothesis 2.

Table 5: Proportion of Startups Operating in Same Industry as Nokia.

Table 6: 2-Sample Test for Equality of Proportions – Industry

Finally, to test Hypothesis 3 which predicts that the startups of founders with

managerial experience from Nokia will be more successful than their counterparts,

I follow the same steps as for Hypothesis 2. First, I plot the proportions of founders

55

with managerial experience against the two samples (successful startups and

control group). As can be seen from Table 7, the proportion of founders with

managerial experience from Nokia is higher in the group of successful startups

(50%) than for the control group (36%).

Table 7: Proportion of Founders with Managerial Experience.

However, as I run a 2-sample test for equality of proportions (Table 8), this

difference proves to be statistically insignificant. Hence, I cannot confirm

Hypothesis 3.

Table 8: 2-Sample Test for Equality of Proportions – Managerial

Experience

56

Table 9 presents the means of transferred resources per subcategory used in the

transferred human resources variable. These means are presented separately for

successful startups and the control group in order get an indication if the measure

on transferred human capital could be improved by i.e., narrowing it down to the

type of factors of human capital that have the biggest contribution to startup

success.

Table 9: Means of Transferred Resources by Subcategory

On average, it seems that the founders of successful startups have transferred

categorically more human capital within each of the subcategory, with the exception

of strategy, where the control group indicates more transferred human capital. In

four of the subcategories, the successful startups had transferred more than 20%

more human capital than the control group, namely Technological Resources

(30%), Human Resources (33%), Logistics (32%), and Patents & Trademarks

(42%). However, even with these four subcategories, the difference was not

statistically significant.

Finally, to take into account the role of control variables in the results, Table 10

compares the control variables between the entire population and the two samples.

Variable Measure Population Successful Startups

Control Group

57

Individual Level

Age

<35 9% 20% 5%

35-44 41% 60% 32%

>44 50% 20% 64%

Education Highly Educated

91% 90% 91%

Gender Male 75% 60% 82%

Female 25% 40% 18%

Entrepreneurship Experience

Having Experience

16% 0% 23%

Motivation

Opportunity-based

81% 100% 73%

Necessity-based

19% 0% 27%

Firm Level

Licensing or Idea Release Agreement

Having agreement 19% 30% 14%

Startup Age Mean (in years)

5 4,6 5,18

More than one founder

Proportion 38% 60% 27%

# of Employees in 2013

Mean 2,22 3,5 1,6

Table 10: Differences in Control Variables between Samples and

Population

As can be seen from the comparison above, there are notable differences between

the two samples with regard to several control variables. On an individual level,

younger founders (below the age of 45) seem to be strongly overrepresented in

successful startups whereas founders of age 45 and older are dominant in the

control group. This is in line with the predictions based on literature – age at around

58

45 seems to be the tipping point after which age starts to contribute negatively to

startup success. There is no real difference in the percentage of founders with a

university-level education between the samples, but it must be noted that the

population in general is very highly educated. In contrast with the expectations

based on the literature, female founders are overrepresented in the group of

successful startups. However, as noted in section 3.2.6.3. on control variables,

empirical evidence on the effects of gender on startup success are mixed. Finally,

as expected, the founders of successful startups are characterized by an

opportunity-based motivation for entrepreneurship: none of them reported

necessity as the primary reason for becoming an entrepreneur.

On the firm level, as predicted, startups with a licensing or idea release agreement

are more prominent in the group of successful startups than in the control group.

The successful startups seem to be slightly younger than their counterparts, but the

difference is less than a year in the mean age. The difference between successful

startups and their counterparts with regard to the proportion of team entrepreneurs

is notable: entrepreneur teams represent well over half of the successful startups

while the same proportion for the control group is less than a third. This seems to

indicate that, as suggested by existing literature, human capital is accumulative

and, hence, founding a startup together with at least one other person contributes

to the success of the startup. However, there is also a difference in the number of

full-time employees that the startups had in 2013, with startups growing the number

of their employees being on average 1,9 full-time employees larger than their

counterparts already in 2013. This is in contrast with the existing literature

suggesting that small startups grow significantly faster than larger ones.

59

PART FIVE: DISCUSSION AND CONCLUSION

DISCUSSION AND CONCLUSION

This chapter will discuss the results and present conclusions from this thesis.

60

5.1. Discussion and Conclusion

Inspired by Sofka et al. (2014), this thesis centers on the human capital of displaced

employees. The specific focus of this thesis is on the effects of displaced

employees’ human capital on their startups. This approach is motivated by the

growing literature on the impact of human capital which, however, fails to isolate

the effects of human capital developed specifically during employment at a single

employer. At the same time, subsidiary closures are nowadays quite common

(Berry, 2013; Mata & Freitas, 2012), and they can create an abundance of skilled

workforce in a specific sector in the local labor market resulting in significantly

increased unemployment (Neittaanmäki & Kinnunen, 2016). Furthermore, losing a

job due to a subsidiary closure has been empirically found to have long-term,

negative effects on an employee’s future earnings (Couch & Placzek, 2010).

Entrepreneurship can be a valid way of continuing to utilize the human capital that

displaced employees accumulated at an MNC, when the job market does not

provide good opportunities for exploiting the human capital. These factors together

prompted the following research question:

How does the human capital that a displaced employee has developed at an

MNC contribute to the success of the employee’s startup?

To answer this question, a theoretical framework is developed based on existing

literature on human capital and entrepreneurship. Even though the theoretical

predictions that are made based on the framework are not supported by the

empirical results, they can guide future research in the area.

Hypotheses 1 and 3 suggest that transferred human capital from an MNC and

especially managerial experience from the MNC contribute to the success of a

displaced employee’s startup. Even though the means and proportion comparisons

suggest that founders of successful startups have transferred more human capital

from the MNC to their startups and held managerial positions at the MNC more

often than their counterparts, these effects are not statistically significant. Hence, I

cannot confirm Hypotheses 1 and 3. Furthermore, my prediction that startups

61

operating in the same industry as the MNC are more successful than their

counterparts is not supported by the empirical findings, where the proportion of

startups operating in the MNCs industry is practically the same for both successful

startups and their counterparts. Hence, Hypothesis 2 is rejected.

Even though the theoretical model of this thesis is not supported by the empirical

findings, it opens up discussion on a gap in existing research, where the effects of

displaced employees’ human capital on an entrepreneurial career have not been

studied previously in a setting that allows for isolating the human capital

accumulated at the MNC. Closing this gap is highly relevant considering the

frequency in which subsidiary closures take place as MNCs change locations, and

at the same time the difficult job markets that subsidiary closures often leave to the

local area.

On a theoretical basis, this thesis builds on the work of Sofka et al. (2014), where

displacement is introduced as a means of transferring human capital from an MNC

subsidiary to the host country. This element has been to a large extent neglected

in the literature (ibid.), and this thesis suggests expanding the recipient side of the

valuable human capital to cover startups of displaced employees in addition to

existing host-country firms.

Furthermore, on a methodological basis, this thesis suggests developing more

accurate measures to establish the amount of human capital that a displaced

employee has accumulated at an MNC and actually transferred to their own startup.

This opens up a discussion on ways to capture the isolated effect of transferred

human capital developed at an MNC instead of relying on measures of human

capital investment, which are not able to measure the transferability aspect.

The empirical part of this thesis suffers from two limitations in particular. On the one

hand, the small sample size constrains the statistical significance of the findings,

resulting in inability to confirm the hypothesis. On the other hand, the method used

for the data analysis does not allow for holding variables constant, thus restricting

the explanatory power of the independent variables: it is impossible to disentangle

the effects of the different variables with the current method. Hence, it could be

62

fruitful for future research to study the same phenomena using a regression

analysis with a significantly larger number of observations.

In addition to empirical limitations, there might be other reasons for the theory not

to hold. First of all, in the case of Nokia, the subsidiary closures took place in the

home country of the MNC instead of a host country. Even though Nokia was a

multinational corporation drawing in foreign human capital in the form of numerous

inpatriates and cooperation with foreign subsidiaries, the focus of e.g. Sofka et al.

(2014) is explicitly on the valuable foreign human capital that displaced employees

hold. Hence, there can be differences in the value and foreignness of the human

capital between MNC subsidiaries in home and host countries.

Furthermore, even though working in a multinational corporation seems to be an

efficient way of building valuable human capital, the context in which that human

capital is built differs greatly from the realities of a startup. Hence, it is possible that

the human capital accumulated at an MNC is difficult to transfer to a startup context

and would be more valuable in another large organization with vast resources and

larger-scale operations.

In sum, the largest contribution of this thesis is that it points to a gap in existing

research on examining the role of human capital that displaced employees have

accumulated at an MNC on the success of their startups and suggests a more

accurate method of measuring transferred human capital in the context of displaced

employees.

For future research, the theoretical predictions will need to be examined using a

regression analysis on a much larger sample in order to be able to draw reliable

conclusions on the theory and practical implications. Furthermore, taking into

consideration the absence of empirical significance, further research could require

a different empirical approach in order to produce significant findings. A potential

solution could be to use a different comparison group, e.g. comparing startups of

displaced employees with other startups that have used the same startup services

or are otherwise similar in nature. This would allow a better understanding of the

specifics of the human capital of displaced employees in relation to other recent

63

entrepreneurs without a similar background. Alternatively, it would be interesting to

compare the value of a displaced employee’s human capital in startups compared

with larger, more established organizations. This would build more insights into the

requirements for efficient transfer of human capital.

Finally, future research could benefit from a more nuanced theory taking into

account the specific factors of human capital developed at an MNC, which would

allow drawing more tangible conclusions on which aspects of a displaced

employee’s human capital are particularly valuable for a startup.

64

REFERENCES

Ackerman, P.L. & Humphreys, L.G. 1990, "Individual differences theory in industrial and organizational psychology" in Handbook of industrial and organizational psychology. Vol. 1, eds. D. Dunnette Marvin & M. Hough Leaetta, 2. ed. edn, Consulting Psychologists Press, Palo Alto, pp. 223-282.

Addison, J.T. & Portugal, P. 1989, "Job Displacement, Relative Wage Changes, and Duration of Unemployment", Journal of Labor Economics, vol. 7, no. 3, pp. 281-302.

Adler, P.S. 2002, "Social Capital: Prospects for a New Concept", The Academy of Management Review, vol. 27, no. 1, pp. 17-40.

Agarwal, R., Echambadi, R., Franco, A.M. & Sarkar, M.B. 2004, "Knowledge Transfer through

Inheritance: Spin-Out Generation, Development, and Survival", Academy of Management Journal, vol. 47, no. 4, pp. 501-522.

Agrawal, A. 2006, "Engaging the inventor: exploring licensing strategies for university inventions and the role of latent knowledge", Strategic Management Journal, vol. 27, no. 1, pp. 63-79.

Alcácer, J. & Chung, W. 2007, "Location Strategies and Knowledge Spillovers", Management Science, vol. 53, no. 5, pp. 760-776.

Almeida, P. & Kogut, B. 1999, "Localization of Knowledge and the Mobility of Engineers in Regional Networks", Management Science, vol. 45, no. 7, pp. 905-917.

Almeida, P. & Phene, A. 2004, "Subsidiaries and knowledge creation: the influence of the MNC and host country on innovation", Strategic Management Journal, vol. 25, no. 8, pp. 847-864.

Arribas, I. & Vila, J. 2007, "Human capital determinants of the survival of entrepreneurial service firms in Spain", International Entrepreneurship and Management Journal, vol. 3, no. 3, pp. 309-322.

Balconi, M. 2002, "Tacitness, codification of technological knowledge and the organisation of industry", Research Policy, vol. 31, no. 3, pp. 357-379.

Baptista, R., Karaöz, M. & Mendonça, J. 2014, "The impact of human capital on the early success of necessity versus opportunity- based entrepreneurs", Small Business Economics, vol. 42, no. 4, pp. 831-847.

Barney, J. 1991, "Firm Resources and Sustained Competitive Advantage", Journal of Management, vol. 17, no. 1, pp. 99.

Barney, J.B., Busenitz, L.W., Fiet, J.O. & Moesel, D.D. 1996, "New venture teams' assessment of learning assistance from venture capital firms", Journal of Business Venturing, vol. 11, no. 4, pp. 257-272.

Bates, T. 1990, "Entrepreneur Human Capital Inputs and Small Business Longevity", The review of economics and statistics, vol. 72, no. 4, pp. 551-559.

Baum, J.R., Locke, E.A. & Smith, K.G. 2001, "A Multidimensional Model of Venture Growth", The Academy of Management Journal, vol. 44, no. 2, pp. 292-303.

65

Bechky, B.A. 2003, "Sharing Meaning across Occupational Communities: The Transformation of Understanding on a Production Floor", Organization Science, vol. 14, no. 3, pp. 312-330.

Becker Gary, S. 1993, Human capital: a theoretical and empirical analysis, with special reference to education, 3. ed. edn, , Chicago.

Becker, G.S. 1962, "Investment in Human Capital: A Theoretical Analysis", Journal of Political Economy, vol. 70, no. 5, pp. 9-49.

Berman, S.L. & Down, J. 2002, "Tacit Knowledge as a Source of Competitive Advantage in the National Basketball Association", The Academy of Management Journal, vol. 45, no. 1, pp. 13-31.

Berry, H. 2013, "When do firms divest foreign operations?", Organization Science, vol. 24, no. 1, pp. 246-261.

Bosma, N., van Praag, M., Thurik, R. & de Wit, G. 2004, "The Value of Human and Social Capital Investments for the Business Performance of Startups", Small Business Economics, vol. 23, no. 3, pp. 227-236.

Bosworth, M. 2014, The upside to being let go by Nokia, BBC, Helsinki.

Bourque, L.B. & Clark, V.A. 1994, "Processing data: the survey example" in Research practice, ed. S. Lewis-Beck Michael, Sage, London, pp. 1-88.

Brown, J.N. 1989, "Why Do Wages Increase with Tenure? On-the-Job Training and Life- Cycle Wage Growth Observed within Firms", The American Economic Review, vol. 79, no. 5, pp. 971-991.

Brown, J.S. & Duguid, P. 1991, "Organizational Learning and Communities-of- Practice: Toward

a Unified View of Working, Learning, and Innovation", Organization Science, vol. 2, no. 1, pp. 40-57.

Brüderl, J., Preisendörfer, P. & Ziegler, R. 1992, "Survival Chances of Newly Founded Business Organizations", American Sociological Review, vol. 57, no. 2, pp. 227-242.

Brush, C.G. 2001, "From Initial Idea to Unique Advantage: The Entrepreneurial Challenge of Constructing a Resource Base and Executive Commentary]", The Academy of Management Executive (1993-2005),vol. 15, no. 1, pp. 64-80.

Bryman, A. & Bell, E. 2015, Business research methods, 4. ed. edn, , Oxford.

Bryman, A. 2006, "Integrating quantitative and qualitative research: how is it done?", Qualitative Research, vol. 6, no. 1, pp. 97-113.

Burt, R.S. 1987, "Social Contagion and Innovation: Cohesion versus Structural Equivalence", American Journal of Sociology, vol. 92, no. 6, pp. 1287-1335.

Carrasco, R. 1999, "Transitions to and From Self‐ employment in Spain: An Empirical

Analysis", Oxford Bulletin of Economics and Statistics, vol. 61, no. 3, pp. 315-341.

Carrington, W.J. 1993, "Wage Losses for Displaced Workers: Is It Really the Firm That Matters?", The Journal of human resources, vol. 28, no. 3, pp. 435-462.

66

Cassar, G. 2006, "Entrepreneur opportunity costs and intended venture growth", Journal of Business Venturing, vol. 21, no. 5, pp. 610-632.

Chakrabarti, A., Vidal, E. & Mitchell, W. 2011, "Business transformation in heterogeneous environments: The impact of market development and firm strength on retrenchment and growth reconfiguration",Global Strategy Journal, vol. 1, no. 1-2, pp. 6-26.

Chandler, G.N. & Hanks, S.H. 1994, "Founder Competence, the Environment, and Venture Performance", Entrepreneurship: Theory & Practice, vol. 18, no. 3, pp. 77-89.

Chandler, G.N. & Hanks, S.H. 1993, "Measuring the performance of emerging businesses: A validation study", Journal of Business Venturing, vol. 8, no. 5, pp. 391-408.

Chandler, G.N. & Jansen, E. 1992, "The founder's self- assessed competence and venture performance", Journal of Business Venturing, vol. 7, no. 3, pp. 223-236.

Cohen, W.M. & Levinthal, D.A. 1990, "Absorptive Capacity: A New Perspective on Learning and Innovation", Administrative Science Quarterly, vol. 35, no. 1, pp. 128-152.

Colombo, M.G., Delmastro, M. & Grilli, L. 2004, "Entrepreneurs' human capital and the start- up size of new technology- based firms", International Journal of Industrial Organization, vol. 22, no. 8, pp. 1183-1211.

Colombo, M.G., Delmastro, M. & Grilli, L. 2004, "Entrepreneurs' human capital and the start- up size of new technology- based firms", International Journal of Industrial Organization, vol. 22, no. 8, pp. 1183-1211.

Compass 2015, 2015 Global Startup Ecosystem Ranking, http://startup-ecosystem.compass.co/ser2015/.

CompTIA 2016, IT Industry Outlook, https://www.comptia.org/resources/it-industry-outlook-2016-final.

Cooper, A.C. & Gimeno-Gascon, F. 1992, "Entrepreneurs, processes of founding and new firm performanc" in The state of the art of entrepreneurshipperformanc, Sexton Donald,L.;Kasarda John,D.; edn, PWS-Kent, Boston, pp. 301-340.

Cooper, A.C., Gimeno-Gascon, F. & Woo, C.Y. 1994, "Initial human and financial capital as predictors of new venture performance", Journal of Business Venturing, vol. 9, no. 5, pp. 371-395.

Cooper, A.C., Gimeno-Gascon, F.J. & Woo, C.Y. 1994, "Initial human and financial capital as predictors of new venture performance", Journal of Business Venturing, vol. 9, no. 5, pp. 371-395.

Corredoira, R.A. & Rosenkopf, L. 2010, "Should auld acquaintance be forgot? the reverse transfer of knowledge through mobility ties", Strategic Management Journal, vol. 31, no. 2, pp. 159-181.

Couch, K.A. & Placzek, D.W. 2010, "Earnings Losses of Displaced Workers Revisited", American Economic Review, vol. 100, no. 1, pp. 572-89.

Davidsson, P. & Wiklund, J. 2000, "Conceptual and empirical challenges in the study of firm growth" in The Blackwell handbook of entrepreneurship, eds. D. Sexton L. & H. Landström, Blackwell Business, Oxford, pp. 26-44.

67

Davidsson, P. & Honig, B. 2003, "The role of social and human capital among nascent entrepreneurs", Journal of Business Venturing, vol. 18, no. 3, pp. 301-331.

Debrulle, J., Maes, J. & Sels, L. 2014, "Start-up absorptive capacity: Does the owner’s human and social capital matter?", International Small Business Journal, vol. 32, no. 7, pp. 777-801.

Denzin, N.K. & Lincoln, Y.S. 2011, The SAGE handbook of qualitative research, 4. ed. edn, , London.

Dokko, G. & Rosenkopf, L. 2010, "Social Capital for Hire? Mobility of Technical Professionals and Firm Influence in Wireless Standards Committees", Organization Science, vol. 21, no. 3, pp. 677-695.

Dokko, G., Wilk, S.L. & Rothbard, N.P. 2009, "Unpacking Prior Experience: How Career History Affects Job Performance", Organization Science, vol. 20, no. 1, pp. 51-68.

Duchesneau, D.A. & Gartner, W.B. 1990, "A profile of new venture success and failure in an emerging industry", Journal of Business Venturing, vol. 5, no. 5, pp. 297-312.

Evans, D.S. & Leighton, L.S. 1989, "Some Empirical Aspects of Entrepreneurship", The American Economic Review, vol. 79, no. 3, pp. 519-535.

Feinberg, S.E. & Majumdar, S.K. 2001, "Technology Spillovers from Foreign Direct Investment in the Indian Pharmaceutical Industry", Journal of International Business Studies, vol. 32, no. 3, pp. 421-437.

Feldman Maryann, P. 1996, Location, location, location: the geography of innovation and knowledge spillovers, Wissenschaftzentrum Berlin für Sozialforschung. WZB, Berlin.

Florin, J., Lubatkin, M. & Schulze, W. 2003, "A Social Capital Model of High- Growth Ventures", The Academy of Management Journal, vol. 46, no. 3, pp. 374-384.

Frese, M., Krauss, S.I., Keith, N., Escher, S., Grabarkiewicz, R., Luneng, S.T., Heers, C., Unger, J. & Friedrich, C. 2007, "Business Owners' Action Planning and Its Relationship to Business Success in Three African Countries", Journal of Applied Psychology, vol. 92, no. 6, pp. 1481-1498.

Gibson, B. & Morgan, G. 1982, Sociological paradigms and organisational analysis: elements of the sociology of corporate life, repr. edn, , London.

Gill, J. & Johnson, P. 2002, Research methods for managers, 3. ed. edn, , London.

Gilson, L.L., Lim, H.S., Luciano, M.M. & Choi, J.N. 2013, "Unpacking the cross‐ level effects of

tenure diversity, explicit knowledge, and knowledge sharing on individual

creativity", Journal of Occupational and Organizational Psychology, vol. 86, no. 2, pp. 203-222.

Gimeno, J., Folta, T.B., Cooper, A.C. & Woo, C.Y. 1997, "Survival of the Fittest? Entrepreneurial Human Capital and the Persistence of Underperforming Firms", Administrative Science Quarterly, vol. 42, no. 4, pp. 750-783.

Groysberg, B., Lee, L. & Nanda, A. 2008, "Can They Take It with Them? The Portability of Star Knowledge Workers' Performance", Management Science, vol. 54, no. 7, pp. 1213-1230.

68

Haber, S. & Reichel, A. 2007, "The cumulative nature of the entrepreneurial process: The contribution of human capital, planning and environment resources to small venture performance", Journal of Business Venturing, vol. 22, no. 1, pp. 119-145.

Hatch, N.W. & Dyer, J.H. 2004, "Human capital and learning as a source of sustainable competitive advantage", Strategic Management Journal, vol. 25, pp. 1155-1178.

Helfat, C.E. 1994, "Firm- Specificity in Corporate Applied R& D", Organization Science, vol. 5, no. 2, pp. 173-184.

Henry, F. 2003, Job Loss in the United States, 1981-2001, National Bureau of Economic Research, Cambridge, Mass.

Hinz, T. & Jungbauer-Gans, M. 1999, "Starting a business after unemployment: characteristics and chances of success ( empirical evidence from a regional German labour

market)", Entrepreneurship & Regional Development; An International Journal, vol. 11, no. 4, pp. 317-333.

Hofer, C.W. & Sandberg, W.R. 1987, "Improving New Venture Performance: Some Guidelines for Success", American Journal of Small Business, vol. 12, no. 1, pp. 11-25.

Hood, J.N. & Young, J.E. 1993, "Entrepreneurship's requisite areas of development: A survey of top executives in successful entrepreneurial firms", Journal of Business Venturing, vol. 8, no. 2, pp. 115-135.

Huckman, R.S. & Pisano, G.P. 2006, "The Firm Specificity of Individual Performance: Evidence from Cardiac Surgery", Management Science, vol. 52, no. 4, pp. 473-488.

Hunter, J.E. 1986, "Cognitive ability, cognitive aptitudes, job knowledge, and job performance", Journal of vocational behavior, vol. 29, no. 3, pp. 340-362.

Hvide, H.K. 2009, "The Quality of Entrepreneurs*", Economic Journal, vol. 119, no. 539, pp. 1010-1035.

Ibarra, H. 1995, "Race, Opportunity, and Diversity of Social Circles in Managerial Networks", The Academy of Management Journal, vol. 38, no. 3, pp. 673-703.

Ibrahim, A.B. & Goodwin, J.R. 1986, "Perceived Causes of Success in Small Business", American Journal of Small Business, vol. 11, no. 2, pp. 41-50.

Iñaki Peña 2002, "Intellectual capital and business start- up success", Journal of Intellectual Capital, vol. 3, no. 2, pp. 180-198.

Jansen, R.J.G., Curşeu, P.,L., Vermeulen, P.A.M., Geurts, J.L.A. & Gibcus, P. 2013, "Information processing and strategic decision- making in small and medium- sized enterprises: The

role of human and social capital in attaining decision effectiveness", International Small Business Journal, vol. 31, no. 2, pp. 192-216.

Jansen, R.J.G., Curşeu, P.,L., Vermeulen, P.A.M., Geurts, J.L.A. & Gibcus, P. 2013, "Information processing and strategic decision- making in small and medium- sized enterprises: The role of human and social capital in attaining decision effectiveness", International Small Business Journal, vol. 31, no. 2, pp. 192-216.

69

Keeley, R.H. & Roure, J.B. 1990, "Management, Strategy, and Industry Structure As Influences on the Success of New Firms: A Structural Model", Management Science, vol. 36, no. 10, pp. 1256-1267.

KETOKIVI, M. & MANTERE, S. 2010, "Two Strategies for Inductive Reasoning in Organizational Research", Academy of Management Review, vol. 35, no. 2, pp. 315-333.

Kiuru, P., Handelberg, J. & Rannikko, H. 2014, Bridge it Up - The impact of startup services offered for employees - Case Nokia's Bridge Program, Aalto University School of Business Small Business Center, Helsinki.

Klemettilä, P. 2015, Nokialta lähteneiden yritykset pääosin toiminnassa – Antcoren

kasvu vahvaa, Kaleva, Oulu.

Klepper, S. 2007, "Disagreements, Spinoffs, and the Evolution of Detroit as the Capital of the U.S. Automobile Industry", Management Science, vol. 53, no. 4, pp. 616-631.

Kletzer, L.G. 1998, "Job Displacement", Journal of Economic Perspectives, vol. 12, no. 1, pp. 115-136.

Kogut, B. 1991, "Country capabilities and the permeability of borders", Strategic Management Journal, vol. 12, pp. 33-47.

Kutner Michael, H., Nachtsheim, C.J. & Neter, J. 2003, Applied linear regression models, 4th ed edn, McGraw-Hill Education - Europe, London.

Kwon, I. & Meyersson Milgrom, E.M. 2014, "The significance of firm and occupation specific human capital for hiring and promotions", Labour Economics, vol. 31, pp. 162-173.

Lappalainen, E. 2013, Nokian Bridge-ohjelma: Menestystarina yhteiskuntavastuusta?, Talouselämä, Helsinki.

Leana, C.R. & Pil, F.K. 2006, "Social Capital and Organizational Performance: Evidence from Urban Public Schools", Organization Science, vol. 17, no. 3, pp. 353-366.

Lecuona, J.R. & Reitzig, M. 2014, "Knowledge worth having in ‘ excess’: The value of tacit and firm‐ specific human resource slack", Strategic Management Journal, vol. 35, no. 7, pp.

954-973.

Lecuona, J.R. & Reitzig, M. 2014, "Knowledge worth having in ‘ excess’: The value of tacit and firm‐ specific human resource slack", Strategic Management Journal, vol. 35, no. 7, pp.

954-973.

Lee, S. & Lee, B. 2015, "Entrepreneur characteristics and the success of venture exit: an analysis of single-founder start-ups in the U.S", International Entrepreneurship and Management Journal, vol. 11, no. 4, pp. 891-905.

Lepak, D.P. & Snell, S.A. 2002, "Examining the Human Resource Architecture: The Relationships Among Human Capital, Employment, and Human Resource Configurations", Journal of Management, vol. 28, no. 4, pp. 517-543.

Littunen, H., Storhammar, E. & Nenonen, T. 1998, "The survival of firms over the critical first 3 years and the local environment", Entrepreneurship & Regional Development, vol. 10, no. 3, pp. 189-202.

70

Macmillan, I.C., Siegel, R. & Narasimha, P.N.S. 1985, "Criteria used by venture capitalists to evaluate new venture proposals", Journal of Business Venturing, vol. 1, no. 1, pp. 119-128.

Mailath, G.J. & Postlewaite, A. 1990, "Workers Versus Firms: Bargaining Over a Firm's Value", The Review of Economic Studies, vol. 57, no. 3, pp. 369-380.

Mailath, G.J. & Postlewaite, A. 1990, "Workers Versus Firms: Bargaining Over a Firm's Value", The Review of Economic Studies, vol. 57, no. 3, pp. 369-380.

Mata, J. & Freitas, E. 2012, "Foreignness and Exit over the Life Cycle of Firms", Journal of International Business Studies, vol. 43, no. 7, pp. 615-630.

McGregor, J. 2006, I Can't Believe They Took The Whole Team, Bloomberg Business.

Meyer Klaus, E. 2009, "When and where does foreign direct investment generate positive spillovers?: A meta-analysis", .

Michael, C. 1998, The foundations of social research: meaning and perspective in the research process, , London.

Mintzberg, H. & Waters, J.A. 1982, "Tracking Strategy in an Entrepreneurial Firm", The Academy of Management Journal, vol. 25, no. 3, pp. 465-499.

Neittaanmäki, P. & Kinnunen, P. 2016, Työttömyys IT-alalla koko Suomessa ja maakunnissa 2006–2015, Jyväskylän Yliopisto, Inoformaatioteknologian tiedekunta, Jyväskylä.

Ng, T.W.H. & Feldman, D.C. 2010, "Organizational Tenure and Job Performance", Journal of Management, vol. 36, no. 5, pp. 1220-1250.

Nonaka, I. 1994, "A Dynamic Theory of Organizational Knowledge Creation", Organization Science, vol. 5, no. 1, pp. 14-37.

OECD , Employment in the ICT sector continues dropping.

Available: http://www.oecd.org/sti/ieconomy/employmentintheictsectorcontinuesdropping.htm [2016, 5/9].

Ong, P.M. & Mar, D. 1992, "Post- Layoff Earnings among Semiconductor Workers", Industrial and Labor Relations Review, vol. 45, no. 2, pp. 366-379.

Pavett, C.M. & Lau, A.W. 1983, "Managerial Work: The Influence of Hierarchical Level and Functional Specialty", The Academy of Management Journal, vol. 26, no. 1, pp. 170-177.

Pavett, C.M. & Lau, A.W. 1983, "Managerial Work: The Influence of Hierarchical Level and

Functional Specialty", The Academy of Management Journal, vol. 26, no. 1, pp. 170-177.

Pennings, J.M., Lee, K. & Van Witteloostuijn, A. 1998, "Human Capital, Social Capital, and Firm Dissolution", The Academy of Management Journal, vol. 41, no. 4, pp. 425-440.

Ployhart, R.E. & Moliterno, T.P. 2011, "Emergence of the Human Capital Resource: A Multilevel Model", Academy of Management Review, vol. 36, no. 1, pp. 127-150.

Podgursky, M. & Swaim, P. 1987, "Job Displacement and Earnings Loss: Evidence from the Displaced Worker Survey", Industrial and Labor Relations Review, vol. 41, no. 1, pp. 17-29.

71

Rauch, A., Frese, M. & Utsch, A. 2005, "Effects of Human Capital and Long‐ Term Human

Resources Development and Utilization on Employment Growth of Small‐Scale Businesses:

A Causal Analysis 1",Entrepreneurship Theory and Practice, vol. 29, no. 6, pp. 681-698.

Rauch, A., Frese, M. & Utsch, A. 2005, "Effects of Human Capital and Long‐ Term Human

Resources Development and Utilization on Employment Growth of Small‐Scale Businesses:

A Causal Analysis 1",Entrepreneurship Theory and Practice, vol. 29, no. 6, pp. 681-698.

Raz, O. & Gloor, P.A. 2007, "Size Really Matters: New Insights for Start- Ups' Survival", Management Science, vol. 53, no. 2, pp. 169-177.

Rees, H. & Shah, A. 1986, "An empirical analysis of self‐employment in the U.K", Journal of

Applied Econometrics, vol. 1, no. 1, pp. 95-108.

Reynolds, P., Bosma, N., Autio, E., Hunt, S., De Bono, N., Servais, I., Lopez-Garcia, P. & Chin,

N. 2005, "Global Entrepreneurship Monitor: Data Collection Design and Implementation 1998– 2003", Small Business Economics; An International Journal, vol. 24, no. 3, pp. 205-231.

Rocha, V., Carneiro, A. & Varum, C. 2015, "What explains the survival gap of pushed and pulled corporate spin- offs?", Economics Letters, vol. 126, pp. 127-130.

Rönnqvist, R., Hakonen, A. & Vartiainen, M. 2015, "The Bridge Program - Participant Perspectives", Aalto University publication series SCIENCE + TECHNOLOGY, [Online], no. 4, pp. 17.01.2016. Available from:https://aaltodoc.aalto.fi/handle/123456789/16082.

Saunders, M. & Tosey, P. 2013, "The Layers of Research Design", Rapport, vol. Winter 2012/13, pp. 58-59.

Saunders, M.1. 2016, Research methods for business students, 7. ed. edn, , Harlow.

Schutjens, V.A.J.M. & Wever, E. 2000, "Determinants of new firm success *", Papers in Regional Science, vol. 79, no. 2, pp. 135-159.

Sexton, D.L. & Bowman, N. 1985, "The entrepreneur: A capable executive and more", Journal of Business Venturing, vol. 1, no. 1, pp. 129-140.

Shane, S. 2000, "Prior Knowledge and the Discovery of Entrepreneurial Opportunities", Organization Science, vol. 11, no. 4, pp. 448-469.

Shane, S. 2000, "Prior Knowledge and the Discovery of Entrepreneurial Opportunities", Organization Science, vol. 11, no. 4, pp. 448-469.

Sicherman, N. & Galor, O. 1990, "A Theory of Career Mobility", Journal of Political Economy, vol. 98, no. 1, pp. 169-192.

Sofka, W., Preto, M.T. & de Faria, P. 2014, "MNC subsidiary closures: What is the value of

employees’ human capital in new jobs?", Journal of International Business Studies, vol. 45, no. 6, pp. 723-750.

Song, J. & Almeida, P. 2003, "Learning-by- Hiring: When Is Mobility More Likely to Facilitate Interfirm Knowledge Transfer?", Management Science, vol. 49, no. 4, pp. 351-365.

Stiglitz, J.E. 2002, "Information and the Change in the Paradigm in Economics", The American Economic Review, vol. 92, no. 3, pp. 460-501.

72

Stuart, R.W. & Abetti, P.A. 1990, "Impact of entrepreneurial and management experience on early performance", Journal of Business Venturing, vol. 5, no. 3, pp. 151-162.

Sturman, M.C. 2003, "Searching for the Inverted U- Shaped Relationship Between Time and Performance: Meta- Analyses of the Experience/ Performance, Tenure/ Performance, and Age/ Performance Relationships", Journal of Management, vol. 29, no. 5, pp. 609-640.

Sullivan, P. 2010, "Empirical evidence on occupation and industry specific human capital", Labour Economics, vol. 17, no. 3, pp. 567-580.

Tan, E. 2014, "Human Capital Theory", Review of Educational Research, vol. 84, no. 3, pp. 411-445.

Taylor, M.P. 1999, "Survival of the Fittest? An Analysis of Self‐ Employment Duration in

Britain", Economic Journal, vol. 109, no. 454, pp. 140-155.

Thompson Arthur, A. & Strickland, A.J. 2002, Strategic Management: concepts and cases, 13. ed. edn, , New York.

Unger, J.M., Rauch, A., Frese, M. & Rosenbusch, N. 2011, "Human capital and entrepreneurial success: A meta-analytical review", Journal of Business Venturing, vol. 26, no. 3, pp. 341-358.

van Praag, C. 2003, "Business Survival and Success of Young Small Business Owners", Small Business Economics, vol. 21, no. 1, pp. 1-17.

van Praag, C. 1999, "Some Classic Views on Entrepreneurship", De Economist, vol. 147, no. 3, pp. 311-335.

Van Praag, C.M. & Cramer, J.S. 2001, "The Roots of Entrepreneurship and Labour Demand: Individual Ability and Low Risk Aversion", Economica, vol. 68, no. 269, pp. 45-62.

Venkatraman, S. 1997, "The distinctiveness domain of entrepreneurship research: an editor's perspective" in Advances in entrepreneurship, firm emergence and growth, ed. A. Katz Jerome, JAI Press, Greenwich, Conn, pp. 119-138.

Venkatraman, N. & Ramanujam, V. 1986, "Measurement of Business Performance in Strategy Research: A Comparison of Approaches", The Academy of Management Review, vol. 11, no. 4, pp. 801-814.

Vivarelli, M. & Santarelli, E. 2007, "Entrepreneurship and the process of firms' entry, survival and growth", Industrial & Corporate Change, vol. 16, no. 3, pp. 455-488.

Wang, H.C., He, J. & Mahoney, J.T. 2009, "Firm‐ specific knowledge resources and competitive

advantage: the roles of economic‐ and relationship‐ based employee governance

mechanisms", Strategic Management Journal, vol. 30, no. 12, pp. 1265-1285.

Weick, K.E. & Roberts, K.H. 1993, "Collective Mind in Organizations: Heedful Interrelating on Flight Decks", Administrative Science Quarterly, vol. 38, no. 3, pp. 357-381.

Weinzimmer, L.G., Nystrom, P.C. & Freeman, S.J. 1998, "Measuring organizational growth: Issues, consequences and guidelines", Journal of Management, vol. 24, no. 2, pp. 235-262.

73

Westhead, P., Ucbasaran, D. & Wright, M. 2005, "Decisions, Actions, and Performance: Do Novice, Serial, and Portfolio Entrepreneurs Differ?*", Journal of Small Business Management, vol. 43, no. 4, pp. 393-417.

Wheelen Thomas, L. & Hunger, J.D. 2006, Strategic Management and Business Policy: Cases, 10. ed. edn, , Upper Saddle River.

Youndt, M.A. & Snell, S.A. 2004, "Human Resource Configurations, Intellectual Capital, and Organizational Performance", Journal of Managerial Issues, vol. 16, no. 3, pp. 337-360.

Zacharakis, A.L. & Meyer, G.D. 2000, "The potential of actuarial decision models: Can they improve the venture capital investment decision?", Journal of Business Venturing, vol. 15, no. 4, pp. 323-346.

Zahra, S.A. & George, G. 2002, "Absorptive Capacity: A Review, Reconceptualization, and Extension", The Academy of Management Review, vol. 27, no. 2, pp. 185-203.

Zander, U. & Kogut, B. 1995, "Knowledge and the Speed of the Transfer and Imitation of Organizational Capabilities: An Empirical Test", Organization Science, vol. 6, no. 1, pp. 76-92.

Zhao, M. 2006, "Conducting R& D in Countries with Weak Intellectual Property Rights Protection", Management Science, vol. 52, no. 8, pp. 1185-1199.

74

APPENDIX A: Industry Division of Respondent Startups