Hitotsubashi University
Institute of Innovation Research

How a laid-off employee becomes an entrepreneur:
The case of Nokia s Bridge program
Byeongwoo Kang
Heikki Rannikko
Erno T. Tornikoski

IIR Working Paper WP#17-15

Dec. 2017

Institute of Innovation Research
Hitotsubashi University
Tokyo, Japan
http://www.iir.hit-u.ac.jp

How a laid-off employee becomes an entrepreneur: The case of Nokia’s
Bridge program

Byeongwoo Kang (corresponding author)*, Heikki Rannikko**, and Erno T. Tornikoski****

Hitotsubashi University, Japan**

Metropolia University of Applied Sciences, Finland***

Grenoble Ecole de Management, France

Abstract: This paper investigates how to support employees to become entrepreneurs. Using
original survey data by Nokia, we show two main findings. First, some contents ofentrepreneurship were effective commonly to employees with an R&D background and to thosewith a non-R&D background while other contents were effective to either employees with an
R&D background or those with a non-R&D background. The former contents are generalknowledge on management, and the later contents are further development of their own
specialties. Second, providing a such program to employees had merits to the program provider.
Our findings have implications for how a firm to design entrepreneurial programs for employees
and to form a business ecosystem around it.
Keywords: Entrepreneurship, Entrepreneur education, Layoffs, Nokia, Spinoff
JEL classification: J63, L26, M131

1.

Introduction
The industrial transition can happen at any moment and anywhere because, today, suchtransitions are speedier and more dynamic than ever before. As a result, large corporations
downsize and de-invest parts of their businesses, which is accompanied by employee layoffs. At
the same time, a country loses an engine for economic growth. This phenomenon raises two
policy issues. One issue is to guide labor to find new opportunities in a new industrial
environment. The other issue is to find a nation’s new growth engine. One of the known actions
for addressing these two issues is to support entrepreneurial activities because new businesses,
especially those with growth potential, are believed to create dynamics in economic activity andaffect economic growth and vitalization (Audretsch, 2004; Shane, 2009).
This paper explores two research questions. The first question is what kind of training iseffective for entrepreneurship of employees with R&D and those without R&D. The second
question is whether any merit exists if a firm funds entrepreneurial activities of employees.
To answers these questions, this study focuses on Nokia’s Bridge program. The Bridge
program was implemented from 2011 to 2014 after Nokia decided on a wide-scale workforce
reduction (Nokia, 2011). Among the five tracks that the Bridge program provided, we focus on
the entrepreneurship track. On the entrepreneurship track, Nokia provided an entrepreneurshipeducation program to laid-off employees and funded grants for their entrepreneurial activities.
For the entrepreneurship program and grant, we conclude that the Bridge program is the bestcase for answering our research questions.
This study provides two insights to further develop theory and practice. The first insight isabout entrepreneurial education. This study shows in detail that certain educational content iseffective for employees to become entrepreneurs, whereas other content is not. This insight willassist in designing effective entrepreneurial programs. The second insight is about the strategy
of a company that would like to encourage corporate spin-offs and to invest startups. Ourfinding shows that merits exist for firms that provide entrepreneurial grants.
The remainder of this paper is organized as follows. In the next section, we review priorstudies on which this study is based and develop hypotheses to test in this study. Next, we
describe our methodological choices, data, and analysis. Then, we show and discuss the findings.
Finally, we conclude by discussing implications for further research and for designing andpracticing entrepreneurship programs.
2.

Literature Review and Hypotheses
This section reviews prior studies on which this study is based. The literature review allows

for identification of what we consider the relevant remaining gap in these studies. Then, wedevelop the hypotheses on the basis of the literature review.
2

2.1.

Entrepreneurial capability and entrepreneurial education
A question in entrepreneurship research exists as to whether generalist characteristics can betaught through education and training.
Subsequent studies investigated this issue. One stream of research provided evidence that
entrepreneurs are not innately determined. Studies indicated that entrepreneurial experience affects
entrepreneurial activities (Gompers et al., 2010; Eesley & Roberts, 2012; Chen, 2013). That finding
implies that entrepreneurial capability is not innately determined and can be developed. Anotherstream of research reported on the effectiveness of entrepreneurial courses and programs forentrepreneur activity. However, the effectiveness is different for each case. Some empirical studies

provided evidence that entrepreneur courses and programs positively affect the entrepreneurial
intentions and skills of university students (Peterman & Kennedy, 2003; Souitaris et al., 2007;von Graevenitz et al., 2010), whereas other studies denied the effects of entrepreneur education(van der Sluis et al., 2008; Oosterbeek et al., 2010). In short, contradictory results exist on
entrepreneurial education and programs.
In this paper, we assume that both results can be derived. Sometimes entrepreneurial
programs have a positive effect on someone who seeks to become an entrepreneur. At othertimes, such programs have no or a negative effect. However, we insist that background of
trainees matters. Both positive and no or negative results can be derived depending on the
background of trainees. Accordingly, we develop the first hypothesis as follows.
H1: The effectiveness of entrepreneurial education varies depending on background of trainees.
By testing hypothesis 1, we investigate the factors of an entrepreneurial program provided by afirm to employees that made the program effective. However, because we lack knowledge on
the preferred content and content type varies in each case, we do not develop a hypothesis
related to each type of content of an entrepreneurial program.
2.2.

Corporate spin-off firms
Prior studies indicated that two strategic merits exist for a parent firm to have corporate

spin-off firms (Parhankangas & Arenius, 2003). First, a company can hope to expand its
business through the uncertain business opportunities of corporate spin-off firms. Firms often
adopt an ambidextrous strategy: setting a short-term strategy for its current market and along-term strategy for its future market. Setting a long-term strategy is difficult because thefuture market does not exist at the current moment and sometimes may exist in a domainbeyond the prediction based on current knowledge. Accordingly, a parent firm utilizes corporate
spin-off firms to fumble into the future. Corporate spin-off firms conduct R&D to develop new3technology seeds for breakthroughs and to explore new applications for both new andconventional technology seeds.
Second, another merit of a corporate spin-off strategy is that firms can construct anecosystem around themselves. When employees start their own businesses, it is difficult toimagine that they do so in fields with which they are not familiar or in which their capabilities
and experience cannot be utilized. Accordingly, employees may start companies in the businessfields of the previous company, enabling a parent firm to form an ecosystem around itself.
Providing entrepreneurial education to employees enables capable employees to become
entrepreneurs around the parent firm.
We assume that a downsizing corporate can achieve those merits Even if the first goal of acorporate spin-off strategy is to lay off its employees, some spin-offs may be critical for businessesof the corporate. Some spin-offs are a key to stop downsizing and survive, and other spin-offs areimportant for future growth. However, spin-offs are startups who faces a lot of challenges.
Accordingly, the corporate must sufficiently support the important spin-offs, so that those spin-offs
are willing to have business relationship with the corporate. Accordingly, we develop the second

hypothesis as follows.
H2: The more a company invest spin-offs, the more the spin-offs keep business relationshipwith the inventing company.

3.

Data

3.1.

Data Source: Nokia’s Bridge Program (Vanksa, 2013)
In 2011, Nokia announced a broad strategic partnership with Microsoft for an emerging

smartphone market. However, the launch of the joint Nokia–Microsoft strategy entailed difficult
decisions regarding wide-scale workforce reductions. Several months before the implementationof these decisions, Nokia’s leadership resolved to adopt a proactive role in diminishing thenegative impacts of the workforce reduction on its employees. To accomplish this goal, theBridge program was established (Nokia, 2011). There were 18,000 employees eligible forsupport and approximately 5,000 employees were in Finland. The summary of Nokia’s Bridge
program is shown in Table 1.
Table 1. Summary of Nokia’s Bridge program- Launch of the joint Nokia–Microsoft strategy failed in 2011.
Background- Nokia decided on a wide-scale workforce reduction.
- Nokia wanted to diminish the negative effects of the reduction.

When

2011–20144

- Finland (Oulu, Tampere, Salo, Capital regions)
Where- Denmark (Copenhagen), India (Bangalore), Romania (Cluj), the United
Kingdom, the United States- Assist individuals and teams in utilizing their capabilities to the fullest- Continue to develop the local economies in which Nokia plays a driving

Aims

role
- Support Nokia’s new strategy and ecosystem- Find a new job within Nokia- Find a new job outside Nokia

Tracks←(The interest of the current study)- Start a new business- Learn something new- Create your own path

Our interest is in the entrepreneurial track (the third track in Table 1). Approximately 10
percent of the eligible employees chose the entrepreneur track. More than 1,000 new companies
were established globally.
In Finland, an estimated 500 individuals, approximately one out of ten employees
dismissed from Nokia in Finland during 2011–2013, chose the entrepreneurship track andbecame entrepreneurs. The Program’s entrepreneurship track has contributed to the start ofapproximately 400 companies set up by those 500 individuals. Through the Bridge Program,Nokia supported the re-employment of dismissed employees in many ways. The mostcomprehensive, detailed, and individual support and advice may have been provided for those
who chose the entrepreneurship track, which aimed to mobilize as much of Nokia’s internal
expertise as possible and to direct employees to use the best national and regional businessservices. New entrepreneurs could also receive a financial grant for starting their business (up to€25,000). Approximately half the new startups were in ICT and mobile applications.
3.2.

Data
As an empirical study, we utilize an original data set that was collected on Nokia

Corporation’s Bridge Program (Nokia, 2011). The data set utilized in this study consists ofsurvey responses from individuals who participated the Bridge program and from financial dataon startup firms. The survey was designed in close co-operation with high-ranking Nokia
representatives.
Information on how the survey was conducted is described in Table 2. The survey was
carried out in May 2013 by the Small Business Center of Aalto University. The target group
included all employees in Finland that had participated in the Bridge program. The survey was5sent to 427 individuals, of which 413 were reached. From these, we received 196 answers,
resulting in a response rate of 47 percent. Similarly, 361 newly founded firms were approached,of which 361 were reached. From these, we received 187 answers, resulting in a response rate of
54 percent. Further restricting the sample to those with available business ids, we used 153
responses.
Table 2 Description of survey data
Survey date

May 2013

Target respondent

All Bridge program participants in Finland

No. of target respondents

427 employees / 361 firms

No. of target respondents reached

413 employees / 361 firms

No. of respondents

196 employees / 187 firms

Response rate

47% (employee perspective) / 54% (firm perspective)

No. of responses used in analysis

153 responses

3.3.

Profile of respondents
This section shows a descriptive analysis of the survey respondents. this subsection isadded to give a sense of the Bridge program to readers. The results are shown in Figures 1, 2, 3,
4, 5, and 6. The total number of observations is different between figures because of one to two
missing answers hereafter. Nevertheless, because only one or two observations out of 153 were
missing, the missing observation did not significantly affect the results. We can see thebackgrounds of the Bridge program participants are diverse in any measure. Having trainees
with diverse backgrounds assists collaboration opportunities between the trainees.
Figure 1. Age
55-64
9 (6%)

Figure 2. Gender

25-34
13(9%)

Female33 (22%)
45-54
46 (30%)Male
118 (78%)

35-44
85 (56%)

Figure 3. Highest education backgroundFigure 4. Entrepreneurial experience before
6

working at Nokia
High school
Elementary school
4 (3%)
1 (1%)
Vocational SchoolYes
12(8%)

8 (5%)

No
141 (92%)

University
139 (91%)22

Figure 5. Position at Nokia
Secretary, assistant3(2%)
Operator/supervisor in production/distribution center
Senior manager****
1(1%)
3(2%)
* Technical position (no subordinate):
researcher, engineer, architect, designer,project/program manager, etc.
Manager***55 (36%)** Non-technical position (no subordinate):analyst, quality specialist, HR consultant,legal councel.

Technical
position*66 (43%)*** Manager (with subordinates):
Any position lower than Senior manager

Nontechnicalposition**25 (16%)**** Senior manager:
vice president, country manager.

Figure 6. Role at Nokia

7

HR

6(4%)
Strategy andbusiness
11(7%)
development

Finance and control
3(2%)

Sales &
marketing17(11%)

R&D
83(54%)

ICM*
33(22%)* ICM:
Integrated Chain Management

4.

Findings and discussion
In the following section, we begin with an analysis of the survey data that focuses on four

aspects: 1) motivation and preparation (Section 4.1), 2) effectiveness of the Bridge program as
recognized by participants (Section 4.2), and 3) participants’ performance after the Bridge
program (Section 4.3). Lastly, in Section 4.4, we test our hypotheses by employing two
statistical analyses.
4.1.

Motivation and preparation
This subsection discusses reasons for becoming an entrepreneur and preparations before

joining the Bridge program.
Table 3 shows the primary reason that each participant in the Bridge program decided to
become an entrepreneur and seek a Nokia startup grant. The most common reason was thatparticipants had the opportunity to become an entrepreneur. This reason indicates thatparticipants had longings to become entrepreneurs and the Bridge program functioned as a
trigger for them to take action. Figure 7 and Table 4 provide support for how they were willing
to become an entrepreneur. As Figure 7 shows, employees had taken various actions before
joining the program by preparing a business plan, investing their own money, organizing a team,making preparations, and others. Some even took actions, such as developing products andprototypes and seeking financial support. Figure 7 indicates that Bridge program participants
had a business idea but did not know how realize the idea. Table 4 shows the business planning
period prepared by Bridge program participants, and that 97 percent of the participants prepared
their business plans up to six months before presenting them to the grant board.
The second common reason was that participants could start a business using theachievements and capabilities obtained at Nokia, although the gap between the first and the8second common reasons is large. This gap represents a positive signal for a company to support
entrepreneur programs for its employees because the company may be able to develop an
eco-system on the basis of its employees’ startups. The last reason, “I was able to leverage
Nokia's technology for new business opportunities not exploited by Nokia,” is in a similar
context although the second and the last common reasons were defined separately.
Moreover, the third common reason was that participants wanted to avoid unemployment.
Scholars have indicated that the correlation between unemployment and entrepreneurship can beeither positive or negative depending on the condition (Verheul et al., 2002). Sometimes a highunemployment rate is considered a reflection of a few business opportunities and is negatively
correlated with entrepreneurship. However, for an individual, (the threat of) unemployment
makes him or her consider self-employment because of the shortage of alternative job
opportunities. Finland is no exception. Our study also shows that the threat of unemployment
made employees decide to become an entrepreneur, as is shown through other evidence on
Finland (Ritsilä & Tervo, 2002).
Table 3. Primary reason for becoming an entrepreneur and seeking the Nokia startup grantReasonCount (Share)

I have for a long time wanted to be an entrepreneur and now have the chance.

62 (41%)

I was able to start a business around an innovation/capability that I developed
at Nokia.

21 (14%)This was the only way out of unemployment for me.

19 (12%)

I found an attractive business opportunity through the Nokia Bridge program.

13 (9%)

I did not want to continue in a salaried job.

9 (6%)I was able to leverage Nokia's technology into a new business opportunity not
exploited by Nokia.
Others5 (3%)
24 (15%)Figure 7. Startup activity engagement before joining the Bridge Program (multiple choice)

9

Yes

No

‐ Prepare a busienss plan
‐ Invest own money‐ Organize start up team
‐ Develop models and prototypes‐ Save money to invest‐ Look for facilities‐ ‐Engage
early
discussionson
Engageinin
early
discussions…
technology licensing/idea releases‐ Apply for financial support‐ Get financial support‐ Buy or rent facilities
0%

10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Table 4. Business planning period

4.2.

PeriodCount (Share)< 1 month

18 (12%)

1–2 months

90 (59%)

3–6 months

41 (27%)> 6 months

4 (3%)

Nokia’s contribution to startups recognized by participantsThis subsection describes Nokia’s contributions that participants recognized. As was notedin subsection 4.2, the total number of observations is slightly different between figures.
The first measurement is whether Bridge program participants received a technology
license from Nokia (Figure 7). Although most (84 percent) participants did not receive atechnology license from Nokia, a few did. However, the survey does not provide information on
the type of technologies that they licensed from Nokia.
The second measurement is the relationship of a startup from the Bridge program with
Nokia (Figure 8). No startup was a competitor to Nokia. Moreover, approximately half (45percent) of the startups from the Bridge program have a relationship with Nokia. In this sense,
Nokia may have formed an eco-system with some of the spin-offs around itself through the
Bridge program.
The third measurement is the importance of the relationship with Nokia that is recognizedby Bridge program participants as affecting their startup’s performance (Figure 9). Many
respondents considered the relationship with Nokia as not important even if approximately half10

of the startups have some kind of business relationship (in Figure 8). We think there are two
reasons for this contradictory result. First, one of the important goals in the Bridge program isfor Nokia to restructure. Accordingly, supporting employees to find new opportunities outside of
Nokia was also important for Nokia. Second, it is important to not intervene too much startups.
Since a corporate spin-off strategy is to find new opportunities, spin-offs need to be creative andthe corporate must let the spin-offs do whatever they want to do.
Figure 7. Tech license from Nokia

Figure 8. Relationship with Nokia

Figure 9. Importance of relationship with Nokia for a startup’s performance11

---------------------------Figure 10---------------------------Figure 10 indicates participants’ assessment of the benefit from the support provided by the
Bridge program to their business development, for which nine items were on the survey, andrespondents were asked to respond with between “1: Totally unbeneficial” and “7: Greatly
beneficial.” As the average score (5.009) indicates, Bridge program participants in generalconsidered the support from the program as beneficial for their business development. The
direct financial support ((9) in Figure 10) shows the most positive result relative to any othersupport. The presentation of overall services and of entrepreneurship information, and theBridge entrepreneurship program process ((1), (2), and (7) in Figure 10) also show positive
results.
---------------------------Figure 11---------------------------Figure 11 indicates participants’ overall assessment of the Bridge program, for which eightitems were on the survey. Respondents were asked to respond with between “1: Totally disagree”
and “7: Totally agree.” As was the case in Figure 2, this survey also shows that, in general,
Bridge program participants enjoyed the program. One point to note in this figure is thecomparison between entrepreneurial coaching and financial support ((7) in Figure 11).
Participants considered that financial support was more important than entrepreneurial coaching.
The significance of direct financial support in the entrepreneurial program is also supported by
the fact that Bridge program participants assessed as important the value of direct financial
support from the program ((5) in Figure 11). Although the current focus is on the effects ofentrepreneurial education on entrepreneurial activity, the significant role of financial support ofentrepreneurial activity is not negligible.

12

4.3.

Participants’ performance after the Bridge programThis subsection describes performance of the Bridge program participants.
Figure 12 shows the Bridge program participants’ establishment type. During the Bridge

program, 127 participants established a company. In addition, eight participants acquired acompany (or share of a company) during the program. In contrast, 17 participants already had acompany when they joined the Bridge program. Given that 12 participants were entrepreneurs
before working at Nokia (Table 8), we assume that some participants established a company
when working at Nokia.
Figure 13 shows the number of founding members. Many participants founded a startup
alone. As previously mentioned, approximately half of the new startups were in ICT and mobile
applications. In such business fields, many startups do not need numerous staff members andmany of them can run their business alone. The largest number of founding members in astartup was 10. Considering the condition that the Bridge program restricted founding members
to a maximum of four of its program participants, that some firms had more than four founding
members implies that individuals outside the Bridge program were invited by program
participants to start a company.
Figure 14 shows the amount of direct financial support from the Bridge program. As
previously mentioned, new entrepreneurs could receive a financial grant of up to €25,000 for
starting their business. Because the Bridge program restricted the number of founding members
from Bridge program participants to four, a startup could obtain grants up to €100,000. However,each startup did not receive equal financing. Figure 14 shows that the financed amount varies
between less than €10,000 and more than €50,000. We assume that Nokia preferred somestartups more than others and that the financed amount correlates to its support. Bridge program
participants spent the direct financial support for various purposes: operations costs (energy,
rents, phones, others), investments in fixed assets, subcontracts, own and others’ salaries, andinvestments in inventory.
Figure 15 shows the activeness of the companies of the Bridge program participants. Morethan 90 percent had survived as of the survey period (May 2013). This survival rate is higher
than that shown in other evidence on survival rates of new firms. For example, OECD (2003)
reported that approximately 70 percent survive more than two years after establishment. Hence,
this measurement supports the concept that the Bridge program might be successful, although
not all firms in the survey operated for more than two years as of the survey date.
Figure 12. Establishment typeFigure 13. Number of founding members

13

(share) Acquisition
during the program
8 (5%)>4:
9 (8%)

Establishment
before the program
17 (11%)4:
10 (9%)

3:
24 (22%)
Alone:
82 (77%)

Establishment during the program
127 (84%)2:
25 (23%)Figure 14. Financial support

Figure 15. Company activeness(total amount before tax)

(as of the survey date: May 2013)€50,000 - €100,000*:
18 (12%)< €10,000:
10 (7%)€10,000- €14,999:
33 (22%)€25,000- €49,999*:
22 (14%)€20,000- €24,999:
25 (16%)

Closed11(7%)

Active (as of survey date)
141 (93%)€15,000- €19,999:
45 (29%)cf. Survival rate after 2 years in Fi: 70% (OECD, 2003)* Total amount when several participants

4.4.

Testing hypotheses

4.4.1. Testing H1
Before testing H1, the case of all staffs is analyzed as a first look. Figure 16 indicates theimportance of the resources obtained during the Bridge program or during employment at Nokia
for a startup’s performance, for which the survey contained 14 items. Respondents were asked
to respond by scoring between “1: Not important” and “7: very important.”---------------------------Figure 16---------------------------The one sample t-test in the current study tests whether each item in the survey wassignificantly different from a hypothesized value. The first model (m1) defines “4” as ahypothesized value and the second model (m2) defines the average score (3.916) of all items as14

a hypothesized value. One reason for adding the second model is to reflect the respondents’scoring tendency.
The two models in Table 5 show slightly different statistical significance for each item.
Table 5 shows that there are what Bridge program participants considered important and what
they considered not important.
First, we observe the items that participants considered to be important. Six
items—technological knowledge, functional knowledge, knowledge of customer needs,
knowledge of industry conditions, R&D, and strategy—were significantly higher than the
hypothesized values in the two models. In other words, the six items were considered important.
In subsequent tables, we analyze the results in detail accordingly their roles and positions at
Nokia.
Second, we observe the items that participants considered unimportant. Five
items—knowledge of country or regional conditions, operations, human resources, relationships
with buyers and suppliers, and patents and trademarks—were significantly lower than the test
value of “4.” In other words, the five items were considered unimportant. The primary reason
that these items were considered unimportant was that a number of startups from the Bridge
programs were in ICT and mobile applications. Because such startups have the Internet as theirmarket, they do not require supply chains, operations, knowledge of specific country or regional
conditions, and patents and trademarks. In the beginning stage of a startup, founders wereenough of a resource to run a company and human resource knowledge was not needed to hire
employees.
Table 5. Result for hypothesis 1 (One sample t-test setting)m1: “4” as a hypothesized value, m2: average (3.916) of all items as a hypothesized value
Items

Mean ± Std. Dev. (n)m1m2Technological knowledge

4.599 ± 2.439 (152)

***

***

Functional knowledge

4.250 ± 2.214 (152)***

Knowledge of customer needs

5.092 ± 1.988 (153)

***

***

Knowledge of industry conditions

4.588 ± 2.095 (153)

***

***

Knowledge of country or regional conditions

3.250 ± 1.981 (152)

***

***

Marketing and sales

4.072 ± 2.072 (152)

R&D

4.392 ± 2.257 (153)

**

***

Operations (e.g., logistics)

3.020 ± 2.018 (152)

***

***

Strategy

4.464 ± 2.045 (151)

***

***

Human resources

3.265 ± 2.141 (151)

***

***

(i.e., knowledge required to create product or service)15

Relationships with buyers, suppliers

3.322 ± 2.224 (152)***

Brand name3.739 ± 2.13 (153)*

Patents and trademarks

2.836 ± 2.054 (152)

***

***

Other

1.539 ± 1.446 (76)

***

***

***

* p < 0.1, ** p < 0.05, *** p < 0.01
We test the first hypothesis as the following manner. We separate trainees into those with
R&D experience and those without R&D experience and employ the one sample t-test to eachtype of trainees. Then, we compare the case of those with R&D experience and that of those
without R&D experience. The results are shown in Tables 6-1 and 6-2. Table 6-1 shows the caseof R&D staff members and Table 6-2 shows the case of non-R&D staff members.1
First, we focus on the case of R&D staff members. In Table 6-1, four items—technologicalknowledge, knowledge of customer needs, knowledge of industry conditions, and R&D—weresignificantly higher than the two hypothesized values in the two models. We reason thatknowledge of customer needs and knowledge of industry conditions had positive effects onparticipants with R&D backgrounds to gain business sense. Meanwhile, five items—knowledgeof country or regional conditions, operations, relationships with buyers and suppliers, humanresources, and patents and trademarks—were significantly lower than the two hypothesizedvalues in the two models. We attribute this result to the characteristics of the startups from theBridge program. More than half of the startups were in ICT standard service sectors. Theirproducts and services are not tangible products but are, instead, digital products. Accordingly,
they have no suppliers and buyers, and their intangible (digital) products and services areprovided online and are not limited to specific regions or countries. Because their products andservices are not limited to specific regions or countries, they did not have to protect themthrough patents and trademarks. Participants recognized human resources as unimportantbecause more than half of all startups were founded by one founder and they were in very earlyphases with respect to hiring and managing employees.
Table 6-1. Result for hypothesis 1 (One sample t-test setting) – R&D staffs
m1: “4” as a hypothesized value, m2: average (3.847) of all items as a hypothesized value
Items

Mean ± Std. Dev. (n)m1m2Technological knowledge

5.159 ± 2.252 (82)

***

***

(i.e., knowledge required to create product or service)
Non-R&D staff members are those whose roles were ICM and operations, sales andmarketing, strategy and business development, HR, and finance and control.

1

16

Functional knowledge

4.181 ± 2.102 (83)Knowledge of customer needs

5.108 ± 1.951 (83)

***

***

Knowledge of industry conditions

4.482 ± 2.126 (83)

**

***

Knowledge of country or regional conditions

2.976 ± 1.879 (82)

***

***

Marketing and sales

3.819 ± 2.096 (83)R&D

5.133 ± 2.117 (83)

***

***

Operations (e.g., logistics)

3.145 ± 1.964 (83)

***

***

Strategy

4.28 ± 2.116 (82)

Human resources

3.325 ± 2.142 (83)

***

**

Relationships with buyers, suppliers

3.133 ± 2.111 (83)

***

***

Brand name3.566 ± 2.085 (83)**Patents and trademarks

3.024 ± 2.124 (83)

***

***

Other

1.422 ± 1.215 (45)

***

***

*

*

* p < 0.1, ** p < 0.05, *** p < 0.01
In Table 6-2, four items—knowledge of customer needs, knowledge of industry conditions,marketing and sales, and strategy—were significantly higher than the two hypothesized values
in the two models. The result was different from that in Table 19-1. Participants from non-R&D
backgrounds did not show positive scores with statistical significance in technology-relevant
items, e.g., technological knowledge, functional knowledge, or R&D. We assume that even if
participants from non-R&D backgrounds were taught such knowledge, the effect was minimal
without a technical background. Instead, capabilities that they used at Nokia seemed important,e.g., marketing and sales and strategies. In contrast, three items—operations, human resources,
and patents and trademarks—were significantly lower than the two hypothesized values in thetwo models. This result is almost the same as the case of participants with an R&D background(Table 6-1). The difference is that two items—knowledge of country or regional conditions andrelationships with buyers and suppliers—had no statistical significance in Table 6-2.
Table 6-2. Result for hypothesis 1 (One sample t-test setting) – Non-R&D staff
m1: “4” as a hypothesized value, m2: average (3.805) of all items as a hypothesized value
Items

Mean ± Std. Dev. (n)Technological knowledge

3.943 ± 2.502 (70)m1

m2

(i.e., knowledge required to create product or service)
Functional knowledge

4.333 ± 2.356 (69)

Knowledge of customer needs

5.071 ± 2.045 (70)

17

**
***

***

Knowledge of industry conditions

4.714 ± 2.065 (70)***

Knowledge of country or regional conditions

3.571 ± 2.061 (70)**Marketing and sales

4.377 ± 2.015 (69)*R&D

3.514 ± 2.111 (70)**

Operations (e.g., logistics)

2.87 ± 2.086 (69)

***

***

Strategy

4.681 ± 1.952 (69)

***

***

Human resources

3.191 ± 2.153 (68)

***

**

Relationships with buyers, suppliers

3.551 ± 2.349 (69)*

Brand name3.943 ± 2.18 (70)

Patents and trademarks

2.609 ± 1.957 (69)

***

***

Other

1.71 ± 1.736 (31)

***

***

***

**

* p < 0.1, ** p < 0.05, *** p < 0.01
In summary, certain content in entrepreneurial education have a positive impact onentrepreneurship. R&D staffs assessed “technological knowledge,” “knowledge of customer
needs,” “knowledge of industry conditions,” and “R&D” positively while non-R&D staffs
assessed “knowledge of customer needs,” “knowledge of industry conditions,” “marketing andsales,” and “operations” positively. Accordingly, we conclude that H1 is supported(effectiveness of entrepreneurial education varies depending on background of trainees). We
will discuss more on the result in the following section.
4.4.2. Testing H2
For the second hypothesis, we employ an ordered logit regression. The dependent variable
is the importance of the relationship with Nokia (Figure 9). The dependent variable ranges from
1 to 7. The independent variable is financial support (Figure 14), which is employed as a proxy
for Nokia’s support of a startup. The values for this variable ranges from 1 to 6. We also add twogroups of control variables. The first control variable group (control group 1) is a startup`srelationship with Nokia (Figure 8). Business relationship types between two companies
naturally decide the importance of the relationship. For example, buyers are important for all
suppliers, especially when the bargaining power of buyers is significant. We control such amatter by adding the relationship with Nokia. The second control variable group (control group2) is the background of Bridge program participants (Figures 1, 2, 3, 5, and 6). These control
variables control personal characteristics and are often added in prior studies (Wagner, 2006;
Silva, 2007). A full description of the variables is shown in Table 7 and the correlation between
variables is shown in Appendix.

18

Table 7. Overview of regression variables
Financial support (proxy of Nokia’s support to a startup)

Independent
variable- < €10,000-1- €10,000–€14,999-2- €15,000–€19,999-3- €20,000–€24,999-4- €25,000–€49,999-5- €50,000–€100,000-6Business relationship with Nokia (Figure 8)- Partners- Dummy- My company is Nokia’s customer (Nokia is my company’s supplier)- Dummy- My company is Nokia’s supplier (Nokia is my company’s customer)- Dummy- My company is Nokia’s consultant- Dummy- Nokia is my company’s consultant- DummyNote: The baseline values are “Others” and “No relationship”
Age (Figure 1)- 25–34 years old-1- 35–44 years old-2- 45–54 years old-3- 55–64 years old-4

Gender (Figure 2)
Control- Male- Dummyvariables

Highest educational background (Figure 3)- Elementary school- Dummy- High school- Dummy- Vocational school- DummyNote: The baseline value is “University (including univ. of appliedscience)”
Position at Nokia (Figure 5):- Senior manager- Dummy- Manager- Dummy- Non-technical position- Dummy- Others- DummyNote: The baseline value is “Technical position”
Role at Nokia (Figure 6)- ICM- Dummy19

- Sales and marketing- Dummy- Strategy and business development- Dummy- HR- Dummy- Finance and control- DummyNote: The baseline value is “R&D”
The regression result is shown in Table 8 for the coefficient and t statistics of the
independent and control variables. The independent variable, Financial Support, has statisticalsignificance (model 1). The coefficient of the independent variable was statistically significant
even when control variables for the business relationship with Nokia were added (models 3, 4,
and 6). This result indicates that Bridge program participants recognized that Nokia’s support
for the program has a positive effect and that their relationship with Nokia was important totheir startup’s performance. Therefore, we conclude that hypothesis 2 is supported.
Additionally, an analysis of the control variables provides an interesting finding. Table 8shows the coefficients of the control variables regarding the types of business relationship with
Nokia (control group 1). The regression result indicates that the coefficient of “a startup is
Nokia’s supplier (i.e., Nokia is the startup’s customer)” was positive and statistically significant
in all regression models. Accordingly, we confirm that we properly control for the impact ofbusiness relation type on the significance recognition of the business relationship. Meanwhile,the coefficients for “a startup and Nokia are business partners” and “a startup is Nokia’s
consultant” were negative and statistically significant in all regression models. We interpret thisresult as indicating that business partner and consultant relationships are weaker than supply
chain relationships.

20

Table 8. Result for hypothesis 2 (ordered logit regression)
DV: importance of relationship with Nokia for their startup’s performance
m1(1) Financial support(2) Business relationship with Nokia: Partners

m2

m3

m4

m5

m6

0.33910.41410.27940.3591

***

***

**

[2.31] **[2.75][3.04][1.99]-1.9424-1.7309-1.6166-1.5553

***

***

**

[-2.33] **[-3.25][-2.80][-2.50](3) Business relationship with Nokia:-1.3467-1.3402-1.5436-1.4148

My company is Nokia’s customer[-1.26][-1.23][-1.03][-0.99](4) Business relationship with Nokia:

7.78348.4745

6.442

7.246

***

***

**

[2.70] ***My company is Nokia’s supplier[3.77](5) Business relationship with Nokia:-2.3003-2.5334-2.3131

***

***

***

[-3.27] ***[4.03][2.41]-2.476

My company is Nokia’s consultant[-3.62](6) Business relationship with Nokia:-3.1851-3.9286-1.8002-2.8345****[-0.97][-1.48]-0.2054-0.4575-0.6748[-0.60][-1.48][-2.27] **0.1287-0.0556-0.2419[0.22][-0.10][-0.45]-0.5508-0.6559

0.0278[-0.00][-0.00][0.00]-14.9866-14.4753-15.0336

Nokia is my company’s consultant[-2.13](7) Age(8) Gender (Female dummy)(9) Highest education: Elementary school(10) Highest education: High school
21[-3.84][-2.57][-3.15]

(11) Highest education: Vocational school(12) Position at Nokia: Senior manager(13) Position at Nokia: Manager(14) Position at Nokia: Non-technical position(15) Position at Nokia: Others(16) Role at Nokia: ICM(17) Role at Nokia: Sales and marketing(18) Role at Nokia: Strategy and business development(19) Role at Nokia: HR(20) Role at Nokia: Finance and control
N

149

149

* p < 0.1, ** p < 0.05, *** p < 0.01
22149[-0.01][-0.02][-0.01]-0.0787-0.32480.1263[-0.06][-0.25][0.11]

1.77022.45793.1353[1.36][1.95] *[2.74] ***0.9894

1.11881.6354[2.00]**[2.28] **[3.50] ***-0.5349-0.262-0.2288[-0.55][-0.28][-0.24]-12.8066-12.8446-13.8937[-0.01][-0.01][-0.01]-0.9023-1.0607-1.2913[-1.40][-1.65] *[-2.18] **-0.2008-0.3545-0.7263[-0.28][-0.50][-1.05]0.93320.81030.3273[1.25][1.10][0.46]

1.6776

1.25331.9069[1.33][1.03][1.72] *-1.3515-1.9645-0.0984[-0.94][-1.34][-0.10]

147

147

147

4.5.

Discussion
Several findings from Nokia’s Bridge program are worth further discussion.
First, from the descriptive analysis of the Bridge program, the program’s characteristics canbe summarized as follows. Participants longed to become entrepreneurs, and the Bridge
program was provided to satisfy that longing. Bridge program participants were diverse in termsof their backgrounds. Some startups from the Bridge program had a technology license fromand a business relationship with Nokia, the provider of the Bridge program. However, more thanhalf the startups had no relationship with Nokia, implying that there were no requirements to bea participant and no conditions on participants’ outputs. In this sense, Bridge program
participants enjoyed the freedom to develop ideas into businesses without being restricted.
Because an innovation economy is one driven by new ideas, it is important to provide anenvironment in which any business idea is not limited.
Second, a corporate spin-off strategy is useful when the corporate downsizes to moderate
negative impressions. Because markets mature at some point and business circumstances change
continuously, restructuring is inevitable. When a firm restructures its business and lays off
employees, it sends a strong negative impression to the outside world. Such a negative impression is
critical because new job seekers may not be willing to join the firm. Historical evidence indicatesthat this strategic spinning-off activity is effective in moderating negative impressions (Chesbrough,2002). In 1983, Xerox attempted to redirect projects at its Palo Alto Research Center (PARC) to be
more closely aligned to its copier and printer business. As Xerox retreated from the computerbusiness, some employees in the computer business had to leave Xerox PARC. Instead, of firingthem, Xerox PARC helped these employees spin off projects using the company’s equipment, whichallowed Xerox PARC to restructure itself without sending a negative message through the firing.

The case of Nokia also shows that providing a corporate spin-off program was merit. As Figure
11, Nokia received positive responses from the participants in general.

Third, by testing the first hypothesis, we showed that participants in the Bridge program
considered certain content of the entrepreneurial programs to be effective for theirentrepreneurial activity. The effectiveness of each content was different between participants
with an R&D background and those with a non-R&D background. The content that bothparticipants—with R&D and with non-R&D backgrounds—commonly scored as “important”
was knowledge of customer needs and industry conditions (Tables 6-1 and 6-2). Such content isknowledge on business. While participants with an R&D background scored technologicalknowledge and R&D as important, those with non-R&D backgrounds scored marketing andsales and operations as important. Such content is in-depth training of their own specialty.
Accordingly, when a corporate prepares entrepreneurial programs to their employees, the23

programs should be two tracks: teaching knowledge on business and strengthening specialty of
employees.
Fourth, by testing the second hypothesis, we showed that higher investments by Nokia
indicated that recipients considered the relationship with Nokia to be more important. Not all
spin-offs from the Bridge program may not be evenly important as approximately half of thestartups had no business relationship with the firm. We assume that it was not an easy decision for

the Bridge program participants to enter into business relationships with Nokia because they musthave known well the situation of Nokia. Relying on a downsizing conglomerate is risky for startups.
However, Nokia could make those spin-offs be willing to have a business relationship with Nokia bysupporting those spin-offs well by strategically investing. As seen in Table 2, Bridge program

participants considered direct financial support to be the most significant benefit for theirbusiness development. In the Bridge program, recipients of financial support spent such funds
as they pleased. Accordingly, a strategic investment is important in a corporate spin-off strategy.
Fifth, the timing to provide entrepreneurial programs is also a determinant of the success ofthe program and outputs. For example, Nokia attempted an entrepreneurial program years
before the Bridge program, but that effort failed. In 2005, Nokia and several large corporations(TietoEnator, Elisa, Finnet, and YLE) in Finland founded the Digital Media Service Innovations
Finland (DIMES) association2 to support IT startups in Finland. Nokia’s intention for launching
DIMES was to form an ecosystem around itself.3 However, the practice was not successful andthe DIMES association stopped operating. When the DIMES association was founded in 2005,
Nokia dominated the mobile phone market. Job seekers with an ICT background in Finland
primarily considered working at Nokia and had no reason to take risks as an entrepreneur. In
contrast, when the Bridge program was launched, Nokia was downsizing its business. Therefore,
not only job seekers but also employees of Nokia were motivated to take risks as entrepreneurs.
Accordingly, we assume that the timing to provide an entrepreneurial program is effective whena parent firm is downsizing.

5.

Conclusion

2

Because DIMES no longer exists, its webpage (http://www.dimes.fi) no longer exists today.
One of the few remaining webpages related to DIMES is at the following links:
https://ec.europa.eu/growth/tools-databases/dem/initiatives/985/digital-media-innovations-finlan
d-dimes (Last access on January 20th, 2017).
https://www.linkedin.com/company/dimes-association-digital-media-service-innovations-finlan
d- (Last access on January 20th, 2017).
3
Interview with a manager at Nokia and the president of DIMES (May 16, 2016 in Tokyo).
24

The ultimate goal of this study is to understand how to guide employees to become
entrepreneurs. Two research questions are proposed in this study. The first question is what kindof training is effective for entrepreneurship of employees with different backgrounds. The
second question is whether any merit exists if a firm provides an entrepreneurial program when
it is downsizing. To answer these questions, we used original survey data from the Bridge
program—an entrepreneurship program that Nokia designed for their employees. We used two
statistical methods to test our hypotheses.
This study has two main findings. First, this paper finds that some entrepreneurial
education content is effective commonly to employees with an R&D background and to thosewith a non-R&D background, but that other contents were effective to either employees with an
R&D background or those with a non-R&D background. From our discussion, we conclude thatteaching general knowledge on business and deepening one’s own specialty were effective
entrepreneurial education. Second, benefits are available for a company that provides anentrepreneurial program to its employee. The benefits were to have a positive impression about
the company and to keep business relations with important spin-offs. In this sense, Nokia’s
Bridge program was useful for both ex-employees and Nokia.
Our findings have a number of implications. First, some entrepreneurship education content
has a positive impact on employees seeking to become entrepreneurs. When designing
entrepreneurship education programs, designers must pay attention the subjects that should beoffered in the programs. Understanding the needs of potential entrepreneurs is important.
Second, companies that provide such entrepreneurship education programs to their employees
also experience positive impacts for themselves. If a company wants to construct an ecosystem
around itself, sponsoring entrepreneurship education programs—if unable to offer them—can beconsidered an ecosystem strategy. Third, supporting entrepreneurship programs is also
important for policy makers. Industrial transition is inevitable, is accelerating, and is becoming
more dynamic. The most important point is to guide labor to find new opportunities in new
industrial environments and to maintain national competitiveness during industrial transitions.
Providing incentives for a company to set corporate spin-off programs could be one of the keys
for a nation to foster new competitiveness in a new industrial environment.

Acknowledgments
The authors thank Aalto University Small Business Center for allowing us to use a valuable
survey dataset for this study. The authors would also like to thank the interviewees for sharing
their experiences and insights with us. This work was supported by JSPS KAKENHI Grant
Number 15K17138. All remaining errors are our own.
25

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Figure 10. Participants’ assessment of the benefit of the support provided by the Bridge program to their business development

28

Figure 11. Participants’ overall assessment of the Bridge program

29

Figure 16. Participants’ assessment of the importance of resources obtained during the Bridge program or during employment at Nokia for a startup’s
performance30Appendix: Overview of correlations between variables in regression
(1)

1

(2)

-0.05271(3)0.01720.72041(4)0.03330.72380.94151(5)0.04710.6856

0.71840.77111(6)0.05280.697

0.9121

0.96970.74381(7)-0.21930.05190.09880.08450.07780.06391(8)-0.17010.07040.0175-0.0033

0.00490.0074-0.04311(9)-0.13960.04150.0319

0.03

0.039

0.031

0.07630.15441(10)0.0042-0.0183-0.0572-0.0666-0.0274-0.0618-0.0763-0.0872-0.01351(11)-0.1376-0.02620.0052-0.00420.03680.0006-0.06810.0895-0.0193-0.0391(12)-0.0923

0.18940.1121

0.09560.08740.104-0.0655

0.1469-0.0432

0.01260.16111(13)-0.22480.08380.06450.06070.07870.0626-0.0187

0.2121

0.495-0.0272

0.32920.21211(14)-0.0444

0.13820.06890.05390.0291

0.0615-0.03180.4543-0.03640.03750.13330.1956-0.07351(15)0.0427-0.2446-0.1629-0.1149-0.1847-0.0975

0.1637-0.1539-0.06-0.0349-0.1739-0.0531-0.1213-0.32761(16)0.02010.07240.0556

0.05240.0679-0.0891

0.1992-0.0753-0.0116-0.0235-0.0337-0.0753-0.0235-0.0634-0.10471(17)-0.0890.10340.07950.0748-0.0774

0.07720.048

0.3026-0.0166-0.0336-0.0481-0.1076-0.03360.4567-0.1496-0.0291(18)-0.1117-0.0451-0.08440.0524-0.05420.054

0.06670.0395-0.0116-0.0235-0.0337-0.0753-0.0235

0.06430.0942-0.0203-0.0291(19)0.00170.07730.07740.06640.06210.00880.0401-0.0363-0.0291-0.0588

0.1028-0.1884-0.0588

0.12320.08930.2496-0.0725-0.05071(20)

0.0067-0.0624-0.0531-0.0664-0.01-0.05960.1

0.0525-0.0217-0.0439-0.063-0.1408-0.0439-0.0470.19470.1529-0.0542-0.0379‐0.0949

The label number corresponds the variable number in Table 8.

311