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The Importance of Specific Human Capital, Planning
and Familiarity in Dutch Small Firm Ownership Transfers:
a Sellers Perspective
Lex van Teeffelen*
University of Applied Sciences Utrecht
Faculty of Economics and Management
P.O. Box 85029, 3508 AA Utrecht
The Netherlands
lex.vanteeffelen@hu.nl
Lorraine Uhlaner
Nyenrode Business University
P.O. Box 130, 3620 AC Breukelen
The Netherlands
l.uhlaner@nyenrode.nl
Martijn Driessen
Entrepreneur Consultancy
Bisonspoor 1216, 3605 KZ Maarssen
The Netherlands
mdriessen@ondernemerstest.nl
About the authors:
Lex van Teeffelen, Msc, is research manager on SME Transfers at the University of Applied Science Utrecht.
As associate professor and senior consultant he develops programs on change management, innovation and SME
transfers. He graduated in Social Psychology at the Free University Amsterdam.
Lorraine Uhlaner, PhD, is professor in Entrepreneurship at the Nyenrode Business University, director of the
international MBA Program and senior research fellow at the Max Planck Institute. Her current research includes
family firms, corporate governance and business transfers.
Martijn Driessen, MBA, PhD, is owner/manager of Entrepreneurship Consultancy specialised on SME’s. He was
awarded both as entrepreneur and researcher on the E-Scan for Entrepreneurs. He graduated in Business
Administration and received his PhD at the Rijksuniversiteit Groningen.
* For information on this study, please contact the first author
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The Importance of Specific Human Capital, Planning
and Familiarity in Dutch Small Firm Ownership Transfers:
a Seller’s Perspective
Abstract
This study looks at the importance of human capital, planning and familiarity in small firm business transfers. We
differentiate amongst two types of human capital - specific and generic - to predict business transfers performance.
A representative dataset, randomly drawn, consists of 112 Dutch small firm owners, who had sold their firm in 2005
and 2006. Hierarchical multiple regressions show that specific human capital, like flexibility, social skills and
market awareness predict transfer performance better than generic human capital like general education and
entrepreneurial experience. Results also show that planning in business transfers is unrelated to objective transfer
performance indicators (transfer time and obtained price relative to asking price) but strongly related to subjective
transfer performance (namely satisfaction by the seller with the transfer). Results also show that familiarity between
seller and buyer rather than kinship or family ties is a key predictor for all three transfer performance indicators.
Keywords
Human Capital, Planning, Familiarity, Family, Performance, SME, Satisfaction, Price, Transfer time, Transfer,
Entrepreneurial Exit, Seller, Buyer, Owner, Small Business.
1. Introduction
In this article we look at the relationship between human capital, planning and transfer
performance from the perspective of the seller. Many studies on human capital and planning look at
the performance of start ups and established small firms. However, studies relating human capital and
planning to entrepreneurial exit are rare. For this reason Brinckmann et al. (2008), Meijaard et al.
(2005) and Wennberg et al. (2009) recommend further exploration. This study relates human capital
and planning to entrepreneurial exit. In entrepreneurial exits there are two main options: sale or
liquidation. In a sale the firm survives even though the previous owner leaves. In a liquidation both
the owner and the firm stop their activities. This study looks at sales of small business, also called
business transfers, which we define as a change of ownership where more than 50% of assets or
shares are transferred. We exclude ownership transfers between spouses (Van Teeffelen and Leroy,
2009). Small businesses are defined as firms with less than 50 employees.
Human capital and small business performance is a subject which is widely studied.
Researchers have established the importance of human capital as a key predictor for small-firm
performance (Aides and Van Praag, 2007; Bhagavatula et al., 2008; Dimov and Shepard, 2004;
Colombo and Grilli, 2005; Haber and Reichel, 2006, Honig, 1998, 2001; Markman and Baron,
2003; Van Praag, 2003; Van der Sluis et al. 2005). However, they still debate which type of
human capital plays a greater role in small firm performance. Some approaches to human capital
distinguish between generic and specific components (Becker, 1975). Generic components
include overall formal education, age and general or professional experience. Specific
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components of human capital refer to education limited to a particular sector, activity or context,
like small business education and specific work experience in a sector. In this study we test
whether generic and/or specific human capital predicts business transfer performance better.
The relationship between planning and small business performance has also been the
subject of many studies. The meta-analysis by Brinckmann et al. (2008) bridges conflicting
findings of past research, showing that business planning improves small business performance
in general, but is strongly affected by the way performance is measured. Whereas objective
performance indicators show a strong relationship between planning and small firm performance,
relationships with subjective performance indicators and small firm performance are much
weaker. However, the study of Brinckmann et al. (2008) does not look at succession planning.
Many experts in the domain of business transfers are strong advocates of succession planning in
ownership transfers (EU Commission, 2002; Flören, 1998; Goldberg and Woodridge, 1993; Kets
de Vries, 1993; Kirby and Lee, 1996; Kommers and van Engelenburg, 2003; Landsberg,1988;
Meijaard 2004; Mandelbaum, 1994; Reece, 2004; Rue and Ibrahim, 1996; Seymour, 1993;
Sharma et al., 2001, Stavrou, 1996). Research on the effects of planning on transfer performance
(Van Teeffelen, 2007) leads to conclusions opposite to that of Brinckmann et al. (2008), namely
that succession planning is strongly related to subjective performance measures and weakly
related if at all to objective performance measures. The present paper includes both objective and
subjective performance measures, in order to clarify this point.
Research on transfer performance has focused mainly on family firms (e.g. Morris et al.
1997; Le Breton-Miller et al., 2004). Though important, studies show that both worldwide (Grant
Thornton, 2005) and in The Netherlands (Meijaard, 2005; Van Teeffelen, 2007) the majority (60-
70%) of all transfers take place between non-family members. The current study extends
research to non-family transfers. We will differentiate between family ties and more general
familiarity between (non-family) sellers and buyers, exploring which aspect predicts transfer
performance.
Apart from the academic interest there is a practical need for research on ownership
transfer. Many governments and policymakers are in need of transfer performance indicators
(TPIs) to devise policies on transfer failure prevention. By recent estimations a third of all
entrepreneurs in western countries will retire in the coming decade (EU, 2002; EU, 2006). The
European Commission considers the expected high rates of exit as an economic threat. Failure to
transfer ownership of these firms is predicted to lead to high rates of unemployment and a
massive loss of tangible and intangible capital.
This paper is organized in four sections. In the second section, we will look into the theory
of human capital, the importance of planning, previous research and our hypotheses. In
remaining sections we present our methods, results, discussion of the findings and their
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theoretical and practical implications.
2. Theory
Human capital and planning are vital to transfer performance according to three succession
models (Le Breton-Miller et al., 2004; Meijaard et al., 2005; Morris et al., 1997). All models
underline the importance of human capital variables like education, experience, entrepreneurial
and managerial capabilities of both the incumbent and the successor. The model of Meijaard et
al. (2005), shown in Figure 1, covers both family and non-family ownership transfers. Boxes II
and V include the human capital variables, respectively, of the predecessor and the successor.
Box III covers various planning variables. We first consider the importance of human capital and
then discuss the importance of planning.
Please insert Figure 1 here
2.1 Human Capital
The concept of human capital is derived the resources-based view (RBV). RBV focuses on
the critical resources of companies to perform, to survive and to compete. It assumes that certain
critical resources predict better firm performance and chances of survival. The resources are
usually categorized as human, financial and physical (Hall, 1992). Barney (1991) also identifies
organizational resources as a fourth type of resource, and which represents formal and informal
planning, informal relationships within and in connection with the firm and external financial
and advisory support.
Human capital has proven to be vital for firm creation, firm performance, growth and
survival in both western and non-western countries (Aides and Van Praag, 2007; Bhagavatula et
al., 2008; Dimov and Shepard, 2004; Colombo and Grilli, 2005; Haber and Reichel, 2006,
Honig, 1998, 2001; Markman and Baron, 2003; Van Praag, 2003; Van der Sluis et al. 2005).
Human capital represents the education, age, gender, experiences, skills and knowledge of
entrepreneurs (Becker, 1975). Human capital is both codified and uncodified knowledge of the
entrepreneurs and is developed by education, training and personal experience. Explicit and
codified knowledge and skills can be transferred. Tacit knowledge like personal experiences,
uncodified skills and the ability to spot opportunities are harder to transfer and copy, because
they are connected to a person, his experience or a specific firm (Dimov and Sheperd, 2004).
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As mentioned in the introduction, researchers distinguish between generic and specific
components of human capital (Becker, 1975). Some studies on small firm growth show a robust
impact of general education (Cooper et al., 1994; Westhead and Cowling, 1995) while others fail
to show this relationship (Storey, 1994; Brüdl and Preisendörfer, 2000). Cooper and Gimeno-
Gascon (1992) and Van der Sluijs (2005) find in their meta-analysis that general education is
related positively to small firm performance. The positive relationship to education is widely
acknowledged, but the impact of general education is still questioned. Colombo and Grilli (2005)
and Dimov and Sheperd (2005) point out that the nature of the education and experience has
been largely neglected in empirical research and show that specific education is a much better
predictor for specific types of firms and their performance.
We observe the same incongruent findings on generic and specific measures of experience.
For instance, Bates (1990) and Brüderl et al. (1992) and Gimeno et al. (1997) find no evidence
that individuals’ prior general experiences of self-employment and managerial experiences have
an impact on the failure rate of new firms. At the same time self-employment and managerial
experience do relate to growth (Brüderl and Preisendörfer; 2000) or economic performance
(Gimeno et al., 1997) of new firms. Again Colombo and Grilli`s (2005) results indicate that
specific experience relevant to the sector predicts small-firm performance better than general
experience. Their findings are backed up by other studies which show that experience and
specific skills relevant to the sector are strongly related to innovation capabilities (Hadjimolis,
2000), opportunity recognition (Uscaban et al., 2008) and small firm performance (Bhagavatula
et al., 2008; Haber and Reichel, 2007).
Markman and Baron (2003) and Driessen (2005) are strong advocates of the importance of
specific human capital. They argue that success of entrepreneurs is related to a specific set of
entrepreneurial skills and competences and agree upon the importance of a set of specific human
capital components: perseverance, flexibility, opportunity recognition or market awareness, self-
efficacy and social skills. Research by Driessen (2005) supports the claim that specific
entrepreneurial competences and characteristics do predict better turnover and a higher rate of
survival in start-ups. De Jong and Van der Velden (2005) observe that the same characteristics
and competences account for the day-to-day successes or failures in business and ownership
transfers alike: risk taking, perseverance, management skills, strategic abilities and a feeling for
the market.
Why should perseverance, flexibility, market awareness, self-efficacy and social skills be
important in selling a firm? We present the following explanation: transfer of ownership is a
complex process combining legal, tax, financial, market situation, organizational and emotional
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aspects involving a buyer, a seller, advisers, staff, customers and suppliers (EU, 2002; Kommers
and Van Engelenburg, 2003). The refusal of banks to finance ownership transfer is an important
reason for transfer failure (Geerts, Herrings and Peek, 2004; Langman and Lugt, 2005). As in
many countries, capital in the Netherlands is primarily provided by banks; private investors and
venture capital are rarely available for small firms. Strictly speaking, financing is a problem of
the buyer. But in selling the firm the timing (market awareness), looking at all options and
adjusting plans (flexibility), finding the right successor (perseverance) and negotiating with both
the purchaser and the bank (social skill and self efficacy) are important since in many cases co-
financing of the seller is essential to capitalize the ownership transfer. We assume that these
specific capabilities are as important for entrepreneurs in transfer situations as generic human
capital like education and general entrepreneurial experience. We thus propose:
Hypothesis 1:
a) Specific human capital (entrepreneurial competencies like perseverance, flexibility
market awareness, self-efficacy and social skills) of the seller is positively associated with
transfer performance.
b) Generic human capital (education and entrepreneurial experience) of the seller is
positively associated with transfer performance.
2.2 Planning
Planning is an organizational resource according to RBV (Barney, 1991). Research on business
planning and small business performance has led to conflicting empirical findings and competing
schools of thought. On the one hand, the planning school (e.g., Ansoff, 1991; Porter, 1985) sees
planning as advantageous to performance, positing that planning increases speed, a more effective
use of resources and pre-conceived alternatives (Delmar and Shane, 2003). On the other hand,
contingency theorists (e.g. Mintzberg,1994; Donaldson, 1985) see planning as (potentially)
detrimental to small firms. According to this view, small firms have less need for formal rules and
procedures. Day-to-day contact with the manager/owner is an efficient mechanism of coordination
and planning, more satisfying and a more efficient use of resources than planning (Hatch, 2006).
Brinckmann et al. (2008) examine the conflicting paradigms and empirical findings on planning
and small business performance by looking at contextual differences and the way performance is
measured. They carefully select and meta-analyze 46 studies from 15 different countries, concluding
that (especially long-term) business planning improves small business performance. They find that
start-ups, which are usually smaller, profit less from planning than established firms. They also find
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that firms in countries with high uncertainty avoidance profit less from planning due to rigidly
sticking to their planning. Finally, the Brinckmann et al. (2008) study also shows that the way
performance is measured affects the pattern of results. Objective performance indicators (i.e. hard-
coded data on sales, growth and survival) are superior to subjective performance indicators (i.e.
those where individual respondents assess outcomes.
The results of Brinckmann et al.’s (2008) study do not necessarily apply however to business
transfer planning in that it excludes short-term (less than a year), operational and functional planning,
the kind of planning which may be pertinent to ownership transfers in small firms. Indeed, studies of
ownership change and (post) transfer performance in small firms find either weak or no effect of
planning with objective performance indicators such as sales growth and survival (Astrachnan and
Kolenko, 1994; Avila, Avila and Naffziger, 2003; Berent et al., 2009; File and Prince 1996; Meijaard
et al., 2005; Morris, Williams and Nell, 1996; Morris et al. 1997). Studies using subjective
performance measurements, on the other hand, find a strong relationship between planning and
satisfaction (Morris et al., 1997; Sharma, Chrisman and Chua, 2003; Van Teeffelen, 2007). In sum,
studies on ownership change show that functional planning in ownership change does not contribute
to objective transfer performance but does improve satisfaction on the part of the entrepreneur who is
selling. These findings, furthermore, are in line with the contingency theory and the predictions of
Mintzberg (1994), which point out that it is more effective for small firms to improvise than to
formalize rules and procedures .
In measuring transfer performance the underlying assumption is that objective and
subjective Transfer Performance Indicators (TPIs) are related (Morris et al., 1997). Theoretically
this is a dubious assumption. Objective measures are rated by outsiders/observers
(consultants/practitioners, researchers), while subjective performance is rated by the
insiders/actors (business owners who are selling). The known actor-observer difference (Jones
and Nisbett, 1971) in social psychology states that consultants/researchers as observers typically
attribute causes to the business owner`s characteristics as the actor, while the business owners as
actor will attribute causes to circumstances. So we may expect that objective and subjective TPIs
will tell us a different story. To further explore this assumption, we test hypotheses using both
objective and subjective TPIs. In contrast with the dominant planning school paradigm we
predict that planning in small businesses ownership is not related to objective TPIs but should be
positively associated with subjective TPIs. To summarize we propose hypotheses 2a en 2b.
Hypothesis 2:
a) Planning by the seller is not related to objective Transfer Performance Indicators
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b) Planning by the seller is positively related to subjective Transfer Performance
Indicators.
2.3 Family ties and familiarity
The vast majority of academic transfer studies to date deal with family transfers. The
importance of family firms for national economies is widely recognized. Family transfers
represent only a minority of small firm ownership transfers however. Several studies show that
generally a third of all SME transfers are family transfers (Grant Thornton, 2005; Howorth,
Westhead and Wright, 2004). In this article, we would like to extend our knowledge by looking
at all kinds of relationships between seller and buyer.
A lesson learned from family transfers is that trust is essential (Howorth, Westhead and
Wright, 2004; Morris et al., 1997; Venter, Boshof and Maas, 2003). Howorth, Westhead and
Wright (2004) show that trust between seller and owner mitigates information asymmetry
between buyer and seller. This helps to negotiate a fair price and the knowledge transfer from
seller to buyer. Venter, Boshoff and Maas, (2003) also show that a positive relationship and trust
are the best predictors for satisfaction with the transfer process in family firms. We expand the
concept of family ties to overall familiarity. That is we assume the better a seller and purchaser
know one other, the better transfer performance is likely to be. We thus predict that familiarity
between seller and purchaser (and not kinship) is positively associated with TPIs.
Hypothesis 3:
Familiarity between seller and purchaser - family and non-family transfers alike - is
positively associated with both objective and subjective Transfer Performance Indicators.
2.4 Other factors
Three other factors may influence transfer performance: the motive for the ownership
transfer, the use of external advisors and the number of employees involved. Since these factors
are not our main focus, we will use these variables as control variables with regard to ownership
transfer.
A recent study of Wennberg et al. (2009) provides a taxonomy for exit routes. Wennberg et
al. (2009) warn against generalizing results from one type of exit to another. With respect to
ownership transfer they make a distinction between harvest sales and distress sales. In the
harvest sale the sale is for reasons of wealth creation and in the distress sale the firm is sold to
avoid liquidation or bankruptcy. We think this distinction is more meaningful than the motive of
retirement mentioned in previous studies (Bruce and Picard, 2006; European Commission, 2002;
Meijaard and Diephuis, 2004) and covers forced sales as opposed to voluntary sales.
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Succession models (Le Breton-Miller et al, 2003; Meijaard et al, 2005; Morris et al., 1997)
suggest that the use of an external advisor in ownership transfer can be beneficial for transfer
performance. Both Meijaard et al (2005) and Morris et al. (1997) find a negative result for the
use of (tax) advisors during the post transfer performance. Morris et al. (1997) explains this by
assuming that the results of (tax) advice favor individual financial gain for the selling party at the
expense of business profits. Though this might be true for long-term post transfer performance,
we think that external advice could improve short-term transfer performance like selling price,
transfer duration and satisfaction.
The number of employees can complicate transfers. Geerts, Herrings and Peek (2004)
show that the number of employees is a major concern of buyers. Meijaard et al. (2005) find that
firm size is the best predictor of post transfer performance. That is, the smaller the SME the
better the post transfer performance.
2.5 Measuring transfer performance
In looking at transfer performance indicators (TPIs) we have to address the issue that
success in transfers is not well defined (Venter, Boshoff and Maas, 2003). Morris et al. (1997)
propose to evaluate and distinguish between the quality of the (personal) experience as
subjective measurement and the effectiveness as seen by others as objective measurement. As
objective measurement Morris et al. (1997) and Meijaard et al. (2005) suggest looking at
duration of a transfer and to post-transfer growth of sales, profit, employees, new products or
survival. These measures are widely used in both research on planning and small firm
performance (Brinckmann et al., 2008) and research of planning and transfer performance. In his
own model and research Morris et al. (1997) uses the smoothness of ownership transfer as a
subjective measure. Similarly, Venter, Boshof and Maas (2003) and Sharma et al. (2003) use
satisfaction as subjective measurement.
Taking a multi-dimensional perspective on performance is advised by scholars like Kaplan and
Norton (1996), Hillman and Keim (2001) and Laitinen (2002). Also small firm researchers like
Ghobadian and O’Regan ( 2006) and Haber and Reichel (2006) are strong advocates of multiple
performance criteria to get a better understanding of the success of small firms.
The models of Morris et al (1997) and Meijaard et al. (2005) suggest that the transfer time
is a TPI. Transfer time provides functional information for policy makers since delay and
postponement (with a long transfer time) are among the main factors for transfer failure (Flören,
2002; Sharma et al., 2001). Another objective measure is the obtained selling price. In terms of
subjective measures we measure satisfaction in accordance with Sharma et al. (2003) and Venter,
Boshoff and Maas (2003).
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3. Method
To summarize, in this study we focus on two objective (transfer time and obtained price)
and one subjective TPI (satisfaction). We expect the three chosen TPIs will show different
patterns. A better transfer performance is defined by:
a) a shorter transfer period;
b) a smaller (negative) difference between the obtained and asking price;
c) greater satisfaction about the transfer.
The rest of the method section includes a description of our sample, the dependent,
independent and control variables and a description of our data analyses.
3.1 Sample
We drew a sample of 3,600 small firms, defined as having less than 50 employees, using the
registers of the Dutch Chambers of Commerce and national codes “registration of new ownership”
and “still in business” in the years of 2005 and 2006. Due to our research design, which required
contacting former business, we could track 1,800 former owners. Of these 1800, were we able to
contact 900 sellers within three calling attempts. These owners were screened to meet the following
criterion: more than 50% of the assets or shares had to have been transferred to non-spouses. Cases
were omitted, furthermore, where there had been only a minority sale of equity, a change due to a
new partner joining the firm (rather than someone leaving) or a change in the legal status of the firm.
This led to a reduced sample of 288 former firm owners meeting the above criteria, 130 of which
participated in the study. This resulted in a response rate of 45% of the final target sample. Due to
missing values our dataset for analysis was reduced to 112.
Prior to sampling we piloted our survey questionnaire with thirty business owners, revising
the final version based on those responses. One important lesson learned was that small-firm owners
were quite reluctant to share objective performance figures on profit, sales growth, costs and assets.
Even information regarding the costs of using advisors in the ownership transfer was withheld. Based
on this feedback, it was decided to omit such questions from the final version of the study.
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Our dataset represents 1% of all estimated transfers per year in The Netherlands (Meijaard
and Diephuis, 2004). The macroeconomic setting in 2005-2006 is a mildly growing economy of 1-
3% (CBS, 2006; 2007).
The dataset is representative for the Dutch SME population on firm size, sector and the gender of the
business owners. The average transfer age of business owners is 53 years. As shown in Table 2 the
fraction of forced sales is 33%, and we can also see that the fraction of family transfers is 30%. The
fraction of transfer between familiar sellers and purchasers is considerably higher: 55%.
3.2 Dependent variables
The three dependent variables include total transfer time, the difference (in percentages) of
the obtained and asked price and satisfaction (see Table 1). Single items measure the first two
dependent variables. Satisfaction is measured by a six item scale (Cronbach’s α = 0.80, see Table 1
and 2), based on items of Venter, Boshoff and Maas (2003) and Sharma et al. (2003). Factor analyses
with Varimax rotation on the six satisfaction items results in a single factor, with factor loadings of
individual items ranging from 0.55 and 0.74.
Please insert Table 1
3.3 Independent variables
To measure generic human capital of the seller, we measure the level of education ranging
from primary school as the lowest in ranking order (1) to a completed university master course (7).
To measure entrepreneurial experience we asked the respondent to indicate the number of years he or
she had owned the business before selling it. For specific human capital we used 10 items of
Driessen’s (2005) E-Scan. Driessen validated his items extensively on three separate datasets. For our
survey we selected initially two of his highest loading items on each of the factors: market awareness,
perseverance, flexibility, social orientation, self efficacy. All items are scored on a 7 point Likert
scale. For all but self efficacy we eventually we selected the item with the highest factor load in our
analysis (0.70 or higher), dropping self efficacy, as it did not load on a separate factor.
To measure planning and preparation we used a 10-item scale (Cronbach’s α = 0.78). Separate
factor analysis with Varimax rotation, show three factors in our items: preparation (items 1 and 6
through 9), planning (items 3 and 4) and decision making (items 2 and 10) all with communalities
above .50.
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To measure familiarity between seller and buyer we asked on a five point Likert scale how
well they knew each other before they started the initial talks on the transfer of ownership.
3.4 Control variables
We introduced our control variables in paragraph 2.3: harvest and distress sales, the use of
advisors and the number of employees. Wennberg et al. (2009) uses objective balance sheet and
profit and loss account performance measures to determine harvest vs.distress sales. We have no data
on direct firm performance so we used a dummy proxy for distress sales: forced sales. Forced sales
are sales motivated by illness/health problems or declining firm performance (0=voluntary, 1=forced
transfers). Our proxy for distress sales is not as accurate as Wennberg’s et al (2009) definition and
note we cannot rule out that firms with ailing owners still perform well, although this is unlikely.
We asked small firm owners what kind of advisors they used and counted the number. A third
of the business owners didn’t use any advisors, but on average they use one advisor. Most frequently
they were advised by an accountant or a bookkeeper.
The number of employees was measured by the number of employees working at the firm
beside the owners in Full Time Equivalents. Socially desirable answering was also included based on
the items “I am always honest”, since it was the highest loading item (> 0.70) on this separate factor.
Finally we distinguished family transfers from non-family transfers with a dummy variable by
informing if the firm was sold to a family member (see Table 1).
3.5 Data analysis
We tested our hypotheses using hierarchical multiple regression analysis. Hierarchical
regression allows us to look at the added predictive value of each set of independent variables, in this
case human capital, planning and familiarity. We first entered the control variables, then human
capital, then planning and finally familiarity. In Table 4 the results are combined. We also tested for
multi-collinearity using VIF-scores. All VIF scores were below a reasonable level (less than 2).
4. Results
Table 2 shows there are no significant correlation between our objective and subjective TPIs.
Also there is weak correlation amongst most of the independent variables. This reduces the risk on
multi-collinearity. The two highest correlations in Table 2 are “family transfers” and “familiarity”
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(r=0.52, p<.01) and “the number of advisors” and “planning” (r=.46, p<.01).
Please insert Table 2
Please insert Table 3
Table 3 shows the results of the multiple hierarchical regression. Hypothesis 1a, that specific
human capital is positively associated with transfer performance, is mostly but not fully confirmed.
Specific human capital is a significant predictor for all three TPIs. Flexibility predicts a shorter
transfer time and a higher degree of satisfaction. Also better social skills predict a shorter transfer
time. But counter to expectations, market awareness is associated with a lower obtained price.
Hypothesis 1b, that generic human capital is positively associated with transfer performance, is
rejected since it predicts a longer transfer time instead of a shorter transfer time and is unrelated to
the other two TPIs.
Hypothesis 2a, that planning is not related to objective TPIs, and Hypothesis 2b, that
planning is positively related to subjective TPIs, are both confirmed. Planning does increase the
objective transfer time slightly but only as a trend (p< .10) and is unrelated to the obtained price. At
the same time planning does predict the subjective TPI satisfaction strongly.
Our analysis also shows that familiarity does predict transfer performance. Familiarity
increases the obtained price and satisfaction, but contrary to our expectations, also increases transfer
time. This means that Hypothesis 3 – familiarity will increase TPIs – is not fully confirmed. Of
particular interest is the family firm variable which, as expected, does not predict TPIs. Thus, it
confirms our prediction that familiarity between seller and buyer, and not kinship, predicts better
transfer performance.
Only one of the control variables has predictive value: when advisors are consulted this
also increases the transfer time so advisors contribute negatively to transfer performance. All the
other control variables show no predictive value, including the number of employees, forced
transfers, family transfers and socially desirable answers.
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5. Discussion
This study looks at the importance of human capital, planning and familiarity in exit
situations. We made a distinction between generic and specific human capital because based on
past research, specific human capital variables were found to have better predictive value with
respect to small-firm performance (Driessen, 2005; Colombo and Grilli, 2005; Dimopv and
Shepperd, 2005). Our results confirm this expectation. In particular, specific human capital, like
flexibility, social skills, market awareness predict transfer performance better than generic
human capital characteristics such as general education and entrepreneurial experience.
Especially flexibility proves to be important since it shortens/reduces the transfer time and
increases satisfaction. Also social skills speed up the transfer. More market awareness leads to a
lower obtained price. This may indicate that when a seller is more aware of the market, he or she
becomes be more realistic in valuation of a proper price.
Therefore, our results confirm that we should look at specific human capital: skills,
experience and competencies of business owners needed in the context of situation. By doing so
we may have captured a part of the tacit knowledge and skills that stick to a person (Dimov and
Sheperd, 2004). However, not all characteristics are significant predictors nor in a positive
direction. Flexibility appears to be the most consistently positive predictors of TPIs.
We proposed a non-planning paradigm for small firms, saying that planning has no effect
on small-firm transfer performance since the manager/owner himself is an efficient mechanism
of coordination. Our results partially confirm this view in the situation of a business transfer.
Planning is unrelated to objective TPIs. Our analyses also point out that planning increases the
transfer time. Perhaps planning may give rise to second thoughts resulting in postponing the
decision to sell the firm. Alternatively the selling party may discover the firm has to prepared
first and changes are necessary to be saleable.
It is clear though that planning does improve the subjective TPI satisfaction. We believe
planning may give the seller a better insight into various possibilities, as well as the steps to take
to sell the firm. This might alleviate the complexities in ownership transfers (EU, 2002;
Kommers and Van Engelenburg, 2003).
Do our findings deviate from Brinckmann’s et al. (2008) study in which planning is
related to small firm performance? Let us first look at the similarities. Our study shows that
objective and subjective performance measures give different results, which is in line with
Brinkmann et al. (2008) findings. Brinckmann’s et al. (2008) results also show that firms in
countries which are more prone to deviate from their plans, benefit more from planning. So
flexibility plays a vital role. We also find that flexibility is the most important entrepreneurial
competence since it speeds up the transfer and increases satisfaction. Finally our study looks at
15
small firms and finds that planning is unrelated to objective transfer performance. Also
Brinckmann et al. (2008) find that start-ups, which are generally smaller than established firms,
profit far less from planning.
Our study also shows differences in outcomes. We find that planning is related to
subjective transfer performance whereas Brinckmann et al.(2008) finds relations with objective
performance measures. We have to keep in mind that our focus, sample and definition of
planning are different from the study of Brinckmann et al. (2008). First, our study looks at
functional planning in the specific situation of entrepreneurial exit. Brinckmann et al. (2008)
excluded operational and functional planning. We think this might explain why, contrary to
Brinckmann et al. (2008), we find planning to be more strongly related to subjective performance
measures than to objective performance measures. Planning with a horizon of a year or less and
in the specific situation of business transfers seems to be related differently to performance than
general business planning with a typically longer time horizon. In sum, looking at the specific
context and situation in which planning is used seems to be of importance.
An equally important outcome of our study is that objective and subjective TPIs are
unrelated and show different results. This should be kept in mind when comparing results from
previous research (e.g. Morris et al.,1997). Our findings reinforce the importance of looking
more carefully at how performance is measured when predicting small firm (transfer)
performance (Haber and Reichel, 2006, Kaplan and Norton, 1996; Hillman and Keim, 2001;
Laitinen, 2002).
In this study, we chose measures addressing aspects of the business transfer. We chose not to
measure post-transfer performance (e.g. sales growth, profit change) largely due to the difficulty in
obtaining such information from our respondents. This is not uncommon in our country where
money, earnings and property are not discussed in public. Neither is it unusual in small firm research.
Ghobadian and O’Regan (2006) also found that privately owned small firms are reluctant to provide
detailed data on performance. Garg et al. (2003) suggest the use of perceived performance measures
in situations where small business owners are reluctant to share their figures. Ghobadian and
O’Regan (2006) argue this is acceptable since perceived responses have been found to be reliable and
highly correlated to objective performance measures. It may be worthwhile, furthermore, to consider
the degree of reliability of balance and profit sheets in the context of business transfer. For instance, it
is in the seller’s interest to boost turnover and profit in the years before selling. The buyer is apt to
invest and/or reduce profits to save on immediate or future taxation. So how comparable are objective
performance measures like balance and profit sheets before and after ownerships transfer?
16
Regarding the importance of familiarity versus family ties in the transfer process, our
outcomes show only effects of familiarity. This highlights the value of social capital in the
context of business transfer. Although a more in-depth treatment of the topic is warranted, our
preliminary findings suggest that trust and the quality of relationships between buyer and seller
may be a significant factor in business transfer success.
Our study also provides some clues about the role of advisors. The high correlation
between advisors and the amount of planning indicates that their role could be potentially both
detrimental (longer transfer time) and advantageous (more planning increases satisfaction). The
negative outcome is in line with previous research (Morris et al., 1997; Meijaard et al. 2005).
The positive outcome has not been previously reported. Given these contradictory findings, we
think we should be careful with conclusions about the role of advisors. We should know what
kinds of advisors are involved and how much they are involved in the actual ownership transfer.
In our study, the majority of advisors used are accountants and bookkeepers. Van Teeffelen
(2009) finds that especially accountants are often not well equipped to advise on ownership
transfers. They have little transfer experience, report role conflicts and are not well connected
with banks and financial institutions.
This study generates some practical implications for accountants, business brokers, sellers
and buyers in the market. Company size, as measured by the number of staff, is neither an
obstacle nor an asset in selling a firm, nor do we find differences between forced and voluntary
sales. There is some indication that a seller using fewer advisors may actually be better off,
speeding up ownership transfer in small firms. Flexibility is the most important entrepreneurial
competence, since it speeds up the transfer and is associated with greater seller satisfaction. More
market awareness helps in getting a more realistic view on the value of the firm, since owners
with more market awareness seem more willing to drop the price.
Our findings also suggest that matchmakers and accountants should look at the close circle
of the selling party. Buyers known previously by the seller may lead to a more satisfied seller
and a better selling price. Note that even though familiarity affects both obtained selling price
and satisfaction, the obtained price is unrelated to satisfaction. This suggests that the added value
in selling off the business is not about money alone. It points in the direction of trust in the
buying party, so that staff, customers and the firm are in good hands (Howorth, Westhead and
Wright, 2004; Venter, Boshoff and Maas, 2003).
17
There are some restrictions to our study. To use TPIs we should have looked at both
succeeded and failed transfers. We only looked at succeeded transfers. However we expect that our
TPIs will be also valid for failed transfers. In failed unsuccessful transfers the transfer duration will
be much longer (second or third try) or infinite (no ownership transfer) if the transfer fails, the
negative difference between obtained and asking price will rise to 100% if there is no sale at all and
satisfaction will be lower since the seller did not realize his initial goal.
Although the sample is representative for The Netherlands, the fact remains that we only look
at one country. And the large majority of SME in our sample are micro firms (0-9 FTE). By itself this
is no problem, since micro firms constitute more than 90% of all firms in many countries (EU, 2009)
and we find no indications that the number of employees effects TPIs. We did not take in medium-
sized firms in our sample. One might argue that for medium-sized firms (50-250 FTE) outcomes
could be different, since they have a higher degree of formalization and a lower flexibility compared
to micro and small firms.
Finally the results suggest that a lack of human capital and unfamiliarity of seller and buyer
leads to transfer failure, but we did not test this on failed transfers yet. In the near future, the
challenge will be to compare successful and failed ownership transfers in the short, medium and long
run, so we may compare them. In addition, comparative research in different countries will give us
answers on the question if specific cultural and economic differences between countries influence
success and failure of small-business transfers in other countries.
18
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23
Figure 1: Model of Meijaard et al. (2005)
With the label “Sex” in box II and III, Meijaard’s model means “Gender”.
24
Tabel 1: List of variables
Dependent variables
transfer time
How long did it take to transfer the ownership of your firm, calculated from the
first inquiries you made about transfer to the actual transfer of equity or shares?
Obtained price
Was the obtained selling price lower, equal or higher than the initial asked price?
What was the deviance in percentages?
Satisfaction:
If you look back, how satisfied are with the following aspects of the transfer?
1. The preparations I have made
2. The total transfertime
3. The amount of potential buyers I negotiated with
4. The abilities of my successor
5. The obtained price
6. The performance of the company at this moment
scores: 1 = dissatisfied thru 5 = satisfied
Independent variables
Generic human captial
What is your educational level? 1= primary school thru academic masters
How long were you owner of the firm? (in years)
Specific human capital
Perseverance (will)
I will endure until all my plans are realised
Flexibilty
I change my plans immediately if the situation changes
Market awareness (environment)
I track well what other firms do in my sector
Social skills
I find it hard to start conversation with people
scores: 1 completly disagree thru 7= completly agree
Planning
Which of the following steps did you take before selling your business?
1. Made inquiries or went to gatherings
2. Made a decision on the date of sale
3. Made a planning in my head
4. Worked out a planning on paper
5. Identyfied stongnesses/weaknesses, opportunities/threaths
6. A valuation was made
7. Improved my business as preparation for the sale
8. Made a sales memorandum
9. Informed my staff
10. Made a profile of potential buyers
scores: 1= not at all thru 5 = completely
Familiarity
1. How well did you know the buyer before you started to negotiate?
scores: 1=not at all thru 5= well % fraction score: 4 and 5
Control variables
Number of employees
How many employees in fte were working in the firm besides the owners?
Family transfer
Did you sell your firm to a family member?
scores: 0=no, 1= yes
Number of advisors
Which advisors did you consult? How many advisors did you consult?
Forced transfers (dummy)
For what reason did you sell the firm? Fraction of illness/declining results
Socially desirable response
1. I always tell the truth
scores: 1 completly disagree thru.7= completly agree
25
Table 2:
Pearson correlations, means and standard deviations of tested variables (n=112)
12345678910 11 12 13 14 15 16
Dependents
1. Transfer time 1.00
2. Obtained price 0.02 1.00
3. Satisfaction 0.03 0.18 1.00
Independents
4. Entrepreneurial experience 0.26 ** 0.04 0.06 1.00
5. Level of education -0.13 0.01 -0.04 -0.19 * 1.00
6. Perseverance 0.02 0.05 -0.07 -0.08 0.06 1.00
7. Flexibility 0.21 -0.01 -0.24 ** -0.04 -0.05 0.15 1.00
8. Market awareness 0.10 0.13 0.01 0.15 0.12 0.02 0.17 1.00
9. Social orientation -0.15 0.02 -0.11 -0.07 0.06 0.02 0.12 0.13 1.00
10. Planning 0.24 ** -0.03 0.29 ** 0.05 0.06 -0.01 -0.06 -0.06 -0.03 1.00
11. Familiarity 0.05 0.24 ** 0.08 0.01 0.05 -0.02 -0.05 0.01 -0.25 ** 0.11 1.00
Controls
12. Number of employees 0.14 -0.01 0.13 0.03 -0.25 ** 0.03 0.08 -0.13 0.00 0.21 * -0.12 1.00
13. Familiy firms (dummy) -0.09 0.12 -0.07 0.24
**
-0.02 0.00 -0.04 -0.05 0.02 -0.26
**
0.52
**
-0.06 1.00
14. Number of advisors 0.31 ** 0.01 0.12 0.11 0.01 0.05 -0.02 -0.05 0.01 0.46 ** -0.19 * 0.16 -0.17 1.00
15. Forced transfers (dummy) -0.15 0.03 -0.01 -0.18 0.07 0.18 0.07 0.04 -0.03 -0.14 -0.13 -0.10 0.00 -0.20 * 1.00
16. Social desiriable answering -0.08 0.09 -0.08 -0.02 0.02 0.02 0.10 0.23 * 0.20 * 0.02 -0.06 -0.06 -0.04 0.01 -0.11 1.00
Mean 1.11 -6.17 23.34 17.20 3.91 2.39 2.94 3.08 5.30 22.60 3.32 3.34 1.01 2.17
Fraction 0.55 0.30 0.33
Standard deviation 1.36 13.36 6.48 12.92 1.39 1.18 1.47 1.70 1.67 9.04 10.13 1.80 0.92 0.97
Std. Cronbach's α0.80 0.78
*p < 0.05 (two tailed) **p < 0.01 level (two tailed)
26
Table 3: Hierarchic regression analyses
Objective tpi's Subjective tpi
Transfer time
Obtained Price
Satisfaction
β β β β VIF β β β β VIF β β β β VIF
1 2 3 4 1 2 3 4 1 2 3 4
Controls
Number of employees 0.08 0.06 0.03 0.05 1.19 -0.01 0.00 0.01 0.03 1.18 0.11 0.14 # 0.08 0.10 1.18
Family transfer (dummy) -0.05 -0.08 -0.05 -0.13 1.55 0.13 # 0.14 # 0.14 0.00 1.56 -0.04 -0.06 0.02 -0.10 1.56
Number of advisors 0.28 *** 0.26 ** 0.21 * 0.22 * 1.37 0.01 0.01 0.02 0.04 1.37 0.10 0.06 -0.06 -0.04 1.37
Forced transfers (dummy) -0.09 -0.10 -0.09 -0.06 1.19 0.01 0.01 0.01 0.07 1.19 0.02 0.06 0.06 0.10 1.16
Social desiriable answers -0.09 -0.10 -0.11 -0.10 1.13 -0.03 -0.06 -0.06 -0.03 1.13 -0.08 -0.06 -0.09 -0.06 1.13
Generic Human Capital
Experience as entrepreneur 0.20 * 0.18 * 0.18 * 1.29 -0.01 -0.01 -0.01 1.29 0.03 0.00 0.00 1.32
Level of education -0.03 -0.05 -0.05 1.19 -0.04 -0.04 -0.05 1.18 -0.01 -0.06 -0.06 1.18
Specific Human Capital
Perseverance -0.06 -0.06 -0.04 1.11 0.00 -0.02 0.00 1.10 0.08 0.07 0.08 1.09
Flexibility -0.23 ** -0.23 ** -0.23 ** 1.10 -0.02 0.02 -0.02 1.10 0.21 * 0.20 * 0.20 * 1.09
Social orientation R -0.15 * -0.15 * -0.15 * 1.09 -0.01 0.02 0.01 1.10 0.06 0.06 0.05 1.10
Market awareness -0.09 -0.10 -0.10 1.19 -0.18 * -0.17 * -0.18 1.17 0.07 0.10 0.10 1.17
Planning 0.13 # 0.15 # 1.42 -0.02 0.01 1.45 0.33 *** 0.34 *** 1.44
Familiarity 0.17 * 1.47 0.29 ** 1.52 0.25 ** 1.49
R-square 0.13 0.25 0.26 0.29 0.02 0.05 0.05 0.10 0.03 0.09 0.17 0.21
∆ R-square 0.13 ** 0.12 ** 0.01 0.02 # 0.02 0.03 0.00 0.05 ** 0.03 0.06 0.12 ** 0.04 *
R = reversed coded # = p< .10 * = p < .05 ** = p< .01 *** = p < .001
