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nt. J. Entrepreneurship and Small Business, Vol. 29, No. 3, 2016
Copyright © 2016 Inderscience Enterprises Ltd.
The influence of functional and relational proximity
on business angel investments
Johannes Herrmann
Stockholm School of Economics,
Sveavägen 65, 113 83 Stockholm, Sweden
Email: johannes.herrmann@alumni.hhs.se
Sofia Avdeitchikova*
The Ratio Institute,
Sveavägen 59, 103 64 Stockholm, Sweden
Email: sofia.avdeitchikova@ratio.se
*Corresponding author
Alexander Hjertström
Stockholm School of Economics,
Sveavägen 65, 113 83 Stockholm, Sweden
Email: alexander.hjertstrom@alumni.hhs.se
Abstract: Business angels are a vital source of capital for innovative start-up
firms. However, many potentially attractive investment opportunities are
rejected during the angel’s decision-making process. Information asymmetry,
risk and distrust in the relationship between the investor and the entrepreneur
result in investment barriers. The concept of proximity has been proposed as a
conceptual foundation to understand how the relationship between angel and
entrepreneur hinders or benefits the investment decision. Specifically,
researchers distinguish between the functional (geographic) and relational
dimensions of proximity. This paper uses structural equation modelling to
examine the influence of proximity on angel investments based on data from
226 investment situations gathered in fall 2014 from 56 business angels and 87
entrepreneurs in Sweden. We find that the relationship between geographic
proximity and the likelihood of a positive investment decision is indeed
positive and significant, supporting prior research; however, it is fully mediated
by relational proximity.
Keywords: business angels; investment decision; functional proximity;
relational proximity; structural equation modelling; mediation.
Reference to this paper should be made as follows: Herrmann, J.,
Avdeitchikova, S. and Hjertström, A. (2016) ‘The influence of functional and
relational proximity on business angel investments’, Int. J. Entrepreneurship
and Small Business, Vol. 29, No. 3, pp.468–490.
Biographical notes: Johannes Herrmann is a graduate of the Stockholm School
of Economics Master’s Program in Business and Management. He also studied
at the San Diego State University, Bocconi University and the University of
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Münster, where he worked in the Institute of Business-to-Business Marketing.
He co-founded and works now at Airinum, a Swedish startup that aims to
tackle the perils of air pollution worldwide.
Sofia Avdeitchikova is a Researcher at The Ratio Institute in Stockholm,
Sweden. She holds a PhD in Entrepreneurship from Lund University. She has
for many years been active as consultant on economic growth issues,
particularly with focus on entrepreneurship policy, and has also served as Head
of Department at Swedish Agency for Growth Policy Analysis. She has
published in several international peer-review journals and anthologies and is
on the editorial board of Venture Capital: An International Journal of
Entrepreneurial Finance.
Alexander Hjertström is a graduate of the Stockholm School of Economics
Master’s Program in Business and Management. He is also an MBA graduate
from the Indian Institute of Management in Ahmedabad. He has extensive
experience from the e-commerce industry around the world and is one of the
co-founders of Airinum, where he works today.
This paper is a revised and expanded version of a paper entitled ‘The influence
of functional and relational proximities on business angel investments’
presented at International Conference for Entrepreneurship, Innovation and
Regional Development, Sheffield, 18–19 June 2015.
1 Introduction
Research suggests that business angels, defined as private individuals investing equity
capital in unquoted ventures with which they have no family connections (Mason and
Harrison, 2000), primarily exhibit local investment patterns. For instance, studies by
Gaston (1989), Lumme et al. (1998) and Sohl (2003), all showed that as many as three of
four investments occur near business angels’ home or work. There are many explanations
for this locality, including the local nature of information on potential deals (Mason,
2007; Wetzel, 1983), the need to monitor investments due to agency risk (Landström,
1992) and business angels’ tendency for post-investment involvement (Mason and
Harrison, 1995). Due to such predominantly local patterns of investing, business angel
investments are considered to be an important vehicle for recycling and retaining wealth
within the region where it was created.
Given the plentiful evidence of the local nature of business angel investing and what
can be considered satisfactory explanations for this locality, the importance of geographic
proximity in business angel investment activity has been generally accepted in the
literature. However, this has meant that the rather substantial share of business angel
investments that nevertheless are conducted over distance has received much less
attention. This is significant because, in addition to contributing to recycling wealth
within regions, business angels also appear to contribute to reallocating wealth between
regions, and based on indications from prior studies, rural regions are less likely to
benefit from this reallocation (Avdeitchikova, 2009; Harrison et al., 2010). In particular,
although the overall share of long-distance angel investment is moderate, the share of
long-distance investments in certain regions can be very substantial, amounting to as
much as 75%–80% according to an earlier Swedish study (Avdeitchikova, 2009), which
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means that such investments are important to understand and explain. Furthermore, given
the recent developments resulting from digitalisation, increased transparency and the
overall decreasing costs of doing business, there might be a reason to revisit the issue of
proximity in business angel investing.
An interesting theoretical development has occurred within the field of innovation
studies, in which researchers have suggested that we can evolve our understanding of the
role proximity plays in individuals and organisations’ decisions to interact if we delve
deeper into the notion of proximity itself. Specifically, researchers have increasingly
called for abandoning a plain, overly simplistic perception of proximity as merely
physical space and adopting a more complex, thorough understanding of proximity as
incorporating both functional (geographic) and relational dimensions (Boschma, 2005;
Moodysson and Jonsson, 2007; Shaw et al., 2000). The latter perspective has been
described in the literature as proximity in terms of social ties, knowledge base
similarities, organisational affinity and adherence to the same standards and rules of
behaviour (Boschma, 2005). In this paper, we draw upon this functional-relational
proximity framework to study business angel investments.
The questions we ask in this paper are as follows:
• How does proximity influence the investment decision in business angel
investments?
• What does the interplay between the functional and relational dimensions of
proximity look like in the investment decision?
By answering these questions, we aim to contribute to an increased understanding of the
role of proximity in business angel investments and therefore of the locational patterns of
business angel activity. We do so using a state-of-the-art structural equation modelling
technique based on data from 226 investment situations gathered in the fall of 2014 from
56 business angels and 87 entrepreneurs in Sweden. To the best of our knowledge, the
functional-relational proximity framework has not been empirically tested before in the
business angel research setting. Moreover, researchers have called for more primary
studies to enrich the empirical basis of the business angel research field (Landström,
2007).
This paper is structured as follows. First, we review the existing literature on the
geography of business angel investments. Second, we present the theoretical framework
of functional vs. relational proximity. Next, we present the methodology of the study
followed by the results, discussion and conclusion sections. We finalise by discussing
some possible implications for knowledge and for practice.
2 Literature review
2.1 Evidence of geographic patterns in business angel investing
Since the early 1980s, many scholars have studied the location patterns of business angel
investing, mainly with regards to the locational preferences of business angels and the
actual tendencies towards local/long-distance investing.
Most researchers who have studied the locational preferences of business angels find
that a relatively large share of investors are prepared to invest outside the geographic
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proximity of their home or workplace. According to, e.g., Freear et al. (1992), Tymes and
Krasner (1993) and Wetzel (1983), approximately one-third of investors prefer to invest
in their immediate geographic proximity (less than 50 miles or a one-hour drive), another
third have broader geographic preferences, and the remainder would consider investing in
any location if an attractive investment opportunity emerges.
Examining the actual investments that business angels make, a slightly different
pattern emerges. In these studies, as many as three out of four investments are conducted
within an investor’s immediate geographic proximity (Landström, 1998; Lumme et al.,
1998; Riding, 1993; Wetzel, 1983). Thus, although many of the studies on the locational
preferences of business angels show that geographic proximity is not particularly
important, studies of the actual investments show that there is a strong tendency towards
local investments.
The prevalence of local investing among business angels has been explained by
several factors. The first is associated with access to information regarding investment
opportunities (Mason, 2007). The informal venture capital market is invisible and thereby
ineffective with regards to accessing information about new investment prospects.
Therefore, most investment opportunities come to investors’ attention through a network
of trusted friends and business associates (Wetzel, 1983) who tend to be geographically
local (Sørheim, 2003). Thus, the closer the potential investment object is to the investor,
the more likely he or she is to become aware of the investment opportunity (Wetzel,
1983).
The second factor is related to the uncertainty regarding an entrepreneur’s intentions
and capabilities and the risk of opportunistic behaviour by that individual (Gallié and
Guichard, 2005; Harrison et al., 1997). A business angel would be more willing to
provide financing to an entrepreneur with whom he or she either has had a previous
social relationship or whose reputation is known. Again, this factor is connected to the
fact that investors are more likely to have previous social relationships with entrepreneurs
from the same region. Even if an investor does not have a previous professional or social
connection to the entrepreneur, the competence and trustworthiness of local actors is
easier to establish (Mason, 2007), and it is easier to understand the subtle signs of
competence and goodwill of individuals who are from the same region as the investor
(Avdeitchikova, 2008a).
Finally, business angels have a tendency to take a ‘hands-on role’ in the companies
that they invest in, which is related both to a value-added contribution in terms of
business expertise and the need to monitor (Harrison and Mason, 1996; Landström, 1992;
Mason, 2007). Because many business angels have other commitments (most are
employed and/or run their own businesses) in addition to investing in other ventures,
there is a limited quantity of resources in terms of the time that they can devote to visiting
entrepreneurs at remote locations (Harrison et al., 2010). Therefore, the hands-on
involvement activity consumes less time if the investment is local.
Currently, however, the prevalence of local investing among business angels may
have decreased somewhat compared with the situation in the 1980s and 1990s. Recent
studies have found a reduced propensity to invest close to home of approximately 50%
(Avdeitchikova, 2008b; Harrison et al., 2010; Wright et al., 2015). While some of this
difference might be due to different national contexts and definitional and
methodological discrepancies, it may also be an indication that something is changing
with regards to proximity in business angel investments. Logically, there is a strong case
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for engaging in non-local investments if an investor can find a way to overcome the
disadvantages of distance outlined above because access to attractive investment
opportunities indeed varies geographically, making it more or less necessary for investors
(particularly those in peripheral regions) to seek opportunities elsewhere.
2.2 Entrepreneurs’ geographic patterns in attracting financing
We currently know very little about both the locational preferences and the actual
geographic behaviour of entrepreneurs in terms of attracting financing. Research
investigating entrepreneurs’ financing behaviour traditionally focuses on other
parameters of the choice of financial sources, such as the cost of capital, control
preferences and rent-seeking, while the topic of geographic patterns and their antecedents
is rarely addressed. Conversely, there is well-developed literature on entrepreneurial team
formation that addresses the issues of social connectedness and the knowledge base
similarity between entrepreneurs and those with whom they choose to cooperate,
including the choice of financiers (Aldrich and Kim, 2012; Ruef et al., 2003), which,
according to the framework presented below, would fall under the definition of relational
proximity (Boschma, 2005).
The problem in the business angel literature is that it treats entrepreneurs as agents
and investors as principals in the investment situation, which implies that the business
angel is exposed to certain risks when making an investment and that he or she will seek
to control that risk (e.g., Landström, 1992). However, such a division does not capture the
exposure of an entrepreneur when attracting external financing, particularly equity
financing. In fact, although a business angel is exposed to risks and uncertainties,
particularly the risk of failure of the venture and the behavioural uncertainty of the
entrepreneur, he or she has the opportunity to differentiate his or her investment portfolio,
thereby addressing the overall risk exposure. Prior research (e.g., Månsson and
Landström, 2006) has found that business angels on average allocate 5% to 20% of their
assets to investments in unquoted ventures, hold portfolios of 4–5 ventures, and
complement their business angel investments with investments in publicly traded stocks,
real estate, art, etc. Thus, the potential consequences of the failure of an investment for an
average business angel are rather limited.
Conversely, an entrepreneur is normally already highly committed to his or her
venture through personal funds, time, reputation, etc. By bringing in an external investor,
he or she may increase this exposure further. Specifically, in the case of external equity
investments, the entrepreneur must share sensitive information regarding the venture that
can be misused by the other party, cede decision-making power and withstand the risk of
potentially costly conflicts (Collewaert, 2012). Therefore, we argue that it is important to
remember the entrepreneur in a discussion on investment decision making; first, because
the potential risk exposure of the entrepreneur makes the relationship between the
entrepreneur and the investor highly relevant, and second, because an investment implies
that a positive decision has been made by the financier and the entrepreneur. Therefore,
the dataset that the empirical analysis in this paper is based on captures both business
angels and entrepreneurs’ investment decisions.
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3 Theoretical framework
3.1 Traditional approaches to understanding the business angel-entrepreneur
relationship
A number of studies within business angel research have examined the individual
relationship between business angels and start-up founders, particularly the mechanisms
that facilitate the exchange of information, knowledge transfer, and risk mitigation, and
how those in turn influence the deal (Dimov, 2007; Florin et al., 2013; Maxwell et al.,
2011; San José et al., 2005). The theoretical approaches that have been applied to
understand this relationship can be summarised in four categories: social capital/network,
human capital, and organisational and geographic approaches.
Below is a summary of the key theoretical approaches and previous studies on
business angels that offer different perspectives on the impact of the relationship on a
funding decision (Table 1). This short excerpt is the result of a systematic literature
review of the business angel field through the lens of applied theoretical frameworks.
Table 1 Business angel research on the angel-entrepreneur relationship
Approach Main findings
Social capital/network The social embeddedness between the business angel and the
entrepreneur and their ability to build a long-term, trusting
relationship is central for an investment to take place (Amatucci and
Sohl, 2007; Harrison et al., 1997; Politis and Landström, 2002;
Sørheim, 2003).
Human capital Shared experiences from the general life background, specific context
knowledge or prior entrepreneurial experience enable information
exchange and create a common understanding between an investor
and entrepreneur (Aram, 1989; Politis and Landström, 2002; Reitan
and Sorheim, 2000; Robinson and Cottrell, 2007).
Organisational Being associated with a professional organisational setting or being
connected through a context such as an association, incubator or even
an event strengthens the credibility in the relationship because it
implies sharing the same reference space and knowledge
(Avdeitchikova, 2008a; Bruneel et al., 2007; Florin et al., 2013; May,
2002).
Geographic Geographic proximity is a significant factor in business angel
investing, although the literature is inconclusive regarding the degree
of significance and whether geographic proximity is a compensatory
or a non-compensatory factor (Landström, 1998; Lumme et al., 1998;
Mason, 2007; Mason and Harrison, 2004; Riding, 1993; Wetzel,
1983).
From this brief review, we can conclude that several aspects appear to be important when
one attempts to understand business angel investments, and it is most likely a
combination of these aspects that best explains the investment decisions in such
situations. We can also observe that the research is inconclusive regarding the role of
geographic proximity in the investment decision, and the empirical evidence is partially
contradictory. In an attempt to reconcile this evidence, we adopt the functional-relational
proximity framework suggested by Moodysson and Jonsson (2007), which is based on
earlier works by Torre and Gilly (2000) and Boschma (2005). To a large degree, this
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approach is based on the theoretical development outlined above; however, what sets it
apart is that it synthesises the explanatory variables along these dimensions into a number
of relational proximities that exist in addition to geographic proximity and that affect
both the need for geographic proximity and the probability of interaction. Thus, this
approach puts the geographic and non-geographic aspects of proximity into the same
model. The next section outlines this theoretical framework and the hypotheses that guide
the empirical analysis.
3.2 Functional-relational proximity framework
In its most basic definition, geographic proximity is the physical distance between two
actors (Howells, 2002). However, physical distance does not necessarily capture distance
as it is perceived by the actors. The effort that it takes to interact appears to be more
important than mere physical distance (Moodysson and Jonsson, 2007). Thus, we adopt
the functional definition of proximity and add the dimensions of time and the cost of
travel and communication to the physical distance.
Conversely, relational proximity refers to the non-tangible dimensions of proximity
that in this case encompass cognitive, social and organisational dimensions, as suggested
by Boschma (2005)1.
• Cognitive proximity is concerned with similarities in the way actors perceive,
interpret and evaluate the world (Letaifa and Rabeau, 2013; Nooteboom, 2000).
Actors are cognitively proximate when they share a similar educational or
professional background and thus have a similar frame of reference, which makes it
easier for them to establish and retain relationships and to carry out advanced
communications (Moodysson and Jonsson, 2007).
• The concept of social proximity stems from research in social embeddedness
(Granovetter, 1985) and describes the level of the relationship between individuals in
terms of trust based on friendship, kinship and experience (Boschma, 2005; Letaifa
and Rabeau, 2013). If the level of trust in a relationship is high, the individuals will
be more likely to put themselves in a potentially vulnerable position and share
sensitive information (Sørheim, 2003).
• In this context, organisational proximity can be described as actors belonging to the
same (broadly defined) organisation and thereby having similar frames of reference
as well as adhering to the same standards and norms of behaviour (Moodysson and
Jonsson, 2007). Individuals who belong to the same organisations are arguably more
likely to engage in interactions, can carry out more advanced communication and can
rely on their counterparts to a greater degree.
Moodysson and Jonsson (2007) made a strong argument for grouping the ‘non-tangible’
dimensions of proximity under one umbrella – relational proximity. First, there is a
certain conceptual overlap between the dimensions that makes it difficult to completely
separate them in the empirical analysis. Second, as the authors explain, the antecedents of
these proximity dimensions are at least partly the same, which means that these different
dimensions often co-exist in practice. We follow this recommendation and use the
constructs of functional and relational proximity in our model. Functional proximity
encompasses one dimension, geographic proximity; thus, these terms are used
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interchangeably and encompass distance, time and cost variables. The framework is
illustrated in Figure 1.
Figure 1 Dimensions of proximity and associated measures
Multidimensional
Proximity
Relational Proximity Functional Proximity
Social Proxmity Cognitive Proximity Organizational Proximity Geographical Proximity
Social Closeness Knowledge Similarity Organizational Affinity AccessabilityKey Measures
Dimensions
Constructs
Framework
3.3 Hypotheses
Based on the discussion earlier in this paper and specifically in the context of business
angel investing, we expect that functional proximity has a direct and positive influence on
the probability of investment. This direct effect would occur because local interaction has
a lower cost than interaction over distance, at least in situations in which face-to-face
contact between the parties is required. Furthermore, being geographically proximate to a
potential investment object, individuals can more easily discover certain information that
otherwise would not have been available, such as subtle signs of others’ trustworthiness,
competence and goodwill (Avdeitchikova, 2008a). Therefore, we hypothesise as follows:
H1 When observed in isolation, functional proximity is significantly and positively
related to the likelihood of a positive investment decision in business angel
investments.
Furthermore, we expect relational proximity to have a direct and positive influence on the
probability of investment. Proximity along the organisational dimension will make
investors and entrepreneurs more likely to meet each other and engage in interaction.
This proximity also provides a certain degree of security regarding the behaviour of the
other party because both parties will have similar frames of reference and will adhere to
the same standards and norms of behaviour. Proximity along the cognitive dimension will
make the investor and the entrepreneur more likely to perceive, understand and determine
the value of information regarding the investment opportunity and the funding
opportunity. The entrepreneur and the investor are also likely to be more certain of the
other’s competence and skills. Proximity along the social dimension allows the investor
and the entrepreneur to view each other as more trustworthy, to perceive the information
received from the other party as more reliable and to require less monitoring in the
post-investment phase. Therefore, we hypothesise as follows:
H2 Relational proximity is significantly and positively related to the likelihood of a
positive investment decision in business angel investments.
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Finally, we expect functional proximity to have an indirect, positive effect on the
probability of investment due to the ability of geography to facilitate other proximity
dimensions; individuals who reside close to each other are more likely to interact, engage
in similar types of activities and share common norms and understandings (Boschma,
2005). Thus, we expect that relational proximity partially mediates the relationship
between functional proximity and the probability of investment. Therefore, we
hypothesise as follows:
H3 Relational proximity partially mediates the relationship between functional
proximity and the investment decision in business angel investments.
The hypotheses can be illustrated as follows (Figure 2):
Figure 2 Hypothesised proximity relation to the financing decision
4 Methodology
4.1 Sample and procedure
The study was conducted by utilising a qualitative pre-study to assess and adapt our
proposed proximity items as well as a survey research design to test our derived
framework in an empirical setting. The pre-study was conducted with 12 participants
with equally distributed groups of:
a experts in the field of informal venture capital and business angels
b active business angels
c founding entrepreneurs of start-ups.
The interviews were semi-structured and focused on identifying proximity factors that the
interviewees perceived in interactions between business angels and founders. The derived
items were then integrated with the dimensions of proximity to test our proposed
framework in a survey design.
The survey was administered electronically, and data were obtained from 56 business
angels and 87 entrepreneurs in Sweden (a response rate of approximately 40%). In total,
the respondents provided data on 111 ‘taken’ and 115 ‘not taken’ investments. The
details are summarised in Table 2.
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Table 2 Distribution of survey responses
Business angels Entrepreneurs Total
Total responses 78 119 197
Share of total population (in %)a 13–19.5 .47–5.61 .76–7.81
Estimated response rate (in %) 32.54 47.98 40.26
Eligible for further analysis 56 87 143
Investment taken 56 55 111
Investment not taken 53 62 115
Total Investment opportunities 109 117 226
Note: aFor an overview of estimates of the total population of Swedish entrepreneurs and
business angels see Söderblom and Samuelsson (2014) and CSES (2012).
The sample collections of previous research in the business angel field have been
criticised because researchers have often employed convenience samples that do not
necessarily reflect the overall business angel market (Avdeitchikova, 2008a). However,
because random samples can be difficult to obtain and are often impractical, researchers
have suggested a variety of sampling methods and recommend a combination of methods
to minimise the resulting sampling bias (Harrison and Mason, 1992; Månsson and
Landström, 2006; Sorheim and Landstrom, 2001). We follow those research suggestions
and combine several distribution methods as well as samples from business angels and
entrepreneurs to obtain a minimally biased sample from the Swedish business angel
investment market. We employed a clustering survey distribution in which individuals
were contacted both directly and through intermediary organisations such as associations,
incubators, and business angel networks (Creswell, 2009). Our directly obtained survey
participants were obtained from various online portals, such as LinkedIn and Angellist,
which further qualify our business angel sample as ‘visible’.
4.2 Questionnaire design and measures
The general structure of our survey was inspired by and modified from Shane and Cable
(2002), who divided their sample of formal venture capitalists and business angels into
two randomly selected groups: one group was asked to think about the most recent
seed-stage investment they made, and the other was asked to think about the most recent
seed-stage investment they evaluated but did not make.
Initially, respondents were asked to identify themselves as either private investors,
entrepreneurs, or neither (ending the survey). We chose the identification of ‘private
investor’ instead of the term ‘business angel’ because the latter is subject to ambiguity if
it is not thoroughly defined (Avdeitchikova et al., 2008). In turn, both business angels and
entrepreneurs were presented with a similar set of questions that measured proximities in
relation to a specific investment opportunity. Thus, one submitted respondent survey
could include the datasets on one actual investment, one investment opportunity that was
evaluated but not invested, or both. Additionally, both parties answered questions to
qualify for eligibility in our sample.
The items measuring the dimensions of proximity were adopted from previous studies
in the social sciences field and our pre-study interviews. The spatial measures of distance,
time and cost were measured by adapting the scale developed by Aguiléra et al. (2012)
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with categorical ultra-local, local, regional and national items. The social tie dimension
was measured using three categorical items of previously knowing the other party
directly, previously knowing the other party through contacts or not previously knowing
the other party at all (Granovetter, 1973; Shane and Cable, 2002). The remaining
measures of education, professional and entrepreneurial experience, organisation,
membership, cooperation and social closeness were measured using a 6-point Likert
scale. Finally, all of the respondents were asked to provide answers regarding their
gender, age and work place (city), which were recorded as control variables. The
measured items with their origin in the literature can be found in the Appendix.
4.3 Data analysis and methods
First, our resulting dataset was restructured and tested based on the assumptions
underlying the further analysis (refer to Section 5.4). Second, confirmatory factor
analysis (CFA) was performed to aggregate our observed variables into constructs that
resemble the proposed proximity constructs of our framework. Last, we constructed
structural models based on the causal relations proposed by the proximity theory using
the maximum likelihood estimation SEM approach. We developed two structural SEM
models to determine the mediation effect of relational proximity on the relationship
between functional proximity and the likelihood of investment.
SEM, which is a “multivariate technique combining aspects of factor analysis and
multiple regression that enables the researcher to simultaneously examine a series of
interrelated dependence relationships among the measured variables and latent constructs
(variates) as well as between several latent constructs” [Hair, (2014), p.546], is an
appropriate analysis method in our research case for several reasons. The framework of
this thesis, based on the proximity theory, includes multiple dependent relationships as
well as latent constructs (Hair, 2014). Thus, both required concepts can be included in a
holistic model rather than using a two-method approach of a separate CFA and multiple
regressions. Moreover, SEM is superior to the latter approach because it accounts for the
measurement error of the observed data and thus should model the observed relationships
more accurately (Hair, 2014). Our analysis includes the examination of a mediation
relationship, for which SEM has been proposed as advantageous to a classical mediation
determination using regression analysis (MacKinnon, 2008). The utilised software for our
analysis is the programming language R and therein, the SEM package lavaan (R Core
Team, 2014; Rosseel, 2014).
4.4 Sample size, reliability and normality assumptions
An often discussed limitation of SEM is its relatively large required sample size to return
stable results (Hair, 2014; Hoyle, 2012). Hair (2014) recommended sample sizes based
on the constructs used in the model and communalities (squared standardised construct
loadings). In the following section, the developed model contains two constructs and
shows average item communalities of high to moderate (>0.5). The recommended
minimum sample size of 150 observations is unambiguously exceeded, with 225
complete observations (Hair, 2014).
The model was tested for overall reliability and construct reliability using Cronbach’s
alpha (Cronbach, 1951; Lavrakas, 2008). All of the items and constructs as well as the
overall reliability exceed the proposed cut-off criterion of 0.7 (Hair, 2014; Nunnally,
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unctional and relational proximity 479
1978) (see Table 3). Moreover, because our sample consists of two heterogeneous
groups, business angels and entrepreneurs, we tested for reliability differences in and
between those groups. To compare the two groups, we test for the null hypothesis that
both groups are equally reliable (Cronbach, 1951; Feldt et al., 1987). The null hypothesis
can be retained with a p-value of 0.215 (significance level p = 0.05); thus, both groups
will be pooled in the future analysis.
Table 3 Cronbach’s alpha for the total sample, business angels and entrepreneurs
N = 225 Total Business angel Entrepreneur
1 Financed .77 .80 .74
Relational .71 .72 .72
2 Education .75 .79 .72
3 Professional experience .74 .79 .70
4 Entrepreneurial experience .75 .78 .72
5 Closeness .74 .77 .71
7 Organisation .74 .77 .70
8 Membership .74 .78 .70
9 Cooperation .74 .78 .71
Functional .94 .93 .96
10 Distance .73 .77 .70
11 Time .73 .77 .70
12 Cost .72 .76 .70
Total .76 .79 .73
Table 4 Univariate normality analysis (with Z-values = skewness | kurtosis/standard error)
N = 225 Abbreviation Mean Std.
deviation Skewness Skewness
Z-value Kurtosis Kurtosis
Z-value
Financed F 1.49 0.50 0.04 0.11 –2.01 –3.11
Education ED 2.99 1.63 0.27 0.84 –1.17 –1.81
Entrepreneurial
experience
EN 3.38 1.63 0.03 0.09 –1.26 –1.96
Professional
experience
PR 3.21 1.59 0.18 0.54 –1.16 –1.80
Cost C 1.53 0.91 1.47 4.53 0.82 1.27
Distance DI 2.09 1.05 0.68 2.09 –0.72 –1.12
Time TI 1.92 0.98 0.78 2.40 –0.46 –0.71
Cooperation CO 2.22 1.63 1.03 3.17 –0.33 –0.51
Membership ME 2.40 1.63 0.73 2.24 –0.91 –1.41
Organisation OR 1.62 1.27 2.03 6.26 2.93 4.54
Closeness CL 1.55 1.77 0.62 1.92 –1.00 –1.55
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The SEM assumes normally distributed data (Hair, 2014). By testing for the univariate
normality of each item, we retain all of the items except for organisation, which exceeds
both the skewness and kurtosis cut-off criteria (West et al., 1995) (see Table 4). In
Mardia’s test for multivariate kurtosis, we find a standardised z-value for kurtosis of 2.80,
which approaches but does not exceed the proposed threshold of 3.00 (Ullman, 2006).
Therefore, we can conclude that the collected dataset approximately fulfils the conditions
of both univariate and multivariate normality.
5 Results
Because the combination of proximity dimensions in the relational construct is not
unequivocally agreed upon, we first tested different structural model designs to determine
the relational construct with the best fit. When observing overall model fits and X², we
find that including social and cognitive proximity but disregarding the organisational
dimension results in the best fit in all of the measured models (see Table 5). Thus, the
following analysis of the measurement and structural model will be based on the
combination of three dimensions (social, cognitive and geographic).
Table 5 Model selection with different relational proximity combinations
Model Proximity
dimensions Path Factor
loadings Significance X2 Fit
1 Cognitive PR → R 1.000 0.002 CFI = 0.969
TLI = 0.955
RMSEA = 0.067
SRMR = 0.059
ED → R 0.932 p < 0.01
EN → R 0.624 p < 0.01
Organisational ME → R 0.572 p < 0.01
Geographic CO → R 0.536 p < 0.01
DI → F 1.000
TI → F 0.947 p < 0.01
C → F 0.817 p < 0.01
2 Social CL → R 0.663 p < 0.01 0.015 CFI= 0.982
TLI = 0.969
RMSEA = 0.069
SRMR = 0.047
Organisational ME → R 1.000
Geographic CO → R 0.650 p < 0.01
DI → F 1.000
TI → F 0.946 p < 0.01
C → F 0.817 p < 0.01
3 Cognitive PR → R 1.000 0.039 CFI = 0.985
TLI = 0.976
RMSEA = 0.054
SRMR = 0.049
ED → R 0.904 p < 0.01
EN → R 0.616 p < 0.01
Social CL → R 0.452 p < 0.01
Geographic DI → F 1.000
TI → F 0.947 p < 0.01
C → F 0.817 p < 0.01
The influence of
f
unctional and relational proximity 481
Table 5 Model selection with different relational proximity combinations (continued)
Model Proximity
dimensions Path Factor
loadings Significance X2 Fit
4 Cognitive PR → R 0.994 p < 0.01 0.000 CFI = 0.939
TLI = 0.917
RMSEA = 0.085
SRMR = 0.064
ED → R 1.000
EN → R 0.705 p < 0.01
Social CL → R 0.728 p < 0.01
Organisational ME → R 0.780 p < 0.01
Geographic CO → R 0.679 p < 0.01
DI → F 1.000
TI → F 0.947 p < 0.01
C → F 0.817 p < 0.01
The resulting path diagram of measurement model 3 is in Figure 3. The factor loadings
are all above the recommended threshold of 0.5, with the exception of the social
closeness dimension (Hair, 2014). However, this factor loading is significant at p < 0.01
and approaches the threshold; it will therefore be retained. The lower value can likely be
explained by the combination of the social and cognitive dimensions in one construct.
The measurement model was additionally tested for indiscriminant validity by combining
all seven indicators into one construct (Hair, 2014). The resulting model fit is in all
aspects inferior to that of the two-construct model; therefore, indiscriminant validity is
assumed.
Figure 3 The confirmatory factor analysis
0.205*
Professional experience
Education
Entrepreneurial experience
Closeness
Relational
Functional
Distance
Time
Cost
C1
C2
C3
S1
G1
G2
G3
1.000
0.921**
0.605**
0.430**
1.000
0.947**
0.817**
0.568
0.458
0.199
0.085
0.877
0.902
0.771
Notes: **p < 0.01; *p < 0.05.
When observing our constructed structural models, we find the model indices to be
within the suggested ranges for a good fit. Although the X2 values appear to indicate a
low fit, these results should not be judged in isolation, and models can display good fits
with significant X2 values (Hair, 2014), see Table 6.
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Table 6 Model fit for structural models
Structural model 1 Structural model 2 Structural model 3
Degrees of freedom 18 19 18
X2 0.039 0.028 0.039
CFI 0.985 0.983 0.985
SRMR 0.049 0.056 0.049
RMSEA 0.054 0.056 0.054
To test our first hypothesis, we construct a structural model that includes the
two constructs of relational as well as functional proximity, which are defined by their
indicators, and the financing decision outcome as the measured dependent variable (see
Figure 4). Functional proximity is defined as the sole determinant of the financing
decision. A significant influence of functional proximity on the financing decision can be
observed if no involvement of relational proximity is assumed. Thus, Hypothesis 1 can be
retained because functional proximity has a significant, positive influence on the
financing decision.
Figure 4 SEM 1 – path diagram of the direct effect of functional proximity
Professional experience
Education
Entrepreneu rial experience
Closeness
Relational
Functional
Distance
Time
Cost
C1
C2
C3
S1
G1
G2
G3
1.000
0.904**
0.616**
0.452**
1.000
0.947**
0.817**
0.568
0.443
0.206
0.094
0.877
0.901
0.772
Finance d
0.080* 0.207*
Notes: **p < 0.01; *p < 0.05.
Our second hypothesis proposes a significant influence of relational proximity on the
financing decision when viewed in isolation (see Figure 5). We construct a new SEM
model with relational proximity influencing the financing decision in isolation. The
resulting effect is moderately strong and significant at p < 0.01. Therefore, we similarly
retain Hypothesis 2 and confirm a significant influence of relational proximity on the
financing decision.
The influence of
f
unctional and relational proximity 483
Figure 5 SEM 2 – path diagram of the direct effect of relational proximity
Professional experience
Education
Entrepreneurial experience
Closeness
Relational
Functional
Distance
Time
Cost
C1
C2
C3
S1
G1
G2
G3
1.000
0.904**
0.616**
0.452**
1.000
0.947**
0.817**
0.568
0.443
0.206
0.094
0.877
0.901
0.772
Finance d
0.080* 0.207*
Notes: **p < 0.01; *p < 0.05.
The third structural model considers the mediating relationship suggested in our
conceptual framework (see Figure 6). The causal relation between functional proximity
and the financing decision becomes insignificant, while a causal link between functional
and relational proximity as well as between relational proximity and the financing
decision can be observed. These results imply a fully mediating effect of relational
proximity with regards to the relationship between functional proximity and the financing
decision. When accounting for the mediation relationship in the SEM model, we obtain
significant results for both the indirect (0.022) as well as the total effect (0.079) of the
mediation. Our mediation analysis was conducted as part of the SEM model with helping
variables accounting for the direct and indirect mediation effects. Therefore, we reject our
Hypothesis 3 in favour of a full mediating effect of relational proximity.
Figure 6 SEM 3 – path diagram of the structural mediation model
Professional experience
Education
Entrepreneurial experience
Closeness
Relational
Functional
Distance
Time
Cost
C1
C2
C3
S1
G1
G2
G3
1.000
0.904**
0.616**
0.452**
1.000
0.947**
0.817**
0.568
0.443
0.206
0.094
0.877
0.901
0.772
Finance d
0.102**
0.056 0.218*
Notes: **p < 0.01; *p < 0.05.
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5 Discussion and implications
In this paper, we aimed to examine the influence of proximity on the investment decision
in business angel investments. We used Moodysson and Jonssons’ (2007) functional-
relational framework as a point of departure and hypothesised that functional proximity is
significantly and positively related to the likelihood of a positive investment decision
when observed in isolation and that this relation is partially mediated by relational
proximity. We studied this relationship based on data from 226 investment situations
gathered in the fall of 2014 from 56 business angels and 87 entrepreneurs in Sweden.
Our results confirm prior research on business angel investors’ geographic investment
patterns (Avdeitchikova et al., 2008; Mason, 2007; Mason and Harrison, 2004). The
investment decision in our model is partially determined by the functional proximity of
the investor and entrepreneur when observed in isolation. The effect is significant but of
low strength, which can be expected because an investment decision is a complex
outcome with many different determinants not considered in our model (Helle, 2006;
Mason and Harrison, 2006; Maxwell, 2011).
Furthermore, our results support conceptual studies in the field of business angels that
have hypothesised a mediating influence of relational factors in the investment
decision-making process (Avdeitchikova, 2008a; Mason, 2007; Riding, 1993). Contrary
to previous research, we find this influence to be fully mediating. This result means that
the initial influence of functional proximity on the investment decision can be fully
explained by the relational factors that mediate it.
How can we understand and interpret these findings? In particular, is geography
irrelevant in business angel investing? Based on the results of this study, our best answer
to this question is that geographic proximity is relevant as far as it facilitates other
proximity dimensions. Although the direct influence of geographic proximity is very
moderate and disappears completely when relational proximity is introduced, it is a
strong predictor of relational proximity. When these proximity dimensions co-exist,
geography matters in business angel investing. Similarly, we argue that, in cases where
relational proximity in an entrepreneur-investor relationship is developed and sustained
over distance, geography can potentially become unimportant in the investment decision.
Thus, although scholars and practitioners are naturally interested in the issue of
whether geographic proximity matters in business angel investing, a better research
question would perhaps concern when geographic proximity matters and for whom it
matters in the context of business angel investing. These questions are in accordance with
prior studies that have repeatedly found that business angels are a heterogeneous group
that exhibit a variety of characteristics, attitudes and behaviours that can change over
time with experience and in different contexts (e.g., Avdeitchikova, 2008b, Harrison
et al., 2010).
5.1 Suggestions for further research
Possible ways to develop knowledge on the situational role and the interaction between
relational and functional proximities are to consider the possible drivers of locality in
business angel investments. For instance:
The influence of
f
unctional and relational proximity 485
• The nature of the knowledge that is required to recognise and evaluate an investment
opportunity. Depending on whether an investment requires local knowledge that has
a strong tacit dimension and is better understood and applied in the local context
(Avdeitchikova, 2008a) or knowledge regarding a certain industry or technology that
can be codified and thereby is more easily transferable (Moodysson and Jonsson,
2007), the role of geographic proximity may be different. Therefore, future studies of
the role of proximity in business angel investments may benefit from integrating the
frameworks of the different knowledge bases (Asheim, 2007; Liu et al., 2013)
developed in the field of economic geography that have been used to explain, for
instance, the localisation patterns of innovation collaborations.
• The investors’ motivations and financial commitment. The literature on business
angel decision making largely assumes that business angels are rational economic
actors who make financial decisions in the presence of uncertainty and risk (e.g.,
Harrison et al., 1997). However, we observe a broadening of the types of investors
and investment behaviours (such as ‘micro investors’ and ‘crowd investors’); thus,
we see a need to focus more on the variety of potential investment motivation
contexts and characteristics and how they can affect the roles and importance of the
proximity dimensions (Agrawal et al., 2011). For instance, in comparing business
angel investments with ‘micro investments’, Avdeitchikova (2008a) found that in
smaller investments, investments that involved no or minimal post-investment
involvement and investments that were motivated by other than financial criteria,
geographic proximity was both less prevalent and considered to be less important.
Harrison et al. (2010) further found that very large investments have a higher
tendency to be conducted over distance, likely by so-called ‘super angels’ (Smith et
al., 2010). Therefore, the question of what we mean when we refer to business angels
is very important because considerable heterogeneity can be expected along different
proximity dimensions.
Furthermore, the entrepreneur side of the relationship is perhaps the most poorly
understood. Here, we have argued that entrepreneurs are exposed to considerable risks
and uncertainties when attracting external financing, and they therefore seek to mitigate
such risks. Although the empirical analysis shows that entrepreneurs’ behaviour in terms
of relying on functional and relational proximities is not significantly different from that
of business angels, the nature of the potential risks and uncertainties may well be
different, particularly when they are related to the proximity aspect. Thus, the
relationship between entrepreneurs’ decision making in business angel investing and
proximity needs to be explored.
A better understanding of when and for whom different proximity dimensions are
important would also allow the development of policy instruments and tools that better
address the needs of certain types of investors and entrepreneurs. For instance, when
developing digital match-making platforms, what may work for crowd investing, in
which investors are often motivated by factors other than financial returns, invest little
money and have very limited decision-making power (and involvement ambitions) in the
venture that they invest in (Agrawal et al., 2011), is likely not the same as what will work
for business angels who invest considerable amounts of money, expect financial returns
and often become actively involved in the strategic leadership of the venture
(Avdeitchikova et al., 2008).
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5.2 Limitations
Our research and its results need to be considered with several limitations. First, our
developed framework has thus far not been empirically applied to the field of business
angels. Past empirical studies of the proximity framework in other fields vary
significantly in their context, utilised proximity dimensions, constructs and results
(Knoben and Oerlemans, 2006), which makes comparisons and analogical use
impractical. Our employed survey scales are shown to be reliable in our context, but they
require further confirmation in future studies. Furthermore, we have not applied a specific
definition of business angels and have allowed individuals, to a certain degree, to
self-select for participation in the study, which remains, although not uncommon,
methodologically problematic, as it reduces comparability with other studies
(Avdeitchikova et al., 2008).
In addition to the previously noted methodological limitations, the scope of our study
limits its generalisability. The survey was conducted solely in Sweden and therefore does
not consider functional or relational considerations in a cross-border setting. In addition,
within national borders, generalisability is likely to be limited to other countries with
homogeneous institutional settings. In countries such as China and the USA, in which
substantial differences in regulations exist between regions, we would not expect the
findings to hold (Mason and Landström, 2012).
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Notes
1 We have not included institutional proximity from the original framework because this
dimension is primarily relevant in multi-national studies, as cross-border transactions often
display significant differences in formal as well as informal institutional norms (Boschma,
2005). Because our study is based on a national survey, institutional influences are expected to
be minimal.
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errmann et al.
Appendix
Type Dimension Function Previous theoretical measure* Pre-study finding of measures Variables measured in survey
[Geographic] Functional
proximity
Geographic Physical distance • Time
• Distance
• Cost
• Time between offic es
• Time between meeting points
• Transport distance
• Convenience of location
• Cost of travelling
[Distance] – physical distance
between offices
[Time] – travelling time between
offices
[Cost] – approximate cost of
travelling between offices
[Cognitive] Cognitive Knowledge and
experience
• Type of education
• Level of education
• Type of role
• Industry experience
• Entrepreneurial level
• Type of education
• Length of education
• Non-professional experience
• Position/role
• Type of job
• Type of skills
• Entrepreneurial experience
[Educational] – similar types of
educational background
[Professional] – similar types work
experience from either job position or
industry
[Entrepreneurial] – similar
entrepreneurial experience
[Social] Social Connectedness • No ties
• Indirect ties
• Direct ties
• Previous contact
• Acquaintanceship
• Friends
• Family or relatives
• Strength of relationship
[Ties] – no connection, connection
through mutual connections or direct
personal connection
[Closeness] – strength of relationship
[Organisational]
Relational
proximity
Organisational Solidarity,
credibility, culture
and value
understanding
• Belonged to same
organisation
• Affinity from previous
organisational branch
• Cooperation or partnership
• Affinity from events
• Affinity from associations,
societies or clubs
• Affinit y from simila r or same
companies
[Organisation] – affinity from
working together in the same company
[Membership] – affinity from a
mutual connection through network or
association
[Cooperation] – affinity from
cooperation through events or similar
activities
