Journal of Business Venturing 17 (2002) 59 – 81
The consequences of conflict between the venture capitalist
and the entrepreneurial team in the United Kingdom from
the perspective of the venture capitalist
Hironori Higashide, Sue Birley*
Management School, Imperial College, Exhibition Road, 53 Princes Gate, London SW7 2PG, UK
Received 1 December 1998; received in revised form 1 February 2000; accepted 1 March 2000
Abstract
This research investigates the factors associated with the nature of conflict in the post-investment
relationship between the venture capitalist (VC) and the entrepreneurial team (EP) in a venture that
was funded by the venture capital firm, and as perceived by the VC. The study hypothesises a
relationship between this perceived conflict and the post-investment performance of the investee firm.
It examines both cognitive and affective conflict in two strategic areas—organisational goals and
policy decisions—and relates them to the performance. The data was collected by a survey of VCs in
the UK and a 60% effective response rate was achieved. The results show that conflict as disagreement
can be beneficial for the venture performance, although at the same time, conflict as personal friction is
negatively associated with performance. These impacts are in general stronger in the conflict related to
organisational goals than to policy decisions. D 2001 Elsevier Science Inc. All rights reserved.
Keywords: Conflict; Venture capitalist; Entrepreneurial team
1. Executive summary
This paper is concerned with the conflict that can arise between the venture capitalist (VC)
and the entrepreneurial team (EP) during the post-investment period. Although it can be
argued that conflict is likely to produce negative outcomes, this is not universally viewed as
undesirable (Ross et al., 1997). Within limits, some conflict serves both to prevent relation-
* Corresponding author. Tel.: +44-20-7-594-9102/3; fax: +44-20-7-594-9204.
E-mail address: [email protected] (S. Birley).
0883-9026/00/$ – see front matter D 2001 Elsevier Science Inc. All rights reserved.
PII: S 0 8 8 3 - 9 0 2 6 ( 0 0 ) 0 0 0 5 7 - 4
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H. Higashide, S. Birley / Journal of Business Venturing 17 (2002) 59–81
ships from stagnating and to flag opportunities for improvement (Jehn, 1995; Amason, 1996).
Nonetheless, at high levels, conflict is generally conceded to be costly to both parties (Reve
and Stern, 1989). Thus, conflict between the venture capital organisation and the investee
company is not necessarily harmful. Indeed, it may be beneficial (Amason, 1996). Moreover,
as found in the relationship between consensus and organisational performance (Bourgeois,
1980; Dess, 1987), conflict may arise in two possible ways (Bourgeois and Eisenhardt, 1988):
1. as the goals of the two organisations begin to diverge and/or
2. as the policies adopted by the investee company are unacceptable to the investor.
Thus, this study examines inter-organisational conflict between the entrepreneur and the
VC as perceived by the VC from two different dimensions: cognitive or affective conflict and
goal or policy conflict.
After initial screening by telephone, pre-tested questionnaires were sent to 174 UK VCs
identified mainly from two sources: British Venture Capital Association 1996/1997 Directory
(BVCA, 1996), and The Venture Capital Report: Guide to Venture Capital in the UK and
Europe (Venture Capital Report, 1996). Two follow-up letters were sent to increase response
rates approximately 3 weeks after the initial mailing and approximately 4 weeks after the first
follow-up. Eighty VCs returned usable questionnaires giving an effective response rate of
60%. However, in the regression analyses, the set of 57 or 58 questionnaires (depending on
the model) is analysed mainly as a result of missing values.
As expected, it was found that conflict as disagreement can be beneficial for the venture
performance, although at the same time conflict as personal friction is negatively associated
with performance. Thus, the past research findings with respect to cognitive and affective
conflict are replicated in the VC–EP relationship. Goal conflict has a greater impact on the
venture performance than policy conflict, and works independently of policy conflict. On the
other hand, goal conflict appears to be a necessary condition to make policy conflict work. In
the sub-dimensions of goal conflict, conflict about product/innovation has the strongest
impact on the venture performance both in beneficial and non-beneficial directions, and
seems to work independently of other sub-dimensions. The strategic advice sub-dimension of
policy conflict factor shows marginal positive association with the venture performance.
The study also produced an unexpected and, on the surface, counter-intuitive finding of a
negative relationship between the VC’s perceived effectiveness and their description of the
performance of the venture. We would suggest that this does not mean that the VC should
cease any involvement with the venture but, rather, that involvement increases as a reaction to
negative performance.
The evidence from this research clarifies the view that, in order for the VC to improve his/
her satisfaction with the venture invested, it is important to manage agency risks well both in
the due diligence and deal, and in the post-investment phase. However, getting the right
entrepreneurial management team upfront in the investment process seems to have been more
crucial for the VC’s satisfaction to date in the UK, compared with managing the risk after the
deal. Moreover, the reduction of uncertainty and ambiguity, which stems from the constructive conflict in the UK VC–EP team relationship, seems to have had limited impact on
the eventual perceived venture performance. Further, VCs should be careful not to interfere in
H. Higashide, S. Birley / Journal of Business Venturing 17 (2002) 59–81
61
the goals and policies of their investee companies since any resultant disagreement could
wipe out any potential positive effects. To the entrepreneurs, we would say beware of VCs
who want to be involved in decision making since such involvement could be detrimental to
their perception of your performance!
2. Introduction
It is generally accepted that the provision of venture capital is often critical to the success
of high growth entrepreneurial firms. Moreover, the relationship between the VC and the EP
usually extends beyond the simple provision of capital. In the post-investment period, the VC
frequently plays an active role with the portfolio company, representing the interests of the
syndicate of venture capital firms either on the board of the venture or in other less formal
ways (Timmons and Bygrave, 1986; MacMillan et al., 1988; Gorman and Sahlman, 1989).
However, whilst the interests of the entrepreneur and the VC can be assumed to be in
alignment during the negotiation of the deal, this is not necessarily the case afterwards.
Indeed, for the VC, the commitments and intentions of the entrepreneur are difficult to gauge
upfront, even after intensive screening and evaluation (MacMillan et al., 1987; Sahlman,
1990). So, for example, as a rational investor the VC may expect the EP to relinquish their
absolute independence in order to maximise the expected shareholder wealth through
corporate growth (Brophy and Shulman, 1992). Moreover, the VC may wish to harvest a
venture’s profits rather than to reinvest in future developments in order to distribute to limited
partners, especially when the venture is financially viable but too small to go public
(Sahlman, 1990). By contrast, the entrepreneur’s motivation to start a venture may not be
solely future wealth maximisation but also other personal needs, such as peer approval and
personal independence (e.g. Birley and Westhead, 1994; Scheinberg and MacMillan, 1988).
In such cases, flotation or sale of the company would not be a consideration. As a result,
conflict may arise. This study is concerned to explore the nature of conflict that may arise
between the VC and the EP and to assess its impact on the performance of the venture as
perceived by the VC.
3. Research hypotheses
Although it can be argued that conflict is likely to produce negative outcomes, this is
not universally viewed as undesirable (Ross et al., 1997). Within limits, some conflict
serves both to prevent relationships from stagnating and to flag opportunities for
improvement (Jehn, 1995; Amason, 1996). For instance, the VC’s playing ‘‘devil’s
advocate’’ is one of the major ways that the lead investor can add value to the venture
by contributing to the avoidance of costly mistakes (Timmons and Sapienza, 1994).
Nonetheless, at high levels, conflict is generally conceded to be costly to both parties
(Reve and Stern, 1989). Thus, we can see that conflict between the venture capital
organisation and the investee company is not necessarily harmful. Indeed, it may be
beneficial (Amason, 1996). Moreover, as found in the relationship between consensus and
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H. Higashide, S. Birley / Journal of Business Venturing 17 (2002) 59–81
organisational performance (Bourgeois, 1980; Dess, 1987), conflict may arise in two
possible ways (Bourgeois and Eisenhardt, 1988):
as the goals of the two organisations begin to diverge and/or
as the policies adopted by the investee company are unacceptable to the investor.
Thus, this study examines conflict between the entrepreneur and the VC from two different
dimensions: harmful or beneficial conflict and goal or policy conflict.
3.1. Harmful or beneficial conflict
Conflict has been broadly defined as perceived incompatibilities (Boulding, 1963),
discrepant views, or interpersonal incompatibilities between two parties, (Jehn, 1995).
Moreover, the construct may have more than one dimension. For example Priem and Price
(1991) dichotomise cognitive task-related conflicts and social–emotional conflicts that arise
from interpersonal disagreements not directly related to the task. Similarly, Amason and
Schweiger (1994) describe cognitive conflict and affective conflict, where cognitive conflict
is the functional, task-oriented conflict which stands for judgmental differences about how
best to achieve common objectives; and affective conflict is the dysfunctional and
emotional conflict which arises from incompatibilities or disputes among decision participants. In a later paper, Amason (1996) notes that an important factor influencing decision
quality is the cognitive capabilities of a top management team. This cognitive capability is
related to the team’s cognitive diversity, which seems to result in the potential for highquality decisions. Cognitive diversity provides a larger set of problems and a larger set of
alternative potential solutions when a team makes complex decisions, so that reconciling
dissimilar solutions leads to effective group discussion and avoids group-think (Hoffman,
1959; Hoffman and Maier, 1961). Thus, it can be argued that groups comprising
individuals with a variety of skills, knowledge, abilities and perspectives are potentially
more effective when solving complex, non-routine problems. Indeed, Bantel and Jackson
(1989) found that top management teams with diverse capabilities with respect to their
functional backgrounds made more innovative, higher-quality decisions than teams with
less diverse capabilities. They concluded that cognitive diversity can be a valuable resource
in the decision making process.
Schweiger and Sandberg (1989) conclude that in order to effectively utilise a team’s
capabilities, the member’s diversified skills and perspectives must be identified and built
into each decision in the most appropriate manner. For example, research effort on how to
build conflict into strategic decision making has focussed on techniques such as devil’s
advocacy and dialectical inquiry, which encourage critical and investigative interaction
designed to produce a single decision from a variety of diverse perspectives (Schweiger et
al., 1986). The primary purpose of these structured problem-solving techniques is to
generate discussions stemming from different and opposing positions (Bantel and Jackson,
1989) so as to produce a synthesis that is qualitatively superior to either of the initial
positions themselves (Churchman, 1971). By contrast, too little task (cognitive) conflict
can lead to inactivity because a sense of urgency is lacking (Van de Vliert and De Dreu,
H. Higashide, S. Birley / Journal of Business Venturing 17 (2002) 59–81
63
1994). However, moderate levels of cognitive conflict are constructive, since they
stimulate discussion of ideas that help groups perform better (Jehn, 1995). In short,
cognitive conflict contributes to decision quality because the synthesis that emerges from
contesting diverse perspectives is generally superior to that from the individual perspectives (Schweiger and Sandberg, 1989; Jehn, 1995). Moreover, it has been shown that
conflict with respect to the inter-personal dimensions of organisational life is associated
with productivity and satisfaction in groups (Gladstein, 1984; Wall and Nolan, 1986).
From this, it can be inferred that there is likely to be a positive relationship between the
results of decisions—corporate performance—and cognitive conflict.
It is likely, however, that there is an optimal level of cognitive conflict beyond or
below which group performance diminishes (Boulding, 1963; Pondy, 1967). For example,
Gersick (1989) found that groups with extreme amounts of continuing discussion and lack
of consensus were unable to move into the next stage of productive work. When
disagreement as cognitive conflict is perceived as personal criticism, it is argued that
such interpretation can turn cognitive disagreement into a full-scale emotional conflict
(Brehmer, 1976). As a result, cognitive conflict and affective conflict often emerge and
exist together (Amason, 1996). So, for example, it is likely that the criticism and debate
necessary for cognitive conflict could be interpreted as political gamesmanship. In such
circumstances, members focus on reducing threats, increasing power, and attempting to
build cohesion rather than working on task-related issues (Jehn, 1995). When one team
tries to gain influence at the expense of another (Eisenhardt and Bourgeois, 1988), the
resulting incredulity triggers personal affective conflict, which could undermine consensus
and jeopardise decision quality (Amason, 1996), and which decreases goodwill and
mutual understanding (Deutsch, 1969). Consequently, members are less receptive and
less capable of gathering, integrating, and adequately assessing valuable information from
other group members (Jehn, 1995; Pelled, 1995). Further, when group members have
interpersonal problems and are angry with one another, feel friction with each other, or
experience personality clashes, they tend to work less effectively and produce sub-optimal
products (Argyris, 1962). A person who is angry or antagonistic simply loses perspective
about the task being performed (Kelley, 1979). The threat and anxiety associated with
this type of relationship conflict also inhibits people’s cognitive functioning in processing
complex information (Staw et al., 1981; Roseman et al., 1994). It follows from the above
argument that there is likely to be a negative relationship between affective conflict
and performance.
Improving performance by processing more information through creating more diverse
viewpoints comes at the expense of group satisfaction and acceptance of the decision
(Eisenhardt and Zbaracki, 1992). Thus, group members may engage in cognitive conflict,
while potentially triggering affective conflict (Amason, 1996). Since this mutation process
can go unnoticed (Deutsch, 1969; Brehmer, 1976), it seems that the cognitive conflict
produces quality decisions but also lowers consensus and affective acceptance (Amason,
1996). This argument is reinforced through research focusing on the impact of structured
conflict-inducing techniques (Schweiger et al., 1986; Schwenk, 1990). From this, it
follows that there is likely to be an interaction between cognitive and affective conflict
and performance.
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Returning to the focus of our study, this leads to the following hypotheses set at the interorganisational level of analysis:
H1. The cognitive conflict level between the VC and the entrepreneur or EP is
positively associated with the venture’s performance, when the affective conflict
level is controlled.
H2. The affective conflict level between the VC and the entrepreneur is negatively
associated with the venture’s performance, when the cognitive conflict level
is controlled.
3.2. Goal or policy conflict
In a rational-comprehensive approach to decision-making, decision-makers gather appropriate information, define organisational goals, and select the optimal route from a
comprehensive list of policy alternatives (Bourgeois, 1980; Eisenhardt and Zbaracki,
1992). However, whether decision-makers are rational or boundedly rational is no longer
particularly controversial since empirical studies have shown that there exist cognitive limits
to the rational model; that many decision phases frequently repeat and often go deeper; and
that the complexity of the problem and the conflict among decision makers often influences
the shape of the decision path (Eisenhardt and Zbaracki, 1992). This is certainly more likely
to be the case in our context as the new venture begins to trade and as circumstances
inevitably change. Indeed, the most prevalent argument is that more complex or turbulent
environments require less rationality (Fredrickson, 1984; Miller, 1987). Thus, we are drawn
to the incremental (or adaptive) view of policy-making which posits that the cognitive limits
to human rationality make a more sequential and incremental approach to strategy-making
not only more realistic but also preferable. Here, goals are not necessarily either established or
agreed upon prior to the consideration of alternatives; rather, goals and policies interact and
adjust in the light of what is currently feasible and politically acceptable (Bourgeois, 1980).
However, such situations are likely to result in changes in either goals or policies upon which
the VC and the entrepreneur may not agree. As a result, conflict may arise and performance
may decline. Indeed, Fredrickson and Iaquint (1989) demonstrated this predicted negative
relationship between rationality and firm performance in an unstable environment and the
predicted positive performance in a stable environment. They also demonstrated the
strength and stability of this relationship over time. By contrast, other research has failed
consistently to demonstrate whether a positive or a negative relationship exists between
consensus either on goals, policies, or both and organisational performance. For example,
Grinyer and Norburn (1977–1978) found consensus on goals for the highest-performing
firms to be negatively related to performance. Bourgeois (1980) showed that consensus on
both ends and means did not yield the highest performance, and instead the highest
performance group had consensus on means but not ends. Dess (1987) found that
consensus on either goals or policies (but not both) to be positively related to organisational performance.
H. Higashide, S. Birley / Journal of Business Venturing 17 (2002) 59–81
65
Since we have assumed that VCs take an active, though non-executive, role in their
investee company, it is reasonable to assume that they will be involved in both means and
ends—policies and goals. Therefore, this research posits that conflict on inter-organisational
goals and on competitive policies are of equal importance (Dess, 1987).
H3. Cognitive conflict on both goals and policies is necessary to explain the expected
positive association between cognitive conflict and venture performance.
H4. Affective conflict on both goals and policies is necessary to explain the expected
negative association between affective conflict and venture performance.
A number of researchers have suggested that the type of task a group performs influences
the relationship between conflict and performance (Brehmer, 1976; Van de Ven and Ferry,
1980). Therefore, it is not surprising that whether or not cognitive conflict is beneficial may
well depend on the type of task the group performs (Jehn, 1995). While routine tasks involve
a low amount of variety in methods and repetitiveness of task process (Hall, 1972), nonroutine tasks require problem solving, have few set procedures, and have a high degree of
uncertainty (Van de Ven et al., 1976). As Brown (1983) noted, even though cognitive conflict
has a positive effect, too much conflict can produce low-quality outcomes for non-routine
tasks. Thus, the amount of disagreement and variety in a group needs to match the level of
variety in the task for the group to be effective (Jehn, 1995).
In any dyad, the decision-making on one party’s specialised field is likely to be more
routine and may involve relatively less debate for the party possessing the higher ability or
expertise. For example, in the VC–EP relationship, VCs are usually unwilling to be involved
in the day-to-day operation matters but regard financial management as one of their most
important roles (Gladstone, 1988; MacMillan et al., 1988). On the other hand, the decisions
about the strategic choice for the venture may include a great deal of debate. Moreover, as the
decision-making becomes more routinised to the one party, the task interdependency
decreases, and eventually the affective conflict may decrease. It is clear from this that the
sub-dimensions of conflict both in policy areas and goals may have different impacts on the
venture’s performance. Thus, in addition to the investigation of the main hypothesis, these
effects are also explored in this study.
4. Methodology
4.1. Research focus
The focus of this research is the population of relatively young investments made by UK
VCs. Our ideal research design would have been to explore conflict in specific dyad
relationships between a VC and an investee team. However, at a very early stage during
the pilot study, it became clear that this would be impossible to achieve. Quite simply, those
VCs willing to participate by completing a questionnaire, or by being interviewed, were not
willing to be identified with a specific client, nor were they willing to make an introduction,
although they were willing to discuss a particular client relationship without identifying the
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client name. Moreover, the minority who were happy to discuss the relationship with a
particular client were not prepared to reveal performance measures once the client had been
identified to the researchers. This reluctance to engage with researchers and to reveal details
of specific relationships is not unusual in Europe. Other researchers have also found the
venture capital community’s negative attitude towards surveys (Muzyka et al., 1996).
Therefore, we decided to explore the hypothesised relationships from the perspective of
the VC. Thus, this study examines the VCs perception of conflict in the relationship and
relates it to their perception of performance. This approach is consistent with that used by
Spinelli and Birley (1998) in their study of conflict in the franchise system.
4.2. The unit of analysis
The unit of analysis is the relationship between the VC and the EP. Although a VC
must have a basic style for the management of investee firms, we have assumed that they
adjust their post-investment involvement style and activities from investment to investment
in accordance both with the perceived agency and business risks, and with the possible
synergetic impacts which the VC thinks can bring benefits for the investment performance. For example, practitioners frequently refer to the importance of flexibility in the
VC’s managing the relationship with the investee firm/management team, especially in the
post-investment phase (Gladstone, 1988). Thus, it can also be assumed that the levels of
conflict between the VC and the EP team should vary from investment to investment.
This is consistent with the approach of MacMillan et al. (1988), Sapienza (1992) and
Sapienza et al. (1996). Further, the instruments adopted in this study to measure the level
of the cognitive and affective conflict have been developed in the intra-organisational
context such as for top management teams (Amason, 1996) and work groups in a large
firm (Jehn, 1995). In this respect, the relationship between the VC and the EP team was
deemed appropriate in order to apply these instruments to the inter-organisational context
in this study.
4.3. Research instrument
It was decided to adopt a survey methodology since there already existed appropriate and
pre-tested instruments for both performance and conflict measures.
4.3.1. Dependent variable
The performance measurement of the venture is taken from Sapienza’s (1992) survey of
the US VC–entrepreneur dyads and Sapienza et al. (1996) survey of UK VCs. It was slightly
modified as a result of the pilot study. It comprises five financial criteria (sales growth rate,
market share, cash generation/consumption, return on investment, value of the company), and
five non-financial criteria (new product/process development, market development, operating
efficiency, personnel development, harvest/exit readiness). Respondents were asked both to
indicate the relative importance of the criteria within the two groups by distributing 100
points, and their satisfaction with the performance on each criterion on a 5-point Likert type
scale. They were then asked to weight the importance of financial versus non-financial
H. Higashide, S. Birley / Journal of Business Venturing 17 (2002) 59–81
67
criteria. The overall weighted average result and a separate overall performance satisfaction
score were averaged and used as the performance measure.
As we have already noted, VCs are very reluctant to provide sensitive specific
financial data of their investee firms. Chandler and Hanks (1993) showed that the above
performance instrument, originally developed by Gupta and Govindarajan (1984) using
subjective measures, has a high disclosure rate, strong internal consistency, and relatively
strong inter-rater reliability. Thus, the respondents’ satisfaction with the performance of
the company is used to serve as a proxy for success (Anderson and Narus, 1990).
Anderson and Narus (1984, p. 66) defined satisfaction as ‘‘a positive affective state
resulting from the appraisal of all aspects of a firm’s working relationship with another
firm.’’ Importantly for this study, Sapienza’s (1992) study demonstrated that there
was no significant difference between the mean performance scores of entrepreneurs
and VCs.
4.3.2. Independent variables
4.3.2.1. Goal conflict. Nine items out of the 12-item instrument used by Bourgeois (1980)
to measure goal consensus were modified to be applied in the context of the VC–EP team
relationship. These are the items that are not italicized in Table 4. In addition, four items
drawn from the literature which deals with relationships between the VCs and the
entrepreneur were added to reflect the possible conflict areas in the relationship (see the
italicized items in Table 4).
4.3.2.2. Policy conflict. The items used in past research (Bourgeois, 1980; Eisenhardt and
Bourgeois, 1988) to measure policy conflict focus upon operational decision making areas
and were inappropriate for this study. However, MacMillan et al. (1988) developed 20 items
to measure the VC’s level of involvement in a venture. This measure has been extensively
utilised in the venture capital literature and adopted in surveys both of the entrepreneur and
the VC in the US (Rosenstein et al., 1993; Ehrlich et al., 1994), and of the entrepreneur in
the UK (Harrison and Mason, 1992). On the basis of the Harrison and Mason (1992)
instrument and our pilot study, the items were modified with reference to Barney et al.
(1996) and Sapienza (1992) so as to reflect this study’s focus on the post-investment period
(see Table 6).
4.3.2.3. Cognitive and affective conflict. Jehn’s (1995) instrument to measure intra-group
conflict is based upon a scale consisting of eight items developed by Rahim (1983). This was
later modified and reduced to seven items by Amason (1996) which, when factor analysed,
showed a two-factor solution indicating a clear distinction between cognitive and affective
conflict. Accordingly, for this research the highest loading items were chosen from Amason’s
(1996) study, giving the following questions:
How many disagreements have there been? — Cognitive conflict.
How much personal friction has there been? — Affective conflict
How many personality clashes have there been? — Affective conflict.
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Table 1
Response rates
Mailouts
Usable returns
Non-eligible
MBO/MBI only
No investment in 1994/95
No investment as lead investor
Not the VC firm/No longer the VC firm
Others
Non-usable
Non-response
Effective response rate
N
%
174
80
100
46
15
6
2
15
2
5
49
9
3
1
9
1
3
28
60
However, during the pilot phase, VCs indicated that they found it difficult to distinguish
between the two affective conflict statements and that, in fact, affective conflict rarely
develops to personality clashes. Therefore, this question was dropped from the main survey.
4.3.3. Control/explanatory variables
The criteria that the VC uses in assessing a business plan is a useful source of
determining the key explanatory variables. Thus, the seven items used to measure
business risk were taken mainly from Muzyka et al. (1996) whose items were used in
the UK (and other European countries), but with reference to MacMillan et al. (1988). In
order to measure the EP’s management competencies, six items based on Muzyka et al.
(1996) and MacMillan et al. (1985) were chosen. The VC’s perceived effectiveness was
measured by first asking respondents to indicate whether they had participated in each of
the roles listed in Table 6 during the post-investment period. They were then asked to
score both the importance and the effectiveness of their involvement in each of the areas
on five-point Likert type scale. The products of importance and effectiveness for each
item are summated.
It was expected that the perceived venture performance would improve as business risk
becomes lower, the EP management competencies higher, and the VC effectiveness higher.
4.3.4. Data collection
After initial screening by telephone, pre-tested questionnaires were sent to 174 UK VCs
between January and March, 1997. They were identified mainly from two sources: the British
Venture Capital Association 1996/97 Directory (BVCA, 1996), and The Venture Capital
Report: Guide to Venture Capital in the UK and Europe (Venture Capital Report, 1996). Two
follow-up letters were sent to increase response rates (Dillman, 1978) approximately 3 weeks
after the initial mailing and approximately 4 weeks after the first follow-up. Eighty VCs
returned usable questionnaires giving an effective response rate of 60% (see Table 1).
However, in the regression analyses, the smaller set of 57 or 58 questionnaires (depending on
the model) is analysed mainly as a result of missing values.
H. Higashide, S. Birley / Journal of Business Venturing 17 (2002) 59–81
69
Table 2
Means, standard deviations, and Pearson product moment correlations
Listwise deletion of missing value for the correlations (n = 57).
Variable
Dependent
variable
1. Venture
performance
Control
variables
2. EP team
competencies
3. Business risk
4. VC effectiveness
Conflict
variables
Goal conflict:
5. Affective
6. Cognitive
Policy conflict:
7. Affective
8. Cognitive
Mean
S.D.
1
2
3.45
0.84
3.25
0.59
3.30
1.83
0.47
0.78
1.43
1.71
0.52
0.59
0.17
0.06
0.20
0.11
1.40
1.61
0.56
0.56
0.22
0.11
0.27
0.18
3
4
5
0.02
0.05
0.39**
0.40**
0.74**
0.03
0.01
0.38**
0.53**
0.82**
0.78**
6
7
0.57**
0.68**
0.85**
0.68**
0.53** 0.33*
0.24 0.10 0.11
* Correlation is significant at the 0.05 level (2-tailed).
** Correlation is significant at the 0.01 level (2-tailed).
The VC was asked to choose a particular investment in which they participated as the lead
investor during 1994/1995 but which was not a management buyout or buyin. This sampling
procedure was used to avoid including only high-performing ventures,1 to allow greater recall
possibilities for the respondents, and to gather data on an investment in which there was some
opportunity for post-investment performance assessment.
5. Results
A multiple regression analysis hierarchical procedure was used in accordance with the
suggestion by Cohen and Cohen (1983). Two models were developed for each set of
hypothesised relationships. The first model (control model) explains the dependent variable
relationship to the control variables and the second (full) model includes both cognitive and
affective variables.
1
A management buyout is the term used for an existing business that is bought by the current management.
The performance of these investments is, generally, better than that of other venture capital investments.
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H. Higashide, S. Birley / Journal of Business Venturing 17 (2002) 59–81
Table 3
Hierarchical regression analysisa
Goal conflict
Control variables
EP ability
Business risk
VC effectiveness
R2
F
Main effect
Goal affective
Goal cognitive
Policy affective
Policy cognitive
R2
DR2
F
No. of cases
Policy conflict
Both
Control
Full
Control
Full
Control
Full
0.560***
0.336***
0.145y
0.607
27.78***
0.554***
0.320***
0.187**
0.563***
0.325***
0.146
0.591
25.48***
0.550***
0.310**
0.218*
0.563***
0.325**
0.146
0.591
25.48***
0.554***
0.293**
0.229*
0.223y
0.319*
58
0.650
0.044
19.35***
58
57
0.239
0.303
0.611
0.020
16.01***
57
57
0.276
0.326*
0.068
0.157
0.651
0.060
13.05***
57
a
Standardised betas are reported in all tables.
y
p < 0.10.
* p < 0.05.
** p < 0.01.
*** p < 0.001.
Clearly, multicollinearity may have a harmful effect in the regression equations in the
models. However, as Berry and Feldman (1985) note, when it exists and there is no
possibility of gathering additional data, the most reasonable course is to recognise its
presence and live with the consequences. Table 2 shows the descriptive statistics and
correlations for the variables in the study2 and, as expected, affective and cognitive conflict
show high correlations since both are expected to happen simultaneously. However, more
detailed assessment tools such as variance inflation factor (VIF), which are provided in SPSS
package, do not show unacceptable multicollinearity problems among variables in the further
analysis. Moreover, the presence of multicollinearity should not make the hypothesis tests
any less conservative (Berry and Feldman, 1985). Therefore, if the parameter estimates for
cognitive and affective conflict are significant, the hypothesis will be supported despite any
multicollinearity that may be present (Amason, 1996).
Hypotheses 1 and 2 state that cognitive conflict is associated with the venture performance
positively, while affective conflict is associated negatively. As illustrated in Table 3, all the
coefficients of affective conflict show negative values, while all of those of cognitive conflict
2
The items in each construct are independent so that a high alpha was not expected. However, all the
coefficient alphas of the constructs in the model are above the 0.70 level recommended by Nunnally (1967),
except the alpha of the business risk construct (0.64), which was deemed acceptable for further analysis.
H. Higashide, S. Birley / Journal of Business Venturing 17 (2002) 59–81
71
Table 4
Goal cognitive conflicta
Items
Factor 1, short Factor 2, long
Factor 3, product Factor 4, control
term orientation term orientation /innovation
/incentives
Communality
Long term
0.40904
profitability
Profit next year
0.81825
Sales growth rate
0.77084
Market share
0.27078
Exit/harvest timing
0.14121
and method
CEO/team rewards
0.22934
CEO/team
0.27810
decision authority
CEO/team
0.03948
personal develop. . .
Cash flow
0.72680
New product
0.21100
development
Innovation/R&D
0.12821
Market penetration
0.07068
Cost efficiency
0.47170
Eigenvalue
5.18890
Cum. %
39.9
0.69070
0.00724
0.10795
0.65609
0.38121
0.18310
0.67024
0.75918
0.08351
0.21558
0.21741
0.10257
0.17996
0.19614
0.02524
0.18093
0.85421
0.71267
0.57044
0.63955
0.34160
0.03546
0.07962
0.09284
0.70860
0.79918
0.67774
0.72590
0.02015
0.18925
0.81224
0.69751
0.09721
0.14072
0.29704
0.84449
0.25582
0.20301
0.69137
0.81870
0.02760
0.70242
0.21980
1.59468
52.2
0.85197
0.48724
0.64510
1.44146
63.3
0.17627
0.02826
0.00744
1.01689
71.1
0.77412
0.73658
0.68703
Items with factor loadings greater than 0.5 appear in italic.
a
Some items in the table are modified for presentation purposes, and thus not exactly the same as in the
actual survey.
are positive. However, some of the coefficients, especially of policy conflict, are not
significant (e.g. in the separate policy conflict model: policy affective, p = 0.175; policy
cognitive, p = 0.109). In addition, in the separate goal conflict model, the coefficient of goal
affective conflict is marginally significant ( p = 0.083), although that of goal cognitive
conflict shows fairly strong significance ( p = 0.014). Thus, roughly speaking the findings
support the directions of both hypotheses, although the impact of goal conflict (affective and
cognitive) on the venture performance appears to be stronger than that of policy conflict.
With reference to hypotheses 3 and 4, both of which are concerned with the interactive
effect of goal and policy conflict, the extreme right column of Table 3 shows that only the
goal cognitive conflict is significant ( p = 0.023). Although the coefficient of goal affective
conflict falls short of significance ( p = 0.139), the change in significant level from that in the
separate goal conflict model ( p = 0.083) is not large. Interestingly, however, the changes in
significant level of policy conflict (both affective and cognitive) between the separate and the
combined model are relatively larger than those in goal conflict (affective, from p = 0.175 to
p = 0.733: cognitive, from p = 0.109 to p = 0.428). Naturally, the coefficients of policy
conflict become smaller, although the signs of the coefficients are still the same as those in
the separate model. On the other hand, the coefficients of goal conflict stay almost at the
same level as in the separate model. These findings seem to show that, both in cognitive and
72
H. Higashide, S. Birley / Journal of Business Venturing 17 (2002) 59–81
Table 5
Goal affective conflict
Items
Factor 1, profitability
orientation
Long term profitability
Profit next year
Sales growth rate
Market share
Exit/harvest timing/method
CEO/team rewards
CEO/team decision authority
CEO/team personal develop
Cash flow
New product development
Innovation/R&D
Market penetration
Cost efficiency
Eigenvalue
Cum. %
0.76860
0.64558
0.62827
0.78423
0.71152
0.33181
0.19167
0.01030
0.45671
0.14552
0.07809
0.67843
0.31501
5.38117
41.4
Factor 2, product
/innovation
0.05179
0.36434
0.56050
0.04470
0.06853
0.10450
0.22919
0.07820
0.50152
0.87684
0.84595
0.17562
0.67474
1.65348
54.1
Factor 3, control
/incentives
0.16714
0.30919
0.17013
0.07470
0.20111
0.79383
0.83703
0.83164
0.29812
0.15452
0.05131
0.02210
0.12426
1.53135
65.9
Communality
0.62137
0.64512
0.73782
0.62260
0.55141
0.75118
0.78988
0.69784
0.54898
0.81390
0.72435
0.49160
0.56994
Items with factor loadings greater than 0.5 appear in italic.
affective conflicts, goal conflict works independently from policy conflict. By contrast, the
goal conflicts are possibly a necessary condition for policy conflict to work. Thus,
hypotheses 3 and 4, which expect inter-dependence between goal and policy conflict, are
partly and very weakly supported by these findings.
Impressive figures in Table 3 are the coefficients and the significance level of the control
(explanatory) variables. As expected, all the coefficients of both the entrepreneur’s ability and
the business risk are significant, and are positively and negatively associated, respectively,
with the VCs’ description of the venture performance. However, contrary to expectation, the
coefficients of the VC effectiveness in the full models are significantly but negatively
associated with the venture performance; in the control models, the coefficients are marginally associated (goal conflict model, p = 0.097; policy conflict model, p = 0.106).
As demonstrated by the R2 values in Table 3, all the regression models do fairly good jobs
in comparison with MacMillan et al.’s (1988) and Sapienza’s (1992) studies, both of which
are concerned with the VC value-added, include performance measures, and run regression
analyses. For example, in MacMillan et al.’s (1988) study, the four identified factors of the
VC involvement did not show any significant correlations with the performance variables
used in their study. Sapienza’s (1992) study proposed two models explaining the venture
performance. One includes six independent variables and results in an R2 of 0.46; the other
with 10 independent variables yields an R2 of 0.51. Further, Sapienza et al.’s (1996) study
conducted in the European VC–EP team context, which investigates the impact of 10
independent variables and a control variable on the VC involvement effectiveness, yields an
R2 of 0.209. However, it is the control variables that dominate any explanation of the
variation in venture performance. For instance, goal conflict and policy conflict contribute to
increasing the R2 by just 0.044 and 0.020, respectively.
H. Higashide, S. Birley / Journal of Business Venturing 17 (2002) 59–81
73
Table 6
Policy cognitive conflict
Items
Factor 1,
strategic advice
Financial advice
0.71999
Management advice
0.71341
Marketing plan
0.72123
Advice on private matters
0.09133
Advice as mentor/coach
0.31778
Business strategy adjustment 0.74539
Recruitment assistance
0.20045
Professional contacts
0.16913
Debt/equity arrangements
0.62992
Advice on short-term crises
0.61140
Industry competition advice
0.46829
Industrial contact assistance
0.02968
Eigenvalue
4.83169
Cum. %
40.3
Factor 2,
networking help
0.00537
0.04090
0.15755
0.75752
0.50619
0.15969
0.88014
0.44610
0.36942
0.39864
0.08592
0.06796
1.42218
52.1
Factor 3, inter-personal
/personnel help
0.33438
0.23962
0.11315
0.34827
0.51895
0.02374
0.00585
0.56109
0.06167
0.15470
0.53695
0.82571
1.06715
61.0
Communality
0.63022
0.56804
0.55779
0.70348
0.62652
0.58168
0.81485
0.54243
0.53708
0.55665
0.51499
0.68729
Items with factor loadings greater than 0.5 appear in italic.
The second part of the analysis involved exploring the presence of any sub-dimensions to
the main constructs used in the analysis. This would provide the opportunity to expand and
possibly strengthen the regression analyses. Factor analysis is extensively used to capture
sub-dimensions of constructs. Of several major alternatives, principal component analysis
(PCA) was chosen for this purpose. In order to decide the number of factors to be
extracted, the criteria of the factors to be extracted, the criteria of the factors having
eigenvalues greater than 1 was used (Hair et al., 1995). In addition, in order to find
Table 7
Policy affective conflict
Items
Factor 1,
strategic advice
Financial advice
0.80807
Management advice
0.79738
Marketing plan
0.53200
Advice on private matters
0.22185
Advice as mentor/coach
0.29841
Business strategy adjustment 0.81280
Recruitment assistance
0.11679
Professional contact
0.11190
Debt/equity arrangements
0.60993
Advice on short-term crises
0.76070
Industry competition advice
0.33086
Industrial contact assistance
0.08657
Eigenvalue
5.75997
Cum. %
48.0
Factor 2,
networking help
0.25030
0.28432
0.63969
0.10741
0.67085
0.16781
0.13542
0.59347
0.14151
0.14857
0.69462
0.72166
1.44784
60.1
Items with factor loadings greater than 0.5 appear in italic.
Factor 3, inter-personal
/personnel help
0.06873
0.01220
0.07689
0.82488
0.51551
0.22085
0.86364
0.53459
0.41163
0.44835
0.05363
0.09911
1.13561
69.5
Communality
0.72035
0.71680
0.69814
0.74117
0.80484
0.73758
0.77785
0.65051
0.56148
0.80175
0.59484
0.53811
74
H. Higashide, S. Birley / Journal of Business Venturing 17 (2002) 59–81
Table 8
Hierarchical regression analysis — goal cognitive conflict sub-dimensionsa
Control variables
EP ability
Business risk
VC effectives
R2
F
Control
Full (1)
Full (2)
Full (3)
Full (4)
Combined
0.560***
0.336***
0.145y
0.607
27.78***
0.518***
0.349***
0.145
0.529***
0.330***
0.134
0.573***
0.303***
0.169*
0.589***
0.307***
0.258**
0.537***
0.303**
0.231*
Main effects
1. Short-term
Affective
Cognitive
2. Long-term
Affective
Cognitive
3. Product/Innovation
Affective
Cognitive
4. Control/Incentives
Affective
Cognitive
R2
DR2
F
No. of cases
58
0.285*
0.210
0.237
0.001
0.240y
0.250*
0.052
0.057
0.200y
0.324**
0.637
0.030
18.22***
58
0.640
0.033
18.49***
58
0.661
0.054
20.29***
58
0.027
0.230
0.146
0.364*
0.655
0.049
19.77***
58
0.093
0.148
0.718
0.111
10.63***
58
Items with factor loadings greater than 0.5 appear in italic.
a
All the affective conflict sub-dimensions are for controlling purpose.
y
p < 0.10.
* p < 0.05.
** p < 0.01.
*** p < 0.001.
simpler and more easily interpretable components, varimax rotation method was used for
all the constructs for the regression analysis (Hair et al., 1995). Factor loadings where
the value was above the 0.5 level are used for interpretation, together with
communality values.
Four sub-dimensions were found for cognitive goal conflict, and were labelled short-term
orientation, long-term orientation, product/innovation, and control/incentives (see Table 4).
However, for the affective goal conflict, items concerned with the short-term and long-term
orientation are categorised into a single factor which was labelled the profitability orientation
dimension (see Table 5).
Table 6 shows three clearly distinct factors for policy conflict, which we have labelled
as strategic advice (factor 1), networking help (factor 2), and inter-personal/personnel
help (factor 3). These results are remarkably consistent with the results for cognitive
conflict (Table 7), with one exception. ‘‘Discussing marketing plans’’ loads on both
H. Higashide, S. Birley / Journal of Business Venturing 17 (2002) 59–81
75
Table 9
Hierarchical regression analysis — goal affective conflict sub-dimensions
Control variables
EP ability
Business risk
VC effective
R2
F
Main effects
1. Profit
Affective
Cognitive
2. Product/Innovation
Affective
Cognitive
3. Control/Incentives
Affective
Cognitive
R2
DR2
F
No. of cases
Control
Full (1)
Full (2)
Full (3)
Combined
0.560***
0.336***
0.145y
0.607
27.78***
0.530***
0.341***
0.145
0.572***
0.296**
0.162y
0.589***
0.307***
0.258**
0.608***
0.272**
0.244**
0.227y
0.211y
0.091
0.013
0.277y
0.354*
0.321**
0.377**
58
0.632
0.025
17.85***
58
0.674
0.068
21.54***
58
0.146
0.364*
0.655
0.049
19.77***
58
0.095
0.138
0.656
0.104
13.01***
58
Items with factor loadings greater than 0.5 appear in italic.
y
p < 0.10.
* p < 0.05.
** p < 0.01.
*** p < 0.001.
strategic advice and networking help. Since this is one other source of advice about
external issues, we decided to interpret it as the same ‘‘networking help’’ effect as factor
3 in Table 6.
In order to examine the impact of these sub-dimensions of conflict, separate regression
analyses were conducted. Then the sub-dimensions of each goal or policy conflict were put
into the single model in order to explore the interactive effect among the sub-dimensions of
conflict. Since omitted variables could have introduced bias, the results of these analyses
should be interpreted with caution (Schul et al., 1983). However, certain insights can be
gained by analysing the individual effects of these sub-dimensions. In the analysis, it was
necessary to control the affective conflict level when examining the impact of cognitive
conflict level, and vice versa. For this purpose, and on the basis of the factor loading
scores, a summated scale corresponding to each factor was constructed. All the alpha
values in the sub-dimensions of goal and policy conflict are well above the 0.7 level of
Nunnally’s (1967) criteria.
In general, and consistent with the main regression model, the sub-dimensions are interrelated. Moreover, those of goal conflict show much stronger support for the impact of
conflict on the performance than those of policy conflict, which seem to have very
76
H. Higashide, S. Birley / Journal of Business Venturing 17 (2002) 59–81
Table 10
Hierarchical regression analysis — policy cognitive conflict sub-dimensionsa
Control Variables
EP ability
Business risk
VC effective
R2
F
Main effect
1. Strategic
Affective
Cognitive
2. Network
Affective
Cognitive
3. Personal(nel)
Affective
Cognitive
R2
DR2
F
No. of cases
Control
Full (1)
Full (2)
Full (3)
Combined
0.563***
0.325***
0.146
0.591
25.48***
0.551***
0.304**
0.209*
0.532***
0.344***
0.153
0.580***
0.328**
0.164y
0.541***
0.323**
0.202y
0.199
0.265y
0.248
0.350*
0.413y
0.259
0.344
0.313
57
0.613
0.023
16.19***
57
0.625
0.019
17.37***
58
0.138
0.043
0.601
0.010
15.35***
57
0.347y
0.211
0.664
0.074
10.33***
57
Items with factor loadings greater than 0.5 appear in italic.
a
Control model for network conflicts is the same as the cognitive conflict.
y
p < 0.10.
* p < 0.05.
** p < 0.01.
*** p < 0.001.
marginal impacts. For goal cognitive conflict, three out of four sub-dimensions are
significantly and positively associated with the venture performance (see Table 8).
Short-term orientation ( p = 0.138) is the exception. However, if the four sub-dimensionalised goal conflicts are used in the same equation, the coefficient of product/innovation
marginally falls short of significance ( p = 0.127), while the other two significant
coefficients in the separate models lose their significance in the combination model.
Almost the same results were obtained in the analysis of goal affective conflict (see
Table 9), implying that the sub-dimension of product/innovation can be beneficial and/or
harmful for the venture performance, and works strongly and independently, while other
goal cognitive sub-dimensions are inter-dependent. In addition, only the cognitive coefficient of control/incentives of the EP team sub-dimension are of significance. That is, it is
not likely that the discussion about this area is seen as harmful to the venture performance.
However, it should be noted that this dimension also shows inter-dependence among the
sub-dimensions.
Among the policy conflict areas, the strategic advice factors in the separate and
combined models tend to show marginally significant associations with the perceived
H. Higashide, S. Birley / Journal of Business Venturing 17 (2002) 59–81
77
Table 11
Hierarchical regression analysis—policy affective conflict sub-dimensions
Control variables
EP ability
Business risk
VC effective
R2
F2
Main effect
1. Strategic
Affective
Cognitive
2. Network
Affective
Cognitive
3. Personal(nel)
Affective
Cognitive
R2
DR2
F
No. of cases
Control
Full (1)
Full (2)
Full (3)
Combined
0.563***
0.325***
0.146
0.591
5.48***
0.540***
0.307**
0.198y
0.547***
0.313**
0.195y
0.580***
0.328**
0.164y
0.522***
0.306**
0.198y
0.342y
0.280
0.242
0.275y
0.275
0.316
57
0.615
0.024
16.29***
57
0.606
0.016
15.71***
57
0.187
0.192
0.138
0.043
0.601
0.010
15.35***
57
0.357y
0.222
0.648
0.057
9.61***
57
Items with factor loadings greater than 0.5 appear in italic.
y
p < 0.10.
** p < 0.01.
*** p < 0.001.
venture performance both in beneficial and non-beneficial directions (see Tables 10 and
11). Interestingly, cognitive (affective) conflict in the personal/personnel factor has the
negative (positive) coefficient, although it is not significant. Contrary to expectation, these
signs of the coefficients are the only exception throughout the regression analysis using the
conflict variable.
6. Discussion
There is a growing literature that examines the question of whether, and when, VCs add
value through their involvement in the business in the post-investment period (Sapienza,1992).
Interestingly, the results are controversial, examined both from the VC’s point of view
(MacMillan et al., 1988) and that of the EP (Timmons and Bygrave, 1986: Rosenstein et al.,
1993), a controversy that is reinforced in the research stream that analyses initial public
offering data (Brophy, 1988; Cherin and Hegert, 1988; Barry et al., 1990). An explanation for
the difference in perception between the VC and the EP is the clear information
asymmetry that arises, resulting in one party making choices that are not known or fully
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H. Higashide, S. Birley / Journal of Business Venturing 17 (2002) 59–81
understood by the other (Sahlman, 1990; Barry, 1994). As a consequence, disagreement
and conflict may arise.
The purpose of this paper was to add to the venture capital literature on the post-investment
relationship between the VC and EP by exploring the nature and extent of conflict and its
perceived impact on the venture performance. As Sapienza (1992) concludes ‘‘. . .the nature
and style of the VC–CEO interactions have a specific impact on the value of the venture
capitalist involvement’’ (p. 22).
As expected, it was found that conflict as disagreement can be beneficial for the venture
performance, although at the same time conflict as personal friction is negatively associated
with performance. Thus, the past research findings with respect to cognitive and affective
conflict are replicated in the VC–EP. Goal conflict has a greater impact on the venture
performance than policy conflict, and works independently of policy conflict. On the other
hand, goal conflict appears to be a necessary condition to make policy conflict work. In the
sub-dimensions of goal conflict, conflict about product/innovation have the strongest impact
on the venture performance both in beneficial and non-beneficial directions, and seem to work
independently of other sub-dimensions. With reference to the policy conflict sub-dimensions,
the strategic advice factor shows marginal positive association with the venture performance.
As found in new venture literature, the competence of the venture management team
has the greatest influence on the venture performance in each model, followed by the
business risk construct. That is, these constructs appear to explain most of the perceived
performance variation in the post-investment relationship between the VC and the
entrepreneur team in the UK.
Contrary to expectation, the effectiveness of the VC’s involvement is negatively associated
with the venture performance. This may imply that their involvement tends to start increasing
when they perceive the venture’s performance as unsatisfactory and where they feel that they
can make an effective contribution. Put simply, it may be possible that the perceived venture
performance is the cause for the VCs increasing their involvement and eventual perceived
effectiveness, rather than that their involvement should be reduced or discontinued! For
example, MacMillan et al. (1988) found that the VC’s activities such as searching for
candidates for the management team, formulating business strategy, and managing crisis and
problems were significantly, but negatively, associated with some of the venture performance
measures adopted in their study (sales, market share, profits and ROI). In fact, rates of
successful turnarounds from ‘‘living dead’’ situations range between 40% and 60%, depending on the size of the venture capital firms. Indeed, during the implementation of a turnaround
strategy about 30% of ventures experience a change of the management (Ruhnka et al.,
1992). Further, in our study, the exceptional unexpected signs of the personal/personnel subdimension in policy conflict may be attributed to whether or not the VC’s assistance in
recruitment, which in fact has the highest loadings in factor analysis both for cognitive and
affective conflict, has been required in the post-investment phase.
The results of this study have significant implications for the practitioner. They suggest
that in order for the VC to improve his/her satisfaction with the venture invested, it is
important to manage agency risks well both in the due diligence and deal negotiations, and in
the post-investment phase. However, getting the right entrepreneurial management team
upfront in the investment process seems to have been more crucial for the VC’s satisfaction
H. Higashide, S. Birley / Journal of Business Venturing 17 (2002) 59–81
79
with subsequent performance compared with managing the risk after the deal. Moreover, the
reduction of uncertainty and ambiguity, which stems from the constructive conflict in the
VC–EP team relationship, seems to have had limited impact on the eventual perceived
venture performance. Further, the results suggest that VCs should be careful not to interfere in
the goals and policies of their investee companies since any resultant disagreement could
wipe out any potential positive effects. To the entrepreneurs, we would say beware of VCs
who want to be involved in decision making since such involvement could be detrimental to
their perception of your performance! It follows from this that one fruitful research avenue for
the future is to explore how VCs can gain the benefits of conflict without the resultant costs
(Eisenhardt and Zbaracki, 1992).
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