The consequences of conflict between the venture

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: S0883 -9026 (00 )00057 -4

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 con-

structive 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) 59–8160

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

H. Higashide, S. Birley / Journal of Business Venturing 17 (2002) 59–81 61

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 partici-

pants. 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 high-

quality 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,


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 perspec-

tives (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.


Returning to the focus of our study, this leads to the following hypotheses set at the inter-

organisational 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 appro-

priate 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 organisa-

tional performance.


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), non-

routine 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


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 perfor-

mance. 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


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.


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.

Table 1

Response rates

N %

Mailouts 174 100

Usable returns 80 46

Non-eligible

MBO/MBI only 15 9

No investment in 1994/95 6 3

No investment as lead investor 2 1

Not the VC firm/No longer the VC firm 15 9

Others 2 1

Non-usable 5 3

Non-response 49 28

Effective response rate 60


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.

Table 2

Means, standard deviations, and Pearson product moment correlations

Listwise deletion of missing value for the correlations (n = 57).

Variable Mean S.D. 1 2 3 4 5 6 7

Dependent

variable

1. Venture

performance

3.45 0.84

Control

variables

2. EP team

competencies

3.25 0.59 0.68**

3. Business risk 3.30 0.47 0.53** 0.33*

4. VC effectiveness 1.83 0.78 �0.24 �0.10 �0.11

Conflict

variables

Goal conflict:

5. Affective 1.43 0.52 0.17 �0.20 �0.02 0.39**

6. Cognitive 1.71 0.59 0.06 �0.11 �0.05 0.40** 0.74**

Policy conflict:

7. Affective 1.40 0.56 �0.22 �0.27 �0.03 0.38** 0.82** 0.57**

8. Cognitive 1.61 0.56 �0.11 �0.18 0.01 0.53** 0.78** 0.68** 0.85**

* Correlation is significant at the 0.05 level (2-tailed).

** Correlation is significant at the 0.01 level (2-tailed).


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.

Table 3

Hierarchical regression analysisa

Goal conflict Policy conflict Both

Control Full Control Full Control Full

Control variables

EP ability 0.560*** 0.554*** 0.563*** 0.550*** 0.563*** 0.554***

Business risk �0.336*** �0.320*** �0.325*** �0.310** �0.325** �0.293**

VC effectiveness �0.145y �0.187** �0.146 �0.218* �0.146 �0.229*

R2 0.607 0.591 0.591

F 27.78*** 25.48*** 25.48***

Main effect

Goal affective �0.223y �0.276

Goal cognitive 0.319* 0.326*

Policy affective �0.239 �0.068

Policy cognitive 0.303 0.157

R2 0.650 0.611 0.651

DR2 0.044 0.020 0.060

F 19.35*** 16.01*** 13.05***

No. of cases 58 58 57 57 57 57a Standardised betas are reported in all tables.y p < 0.10.

* p < 0.05.

** p < 0.01.

*** p < 0.001.


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

Table 4

Goal cognitive conflicta

Items

Factor 1, short

term orientation

Factor 2, long

term orientation

Factor 3, product

/innovation

Factor 4, control

/incentives Communality

Long term

profitability

0.40904 0.69070 0.00724 0.10795 0.65609

Profit next year 0.81825 0.38121 0.08351 0.17996 0.85421

Sales growth rate 0.77084 0.18310 0.21558 0.19614 0.71267

Market share 0.27078 0.67024 0.21741 0.02524 0.57044

Exit/harvest timing

and method

0.14121 0.75918 �0.10257 0.18093 0.63955

CEO/team rewards 0.22934 0.34160 0.07962 0.70860 0.67774

CEO/team

decision authority

0.27810 0.03546 0.09284 0.79918 0.72590

CEO/team

personal develop. . .0.03948 0.02015 0.18925 0.81224 0.69751

Cash flow 0.72680 0.09721 0.29704 0.25582 0.69137

New product

development

0.21100 0.14072 0.84449 0.20301 0.81870

Innovation/R&D 0.12821 �0.02760 0.85197 0.17627 0.77412

Market penetration �0.07068 0.70242 0.48724 0.02826 0.73658

Cost efficiency 0.47170 0.21980 0.64510 0.00744 0.68703

Eigenvalue 5.18890 1.59468 1.44146 1.01689

Cum. % 39.9 52.2 63.3 71.1

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.


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 margin-

ally 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.

Table 5

Goal affective conflict

Items

Factor 1, profitability

orientation

Factor 2, product

/innovation

Factor 3, control

/incentives Communality

Long term profitability 0.76860 0.05179 0.16714 0.62137

Profit next year 0.64558 0.36434 0.30919 0.64512

Sales growth rate 0.62827 0.56050 0.17013 0.73782

Market share 0.78423 0.04470 0.07470 0.62260

Exit/harvest timing/method 0.71152 0.06853 0.20111 0.55141

CEO/team rewards 0.33181 0.10450 0.79383 0.75118

CEO/team decision authority 0.19167 0.22919 0.83703 0.78988

CEO/team personal develop �0.01030 0.07820 0.83164 0.69784

Cash flow 0.45671 0.50152 0.29812 0.54898

New product development 0.14552 0.87684 0.15452 0.81390

Innovation/R&D �0.07809 0.84595 0.05131 0.72435

Market penetration 0.67843 0.17562 �0.02210 0.49160

Cost efficiency 0.31501 0.67474 0.12426 0.56994

Eigenvalue 5.38117 1.65348 1.53135

Cum. % 41.4 54.1 65.9

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 6

Policy cognitive conflict

Items

Factor 1,

strategic advice

Factor 2,

networking help

Factor 3, inter-personal

/personnel help Communality

Financial advice 0.71999 0.00537 0.33438 0.63022

Management advice 0.71341 0.04090 0.23962 0.56804

Marketing plan 0.72123 0.15755 0.11315 0.55779

Advice on private matters 0.09133 0.75752 0.34827 0.70348

Advice as mentor/coach 0.31778 0.50619 0.51895 0.62652

Business strategy adjustment 0.74539 0.15969 0.02374 0.58168

Recruitment assistance 0.20045 0.88014 �0.00585 0.81485

Professional contacts 0.16913 0.44610 0.56109 0.54243

Debt/equity arrangements 0.62992 0.36942 �0.06167 0.53708

Advice on short-term crises 0.61140 0.39864 0.15470 0.55665

Industry competition advice 0.46829 0.08592 0.53695 0.51499

Industrial contact assistance 0.02968 0.06796 0.82571 0.68729

Eigenvalue 4.83169 1.42218 1.06715

Cum. % 40.3 52.1 61.0


Table 7

Policy affective conflict

Items

Factor 1,

strategic advice

Factor 2,

networking help

Factor 3, inter-personal

/personnel help Communality

Financial advice 0.80807 0.25030 0.06873 0.72035

Management advice 0.79738 0.28432 0.01220 0.71680

Marketing plan 0.53200 0.63969 0.07689 0.69814

Advice on private matters 0.22185 0.10741 0.82488 0.74117

Advice as mentor/coach 0.29841 0.67085 0.51551 0.80484

Business strategy adjustment 0.81280 0.16781 0.22085 0.73758

Recruitment assistance 0.11679 0.13542 0.86364 0.77785

Professional contact 0.11190 0.59347 0.53459 0.65051

Debt/equity arrangements 0.60993 0.14151 0.41163 0.56148

Advice on short-term crises 0.76070 0.14857 0.44835 0.80175

Industry competition advice 0.33086 0.69462 0.05363 0.59484

Industrial contact assistance 0.08657 0.72166 0.09911 0.53811

Eigenvalue 5.75997 1.44784 1.13561

Cum. % 48.0 60.1 69.5



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

Table 8

Hierarchical regression analysis — goal cognitive conflict sub-dimensionsa

Control Full (1) Full (2) Full (3) Full (4) Combined

Control variables

EP ability 0.560*** 0.518*** 0.529*** 0.573*** 0.589*** 0.537***

Business risk �0.336*** �0.349*** �0.330*** �0.303*** �0.307*** �0.303**

VC effectives �0.145y �0.145 �0.134 �0.169* �0.258** �0.231*

R2 0.607

F 27.78***

Main effects

1. Short-term

Affective �0.285* �0.237

Cognitive 0.210 0.001

2. Long-term

Affective �0.240y �0.052

Cognitive 0.250* 0.057

3. Product/Innovation


Cognitive 0.324** 0.230

4. Control/Incentives

Affective �0.146 0.093


R2 0.637 0.640 0.661 0.655 0.718

DR2 0.030 0.033 0.054 0.049 0.111

F 18.22*** 18.49*** 20.29*** 19.77*** 10.63***

No. of cases 58 58 58 58 58 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.


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 inter-

related. 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

Table 9

Hierarchical regression analysis — goal affective conflict sub-dimensions

Control Full (1) Full (2) Full (3) Combined

Control variables

EP ability 0.560*** 0.530*** 0.572*** 0.589*** 0.608***

Business risk �0.336*** �0.341*** �0.296** �0.307*** �0.272**

VC effective �0.145y �0.145 �0.162y �0.258** �0.244**

R2 0.607

F 27.78***

Main effects

1. Profit


Cognitive 0.211y �0.013

2. Product/Innovation

Affective �0.321** �0.277y

Cognitive 0.377** 0.354*

3. Control/Incentives

Affective �0.146 0.095


R2 0.632 0.674 0.655 0.656

DR2 0.025 0.068 0.049 0.104

F 17.85*** 21.54*** 19.77*** 13.01***

No. of cases 58 58 58 58 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.


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-dimensio-

nalised 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 coeffi-

cient 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

Table 10

Hierarchical regression analysis — policy cognitive conflict sub-dimensionsa


Control Variables

EP ability 0.563*** 0.551*** 0.532*** 0.580*** 0.541***

Business risk �0.325*** �0.304** �0.344*** �0.328** �0.323**

VC effective �0.146 �0.209* �0.153 �0.164y �0.202y

R2 0.591

F 25.48***

Main effect

1. Strategic

Affective �0.199 �0.248

Cognitive 0.265y 0.350*

2. Network

Affective �0.344 �0.413y


3. Personal(nel)

Affective 0.138 0.347y

Cognitive �0.043 �0.211

R2 0.613 0.625 0.601 0.664

DR2 0.023 0.019 0.010 0.074

F 16.19*** 17.37*** 15.35*** 10.33***

No. of cases 57 57 58 57 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.


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

Table 11

Hierarchical regression analysis—policy affective conflict sub-dimensions


Control variables

EP ability 0.563*** 0.540*** 0.547*** 0.580*** 0.522***

Business risk �0.325*** �0.307** �0.313** �0.328** �0.306**

VC effective �0.146 �0.198y �0.195y �0.164y �0.198y

R2 0.591

F2 5.48***

Main effect

1. Strategic

Affective �0.242 �0.342y

Cognitive 0.275y 0.280

2. Network

Affective �0.275 �0.187


3. Personal(nel)

Affective 0.138 0.357y

Cognitive �0.043 �0.222

R2 0.615 0.606 0.601 0.648

DR2 0.024 0.016 0.010 0.057

F 16.29*** 15.71*** 15.35*** 9.61***

No. of cases 57 57 57 57 57

Items with factor loadings greater than 0.5 appear in italic.y p < 0.10.

** p < 0.01.

*** p < 0.001.


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%, depend-

ing 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 sub-

dimension 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


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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Documents

The consequences of conflict between the venture