Unleashing synergies in strategic networks of SMEs: The influence of partner fit on corporate entrepreneurship

Abstract

An increasing number of firms participate in inter-firm networks to improve their innovation, renewal, and venturing; efforts that can be termed ‘corporate entrepreneurship’. This article contributes to the literature by developing and testing explanations for how partner fit (that is, complementary capabilities and organizational compatibilities) influences networking firms’ corporate entrepreneurship. Using survey data on SMEs engaged in strategic networks with multiple partners, we found support for an argument suggesting that partner fit requires mediating variables to unleash the potential synergies of partner fit for corporate entrepreneurship. Specifically, our results suggest partner fit triggers resource leverage through processes of both combining resources and accumulating resources from interaction with network partners. Thus, a positive indirect relationship between partner fit and corporate entrepreneurship is present.

Keywords

corporate entrepreneurship inter-organizational relationships networks partner fit resource leverage

Introduction

A popular forum for small- and medium-sized enterprises (SMEs) to strengthen innovation for competitiveness is to participate in formal strategic networks where intentionally formed groups of firms operating in the same industry undertake direct interactions to realize potential synergies (Hagedoorn and Schakenraad, 1994; Human and Provan, 1997; Thorgren et al., 2009a; Wincent, 2008; Wincent et al., 2009; Wincent et al., 2010a). These networks benefit SMEs because they can share and combine information, skills, and resources to develop advanced innovation and reduce costs and share risks (Van Gils and Zwart, 2009). Participating firms are thus, given the opportunity to remain competitive in complicated and highly volatile environments. The European Union (EU) and other policymakers have considered it important to support these strategic networks. Various policy programmes have attracted SMEs that seek to improve their efforts to identify and realize new business opportunities; for example, through developing new products or production processes and entering new markets (see Chaston, 1995; Hanna and Walsh, 2008; Thorgren et al., 2009b; Wincent et al., 2010b). As such, a prominent motive for SMEs to join such a network is their potential to improve corporate entrepreneurship (CE).

The literature suggests that ‘partner fit,’ in terms of high capability complementarity (i.e., partners have different capabilities) and high compatibility (i.e., partners’ organizational cultures, management, and operating styles are similar), can explain why firms are generally motivated to engage in interorganizational relationships. Partner fit (Harrison et al., 2001; Kale et al., 2000) presents opportunities for firms to create excess value and realize the potential of such synergy (see Chung et al., 2000; Hitt et al., 2000). Although they have not specifically addressed CE, prior studies have demonstrated various positive effects of partner fit. Ozorhon et al. (2008) found a positive relationship to international joint venture (IJV) performance, while a study by Kwon (2008) further specified that the positive effects on IJV performance can be indirect and mediated by social capital. In a similar vein, a study by Sarkar et al. (2001) indicated that the relationship between inter-firm diversity/compatibility and alliance performance can be mediated by relationship capital. Further, in a conceptual article, Nielsen (2005) suggested knowledge embeddedness is a mediating variable in creating synergies in strategic alliances. Thus, conceptual and empirical evidence indicate that synergies related to partner fit can be beneficial to interorganizational relationships. However, because of the demonstrated mediation effects any potential synergy benefits may act through more complex indirect causal chains rather than being direct effects.

Despite the potential in strategic networks to improve participating firms’ CE through multilateral exchanges and interaction (see Antoncic and Hisrich, 2004; Simsek et al., 2003; Teng, 2007), no studies have examined the role of potential partner synergies by testing the relationship between the degree of partner fit and a firm’s level of CE. This neglect is unfortunate because the idea behind strategic networks is related to exploiting partner synergies. By overlooking partner fit, research may have neglected a significant factor that could explain why SMEs can still engage in CE activities, which are known for being resource demanding (see Teng, 2007; Wincent, 2008). In this study, we address this notion and extend previous literature by asking a two-part question: If partner fit does indeed enhance networking firms’ CE, how does it do so? Using survey data on firms operating in strategic networks, we examined the mechanisms related to partner fit in this particular context.

Theoretical background

CE is defined as ‘the sum of a company’s innovation, renewal, and venturing efforts’ (Zahra, 1995: 227). As such, and consistent with prior literature (Simsek et al., 2007; Zahra, 1996; Zahra et al., 2009), CE captures innovation activities (new products, production processes, and organizational methods); renewal activities (business changes); and venturing activities (market expansion). In this study we examine one aspect of CE, namely firms’ behavior in terms of innovativeness, risk taking, and proactiveness related to R&D activities with a particular focus on new products and services to be exploited within the boundaries of the existing firm.

Using a resource-based framework (Teng, 2007), we investigate the role of partner synergies for enhanced CE through engaging in strategic networks. Our argument is that SMEs which typically have fewer resources than large firms can also enhance their CE because the processes used to leverage resources allow existing resources to be ‘energized;’ thus, greater resources are secured (Hamel and Prahalad, 1993, 1994). Our approach is thus grounded in the entrepreneurship literature. Stevenson and Jarillo (1990: 23) viewed entrepreneurship as a process where ‘individuals – either on their own or inside organizations – pursue opportunities without regard to the resources they currently control,’ advocating that the ideas of resource leverage and entrepreneurship are closely linked. This implies that a firm’s ability to leverage resources will impact on how well equipped it is to engage in CE activities.

Hamel and Prahalad (1994) suggested several ways resources can be leveraged: concentrating, accumulating, combining, conserving, and recovering of resources. In the context of strategic networks, two of these ways are particularly prominent: accumulating and combining resources (Das and Teng, 2000; Hamel, 1991). In strategic networks, accumulating resources refers to how participating firms gain access to partners’ resources and internalize them to their own firm (Hamel and Prahalad, 1993). Leveraging resources through combination occurs when partners’ complementary skills and resources are joined to create added value for each participating firm (Hamel, 1991). The logic underlying leveraging resources in strategic networks therefore, is founded on the potential synergies that exist among partners.

To this background, we posit that partner fit captures potential partner synergies and refers to how partners mesh with one another on two dimensions: complementarity and compatibility. Complementarity refers to the lack of similarity or overlap between partners’ resources in relation to each firm’s capabilities (Kale et al., 2000; Mowery et al., 1996). Compatibility refers to the similarity between partners’ organizational cultures and management and operating styles (Kale et al., 2000; Sarkar et al., 2001). We next hypothesize a series of relationships that capture how partner fit may indirectly influence CE through resource leverage, which is illustrated in Figure 1.

Figure 1.

Model of Resource Combining and Resource Accumulating Processes for Strengthened Corporate Entrepreneurship

Hypotheses

Partner fit and relational capital

To elaborate on the resource accumulation process in enhancing CE, we first hypothesize that in strategic networks, partner fit is positively related to relational capital. Relational capital is the social dimension of a relationship based on friendship, closeness, and trust (see Lincoln and Miller, 1979). Interdependence and knowledge transfer are key steps for accumulating resources for strengthened CE by accessing and transferring resources from competitive partners. Prior research however, provides strong arguments that developing relational capital is needed to enable such steps because developing interdependence and knowledge transfer depends much on partners’ willingness and motivation to transfer resources to one another (Emerson, 1962; Larsson et al., 1998; Norman, 2002). Partner fit does not directly create such willingness and motivation. For motivated and willing partners, the need to establish a high quality social relationship comes first. This is particularly challenging in a multi-partner context as firms are more loosely connected and flexible in entering and exiting cooperative relationships (as opposed to dyadic alliances).

Prior research does indicate that partner fit may positively influence building relational capital. Through partner fit, firms can gain access to desired resources (see Chung et al., 2000; Das and Teng, 2000; Dyer and Singh, 1998) which may be difficult to acquire on the traditional market (see Mowery et al., 1996). The exchanged resources are more critical in a relationship with higher partner fit than one with lower partner fit because firms do not possess similar resources. Further, the organizations are similar enough in culture, management, and operating styles that they can realize potential in their complementary resources. Partners may thus be motivated to build a high-quality relationship in which a social dimension is important. In this sense, partners may not wish to risk destroying the relationship through opportunistic actions, conflicting intentions, or unwanted behavior due to the potential inherent in having established a relationship with high partner fit. Building relational capital therefore, may be stimulated (see Inkpen and Currall, 1998). So, we hypothesize:

Hypothesis 1. Partner fit has a positive association with relational capital among partners in strategic networks.

Relational capital and interdependence

In strategic networks, we expect relational capital to be positively associated with interdependence. High interdependence occurs in a relationship when partners find the exchanged resources to be critical for their firm, particularly when it is simultaneously difficult to obtain similar resources from another source (see Emerson, 1962). The kind of knowledge that can be used to accumulate resources for strengthened CE must be very valuable, targeted, and codified for the firm’s production, organization, and learning. Thus, knowledge transfer requires effort from both the knowledge source and the knowledge recipient. We argue here that relational capital may not sufficiently create such efforts directly. We do, however, recognize that relational capital may develop interdependence between partners, which in turn may stimulate the efforts necessary for knowledge transfer.

Arguing for a positive relationship between relational capital and interdependence is based on the notion that relational capital, with its associated trust, friendship and closeness, can provide partners with resources that are difficult to acquire in strictly instrumental relationships (Uzzi, 1997). Moreover, relational capital can potentially provide emotional support, personal feedback, and confirmation of individuals’ belongingness and so, thus facilitating individual competence and developing confidence along with enhanced social well-being (Kram and Isabella, 1985). Benefits generated from this social dimension could make partners more willing to contribute to the partnership. Consequently, relational capital may increase the value of the relationship while reducing the probability of finding another relationship that can replace the established partnership (Zaheer et al., 1998). In other words, the social dimension can lead to interdependence. Thus, we hypothesize:

Hypothesis 2. Relational capital has a positive association with interdependence among partners in strategic networks.

Interdependence and knowledge transfer

In a next step, we expect a positive association between interdependence and knowledge transfer among partners in strategic networks. External knowledge and information acquired through inter-organizational relationships is often embodied in know-how and practices, which tend to be sticky (Simonin, 1999; von Hippel, 1994). Transferring such knowledge may be difficult, especially if the recipient lacks absorptive capacity or if the source is reluctant to share (Norman, 2002; Szulanski, 1996). From a source perspective, interdependent partners are by definition motivated to maintain the relationship (see Emerson, 1962). Extending this notion, we argue that partners may be less protective of their own knowledge when they depend on partners’ resources (Norman, 2002). This follows the norm of reciprocity; someone who receives much has incentives to give much (Gouldner, 1960; Homans, 1958). Hence, firms in an interdependent partnership will be highly motivated to share knowledge and ensure that their partners are able to absorb the knowledge. From a recipient perspective, inherent to the concept of interdependence is firm motivation to maintain exchanges. They may be more motivated to absorb knowledge from strategic network partners when that knowledge is difficult to acquire elsewhere in the market. From these motivations to mutually transfer knowledge, we hypothesize:

Hypothesis 3. Interdependence has a positive association with knowledge transfer among partners in strategic networks.

Knowledge transfer and corporate entrepreneurship

Ultimately, we expect that knowledge transfer among partners in strategic networks facilitates creating and enabling CE for networking firms in the process of accumulating their own resources. From a resource-based framework, knowledge transfer can bolster the firm’s resource base, which will enable entrepreneurship processes (Stevenson and Jarillo, 1990; Teng, 2007). Knowledge transfer in a strategic network will include both explicit and sticky information, some of which is based on firm know-how and practices (Simonin, 1999; von Hippel, 1994). This transfer of information allows firms to accumulate own resources that enable them to carry out CE (including innovation, renewal, and venturing activities). Much of this information may be difficult to acquire in the traditional market (Mowery et al., 1996). Thus, firms will be provided with rare opportunities for CE. Thus, we hypothesize:

Hypothesis 4. Knowledge transfer has a positive association with corporate entrepreneurship among partners in strategic networks.

Partner fit and joint combinatory efforts

As described in the theoretical background, firms may also strengthen their CE by participating in strategic networks that offer opportunities to combine their resources with partners’ resources. To ensure that combining resources will materialize, partners can sign contracts that outline the terms and objectives for their joint activities. Critical for any contract to be signed at all, however, is that the partners perceive they can gain something from joint efforts with partners, given each firm’s resource profile.

Drawing upon resource leverage research (Hamel and Prahalad, 1993, 1994), combinatory efforts refer to activities where partners jointly and deliberately explore options and combine resources to get more out of resources than they possess individually (note that this differs from knowledge transfer where firms learn and absorb knowledge from partners, which they can add to their existing resource base for strengthened CE). These joint combinatory efforts can include activities targeted at mutual business development efforts such as combining resources for new products, production marketing, and so on. Extending partner fit for exploratory purposes, as previously highlighted by Kale et al. (2000), we argue that partner fit is an important factor that determines whether firms will engage in joint combinatory efforts. Partner fit can provide firms with more options to integrate the resources they have to create synergy and added value. Given the several attractive opportunities to combine resources, we suggest a positive relationship between partner fit and partners’ joint combinatory efforts.

Hypothesis 5. Partner fit has a positive association with joint combinatory efforts among partners in strategic networks.

Joint combinatory efforts and corporate entrepreneurship

As the second step in this process where resources are leveraged by combining partner resources (in contrast to the accumulating process), we argue that joint combinatory efforts among partners in strategic networks will have a positive influence on firms’ extent of CE activities. Joint combinatory efforts have the potential to amplify the value of firm resources (Hamel and Prahalad, 1994). When partners find ways to combine their resources to create added value, they can form contracts to initiate and carry out the combinatory effort. Thus, joint combinatory efforts can be achieved without the development of relational capital, interdependence and knowledge transfer. By jointly and deliberately combining resources, the boundaries for a firm’s achievements in terms of innovation renewal, and venturing are extended. By combining resources, the array of possibilities for new products, production processes, and organizational methods broadens. Higher levels of joint combinatory efforts may lead partners to identify and act upon new opportunities to use the possible combinations of resources and motivate them to expand their current repertoire of actions. As such, we posit a positive relationship between joint combinatory efforts and CE.

Hypothesis 6. Joint combinatory efforts have a positive association with corporate entrepreneurship among partners in strategic networks.

Methods

Sample and data

We examined the hypotheses using firms in two strategic networks that shared several similarities. They included firms related to the wood industry in a rural region of Sweden and were formed in 1996 and 1999. Thus, they enabled some isolation of the hypothesized relationships from exogenous effects related to industry and geographical location. Each network aimed to strengthen the participating firms by providing a platform for cooperative relationships and joint projects; they focused on business development, including product and production development and marketing programs. Network projects were carried out in sub-groups, usually groups of three firms, with the shared network trademark used when communicating with third parties. Firms had the opportunity to meet prospective partners in the network, but were free to decide if they wanted to be active and engage in a joint project. In the first year, the average number of employees among participating firms was 10, and sales ranged from less than one million Swedish kronor (SEK) to 163 million SEK, with a mean of 16 million SEK (1 SEK ≈ $0.14 USD when data was collected).

Each network had a formal organization, including membership registers. With authorization from the networks’ contact personnel, we contacted each member firm to invite them to participate in the study. If they agreed, feedback was promised and confidentiality of single responses was assured. Data were collected for two periods: the period 2000 to 2002 (T1) and the period 2002 to 2004 (T2). In 2002, we identified 54 active firms in the networks: 24 firms in one network and 30 firms in the other. In 2004, the number of firms still active in the networks was 41; 19 and 22, respectively. This study only targeted active network firms during the entire period, 2000–2004 so our final sample consisted of 41 member firms which represented a 100 percent response rate.

The targeted respondent of each member firm was the chief executive officer, firm owner, or other person either responsible for or most knowledgeable about the firm’s network participation. Part I of the interviews included open-ended questions on the firm and its participation in the network (e.g., which other firms they worked with and how they did this). This provided richer information about the network activities and an indication whether the respondents were sufficiently knowledgeable to provide accurate answers. Part II consisted of a structured questionnaire including the constructs examined in this study. During this part, the researcher only listened to the respondents reasoning and to any kind of additional information shared when filling in the questionnaire. The researcher did, however, not ask additional questions to the ones posed in the questionnaire and did not engage in any kind of discussions with the respondents which may have influenced their answers.

Methods of analysis

Considering the several hypothesized relationships with which the constructs are interrelated, we used structural equation models to test our model. Such a technique is important both to examine the multivariate impact of the constructs involved in the proposed relationships and to evaluate possible logics mis-specifications. The research design, including sampling logics and techniques for analyses, is not uncommon in study settings similar to the one described (see Hult et al., 2002).

Following the two-step procedure suggested by Anderson and Gerbing (1988) to assess the quality of our measurements and to test our hypotheses, we began by evaluating the measurement models, validity, and reliability. We verified factorial structures and necessary psychometric evidence of convergent and discriminant validity by confirmatory factor analysis using structural equation modeling and AMOS. We then proceeded to test structural equation models by maximum likelihood estimation. Owing to the modest sample size in relation to the number of items, we calculated and analyzed composite constructs (Bentler and Chou, 1987). We also performed a bootstrapping procedure to estimate the potential effect of the sample size. We calculated the Bollen-Stine p-value to determine whether there was statistically significant probability that the results established by the sample we used differed from those of a larger sample (Bollen and Stine, 1992).

We tested our hypotheses by simultaneously estimating structural equations in AMOS. We used lagged multiple regressions to test the model. The dependent variable was predicted by the independent variable as measured at t - 1, which is a recommended approach for controlling for endogenous influence (Cohen and Cohen, 1983). Owing to the lagged regression design, we were able to control for the effect on T2 of CE as measured at T1. In addition to being consistent with scholarly recommendations, such a time lag is also consistent with perspectives suggesting effects on CE to be lagged (Zahra, 1993). In evaluating the structural model, we followed recommendations to interpret multiple model fit indices (Bollen, 1990) and considered special indices that have been deemed appropriate for studies with modest sample sizes. In addition to estimating the sample size sensitive chi-square statistics where acceptable values should be above .05, therefore, we also evaluated the less sensitive comparative fit index (CFI) where indication of good model fit is above .90, the incremental fit index (IFI) where acceptable values should be above .90, and the root mean square error of approximation (RMSEA) where acceptable values should be below .08 (Browne and Cudeck, 1993).

Measures

Unless otherwise noted, measures were based on data from the questionnaires including a 5-point Likert scale, ranging from 1=strongly disagree to 5=strongly agree. The scale for the dependent variable, corporate entrepreneurship, was rooted in the original CE scale developed by Miller and Friesen (1982), with refinements from the work of Zahra and Covin (1995). On the basis of their scales, we used six items to assess member firms’ actions related to risk taking, proactiveness, and innovativeness during the previous two years. As we were aware of the limited possibility of using cross-sectional data to determine causality of the proposed influences on CE, and because we needed to control for endogenous effects (Cohen and Cohen, 1983), we collected longitudinal data by contacting each firm at two points in time (T1 and T2). This approach ensured that any influences of partner fit and the relational variables on CE were in the proposed direction and that no reversed causation existed. CE research has been criticized for not analytically demonstrating that CE may lead to partnering. Our approach avoided capturing inflated influences and ensured we found causal influences as we hypothesized a priori.

Our model incorporated five constructs to assess direct or indirect influences on CE, all of which were measured at T1. All but joint combinatory efforts were measured using multi-item constructs. For these constructs, Tables 1 and 2 detail all items pertaining to the independent constructs along with the results of confirmatory factor analysis. We assessed partner fit with four items. This scale was built on one previously used by Kale et al. (2000). The measurement is related to the complementary and compatible resources, management, culture, and competences between the firm and its cooperating partners. We performed confirmatory factor analyses to examine the potential and proposed higher-order representation of partner fit as represented by complementarity and compatibility. The results (Table 1) show support for a two-factorial construct of partner fit. The correlation between complementarity and compatibility is .76, which also suggests that these two first-order constructs form a second-order construct. Evaluation by fit indices supports the model’s ability to represent the data well (χ² = .42; d.f. = 1; p = .52; CFI = 1.00; IFI = 1.00; and RMSEA = .00).

Table 1.

Confirmatory Factor Analysis of Partner Fit^a,b

Construct	Item descriptions	Loading
Complementarity	There is high complementarity (i.e., differences but necessary ones) between the resources/capabilities of the other companies and ours.	.76
	There is high similarity/overlap between the core capabilities of the other companies and ours.^c	.58
Compatibility	The organizational cultures of the other companies and ours are compatible with each other.	.94
	The management and operating styles of the other companies and ours are compatible with each other.	.90
Goodness-of-fit	χ² = .42; d.f. = 1; p = .52; IFI = 1.00; CFI = 1.00; RMSEA = .00; .00 < C.I. 90% RMSEA < .36

Notes: ^a All items are significant at p < .05. ^b Standardized loadings. ^c Reversed item.

Table 2.

Confirmatory Factor Analysis^a,b

Construct	Item descriptions	Time 1 Loading	Time 2 Loading
Corporate entrepreneurship	Our company has introduced many products or services.	.63	.75
	Our company has made dramatic changes in the mix of its products or services.	.83	.70
	This company has shown a strong commitment to research and development (R & D), technological leadership, and innovation.	.85	.79
	This company has followed strategies that allowed it to exploit opportunities in its external environment.	.83	.63
	This company has shown a strong proclivity for high risk projects (with chances of very high return).	.72	.48
	This company has emphasised bold, wide-ranging acts to position its products/services.	.79	.86
Partner fit	Complementarity	.71	–
	Compatibility	.82	–
Relational capital	There is close, personal interaction between the partners at multiple levels.	.84	–
	There is mutual respect between the partners at multiple levels.	.93	–
	There is mutual trust between the partners at multiple levels.	.95	–
	There is personal friendship between the partners at multiple levels.	.98
	There is high reciprocity among the partners.	.79	–
Interdependence	The company has shown a willingness to contribute with information, assistance, and guidance to other member firms in the cooperative network.	.60	–
	This company has shown a willingness for investing substantial resources from the firm to the cooperative partners in the cooperative network.	.71	–
	Our partners have provided us with significant resources difficult to acquire in other ways.	.97	–
	It would have been difficult to replace the partners (within the projects carried out).	.83	–
Knowledge transfer	This company has learnt a great deal about the other firms’ technology and/or process knowledge.	.78	–
	This company has decreased the initial technological dependence on the other firms.	.39	–
	The technological and/or process knowledge of the other firms has been absorbed by this company.	.82	–
Goodness-of-fit	χ² = 310.00; d.f. = 278; p = .09; IFI = .95; CFI = .95; RMSEA = .05; .00 < C.I. 90% RMSEA < .08

Notes: ^a All items are significant at p < .05. ^b Standardized loadings.

We assessed relational capital with five items focused on characteristics of the relationship between the firm and its partners concerning qualities such as respect, trust, and friendship. This scale was adopted from the work of Kale et al. (2000). We measured interdependence using four items that reflect the motivation to engage in the relationship and the availability of similar exchanges with others (Emerson, 1962). We assessed knowledge transfer using three items. This scale was modified from one used by Simonin (1999) relating to a firm’s learning and absorption of knowledge through interfirm links. The variables of partner fit, relational capital, interdependence, and knowledge transfer capture the firms’ self-assessments of their partnering relationships. Respondents were instructed to answer these questions with the partners with whom they had actively worked in mind. This implies that the measures capture a comprised assessment of partners in the sub-group in which they have been active (usually consisting of three firms), rather than all firms active in the network. We assessed joint combinatory efforts by asking respondents to estimate the extent to which they and their partners assigned for joint business development projects by combining mutual resources and then used the average number of hours in joint efforts as a measurement.

Table 2 reports the confirmatory factor analysis of the study’s constructs. Overall fit statistics supported the factorial structure and measurement properties. All estimated loadings were significant (p < .05) and substantial (coeff. >.40), which provides evidence of convergent validity. Model chi-square was not significant (χ² = 310.00; d.f. = 278; p = .09), and absolute and relative fit measures indicated that the model represents the data well (i.e., CFI = .95; IFI = .95; and RMSEA = .05). Furthermore, when using small sample sizes and relatively large numbers of items, it has been recommended that researchers pay attention to the relationship between chi-square and degrees of freedom. The ratio should not exceed five (Marsh and Hocevar, 1985) and preferably not two (Byrne, 1989). The ratio of chi-square and degrees of freedom was 1.12 for the estimated model, which is well within recommended levels, suggesting a reasonable representation of data. Overall, we found evidence that the model represents the data well.

Results

Table 3 presents descriptive statistics, correlations, reliabilities, and average variance extracted (AVE) of the observed composite constructs. Considering the limited number of items used for measuring the constructs in this study, it is satisfactory to see that all constructs besides knowledge transfer (alpha coefficient 0.67) had an alpha coefficient equal to or greater than .70, which is the traditional conservative threshold for unidimensionality. As Cronbach’s alpha is dependent upon the number of items used for assessing a construct, a value of 0.67 could be considered acceptable and not a threat for unidimensionality. We also performed an exploratory factor analysis (EFA) and noticed that all individual items loaded on the expected factor with acceptable loadings, which further strengthened the use of these specific items. We found no threats for multicollinearity, as all correlations were low or moderate. We also received some initial support for the hypothesized relationships in that the hypothesized directions of relationships and evident correlations appear consistent. AVE for each construct exceeded the recommended minimum levels for measurements to display convergent validity (Fornell and Larcker, 1981). By comparing AVE with the shared variance for all possible pairs of composite constructs, we also found support for discriminant validity (Anderson and Gerbing, 1988).

Table 3.

Descriptive Statistics and Correlations^a

Variables	Mean	s.d.	AVE	1	2	3	4	5	6	7
1. Corporate entrepreneurship (T1)	2.83	.95	.78	(.90)
2. Corporate entrepreneurship (T2)	2.55	.71	.70	.47***	(.84)
3.Partner fit	3.18	.83	.76	−.22	−.18	(.82)
4.Relational capital	4.01	.95	.90	.12	−.14	.51**	(.95)
5.Interdependence	3.10	.85	.78	.33*	.32*	−.12	.22	(.86)
6.Knowledge transfer	2.50	.93	.66	.24	.52***	.29	.23	.31	(.67)
7. Joint combinatory efforts	1.27	1.97	−	.08	.30	.31*	.11	.08	.27	(−)

Notes: a Cronbach’s alpha reliabilities are shown along the diagonal. * p < .05. ** p < .01. *** p < .001.

Before estimating the hypothesized model, we estimated competing models to check for mis-specification errors. In checking for non-hypothesized paths between constructs, we also examined possible influences of joint combinatory efforts to relational capital, interdependence, and knowledge transfer. In a next step, we estimated the hypothesized model; controlled for mis-specification as described above; and examined absolute and relative goodness-of-fit indices. We found that the hypothesized model reasonably represented the data (χ² = 5.86; d.f. = 7; p = .56; IFI = 1.00; CFI = 1.00; RMSEA = .00; .00 < 90% CI RMSEA < .17), which further supported the use of our model. Nested model tests revealed the hypothesized model is superior to a non-mediation model (χ² = 11.83; d.f. = 8; p = .16; IFI = .94; CFI = .93; RMSEA = .11; .00 < 90% CI RMSEA < .23; Δχ² = 6.0; Δd.f. = 1; p = .01) as well as to a partial mediating model (χ² = 4.06; d.f. = 6; p = .67; IFI = 1.00; CFI = 1.00; RMSEA = .00; .00 < 90% CI RMSEA < .16; Δχ² = 1.8; Δd.f. = 1; p = .18). The superiority is evident by statistical significance (in the first case) and by being more parsimonious (in the second case).

To further evaluate the proposed model, we performed nested model tests comparing the relative strength of the two proposed processes. We sought to test if a model with one of the processes explained the data better compared to a model containing simultaneous influences from both processes. To test this, we excluded the final path in the respective process to CE (knowledge transfer → CE and joint combinatory efforts → CE) and compared three models: the hypothesized model, the resource combination model, and the resource accumulation model. Results revealed that the hypothesized model that included both processes simultaneously was superior to a model that included only the influence on CE from a resource combination process (χ² = 18.13; d.f. = 8; p = .02; IFI = .85; CFI = .81; RMSEA = .18; .07 < 90% CI RMSEA < .29; Δχ² = 12.3.; Δd.f. = 1; p < .001) or only the influence from a resource accumulation process (χ² = 10.18; d.f. = 8; p = .25; IFI = .97; CFI = .96; RMSEA = .08; .00 < 90% CI RMSEA < .21; Δχ² = 4.3; Δd.f. = 1; p = .04). These results further support the relevance of using the hypothesized model before any other model.

Table 4 displays the results of the structural model test. For the hypotheses capturing indirect effects on CE through accumulating resources (Hypotheses 1–4), our findings support all the hypotheses. Hypothesis 1 predicted that partner fit has a positive association with relational capital. The coefficient was positive and significant (β = .59; p < .001), providing support for Hypothesis 1. Hypothesis 2 predicted that relational capital has a positive association with interdependence, which was also supported by findings (β = .34; p < .05). Hypothesis 3 stated a positive association between interdependence and knowledge transfer. The positive and significant coefficient supports Hypothesis 3 (β = .38; p < .05). Findings also support Hypothesis 4, which predicted that knowledge transfer is significantly and positively related to CE (β = .36; p < .001).

Table 4.

Results from Path Estimation

Independent variables		Dependent variables	Coefficients^a
Partner fit	→	Relational capital	.59***	(.16)
Relational capital	→	Interdependence	.34*	(.15)
Interdependence	→	Knowledge transfer	.38*	(.16)
Knowledge transfer	→	Corporate entrepreneurship (T2)	.36***	(.10)
Partner fit	→	Joint combinatory efforts	.74*	(.36)
Joint combinatory efforts	→	Corporate entrepreneurship (T2)	.09*	(.04)
Corporate entrepreneurship (T1)	→	Corporate entrepreneurship (T2)	.25**	(.10)
Goodness-of-fit	χ² = 5.86; d.f. = 21; p = .56; IFI = 1.00; CFI = 1.00;
	RMSEA = .00; .00 < C.I. 90% RMSEA < .17

Notes: a Standard error in parentheses. * p < .05. ** p < .01. *** p < .001.

Support was also found for the hypotheses that captured indirect effects on CE through combining resources (Hypotheses 5 and 6). Hypothesis 5 predicted that partner fit is positively associated with joint combinatory efforts, which the results supported (β = .74; p < .05). Finally, results also supported Hypothesis 6, which predicted that joint combinatory efforts has a positive association with CE (β = .09; p < .05). As suggested, we were also interested in detecting any occurrence of strengthened influences on CE across time. We therefore, performed a post hoc analysis where we replaced the CE variable used for the tests with a CE variable drawn from the earlier wave from the survey where the other independent variables were collected. These additional tests revealed that neither of the two processes were significantly related (p >.05) to CE at T1. Thus, our results only support lagged effects from the independent variables on CE.

To test for potential effects of the relatively modest sample used, we performed Bollen and Stine’s (1992) procedure of drawing 200 bootstrap samples from the data to compute the Bollen-Stine p-value. Because the usual maximum likelihood-based p-value for estimating model fit can be misleading in smaller samples, we also assessed the p-value for the chi-square statistic using Bollen-Stine bootstrap. We found support for the fact that parameter estimates are unaffected by sample size at the p-value of .71. Because the p-value exceeds the critical level of .05, we can conclude that the model fit the data well (Bollen and Stine, 1992).

In addition to the statistical support of the hypotheses, the data collected from interviews with the respondents further validated that both resource combining and resource accumulating processes took place in the sampled networks. Two of these examples will be described here for illustrative purposes. Starting with resource combining, one example was noticed in cooperation among several smaller firms that produced different kinds of wooden houses. These firms made use of different production technologies and within the frame of the network they formed an agreement that they would combine each other’s individual strengths to identify new business opportunities and create new business concepts. Because of their combination of technologies, the firms managed to enter into a contract with a large constructor abroad. This deal resulted in a completely new kind of wooden house custom-made for the Alps. In this process, the firms did not transfer knowledge but combined their different technologies (one firm made the roof, one made the interior design, one made the beams, etc.).

One example of the accumulation process captures the chain from partner fit to CE. In one of the networks, representatives from all participating firms were invited to meetings where plans for joint activities were set up. The firms had no previous experience of each other. At start, these interactions between firm representatives concerned rather general issues. With time, the firms noticed that they shared several problems and displayed organizational culture similarities. They also identified how they could assist each other by possessing distinct capabilities. As the firm representatives became acquainted with each other on a more personal level, they also initiated discussions on more sensitive and relevant issues for their firms. This also raised their apprehension of the value of being committed with these partners. As the firms were drawn closer to each other this resulted in more specific and relevant knowledge transfer, which evidently had an effect on their CE. For one of the firms, co-operation facilitated the process of updating their product portfolio as their partners’ knowledge of quality, supply, and demand of other kinds of wood than previously used inspired them to change their material to follow a new product trend.

Discussion

Review of results

By specifying and testing a model for how partner fit can enhance networking firms’ CE, we contribute to the inter-organizational relationship literature and studies of CE. Research has provided understanding of how partner fit may determine how inter-organizational relationships are formed (Chung et al., 2000) and an account of how partner fit may be positive for alliance performance (Sarkar et al., 2001). Prior literature leaves open the question of how partner fit is related to CE in firms that engage in multi-partner networks where the main objective is to strengthen participating firms’ ability to innovate and compete. Addressing this question, we examined the effects of partner fit on CE in firms operating in strategic networks. More specifically, we tested predictions that partner fit has indirect positive effects on CE by leveraging resources by accumulating and combining resources. It is to be noticed, however, that this study focused on one aspect of CE: firms’ behavior in terms of innovativeness, risk taking, and proactiveness related to R&D activities. Further, these activities have a particular focus on new products and services to be exploited within the boundaries of the existing firm.

In terms of resource accumulation, our results show that by participating in a strategic network, partner fit is directly related to the extent of relational capital and knowledge transfer. Further, we found support for a positive chain starting with partner fit and connecting to relational capital, to interdependence, to knowledge transfer and, finally, to higher CE. In a prior study, Kale et al. (2000) found a positive association between relational capital and learning (which is similar to our knowledge transfer construct) without including the mediating variable of interdependence. In addition, they did not find a significant relationship between partner fit and learning their study was conducted, however, on dyadic strategic alliances. In our study, which uses strategic networks with more loosely connected, multiple partners, other mechanisms are at play. By joining a strategic network, firms have not automatically agreed that they will transfer knowledge to other firms. Here, we find it less likely that just building relational capital would result in knowledge transfer. By building relational capital in these networks, however, the partners are drawn closer to one another and the interdependence created motivates the partners to transfer knowledge. Also, in this context, partner fit makes knowledge transfer possible for partners. Regarding resource combination as a way to leverage resources necessary for CE activities, our results support the predictions that partner fit is positively associated with joint combinatory efforts among partners in strategic networks and further, that such efforts have a positive, direct effect on CE. In sum, our study demonstrates how partner fit has positive, indirect effects on networking firms’ CE by accumulating and combining resources.

Implications for research and practice

This study has implications for both theory and practice. This research extends understanding of how SMEs realize potential synergies from joining together in a strategic network with multiple partners by examining the relationship between partner fit and CE. Previous research has suggested partner fit can explain how dyadic alliances are formed and how they perform (Chung et al., 2000; Sarkar et al., 2001). It has, however, become common for SMEs to enter multi-partner networks where the objective is to strengthen the participating firms’ CE (Hanna and Walsh, 2008). This is the first study to examine the relationship between partner fit and CE in the context of strategic networks with multiple partners. We therefore, extend research on partner fit by introducing both a new dependent variable and a new interorganizational context. By finding a positive, indirect relationship between partner fit and CE, our results support prior arguments that partner fit produces beneficial outcomes. Further, our findings support studies showing that positive effects from partner fit are mediated by variables that realize the potential of fit (Kwon, 2008; Nielsen, 2005; Sarkar et al., 2001).

The tested mediating variables suggest that partner fit has positive effects on CE because resources can be accumulated and combined; thus, it is possible to leverage resources for CE activities. This means that partner fit can amplify the value of firm resources and the firm can engage in actions that go beyond its own resources. Thus, firms can more effectively engage in CE activities such as innovation, renewal, and venturing (Zahra, 1995). This is consistent with Teng’s (2007) use of a resource-based framework for understanding the role of strategic alliances for CE, with Stevenson and Jarillo’s (1990) view of entrepreneurship processes as a mismatch between aims and resources and Hamel and Prahalad’s (1993, 1994) suggestions on how to leverage resources. Our study brings these views and ideas together enhancing our understanding of why partner fit may have positive effects on networking firms’ CE.

Finally, we contribute to studies on CE antecedents. By examining the effects of partner fit on CE, we extend the traditional CE literature, which has turned to both external and internal variables. External variables include environmental dynamism, hostility, uncertainty, heterogeneity, technological opportunities, and demands (Antoncic and Hisrich, 2004; Covin and Slevin, 1991; Zahra, 1993), while internal variables include internal management systems, organizational resources, and support (Greene et al., 1999; Poon et al., 2006) to explain CE. So, the traditional model on CE could benefit from including interorganizational relationships in addition to internal and external variables.

Our study also has practical implications. For EU and other policymakers that support strategic networks for improved work with developing new products or production processes and entering new markets, this study illustrates the importance of carefully considering how the networks are configured (cf. Thorgren et al., 2010). This study suggests that firms partnering within the network should be similar enough in organizational culture and management style, but dissimilar enough in capabilities for synergies to be unleashed and developed into CE. Accordingly, when networks are formed and financially supported policy makers may raise awareness of the importance of and alertness to achieving fit between partners on the dimensions of capabilities (dissimilar) and organizational cultures (similar). If there is no such fit, there is a risk that synergies do not exist to the same extent or that conditions do not allow them to be unleashed. In addition, our results provide some potentially useful insights for managers who want to participate in strategic networks as a means to strengthen their firm’s CE, which can be achieved in several ways. For example, the firm can gain benefits from being alert to taking part in joint efforts that actively seek to combine resources that each independent firm can offer. In addition, establishing informal governance through relational capital and interdependence provides a greater chance that the firm can expect knowledge transfer from its partners. This can, in turn, accumulate the firm’s resources and enable more CE activities to be carried out.

Limitations and directions for future research

The present study has some limitations that are important to note. First, we acknowledge that our measures present some limitations. The study is based on self-reports; as such, respondent bias is possible. Some respondents may not have been knowledgeable enough to answer the questions, and their perceptions may not necessarily be consistent with those of others within the firm. This issue may inadequately inflate organizational-level constructs. Although bias is still possible, we made significant efforts to avoid such bias: respondents were engaged both at a strategic and operational level in the network cooperation, and a researcher listened to respondents’ reasoning when they completed the questionnaire. Nevertheless, future research using alternative measures and/or multiple respondents within the same firm would increase the robustness of the results. The risks of a common method bias are however, reduced by the lagged regression design. Moreover, our measures of partner and relationship characteristics could be improved. Future studies should consider measures that capture each individual partner link. We examined the relationship between partner fit and CE in two strategic networks. These networks consisted of Swedish firms related to the wood industry. Data from other samples or from populations of other kinds of networks may generate different results, particularly with respect to differing business cultures and regulations. We believe it may be fruitful to test whether relationships differ across networks and the extent different network environments influence CE. Thus, future research that includes other networking samples could be helpful. With our presence during data collection and carefully selecting networks and member firms, we aimed to ensure validity. Testing our hypotheses in this specific research setting provides confidence in how the indirect effects from partner fit on CE operates, but care must be taken in generalizing the results to other contexts. Finally, as we only focused on one aspect of CE (innovative, proactive and risk-taking behavior) the results may not hold for other aspects of CE such as deliberate new venture creation.

In summary, this research suggests that firms can use interorganizational relationships to secure resources for carrying out CE activities. Partner fit in strategic networks provides opportunities for firms to accumulate and combine their resources in order to engage in entrepreneurship processes. Our results suggest that this has potentially important implications for theory and organization in strategic networks.

Footnotes

Funding

This work was supported by Handelsbanken’s Research Foundation (grant number P2007-0043:1).

Sara Thorgren is assistant professor in entrepreneurship at Luleå University of Technology. Her research interest includes interorganizational relationships with a particular focus on trust processes and entrepreneurship in contexts of strategic relationships.

Joakim Wincent is Professor of Entrepreneurship at Luleå University of Technology. His current research focuses largely on strategic interorganizational relationships, open innovation, competition, emotion and stress management in innovative processes.

Daniel Örtqvist is associate professor of entrepreneurship at Luleå University of Technology. His academic research mainly concerns the entrepreneurial identity, the establishment of new ventures and aspects of organized cooperation.

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