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Influence factors of trust building in cooperation agreements
Journal of Business Research 67 (2014) 710–714
Contents lists available at ScienceDirect
Journal of Business Research
Influence factors of trust building in cooperation agreements☆ Jesús David Sánchez de Pablo González del Campo a,⁎, Isidro Peña García Pardo a,1, Felipe Hernández Perlines b,2 a b
Faculty of Law and Social Sciences, University of Castilla-La Mancha, 13071 Ronda Toledo s/n, Ciudad Real, Spain Faculty of Law and Social Sciences, University of Castilla-La Mancha, 45071 Cobertizo San Pedro Martir, s/n, Toledo, Spain
a r t i c l e
i n f o
Article history: Received 1 April 2013 Received in revised form 1 October 2013 Accepted 1 November 2013 Available online 9 December 2013 Keywords: Cooperation agreements Reputation Cooperation experience Trust
a b s t r a c t Trust is a key success factor in cooperation agreements. Therefore, identifying the factors that make the greatest contribution toward building trust is fundamental for an understanding of cooperation agreements. This paper analyzes two factors that might contribute to generating trust for successful agreements during the initial stages of the relationship. These factors are the partner's reputation and prior partnering experience. The study aims to confirm and complete the understanding of the relationship between these two variables and the success of cooperation agreements, by examining the indirect effect, through trust building, that these factors have on the success of agreements. The study analyzes these relationships by applying a structural equation model on the basis of partial least squares (PLS) methodology. The total impact of previous cooperation experience and the partner's reputation on the success of cooperation agreements is strong, positive, and significant. © 2013 Elsevier Inc. All rights reserved.
1. Introduction The current economic environment is highly complex and dynamic (Collet & Philippe, 2013). Securing a favorable competitive position on the basis of an individual dominant capability is therefore becoming increasingly difficult. Companies can, however, create routines in their cooperation agreements that yield relational rents (Dyer & Singh, 1998). The literature on cooperation agreements discusses key factors as regards the success of this business partnership model. Many of these studies use diverse theoretical approaches for justification and development, and show the relevance of a large number of factors that are very difficult to integrate into a model. Variables may, however, exist that, in addition to having a direct influence on the success of a cooperation agreement, also have an effect on other variables that indirectly improve success rates. The main contribution of this paper is, therefore, an illustration of the factors with the greatest significance and relevance in the literature, and an analysis of the relationship between them as regards their impact on success. For two different companies, an evaluation of their success when they attain the same result may also be different. The best means to
☆ The authors thank Pedro Jiménez Estévez from the University of Castilla-La Mancha, for his valuable comments on this paper. ⁎ Corresponding author. Tel.: +34 926295300; fax: +34 926293429. E-mail addresses: [email protected] (J.D. Sánchez de Pablo González del Campo), [email protected] (I. Peña García Pardo), [email protected] (F. Hernández Perlines). 1 Tel.: +34 926295300; fax: +34 926293531. 2 Tel.: +34 926295300; fax: +34 926295161. 0148-2963/$ – see front matter © 2013 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.jbusres.2013.11.032
assess the success of cooperation agreements is therefore the use of subjective variables. This study centers on two operational measures: the partner's level of satisfaction (Mohr & Spekman, 1994) and the achievement of the objectives of the agreement (Phillips, Lawrence, & Hardy, 2000). The most determinant factor in the success of cooperation is trust (Gulati & Higgins, 2003; Stuart, 2000; Thorgren & Wincent, 2011). Understanding trust generation in cooperation agreements and their success is therefore important. Previous experience of cooperation and the partner's reputation are factors with a substantial social component that could help build trust during the initial stages of an agreement. Previous works show the impact of previous cooperation experience (Heimeriks & Duysters, 2007) and the partner's reputation (Anand & Khanna, 2000; Saxton, 1997) on the success of cooperation agreements. The aim of this paper is to analyze the impact of these factors on trust, and thus their indirect impact on the success of cooperation agreements. A review of these relationships leads to the conclusion that companies can initiate good cooperation agreements if they have prior experience in cooperation and encounter partners with good reputations. Most existing empirical studies analyze high-technology industries (Haeussler, Patzelt, & Zahra, 2012; Stuart, 2000). Business cooperation in mature industries with a low technological intensity, however, receives little attention in the literature. But cooperation agreements can also be effective in mature industries, owing to the high volatility of such environments. This paper therefore studies the Spanish agrofood industry in order to bridge the gap in research, and since this sector records R&D expenditure as a percentage of Gross Value Added that is much lower than the average in other industries.
J.D. Sánchez de Pablo González del Campo et al. / Journal of Business Research 67 (2014) 710–714
2. Trust as a principal determinant of the success of cooperation agreements
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H2. The partner's reputation has a positive relation, directly and indirectly through trust development, with the success of the cooperation agreement.
One of the topics that scholars most frequently analyze regarding cooperation agreements is the link between trust and the success of cooperation agreements. Previous studies show that mutual trust between partners is a common factor in many successful cooperation agreements (Das & Teng, 1998). Gambetta (1988, p. 217) defines trust as the probability that one economic actor will make decisions and take actions that will be beneficial, or at least not detrimental, to another. Trust has two main components. The first is trust before initiating the agreement. If a company has previous experience with its partners then the two partners will already have inter-organizational trust. The second component is trust during the development of an agreement. As the agreement is developing, trust will increase between the companies if all parties fulfill expectations (Ring & Van de Ven, 1994). The principal benefits that trust generates in cooperation agreements are: a reduction in transaction costs, mitigation of the risk of investing in specific assets, and the facilitation of the decision-making process. These aspects give rise to commitment, and allow the partners to assume more risks, which generates value (Thorgren & Wincent, 2011). A high level of trust allows partners to share knowledge, resources, and capabilities, which has a positive impact on agreement outcomes because partners feel safe from opportunistic behavior from each other (Stuart, 2000). Moreover, an adequate level of trust helps predict the partner's behavior during the initial stages of an agreement, and fosters desirable behavior as the agreement develops (Das & Teng, 1998). A high level of trust therefore allows companies to meet their cooperation goals and improve satisfaction with their partners.
Some studies show a positive relationship between previous experience of cooperation and the outcomes of agreements. These relationships may be linear (Anand & Khanna, 2000; Heimeriks & Duysters, 2007) or curvilinear (Sampson, 2005). From an evolutionary perspective, when organizations develop consecutive cooperation agreements, they accumulate a collective knowledge of inter-organizational activities (Zollo, Reuer, & Singh, 2002), and a specific knowledge of organizational culture, management systems, and the partner's capabilities and weaknesses (Reuer & Ariño, 2007). This tacit knowledge reduces coordination and development efforts in new agreements, and thus makes building trust with partners easier. Xia (2011) confirms that developing cooperation agreements improves both trust and mutual dependence in future agreements, as well as the likelihood of survival. Furthermore, according to the resource-based view (RBV), previous experience of cooperation generates cooperation management capabilities that enable firms to forge new alliances (Gulati, Lavie, & Singh, 2009; Ring & Van de Ven, 1994) and increase the chance of success in new partnerships (Sampson, 2005). Cooperation therefore generates relational rents (Dyer & Singh, 1998) that create value for the company (Anand & Khanna, 2000) through the trust they bring. The experience of cooperation obviously improves the efficiency of new cooperation agreements, through the learning that takes place and the trust that develops from fulfilling previous objectives.
3. The influence of reputation and experience of cooperation on trust
H3. Previous experience of cooperation has a positive relationship with trust building in the cooperation agreement.
3.1. The importance of reputation in cooperation agreements
H4. Previous experience of cooperation has a positive relationship, directly and indirectly through trust development, with the success of the cooperation agreement.
Companies can cut transaction costs during the initial stages of a cooperation agreement through trust between partners if their partner's reputation is adequate. A positive reputation can reduce information asymmetries and help increase the number of cooperation agreements with this partner (Houston, 2003). Reputation can therefore be a substitute for costly mechanisms that verify intentions and monitor a partner's actions. Moreover, partners sometimes perceive positive reputation as being more important than the threat of legal sanctions to ensure cooperation in alliances. Arend (2006) shows that the variables to do with reputation indicate how companies value the importance of their partner's skills in fulfilling their own objectives. If the information about the partner (i.e., features of the organizations concerning the quality of their products, their management, or their financial status) is positive, then both the partner's image and reputation will be positive (Mora, Montoro, & Guerras, 2004). When entering into cooperation agreements, companies will seek partners with good reputations, since this reputation transmits trust during the initial stages of an agreement and is therefore conducive to an effective start to the relationship (Das & Teng, 1998). A good reputation is indicative of quality and legitimacy (Dacin, Oliver, & Roy, 2007). Thus, when a company is in an alliance, carries out activities well, and other firms can observe positive cooperation behaviors, both reputation and trust between partners increase. A firm's reputation is therefore an important resource, which can both attract alliance partners and contribute to alliance success (Saxton, 1997). H1. The partner's reputation has a positive relation with trust building in the cooperation agreement.
3.2. The importance of previous experience of cooperation in the cooperation agreement
4. Methodology 4.1. Sample design The population under study is that of agro-food companies that engage in cooperation agreements. These agreements come from a review of national economic newspapers for the period from January 2001 to December 2005, and from singling out agro-food businesses planning to sign cooperation agreements or having just done so. One of the main dilemmas during the study of alliances is that of deciding on the unit of measurement. This study analyzes cooperation from the point of view of the company, since its objective is to ascertain the perception of Spanish agro-food companies that decide to cooperate. This research disregards alliance as the unit of analysis, since this study does not seek to evaluate companies from other sectors (in diversified agreements) or other countries (international agreements), which may distort the aims of the study. A series of mailings of questionnaires comprise the data collection procedure. Senior executives such as CEOs, marketing managers, general managers, and managing directors, who are knowledgeable about cooperation agreements, complete and return these questionnaires, of which 52 are valid, giving a response rate of 18.5%. 4.2. The model A structural equations model (SEM) tests the hypotheses. SEM permits the specification of the relationships between the constructs
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(structural model) and the goodness of their definition (measurement model). More specifically, this research uses the partial least squares (PLS) technique, via the SmartPLS 2.0 program (Ringle, Wende, & Will, 2005). This study uses a variance-based PLS approach rather than covariance-based methods, as PLS only demands fairly modest sample sizes and distribution restrictions. The size of the sample (52 companies) is adequate because this size fulfills the requirements of the rule of ten (Hair, Ringle, & Sarstedt, 2011). The authors use the Rodríguez and Wilson (2002) scale to measure trust, with respondents rating their degree of trust (from 1 to 7) through the use of three items: (a) I have full trust in the information provided by our partner (TRUST1); (b) our partner is perfectly honest and credible (TRUST2); and c) we have the expectation that the relationships with our partners will be beneficial (TRUST3). The items form a single factor following factor analysis. Trust therefore behaves as a reflective construct. This research analyzes the partner's reputation using a 5-item scale, taking Saxton (1997) as its basis. Respondents compare their partner(s) to other companies in the same sector using a 7-point Likert scale assessing the quality of products or services (REPUT1), ability to attract skilled workers (REPUT2), relations with the other companies in their environment (REPUT3), level of environmental responsibility (REPUT4), and relations with customers (REPUT5). Factor analysis yields a single factor for reputation. Partner's reputation is therefore a reflective construct. This study analyzes the previous experience of cooperation through two items and the use of a 7-point Likert scale: I am pleased with the results from such relationships (RS) and satisfied with the behavior of partners in agreements (PBS). The latent variable is formative because factor analysis yields more than one factor. This study uses two operational measures to analyze the success of the cooperation agreement. – Satisfaction (SAT) is a subjective concept that depends on many factors with applications in numerous fields. This study uses a 7-point Likert scale to assess seven items that analyze satisfaction as regards different aspects of the agreement, such as the partner's behavior, performance, and the overall functioning of the cooperation agreement. The factor loading is unidimensional, and the study therefore uses the average value of these items. – For degree of objective compliance (DOC), a scale of five items that seeks to collect companies' reasons for entering into cooperation agreements forms the basis for the measurement of this variable. Items are: transfer of knowledge and learning, access to resources and complementary capabilities, increase in competitive power, cost reduction/increase in efficiency, and customer satisfaction (Duysters and Hagedoorn 2000; Saxton, 1997). The aim is to discover the extent of compliance with these actions on a 7-point Likert scale ranging from 1 (very low degree of compliance) to 7 (very high degree of compliance). The factorial loading is unidimensional. The study therefore employs the average value of these items. Finally, factor analysis places the two indicators in separate factors, thereby signifying that the success of the cooperation agreement is formative. 5. Analysis of results
Table 1 Measurement model. Latent variable
CR
AVE
Measurement variable
Standardized loading (t-statistics)
Trust
0.94
0.83
Partner's reputation
0.92
0.69
Trust1 Trust2 Trust3 Reput1 Reput2 Reput3 Reput4 Reput5
0.87 (20.66) 0.95 (82.49) 0.91 (38.77) 0.71 (8.81) 0.91 (23.62) 0.85 (27.32) 0.82 (23.47) 0.87 (21.31)
This research ensures adequate content validity by taking its cue from an exhaustive literature review, selecting scales that previous works already validate in order to develop a pre-test, and considering the opinions of several experts in the field of study. This study also uses the tstatistic to determine the significance of the weights of the indicators of these constructs through the bootstrapping function of the SmartPLS 2.0 program. The measurement model offers evidence of convergent and discriminant validity, yielding factor loading values that are higher than 0.7 and significant (Hair, Anderson, Tatham, & Black, 1998). For reflective constructs, on the other hand, analysis of the reliability and construct validity is necessary. The model is reliable because the latent variables represent a composite reliability (CR) that exceeds 0.7; the value that Fornell and Larcker (1981), and Hair et al. (1998) recommend (See Table 1). This study analyzes convergent validity by using Average Variance Extracted (AVE). Hair et al. (1998) claim that a model has convergent validity when AVE values are greater than 0.50. Fornell and Larcker (1981) assert that, as a means of evaluating discriminant validity, the AVEs of the latent variables should be greater than the square of the correlations among the latent variables, which indicates that the latent variable components and their block of indicators have a greater share of the variance than another component representing a different block of indicators. The model has discriminant validity (see Table 2). 5.1.2. Structural model Prior to hypothesis testing, a check of the quality of the structural equations confirms the model's predictive relevance and its goodness of fit because all the latent variables have positive cv-redundancy and cv-communality indices (Hair et al., 2011). 5.2. Hypothesis testing The hypothesis testing consists of analyzing the relationships between the constructs in the model, its path coefficients, and their t-values (Fig. 1). The results of the analysis provide empirical support for all the hypotheses. The aim of this study is to discover the total effect of the variables on the success of cooperation agreements, and, more specifically, the effect of the partner's reputation and the previous experience of cooperation on the generation of trust. The significance of the direct effect of the partner's reputation and the previous experience of cooperation Table 2 Discriminant validity assessment of PLS models.
5.1. Analysis of the reliability and validity of the model Prior to the hypothesis testing, an assessment of the properties of the model's measure variables in two phases is instructive. These phases are: (1) Measurement model; and (2) Structural model. 5.1.1. Measurement model For the formative constructs, content validity reveals whether the measuring instrument provides real answers to the study objectives.
Trust Previous Partner's Success of experience of reputation cooperation cooperation agreement Trust Previous experience of cooperation Partner's reputation Success of cooperation agreement
0.91 0.61 0.48 0.62
– 0.18 0.61
0.83 0.43
–
The diagonal shows the square root of the variance that the constructs and their measures share (AVE). Off-diagonal elements are the correlations between the constructs.
J.D. Sánchez de Pablo González del Campo et al. / Journal of Business Research 67 (2014) 710–714
RS
713
PBS
0.67**
0.37 PREVIOUS EXPERIENCE OF COOPERATION
0.25***
0.54*** TRUST1
DOC
0.87** TRUST2
0.55***
TRUST R2= 0.51
0.95**
SUCCESS OF 0.57*** COOPERATION AGREEMENT R2 = 0.63 0.54***
0.91**
0.38***
TRUST3
0.13 SAT
PARTNER´S REPUTATION 0.87**
0.71*** 0.91*** REPUT1
0.85***
REPUT3
REPUT2
0.82*** REPUT4
REPUT5
Fig. 1. Analyzing the proposed model.
on trust provide empirical support for H1 and H3. High levels of the partner's reputation thus lead to an increase in trust in the current agreement (sv = 0.38; t = 5.61), while a high satisfaction with previous agreements leads to greater trust in the current agreement (sv = 0.54; t = 9.47). Table 3 shows the total effect on both relationships. The total impact is thus sufficiently strong to consider the indirect effect of the partner's reputation on the success of the cooperation agreement (sv = 0.34) positive and significant (t = 3.57). H2 therefore receives empirical support from the results. Similarly, the total impact of previous experience of cooperation on the success of the cooperation agreement is strong (sv = 0.55), positive, and significant (t = 6.55), and H4 therefore receives empirical support. This study thus verifies that both variables have an indirect effect on the success of cooperation agreements through trust. Hypothesis testing confirms the direct, significant, and positive relationship between previous experience of cooperations and the success of the cooperation agreement. In contrast, the analysis fails to demonstrate a direct and significant relationship between the partner's reputation and the success of the cooperation agreement.
Table 3 Decomposition of effects. Path
Previous experience of cooperation → trust Previous experience of cooperation → success of cooperation agreement Partner's reputation → trust Partner's reputation → success of cooperation agreement ⁎ p b 0.05. ⁎⁎⁎ p b 0.001.
Standardized values (t-values) Total effects
Direct effects
0.54 (9.44⁎⁎⁎) 0.55 (6.55⁎⁎⁎) 0.38 (4.82⁎⁎⁎) 0.34 (3.57⁎⁎⁎)
0.54 (9.44⁎⁎⁎) 0.25 (2.61⁎) 0.38 (4.82⁎⁎⁎) 0.13 (1.27)
Indirect effects
0.30 (3.29⁎⁎⁎)
0.21 (2.02⁎)
6. Conclusion and discussion Although numerous empirical studies demonstrate the influence of trust on the success of cooperation agreements, this paper enriches the literature by identifying factors that contribute toward actually improving trust. After analyzing the effect of the partner's reputation and previous experience of cooperation on trust, the present study obtains empirical support for the proposed hypotheses (H1 and H3). Moreover, when contemplating the relationships in the model between variables that determine the success of cooperation agreements, the study finds empirical support for all relationships (H2 and H4). The paper's main contribution is therefore to show a strong mediating effect of trust in the relationships between two key factors (the partner's reputation and previous experience of cooperation) and the success of a cooperation agreement. The results lead to a series of practical recommendations that may be truly useful for the upkeep and management of cooperation agreements. The initial stages of the agreements are relevant to developing agreements with partners who have good reputations. Furthermore, the accumulation of previous links between partners increases the chance of success of such agreements. These conclusions are similar to those of previous studies that reflect the positive influence of the partner's reputation (Anand & Khanna, 2000; Houston, 2003; Saxton, 1997; Stuart, 2000) and previous experience of cooperation (Chung, Singh, & Lee, 2000; Heimeriks & Duysters, 2007; Xia, 2011) on trust, and on the success of cooperation agreements. Nevertheless, although the partner's reputation has a significant relationship with the success of cooperation agreements, this relationship is not direct, instead occurring through the mediator, trust between the two parties. Arend (2009) is unable to find a clear relationship between reputation and cooperation, alliance length, and overall partner welfare. Real players make decisions with a rationale that is often difficult to predict ex ante, yet can cause major unforeseen outcomes ex post. In complex environments, reputation cannot always guarantee that partners will refrain from behaving opportunistically (Zhang, Jia, & Wan, 2012), and a higher degree of trust is necessary between partners to improve the success of cooperation agreements.
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A further contribution of this paper is to broach new territory in terms of sector because few empirical studies focus their entire analysis of cooperation agreements on mature industries. Companies in mature industries will therefore have more knowledge about the possibility of increasing trust during the initial stages of an agreement. This knowledge is especially crucial in the Spanish agro-food industry because this sector has little experience of cooperation – 34.6% of the companies in the sample have never entered into a cooperation agreement – , and partners with experience in cooperation are few and far between. Nevertheless, cooperation is a necessary strategy given the current state of this sector, whereby innovation is relevant if companies are to develop their activities efficiently. R&D activities are extremely expensive, however, and companies often lack the resources necessary or the capacity to carry them out alone. This paper identifies two factors that contribute toward increasing the trust during the initial stages of an agreement and, indirectly, the agreement's success. Future research should analyze the determinants of trust building during development and the influence of these determinants on success. The final aim of this analysis would be to integrate both components of trust into a single study, thus obtaining a more holistic and complete vision of the key factors in trust generation. One of the main limitations of this study is the cross-sectional nature of the analysis. Although this methodology provides precise information on the perception of partners as regards agreements, the technique is void of insight into the evolution of these perceptions over time. Future research should be longitudinal in order to verify whether the perceptions of companies in agreements change, and identify the most relevant issues during each stage of the cooperation agreement. Finally, work is already underway on follow-up, multi-industry research in order to validate the results of the current study. References Anand, B. N., & Khanna, T. (2000). Do firms learn to create value? The case of alliances. Strategic Management Journal, 21(3), 295–315. Arend, R. J. (2006). SME-Supplier alliance activity in manufacturing: Contingent benefits and perceptions. Strategic Management Journal, 27(8), 741–763. Arend, R. J. (2009). Reputation for cooperation: Contingent benefits in alliance activity. Strategic Management Journal, 30, 371–385. Chung, A. S., Singh, H., & Lee, K. (2000). Complementarity, status similarity and social capital as drivers of alliance formation. Strategic Management Journal, 21(2), 1–22. Collet, F., & Philippe, D. (2013). From Hot cakes to cold feet: A contingent perspective on the relationship between market uncertainty and status homophily in the formation of alliances. Journal of Management Studies. http://dx.doi.org/10.1111/joms.12051 (Accepted Article). Dacin, M. T., Oliver, C., & Roy, J. P. (2007). The legitimacy of strategic alliances: An institutional perspective. Strategic Management Journal, 28(3), 169–187. Das, T. K., & Teng, B.S. (1998). Between trust and control: Developing confidence in partner cooperation in alliances. Academy of Management Review, 23(3), 491–512.
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Contents lists available at ScienceDirect
Journal of Business Research
Influence factors of trust building in cooperation agreements☆ Jesús David Sánchez de Pablo González del Campo a,⁎, Isidro Peña García Pardo a,1, Felipe Hernández Perlines b,2 a b
Faculty of Law and Social Sciences, University of Castilla-La Mancha, 13071 Ronda Toledo s/n, Ciudad Real, Spain Faculty of Law and Social Sciences, University of Castilla-La Mancha, 45071 Cobertizo San Pedro Martir, s/n, Toledo, Spain
a r t i c l e
i n f o
Article history: Received 1 April 2013 Received in revised form 1 October 2013 Accepted 1 November 2013 Available online 9 December 2013 Keywords: Cooperation agreements Reputation Cooperation experience Trust
a b s t r a c t Trust is a key success factor in cooperation agreements. Therefore, identifying the factors that make the greatest contribution toward building trust is fundamental for an understanding of cooperation agreements. This paper analyzes two factors that might contribute to generating trust for successful agreements during the initial stages of the relationship. These factors are the partner's reputation and prior partnering experience. The study aims to confirm and complete the understanding of the relationship between these two variables and the success of cooperation agreements, by examining the indirect effect, through trust building, that these factors have on the success of agreements. The study analyzes these relationships by applying a structural equation model on the basis of partial least squares (PLS) methodology. The total impact of previous cooperation experience and the partner's reputation on the success of cooperation agreements is strong, positive, and significant. © 2013 Elsevier Inc. All rights reserved.
1. Introduction The current economic environment is highly complex and dynamic (Collet & Philippe, 2013). Securing a favorable competitive position on the basis of an individual dominant capability is therefore becoming increasingly difficult. Companies can, however, create routines in their cooperation agreements that yield relational rents (Dyer & Singh, 1998). The literature on cooperation agreements discusses key factors as regards the success of this business partnership model. Many of these studies use diverse theoretical approaches for justification and development, and show the relevance of a large number of factors that are very difficult to integrate into a model. Variables may, however, exist that, in addition to having a direct influence on the success of a cooperation agreement, also have an effect on other variables that indirectly improve success rates. The main contribution of this paper is, therefore, an illustration of the factors with the greatest significance and relevance in the literature, and an analysis of the relationship between them as regards their impact on success. For two different companies, an evaluation of their success when they attain the same result may also be different. The best means to
☆ The authors thank Pedro Jiménez Estévez from the University of Castilla-La Mancha, for his valuable comments on this paper. ⁎ Corresponding author. Tel.: +34 926295300; fax: +34 926293429. E-mail addresses: [email protected] (J.D. Sánchez de Pablo González del Campo), [email protected] (I. Peña García Pardo), [email protected] (F. Hernández Perlines). 1 Tel.: +34 926295300; fax: +34 926293531. 2 Tel.: +34 926295300; fax: +34 926295161. 0148-2963/$ – see front matter © 2013 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.jbusres.2013.11.032
assess the success of cooperation agreements is therefore the use of subjective variables. This study centers on two operational measures: the partner's level of satisfaction (Mohr & Spekman, 1994) and the achievement of the objectives of the agreement (Phillips, Lawrence, & Hardy, 2000). The most determinant factor in the success of cooperation is trust (Gulati & Higgins, 2003; Stuart, 2000; Thorgren & Wincent, 2011). Understanding trust generation in cooperation agreements and their success is therefore important. Previous experience of cooperation and the partner's reputation are factors with a substantial social component that could help build trust during the initial stages of an agreement. Previous works show the impact of previous cooperation experience (Heimeriks & Duysters, 2007) and the partner's reputation (Anand & Khanna, 2000; Saxton, 1997) on the success of cooperation agreements. The aim of this paper is to analyze the impact of these factors on trust, and thus their indirect impact on the success of cooperation agreements. A review of these relationships leads to the conclusion that companies can initiate good cooperation agreements if they have prior experience in cooperation and encounter partners with good reputations. Most existing empirical studies analyze high-technology industries (Haeussler, Patzelt, & Zahra, 2012; Stuart, 2000). Business cooperation in mature industries with a low technological intensity, however, receives little attention in the literature. But cooperation agreements can also be effective in mature industries, owing to the high volatility of such environments. This paper therefore studies the Spanish agrofood industry in order to bridge the gap in research, and since this sector records R&D expenditure as a percentage of Gross Value Added that is much lower than the average in other industries.
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2. Trust as a principal determinant of the success of cooperation agreements
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H2. The partner's reputation has a positive relation, directly and indirectly through trust development, with the success of the cooperation agreement.
One of the topics that scholars most frequently analyze regarding cooperation agreements is the link between trust and the success of cooperation agreements. Previous studies show that mutual trust between partners is a common factor in many successful cooperation agreements (Das & Teng, 1998). Gambetta (1988, p. 217) defines trust as the probability that one economic actor will make decisions and take actions that will be beneficial, or at least not detrimental, to another. Trust has two main components. The first is trust before initiating the agreement. If a company has previous experience with its partners then the two partners will already have inter-organizational trust. The second component is trust during the development of an agreement. As the agreement is developing, trust will increase between the companies if all parties fulfill expectations (Ring & Van de Ven, 1994). The principal benefits that trust generates in cooperation agreements are: a reduction in transaction costs, mitigation of the risk of investing in specific assets, and the facilitation of the decision-making process. These aspects give rise to commitment, and allow the partners to assume more risks, which generates value (Thorgren & Wincent, 2011). A high level of trust allows partners to share knowledge, resources, and capabilities, which has a positive impact on agreement outcomes because partners feel safe from opportunistic behavior from each other (Stuart, 2000). Moreover, an adequate level of trust helps predict the partner's behavior during the initial stages of an agreement, and fosters desirable behavior as the agreement develops (Das & Teng, 1998). A high level of trust therefore allows companies to meet their cooperation goals and improve satisfaction with their partners.
Some studies show a positive relationship between previous experience of cooperation and the outcomes of agreements. These relationships may be linear (Anand & Khanna, 2000; Heimeriks & Duysters, 2007) or curvilinear (Sampson, 2005). From an evolutionary perspective, when organizations develop consecutive cooperation agreements, they accumulate a collective knowledge of inter-organizational activities (Zollo, Reuer, & Singh, 2002), and a specific knowledge of organizational culture, management systems, and the partner's capabilities and weaknesses (Reuer & Ariño, 2007). This tacit knowledge reduces coordination and development efforts in new agreements, and thus makes building trust with partners easier. Xia (2011) confirms that developing cooperation agreements improves both trust and mutual dependence in future agreements, as well as the likelihood of survival. Furthermore, according to the resource-based view (RBV), previous experience of cooperation generates cooperation management capabilities that enable firms to forge new alliances (Gulati, Lavie, & Singh, 2009; Ring & Van de Ven, 1994) and increase the chance of success in new partnerships (Sampson, 2005). Cooperation therefore generates relational rents (Dyer & Singh, 1998) that create value for the company (Anand & Khanna, 2000) through the trust they bring. The experience of cooperation obviously improves the efficiency of new cooperation agreements, through the learning that takes place and the trust that develops from fulfilling previous objectives.
3. The influence of reputation and experience of cooperation on trust
H3. Previous experience of cooperation has a positive relationship with trust building in the cooperation agreement.
3.1. The importance of reputation in cooperation agreements
H4. Previous experience of cooperation has a positive relationship, directly and indirectly through trust development, with the success of the cooperation agreement.
Companies can cut transaction costs during the initial stages of a cooperation agreement through trust between partners if their partner's reputation is adequate. A positive reputation can reduce information asymmetries and help increase the number of cooperation agreements with this partner (Houston, 2003). Reputation can therefore be a substitute for costly mechanisms that verify intentions and monitor a partner's actions. Moreover, partners sometimes perceive positive reputation as being more important than the threat of legal sanctions to ensure cooperation in alliances. Arend (2006) shows that the variables to do with reputation indicate how companies value the importance of their partner's skills in fulfilling their own objectives. If the information about the partner (i.e., features of the organizations concerning the quality of their products, their management, or their financial status) is positive, then both the partner's image and reputation will be positive (Mora, Montoro, & Guerras, 2004). When entering into cooperation agreements, companies will seek partners with good reputations, since this reputation transmits trust during the initial stages of an agreement and is therefore conducive to an effective start to the relationship (Das & Teng, 1998). A good reputation is indicative of quality and legitimacy (Dacin, Oliver, & Roy, 2007). Thus, when a company is in an alliance, carries out activities well, and other firms can observe positive cooperation behaviors, both reputation and trust between partners increase. A firm's reputation is therefore an important resource, which can both attract alliance partners and contribute to alliance success (Saxton, 1997). H1. The partner's reputation has a positive relation with trust building in the cooperation agreement.
3.2. The importance of previous experience of cooperation in the cooperation agreement
4. Methodology 4.1. Sample design The population under study is that of agro-food companies that engage in cooperation agreements. These agreements come from a review of national economic newspapers for the period from January 2001 to December 2005, and from singling out agro-food businesses planning to sign cooperation agreements or having just done so. One of the main dilemmas during the study of alliances is that of deciding on the unit of measurement. This study analyzes cooperation from the point of view of the company, since its objective is to ascertain the perception of Spanish agro-food companies that decide to cooperate. This research disregards alliance as the unit of analysis, since this study does not seek to evaluate companies from other sectors (in diversified agreements) or other countries (international agreements), which may distort the aims of the study. A series of mailings of questionnaires comprise the data collection procedure. Senior executives such as CEOs, marketing managers, general managers, and managing directors, who are knowledgeable about cooperation agreements, complete and return these questionnaires, of which 52 are valid, giving a response rate of 18.5%. 4.2. The model A structural equations model (SEM) tests the hypotheses. SEM permits the specification of the relationships between the constructs
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(structural model) and the goodness of their definition (measurement model). More specifically, this research uses the partial least squares (PLS) technique, via the SmartPLS 2.0 program (Ringle, Wende, & Will, 2005). This study uses a variance-based PLS approach rather than covariance-based methods, as PLS only demands fairly modest sample sizes and distribution restrictions. The size of the sample (52 companies) is adequate because this size fulfills the requirements of the rule of ten (Hair, Ringle, & Sarstedt, 2011). The authors use the Rodríguez and Wilson (2002) scale to measure trust, with respondents rating their degree of trust (from 1 to 7) through the use of three items: (a) I have full trust in the information provided by our partner (TRUST1); (b) our partner is perfectly honest and credible (TRUST2); and c) we have the expectation that the relationships with our partners will be beneficial (TRUST3). The items form a single factor following factor analysis. Trust therefore behaves as a reflective construct. This research analyzes the partner's reputation using a 5-item scale, taking Saxton (1997) as its basis. Respondents compare their partner(s) to other companies in the same sector using a 7-point Likert scale assessing the quality of products or services (REPUT1), ability to attract skilled workers (REPUT2), relations with the other companies in their environment (REPUT3), level of environmental responsibility (REPUT4), and relations with customers (REPUT5). Factor analysis yields a single factor for reputation. Partner's reputation is therefore a reflective construct. This study analyzes the previous experience of cooperation through two items and the use of a 7-point Likert scale: I am pleased with the results from such relationships (RS) and satisfied with the behavior of partners in agreements (PBS). The latent variable is formative because factor analysis yields more than one factor. This study uses two operational measures to analyze the success of the cooperation agreement. – Satisfaction (SAT) is a subjective concept that depends on many factors with applications in numerous fields. This study uses a 7-point Likert scale to assess seven items that analyze satisfaction as regards different aspects of the agreement, such as the partner's behavior, performance, and the overall functioning of the cooperation agreement. The factor loading is unidimensional, and the study therefore uses the average value of these items. – For degree of objective compliance (DOC), a scale of five items that seeks to collect companies' reasons for entering into cooperation agreements forms the basis for the measurement of this variable. Items are: transfer of knowledge and learning, access to resources and complementary capabilities, increase in competitive power, cost reduction/increase in efficiency, and customer satisfaction (Duysters and Hagedoorn 2000; Saxton, 1997). The aim is to discover the extent of compliance with these actions on a 7-point Likert scale ranging from 1 (very low degree of compliance) to 7 (very high degree of compliance). The factorial loading is unidimensional. The study therefore employs the average value of these items. Finally, factor analysis places the two indicators in separate factors, thereby signifying that the success of the cooperation agreement is formative. 5. Analysis of results
Table 1 Measurement model. Latent variable
CR
AVE
Measurement variable
Standardized loading (t-statistics)
Trust
0.94
0.83
Partner's reputation
0.92
0.69
Trust1 Trust2 Trust3 Reput1 Reput2 Reput3 Reput4 Reput5
0.87 (20.66) 0.95 (82.49) 0.91 (38.77) 0.71 (8.81) 0.91 (23.62) 0.85 (27.32) 0.82 (23.47) 0.87 (21.31)
This research ensures adequate content validity by taking its cue from an exhaustive literature review, selecting scales that previous works already validate in order to develop a pre-test, and considering the opinions of several experts in the field of study. This study also uses the tstatistic to determine the significance of the weights of the indicators of these constructs through the bootstrapping function of the SmartPLS 2.0 program. The measurement model offers evidence of convergent and discriminant validity, yielding factor loading values that are higher than 0.7 and significant (Hair, Anderson, Tatham, & Black, 1998). For reflective constructs, on the other hand, analysis of the reliability and construct validity is necessary. The model is reliable because the latent variables represent a composite reliability (CR) that exceeds 0.7; the value that Fornell and Larcker (1981), and Hair et al. (1998) recommend (See Table 1). This study analyzes convergent validity by using Average Variance Extracted (AVE). Hair et al. (1998) claim that a model has convergent validity when AVE values are greater than 0.50. Fornell and Larcker (1981) assert that, as a means of evaluating discriminant validity, the AVEs of the latent variables should be greater than the square of the correlations among the latent variables, which indicates that the latent variable components and their block of indicators have a greater share of the variance than another component representing a different block of indicators. The model has discriminant validity (see Table 2). 5.1.2. Structural model Prior to hypothesis testing, a check of the quality of the structural equations confirms the model's predictive relevance and its goodness of fit because all the latent variables have positive cv-redundancy and cv-communality indices (Hair et al., 2011). 5.2. Hypothesis testing The hypothesis testing consists of analyzing the relationships between the constructs in the model, its path coefficients, and their t-values (Fig. 1). The results of the analysis provide empirical support for all the hypotheses. The aim of this study is to discover the total effect of the variables on the success of cooperation agreements, and, more specifically, the effect of the partner's reputation and the previous experience of cooperation on the generation of trust. The significance of the direct effect of the partner's reputation and the previous experience of cooperation Table 2 Discriminant validity assessment of PLS models.
5.1. Analysis of the reliability and validity of the model Prior to the hypothesis testing, an assessment of the properties of the model's measure variables in two phases is instructive. These phases are: (1) Measurement model; and (2) Structural model. 5.1.1. Measurement model For the formative constructs, content validity reveals whether the measuring instrument provides real answers to the study objectives.
Trust Previous Partner's Success of experience of reputation cooperation cooperation agreement Trust Previous experience of cooperation Partner's reputation Success of cooperation agreement
0.91 0.61 0.48 0.62
– 0.18 0.61
0.83 0.43
–
The diagonal shows the square root of the variance that the constructs and their measures share (AVE). Off-diagonal elements are the correlations between the constructs.
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RS
713
PBS
0.67**
0.37 PREVIOUS EXPERIENCE OF COOPERATION
0.25***
0.54*** TRUST1
DOC
0.87** TRUST2
0.55***
TRUST R2= 0.51
0.95**
SUCCESS OF 0.57*** COOPERATION AGREEMENT R2 = 0.63 0.54***
0.91**
0.38***
TRUST3
0.13 SAT
PARTNER´S REPUTATION 0.87**
0.71*** 0.91*** REPUT1
0.85***
REPUT3
REPUT2
0.82*** REPUT4
REPUT5
Fig. 1. Analyzing the proposed model.
on trust provide empirical support for H1 and H3. High levels of the partner's reputation thus lead to an increase in trust in the current agreement (sv = 0.38; t = 5.61), while a high satisfaction with previous agreements leads to greater trust in the current agreement (sv = 0.54; t = 9.47). Table 3 shows the total effect on both relationships. The total impact is thus sufficiently strong to consider the indirect effect of the partner's reputation on the success of the cooperation agreement (sv = 0.34) positive and significant (t = 3.57). H2 therefore receives empirical support from the results. Similarly, the total impact of previous experience of cooperation on the success of the cooperation agreement is strong (sv = 0.55), positive, and significant (t = 6.55), and H4 therefore receives empirical support. This study thus verifies that both variables have an indirect effect on the success of cooperation agreements through trust. Hypothesis testing confirms the direct, significant, and positive relationship between previous experience of cooperations and the success of the cooperation agreement. In contrast, the analysis fails to demonstrate a direct and significant relationship between the partner's reputation and the success of the cooperation agreement.
Table 3 Decomposition of effects. Path
Previous experience of cooperation → trust Previous experience of cooperation → success of cooperation agreement Partner's reputation → trust Partner's reputation → success of cooperation agreement ⁎ p b 0.05. ⁎⁎⁎ p b 0.001.
Standardized values (t-values) Total effects
Direct effects
0.54 (9.44⁎⁎⁎) 0.55 (6.55⁎⁎⁎) 0.38 (4.82⁎⁎⁎) 0.34 (3.57⁎⁎⁎)
0.54 (9.44⁎⁎⁎) 0.25 (2.61⁎) 0.38 (4.82⁎⁎⁎) 0.13 (1.27)
Indirect effects
0.30 (3.29⁎⁎⁎)
0.21 (2.02⁎)
6. Conclusion and discussion Although numerous empirical studies demonstrate the influence of trust on the success of cooperation agreements, this paper enriches the literature by identifying factors that contribute toward actually improving trust. After analyzing the effect of the partner's reputation and previous experience of cooperation on trust, the present study obtains empirical support for the proposed hypotheses (H1 and H3). Moreover, when contemplating the relationships in the model between variables that determine the success of cooperation agreements, the study finds empirical support for all relationships (H2 and H4). The paper's main contribution is therefore to show a strong mediating effect of trust in the relationships between two key factors (the partner's reputation and previous experience of cooperation) and the success of a cooperation agreement. The results lead to a series of practical recommendations that may be truly useful for the upkeep and management of cooperation agreements. The initial stages of the agreements are relevant to developing agreements with partners who have good reputations. Furthermore, the accumulation of previous links between partners increases the chance of success of such agreements. These conclusions are similar to those of previous studies that reflect the positive influence of the partner's reputation (Anand & Khanna, 2000; Houston, 2003; Saxton, 1997; Stuart, 2000) and previous experience of cooperation (Chung, Singh, & Lee, 2000; Heimeriks & Duysters, 2007; Xia, 2011) on trust, and on the success of cooperation agreements. Nevertheless, although the partner's reputation has a significant relationship with the success of cooperation agreements, this relationship is not direct, instead occurring through the mediator, trust between the two parties. Arend (2009) is unable to find a clear relationship between reputation and cooperation, alliance length, and overall partner welfare. Real players make decisions with a rationale that is often difficult to predict ex ante, yet can cause major unforeseen outcomes ex post. In complex environments, reputation cannot always guarantee that partners will refrain from behaving opportunistically (Zhang, Jia, & Wan, 2012), and a higher degree of trust is necessary between partners to improve the success of cooperation agreements.
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A further contribution of this paper is to broach new territory in terms of sector because few empirical studies focus their entire analysis of cooperation agreements on mature industries. Companies in mature industries will therefore have more knowledge about the possibility of increasing trust during the initial stages of an agreement. This knowledge is especially crucial in the Spanish agro-food industry because this sector has little experience of cooperation – 34.6% of the companies in the sample have never entered into a cooperation agreement – , and partners with experience in cooperation are few and far between. Nevertheless, cooperation is a necessary strategy given the current state of this sector, whereby innovation is relevant if companies are to develop their activities efficiently. R&D activities are extremely expensive, however, and companies often lack the resources necessary or the capacity to carry them out alone. This paper identifies two factors that contribute toward increasing the trust during the initial stages of an agreement and, indirectly, the agreement's success. Future research should analyze the determinants of trust building during development and the influence of these determinants on success. The final aim of this analysis would be to integrate both components of trust into a single study, thus obtaining a more holistic and complete vision of the key factors in trust generation. One of the main limitations of this study is the cross-sectional nature of the analysis. Although this methodology provides precise information on the perception of partners as regards agreements, the technique is void of insight into the evolution of these perceptions over time. Future research should be longitudinal in order to verify whether the perceptions of companies in agreements change, and identify the most relevant issues during each stage of the cooperation agreement. Finally, work is already underway on follow-up, multi-industry research in order to validate the results of the current study. References Anand, B. N., & Khanna, T. (2000). Do firms learn to create value? The case of alliances. Strategic Management Journal, 21(3), 295–315. Arend, R. J. (2006). SME-Supplier alliance activity in manufacturing: Contingent benefits and perceptions. Strategic Management Journal, 27(8), 741–763. Arend, R. J. (2009). Reputation for cooperation: Contingent benefits in alliance activity. Strategic Management Journal, 30, 371–385. Chung, A. S., Singh, H., & Lee, K. (2000). Complementarity, status similarity and social capital as drivers of alliance formation. Strategic Management Journal, 21(2), 1–22. Collet, F., & Philippe, D. (2013). From Hot cakes to cold feet: A contingent perspective on the relationship between market uncertainty and status homophily in the formation of alliances. Journal of Management Studies. http://dx.doi.org/10.1111/joms.12051 (Accepted Article). Dacin, M. T., Oliver, C., & Roy, J. P. (2007). The legitimacy of strategic alliances: An institutional perspective. Strategic Management Journal, 28(3), 169–187. Das, T. K., & Teng, B.S. (1998). Between trust and control: Developing confidence in partner cooperation in alliances. Academy of Management Review, 23(3), 491–512.
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