Week 6 DQ/Relationship governance and learning in partnerships.pdf
Relationship governance and learning in partnerships
Marko Kohtamäki Department of Management, University of Vaasa, Vaasa, Finland
Purpose – Relationship learning is a topic of considerable importance for industrial networks, yet a lack of empirical research on the impact of relationship governance structures on relationship learning remains. The purpose of this paper is to analyze the impact of relationship governance structures on learning in partnerships.
Design/methodology/approach – This paper contributes to the closure of the research gap by examining sample data drawn from 42 interviews on the subject of 199 customer-supplier relationships within the Finnish metal and electronics industries. As a method, the paper applies cluster analysis and analysis of variance mean-comparison.
Findings – The results of this paper show that balanced hybrid governance structures explain learning in partnerships, which suggests that certain combinations of relationship governance mechanisms (price, hierarchical, and social mechanism) produce the best learning outcomes in partnerships. Results suggest that managers should use hybrid relationship governance structures when governing their supplier partnerships.
Research limitations/implications – The paper has some limitations such as limited sample size, cross-sectional data, and difficulties due to measuring social phenomenon such as learning. Owing to the interview method being applied, research is bound to apply a sample data drawn from companies that operate in the west coast in Finland. These limitations need to be considered when applying the results.
Practical implications – The results encourage managers to use different governance mechanisms simultaneously when managing their company’s supply chain partnerships. The result emphasizes the role of active relationship management.
Originality/value – The paper is one of the first to empirically show that relationship learning is best facilitated by using various relationship governance mechanisms simultaneously. Trust needs to be complemented by hierarchical and possibly by price mechanism.
Keywords Customer relations, Supplier relations, Learning, Partnership, Finland
Paper type Research paper
1. Introduction The imperfect nature of industrial markets favors the use of more sophisticated mechanisms of relationship governance than mere competitive bidding to drive learning and innovation, within partnerships and business networks (Ahmadjian and Lincoln, 2001; Knight, 2002). Competitive bidding cannot foster learning, when supplier switching times are long. Thus, in partnerships, competition or, in particular, competitive bidding is inefficient in terms of learning (Krause et al., 2000). Therefore, the interplay between price, hierarchical, and social governance mechanisms is particularly interesting in partnerships (Adler, 2001; Ghoshal and Moran, 1996). Following on Adler’s (2001) model, the present study proposes that relationship
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This paper emerged from the research projects Dynamo and System. The financial support of the Finnish Funding Agency for Technology and Innovation and the companies involved in this project is gratefully acknowledged.
Relationship governance and
The Learning Organization Vol. 17 No. 1, 2010
pp. 41-57 q Emerald Group Publishing Limited
0969-6474 DOI 10.1108/09696471011008233
learning is best facilitated by the simultaneous use of different relationship governance mechanisms and that certain combinations of these mechanisms increase relationship learning more than others do.
This research contributes to the current knowledge of partnerships by increasing understanding about the impact of relationship governance structures on learning in partnerships, which previous literature contends to be an important research gap (Nooteboom and Gilsing, 2004). Indeed, the research on relationship governance (Adler, 2001) has neglected the relationship learning view, while the scholars focusing on relationship learning have overlooked the governance viewpoint. This paper addresses the research gap by combining these literature streams into a coherent research model that explains how different combinations of relationship governance mechanisms (price, hierarchical, and social) have an impact on relationship learning. This study will also contribute by increasing our knowledge as to how supply chain partnerships should be governed in order to facilitate learning. While a vast amount of previous literature contends that learning requires trust (Dodgson, 1993; Rousseau et al., 1998), the present paper intends to study whether learning can be enhanced by combining trust (a social mechanism), relationship management (a hierarchical mechanism), and competition between the suppliers (a price mechanism; Adler, 2001).
2. Relationship governance and learning Learning in partnerships This study approaches relationship learning by applying organizational learning theory (Fiol and Lyles, 1985). Since learning is context dependent (Holmqvist, 2003; Knight, 2002), it needs to be studied in both partnerships and networks. The argument is that the level of organizational integration, e.g. trust, between the organizational members affects learning and, thus, learning is different in teams than it is in inter-organizational networks or partnerships.
Previous literature provides various definitions of relationship learning. The present study defines the relationship learning according to Selnes and Sallis (2003, p. 80) as:
[. . .] a joint activity between a supplier and a customer in which the two parties share information, which is then jointly interpreted and integrated into a shared relationship-domain – specific memory [. . .]
This definition of relationship learning underlines knowledge sharing, shared interpretation and the development of activities in a partnership alongside other definitions (Håkansson et al., 1999; Dyer and Hatch, 2004; Inkpen, 1996; Knight, 2002).
Relationship governance and learning Following the previous definitions of partnerships, the present study defines partnerships and networks as an intermediate form between markets and hierarchies (Thorelli, 1986; Ritter, 2007; Williamson, 1985). In other words, a vertical partnership is a customer-supplier relationship, which is long, integrated, and deeply rooted in the social relationships between the individuals that are active in the relationship (Macaulay, 1963; Sako, 1992; Ritter, 2007).
Recent theory developments in the study of relationship governance argue that the most effective partnership governance structure is a hybrid, in which the customer employs several relationship governance mechanisms simultaneously to govern
a single supply relationship (Figure 1; Adler, 2001; Heide, 1994; Ritter, 2007; Kohtamäki et al., 2006). The three relationship governance mechanisms that previous studies apply are termed price, hierarchical, and social mechanism (Adler, 2001; Powell, 1990; Bradach and Eccles, 1989; Hines, 1995; Heide, 1994).
Previous empirical research has commonly operationalized network governance in terms of sourcing policy, whether the customer applies single, dual, or multiple sourcing in their procurement policy (Dyer and Ouchi, 1993, pp. 55-8; Hines, 1995, 1996). This study adopts a more sophisticated approach and applies multiple indicators to define and measure each governance mechanism. In this study, relationship governance refers to a governance structure of a supplier relationship, which is constructed using a combination of price, hierarchical, and social mechanisms. The theory contends that a customer can steer the behavior of its suppliers by applying these mechanisms in different combinations (Adler, 2001). The following section describes the individual governance mechanisms in more detail, while the subsequent sections develop on their different combinations and their impact on relationship learning.
Price as a mechanism of relationship governance refers to utilizing the competition between suppliers in the market to steer the relationship. Competition is known as an efficient mechanism, which is utilized not only in markets, but also in hierarchies and networks (Dyer and Hatch, 2004; Krause et al., 2000; Powell, 1990; Swedberg, 1994). However, when switching to an alternative partner becomes time-consuming and costly due to the unique resources and capabilities of the supplier, the market works
Figure 1. Effects of relationship
governance structures on learning in partnerships
Relational contracting Hybrid
R elationship learning
Note: Low social relationship governance in lower left triangles and high social relationship governance in upper right triangles Source: Adler (2001)
Relationship governance and
imperfectly and other governance mechanisms are required to ensure learning and development in the relationship (Kohtamäki and Kautonen, 2008). Various scholars describe Toyota’s successful dual or multiple supplier policy within its supplier network, which utilizes competition without a constant need to change suppliers (Dyer and Hatch, 2004; Sako, 2004; Dyer and Nobeoka, 2000). Dual or multiple sourcing enables a customer to use competition without sacrificing the long-term relationship, which facilitates development and learning in the relationship (Hines, 1995). Competition can prove a catalyst for developmental work, while the partners’ belief in the continuity of the relationship motivates the development.
Gerlach (1992) defines the hierarchical governance mechanism as the “visible hand” of the manager in the organization. In this study, hierarchical governance refers to mechanisms such as the customer’s use of authority in the relationship and the hierarchical structures and processes that apply to the business relationship (Nishiguchi and Beaudet, 1998; Bensaou, 1999; Håkansson and Lind, 2004). Thus, when using hierarchical relationship governance, the customer steers, but also forces the development of the business relationship. Researchers have provided examples of customers’ use of authority and hierarchical structures. For example, Dyer and Hatch (2004) describe three methods, which Toyota applies to support supplier development: supplier association, consulting groups, and learning teams. This means that Toyota facilitates supplier learning with conferences and smaller learning forums, e.g. learning teams, but also provides a consulting service to its suppliers (Sako, 2004; Dyer and Nobeoka, 2000). These results suggest that Toyota does not only try to develop trusting relationships with its suppliers, but seeks to actively facilitate learning in its partnerships and supplier network. Our study follows the view by analyzing the role of hierarchical relationship governance in partnership learning.
A whole stream of literature has examined trust and social governance in business relationships (Adler, 2001; Granovetter, 1985; Ouchi, 1980). In this context, social governance refers to trust (Zaheer et al., 1998), open interaction and a feeling of shared destiny (Adler, 2001; Ghoshal and Moran, 1996). A number of studies emphasize the significance of these phenomena for learning in relationships (Håkansson et al., 1999; Selnes and Sallis, 2003). However, as learning needs to be focused in order to create value for a particular business relationship, trust alone is an inadequate governance mechanism and needs to be supported by other mechanisms (Adler, 2001; Kohtamäki and Kautonen, 2008).
The role of relationship governance structures on learning Based on Adler’s (2001) model, the present study suggests that learning in relationships is best facilitated by a combination of price, hierarchy, and the social relationship governance mechanisms, rather than a sole reliance on any one of these single mechanisms. In the following discussion of the impact of different combinations of governance mechanisms on relationship learning, the degree of each governance mechanism in a particular governance structure is simply regarded as being either high or low. Figure 1 displays eight different combinations of the three governance mechanisms, that is, eight alternative relationship governance structures. This study proposes that they have a varying impact on learning in business relationships. Since, the conceptual evidence in previous literature is not clear enough to warrant a formal hypothesis, the following discussion declines to construct formal hypotheses but
instead presents preliminary conceptual evidence as a basis for the subsequent exploratory empirical analysis.
Figure 1 suggests that governance structures are constructed on the basis of price, hierarchical, and social mechanisms. Thus, the present study suggests there are basically four different combinations of relationship governance mechanisms, as in the remainder of the eight clusters the customer either applies a single mechanism (price, hierarchical, or social) or does not apply any of them (a laissez-faire approach). The four clusters, in which a customer uses two or three different mechanisms simultaneously, are here termed relational governance, supportive hierarchical governance, low-trust hybrid governance, and hybrid governance.
By relational governance, the model refers to a combination of price and social mechanism (Macaulay, 1963). Theory suggests that just as competitive bidding may force the supplier to develop the customer relationship (Krause et al., 2000); trust could increase its partners’ willingness to share knowledge within it (Håkansson et al., 1999). On the other hand, unreasonable use of competitive bidding could lead to a decrease in a supplier’s commitment to the relationship, and thus unwillingness to invest in relationship development. The findings of the previous studies recommend dual or multiple supplier policies, which are able to simultaneously produce competition, stability, and trust in the relationship (Dyer and Hatch, 2004; Hines, 1995; Dyer and Ouchi, 1993).
Previous studies also suggest that the combination of hierarchical and social mechanisms can be effective in terms of relationship learning (Adler, 2001; Kohtamäki et al., 2006). Relationship learning may require an open and trusting atmosphere, but also a little pressure created by the customer. While previous scholars show that mutual learning requires trust between the partners (Takeuchi and Nonaka, 1995; Selnes and Sallis, 2003), Adler’s (2001) model argues that partnerships should be managed and facilitated (Möller et al., 2005). This suggests that hierarchical governance is fundamental in partnerships (van der Meer-Kooistra and Vosselman, 2000), but its use should be delicate, so that it will not cause distrust (Ghoshal and Moran, 1996). Hence, a customer should have sufficient competence to apply hierarchical steering without causing distrust.
The paper defines the third combination of governance mechanisms as a low-trust hybrid (Adler, 2001). In this alternative, the combination of price and hierarchical mechanism affects learning in partnerships. When talking of this low-trust hybrid relationship governance structure, the researcher is referring to a business relationship, which is governed by hierarchical structures and some competition, but not by trust, perhaps due to the loosely coupled organization of the relationship. This particular relationship governance structure might not be efficient in terms of new knowledge creation, because learning requires trust, but could well be efficient in terms of keeping the overall costs of the relationship down.
The fourth alternative relationship governance structure is here termed a hybrid (Heide, 1994; Hines, 1995; Håkansson and Lind, 2004; Sako, 2004). In a hybrid governance structure, the customer applies all three governance mechanisms simultaneously. The present study suspects that the hybrid governance structure facilitates relationship learning and relationship performance, by providing a moderate level of competition and hierarchical direction, as well as an open atmosphere in which to share and develop knowledge and learning within the partnership.
Relationship governance and
In summary, the present study focuses on the impact of relationship governance structures on relationship learning by applying Adler’s (2001) model of relationship governance. The study explores which kinds of relationship governance structures can be discerned within 199 business relationships in order to see how various combinations of governance mechanisms affect relationship learning.
3. Research methodology and data Data collection The study uses cluster analysis to analyze sample data from 199 customer-supplier relationships. The data were collected from 26 (45 percent medium-sized/55 percent large) business units in the metal and electronics industries in Finland. Data were collected in interviews of 42 supply directors (three respondents), supply managers (26 respondents), or strategic buyers (13 respondents). Most of the respondents (39 of 42), analyzed five relationships each, while the rest (three respondents) analyzed a few individual relationships by using a web-based questionnaire. The researcher controlled for the potential effect of the respondent’s role within the organization (director, supply manager, and strategic buyer) on their responses, by comparing the responses of directors, managers, and buyers on the key study variables by using t-test. However, the test yielded no statistically significant differences between the respondents in different roles. The companies were chosen from western Finland for research economic reasons, as the data was collected in personal interviews and the researcher had to travel to all the respondent companies.
Measures Previous studies (Selnes and Sallis, 2003; Kohtamäki and Kautonen, 2008; Krause et al., 2000) contributed to the development of the items in the questionnaire, which uses Likert-scale measures (1, fully disagree; 5, fully agree; Appendix 1). The researcher transferred items into four different composite variables (price, hierarchical, social governance mechanisms, and relationship learning) for the cluster analysis and mean comparisons. The study tested the items by using partial least squares approach. Researcher tests the constructs by using Cronbach’s alpha, composite reliability and average variance extracted (AVE). The researcher also tests both item and construct discriminant validity, inspect skewness, and kurtosis values of all constructs as well as checks the data for possible common method bias and multicollinearity.
The main determinants of the price mechanism are internal competition within the network, potential suppliers in the market and the development of a competitive atmosphere among the suppliers (Hines, 1996). The four variables measuring the price mechanism were developed on the basis of Kohtamäki et al. (2008) (Krause et al., 2000). Items measuring price were: frequency of bidding; number of potential suppliers in the market; number of suppliers for a given component; and development of a competitive atmosphere in the relationship.
Previous studies define hierarchical governance as consisting of several different variables, which measure both the customer’s use of authority and hierarchical structures in the relationship (Hines, 1996; Ellram, 2002). Measures of this dimension were modified on the basis of Kohtamäki et al. (2008) (Krause et al., 2000). This study measures hierarchical governance by using five variables: level of quality and management system requirements; urge to affect supplier’s procedures; supplier’s
involvement in customer’s production and quality meetings; use of supplier auditing; and exactness of instructions given to supplier.
Previous empirical research has studied social governance extensively and scholars have used various scales to report their findings. This research applies the scale used by Selnes and Sallis (2003) (Kohtamäki et al., 2008), which reflects the two dimensions of social governance defined as having a shared purpose and trust. Four variables measure social governance: development of shared understanding; level of strategic discussions with the supplier; customer’s willingness to develop trust in the relationship; and willingness to seek a common understanding.
The present study measures learning with four items based on the conceptualizations of Selnes and Sallis (2003). The variables are: development of new ideas in the relationship; economic value of new ideas in the relationship; shared problem solving and knowledge sharing; and explication of the most conflicting problems.
The reliability of the constructs was measured by deriving values for Cronbach’s alpha (threshold value 0.6), composite reliability (0.7), and AVE (0.5). Almost all the constructs show fairly satisfactory Cronbach’s alpha, composite reliability and AVE values (Chin, 1998; Cool et al., 1989), although AVE value for price governance were a little low and below the threshold (0.5). As all the items, except one measuring price, exceed the typical threshold value set for the item loading (0.6) and the loading of each item with their respective construct is statistically significant, researcher can safely conclude satisfactory item discriminant validity. As the price mechanism achieved fairly satisfactory Cronbach’s alpha and composite reliability values, researcher decided to keep all the items in order to maintain the construct’s theoretical consistency. All constructs showed satisfactory discriminant validity as AVE values exceeded the squared latent variable correlations (Cool et al., 1989) even if the low AVE value of price governance suggest that those measures need development in future studies (Chin, 1998).
The researcher also decided to test the skewness and kurtosis of each construct and found every construct exceeding the typical threshold. The data were also tested for common method bias using Harman’s (1976) one factor test, which the researcher conducted by using principal axis factoring and interpreting the unrotated factor solution (Podsakoff and Organ, 1986). The test showed that common method variance was not present in the data as the items loaded on four factors, which accounted for 61 percent of the total variance of which the first factor accounted for only 33 percent. Finally, researcher analyzed the data due to possible multicollinearity of the constructs, but the correlation matrix (Appendix 2) and vif-value shows that in this dataset multicollinearity does not create a problem. Vif-value for all the constructs remained well below 2, while the typical threshold is 10 (Tabachnick and Fidell, 2007). In summary, based on the statistical tests reported above, the items and constructs appear suitable for further analysis (Table I).
Methods and data analysis The present study analyzes the data in two phases. The first phase of the analysis applies cluster analysis in order to find the clusters consisting of business relationships governed by similar relationship governance structures and, thus, differing from other clusters. In the second phase, these clusters of business relationships are mean