and learning in partnerships
Department of Management, University of Vaasa, Vaasa, Finland
Purpose â€“ 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 â€“ 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 â€“ 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 â€“ 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 â€“ The results encourage managers to use different governance mechanisms
simultaneously when managing their companyâ€™s supply chain partnerships. The result emphasizes
the role of active relationship management.
Originality/value â€“ 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
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 Adlerâ€™s (2001) model, the present study proposes that relationship
The current issue and full text archive of this journal is available at
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.
The Learning Organization
Vol. 17 No. 1, 2010
q Emerald Group Publishing Limited
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 â€“ specific memory […]
This definition of relationship learning underlines knowledge sharing, shared
interpretation and the development of activities in a partnership alongside other
definitions (HaËškansson 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; KohtamaÂ¨ki
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
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
Effects of relationship
governance structures on
learning in partnerships
Note: Low social relationship governance in lower left triangles and high social
relationship governance in upper right triangles
Source: Adler (2001)
imperfectly and other governance mechanisms are required to ensure learning and
development in the relationship (KohtamaÂ¨ki and Kautonen, 2008). Various scholars
describe Toyotaâ€™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 partnersâ€™ belief
in the continuity of the relationship motivates the development.
Gerlach (1992) defines the hierarchical governance mechanism as the â€œvisible handâ€
of the manager in the organization. In this study, hierarchical governance refers to
mechanisms such as the customerâ€™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; HaËškansson 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
customersâ€™ 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 (HaËškansson 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; KohtamaÂ¨ki
and Kautonen, 2008).
The role of relationship governance structures on learning
Based on Adlerâ€™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 partnersâ€™ willingness to share knowledge within it (HaËškansson et al., 1999).
On the other hand, unreasonable use of competitive bidding could lead to a decrease in a
supplierâ€™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
Previous studies also suggest that the combination of hierarchical and social
mechanisms can be effective in terms of relationship learning (Adler, 2001; KohtamaÂ¨ki
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), Adlerâ€™s (2001) model argues that partnerships should be
managed and facilitated (MoÂ¨ller 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; HaËškansson 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.
In summary, the present study focuses on the impact of relationship governance
structures on relationship learning by applying Adlerâ€™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
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 respondentâ€™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.
Previous studies (Selnes and Sallis, 2003; KohtamaÂ¨ki 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 Cronbachâ€™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 KohtamaÂ¨ki 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 customerâ€™s use of authority and hierarchical
structures in the relationship (Hines, 1996; Ellram, 2002). Measures of this dimension
were modified on the basis of KohtamaÂ¨ki 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 supplierâ€™s procedures; supplierâ€™s
involvement in customerâ€™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) (KohtamaÂ¨ki 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; customerâ€™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
The reliability of the constructs was measured by deriving values for Cronbachâ€™s
alpha (threshold value 0.6), composite reliability (0.7), and AVE (0.5). Almost all the
constructs show fairly satisfactory Cronbachâ€™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 Cronbachâ€™s alpha and composite reliability values, researcher
decided to keep all the items in order to maintain the constructâ€™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 Harmanâ€™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
compared in terms of learning in order to discover which kinds of governance
structures increase learning in business relationships.
This study applies non-hierarchical cluster analysis and the k-means method. In the
k-means method the cases are grouped into homogenous groups (Ketchen and Shook,
1996), while the number of clusters is given by the researcher. In this study, cases are
clustered by using the composite variables of the three governance mechanisms (price,
hierarchy, and social). During the analysis, various cluster solutions were tested, but
the researchers decided to apply a four-cluster solution, as it was the most informative
and clear from the point of view of results.
As the cluster analysis recognizes some groups and ignores some potential ones, it
means that the ones being found are interpreted as viable. According to the
configurational contingency approach, only those forms which are viable can be
identified in the empirical world (Gerdin and Greve, 2004). Thus, if some combination
of governance structure is not found in the empirical world, the approach would
suggest that such a combination is not viable.
After the cluster analysis, the study compares the resulting groups by using
one-way analysis of variance (ANOVA) mean-comparison. Resulting groups are
mean-compared in terms of learning by using both the four individual learning items
and the respective composite variable, to study whether relationship learning varies
statistically significantly between different clusters. The study uses also post hoc
analysis (Scheffeâ€™s test) to test how learning varies between each recognized cluster
(Tabachnick and Fidell, 2007). This analysis shows which clusters differ from each
other in terms of relationship learning. During the analysis, the study applies SPSS
(version 15) to conduct the cluster analysis and mean comparisons.
Table II shows the results of the cluster analysis. In the analysis, researchers found
four clusters, which clearly varied in terms of the relationship governance structures
used in the cases. These clusters, which consist of relationships that are governed by
Relationship governance structure (clusters)
Governance mechanisms Social Market Supportive hierarchical Hybrid
Price governance 2.17 3.68 2.43 3.74
Hierarchical governance 2.40 2.67 3.89 3.79
Social governance 2.94 2.57 4.15 4.09
Number of cases in a given cluster 31 30 81 54
Note: Figures are average scores of respondentsâ€™ responses on a Likert scale of 1-5
Average scores of
mechanisms of different
Skewness Kurtosis Cronbachâ€™s alpha Composite reliability AVE
Price governance 0.12 0.26 0.67 0.71 0.41
Hierarchical governance 20.34 20.30 0.75 0.82 0.50
Social governance 20.64 0.11 0.77 0.86 0.60
Relationship learning 20.42 0.17 0.80 0.87 0.62
Cronbachâ€™s alpha and
values of all the
various relationship governance structures, are here termed: social, market, supportive
hierarchical, and hybrid. While clusters are reported in columns, rows present the three
governance mechanisms, which were used as criteria when clustering the cases.
In the cluster of deep-rooted social governance, the relationships are governed only
by using an intermediate social mechanism. The results show that in this cluster where
the values of all the governance mechanisms stay below three, the value of the social
mechanism is only very slightly below. It seems that in these relationships, the
customer is either incapable or unwilling to use either price or hierarchical mechanisms
to govern the supplier relationship. The second cluster includes market-governed
supplier relationships. Customers govern these relationships by using a strong price
mechanism, but the use of social and hierarchical relationship governance is at a low
level. It seems that in these partnerships, the customer intends to use the threat of
competition to force the supplier to develop the customer relationship. The third cluster
consists of supplier relationships governed by using supportive hierarchical
governance. By supportive hierarchical governance, the researcher means that the
supplier relationships are governed by using strong hierarchical and social governance
mechanisms. In these partnerships, customers seem to be able to use both structures
and requirements simultaneously without causing distrust. Governance is
two-dimensional, showing that companies use various mechanisms simultaneously.
Finally, the fourth cluster consists of hybrid governed supplier relationships. In these
relationships, customers are willing and able to apply all three mechanisms
simultaneously in a balanced manner.
After the cluster analysis, the researcher mean-compared the four groups in terms of
learning. In Table III, the last column on the right describes the value of the composite
variable of learning formed from the four individual items. This study applies the
ANOVA post hoc test (Scheffe) to analyze the differences between clusters. Scheffeâ€™s
test enables researchers to compare learning between each cluster in order to interpret
of new ideas
value of new
Explication of the
1. Social 2.23 2.13 2.42 3.06 2.46
2. Hybrid 3.26 2.70 3.54 4.35 3.46
3. Market 2.03 1.93 2.27 3.20 2.36
hierarchical 2.92 2.77 3.71 4.17 3.39
Average 2.77 2.53 3.25 3.90 3.11
Scheffeâ€™s test (a) (b) (a) (a) (a)
Notes: Scores are averages of the respondentsâ€™ responses measured on a Likert scale from 1 to 5;
(a) all the differences between relationships governed by social, market, hybrid, and supportive
hierarchical relationship governance structures are statistically significant at a significance level of
,0.05, except the difference between social and market-governed relationships and hybrid and
supportive hierarchically governed relationships; (b) the difference between relationships governed by
social and supportive hierarchical governance structures and between hybrid and market-type
relationship governance structures are statistically significant at a significance level of ,0.05, but the
difference between social and market-governed, social and hybrid-governed, market and supportive
hierarchically governed relationships is not
Average scores of
different groups in terms
how learning differs between all the different clusters (i.e. between social and market, or
hybrid and supportive hierarchical). Analysis based on the composite variable shows
that learning does not vary statistically significantly between supportive hierarchical
and hybrid governed clusters and between social and market governed clusters.
However, learning does vary statistically significantly in all the other combinations,
such as hybrid and social, hybrid and market, supportive hierarchical and social, and
supportive hierarchical and market. These results support the interpretation that
learning is highest in hybrid and supportive hierarchically governed clusters of
relationships and lowest, in social and market-governed clusters of relationships.
Interestingly, learning is actually higher in social than in market-governed
relationships. However, this difference is not statistically significant. Observations
are by far similar, whether one looks at results of the composite variable or three of the
four single items (development of new ideas, shared problem solving and knowledge
sharing and explication of the most conflicting problems). However, results slightly
differ when looking at one of the learning items (economic value of new ideas). With this
particular item, the results differ slightly from the other items, as the differences
between social and hybrid and market and supportive hierarchically governed
relationships are not statistically significant, while they are with the rest of the items.
However, also with this item the differences between social and supportive
hierarchically governed relationships and between hybrid and market-governed
relationships are statistically significant, which supports researcherâ€™s interpretation of
the results. Table III shows all the results of the mean-comparisons.
Finally, the analysis shows that in partnerships, companies often use various
relationship governance structures to govern a partnership. The results provide
evidence that supportive hierarchical and hybrid forms of governance are more
effective in terms of learning than social or market governance. The results suggest
that in order to support learning in the partnership, customers need to develop
hierarchical structures, require developmental efforts from the suppliers and to
maintain strong social relationships. In summary, Figure 2 shows the empirically
found clusters (in italic) with the average scores of relationship learning.
5. Conclusions and discussion
The effect of governance structures on learning in partnerships
The present study stresses the impact of relationship governance on learning in
partnerships. As, according to prior studies, learning is important for business
performance, and as industrial networks cannot often be governed only by using
competitive bidding due to long partner switching times, learning needs to be
facilitated by using other forms of relationship governance, such as social and
hierarchical governance (Adler, 2001). As some of the prior studies have emphasized
the role of trust on learning (HaËškansson et al., 1999), the present study argues that trust
needs to be complemented by the use of at least a hierarchical mechanism. The results
emphasize the role of both relationship management and trust.
The empirical analysis shows that relationship governance structures have an
impact on learning in the partnership. Learning is highest in partnerships governed by
supportive hierarchical or hybrid governance structures in comparison to market and
socially governed ones. Again, supportive hierarchical governance refers to supply
relationships, in which the customer applies both hierarchical and social governance
mechanisms, while hybrid governance structures signify a relationship utilizing the
three mechanisms (namely price, hierarchical, and social). In contrast, in
market-governed relationships, the customer only uses the price mechanism, while
in the socially governed relationships, the customer applies only a social mechanism at
an intermediate level. This result parallels those of previous empirical studies. First,
the result supports Adlerâ€™s (2001) model of organization of an economic system
indicating that parties often apply the various mechanisms simultaneously and, thus,
gain learning in their supply partnerships. This particularly contributive empirical
result suggests that customers need competencies to apply various mechanisms
simultaneously. Customers need to be able to balance different mechanisms in order to
utilize them simultaneously (Gustafsson, 2002; KohtamaÂ¨ki et al., 2006); according to
Barringer and Harrison (2000), managing partnerships is like â€œwalking a tightrope.â€
The results seem to suggest placing emphasis on hierarchical and social
governance. However, the results do not preclude the advantages of the market
mechanism, when it is used in a balanced way, as in hybrid-governed relationships,
which were found to be the most efficient in terms of learning. However, these results
do question the efficacy of an extreme market mechanism in partnerships (Krause et al.,
2000). In partnerships, the threat of competition on its own is apparently not
sufficiently credible to increase development effort, but an unfair, unsystematic, and
Effects of relationship
governance structures on
learning in partnerships
Notes: Empirically found clusters in italic, with the average scores of learning;
low social relationship governance in lower left triangles and high social
relationship governance in upper right triangles
implicit use of competitive bidding can cause distrust, which, in turn, can discourage
information sharing and even prohibit learning.
These results seem to highlight the significance of social governance. Owing to the
high instance of social governance in all the groups of high partnership learning, trust
and the feeling of shared purpose seem to play a significant role in supporting learning.
According to the results, an increase in the level of social governance leads to an
increase in partnership learning. These results demonstrate support for the previous
research results of, for example, Zaheer et al. (1998) (HaËškansson et al., 1999) who
emphasized the significance of trust in business relationships.
The results place emphasis on network management by suggesting that social
mechanisms should be complemented by the use of hierarchical mechanisms in order
to gain learning. These results provide support for some prior studies (Krause et al.,
2000; Liker and Choi, 2004; Sako, 2004) that have also suggested a few practical tools to
assist suppliers in their development (Dyer and Hatch, 2004; Sako, 2004). According to
those studies, various methods, such as supplier associations, consulting groups, and
learning teams can help to realize the development potential of suppliers.
The result of this study is particularly contributive to management and
organizational learning theory, as it suggests that in the unique context of
partnership, ability to manage relationship by applying various mechanisms
simultaneously results in increased learning. Thus, learning should be facilitated by
using various governance mechanisms simultaneously. This is one of the first studies
that demonstrate this result by using empirical data in the context of partnership.
How to govern partnerships?
The present study suggests that the partnership governance structure should be
balanced â€“ utilizing at least the hierarchical and social governance mechanisms. The
customer should be able to put pressure upon the supplier to develop relationship
processes, without the suppliers feeling that the customer is only doing it for
opportunistic reasons, in other words, a win-win outcome is available to both the
customer and supplier.
These results mean that industrial customers need a management system which
defines the goals, implementation, and follow-up processes of relationship
development. This system needs to be built up together with the supplier. This
shared planning and implementation of relationship management systems will support
the development of trust and a feeling of shared purpose. These ideas seem to integrate
the relationship governance approach that has been applied in this study and the ideas
of the IMP group (Ford and HaËškansson, 2006), which suggest that reciprocal
interaction is the key to learning and development in a business relationship. Since this
study suggests that a customer should be able to simultaneously manage the
relationship and develop trust, and as the study suggests that this could be done by
engaging the suppliers in a shared planning and development process, it seems that
there is a call for theory that emphasizes shared relationship management and joint
Limitations and research implications
Although the results of this study are important, this research does have some
limitations. First, the dataset is a sample from Finnish companies from the metal and
electronic industries that operate in the west coast in Finland. Larger and perhaps
comparative international research data is needed to test the research model and the
generalizability of these results. Second, the data is cross-sectional, which suggests
that the results of this study should be tested with longitudinal data in order to capture
the development of the relationships over time â€“ and, indeed, the actual learning
process. Third, as the measurement of these phenomena is difficult, qualitative
research is needed to verify the findings, but also to create knowledge concerning the
mechanisms of learning in partnerships. However, despite the limitations, the present
study gives an interesting and theoretically contributive viewpoint and provides a
basis for future studies of the relationship between partnership governance structures
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Items and variables Mean SD Loading
Bids from competitors of this supplier are frequently
requested 2.58 0.98 0.67
There are numerous potentially substitutive
suppliers 3.21 1.19 0.81
Similar or closely comparable components have
several suppliers for us (multiple source) 2.78 1.37 0.33
Supplier is reminded of the highly competitive
situation constantly, which is done in order to have a
highly competitive atmosphere 3.16 1.16 0.72
We present very specific requirements for the
supplierâ€™s quality and management systems 3.92 1.15 0.64
We intend to influence the supplier in a very active
manner 3.88 0.99 0.64
Supplierâ€™s representatives actively participate in
production or development meetings 3.20 1.26 0.79
We audit supplierâ€™s processes using a specific
method 2.80 1.50 0.71
Supplier has been given very specific written
instructions on how to react to delivery problems 3.43 1.22 0.68
Customer tries to develop trust and a feeling of
community by systematically organizing different
shared meetings and training in which the
participants are urged to develop a shared
understanding 3.12 1.19 0.77
Customer discusses all the relevant issues related to
supplierâ€™s operations and strategies with the supplier 3.52 1.27 0.86
Customer attempts to develop trust by acting in a
trustworthy manner themselves 4.18 0.86 0.69
Problems in the relationship are dealt with
constructively, because the customer wants to seek a
shared understanding 4.02 1.07 0.77
In this relationship new ideas for development are
often born 2.77 1.03 0.85
Some of these ideas have major economic
significance for the customerâ€™s and/or supplierâ€™s
business 2.53 1.06 0.78
In this relationship, we solve problems together and
share knowledge actively 3.25 1.10 0.85
In this relationship we dare to discuss even the most
contentious problems so that they can be solved 3.90 1.06 0.78
Note: All the variables were measure on a Likert scale from 1 to 5 (1, fully disagree; 5, fully agree)
List of variables used
in the study
About the author
Marko KohtamaÂ¨ki works as a Research Director in Research Group of â€œStrategy, Networks and
Enterpriseâ€ at the University of Vaasa, Finland. He takes special interest in business networks
and strategic management. Marko KohtamaÂ¨ki can be contacted at: [email protected]
Price governance 1
Hierarchical governance 0.02 1
Social governance 20.10 0.67 * 1
Relationship learning 0.06 0.54 * 0.64 * 1
Note: *Correlation is significant at the 0.01 level (two-tailed)
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