A multi-criteria decision support model for evaluating the performance of partnerships
Introduction
The new business environment is characterized by increased competition due to globalization, high customers' expectations, limited natural resources and rapid change in technologies and markets. One approach to remain competitive is through establishing a partnership. Partnership is an inter-firm relationship which is characterized by asset, information and risks/rewards sharing, and joint decision-making (Daugherty, 2011, Lambert et al., 1996, Webster, 1992).
There are different drivers for entering into a partnership. The most common drivers are cost reduction, customer service improvement, marketing advantage, product development, product diversification and joint investment (Cruijssen et al., 2007, Ellram, 1995, Hoffmann and Schlosser, 2001, Lambert et al., 1996). There may be more than one driver for each partner with different importance. Partnerships require intensive time and effort, reduce autonomy, and can result in more complexity and opportunistic behavior because of information asymmetry (Kwon & Suh, 2005). A high percentage (about 40–70%) of partnerships fail to achieve their drivers (Das and Teng, 2000).
The Transaction Cost Economics (TCE) and the Resource-based View (RbV) are widely cited theoretical approaches for explaining the effects of partnership on performance (Combs and Ketchen, 1999, Geyskens et al., 2006, Hoffmann and Schlosser, 2001, Markus, 2004). Based on these theories similarity (Brinkerhoff, 2002), compatibility (Maheshwari, Kumar, & Kumar, 2006), mutuality (Hoffmann & Schlosser, 2001), joint decision-making (Mohr & Spekman, 1994), information sharing (Hua & Cong, 2011), risk/reward sharing (Poppo & Zenger, 2002), trust and commitment (Morgan & Hunt, 1994) are identified as the major factors affecting partnership performance.
The partnership development process includes four main stages: (1) assessing the drivers/needs for partnership, (2) selecting a partner, (3) establishing the right level of partnership, and (4) maintaining/evaluating the ongoing partnership (Ellram, 1991, Hoffmann and Schlosser, 2001, Kim et al., 2010). Different criteria need to be considered to evaluate each stage of partnership. In the literature, few studies focused on evaluating the maintenance stage. Evaluating an ongoing partnership in the maintenance stage consists of both the evaluation of partnership performance (outcomes) and the factors affecting the performance (components). In this study, we focus on the evaluation of partnership performance in the maintenance stage.
Several studies (e.g. Glaister and Buckley, 1998, Hoffmann and Schlosser, 2001, Wilson, 1995) used a single criterion, which was managers' satisfaction and their perception on achieving the overall driver, to evaluate the partnership performance. This evaluation measure could be biased and hard to interpret (Carter, Kaufmann, & Michel, 2007). In addition, using one or even several criteria independently (e.g. Rezaei et al., 2015, Ryu et al., 2009, Vereecke and Muylle, 2006) cannot capture the overall partnership performance because the importance and interdependencies of the measures are not considered. The lack of a systematic approach to evaluate partnership performance has been identified among the reasons for partnership failure (Hoffmann and Schlosser, 2001, Holmberg and Cummings, 2009).
In order to comprehensively evaluate partnership performance, the drivers, as well as their relevant measures and importance must be considered in the model. There are several difficulties in considering multiple criteria for evaluating an ongoing partnership. First, the importance of these criteria may not be the same for each partner. Second, some of the criteria may be interrelated. Third, some criteria may be hard to estimate quantitatively. Lambert (1997) and Simatupang and Sridharan (2005) developed a multi-dimensional index for the establishment and maintenance stages respectively, however, the importance and the interdependencies of the measures were not considered. Recently, Chen and Wu (2010) and Verdecho, Alfaro-Saiz, Rodriguez-Rodriguez, and Ortiz-Bas (2012) incorporated the importance and interdependences of the measures, however, they did not evaluate partnership in different periods and did not consider different drivers of entering into partnerships.
The objective of this study is to bridge the gap in the literature for evaluating an ongoing partnership in different periods in the maintenance stage using multi-criteria decision analysis methods, while considering the importance of partnership drivers and measures and their interdependencies as well as uncertainties in the estimation of some measures.
Section snippets
Literature review
The performance of a partnership has been investigated using both theoretical approaches and mathematical models. The Transaction Cost Economics theory suggests selecting an inter-firm relationship that minimizes the sum of fixed and continual transaction costs (Geyskens et al., 2006). Based on the TCE theory, partnership can significantly reduce the costs of selecting and monitoring a supplier in long-term transactions with high uncertainty, and low to medium asset specifity and frequency (
Modeling approach
To develop a model for partnership evaluation it is necessary to define the evaluation criteria. There are a variety of criteria for evaluating partnerships. Criteria such as financial factors (Luo & Chen, 1995) and operational efficiency (Kim & Park, 2002) have been used frequently. Simatupang and Sridharan (2005) used three operational measures including fulfilment rate, inventory, and responsiveness to estimate partnership performance for supplier–buyer partnership. Sodhi and Son (2009) used
Case study
The proposed decision support model is applied to a partnership in the forest industry in Canada. The forest industry in developed countries has been losing its competitiveness due to the emergence of new alternative products and low-cost competitors (Sathre & Gustavsson, 2009). Partnership with other companies within and outside the forest industry is identified as a strategy to remain competitive (Chambost et al., 2009, FPAC, 2011).
The case study is related to a partnership between a forest
The interdependency of the drivers and the measures
The results of the ISM analysis revealed a few more interdependencies among the measures for company A. However, there was no new interdependencies for measures defined by company B. Table 6 shows Reachability Matrix (M*) with new interdependencies between the measures for company A.
The measures' importance and partnership performance indexes
The final importance of the drivers and the measures for each partner considering all the interdependencies is summarized in Tables 7 and 8, respectively. The importance is given for both initial importance and the
Discussion
The result of the ISM analysis revealed some new interdependencies among measures, which otherwise could be overlooked by the decision makers. For example, because of these new interdependencies the importance of the value of the joint projects decreased while the importance of production flexibility increased.
In the second step, we used ANP to get the importance of the drivers and the measures. We reduced the number of pairwise comparisons in ANP significantly by using Incomplete Pairwise
Conclusions
This paper presented a comprehensive and systematic approach to evaluate the performance of an ongoing partnership. From a methodological point of view, the proposed multi-criteria model incorporates partnership's drivers and their importance in the process of estimating the importance of the performance measures which were overlooked in similar models developed for assessment in the partnership selection stage (e.g. Chen and Wu, 2010, Simatupang and Sridharan, 2005, Verdecho et al., 2012).
Acknowledgments
The authors are grateful for the financial support by the Natural Sciences and Engineering Research Council of Canada (Discovery Research Grant RGPIN 249986-04) for partial funding of this research and the Strategic Research Network on Value Chain Optimization (NSERC Grant NETPG 387200-09) to provide graduate research funding to the first author,
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