An Examination of Interorganizational Relationship Magnitude and Its Role in Determining Relationship Value

As the business environment becomes more complex, organizations within a supply chain realize that in many instances they can benefit from closer, long-term relationships. However, researchers in this area agree that there is no one relationship that is appropriate or necessary for all situations. Since there exists a wide range of relationships within a supply chain, businesses need to manage the development and maintenance of each relationship in their “portfolios.” This entails recognizing the motivating factors that drive companies to a particular relationship, determining the appropriate amount of resources that will be dedicated based on the targeted relationship, and measuring the benefits to ensure they are achieving value from the relationship.

Although there is a great deal of research on interorganizational relationships in the marketing and logistics literature, little empirical research has been conducted on the concept of relationship magnitude (the extent or degree of closeness or strength of the relationship) and none has addressed how to determine and select the “optimal” relationship magnitude for particular business situations. This dissertation strives to fill this gap by understanding and explaining the phenomenon of relationship magnitude and its relationships with situational drivers (i.e., how companies determine the level of intensity), the type of relationship chosen, and the value of the relationship (i.e., how the level of intensity affects the evaluation of value).

Using the extant literature along with qualitative interviews, a theoretical model was constructed and then tested through a mail survey sent to 588 customers of transportation providers. The survey data were analyzed using structural equation modeling to simultaneously test the five hypotheses and the contention that relationship magnitude is a second order construct comprised of the dimensions of trust, commitment and dependence. The fit of the second order model was good (RMSEA of .073, CFI of .905, CMIN of 2.715) and all hypothesized paths were significant, thus supporting the theory of this dissertation.