Salesforce Compensation: An Analytical and Empirical Examination of the Agency Theoretic Approach
Since the papers of Basu et al. (1985) and Lal and Srinivasan (1993), marketing academics have been interested in the design and implementation of optimal compensation plans. The literature has focused on agency theory as a foundation to help describe and understand this process. Although there has been much theoretical work on this topic, empirical evidence to support this theory remains sparse. Studies by Coughlan and Narasimhan (1992) and John and Weitz (1988, 1989) have found some early evidence that supports agency theory. In this paper we revisit the issue of salesforce compensation on both theoretical and empirical fronts. On the theory side we build a game theoretic model of salesforce compensation that accounts for risk aversion on the part of both the principal and the agent. We further show that accounting for the firm size within the analytical framework yields new insights into the nature of compensation design. The results obtained from our model, while substantiating past findings, offer some new insights into the compensation design process. In particular we find that firm demographics play an important role in the design of the optimal compensation scheme. We then use two datasets collected ten years apart by the Dartnell Corporation to investigate and test hypotheses generated by our model and the extant literature. Our results show that the basic tenets of agency theory continue to hold over time and that our new theoretical hypotheses are consistent with the data. Using the two datasets we also find that inter-temporal changes in salesforce compensation coincide with the advent and adoption of new technologies. Our research thus adds to our substantive knowledge of the drivers of salesforce compensation, while adding to the theoretical structure through taking account of the possibility of principal risk aversion. Copyright Springer Science + Business Media, Inc. 2005
Volume (Year): 3 (2005)
Issue (Month): 1 (January)
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