Effect of technology on sales performance: progressing from technology acceptance to technology usage and consequence.

The growth in customer relationship management (CRM) software deployment has paralleled the transition from transactional marketing to relationship marketing. In organizations, a go-to-market strategy relies heavily on salespeople, CRM management has primarily been the responsibility of the

sales force, and research has traditionally had its roots in understanding sales force automation (SFA) (Tanner et al. 2005). Now SFA research is being supplanted by broader, enterprise-wide CRM research. While research frameworks have been developed for understanding adoption issues or general technology acceptance issues, there has been no research into the performance effect of the technology after the technology is installed and trained. Further, the adoption research has, to date, worked under the assumption that more overall usage of technology is better. Consistent with the recommendations of Leigh and Marshall (2001) to examine issues surrounding implementation of CRM, we research the relationship between the operational usage of a CRM system and its effect on the objective performance of a salesperson. We look for optimal points of usage to maximize performance. This should be an issue that is of tremendous importance to practitioners looking to maximize their return on investment in CRM technology.

Most research to date has focused on technology acceptance, and not on the productive effect of technology usage. Deservedly, researchers have examined the acceptance of technology, and several models have been proposed in the literature. These models include the Technology Acceptance Model (TAM) (Davis 1986), and its extension (TAM2) (Venkatesh and Davis 2000), and models based on the Theory of Reasoned Action (Davis, Bagozzi, and Warshaw 1989), Innovation Diffusion Theory (Moore and Benbasat 1991), Triandis model (Thompson, Higgins, and Howell 1991), Motivation (Davis et al. 1992), Theory of Planned Behavior (Taylor and Todd 1995), Social Cognitive Theory (Compeau and Higgins 1995; Compeau, Higgins, and Huff 1999), and, recently, the Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh et al. 2003). Each model has the same dependent variable, usage, but uses various antecedents to understand acceptance of technology.

An implicit assumption in all the above models is a positive and linear relationship between performance and usage (Figure 1). There is an underlying assumption that technology utilization is a proxy of its perceived effectiveness. Sometimes, it is even explicitly stated. To quote Heine, Grover, and Malhotra, "It is assumed that increased utilization is a desirable behavior and implies better performance" (2003, p. 191). But this critical assumption that usage is a proxy to performance has not been tested in the literature.