The Acceptance of Information Technology in the Sales Force
The Acceptance of Information Technology in the Sales Force
Authors: Niels Schillewaert, Michael J. Ahearne, Ruud T. Frambach, Rudy K. Moenaert
The introduction of information technology into (sales and marketing) organizations has been shown on many occasions to result in superior returns for a company (e.g. enhanced productivity, customer communication and relationships) when these technologies are embraced by the individual target users (Bhattacherjee 1998; Srinivasan 1985; Davis et al. 1989; Leonard-Barton and Deschamps 1988). Still, in sales force contexts the acceptance of technological innovations at the individual level (i.e. the field salespeople), has not been investigated previously. The marketing literature on innovation adoption has primarily focused either on consumer markets (e.g. Ozanne and Churchill 1971; Gatignon and Robertson 1985; Steenkamp et al. 1999) or on adoption at the organizational/departmental level (e.g. Robertson and Gatignon 1986; Gatignon and Robertson 1989; Frambach et al. 1997; Moriarty and Swartz 1991).
Conversely, the information systems literature contains an extensive and long standing tradition of research that focuses on explaining the acceptance of information technology from the users (i.e. individual's) perspective (e.g. DeLone and McLean 1992; Zmud 1979; Ives and Olson 1984; Davis et al. 1989; Doll and Torkzadeh 1988; Trevino and Webster 1992). Perhaps the best contributions in predicting and explaining user acceptance of computer technology in organizational contexts have been made by the Technology Acceptance Model (TAM) (e.g. Davis et al. 1989; Venkatesh and Davis 2000). The central thesis of the TAM is that individual computer acceptance is determined by two instrumental beliefs: perceived usefulness (i.e. the extent to which a person believes that using the system will improve his/her job performance) and perceived ease of use (i.e. the extent to which a person considers that using the system will be free of effort). Over the years, strong empirical support has accumulated in favor of TAM (e.g. Igbaria et al. 1996; Davis 1989; Trevino and Webster 1992; Igbaria 1993; Adams et al. 1992; Doll et al. 1998). Therefore, this model represents the current thinking in the field of information systems about user acceptance of computer technology.
Whereas some research has been done to model the effects of different external variables, TAM needs to be broadened to encompass other important theoretical constructs which need to be tested within an integrated nomological net of variables. Such integrated models depart from prior research on innovation adoption and computer acceptance, which has focused primarily on either first order effects of acceptance determinants (e.g. Rogers 1995; Thompson et al.1991) or antecedents of perceived usefulness or ease of use separately (e.g. Venkatesh and Davis 2000; Venkatesh 1996; Venkatesh and Davis 1996; Karahanna and Straub 1999). Against this background, a major contribution of our study is that it develops and tests a theoretically integrated model which explains salespeoples computer acceptance behavior.
Moreover, observations in practice suggest implementation failure rates of sales technology as high as 75% (Petersen 1997; Siebel and Malone 1996; Blodgett 1995; Lee 1998) and indicate that a major reason may be that salespeople are among the most technophobic and resistant of all white collar workers (e.g. Parthasarathy and Sohi 1997; Bresnahan 1998; Mills 1995). Harris and Pike (1996), for example, report that almost one out of five sales reps in the agribusiness never use a computer in their work. Given this situation, we assert that a sales reps personal innovativeness in the domain of information technology is key in understanding and explaining the acceptance of technology in the context of personal selling. Counter to the assertions of the TAM, we develop hypotheses maintaining that innovative salespeople will not only hold different belief structures in terms of using sales technology, but also that a sales reps individual innovativeness will influence acceptance behavior over and above these held beliefs. Hence, we asses whether organizations should actively identify and target those salespeople, within their sales organization, that are high in personal innovativeness during the implemention of sales technology (Agarwal and Prasad 1998).
Despite the extensive study of TAM, the impact of social influences and norms on acceptance remains one of the poorly understood aspects of technology acceptance (Davis et al. 1989; Venkatesh and Davis 2000). The prior studies on TAM investigate the role of social influences from a general standpoint, namely that of ifimportant otherslt. Consequently, these social influences are not adapted to a personal selling context. In this study, we adapt and disentangle these influences to a sales setting as we hypothesize that these effects are differential depending on the source (e.g. customers, competitors, supervisors and colleagues).
An additional limitation of current research is that both the TAM and the traditional innovation adoption literature take a narrow view on acceptance. This criterion variable is traditionally conceptualized as the mere iefrequency of usele (e.g. Davis et al. 1989) or as a isdichotomous (single) adoption decisionlm (e.g. Frambach et al 1998; Gatignon and Robertson 1985). Virtually no studies have measured and examined acceptance as the extent of adoption. where the innovating unit goes through an implementation and confirmation stage (Rogers 1995; Westphal et al. 1997). Hence, an additional objective of this study is to conceptualize and measure individual technology acceptance.
We examine the issue of salesperson technology acceptance using both a direct and an unobtrusive measure of acceptance. The direct measure of acceptance consists the salespersons reports about his/her own acceptance behavior, while the unobtrusive measure is an assessment of the sales reps acceptance behavior as perceived by a second source, namely the focal sales reps sales manager. After establishing adequate levels of interrater agreement (i.e. between the sales rep and his/her sales manager), both measures are aggregated to form a reliable measure of acceptance. Hence, we reduce the effects of common method bias as an explanation for a sales reps technology acceptance.
As a context for our research, we focus on Sales Automation (SA) technology. SA- applications are defined here as an umbrella term describing computerized systems, which are specifically designed to support individual field sales representatives. In the sections that follow, we begin with a discussion of the focal constructs and develop our research hypotheses. In the method section, we outline the sample and data acquisition procedure as well as the measure development. Next, we test our research model using multiple respondent data from a cross sectional sample of sales reps (N=168) and their (field) sales managers. Finally, we discuss the implications of our findings and provide suggestions for future research.