The adoption of information technology in the sales force

Abstract

The purpose of this article is to explain why salespeople adopt information technology. The results from a cross-sectional study of 229 salespeople indicate that putting sales technology to use strongly depends on salespeople's perceptions about the technology enhancing their performance, their personal innovativeness and organizational efforts in terms of user training. Throughout the adoption process companies also need to target sales line managers–next to end users–because salespeople clearly comply with the expectations of their supervisors. Finally, the threat from competing sales professionals or peers who use similar sales technology seems to be of secondary importance for individual sales technology adoption.

Introduction

This paper seeks to explain the adoption of information technology by salespeople. Many companies implement sales technology in their sales forces in hopes of enhancing productivity, communication and customer relationships (e.g. Campbell, 1998, Goldenberg, 1996, Moncrief et al., 1991). Anecdotal evidence indicates that implementing sales technology in field organizations is often problematic, however, with implementation failure rates in sales organizations as high as 75% (Kaydo, 1999, Petersen, 1997). Moreover, automating a sales force is costly with automation budgets for mid-sized sales forces ranging from $2.5 to $6.25 million with an average annual operating budget of $1.25 million (Siebel & Malone, 1996). Automating the sales force is actually “easier said than done.” One of the risks of introducing sales technology is that individual salespeople resist using the technology as they seem to be rather technophobic or self-willed (e.g. Parthasarathy & Sohi, 1997). Hence, once organizational decision-makers have decided to adopt sales technology, their focus should shift to putting the innovation to use at the individual level (Gopalakrishnan & Damanpour, 1997) if they want sales technology to result in superior returns (Bhattacherjee, 1998).

It is surprising that only a few marketing studies have dealt with adoption of sales technology within sales organizations. From an academic point of view consumer adoption (e.g. Steenkamp, ter Hofstede, & Wedel, 1999) organizational adoption (e.g. Frambach et al., 1998, Gatignon & Robertson, 1989), or salespeople's adoption of selling new products (Anderson & Robertson, 1995) have received principal attention. This paper extends the Technology Acceptance Model (TAM) (see Fig. 1) (e.g. Davis et al., 1989, Venkatesh & Davis, 2000) to explain sales technology adoption within sales organizations. Our study fully embeds personal dispositional IT-innovativeness within TAM, disentangles the impact of external as well as internal social influences (e.g. customers, competitors, supervisors and colleagues) on technology adoption and measures salesperson technology adoption using both a direct (i.e. the salesperson's own appraisal) and a second source measure of adoption (i.e. the sales rep's technology adoption as perceived by the focal sales rep's sales manager).

As a context our research focusses on Sales Automation (SA) technology and tests a model using a cross-sectional survey among 229 salespeople. SA is used as an umbrella term here describing computerized systems specifically designed to support individual field sales representatives. SA-systems do not comprise general office tools (e.g. word processing and presentation) or separate e-mail and WWW applications. For example, SA-technology may contain applications such as: (1) contact and account management (e.g. call history, buying information), (2) time management, (3) prospecting (e.g. lead tracking), (4) price/product configurator, (5) sales (funnel) analysis, (6) order management (e.g. order entry and status).

The remainder of the paper begins with a discussion of the focal constructs and then develops the research hypotheses. Next the sample and data acquisition procedure as well as the measure development and the empirical test of our hypothesized model are discussed. Finally, implications and suggestions for future research are provided.