management science - SSRN

MANAGEMENT SCIENCE

informs

Vol. 54, No. 4, April 2008, pp. 671–685 issn 0025-1909 eissn 1526-5501 08 5404 0671

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doi 10.1287/mnsc.1070.0783 © 2008 INFORMS

High Touch Through High Tech: The Impact of Salesperson Technology Usage on Sales Performance via Mediating Mechanisms Michael Ahearne, Eli Jones

Bauer College of Business, University of Houston, Houston, Texas 77204 {[email protected], [email protected]}

Adam Rapp

College of Business Administration, Kent State University, Kent, Ohio 44242, [email protected]

John Mathieu

Department of Management, University of Connecticut, Storrs, Connecticut 06269, [email protected]

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ales technology has been touted as a primary tool for enhancing customer relationship management. However, empirical research is sparse concerning the use of information technology (IT) and its effects on the relationship between salespersons and customers. Using an interdisciplinary research approach, we extend tasktechnology-fit (TTF) theory by examining the mechanisms through which use of IT by the sales force influences salesperson performance. We test a model that incorporates salespersons’ customer service, attention to personal details, adaptability, and knowledge—key marketing constructs that could mediate IT’s impact on salesperson performance. Results in a pharmaceutical sales setting indicate that IT use can improve customer service and salespersons’ adaptability, leading to improved sales performance. Key words: customer relationship management; relationship marketing; sales technology History: Accepted by Barrie R. Nault, information systems; received December 2, 2004. This paper was with the authors 1 year and 6 months for 4 revisions. Published online in Articles in Advance February 1, 2008.

Introduction

personnel (e.g., salespeople) in their selling and/or administrative activities (Morgan and Inks 2001). The promise of SFA/CRM systems is that the technology will enhance salespeople’s effectiveness by enhancing their ability to share market (e.g., customer and competitor) intelligence with their colleagues, manage their customer contacts, create more impactful sales presentations, and submit sales call reports, sales forecasts, and internal claims for expense reimbursements (Gohmann et al. 2005). Although the relationship between IT use and salesperson performance remains primarily unsubstantiated, many organizations spend considerable human and financial resources in equipping their sales forces with IT. The cost of automating a sales force can be upwards of $3,500 per salesperson (Girard 1998), and more than 60% of all SFA projects have been unsuccessful (Rivers and Dart 1999). Meanwhile, companies continue to invest in SFA technology in the hopes that improving the speed and quality of information flow among the salespersons will ultimately deliver dividends (Speier and Venkatesh 2002). Marketing and information systems (IS) researchers alike have called for more research in the area of technology use and

Many organizations believe that supplying information technology (IT) to their employees will enable them to excel in a fluid business environment. Indeed, the “technology-enabled organization” has emerged, and the prevailing wisdom is that IT use will be a key driver of growth and profitability (Sawhney and Zabin 2002). However, research also has suggested negative aspects of technology use, such as how knowledge sharing can hurt task performance in a team situation (Haas and Hansen 2005), and how technology use can increase employee stress and turnover (Speier and Venkatesh 2002). More specifically, technologies such as sales force automation (SFA) and customer relationship management (CRM) tools have garnered much attention from academics and practitioners in recent years, because companies have made significant investments in them with the hope that IT will improve performance, customer service, customer satisfaction, and business relationships (Moncrief et al. 1991). SFA/CRM systems are suites of applications designed to automate collection, assimilation, analysis, and distribution of information by assisting back-office staff and front-office 671

Electronic copy available at: http://ssrn.com/abstract=1584728

Ahearne et al.: The Impact of Salesperson Technology Usage on Sales Performance via Mediating Mechanisms

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Management Science 54(4), pp. 671–685, © 2008 INFORMS

Figure 1

Hypothesized Model

Salesperson behaviors and characteristics Salesperson behaviors Salesperson call activity

Salesperson experience Customer service

H6

H2 Attention to personal details

H7

H3 Technological usage

Salesperson performance H4

H5

Salesperson characteristics H8

Knowledge

H9

Sources of data

Adaptability

Archival records System data Sales rep Customer

H1

its impact on salesperson performance (Leigh and Marshall 2001, Marshall et al. 1999) and organizational performance generally (Venkatesh 2006). Existing IS/IT research at the individual level (Millman and Hartwick 1987, Sharda et al. 1988, Sulek and Maruchek 1992) argues that implementation of information systems has a positive impact on individual (decision) performance through a sequential process involving attitudes and behaviors. Although the academic literature has addressed conceptual issues associated with IT and its implementation among salespeople (Jones et al. 2002, Erffmeyer and Johnson 2001, Pullig et al. 2002, Rivers and Dart 1999, Speier and Venkatesh 2002), relatively few articles have examined the consequences of SFA/CRM (Ahearne et al. 2004, Ko and Dennis 2004, Sundaram et al. 2007). Our research goes beyond examining whether IT affects performance and helps researchers and practitioners understand why and how IT has such impact in the context of key marketing constructs. The model (see Figure 1) that we advance specifies multiple

mediating mechanisms tying salespersons’ IT use with performance.

Conceptual Background

Technologies such as computer systems (hardware, software, and data) and user support services (training, help lines, etc.) are provided to assist users in their job tasks. In the IS/IT literature, a variety of models have been advanced to predict IT usage and its influence on performance. At an organizational level, researchers argue that “the extent to which the expected benefits of an innovation are realized is largely reflected in the success by which an innovation has been incorporated within the organization’s operational and/or managerial work system” (Zmud and Apple 1992, p. 148). Also, IS researchers have examined how IT affects individual-level performance. Perhaps the most well-known model stems from the technology-to-performance chain research: task-technology-fit (TTF) theory, which predicts that individuals’ use of IT affects their performance and

Electronic copy available at: http://ssrn.com/abstract=1584728

Ahearne et al.: The Impact of Salesperson Technology Usage on Sales Performance via Mediating Mechanisms Management Science 54(4), pp. 671–685, © 2008 INFORMS

that the performance benefits will be greater if the IT fits the task (Goodhue and Thompson 1995). Using experimental data, Marcolin et al. (2000) found that user competence should be incorporated into the tasktechnology-fit model. However, the mediating factors linking such compatibility with performance have remained largely unexplored. In addition, TTF theory has been studied primarily using user judgments (ratings) of IT use and compatibility, which are susceptible to a number of biases (Goodhue and Thompson 1995). In summary, IS/IT models have focused primarily on technology use as the ultimate criterion of interest (see Venkatesh 2006). A few researchers have extended those models to specifically examine the impact of IT use on performance at both the organizational and individual levels, but have typically done so from just the IT-user’s perspective. Moreover, the prevailing wisdom is based primarily on experimental data and sampling frames of in-house, salaried employees. Although IS researchers have applied and extended TTF theory at the individual (Marcolin et al. 2000), group (Dennis et al. 2001), and organizational (Devaraj and Kohli 2003) levels, we extend this theory by explaining how IT usage affects salesperson performance via specific mediating mechanisms; in particular, through individual characteristics and behaviors that have been examined in the marketing literature. This interdisciplinary research approach can contribute to our understanding of IT’s impact on job outcomes, particularly in the context of boundary-spanning, customer-facing employees such as salespeople. Field salespeople are often separated from the selling organization (and its direct influences), are in direct contact with customers, and need to manage their multiple time demands. They are incentivized to approach many customers in a geographic territory and to persuade those customers to purchase the company’s products and/or services. Published studies targeting this specific stream of investigation have generated mixed results. For example, Igbaria and Tan (1997) find only positive consequences of sales technology, whereas Avlontis and Panagopoulos (2005) did not find a link to salesperson performance. In fact, Speier and Venkatesh (2002) found that sales technology implementation may actually hinder a sales force, resulting in significant increases in turnover and decreases in perceptions of organizational commitment and job satisfaction of salespeople. Technology’s Influence on Salesperson Performance SFA/CRM tools are specifically designed to help salespeople meet their objectives in managing customer relationships by making repetitive selling tasks such as administrative work more efficient,

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thus enabling more face-to-face relationship-building time with customers. As proposed by Goodhue and Thompson (1995), a core element of TTF theory is that the performance benefits will be greater if the technology fits the task. Assuming that the technology in our study fits the selling task, SFA becomes a tool for improving communication between sellers and buyers due to its storage capacity of key information, which salespeople can draw upon to facilitate purchase transactions. Utilizing customer purchase history and preferences, salespeople can tailor presentations to appeal to specific buying needs. In short, we anticipate that the use of IT will enhance performance in this setting. Naturally, this represents a foundational relationship that must exist to warrant testing the role of mediators (Mathieu and Taylor 2006). Therefore, we hypothesize the following: Hypothesis 1 (H1). Higher IT usage by salespeople is associated with higher salesperson performance. Technology’s Influence on Salespeople’s Characteristics and Behaviors The focal thesis of this investigation is that we believe that the use of IT tools does not influence performance directly, but rather through a variety of mediating mechanisms that occur during salespersoncustomer exchanges. For example, inspired by past research (Sproull and Kiesler 1986), Huber (1990, p. 50) claims that the communication capabilities of advanced IT enable individuals to record and index information more reliably, and control access in a communication event or network. Furthermore, recent advances in mobile technologies facilitate sales representatives’ use of and access to these electronic media, at any time, from any place, and to communicate information in almost any form (Jarvenpaa and Ives 1994). This suggests that IT increases the richness, the complexity, and the mobility of knowledge and information because of increased communication speed and access to information (Jarvenpaa and Ives 1994). Also, studies suggest that IT increases personal effectiveness (Igbaria and Tan 1997, Millman and Hartwick 1987), improves decision making, and enhances communication processes (Good and Stone 1995, Huber 1990). Regarding salesperson performance, marketing researchers have classified the influences on performance into three general categories: (1) salespeople’s behaviors, (2) task characteristics, and (3) salesperson characteristics (Kohli 1989). In this sales setting, the tasks identified are associated with calling on customers to build long-term relationships. Thus, our overarching hypothesis is that higher technology usage by salespeople is associated with higher salesperson performance via technology’s impact on their behaviors and characteristics—in particular, customer

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service, attention to personal details, salesperson knowledge and adaptability. These specific intervening variables are especially germane to the selling context and the way in which technology should indirectly influence salesperson performance. Delivering quality customer service has emerged as a strategic imperative, one that is increasingly tied to a firm’s IT resources and capabilities. In situations where an automated sales system fits the task, usage should improve the level of customer service. Theoretically, IT-savvy sales representatives can build stronger customer relationships and provide better customer service through process innovations (Duncan and Moriarty 1998). In support of this idea, Zeithaml et al. (1988) posit that one possible consequence of salespeople’s use of IT is increased responsiveness and reliability—two primary components of service quality.1 Reliability is an attribute of quality and is associated with the dependability and consistency of the salesperson (Parasuraman et al. 1985). IT usage should promote reliability through the storage and retrieval of key customer concerns and detailed notes regarding the customer’s interests, adding to the perceptions of the salesperson’s reliability. Dependable information allows customers to make informed decisions about the impact of buying or not buying the salesperson’s product or service. Equally important, responsiveness concerns the willingness or readiness of salespeople to provide service to customers in a timely manner (Parasuraman et al. 1985). IT enables salespeople to more quickly access important databases (e.g., order processing and shipping information), thereby improving the speed at which salespeople respond to customers’ needs even while face-to-face with the customer. Accordingly, we hypothesize the following: Hypothesis 2 (H2). Higher IT usage by salespeople is associated with higher perceptions of customer service as determined by the customer. Technology should also facilitate the salesperson’s ability to build stronger customer relationships via storage of and access to the personal, detailed information gathered about each customer over time. This information can be used to better prepare for sales calls prior to face-to-face meetings with customers. For example, salespeople can store customers’ birthdays, anniversaries, preferred buying times, office hours, or similar information in their contact manager. This information is easily accessible to the salesperson, thereby enabling him or her to better prepare 1 Other service-quality facets are tangibles, assurance, and empathy. We focus on reliability and responsiveness because technology should affect the salesperson’s ability to provide consistent and expedient service.


for the sales call thus making it more personalized. Information communication has been defined as “the formal as well as informal sharing of meaningful and timely information between firms” (Anderson and Narus 1990, p. 44) and the “glue that holds together” business relationships (Mohr and Nevin 1990, p. 36). It logically follows that technology should permit salespeople to serve customers faster and more reliably, while enabling the salesperson to form closer bonds with his or her customers through the retrieval of key personal information. Therefore, we expect the following: Hypothesis 3 (H3). Higher IT usage by salespeople is associated with higher perceptions of attention to personal details as determined by the customer. Competence in service is achieved when customers regard the salesperson as knowledgeable and capable. Prior research on salespeople’s knowledge can be divided into two categories: declarative knowledge and procedural knowledge. Declarative knowledge is the content of categories that contain a set of prototypes representing the essential characteristics of category members (Szymanski 1988, Weitz et al. 1986). Procedural knowledge, also known as behavioral scripts, contains information about the sequences of behavior appropriate to particular situations (Cohen and Bacdayan 1994). The importance of informationgathering skills and activities is well recognized in the personal selling literature (Moncrief 1986). For example, Sujan et al. (1988) suggest that salespeople’s effectiveness and knowledge can be enhanced by providing them with market research information and encouraging them to use that information. Due to its storage, retrieval, and network capacities, IT has the potential to enable and facilitate information acquisition, dissemination, and utilization (Huber 1991). By drawing upon the information available through computers and databases, a salesperson can both improve the content of his/her sales presentations and demonstrate a higher level of knowledge. For instance, a sales representative can search online databases or the Internet for customer- and businessrelated information, thus improving his or her knowledge of potential customer needs. IT allows sales representatives to draw upon an expansive (computerized) organizational memory of people and databases and to use it to update their beliefs and knowledge about business relationships (Huber 1991). Electronic communication media can link a salesperson to other professionals within and across organizational boundaries, which implies that sales representatives who exhibit high levels of information technology usage have access to a more expansive base of external and organizational information sources, knowledge, and people, compared to


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those who use technology less. Marshall et al. (1999) support this reasoning, stating that intelligence and information gathering occur more through the use of technology. Therefore,

repositories and nodes, the odds of finding the solution to a particular customer’s idiosyncrasies, specific requests, or problems are increased. Thus, we expect the following:

Hypothesis 4 (H4). Higher IT usage by salespeople is associated with higher perceptions of salesperson market and product knowledge as determined by the customer.

Hypothesis 5 (H5). Higher IT usage by salespeople is associated with higher perceptions of adaptive selling as determined by the salesperson.

Adaptive relationship management tenets suggest that not all customers should be treated alike or given equal priority (Sawhney and Zabin 2002). Salespeople’s smart-selling behaviors are characterized by altering sales approaches across and during customer contacts (Spiro and Weitz 1990, Sujan et al. 1988, Weitz et al. 1986) and engaging in sales call planning (Sujan et al. 1994). Through the practice of adaptive selling, salespeople exploit the unique opportunities of personal selling. Salespeople can research the customer and tailor a sales presentation for that customer, as opposed to using a scripted presentation for all customers. In addition, with proper preparation before the sales call, salespeople can sense customer reactions during the call and can make instant adjustments (Spiro and Weitz 1990, Weitz et al. 1986). Similarly, salespeople can judge the suitability of specific sales behaviors and alter their approach to fit the circumstance (Sujan et al. 1994). Salespeople report that sales technology helps make sales calls more professional (Marshall et al. 1999). As suggested by Spiro and Weitz (1990), the collection of information about the sales situation to facilitate adaptation to the customer is part of salesperson capabilities. Technology can enable such information collection, and, thus, enable adaptive selling. SFA databases and applications, for example, often have capabilities that allow sales representatives to keep detailed records about clients and calls. Calendaring and routing tools enable sales representatives to effectively manage time, set up appointments accurately, and engage in weekly planning. With vast amounts of information at their fingertips, as well as search and analysis capabilities at the click of a button, sales representatives can tailor their sales messages to a specific customer. Salespeople who use sales technology for planning provide better and more detailed information and, in turn, can think differently about their sales strategies and approaches. For instance, reviewing the account history in the sales database right before the actual face-to-face sales call enhances a salesperson’s ability to plan the appropriate sales strategy and to determine which products to emphasize during the sales call based on the customer’s previously stated preferences (which are stored in the selling company’s CRM database). Finally, because database and networked applications provide access to information

The Effects of Salesperson Behaviors and Characteristics on Salesperson Performance Next, we examine the salesperson-customer interface by looking at the impact of customer service, attention to personal details, knowledge, and adaptive selling on salesperson performance. Although several of these constructs and their relationships to performance have been examined in previous research with varying results, by including these characteristics and behaviors we can verify whether mediating relationships from IT to performance are present. Atuahene-Gima (1995) defines service quality as the activities undertaken by the firm to enhance the intangible aspects of the organization, such as delivery, expertise, and the appearance of contact personnel and the sales force. This definition underscores the salesperson’s role as a service provider through his or her interactions with customers. Indeed, Bitner (1990) argues that during the service encounter employee (salesperson) behavioral performance is the service, as customers perceive it. Several authors have highlighted the importance of the service provider’s role in satisfying the customer (Zeithaml et al. 1988). Research has shown that employees’ attitudinal and behavioral responses can affect customers’ perceptions of the service encounter and their judgments about service quality (Bitner 1990), which influence buying behavior and lead to gains in performance. Finally, service quality has been shown to enhance customer satisfaction, retention, and repeat purchasing. Hypothesis 6 (H6). Higher perceptions of a salesperson’s customer service are associated with higher salesperson performance. Crosby et al. (1990) suggest that the salesperson is essentially “the company” to the customer, because the salesperson is the primary, if not the sole, contact point for customers. Therefore, the quality of the salesperson-customer relationship is directly influenced by the salesperson. Humphreys and Williams (1996) propose that customer orientation focuses not only on what buyers receive (technical product attributes), but also on how buyers and sellers interact (interpersonal process attributes). The authors find that interpersonal process attributes of

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the total market offering can be significant determinants of customer service, which in turn underscores the role of salesperson behavior in optimizing customer value and satisfaction. Thus, we posit the following:

leading to growth in performance (Bitner et al. 1994). Thus,

Hypothesis 7 (H7). Higher perceptions of a salesperson’s attention to personal details are associated with higher salesperson performance.

Research Method

To ensure stable relationships, it is crucial that customers hear a message that is specific to their needs and requirements. It is well known that salespeople— as external representatives of the firm—are primarily responsible for giving clear, well-considered presentations, responding to customers’ questions, and being knowledgeable about the products and services offered (Behrman and Perreault 1984). Knowledgeable salespeople are familiar with the product or service, have an understanding of customer needs and expectations, and learn critical information about customers. According to Saxe and Weitz’s (1982) definition of customer-oriented selling, a salesperson should know how a product will meet a customer’s explicit and implicit needs, and should know how to satisfy a customer when the product does not perform as expected. A salesperson’s procedural knowledge can lead to a strong customer orientation, thereby resulting in higher levels of performance. Because cognitive selling research tenets suggest that more effective salespeople have a more distinctive knowledge base and set of scripts for different selling situations, it logically follows that the more knowledgeable a salesperson, the greater the influence on his or her performance. Hence: Hypothesis 8 (H8). Higher salesperson knowledge is associated with higher salesperson performance. Hartline and Ferrell (1996) define adaptability as the ability of customer-contact employees to adjust their behaviors to the interpersonal demands of the service encounter. Salespeople demonstrate high levels of adaptive selling behaviors when they use different sales presentations across sales encounters and make adjustments during those encounters. Although this relationship has been examined in previous research (Brown and Peterson 1994, Goolsby et al. 1992, Sujan et al. 1994), varying results demonstrate the need for further research. For example, Goolsby et al. (1992) found inconsistent results of adaptiveness traits of salespeople and relationships with different dimensions of performance. In general, however, theory supports the premise that salespeople who adapt their behaviors during customer interactions are more likely to fulfill the needs and requests of their customers and thereby increase customer satisfaction

Hypothesis 9 (H9). Higher salesperson adaptability is associated with higher salesperson performance.

Setting and Sample We chose the pharmaceutical industry as the setting for this research. Pharmaceutical sales representatives—often referred to in the industry as “detailers”—carry information about drugs to physicians, encouraging the physician to accept and prescribe their company’s products (drugs), rather than their competitors’ products, to their patients. Because new drugs are continually being developed by pharmaceutical companies and approved by governing agencies, a busy physician faces a difficult challenge in trying to keep current with the drug industry through reading or through medical associations, and hence he or she must rely on the information provided by the pharmaceutical sales representative. It is important to note that the salespeople maintain faceto-face contact with the customer, but use technology for retrieval of prior contact information and for planning purposes. Pharmaceutical sales representatives function in an environment where a high level of customer contact is necessary. Industry records suggest that these sales representatives meet or call on doctors 25 times annually. This implies that the doctors become very familiar with the sales representatives’ behaviors and characteristics. This setting also met three major conditions necessary for our research: (a) there was a broad array of information technology applications available to the sales force; (b) the use of technologies was voluntary, such that variance in information technology usage among sales representatives existed; and (c) the company’s sales force was large enough to allow for advanced statistical analyses. Data were collected from three separate sources: (a) written salesperson surveys; (b) written customer (doctor) surveys; and (c) archival technology usage, call activity, and performance data from company records. Our sample was drawn from the female health care segment of a medium-sized pharmaceutical company. Changes in the pharmaceutical industry in recent years have caused many companies to realign their sales forces into specialized segments so they can provide customers with better and more accurate product information. In the present company, the sales representatives are responsible for marketing directly to physicians, rather than to managed-care organizations or hospitals. Importantly, our sample was collected in a longitudinal manner. Data were collected from three


separate sources over three time periods. First, salespersons’ objective technology usage was cumulated for six months constituting period one. Second, we administered surveys to the sales force representing period two. Third, we obtained each salesperson’s call activity from company call records for approximately three months following the employee survey data collection. Salespersons’ level of customer service, attention to personal detail, and knowledge were assessed by the customers (physicians) during this period. Finally, each salesperson’s sales (percent to quota) were obtained from company records for the three-month period following the survey-based data collection. Sales Representatives Each sales representative is responsible for a specific geographical area and a specific specialty. Sales representatives are responsible for six products, including an estrogen replacement drug and several types of female contraceptives. All sales representatives receive training for each of these product lines and receive support from top management. A division sales manager supervises several sales representatives. On average, about 85% of sales representatives’ compensation derives from their salary; the remaining 15% comes from commissions based on individual performance. To obtain the salesperson segment of the data, we surveyed all 254 sales representatives of the female health-care division of the company. Of these, there were 231 (91%) usable responses obtained. All of the respondents completed and returned a copy of a questionnaire mailed directly to them by the researchers. A strong management endorsement of questionnaire completion via e-mail and telephone, coupled with two waves of mailings, led to the high response rate. After we matched the customer data (a minimum of two customers per sales representative), the technology use data, sales performance data, and salesperson data, we were left with a sample of 137 usable cases. This subsample was representative of the initial sample population. It was 40% male, with an average age of 36.1 (SD = 86), and 91% reported their ethnicity as white. On average, they had previously worked for 2.9 other firms (SD = 36), had 12.3 years of business experience (SD = 78), and had 9.7 years of experience in sales (SD = 76). They reported an average sales experience with this organization of 6.7 years (SD = 47). Eighty percent of the sample had a bachelor’s degree and the remaining 20% held an advanced degree. Customers The customers (physicians) were contacted by a research university and were offered $30 in compensation for their responses. To ensure that the sales

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representatives had a high level of contact with the physician and that the physician wrote an adequate number of prescriptions in the category, we only considered high-prescribing physicians. Three thousand questionnaires were mailed to physicians (approximately 5% were returned due to incorrect addresses) and 842 usable questionnaires were obtained. Each physician was asked to report on salespeople in a particular drug category (female oral contraceptives). Because the survey included two primary competitors, the study was blinded so that customers were unaware of the sponsoring company in order to minimize respondent bias. Sixty-eight percent of the physician respondents were male, and their average medical practice experience was approximately 17 years. These characteristics are in line with known population values. Sales Force Automation Technology The Siebel Pharma Sales software utilized by the sales force was designed to fit the pharmaceutical sales setting and was further customized to fit the sales process of the focal company in this study. Thus, although not directly measured, we believe that the task-technology fit is present in our study and would generate a significant relationship between IT use and performance. In fact, the leading implementation company specializing in pharmaceutical SFA/CRM implementations further customized the software to fit the sales process of the focal company in this study. The software is designed to support the pharmaceutical salesperson in all major tasks such as precall planning (planning what to say and how to persuade a customer when they meet them), postcall reporting (keeping accurate records of all contacts and sales to customers), territory analysis (analyzing which customers are most important and when to contact them), communication with other salespeople and sales managers, and resources to support updates in product information and company marketing activities. Sales representatives and their managers received training on the technology prior to implementation and follow-up sessions after the system implementation.

Qualitative Grounding of Measures

The scale development progressed through two stages. First, existing scales were adapted and extended to ensure that they were applicable to a pharmaceutical sales-representative setting. Following this, construct definitions and items were discussed with company representatives to confirm their applicability to the pharmaceutical sales context, and any necessary rewording was completed. The overarching objectives of this preliminary phase was to identify construct domains, to generate sample items for

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new constructs, to check the face validity of existing measures in a sales setting, and to assess the nomological “sense” of our conceptual model (Churchill 1979). We began our study with an extensive literature review, combined with an exploratory qualitative grounding of our constructs. Both salespeople and managers were asked to discuss what they felt constituted customer service, as well as salesperson knowledge, personal details, and adaptability in their industry. Each participant freely listed items and was asked follow-up questions when items were unclear or warranted additional explanation. To gain relevancy of the items in a pharmaceutical context, we repeated the process with different members of the pharmaceutical industry. We interviewed 15 pharmaceutical salespeople, 32 physicians, and 8 of the physicians’ administrative assistants. The underlying reason for interviewing both physicians and their administrative assistants was to gain a customer’s perspective. Based on these interviews, a list of Likerttype scale items was produced, and was reviewed by a separate panel of five researchers, who made sure that items were appropriately worded and clear. A formal pretest of the questionnaire was conducted with over 30 additional physicians. The measures were amended, based on our findings, to create a final set of items for each construct. All resulting scales were seven-point Likert, with anchors 1 = strongly disagree and 7 = strongly agree. All constructs were treated as reflective in nature and all items appear in the online appendix, which is provided in the e-companion.2

Measures Archival IT Use In cooperation with the IT specialists at the midsized pharmaceutical company, a software product that runs alongside the Siebel CRM software and tracks technology usage by sales representatives was installed. It is important to employ archival measures of actual IT use, because Collopy (1996) reported that there was a 32% difference in reports of average IT use when self-assessed respondents were compared to interactive, logged users. The usage-tracking software creates a time stamp when a sales representative begins using the Siebel program and another time stamp when the sales representative completes his or her session. The software also time-stamps and tracks exact screen usage within the 90+ screens available to the sales representative. From these time stamps, the system generates reports on the number of unique 2 An electronic companion of this paper is available as part of the online version that can be found at http://mansci.journal.informs. org/.


screen hits (for each of the 90+ screens) and total time spent per screen (for each of the 90+ screens) over a given time period (e.g., one week, one month, ). These 90+ screens allow sales representatives to perform such tasks as call recording and note taking, territory analysis, customer profiling, product information gathering, competitive product profiling, call planning, and territory management. These screens deliver insights about the customer and sales environment through digital dashboards and consultative reviews to which the salespeople can refer when preparing for each sales call. Every time a sales representative synchronizes with the company system to report their call activity or to download new customer data, the software transfers the sales representative’s usage information to the company’s database. Sales representatives were unaware that the system was tracking their usage patterns. This is the company policy, and it was quite clear, based on discussions with senior sales management, sales training staff, and sales representatives themselves, that the salespeople did not know that the tracking was in place. The company uses the data to track general trends in behavior, and although data are available, it is their policy not to view data at the individual representative level. This system had been in place for over six months without any major problems at the time of the study. To develop the appropriate measure of technology use, we met with pharmaceutical salespeople and discussed overall information technology usage. We recorded and coded responses from these individuals and then compared them to screens that the technology producer considered core usage screens for sales planning. This approach allowed us to remove any entry-level or gateway screens (i.e., log-in and menu) as well as any administrative screens that were not related to salesperson performance. In order to verify that a global measure of technology usage was most appropriate, we ran a maximumlikelihood factor analysis using screen-level data to test whether there were several underlying dimensions to our technology usage measure. After removing gateway and administrative screens, the core usage screens factored to a single underlying dimension. Interviews with sales representatives, technology support personnel, and technology designers of the system further supported the use of a global measure of technology utilization. System experts confirmed that although the system is used for multiple purposes in the sales process (e.g., precall planning, customer analysis, territory analysis, etc…); the screens used to execute these tasks are by no means mutually exclusive, and in most cases overlap. Based on the above information, we then used total time spent on the application (i.e., sum of all time


spent on core screens excluding gateway and administrative screens) and total screen hits on the application (i.e., sum of all screen hits on core screens excluding gateway and administrative screens) as indicators of the latent technology usage construct (r > 070, p < 001). We believe that by employing both measures we are achieving a more accurate representation of the salesperson’s technology usage.3 In addition and as suggested by Griffith and Northcraft (1994), we believe that this approach permits us to explore whether the core features of the technology are responsible for the technology’s effects. Survey Measures Salespersons. Adaptive selling can be defined as “the altering of sales behaviors during a customer interaction or across customer interactions based on perceived information about the nature of the selling situation” (Weitz et al. 1986, p. 175). Based on a scale developed by Spiro and Weitz (1990), we selected two items that were specifically applicable to our setting. These pertained to sales representatives’ adaptive selling behaviors and to their actual adaptive behavior ( = 086). Customers. Salespersons’ attention to personal details was rated by physicians using a three-item scale that demonstrated an acceptable level of internal consistency ( = 087). The scale was first averaged for each customer, and then, based on the average variance across customers per salesperson, we calculated an agreement index of rwg = 078 for these ratings (James et al. 1984). This provides evidence that different customers rated each salesperson’s attention to personal details similarly. Salesperson knowledge was assessed by the customer in a five-item scale that encompassed several aspects of knowledge, including both product and market knowledge ( = 091). The scale was first averaged for each customer, and then based on the average variance across customers per salesperson, we calculated an agreement index of rwg = 075 (James et al. 1984). Customer service was measured as a composite of responsiveness and reliability. Physicians were presented with four items about the responsiveness of the salesperson ( = 092) and three items regarding the reliability ( = 088) of their sales representative. We employed these two subscales as substantively based item parcels indicators in the analyses (cf. Landis et al. 2000). A parcel indicator is 3

Note that if one decomposes the technology measure into separate components by functionality (accounts, activities, and analysis), the individual components demonstrate strong correlations with one another. These highly correlated dimensions exhibit similar relationships to the mediating variables. Accordingly, we employ the use of an omnibus measure of technology usage.

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defined as an index comprised of the sum (or average) of two or more items, responses, or behaviors. One parcel represented the responsiveness indicator, whereas the alternate parcel represented the reliability parcel. Collectively, they exhibited an = 077. Moreover, customers evidenced high agreement for salesperson customer service (i.e., we calculated an average rwg = 073; (James et al. 1984)). Archival Sales Performance Representatives’ sales, relative to quota, were obtained from company records and used as our criterion. The measure used was the percentage of sales quotas that were achieved across products in the female health care division. Percent of quota, or total sales divided by the expected sales target, is a strong measure of sales representative performance because it controls for potential contaminating factors such as territory size (Churchill 1979). Sales representatives’ quotas are set annually by a consulting company, in conjunction with corporate sales management, and are based on market information and company records of past performance. Quotas are discussed with sales representatives to ensure that the representative understands the methods used to set their annual quota. Covariates Because past research posits a direct, positive relationship between effort and performance (e.g., Brown and Peterson 1994), and between experience and performance (e.g., Churchill et al. 1985), we included measures of salesperson experience and call activity as covariates. Salesperson experience was measured as the salesperson’s time employed in their sales field. Level of call activity was assessed as the total number of sales calls made to physicians within a salesperson’s territory. Objective call activity or the number of sales calls is tracked by the company and validated by the Food and Drug Administration because it is necessary for physicians to “sign-off” every time a sales representatives drops off product samples. Although not all calls involve sample drops to customers, there is a strong correlation between reported sales calls and sampling (0.90+). We used actual sales calls reported by sales representatives, rather than sample drops because the measure is more inclusive of all sales interactions with customers.

Analysis

To examine the proposed effects of technology use, we employed structural equation modeling via AMOS 5.01 with the maximum-likelihood estimation method testing the model illustrated in Figure 1. Following the two-step procedure outlined by Anderson


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and Gerbing (1988), we first fit a confirmatory factor analysis (CFA) measurement model followed by several structural models to test our hypotheses. To test the mediating role of customer service, knowledge, attention to personal details, and adaptability, we followed Mathieu and Taylor’s (2006) procedures and estimated a series of models. Structural equation modeling techniques have long been advocated as preferable to regression techniques for testing mediational relationships because they permit one to model both measurement and structural relationships and yield overall fit indices (Baron and Kenny 1986). All models tested include the call activity and experience measures as covariates. We report the standardized root mean square residual (SRMR), and the comparative fit index (CFI) (Bentler 1990) as model fit indices. We consider models with CFI values < 090 and SRMR values > 010 as deficient, those with CFI ≥ 090 to < 095, and SRMR > 008 to ≤ 010 ranges as acceptable, and ones with CFI ≥ 095 and SRMR ≤ 008 ranges as excellent (Mathieu and Taylor 2006). Based on the noncentrality parameter estimate, our hypothesized structural model exhibited a power estimate greater than 0.90 (Kim 2005). We adopted the conventional p < 005 as evidence of statistical significance.

and customer service were all obtained from customers and may be subject to common method or same source effects. Consequently, we introduced an additional first-order source factor to the CFA. All indicators derived from customer ratings were freed onto this factor, and the source factor was constrained to be uncorrelated with all substantive factors (Podsakoff et al. 2003). This CFA model revealed an excellent fit [ 2 = 1847 (84), p < 001; CFI = 095; SRMR = 0055]. Consequently, we will employ the CFA model, including the source factor as the base for our substantive model tests. Structural Models We next fit several different structural models to test the mediating effects depicted in our conceptual framework as outlined by Mathieu and Taylor (2006). Table 2 presents a summary of the model fit indices and associated parameter estimates. In effect, we isolate the direct and indirect effects for the technology usage antecedent. We first fit a direct effect model estimating the direct path of technology usage to performance, with no paths leading to or stemming from the mediator variables (although all mediators remain as latent variables in the model). This direct effect model exhibited deficient fit indices [ 2 = 2922 (106), p < 001; CFI = 091; SRMR = 009]. This indicates that technology usage has a significant relationship with at least one of the mediating variables, or one of the mediating variables relates significantly to performance. Notably, results of this analysis revealed that technology usage related significantly with performance ( = 017, p < 005). This provides support for Hypothesis 1. Moreover, call activity exhibited a significant relationship with performance ( = 023, p < 001), although experience did not ( = 007, ns). Proportion of variance explained in performance was R2Performance = 009. Next, we fit a no direct effect model that includes parameter estimates from technology usage to all the mediating constructs and from the mediating constructs to performance, but excludes the direct effect

Results Measurement Models The intercorrelations among the latent constructs are included in Table 1. We first fit a CFA model to our data, which yielded an acceptable fit ( 2 = 2703 (94), p < 001; CFI = 091; SRMR = 0064). Fornell and Larcker (1981) tests for discriminant validity were all found to be acceptable with results reported for composite reliability and average variance extracted for all latent variables (see Table 1). Furthermore, all factor loadings were significant (p < 001), and composite reliabilities exceeded the 0.60 benchmark (Bagozzi and Yi 1988). Nevertheless, recall that the measures of salesperson knowledge, attention to personal details, Table 1

Construct Intercorrelations, Means, Standard Deviations and Reliabilities

Variable 1. 2. 3. 4. 5. 6. 7. 8.

Tech usage∗ Customer service Personal details Knowledge Adaptability Performance Call activity Experience

1

2

1 008 009 010 014 013 −006 −006

1 043 056 011 033 009 001

3

1 018 012 030 013 011

4

1 016 011 −008 016

5

1 027 019 004

6

1 023 009

7

1 005

Mean (SD) 000 405 273 518 602 9310 1550 643

(1.0) (1.4) (1.2) (1.1) (1.2) (56.9) (7.7) (3.6)

CR

AVE

CA

— 0.71 0.87 0.93 0.81 — — —

— 0.57 0.70 0.72 0.68 — — —

— 0.77 0.87 0.91 0.86 — — —

Notes. Technology usage, performance, and call activity are objective measures; therefore, scale reliability tests are not applicable. CR, composite reliability; AVE, average variance extracted; CA, Cronbach alpha. p < 005 for values greater than 0.17 n = 137. ∗ Values for technology usage were standardized.


Table 2

Parameter Estimates, Model Fit Statistics and Variance Explained Direct effect model∗

Hypothesized no direct effect model

Full model

H1: Technology use −→ Performance H2: Technology use −→ Customer service H3: Technology use −→ Att to pers details H4: Technology use −→ Knowledge H5: Technology use −→ Adaptability H6: Customer service −→ Performance H7: Att. to pers. details −→ Performance H8: Knowledge −→ Performance H9: Adaptability −→ Performance Salesperson exp −→ Performance Call activity −→ Performance

0169∗∗ — — — — — — — — 0071 0231∗∗

— 0409∗∗ 0137 0343∗∗ 0194∗∗ 0382∗∗ 0193∗∗ 0026 0187∗∗ 0044 0216∗∗

−0188 0450∗∗ 0155 0359∗∗ 0196∗∗ 0537∗∗ 0222∗∗ 0108 0185∗∗ 0047 0225∗∗

Chi square Degrees of freedom CFI SRMR

292.2 106 091 009

222.6 99 094 007

220.4 98 094 007

R2

— — — — 0086

0167 0019 0118 0037 0290

0202 0024 0129 0038 0300

Customer service Att. to pers. details Knowledge Adaptability Performance ∗

Standardized parameter estimates. Significant at p < 005 level.

∗∗

from technology to performance. This also represents our hypothesized structural model and demonstrated acceptable fit indices [ 2 = 2226 (99), p < 001; CFI = 094; SRMR = 007]. As shown in Table 2, significant relationships were all in the hypothesized directions. Specifically, technology use was related significantly to customer service, salesperson knowledge, and adaptability, respectively (H2: = 041, p < 001; H4: = 034, p < 001; H5: = 019, p < 005). Alternatively, technology usage failed to relate significantly to salespersons’ attention to personal details (H3: = 014, ns). Furthermore, customer service, attention to personal details, and salesperson adaptability each related significantly with salespersons’ performance, respectively (H6: = 038, p < 001; H7:

= 019, p < 001; H9: = 019, p < 005). Surprisingly, the relationship between salesperson knowledge and performance was not significant (H8: = 003, ns), nor was that between salesperson experience and performance ( = 004, ns). Finally, the relationship between salesperson call activity and salesperson performance was significant in this model ( = 022, p < 001). The proportions of variance of the endogenous variables accounted for by the hypothesized influences were as follows: R2customer service = 017; R2Attention to details = 002; R2Knowledge = 012; R2Adaptability = 004; and R2Performance = 029. Last, we fit a full structural model that adds a direct path from technology usage to performance to the hypothesized model. Although the full structural model also exhibited acceptable fit [ 2 = 2204 (98), p < 001; CFI = 094; SRMR = 007], it did not evidence

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a significant improvement over the hypothesized model ( 2 1 = 22, ns.), nor was the direct effect significant ( = −019, ns). In summary, the various model tests provide us with valuable information about the mediating mechanisms inherent in our model. From the direct effect only model we determined that IT use related positively to sales performance and that the mediating variables play an important role in our model. From the no direct effect i.e., hypothesized model we determined that the relationships between technology usage and performance was transmitted primarily through salespersons’ customer service (Sobel = 249; p < 001) and adaptability (Sobel = 167; p < 010), rather than knowledge (Sobel = 106; ns) or attention to personal details (Sobel = 094; ns). Finally, the full model informed us that the influence of technology usage was fully mediated when these four sales behaviors were considered. Accordingly, on the basis of parsimony, and the fact that the IT-usage variable did not exhibit a significant effect with the criterion in the full model, we conclude that the hypothesized model is preferable (Anderson and Gerbing 1988). Exploratory Analyses Although we concluded that the hypothesized model fit better than the alternatives that we considered, its fit values suggested that there was still room for improvement. Given the causal sequence of data collection and the overall theoretical framework that we advanced earlier, we considered whether adding additional parameters would yield a better model fit. We determined that greater fit could be achieved by modeling a relationship between salespersons’ attention to personal details and customer service, and another relationship between call activity and salespersons’ adaptability. Within each pair of variables, one might introduce a unidirectional parameter, reciprocal paths, or permit disturbance or error terms to correlate, under the assumption that some omitted variable might be exerting influence. Given that we did not hypothesize such relationships a priori, nor do we have firm justification for suggesting a causal priority among these variables (see Mathieu and Taylor 2006), which approach to employ is debatable. In any case, modeling these relationships in some fashion would yield an excellent model fit and suggests fruitful directions for future research.

Discussion

One of the main research questions that has intrigued both researchers and practitioners alike in recent years is the effect that technology has on the performance of salespeople (Erffmeyer and Johnson 2001, Leigh and Marshall 2001). IS research has made significant progress in understanding psychosocial determinants

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of IT use and employee (user) satisfaction resulting from such use. Our research sought to extend TTF theory by focusing on the effects of IT use on salespeople’s behaviors and characteristics as key mediators of the relationship between IT and salesperson performance. In other words, we have endeavored to understand why and how IT has an impact on performance through which mediating mechanisms. These multiple sources of data overcome limitations of samesource biases and make our findings less susceptible to alternative explanations. Consistent with our predictions and those offered by IS researchers, technology use did yield positive and significant outcomes. Specifically, in our empirical test we discovered that increased technology was associated with sales performance via customer service and salesperson adaptability. Although exhibiting a significant correlation with performance, surprisingly, the influence of salespersons’ attention to personal details was not statistically significant in our model. However, attention to personal details was correlated significantly with overall perceptions of customer service, and our exploratory analyses suggested that there may well be some form of relationship between the two constructs. IT use was found to significantly affect salespersons’ knowledge. This positive relationship confirms the assumption that using the IT system helps salespeople update their knowledge about the market and about their specific products. Interestingly, however, knowledge did not exhibit a statistically significant relationship with performance. Here again, perhaps knowledge manifests its influence indirectly through other mechanisms such as customer service. Alternatively, because the knowledge levels of the current sample of salespeople were rated as fairly high and very consistent across people, this nonsignificant result may be attributable to a restriction of range. Previous research suggests that salesperson adaptability can influence performance. In fact, salespersons’ adaptability has been positively associated with self-assessed sales performance (Boorom et al. 1998). However, adaptability was not significantly correlated with managers’ ratings of sales performance in the Spiro and Weitz (1990) study. Given that both self-ratings and manager ratings of performance are susceptible to a variety of rating biases, it appears that our findings provide additional insight into these contradictory findings. We found support for the assumption that salesperson adaptability does positively influence archival indices of salesperson performance. To understand how organizations can realize the desired results, such as increased sales through technology use, it is important to conceptualize the details (mediators) of how technology usage leads to those


impacts, so that the process can be better managed. Our results indicate that salespeople achieve high levels of performance, not purely by technology, but rather through facilitating mechanisms that are directly influenced by technology usage. Interestingly, it is the increases in other factors, such as adaptability and customer service, that increase salesperson performance. Managerial Implications Our study has several implications and findings that can be translated into strategic actions for sales management and IT executives. Our findings suggest that overall usage of SFA tools can have a significant influence on salespeople’s adaptability to customers’ needs and salespeople’s ability to better serve customers— two consequences that enhance salesperson performance. This is important because it helps managers persuade their salespeople to use technology more during the sales process. Moreover, it is evident that the use of technology can lead to several positive outcomes for salespeople. Because sales departments’ priorities have moved to improving relationships and improving the quality and uniqueness of the sales presentation (Rivers and Dart 1999), technology may be a viable option in this regard. The use of the right IT system can help salespeople build stronger customer relationships. This being the case, firms must decide how to deploy their technology resources to maximize their customer relationships, and must also determine how to get their salespeople to use those resources. If sales managers and salespeople understand how a particular IT system can provide the company with a competitive advantage, such as our study suggests, the dollars invested will be well spent. The pharmaceutical industry provides a good illustration of this point. Although the precise magnitudes of effects observed in our study were somewhat modest, recent statistics indicate that there were $251.8 billion in prescription sales to U.S. pharmacies in 2005 (Blum 2007). Taking into consideration that generic drugs accounted for 56% of prescriptions in 2005, while generating 13% of drug sales, there were approximately $219.1 billion in brand-name prescription drugs sold to U.S. pharmacies in 2005. Based on our findings, the direct effect of technology usage and covariates explained approximately 9% of the variance in performance, which translates to roughly $19.7 billion in sales. Adding in the mediating factors that we examined accounted for approximately another 20% of variance in sales, or roughly another $44 billion if extended to the industry as a whole. The relationships between technology use, customer service, attention to personal details, adaptability, salesperson knowledge, and salesperson performance


provide a strong justification for the implementation of IT among the sales force. This study helps sales managers recognize some of the intangible benefits associated with SFA and CRM systems, which has heretofore been elusive (Rivers and Dart 1999). It may also be interesting to note that once salespeople begin to see the positive outcomes associated with their use of the technology systems, they may be more willing to invest more time and effort into using the system. This implies that organizations that utilize SFA or CRM technologies should consider recruiting salespeople that have the ability to incorporate the technologies into their daily routines. Study Limitations and Future Research Although we discovered some of the positive outcomes and consequences of employing the use of information technology, as with most research, this study does have some limitations. Perhaps the single most important limitation of our study is the singlecompany frame. It would be interesting to investigate the relationship between IT and salesperson performance in other sales situations and industries to determine generalizability. For instance, the effect of technology may be more apparent in sales situations in which sales calls are more elaborate or in which the customer witnesses the use of the technology. Note, however, that we chose to focus on a single site because doing so enabled us to control for extraneous and contextual factors (e.g., market and organizational factors). Insufficient consideration of the organizational context, and pooling data from different firms are potential explanations for the mixed findings in the area of IT and salesperson performance (Avlontis and Panagopoulos 2005). Hence, future research is needed to replicate and extend our findings. Our research suggests that there are several positive relationships between IT and salesperson performance, but it is certainly not definitive. Although the impact of technology was always found to be positive, it does not rule out an IT-performance paradox at the level of the individual salesperson. IT may be stronger or weaker in other sales contexts. Importantly, our research assumes a task-technology fit; however, we did not conclusively verify this fit. An additional limitation, as well as an area in need of further research, concerns the direction of causality suggested in the findings. Although we employed multiple data sources in a longitudinal design to test the relationships within our model, purely causal inferences remain difficult to make (Mathieu and Taylor 2006). Thus, affirmation of the causality of our proposed relationships through additional longitudinal and/or experimental studies is needed. Our review of past literature indicates other important issues that require future research. Researchers

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need to investigate additional mediators of technology use that we may have omitted, as well as potential moderators that may enhance or detract from relationships. For example, the length of the relationship with the customer may strengthen or weaken some of the proposed relationships. Also, better understanding of the timing and longitudinal nature of these outcomes is needed, as well as research that partitions the technology usage measure into finer components. Overall, we have offered an initial framework for understanding how technology can improve salespeople’s and their companies’ efforts in serving customers. Our study provides an example for how interdisciplinary research can be conducted on the effects of IT in a variety of fields. More specifically, as we have demonstrated, researchers could create contextual extensions of TTF theory to determine how individuals’ use of IT should lead to benefits.

Electronic Companion

An electronic companion to this paper is available as part of the online version that can be found at http:// mansci.journal.informs.org/. Acknowledgments

The authors thank the Institute for the Study of Business Markets at Pennsylvania State University for its financial support of this study, and Blake Ives for his helpful comments.

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