Effect of Technology on Sales Performance: Progressing from Technology Acceptance toTechnology Usage and ConsequenceAuthor(s): Michael Ahearne, Narasimhan Srinivasan, Luke WeinsteinSource: The Journal of Personal Selling and Sales Management, Vol. 24, No. 4, CustomerRelationship Management: Strategy, Process, and Technology (Fall, 2004), pp. 297-310Published by: M.E. Sharpe, Inc.Stable URL: http://www.jstor.org/stable/40471971 .Accessed: 22/10/2011 10:57

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EFFECT OF TECHNOLOGY ON SALES PERFORMANCE: PROGRESSING FROM TECHNOLOGY ACCEPTANCE TO

TECHNOLOGY USAGE AND CONSEQUENCE Michael Ahearne, Narasimhan Srinivasan, and Luke Weinstein

Technology plays an ever-increasing role in personal selling and customer relationship management (CRM). Over the past decade, many models examining the acceptance of technology have been proposed and refined, contributing signifi- cantly to our knowledge of technology adoption. An implicit assumption made in such models is that increasing the usage of technology is better - that is, more usage is better than less usage. This is a critical assumption that has not been tested in the literature. What if technology has diminishing returns? We propose that it is time to progress to a Technology Performance Usage Model (TPUM) and to look for usage levels that lead to optimum effect on performance. Our model is tested using a sample of 131 salespeople in an operational CRM context. Results show a curvilinear relationship between a salesperson's prime task performance (measured as sales percent to quota) and their usage of the "enabling" CRM technology. Initially, the CRM technology is enabling on sales performance, but diminishing return sets in, and beyond a point, a disabling effect on sales performance can be observed. This finding offers a valuable insight to practitio- ners and provides a strategic direction - achieving and maintaining a particular level of technology usage to optimize prime task performance.

The growth in customer relationship management (CRM) software deployment has paralleled the transition from trans- actional marketing to relationship marketing. In organiza- tions, 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 re- search into the performance efifect of the technology after the technology is installed and trained. Further, the adoption re- search has, to date, worked under the assumption that more overall usage of technology is better. Consistent with the rec- ommendations of Leigh and Marshall (2001) to examine is-

Michael Ahearne (Ph.D. Indiana University), Associate Professor of Marketing, Department of Marketing & Entrepreneurship, Bauer College of Business, University of Houston, mahearne@uh.edu. Narasimhan Srinivasan (Ph.D., State University of New York at Buffalo), Associate Professor of Marketing, University of Connecti- cut, narasimhan.srinivasan@uconn.edu. Luke Weinstein (MBA, M.S. Engineering, University of Pennsyl- vania), Doctoral Candidate in Marketing, University of Connecti- cut, luke.weinstein@uconn.edu. Authors are listed alphabetically and made equal contributions. The authors thank the Special Issue Guest Editors and the three review- ers for their comments and suggestions.

sues 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 accep- tance, and not on the productive effect of technology usage. Deservedly, researchers have examined the acceptance of tech- nology, and several models have been proposed in the litera- ture. These models include the Technology Acceptance Model (ΤΑΜ) (Davis 1986), and its extension (TAM2) (Venkatesh and Davis 2000), and models based on the Theory of Rea- soned Action (Davis, Bagozzi, and Warshaw 1989), Innova- tion 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 posi- tive and linear relationship between performance and usage (Figure 1). There is an underlying assumption that technol- ogy utilization is a proxy of its perceived effectiveness. Some- times, it is even explicitly stated. To quote Heine, Grover, and Malhotra, "It is assumed that increased utilization is a

Journal of Personal Selling & Sales Management, vol. XXIV, no. 4 (fall 2004), pp. 297-310. © 2005 PSE National Educational Foundation. All rights reserved.

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Figure 1 Expected Performance Plotted Against

Technology Usage Time

ci s'

I /

^-- > Usage

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. We recognize that the focus of past research has been the

acceptance of technological innovations, and the objective was to accelerate the acceptance of technology. But, what hap- pens after any technology is accepted? Would increased usage always be desirable? Does technology always improve perfor- mance? Initially, there are acquisition and setup costs, train-

ing costs, and maintenance costs. The positive returns would start flowing in only after some time. How does technology usage affect performance over an extended period of time af- ter the initial setup period, training is completed, and users are now using the system regularly? Is it possible to evaluate the returns from a specific technology? These important ques- tions are very relevant to managers. The purpose of this study is to examine the effect of operational CRM technology us-

age on salespersons' performance after they have been using the technology for at least six months. It is consistent with the research priorities in sales strategy and performance re- cently identified by Leigh and Marshall (2001): information technology (IT), sales/service/support systems, and selection/ development of salespeople.

BACKGROUND

When TAMs were first introduced in the literature around the mid-1980s, the business environment was very different than it is today. The computer industry was mainframe ori- ented, with an installed base of approximately 19 million per- sonal computers (PCs) in the United States (Juliussen 2002). Users generally interacted with computers using unfriendly

command-driven character-based interfaces. There were lim- ited applications available. Applications were generally expen- sive, and the competition in any application field was relatively limited. Indeed, many of the empirical studies used in testing the early acceptance models measured the acceptance and

usage of word-processing applications (Davis 1989; Davis, Bagozzi, and Warshaw 1989, 1992). Today, the typewriter has become a relic, and there is pervasive use of word proces- sors, and this too has long ceased to be an innovation.

Over the past couple of decades, technology has evolved and is significantly different than when acceptance models were first introduced. Today, PCs have become pervasive in business, with an installed base of approximately 201 million (Juliussen 2002). Users interact with friendly graphical inter- faces that are increasingly easier to use. Many vendors pro- vide a variety of application software, and prices keep dropping dramatically. Key competitive factors in a user's choice of an

application include product functionality and ease of use - two key antecedents of TAMs. With increasing adoption of new technology just to stay competitive, businesses have to

necessarily invest heavily in training their employees with new

technology (Marshall et al. 2000). Although we have certainly learned a great deal from TAMs

in the literature, the specific technologies examined have been

evolving, and we can still find examples of technology imple- mentation failures in recent CRM literature (e.g., Jones, Sundaram, and Chin 2002; Rivers and Dart 1999; Speier and Venkatesh 2002). Beyond acceptance, the productive ef- fect of technology usage on performance is a very important issue today.

A major contribution of this study is the critical assess- ment of the implicit assumption of a positive and linear rela-

tionship between technology usage and performance. First, every salesperson knows that time is a scarce resource to be

judiciously allocated among various tasks. Second, technol-

ogy cannot substitute several necessary tasks, such as travel and personal discussion. Third, a linear relationship can only hold for a limited range, as substitutability will otherwise elimi- nate all expensive inputs with low marginal productivity - that is, every resource has diminishing returns. Otherwise, technology can be used increasingly, and salespeople can be eliminated. However, this is not likely to happen. There is a limit to what technology can do, and we recognize that tech-

nology is only a tool, and the substance of personal selling still resides with salespeople.

What if the relationship between technology usage and per- formance is curvilinear? (See Figure 2.) A meta-analysis of time and performance relationships shows similar curvilinear pat- terns (Sturman 2003). BefDre we can test for possible media- tors and moderators that affect the relationship, we must first understand how and when the use of sales technology leads to increased effectiveness and efficiency (Tanner et al. 2005).

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Let us consider an operational CRM context - a term coined by Tanner et al. (2005) to describe that portion of CRM us-

age for sales process management, such as lead generation, contact, scheduling, performance tracking, and other func- tions in a sales context. Intuitively, it is reasonable to expect a salesperson using a CRM system to improve his or her per- formance by effectively increasing his or her usage of CRM

technology from, say, three to four hours per week. But if that same salesperson increased his or her usage to 30 hours a week, that salesperson would be increasing his or her CRM

usage at the expense of other work tasks that must be per- formed. As with any business that attempts to optimize its use of resources, a salesperson needs to optimize his or her allocation of a key resource - time - among various tasks, in order to maximize his or her sales performance.

It is natural that there would be heterogeneity in technol-

ogy usage across employees. Anecdotal evidence has suggested that salespeople are among the most technophobic and resis- tant of all white-collar workers (Mills 1995; Parthasarathy and Sohli 1997), though this should be changing as technol-

ogy becomes more pervasive. Some people take enthusiasti- cally to new technologies and are innovators. Others prefer the old way of doing business and are laggards. Hence, it is

quite conceivable that salespeople may often overuse or under- use CRM technology. Overuse may come at the expense of other sales tasks that may have a greater effect on performance. Underutilization would imply that the salesperson is less ef- fective than he or she could be, if only he or she used the tools of the CRM technology to a greater degree.

If there is a curvilinear relationship between technology usage and performance, it has very important implications for the effective use of technologies in personal selling. To what degree of usage is technology enabling? What are the factors that can help us predict when technology usage will have a positive effect on performance? Does technology have

diminishing returns? What are the factors that will predict when usage will have a disabling effect on performance? To answer such questions, researchers have to move beyond tech-

nology acceptance and usage contexts. We need to study situ- ations after any particular technology is adopted and installed

by an organization, and salespeople have been trained to ef-

fectively use the system.

THEORETICAL FOUNDATIONS AND MODEL

Improving human performance in organizations is a primary goal for organizations to increase competitiveness (Marshall et al. 2000). Organizations spend significant amounts of

money on technology implementation and look to get in- creased productivity and performance from their investments (Marshall et al. 2000). To meet these goals, researchers need

Figure 2 Proposed Curvilinear Relationship of Performance and

Technology Usage Time

Enabling f , . '

j λ ν Disabling

ι ν ' >

Usage

Note: Assumes technology is installed, training is over, and CRM is in use.

to better understand how technology acceptance and usage leads to improved performance. The TAMs provide us with a rich foundation of theory and important constructs regard- ing usage, as outlined in Table 1 . The next logical step is to study the effect of technology usage variance on actual per- formance.

The major contribution of this research is to examine if

technology, such as capital and labor, has diminishing mar-

ginal productivity in the corporate production process. Time of salespeople is not unlimited and has to be allocated across various tasks, with differing effects on performance. Currently, the differential allocation of time on technology is voluntar- ily decided by each salesperson, with no recommendations or

process training on how to optimize the allocations. Histori- cally, time management for salespeople focused on the best way to ration a salespersons time among the accounts that make up his or her territory (e.g., Zoltners, Sinha, and Chong 1979). Today, as competition has intensified, more is expected of salespeople (e.g., Nonis and Sager 2003), leading to more stress in salespeople. Time management can now be seen as a cluster of behaviors that are deemed to facilitate productivity and alleviate stress (Lay and Schouwenburg 1993). In this study, we deal only with time spent on specific CRM tech-

nology and its effect on performance. This is not a broad time management study.

In the early 1990s, Microsoft chairman Bill Gates predicted that business technology eventually would allow people to do a full day's work in four or five hours, freeing them to spend more time on leisure pursuits or with friends and family. In- stead, by the mid-1990s, the average workweek was nearly an hour longer than in the early 1980s, and a survey found that

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Table I Review of Technology Acceptance Models in the Literature

Topic Reference Key Conclusions

Technology Acceptance Model (ΤΑΜ) Davis ( 1 989); Usage can be predicted from intentions. Davis, Bagozzi, and Warshaw ( 1 989); Key antecedents of usage are: Taylor and Todd ( 1 995) · perceived ease of use,

• perceived usefulness.

Theory of Reasoned Action (TRA) Davis, Bagozzi, and Warshaw ( 1 989); Subjective norm from TRA is examined, but mixed (an acceptance model) Jones, Sundaram, and Chin (2002) results on significance. Triandis (an acceptance model of Thompson, Higgins, and Howell (1991) Key constructs that were significant antecedents PC utilization) of usage were:

• social factors (similar to subjective norm), • complexity of use (similar to ease of use), • job fit (similar to perceived usefulness), • long-term consequences.

Innovation Diffusion Theory (IDT) Moore and Benbasat (1991) Looked only at adoption versus nonadoption and (an acceptance model) added new constructs of

• observability, • trialability, • voluntariness.

Motivational Model (MM) Davis, Bagozzi, and Warshaw (1992) Enjoyment is an antecedent of intention and usage. (an acceptance model)

Theory of Planned Behavior (TPB) Taylor and Todd ( 1 995) Subjective norm significant, made up of (an acceptance model) · peer influence,

• superior's influence. Adds perceived behavioral control with antecedents of • self-efficacy, • facilitating conditions.

Social Cognitive Theory (SCT) Compeau and Higgins (1995); Self-efficacy is an antecedent of usage, both directly (an acceptance model) Compeau, Higgins, and Huff (1999) and mediated through anxiety and affect. ΤΑΜ extended with Task-Technology Fit Dishaw and Strong (1999); TTF does not directly affect usage but is mediated (TTF) Mathieson and Keil (1998) through ease of use.

Technology Acceptance Model Extended Jones, Sundaram, and Chin (2002); Adds to the ΤΑΜ (Davis 1 989) by including (TAM2) Venkatesh and Davis (2000) antecedents of perceived usefulness of subjective

norm, image, job relevance, output quality, and result demonstrability. Moderators of intention to use are experience and voluntariness.

Unified Theory of Acceptance and Venkatesh et al. (2003) Summarizes the literature nicely and proposes a Use of Technology (UTAUT) unifying framework. Key antecedents of intention

and thus usage are: • performance expectancy (usefulness), • effort expectancy (ease of use), • social influence (subjective norms), • facilitating conditions.

Moderators are: • gender, •age, • experience, • voluntariness.

Technology Addiction Brown ( 1 99 1 ; 1 993); Charlton (2002); Finds the existence of a form of behavioral Griffiths (1998) addiction in the use of computers and the Internet.

Addiction has six components: salience, mood modification, tolerance, withdrawal symptoms, conflict, and relapse.

Learning Orientation and Performance Ahearne et al. (2004); Shows the link between learning orientation and Orientation Sujan,Weitz, and Kumar (1994) performance orientation to the adoption of

technology and performance in an SFA context.

Complexity Teo and Lim (1996) Study of factors associated with usage. Adds complexity but in the context of being difficult to use, the opposite of ease of use.

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more than 68 percent of respondents felt at least somewhat more overwhelmed at work today (Carey 1996). With the

speed of technology, managers expect more tasks to be com-

pleted by employees in the same amount of time. At the most basic level, employees have two resources, time and energy, and these resources are under the employee s control (Brown and Leigh 1996). So with increased expectations and demands, salespeople must manage these resources to optimize their effectiveness, but with few, if any, tools to make objective observations or decisions in this resource management. In the CRM world, others have touched on this issue, such as

Engle and Barnes (2000), suggesting that too much use and

emphasis on the planning function of CRM systems may not be productive.

Similarly, self-regulatory capabilities are critical for success in boundary-spanning roles such as personal selling (Brown, Leigh, and Jones 2005). Self-regulatory effectiveness is defined as the ability to monitor and control ones own goal-directed behaviors and performance (Bagozzi and Dholakia 1999). In

complex task environments such as sales, individuals must

pursue multiple goals simultaneously (Austin and Vancouver 1996; Bagozzi and Dholakia 1999). Attention and effort can be channeled from the most productive tasks to those that are less instrumental for attaining central goals (Brown, Leigh, and Jones 2005). While research has shown that self-regula- tory skills can be enhanced through training in areas such as time management (Leach 1999), how do we train salespeople with self- regulatory skills on the usage of CRM systems, when we do not understand the effect of such usage on their perfor- mance?

We claim that one possible benefit of CRM implementa- tions would be more effective use of the salesperson's time (Tanner et al. 2005), but we do not have sufficient empirical data to support this claim, or how it may be moderated by a

salespersons technology expertise. Absent tools to objectively optimize their management of time and effort resources, sales-

persons will make subjective estimates to prioritize and opti- mize usage of these resources. We theorize that affective orientations to tasks from which salespeople get internal re- wards could have enabling effects in accepting and using tech-

nology, whereas resistance to avoiding tasks that are not as

enjoyable as others could contribute to a disabling effect, as would the limitation of time. This theory is only being of- fered as a way of understanding why different individuals

might have differential utilization of technology. Furthermore, this explanation is offered only as a background for under-

standing affinity or disaffinity to technology and is not the focus of the research. Our current study deals with the explo- ration of the diminishing returns of technology usage.

To assume that performance would continue to increase

linearly with technology usage is intuitively not appealing. To illustrate, a successful outside salesperson who adopted

and used a CRM system for 30-plus hours per week clearly would have little time to make sales calls and close transac- tions. This would have a severe negative effect on the salesperson's performance. Yet the same salesperson using the CRM technology for, say, three hours per week, might see an increase in his or her productivity, calls per week statistics, and sales as a percent of quota performance. Maybe this same salesperson using the CRM for four hours per week would see even further increases in their performance. So the ques- tion is, at what point of usage does performance start to de- cline? To capture the enabling and disabling aspects of

technology usage, we propose a quadratic model that can be expressed as

Performance - (X + βλ *

Usage + β2 *

Usage2.

The above model is a generalized way of capturing the

enabling effect of technology if β{ is positive and significant, and if/?2 is negative and significant, similar to the Bass (1969) model. It is powerful in that it captures the net effect of vari- ous factors that can influence βχ and ßr

There would be a minimal level of sales performance ex-

pected for each salesperson when there is little use of the spe- cific technology under investigation. The coefficient βχ is

expected to be positive, denoting the enabling effect - that is, initially using technology improves performance. However, technology is expected to have diminishing returns. Thus, the coefficient β2 is expected to be negative, denoting the dis-

abling effect - that is, beyond a particular point, more usage would detract from performance - because other essential tasks with greater marginal productivity would have to be

neglected or sacrificed. High levels of convenience using tech-

nology or high levels of "relative enjoyment" (relative to other tasks performed by salespeople) using technology, self-effi- cacy, ease of use, usefulness, or social factors are additional

facilitating factors that may increase usage of technology in sales situations.

Certain tasks are more convenient to perform than other tasks. For example, a salesperson might find it more conve- nient to work with his or her operational CRM technology instead of getting into a car and driving to visit a customer. It could be even more convenient if the salesperson is tired or

expects to encounter objections from a prospective customer. This increases the positive effect of working with the CRM

technology, a task that an employer would typically still re-

quire the salesperson to perform, and allows the salesperson to partially avoid an important alternate task with possible negative effect.

The user will tend to spend more time on those tasks from which he or she gets more enjoyment, instead of being ever

vigilant about which tasks will contribute the most to perfor- mance. Because time is a scarce resource for any salesperson, a salesperson spending a lot of time on an enjoyable task would

302 Journal of Personal Selling & Sales Management

have less time to spend on tasks that may have a relatively greater effect on performance. Hence, there exists a need to balance use of any resource with its marginal productivity, that is, use technology as long as it has a positive effect on performance, but not beyond that. For this to happen, one has to first establish a curvilinear relationship, then a feed- back system, and, finally, a corrective mechanism that guides a salesperson to optimally use any technology.

METHODOLOGY

Sample and Site Selection

We conducted our research within the female health-care di- vision of a mid-sized pharmaceutical firm. This division of the company had a Siebel CRM system in place for more than one year prior to the implementation of our research. Siebel is widely considered to be the market leader in CRM systems in the pharmaceutical industry, holding a dominating share of the market. The configuration of the Siebel system in this study is typical for the pharmaceutical industry. The Siebel system included sales tools designed to facilitate call planning, postcall record keeping, information gathering, intrafirm communi- cation, and customer account management.

Our analysis sample consists of 131 sales representatives. Approximately 54 percent of them are female, and the me- dian age is in the range of 26-35 years. The average experi- ence in a sales job is 8.5 years (s.d. = 5.7), the average tenure within the company is 5.8 years (s.d. = 4.4), and the sales- people worked in their territory an average of 2 A years (s.d. = 1.4). All sales representatives were college graduates, with ap- proximately 1 5 percent having advanced degrees.

We considered two major conditions as necessary for our research: (1) the use of technologies was voluntary such that variance in IT usage among sales representatives existed, and (2) the technology had been in place long enough to provide sufficient familiarity and establish stable patterns of use among the sales force. These were necessary because our primary in- vestigation was to measure the consequence of technology usage on sales performance. We wanted to ensure that the technology was adopted, salespeople had been trained, and there was heterogeneity of usage among the salespeople. Choosing the respondents from a single firm (as opposed to a cross-sectional study using various firms) is an advantage in the present setting, as it controls for confounding external effects due to the variability in market contexts (e.g., com- petitive situations) and organizational factors (e.g., informa- tion systems and sales management practices). The limitation of investigating salespeople from any single firm can, how- ever, lead to a question of representativeness of the firm and the generalizability of results. However, what is being tested in our model is the theoretical relationship between degree of

technology usage and its relationship to performance, and greater homogeneity within the sample would ensure that extraneous factors do not confound the results. Therefore, in this study's context, having a single firm is an advantage.

Measures

The testing of the proposed relationship between IT usage and sales performance depends on two critical variables: tech- nology usage and performance. In past research on technol- ogy usage, researchers have relied almost exclusively on self- reported measures of technology usage (Jones, Sundaram, and Chin 2002; Speier and Venkatesh 2002). In our study, we sought an objective measure of sales representative technology usage. In an effort to accurately track the technology usage of sales representatives, we worked with a company that special- izes in Siebel technology implementation in the pharmaceuti- cal industry. In cooperation with the IT specialists at the mid-sized pharmaceutical company, a software product was installed that runs alongside the Siebel CRM software that tracks usage of technology by sales representatives. The soft- ware program places a time stamp when a sales representative begins using the Siebel software and a time stamp when the sales representative completes his or her session. The software also time stamps and track exact screen usage within 90-plus screens available to the sales representative. Every time a sales representative synchronizes with the company system to re- port their call activity or to download new customer data, the software also transfers the sales representative's usage informa- tion to the company's database. The usage figures did not in- clude tracking time spent in administrative activities (such as the upload/download functions), but focused on screens for territory analysis activities, precall planning and preparing ac- tivities, and postcall planning and reporting activities. The CRM system had been in place for more than one year, and the data capture system had been in place for more than six months without any major problems. We only used data for salespeople who had been using the CRM system for at least six months after training to eliminate any confounding on the data from initial training. As noted earlier, system usage is voluntary, and usage is not even required for minimal report- ing purposes. However, we eliminated seven users from our sample with fewer than 50 hits on the system in the three- month period measured, in order to eliminate users who only accessed the system minimally or who chose not to use the system at all. Adoption of the technology is a precursor to our study about usage, and this fits our requirement. This left a sample of 131 salespeople for conducting our analysis.

Sales representatives were unaware that the parent com- pany was tracking, or had the ability to track, their CRM usage patterns. This was the company policy. It was quite clear, based on discussions with senior sales management and

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sales representatives, that the salespeople did not know about the usage tracking.

Data from this system were collected for a three-month pe- riod for all sales representatives. A measure of the total number of system hits (or total screens used in a three-month period) was used as a measure of salesperson technology utilization (av- erage hits = 283; s.d. = 228). We used hits rather than total time because, with total time on the system, there is a possi- bility of sales representatives leaving a screen running with- out using the software. One should note, however, that these measures of hits and time are highly correlated (0.98). Per-

haps we did not have to worry on that account. An added

advantage to using hits is that it captures the wider usage across multiple screens and, also, multiple access to the same functional screens, if it was seen to be beneficial. Further, we ran our analysis using hits and separately using time as our measure of usage. Our results were significant irrespective of whether we measured usage with using hits or time.

We obtained the salespersons performance from company records. Salesperson performance was operationalized using the percent of quota per sales representative based on achieved sales levels over the three-month period corresponding to the

technology usage data collection. We did not attempt to con- trol for sales performance prior to the implementation of the CRM system for two reasons. First, since the Siebel CRM sys- tem was implemented, there have been territory realignments along with the addition of many new salespeople. Second, there was a different CRM system in place prior to the Siebel system and thus prior performance could only allow us to look at incremental gain of the new system versus the old system. How- ever, as Churchill et al. (1985) noted, percent of quota is a

strong measure of performance, as it controls for externalities such as differences across territories, including territory size, market potential, and economic conditions, and the percent- to-quota measure should, in part, control for prior performance. Sales quota was calculated based on the volume of product sold (prescriptions) by customers (physicians) in a salesperson's territory (a company-defined set of physicians), as compared to a target quota that is set at the beginning of the year by an external organization specializing in sales force compensation. Because prescription information in the pharmaceutical in-

dustry is accurately tracked to the physician level (due in large part to the fact that the industry is heavily regulated by the Food and Drug Administration), with the vast majority of all

pharmacies reporting customer-prescribing data to two major secondary data houses (NDC and IMS), this represents an accurate picture of a sales representative's performance.

Results

In order to test for the presence of a curvilinear relationship between IT usage and job performance, as represented in Fig-

ure 2, two regressions were run. First, a simple linear regres- sion with technology usage as the independent variable and

job performance as the dependent variable was run. Second, a multiple regression with both usage and usage1 as indepen- dent variables and job performance as the dependant variable was run. (See Table 2 for the regression results.)

First, we observe that the linear relationship between IT

usage and job performance is not significant (p > 0.05). This

clearly calls into question the assumption made in past re- search that the relationship is linear and provides evidence for its negation. Second, support is found for a curvilinear

relationship. The effect of IT usage, βχ, is positive and signifi- cant (B = 0.64, ρ < 0.05), and the effect oiusage2^^ is nega- tive and significant (B = -0.55, ρ < 0.05), supporting our

hypothesis. By including the quadratic term, /?2, for technol-

ogy usage, the variance explained in salesperson job perfor- mance significantly increased (p < 0.05) from 1.2 percent to 4.2 percent. Although the amount of variance explained in

job performance (percent quota) is rather low, the following points should be considered when interpreting this relation-

ship. First, the effect is "robust," because it links two different data sources (both of which are objective). Second, this 4.2

percent of variation in salesperson performance is explained only by IT usage. Third, this is the incremental increase in

performance beyond what was accomplished by the older SFA

technology that was replaced with the current technology. Fourth, the amount of variance explained in salesperson per- formance compares favorably to the individual contributions made by other variables in previous sales studies. The vari- ance explained is consistent with prior literature findings that "no single determinant can explain a very large proportion of the variance in objective sales performance" (Churchill et al. 1985, p. 1 16). Furthermore, Churchill et al. found that "the

ability of individual determinants to predict performance seems rather unimpressive" (1985, p. 110). Our study fo- cused on the incremental difference in performance due to the updating of the CRM technology. It does not capture the entire effect of technology but only the capacity of the update (in the CRM technology studied) to improve performance. The 4.2 percent variance explained compares well to the other

findings in the literature, ranging from 3 to 9 percent (Churchill et al. 1985; Vinchur et al. 1998).

By examining the plot of the effects of IT usage on sales-

person job performance in Figure 3a, one can see that the ideal system usage (488 hits), or the point at which usage maxi- mizes job performance, is higher than the average current sys- tem usage (283 hits). A histogram of IT usage is shown in

Figure 3b. One should note that about 20 percent of the sales-

people in the sample use the system above the ideal level of 488 hits. One has to be cautious with using "hits," as it is

possible, but not probable, that if the sales training is effec- tive, salespeople having more "hits" are navigating a lot before

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306 Journal of Personal Selling & Sales Management

finding what they want, and there are still learning-curve ef- fects that have not stabilized. However, as training has been

provided, and a year has passed, we do not expect this pos- sible alternative viewpoint to be likely.

MANAGERIAL IMPLICATIONS

Most providers of CRM software have their roots in SFA, and so "the bulk of CRM functionality is designed to enhance sales and sales management functionality" (Shoemaker 2001, p. 178). Our research shows that there is a positive payoff to SFA/CRM, but that the enabling effect tapers off, and the relationship between technology usage and performance is curvilinear. This is a powerful implication. For in our par- ticular sample, on average, it appears that increasing the usage of technology will have a positive effect on performance. Tech-

nology usage should be encouraged until the optimum point - that is, the point of inflection - where the positive effect tapers off to zero. How can we identify this optimum point? We need to keep track of technology usage and update the system to incorporate changes to the expert system that may be in use, or to provide valuable information to the manager to guide salespeople about their technology usage.

Tracking technology usage poses an interesting conflict for managers between confidential monitoring of technology use versus privacy concerns of their users. Although outside the scope of the present study, we recognize that it is an impor- tant issue, and firms will need to decide how to respond to this conflict. The sales management literature has underscored the importance of trust among salespersons and sales manag- ers (e.g., Flaherty and Pappas 2000; McNeilly and Lawson 1999; Rich 1997). If a firm had an open policy of tracking technology use, the salesperson would know when the opti- mum point is reached, but there is also the possibility that the situation can be "gamed" - that is, if one is rewarded for logging on a certain number of times, or keeping the screen on for a certain amount of time, these can even be automated. The potential for salespersons manipulating the use of tech- nology, when there is the open policy of tracking such use, assumes an adversarial relationship between employees and management. Similarly, advising salespersons to increase or decrease their use of technology surrenders the confidential- ity of monitoring such use and may add to the lack of trust between salespersons and sales managers.

If a firm wishes to keep the monitoring confidential, the manager can use the information for periodic discussions about how particular salespeople can improve their perfor- mance by using technology to a greater or lesser degree. This way the overusers can also be identified, and, in their case, the manager could counsel them to perhaps spend less time with technology or to improve on some other aspect of their job requirements.

Alternately, underusers can be identified and sent in for additional training so that they are able to get to the opti- mum point faster. Overusers can also be identified and sent for training to reduce their technology usage, and evaluate whether there are sufficient productivity gains from the ex- tent of technology use. A feedback mechanism and a guiding system are future developments that could vastly assist in im-

proving overall performance. We recognize that individual firms will have their own policies, both in the openness of monitoring, as well as in human resources training and de- velopment. Also, some trainees will be quick learners and com- fortable with technology, whereas some others may be slower in acquiring and using technology to improve their perfor- mance. Would the optimum point be reached quicker for the fast learners with greater technology expertise? Would the re- lationship be different for different salespeople based on their technology expertise? Only a close monitoring of technology usage and its effect on performance will yield usable results to managers, and periodic updating of the study suggested here would be extremely useful. We address the issue of the effects of heterogeneous technology expertise across the sales force further in the next section.

To achieve the above recommendation of identifying the optimum point of usage, it is conceivable that during peri- odic sales meetings, the salespeople at or near the optimum be provided a forum to discuss their use of technology, so that they become exemplars for the others to emulate. Behav- ioral modification through such recognition can be quite ef- fective. By anecdote, instances of where technology played a positive role can be identified and used in training sessions. Each firm may use its own antecedents of technology use that it observes in its salespeople. Equally important is the identi- fication of factors that contribute to the disabling effect, so that people are persuaded not to overuse technology when marginal productivity declines.

Although we have a situation in which the benefits of tech- nology usage are captured through performance, Rivers and Dart (1999) find that formal quantification of costs and ben- efits is uncommon. Erffmeyer and Johnson (2001) state that systematic formal planning and evaluation practices were not widely adopted. Positive outcomes were improved commu- nication with clients and access to information. Negative ex- periences included underestimation of cost and amount of training required. In this study, though we did not specifi- cally look at quantification of costs and benefits of opera- tional CRM, a long-term strategic payoff is improved performance, and such returns are recurring.

Productivity as measured in payback period of operational CRM (SFA) can be found in the literature. Gillan (1992) reported a payback period of 18 months for a $1.5 million SFA; Taylor (1993) found about two-thirds of respondents in a survey reported a payback period less than eight months.

Fall 2004 307

There are also other returns. Verity (1993) finds that benefits include the reduction of errors associated with manual pro- cessing, reduced support costs, and improved close rates. Tay- lor (1994) reports that SFA helps salespeople with faster access to information and also reduces the number of follow-ups for more information. In our present study, the nonquantified benefits seem very plausible, but we primarily concentrated on the effect of technology usage on the prime task sales per- formance.

LIMITATIONS AND FUTURE RESEARCH

The empirical finding of the curvilinear relationship between

prime task sales performance and enabling technology usage is an important contribution to the literature. A linear rela-

tionship between technology usage and performance is not tenable. Technology has diminishing returns, and a curvilin- ear relationship is supported. We need to conduct further re- search to determine what the antecedents to the optimal performance usage levels are, rather than antecedents to adop- tion (increased usage). We need to determine what the ante- cedents are for the initial enabling linear relationship found

in/?l5 plus the antecedents for the disabling effects found in

ßr Are these antecedents the same for each part of the curve, unique to each part of the curve, or do they share some com- mon antecedents? Once we understand these antecedents, we will be able to recommend to technology users and their man-

agers how to utilize more of the technology or use it more

effectively, or we could recommend when to limit further us-

age of the technology. The TAMs have provided us with a rich set of antecedents of acceptance, and some of these anteced- ents may be the same for the performance usage relationship.

The point of inflection, or the optimum point, is not a

stationary point. As the system is updated and more people use the system effectively, the optimum point is likely to be

approached sooner. This will continue until, perhaps, the tech-

nology gets outmoded. Then, there will be a new learning curve for the improved technology, and the cycle begins once

again. In one way, the model that we have tested may be seen to be a test of the effectiveness of the particular technology that is in use at the particular firm. However, because the

technology we study is the market leader in the pharmaceuti- cal industry as well as in many other industries, it is very likely that the pattern of results will hold true for any major technology.

We also need to examine the effect of individual technol-

ogy expertise on the relationship. We do not believe that the

relationship will be the same for salespersons with different levels of expertise. Figure 4 proposes a possible moderating effect of expertise on the relationship. We believe that expert users will reach their individualized optimum technology us-

age by using the system more than an average user. While this

Figure 4 Proposed Moderating Effect of Technology Expertise on

the Relationship of Performance and Technology Usage Time

^ Enabling

!' ' Disabling

8 ! ' V- -<7 i

- > Usage

Ψ Technology Expertise Level

Expert User

Average Skill User Unskilled User/ Technophobe

Note: Assumes technology is installed, training is over, and CRM is in use.

seems counterintuitive, it is because expert users have the ca-

pability to use more advanced features of a CRM system. The advanced features should also be more productive features, meaning the extra usage time could be spent on using ad- vanced productive features of the technology. However, we also hypothesize that the expert user would get a greater rela- tive improvement in his or her performance than an average skilled user. Further, we suspect that unskilled or technophobic users would not achieve any productivity benefits or the rela-

tionship would not be significant. Future research on this

proposed moderating effect is needed. Such insight into the

differing relationships between different expertise classes of users could provide us with a basis for conducting further research into how, or if, technology products should inter- face differently with different classes of users. Current CRM software and software training is marketed and provided in a "one size fits all" package. Validation of our hypothesis could lead to different user interfaces, or adaptive user interfaces, based on the expertise of the user.

More empirical studies will be needed to further validate the Technology Performance Usage Model (TPUM), especially with respect to its external validity in other technology areas, and across various firms in different industries. This valida- tion also includes the development and test of measures not currently found in the literature to be able to characterize and

308 Journal of Personal Selling & Sales Management

discriminate between the positive and negative phases of tech- nology usage. In effect, we are calling for a greater quantifica- tion of the costs and benefits of technology implementation and usage. Such results will also assist in system upgrade deci- sions, based on the expected cost-benefit ratio.

The current study uses cross-sectional data. While we pro- pose that the causal relationship is that underuse or overuse of the CRM technology leads to reduced sales performance, per- haps technology use is driven by performance. However, there is no obvious rationale as to why performance would drive technology usage. It is more reasonable to assume that tech- nology enhances performance. However, a longitudinal study is needed to validate the causality of the relationship. Further, research on the specific effect of individual screens or groups of screens (i.e., call planning versus analysis versus calendar- ing, etc.) will enhance our understanding of the differential effect of the various components of the CRM technology. Longitudinal follow-up studies could also more clearly iden- tify any lagged effects from technology usage. A longitudinal study that includes the time of initial installation of a CRM system could also provide valuable insight into the initial dip in performance typically related to the introduction of new enterprise-wide software technologies. At this time, we do not have sufficient data to econometrically determine the length of a lag, if any, between technology usage and performance. We feel comfortable in assuming that most of the effect is captured in our data by using a large window of time.

We could also look at breaking out individual components of usage, such as precall planning, territory analysis, postcall planning and reporting, and so on, to see if there are optimal usage points for the individual components. For example, Engle and Barnes (2000) have suggested that the sales force must be trained in how to use the planning functions more efficiently in less time. Similarly, they suggest that the focus of usage should be on their use in actual selling and customer interaction situations.

There is evidence (Erffmeyer and Johnson 2001) that us- ers, both management and salespeople, have higher satisfac- tion (85 percent and 80 percent, respectively, in the above study) than do customers (only 50 percent). So, finally, the focus has to be to return to the customer and the customer's satisfaction, using technology as a tool and monitoring its effect on performance. Unfortunately, we did not have an opportunity to assess customer evaluations in this study.

Although we have been able to document a curvilinear re- lationship between technology performance and usage, spe- cific factors characterizing the enabling phase and those indicating the disabling phase need to be further investigated. Future research can explore the influences of specific factors

on/Jj and/?2, as there is always the omitted variable bias in any empirical model. The simplicity and elegance of the proposed model are positive features that are useful to retain. However,

if specification of other variables does affect the coefficients in any meaningful way, a future stream of research emulating the development of the Bass model could well be anticipated.

Our model also does not directly address training. A key stated assumption is that the technology is in place, and sales- people are studied after they have been trained. However, appropriate training could be a key moderator to the perfor- mance usage curvilinear relationship. What about other mod- erators, such as the use of technology usage in evaluation, or in counseling? Research can be conducted on how to modify training, reward systems, or even the technology to optimize usage of an individual user to gain maximum performance. Using appropriate Bayesian techniques (Rossi and Allenby 2003), it may be possible to model an individual user's per- formance usage relationship curve. Then it would be possible to use this model as an input to a software technology prod- uct with a built-in expert system, which optimizes the pro- ductivity and performance of the individual user. Also, it might be possible to customize the training or reward systems for each individual user.

Finally, a broader time management study that takes into account not only the effect of time spent on CRM technol- ogy but also the effect of CRM technology on differential time allocations by salespersons to achieve specified goals of an organization would be useful. Prior literature in another area, information search studies, using gross measures of time, and number of activities (Ratchford and Srinivasan 1993) revealed diminishing returns to time expenditures. Results of the present study reveal a similar pattern of positive but di- minishing returns. Hence, a managerial study on differential time allocation to various sales activities (such as prospect- ing, analysis, follow-up, etc.) could be conducted to evaluate and improve CRM technology in order to optimize a sales- person's time.

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