The Impact of Personality Variables, Prior …personal.cb.cityu.edu.hk/mkwydou/The Impact...

The Impact of Personality Variables,Prior Experience, and Training

on Sales Agents’ Internet Utilizationand Performance

Rajesh GulatiDennis N. Bristow

Wenyu Dou

ABSTRACT. Emerging literature on the impact of the Internet on busi-ness-to-business (B2B) marketing has primarily focused on examiningthis issue from the perspective of manufacturers and buyers. This studyfocuses on the sales agent, a third prominent actor in B2B markets, andtests a conceptual model that relates a sales agent’s personality, demo-graphic, and user-situational constructs to that sales agent’s Internet uti-lization for selling activities. Further, the model tested in this studyrelates a sales agent’s Internet utilization to perceived sales performance.Findings in this study indicate that internal locus of control, learning ori-entation, and sales related Internet training relate positively to a salesagent’s Internet utilization, and that a sales agent’s age relates negativelyto Internet utilization. Further, the results support a positive relationshipbetween a sales agent’s Internet utilization and sales performance. Thisstudy emphasizes that the Internet can be a productive tool for sales agents.

Rajesh Gulati is Assistant Professor of Marketing, G. R. Herberger College of Busi-ness, St. Cloud State University, St. Cloud, MN 56301 (E-mail: [email protected]).

Dennis N. Bristow is Associate Professor of Marketing, G. R. Herberger College ofBusiness, St. Cloud State University, St. Cloud, MN 56301.

Wenyu Dou is Assistant Professor of Marketing, G. R. Herberger College of Busi-ness, St. Cloud State University, St. Cloud, MN 56301.

Journal of Business-to-Business Marketing, Vol. 11(1/2) 2004http://www.haworthpress.com/web/JBBM

2004 by The Haworth Press, Inc. All rights reserved.Digital Object Identifier: 10.1300/J033v11n01_09 153

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The implications of the results of this study for sales agents with respect totraining and recruitment are discussed and avenues for future researchare suggested. [Article copies available for a fee from The Haworth DocumentDelivery Service: 1-800-HAWORTH. E-mail address: <[email protected]> Website: <http://www.HaworthPress.com> © 2004 by The HaworthPress, Inc. All rights reserved.]

KEYWORDS. Internet utilization, locus of control, learning orienta-tion, experience, training, performance, sales agents

INTRODUCTION

The Internet is a unique communication medium (Hoffman &Novak, 1996) that facilitates storage and exchange of information avail-able interactively at a marginal cost (Peterson et al., 1997). Not surpris-ingly, the Internet has found extensive application in both consumer andbusiness markets. The increase in the use of Internet resources in busi-ness-to-business (B2B) markets has truly been spectacular. Estimatesregarding Internet B2B sales in 2004, for example, range from $2.7 tril-lion (Blackmon, 2000) to $7.3 trillion (Shaw, 2001). Realization of theimportance of the Internet in business-to-business marketing has re-sulted in increased efforts on the part of researchers to better understandits impact. Studies have initiated investigations regarding this issuefrom the perspective of suppliers (e.g., Avlonitis & Karayanni, 2000;Deeter-Schmelz et al., 2001), buyers (e.g., Kennedy & Deeter-Schmelz,2001), and relationships between suppliers and buyers (e.g., Ling &Yen, 2001). Topics that have been studied include trends in B2B mar-keting (Sharma, 2002), strategic utilization of the Internet by firms(Sadowski et al., 2002), issues a firm should consider before developinga web business (Wilson & Abel, 2002), evaluation of B2B web-sites(Leong & Ewing, 2002), and management of channels of distribution(Webb, 2002).

As is evident from the literature cited above, extant research inbusiness-to-business marketing has largely focused on the role theInternet plays as an innovative marketing channel that provides a di-rect link between suppliers and buyers. Direct (proprietary) salesforces and independent intermediaries such as sales agents representthe other marketing channels that suppliers in business markets utilizeto sell their products and services. Notwithstanding the trend where an

154 JOURNAL OF BUSINESS-TO-BUSINESS MARKETING

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increasing number of suppliers are opting to sell over the Internet, thetwo alternative channels represent valuable links in business markets.However, scholars have paid scant attention to the impact the Internetmay have either on the solvency or on the operations of direct salesforces or independent marketing intermediaries such as sales agents.This study addresses one of these gaps in current business-to-businessmarketing literature by examining one possible influence the Internethas on sales agents’ operations.

More than half a million independent intermediaries comprisingmanufacturer representative, broker or agent firms operate as distribu-tion channels in North America today (source: Manufacturer Represen-tatives Educational Research Foundation, 2002). The presence of theseintermediaries such as sales agents in business markets offers supplierswith a viable and sometimes more profitable alternative to employingand compensating a direct (proprietary) sales force (cf. Anderson &Weitz, 1992; Weiss & Anderson, 1992). Such intermediaries are, there-fore, an important population from the perspective of both researchersand principals in business markets.

The relatively meager literature addressing the impact of the Interneton independent intermediaries include some conceptualizations (e.g.,Learner & Storper, 2001; Stead & Gilbert, 2001) and empirical studies(e.g., Gulati, Bristow, & Dou, in press) that focus on Internet mediateddisintermediation of independent sales agents. Some relevant issuesthat researchers have yet to address include examining (a) factors thatinfluence the adoption and utilization of Internet resources by interme-diaries such as independent sales agents in business markets, and (b) theconsequences to these intermediaries of such adoption and utilization.This study addresses the above research gap in the business-to-businessmarketing literature by investigating the influence of one plausible setof individual and situational determinants on, and one possible conse-quence of, an independent agent’s use of Internet resources.

Utilizing the emerging literature addressing the impact of the Interneton marketing, findings from information systems research, interviewswith sales agents, and related theories, this study first conceptualizesand then empirically tests a model that seeks to answer the following re-search questions:

1. What relationships, if any, exist between an independent salesagent’s (a) internal locus of control orientation, (b) learning orien-tation, (c) age, (d) sales related Internet training, and (d) non-sales

Gulati, Bristow, and Dou 155

related Internet use and the extent to which the sales agent utilizesthe Internet for selling activities?

2. What is the relationship, if any, between an independent salesagent’s utilization of the Internet for selling activities and the con-sequent performance the agent attributes to such Internet utiliza-tion?

The above research questions, therefore, identify certain plausibledeterminants of a sales agent’s Internet utilization. The rationale behindselecting these specific determinants is as follows. A review of litera-tures addressing factors that influenced (a) attitudes toward technologyas well as technology adoption and use (e.g., Agarwal & Prasad, 1999;Alavi & Joachimsthaler, 1992; Harrison & Rainer, 1992; Howell &Shea, 2001; Igbaria, Guimaraes, & Gordon, 1995; Morris & Venkatesh,2000; Taylor & Todd, 1995; Thompson, Higgins, & Howell, 1994), and(b) learning (e.g., Colquitt & Simmering, 1998; Lang & Wittig-Berman, 2000; Sujan et al., 1994) indicated that plausible variables in-fluencing Internet use could be classified into three categories–person-ality traits, demographic variables, and user-situational variables. Thisreview also identified two personality traits, i.e., learning orientationand locus of control (e.g., Colquitt & Simmering, 1998; Lang &Wittig-Berman, 2000; Lengnick-Hall & Sanders, 1997; Sujan et al.,1994), one demographic variable, age (e.g., Kerr & Hiltz, 1988; Morris &Venkatesh, 2000) and two-user situational variables, experience andtraining (e.g., Agarwal & Prasad, 1999; Alavi & Joachimsthaler, 1992;Igbaria, Guimaraes, & Davis, 1995; Igbaria, Pavri, & Huff, 1989) asprominent variables that may influence adoption and use of technologysuch as the Internet. This study, therefore, incorporated all three catego-ries of variables and selected the above stated specific variables withineach category to reflect the emphasis placed on them in previous studies.

We recognize the possibility that other variables, not addressed bythis study, may also influence an intermediary’s Internet utilization. Wealso acknowledge the existence of powerful theoretical frameworks thathave guided research on adoption and utilization of information tech-nology, including the theories addressing the adoption and diffusion ofinnovation (e.g., Rogers, 1995), the theory of reasoned action (Ajzen &Fishbein, 1980), the theory of planned behavior (Ajzen & Madden,1986), and the technology acceptance model (Davis, 1989). However,the purpose of this paper does not involve testing any one or more ofthese theoretical frameworks in the context of a sales agent’s Internetutilization. Rather, this study utilizes related findings and focuses on


identifying and testing for direct relationships (a) between selectedvariables and an intermediary’s Internet utilization, and (b) the impactsuch utilization has on the intermediary’s performance as implied bythe research questions.

The next section introduces the conceptual model that relates a salesagent’s personality characteristics, experience, and training to that salesagent’s extent of Internet utilization and consequent performance (ab-breviated as the Sales Agent’s IUP model). The constructs the model in-cludes are defined and the hypothesized relationships the model impliesare developed. Subsequently, descriptions of the procedures involved indeveloping the survey instrument, collecting the data, and analyzing theprocured data are presented. The findings of this study are then pre-sented. This is followed by a discussion of the implications that flowfrom the results of the study. The final section lists some limitations ofthis study and suggests avenues for related future research.

CONCEPTUAL FRAMEWORK AND HYPOTHESES

The Sales Agent’s Internet Utilization and Performance model (i.e.,the Sales Agent’s IUP model, Figure 1) forwards several constructs thatinfluence directly the extent to which a sales agent utilizes the Internetfor selling activities. As Figure 1 depicts, a sales agent’s internal locusof control orientation and learning orientation are two personality traitsthat relate positively to that sales agent’s Internet utilization. A demo-graphic variable, i.e., a sales agent’s age, is depicted in the IUP model asinfluencing a sales agent’s Internet utilization negatively. Further, themodel depicts that two user-situational variables, i.e., a sales agent’sprior non-sales related Internet use and sales related Internet training in-fluence positively the sales agent’s use of the Internet for selling activi-ties. Finally, the Sales Agent’s IUP model depicts that a sales agent’sInternet utilization is directly and positively related to that agent’s per-ception regarding the impact such utilization has on performance.

Internet Utilization: Both business literature and academic journalsemphasize that the Internet is an innovative medium that facilitatesbetter communication between individuals or firms (e.g., Dos Santos &Kuzmitz, 2000; Hoffman & Novak, 1996; Pease, 2000; Peterson et al.,1997). In describing how the Internet can be utilized by salespersons,Weitz et al. (2001) state that the Internet aids salespersons in severalselling activities such as prospecting, information gathering, and com-municating with principals and customers. In the context of sales


agents, Gulati, Bristow, and Dou (in press) define Internet utilization asthe extent to which independent sales agents use the Internet to establishand maintain communication linkages with their principals, prospects,and current customers. This study adapts this definition of Internet utili-zation. For the purpose of this study, Internet utilization refers to the ex-tent the Internet is used for performing selling activities such as prospect-ing and providing service to customers.

Learning Orientation and Internet Utilization: An individual’s learn-ing orientation is an intrinsic personality trait that motivates the individ-ual to learn in order to improve his or her abilities and become morecompetent at performing relevant tasks (Dweck & Legget, 1988; Lang& Wittig-Berman, 2000; Sujan et al., 1994). Prior research has relatedlearning orientation positively to (a) an individual’s belief that abilitiescan be acquired (Elliot & Dweck, 1988), (b) a student’s functioning andlearning (Lengnick-Hall & Sanders, 1997), (c) a salesperson’s ability todevelop and apply selling knowledge and also to work hard at his or herjob (Sujan et al., 1994), and (d) an individual’s motivation to learn(Colquitt & Simmering, 1998).

The above findings suggest that an individual with a high learningorientation is able to adapt and master new environments and situations


Learning Orientation

Internal Locus ofControl

Age

Sales Related InternetTraining

Prior InternetExperience

Internet Utilization Perceived Performance( )�

FIGURE 1. A Conceptual Model Depicting the Impact of a Sales Agent’s Per-sonality, Demographic, and User-Situational Constructs on Internet Utilizationand Perceived Performance (The Sales Agent’s IUP Model)

with relative ease. We now apply these findings to an independent salesagent’s utilization of the Internet. A sales agent who possesses a highlearning orientation, in comparison to another sales agent who does nothave the same degree of learning orientation, should be more willingand able to expend effort in learning to utilize Internet resources forbusiness purposes. This sales agent should also utilize the Internet, arelatively new communication tool, to a greater extent in communicat-ing with customers. The Sales Agent’s IUP model (see Figure 1) depictsthis relationship through a direct arrow leading from the constructlearning orientation to Internet utilization.

H1: The higher is an independent sales agent’s learning orientation,the greater is the sales agent’s Internet utilization for selling ac-tivities.

Locus of Control and Internet Utilization: Rotter (1966) defines lo-cus of control as a personality trait that determines the extent to whichan individual believes that his or her behavior directly influences theevents that follow. Individuals with an internal locus of control, for ex-ample, believe that (a) they have direct influence over the events in theirlives, and (b) outcomes are related to their efforts (Lefcourt, 1991). In-dividuals with external locus of control, on the other hand, believe thatevents in their lives are caused by forces not within their control. Ac-cording to Rotter (1966), internal locus of control is a personality traitthat positively influences learning and is capable of influencing behav-ior in many situations.

Previous research findings that have looked at the relationships be-tween locus of control and organizational behavior have linked internallocus of control positively to an individual’s desire for autonomy, abil-ity to process complex tasks, and responsibility (Abdel-Halim, 1980;Spector, 1982, 1986). Research also suggests that individuals with in-ternal locus of control orientation set higher goals for themselves(Hollenbeck & Klein, 1987; Phillips & Gully, 1997), are more likely toengage in managing issues themselves (Spector, 1982, 1986), are bettersuited to jobs that require initiative and problem solving capabilities(Abdel-Halim, 1980), and are more likely to view innovations as oppor-tunities (Howell & Shea, 2001).

The above findings can be usefully applied in postulating a relation-ship between internal locus of control orientation of independent salesagents and their Internet utilization. Sales agents with an internal locusof control are likely to view the availability of Internet resources as an


opportunity, are likely to take the initiative in adopting and utilizing theInternet, and are likely to be able to utilize the Internet resources in aconstructive manner. This posited relationship is depicted in Figure 1by a direct arrow linking locus of control and Internet utilization.

H2: For an independent sales agent, an internal locus of control ori-entation relates positively to that sales agent’s Internet utiliza-tion for selling activities.

Age and Internet utilization: Prior research addressing the impact ofage on acceptance and use of new technology largely supports a nega-tive relationship between age and the other variables (e.g., Kerr & Hiltz,1988; Morris & Venkatesh, 2000). For instance, Morris and Venkatesh(2000) found that an employee’s age was negatively related to bothshort term and long term utilization of a new software. In a relatedstudy, Harrison and Rainer (1992) found that younger individuals weremore skilled at using computers. Elsewhere, employee age has beenlinked to a resistance to change in the work environment and an inclina-tion for familiar tasks (Myers & Conner, 1992; Sharit & Czaja, 1994).However, some studies have failed to find a significant relationship be-tween age and technology acceptance and use. For example, Zinkhan,Joachimsthaler, and Kinnear (1987) did not find a significant relation-ship between age and the use of a marketing decision support system.Agarwal and Prasad (1999) failed to find a negative relationship be-tween an employee’s length of tenure and the employee’s beliefs aboutthe usefulness and ease of use of an information technology innovation.

For an independent sales agent, the Internet represents a new technol-ogy and the utilization of Internet resources for selling purposes pre-sents a departure from the customary way of doing business. Hence, thefindings in literature with regard to the relationship between age andnew technology adoption and use should apply to the sales agent also.In accordance with the bulk of research that suggests a negative rela-tionship, the Sales Agent’s IUP model depicts a negative relationshipbetween a sales agent’s age and his/her Internet utilization.

H3: A sales agent’s age relates directly and negatively to that salesagent’s Internet utilization for selling purposes.

Training and Internet Utilization: As training provides relevant in-formation about various facets of a new technology, it helps in reducinguncertainty and anxiety about that innovation (Agarwal & Prasad,


1999). Reduced uncertainty and increased knowledge facilitates accep-tance and use of new technologies. Agarwal and Prasad (1999) foundthat an individual’s training on an information technology innovationwas related positively to that individual’s beliefs regarding usefulnessand ease of use of that technology. Other findings in information sys-tems literature strongly suggest a positive and direct relationship be-tween computer training and subsequent use (Igbaria, Guimaraes, &Davis, 1995; Igbaria, Pavri, & Huff, 1989). In a similar vein, a meta-analysis conducted by Alavi and Joachimsthaler (1992) found strongsupport in literature for a positive relationship between training andsuccessful decision support system implementation including its utili-zation.

Applied to an independent sales agent, these findings suggest that asales agent who receives training in utilizing the Internet for businesspurposes would have more information about such use, will have lessuncertainty and anxiety with respect to utilizing the Internet resourcesavailable, and should utilize the Internet more fully in selling activities.The Sales Agent IUP model indicates this relationship through a directand positive path between training and Internet utilization.

H4: Sales related Internet training is related directly and positively toa sales agent’s Internet utilization for selling activities.

Experience and Internet Utilization: The relationship between priorexperience and behavior is well established in social psychology litera-ture (e.g., Ajzen & Fishbein, 1980; Bagozzi, 1981; Fishbein & Ajzen,1975). Findings in information systems literature have linked prior ex-perience with computers positively to subsequent computer usage(Igbaria, Guimaraes, & Davis, 1995; Igbaria, Pavri, & Huff, 1989), andcomputer skills (Harrison & Rainer, 1992). Alavi and Joachimsthaler(1992), in their meta-analytic study, found support for a positive rela-tionship between prior experience in using decision support systems(DSS) and subsequent successful DSS implementation. Agarwal andPrasad (1999) hypothesized and established that an individual’s priorexperience with similar technologies was related positively with that in-dividual’s beliefs about the usefulness and ease of use of an informationtechnology innovation.

We argue that a similar relationship exists between an independentsales agent’s prior use of Internet resources for non-selling activitiesand that sales agent’s subsequent use of the Internet for selling pur-poses. A sales agent’s prior exposure to the Internet is likely to lead to


more favorable intentions and stronger beliefs about the utility of theInternet for business purposes. Such intentions and beliefs are likely toresult in greater Internet utilization on the part of the sales agent in sell-ing activities. The Sales Agent’s IUP model, accordingly, depicts a pos-itive and direct relationship between non-sales related Internet use andInternet utilization for selling activities.

H5: The greater is an independent sales agents prior non-sales expe-rience with the Internet, the greater is that sales agent’s Internetutilization for selling activities.

Internet Utilization and Performance: In business-to-business mar-kets, buyer and seller firms that transact online are benefitting from in-creased efficiencies and effectiveness because of cost savings andimproved productivity (Deeter-Schmelz et al., 2001; Sharma, 2002).The characteristics of the Internet such as interactivity, unlimited infor-mation storage capacity, low cost, and speed of information transfer(Peterson et al., 1997) that provide these firms with such advantagesshould also assist intermediaries in conducting their business. Gulati,Bristow and Dou (in press), for example, concluded from their studythat sales agents who utilized the Internet for business purposesachieved better information exchange with their principals. We postu-late that sales agents who utilize the Internet to prospect, communicatewith and provide service to their customers will be able to perform theseactivities more effectively. The Sales Agent’s IUP model indicates thisrelationship by a direct path relating Internet utilization and perfor-mance.

H6: An independent sales agent’s Internet utilization for selling ac-tivities is directly and positively related to sales performance.

METHOD

Data for this study was collected through a survey of independentsales agents who belong to a national manufacturer’s agents associa-tion. The survey instrument gathered information pertaining to salesagents’ personality, age, and user-situational variables in addition totapping their perceptions regarding Internet use and consequent perfor-mance.


Participants: The survey participants were independent sales agentswho were randomly selected from the membership roster of a nationalmanufacturer’s agents association. Out of a total of 1,500 surveys dis-tributed, 335 useable surveys were returned, resulting in a response rateof approximately 22 percent. An evaluation of early and late respon-dents revealed no response bias (see Armstrong & Overton, 1979). Theaverage age of participating sales agents was 51 years, and approxi-mately 77% had completed a college education. These sales agents had,on average approximately 19 years of experience and employed 4 sales-people. The average annual sales achieved by the participants was $9.4million.

Development of Survey Instrument and Testing: The process adoptedfor developing the survey instrument included in-depth discussionswith the representatives from the national manufacturer’s association inorder to better understand the operations of member sales agents andcomprehend their Internet use. Additionally, marketing academicianswere consulted to obtain their inputs regarding the issues of interest tothe researchers. In order to obtain the item-sets that represented the con-structs of interest, the researchers consulted various available constructscales and reviewed the literatures in marketing, social psychology, andinformation systems. Construct measures that exhibited desirable reli-ability and validity were adapted to suit the current investigation.Where such scales could not be identified from the literature, thenomological nets for constructs were developed by tapping into the ex-pertise and experience of manufacturer’s association representativesand through consultation with academicians. These procedures led tothe selection of the item-sets that represented the constructs in the SalesAgent’s IUP model.

A sales agent’s learning orientation was represented by a 6-item setadapted from a scale developed by Sujan, Weitz, and Kumar (1994)(Cronbach Alpha = .81). For example, the Learning Orientation Scaleitem “An important part of being a good salesperson is continually im-proving your sales skills,” was adapted in this study as “I continuallywork to improve my selling skills.” Similarly, the LOS item, “It is im-portant for me to learn from each selling experience” was adapted as “Ilearn something from each selling experience” in this study. A 4-itemset, adapted from scales developed by Rotter (1966), MacDonald andTseng (1971), and Spector (1988) (Cronbach Alpha = .82 to .87 acrossseveral studies) was used in this study to measure a sales agent’s inter-nal locus of control orientation. For instance, the following internal lo-cus of control scale items, “It isn’t wise to plan too far ahead because


most things turn out to be a matter of good or bad fortune anyhow” and“Many times I feel that I have little influence over things that happen tome,” were adapted as “I can pretty much determine what happens in mylife”, and “My life is determined by my own action,” respectively. Anagent’s sales related Internet training was represented by a 3-item set,and a 4-item set was generated in order to assess a sales agent’s priornon-sales related Internet experience.

The 6-item Internet Utilization scale developed by Gulati, Bristow,and Dou (in press) was adapted to this study by eliminating one item(my firm uses the Internet to communicate with our principals) that wasdeemed inappropriate for the sample used in this study. An 8-item setwas identified as being representative of a sales agent’s Internet relatedperformance perceptions. Interviews with representatives of the na-tional manufacturer’s agents association revealed that sales agentsmade all strategic, operational, and tactical decisions pertaining to theirbusiness operations, and correspondingly, equated their work relatedactivities to those of the firms they owned. Accordingly, statements thatmeasured a sales agent’s (a) Internet utilization, and (b) performanceperceptions were worded to reflect the centralized nature of a salesagent’s operations (see Table 1). The chronological age of the partici-pating sales agents served as an objective measure of the final constructdepicted in Figure 1.

The 7 constructs depicted in the Sales Agent’s IUP model, therefore,were initially represented in the survey instrument by thirty-one items(see Table 1). All the survey items were written into a 7-point Likerttype format and were reviewed by marketing academicians with exper-tise in the areas of professional selling, e-commerce/Internet marketing,consumer behavior, and marketing research. The reviewers examinedthe survey items for potential problems in wording, phrasing, under-standability, or redundancy. This review process resulted in the revisionof several items. The revised items and 8 demographic questions weresubsequently reviewed by several manufacturers’ representatives. Thisprocedure revealed no problems with the wording or understanding ofthe various item-sets.

Survey Administration: In order to maximize response rate, the re-searchers sought the assistance of the president and director of themembership of the national manufacturer’s agents association. With thecooperation of these individuals, a letter explaining the nature of the re-search and indicating the association’s support of the study was drafted.This letter served as the cover page of the survey instrument. Addi-tionally, this letter was also included in an issue of the association’s


TABLE 1. Summary of Findings for the Respecified Measurement Model

Constructs and ItemsCronbach's

AlphaSq.

Multiple RStd.

Loadingst-

Value p <


I continually work to improve my selling skills

I continually work to improve my product knowledge

I learn something from each selling experience

I am always learning something new about my customers

Learning how to be a better manufacturer's rep is of fundamental importance to me

There are few new things to learn about selling*

∝0.826

0.54

0.51

0.40

0.47

0.54

0.74

0.71

0.63

0.69

0.73

14.51

13.91

11.87

13.27

14.44

.001

.001

.001

.001

.001

Internal Locus of Control

I am almost certain to make my plans work

I can pretty much determine what happens in my life

When I get what I want, it's usually because I worked hard for it

My life is determined by my own actions

∝0.803

0.37

0.55

0.59

0.55

0.60

0.74

0.77

0.74

11.17

14.39

15.13

14.40

.001

.001

.001

.001

Internet Utilization

My firm uses the Internet to find information about our customers

My firm uses the Internet to find information about prospects

My firm uses the Internet to communicate with prospects

The Internet is a tool that my firm uses to communicate with our customers*

My firm uses the Internet to provide follow-up service to our customers*

∝0.840

0.85

0.81

0.40

0.92

0.90

0.63

20.96

20.25

12.45

.001

.001

.001

Sales Related Internet Training

I have had formal training in the use of e-mail as a form of communication in selling

I have had formal training in the use of the World Wide Web to find information relevant to selling

I have had formal training in using the Internet to transfer documents

∝0.911

0.79

0.92

0.64

0.89

0.96

0.80

20.13

22.92

17.34

.001

.001

.001

165

TABLE 1 (continued)

Constructs and ItemsCronbach's

AlphaSq.

Multiple RStd.

Loadingst-

Value p <

Prior Internet Experience

In the past, I have used the Internet to participate in chat rooms

In the past, I have used the Internet for on-line entertainment (i.e., games, audio, video)

In the past, I have used the Internet to help me make purchase decisions

In the past, I have used the Internet as a communication tool*

∝0.550

0.21

0.26

0.41

0.46

0.51

0.64

7.01

7.75

9.40

.001

.001

.001

Perceived Performance

Using the Internet has resulted in an increase in sales for my firm

Using the Internet has enabled my firm to provide better service to our customers

Using the Internet has made my firm more effective

Using the Internet has enabled my firm to reduce our selling costs

Using the Internet has increased the efficiency of my firm

On average, using the Internet has reduced the amount of time required for my firm to make asale

Using the Internet has enabled my firm to do a better job of prospecting*

Using the Internet has enabled my firm to locate prospects more quickly*

∝0.916

0.57

0.78

0.90

0.42

0.77

0.44

0.76

0.88

0.95

0.65

0.88

0.66

16.07

20.29

23.08

12.99

20.13

13.43

.001

.001

.001

.001

.001

.001

*Denotes items that were removed from the analyses during scale purificiation.

Descriptive Goodness of Fit Indices:

x2 (N = 335), p = .00 595.91RMR 0.17GFI 0.87AGFI 0.84NFI 0.87CFI 0.92AIC 719.68RMSEA 0.067

166

monthly agency sales magazine which was sent to all association mem-bers approximately 2 weeks prior to the distribution of the survey in-strument. The agency administrators also e-mailed the member salesagents, giving them an advance of the questionnaire they might receivevia the U.S. mail.

The survey instrument was mailed out (first class via the UnitedStates Post Office) to the 1,500 randomly selected sales agents threedays after the e-mail message was distributed. A week and a half later,the selected sales agents received another e-mail from the director ofmembership of the manufacturers’ agents thanking them for their par-ticipation and requesting them to complete the survey if they had not al-ready done so. This e-mail message also included contact informationso that sales agents could request a second copy of the survey instru-ment if they had not yet received it.

MEASURE PURIFICATION AND ANALYSIS

A correlation matrix of the thirty items representing the 7 constructsdepicted by the Sales Agent’s IUP model (Figure 1) was generated aftervalidating the data-set (i.e., ascertaining the correctness of the re-sponses, reverse coding, etc.). An examination of the inter-item correla-tions was undertaken to assess (a) the pattern of correlations betweenitems representing unique constructs, and (b) the pattern of correlationsbetween items representing distinct constructs. The procedure listed byGerbing and Anderson (1988) was used to assess the dimensionality ofthe 6 constructs in the Sales Agent’s IUP model measured by multi-itemscales. These items, therefore, were subjected to principal componentanalysis using the Kaiser criterion (eigenvalue � 1) with varimax rota-tion.

The resulting 8-factor structure was examined to assess the loadingsand cross-loadings of the 30 items. Six items either (a) cross-loaded onmore than one factor (loadings > .4), and/or (b) loaded on a factor thatcould not be identified. Further, these items exhibited low loadings(loading < .5) on the factor (construct) they represented. A re-examina-tion of the correlation matrix indicated that these 6 items had statisti-cally significant correlations with items representing other constructsbut weak correlations with items representing the same constructs. Af-ter examining the content of these items, it was determined that theunderstandability of each of these items may be suspect, and that eachwas a poor representative of the associated construct. Consequently,


these 6 items were deleted from further analysis. A second principalcomponent analysis with the twenty-four remaining items yielded a6-factor structure with eigenvalues > 1 (total variance explained =69%). The loadings of the twenty-four items corresponded to the con-structs they represented, suggesting that all the construct measures wereunidimensional.

To purify and further assess the unidimensionality of the con-struct-measures, a confirmatory factor analysis was conducted usingLISREL 8.3 (see Anderson & Gerbing, 1988; Gerbing & Anderson,1988). Table 1 summarizes the findings of the measurement model inwhich 24 items were hypothesized to represent 6 constructs in the SalesAgent’s IUP model (Figure 1). For the measurement model analyzed,the following values were observed for the various fit indices: X2 (237N = 335) = 595.91; p = .00; Standardized RMR = .06; GFI = .87; AGFI =.84; NFI = .87; NNFI = .90; CFI = .92; and RMSEA = .067. The model,therefore, exhibited acceptable values for a majority of fit indices (seeBentler & Bonnet, 1980; Williams & Hollahan, 1994). Additionally, allbut one standardized loading exceeded .50, and the squared multiplecorrelations were adequate suggesting an acceptable model fit. The sta-tistically significant standardized loadings exhibited by the twenty-fouritems representing the six constructs (see Table 1) established the con-vergent validity of the measures (see Anderson & Gerbing, 1988).

Chi-square difference tests were conducted to determine the discrimin-ant validity of the 6 multi-item constructs in the Sales Agent’s IUPmodel. Table 2 presents the results of these tests. The X2 differences be-tween all possible pairs of constructs are statistically significant (Over-all α = .05; critical α = .0034137; critical X2

(1 d.f.; p = .001) = 10.828),implying that the item-sets representing the various constructs exhibitdiscriminant validity (see Anderson & Gerbing, 1988; Bagozzi & Phil-lips, 1982). Table 1 and Table 2 together indicate that the 6 multi-itemmeasures possess both convergent and discriminant validity, i.e., themeasures exhibit construct validity (Kerlinger, 1986).

Table 1 also depicts (a) the purified item sets that represent the 6unidimensional constructs in the Sales Agent’s IUP model, and (b) thereliability of those item-sets. Five of the six measures exhibit good con-sistency (Cronbach α). The item-set representing a sales agent’s non-sales related Internet experience had an unacceptably low reliability (cf.Nunnally, 1978). This construct was deleted from the subsequent pathanalysis undertaken to test the relationships hypothesized in the IUPmodel. The relationship expressed in Hypothesis 5, therefore, could not


be formally tested. Table 3 reports the correlation matrix for the con-struct-measures computed by summating the item-sets representing the6 constructs and the single item objective measure of a sales agent’sage.

This study followed the two-step structural equation modeling proce-dure advocated by Anderson and Gerbing (1988). After purifying themeasures as described above, a covariance matrix of the 5 summatedconstruct measures and one single item measure (age) was generated.The linear relationships posited by the Sales Agent’s IUP model weretested using a path-analytic technique in LISREL 8.3. The following val-ues were observed for the various fit indices: X2 (4 N = 335) = 7.76, p =.10; Standardized RMR = .024; GFI = .99; AGFI = .96; NFI = .98; CFI =.99; and RMSEA = .052. Based on the Chi-square value, and the valuesof various fit indices, the path model indicated an good fit for the testedmodel.

Table 4 reports the standardized path coefficients along with theirt-values and statistical significance for the various hypothesized linearrelationships. Figure 2 depicts the various paths that were tested along


TABLE 2. Assessing Discriminant Validity: Chi-Square Difference Tests

Models/Construct Pairs Model ¥ 2 ∆Model ¥ 2 p-value

Unconstrained Measurement Model (d.f. = 237) 595.91

Constrained Models (d.f. = 238)

Internal Locus of Control and Learning Orientation

Internet Utilization and Learning Orientation

Internet Utilization and Internal Locus of Control

Sales Training and Learning Orientation

Sales Training and Internal Locus of Control

Sales Training and Internet Utilization

Non-Sales Related Internet Usage and Learning Orientation

Non-Sales Related Internet Usage and Internal Locus of Control

Non-Sales Related Internet Usage and Internet Utilization

Non-Sales Related Internet Usage and Sales Training

Perceived Performance and Learning Orientation

Perceived Performance and Internal Locus of Control

Perceived Performance and Internet Usage

Perceived Performance and Sales Training

Perceived Performance and Non-Sales Related Internet Usage

806.45

1,066.35

964.50

1,128.37

977.56

1,053.07

668.34

672.41

650.70

663.63

1,096.63

976.69

861.63

1,308.19

650.81

210.54

470.44

368.59

532.46

381.65

457.16

72.43

76.50

47.09

67.72

500.72

380.78

265.72

712.28

54.9

<.001

<.001

<.001

<.001

<.001

<.001

<.001

<.001

<.001

<.001

<.001

<.001

<.001

<.001

<.001

Note: Critical α = .0034137; Critical X2(1 d.f., p = .001) = 10.828; p = significance level.


TABLE 3. Correlation Matrix of Constructs Included in the Sales Agent’s IUPModel

Construct

LearningOrienta-

tion

InternalLocus ofControl

Age SalesRelatedInternetTraining

PriorInternetExperi-ence

InternetUtilization

PerceivedPerfor-mance

Learning Orientation 1.00


.456** 1.00

Age .048 .024 1.00

Sales RelatedInternet Training

.094 .017 .002 1.00


�.112* �.094 �.292** .208** 1.00

InternetUtilization

.286** .239** �.185** .249** .308** 1.00

PerceivedPerformance

.214** .107* �.072 .242** .332** .642** 1.00

Note: (*) implies correlation is significant at the .05 level (2-tailed); (**) implies correlation is significant at the .01 level(2-tailed).

TABLE 4. Path Model Estimation Results

Direct RelationshipsStd. Path

Coeff.T-

Value2-TailedSig. p<


Learning Orientation → Internet Utilization g11 .21 3.68 .002

Internal Locus of Control

Locus Orientation → Internet Utilization g12 .15 2.59 .01

Age

Age → Internet Utilization g13 �.20 �3.97 .002

Sales Related Internet Training

Sales Related Internet Training → Internet Utilization g14 .23 4.60 .002

Internet Utilization

Internet Utilization → Perceived Performance β21 .64 15.31 .002

with their standardized path coefficients. As Table 4 and Figure 2 indi-cate, the analysis supported the hypothesized relationships that weretested. The next section discusses the results of this study.

RESULTS

The path-analytic model (see Figure 2) tested the relationships be-tween 4 exogenous and 2 endogenous constructs depicted in the SalesAgent’s IUP model. A sales agent’s learning orientation related posi-tively and significantly to sales related Internet utilization (g11 = .21, t =3.68, p < .002), supporting Hypothesis 1. The results indicate that, forsales agents who participated in this study, the personality trait learningorientation was related directly and positively to the extent to whichthey utilized the Internet for selling purposes. A positive and statisti-cally significant path (g12 = .15, t = 2.59, p < .01) between a sales agent’sinternal locus of control orientation and Internet utilization suggestedsupport for Hypothesis 2. For the sampled sales agents, then, the extentto which these agents perceived that they had control over future conse-quences influence positively the degree to which they utilized theInternet for selling purposes.


Sales Related InternetTraining


Age



Internet Utilization Perceived Performance

γ11 =

.21, t =3.68γ

12 = .15, t = 2.59

γ13 = .20, t = 3.97� �

γ 14= .23, t = 4.60

β21 = .64, t = 15.31

FIGURE 2. Path Diagram Depicting Standardized Path Coefficients in theSales Agent’s IUP Model

The path analysis also supported Hypothesis 3 which posited a directand positive relationship between a sales agent’s age and Internet utili-zation (g13 = �.20, t = �3.97, p < .002). This result establishes that par-ticipating sales agents who were older did not utilize the Internet orutilized the Internet to a lesser extent in soliciting customers. The statis-tically significant positive path coefficient leading from sales relatedInternet training to Internet utilization (g14 = .23, t = 4.60, p < .002) indi-cates support for Hypothesis 4. Among the sales agents who respondedto the survey instrument, then, those that received more training in us-ing the Internet for selling activities ended up utilizing the Internet to agreater extent than others.

Hypothesis 6, which posits a direct and positive relationship between asales agent’s Internet utilization and perceived performance attributableto Internet use was also supported by the path analysis (g16 = .64, t =15.31, p < .002). This result implies that responding sales agents whoutilized the Internet to a greater extent for selling activities also per-ceived higher improvements in sales performance.

The path model (see Figure 2) also indicates the relative influence ofthe various personality, demographic, and user-situational constructs(see Loehlin, 1992). For example, among the relationships tested, auser-situational variable, i.e., Internet related training, was moststrongly related to Internet utilization (g14 = .23) followed by a salesagent’s learning orientation, a personality trait (g11 = .21). Also, theanalysis found that the positive influence of Internet related training(g14 = .23) was greater than the negative influence age (g13 = �.11) hadon Internet utilization. A meta-analysis by Alavi and Joachimsthaler(1992) came to a similar conclusion regarding the primacy of user-situ-ational variables over other variables in the context of DSS implemen-tation. In addition to the above findings, the results of the path analysisrevealed that the four exogenous variables had statistically significantindirect relationships with perceived performance through their directrelationships with Internet utilization. The next section discusses impli-cations that follow from the test of various relationships depicted in theSales Agent’s IUP model.

DISCUSSION AND IMPLICATIONS

A direct implication of the findings in this study is that sales agentscan enhance their sales performance by utilizing the Internet fully for


selling purposes. More specifically, sales agents who use Internet re-sources to prospect for, communicate with, and provide service to po-tential and current customers gain by building better relationships withtheir clients and this translates into enhanced sales for such sales agents.This implication that the Internet can be usefully utilized by sales agentsstands in contrast to literature that suggests the possibility that salesagents may be disintermediated due to the Internet (e.g., Learner &Storper, 2001; Stead & Gilbert, 2001), or indicates that sales agents arefearful of being disintermediated due to the Internet (Gulati, Bristow, &Dou, in press). In other words, the findings of this study suggest thatsales agents should view the Internet revolution as a positive develop-ment that can potentially advance their business goals.

The findings of this study regarding the influence of Internet relatedtraining on Internet utilization offers some enabling suggestions forsales agents who may be interested in utilizing the Internet more fully intheir business operations. By participating in training related to the useof the Internet, and by providing such training to their salespeople, salesagents can increase the extent to which Internet resources are used intheir firms to accomplish selling activities. This should have positiveramifications for sales agents in terms of increased sales and better cus-tomer service and support.

The finding that age is related negatively to Internet utilization, whentaken in isolation, suggests that older sales agents have a harder time ad-justing to the Internet age and are unlikely to use the Internet resourcesavailable. However, evidence in this study that training relates posi-tively to Internet utilization suggests that, even for older sales agents,participation in Internet related training may reduce their doubts andhesitation and facilitate greater Internet utilization for selling activitiesby these agents. The relationship of training and age on Internet utiliza-tion of sales agents may also have some ramifications for the salespeo-ple that work for these sales agents. Specifically, sales agents who wanttheir salespeople to utilize Internet resources for selling activitiesshould keep in mind that older salespeople may have a harder time ac-cepting and adopting the Internet. Correspondingly, these older sales-people may require more input in the form of Internet related training sothat they may overcome their reluctance to utilize the Internet to estab-lish links with and provide service to prospects and customers.

Although the low reliability of the item-set that measured a salesagent’s non-sales related Internet experience precluded a formal test ofthe relationship between this construct and the sales agent’s Internet uti-lization, an exploratory path analysis that included this construct indi-


cated a positive, statistically significant relationship between the twoconstructs. A confirmation of this result with a more reliable measure ofnon-sales related Internet experience would imply that by recruitingsalespeople that have some experience in using Internet resources, salesagents can further ensure that salespeople utilize the Internet resourcesto the fullest. Findings of this study regarding the influence of personal-ity variables have additional implications for the recruitment of newsalespeople. Sales agents should select salespeople with an internal lo-cus of control and a learning orientation. Such salespeople could be ex-pected to readily adopt the Internet as a tool for selling and providingservice to customers.

LIMITATIONS AND FUTURE RESEARCH

Any conclusions regarding causal linkages in this study should bemade with caution as this study utilized cross-sectional data to test pro-posed relationships. Also, the data was generated through self-reports,and therefore may be biased to an extent. This study utilized a list of salesagents belonging to one national manufacturer agents’ association. Al-though the selection of a random sample adds to the generalizability ofthe results, the conclusions of this study cannot be safely extended be-yond the members of the agents’ association. Another limitation derivesfrom the fact that some constructs in the Sales Agent’s IUP model havebeen conceptualized specifically for this study, and therefore do nothave previously validated measures.

This study restricted itself to examining the influence of selected per-sonality, demographic, and user-situational variables on the independ-ent sales agent’s Internet utilization. Several personality traits such asself-efficacy, perceived behavioral control, and risk adverseness, otherdemographic variables such as education, and certain user-situationalvariables such as involvement that have been linked in literature to newtechnology adoption and implementation either directly or indirectly(see Alavi & Joachimsthaler, 1992; Taylor & Todd, 1995; Venkatesh &Davis, 1996) were not included in the Sales Agent’s IUP model. Theconceptual model tested in this study, therefore, was not comprehen-sive. The exclusion of certain variables from the model, however, doesnot negate any of the findings in this study. This study measuredInternet related training in terms of the extent to which sales agents re-ceived such training; the quality of the training received was not mea-sured here. Hence, the variable, Internet training, addressed only one


dimension of training. Conceivably, the quality of training imparted, aswell as the receptivity of sales agents to such training, could also influ-ence the degree to which Internet training is useful for sales agents.

One area for future research involves testing the influence of otheranteceding variables on a sales agent’s Internet utilization. This mayshed further light on the relative influence of different variables onInternet utilization and may identify other controllable variables thatsales agents can use to better exploit this marketing channel to their ad-vantage. In addition, we would encourage researchers to test the SalesAgent’s IUP and other plausible models on dependent variables such assales agent’s efficiency and satisfaction. Finally, further validation ofthe constructs and relationships tested in this study is warranted.

This study concerns itself with examining the influence of Internet uti-lization on perceived sales performance. An extension of this studywould involve testing for the influence of Internet utilization on a salesagent’s performance vis-à-vis their principals. We also encourage re-searchers to further replicate and extend the findings of this study. Whilethe population of sales agents utilized for this study consisted of salesagents belonging to over 100 different industries, the participants did infact represent a rather specialized type of professional salesperson and aselect type of channel relationship. Future research can test the modelproposed here on a more diversified sample of sales professionals.

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