Montclair State University Montclair State University
Montclair State University Digital Montclair State University Digital
Commons Commons
Department of Marketing Faculty Scholarship
and Creative Works
Department of Marketing
1-1-2014
Does Information and Communication Technology Improve Job Does Information and Communication Technology Improve Job
Satisfaction? The Moderating Role of Sales Technology Satisfaction? The Moderating Role of Sales Technology
Orientation Orientation
Yam Limbu
Montclair State University
, [email protected].edu
C Jayachandran
Montclair State University
, jayachandrc@montclair.edu
Barry J. Babin
Louisiana Tech University
Follow this and additional works at: https://digitalcommons.montclair.edu/marketing-facpubs
Part of the Marketing Commons
MSU Digital Commons Citation MSU Digital Commons Citation
Limbu, Yam; Jayachandran, C; and Babin, Barry J., "Does Information and Communication Technology
Improve Job Satisfaction? The Moderating Role of Sales Technology Orientation" (2014).
Department of
Marketing Faculty Scholarship and Creative Works
. 183.
https://digitalcommons.montclair.edu/marketing-facpubs/183
This Article is brought to you for free and open access by the Department of Marketing at Montclair State
University Digital Commons. It has been accepted for inclusion in Department of Marketing Faculty Scholarship
and Creative Works by an authorized administrator of Montclair State University Digital Commons. For more
information, please contact digitalcommons@montclair.edu.
Does information and communication technology improve job
satisfaction? The moderating role of sales technology orientation
Yam B. Limbu
a,
⁎
, C. Jayachandran
b,1
, Barry J. Babin
c,2
a
Department of Marketing, School of Business, Montclair State University, 1 Normal Ave., Montclair, NJ 07043, USA
b
Center for International Business, Department of Marketing, School of Business, Montclair State University, 1 Normal Ave., Montclair, NJ 07043, USA
c
Marketing & Analysis Department, Louisiana Tech University, Ruston, LA 71272, USA
abstractarticle info
Article history:
Received 20 October 2012
Received in revised form 13 May 2013
Accepted 9 January 2014
Available online 12 July 2014
Keywords:
Information and communication technology
Job satisfaction
Administrative performance
Outcome performance
Technology orientation
Empirical research concerning the role of information and communication technology (ICT) in shaping business-
to-business salesforce job satisfaction remains relatively scarce. The authors propose and empirically test a causal
model that theoretically represents structural relationships among factors comprising ICT and eventual salesper-
son job satisfaction. Study results indicate that ICT indirectly influences job satisfaction through salesforce admin-
istr ative performance. While ICT infrastructure, training, and support positively relate to administrative
performance, none of them influence outcome performance significantly. In addition, salesperson technology ori-
entation moderates the effect of both ICT infrastructure and support on job satisfaction. Managerial insights and
implications from the research are discussed.
Published by Elsevier Inc.
1. Introduction
Firms continue to invest substantial resources in information and
communication technology (ICT) infrastructure, training, and sup-
port with the hope of enhancing the capabilities of their salesforce.
Modern technology augments business-to-business (B2B) salesforce
activities with better ICTs than ever before. Information technology
is fundamental to a firm's growth and represents a primary tool for en-
hancing B2B salesforce performance. Research shows that communica-
tion technologies enhance salesforce activities including employing
market intelligence, managing their customer contacts, submitting
sales call reports, and developing sales forecasts (Gohmann, Guan,
Barker, & Faulds, 2005). While the sales literature contains a fair amount
of research on adoption and usage of information technology applica-
tions, the role that ICTs play in shaping salesperson job satisfaction
remains understudied.
The current study presents unique contributions to sales technology
literature in four distinct ways. First, an extensive literature provides
evidence concerning sales technologies including salesforce automation
(SFA) and customer relationship management (CRM). SFA involves the
use of computer hardware and software applications to convert manual
sales activities to electr onic processes (Erf fmeyer & Johnson, 2001;
Rivers & Dart, 1999). CRM uses technology to manage customer interac-
tions and transactions (Zoltners, Sinha, & Zoltners, 2001). However,
Hunte r and Perreault (2006) and Marshall, Moncrief, Rud d, and Lee
(2012) argue that today's salespeople use a wide range of technologies
that may go beyond the classification as either CRM or SFA. Therefore,
Hunter and Perreault (2006; 2007) coin the term sales technology (ST)
as a broader concept including all information technologies salespeople
use in performing the selling function. However, this definition does not
explicitly describe just what all the various technologies might be. Sim-
ilarly, others (Erffmeyer & Johnson, 2001; Honeycutt, 2005) argue that
SFA refers to different things to different people or firms. For example,
one organization may deem a tablet computer as an SFA tool, but anoth-
er may not. These definitions focus on “information technologies” and
do not address the more general case of “communication technologies.”
In practice, communication technologies are critical in B2B selling
environments. Today, the professional salesperson has at his or her dis-
posal a range of communication devices and applications including tab-
let computer/smartphone apps, blogs, wikis, social media networking
sites, data warehouses, and various near-field communication capabili-
ties. Hence, the present generation of salespeople employs a variety of
communication technologies including social media and mobile
Internet technology that go beyond the traditional boundaries of CRM,
SFA, and ST (Hunter & Perreault, 2006; Marshall et al., 2012). For example,
pharmaceutical sales representatives are currently practicing e-detailing,
Industrial Marketing Management 43 (2014) 1236–1245
⁎ Corresponding author. Tel.: +1 973 655 3361; fax: +1 973 655 7673.
E-mail addresses: limbuy@mail.montclair.edu (Y.B. Limbu),
jayachandrc@mail.montclair.edu (C. Jayachandran), bbabin@latech.edu (B.J. Babin).
1
Tel.:+19736557523.
2
Tel.:+13182574012.
http://dx.doi.org/10.1016/j.indmarman.2014.06.013
0019-8501/Published by Elsevier Inc.
Contents lists available at ScienceDirect
Industrial Marketing Management
which uses a rich variety of new and intricate ICTs, including electronic
video conferencing, interactive voice response, online video presenta-
tions, and social media (Alkhateeb & Doucette, 2008).
Our study takes a holistic approach in understanding the role of ICT
on salesforce performance and job satisfaction. ICT is a well-defined and
widely used construct in the information system literature and encom-
passes computer and communication technologies, shareable technical
platforms and databases, networking technologies, broadcast media,
and audio and video processing and transmission (Chung & Hossain,
2010; Davenport, Hammer, & Metsisto, 1989; Ross, Beath, & Goodhue,
1996; Schaper & Pervan, 2007; Weill, Broadbent, & Butler, 1996).
Because ICT plays an important role in any organization and the use of
novel and sophisticated ICTs continue to emerge in B2B selling, it is un-
doubtedly an important topic that deserves sales researchers' attention.
Researchers indeed call for more substantive research dealing with sales
ICT practice and its effectiveness (Brady, Fellenz , & Brookes, 2008;
Marshall et al., 2012).
Second, the study explores the effects of previously untapped con-
structs related to ICT such as a firm's ICT infrastru cture and support.
As a firm's investment in sales ICTs continues to grow especially in
B2B industries, the use of ICTs has become an integral pa rt of a
salesperson's daily routine. A firm's investment in ICT can influence
sales performance and job satisfaction. However, any such influence is
likely moderated by salesperson technology orientation. Therefore, the
study sheds light on the efficacy of salesforce ability and propensity to
use ICTs as a driving force of job satisfaction. Given that salesforce
turnover can be very costly and poses a serious challenge to B2B firms,
understanding the influence of ICT and fi rms' job enrichment efforts
such as technology infrastructure and support on salesforce job satisfac-
tion and retention has become crucial to firms' ability to sustain sales
performance.
Third, previous research relies primarily on sales volume (in units
or dollars) as a principal indicator of salesperson performance
(e.g., Ahearne, Hughes, & Schillewaert, 2007; Ahearne, Jones,
Rapp, & Mathieu, 2008; Morris, Davis, Allen, Avila, & Chapman,
1991). Lately, research suggests two key dimensions of salesperson
performance: administrative performance (nonselling performance
or sales-related administrative performance such as call planning
and r eporting) and outcome performance (selling performance
such as generating sales volume and sales revenue) (e.g., Baldauf,
Cravens, & Pie rcy, 2005; Sundaram, Schwarz, Jones, & Chin, 2007).
To date, little is known about the differential antecedents and out-
comes of the two perfo rmance dimensions. Sundaram et al. (2007)
underline the importance of salesperson administr ative perfor-
mance and emphasize a need for f urther r esearch in to the effects
of sales technology on th e di fferent dimensions of sales perfo r-
mance. Thus, the current study addresses this issue.
Finally, despite decades of research, the relationship between job
performance and job satisfaction remains a topic of ongoing controver-
sy. Extant literature offers two opposite points of view concerning this
relationship (i.e., job satisfaction leads to job performance or job perfor-
mance leads to job satisfaction). Over the last few decades, researchers
gravitated toward the view that job performance exhibits a weak but
positive relationship with job satisfaction (e.g., Brown & Peterson,
1993,1994). In light of such conflicting findings regarding the perfor-
mance–satisfaction relationship, the secondary goal of our study is to
examine whether different dimensions of job performance (e.g., admin-
istrative and outcome) have different impacts on job satisfaction.
2. Literature review
Prior research on salesforce technology in B2B personal selling can
be grouped into three streams. As shown in Table 1, extant research
focuses on SFA (e.g., Erffmeyer & Johnson, 2001; Rivers & Dart,
1999). These studies report a strong impact of SFA on sales perfor-
mance (e.g., Jelinek et al., 2006; Ko & Dennis, 2004) and sales
productivity through better account prospecting, development,
and buyer profiling (Pullig, Maxham, & Hair, 2002). The second
cluster of studies investigates the ef ficacy of sales-based CRM
(e.g., Ahearne et al., 200 7; Zoltners et al., 200 1) and it s impact on
salesperson performance (e.g., Ahearne et al., 2004; Ahearne
et al., 2008) and call pro ductivity (Ahearne et al., 2007). The t hird
line of research introduces the term sales technology, which includes
a range of information technologies meant to facilitate or enable
performance of sales tasks (Hunter & Perreault, 2006; 2007). The
studies suggest a direct impact of sales technology on internal role
performance and an indirect effect on per formanc e with customers .
Most sales research studies rely on a narrow concept of sales tech-
nology with a focus on a specific sales technology package and its im-
pact on sales effectiveness. However, not all B2B salespersons access
and use such information technology resources (e.g., SFA and CRM).
Rather, in most cases, firms install and maintain a common set of ICTs
that are used by employees, including the salesforce. Considering the
crucial role of ICTs in B2B selling, it is critical to have a general under-
standing of the holistic contribution of ICT to salesforce performance
and job satisfaction.
3. Proposed model and hypotheses
The proposed causal model (see Fig. 1) is grounded in several bodies
of knowledge. These include the social exchange theory, which assumes
that human behavior is an exchange of rewards between actors; the job
characteristics model of work motivation, which illustrates the potential
impact of job characteristics on job outcomes; and the balance theory,
which is a psychological theory that highlights the desire for consisten-
cy. Fig. 1 posits relationships between three ICT-related exogenous con-
structs (i.e., infrastructure, training, and support) and three endogenous
constructs (i.e., administrative performance, outcome performance, and
job satisfaction). ICT factors exert direct and indirect effects on job satis-
faction through administrative performance and outcome performance.
Furthermore, technology orientation moderates the re lationship be-
tween ICT factors and job satisfaction.
In this study, ICT infrastructure is defined as a salesperson's per-
ceptions of a firm's investment in sales-relat ed ICT resources in-
cluding hardware, software, staffing, and sophist icated Interne t
applications. ICT training re fers to t he exte nt t o which salespeople
believe t hat they receive sufficient sales-related ICT training to use
ICT tools. ICT support involves a sale sperso n's perceptions regarding
the firm's provision of inputs that are needed to engage in the effi-
cient use of ICT resources. Support may include the availability of
specialized personnel mannin g a help desk, an information cente r
to answer users' questions regarding ICT usage, troubleshooting ca-
pabilities, and hands-on support to users before and during usage
(Bhattacherjee & Hikmet, 2008). In line with Sundaram et al.
(2007),wedefine administrative performance as the extent to which
ICTs affect the quality of salesperson planning, time management, and
reporting. “Submitting required reports on time” is an example of sal es-
person administrative performance (Hunt er & Perreault, 2007). Outcome
performance refers to “the extent to which the technology affects the qual-
ity of the salesperson's ability to produce key sales results” (Sundaram
et al., 2007, p. 111) and represents quantitative results of the salesperson's
efforts (Baldauf et al., 2005). In this study, technology orientation is defined
as a salesperson's propensity and analytical skills needed to use firm-
specificICTsinperformingsalestasks(Hunter & Perreault, 2006).
3.1. Impact of ICT factors on salesperson performance and Job satisfaction
Job satisfaction represents an individual's psychological well-being
on the job (Singh, Goolsby, & Rhoads, 1994). Various models propose,
test, and try to explain the impact of job design on employee job satis-
faction. For example, Hackman and Oldham's (1976) job characteristics
model (JCM) suggests that various job characteristics (e.g., skill variety
1237Y.B. Limbu et al. / Industrial Marketing Management 43 (2014) 1236–1245
and task significance) influence job satisfaction through psychological
state s including feelings of meaningfulness and responsibility. The
propositio n that perceived job charac teristics influence perceptual
processes, motivation, work performance, and job satisfaction is well
researched in organizational literature (Glisson & Durick, 1988; Loher,
Noe, Moelle r, & Fitzgerald, 1985; Thatcher, Stepin a, & Boyle, 2002;
Ting, 1997). Job enrichment efforts include firms' endeavors to make
the jobs more interesting, challenging, and significant by adding dimen-
sions such as variety, autonomy, feedback, and control (Umstot, Bell, &
Mitchell, 1976). Job enrichment is deemed to improve job satisfaction
by providing greater capabilities for employee achievement, growth,
and recognition (Loher et al., 1985; Neuman, Edwards, & Raju, 1989).
The current study focuses on ICT-related job characteristics or enrich-
ment efforts that influence job satisfaction. The study proposes that a
firm's investment in ICT infrastructure, training, and support favorably
influences salesforce perceptions of enrichment efforts and organiza-
tional backing. In turn, these factors eventually influence salesperson
motivation and job satisfaction. Such corporate decisions are generally
based on the rationale that a salesperson's perceptions of firm commit-
ment and continuous assistance rendered to perform the tasks directly
influence the way they perform and feel about their job.
Prior research relying on a resource-based view of information
technology suggests that investment in information technology re-
sources relates positively to a firm's success (Bharadwaj, 2000; Keen,
1991; McKenney, 1995). This suggests that firms can enhance salesforce
productivity by improving salesforce-oriented ICT infrastructure. Anoth-
er line of literature suggests that the extent of skills utilization (i.e., the
degree to which jobs allow individuals to use their skills and abilities)
is a strong predictor of job satisfaction (Gerhart, 1987; Glisson &
Durick, 1988). Since an effective use of ICT involves skills, one can
argue that a firm's investment in ICT resources has a positive influence
on employee job satisfaction. Theoretically, social exchange theory
holds that an individual is likely to reciprocate with a positive attitude
and behavior when the individual receives positive inducements from
Table 1
Review of major empirical models in the sales technology literature.
Authors Sales
technology
Exogenous variables Endogenous variables
Speier & Venkatesh (2002) SFA Individual traits, role perceptions, organization characteristics,
professional state
Individual perceptions of technology, person–technology fit,
subjective outcomes, objectives
Jones, Sundaram, & Chin
(2002)
SFA, TAM
a
Salesperson attitudes and characteristics, normative and control
beliefs
Infusion, intention to use
Ko & Dennis (2004) SFA System usage, experience, expertise Sales performance
Ahearne, Jelinek, & Rapp
(2005)
SFA Usage Salesperson performance
Rangarajan, Jones, & Chin
(2005)
SFA Task complexity, perceived usefulness Role ambiguity, role conflict, effort, infusion
Schillewaert, Ahearne,
Frambach, & Moenaer
(2005)
SFA, TAM Social influence, salesperson characteristics, organizational
facilitators, subjective social norm
Usefulness, ease of use, usage
Jelinek, Ahearne, Mathieu,
& Schillewaert (2006)
SFA Individual differences, organizational factors, contextual influences Intention to adopt, adaptation, sales performance
Park, Kim, Dubinsky,
& Lee (2010)
SFA Usage Information proceeding, adaptive selling behavior, sales
performance, relationship quality
Eggert & Serdaroglu (2011) SFA Supervisor support, facilitating conditions Technology usage, sales performance
Ahearne et al. (2007) CRM Acceptance Sales performance
Ahearne, Srinivasan,
& Weinstein (2004)
CRM Usage Sales performance
Sundaram et al. (2007) SFA, CRM Prior attitude toward information technology Prior intention to use, routinization, infusion, sales performance
Rapp, Agnihotri, & Forbes
(2008)
SFA, CRM Usage, experience Effort, adaptive selling, sales performance
Hunter & Perreault (2006) ST Internal technology support, customer approval, sales experience Sales technology orientation, information effectiveness, planning,
adaptive selling behaviors, salesperson performance
Hunter & Perreault (2007) ST Training, customer's information technology expectations Usage, relationship-forging tasks, sales performance
Mathieu, Ahearne, & Taylor
(2007)
ST Work experience, leader commitment, empowering leadership Self-efficacy, usage, sales performance
Ahearne et al. (2008) ST Usage Sales performance
Onyemah, Swain, & Hanna
(2010)
ST Perceived technological savvy of sales manager, coworkers, and
competitors
Perceived monitoring of salesperson's activities, technology usage,
sales performance
Lapierre & Denier (2005) ICT Organizational culture, change processes, control system,
managerial style
ICT adoption, communication effectiveness
Robinson, Marshall, & Stamps
(2005a)
TAM Perceived control, length of work experience, personal
innovativeness, support services, organizational innovativeness
Perceived ease of use, perceived usefulness, attitude, behavioral
intention
Robinson, Marshall, & Stamps
(2005b)
TAM Perceived usefulness, perceived ease of use Attitude, behavioral intentions, adaptive selling, job performance
Shim & Viswanathan (2007) TAM System features Perceived ease of use, perceived usefulness
a
TAM refers to technology acceptance model.
Fig. 1. Conceptual framework of information and communication technology an d job
satisfaction.
1238 Y.B. Limbu et al. / Industrial Marketing Management 43 (2014) 1236–1245
others (Blau, 1983; Gouldner, 1960). The theory puts forward that all
human relationships are based on a subjective cost–benefit analysis
and the comparison of alternatives (Hormans, 1958). In a workplace
setting, social exchange relationships are likely to take place when em-
ployers take care of employees and promote effective work behavior
and positive employee attitudes (Cropanzano & Mitchell, 2005). Apply-
ing this theory in a personal selling context, one would expect a firm's in-
vestmen t in salesfo rce job enrichment to be a significant determinant of
salespersons' psychological outcomes (e.g., affective reaction toward
one's job). Thus, this study predicts the following:
H1a. Infrastructure investment relates positively to job satisfaction.
H1b. Infrastructure investment relates positivel y to administrative
performance.
H1c. Infrastructure investment relates po sitively to ou tcome
performance.
Previous research suggests that salesforce training is positively asso-
ciated with improved salesforce productivity (e.g., Farrell & Hakstian,
2001; Roberts, Lapidus, & Chonko, 1994; Roman, Ruiz, & Munuera,
2002). According to the social exchange theory, social attitudes and be-
haviors are the results of an exchange process between two parties that
often go beyond economic exchange (Blau, 1983; Gouldner, 1960). The
relationship between a firm's provision of ICT training to salesforce and
the ICT-enabled performance (e.g., administrative and sales output) can
be viewed as a product of social exchange. Because ICT training can fa-
vorably affect the salesperson's perceptions of organizational support
(an employee's perception that the organiza tion cares for his or her
well-being and is supportive of his or her concerns [ Eisenberger,
Huntington, Hutchison, & Sowa, 1986]), salespeople who receive higher
levels of training are more likely to reciprocate with favorable job-relat-
ed performance and attitude toward their jobs. Thus, we posit a direct
effect of ICT training on administrative performance, outcome perfor-
mance, and job satisfaction.
H2a. Training relates positively to job satisfaction.
H2b. Training relates positively to administrative performance.
H2c. Training relates positively to outcome performance.
Social exchange theorists suggest that organizational support is a
strong predictor of workplace outcomes including attachment and feel-
ings of obligation to the organization, commitment, reduced absenteeism,
organizational citizenship behavior, and job performance (Eisenberger,
Armeli, Rexwinkel, Lynch, & Rhoades, 2001; Eisenberger, Cummings,
Armelo, & Lynch, 1997; Eisenberger, Fasolo, & Davis-LaMastro, 1990;
Eisenberger et al., 1986; Lynch, Eisenberger, & Armeli, 1999; Riggle,
Edmondson, & Hansen, 2009; Shore & Wayne, 1993). Organizational
support theory, a social exchange theory, may be useful to understand
the relationship between ICT support and salesforce behavior. This
theory holds that employees increase their effort in carrying out or-
ganizational tasks to the degree that the organization is perceived
to be willing and able to reciprocate with desirable socioemotional
resources (Aselage & Eisenberger, 2003). Perception of organiza-
tional support influences job satisfaction in several ways including
addressing socioemotional needs, increasing performance–reward
expectancies, and sign aling the availabilit y of aid whe n needed
(Rhoades & Eisenberger, 2002). Because organizational support re-
duces workplace t ensions by satisfy ing the workfo rce's needs f or
emotional support (Rhoades & Eisenberger, 2002), organizational
support theory suggests that managerial actions and attitudes
concerning ICTs influence a salesperson's behavior. Salespeople who per-
ceive a higher level of organizational ICT support tend to consider their
job as more pleasant, to feel happier at work, and to be obligated to
help the organization to meet its goals, and they may demonstrate a
stronger commitment to use ICT tools for improved performance.
These, in turn, positively influence job performance and satisfaction
(Rhoades & Eisenberger, 2002; Trauth & Cole, 1992). Thus, based on
the above discussion, we predict that a firm's ICT support positively re-
lates to salesperson administrative performance, outcome performance,
and job satisfaction.
H3a. Support relates positively to job satisfaction.
H3b. Support relates positively to administrative performance.
H3c. Support relates positively to outcome performance.
3.2. The relationship between job performance and job satisfaction
The balance theory can be useful in providing theoretical support for
the job performance–job satisfaction link. This theory holds that an indi-
vidual is expected to form positive or negative feelings after obtaining
and assessing his or her job performance (Locke, 1976). If her or his job
performance is conceived to be positive or rewarding, job satisfaction is
likely to emerge (Locke, 1970). Consistent with this theory, previous
studies suggest a mild, positive relationship between job performance
and job satisfaction (e.g., Bagozzi, 1980; Behrman & Perreault, 1984;
Christen, Iyer, & Soberman, 2006). Therefore, we hypothesize the posi-
tive effects of a salesperson's administrative and outcome performance
on job satisfaction. We predict that both administrative performance
(e.g., planning, time management, and reporting) and outcome perfor-
mance (e.g., help to increase market share) are key measures of sales
success that ultimately improve salesforce job satisfaction.
H4a. Administrative performance relates p ositively to sales jo b
satisfacti on.
H4b. Outcome performance relates positively to job satisfaction.
3.3. The moderating effect of salesperson technology orientation
Organizational ICT capability (e.g., infrastructure, training, support)
and provision of sales technology alone may not guarantee improved
salesforce job performance and job satisfaction. Salesperson technology
orientation (i.e., the predispositio n to embrace sales technologie s in
their tasks) can play a key role in shaping relationships between ICT ca-
pabilities and salesforce job satisfaction. Devaraj and Kohli (2003) argue
that the driver of the impact of information technology is not the invest-
ment in technology, but the actual application of technology. Sales liter-
ature shows that technology orientation has a strong effec t on how
effectively a salesperson uses information about customers and shapes
sales performance (Hunter & Perreault, 2006; Sundaram et al., 2007).
We, therefore, posit that salespeople with higher levels of technology
orientation should be more willing and able to use new and sometimes
sophisticated technologies to perform their jobs. Those lacking a high
technological orientation may not be able to leverage the technologies
for success. As a result, the salespeople who are technologically oriented
may be able to perform some tasks more efficiently than those who
have low levels of technology orientation. The greater feelings of effica-
cy resulting from enhanced performance should manifest themselves in
higher job satisfaction. Therefore, we hypothesize the following:
H5a. Technology orientation moderates the relationship between in-
frastructure and job satisfaction in such a way that a firm's ICT infra-
structure will be a significant positive predictor (not significant) of job
satisfaction when salesperson technology orientation is high (low).
H5b. Technology orientation moderates the relationship between
training and job satisfaction in such a way that a firm's ICT training
will be a significant positive predictor (not significant) of job satisfac-
tion when salesperson technology orientation is high (low).
1239Y.B. Limbu et al. / Industrial Marketing Management 43 (2014) 1236–1245
H5c. Technology orientation moderates the relationship between sup-
port and job satisfaction in such a way that a firm's ICT support is a sig-
nificant positive predictor (not significant) of job satisfaction when
salesperson technology orientation is high (low).
4. Methodology
4.1. Sample and procedure
A global marketing research company located in a large city in India
collected data for this study by surveying area pharmaceutical sales repre-
sentatives. The Indian pharmaceutical industry is continuously growing
and currently ranks third in terms of global volume of production.
Among the reasons for choosing pharmaceutical sales reps are as follows:
(1) a broad array of ICTs are available; (2) ICT infrastructure, training, and
support vary across pharmaceutical companies; (3) pharmaceutical reps
are prototypical professional salespeople; and (4) the use of technology
is not completely within an employer's control (Speier & Venkatesh,
2002; Sundaram et al., 2007).
Using a list of pharmaceutical companies obtained from the Indian
Drug Manufacturers' Association, 30 large- and medium-sized pharma-
ceutical firms' marketing executives were approached and asked for a
company-wide list of field sales representatives. Fourteen of them
obliged, and this resulted in a master list of 672 active sales representa-
tives. Of this master list, 550 representatives who had a minimum of
three years of experience were qualified to participate in the study. Ex-
perienced field investigators drawn from a pool of field research staff of
the research firm administered the survey at multiple locations based in
five major cities. Before the survey was administered, the questionnaire
was pretested for readability, clarity, and sensibility by using a repre-
sentativ e sample of 10 pharmaceut ical sales representatives. Partici-
pants were informed that their participation was voluntary and were
assured of confidentiality of all the information provided. The survey
questionnaire was prepared, pretested, and administered in English. Be-
fore responding to the survey, the respondents were instructed to think
about recent meetings with physicians and answer the questions based
on that recollection. Any respondent not reporting personal contact
with a physician within two months was excluded from analyse s,
resulting in 372 completed qualifying questionnaires. Of those, 14
cases were excluded because of incomplete responses, resulting in 358
useable responses. The result is a 67% response rate.
Table 2 displays a profile of the sample. The respondents' average
experience in a pharmaceutical sales job was six years (SD = 6.7). The
study sample consists of approximately 92% of men with a median
reported age between 24 and 52 years. Most res pondents represent
foreign-owned (non-Indian) companies (66.4%). A majority of them
represent prescription drugs (51.1%), with 11.2% repr esenti ng over-
the-counter drugs and 31.8% representing both. The physicians they
call on include general practitioners (34.6%), specialists (42.2%), and
a mixture of both (15.4%) . Specialists we re composed of dentists ,
ophthalmologists, pediatricians, etc.
4.2. Measurement
Table 3 presents a summary of measurement items adapted from
scales widely applied in previous studies. Job satisfaction was measured
by the three-item scale of Cammann, Fichman, Jenkins, and Klesh (1983).
Sample items include “All in all, I am satisfied with my job” and “In gen-
eral, I don't like my job (reverse item).” A seven-point scale using a
slightly modified version of the four-item scale of Sundaram et al.
(2007) measured salesperson outcome performance. Respondents
were directed to assess “to what extent has ICT affected the quality of
your performance” (e.g., Behrman & Perreault, 1982; Hunter, 2004). Ad-
ministrative performance was assessed with a three-item scale adapted
from Sundaram et al. (2007) (see Behrman & Perreault, 1982; Hunter
& Perreault, 2007). The measure assesses the quality of the salesperson's
call planning, time management, and reporting to supervisors. To assess
ICT infrastructure, we adapted four items from Chen and Tsou (2007).
This measure emerges from the rich information system literature
(Bharadwa j, 2000; Ross et al., 1996; Weill et al., 1996). Based on item re-
liability, modification indices, and tests of discriminant validity from con-
firmatory factor analysis (CFA), we deleted one item: “Our company has
emphasized ICT staffing and training.” One additional item, “Our compa-
ny has embraced sophisticated communication applications,” was added
to reflect the nature of our study.
ICT training was measured wit h a three-item scale based on the
work of Roberts et al. (1994) and Johlke (2008). The measure of
Hunter and Perreault (2006) was adapted to assess ICT support. One re-
verse item, “I feel that I need more help with ICT than I get,” with a large
number of high modification indices and a poor factor loading was de-
leted. Salesperson technology orientation was measured by the scale
of Hunter and Perreault (2006) which is composed of three items, in-
cluding “I extensively use ICTs to perform my job” and “Itrytolinkdif-
ferent ICTs so that they work together well.”
5. Results
The hypoth esized relationsh ips were estimated using structural
equation modeling with the use of maximum likelihood estimation.
The assessment of the hypothesized model follows the two-stage ana-
lytic technique with a thorough assessment of the measurement theory
via CFA preceding an assessment of the implied structural theory (see
Hair, Back, Babin, Anderson, & Tatham, 2010). The CFA was used to as-
sess construct validity and measurement theory quality (fit). Afterward,
the structural relations among ICT factors, job performance, and job sat-
isfaction were evaluated. In addition, we used a multigroup analysis in
AMOS for testing the moderating effect of technology orientation.
5.1. Measurement model
Aconfirmatory factor analysis with six latent constructs and 20
items was performed to determine the mod el fitness. The
goodness-of-fit indices for the measurement model are: χ
2
(155)
=
260.197, p b .001; goodness-of-fit index (GFI) = .93; root mean
square error o f approximation (RMSEA) = .044; and comparative
fit index (CFI) = .98. All indices are in line with a reason able fitfor
a model of this complexity and this sample size as indicated in Hair
et al. (2010, p. 654). We, therefore, interpret the overall model fit
as accep table and turn toward other indicators of construct validity.
As shown in Table 3, all factor loadings are significant and higher
than .5, which provides an initial support for convergen t validity
(Gerbing & Anderson, 1988). Average variance extracted (AVE) esti-
mates for all hypothesized constructs are higher than .50, and construct
reliabilities are higher than the recommended level of .70 (Hair et al.,
2010). Thus, taking loadings, AVE, and construct reliabilities together,
Table 2
Sample characteristics.
Sample size 358 Pharmaceutical sales representatives
Average work experience 6 years
Gender Male = 91.9%
Female = 8.1%
Age Median age = between 24 and 52 years
Company types Foreign-owned company = 66.4%
Domestically-owned company = 29.1%
Drug types Prescription drugs = 51.1%
Over-the-counter (OTC) = 11.2%
Both = 31.8%
Physicians visited General = 34.6%
Specialist = 42.2% (dentist, ophthalmologist,
pediatrician, gynecologist, cardiologist, neurologist)
Both = 15.4%
1240 Y.B. Limbu et al. / Industrial Marketing Management 43 (2014) 1236–1245
they provide initial support for the convergent validity of the measure-
ment model. Since the AVE by each latent variable's measure was larger
than the squared interconstruct correlation (see Table 4), discriminant
validity is also demonstrated (Fornell & Larcker, 1981). The indicators
have more in common with their respective constructs than they do
with other constructs.
Since our independent and dependent variables come from the same
data source, we acknowledged the possibility of common method bias
(CMB). Therefore, we tested for the possibility of CMB using Harman's
single-factor test on items included in our measurement model. The
result of a principal component factor analysis revealed that the first
factor explained only 29% of the variance, which shows that CMB does
not appear to be a serious problem in the data (Podsakoff & Organ,
1986). In addition, a solution including a measurement factor does not
significantly improve fit, pro viding further evidence of a lack of CMB
(Hair et al., 2010).
5.2. Hypothesis testing
We next turn to the structural model. The fit indices for the hypoth-
esized model are once again in line with the guidelines from Hair et al.
(2010, p. 654) (χ
2
(156)
= 263.4, p b .001; GFI = .93; RMSEA = .044;
CFI = .97). Given good fit, we turn toward the individual hypotheses.
5.2.1. Direct effects
Table 5 presents the results of hypothesis testing. Hypotheses
1a–1c predict that ICT infrastructure is positively related to job sat-
isfaction, administrative performance, and outcome performance,
respectively. Results do not support a direct effect of infrastructure
on job satisfaction (β = .067, t = .554, p N .05) and outcome performance
(β = .088, t = .828, p N .05). However, infrastructure significantly pre-
dicts administrative performance (β = .363, t = 3.28, p b .01).Hence,
hypothesis 1b is supported, but hypotheses 1a and 1c are not. Hypotheses
2a–2c posit a positive impact of ICT training on job satisfaction, adminis-
trative performance, and outcome performance, respectively. Results
do not support the hypothesized training–job satisfaction relationship
(β = .045, t = .159, p N .05) and training–outcome perf ormance
(β = .275, t = 1.12, p N .05) relationships. However, as shown in
Table 5, tr aining signifi cant ly and positively relates to administra-
tive performance (β = .592, t = 2.32, p b .05). Thus, these r esults
indicate support for hypothesis 2b, but not for hypotheses 2a and
2c. Consistent with hypothesis 3b, ICT support positively enhances
sales administrative performance (β = .446, t =2.13,p b .05);
however, it do es not in fluence job satisfaction ( β = .009, t = .041,
p
N .05) or outc ome performance (β = .252, t = 1.08, p N .05).
Thus, hypotheses 3a and 3c are not supported. As predicted by
hypothesis 4a, administrative performance posit ively relat es to
salesperson job satisfaction (β = .215, t = 2.01, p b .05). Similarly,
outcome pe rform ance positively impacts job satisfaction (β = .387,
t = 3.96, p b .001), supporting hypothesis 4b.
5.2.2. Indirect effects
The previous section examined the direct effects of ICT on job perfor-
mance and job satisfaction indicating that ICT factors are significant deter-
minants of administrative performance and that, in turn, administrative
performance relates positively to job satisfaction. To further test the sig-
nificance of the indirect effects, we estimate the indirect effects of ICT fac-
tors on job satisfaction through administrative performance using the
bootstrap estimation procedure in AMOS. Results reveal a positive and
Table 3
Measurement items, standardized CFA factor loadings, average variance extracted (AVE), and composite reliabilities.
Constructs and items Factor loading Composite reliability AVE
Job satisfaction 0.85 0.65
All in all, I am satisfied with my job. 0.775
In general, I like working at my company. 0.889
In general, I don't like my job. (r) 0.747
Infrastructure 0.89 0.67
Our company has allocated a generous budget for purchasing ICT hardware. 0.761
Our company has allocated a generous budget for purchasing ICT software. 0.772
Our company has embraced sophisticated Internet applications. 0.867
Our company has embraced sophisticated communication applications. 0.859
Training 0.78 0.55
I have received enough ICT training to do my job well. 0.640
The ICT training that I have received is useful. 0.788
The ICT training that I have received helped me to become more efficient and productive. 0.784
Support 0.87 0.69
My company adequately equips me with ICT tools. 0.826
My company has supplied all information and communication technologies that are needed to perform my job well. 0.843
My company has adequately supported me on the use of ICT in my sales job. 0.821
Administrative performance 0.88 0.71
ICT helped in managing my time. 0.857
ICT improved my planning ability. 0.828
ICT improved my reporting ability to my supervisor. 0.837
Outcome performance 0.82 0.53
To what extent has ICT affected the quality of your performance with regard to:
Contributing to company's emphasis on increasing market share. 0.751
Help in identifying major accounts in the territory and promote sales to them. 0.682
Assist in generating sales of company's new products. 0.711
Help in meeting sales targets. 0.761
Technology orientation 0.81 0.59
I extensively use ICTs to perform my job. 0.840
I try to link different ICTs so that they work together well. 0.817
Compared to others in salespeople, I am technology oriented. 0.626
Table 4
Interconstruct correlations and descriptive statistics.
123456
1. Infrastructure 1.00
2. Training 0.79 1.00
3. Support 0.73 0.74 1.00
4. Outcome performance 0.52 0.58 0.58 1.00
5. Administrative performance 0.47 0.71 0.73 0.51 1.00
6. Job satisfaction 0.36 0.43 0.42 0.48 0.43 1.00
Mean 4.95 5.05 5.02 5.42 5.34 5.12
Standard deviation 1.28 1.31 1.30 0.91 1.28 1.18
Note: all correlations are significant at the .01 level.
1241Y.B. Limbu et al. / Industrial Marketing Management 43 (2014) 1236–1245
significant indirect effect of infrastructure on job satisfaction (β =.356,
p b .01) through administrative performance. Similarly, the indirect ef-
fects of support on satisfaction (β =.147,p b .05) and training on satis-
faction (β = .210, p b .05) via administrative performance are
significant.
5.2.3. Moderating effects
Because the direct effects of ICT factors on job satisfaction are not sig-
nificant, we were interested to gain further insights into whether a
salesperson's technology orientation can moderate the relationships in
the model. Before performing moderation analysis using the multigroup
analysis in AMOS, we split the sample into high and low technology ori-
entation groups by following the median split procedure. Afterward, we
tested invariance between two groups by following conventional proce-
dures (e.g., Hair et al., 2010). First, we compared a constrained model
that imposes equality constraints on the paramet ers across groups
with an unconstrained baseline model that allows these parameters to
vary freely across groups. Second, a chi-square difference test for
moderation examines the detriment in fit associated with the structural
invariance constraint. Results show that the unconstrained and
constrained models differ in fi t(Δχ2 = 37.947, df = 3, p b .05),
suggesting technology orientation as a moderator that alters the rela-
tionship between ICT factors and job satisfaction.
Table 6 presents a summary of the results of the individual variable
moderation analysis. Significant chi-square differences exist for
invariance constraints on the infrastructure → job satisfaction (Δχ2=
4.84, df = 1, p b .05) and support → job satisfaction (Δχ2 = 2.82, df =
1, p b .1) coefficients. Thus, consistent with hypothesis 5a, we find a signif-
icant positive influence of infrastructure on job satisfaction in the high
technology orientation group (β = .798, p b .05). However , for the low
technology orientation group, infrastructure is not a significant predictor
of job s atisfaction (β = .075, p N .05). This suggests t hat th e influ-
ence of ICT infrastructure on the salesforce's job satisfaction is sig-
nifi
cantly higher when their technology orientation is high than
low. Similarly, as predicted i n hypothesis 5c, ICT support is a stron-
ger predict or of job satisfact ion whe n te chnolog y orie ntatio n is high
(β = .508, p b .10) than low (β = .088, p N .05). In other words, ICT
support is more import ant for the salesforce with higher lev els of
technology orientation. Contrary to the predict ion (hypot hesis
5b), technology orientation does not mode rate the relationsh ip
between ICT training and job satisfaction. This means that the
role of ICT training is not significantly different bet ween the
high (β = .211, p N .05) versus the low (β = .042, p N .05) tech-
nology orientation groups. Overall, the results support a moder-
ating role of tec hnology orientation on the relationship between
ICT factors and job satisfac tion.
6. Discussion and conclusion
While the existing research confirms various determinants of em-
ployee job satisfaction, our study specifically examines whether B2B
salesforce perceptions of firm job characteristics/job enrichment efforts
in regard to ICT infrastructure, training, and support influence salesforce
job satisfaction. The study findings demonstrate that ICT has not only be-
come an integral part of a B2B sales representative's routine, but it also
enhances salesforce performance and job satisfaction. ICTs have positive
indirect effects on the salesforce's job satisfaction through administrative
performance. This means that ICT infrastructure, training, and support
improve sales-related administrative tasks or the nonselling activities,
whichinturninfluence a salesperson's job satisfaction. While companies
continuously experience enormous challenges to effectively leverage
sales technologies to increase revenue, sales managers are increasingly
under pressure to justify the investments made in sales technologies
(Peters en, 1997; Speier & Venkatesh, 2002; Thetgyi, 2000). The findings
of this study help justify continuous investment in salesforce technology,
training, and support. As new and sophisticated ICTs continue to emerge,
B2B firms cannot afford to solely rely on traditional sales technologies
(e.g., SFA and CRM). Rather, salespeople should be encouraged to adopt
and use a rich variety of new and emerging ICTs, including social
media, blogs, wireless and cellular networking, mobile applications, etc.
In light of the extensive use of ICTs in managing salesforce activ-
ities in B2B selli ng (e.g., pharmaceutical industry), the firm should
recruit salespeople who have a posi tive mind-set and ability to
learn about using ICT resources. Furthermore, training should ad-
dress the inhibitions that already exist among sales representatives
who do not score high on technological orientation. The efficient
Table 5
Structural parameter estimates.
Estimate S.E. t-Value p
Job satisfaction ← Infrastructure .067 .124 .554 .579
Administrative performance ← Infrastructure .363 .113 3.276 .001
⁎⁎
Outcome performance ← Infrastructure .088 .086 .828 .408
Job satisfaction ← Training .045 .319 .159 .873
Administrative performance ← Training .592 .320 2.316 .021
⁎
Outcome performance ← Training .275 .245 1.121 .262
Job satisfaction ← Support .009 .198 .041 .967
Administrative performance ← Support .446 .209 2.130 .033
⁎
Outcome performance ← Support .252 .166 1.083 .279
Job satisfaction ← Administrative performance .215 .097 2.014 .044
⁎
Job satisfaction ← Outcome performance .387 .102 3.962
⁎⁎⁎
⁎⁎⁎
p b .001.
⁎⁎
p b .01.
⁎
p b .05.
Table 6
Summary of moderation analysis.
Parameter estimates (t-Value) Tests for invariance
Hypothesized paths Technology orientation Δχ2(Δdf = 1) Significance
High Low
Infrastructure → job satisfaction .798 (2.34)** .075 (.828) 4.84 **
Support → job satisfaction .508 (1.919)* .088 (.514) 2.82 *
Training → job satisfaction .211 (.733) .042 (.313) 0.83 n.s.
**Significant at 0.05 level, *at 0.10 level, n.s. = not significant.
1242 Y.B. Limbu et al. / Industrial Marketing Management 43 (2014) 1236–1245
use of help desks and around-the-clock assistance alon g with
mentoring may be helpful i n this regard.
The findings indicate that the salesforce's propensity and proficiency
in the use of ICT tools favorably affect sales tasks and job satisfaction.
Salespeople with higher levels of technology orientation show superior
overall job satisfaction as ICT infrastructure and support levels increase.
This means that besides equipping and providing continuous technical
support to field salespeople, firms may want to enhance salesperson
predisposition to adopt, use, and integrate ICT tools in their daily profes-
sional activities. Studies also show that salespeople, especially older
salespeople, sometimes show technophobia and use only a fraction of
the available technology tools (Donaldson & Wright, 2004; Greenberg,
2004; Parthasarathy & Sohi, 1997; Speier & Venkatesh, 2002). Salespeo-
ple have an additional reason to enhance their technology orientation as
more of today's B2B buyers use online tradin g platforms and expect
suppliers to be on the same page with the use of technology to maintain
and build relationships between the two parties. An important manage-
rial implication of this phenomenon would be to deliberately recruit
technologically adept salespeo ple who have the propensity to le arn,
adopt, and use ICTs appro priate today and the ability to keep pace
with technology throughout their sales career.
One original finding of the present study is the fact that the ICTs have
a greater and more favorable effect on administrative performance than
on outcome performance. This is consistent with th e conventional
wisdom that ICTs are used more for facilitating selling functions rather
than directly generating sale s volume. To improve outcome perfor-
mance, special efforts should be directed at developing sales-oriented
ICT infrastructure, training, and support that directly facilitate selling ac-
tivities. Having said this, sales managers cannot undermine the role of
administrative performance because sales-related administrative jobs
not only are complementary to sales tasks but also serve as a critical re-
source to build customer relationships and generate sales.
Another interesting result of this study is the relationships between
the dimensions of job performance and job satisfaction. Salesperson
outcome performance has a greater positive effect on job satisfaction
than administrative performance. This result provides an additional jus-
tification why firms need to improve their sales-generating ICTs. In ad-
dition, salesforce ICT training programs should equally emphasize
outcome-oriented selling proficiencies as well as administrative skills.
By drawing on th e social exchange theory, the job characteristics
model, and the balance theory, our study contributes to the marketing
literature by proposing and empirically testing the relationship be-
tween the sales ICT-related organizational capabilities and job enrich-
ment efforts (i.e., infrastructure , training, and support) and job
satisfaction. Contrary to some information system studies proposing a
direct effect of information technology on job satisfaction (e.g., Carey,
1992; Counte, Kjerulff, Salloway, & Campbell, 1985; Henry & Stone,
1995), the results of this study suggest that sales technology does not
directly influence job satisfaction unless the focus rests solely on those
high in technology orientation. One explanation may be that the profes-
sional nature of pharmaceutical salespeople requires technology to be
leveraged into performance. If not, satisfaction does not increase. Sales-
person administrative performance facilitates the impact of ICT on job
satisfaction. In addition, salesperson technology orientation moderates
the impact of ICT infrastructure and support on job satisfaction. In line
with Brady et al. (2008), we call for additional research in the area of
ICT and its potential impact on marketing practice. The current study
takes a holistic approach and relies on ICT as a broader and a more com-
prehensive term than SFA, CRM, or ST and enriches the sales technology
literature by establishing its role on B2B salesforce's job satisfaction.
7. Limitations and future research
The study design presents some limitations. The data collection took
place in India at one specific point in time using only pharmaceutical
sales representatives. Al though several emerging markets like India
have limitations of broadband technology, the penetration of wireless
communication a nd handheld information technology tools could
make up for the defi ciencies of technology infrastructure. However,
future studies can u se a longitudinal and cross-cultural research ap-
proach in collecting data from a more diverse range of salespeople
across different cultural and geographic markets. Many other variables
may play a role. An interesting avenue for future research would be to
explore how a salesperson's individual characteristics such as the need
for information technology usage (i.e., individual's motivation to use in-
formation technology), perceived innovativeness, and self-ef
ficacy may
play a role in the relationship between ICT and job satisfaction. Clearly,
additional research is needed to determine whether demographic
variables (i.e., age and gender) and education influence ICTs' effects on
job satisfaction.
Herzberg, Mausner, and Snyderman (1959) suggest that certain mo-
tivational factors in the workplace cause job satisfaction, while deficien-
cies in hygiene factors cause dissatisfaction. As evidenced by the
findings of our study, the ICT does not directly cause job satisfaction
when the sales person's technology orientation is low, and hence, one
may argue that, in fact, as a hygiene factor, ICT training may not contrib-
ute to satisfaction. Thus, as noted by one of the reviewers of this paper,
another interesting direction for future research would be to empirically
examine th e two-dimensional paradigm of the motivation–hygiene
theory.
References
Ahearne, M., Hughes, D. E., & Schillewaert, N. (2007). Why sales reps should welcome in-
formation technology: Measuring the impact of CRM-based IT on sales effectiveness.
International Journal of Research in Marketing, 24(4), 336–349.
Ahearne, M., Jelinek, R., & Rapp, A. (2005). Moving beyond the direct effect of SFA adop-
tion on salesperson performance: Training and support as key moderating factors.
Industrial Marketing Management, 34,379–388.
Ahearne, M., Jones, E., Rapp, A., & Mathieu, J. (2008). High touch through high tech: The
impact of salesperson technology usage on sales performance via mediating mecha-
nisms. Management Science, 54(4), 671–685.
Ahearne, M., Srinivasan, N., & Weinstein, L. (2004). Effects of technology on sales perfor-
mance: Progressing from technology acceptance to te chnology usage and conse-
quences. Journal Personal Selling Sales Management, 24(4), 297–310.
Alkhateeb, F. M., & Doucette, W. R. (2008). Electronic detailing (e-detailing) of pharma-
ceuticals to physician s: A review. Internati onal Journal of Pharmaceutical and
Healthcare Marketing, 2(3), 235–245.
Aselage, J., & Eisenberger, R. (2003). Perceived organizational support and psychological
contracts: A theoretical integration. Journal of Organizational Behavior, 24(5),
491–509.
Bagozzi, R. P. (1980). Performance and satisfaction in the industrial salesforce: An exam-
ination of their antecedents and simultaneity. Journal of Marketing, 44(2), 65–77.
Baldauf, A., Cravens, D. W., & Piercy, N. F. (2005). Sales management control research —
Synthesis and an agenda for future research . Journal of Personal Selling & Sales
Management, 25(1), 7–26.
Behrman, D. N., & Perreault, W. D., Jr. (1982). Measuring the performance of industrial
salespersons. Journal of Business Research, 10(3), 355–370.
Behrman, D. N., & Perreault, W. D., Jr. (1984). A role stress model of the performance and
satisfaction of industrial salespersons. Journal of Marketing, 48(4), 9–21.
Bharadwaj, A. S. (2000). A resource-based perspective on information technology capabil-
ity and firm performance: An empirical investigation. MIS Quarterly
, 24(1), 169–196.
Bhattacherjee, A., & Hikmet, N. (2008). Reconceptualizing organizational support and its
effect on information technology us age: Evidence from the health care sector.
Journal of Computer Information Systems, 48(4), 69–76.
Blau, P.M. (1983). Exchange and power in social life. New York: John Wiley.
Brady, M., Fellenz, M. R., & Brookes, R. (2008). Researching the role of information and
communications technology (ICT) in contemporary marketing practices. Journal of
Business & Industrial Marketing, 23(2), 108–114.
Brown, S. P., & Peterson, R. A. (1993). Antecedents and consequences of salesperson job
satisf action: Meta-analysis and assessment of causal effects. Journal of Marketing
Research, 30(1), 63–77.
Brown, S. P., & Peterson, R. A. (1994). The effect of effort on sales performance and job sat-
isfaction. Journal of Marketing, 58(2), 70–80.
Cammann, C., Fichman, M., Jenkins, G. D., & Klesh, J. R. (1983). Assessing the attitudes and
perceptions of organizational members. In S. E. Seashore, E. E. LawlerIII, P. H. Mirvis, &
C. Cammann (Eds.), Assessing organizational change: A guide to methods, measures, and
practices (pp. 71–138). New York, NY: Wiley.
Carey, J. M. (1992). Job satisfaction and visual display unit (VDU) usage: An explanatory
model. Behaviour & Information Technology, 11(6), 338–344.
Chen, J., & Tsou, H. (2007). Information technology adoption for service innovation prac-
tices and competitive advantage: The case of financial firms. Information Research: An
International Electronic Journal, 12(3), 314.
1243Y.B. Limbu et al. / Industrial Marketing Management 43 (2014) 1236–1245
Christen, M., Iyer, G., & Soberman, D. A. (2006). Job satisfaction, job performance, and ef-
fort: A reexamination using agency theory. Journal of Marketing, 70(1), 137–150.
Chung, K. S. K., & Hossain, L. (2010). Towards a social network model for understanding
information and communication technology use for general practitioners in rural
Australia. Computers in Human Behavior, 26(4), 562–571.
Counte, M.A., Kjerulff, K. H., Salloway, J. C., & Campbell, B. (1985). Implementing computer-
ization in hospitals: A case study of the behavioral and attitudinal impacts of a medical
information system. Journal of Organizational Behavior Management, 6(3/4), 109–122.
Cropanzano, R., & Mitchell, M. S. (2005). Social exchange theory: An interdisciplinary re-
view. Journal of Management, 31(6), 874–902.
Davenport, T. H., Hammer, M., & Metsisto, T. J. (1989). How executives can shape their
companies' information systems. Harvard Business Review, 67(5), 130–134.
Devaraj, S., & Kohli, R. (2003). Performance impacts of information technology: Is actual
usage the missing link? Management Science, 49(3), 273–289.
Donaldson, B., & Wright, G. (2004). Salesforce automation in the UK pharmaceutical in-
dustry: Why is the strategic potential of these systems not being realised in practice?
Journal of Medical Marketing, 4(3), 251–263.
Eggert, A., & Serdaroglu, M. (2011). Exploring the impact of sales technology on salesper-
son performance: A task-based a pproach. The Journal of Marketing Theory and
Practice, 19(2), 169–185.
Eisenberger, R., Armeli, S., Rexwinkel, B., Lynch, P. D., & Rhoades, L. (2001). Reciprocation
of perceived organizational support. Journal of Applied Psychology, 86(1), 42–51.
Eisenberger, R., Cummings, J., Armelo, S., & Lynch, P. (1997). Perceived organizational sup-
port, discretionary treatment, and job satisfaction. Journal of Applied Psychology, 82(5),
812–820.
Eisenberger, R., Fasolo, P., & Davis-LaMastro, V. (1990). Perceived organizational support
and employee diligence, commitment, and innovation. Journal of Applied Psychology,
75
(1), 51–59.
Eisenberger, R., Huntington, R., Hutchison, S., & Sowa, D. (1986). Perceived organizational
support. Journal of Applied Psychology, 71(3), 500–507.
Erffmeyer, R. C., & Johnson, D. A. (2001). An exploratory study of sales force automation
practices: Expectations and realities. Journal of Personal Selling & Sales Management,
21(2), 167–175.
Farrell, S., & Hakstian, A.R. (2001). Improving sales force performance: A meta-analytic in-
vestigation of th e effectiveness and utility of personnel selection procedur es and
training interventions. Psychology & Marketing, 18(3), 281–316.
Fornell, C., & Larcker, D. (1981). Evaluating structural equations models with unobserved
variables and measurement error. Journal of Marketing Research, 18(1), 39–50.
Gerbing, D. W., & Anderson, J. C. (1988). An updated paradigm for scale development in-
corporating unidimensionality and its assessment. Journal of Marketing Research,
25(2), 186–192.
Gerhart, B. (1987). How important are dispositional factors as determinants of job satis-
faction? Impli cations for job design and other personnel programs. Journal of
Applied Psychology, 72(3), 366–373.
Glisson, C., & Durick, M. (1988). Predictors of job satisfaction and organizational commit-
ment in human service organizations. Administrative Science Quarterly, 33(1), 61–81.
Gohmann, S. F., Guan, J., Barker, R. M., & Faulds, D. J. (2005). Perceptions of sales force au-
tomation: Differences between sales force and management. Industrial Marketing
Management, 34(4), 337–343.
Gouldner, A. W. (1960). The norm of reciprocity: A preliminary statement. American So-
ciological Review, 25(2), 161–178.
Greenberg, P. (2004). CRM at the speed of light Essential customer strategies for the 21st
century (Third Edition ). McGraw-Hill Osborne Media.
Hackman, J. R., & Oldham, G. R. (1976). Motivation through the design of work: Test of a
theory. Organizational Behavior and Human Performance, 16(2), 250–279.
Hair, J. F., Back, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2010).
Multivariate data
analysis. New Jersey: Prentice Hall.
Henry, J., & Stone, R. (1995). Computer self-efficacy and outcome expectancy: The effect
on the end-user's job satisfaction. Computer Personnel, 16(4), 15–34.
Herzberg, F., Mausner, B., & Snyderman, B. B. (1959). The motivation to work (2nd ed.).
New York: John Wiley & Sons.
Honeycutt, E. D. (2005). Technology improves sales performance—doesn't it?: An intro-
duction to the special issue on selling and sales technology. Ind ustri al Marketing
Management, 34(4), 301–304.
Hormans, G. C. (1958). Social behavior as exchange. American Journal of Sociology, 63(6),
597–606.
Hunter, G. L. (2004). Information overload: Guidance for identifying when information
becomes detrimental to sales force performance. Journal of Personal Selling & Sales
Management, 24(2), 91–100.
Hunter, G. K., & Perreault, W. D., Jr. (2006). Sales technology orientation, information effec-
tiveness, and sales performance. Journal of Personal Selling & Sales Management, 26(2),
95–113.
Hunter, G. K., & Perreault, W. D., Jr. (2007). Making sales technology effective. Journal of
Marketing, 71(1), 16–34.
Jelinek, R., Ahearne, M., Mathieu, J., & Schillewaert, N. (2006). Alongitudinalexamination
of individual, organizational, and contextual factors on sales technology adoption and
job performance. The Journal of Marketing Theory and Practice, 14(1), 7–23.
Johlke, M. C. (2008). Sales presentation skills and salesperson job performance. Journal of
Business & Industrial Marketing, 21(5), 311–319.
Jones, E., Sundaram, S., & Chin, W. (2002). Factors leading to sales force automation use: A
longitudinal analysis. Journal of Personal Selling & Sales Management, 22(3), 145–156.
Keen, P. G. W. (1991). Shaping the future: business design through information technology.
Cambridge, MA: Harvard Business Press.
Ko, D. -G., & Dennis, A.R. (2004). Sales force automation and sales performance: Do expe-
rience and expertise matter? Journal Personal Selling & Sales Management, 24(4 ),
311–322.
Lapierre, J., & Denier, A. (2005). ICT adoption and moderating effects of institutional fac-
tors on salesperson's communication effectiveness: A contingency study in high-
tech industries. Technovation, 25(8), 909–927.
Locke, E. A. (1970). Job satisfaction and job performance: A theoretical analysis.
Organizational Behavior & Human Performance, 5(5), 484–500.
Locke, E. A. (1976). The nature and causes of job satisfaction in handbook of industrial and
organizational psychology. In M.D. Dunnette (Ed.), Chicago: Rand McNally.
Loher, B. T., Noe, R. A., Moeller, N. L., & Fitzgerald, M. P. (1985). A meta-analysis of the re-
lation of job characteristics to job satisfaction. Journal of Applied Psychology, 70(2),
280–289.
Lynch, P. D., Eisenberger, R., & Armeli, S. (1999). Perceived organizational support: Inferi-
or versus superior performance by wary employees. Journal of Applied Psychology,
84(4), 467–483.
Marshall, G. W., Moncrief, W. C., Rudd, J. M., & Lee, N. (2012). Revolution in sales: The im-
pact of social media and related technology on the selling environment. Journal of
Personal Selling & Sales Management, 32(3), 349–363.
Mathieu, J., Ahearne, M., & Taylor, S. R. (2007). A longitudinal cross-level model of leader
and salesperson influences on sales force technology use and performance. Journal of
Applied Psychology, 92(2), 528–537.
McKenney, J. L. (1995). Waves of change: Business evolution through information technolo-
gy. Cambridge, MA: Harvard Business School Press.
Morris, M. H., Davis, D. L., Allen, J. W., Avila, R. A., & Chapman, J. (1991). Assessing the re-
lationship among performance measures, managerial practices, and satisfaction
when evaluating the sales force: A replication a nd extension. Journal of Per sonal
Selling & Sales Management, 11(3), 25–35.
Neuman, G. A., Edwards, J. E., & Raju, N. S. (1989). Organizational development interven-
tions: A meta-analysis of their effects on satisfaction and other attitudes. Personnel
Psychology, 42(3), 461–489.
Onyemah, V., Swain, S. D., & Hanna, R. (2010). A social learning perspective on sales tech-
nology usage: Preliminary evidence from an emerging economy. Journal of Personal
Selling & Sales Management
, 30(2), 131–142.
Park, J. E., Kim, J., Dubinsky, A. J., & Lee, H. (2010). How does sales force automation influ-
ence relationship quality and performance? The mediating roles of learning and sell-
ing behaviors. Industrial Marketing Management, 39(7), 1128–1138.
Parthasarathy, M., & Sohi, R. S. (1997). Sales force automation and the adoption of tech-
nological innovations by salespeople: Theory and implications. Journal of Business
and Industrial Marketing, 12(3), 196–201.
Petersen, G. S. (1997). High impact sales force automation. Boca Raton, FL: St: Lucie Press.
Podsakoff, P.M., & Organ, D. W. (1986). Self-reports in organization research: Problems
and prospects. Journal of Management, 12(4), 531–544.
Pullig, C., Maxham, J . G., & Hair, J. F. (2002). Salesforce automation systems: An ex-
ploratory examination of organizational factors assoc iated with effective im-
plementation and sales force productivity. Journal o f Business Research, 55,
401–415.
Rangarajan, D., Jones, E., & Chin, W. (2005). Impact of sales force autom ation on
technology-related stress, effort, and technology usage among salesp eople .
Industrial Marketing Management, 34(4), 345–354.
Rapp, A., Agnihotri, R., & Forbes, L. P. (2008). The sales force technology-performance
chain: The role of adaptive selling and effort. Journal of Personal Selling & Sales
Management, 28(4), 335–350.
Rhoades, L., & Eisenberger, R. (2002). Perceived organizational support: A review of the
literature. Journal of Applied Psychology, 87(4), 698–714.
Riggle, R. J., Edmondson, D. R., & Hansen, J.D. (2009). A meta-analysis of the relationship
between perceived organizational support and job outcomes: 20 years of research.
Journal of Business Research, 62(10), 1027–1230.
Rivers, L. M., & Dart, J. (1999). The acquisition and use of sales force automation by
mid-sized manufacturers. Journal of Personal Selling & Sales Management,
29(2), 59–73.
Roberts, J., Lapidus, R. S., & Chonko, L. B. (1994). An exploratory examination of situational
variables, effort, and salesperson performance. Journal of Mar keting Theory and
Practice
, 2(3), 70–93.
Robinson, L ., Marshall, G. W., & Stamps, M. B. (2005a). Sales force use of technol-
ogy: Antecedents to technology acceptance. Journal of Business Research,
58(12), 1623–1631.
Robinson, L., Marshall, G. W., & Stamps, M. B. (2005b). An empirical investigation of tech-
nology acceptance in a field sales force setting. Industrial Marketing Management,
34(4), 407–415.
Roman, S., Ruiz, S., & Munuera, J. L. (2002). The effects of sales training on sales force ac-
tivity. European Journal of Marketing, 36(11/12), 1344–1366.
Ross, J. W., Beath, C. M., & Goodhue, D. L. (1996). Develop long-term competitiveness
through IT assets. Sloan Management Review, 38(1), 31–45.
Schaper, L. K., & Pervan, G. P. (2007). ICTs & OTs: A model of information and communi-
cations t echnology acceptance and utilisation by occupational therapists.
International Journal of Medical Informatics, 76(1), S212–S221.
Schillewaert, N., Ahearne, M. J., Frambach, R. T., & Moenaer, R. K. (2005). The adoption of in-
formation technology in the sales force. Industrial Marketing Management, 34(4),
323–336.
Shim, S. J., & Viswanathan, V. (2007). User assessment of personal digital assistants used
in pharmaceutical detailing: System features, usefulness and ease of use. The Journal
of Computer Information Systems, 48(1), 14–21.
Shore, L. M., & Wayne, S. J. (1993). Commitment and employee behavior: Compar-
ison of affective commitment and continuance commitment with perceived
organizational support. J ournal of Applied Psy chology, 78(5), 774–780.
Singh, J., Goolsby, J. R., & Rhoads, G. K. (1994). Behavioral and psychological consequences
of boundary spanning burnout for cust omer service r epresentatives. Journal of
Marketing Research, 31(4), 558–569.
1244 Y.B. Limbu et al. / Industrial Marketing Management 43 (2014) 1236–1245
Speier, C., & Venkatesh, V. (2002). Thehiddenminefields in the adoption of sales force au-
tomation technologies. Journal of Marketing, 66(3), 98–111.
Sundaram, S., Schwarz, A., Jones, E., & Chin, W. W. (2007). Technology use on the front
line: How information technology enhances individual performance. Journal of the
Academy of Marketing Science, 35(1), 101–112.
Thatcher, J. B., Stepina, L., & Boyl e, R. (2002). Turnover of information technology
workers: Examining empirically the influen ce of attitudes, job characteristics,
and external markets. Journal of Management Inform ation S ystems, 19(3),
231–261.
Thetgyi,O.(2000).Radical makeovers. Sales and Marketing Managem ent, 152(4),
78–85.
Ting, Y. (1997). Determinants of job satisfaction of federal government employees. Public
Personnel Management., 26(3), 313–334.
Trauth, E. M., & Cole, E. (1992). The organizational interface: A method for supporting end
users of packaged software. MIS Quarterly, 16(1), 35–53.
Umstot, D.D., Bell, C. H., & Mitchell, T. R. (1976). Effects of job enrichment and task goals
on satisfaction and productivity: Implications for job design. Journal of Applied
Psychology, 61(4), 379–394.
Weill, P., Broadbent, M., & Butler, C. (1996). Exploring how firms view IT infrastructure.
Working paper. The University of Melbourne.
Zoltners, A. A., Sinha, P., & Zoltners, G. A. (2001). The complete guide to accelerating sales
force performance. New York: AMACOM.
Yam B. Limbu is an Assistant Professor of Marketing at Montclair State University. His re-
search interests include consumer behavior, Internet marketing, pharmaceutical and
healthcare marketing, communication strategies, and sales management. His publications
have appeared in the Journal of Business-to-Business Marketing, Marketing Education Re-
view, Journal of Research in Interactive Marketing, Journal of Current Issues and Research in
Advertising, and Journal of Promotion Management.
C. Jayachandran
is a Professor of Marketing and International Business. His research inter-
ests include entry modes into emerging markets, innovation, healthcare, and servicesmar-
keting. His recent publi cations appeared in the International Journal of Innovation and
Research, Asian Case Research Journal, Journal of Consumer Marketing, and International Jour-
nal of Information Systems and Social Change.
Barry J. Babin is the Max P. Watson, Jr. Endowed Professor and Chair of the Department of
Marketing and Analytics at Louisiana Tech University. His research appears in many pres-
tigious journals including The Journal of the Academy of Marketing Science, The Journal of
Marketing, The Journal of Retailing, The Journal of Consumer Research, The International Jour-
nal of Wine Business Research, and The Journal of Business Research.Heiscoauthorofseveral
books including CB: A Value Based Approach, Business Research Methods, Sales Manage-
ment: Building Customer Relationships and Partnerships, Multivariate Data Analysis, and
Exploring Marketing Research.
1245Y.B. Limbu et al. / Industrial Marketing Management 43 (2014) 1236–1245
