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Behaviour & Information Technology

ISSN: 0144-929X (Print) 1362-3001 (Online) Journal homepage: https://www.tandfonline.com/loi/tbit20

The impact of information technology on individual and firm marketing performance

Robert W. Stone , David J. Good & Lori Baker-Eveleth

To cite this article: Robert W. Stone , David J. Good & Lori Baker-Eveleth (2007) The impact of information technology on individual and firm marketing performance, Behaviour & Information Technology, 26:6, 465-482, DOI: 10.1080/01449290600571610

To link to this article: https://doi.org/10.1080/01449290600571610

Published online: 25 Jun 2008.

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The impact of information technology on individual and firm marketing performance

ROBERT W. STONE*{, DAVID J. GOOD{ and LORI BAKER-EVELETH{

{Department of Business, University of Idaho, Moscow, ID 83843, USA {Department of Marketing, Seidman School of Business, Grand Valley State University,

Marketing 920 EC, 301 W. Fulton Street, Grand Rapids, MI 49504-6495, USA

The perceived impacts of information technology use on firm marketing organization

performanceare examined.A theoretical modelis presented linking organizationaland end-

user traits, information quality, system/service quality, industry traits, and tasks performed

using a system to perceptions of organizational performance impacts through ease of system

use, perceived individual performance impacts (i.e. perceived usefulness), attitudes toward

using the system, and system use. The empirical examination uses a mail survey of US

marketing executives to collect the data. The quantitative technique used is structural

equation modeling. The results indicate that measures of organizational traits, individual

traits, information quality, system/service quality, and tasks performed using the system

impact perceived performance of the marketing organization mediated individual

performance impacts (i.e. perceived usefulness), attitudes toward using the system, and

systemuse.Managerialimplicationsandconclusionsarepresentedbasedupontheseresults.

Keywords: Information technology; Marketing performance; Technology acceptance

model

1. Introduction

The primary issue with any strategic tool is the degree that

its usage benefits the user. Yet, strategic tools are often

employed with little concrete understanding of the advan-

tages they engender. For example, information technology

(IT) is a widely discussed and implemented organizational

tool (Torkzadeh and Doll 1999, Sorensen and Buatsi 2002)

despite difficulty in measuring its value (O’Brien 1997).

Still, the belief that technologies provide advantages

(e.g. technology is always good) is a driving force in

many organizations, as its use is prevalent (Stites 1999,

Wipperfuth 1999) among marketers as a strategic tool

(Good and Stone 2000).

The cost of technology is enormous (Grover et al. 1998,

Legris et al. 2003), often accounting for over 2% of revenues

(Macmillan 1997). Yet, while IT is credited with enhancing

productivity (Anandarajan et al. 2000), it remains less clear

to what degree productivity from using IT is rewarded in a

competitive environment since a variety of components

must be utilized effectively to ensure quality usage (Sorensen

and Buatsi 2002). As a result, a contemporary theme in

information technology research focuses on understanding

linkages between IT and its impact on performance

(Griswold 1998). For example, when Prudential Insurance

invested 100 million dollars several years ago to equip over

10,000 agents with laptop computers (Ostermiller 1999), the

underlying implied purpose was to improve the productivity

of the sales force. While management surely believed this

was a rewarding strategy, understanding the degree to which

such investments assist the individual and firm (Bresnahan

1998, Fleming 1999) remains chiefly unexplored (Good and

Stone 2000). In fact, while it is popular to propose that the

usage of technology provides high returns, evidence suggests

this is not always true (Macmillan 1997, Grover et al. 1998).

Technologies can, in fact, have uncertain, little, or no impact

on profitability (O’Sullivan 1998), calling into question how

such strategic decisions are assessed (Torkzadeh and Doll

1999) and the reassessment of IT value (Tallon et al. 2000).

Providing a linkage between IT use and impacts on

*Corresponding author. Email: [email protected]

Behaviour & Information Technology, Vol. 26, No. 6, November – December 2007, 465 – 482

Behaviour & Information Technology ISSN 0144-929X print/ISSN 1362-3001 online ª 2007 Taylor & Francis

http://www.tandf.co.uk/journals DOI: 10.1080/01449290600571610

organizational performance is the focus for the study

presented below.

By integrating the widely studied (McGill et al. 2003)

DeLone and McLean (1992, 2003) model with Davis’

Technology Acceptance Model (Davis 1989), this research

provides insight into how marketers can successfully utilize

IT within their strategic mission. This integrated model

proposes that among marketers, the general constructs of

organizational, individual, and industry traits, information

quality and system and service quality, and the tasks

performed using the information system influence users’

perceptions of the ease of use as well as their perceptions of

system usefulness (i.e. perceived impacts on the individual’s

performance). Perceptions of ease of system use and

usefulness impact users’ attitudes toward the system, which,

in turn, impact system use and ultimately perceived

organizational performance. The study is presented in the

following order: the rationale for the study, the theoretical

model and hypotheses, the methodology, managerial

implications and finally conclusions.

2. The rationale for the study

In a quest to improve performance, organizations are

increasingly relying on, and investing in, information

technologies (Motiwalla and Fairfield-Sonn 1998). In this

framework, IT is seen as an ‘enabler’ providing businesses

the ability to significantly broaden offerings (Anandarajan

et al. 2000) which then provide marketers an array of new

advantages (Min and Keebler 2002) that can dramatically

alter the business arena (Sorensen and Buatsi 2002). The

increased reliance, expenditures, and costs of technologies

(Ryan et al. 2002) and the need to seek high-return IT

strategies (Hoffman 2002) drive the desire to evaluate the

performance of specific IT investments and the factors

that influence perceptions of information system success

(Ishman et al. 2001). For example, to what degree can the

firm positively influence elements that ultimately impact

the ability of IT to improve the performance perceptions of

the technology user and his/her firm? In this regard, the call

to create critical success factors (CSF) where specific

performance outcomes are sought from IT investments is

growing (Peffers and Gengler 2003) in an environment

where linkages should exist between the firm and IT

productivity, although little is known about performance in

this context. If, however, IT investments are unable to

recoup costs (Berndt and Morrison 1995, Motiwalla and

Fairfield-Sonn 1998) and a primary objective is to

maximize risk and reward trade-offs (Benaroch 2002), the

worthiness of information technologies comes into serious

question (Mahmood and Mann 1993, Shaw 1994, Bryn-

jolfsson and Yang 1996), specifically as they relate to the

unique needs of the users (McGill et al. 2003). It is within

this context that the study is focused.

A theoretical framework integrating previous research

(DeLone and McLean 1992, 2003, Davis 1989, Goodhue and

Thompson 1995) is tested in a contemporary performance

context within a critical operational unit (i.e. marketing) of a

business firm. To this end, the study explores issues that

theoretically impact perceptions of individual and organiza-

tional performance and provides implications of both

academic and practical importance regarding strategic

placement of IT. It is important to acknowledge that the

impacts studied are perceptual in nature. In addition, the

focus is on the determinants of using an existing system (as

opposed to implementing a new system) and the perception of

enhanced performance resulting from its use (Bhattacherjee

2001). Such an approach is not unique, as the study of

perceived impacts on information system success in the

context of user-developed applications (McGill et al. 2003)

provides a logical basis for the exploration of IT users’

specific uses of IT and impacts from this use.

The theoretical model, in general form, is displayed in

figure 1. It is operationalized and discussed in a specific

form by the empirical study. The proposed relationships in

the general model offer marketers and organizations

significant competitive advantages since these are at least

partially controllable by management. As a result, mean-

ingful implications for managers regarding IT and its use to

facilitate performance as perceived by users are possible.

For instance, marketers and senior management can alter

the resource assignments to these variables (e.g. tasks

performed, system/service quality) if these modifications

increase the perceived performance of the organization.

Hence, the findings offer a number of strategic implications

to marketers and senior management as well as more

detailed theoretical and empirical explorations into quali-

ties that influence perceptions of performance using IT.

3. Theoretical foundation linking IT, performance, and the

marketing professional

Information technology systems are increasingly being

required to perform more sophisticated activities (Sorensen

and Buatsi 2002). For instance, creating data warehouses

provides a strategic tool that fosters long-term information

(Williams 1999, Wixom and Watson 2001) that can assist

marketers in understanding customer motivations and

needs. The success of these systems is dependent on

whether they get used. A belief of enhanced individual

performance by accessing customer information will

encourage some to use the system. In addition, if marketers

perceive the system to be easy to learn and use, they will

have positive attitudes about the system and are more likely

to use it (Davis 1989, Gefen and Straub 2000, Clay et al.

2005). Correspondingly, properly structured IT systems

enhance the ability of a firm to reach across international

marketplace barriers (Harrison-Walker 2002).

466 R. W. Stone et al.

Important to this investigation is the information

technology productivity paradox. This paradox notes that

measurement of worker productivity does not escalate

consistently with advancing IT investment (Brynjolfsson

1993, Motiwalla and Fairfield-Sonn 1998), suggesting a

form of ‘technological disequilibrium’. As management

increasingly becomes concerned about IT value (Macmillan

1997), linkages of perceived performance must be made to

factors the firm controls. Otherwise, the enormous interest

in understanding IT performance (DeLone and McLean

1992, 2003, Goodhue and Thompson 1995, Grover et al.

1998) may escalate into a costly client interaction mechan-

ism (Macmillan 1997). In such an environment, the need

for marketers to automatically escalate IT offerings may

not always be the correct course of action.

An important theoretical model which frames this study

is the Technology Acceptance Model (TAM) (Davis, 1989,

Davis et al. 1989). Adapted and applied from the theory of

reasoned action which is concerned with understanding and

predicting human behaviour (Ajzen and Fishbein 1980,

Davis et al. 1989, Chau and Hu 2001), TAM traces the

impact external variables have on the beliefs, attitudes, and

intention to use computer technology (Davis et al. 1989,

Legris et al. 2003). Two primary beliefs impacting system

adoption are perceived system usefulness and ease of use

(Davis 1989, Davis et al. 1989, Igbaria and Tan 1997). The

model has been widely used to determine successful

prediction of IT acceptance by identifying causal relation-

ships of an individual’s perception of usefulness and ease of

use, to their behavioural intention to use IT, and the

subsequent action of using and accepting the technology

(Davis 1989, Davis et al. 1989, Adams et al. 1992,

Venkatesh and Davis 1996). Usefulness assesses the indivi-

dual’s perception of how IT will improve the individual’s

performance and ease of use assesses the individual’s

perception of the amount of effort needed to use the system

(Davis 1989, Davis et al. 1989, Venkatesh and Davis 1996).

Initial acceptance of technology is critical to the eventual

success of the system. The marketers in this study were

successfully using the system and had moved beyond initial

acceptance. The framework of TAM needed further

extension to assess the perceived impact and effectiveness

(i.e. success) at the firm level and for a system beyond initial

acceptance. However, it is difficult to assess gains or the

value in information technology because inappropriate

measures are used to assess the linkages between perfor-

mance and IT (Motiwalla and Fairfield-Sonn 1998). For

this reason, viewing IT success within a specific context of

unique users (Kallman and O’Neill 1993, McGill et al. 2003)

is a requirement for understanding performance criteria.

For example, depending upon the task being performed, the

usage of IT among accountants (Henry 1999) should be

different than plant managers scheduling personnel shifts

(DuCote and Malstrom 1999). This study explores the

perceived impacts of IT on firm performance within the

specific context of marketing. In this regard, McGill et al.

Figure 1. The general model of IT impacts on marketing performance.

Marketing performance 467

(2003) note that to understand technological success, it is

necessary to apply a litmus test applicable to individual

users and their needs. That is, success factors are not

universal among all populations. It is necessary to examine

these measures among the unique users who populate a

particular environment. Therefore, not only is this research

grounded in the existing literature but also upon the

practicality of marketers needing to assess the growing

investment of information technology within their domain.

As a result, marketers represent key users of IT who

should be examined in the context of the proposed model

based on two distinctive qualities. The first quality is that

information technology is seen as providing a key source of

competitive advantage for users (Sriram and Krishnan

2003). Since information technology is reputed to provide a

competitive advantage, marketers view IT as a critical

opportunistic tool (Harrison-Walker 2002, Osmonbekov

et al. 2002, Sorensen and Buatsi 2002). Second, this popula-

tion is historically concerned about both internal and external

performance factors. That is, the success of the firm depends

upon marketers’ abilities to be internally and externally

successful (e.g. sell customers products, and secure internal

support for strategic efforts). Examples of IT usage among

marketers include the utilization of e-commerce strategies and

actions, managing just-in-time inventories, managing custo-

mer relationship databases, sales order processing and

automated sales training programs. In this domain, marketers

can provide key insights into organizational success and

linkages with IT. In a major difference with other studies, this

concern should ensure their use of technology is not solely

guided by narrowly defined, internal needs.

To understand IT performance impacts, DeLone and

McLean (1992, 2003) proposed a theoretical framework

linking perceptions of information and system and service

quality to impacts on the user’s performance through the

degree of system use and satisfaction with the system. In

their model, individual performance impacts are linked to

firm performance by noting that the individual perfor-

mance impacts can be summated across all appropriate

users in the organization. Understanding individual users

(Doll et al. 1994) and the overall DeLone and McLean

(1992) model underscore the key role the organization, the

individual, and a coalescing of these factors play in

enriching performance through IT.

Other researchers have empirically tested various aspects

of the DeLone and McLean model (McGill et al. 2003) and

these results illustrate consistency over several paths in the

model. It has been shown that the paths from system/

service quality and information quality have the hypothe-

sized influences on perceived usefulness (Seddon and Kiew

1996, Roldan and Millan 2000, McGill et al. 2003). Yet, no

similar consistent results have been found for the paths

between system/service quality and information quality to

system use. Most information systems offer more than just

information, they provide support. Service quality has been

suggested as an additional antecedent to information and

system/service quality and is related to the responsiveness,

assurances, and empathy of support provided with the

system. The recent inclusion of service quality measuring

the IT group’s reliability and responsiveness to maintaining

the system (DeLone and McLean 2003) has also been

hypothesized to influence perceived usefulness and ease of

use (Li 1997, Wixom and Watson 2001). Past research

indicates that usefulness and ease of use impact system use

(Igbaria and Tan 1997). However, no research has consis-

tently found a meaningful path in the reverse direction

(McGill et al. 2003). Impacts on individual performance

from use have been shown (Igbaria and Tan 1997) as well

as from perceived usefulness (Roldan and Millan 2000,

McGill et al. 2003). The final path, from individual impacts

to organizational impacts, has also been demonstrated to

be meaningful (Roldan and Millan 2000).

While the DeLone and McLean (1992) model is an impor-

tant contribution in understanding IT performance, the

authors note a theme consistent with Brynjolfsson (1993)

that problems exist in measuring constructs ‘at different

levels’ (p. 61). Implying that while these constructs appear to

be solid individual indicants of performance, their applica-

tion and use differ based on environmental conditions. For

instance, their examination is limited in that the only

antecedents in the model are information and system/service

quality (DeLone and McLean 1992). However, in many

situations, there are other variables that impact system use

and satisfaction. Satisfaction with a system can be influ-

enced by perceptions of usefulness whereas system use can

be influenced by perceived ease of its use. In the revised

Information Systems Success Model the use of the system

provides multiple impacts that have been simplified into a

variable of net benefits (e.g. work group impacts, organiza-

tional impacts, individual impacts) (DeLone and McLean

2003). An easy to use system will be perceived as useful

resulting in continued or greater system use, which can ulti-

mately impact perceptions of performance with the system.

Incorporating information system and service quality

antecedents from the revised Information System Success

Model with the external antecedents, perceived usefulness

and ease of use from TAM provides a fuller examination of

factors impacting system success or performance using the

system. It is this integrated model that provides the

theoretical foundation for the research.

4. The hypotheses

The theoretical framework is summarized by a series of

hypotheses that relate to both the general model displayed

in figure 1 and the empirical study. The hypotheses are

grouped according to constructs in the general model and

stated in terms of the measures of these general constructs

468 R. W. Stone et al.

as they are used in the empirical study. These measures are

discussed in detail in a later section.

4.1 Organizational and individual traits

Hypotheses 1 – 10 propose that the characteristics of the

organization and the individual impact the degree to which

an information system is perceived as useful and easy to use

by marketers. Hypotheses 1 and 2 parallel the logic of

Guimaraes, Igbaria and Lu (1992) in their contention that

the organization strongly influences IT usage and satisfac-

tion. This satisfaction is frequently due to the perceived

usefulness of the system. A firm that supports the adoption

and usage of IT should expect that the use and perceived

usefulness of the system would increase due to this support.

Because IT is a strategic organizational effort (DeLone and

McLean 1992, Goodhue and Thompson 1995, Kesner

1999), the firm’s traits should theoretically influence system

satisfaction and perceived usefulness. Particularly true

among non-technical system users (e.g. marketers), these

hypotheses focus on the ability of the organization and its

management to influence the environment in which systems

are used.

The measures selected to operationalize organizational

traits (i.e. innovative climate, computer training and

computer staff support) reflect the unique background

and professional inclinations of marketing. Innovative

climate was selected because of the need for marketers to

find ‘new’ approaches (e.g. in a competitive situation) to

their responsibilities as well as existing contentions that the

organization’s environment influences its usage of technol-

ogy (Shibata et al. 1991, Headrick and Morgan 1999).

Given the increasing reliance on IT to accomplish unique

tasks as well as organizational and performance outcomes

among marketers (Good and Schultz 1997), a demand

exists for the organization to link technology and innova-

tion (Motiwalla and Fairfield-Sonn 1998, Weston 1998).

The variables of computer training and staff support were

selected because managerial support of information sys-

tems is often key to successful organizational adoption

(Williams 1999). Furthermore, training reflects a logical

inclusion critical to the success of IT users (Watson 1999),

particularly for marketers who often lack extensive

computer training. Thus, supporting IT users (e.g. training

and staff support) assists the perceived usefulness and ease

of ease of the technology.

Hypotheses 7 – 10 ten focus on the influence of individual

traits (i.e. technological leadership and end-user computer

experience) on perceived system ease of use and usefulness

(i.e. individual performance impacts). Previous research

confirms linkages between the individual and IT is crucial

(Igbaria et al. 1995). Furthermore, because technology

leadership provides real advantages (Whitford 1999),

focusing on measures of technological leadership among

‘non-technical’ personnel reflects the degree to which

marketers can build and use this area of expertise. Given

the assumption that technology constantly expands its

offerings, such leadership provides opportunities to en-

hance system usefulness. Such leadership should also

influence the individual’s willingness to experiment with

the system which influences their perception of the system’s

ease of use. Similarly, past experience of users should

logically influence their perceptions of system ease of use

and usefulness. For these reasons, the measures used to

assess individual traits are the user’s technological leader-

ship and previous computer experience.

Hypothesis 1 (H1): An innovative organizational climate

has a positive impact on the end-user’s perceived indi-

vidual performance impacts (i.e. perceived usefulness).

Hypothesis 2 (H2): An innovative organizational climate

has a positive impact on the end-user’s perceptions of

system ease of use.

Hypothesis 3 (H3): Computer training has a positive

impact on the end-user’s perceived individual perfor-

mance impacts (i.e. perceived usefulness).

Hypothesis 4 (H4): Computer training has a positive

impact on the end-user’s perceptions of system ease of use.

Hypothesis 5 (H5): Computer staff support has a positive

impact on the end-user’s perceived individual perfor-

mance impacts (i.e. perceived usefulness).

Hypothesis 6 (H6): Computer staff support has a positive

impact on the end-user’s perceived ease of system use.

Hypothesis 7 (H7): Technological leadership has a

positive impact on the end-user’s perceived individual

performance impacts (i.e. perceived usefulness).

Hypothesis 8 (H8): Technological leadership has a positive

impact on the end-user’s perceived ease of system use.

Hypothesis 9 (H9): End-user’s computer experience has a

positive impact on the end-user’s perceived individual

performance impacts (i.e. perceived usefulness).

Hypothesis 10 (H10): End-user’s computer experience has

a positive impact on the end-user’s perceived ease of

system use.

4.2 Information and system/service quality

Hypotheses 11 – 14 address the issue of perceived quality,

both in terms of the specific information these systems

provide users and the delivery mechanisms used by the

systems. Founded on the understanding that the goal of IT

is to enhance performance of system users (Gasson 1999),

quality is a difficult construct to measure due to differences

in users’ context of system use. Yet, interest in the construct

remains high (Paul et al. 1999). In this vein, hypotheses 11

and 12 note that the quality of what information the system

offers is important to users (Ang and Koh 1997), especially

marketers who theoretically use technology to gain a

Marketing performance 469

competitive advantage. In fact, because demands for access

to quality information (Schroeder 1987) have not dimin-

ished as a critical component of IS success (Ang and Soh

1997), it is anticipated that this need is a core requirement

for marketers. Since the applications of IT offerings should

be unique to specific users (Shayo et al. 1999), these

hypotheses propose that marketers see IT as a basic

provider of usable information. It would be expected that

the higher the quality of the information, the less effort that

must be expended to obtain information needed to perform

the user’s job tasks. The information then enhances

usefulness and ease of system use. Information quality is

measured by a construct with the same label.

It is logical for the perceptions of system/service quality

(i.e. computer system/service quality) to enrich the per-

ceived usefulness and ease of use for the system.

Hypotheses 13 and 14 reflect the quality of a system/service

as perceived by end-users that are not IT professionals. If

the system is perceived usable (e.g. easy to use; well

maintained), marketers, who are not IT professionals, will

find it functional and easy to operate. Further, users’

perceptions affect their choice of product. In fact, the

quality of computer systems/services has become such an

issue that users often select the product based on the least

number of potential problems (Scott 1998).

Hypothesis 11 (H11): Information quality has a positive

impact on the end-user’s perceived individual perfor-

mance impacts (i.e. perceived usefulness).

Hypothesis 12 (H12): Information quality has a positive

impact on the end-user’s perceived ease of system use.

Hypothesis 13 (H13): Computer system/service quality

has a positive impact on the end-user’s perceived

individual performance impacts (i.e. perceived useful-

ness).

Hypothesis 14 (H14): Computer system/service quality

has a positive impact on the end-user’s perceived ease of

system use.

4.3 Industry traits and tasks performed

Hypotheses 15 – 20 address the environment in which

marketers apply IT (i.e. customer knowledge, industry

outlook, and tasks performed). Hypotheses 15 and 16

consider the importance of customer knowledge on

perceived system usefulness and ease of system use.

Hypotheses 17 and 18 examine the impact of industry

outlook on perceived usefulness and ease of use, while

hypotheses 19 and 20 detail the role of the tasks performed

on these two measures.

Given the high level of customer knowledge and outlook

for the industry (e.g. growth and profit potential), it is

critical that marketer’s utilize information as a strategic

link to the customer environment (Raval 1999). The

Vanguard Group, for instance, found that creating a

strong technological infrastructure has become a critical

link to their customer base (Groenfeldt 1996), suggesting

that technology needs to be flexible and responsive to the

marketing environment (Mertins and Arlt 1999). Hence,

organizations operating in a ‘growing and prospering’

environment where customers have significant knowledge

should link the perception of usefulness and ease of system

use. As with the Vanguard example, the need to use IT

embraces the need for perceived usefulness with the system

and ease of system use. Hence, customer knowledge and

industry outlook are expected to have positive impacts on

perceived system usefulness and ease of system use.

The tasks performed with a specific application of IT are

an area fertile for understanding technology users (Blili

et al. 1998). The relationship between IT and the skill level

of the workforce (Lal 1996) supports linkages between tasks

performed and perceived system usefulness and ease of use.

Since the task to be performed is an important considera-

tion (Igbaria et al. 1998), it is expected the design of IT to

provide specific returns for users as the tasks should be

related to anticipated outcomes (Henry and Martinko

1997). Linkages, however, should exist between the task

and the use of technology (Changki et al. 1998), suggesting

that the better the system is able to perform needed tasks,

the greater its perceived usefulness and ease of use.

Hypothesis 15 (H15): Customer knowledge levels have

positive impacts on the end-user’s perceived individual

performance impacts (i.e. perceived usefulness).

Hypothesis 16 (H16): Customer knowledge levels have

positive impacts on the end-user’s perceived ease of

system use.

Hypothesis 17 (H17): Industry outlook has a positive

impact on the end-user’s perceived individual perfor-

mance impacts (i.e. perceived usefulness).

Hypothesis 18 (H18): Industry outlook has a positive

impact on the end-user’s perceived ease of system use.

Hypothesis 19 (H19): The tasks performed using the

system have impacts on the end-user’s perceived indivi-

dual performance impacts (i.e. perceived usefulness).

Hypothesis 20 (H20): The tasks performed using the

system have impacts on the end-user’s perceived ease of

system use.

4.4 Ease of system use and perceived individual performance

impacts

The end-user’s perception of system usefulness depends, at

least partially, on the individual using the system. The user

must expend effort on learning a new system or new

functions for an existing system. For some end-users it is a

continuing process of adapting the system for new uses. If

the system is difficult to use, end-users will expend only

470 R. W. Stone et al.

enough effort on the system to accomplish what must be

done. In this situation, end-users are unlikely to search for

novel or advanced uses for the system. As a result, these

end-users will perceive little usefulness in the system. On the

other hand, if the system is relatively easy to use, end-users

can learn to use and adapt the system to novel uses

expending little effort. For these end-users, the perceived

usefulness of the system would be expected to be relatively

large. This relationship has been hypothesized by numerous

authors (Davis 1989, Davis et al. 1989, Legris et al. 2003)

and is summarized in hypothesis 21.

Hypothesis 21 (H21): The end-user’s perceived ease of

system use has positive impacts on the end-user’s

perceived individual performance impacts.

4.5 Ease of system use, perceived individual performance

impacts, and attitude toward using the system

As Udo and Ebiefung (1999) note, measures of technolo-

gical productivity are often too limited. In response, this

study utilizes a unique population (i.e. marketers) and

explores the impact of IT on the individual’s satisfaction,

use, and perceived organizational performance impacts

with the system. Specifically, hypothesis 22 explores the

impact of ease of system use on system satisfaction (i.e.

attitude toward using the system). Hypothesis 23 examines

the role of perceived usefulness (i.e. perceived individual

performance impacts) impacting system satisfaction.

Hypothesis 22 (H22): The end-user’s perceived ease of

system use has positive impacts on the end-user’s system

satisfaction (i.e. attitude toward using the system).

Hypothesis 23 (H23): The end-user’s perceived individual

performance impacts have positive impacts on the end-

user’s system satisfaction (i.e. attitude toward using the

system).

4.6 System satisfaction and system use

Hypothesis 24 investigates the relationship between the

end-user’s system satisfaction and system use. Greater

satisfaction with the system should encourage its usage. In

fact, satisfaction and usage have been shown to be related

(Khalil and Melkordy 1999, McGill et al. 2003), although

there is much about this relationship that remains unclear

(Changki et al. 1998). This research follows the work of

several researchers that propose a meaningful and positive

relationship between user satisfaction and use of the system

in that satisfaction encourages use (Davis et al. 1989).

Hypothesis 24 (H24): End-user system satisfaction

(i.e. attitudes toward using the system) positively impacts

the end-user’s degree of system use.

4.7 System use and organizational performance impacts

Corresponding with the literature (DeLone and McLean

1992, 2003, Goodhue and Thompson 1995), hypothesis 25

examines the perceived performance impacts of IT use on

the organization. It is the nature of such impacts to be

altered depending upon the user (e.g. accounting, market-

ing, engineering), the nature of IT investment (Grover et al.

1998), the level of revenues consumed (Macmillan 1997),

and expected performance. This suggests that understand-

ing the perceived impacts of IT on performance (Griswold

1998) is critical. It also makes sense that the use of IT

should contribute to perceptions of performance (Rogers

et al. 1996). Thus, system usage is expected to have mean-

ingful and positive impacts on perceived organizational

performance.

Hypothesis 25 (H25): End-user system use has positive

impacts on perceived organizational performance im-

pacts from using the system.

5. The research method

5.1 The sample

The sample was collected using a national (i.e. United

States) mail survey. The target population was individuals

who had classified themselves as a marketing executive on a

purchased mailing list. The selection of marketers as the

target population was made so the study could focus on IT

users who are managers having the job driven qualifications

noted previously.

The questionnaire used was developed with items

designed to measure the constructs required by the model.

A preliminary version of the questionnaire was pre-tested

using a group of 20 marketing executives from the United

States. These 20 individuals provided feedback regarding

appropriateness, coverage, and readability of the items. A

total of 1200 questionnaires were mailed to marketers

selected in a systematic random fashion from the mailing

list. The usable returns numbered 403 for a response rate of

approximately 33.58%. Due to mailing list restrictions, no

additional attempts to contact the individuals on the

mailing list were made. The survey respondents were self-

identified as marketers. Realizing that these individuals are

marketing executives who have limited time and who may

interpret the items as dealing with key, confidential matters,

the return rate is excellent (Good and Stone 1995).

In order to test for the possible presence of non-response

bias in the sample, a holdout group was formed. The

questionnaires placed in the holdout group were selected by

setting a cutoff date before mailing the questionnaire. Any

questionnaires returned after this date were not considered

part of the sample and were placed in the holdout group

Marketing performance 471

(Rainer and Harrison 1992). A total of 28 questionnaires

were put in the holdout group, leaving 375 responses in the

sample. Due to the focus of the research being rooted in

computer use, additional steps were taken to assure a

representative sample for use in the study. All question-

naires returned by individuals reporting no previous

computer experience or who reported no current computer

use at work were eliminated from the sample and hold-out

group. This reduced the sample size by 19 and the number

of returns in the holdout group by 3.

The sample and holdout group are characterized by

values on several variables that are displayed in table 1. The

respondents self-reported all the demographic values that

are reported. The questionnaire items either provided

categories for the respondent to check or requested a single

numeric value (e.g. years of prior computer experience).

Within the sample, 74% of the respondents were male and

26% female, while in the holdout group these values were

80% and 20%. The firms employing the sample respon-

dents had, on average, 570 employees with a range from 5

to 8000 employees and in the holdout group this average

was 382 employees with a minimum of 10 and a maximum

of 725 employees. The average respondent age in the

sample was 43 years with a range from 20 to 71 years, while

in the holdout group these values were 48 years with a

range from 28 years to 69 years. The respondents were also

asked to report their highest level of education obtained.

Among the respondents in the sample, 6% reported high

school, 13% reported a 2-year college, 55% a 4-year

college, 22% a masters degree, and 4% a doctorate. In

the holdout group, these percentages were 4% high school,

2-year college, and doctorate, 52% 4-year college, and 36%

a masters degree. The years of computer use averaged 12

years for the respondents in the sample, with a minimum

and a maximum of 1 year and 37 years. In the holdout

group, these values were 13 years ranging from a minimum

of 5 years to a maximum of 28 years. Daily computer use

was measured by the number of times in a day

the respondent used a computer system at work. For the

respondents in the sample, the average was 24 times and the

range was from 1 to 103 times daily. For the holdout group,

the times per day the computer was used averaged 18 times

with extreme values of 1 time and 100 times.

5.2 The measures

The constructs defined in the theoretical model were

operationalized by several measures. These measures were

stated in the hypotheses presented earlier. Each measure

was formed from two or more questionnaire items. All

these items are shown in table 2.

All the measures were formed using questionnaire items

that were either developed by the authors or modified from

previously published scales to the technology-oriented

environment in which the instrument was utilized. Speci-

fically, innovative climate originated from work done on

franchisee innovation by Koys and DeCotiis (1991) and

work by Strutton et al. (1993). These measures had original

reliabilities of 0.768 and 0.82. The technological leadership

measure was adapted from a scale designed to assess

opinion leadership developed by King and Summers (1970)

and Goldsmith and Hofacker (1991). These scales had

reliabilities reported as 0.79 – 0.90. Computer staff support,

ease of system use, and individual performance impacts

were adopted from work done by Good and Stone (1999)

with previously reported reliabilities of 0.92, 0.96, and 0.95.

Information quality was adapted from the information

usage (company sources) scale tested by Crosby and

Table 1. The demographics for the sample and holdout group.

Gender

Male (%) Female (%)

Sample 74 26

Holdout group 80 20

Number of employees

Average Minimum Maximum

Sample 570 5 8000

Holdout group 382 10 725

Respondent age

Average

(years)

Minimum

(years)

Maximum

(years)

Sample 43 20 71

Holdout group 48 28 69

Highest education level

High

school

(%)

2-year

college

(%)

4-year

college

(%)

Masters

degree

(%)

Doctorate

(%)

Sample 6 13 55 22 4

Holdout group 4 4 52 36 4

Years of computer use

Average

(years)

Minimum

(years)

Maximum

(years)

Sample 12 1 37

Holdout group 13 5 28

Daily computer use (number of times per day)

Average Minimum Maximum

Sample 24 1 103

Holdout group 18 1 100

472 R. W. Stone et al.

Table 2. The confirmatory factor analysis indicants, standardized path coefficient, measures, and their psychometric properties.

Measures and their indicants (construct in the general model)

Path

coefficient

Composite

reliability

Shared

variance

Innovative climate (organizational traits) 0.94 76%

My business firm:

1. encourages me to find new ways around old problems. 0.84**

2. encourages me to develop my own ideas. 0.86**

3. encourages me to improve upon its methods. 0.90**

4. talks up new ways of doing things. 0.87**

5. likes me to try new ways of doing things. 0.89**

Technological leadership (individual traits) 0.94 81%

6. Usually, I am one of the first among my professional associates to adopt a new

technology when it is available.

0.89**

7. Compared to my professional associates, I use new computer technologies sooner. 0.92**

8. In general, I am the first of my business associates to know about new computer equipment. 0.91**

9. I use new technologies even if other professional associates do not. 0.88**

Computer training (organizational traits) 0.84 72%

In my business firm:

10. computer training is readily available. 0.84**

11. the computer training provided is always excellent. 0.87**

Computer staff support (organizational traits) 0.89 73%

12. The computer technician quickly helps solve problems. 0.82**

13. The computer technician can always solve my problems. 0.85**

14. When I don’t know how to do something, the computer staff can always help. 0.88**

Information quality (information quality) 0.89 68%

The computer systems at work provide:

15. up-to-date information. 0.83**

16. the information I need on time. 0.91**

17. sufficient information. 0.76**

18. information that is clear. 0.78**

Ease of system use 0.88 78%

19. I find the computer easy to use. 0.90**

20. I find it easy to get the computer to do what I want it to do. 0.87**

Computer system/service quality (system/service quality) 0.93 88%

The computer systems at work are excellent in terms of:

21. the promptness of maintenance and repair. 0.90**

22. the quality of maintenance and repair. 0.97**

End-user previous computer experience (individual traits) 0.84 72%

23. I have used computers throughout my career. 0.84**

24. I have used computer systems over a long period of time. 0.86**

Customer knowledge (industry traits) 0.78 64%

25. Our customers possess a great deal of market information. 0.81**

26. Our customers have knowledge about the market 0.79**

System satisfaction (attitude toward using the system) 0.96 84%

27. Overall, I am content with the computer systems at work. 0.92**

28. Overall, I am pleased with how the computer systems at work facilitate my work. 0.91**

29. Overall, the computer systems ‘fit well’ what I need at work. 0.89**

30. Overall, I am satisfied with the computer systems at work. 0.95**

Perceived individual performance impacts (perceived usefulness) 0.93 77%

In my business/firm, computer systems:

31. improve my work performance. 0.92**

32. help make me more successful. 0.90**

33. improve the quality of my work. 0.89**

34. help me do a better job. 0.79**

Perceived organizational performance impacts 0.91 72%

The computer systems at work:

35. are successful by improving organizational performance. 0.83**

36. lead to a more successful organization. 0.91**

(continued )

Marketing performance 473

Stephens (1987) with reliabilities of 0.84 and 0.81 and the

work of Doll and Torkzadeh (1988) with reliabilities above

0.90. System satisfaction was adapted from a job satisfac-

tion scale used by Dubinsky et al. (1986), with a reliability

of 0.83. The industry outlook scale was adapted from

Burke’s (1984) market attractiveness scale. Its original

reliability was reported as 0.92. Customer knowledge was

developed from Butaney and Wortzel’s (1988) examination

of market power of customer knowledge. Its reported

reliability was 0.74. Computer training, computer system/

service quality, end-user previous computer experience,

firm performance impacts, and tasks performed were

created by the authors after discussions with marketing

executives and a thorough review of the literature. The

system usage construct was measured by the respondent’s

self-reported percentage of time spent using the system.

5.3 Non-response bias

As in any study involving a survey, the possible presence of

non-response bias is a concern. Comparing the holdout

group to the sample for possible differences in the

demographic values is one examination for the presence

of this bias. A second is to perform a similar comparison

for the summated measures used in the study. In this

research, both examinations were performed.

The demographics not used in the estimation of the

theoretical model were compared between the sample and

the holdout group using t-tests. The specific variables and

t-values were: gender (0.68); number of employees (2.90);

respondent age (72.45); educational level of the respon- dent (71.52); years of computer use (70.61); and the number of times each day the system is used (1.02).

Meaningful differences across the demographics were

identified for the number of employees and respondent

age. These differences indicated that the respondents in the

sample worked in larger organizations and were on average

younger than the individuals whose responses were in the

holdout group. The magnitude of this difference in terms of

the number of employees was 570 employees on average for

the firms employing the respondents in the sample and 382

for the firms employing the executives in the holdout group.

Similarly, the average age of the respondents in the sample

was 43 years, while in the holdout group this average was

48 years.

The summated measures were also compared across the

sample and the holdout group for meaningful differences

using multiple analysis of variance. As a group, no mean-

ingful differences were found between the holdout group

and the sample. The Wilks Lambda was 1.03. The results of

the tests for the individual summated measures also showed

no significant differences between the sample and the hold-

out group, except for the degree of system use and ease of

system use. For these measures, the differences were sig-

nificant at a 5% level. In terms of direction, the non-

respondents simulated by the holdout group perceived the

system to be harder to use and spent a lower percentage of

their time using the system than the respondents in the

sample. The actual F-statistics (1 and 334 degrees of freedom)

for the individual test of each measure were: innovative

climate (0.51); technological leadership (3.00); computer

training (0.36); computer staff support (0.00); information

quality (3.13); ease of system use (6.14) computer system/

service quality (0.81); end-user computer experience (1.48);

industry outlook (0.00); customer knowledge (0.20); tasks

performed (1.78); system use (4.23); system satisfaction

(0.32) perceived individual performance impacts (3.49); and

perceived firm performance impacts (1.65).

The meaningful differences in these summated variables

between the holdout group and the sample require

additional investigation and explanation. The differences

indicated that the respondents perceived their information

Table 2. (Continued ).

Measures and their indicants (construct in the general model)

Path

coefficient

Composite

reliability

Shared

variance

37. lead to higher quality of work. 0.88**

38. improve the marketplace success of the firm. 0.78**

Industry outlook (industry traits) 0.80 59%

39. The prospect for future profit is good. 0.63**

40. The average industry growth is high. 0.81**

41. The average industry pretax profits are high. 0.84**

Tasks performed (tasks performed) 0.86 67%

Indicate the degree to which computer technology in your business/firm has been important in aiding

performance in the following areas:

42. providing information for effective communication. 0.76**

43. improving communication between my firm and customers. 0.83**

44. Improving communication between members of my business/firm (e.g. sales force to manufacturing). 0.86**

**Statistically significant at a 1% level.

474 R. W. Stone et al.

system to be easier to use and made greater use of it when

compared to the simulated non-respondents. These means

were 7.09 and 5.97 for the summated ease of use measure

and 40.73% and 24.94% for system use. These results

appear consistent with the differences in the demographic

variables. Relative to the nonrespondents, the respondents

worked in larger organizations where IT might be more

available. Further, the respondents were, on average,

slightly younger and perhaps more willing to use IT. Based

on these differences, the respondents probably had a

greater interest in the topic of the survey and responded

sooner and at a higher rate.

Given the purpose of the study, these differences do not

necessarily present a ‘bad’ bias. The sample contains

respondents who make relatively frequent use of the system

they were asked to evaluate. As a result, they know how to

use the system and perceive it relatively easy to use. This

contention is further supported by the fact that the

respondents in the sample had on average greater previous

computer experience (i.e. 7.02 years) than the individuals in

the holdout sample (i.e. 6.18 years). Since it is desirable to

have answers in the sample from respondents who are

knowledgeable about the system and make use of it, these

differences need not be serious. As a result, it can be

concluded that non-response bias is not a serious problem

for the study.

5.4 The psychometric properties of the measures

The next stage in the empirical analysis was to evaluate the

psychometric properties of the measures. The analysis was

based on the results from a confirmatory factor analysis

using a structural equations approach in Calis (i.e.

Covariance Analysis of Linear Structural Equations) in

PC SAS version 8. In the analysis, each measure was

exogenous in the model and scaled by setting its standard

deviation equal to one. The measures were also allowed to

pair-wise correlate. The individual items in each measure

were reflective and impacted by a random disturbance term.

Each disturbance term was free to vary with a path between

it and the indicant set equal to one. The estimation

procedure used was maximum likelihood.

The results from the analysis are summarized by several

statistics. The goodness of fit index was 0.85 and adjusted

for degrees of freedom it was 0.81. The root mean square

residual was 0.04. The chi-square statistic was statistically

significant at a 1% level and had a value of 1298.81 with

841 degrees of freedom. The normed chi-square statistic

was 1.54. Bentler’s comparative fit index was 0.96. The

incremental fit indexes (i.e. Bentler and Bonett’s normed

and nonnormed indexes and Bollen’s normed and non-

normed indexes) ranged from 0.87 to 0.96. These results

and the relatively large sample size, even with the

undesirable significant chi-square statistic, imply a good

fit between the model and the data (Hair et al. 1992).

Using the results of the confirmatory factor analysis, the

psychometric properties of the measures were evaluated

and shown in table 2. Since the standardized path between

each indicant and its measure was at least as large as 0.63,

item reliability was satisfied (Rainer and Harrison 1993).

Because the composite reliability coefficients ranged from

0.78 to 0.96, composite reliability was satisfied (Nunnally

1978). All the average percentages of shared variance were

59% or greater, demonstrating satisfactory levels of this

trait (Rivard and Huff 1988). Due to these desirable values,

it can be concluded that convergent validity was satisfied

for each measure (Igbaria and Greenhaus 1992, Rainer and

Harrison 1993).

Discriminant validity was also examined using the results

from the confirmatory factor analysis. The examination

compared the squared correlation between each pair of

measures to their average percentage of shared variances.

Discriminant validity is satisfied if, for each measure pair,

the average percentages of shared variance are greater than

the corresponding squared correlation (Fornell and Larcker

1981). The squared correlations ranged from 0.00 to 0.36

and are reported in table 3. Since these squared correlations

were less than all the average percentage of shared

variances already reported, discriminant validity was

satisfied (Fornell and Larcker 1981). These results, coupled

with convergent validity, imply that the measures satisfied

construct validity (Rainer and Harrison 1993). Thus, the

developed measures had desirable psychometric properties.

5.5 Estimation of the model

The empirical specification of the model was developed as

reflective in nature, with the path between a measure and its

indicant pointing from the measure to the indicant. Each of

these paths was free to vary, with the exception of one

indicant for each endogenous measure that was used to

scale the measure. Further, each indicant and endogenous

measure was impacted by a disturbance term that was free

to vary. The resulting model was estimated using a

structural equations approach (i.e. Calis in PC SAS version

8) and maximum likelihood estimation.

The overall fit of the model to the data is described by

several summary statistics displayed in table 4. The

goodness of fit measure was 0.81 while this index adjusted

for the degrees of freedom in the model was 0.78. The root

mean square residual was 0.15. The chi-square statistic was

1748.61 with 876 degrees of freedom and was statistically

significant at a 1% level. The normed chi-square statistic

was 2.00 and Bentler’s comparative fit index was 0.92. The

incremental fit indexes ranged from 0.84 to 0.92. Consider-

ing the sample size and the complexity of the model, these

Marketing performance 475

statistics indicate an acceptable fit between the model and

the data (Hair et al. 1992).

The details of the estimated measurement model are

shown in figure 2 using the estimated standardized path

coefficients. Viewing these results, each path between a

measure and its indicant that was free to vary was

significantly different from zero at a 1% level. The details

of the estimated structural model are also displayed in

figure 2, using standardized path coefficients. The paths of

the antecedents to perceived individual performance

impacts (i.e. perceived usefulness) and the ease of system

use are examined first. Within the organizational traits

group of antecedents, the innovative climate measure had

the predicted positive impact on perceived individual

performance impacts (H1). Computer training had the

predicted significant impact on ease of system use (H4). For

the two measures in the individual traits group, the degree of

technological leadership had the hypothesized influence on

ease of system use (H8). Furthermore, amount of end-

user computer experience had the predicted impacts on the

ease of system use (H10) and perceived individual perfor-

mance impacts (H9).

Table 3. The squared correlations among the measures.

Innovative

climate

Technological

leadership

Computer

training

Computer

staff support

Innovative climate 1.00

Technological leadership 0.04 1.00

Computer training 0.14 0.04 1.00

Computer staff support 0.17 0.04 0.34 1.00

Information quality 0.14 0.02 0.13 0.14

Ease of system use 0.03 0.36 0.12 0.05

Computer system/service quality 0.11 0.03 0.16 0.26

End-user previous computer experience 0.06 0.34 0.04 0.01

Customer knowledge 0.08 0.00 0.03 0.03

System satisfaction 0.17 0.06 0.17 0.16

Perceived individual performance impacts 0.16 0.12 0.07 0.06

Perceived firm performance impacts 0.26 0.08 0.13 0.22

Industry outlook 0.13 0.01 0.08 0.10

Tasks performed 0.30 0.08 0.16 0.17

System use 0.01 0.13 0.03 0.00

Information quality Ease of system use

Computer

system/service

quality

End-user

computer

experience

Information quality 1.00

Ease of system Use 0.05 1.00

Computer system/service quality 0.14 0.10 1.00

End-user computer experience 0.01 0.25 0.03 1.00

Customer knowledge 0.02 0.00 0.00 0.01

System satisfaction 0.35 0.17 0.19 0.05

Perceived individual performance impacts 0.12 0.13 0.06 0.15

Perceived organizational performance impacts 0.34 0.08 0.12 0.03

Industry outlook 0.12 0.01 0.07 0.08

Tasks performed 0.14 0.05 0.13 0.09

System use 0.00 0.14 0.04 0.18

Customer

knowledge

System

satisfaction

Perceived individual

performance impacts

Perceived organizational

performance impacts

Customer knowledge 1.00

System satisfaction 0.00 1.00

Perceived individual performance impacts 0.02 0.13 1.00

Perceived organizational performance impacts 0.02 0.25 0.31 1.00

Industry outlook 0.01 0.10 0.10 0.12

Tasks performed 0.07 0.25 0.19 0.35

System use 0.00 0.04 0.05 0.02

Industry outlook Tasks performed System use

Industry outlook 1.00

Tasks performed 0.14 1.00

System use 0.03 0.06 1.00

476 R. W. Stone et al.

The information quality measure had the predicted

influences on both ease of system use (H12) and perceived

individual performance impacts (H11). The system/service

quality measure had as predicted a positive impact on the

ease of system use (H14). For the industry traits category,

none of the measures had the predicted influences on either

ease of system use or perceived individual performance

impacts. The tasks performed using the system measure

had the predicted impacts on perceived individual perfor-

mance impacts (H19).

Also shown in figure 2 are the paths among the endo-

genous variables of ease of system use, perceived individual

performance impacts, system satisfaction, system use, and

perceptions of organizational performance impacts. All of

these estimated paths were significant as hypothesized. Ease

of system use had meaningful impacts on both perceived

individual performance impacts (H21) and system satisfac-

tion (H22). Additionally, perceived individual performance

impacts had the hypothesized positive, meaningful impact

on system satisfaction (H23). System satisfaction had the

predicted positive influence on system use (H24). In turn,

system use had a positive, meaningful influence on per-

ceived organizational performance impacts (H25).

6. Managerial implications

The managerial implications of key interest are those

represented by the paths from the antecedents to ease of

system use and perceived individual performance impacts

(i.e. perceived usefulness). This is in part because all the

hypothesized paths among the endogenous variables in the

model (i.e. those paths from ease of system use and per-

ceived individual performance impacts forward to per-

ceived organizational performance impacts) were significant

as predicted. Furthermore, these relationships are impor-

tant because all of the antecedents are at least partially

controllable by management.

If the organization can create an environment encoura-

ging innovation, it will have positive impacts on perceived

organizational performance through positive impacts on

the system’s perceived usefulness mediated by system satis-

faction and use. This may well be because the innovative

climate encourages uses of the system that have potential

for significant performance gains, if successful. Encouraging

such innovation among employees provides an environ-

ment to attempt and learn about such appropriate risk-

taking. Another organizational trait is computer training.

Providing computer training on the system improves end-

users’ perceptions of the system’s ease of use and ultimately

positively influencing perceived organizational performance

through system satisfaction and use. Such a result is logical

in that appropriate training in the use of the system should

make users perceive it easier to use. This is the logic behind

providing computer training in the organization.

The characteristics of marketers recruited and retained

by the organization can also influence their perception of

the organizational performance impacts possible from the

use of the system. Recruiting and retaining marketers and

managers who are early adopters of technology (i.e. display

technological leadership) have positive impacts on perceived

organizational performance from system use through

perceptions that the system is easy to use which improves

system satisfaction and, in turn, increases the degree of

system use. In addition, hiring marketers with significant

prior computer experience has meaningful impacts on

perceived organizational performance through both ease of

system use and perceived impacts on individual perfor-

mance. Thus, by recruiting and retaining marketers and

managers with meaningful prior computing experience and

with a predilection toward using and adapting new tech-

nologies, organizations can improve, at least perceptually,

organizational performance.

The characteristics of the information provided by the

system and the system itself impact perceived organiza-

tional performance impacts. If the system provides infor-

mation that is of high quality (i.e. timely and sufficient), it

has positive impacts on perceived organizational perfor-

mance. These impacts occur through improvements in ease

of system use and perceived individual performance

impacts improving system satisfaction, system use, and

ultimately perceived organizational performance. The

quality of the system (e.g. maintenance and repair) also

influences perceived organizational performance through

making the system easier to use for the marketer. A system

that is easy to use increases system satisfaction, system use,

and has perceived organizational performance impacts.

The tasks performed using the system influence perceived

organizational performance through improving perceived

individual performance. In this study, the tasks performed

using the system focused on improving communications.

The empirical results indicted that using the system to

improve communications has positive impacts on perceived

individual performance of marketers, system satisfaction,

Table 4. The summary statistics of the model’s fit.

Statistic Value

Goodness of fit index 0.81

Adjusted goodness of fit index 0.78

Root mean square residual 0.15

Chi-square statistic 1748.61**

Degrees of freedom 876

Normed chi-square statistic 2.00

Bentler’s comparative fit index 0.92

Bentler & Bonett’s nonnormed index 0.91

Bentler & Bonett’s normed index 0.86

Bollen’s normed index 0.84

Bollen’ nonnormed index 0.92

**Statistically significant at a 1% level.

Marketing performance 477

Figure 2. The estimated standardized path coefficients.

478 R. W. Stone et al.

system use, and ultimately perceived organizational perfor-

mance. Finally, it should also be noted that any antecedent

significantly impacting ease of system use also has a signifi-

cant impacts on perceived organizational performance

impacts through perceived individual performance impacts.

It is important for management to track the success of IT

investments (Skok et al. 2001), as the value of technology is

increasingly becoming a critical managerial issue (Hoffman

2002, Berry 2003). In this spirit, the antecedents to ease of

system use and perceived individual performance impacts,

grouped into the categories shown in figure 1, are examined

in a very specific user context. Several interesting implica-

tions can be reached based on the empirical results. Overall,

these results indicate that the firm and its management can

influence perceived organizational performance from sys-

tem use through the careful management of organization

and individual traits, information and system/service

quality, and the tasks to which IT is applied.

There are examples in the literature of information

systems failing to reach full impact due to human and

organizational factors (Irani et al. 2001) as well as specific

concerns raised about individual objectives IT is expected

to accomplish (Peffers and Gengler 2003). Such factors play

a role in encouraging system success as measured by

perceived organizational performance impacts. In an

examination of the individual results, the positive relation-

ship between the organizational trait of innovative climate

and perceived individual performance impacts implies that

management can use heightened innovative opportunities

within the firm to drive performance results both on indivi-

dual and organizational levels. Thus, firms can encourage

and/or facilitate IT performance impacts by embracing a

climate of innovation. For example, consider the marketer

who is encouraged to discover new methods for computer

mapping of customer territories. Such an opportunity

apparently influences the marketer’s perceived performance

and organizational performance. Organizations able to

expand the ‘mind-set’ of users to include a setting where

innovation is encouraged can also encourage performance

enhancing computer technology applications.

The empirical results also found that computer training

provided by management impacts perceived individual and

organizational performance impacts through making the

system easier to use. This is the typical motivation of orga-

nizations when providing computer training. These results

indicate that such training does improve perceptions of ease

of use, perceived individual performance, system satis-

faction, the degree of use, and ultimately organizational

performance.

The positive linkages between the individual trait of end-

user computer experience to both ease of system use and

perceived individual performance impacts support the con-

tention that among marketers, past users are more likely to

be current users. Furthermore, past users breed current use,

impacts on perceived individual performance, and ease of

system use. These latter two measures ultimately impact

organizational performance through system satisfaction

and use. Thus, there is value in recruiting personnel who

have appropriate experiences with computer technology.

As a result, the specifics regarding the execution of IT in the

organization need to be addressed by chiefly the functional

users (Ross and Weill 2002), such as marketers.

The meaningful relationships between information quality

and ease of system use and perceived individual perfor-

mance impacts indicate that ultimately perceived marketing

organization impacts depend partially on having a compu-

ter system that provides quality information. The positive

relationship of the computer system/service quality mea-

sure to system ease of use confirms that computer system/

service quality influences perception of performance at the

individual and organizational levels. For the organization,

this indicates that it is not enough to have a good ‘product’

(i.e. information) from computer technology. The organi-

zation must also have support mechanisms that embrace

the ability to work with the technology. For example,

management may need to ensure that computer systems are

easy for marketers to manage and seldom need mainte-

nance. These characteristics, based on the empirical results,

would encourage marketers to use the system and improve

their perceptions of their own and organizational perfor-

mance through system satisfaction and use.

The positive relationship between the tasks performed

and perceived individual performance impacts confirms

that as the ability of IT to perform tasks for the marketer

escalates, this will improve individual performance and

ultimately organizational performance. Since an underlying

goal is to use IT to build performance, this indicates IT

professionals have a critical role in ensuring that the com-

puter systems are able to accomplish specific objectives or

tasks. Thus, while the tasks selected for this study certainly

reflect the need of marketers, it appears that it is important

to understand the tasks the organization expects the tech-

nology to accomplish.

The lack of meaningful relationships for customer

knowledge and industry outlook to ease of system use

and perceived individual performance impacts indicate that

industry traits have no influence on perceived performance.

These results are surprising given that the executives studied

are marketers who interact significantly with the environ-

ment (e.g. the industry) of the organization. Additional

study is needed to fully understand these insignificant

relationships. Similarly, the organizational trait of compu-

ter staff support had no meaningful impacts in the model.

There are several potential explanations for these results

which require additional investigation. Possibilities include

the types of computer systems and programs used as well as

the types of support offered. There were several other

insignificant paths in the model as well. The innovative

Marketing performance 479

climate organization trait did not impact the ease of system

use in a meaningful way. This result coupled with the

significant relationship between innovative climate and

perceived individual performance impacts may well imply

that innovation in the organization does not influence sys-

tem usability, but only perceived usefulness of the system.

Conversely, computer training and technological leadership

had meaningful impacts on ease of system use, but not

perceived individual performance impacts.

7. Conclusions

Understanding to what degree a strategic tool benefits the

organization is a critical issue. It has been suggested that

information systems need to link strategy to non-financial

measures of success including traditional variables such as

customer service and market performance (Stivers and Joyce

2000). Yet, in an environment where pressures are mounting

to understand the specific value of IT (Tallon et al. 2000),

little is known about how information technologies can be

cultivated to the advantage of marketers. Despite being a

widely discussed managerial issue in recent years (Torkzadeh

and Doll 1999), measuring the value of IT is extremely

difficult and something about which little is known (O’Brien

1997). Yet, the use of information technologies remains

prevalent (Stites 1999, Wipperfuth 1999) among marketers

(Harrison-Walker 2002, Osmonbekov et al. 2002; Sorensen

and Buatsi 2002), often at enormous costs (Ostermiller

1999). It is the need to better understand performance

impacts of IT that provided the focus of this study.

Modifying the Technology Acceptance Model (Davis 1989,

Davis et al. 1989), the DeLone and McLean (1992) model and

Goodhue and Thompson’s (1995) task-technology-individual

fit proposal, this research empirically explored the organiza-

tional, individual, information, system, industry, and task

traits that influence perceived organizational performance

impacts from IT use mediated by ease of system use and

perceived individual performance impacts, system satisfac-

tion, and system use. It was found that through the diligent

marshalling of technological, environmental, and human

resources, management can enhance the impact IT has on

perceived marketing organization performance.

Many of the relationships in the theoretical model have

been examined previously in the literature. It is worthwhile

to compare the results presented here for the marketing

organization and marketer to the more general results in

the literature. As discussed earlier, relationships between

both system/service quality and information quality to

perceived usefulness have been previously acknowledged.

Yet, relationships from these same two variables to system

use have not been previously found. The results from this

research for marketing executives expand these results.

Information quality was found for marketing executives to

have a meaningful impact on perceived usefulness while

system/service quality’s influence on perceived usefulness

was indirect through ease of use.

It has been previously reported that no relationships are

found from information quality and system/service quality

to system use. However, these results indicated that for

marketing executives, these relationships exist through

perceived useful and system satisfaction. Furthermore, it

also has been previously demonstrated that ease of use and

perceived usefulness positively impact system use. In this

model estimated for marketing executives these relation-

ships exist, but mediated by system satisfaction. Similarly,

the ultimate impacts in the model to perceived organiza-

tional performance impacts from system do occur, but the

causal linkages are different.

Some of the differences between these results for market-

ers and those reported in the literature could well be due to

application of the TAM to marketers and the marketing

organization. This raises questions requiring additional

investigation. The research presented here examines com-

puter system use and its impacts on performance for the

marketer and in the marketing organization. An interesting

question is how will these results differ based on the need

for, and use of IT in other areas of the business organi-

zation. Furthermore, given marketers’ traditional focus on

factors outside of the business organization, the lack of

significant relationships of the externally oriented antece-

dents (i.e. industry outlook and customer knowledge) in the

model is surprising. These results require additional study

among marketers as well as among executives in other

business areas to compare the results to those from

marketers. These studies would add knowledge regarding

the acceptance, use, and performance impacts of informa-

tion technology in a variety of organizational areas.

References

ADAMS, D.A., NELSON, R.R. and TODD, P.A., 1992, Perceived usefulness,

ease of use and usage of information technology: a replication. MIS

Quarterly, 16(2), 227 – 247.

AJZEN, I. and FISHBEIN, M., 1980, Understanding Attitudes and Predicting

Social Behavior (Englewood Cliffs, NJ: Prentice-Hall).

ANANDARAJAN, M., IGBARIA, M. and ANAKWE, U.P., 2000, Technology

acceptance in the banking industry: a perspective from a less developed

country. Information Technology & People, 13(4), 298 – 312.

ANG, J. and KOH, S., 1997, Exploring the relationship between user

information satisfaction and job satisfaction. International Journal of

Information Management, 17, 169 – 177.

ANG, J. and SOH, P.H., 1997, User information satisfaction, job satisfaction

and computer background: an exploratory study. Information &

Management, 32, 255 – 266.

BENAROCH, M., 2002, Managing information technology investment risk: a

real options perspective. Journal of Management Information Systems,

19(2), 43 – 84.

BERNDT, E.R. and MORRISON, C.J., 1995, High-tech capital, economic and

labor composition in U.S. manufacturing industries: an exploratory

analysis. Journal of Econometrics, 65(1), 9 – 43.

BERRY, J., 2003, ROI or your money back. Computerworld, 37(5), 42.

480 R. W. Stone et al.

BHATTACHERJEE, A., 2001, Understanding information systems continu-

ance: an expectation – confirmation model. MIS Quarterly, 25, 351 – 370.

BLILI, S., RAYMOND, L. and RIVARD, S., 1998, Impact of task uncertainty,

end-user involvement, and competence on the success of end-user

computing. Information & Management, 33, 137 – 153.

BRESNAHAN, J., 1998, What good is technology? CIO, 11, 24 – 30.

BRYNJOLFSSON, E., 1993, The productivity paradox of information

technology. Communication of the ACM, 36, 67 – 77.

BRYNJOLFSSON, E. and YANG, S., 1996, Information technology and

productivity: a review of the literature. Advances in Computers, 43,

179 – 214.

BURKE, M.C., 1984, Strategic choice and marketing managers: an

examination of business-level marketing objectives. Journal of Marketing

Research, 21, 345 – 359.

BUTANEY, G. and WORTZEL, L.H., 1988, Distributor power versus manu-

facturer power: the customer role. Journal of Marketing, 52, 52 – 63.

CHANGKI, K., KUNSOO, S. and JINJOO, L., 1998, Utilization and user

satisfaction in end-user computing: a task contingent model. Information

Resources Management Journal, 11, 11 – 24.

CHAU, P.Y.K. and HU, P.J., 2001, Information technology acceptance by

individual professionals: a model comparison approach. Decision

Sciences, 32(4), 699 – 719.

CLAY, P., DENNIS, A.R. and DONG-GIL, K., 2005, Factors affecting the loyal

use of knowledge management systems. Proceedings of the 38th Hawaii

InternationalConferenceonSystemSciences,4 – 8January,Wikoloa,Hawaii.

CROSBY, L.A. and STEPHENS, N., 1987, Effects of relationship marketing on

satisfaction, retention, and prices in the life insurance industry. Journal

of Marketing Research, 24, 404 – 411.

DAVIS, F.D., 1989, Perceived usefulness, perceived ease of use and user

acceptance of information technologies. MIS Quarterly, 13(3), 319 – 340.

DAVIS, F.D., BAGOZZI, R. and WARSHAW, P.R., 1989, User acceptance of

computer technology: a comparison of two theoretical models. Manage-

ment Science, 35(8), 982 – 1003.

DELONE, W.H. and MCLEAN, E.R., 1992, Information systems success:

the quest for the dependent variable. Information Systems Research, 3,

60 – 94.

DELONE, W.H. and MCLEAN, E.R., 2003, The DeLone and McLean model

of information systems success: a ten year update. Journal of Manage-

ment Information Systems, 19(4), 9 – 30.

DOLL, W.J. and TORKZADEH, G., 1988, The measurement of end-user

computing satisfaction. MIS Quarterly, 12, 259 – 273.

DOLL, W.J., XIA, W. and TORKZADEH, G., 1994, A confirmatory factor

analysis of the end-user computing satisfaction instrument. MIS

Quarterly, 18, 453 – 461.

DUBINSKY, A.J., HOWELL, R.D., INGRAM, T.N. and BELLENGER, D., 1986,

Salesforce socialization. Journal of Marketing, 50, 192 – 207.

DUCOTE, G. and MALSTROM, E.M., 1999, A design of personnel scheduling

software for manufacturing. Computers & Industrial Engineering, 37,

473 – 476.

FLEMING, C., 1999, How do you plan for technology investments? Credit

Union Magazine, 65, 13 – 14.

FORNELL, C. and LARCKER, D.F., 1981, Evaluating structural equation

models with unobservable variables and measurement error. Journal of

Marketing Research, XVIII, 39 – 50.

GASSON, S., 1999, The reality of user-centered design. Journal of End-User

Computing, 11, 5 – 15.

GEFFEN, D. and STRAUB, D., 2000, The relative importance of perceived

ease of use in IS adoption: a study of e-commerce adoption. Journal of

the Association for Information System, 1(8), 1 – 28.

GOLDSMITH, R.E. and HOFACKER, C.F., 1991, Measuring consumer

innovativeness. Journal of the Academy of Marketing Science, 19, 209 –

221.

GOOD, D.J. and SCHULTZ, R.J., 1997, Technological teaming as a marketing

strategy. Industrial Marketing Management, 26, 413 – 422.

GOOD, D.J. and STONE, R.W., 1995, Computer technology and the

marketing organization: an empirical investigation. Journal of Business

Research, 34, 197 – 209.

GOOD, D.J. and STONE, R.W., 1999, Working smarter: the impact of

technology on marketer motivation. Participation & Empowerment: An

International Journal, 7, 56 – 67.

GOOD, D.J. and STONE, R.W., 2000, The impact of computerization on

marketing performance. Journal of Business & Industrial Marketing, 15,

134 – 177.

GOODHUE, D.L. and THOMPSON, R.L., 1995, Task – technology fit and

individual performance. MIS Quarterly, 19, 213 – 236.

GRISWOLD C. III, 1998, Evaluating IT investments. Health Management

Technology, 19, 24 – 26.

GROENFELDT, T., 1996, Vanguard’s new role: IT leader. Computerworld,

November, F9 – F12.

GROVER, V., TENG, J.T.C. and FIEDLER, K.D., 1998, IS investment priorities

in contemporary organizations. Communications of the ACM, 41, 40 – 48.

GUIMARAES, T., IGBARIA, M. and LU, M., 1992, The determinants of DSS

success: An integrated electronic meeting systems at IBM: lessons

learned and success factors. MIS Quarterly, 14, 409 – 430.

HAIR, J. Jr., ANDERSON, R.E., TATHAM, R.L. and BLACK, W.C., 1992,

Multivariate Data Analysis With Readings (New York: MacMillan

Publishing Company).

HARRISON-WALKER, L.J., 2002, If you build it, will they come? Barriers to

international e-marketing. Journal of Marketing Theory and Practice,

10(2), 12 – 21.

HEADRICK, R.W. and MORGAN, G.W., 1999, Measuring the impact of

information systems on organizational behavior. Journal of End User

Computing, 11, 16 – 21.

HENRY, J.W. and MARTINKO, M.J., 1997, An attributional analysis of the

rejection of information technology. Journal of End User Computing, 9,

3 – 17.

HENRY, P.F., 1999, Accounting news. Accounting Technology, 15, 10 – 11.

HOFFMAN, T., 2002, Study ties frugal IT spending to good financial

performance. Computerworld, 26(51), 22.

IGBARIA, M. and GREENHAUS, J.H. 1992, Determinants of MIS employee’s

turnover intentions: a structural equation model. Communications of the

ACM, 35, 35 – 49.

IGBARIA, M., IIVARI, J. and MARAGAHH, H., 1995, Why do individuals use

computer technology? A Finnish case study. Information & Management,

29, 227 – 239.

IGBARIA, M. and TAN, M., 1997, The consequences of information

technology acceptance on subsequent individual performance. Informa-

tion & Management, 32(3), 113 – 121.

IGBARIA, M., ZINATELLI, N. and CAVAYE, A.L.M., 1998, Analysis of

information technology success in small firms in New Zealand.

International Journal of Information Management, 18, 103 – 119.

IRANI, Z., SHARIF, A.M. and LOVE, P.E.D., 2001, Transforming failure into

success through organisational learning: an analysis of a manufacturing

information system. European Journal of Information Systems, 10, 55 – 66.

ISHMAN, M.D., PEGELS, C. and SANDERS, L., 2001, Managerial information

system success factors within the cultural context of North America and

former Soviet Republic. Journal of Strategic Information Systems, 10,

291 – 312.

KALLMAN, E.A. and O’NEILL, K., 1993, The critical success factors of

distributed computing are organizational, not technological. Journal of

Information Technology Management, 4, 112 – 123.

KESNER, R.M., 1999, IT planning and procurement—the underlying

architectural process. Information Strategy, 16, 7 – 14.

KHALIL, O.E. and MELKORDY, M.M., 1999, The relationship between user

satisfaction and system usage: empirical evidence from Egypt. Journal of

End User Computing, 11, 21 – 28.

KING, C.W. and SUMMERS, J.O., 1970, Overlap of opinion leadership across

consumer product categories. Journal of Marketing Research, 7, 43 – 50.

Marketing performance 481

KOYS, D.J. and DECOTIIS, T.A., 1991, Inductive measures of psychological

climate. Human Relations, 44(March), 265 – 285.

LAL, K., 1996, Information technology, international orientation and

performance: a case study of electrical and electronic goods manufactur-

ing firms in India. Information Economics and Policy, 8, 269 – 280.

LEGRIS, P., INGHAM, J. and COLLERETTE, P., 2003, Why do people use

information technology? A critical review of the technology acceptance

model. Information & Management, 40(3), 191 – 204.

LI, E.Y., 1997, Perceived importance of information system success factors:

a meta analysis of group differences. Information & Management, 32,

15 – 28.

MACMILLAN, H., 1997, Managing information systems: three key principles

for general managers. Journal of General Management, 22, 12 – 23.

MAHMOOD, A. and MANN, G., 1993, Measuring the organizational impact

of information technology investment: an exploratory study. Journal of

Management Information Systems, 32, 97 – 122.

MCGILL, T., HOBBS, V. and KLOBAS, J., 2003, User-developed applications

and information systems success: a test of the DeLone and McLean

model. Information Resources Management Journal, 16(1), 24 – 45.

MERTINS, K. and ARLT, R., 1999, Supporting order control in decentralized

manufacturing structures. Computers & Industrial Engineering, 37, 35 – 38.

MIN, S., SONG, S. and KEEBLER, J.S., 2002, An internet-mediated market

orientation (IMO): building a theory. Journal of Marketing Theory and

Practice, 10(2), 1 – 11.

MOTIWALLA, L. and FAIRFIELD-SONN, J., 1998, Measuring the impact of

expert systems. Journal of Business and Economic Studies, 4, 1 – 17.

NUNNALLY, J., 1978, Psychometric Methods, 2nd edition (New York:

McGraw-Hill).

O’BRIEN, T., 1997, Redefining IT value. Informationweek, 7 April, 71 – 76.

O’SULLIVAN, O., 1998, Technology spending’s uncertain payback. USBAN-

KER, 108, 32 – 44.

OSMONBEKOV, T., BELLO, D.C. and GILLILAND, D.I., 2002, Adoption of

electronic commerce tools in business procurement; enhanced buying

center structure and processes. Journal of Business & Industrial Market-

ing, 17(2 – 3), 151 – 166.

OSTERMILLER, M., 1999, Prudential makes $100 million technology

investments in agents. Best’s Review, 9, 77.

PAUL, D.L., PEARLSON, K.E. and MCDANIEL, R.R. Jr., 1999, Assessing

technology barriers to telemedicine: technology-management implica-

tions. IEEE Transactions on Engineering Management, 46(3), 279 – 288.

PEFFERS, K. and GENGLER, C.E., 2003, How to identify new high-payoff

information systems for the organization. Association for Computing

Machinery: Communications of the ACM, 46(1), 83 – 88.

RAINER, R.K. Jr. and HARRISON, A.W., 1993, Toward development of the

end user computing construct in a university setting. Decision Sciences

Journal, 24, 1187 – 1202.

RAVAL, V., 1999, Information strategy in service-focused organizations.

Information Strategy, 16, 36 – 40.

RIVARD, S. and HUFF, S., 1988, Factors of success for end-user computing.

Communications of the ACM, 31, 552 – 561.

ROLDAN, J.L. and MILLAN, A.L., 2000, Analysis of the information systems

success dimensions interdependence: an adaptation of the DeLone and

McLean model in the Spanish EIS field. BITWorld 2000 (conference

proceedings) 1 – 3 June, Mexico City, Mexico.

ROGERS, D.S., DAUGHERTY, P.J. and ELLINGER, A.E., 1996, The relationship

between information technology and warehousing performance. Logis-

tics and Transportation Review, 32, 409 – 421.

ROSS, J.W. and WEILL, P., 2002, Six decisions your IT people shouldn’t

make. Harvard Business Review, 80(11), 84 – 91.

RYAN, S.D., HARRISON, D.A. and SCHKADE, L., 2002, Information-

technology investment decisions: when do costs and benefits in the

social subsystems matter? Journal of Management Information Systems,

19(2), 85 – 127.

SCHROEDER, C., 1987, Improved information access: the next challenge.

Inform, 1, 6 – 7.

SCOTT, J.T., 1998, Why I love my Mac. Office Systems, 15, 48 – 49.

SEDDON, P.B. and KIEW, M.Y., 1996, A partial test and development of

DeLone and McLean’s model of IS success. Australian Journal of

Information Systems, 4(1), 90 – 109.

SHAYO, C., GUTHRIE, R. and IGBARIA, M., 1999, Exploring the measure-

ment of end user computing success. Journal of End User Computing, 11,

5 – 14.

SHAW, S.J., 1994, Productivity paradox or payoff. Technology Review,

February – March, 2 – 13.

SHIBATA, G., TSE, D., VERTINSKY, I. and WEHRUNG, D., 1991, Do norms of

decision-making styles, organizational design and management affect

performance of Japanese firms? An exploratory study of medium and

large firms. Managerial and Decision Economics, 12, 135 – 147.

SKOK, W., KOPHAMEL, A. and RICHARDSON, I., 2001, Diagnosing informa-

tion systems success: Importance-performance maps in the health club

industry. Information & Management, 38, 409 – 419.

SORENSEN, O.J. and BUATSI, S., 2002. Internet and exporting: the case of

Ghana. Journal of Business & Industrial Marketing, 17(6), 481 – 500.

SRIRAM, R.S. and KRISHNAN, G.V., 2003, The value relevance of IT

investments on firm value in the financial services sector. Information

Resources Management Journal, 16(1), 46 – 61.

STITES, J., 1999, As black technology entrepreneurs organize, they are

spreading the word about the benefits of ‘digital freedom’. New York

Times, 22 February, C-4.

STIVERS, B.P. and JOYCE, T. 2000, Building a balanced performance

management system. SAM Advanced Management Journal, 65, 2 – 22.

STRUTTON, D., STRATON, L.E. and LUMPKIN, J.R., 1993, The influence of

psychological climate on conflict resolution strategies in franchise

relationships. Journal of the Academy of Marketing Science, 21, 207 –

216.

TALLON, P., KRAEMER, K.L. and GURBAXANI, V., 2000, Executives’

perceptions of the business value of information technology: a process-

oriented approach. Journal of Management Information Systems, 16(4),

145 – 173.

TORKZADEH, G. and DOLL, W.J., 1999, The development of a tool for

measuring the perceived impact of information technology on work.

Omega, 27, 327 – 339.

UDO, G.G. and EBIEFUNG, A.A., 1999, Human factors affecting the success

of advances manufacturing systems. Computers & Industrial Engineering,

37, 297 – 300.

VENKATESH, V. and DAVIS, F.D., 1996, A model of the antecedents of

perceived ease of use: development and test. Decision Sciences, 27, 451 –

481.

WATSON, S., 1999, Train to retain. Computerworld, 33, CW22 – CW25.

WESTON, R., 1998, Innovation in the bottom line. Informationweek, 700,

495 – 496.

WHITFORD, D., 1999, A new MBA for the e-corp: half-manager. Fortune,

139, 189 – 192.

WILLIAMS, C., 1999, Enhancing the information harvest. Bobbin, 40, 78 – 81.

WIPPERFUTH, H., 1999, Start-up foresees exchange pits in cyberspace. The

Investment Dealers’ Digest, 19 April, 12.

WIXOM, B.H. and WATSON, H.J., 2001, An empirical investigation of the

factors affecting data warehousing success. MIS Quarterly, 25(1), 17 – 41.

482 R. W. Stone et al.