2 28 examining the effect of user satisfaction on system usage and individual performance taiwan
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International Journal of Information Management 32 (2012) 560573
Contents lists available atSciVerse ScienceDirect
International Journal of Information Management
j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / i j i n f o m g t
Examining the effect of user satisfaction on system usage and individual
performance with business intelligence systems: An empirical study of Taiwans
electronics industry
Chung-Kuang Hou
Chia-Nan University of Pharmacy and Science, Department of Information Management, 60 Erh-Jen RD., Sec. 1, 717 Jen-Te, Tainan 711, Taiwan, ROC
a r t i c l e i n f o
Article history:
Available online 4 April 2012
Keywords:
End-user computing satisfaction
Business intelligence
User satisfaction
System usage
Individual performance
a b s t r a c t
The advent of new information technology has radically changed the end-user computing environment
over the past decade. To enhance their management decision-making capability, many organizations
have made significant investments in business intelligence (BI) systems. The realization of business ben-
efits from BI investments depends on supporting effective use of BI systems and satisfying their end
user requirements. Even though a lot of attention has been paid to the decision-making benefits of BI
systems in practice, there is still a limited amount of empirical research that explores the nature of end-
user satisfactionwith BI systems. End-user satisfaction andsystem usage have been recognized by many
researchers as critical determinants of the success of information systems (IS). As an increasing num-
ber of companies have adopted BI systems, there is a need to understand their impact on an individual
end-users performance. In recent years, researchers have considered assessing individual performance
effects from IS use as a key area of concern. Therefore, this study aims to empirically test a framework
identifying the relationships between end-user computing satisfaction (EUCS), system usage, and indi-
vidual performance. Datagatheredfrom 330end usersof BI systemsin the Taiwanese electronicsindustry
were used to test the relationships proposed in the framework using the structural equation modeling
approach. The results provide strong support for our model. Our results indicate that higher levels of
EUCS canlead to increasedBI system usage and improved individual performance, andthat higherlevelsof BI system usage will lead to higher levels of individual performance. In addition, this studys findings,
consistent with DeLone and McLeans IS success model, confirm that there exists a significant positive
relationship between EUCS and system usage. Theoretical and practical implications of the findings are
discussed.
2012 Elsevier Ltd. All rights reserved.
1. Introduction
Today, many organizations continue to increase their invest-
ment in implementing various types of information systems (IS),
such as enterprise resource planning (ERP) and customer relation-
ship management (CRM), primarily because of the belief that these
investments will lead to increasedproductivity foremployees (Jain
& Kanungo, 2005). Evaluating individual employee performancefrom IS use has been an ongoing concern in IS research (Goodhue
& Thompson, 1995).However, previous studies that examined the
relationship between IS usage and individual performance effects
havereportedcontradictory results thatrange frompositiveto non-
significant, to even a negative relationship. For instance,Goodhue
Correspondence address: Department of Information Management, Chia-Nan
University of Pharmacy and Science, 60 Erh-Jen RD., Sec. 1, 717 Jen-Te, Tainan,
Taiwan, ROC. Tel.: +886 6 2664911x5311; fax: +886 6 3660607.
E-mail addresses:[email protected], [email protected]
and Thompson (1995)explored the role of task-technology fit on
individualperformance effects andindicateda positive relationship
between IS use and individual performance. Conversely, Pentland
(1989)found a negative relationship between IS use and individ-
ual performance.Lucas and Spitler (1999)found that IS use has no
impact on individual performance.
Many researchers have recognized user satisfaction as a crit-
ical determinant of the success of IS (Bailey & Pearson, 1983;DeLone & McLean, 1992; Doll & Torkzadeh, 1988; Igbaria & Tan,
1997). When data computing in organizations has transformed
from transactional data processing into end-user computing (EUC),
Doll and Torkzadeh (1988) have developed a 12-item and five-
factor instrument for measuring end-user computing satisfaction
(EUCS) in the EUC environment. Even though EUCS instrument has
already been widely applied and validated for various IS appli-
cations (e.g., decision support systems (McHaney, Hightower, &
White, 1999; Wang, Xi, & Huang, 2007), ERP systems (Somers,
Nelson, & Karimi, 2003),and online banking systems (Pikkarainen,
Pikkarainen, Karjaluoto, & Pahnila, 2006),it has not been validated
0268-4012/$ see front matter 2012 Elsevier Ltd. All rights reserved.
doi:10.1016/j.ijinfomgt.2012.03.001
http://localhost/var/www/apps/conversion/tmp/scratch_3/dx.doi.org/10.1016/j.ijinfomgt.2012.03.001http://www.sciencedirect.com/science/journal/02684012http://www.elsevier.com/locate/ijinfomgtmailto:[email protected]:[email protected]://localhost/var/www/apps/conversion/tmp/scratch_3/dx.doi.org/10.1016/j.ijinfomgt.2012.03.001http://localhost/var/www/apps/conversion/tmp/scratch_3/dx.doi.org/10.1016/j.ijinfomgt.2012.03.001mailto:[email protected]:[email protected]://www.elsevier.com/locate/ijinfomgthttp://www.sciencedirect.com/science/journal/02684012http://localhost/var/www/apps/conversion/tmp/scratch_3/dx.doi.org/10.1016/j.ijinfomgt.2012.03.001 -
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with users of business intelligence (BI) systems. BI systems were
designed to provide decision-makers with actionable information
deliveredat therighttime, atthe right place,and in thecorrect form
to make the right decisions (Negash & Gray, 2004). Given these
goals, attributes measured by EUCS such as timeliness, accuracy,
content, etc., are relevant to an evaluation of BI systems. Since an
increasing number of companies have adopted BI systems, there
is a need to understand the impact of EUCS on individual job per-
formance. DeLone and McLean (2003) propose that higher levels
of individual satisfaction with using an IS will lead to higher levels
of intention to use, which will subsequently affect the use of the
system. Most studies investigating system usage at the individual
level terminate at the user acceptance of the computer technol-
ogy rather than at the performance outcome (Dasgupta, Granger, &
McGarry, 2002). Themainreason could beattributed tothe conven-
tional wisdom thatmore use leads to better performance. However,
empirical studies that examined the relationship between IS usage
and individual performance effects have reported contradictory
results ranging from positive to non-significant, to even a negative
relationship. Therefore, the purpose of this study is to investigate
whether it is appropriate to adopt the EUCS instrument to mea-
sure user satisfaction with BI systems. Furthermore, this study also
examines the following research question: How does EUCS influ-
ence system usage and individual job performance? In this paper,
we present a model that identifiesthe relationships between EUCS,
system usage, and individual performance.Drawing on Igbaria and
Tans (1997)nomological net model, we propose that EUCS has a
positive impact on individual performance both directly and indi-
rectly through system use. Operational measures for the constructs
are developed and tested empirically, using data collected from
330 respondents in the Taiwanese electronics industry to a sur-
vey questionnaire. Structural equation modeling is used to test
the hypothesized relationships. The structure of this paper is orga-
nized as follows. In Section2,we review the related literature on
BI systems, EUCS, and performance measures to provide the nec-
essary background information for the study. Section 3presents
the research framework and develops the hypothesized relation-
ships, while Section 4 describes the research methodology. Section5 presents thedata analysis andresults,which arediscussed in Sec-
tion 6. Section 7 presents implications for practice andresearch,and
the final section describes the limitations of the study.
2. Literature review
2.1. Business intelligence (BI) system
Today, many organizations have already implemented ERP sys-
tems, considered to be one of the most significant and necessary
business software investments for firms. ERP systems offer orga-
nizations the advantage of providing a single, integrated software
system that links their core business activities such as operations,manufacturing, sales, accounting, human resources, and inventory
control (Lee, 2000; Newell, Huang, Galliers, & Pan, 2003; Parr &
Shanks, 2000).As more companies implement ERP systems, they
have accumulated massive amounts of data in their databases.
Although ERP systems are good at capturing and storing data, they
offer very limited planning and decision-making support capabil-
ities (Chen, 2001).It is widely accepted that ERP should provide
better analytical and reporting functions to aid decision-makers
(Chou, Tripuramallu, & Chou, 2005).According to Aberdeens sur-
vey report, business intelligence (BI) applications have the highest
percentage of planned implementations by companies using ERP
systems (AberdeenGroup, 2006).
AsMikroyannidis and Theodoulidis (2010)explain, the BI sys-
tem is a collection of techniques and tools, aimed at providing
businesses with the necessary support for decision making (p.
559).Moss and Atre (2003)also define BI as being a collection of
integrated operational as well as decision support applications and
databasesthat provide thebusinesscommunity with easy accessto
business data (p.4). As such, BI systems canbe regarded as thenext
generation of decision support systems (Arnott & Pervan, 2005).
Therefore, BI systems can provide real-time information, create
rich and precisely targeted analytics, monitor and manage business
processes via dashboards that display key performance indicators,
and display current or historical data relative to organizational or
individual targets on scorecards.
In recent years, several major ERP software vendors such as
SAP and Oracle have started to offer extended products, such
as BI applications, because they have realized the shortcomings
of their systems in providing decision-making support. Accord-
ing to results from the 2009 IT spending survey from Gartner, BI
continues to be the top spending priority for chief information
officers (CIOs) in order to raise enterprise visibility and trans-
parency, particularly sales and operational performance (Gartner,
2008).Furthermore, more than half of the respondents in another
survey byInformationAge (2006)stated that improving decision-
making and better corporate performance management are the
two main drivers of BI investment. Companies that adopt BI sys-
tems can empower their employees decision-making capabilities
in a faster and more reliable way. Therefore, BI can deliver better
business information by offering a powerful grip on organizational
data. Since a BI system includes technology for reporting, analy-
sis, and sharing information, it can be integrated into ERP systems
to maximize the return-on-investment (ROI) of ERP (Chou et al.,
2005).
2.2. End-user computing satisfaction
Cotterman and Kumar (1989) defined an end user as any person
who has an interaction with computer-based IS as a consumer of
information.Turban et al. (2007)briefly discuss how the end-user
canbe atany levelin anorganization orin anyfunctionalarea. Many
researchers have emphasized user satisfaction as a measure of ISsuccess in organizations (Bailey& Pearson,1983;DeLone & McLean,
1992; Doll & Torkzadeh, 1988; Ives, Olson, & Baroudi, 1983 ). Of
course, the definition of user satisfaction has evolved with the
changes in the IS environment (Simmers & Andandarajan, 2001).
Early research on user satisfaction was conducted in the transac-
tional data processing environment (e.g., Bailey and Pearson, 1983;
Ives et al., 1983),in which users interact with the computer indi-
rectly with the assistance of an analyst or a programmer ( Doll &
Torkzadeh, 1988).User satisfaction was defined as the extent to
which users believe that the information system available to them
meets their information requirement(Ives et al., 1983, p. 785).Sub-
sequent research on user satisfaction has been conducted in the
end-user computing (EUC) environment, in which users interact
directly with the application software to enter information or pre-pareoutput reports (Doll& Torkzadeh,1988). Inthis context of EUC,
user satisfaction has been defined as an affective attitude towards
a specific computer application by someone who interacts with the
application directly (Doll & Torkzadeh, 1988, p. 261).
The literature has developed several instruments to measure
user satisfaction, including a 13-item user information satisfaction
(UIS) instrument from Ives et al. (1983), Bailey and Pearsons
(1983)39-item computer user satisfaction instrument, and Doll
and Torkzadehs (1988)12-item EUCS instrument. Based on the
UIS instrument of Ives et al.,Doll and Torkzadeh (1988)developed
a 12-item and 5-factor instrument for measuring EUCS, which has
been widely applied and validated, and found to be generalizable
across several IS applications (Gelderman, 1998; Igbaria, 1990). For
example, the instrument has been tested in an ERP environment
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Table 1
Overview of EUCS model research in IS.
Author IS applications Sample source/size Findings
McHaney et al. (1999) Representational decision
support systems (DSS)
123 respondents from 105 different
individuals/companies in the United
States
They conducted a test-retest study and found
that the EUCS instrument remained internally
consistent and stable.
Xiao and Dasgupta (2002) Web-based portals 332 full-time and part-time students at
a large mid-Atlantic university in the
United States
They found that with the exception of one item
that measured sufficiency of information, the
rest of the items (11 items) in the EUCS model
were valid.McHaney et al. (2002) Typical business software
applications
342 knowledge workers in Taiwan They found that the EUCS instrument was a
valid and reliable measure in Taiwanese
applications.
Somers et al. (2003) Enterprise resource
planning (ERP) systems
407 end users from 214 organizations
in the United States
They confirmed that the EUCS instrument
maintains its psychometric stability when
applied to users of ERP applications.
Abdinnour-Helm et al. (2005) A web site 176 students in a lab simulation They found that the EUCS was a valid and
reliable measure in the Web environment, but
one of the sub-factors, timeliness, will need
further refinement.
Pikkarainen et al. (2006) Online ban king systems 268 sys tem us er s in Finland Th eyte sted an dthen mo dified the EUCS mo del.
Their findings indicated that the modified
EUCS model can be utilized in analyzing user
satisfaction with the online banking.
Wang et al. (2007) Group decision support
systems
156 undergraduate students in China
in an experiment
They found that the EUCS model exhibits
acceptable fit to the sample data and the
reliability and validity of the model are also
validated.
Deng et al. (2008) Enterprise wide
applications
2,648 respondents from five world
regions including the United States,
Western Europe, Saudi Arabia, India,
and Taiwan
They used multi-group invariance analysis to
test the model across five national samples and
found that the EUCS instrument provided
equivalent measurement across national
cultures.
and was found to be reliable (Law & Ngai, 2007; Somers et al.,
2003). Table 1 provides a summary of the EUCS research in IS. EUCS
is a multi-faceted construct that consists of five subscales: content,
accuracy, format, ease of use, and timeliness (seeFig. 1).Although
past research has demonstrated the validity and reliability of the
EUCS instrument (Doll & Xia, 1997; Doll, Xia, & Torkzadeh, 1994;
Hendrickson, Glorfeld, & Cronan, 1994; McHaney & Cronan, 1998;
McHaney et al., 1999),some studies involved only student groups,or groups of users within a single organization. The responses
from students may not reflect the real-world situation. Besides,
the results from a single organization may not be generalizable
(OReilly, 1982).
2.3. System usage
Over the past decade, the system usage (synonymous with
use) construct has played a critical role in IS research (Barkin &
Dickson, 1977; Bokhari, 2005;Schwarz & Chin, 2007). Burton-Jones
Content
Timeliness
Accuracy
Format
Ease of use
End-User
Computing
Satisfaction
(EUCS)
Fig. 1. End-user computing satisfaction model byDoll and Torkzadeh (1988).
and Straub (2006) state that system usage has been employed
in scholarly studies across four domains, including IS success
(DeLone & McLean, 1992; Goodhue, 1995), IS acceptance (Davis,
1989; Venkatesh, Morris, Davis, & Davis, 2003),IS implementation
(Hartwick & Barki, 1994; Lucas, 1978),and IS for decision-making
(Barkin & Dickson, 1977; Yuthas & Young, 1998).Ives et al. (1983)
argued that system usage can be used as a surrogate indicator of
IS system success.Goodhue and Thompson (1995)defined systemusage as the behavior of employing the technology in completing
tasks (p. 218) and conceptualized it as the extent to which the
information system has been integrated into each individuals work
routine (p. 223). In a review of technology acceptance model liter-
ature,Lee, Kozar, and Larsen (2003) found that the frequency of
use, amount of time using, actual number of usages, and diver-
sity of usage were more commonly used for measuring system
usage. Similarly, Burton-Jones and Straub (2006) report that the
most common measures of system usage include the extent of use,
frequency of use, duration of use, decision to use (use or not use),
voluntariness of use (voluntary or mandatory), features used, and
task supported.
2.4. Individual performance impact of IS
Individual performance impact of IS refers to the actual perfor-
mance of an individual using an IS. DeLoneand McLean(1992) note
that an individual performance impact could also be an indication
that an IS has given the user a better understanding of the decision
context, has improved his or her decision-making productivity, or
has changed the users perception of the importance or usefulness
of the IS. A number of prior studies have measured individual per-
formance impact of IS, including improved individual productivity,
increased job performance, enhanced decision-making effective-
ness, and strengthened problem identification capabilities (DeLone
& McLean, 1992).For example,Gattiker and Goodhue (2005)con-
ducted an empirical investigation of the impact of ERP systems on
business processes andfoundthat theadoptionof ERPsystems was
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C.-K. Hou / International Journal of Information Management 32 (2012) 560573 563
End-user ComputingSatisfaction
Content
Accuracy
Format
Ease of use
Timeliness
System Usage
Frequency of
system usage
Duration of use
Individual Performance
Decision-making quality Job performance
Individual productivity
Job effectiveness
Problem identification speed
Decision-making speed
The extent of analysis in
decision-making
H1aH2
H3
Control variables
Firm size
Elapsed time
since adoption ofthe BI system
H1b
Fig. 2. The research model.
positively associated with improved business processes and might
include higher quality data for decision making, efficiency gains in
business processes, and better coordination among different units
within an organization. In their study of executive informationsystem (EIS), Leidner and Elam (1993)found that the frequency
and duration of EIS use were shown to increase the impact of
decision-making at the individual level, such as decision-making
speed, problem identification speed, and the extent of analysis in
decision-making. Furthermore, Igbaria and Tan (1997) proposed
that system usage has a direct positive effect on individual per-
ceived performance impacts (i.e., perceived impact of computer
systems on decision-making quality, performance, productivity,
and effectiveness of the job).
3. The research model and hypotheses
3.1. The research model
Fig. 2 presents the research model developed in this study.
The research model proposes that end-user computing satisfaction
(EUCS) will have a positive impact on individual performance both
directly and indirectly through BI system usage. EUCS is concep-
tualized as a higher-order construct composed of five first-order
factors: content, accuracy, format, ease of use, and timeliness. Indi-
vidual performance is a concept that has been operationalized in
the existing literature (Igbaria & Tan, 1997; Leidner & Elam, 1993).
Through theliterature support, we develop andtest three hypothe-
ses representing (a) the relationship between EUCS and system
usage (b) the relationship between system usage and individual
performance, and (c) the relationship between EUCS and individual
performance.
3.2. Hypotheses
3.2.1. EUCS and system usage
Previous studies examining the relationship between user sat-
isfaction and system usage have found moderate support for this
relationship at the individual level (Iivari, 2005).User satisfaction
is strongly associated with system usage, as measured by system
dependence (Kulkarni, Ravindran, & Freeze, 2007),the frequency
and duration of use of the system (Guimaraes & Igbaria, 1997;
Yuthas & Young, 1998), the number of different software appli-
cations and business tasks for which IS is used (Igbaria & Tan,
1997), and the intention to use (Chiu, Chiu, & Chang, 2007; Halawi,
McCarthy, & Aronson, 2007). Parikh and Fazlollahi (2002) proposed
that higher levels of user satisfaction lead to positive attitudes
toward using the system, and in turn, increase the actual use of the
system in voluntary situations.DeLone and McLean (2003)argue
that increased user satisfaction will lead to a higher intention to
use, which will subsequently affect the use of the system. In otherwords, dissatisfied users might opt to discontinue to using the sys-
temand seek other alternatives(Szajna& Scamell, 1993). The study
expected that EUCS would have a significant influence on BI sys-
tem usage. In addition, some researchers suggested that increased
system usage leads to a higher level of user satisfaction ( Baroudi,
Olson, & Ives, 1986; Lee, Kim, & Lee, 1995; Torkzadeh & Dwyer,
1994).Therefore, we propose the following hypotheses:
Hypothesis 1a. Higher levels of EUCS will lead to higher levels of
BI system usage.
Hypothesis1b. Higher levelsof BIsystem usage will lead tohigher
levels of EUCS.
3.2.2. System usage and individual performanceThe relationship between the use of an IS and individual per-
formance effects is complex (Jain & Kanungo, 2005).Prior studies
have produced mixed findings in terms of the impact of IS usage
on individual performance. Some studies have found that IS usage
is positively associated with individual performance (Goodhue
& Thompson, 1995; Igbaria & Tan, 1997; Leidner & Elam, 1993;
Torkzadeh & Doll, 1999), while others find that IS usage has no
impact (Lucas & Spitler, 1999), or even a negative impact, on
individual performance (Pentland, 1989; Szajna, 1993).While the
evidence about the relationship between IS usage and individual
performance effects is mixed, it is logical to expect that an IS will
not contribute to any performance effects unless it is used. In other
words, an IS must be utilized before it can deliver performance
effects (Goodhue & Thompson, 1995). Therefore, it is expected that
with increase in IS usage, there will be an improvement in indi-
vidual performance effects and, therefore, a positive relationship
should exist between system usage and individual performance
effects of IS. Therefore, we proposed:
Hypothesis 2. Higher levels of BI system usage will lead to higher
levels of individual performance.
3.2.3. EUCS and individual performance
Prior research has supported the positive impact of user sat-
isfaction on individual performance (Gatian, 1994; Guimaraes &
Igbaria, 1997; Igbaria & Tan, 1997). For example, in their study
of client/server systems,Guimaraes and Igbaria (1997)found that
end-user satisfaction has a positive effect on end-user jobs (i.e.,
accuracy demanded by the job, feedback on job performance,
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skill needed on the job, etc.). Igbaria and Tan (1997) found that
user satisfaction has the strongest direct effect on individual per-
ceived performance effects, but identified a significant role for
system usage in mediating the relationship between user satis-
faction and individual impact. Additionally, DeLone and McLean
(1992) proposed that user satisfaction will affect individual impact
(performance) (i.e., decision effectiveness, problem identification,
information understanding, individual productivity, etc.). There-
fore, thestudyexpected that EUCS wouldhave a significant positive
influence on individual performance, leading to the following
hypothesis:
Hypothesis 3. Higher levels of EUCS will lead to higher levels of
individual performance.
3.2.4. Control variables and individual performance
To have a clear understanding of the effects of user satisfaction
on individual performance, we control forother variables that have
the potential to influence the individual performance. Two control
variables, firm size and elapsed time since adoption of the BI sys-
tem, are included in the research framework. Firm size was used as
the control variable, since it was used in prior IS literature to proxy
for the size of the organization resource base which can influence
IT performance (Hunton, Lippincott, & Reck, 2003; Ravichandran &Lertwongsatien, 2005; Tippins & Sohi, 2003).Firm size was mea-
sured by number of employees working in a firm (Elbashir, Collier,
& Davern, 2008; Zhu & Kraemer, 2002). Large firms with more capi-
talresources caninvest in differentactivitieswhichsupportIT, such
as employee training (Subramani, 2004).Therefore, users in large
firms are generally more satisfied with the systems than those in
smaller firms (Lees, 1987).
Elapsed time since adoption of the BI system was used as the
control variable as suggested by Bradford and Florin (2003).This
variable is calculatedas the length of timesince the implementation
ofBI systems. Theearlier firms implementan IS,the more employee
learning that takes place and the greater the chance of realization
of business benefits from IS investments (Purvis, Sambamurthy, &
Zmud, 2001).Additionally, the longer the time elapsed, the morecomfortable employees are with the system and the greater the
employee satisfaction with the system (Bradford & Florin, 2003).
4. Research methodology
4.1. Subjects
The study focuses on the electronics industry, which has been
the most dynamic sector in East Asia since the 1980s and has
been widely recognized as a key driver of economic growth in its
role as a technology enabler for the whole electronics value chain
(Tung, 2001). Taiwans electronics industry is divided into seven
sectors (semiconductor, photoelectricity, computer and periph-
eral equipment, electronics, software and Internet, IC design,and other electronics industries). Electronics products form an
increasingly vital part of a whole range of products, ranging from
electronic devices and systems (e.g., personal computers [PCs],
mobile phones) to solutions and services (e.g., Internet providers,
broadcasting services) (Lee, 2001;Lin, Chen, Lin, & Wu,2006; Tung,
2001).Furthermore, because of the trend of globalization and the
impact of cost reduction, electronics companies in Taiwan need to
improve the speed of their business process, shorten their time of
delivery, and reduce their manufacturing costs to satisfy their cus-
tomers requirements. Therefore, Taiwanese manufacturers have
adopted many IT applications (e.g., ERP systems) and electronic
commerce systems to enhance their competitiveness in the global
market (Chen, Wang, & Chiou, 2009). For example, ERP systems
have become critical to enhancing the competitive advantage of
Taiwans semiconductor industry by integrating internal informa-
tion, increasing the speed of business processes, andreducing costs
in manufacturing, human resources, and management (Lin et al.,
2006). As morecompanies implementERP systems, theyhave accu-
mulated massive amounts of data on their databases. Although ERP
systems are good at capturing and storing data, they offer very
limited planning and decision-making support capabilities (Chen,
2001).According to Aberdeens survey report, BI applications have
the highest percentage of planned implementations by companies
using ERP systems (AberdeenGroup, 2006).The main purpose of
BI implementation is to enhance the analysis capabilities of busi-
ness information stored in ERP systems to support and improve
managementdecision-making (Elbashir et al., 2008). Therefore, the
electronics industry is likely to be fruitful ground to address our
research objectives.
4.2. Construct measurement
The items used to operationalize the constructs were adapted
from relevant previous studies. All scale items were rephrased to
relate specifically to the context of BI systems and were measured
using a seven-point Likert-type scale (from 1 = strongly disagree
to 7 = strongly agree). To ensure the content validity of scales, a
pre-test was conductedwith fiveindustrial experts andten experi-
encedBI users in Taiwan. They were asked to evaluate the clarity of
wording and the appropriateness of the items in each scale. Based
on the feedback received, we modified the wording of some ques-
tions and instructions. In addition, we adapted a 12-item scale
specifically for measuring end-user computing satisfaction from
Doll & Torkzadehs (1988)EUCS instrument, which consists of five
componentscontent (four items), accuracy (two items), format
(two items), ease ofuse (two items), andtimeliness(twoitems).The
EUCS instrument by Doll and Torkzadeh has been widely used and
empirically validated through confirmatory factor analysis (e.g.,
Abdinnour-Helm et al., 2005; Doll et al., 1994; Hendrickson et al.,
1994; McHaney et al., 2002).
The measures of system usage used widely in the literature
include frequency of use, duration of use, and extent of useby the individual (Davis, 1989; Hartwick & Barki, 1994; Igbaria,
Guimaraes, & Davis, 1995; Leidner & Elam, 1993; Mathieson,
Peacock, & Chin, 2001; Venkatesh & Davis, 2000).In this study, BI
system usage was measured by (1) frequency of use, which was
measured on a seven-point scale ranging from 1 ( less than once a
week) to 7 (more than 4 times a day); and (2) the duration of use
by the individual, which asked individuals to indicate how much
time was spent on the system per week using a seven-point scale
ranging from 1 (less than 10 min) to 7 (more than 2 h).
Individual performance was assessed using 14 items. Four of
these items measuring the perceived impact of BI systems on
job performance, individual productivity, job effectiveness, and
decision-makingqualitywere adapted fromIgbaria and Tan (1997).
Ten additional items measuring the perceived impact of BI sys-tems on the speed of problem identification and decision-making,
and the extent of analysis in decision-making were adapted from
Leidner and Elam (1993). Each item was measured on a seven-
point Likert-type scale ranging from 1 (strongly disagree) to 7
(strongly agree). The speed of problem identification refers to the
length of time between when a problem first arises and when it is
first noticed (Leidner & Elam, 1993).The respondents were asked
to assess the extent to which BI systems helped them identify
potential problems faster, notice potential problems before they
become serious crises, and sense key factors affecting their area
of responsibility. The speed of decision-making refers to the time
between when a decision-maker recognizes the need to make a
decision to the time when he or she renders judgment (Leidner
& Elam, 1993, p. 142).The respondents were asked to indicate the
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Table 2
Profile of the respondents and their organizations (n = 330).
Categories Frequency Percentage
Gender
Male 170 51.5
Female 160 48.5
Age (year)
Under 30 60 18.2
3039 196 59.4
Over 40 74 22.4
Educational level
Bachelors degree 193 58.5
Masters degree 98 29.7
Vocational/technical school 35 10.6
Senior high school 2 0.6
Doctoral degree 2 0.6
Industry segmenta
Photoelectric and optical related
industry
95 28.8
Semiconductor industry 92 27.9
Electronics-related industry 50 15.2
Computer and consumer
electronics manufacturing
industry
39 11.8
IC design house 34 10.3
Software and Internet-relatedindustry
27 8.2
Others (e.g., equipment firms,
material firms)
8 2.4
Number of employees
100 or less 13 3.9
101499 66 20.0
500999 42 12.7
10004999 154 46.6
50009999 12 3.6
Over 10,000 43 13.0
Annual revenue (NT$ millions)
More than 2000 189 57.3
500 to below 2000 62 18.8
100 to below 500 41 12.4
Below 50 27 8.1
50 to below 100 11 3.3
Work position
Non-management/professional
staff
211 63.9
Middle-level management 64 19.4
First level supervisor 47 14.2
Top-level
management/executives
8 2.4
Organizations BI Softwareb
Oracle 102 30.9
SAP 90 27.3
Microsoft 68 20.6
Data systems (Taiwan) 59 17.9
In-house development 53 16.1
Other suppliers 24 7.3
Business objects 14 4.2
Cognos 14 4.2
Hyperion 12 3.6SAS 1 0.3
Elapsed time since adoption of the BI system (year)
Less than 1 29 8.8
13 48 14.5
35 37 11.2
510 120 36.4
Over 10 96 29.1
BI experience (year)
Less than 1 65 19.7
14 128 38.8
Table 2 (Continued)
Categories Frequency Percentage
Over 5 137 41.5
Duration of BI use each week (min)
Less than 20 78 23.6
2040 31 9.4
4090 37 11.2
90120 20 6.1
Over 120 164 49.7
Frequency of system usage
Less than once a week 45 13.6
About once a week 10 3.0
2 or 4 times a week 55 16.7
About once a day 64 19.4
2 or 3 times a day 30 9.1
More than 4 times a day 126 38.2
Note: US$1NT$32.84.a Some organizations belong to more than one industry segment.b Some organizations had one or more BI systems implemented.
degree to which BI systems had helpedthem make a decision more
quickly, shorten the time frame for making decisions, and spend
less time in meetings. The extent of analysis in decision-making
refersto theextent of analysis in decision-makingin situationdiag-nosis, alternative generation, alternative evaluation, and decision
integration (Leidner & Elam, 1993).To measure the extent of anal-
ysis in decision-making, the respondents were asked to assess the
degree to which BI systems had helped them spend significantly
more time analyzing data before making a decision, examine more
alternatives in decision-making, use moresources of information in
decision-making, and engage in more in-depth analysis. Perceptual
measures of performance are chosen because most measurements
of individual performance are intangible or qualitative and, hence,
it is difficult to be precise about their actual value as objective
measures. Tallon et al. (2000) argue that perceptual measures have
begun to be adopted in IS research (e.g., DeLone andMcLean, 1992;
Mahmood and Soon, 1991; Sethi and King, 1991).To ensure data
reliability, we conducted a pilot study with 30 executives fromfour Taiwanese electronics companies. Each participant was asked
to complete the questionnaire, evaluate the instrument and com-
menton its clarity andunderstandability(Moore & Benbasat,1991).
Cronbachs alpha coefficient was used to measure the internal con-
sistency of the multi-item scales used in the study. The value of
Cronbachs alpha for each construct was greater than 0.7, indicat-
ing satisfactory reliability level above the recommended value of
0.6 (Nunnally, 1978).Based on the feedback received, we modified
thewording of some questionsand instructions. The feedback from
the pilot study was incorporated into the final version of the ques-
tionnaire.Appendix Alists the final scale items used to measure
each construct and their reference sources. The 30 executives who
assisted in the pilot study were not included in the sampling frame
of the subsequent study.
4.3. Data collection
Due to the limited time the managerial respondents could
offer, a mail survey approach was adopted to allow respondents to
complete the surveys at their convenience. The sample was drawn
from a report published by 2010 Common Wealth Magazine,
which lists Taiwans top 1000 manufacturers, including electronics
companies ranked by annual revenue. Initial telephone screening
interviews were conducted with IS executives or senior managers
from Taiwans electronics companies to confirm that the selected
companies are using BI systems. Of these, 552 companies qualified
and agreed to participate in the mail survey. A contact person was
identifiedat each company andthat person was asked to distribute
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Table 3
Fit indices for the measurement model.
Fit indices Recommended
valueaFirst-order factor
model
Second-order
factor model
Chi-square/degrees of freedom 3.0 2.315 2.706
Normalized Fit Index (NFI) 0.90 0.950 0.977
Goodness-of-Fit Index (GFI) 0.90 0.905 0.951
Adjusted Goodness-of-Fit Index (AGFI) 0.80 0.862 0.900
Non-normed Fit Index (NNFI) 0.90 0.961 0.974
Root mean square error of approximation (RMSEA) 0.08 0.063 0.072a Recommended values for concluding good fit of model to data (Hair et al., 1998; Segars & Grover, 1993).
the questionnaire to a key end user who has plenty of experience
and knowledge in BI systems at any level in an organization. This
was done to avoid concern about common respondent bias in
survey research. A total of 552 survey packages were sent out.
The survey package contained a cover letter, questionnaire, and
a stamped return envelope. The questionnaire consisted of three
parts. The first part involved demographic questions about the
respondent, their organization, and the extent to which they used
BI systems. The second part included 12 questions that were
adopted from Doll and Torkzadehs EUCS instrument, designed to
measure the respondents satisfaction with BI systems. The third
part of the questionnaire included 14 items measuring individualperformance that were adapted fromIgbaria and Tan (1997)and
Leidner and Elam (1993). Seven-point Likert-type scales were used
to score the responses with 7 standing for strongly agree and 1
for strongly disagree.
A total of 335 completed questionnaires were returned. How-
ever, five responses had to be discarded due to incomplete data.
There were 330 valid responses and the response rate was 54.3%.
The demographics of the respondents surveyed are shown in
Table 2. The respondents included 170 males (51.5%) and 160
females (48.5%), 59.4% were between 30 and 39 years old, and
88.7%had at least a bachelors degree. Thedistribution of theindus-
try segments in our sample included 28.8% in photoelectric and
optical industry, 27.9% in the semiconductor industry, 15.2% in
electronic-related industry, 11.8% in computer and consumer elec-
tronics manufacturing industry and 10.3% in IC design house. As
for the size of the firm in terms of the number of employees, 63.3%
of the responses can be classified as large firms (more than 1000
employees), 12.7% of the responses as medium firms (501999
employees), 20.0% of the responses as small (101499 employ-
ees), and the remaining 3.0% had less than 100 employees. The job
position of respondents included top-level managers (2.4%), mid-
dle managers (19.4%), supervisors (14.2%), and professional staff(63.9%). Most of the participants worked in the IT department
(20.6%), followed by those in the R&Ddepartment (15.5%), the sales
department (9.7%) and the purchasingdepartment (8.8%). Concern-
ing BI usage experience, those who have accumulated more than 5
years comprised the majority, at approximately 41.5%. Nearly half
(49.7%) of the respondents used BI systems more than 120 min per
week. More than 38%reportedusing BI systems an average of more
than four times per day.
To examine the possible presence of a non-response bias, we
tested for statistically significant differences in the responses of
Table 4
Measurement model: factor loadings, reliability and validity.
Constructs Indi cators Construct reliability and validity
Factor loadings Convergent validity
(t-value)
Cronbachs alpha Composite
reliability (CR)bAverage variance
extracted (AVE)c
Contents (C) C1 0.857a 0.937 0.931 0.772
C2 0.876*** 30.272
C3 0.865*** 20.354
C4 0.917*** 22.321
Accuracy (A) A1 0.921a 0.919 0.919 0.851
A2 0.924*** 26.700
Format (F) F1 0.869a 0.892 0.888 0.798
F2 0.918*** 22.320
Ease of use (EU) EU1 0.987a 0.955 0.955 0.914
EU2 0.925*** 32.858
Timeliness (T) T1 0.950a 0.938 0.936 0.880
T2 0.927*** 27.977
Individual performance
(INDP)
INDP1 0.886*** 14.418 0.954 0.912 0.603
INDP2 0.924*** 14.783
INDP3 0.912*** 14.626
INDP4 0.673a
INDP5 0.645*** 15.212
INDP6 0.661*** 15.175
INDP7 0.670*** 13.945
System usage (SU) SU1 0.895a 0.909 0.934 0.876
SU2 0.976*** 14.651
a Loadings are specified as fixed to make the model identified.b CR= (
i
2)/[(
i)2+
Var(j)].
c AVE =
i2/[
i2+
Var(i)], where iis thestandardizedfactorloadingsfor theindicators for a particular latent variablei, Var(i) is theindicatorerror variances
(Fornell & Larcker, 1981).***
Significance level:p
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Table
5
Means,standarddeviations,factorcorrelationsa
nddiscriminantvalidityforthemeasurementmodel.
Constructs
Mean
S.D.
Correlationmatrix
1
2
3
4
5
6
7
8
9
1
Content
5.373
0.9
08
(0.878)
2
Accuracy
5.2
27
1.0
33
0.776**
(0.9
22)
3
Format
5.1
98
1.059
0.7
17**
0.6
94**
(0.8
93)
4
Easeofuse
5.0
45
1.158
0.6
29**
0.6
22**
0.7
40**
(0.956)
5
Timeliness
5.2
19
1.1
09
0.6
88**
0.7
25**
0.6
07**
0.6
37**
(0.9
38)
6
Individualperformance
5.3
45
0.9
28
0.5
97**
0.5
16**
0.5
35**
0.4
40**
0.5
00**
(0.776)
7
System
usage
4.578
2.0
15
0.3
16**
0.2
80**
0.1
66**
0.2
29**
0.2
82**
0.2
83**
(0.9
35)
8
Firm
size
4.970
1.8
36
0.056
0.0
32
0.073
0.0
35
0.0
26
0.057
0.0
26
na
9
ElapsedtimesinceBIadoption
3.6
20
1.2
80
0.3
41**
0.2
29**
0.2
02**
0.1
85**
0.2
13**
0.1
98**
0.3
41**
0.1
93**
na
Note:Diagonalsinparenthesesaresquareroots
oftheaveragevarianceextractedfrom
observed
variables(items);off-diagonalsarecorrelations
betweenconstructs.na:Averagevarianceextractedarenotapplicabletothe
single-item
constructs.
**
p