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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C.-K. Hou / International Journal of Information Management 32 (2012) 560573 561

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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562 C.-K. Hou / International Journal of Information Management 32 (2012) 560573

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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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

Documents

2 28 Examining the Effect of User Satisfaction on System Usage and Individual Performance Taiwan