An Empirical Analysis

7/30/2019 An Empirical Analysis

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Computers & Education 49 (2007) 12241245

www.elsevier.com/locate/compedu

0360-1315/$ - see front matter 2006 Elsevier B.V. All rights reserved.

doi:10.1016/j.compedu.2006.01.010

An empirical analysis of the antecedents of web-based

learning continuance

Chao-Min Chiu a,, Szu-Yuan Sun b, Pei-Chen Sun c, Teresa L. Ju d

a Department of Information Management, National Central University, No. 300, Jungda Road, Jhongli City,

Taoyuan 320, Taiwan, ROCb Department of Information Management, National Kaohsiung First University of Science and Technology,

1 University Road, Yenchao, Kaohsiung 824, Taiwan, ROCc National Kaohsiung Normal University, 116 Ho-Ping First Road, Kaohsiung 802, Taiwan, ROC

d Department of Information Management, Shu-Te University, No. 59, Hun Shan Road, Hun Shan Village,

Yen Chau, Kaohsiung, Taiwan, ROC

Received 5 December 2005; received in revised form 21 January 2006; accepted 30 January 2006

Abstract

Like any other product, service and Web-based application, the success of Web-based learning

depends largely on learners satisfaction and other factors that will eventually increase learners inten-

tion to continue using it. This paper integrates the concept of subjective task value and fairness theory

to construct a model for investigating the motivations behind learners intention to continue using

Web-based learning. The model theorizes that four components of subjective task value (i.e., attain-

ment, utility, intrinsic, and cost) and three dimensions of fairness (i.e., distributive, procedural, and

interactional) aVect learners satisfaction. We also argue that satisfaction and four distinct compo-

nents of subjective task value inXuence learners intention to continue using Web-based learning. The

hypothesized model is validated empirically using data collected from 202 learners of a Web-based

learning program designed for continuing education. The results showed that attainment value, utility

value, intrinsic value, distributive fairness, and interactional fairness exhibited signiWcant positive

* Corresponding author. Tel.: +886 3 4267251; fax: +886 3 4254604.

E-mail address:[email protected] (C.-M. Chiu).
mailto:%[email protected]:%[email protected]:%[email protected]


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C.-M. Chiu et al. / Computers & Education 49 (2007) 12241245 1225

eVects on satisfaction. Utility value and satisfaction play signiWcant roles in shaping learners intention

to continue using Web-based learning.

2006 Elsevier Ltd. All rights reserved.

Keywords: Continuance intention; Fairness; Satisfaction; Subjective task value; Web-based learning

1. Introduction

The proliferation of network access and advances in Internet/Web technology, in conjunction

with social demands for improved access to higher education has facilitated the rapid growth ofonline learning or electronic learning (e-learning) (Lorenzetti, 2005). e-Learning also enables orga-

nizations to reduce the total cost of and increases the eY

ciency of training. According to Interna-tional Data Corporation (IDC), the international e-learning market is growing by leaps and

bounds. IDC estimated the international e-learning market to grow from US$ 6.6 billion in 2002to US$ 23.7 billion in 2006, at a compound annual rate of 35.6%. Cortona Consulting said that thee-learning market could reach $50 billion in 2010. 70% of universities in the USA are now provid-

ing e-learning courses, according to the research of Market Data Retrieval. Concurrent with theorganizational and universities interest in e-learning, a large number of academic papers

(Arbaugh, 2002; Carswell & Venkatesh, 2002; Chiu, Hsu, Sun, Lin, & Sun, 2005) have been pub-

lished on e-learning. These developments reXect the signiWcance of e-learning among scholars and

practitioners.The goal of this study is to explore individuals intention to continue using Web-based learning

in a voluntary setting. Web-based learning refers to learning delivered through a Web browserover the public Internet, private intranet or extranet. It is considered as a major subcomponent ofthe broader term e-learning. Like any Web-based application, the success of Web-based learning

depends largely on user satisfaction and other factors that will eventually increase users intentionto continue using it (continuance intention). The importance of continued use (continuance) is evi-

dent from the fact that customer turnover can be costly, especially given that it cost more toacquire new customers than to retain existing ones (Hart, Heskett, & Sasser, 1990; Reichheld &

Schefter, 2000). In view of this, Web-based learning scholars and practitioners should look forways to increase learners satisfaction levels and continuance intention.

Technology acceptance model (TAM) (Davis, 1989) is one of the most widely used models for

explaining an individuals behavioral intention and actual use of information technology (IT).Several recent studies drawing upon TAM have examined the eVects of the two salient beliefs

about technological characteristics, namely perceived usefulness and ease of use, on learners atti-tude or behavioral intention in the context of Web-based learning or distance learning (Gong, Xu,

& Yu, 2004; Lee, Cho, Gay, Davidson, & IngraVea, 2003; Stoel & Lee, 2003). However, those stud-ies ignored the potentially important impacts of value and fairness. While the importance of tech-

nological characteristics cannot be denied, having well-designed Web-based learning sites/systemsdoes not guarantee the success of Web-based learning. This is because value and fairness issues

appear to be signiWcant in guiding a learners overall assessment of Web-based learning, thusinXuencing the learners continuance decision.


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Web-based learning can be considered as an exchange of time, eVort, and money (inputs) forreceiving skills, knowledge, grades, credits, or degrees (outputs) in return. According to Zeithaml

(1988), it is the overall assessment of what is received and what is given that shapes learners inten-

tion to continue using Web-based learning. Researchers have conceptualized value as a functionof a get component, i.e., the beneWts an individual receives, and a give component, i.e., an indi-viduals monetary and non-monetary costs in acquiring and using a product or service (Parasur-

aman & Grewal, 2000; Sirdeshmukh, Singh, & Sabol, 2002). BeneWts include the extrinsic andintrinsic utility provided by the ongoing relationship with a service provider (Gwinner, Gremier, &Bitner, 1998). Literature in the marketing suggests that value (beneWts and costs) drives satisfac-

tion, loyalty, behavioral intention to remain loyalty, and repurchase intention (Bolton & Drew,1991; Neal, 1999; Patterson & Spreng, 1997). Value theorists argue that value is a centrally held

and enduring belief and plays a central role in our everyday life decisions (Homer & Kahle, 1988;Rokeach, 1968). Equity theory theorizes that individuals seek a fair balance between input (what

is given) and output (what is received). According to Adams (1965), individuals become satisW

edand motivated whenever they feel their inputs are being fairly rewarded. Literature in the market-

ing and organization justice has aYrmed the importance of fairness considerations in the assess-ment of satisfaction (Maxham & Netemeyer, 2002; Smith, Bolton, & Wagner, 1999; Tax, Brown,& Chandrashekaran, 1998). Accordingly, a more complete study of the motivations underlying

individuals intention to continue using Web-based learning should address issues related to valueas well as fairness. To this end, two theories are applied and integrated: expectancy-value model of

achievement motivation (Eccles et al., 1983) and fairness theory.Eccles et al.s (1983) expectancy-value model of achievement motivation, which is based on

Atkinsons (1964) expectancy-value model, links individuals choice, persistence, and performanceto expectancy for success and subjective task value. Eccles et al.s model outlines four motivational

components of subjective task value: attainment value, intrinsic value, utility value, and cost. Themodel suggests that value related variables are likely to be more inXuential in situations of choice.Eccles and her colleagues have shown that subjective task value predicts course plans and enroll-

ment decisions in mathematics, physics, and English courses (Eccles, 1987; Eccles, Adler, & Meece,1984; Meece, WigWeld, & Eccles, 1990). This study follows Eccles et al. (1983) in arguing that sub-

jective task value inXuences learners satisfaction and Web-based learning continuance intentionthrough attainment value (the importance of doing well on Web-based learning), intrinsic value

(playfulness of Web-based learning), utility value (helpfulness of Web-based learning to learnerscurrent and future career goals), and cost (negative aspects of engaging in Web-based learning).

The concept of fairness or justice has long been studied in philosophy, political science, religion,

organizational sciences, and economics (Yilmaz, Sezen, & Kabadayi, 2004). Fairness theory has itsorigins in equity theory. Equity theory (Adams, 1965) is a model of motivation that explains why

people strive for fairness or justice in social exchange processes. According to Adamss (1965)equity theory, an individuals perception of the fairness of exchange relationships is determined by

comparing the output/input ratio for oneself with that of referent others. When the ratios areequal, people are satisWed. Adams (1965) argued that people become demotivated, reduce input

and/or seek change whenever they feel their inputs are not being fairly rewarded. Marketing andorganizational justice researchers (Blodgett, Hill, & Tax, 1997; NiehoV & Moorman, 1993;

Ramaswami & Singh, 2003; Tax et al., 1998) have identiWed three important dimensions of fair-ness: distributive, procedural, and interactional. This study follows prior research in arguing that


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fairness inXuences learners satisfaction with Web-based learning through distributive fairness(fairness of the grades learners receive), procedural fairness (fairness of the procedures that pro-

duce the grades), and interactional fairness (fairness of the instructors treatment during online

interaction).This paper makes two key contributions. First, although empirical evidence has demonstrated

that subjective task value and fairness matter for individuals satisfaction or continuance inten-

tion, fundamental gaps remain in the understanding of value and fairness components that mightexplain individuals satisfaction and continuance intention in the context of Web-based learning.This study develops the measures for the components of subjective task value and fairness and

tests their reliability and validity, and empirically examines their inXuences on satisfaction andcontinuance intention. Second, to the best of our knowledge, this is the Wrst study that examines

the integrated inXuence of subjective task value and fairness on learners satisfaction and intentionto continue using Web-based learning. In sum, by explicating the unique role of subjective task

value and fairness, this paper aims at contributing to the continued development and success ofWeb-based learning in general.

2. Theoretical background

2.1. Web-based learning

Web-based learning is a major subcomponent of the broader term e-learning or distance learn-

ing. Web-based learning is often called online learning. e-Learning refers to learning that contentis delivered via the Internet, intranet, extranet, audio or video tape, satellite TV, and CD-ROM

(Kaplan-Leiserson, 2000). e-Learning applications and processes include Web-based learning,computer-based training, virtual classrooms and digital collaboration. Distance learning refers tolearning situation in which the instructor and learners are separated by time, location, or both

(Perraton, 1988). Learning courses are delivered to remote locations via written correspondence,

text, graphics, audio- and videotape, CD-ROM, online learning, audio- and videoconferencing,

interactive TV, and FAX. The deWnition of distance education is broader than and entails the deW-nition of e-learning.

Numerous studies have discussed the reasons that learners choose Web-based learning or dis-tance learning. Lorenzetti (2005) reported that time Xexibility is a major draw for students tochoose to study online. Other reasons include less conXict with work (Dutton, Dutton, & Perry,

2002), the convenience of not having to travel to and attending a traditional face-to-face class-room (Motiwalla & Tello, 2000; Wegner, Holloway, & Garton, 1999), and the Xexibility of access-

ing course materials anytime and anywhere (wherever Internet connection is possible) (Hong, Lai,& Holton, 2003). Willis (1993) stated that adult learners pursue distance learning for a wide vari-

ety of reasons: constraints of time, distance, and Wnances (cost eVective).

There have been considerable studies interested in exploring factors that inXuence learner satis-

faction or outcomes in the context of Web-based learning or distance learning. For example,Carswell and Venkatesh (2002) examined the inXuence of technological characteristics (e.g., visi-bility, result demonstrability) on learners outcomes (i.e., intention, acceptance, and performance)

in an asynchronous distance learning environment. Martins, Steil, and Todesco (2004) examined


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the eVect of innovation characteristics (e.g., observability and trialability) on adoption of theInternet as a learning tool. Other studies focused on the impact of personal cognition such as self-

eYcacy (Lim, 2001; Wang & Newlin, 2002), computer conWdence (Osborn, 2000), and locus of

control (Morris, Wu, & Finnegan, 2005). Arbaugh (2002) suggested examining the eVects of tech-nological characteristics (e.g., perceived Xexibility) and behavioral characteristics (e.g., virtualimmediacy behaviors) on student learning in and satisfaction with Web-based courses.

Prior studies have shown that technological and personal cognition factors play signiWcantroles in Web-based learning, e-learning, or distance learning. However, those studies ignored thepotentially important impact of value and fairness. According to Bandura (1986), of all the cogni-

tive forces that guide human behavior and stand at the core of social cognitive theory is self-eYcacy. Bandura (1986) suggests that individuals who have high levels of self-eYcacy should be

more likely to perform related behavior in the future. Web-based learning requires investment oftime, eVort, and money in developing knowledge or receiving grades and credits. Accordingly,

individuals who have high levels of self-eY

cacy may have low intention to continue using Web-based learning if they think that Web-based learning has low levels of value and fairness. The

overall assessment of the value and fairness of Web-based learning thus plays an important role inshaping an individuals satisfaction and intention to continue using Web-based learning. Thequestion why do individuals spend money and their valuable time and eVort on Web-based

learning, should be addressed from the perspectives of both value and fairness. Consequently, thesubjective task value concept in the expectancy-value model of achievement motivation and fair-

ness theory are applied and integrated for addressing our research question.

2.2. Subjective task value

Expectancy-value model of achievement motivation (Eccles et al., 1983) posits that individualsperformance, persistence, and choice are most directly predicted by their expectancies for successon the tasks and the subjective task value they attach to success on the tasks. Eccles et al. (1983)

deWned diVerent components of subjective task value: attainment value, intrinsic value, utilityvalue, and cost. Attainment value is the personal importance of doing well on the task. According

to Eccles et al. (1983), it incorporates a variety of dimensions, including perceptions of the tasksability to conWrm salient and valued characteristics of the self (e.g., masculinity, femininity, compe-

tence), to provide a challenge, and to oVer a forum for fulWlling achievement, power and socialneeds. Intrinsic value is the enjoyment an individual gets from performing the activity, or the sub-

jective interest an individual has in the subject. Utility value is how well a task relates to current

and future goals, such as career goals. Finally, cost is conceptualized in terms of the negativeaspects of engaging in the task (e.g., performance anxiety and fear of both failure and success), as

well as both the amount of eVort that is needed to succeed and the lost of opportunities resultingfrom making one choice rather than another.

In studying how expectancies and subjective task value inXuence elementary through secondaryschool students performance and choice, Eccles and her colleagues (1983) showed that compo-

nents of subjective task value predicted both intentions and actual decisions to keep taking math-ematics and English in the context of traditional classroom education. WigWeld and Eccles (1989)found that components of subjective task value predicted junior and senior high school students

intention to keep taking mathematics. Battle and WigWeld (2003) also showed that components of


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subjective task value predicted intentions to attend graduate school. However, the possible impactof the components of subjective task value is still unclear in the Web-based learning context. No

empirical work has been done to address this issue. This begs the question whether an

individuals assessment of what is received (attainment value, intrinsic value, and utility value) andwhat is given (cost) is strong enough to stimulate or inhibit his or her intention to continue usingWeb-based learning.

2.3. Fairness theory

The earliest inXuential theories of fairness or justice were the rule of distributive justice(Homans, 1961) and the equity theory (Adams, 1965). Homans (1961) simple formula for justice

stressed the diVerence between the rewards people received for investments. Researchers have con-ceptualized three types of fairness: distributive, procedural, and interactional. Distributive fairness

refers to the perceived fairness of the outcomes an individual receives (Homans, 1961). Proceduralfairness refers to the perceived fairness of the procedures that produce the outcome (Thibaut &

Walker, 1975). Thibaut and Walker (1975) identiWed two types of control as essential determi-nants of procedural fairness: control over the presentation of evidence (process control) and con-trol over the Wnal decision (decision control). Leventhal (1980) suggested six procedural fairness

rules, including consistency, bias suppression, accuracy, correctability (an appeal mechanism toappeal decisions), representativeness, and ethicality. Finally, Bies and Moag (1986) separated out

interpersonal aspect of procedural justice, referred to as interactional fairness, which emphasizesthe perceived fairness of the interpersonal treatment received during the implementation of a pro-

cedure (Bies & Moag, 1986). Bies and Moag (1986) identiWed truthfulness (honesty and avoidingdeception), courtesy, respect for individual rights, propriety of behavior (avoiding prejudice), and

justifying decisions as typifying fair treatment.Learners spend valuable time and eVorts on Web-based learning thus expect that the grades

they receive, the procedures for evaluating their inputs, and the treatment by instructors during

online interaction are fair. Fairness has been studied to a limited degree in marketing in conjunc-tion with relationship quality (Kumar, Scheer, & Steenkamp, 1995), organizational citizenship

(NiehoV & Moorman, 1993), work outcomes (Ramaswami & Singh, 2003), and service recovery(Blodgett et al., 1997). Research has shown the important role of fairness in explaining job satis-

faction (Moorman, 1991), satisfaction with complaint handling (Tax et al., 1998), satisfaction withservice encounter (Smith et al., 1999), satisfaction with recovery (Maxham & Netemeyer, 2002),and reseller satisfaction (Yilmaz et al., 2004). However, the possible impact of three dimensions of

fairness is still unclear in the Web-based learning context. No empirical work has been doneto address this issue. This begs the question whether an individuals perceptions of fair

treatment in the Web-based learning process is strong enough to enhance his or her satisfactionwith Web-based learning.

3. Research model and hypotheses

The study draws on the concept of subjective task value in expectancy-value model of achieve-

ment motivation and fairness theory to investigate the inXuences of value and fairness on learners


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satisfaction and continuance intention in the context of Web-based learning. Following prior

research (Blodgett et al., 1997; Moorman, 1991; NiehoV & Moorman, 1993; Tax et al., 1998), wedeWne fairness with three distinct dimensions: distributive, procedural, and interactional Following

Eccles et al. (1983), we identiWed four components of subjective task value: attainment value, utilityvalue, intrinsic value, and cost. The proposed theoretical model is shown in Fig. 1. The dependent

variable is Web-based learning continuance intention, which refers to the subjective probabilitythat an individual will continue using Web-based learning. The basic assumption is that Web-based

learning continuance intention is determined by attainment value, utility value, intrinsic value, cost,and satisfaction. Satisfaction, in turn, is jointly determined by distributive fairness, procedural fair-

ness, interactional fairness, attainment value, utility value, intrinsic value, and cost.

3.1. Distributive fairness

In this study, distributive fairness refers to learners perceived fairness of the grades they receive.

Equity theory (Adams, 1965) postulates that individuals who are fairly rewarded experience satis-

faction and will be motivated to engage in a certain behavior. According to Kumar et al. (1995),

distributive fairness is helpful in building good relationships between instructors and learners,which in turn will lead to learners satisfaction. While the inXuence of distributive fairness onlearners satisfaction has not been explicitly examined in the Web-based learning research, support

for the relationships can be found in other settings. For example, some studies found that distribu-tive fairness exerted a signiWcant inXuence on satisfaction with job (Lind & Tyler, 1988; Moorman,

1991), service encounter (Smith et al., 1999), and complaint handling (Tax et al., 1998). Therefore,the following hypothesis is proposed.

H1: Distributive fairness is positively related to learners satisfaction with Web-based learning.

3.2. Procedural fairness

In this study, procedural fairness refers to the perceived fairness of the procedures that produce

the grade. Folger and Greenberg (1985) argued that how outcomes are determined may be more

Fig. 1. Research model for Web-based learning continuance intention.

ProceduralFairness

H9

H1

H2

H3

H4

H5

H6 H7

H8 H10

AttainmentValue

Interactional

Fairness

Satisfaction

UtilityValue

Intrinsic

Value

ContinuanceIntention

DistributiveFairness

Cost

H11

H12


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important than the actual outcomes. Prior research indicates that if individuals believe that theprocedures used to produce the outcomes are fair, they are likely to be satisWed with the outcomes,

even if the outcomes are considered unfair (Lind & Tyler, 1988). Both organizational justice theo-

rists (Folger & Konovsky, 1989) and consumer behavior researchers (Maxham & Netemeyer,2002) suggested that perception of procedural fairness enhances the probability of maintaining along-term overall satisfaction between parties. According to Seiders and Berry (1998), grading

process is an integral part of the Web-based learning service, thus the instructors can enhancelearners satisfaction with Web-based learning by engaging activities that enhance learner percep-tions of procedural justice. Prior research has found relationships between procedural fairness and

satisfaction with variables such as pay (Folger & Konovsky, 1989), reseller (Yilmaz et al., 2004),service encounter (Smith et al., 1999), and complaint handling (Tax et al., 1998). Accordingly, this

study theorizes that if the instructors grading process is perceived by learners as fair, they aremore satisWed with Web-based learning.

H2: Procedural fairness is positively related to learners satisfaction with Web-based learning.

3.3. Interactional fairness

In this study, interactional fairness refers to the extent to which learners feel they have been

treated fairly regarding their online interaction with the instructor throughout the Web-basedlearning process. Since interaction between learners and instructors plays an important role in the

online learning process (PalloV& Pratt, 1999), instructors can enhance learners satisfaction withthe Web-based learning by engaging activities that enhance learner perceptions of interactional

fairness. This notion has some support in other settings. Maxham and Netemeyer (2002) foundeVects of interactional fairness on satisfaction with recovery and overall Wrm satisfaction. Tax

et al. (1998) reported a strong eVect of interactional fairness on satisfaction with complaint han-dling, while Smith et al. (1999) demonstrated that positive perceptions of interactional fairness sig-niWcantly enhanced customer satisfaction with service counter. Therefore, the following

hypothesis is proposed.H3: Interactional fairness is positively related to learners satisfaction with Web-based learning.

3.4. Attainment value

In this study, attainment value represents the importance of doing well on Web-based learningin terms of the learners self-schema and core personal values. WigWeld and Eccles (1992) argued

that tasks will have higher attainment value to the extent that they allow the individual to conWrmsalient aspects of his or her self-schema. Attainment value is similar to the notions of integrated

regulation of extrinsic motivation in self determination theory (Ryan & Deci, 2000). In integratedregulation, individuals assimilate identiWed importance of a behavior into their coherent sense of

self, and thus fully accept it as their own. Ryan and Deci (2000) argued that when identiWcations

have become fully integrated, one will behave with a true sense of volition and willingness. In gen-

eral, students will be more likely to engage in a task, expend more eVort on it, and do better on itwhen they value it (WigWeld, 1994). Meece et al. (1990) found that the attainment value junior highschool students attached to math competence predicted their intentions to continue taking math.

Therefore, the following hypotheses are proposed.


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H4: Attainment value is positively related to learners satisfaction with Web-based learning.H5: Attainment value is positively related to learners intention to continue using Web-based

learning.

3.5. Utility value

In this study, utility value is how well taking Web-based learning courses relates to current andfuture career goals, such as solving problems at work, promotion, and pursuing advanced studies.Utility value of an activity can be tied to the construct of extrinsic motivation in self determination

theory (Deci & Ryan, 1985), in that it taps more instrumental reasons for engaging in a task.Extrinsic motivation refers to doing something because it leads to a separable outcome. Eccles and

WigWeld (2002) argued that a learning activity can have positive values to a person because it facil-itates important future goals, even if he or she is not interested in the learning activity for its own

sake. Ryan and Deci (2000) argued that students can perform extrinsically motivated actionswith willingness that reXects an inner acceptance of the value or utility of task. Utility value has

long been identiWed as a major inXuence on students achievement choices and behaviors. In astudy of college womens value orientations toward family, career, and graduate school, Battleand WigWeld (2003) found that utility value predicted intentions to attend graduate school. Sev-

eral researchers have found that students perceptions of the utility of mathematics are stronglyrelated to their intentions to continue or discontinue their mathematical study (Brush, 1980; Fen-

nema & Sherman, 1977). Sullins, Hernandez, Fuller, and Tashiro (1995) found that value factorssigniWcantly predicted intention to enroll in future biology courses.

In consumer behavior, perceived value has been deWned as the consumers overall assessmentof the utility of a product based on perceptions of what is received and what is given ( Zeithaml,

1988). A number of studies have demonstrated the eVect of personal value on satisfaction andbehavioral intention. Spreng, Dixon, and Olshavsky (1993) argued that value should be a directantecedent of satisfaction. Bojanic (1996) suggested that high levels of perceived value result

in purchase and ultimately higher levels of customer satisfaction. Patterson and Spreng(1997) found that perceived value had a signiWcant eVect on customer satisfaction in the context of

business-to-business professional services. Therefore, the following hypotheses are proposed.H6: Utility value is positively related to learners satisfaction with Web-based learning.H7: Utility value is positively related to learners intention to continue using Web-based learn-

ing.

3.6. Intrinsic value

This study follow Davis, Bagozzi, and Warshaws (1992) deWnition of intrinsic value as theextent to which the activity of using a Web-based learning is perceived to be personally enjoyable

in its own right aside from the instrumental value of the technology. According to self

determination theory, learners are self-determining and intrinsically motivated in Web-based

learning when they are interested in it or enjoy doing it. Triandis (1980) argues that aVect (e.g., thefeelings of joy, elation, and pleasure) has an impact on behavioral intentions. Hsu and Chiu (2004)extended and empirically validated the theory of planned behavior (Ajzen, 2002) in the electronic

service (e-service) context. The results showed that perceived playfulness had a signiWcant eVect on


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individuals satisfaction with e-service. WigWeld and Eccles (1989) indicated that junior and seniorhigh school students interest in math predicted their intentions to keep taking math. In a study of

college students course performance and future enrollment intentions, Bong (2001) found that

students who were intrinsically interested in topics covered in the present course would be morewilling to take similar courses in the future. Therefore, the following hypotheses are proposed.

H8:Intrinsic value is positively related to learners satisfaction with Web-based learning.H9: Intrinsic value is positively related to learners intention to continue using Web-based

learning.

3.7. Cost

Cost refers to negative aspects of engaging in Web-based learning. In this study, we focus onrestriction on learners interactions with other learners and instructors, and feeling of isolation

or not being part of the learning community. Online learning is very diV

erent from face-to-faceinstruction. Learners are geographically separated and thus most of the time, the interaction

among learners is based on asynchronous and text-based communication, which can lead torestriction on socialization or felling of isolation. Online learning students tend to suVer fromisolation a signiWcant factor contributing to withdrawal from study (Bennett, Priest, & Macph-

erson, 1999). Kahl and Cropley (1986) suggested distance learning students feel more isolatedthan face-to-face students and experience lower levels of self-conWdence as a result. This has a

signiWcant eVect on the distance learning students motivation and learning and even can leadthem to drop out. Arbaugh (2000) found that perceived interaction diYculty was negatively cor-

related with students satisfaction with Web-based MBA courses. Billings, Conners, and Skiba(2001) found that students felt somewhat isolated from other students and instructors in the

Web-based learning environment. Isolation was negatively related to satisfaction with Web-based courses. Battle and WigWeld (2003) found that cost was a signiWcant and negative predic-tor of college womens intentions to attend graduate school. Therefore, the following hypotheses

are proposed.H10: Cost is negatively related to learners satisfaction with Web-based learning.H11: Cost is negatively related to learners intention to continue using Web-based learning.

3.8. Satisfaction

Satisfaction is an individuals feelings of pleasure or disappointment resulting from comparing

the perceived performance (or outcome) of Web-based learning in relation to his or her expecta-tions. Oliver (1980) theorized that satisfaction is positively correlated with future intention, both

directly and indirectly via its impact on attitude. In the Wnal step of satisfaction formation pro-cesses, satisfaction determines intentions to patronize or not to patronize the store in the future

(Swan & Trawick, 1981). McGorry (2003) argued that student satisfaction with online learning

courses is likely to determine whether the student takes subsequent courses. Chiu et al. (2005)

reported that learners satisfaction with e-learning was signiWcantly associated with their continu-ance intention. Therefore, the following hypothesis is proposed.

H12: Satisfaction is positively related to learners intentions to continue using Web-based

learning.


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4. Research methodology

4.1. Measurement development

Measurement items were adapted from the literature wherever possible. A pretest of thequestionnaire was performed using six experts in the IS area to assess its logical consistency,

ease of understanding, sequence of items, and contextual relevance. The comments collectedfrom these experts led to several minor modiWcations of the wording and the item sequence.Furthermore, a pilot study was conducted involving 20 master students who ever took Web-

based learning course. Comments and suggestions on the item contents and structure of theinstrument were solicited. The questionnaire was further modiWed based on the comments and

suggestions.Items for measuring attainment value were adapted from Eccles et al. (1983) and Battle and

WigW

eld (2003). The items measured the importance of doing well on Web-based learning. Itemsfor measuring utility value were also adapted from Eccles et al. (1983) and Battle and WigWeld

(2003). These items focused on how well taking Web-based learning courses relates to learnerscurrent and future career goals, such as getting a job, solving problems at work, promotion, andpursuing advanced studies. Items for intrinsic value reXect whether Web-based learning is interest-

ing, enjoyable and fun, similar to those applied by Battle and WigWeld (2003) and Moon and Kim(2001). Items for measuring cost were based on Eccles et al.s (1983) concept. These items mea-

sured four negative eVects of Web-based learning. Distributive fairness was measured with itemsadapted from Folger and Konovsky (1989). The measure focused on the extent to which the grade

received by a learner was perceived to be related to his or her eVort, performance, and the amountof time spent. Procedural fairness was assessed with items adapted to reXect the degree to which

fair procedures were used by the instructor in arriving at the grading decisions, following Folgerand Konovsky (1989) and Moorman (1991). Interactional fairness was measured with items basedon Folger and Konovsky (1989) and Moorman (1991), focusing on the instructors interaction

with learners. SpeciWc items asked whether the instructor showed concern to the learners ques-tions, whether the instructor treated the learner with dignity and respect, and whether the instruc-

tor dealt with the learner in a truthful manner. Items related to satisfaction were adapted fromOliver (1980) and Oliver and Swan (1989). Items for measuring continuance intention were

adapted from prior work by Mathieson (1991) and Bhattacherjee (2001). For all the measures, aseven-point Likert scale was adopted with anchors ranging from strongly disagree (1) to stronglyagree (7).

4.2. Survey administration

Data were gathered from students of a Web-based learning program provided by National

Kaohsiung Normal University (NKNU) in Taiwan. The program provides credits for individualsinterested in special education and computer skills. The Web-based learning service is a Unix-

based system and its learning is delivered via blend of asynchronous and synchronous technolo-gies. Synchronous Web-based learning service consists of real-time interaction between learnersand instructors, facilitated by tools such as Web Meeting (videoconferencing) and chat rooms.

Asynchronous Web-based learning service is a self-study that may be supplemented by non-real


12/22


time interaction with the instructor, for instance through email, message boards, and forums. Thesurvey began on October 4, 2004 and ended on November 25, 2004. The Wrst page of the question-

naire explained the purpose of this study and ensured the conWdentiality. A total of 600 surveys

were distributed to individuals who at least took one course oVered by the NKNU Web-basedlearning service. A total of 221 surveys were returned. The exclusion of responses from incompletequestionnaires resulted in a total of 202 usable questionnaires (a net response rate of 33.7%).

Among the respondents, 69% were female and 31% were male. Fifty-six percent have earned abachelors degree and 25% have received a masters degree. Among the respondents, 80% evertook one Web-based course, 15% ever took two or three Web-based courses, and 5% ever took at

least four Web-based courses. On average, the respondents were 28 years of age and spent 17 h perweek in using the Internet.

4.3. Data analysis

Data analysis utilized a two-step approach as recommended by Anderson and Gerbing (1988).

The Wrst step involves the analysis of the measurement model, while the second step tests the struc-tural relationships among latent constructs. The aim of the two-step approach is to assess the reli-ability and validity of the constructs before their use in the full model.

ConWrmatory factor analysis (CFA) was applied to assess the construct validity of the nine

scales (attainment value, utility value, intrinsic value, cost, distributive fairness, procedural fair-

ness, interactional fairness, satisfaction, and continuance intention) with LISREL. Each itemwas modeled as a reXective indicator of its latent construct. The nine constructs were allowed to

co-vary freely in the CFA model. Model estimation was done using the maximum likelihoodapproach, with the item correlation matrix as input. Table 1 presents the results of the CFA

analysis.For measurement models to have suYciently good model Wt, the 2 value normalized by degrees

of freedom (2/df) should not exceed 3, goodness-of-Wt index (GFI), Adjusted GFI (AGFI) should

exceed 0.8 (Doll, Xia, & Torkzadeh, 1994), non-normed Wt index (NNFI) and comparative Wtindex (CFI) should exceed 0.9, and root mean square error of approximation (RMSEA) should

not exceed 0.08. As shown in Table 2, the Wt indices are within accepted thresholds, except forAGFI, which is slightly lower than the commonly cited threshold: 2 to degrees of freedom ratio

of 1.45 (2D797; dfD551), GFID0.82, NNFID0.95, CFID0.95, and RMSEAD0.047. Accordingto Boudreau, Gefen, and Straub (2001) and Gefen, Karahanna, and Straub (2003), it is common

that not all Wt indices are beyond acceptable thresholds in LISREL. Hence, this model Wtted rea-

sonably well with the data.Reliability was examined using the composite alpha values. As shown in Table 1, all the values

were above 0.7; this indicates a commonly acceptable level for explanatory research. Additionally,convergent validity of the resulting scales was veriWed by using two criteria suggested by Fornell

and Larcker (1981): (1) all indicator loadings should be signiWcant and exceed 0.7 and (2) averagevariance extracted (AVE) by each construct should exceed the variance due to measurement error

for that construct (i.e., AVE should exceed 0.50). For the current CFA model, only two of the 36loadings were slightly below the 0.7 threshold (see Table 1). AVE ranged from 0.54 to 0.81 (see

Table 3), greater than variance due to measurement error. Hence, the convergent validity wasstrongly supported.


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Ta e 1

Summary of measurement scales

Construct Measure Mean SD Loading

Attainment value (AV) Composite alphaD 0.89

AV1 The amount of eVort it took through Web-based

learning was worthwhile to me

5.77 0.85 0.79

AV2 Web-based learning made me a more knowledgeable

person

6.00 0.78 0.93

AV3 Web-based learning broadened my view 6.02 0.77 0.89

AV4 Being successful at Web-based learning conWrmed

my competence

5.77 0.95 0.63

Utility value (UV) Composite alphaD 0.83

UV1 What I learned through Web-based learning was

helpful for me to get a job

5.28 1.11 0.78


useful for resolving my problems at work

5.55 0.94 0.74


useful for my promotion

4.86 1.15 0.62

UV4 The credits I got through Web-based learning were

useful for pursuing advanced studies

5.70 0.95 0.79

Intrinsic value (IV) Composite alphaD 0.92

IV1 I think Web-based learning is interesting 5.40 0.98 0.78

IV2 I think Web-based learning is enjoyable 5.29 1.00 0.95

IV3 I think Web-based learning is fun 5.27 1.00 0.92

Cost (C) Composite alphaD 0.90

C1 Web-based learning reduced opportunities for

interaction among learners

4.80 1.35 0.78

C2 Web-based learning reduced opportunities of

studentteacher dialogue

4.83 1.35 0.81

C3 Web-based learning reduced the sense of being part

of the learning community

5.21 1.26 0.87

C4 Web-based learning reduced opportunities for

discussing with and learning from other learners

4.85 1.39 0.86

Distributive fairness (DF) Composite alphaD 0.91

DF1 The grade I received was fair considering my eVort 4.88 1.10 0.80

DF2 The grade I received was fair considering my

performance

4.84 1.10 0.87

DF3 The grade I received was fair considering the

amount of time I spent

4.94 1.04 0.93

DF4 Overall, the grade I received was fair 5.02 1.00 0.81

Procedural fairness (PF) Composite alphaD 0.91

PF1 The instructor collected accurate information

necessary for making the grading decisions

4.73 1.13 0.84

PF2 The instructor provided opportunities to appeal or

challenge the grading decisions

4.76 1.21 0.80

PF3 The instructor generated standard so the grading

decisions could be made with consistency

4.74 1.09 0.86


14/22


Finally, discriminant validity of the resulting scales was assessed using the guideline suggested

by Fornell and Larcker (1981): the square root of the AVE from the construct should be greaterthan the correlation shared between the construct and other constructs in the model. Table 3 liststhe correlations among the constructs, with the square root of the AVE on the diagonal. All the

diagonal values exceed the inter-construct correlations; hence the test of discriminant validity wasacceptable. Therefore, we conclude that the measures have demonstrated suYcient construct

validity.The structural model reXecting the assumed linear, causal relationships among latent constructs

was tested with the data collected from the validated measures. The structural model yielded anacceptable Wt: 2/dfD1.45 (2D802; dfD554), GFID0.82, AGFID0.78, CFID0.95, NNFID0.94,

and RMSEAD0.047.The test results of the structural model are summarized in Fig. 2. Seven out of the 12 paths

exhibited a P-value less than 0.05, while the remaining Wve were not signiWcant at the 0.05 level of

signiWcance. The paths from distributive fairness, interactional fairness, attainment value, utility

Table 1 (continued)

Construct Measure Mean SD Loading

IF4 The instructor provided me with timely feedback to

my questions

5.05 1.12 0.86

IF5 The instructor provided appropriate answers or

suggestions to my questions or problems

5.08 1.05 0.89

Satisfaction (S) Composite alphaD0.91

S1 My decision to use Web-based learning is a wise one 5.69 0.91 0.70

S2 I think Web-based learning is a good idea 5.57 0.96 0.77

S3 I am pleased with the experience of using Web-based

learning

5.49 0.92 0.93

S4 I am satisWed with the experience of using Web-

based learning

5.42 0.98 0.90

Continuance intention (CI) Composite alphaD0.93

CI1 I intend to continue using the Web-based learning inthe future

5.50 0.98 0.93

CI2 I will continue using the Web-based learning as

much as possible in the future

5.20 1.14 0.83

CI3 I will continue using the Web-based learning in the

future

5.41 0.99 0.93

Table 2

Model Wt indices for the measurement model

Model Wt indices Results Recommended value

2 statistic 2/df 1.45 63

GFI 0.82 70.8

AGFI 0.78 70.8

CFI 0.95 70.9

NNFI 0.95 70.9

RMSEA 0.047 60.08


15/22


value, and intrinsic value to satisfaction were signiWcant, while procedural fairness and costshowed no signiWcant inXuence on satisfaction Consequently, hypotheses 1, 3, 4, 6 and 8 were sup-

ported empirically while hypotheses 2 and 10 were not supported. Utility value signiWcantly andpositively aVected continuance intention, while attainment value, intrinsic value, and cost did not

exhibit signiWcant positive eVects on continuance intention. Hypothesis 7 was supported whilehypotheses 5, 9, and 11 were not supported. Satisfaction exhibited a strong positive eVect on con-

tinuance intention, supporting hypothesis 12.The explanatory power of the research model is also shown in Fig. 2. The R2 value for continu-

ance intention shows that attainment value, utility value, intrinsic value, cost, and satisfaction

account for 66% of variance of continuance intention. The R2 value for satisfaction shows that

Ta e 3

Correlations and AVE

Diagonal elements (in bold) are the square root of the average variance extracted (AVE). OV-diagonal elements are the

correlations among constructs. For discriminant validity, diagonal elements should be larger than oV-diagonal ele-ments.

AV, attainment value; UV, utility value; IV, intrinsic value; C, cost; DF, distributive fairness; PF, procedural fairness;

IF, interactional fairness; S, satisfaction; CI, continuance intention.

Construct AVE Construct

AV UV IV C DF PF IF S CI

AV 0.67 0.82

UV 0.54 0.64 0.73

IV 0.79 0.63 0.62 0.89

C 0.69 0.02 0.14 0.10 0.83

DF 0.73 0.38 0.34 0.49 0.03 0.85

PF 0.68 0.35 0.34 0.44 0.05 0.71 0.82

IF 0.79 0.35 0.38 0.44 0.09 0.52 0.64 0.89

S 0.69 0.62 0.53 0.63 0.00 0.62 0.54 0.50 0.83

CI 0.81 0.60 0.59 0.63 0.02 0.62 0.53 0.48 0.82 0.90

Fig. 2. SEM analysis of research model.

Procedural

Fairness

0.00

0.48*

-0.10

0.14*

0.20*0.01

0.19*0.15*

0.15* 0.05

Attainment

Value

Interactional

Fairness

Satisfaction

R2

= 0.72

Utility

Value

IntrinsicValue

Continuance

Intention

Distributive

Fairness

Cost

0.01

0.81*

*p-value < 0.05

R2

= 0.66


16/22


distributive fairness, procedural fairness, interactional fairness, attainment value, utility value,intrinsic value, and cost account for 72% of variance of satisfaction.

5. Discussion and conclusions

Creating perceptions of value is a primary means of enhancing learners feeling of satisfactionand developing long-term relationship with them, which is as important as learners perceptions offairness. This study provides an initial step toward understanding the integrated inXuence of sub-

jective task value and fairness on learners satisfaction and continuance intention in the context ofWeb-based learning.

The Wndings indicated that learners who experienced higher levels of distributive fairness andinteractional fairness were more satisWed with Web-based learning. Contrary to our expectations,

we found that procedural fairness did not have a signiW

cant impact on satisfaction ThisW

nding isinconsistent with Folger and Greenbergs (1985) argument that how outcomes are determined

may be more important than the actual outcomes. One possible explanation for the insigniWcantrelationship between procedural fairness and satisfaction is that higher levels of distributive fair-ness and interactional fairness compensate for lower levels of procedural fairness. In other words,

learners may be satisWed with the Web-based learning when a low quality of procedures were usedby the instructor to arrive at grading decisions, provided that the instructor dealt with them in a

truthful manner, the instructor showed concern to their questions or problems, and the gradesthey received were fair. The grading of the National Kaohsiung Normal Universitys (NKNU)

Web-based learning service is based on learners midterm and Wnal exams, term reports, and par-ticipation in online interaction (oYce hours). Learners would receive the Wnal grades through mail

two or three weeks latter after the semester ended. To enhance learners satisfaction, this studysuggests that the NKNU Web-based learning system should provide a real-time feedback mecha-nism that allows learners to access the details about grading process. For example, the mechanism

could show learners grades of each part (e.g., exams, reports, and online interaction) and theirranking in the whole class for each part. Instructors of the NKNU Web-based learning service

were scheduled to oVer six times of online interaction during the semester and each interactionshould last three hours. The Wndings indicated that even though each instructor had about 50

learners, he or she could still oVer fair treatment to learners during online interaction, which inturn increased learners satisfaction with Web-based learning. The Wndings suggest that a learner

should collect information about whether or not instructors are fair in grading and treating learn-

ers during online interaction before selecting a course in order to enhance his or her satisfactionwith the Web-based course. The Wndings also suggest that Web-based learning providers (academ-

ics) could provide some reward mechanisms to stimulate instructors to deal with learners in atruthful manner and show concern and respect to learners during online interaction.

Additionally, the Wndings indicated that the inXuences of attainment value, utility value, andintrinsic value on satisfaction were signiWcant, whereas the inXuence of cost was not signiWcant.

One possible explanation for the Wnding is that learners participating in Web-based learning donot really expect extensive interaction. The Wndings suggest that learners may be satisWed with theWeb-based learning even when negative aspects of it existed, provided that doing well on it is

important to them, taking Web-based learning courses relates to their current and future career


17/22


goals, and it is interesting, enjoyable and fun. Prensky (2001) argued that computer games givefun in the learning process and thus motivate individuals to learn. Klaila (2001) argued that e-

learning consumers should expect programs that incorporate the same innovative tools and tech-

niques used in the computer gaming industry such as graphics, interaction, and skill-buildingchallenges to deliver an educational experience thats compelling, informative, and fun. Nealand Normore (2005) suggested that surprising and challenging situations, collaboration, interac-

tivity, and Xexibility of the program make e-learning fun. Accordingly, developers and designers ofWeb-based learning sites can employ aforementioned elements to reduce monotony, exploit learn-ers playful characteristics, and increase their satisfaction. To enhance satisfaction, learners could

take courses from Web-based learning academics working with corporations to oVer courses thatare practical and helpful to their careers.

The key Wnding of this study is that only utility value and satisfaction make signiWcant contri-butions to learners intention to continue using Web-based learning. The Wnding suggests that sat-

isfaction plays the major role in shaping learners intention to continue using Web-based learning.Utility value explained a signiWcantly greater percentage of variance of learners continuance

intention than did attainment value, intrinsic value, and cost. The Wnding suggests that learners inour study put more emphasis on the utility of learning activities than on the importance, interest,and negative aspects as they evaluate the value of Web-based learning. In other words, learners are

goal-oriented and concern extrinsic reasons for engaging in Web-based learning. To increaselearners intention to continue using Web-based learning, the providers should oVer courses that

could help learners develop their skills and knowledge and degrees that meet learners career goals.As shown in Table 4, attainment value and intrinsic value had positive and indirect eVects on

continuance intention through satisfaction. The results indicate a strong mediation of satisfaction inthe eVects of attainment value and intrinsic value. It implies that while attainment value and intrin-

sic value may increase learners satisfaction with Web-based learning, they may not be strongenough to increase learners continuance intention directly. Web-based learning providers shoulddevelop a long-term strategy to enhance learners satisfaction and maintain or enhance users loyal-

ties to Web-based learning services. Additionally, distributive fairness was found to be the secondfactor inXuencing continuance intention in terms of total eVect (direct plus indirect eVect), while

utility value was found to be the third factor. The results suggest that learners are more concernedwith the fairness of the outcomes than the utility of Web-based learning. The results also suggest

that individuals who are fairly rewarded not only experience satisfaction but also will be motivated

Table 4

EVects of fairness and value on continuance intention

Construct Direct eVect Indirect eVect Total eVect

Distributive fairness 0.00 0.389 (0.480.81) 0.389

Procedural fairness 0.00 0.081 (0.100.81) 0.081

Interactional fairness 0.00 0.113 (0.140.81) 0.113

Attainment value 0.01 0.162 (0.200.81) 0.172

Utility value 0.15 0.154 (0.190.81) 0.304

Intrinsic value 0.00 0.122 (0.150.81) 0.122

Cost 0.01 0.041 (0.050.81) 0.051

Satisfaction 0.81 0.00 0.810


18/22


to continue using Web-based learning. Accordingly, an interesting area for future research is toexamine the direct eVects of the three dimensions of fairness on Web-based learning continuance

intention. Finally, the results suggest that failure to include the inXuence of distributive fairness,

interactional fairness, attainment value, utility value, and intrinsic value may lead to inappropriateconclusions and limit the explanatory power of learners satisfaction with Web-based learning.

Although the Wndings are encouraging and useful, the present study has certain limitations.

First, whether our Wndings could be generalized to all types of Web-based learning is unclear. Thisstudy focuses on Web-based learning for continuing education. Factors inXuencing satisfactionand continuance intention of part-time students might be diVerent from those of full-time stu-

dents. Further research is necessary to verify the generalizability of our Wndings. Second, theresults may have been impacted by self-selection bias. Our sample comprised only active users of

Web-based learning. Individuals who had already ceased to participate in Web-based learningmight have diVerent perceptions about the inXuence of components of subjective task value and

dimensions of fairness, and so may have been diV

erently aV

ected by them. Therefore, the resultsshould be interpreted as only explaining satisfaction and continuance intention of active users of

Web-based learning. Whether the results can be generalized to non-participants or to disaVectedusers will require additional research. Third, the eVects of fairness and value components on satis-faction and continuance intention may change with increasing user experience over time. For

example, attainment value might exert its inXuence on learners continuance intention at the earlyadoption stage. Its inXuence on continuance intention might decrease as learners have more expe-

rience in using Web-based learning (take more Web-based courses). Yet, the learners responses inour research were cross-sectional data and did not present an opportunity to examine the long-

term trend of these hypothesized relationships. Further longitudinal studies are recommended tovalidate our research model in this regard. Finally, the program oVered by NKNU is for any indi-

vidual who has a need for continuing education Learners participated in the NKNU Web-basedlearning in a voluntary setting (i.e., under their full volitional control). Some organizations or com-panies oVered online learning in which individuals participated in the learning programs in a man-

datory setting (i.e., based on the organizations regulation or other normative considerations). OurWndings may not be generalized to the mandatory setting. Further research is necessary to verify

the diVerences between the voluntary and mandatory settings.Finally, DeLone and McLeans (2003) IS success model posits that information quality, system

quality and service quality are major determinants of users satisfaction with and intention to useinformation systems. Later studies could explore the eVect of information quality, system qualityand service quality on learners satisfaction with and intention to continue using Web-based learn-

ing. Theory of planned behavior (TPB) (Ajzen, 2002) theorizes that attitude, subjective norm, per-ceived controllability, and self-eYcacy are important predictors of behavioral intention.

Therefore, future research could examine the inXuences of attitudinal beliefs, subjective norm, per-ceived controllability, and self-eYcacy on Web-based learning continuance intention.

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