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Knowledge Mobilization Through E-Learning Systems:Understanding the Mediating Roles of Self-Efficacy andAnxiety on Perceptions of Ease of UseAshok Jashapara a & Wei-Chun Tai ba Royal Holloway, University of London , London, United Kingdomb Southern Taiwan University , Tainan, TaiwanPublished online: 11 Jan 2011.

To cite this article: Ashok Jashapara & Wei-Chun Tai (2011) Knowledge Mobilization Through E-Learning Systems:Understanding the Mediating Roles of Self-Efficacy and Anxiety on Perceptions of Ease of Use, Information SystemsManagement, 28:1, 71-83, DOI: 10.1080/10580530.2011.536115

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Information Systems Management, 28:71–83, 2011Copyright © Taylor & Francis Group, LLCISSN: 1058-0530 print / 1934-8703 onlineDOI: 10.1080/10580530.2011.536115

Knowledge Mobilization Through E-Learning Systems:Understanding the Mediating Roles of Self-Efficacyand Anxiety on Perceptions of Ease of Use

Ashok Jashapara1 and Wei-Chun Tai21Royal Holloway, University of London, London, United Kingdom2Southern Taiwan University, Tainan, Taiwan

Knowledge mobilization is about translating new knowledgeinto practice. Virtual learning processes play an important rolein diffusing knowledge. This study addresses the personal andcognitive dimensions of virtual learning. A survey (n = 403) wasconducted to understand how individual characteritics influenceperceptions of ease of use of e-learning systems. Our findings showthat e-learning system self-efficacy and computer anxiety medi-ate the effects of personal innovativeness with IT and computerplayfulness on perceived ease of use.

Keywords knowledge mobilization; research utilization; absorptivecapacity; e-learning; self-efficacy; computer anxiety; per-ceived ease of use

INTRODUCTIONKnowledge mobilization and research utilization can be

regarded as mutually constituted terms; one in management andthe other in healthcare. While both terms lack conceptual clarity,they are both about translating knowledge or evidence into prac-tice. An important part of the translation process is the firm’sabsorptive capacity; its ability to recognize new knowledge,assimilate and apply it (Lane, Koka, & Pathak, 2006). This isknowledge in action and is primarily based on different formsof learning; exploratory, transformative and exploitative (Zahra& George, 2002). While much current research has focused onthe relational and situated nature of learning (Easterby-Smith,Crossan, & Nicolini, 2000; Wenger, Mcdermott, & Snyder,2002), there has been limited emphasis on the personal and cog-nitive aspects of learning. With the dynamic growth of onlinecourses, e-learning has become a convenient and flexible wayto share valuable knowledge across physical, economic, andpolitical boundaries. E-learning has provided individuals withthe opportunity to learn anytime or anywhere (Boisvert, 2000)and share their learning across organizations (Jashapara, 2005).To this end, our article explores what are the key individual

Address correspondence to Ashok Jashapara, Royal Holloway-University of London, School of Management,Egham, Surrey TW200EX, United Kingdom. E-mail: ashok.jashapara@rhul.ac.uk

characteristics that successfully engage users and how theyare interrelated. If users are daunted by technology, they areunlikely to engage with new knowledge and translate it intopractice (Lucas, 1975).

Many models have been developed to predict users’ accep-tance behaviors towards technology in the IS literature. Davis’(1989) technology acceptance model (TAM) is one of the mostwidely accepted models. The model includes two importantvariables that affect users’ intentions to use a system: perceivedease of use and perceived usefulness. Research has suggested theimportance of investigating factors affecting the two variablesand, thus, several antecedents have been provided by research(Venkatesh & Davis, 1996; Wu & Li, 2007). Among them, indi-vidual difference is one of the most important factors that affectsthe two variables (Hong, Thong, Wong, & Tam, 2001).

More recently, researchers have identified IT-specific indi-vidual differences drawing on situational individual differences.These include dynamic IT-specific individual differences (i.e.,computer self-efficacy and computer anxiety), computer expe-rience, and stable IT-specific individual traits (i.e., computerplayfulness and personal innovativeness with IT). The principaldistinction between dynamic and stable individual differencesis that dynamic individual difference refers to characteris-tics that are more malleable over time (Thatcher & Perrewe,2002). Although IT-specific individual difference is a signif-icant construct affecting perceived ease of use (Venkatesh,2000), findings from existing studies are inconsistent or unclear(Agarwal, Sambamurthy, & Stair, 2000; Reinicke & Marakas,2005; Venkatesh, 2000). Research has suggested a clear needfor further study on these relationships (Agarwal et al., 2000;Chen, Gully, Whiteman, & Kilcullen, 2000).

In addition, identifying the precise nature of individual dif-ferences of perceived ease of use in e-learning settings ishelpful in better understanding users’ perceived learning out-comes. As e-learning provides an alternative mode of coursecontent delivery, universities have a stake in optimizing thisform of learning. Although the use of information technologyand e-learning provides several advantages compared to tradi-tional classroom settings such as learning in flexible locations,

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72 A. JASHAPARA AND W.-C. TAI

there are also some limitations and disadvantages of e-learning,such as students’ possible discomfort and anxiety (Zhang, Zhao,Zhou, & Nunamaker, 2004). These potential disadvantages mayhave a significant influence on learners’ learning effectiveness.Consequently, an investigation of learning outcomes is anotherimportant strand in e-learning research.

However, research results on the effect of technologyupon learning outcome have been confused (Benbunan-Fich& Arbaugh, 2006). Some research reports that computershelp to improve learners’ learning outcomes (Alavi, 1994;Alavi, Wheeler, & Valacich, 1995; Maki, Maki, Patterson, &Whittaker, 2000; Zhang et al., 2004). However, other researchshows that computers may not improve, and may even reducelearners’ learning effectiveness and performance (Brown, 1998;Piccoli, Ahmad, & Ives, 2001; Maleck, Fischer, Kammer,Zeiler, Mangel, Schenk, & Pfeifer, 2001). TAM’s perceivedusefulness has been conceptualized as “the degree to which aperson believes that using a particular system would enhancehis or her job performance” (Davis, 1989, p. 320). It is a conceptof relative advantage in that users view a system as provid-ing greater benefits than other ways of performing the sametask (Agarwal & Prasad, 1998). In e-learning system contexts,the perceived usefulness can be considered similar to perceivedlearning outcomes. The findings of a number of studies haveconverged to show that perceived ease of use has a positiveeffect on perceived usefulness and individual difference is animportant determinant in the perceived ease of using systems(Agarwal & Prasad, 1999; Mathieson, 1991; Saade & Bahli,2005; Taylor & Todd, 1995b). Taken together, we propose thata better understanding of individual differences and perceivedease of use will help explain the inconsistent results found inearlier literature on e-learning learning outcomes.

The primary aim of this article is to develop and test amore integrative model of e-learning systems. The principalresearch gap is how individual difference variables interactwith one another and influence perceived ease of use. Ourproposed research model, thus, looks at the combined role ofsituational, stable, and dynamic individual differences, espe-cially any mediating relationships. An understanding of suchrelationships would allow trainers to design e-learning or othercomputer based programs more effectively. The main researchquestion in this study is “In terms of perceived ease of use,does e-learning system self-efficacy and computer anxiety playan important mediating role on computer playfulness, personalinnovativeness with IT, and computer experience?”

DEFINITION OF E-LEARNINGThere are a diverse range of perspectives and definitions

of e-learning, and many of them depend on the epistemolog-ical backgrounds of the researchers. For instance, objectivitistand constructivist perspectives provide very different theo-ries of learning (Jonassen, 1991). Constructivists argue thatindividuals generate knowledge and meaning through theirexperiences. Knowledge is constructed in social contexts, and

it is appropriated by individuals. Constructivist theories tendto diverge depending on the emphasis on collaboration or cog-nitive information processing abilities. A further extension ofconstructivist theories are sociocultural conceptions that placegreater importance on the cultural dimensions of learning;the norms, values and beliefs underlying social contexts andinteractions (Leidner & Jarvenpaa, 1995).

Learning is conceived as a passive process from an objec-tivist perspective where learners absorb knowledge uncriticallyfrom instructors (Ahmad, Piccoli, & Ives, 1998). Knowledge isan object acquired from the external world. The aim of learningis to acknowledge the external reality and to change learners’behavior accordingly (Li, 1996, Nunes & McPherson, 2007).Learners are passive and compliant recipients of knowledge(Watkins, 2000). From this viewpoint, the aim of e-learning sys-tems is to broadcast instructors’ knowledge and instructionalmaterials to learners as efficiently and effectively as possi-ble. In such an instructor-centered learning process, the role ofe-learning systems is to enable instructors to monitor and con-trol learners’ learning processes. In addition, e-learning systemsare intended to allow instructors to better understand learners’needs and their knowledge levels in order to develop effectivelearning activities (Kanuka & Anderson, 1999).

Constructivists argue that knowledge is not acquiredpassively from an external reality. Instead, knowledge is con-structed by a learner through actively exploring and experienc-ing an object and then developing his or her own meanings inrelation to that object (Watkins, 2000). A learner controls thepace of learning himself or herself and an instructor play therole of supporting the learner rather than the role of directingthe learner (Ahmad et al., 1998). Constructivists consider thatlearners can learn more effectively if they discover informa-tion and knowledge themselves (Leidner & Jarvenpaa, 1995).According to these assumptions, the role of e-learning systemsis to maximize the availability and accessibility of informa-tion and knowledge in order for learners to construct their ownknowledge (Li, 1996). Learners also need to immerse them-selves in the real-world context relevant to their learning inorder to learn better (Leidner & Jarvenpaa, 1995). For exam-ple, learners will learn about forecasting better when they areactually doing the task of forecasting (Soloway & Pryor, 1996).Thus, information technology should enable instructors andlearners to integrate real-world contexts within the learningprocesses (Vrasidas, 2004).

Collaborative learning theories argue that knowledge is cre-ated through the interaction of two or more people engagedin a common task and sharing their diverse experiences.Consistent with constructivism, the collaborative model oflearning assumes that knowledge is created by learners ratherthan transferred from teachers (Whipple, 1987). The moreknowledge is shared, the more knowledge is learned by learners(Li, 1996). Collaborative learning involves active learning andconstruction of knowledge, cooperation and teamwork in learn-ing, and learning via problem solving (Alavi, 1994). Based on

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KNOWLEDGE MOBILIZATION THROUGH E-LEARNING SYSTEMS 73

these assumptions, information technology plays a role in facil-itating various kinds of interactions, participation, discussionsand supporting problem solving processes (Vrasidas, 2000;Vrasidas, 2004).

Cognitivism argues that memory is central to learning; ourprior knowledge and our mental abilities to process informa-tion (Sievanen, 2004). A learner’s mind filters input from theworld and gives the input interpretations based on the learner’sprior experiences (Jonassen, 1994). As a student’s informa-tion processing capability is limited, his or her attention canbe selective. Thus, the different methods of directing learn-ers’ attention becomes significant to achieve better learningoutcomes, such as the provision of topic outlines, explana-tion of learning goals, and use of hypermedia (Leidner &Jarvenpaa, 1995). In addition, since an individual’s mem-ory load is limited, e-learning should allow learners accessto reference aids when they learn (Wilson & Myers, 2000).Furthermore, instruction that matches a learner’s learningstyle is significant for learning (Leidner & Jarvenpaa, 1995).Based on these perspectives, e-learning systems need to pro-vide the functions that enable learners to concentrate, gainaccess to related information, and have personalized learn-ing methods.

Sociocultural learning theory focuses on social contexts andhow learning occurs through imitation, observation and mod-eling (Vygotsky, 1978). Higher order functions of learningoccur through social interaction, cognition, and communica-tion. Learning is a process of appropriating ways of thinkingfrom social interactions such as language and gesture, whichare internalized and transformed. Consistent with construc-tivism, sociocultural theories assume that knowledge is createdby learners, while emphasizing that knowledge is linked tightlywith learners’ historical and cultural backgrounds (Vogel,Davison, Shroff, & Qureshi, 2001). Learning occurs not only inthe contexts of formal classrooms, but also through social com-munity (Holmes & Gardner, 2006). Although learners begin tomake meaning on their own terms within their own culture,it is suggested that different cultures should be honored andbe encouraged to exist together without imposing any form ofcultural hegemony (Vogel et al., 2001).

E-LEARNING AND KNOWLEDGE MOBILIZATIONKnowledge resources need to be effectively mobilized for

organizations to sustain competitive advantage (Joshi, Sarker, &Sarker, S., 2007). E-learning systems which include both syn-chronous and asynchronous technology have played an impor-tant role in knowledge mobilization (Dede, 1999). Knowledgemobilization refers to the extent to which a person who needsknowledge for a specific task can be effectively matched withothers who possess that knowledge (Gosain, 2007). There aresome important perspectives on knowledge mobilization.

Firstly, the activity of knowledge transfer is central to theknowledge mobilization efforts of an organization (Joshi et al.,

2007). Transfer can occur through formal or informal pro-cesses. Formalized knowledge sharing views knowledge asan artifact or commodity that can be structured and trans-ferred using deliberate processes (Röpke, 2006). In contrast,informal knowledge sharing occurs through personal linkagesheld in communities of practice. Here knowledge is tacit,socially constructed and held collectively. Secondly, the con-ception of knowledge mobilization regards knowledge in termsof situational needs (Keen & Tan, 2007). It focuses on howto get the right information to the right person in the rightformat at the right time in order to support decision mak-ing (Delgado, Pérez-Pérez, & Requena, 2005). In order forknowledge to be utilized effectively, knowledge must be depen-dent on the users’ context. Based on these perspectives, weargue that e-learning systems can support knowledge mobiliza-tion as they enable effective knowledge transfer processes andallow learners to actively construct their knowledge throughsocial interactions rather than passively assimilate information(Dede, 1999).

RESEARCH MODELIn Figure 1, we propose a research model for our study based

on theoretical and empirical findings found in the existing liter-ature. This is based on integrating the Technology AcceptanceModel (TAM) and Self-efficacy Theory, and includes constructsof computer anxiety, personal innovativeness, computer play-fulness, and computer experience. TAM has been applied totest various information technologies, including e-mail, WWW,digital library, and social network sites which are significantlyrelevant to e-learning systems (Sledgianowski & Kulviwat,2008; Davis, 1989; Hong et al., 2001; Moon & Kim, 2001).TAM also has been applied to investigate e-learning systems(Saade & Bahli, 2005). In addition, research has suggestedthat TAM and self-efficacy play important roles in predictinge-learning system usage (Ong, Lai, & Wang., 2004). Thus, wepropose TAM and Self-efficacy Theory be used as our the-oretical background for examining individual difference andperceived ease of use of e-learning systems. The following sec-tion presents each of these hypotheses and their surroundingliterature in more detail.

Dynamic Individual Difference Variables as Predictors ofPerceived Ease of UseSystem Self-efficacy

Computer self-efficacy is an individual difference variableinvolving a personal judgment about one’s ability to use acomputer or application (Compeau & Higgins, 1995). Self-efficacy does not have a stable disposition and research suggeststhat computer self-efficacy is a multileveled and multifacetedconstruct that can be divided into two separate categories: gen-eral computer self-efficacy and system specific self-efficacy(Marakas, Yi, & Johnson, 1998). System specific self-efficacy

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Computer Anxiety

E-learning SystemSelf-Efficacy

PersonalInnovativeness with IT

Computer Playfulness

Stable Individual Difference

DynamicIndividual Difference

Uses’ Perception

PerceivedEase of Use

H2 a,b,d,e

Mediation: H3 a,b,d,e

H1 a, b

ComputerExperience

Mediation: H3 c,f

H2 c,f

SituationalIndividual Difference

FIG. 1. Research model: Individual difference and perceived ease of use.

refers to the use of a specific information system or application(Hwang & Yi, 2002).

The theoretical relationship between general computer self-efficacy and perceived ease of use can be based on TAMindicating that “self-efficacy is similar to perceived ease of use”(Davis, 1989, p. 321). In addition, according to self-efficacy the-ory, it is suggested that self-efficacy beliefs impact on how anindividual thinks, feels, motivates herself, and acts (Bandura,1997a). Consistent with these two theories, the relationship hasbeen supported by empirical studies (Venkatesh & Davis, 1996;Lewis, Agarwal, & Sambamurthy, 2003), including the studiesof e-learning systems. For example, Ong et al. (2004) surveyed140 engineers and proposed that general computer self-efficacyhas an impact on perceived ease of use of e-learning systems.Agarwal et al. (2000) has empirically evaluated both generalcomputer and system specific self-efficacy. The results illus-trate that system specific self-efficacy has a stronger effect onperceived ease of use than general computer self-efficacy. Yiand Hwang (2003) verify that system specific self-efficacy sig-nificantly influences perceived ease of use. Thus, we test thefollowing hypothesis:

H1a: E-learning system self-efficacy will have a positive effecton perceived ease of use of e-learning systems.

Computer AnxietyComputer anxiety refers to “the tendency of individuals to be

uneasy, apprehensive, or fearful about current or future use ofcomputers in general” (Igbaria & Parasuraman, 1989, p. 375).

The anxieties include fear, intimidation, and hostility towardscomputers and worries about the possible use of computers.Individuals fear that they will damage computers and will beembarrassed or look stupid while using computers (Barbeite &Weiss, 2004; Heinssen, Glass, & Knight, 1987). People whoare more anxious about computers may behave with higher lev-els of rigidity than those who are relatively less anxious aboutcomputers (Igbaria, 1994). These negative reactions towardscomputers or information systems result in consequences rang-ing from moderate discomfort to extreme avoidance (Todman,2000). People with computer anxiety may choose not to usecomputers if they had choices (Karahanna, Ahuja, Srite, &Galvin, 2002). Computer anxiety is regarded as a form ofstate anxiety that may be improved via appropriate interven-tions or conditions (Brown, Deng, Poole, & Forducey, 2005;Igbaria & Iivari, 1995; Thatcher & Perrewe, 2002; Vician &Davis, 2002).

The proposed relationship between computer anxiety andperceived ease of use is supported by classical theories of anx-iety suggesting the negative impact of anxiety on cognitiveresponses (Venkatesh, 2000). Consistent with the theoreticalsupport, some empirical studies have reported the significanteffect of computer anxiety on perceived ease of use (Igbaria& Iivari, 1995). The more a person believes that he or she haslow levels of computer anxiety, the more he or she perceives ane-learning system to be easy to use. Based on such evidence, wepropose the following hypothesis:

H1b: Computer anxiety will have a negative effect on perceivedease of use of e-learning systems.

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ANTECEDENT VARIABLES AS PREDICTORS OFDYNAMIC INDIVIDUAL DIFFERENCE VARIABLES

Stable IT-specific Individual DifferencesResearch has found that personal innovativeness with IT

and computer playfulness is well-established stable IT-specificindividual traits (Yager, Kappelman, Maples, & Prybutok,1997; Agarwal & Prasad, 1998; Martocchio & Webster, 1992;Thatcher & Perrewe, 2002).

Drawn from Innovation Diffusion Theory (Rogers, 1995),personal innovativeness with IT is defined as “the willingnessof an individual to try out any new information technology”(Agarwal & Prasad, 1998, p. 206). In an organization, someindividuals are more likely to accept innovations than others(Frambach & Schillewaert, 2002). Research has conceptual-ized innovative individuals as those who are early adopters ofa new technology or innovation (Lewis et al., 2003). They arecategorized by the relative speed of adoption of an innovation(Brancheau & Wetherbe, 1990).

In addition, playfulness refers to a person’s internal dis-position to bring a playful quality to an object according tohis or her own meanings in relation to an artifact (Trevlas,Grammatikopoulos, Tsigilis, & Zachopoulou, 2003). In the ISarea, computer playfulness is defined as the degree of cognitivespontaneity in computer interactions (Webster & Martocchio,1992). Motivation can be operationalized as extrinsic and intrin-sic (Lin, 2007) and computer playfulness has been described asan intrinsic motivation. A person with greater levels of computerplayfulness will experience less effort at a task than someonewith low levels of playfulness (Venkatesh, 2000).

The relationship between stable IT-specific individualdifference and self-efficacy arises from Bandura’s (1997b) self-efficacy theory, which argues that the effect of individual per-sonality on performance derives from its effect on self-efficacy.In addition, empirical research has showed that computer play-fulness negatively affects computer anxiety and positively influ-ences computer self-efficacy (Martocchio & Webster, 1992,Webster & Martocchio, 1992). Likewise, personal innovative-ness with IT has a strong, negative relationship with computeranxiety and a positive effect on computer self-efficacy (Agarwalet al., 2000, Thatcher & Perrewe, 2002). Thus, the followinghypotheses are proposed:

H2a: Personal innovativeness with IT will have a positiveeffect on e-learning system self-efficacy.

H2b: Computer playfulness will have a positive effect one-learning system self-efficacy.

H2d: Personal innovativeness with IT will have a negativeeffect on computer anxiety.

H2e: Computer playfulness will have a negative effect oncomputer anxiety.

Situational Individual DifferenceComputer experience has been defined as “the degree to

which a person understands how to use a computer” (Potosky

& Bobko, 1998, p. 338). That is, experienced users are thosewho know sufficiently about a specific computer or system inorder to use it. This construct has been shown as a critical indi-vidual difference variable in determining a person’s behavior,computer skills, computer knowledge, computer ability, beliefs,and usage (Hasan & Ali, 2004; Harrison & Rainer, 1992;Fagan, Neill, & Wooldridge, 2003; Taylor & Todd, 1995a). Theassumption might be that an experienced person may be morefamiliar with the computer and his or her usage may be moreefficient (Taylor & Todd, 1995a).

The relationship between experience and self-efficacy hasstrong theoretical support. Bandura (1997b) identifies the ideathat self-efficacy is formed of four aspects: enactive mas-tery experiences (personal experience), vicarious experiences,verbal persuasion and allied types of social influence, and phys-iological and affective states. The relationship between systemspecific self-efficacy and computer experience has also beenempirically supported (Johnson, 2005). In addition, Igbaria(1993) has found that among several determinants of computeranxiety, computer experience has the strongest effect on com-puter anxiety. The relationship also can be confirmed from anempirical study showing that the online learning students haveless computer anxiety than the traditional classroom learningstudents (Maki et al., 2000). Thus, we propose:

H2c: Computer experience will have a positive effect one-learning system self-efficacy.

H2f : Computer experience will have a negative effect oncomputer anxiety.

Mediation EffectsAs discussed earlier, e-learning system self-efficacy and

computer anxiety (dynamic individual differences) influencethe perception of ease of use. In addition, personal innovative-ness with IT, computer playfulness and computer experienceinfluence e-learning system self-efficacy and computer anx-iety. Thus, we propose that dynamic individual differencesserve as mediators for computer experience or stable individualdifferences interacting with perceived ease of use.

According to self-efficacy theory (Bandura, 1997b), efficacyperceptions can be affected by differences in individual person-ality (Gist & Mitchell, 1992). Empirical research has confirmedthe relationship and shown that personality positively influ-ences application specific self-efficacy (Johnson, 2005). Selfefficacy theory also shows a link between personality and anx-iety (Bandura, 1986). Personality has been conceptualized as abroad trait (Chen et al., 2000) whereas stable IT-specific indi-vidual difference is seen as a situation-specific trait (Thatcher& Perrewe, 2002). Although both situation-specific and broadtraits shape an individual’s self-efficacy perception, stable IT-specific individual difference is suggested as having a morepervasive influence on computer self-efficacy than personality,because personality lacks specific targets (Thatcher & Perrewe,2002).

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76 A. JASHAPARA AND W.-C. TAI

Additionally, in the psychology literature, research indicatesthat trait-like individual differences (such as personality andcomputer playfulness) are more distant from task performancethan state-like individual differences (such as specific self-efficacy and state anxiety), and that the effects of trait-like indi-vidual differences on task performance are more indirect thanstate-like individual differences (Chen et al., 2000). Moreover,according to Self-efficacy theory, the influence of individualtraits and experiences on performance is from its influence onself-efficacy (Bandura, 1997b). In fact, in the field of work moti-vation, it has been found that the effects of several personalitytraits on performance are partly or wholly mediated by a per-son’s self-efficacy (Locke & Latham, 2004). Thus, we proposethat e-learning system self-efficacy and computer anxiety playimportant mediating roles. In keeping with this reasoning, thefollowing relationships are expected:

H3a: E-learning system self-efficacy will mediate the effect ofpersonal innovativeness with IT on perceived ease of useof e-learning systems.

H3b: E-learning system self-efficacy will mediate the effectof computer playfulness on perceived ease of use ofe-learning systems.

H3c: E-learning system self-efficacy will mediate the effectof computer experience on perceived ease of use ofe-learning systems.

H3d: Computer anxiety will mediate the effect of personalinnovativeness with IT on perceived ease of use ofe-learning systems.

H3e: Computer anxiety will mediate the effect of computerplayfulness on perceived ease of use of e-learning sys-tems.

H3f : Computer anxiety will mediate the effect of com-puter experience on perceived ease of use of e-learningsystems.

RESEARCH METHODOLOGYAll constructs in the research model were measured using

existing or new multi-item scales (See Appendix for new scaleitems). Two constructs were developed based on existing scales(computer anxiety and computer experience), one constructwas newly developed (e-learning system self-efficacy), andthe remaining constructs used existing scales. The TAM is awell researched model and its various constructs have beendeveloped, adopted, and validated by numerous studies. Thus,the scale items used to measure perceived ease of use wereadopted from previously validated inventories (Venkatesh &Davis, 2000). Likewise, personal innovativeness with IT wasadapted from Agarwal & Prasad (1998) and computer play-fulness was adapted from Webster and Martocchio (1992).The measure of computer anxiety was drawn from previousstudies (Barbeite & Weiss, 2004; Thatcher & Perrewe, 2002)and computer-based communication anxiety was added. Fouritems were based on existing scales and two items were newly

developed. Computer experience was developed based on pre-vious studies and items pertaining to e-learning systems werechosen (Bozionelos, 2004; Hasan & Ali; 2004, Potosky &Bobko, 1998).

E-learning system self-efficacy was a new scale developedfollowing the Marakas et al. (1998) framework for the construc-tion of system self-efficacy instruments. Thus, the followingcriteria were used for scale development. The items weredesigned to assess a subject’s belief about his or her ability toperform a task rather than their past performance. Secondly, thetasks cited were specific to e-learning rather than generalizedcomputer tasks (such as turning off a computer) to avoid usingskills outside the computing domain. Lastly, the items were ran-domly distributed in the questionnaire. In order to identify aset of e-learning related tasks and to develop e-learning systemself-efficacy measures, current literature was reviewed (Roffe,2002; Zhang et al., 2004) as well as interviewing the Director ofe-learning systems at a university in Taiwan. In order to ensureclarity and validity among construct items, external reviewwas conducted on a number of constructs by three e-learningpractitioners and six academics. All constructs were measuredusing seven-point Likert scales except e-learning system self-efficacy. In order to test e-learning system self-efficacy, researchhas suggested a two-step process (Compeau & Higgins, 1995).First, respondents indicated “yes” or “no” for their ability touse e-learning systems to complete a task. For each positiveresponse, respondents were asked to assess their level of con-fidence, ranging from 1 (not at all confident), 5 (moderatelyconfident), and 10 (totally confident).

The target sample comprised students at a university inTaiwan. The use of a single university for the survey was impor-tant in order to acquire responses from students with the sameconceptualization of an “e-learning system.” To ensure that thesample was representative of the university’s demographics, thisstudy stratified respondents by proportions in each school. Thesurvey was conducted when students had finished their class.The researcher asked for volunteers and took a few minutes todescribe the purpose of the survey and to explain how the ques-tionnaire was to be completed. The respondents could recordtheir feedback on any aspect of the survey on the last page of thequestionnaire. Four-hundred twenty responses were received.Due to incomplete data, 403 completed responses were used inthe analysis. On average, respondents had 8.6 years experienceof using computers. The sample consisted of 204 men and 199women. The age of the respondents ranged from 18 to 64 years.The average was 23 years old and around 81.6% of respondentswere under 25 years old. Almost 62% of respondents gener-ally used the university’s e-learning system 2–6 times or morea week, 14% used the system around once a month, and 25%used the system around once over a month.

DATA ANALYSIS AND RESULTSThe reliability of each construct was measured using

Cronbach’s alpha in terms of internal consistency. The values of

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all constructs were above 0.75 considered acceptable for fieldresearch (Nunnally, 1978). Thus, the results indicate adequateinternal reliability for all constructs.

Convergent validity was assessed by factor analysis, whichwas conducted using principal component factors and varimaxrotation. To ensure construct validity, items were eliminated inthis study according to the following two criteria: (1) loadingsof less than 0.45 on the “parent” factor, or (2) loadings greaterthan 0.45 on the “foreign” factors (Comrey, 1973; Kankanhalli,Tan, & Wei, 2005). Following this criteria, five items were omit-ted from the original 33 items. One item was omitted fromcomputer anxiety and four items were omitted from computerplayfulness. After item deletion, the reliability for computeranxiety and computer playfulness was found to be at an accept-able level. The six factors of the remaining 28 items explained atotal cumulative variance of 64.31% in the data set. The resultsshow that all the items load higher onto their intended con-struct than onto other constructs. That is, the entire measureditems load on the correct latent constructs indicating adequateevidence for convergent validity.

Discriminant validity was assessed by the average varianceextracted (AVE) approach. The AVE expresses the value ofvariance for the measured indicators explained by their latentconstruct (Koufteros, 1999). Table 1 presents the results of theAVE analysis and the shared correlation matrix. The diago-nal values in bold are square root of the AVE for each ofthe constructs, and the others represent the shared correlations.The square root of the AVE for all constructs was greaterthan the inter-construct correlations. The results demonstratedthat the discriminant validity was adequate (Fornell & Larcker,1981). In conclusion, our analysis shows considerable evidencefor high levels of reliability and validity of all the constructs inthis study.

Once the measurement model was found to be satisfactory,we continued with our assessment of the structural model. Thetesting of our structural model was conducted using variousindices provided by LISREL 8.30. All indices of goodness of

TABLE 1Correlations and average variance extracted of constructs

PIIT CP CE CA SSE PEOU

PIIT 0.75CP 0.27 0.79CE 0.50 0.15 0.65CA −0.54 −0.35 −0.37 0.60SSE 0.53 0.28 0.50 −0.57 0.73PEOU 0.50 0.25 0.41 −0.50 0.54 0.81

The diagonal values in bold are square root of the average varianceextracted (AVE) for each of the constructs.

PIIT = Personal innovativeness with IT; CP = Computer play-fulness; CE = Computer experience; CA = Computer anxiety;SSE = E-learning specific self-efficacy; PEOU = Perceived easeof use.

TABLE 2Fit indices for structural model

RecommendedGoodness-of-fit measurement value Outcome

χ2/df 2–5 2.92Goodness-of-fit (GFI) > 0.80 0.85Adjusted goodness-of-fit

(AGFI)> 0.80 0.82

Normalized fit index (NFI) > 0.80 0.84Non-normalized fit index

(NNFI)> 0.80 0.87

Root mean square residual(RMSR)

< 0.1 0.15

Root mean square error ofapproximation (RMSEA)

< 0.08 0.069

Comparative fit index (CFI) Approaching 1; 0.89Higher values,higher

Incremental Fit Index (IFI) Goodness 0.89

fit and their recommended values (Wu, Wang, & Tai, 2004;Deery, Iverson, & Walsh, 2006; Hadjistavropoulos, Frombach,& Asmundson, 1999; Doll, Xia, & Torkzadeh, 1994) are sum-marised in Table 2. RMSR slightly exceeded its recommendedvalue but it was close enough not to cause any concern. Takentogether, the hypothesis model exhibited good levels of fit toallow path coefficients of the research model to be explored.

The statistical results of non-mediation hypotheses areshown in Table 3. The analysis for testing hypotheses includespath coefficients (Beta) and t-values for each equation. The

TABLE 3Non-mediation hypotheses testing

Path

Independent Dependent Beta t-value Outcomes

H1a SSE PEOU 0.37 6.34∗∗∗ SupportedH1b CA PEOU −0.40 −6.23∗∗∗ SupportedH2a PIIT SSE 0.34 5.15∗∗∗ SupportedH2b CP SSE 0.18 3.73∗∗∗ SupportedH2c CE SSE 0.39 5.98∗∗∗ SupportedH2d PIIT CA −0.55 −6.70∗∗∗ SupportedH2e CP CA −0.29 −5.25∗∗∗ SupportedH2f CE CA −0.08 −1.13(n.s.) Not

supported

n.s = non-significant, ∗P < 0.05, ∗∗P < 0.01, ∗∗∗P < 0.001.PIIT = Personal innovativeness with IT; CP = Computer playful-

ness; CE = Computer experience; CA = Computer anxiety; SSE =E-learning specific self-efficacy; PEOU = Perceived ease of use.

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first step is to test hypothesis 1 assessing whether the proposedmediation variables (e-learning system self-efficacy and com-puter anxiety) have significant influences on perceived easeof use. H1a hypothesized that e-learning system self-efficacy(SSE) has a significantly positive effect on perceived ease ofuse (PEOU) of e-learning systems and hypothesis of H1b isthat computer anxiety (CA) has a significantly negative effecton PEOU. As expected, the results verified these two hypothe-ses (β = 0.37, P < 0.001 for SSE and β = −0.40, P < 0.001for CA). Therefore, students with higher e-learning system self-efficacy and lower computer anxiety thought the e-learningsystem was easier to use.

In addition, the effects of personal innovativeness with IT(PIIT), computer playfulness (CP), and computer experience(CE) on e-learning system self-efficacy were tested. The resultsshowed that all relationships were significant (β = 0.34 andP < 0.001 for PIIT, β = 0.18 and P < 0.001 for CP, andβ = 0.39 and P < 0.001 for CE), supporting H2a, H2b, and H2c.Then, the relationships between the three antecedent predictorsand computer anxiety were tested. The results demonstrated thatboth personal innovativeness with IT and computer playfulnesshad a negative effect on computer anxiety (β = −0.55 andP < 0.001 for PIIT and β = −0.29 and P < 0.001 for CP).However, results showed that computer experience does notdirectly affect computer anxiety (β = −0.08).

Next, mediation hypotheses were tested. Three steps werefollowed to assess the mediating power of dynamic individualdifference variables (Baron & Kenny, 1986). (1) we tested sep-arately the direct effects of the mediator variable (SSE or CA)and the independent variables (PIIT, CP, and CE) on perceivedease of use; (2) we tested the paths from computer experience,personal innovativeness with IT, and computer playfulness withthe mediator variable, and (3) we simultaneously tested pathsfrom the mediator variable, computer experience, personalinnovativeness with IT, and computer playfulness to perceivedease of use. Support for the mediation relationship could fail atany of these three steps.

The first step was to test the mediating power of e-learningsystem self-efficacy. As described in Table 4, e-learning sys-tem self-efficacy (β = 0.63, P < 0.001) had a significant effecton perceived ease of use. Computer experience (β = 0.24,P < 0.001), personal innovativeness with IT (β = 0.36,P < 0.001), and computer playfulness (β = 0.13, P < 0.05)also had a significant effect on perceived ease of use. Thus,all tests in Step 1 passed. In addition, all computer experi-ence (β = 0.40, P < 0.001), personal innovativeness with IT(β = 0.34, P < 0.001), and computer playfulness (β = 0.18,P < 0.001) had a significant effect on e-learning system self-efficacy. That is, no variable failed the tests in Step 2.

In step 3, when the antecedent predictors were modeled withe-learning system self-efficacy, the effects of computer playful-ness and computer experience on perceived ease of use becamenon-significant. However, the effect of personal innovativenesswith IT on perceived ease of use still existed, but decreased from

TABLE 4Mediation hypotheses testing (e-learning system self-efficacy)

Beta t-value

SSE → PEOU 0.63 10.63∗∗∗CE → PEOU 0.24 3.54∗∗∗PIIT → PEOU 0.36 5.01∗∗∗CP → PEOU 0.13 2.49∗

E-learning system self-efficacyCE 0.40 6.06∗∗∗PIIT 0.34 5.15∗∗∗CP 0.18 3.84∗∗∗

Perceived ease of useSSE 0.44 5.83∗∗∗CE 0.04 0.62 (n.s.)PIIT 0.22 3.16∗∗CP 0.05 0.89 (n.s.)

n.s = non-significant, ∗P < 0.05, ∗∗P < 0.01, ∗∗∗P < 0.001.PIIT = Personal innovativeness with IT; CP = Computer playful-

ness; CE = Computer experience; CA = Computer anxiety; SSE =E-learning specific self-efficacy; PEOU = Perceived ease of use.

a highly significant explanation power (P < 0.001) to a weakerexplanation power (P < 0.01). Hence, in short, e-learning sys-tem self-efficacy completely mediated the effects of computerexperience and computer playfulness on perceived ease of useand partially mediated the effect of personal innovativeness withIT on perceived ease of use.

The second test examined the mediating power of computeranxiety. In Step 1, computer anxiety (β = −0.68, P < 0.001)had a significant effect on perceived ease of use. Computerexperience (β = 0.24, P < 0.001), personal innovativeness withIT (β = 0.36, P < 0.001), and computer playfulness (β = 0.13,P < 0.05) also had a significant effect on perceived ease of use(see Table 5).

In step 2, both personal innovativeness with IT and computerplayfulness had a significant influence on computer anxiety(β = −0.59 and P < 0.001 for PIIT, and β = −0.30 andP < 0.001 for CP). Therefore, personal innovativeness with ITand computer playfulness passed the test of step 2. However,computer experience failed the test of step 2. There was no sig-nificant relationship between computer anxiety and computerexperience. The failure indicated that computer experience doesnot have a significant influence on computer anxiety, and thatcomputer anxiety cannot act as a mediator for computer expe-rience. Thus, the hypothesis (H3f) was not supported that com-puter anxiety will mediate the effect of computer experience onperceived ease of use.

In step 3, when the antecedent predictors were modeled withcomputer anxiety, the effect of computer playfulness and per-sonal innovativeness with IT on perceived ease of use becamenon-significant. That is, support was found for hypothesis 3dand hypothesis 3e that computer anxiety completely mediated

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TABLE 5Mediation hypotheses testing (computer anxiety)

Beta t-value

CA → PEOU −0.68 −10.30∗∗∗CE → PEOU 0.24 3.54∗∗∗PIIT → PEOU 0.36 5.01∗∗∗CP → PEOU 0.13 2.49∗

Computer anxietyCE −0.04 −0.58 (n.s.)PIIT −0.59 −6.80∗∗∗CP −0.30 −5.21∗∗∗

Perceived ease of useCA −0.59 −6.05∗∗∗CE 0.24 3.66∗∗∗PIIT −0.02 −0.18 (n.s.)CP −0.05 −0.95 (n.s.)

n.s = non-significant, ∗P < 0.05, ∗∗P < 0.01, ∗∗∗P < 0.001.PIIT = Personal innovativeness with IT; CP = Computer playful-

ness; CE = Computer experience; CA = Computer anxiety; SSE =E-learning specific self-efficacy; PEOU = Perceived ease of use.

TABLE 6Results for mediation hypotheses testing

Hypotheses Mediators Antecedents Outcomes

H3a SSE PIIT Partial mediationH3b SSE CP Completely mediationH3c SSE CE Completely mediationH3d CA PIIT Completely mediationH3e CA CP Completely mediationH3f CA CE No mediation,

failed in step 2

PIIT = Personal innovativeness with IT; CP = Computer play-fulness; CE = Computer experience; CA = Computer anxiety;SSE = E-learning specific self-efficacy; PEOU = Perceived easeof use.

the effects of personal innovativeness with IT and computerplayfulness on perceived ease of use.

In summary, four mediation hypotheses were supported, onewas partially supported, and one was not supported. Table 6summarizes the results and outcomes of the test of six mediationhypotheses from hypothesis 3a to hypothesis 3f.

DISCUSSIONOur results confirm most mediation relationships that

e-learning system self-efficacy and computer anxiety act asmediators. Firstly, both e-learning system self-efficacy andcomputer anxiety mediate the effect of computer playfulness onperceived ease of use. Secondly, computer anxiety mediates theeffect of personal innovativeness with IT on perceived ease of

use, while e-learning system self-efficacy only partially medi-ates the effect of personal innovativeness with IT on perceivedease of use. Thirdly, e-learning system self-efficacy mediatesthe effect of computer experience on perceived ease of use.However, inconsistent with a prior study (Hackbarth, Grover,& Yi, 2003), computer anxiety fails to mediate the effect ofcomputer experience on perceived ease of use.

From a theoretical perspective, we provide several implica-tions. Firstly, this study expands the integrative understandingof the relationships among situational, stable, and dynamicindividual differences with respect to their interactions withperceived ease of use. In particular, it has identified the medi-ating role played by dynamic individual differences betweentheir antecedent variables and perceived ease of use. A for-mer study has provided evidence about the mediating role ofbeliefs (beliefs about ease of use and beliefs about useful-ness) between individual differences and behavior (Agarwal &Prasad, 1999). This study has contributed to our understand-ing of the mediating role of system specific self-efficacy andcomputer anxiety in the relationships between individual differ-ences and the belief of ease of use. In addition, the relativelylow level of computer playfulness is noted (mean = 4.44). Thismeans that respondents were only in partial agreement that theyfelt inventive, imaginative, and original when they interactedwith e-learning systems. The results reveal potential challengesfor creative learning using e-learning systems. Further researchcould explore whether the design of e-learning systems influ-ences students’ creative thinking. Moreover, the relationshipbetween computer anxiety and computer experience was notfound in this study. Even though this is similar to findingsin some studies (Rosen, Sears, & Weil, 1987; Marcoulides,1998; Todman & Lawrenson, 1992), results remain inconclu-sive with other studies (Necessary & Parish, 1996). As thesample in this study exhibited relatively high levels of com-puter experience (mean = 5.97), it is reasonable to assume thatcomputer experience would not be a significant predictor ofcomputer anxiety. The explanation may be that computer expe-rience plays an important factor in computer anxiety principallywhere users lack familiarity with computers (Loyd & Gressard,1984). When users become familiar with computers, computerexperience no longer acts as a major factor in computer anxiety.This confirms results of a survey on computer anxiety show-ing students tend to possess middle to low levels of computeranxiety (Shaw & Giacquinta, 2000). The computer anxiety situ-ation does not appear to have changed over the past seven years(King, Bond, & Blandford, 2002) or fifteen years (Durndell &Lightbody, 1994). Further research is needed to explore howanxiety changes over time.

From a practical perspective, an understanding of individualdifferences is required in order to deliver an effective e-learningstrategy. In particular, this research focuses specifically one-learning systems rather than more generally on computersfound in other studies. The findings are particularly importantfor e-learning practitioners and developers. Our results suggest

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that both self-efficacy and anxiety are important factors thatinfluence users’ perception of ease of use. This finding pro-vides practitioners with a new dimension for developing andimplementing e-learning systems. Although people may havedifferent levels of computer experience, innovativeness, andplayfulness in e-learning settings, self-efficacy and anxiety playa crucial role in influencing them. Thus, any training programthat focuses on alleviating computer anxiety or improving self-efficacy prior to e-learning interventions is likely to improve anindividual’s overall learning ability.

E-learning system self-efficacy partially mediates the influ-ence of personal innovativeness with IT on perceived ease ofuse. People with lower personal innovativeness with IT arelikely to perceive systems as difficult to use, resulting in non-use of the system. Unlike self-efficacy, personal innovativenesswith IT is harder to teach in a learning environment, as it is astable individual difference. If individual have low thresholdsfor trying new technologies and high levels of computer anxi-ety, a traditional classroom setting may be more effective thane-learning interventions. Hence, the dimensions of self-efficacyand anxiety are important parameters in knowledge manage-ment systems to help better understand the dynamics of humaninteraction with technological artifacts.

CONCLUSIONKnowledge mobilization and research utilization are pro-

cesses that attempt to narrow the gap between knowledge orresearch evidence and practice. This translation problem isabout acquiring, sharing and applying valuable new knowledgein practice. e-learning systems play an important role in themobilization process and can be considered as boundary objectsthat either aid or hinder translation of knowledge into prac-tice (Alavi & Tiwana, 2003). They can create electronic fencesand foster rigidities among users (Newell, Scarbrough, & Swan,2001). But more fundamentally, it is the personal characteris-tics of users relative to technology that determines the successof these virtual learning systems.

E-learning systems enable the creation, distribution andfuture application of valuable knowledge via a variety of onlinedelivery mechanisms (Wild, Griggs, & Downing, 2002). Whatour research shows is that individuals have different abilitiesin their use of information technology and it is recognitionof these very differences that leads to optimal virtual learningin organisations. Without this recognition, e-learning systemsbecome little more than ‘ghost’ technologies that nobody visits(Walsham, 2001).

In order to explain the cognitive aspects of knowledgemobilization, we have focused on how IT related individualdifferences influence learners’ perceived ease of use. Not onlyhas this article clarified inconsistent findings from previousresearch, it has also provided an integrated understanding ofthe role of individual differences on perceived ease of use.Specifically, this study presents a detailed model integratingvarious IT-specific individual differences and their complex

relationships with perceived ease of use. Our results show thatdynamic individual differences play an important role in medi-ating situational individual differences, but may only partiallymediate stable IT-specific individual traits in some cases. Morefundamentally, our findings suggest that both self-efficacy andanxiety are very important variables in determining perceivedease of use. Nevertheless, it would be wrong to neglect individ-uals’ traits, such as personal innovativeness with IT as they canact as potential de-motivators.

Traditional accounts of the spread of management ideasshow that they underplay the active role recipients play intranslating them into practice (Morris & Lancaster, 2006). Thisactive role plays a fundamental part in the acquisition andassimilation of new knowledge through virtual learning envi-ronments. Our findings show that e-learning systems need toadopt special measures for individuals with low levels of confi-dence with IT and high levels of anxiety. Without sensitivity tothese personal characteristics, knowledge mobilization is morelikely to be perceived as rhetoric rather than reality in organiza-tions with a consequent erosion of its absorptive capacity (Laneet al., 2006).

LIMITATIONSThere are several limitations in this study. Firstly, the results

of study are focused on a single e-learning system in an aca-demic setting. Thus, the results may have limited generalizationto other non-academic contexts. Secondly, there might be otherindividual difference variables affecting perceived ease of usethat are not included in our model. Further research could exam-ine new individual difference variables and incorporate theminto a modified version of our research model. Thirdly, ourfindings and model are based on a Taiwanese sample. Samplesfrom different cultures or contexts may lead to different results.Lastly, we have explored the cognitive rather than the relationaldimensions of knowledge mobilization.

AUTHOR BIOSAshok Jashapara is an internationally recognised expert in

the field of knowledge management. His research examinesphilosophical approaches to organisational knowledge andknowledge transfer and integrates the disparate literaturesof organizational learning, information systems and strategicmanagement. He has secured research funding from diverseagencies such as the ESRC, EU, and the United Nations. Heis a Founder Member of the iCOM Research Group at RoyalHolloway, University of London.

Wei-Chun Tai is Assistant Professor of Information andCommunication at Southern Taiwan University. He receivedhis PhD in Information Management from Royal Holloway,University of London. His current research interestsinclude information system/technology adoption andimplementation, competence, e-business, and knowledgemanagement.

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APPENDIX: Measures and Items

Construct Items

E-learning systemself-efficacy

SSE1. I believe I have the ability to install the software that the e-learning systems need.SSE2. I believe I have the ability using e-learning systems to listen a course.SSE3. I believe I have the ability to communicate with others on discussion boards of the e-learning

systems.SSE4. I believe I have the ability to use e-learning systems to upload my assignments.SSE5. I believe I have the ability to use online chat room of the e-learning systems to interact with others.SSE6. I believe I have the ability to use the e-learning systems to download learning materials.

Computer anxiety CA1. I am scared that I may hit the wrong key and destroy large amounts of information.CA2. I am scared of using text-based on-line chat room to participate in group discussions.CA3. I am scared of using a computer mike to participate in group discussions.CA4. I avoid using the e-learning systems because I have difficulty in understanding computer

terminology.CA5. I fear making mistakes in the e-learning systems that I cannot correct.CA6. I feel apprehensive about using the e-learning systems.

Computerexperience

CE1. I regularly use E-mail.CE2. I regularly install software on personal computers, such as Real Player and Adobe Reader.CE3. I regularly use word processing software package, such as Microsoft Word.CE4. I regularly use online communication applications, such as Microsoft Messenger.CE5. I regularly use internet browsers to visit websites, such as Netscape and Internet Explorer.CE6. I regularly use Internet to download data or files, such as MP3.

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