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Venkatesh/User Perceptions & Intrinsic Motivation

RESEARCH ARTICLE

CREATION OF FAVORABLE USER PERCEPTIONS:

EXPLORING THE ROLE OF INTRINSIC MOTIVATION1

By: Viswanath Venkatesh IntroductionDepartment of Decision and Information

TechnologiesRobert H. Smith School of BusinessVan Munching HallUniversity of MarylandCollege Park, MD [email protected]

Abstract

A key issue facing information systemsresearchers and practitioners has been the diffi-culty in creating favorable user reactions to newtechnologies. Insufficient or ineffective traininghas been identified as one of the key factorsunderlying this disappointing reality. Among thevarious enhancements to training being exam-ined in research, the role of intrinsic motivationas a lever to create favorable user perceptionshas not been sufficiently exploited. In thisresearch, two studies were conducted to com-pare a traditional training method with a trainingmethod that included a component aimed atenhancing intrinsic motivation. The resultsstrongly favored the use of an intrinsic motivatorduring training. Key implications for theory andpractice are discussed.

Keywords: User acceptance, adoption, training,motivation, Technology Acceptance Model

ISRL Categories: AA01, AA07, AC0401, AI0108,A10117

1Allen Lee was the accepting senior editor for thispaper.

Sometimes when I am at my computer, I say to mywife, "1’11 be done in just a minute" and the nextthing I know she’s standing over me saying, "It’sbeen an hour!" (Collins 1989, p. 11).

Investment in emerging information technologyapplications can lead to productivity gains only ifthey are accepted and used. Several theoreticalperspectives have emphasized the importance ofuser perceptions of ease of use as a key factoraffecting acceptance of information technology.Favorable ease of use perceptions are necessaryfor initial acceptance (Davis et al. 1989), whichof course is essential for adoption and continueduse. During the early stages of learning and use,ease of use perceptions are significantly affectedby training (e.g., Venkatesh and Davis 1996).Investments in training by organizations havebeen very high and have continued to growrapidly. Kelly (1982) reported a figure of $100B,which doubled in about a decade (McKenna1990). In spite of such large investments in train-ing, only 10% of training leads to a change inbehavior On trainees’ jobs (Georgenson 1982).Therefore, it is important to understand the mosteffective training methods (e.g., Facteau et al.1995) and to improve existing training methodsin order to foster favorable perceptions amongusers about the ease of use of a technology,which in turn should lead to acceptance andusage. Prior research in psychology (e.g., Deci1975) suggests that intrinsic motivation duringtraining leads to beneficial outcomes. However,traditional training methods in information sys-tems research have tended to emphasize imparting

MIS Quarterly VoL 23 No. 2, pp. 239-260~June 1999 239


knowledge to potential users (e.g., Nelson andCheney 1987) while not paying Sufficient atten-tion to intrinsic motivation during training. Thetwo field experiments in this research attempt tocreate an enhanced level of intrinsic motivationamong users to foster favorable u,ser perceptionsabout a system. Specifically, this researchattempts to induce a state of playfulness, oneform of intrinsic motivation (see Webster andMartocchio 1992), during training with theobjective of causing positive user perceptionsabout the ease of use of the target technology. Agame-based training (GBT) method, developedas part of this research, is compared to a tradi-tional training approach. The TechnologyAcceptance Model (Davis et al. 1989) is used the theoretical perspective to compare user reac-tions and learning across the two different train-ing methods.

Theoretical Development

Background

Technology Acceptance ModelThe Technology Acceptance Model (TAM) (Figure1) (Davis 1989; Davis etal. 1989) explains useracceptance of a technology based on user percep-tions. TAM was developed from the social psychol-ogy theory of reasoned action (TRA) (Ajzen Fishbein 1980; Fishbein and Aizen 1975). TAMsuggests that two specific behavioral beliefs, per-ceived ease of use (EOU) and perceived usefulness(U), determine an individual’s behavioral intentionto use(BI) atechnology.2 Further, it is suggested that

2The original conceptualization of TAM was in keepingwith TRA, where beliefs (U and EOU) were hypothe-sized to be completely mediated by attitude towardusing technology (affective response reflecting theextent to which a person likes/dislikes using a tech-nology). However, based on empirical evidence, TAMwas refined to exclude attitude toward using technol-ogy because it did not fully mediate the effect of per-ceived usefulness on behavioral intention to use. Thepartial mediation of the effect of perceived usefulnessby attitude toward using technology was conceptuallyexplained to be the result of people intending to per-form a behavior in the workplace even if they did nothave a positive attitude (affect) toward the behavior.Even recent research (e~g., Venkatesl~ and Davis 1996)has successfully applied this modified/refined concep-tualization of TAM.

the effect of external variables (e.g., design char-acteristics of the system) on intention are mediat-ed by these two key beliefs. Perceived ease of useis the extent to which a person believes that usinga technology will be free of effort. Given that per-ceived ease of use is ~lefined in terms of effort,users can be expected to report their evalua-tion/assessment of the process of using a system,thus leading perceived ease of use to be a processexpectancy. Perceived usefulness is the extent towhich a person believes that using a technologywill enhance her/his productivity. In contrast toperceived ease of use, which is a processexpectancy, perceived usefulness is an outcomeexpectancy. Perceived usefulness is expected tobe influenced by perceived ease of use becauseother things being equal, the easier a technologyis to use, the more useful it can be. The directeffect of perceived ease of use on behavioralintention to use is stronger in the early stages oflearning and behavior..With time/experience, theeffect becomes indirect operating through per-ceived usefulness (see Davis etal. 1989).Significant empirical evidence (Adams et al.1992; Chin and Gopal 1993; Davis 1989, 1993;Davis et al. 1989; Davis and Venkatesh 1995,1996; Gefen and Straub 1997; Hendrickson et al.1993; Igbaria et al. 199,7; Mathieson 1991 ; Segarsand Grover 1993; Subramanian 1994; Szajna1994, 1996; Taylor and Todd 1995; Trevino andWebster 1992; Venkatesh and Davis 1994, 1996,forthcoming; Venkatesh and Morris forthcoming)has accumulated in support of TAM.

MotivationSimilar to the use of TRA to study human behav-ior, motivational pergpectives have also beenwidely used to understand behavior. In addi-tion to the Technology Acceptance Model,there is also a motivation-oriented perspective,advanced by Davis affd his colleagues (Davis etal. 1992), to predict technology acceptance andusage behavior. Consistent with other motivationresearch (see Vallerand 1997 for a recentreview), Davis et al. (1992) found intrinsic extrinsic motivation to be key drivers of behav-ioral intention to use. Intrinsic motivation refersto the pleasure and inherent satisfaction derivedfrom a specific activity (Vallerand 1997), whileextrinsic motivation emphasizes performing abehavior to achieve a specific goal (e.g., rewards)(Deci and Ryan 1987).

240 MIS Quarterly VoL 23 No. 2~June 1999


External

Variables

PerceivedUsefulness

(u)

/Perceived

Ease of Use

(EOU)

Behavioral

Intention to

Use (BI)

ActualSystem

Use

TrainingBeginning with early research on TAM (i.e., Daviset al. 1989), training has been seen to be an impor-tant factor affecting user perceptions about a tech-nology. In addition to user acceptance research,training has also received considerable attentionin other areas of information systems research.Several methods of training have been proposedand tested for effectiveness. Nelson and Cheney(1987), building on the work of Sprague andCarlson (1982), identify seven different methods software training: tutorial, courses/lectures/semi-nars, computer-aided instruction, interactive train-ing manual, resident expert, help component, andexternal training. Similarly, other researchdescribes three training methods: on-line, docu-ment-based, and instruction-based training (Czajaet al. 1986). In the research identified above andother related research (e.g., Davis and Bostrom1993; Eason 1982; Kalen and AIIwood 1991;Rockart and Flannery 1983; Webster andMartocchio 1995), the emphasis of the training

method has been on communication of knowl-edge to users. Information systems research (e.g.,Compeau and Higgins 1995a) and psychologyresearch (e.g., Gist et al. 1989)3 have focused onimproving such training methods from the stand-point of the knowledge communication process.4

Typically, training provides users with conceptu-al and procedural knowledge necessary to putthe technology to effective use. As Kalen andAIIwood (1991, p. 88) point out

...formal training should not stop at a key-stroke level of merely providing the userswith sufficient procedural knowledge to per-form certain work tasks, but should also giveusers a good conceptual understanding ofthe system they are using.

3Gist et al. (1989) studied outcome variables such satisfaction and frustration.

4Both Compeau and Higgins (1995a) and Gist et al.(1989) discuss behavior modeling as a better approachto training.

MIS Quarterly VoL 23 No. 2~June 1999 241


Along these lines, Glaser (1990) favors a methodof describing concepts first to subjects. This facil-itates the development of a schema to help userslearn procedures at a later time. In contrast,researchers who favor a procedure-first trainingmethod suggest that users prefer learning the pro-cedures first because it helps them generalizeconcepts at a later time (e.g., Wright 1988).There also exists research that reveals equivocalresults across concepts-first and procedures-firsttraining methods (e.g., O~fman and Mandviwalla1994). In sum, training research provides severaldifferent training methods emphasizing knowl-edge transfer as measured by learning outcomes.

Hypotheses Development

We often hear of people "surfing the net" andbeing "addicted to the net." The Internet providesan alternative form of entertainment throughnewsgroups, chatlines, multi-user fantasy, androle-playing games. Recently, Business Week(Cortese 1997) presented an extensive specialreport on the growth of the Internet as a place to"hang out." Thus, current and future forms ofcomputer technology will be used for both workand fun, causin~ a reduced distinction betweenwork and play (Starbuck and Webster 1991 ). Thetraditional view of play is that it is unproductive,pleasant, and involving. In contrast, the modernconceptualization presents two different con-cepts: unproductive play (something that isunproductive, pleasant, and involving) and play-ful work (something that is productive, pleasant,and involving) (see Starbuck and Webster 1991for a discussion). Systems are not necessarilybuilt from the "joy angle" despite people increas-ingly spending time on computers expecting it tobe a pleasurable experience (Brody 1992). Whiletraining methods have focused on improvinglearning outcomes, very little attention has beendevoted to enhancing intrinsic motivation in thetask during training.

Prior research suggests that motivation in gen-eral is an important factor driving perceptionsand behavior, even in training contexts.Effective training programs should address thepotential lack of self-motivation of trainees byincluding a motivational component (McCombs1984). Olfman and Bostrom (1991) compared

the effect of construct-based and application-based training interventions on motivation touse. Perceived usefulness was used as a surro-gate measure for motivation to use. From aTAM perspective, perceived usefulness is anoutcome expectancy and a measure of extrinsicmotivation (see Davis et al. 1992). The currentresearc[~ extends this line of inquiry in animportant way by focusing on intrinsic motiva-tion and how that can be leveraged duringtraining to create favorable user acceptance.The role of intrinsic motivation in training isimportant since it leads to beneficial outcomes(Deci et al. 1991). For example, intrinsic moti-vation in the early stages of learning leads tosustained behavior (Vallerand and Bissonnette1992). Thus, paying attention to the role ofintrinsic motivation during the training processis critical. One specific intrinsic motivator isplayfulness (see Bloch and Bruce 1984;Hirschman 1983; Mihalich 1982; Osborne1979; Unger and Kernan 1983). Prior researchon playfulness has primarily studied it as atrait. 5 Webster and Martocchio (1992) examineplayfulness as a trait in keeping with Lieberman(1977).6 However, they suggest the importanceof studying playfulness as both a trait and astate just as mood has been examined as botha trait (e.g., George 1989) and a state (e.g.,George 1991). In keeping with the idea of play-fulness as a state, this research attempts toinduce a state of playfulness as an intrinsicmotivator during the training process with thespecific objective of enhancing perceived easeof use and the role it plays in determiningbehavioral intention to use. The remainder ofthis section discusses the potential impact of agame-based training intervention aimed at lever-aging playfulness (intrinsic motivation) on TAMconstructs (user perceptions and intentions), the

SBostrom et al. (1990) explain the distinction betweentrait and state variables, with particular attention totheir role in IS research. A trait is an enduring and sta-tic individual difference variable (e.g., personalityvariables) that has an impact on individual informationprocessing in a wide range of settings and activities. Incontrast, states are more dynamic (vary over time)influences on performance (e.g., motivation).

6More recently, Webster and Ho (1997) have examinedhow presentations can be improved from this stand-point.



relationships among TAM constructs, and learn-ing outcomes.

Effect on TAM ConstructsPast research has shown several positive conse-quences of intrinsic motivation. A motivationalskills training component combined with a tradi-tional curriculum favors positive perceptions ofself-efficacy and personal causation leading to amore positive orientation (McCombs 1984).Intrinsic motivation in a task environment hasalso been associated with willingness to spendmore time with the task, lower levels of anxiety,positive mood, and greater learning (e.g.,Csikszentmihalyi 1975; Venkatesh and Speierforthcoming). In a piano-playing context, it wasfound that positive student perceptions of theintrinsic motivation of the teacher had a signifi-cant positive influence on intrinsic motivation ofthe students, their affective reactions and theirproximal (volitional) behavior (Wild et al. 1992).In the context of TAM, introducing intrinsicmotivation through training can be expected tobe mediated by the beliefs: perceived ease ofuse and/or perceived usefulness. This stems fromthe fundamental thesis of TAM (and TRA) thatthe effect of external variables on behavioralintention to use are mediated by beliefs. Thisgeneral hypothesis has been extensively validat-ed in psychology research (see Ajzen andFishbein 1980) and in TAM research (e.g., Daviset al. 1989). Specifically, the effect of traininginterventions have been shown to be mediatedby perceived ease of use (e.g., Venkatesh andDavis 1996).

From a standpoint of its definition, theoreticaldevelopment, and operationalization (Davis1989), perceived ease of use is a constructfocused on an individual’s perception about thelevel of effort needed to use a system, and repre-sents the user’s subjective evaluation of theprocess of interaction with a system. One of thebuilding blocks of the perceived ease of use con-struct is self efficacy theory (Bandura 1977a,1982) which suggests two types of expectations,efficacy and outcome, determine behavior.Bandura (1977b) defines an efficacy expectationas "the conviction that one can successfully exe-cute the behavior required to produce the out-comes" (p. 79). An outcome expectancy defined as "a person’s estimate that a given

behavior will lead to certain outcomes" (Bandura1977b, p. 79). Perceived ease of use is said to berelated to efficacy beliefs (Venkatesh and Davis1996) and thus, a process-oriented construct or process expectancy. Gattiker (1992), in a frame-work of training, suggests that individual motiva-tion will have an impact on substantive com-plexity. Based on intrinsic motivation researchand user acceptance research, it can be expectedthat for a given objective level of effort, greaterlevels of intrinsic motivation during training willhave a favorable impact on perceptions of effort(perceived ease of use).

HI: Perceived ease of use will be higher amongusers in the game-based training interven-tion compared to users in the traditionaltraining intervention.

The primary purpose of the game-based trainingis to enhance perceived ease of use. However, itis also important to understand the effect of suchan intervention on perceived usefulness. In addi-tion to perceived usefulness being a key determi-nant of acceptance, it is important to understandthe effect of game-based training on perceivedusefulness because of potential negative effectsof game-based training. It may be possible forusers to dismiss a game-based training interven-tion as being non-work related ("just a game")and thus, while having higher levels of perceivedease of use, users may have lowered perceivedusefulness. The theoretical underpinnings andoperationalization of perceived usefulness sug-gest that it is a construct focused on outcomeexpectancy (Davis 1989). Consistent with such view, from the perspective of self-efficacy theory(Bandura 1977a), perceived usefulness is outcome belief (in contrast to perceived ease ofuse, which is an efficacy belief). While traininghas been shown to have a strong influence onperceived ease of use, (e.g., Venkatesh andDavis 1996), perceived usefulness is not signif-icantly influenced by training and is morestrongly determined by a matching processbetween a technology’s capabilities and a per-son’s job needs (see Venkatesh and Davis, forth-coming). Their finding across multiple studiessupports the notion that the impact of the twodifferent training interventions on perceivedusefulness will not be different since they foundthat much direct hands-on experience was not



necessary for the formation of stable perceivedusefulness. Further, given that Davis et al.(1992) explained perceived usefulness to similar to extrinsic motivation, it minimizes thepossibility of an intrinsic motivator influencingperceived usefulness. Thus, it is expected thatusers in both training groups will have similarlevels of perceived usefulness.

H2: Perceived usefulness will be equivalentbetween users in the two different types oftraining interventions/

According to TAM, behavioral intention to use isdetermined by perceived ease of use and per-ceived usefulness (BI = EOU + U). Based on thisbasic TAM hypothesis and the earlier hypothesisthat perceived ease of use will be higher in game-based training (H1), it is expected those userswill also exhibit higher levels of behavioral inten-tion to use compared to users who underwenttraditional training.8

H3: The behavioral intention to use will behigher among users in the game-basedtraining intervention compared to users inthe traditional training inter(,ention.

Effect on TAM RelationshipsA fundamental hypothesis of TAM is that theeffect of external variables on intention is com-pletely mediated by beliefs (EOU and U). Thismediation of external variables by beliefs is con-sistent with TRA. As indicated earlier, thishypothesis of mediation has been tested exten-sively in psychology research and TAM researchand is now an integral part of the model. Theeffect of several external variables (includingtraining) has been shown to be mediated by per-ceived ease of use and perceived usefulness.However, in this research, the basic assumptionof complete mediation by TAM constructs ischallenged. Typically, following training inter-ventions emphasizing knowledge transfer, out-

7Please note that the hypothesis is presented in its nullform. However, in this case, there is strong evidenceto suggest that there will be no differences across inter-ventions.

8This effect is expected despite the non-significant dif-ference across interventions on perceived usefulness(H2).

come expectancy (i,e., perceived usefulness)emerges as the strongest determinant of behav-ioral intention to use while process expectancy(i.e., perceived ease of use) is less important as determinant of behavioral intention to use. Thecurrent work argues that by providing an appro-priate framing effect (Tversky and Kahneman1982) using an intrinsic motivator (game-basedtraining, in this case), it is possible not only tocreate a positive impact on perceived ease of use(H1) but also to have a strong impact on theeffect of perceived ease of use on behavioralintention.

Based on social influence theory (Salancik andPfeffer 1978), prior research suggests that attitudetoward a task may quite simply be influenced bylabeling a task as "work" or "play" (Webster andMartocchio 1993). Tt~is effect of labels on atti-tude was supported by Sandelands (1988).Further, research on cognitive categorization(Rosch 1975, 1978) suggests that "play" causesthe focus to be on the means while "work" caus-es the focus to be on the ends (Sandelands 1988).The notion of play and means is related toprocess expectancy (i.e., perceived ease of use)while the idea of work and ends is related to out-come expectancy (i.e., perceived usefulness).Similarly, cognitive evaluation theory suggeststhat intrinsic motivation will lead to higher levelsof effort (Deci 1975). The underlying cognitivephenomenon is termed locus of causality, whichis the basic cognitive mechanism driving the per-formance of any behavior. Locus of causality canbe internal or external. When intrinsic motiva-tion is higher, locus of causality is said to beinternal to the individual and the behavior is dri-ven by intrinsic needs and rewards. Traditionaltraining interventions tend to emphasize extrinsicvalue in using a technology in terms of "what atechnology can do," causing the locus of causal-ity to be external and more focused on outcomeexpectancy (i.e., perceived usefulness). On theother hand, game-based training is expected toshift the locus of causality (from external tointernal) leading to greater emphasis beingplaced on process expectancy (i.e., perceivedease of use).

Research in the area of behavioral decision theo-ry also supports this possibility and explains the



compatibility hypothesis as follows: the morecompatible the stimulus attribute is with theresponse mode, the more heavily the particularattribute will be weighted in judgment andchoice (Slovic et al. 1990). This hypothesis hasalso been supported in other decision-makingresearch (e.g., Slovic et al. 1982). In the contextof this research, a traditional training methodcauses respondents to have a greater degree ofcompatibility with outcome expectancy and,therefore, weight perceived usefulness more thanperceived ease of use in determining behavioralintention to use. In contrast, a game-based train-ing method can be expected to cause a greaterdegree of compatibility with process expectancybecause of enhanced levels of intrinsic motiva-tion, thus causing users to place greater weighton perceived ease of use rather than perceivedusefulness in determining behavioral intention touse. Other related research in decision makingsuggests that different types of framing of theproblem/decision space can lead to differentpreferences (Tversky and Kahneman 1982).Similarly, research in the area of social cognitionsuggests that positive priming can result in shift-ing judgments (Murphy and Zajonc 1993). In thiscase, the positive priming/framing is expected tocause the judgment about perceived ease of useto be more significantly emphasized in a game-based training intervention.

H4: The influence of perceived ease of use onbehavioral intention to use (EOU-BI) willbe higher among users in game-basedtraining when compared to users in tradi-tional training.

TAM suggests that perceived ease of use has adirect effect on behavioral intention to use andan indirect effect through perceived usefulness.Empirical evidence suggests that the effect of per-ceived ease of use .during the early stages oflearning and use is primarily direct and lessstrongly indirect (through perceived usefulness),and it is only with significant experience that theindirect effect becomes stronger (e.g., Davis et al.1989; Szajna 1996). Consistent with this TAMhypothesis, it is expected that the perceived easeof use/perceived usefulness relationship will besignificant, although modest. However, since thestudies in this research are being conducted to

examine the role of intrinsic motivation duringearly stages of learning and use, no significantdifference in the relationship is expected acrossinterventions.

H5: The perceived ease of use-perceived use-fulness (EOU-U) relationship will equivalent across the two types of traininginterventions.9

In a workplace environment, irrespective of thetype of training intervention, it can be expectedthat perceived usefulness will be a key factordetermining behavioral intention to use.However, given the nature of the process under-lying the formation of perceived usefulness (i.e.,a matching between product features and jobtasks) per Davis and Venkatesh (forthcoming), can be expected that users in the two differenttypes of interventions will place similar emphasison perceived usefulness.

H6: There will be no difference in the per-ceived usefulness-behavioral intention touse (U-BI) relationship across the two typesof training interventions.1°

Further, given the differential influence of train-ing interventions on the perceived ease ofuse/behavioral intention to use relationship (H4),the variance in behavioral intention to useexplained by perceived ease of use and per-ceived usefulness will be higher in the case ofgame-based training than traditional training,u

H7." The variance in behavioral intention to useexplained by perceived ease of use and per-ceived usefulness will be higher for thegroup of users in the game-based trainingintervention when compared to users in thetraditional training intervention.

9As with H2, please note that the hypothesis is pre-sented in the null form. Once again, as with 1-12, inthis case also, there is strong reason to expect no dif-ferences across interventions.

~°The issues related to a null hypothesis that applied toH2 and H5 apply in this case as well.

UThis effect is expected despite the non-significantimpact of training on the U-BI relationship (H6).



Method

In order to study the effect of game-based train-ing on user perceptions and learning, two studiesare conducted among business professionals.The target system is a Virtual Workplace System(VWS). The game-based training intervention designed on the basis of an Internet fantasy role-playing game.

SystemThe Virtual Workplace System12 is an Internet-based application aimed at allowing a largenumber of users at different physical locations totelecommute (interact real-time with each other,exchange information, and work together, asthey would in a conventional office setting).There has been an increasing number of tech-nologies supporting telecommuting in organiza-tions (e.g., Microsoft Net Meeting). Target usersfor the system are knowledge workers who spenda large part of their work day using a computerand attending group/team meetings related tovarious projects. The potential advantages (at anorganizational level) of being able to create workplace in virtual space (cyberspace) are: (1) is an inexpensive technology allowing individu-als to work from home, which provides flexibili-ty in terms of working hours, (2) it allows organi-zations to hire valuable knowledge workers with-out being hindered by issues of relocation andalso retain key employees who want to move toa different geographical location, and (3) it alsoprovides for more effective use of (physical)office space. For the knowledge workers, at ajob/task level, the system provides: (1) the scopefor an increased number of different-place, same-time computer-supported meetings among mem-bers of teams and (2) the ability to share infor-mation (using electronic mail, file transfer utili-ties, etc.), to participate in meetings, and to per-form the routine office activities (typing a letter,cutting, pasting, printing, and sharing files, etc.)on the computer itself without having to actuallybe at the same physical location as other knowl-edge workers in the team, group, department, ororganization.

12This system was built as part of this research stream.Technical details are available on request.

Game-Based Training (GBT)

As discussed earlier, with traditional training, thegoal is to impart knowledge to users. Bruner(1962) suggested that the two criteria to deter-mine whether some information is worth teach-ing are: (1) the knowledge "gives a sense delight" and (2) the knowledge "bestows the giftof intellectual travel" (p. 109). Malone (1981a,1981 b) studied computer games and investigatedhow games can be used to make learning withcomputers a more interesting and enjoyableexperience. Malone (1981 a) identified three dis-tinct components of intrinsic motivation: fantasy,challenge and curiosity. In the GBT employed inthis research, intrinsic motivation during trainingis leveraged by introducing a fantasy dimension.There has been very little research focusing onintrinsically interesting activities emphasizingfantasy (Sansone et al. 1989). The fantasy dimen-sion provides cognitive meanings and represen-tations necessary for intrinsic motivation (e.g.,Lepper 1985; Parker and Lepper 1987) and dis-covery-based learning (Bruner 1962). Sansone al. (1989) also found that fantasy emphasis (com-pared to skill emphasis) led to higher levels ofenjoyment, flow, and interest in further informa-tion. From the perspective of Olfman andBostrom (1991), GBT and the traditional trainingintervention in this research are application-based training interventions.

The GBT, by leveraging fantasy,~3 attempts to cre-ate an enhanced state of playfulness amongusers, thus making the training program moreintrinsically motivating while continuing to pro-vide adequate information to facilitate knowl-edge acquisition. This is in contrast to a concept-first or procedure-first training method, the typi-cal training method used in IS research aimedsolely at knowledge transfer. The GBT employs agame (i.e., Multi-User Dungeon) to which fea-tures of the system being .studied were related.Multi-User Dungeon (MUD) is a multi-user gameon the lnternet. MUDs are built on the concept ofthe popular fantasy role-playing game"Dungeons and Dragons." MUDs are based onthe metaphor of physical space (Bruckman 1992)described in a text-based virtual space (i.e., the

13The game-based training intervention (GBT) can alsobe related to a narrative (story as a mental represen-tation) (e.g., Orr 1990).

246 MIS Quarterly Vol. 23 No. 2~June 1999


real world, buildings, and offices are described inthe form of text as one would see it in reality).Every person (player of the game) creates her/hisown character. A character is the representationof the user in the virtual world. Such user-creat-ed characters serve as a front for users to interactin the virtual world. It includes a name, gender,and even a description. MUDs employ different-place, same-time online interaction similar tocommunication among users who are telecom-muting. The growth of Internet communities suchas those on MUDs has been tremendous. A spe-cial report in Business Week (Cortese 1997) sug-gests that there has been a significant growth ofchatlines and interactive communication on theInternet. Thus, there appears to be significantpotential for "interactive text communication."Hagel and Armstrong (1997) go a step further pointing out how MUDs are a tremendoussource of addiction on college campuses, and itis only a matter of time before effective simula-tions of such virtual communities are used to cre-ate business applications.

The GBT will serve as stimulus to enhance intrin-sic motivation consistent with the basic frame-work of Deci (1975), which suggests that theenvironment is one of the stimuli that can causeintrinsic motivation, leading to more goal-direct-ed behavior. This intervention is also consistentwith other prior research that has primarilyfocused on identifying a context that would besupportive of intrinsic motivation (e.g., Deci andRyan 1987). Further, as suggested by Gattiker(1992), developmental factors (in this case, userperceptions and prior experiences with games)will interact with the training environment andaffect the evaluation of the situation. Also, otherwell-established paradigms of research (job-char-acteristics model) (Hackman and Oldham 1976)suggest that increased levels of task variety, as isthe case in the GBT, will lead to enhancement ofmotivation.

Manipulation ChecksTwo manipulation checks were included. First,perceived enjoyment (intrinsic motivation) wasmeasured using three items adapted from thescale of Davis et al. (1992). It is expected thatsubjects in the game-based training interventionwould test higher on this scale. Second, subjects

completed a 45-minute knowledge test at theend of the training program. More positive learn-ing outcomes are typically associated with intrin-sic motivation (e.g., Czikszentmihalyi 1975).Consistent with general intrinsic motivationresearch, it has been suggested that positivelearning can result from playfulness (Lieberman1977). In contrast, other research has suggestedthat playfulness causes increased time to com-plete tasks (Sandelands 1988). Starbuck andWebster (1991) potentially reconcile these con-trasting ideas by suggesting that playfulness maylead to increased time spent on tasks but resultsin higher quality of output. Although Websterand Martocchio (1993) did not find support fortheir hypothesis that labeling as play will havehigher learning outcomes than labeling as work,they did find that the interaction of age and playwas significant, with better learning resultingamong younger employees in play labeling.While the game-based training was expected tohave no positive effect on learning outcomesbecause of the equivalency of the different inter-ventions in terms of total time and hands-onexperience, it was important to ensure that noneof the demerits of playfulness (e.g., wasted time)cause a lowered level of learning.

Study 1

SubjectsParticipants were solicited via a letter directlyaddressed to the individuals selected at randomfrom a list of business professionals (knowledgeworkers) in the geographical area close to theauthor’s place of employment. A total of 320 let-ters, announcing the training on the VirtualWorkplace System, were mailed. A time commit-ment of one day (about eight hours excludingbreaks) was sought. Eighty-two individualsagreed to participate and the actual attendancewas recorded at 69. Only one participant hadtelecommuting experience and all but four hadused electronic mail in the past and were basi-cally familiar with the idea of telecommuting.Also, all except two subjects reported being unfa-miliar with MUDs and Dungeons and Dragons.The subjects were employed in a wide range ofindustries with an average work experience ofjust over seven years (the range was from four to19 years). The average age of the subjects was

MIS Qua~terlj~VoL 23 No. 2~June 1999 247


41.2 (the range was from 29 to 51 ). Thirty-one the 69 (45%) participants were female.

ProcedureFour of the individuals involved with the devel-opment of the Virtual Workplace System con-ducted the training. None of the trainers wereaware of any of the research objectives. All sub-jects were randomly assigned to one of the twotraining interventions: traditional lecture-basedtraining or game-based training. This was done inan attempt to ensure that the effect of the inter-ventions was not specific to those with higherlevels of microcomputer playfulness from a traitstandpoint, thus minimizing systematic biases.Pre-experimental microcomputer playfulnesswas measured using the short seven-item scale ofWebster and Martocchio (1992). There was significant difference in microcomputer playful-ness across treatment groups.

Table 1 presents a graphical representation of thetwo training interventions. The game-based train-ing intervention comprised three two-hour ses-sions, with 30-minute breaks between sessions,conducted on the same day. The subjects com-pleted a 45-minute knowledge test at the end ofthe training. In order to compare ’the impact ofthe game-based training intervention with a tra-ditional lecture-based training approach, a sec-ond training intervention was employed. Thistraining intervention was designed to be a con-cept-first training on the concepts, features, andprocedures of the system. Extensive hand-outs,both on the concepts and procedures of the sys-tem, were used to aid the subjects’ learningprocess. The lecture and hands,on use duringboth types of training interventions required sub-jects to accomplish a specific set of tasks, whichhelped users understand the features and proce-dures of the system, and also fostered uger inter-action. Similar to the game-based training inter-

Traditional 2- our lecture 30-min.Training on the system break

Game-Based 2-hour 30-min.Training hands-on training break

on the game(playing the game’,

vention, the traditional lecture-based traininglasted six hours with 30-minute breaks betweeneach two-hour session. The traditional trainingintervention, therefore, closely mirrored an exec-utive training program on a new software prod-uct. The subjects in the traditional training inter-vention also completed a knowledge test at theend of the training.

The total hands-on experience in both interven-tions was equivalent (four hours). In order ensure equivalence between the two hours ofgame play in the game-based training and thefirst two hours of hands-on use in traditionaltraining, the activities in which subjects wereinvolved were made as similar as possible. In thegame-based training intervention, the subjectswere briefly introduced to the game and navigat-ed through the virtual world of the game, inter-acted with other users of the game (other sub-jects), and played the game (killing monstersand gathering treasures). During the first twohours of hands-on use during traditional train-ing, subjects navigated through the virtual work-space and interacted with others on the system.During the second two hours of hands-on use inboth interventions, the activities were job-ori-ented. Thus, the key difference across interven-tions was the game context in the game-basedtraining intervention.

All subjects completed a TAM questionnaire fol-lowing training. Four-item scales, adapted fromDavis (1989), were used to measure perceivedease of use and perceived usefulness. Two itemswere used to measure behavioral intention to use(see Davis et al. 1989). The items included in theinstrument were aimed at testing TAM in the con-text of the two training interventions. The sub-jects also completed a 45-minute test aimed atassessing the knowledge acquired during thetraining process. In keeping with Kalen and

2-hour actual use 30-min.of the system break

2-hour lectureon the system

2-hour actual use 45-min.of the system test

30-min. 2-,hour actual use 45-min.break of the system test



AIIwood’s (1991) suggestion for future research,this will allow a comparison of the different train-ing interventions to determine "how much andwhat type of instructor activities and user inter-action with the application program result in themost effective learning" (p. 89).

ResultsThe psychometric properties of the scales wereconsistent with past research. Reliability esti-mates (Cronbach alpha) for EOU, U, and were all over 0.90, suggesting a high degree ofreliability. Convergent and discriminant validitywere supported by results from factor analyseswith cross-loadings lower than 0.20. The resultsfrom the manipulation checks were as expect-ed--enjoyment measures were significantlyhigher among the subjects in the game-basedtraining (M = 6.1; SD = 0.6) compared to thetraditional training (M = 4.1; SD = 0.9). Theknowledge test scores in the game-based train-ing (M = 86.8; SD = 7.1) were statisticallyequivalent to the traditional training (M = 87.9;5D = 6.8).

Table 2 presents a comparison of EOU, U, andBI across training interventions. Clearly, userswho participated in the game-based trainingintervention had higher levels of EOU (H1) andwere more likely to use the system as indicatedby BI in comparison to users in the traditionaltraining intervention (H3). The impact on U wasnot statistically significantly different acrossinterventions (H2).TM This pattern of mean differ-ences was confirmed by ANOVAs followed byTukey’s test.

Regression analyses were performed to study theeffect of the training interventions on TAM. Thedata were analyzed for the subjects (n = 34)

’4In order to examine the differential impact of the twotraining interventions on EOU and U on BI, data fromthe two interventions were pooled and a dummy vari-able, TRAINING, was introduced (0 = Traditionaltraining; 1 = Game-based training).. The results indi-cated that TRAINING had a significant impact onEOU consistent with Venkatesh and Davis (1996),suggesting that users in the game-based training inter-vention found the system to be significantly easier touse when compared to users in the traditional train-ing. TRAINING had no effect on U.

who went through the traditional training (TT)and the group of subjects (n = 35) who wentthrough GBT. Table 3 presents a summary of thecomparison of training interventions. In bothtraining interventions, consistent with TAM (e.g.,Davis et al. 1989), BI was determined by EOUand U. Users in the GBT took into account EOU([3 = 0.58) more in determining BI than did usersin the traditional training intervention ([3 = 0.31),thus supporting H4. This was also confirmed bypooling the data across training interventionsand testing for moderation of the EOU-BI rela-tionship by training. 15 This positive moderationeffect, as mentioned earlier, challenges the basicTRA/1-AM hypothesis that all external variableswill be completely mediated by TAM constructs.In the traditional training intervention, the over-all effect of EOU ([[3 = 0.51) and the overalleffect of U on BI ([3 = 0.61) indicated that emerged as a stronger overall predictor of BI.While U remained a strong determinant of BI (]3= 0.66) in GBT, the overall effect of EOU on BIwas higher in GBT compared to traditionaltraining ([3 = 0.83), challenging the basic TAMtenet (and accumulated TAM research) that U the strongest predictor of BI. However, the effectof EOU on U while higher in the GBT ([3 = 0.38)was not statistically significantly different fromtraditional training ([3 = 0.32), confirming H5.The overall effect of U on BI (]3 = 0.61 vs. ]3= 0.66) was also not statistically significantacross interventions, confirming H6. The vari-ance in BI explained in the GBT was 67%,which was statistically significantly higher thanthe variance explained in the traditional training(49%), supporting H7.

Study 2

The second study was conducted to address onepotential limitation of the first study. A potential

151n order to examine the differential impact of the twotraining interventions on the different TAM relation-ships, data from the two interventions were pooledand a dummy variable, TRAINING, was introduced (0= Traditional lecture-based training; 1 = Game-basedtraining). The moderating effect of TRAINING on theEOU-BI relationship revealed a significant interactioneffect, while the interaction effect in the case of theEOU-U and U-BI relationships were non-significant.



Table 2. Study 1: Comparison of TAM Constructs Across Training Interventions

Perceived Ease of Use Perceived Usefulness Intention to Use

Mean Stdo Dev. Mean Std. Dev. Mean Std. Dev.

Traditional Training 4.6 0.8 5.1 0.7 5.1 0.9

5.7 0.7 5.3 0.9 6.0 0.6Game-Based Training

VarianceExplained

in Intention

Game-BasedTraining

ill IP’_*~ ~Ti | _=~1 ~ [o] iF-’] I"11 J~-’~-’,H i[o~-~1 I~,

Effect of EOU on Intention

i i~-I I i[~ I I’~ (:.] i,v~:.] i I | [H ikl

Effect of U on Intention

Direct Indirect Total Direct Total

Traditional 0.49 0.31 0.32 * 0.61 0.51 0.61 0.61Training

0.58 0.38 * 0.66 0.83 0.66 0.660.67

confound in the first study is that the hands-ontraining was not equivalent across the two inter-ventions. In the first study, although specificefforts were made (in the first two hours of hands-on experience during traditional training) toshow subjects various aspects of navigationthrough the Virtual Workplace System and pro-mote interaction among subjects, it can be arguedthat the "actual system use" in the traditionaltraining was four hours while in the game-basedtraining it was only two hours because two hourswere spent on the game and only two hours onthe actual system. Thus, it is possible that thesubjects in the traditional training interventionformed a lower yet more accurate perceived easeof use, especially if only two hodrs were neces-sary to learn the system. In order’to address thisissue, an additional traditional training interven-tion with only two hours of total hands-on systemuse was introduced. Next, to address potentialorder effects (game-first vs. lecture-first) in theGBT (in the first study, the GBT was a game-firstmethod), in this study, a lecture-first GBT wasalso included.

SubjectsParticipants for this study were invited similar tothe first study by mailing out 500 letters. A total

of 212 individuals agreed to participate, the actu-al attendance was recorded at 148, and 146usable responses were obtained. Only seven par-ticipants had telecommuting experience, and allbut three had used electronic mail in the past andwere basically familiar with the idea of telecom-muting. Also, all except nine subjects reportedbeing unfamiliar with MUDs and Dungeons andDragons. The subjects were employed in a widerange of industries with an average work experi-ence of just over 14 years (the range was fromthree to 31 years). The average age of the sub-jects was 44.1 (the range was from 28 to 54).There were 40 (27%) female participants.

ProcedureIn this study, there were two different traditionaltraining interventions and two different game-based training interventions. Table 4 representsthe interventions in the second study. While tra-ditional training intervention #1 (TT1) paralleledthe traditional training in the first study, tradition-al training intervention #2 (TT2) included onlyone two-hour session of hands-on use. Similarly,game-based training intervention #1 (GBT1) wasthe same as the GBT that subjects received in thefirst study. A second game-based training inter-vention (GBT2) was introduced to counterbalance

250 MIS Quarterly Vol. 23 No. 2/June 1999


Traditional 2-hour lectureTraining #1 on the system

Traditional 2-hour lecture 30-min.Training #2 on the system break

Game-Based 2-hour hands-on 30-min.Training #1 training on the game break

(playing the game)

Game-Based 2-hour lecture 30-minTraining #2 on the system break

2-hour actual use 2-hour actual useof the system of the system

2-hour actual 45-min.use of the system test

2-hour lecture 30-min. 2-hour actual useon the system break of the system

45-min.test

2-hour hands-on 30-min. 2-hour actual use 45-min.training on the game break of the system test(playing the game)

the order of the lecture and game-play. In all fourinterventions, similar to the first study, the hands-on experience, guided by a set of exercises, was inthe context of interaction with other users.

The training was conducted similar to the firststudy with two trainers in each session. Subjectswere randomly assigned to one of the four train-ing interventions: TT1, TT2, GBT1, or GBT2. Inthis study, to control for prior knowledge/experi-ence with computers and the trait of playfulness,computer self-efficacy (Compeau and Higgins1995b) and microcomputer playfulness (Websterand Martocchio 1992) were measured. Therewas no significant difference in computer self-efficacy or microcomputer playfulness (trait)across treatment groups. Similar to the first study,following the training, subjects completed a TAMquestionnaire and completed a 45-minute testaimed at measuring learning outcomes.

ResultsThe psychometric properties of the scales wereconsistent with the first study and prior research.Cronbach alpha estimates for EOU, U, and BIexceeded 0.90, supporting a high degree of reli-ability. Convergent and discriminant validitywere supported by results from factor analyseswith cross-loadings lower than 0.20. The resultsfrom the manipulation checks were also consis-tent with the first study. Specifically, enjoymentmeasures were significantly higher among thesubjects in the game-based training (GBT1 : M 6.0, SD = 0.7; GBT2: M = 6.1, SD -- 0.6) com-pared to the traditional training (TTI: M = 4.3,SD = 0.8; TT2: M = 3.9, SD = 0.9). The knowl-

edge test scores in the game-based training(GBT1 : M = 85.2, SD = 6.6; GBT2: M = 79.6, SD= 8.5) were statistically equivalent to the tradi-tional training (TT1 : M = 84.8, SD = 7.2; TT2: =86.7, SD = 9.0).

Table 5 presents a comparison of EOU, U, and BIacross the four training interventions. Users par-ticipating in both game-based training interven-tions possessed higher levels of FOU (H1) andwere more likely to use the system as indicatedby higher levels of BI in comparison to users inthe traditional training groups (H3). The impacton U was not statistically significantly differentacross interventions (H2)?6 This pattern of meandifferences was confirmed by ANOVAs followedby Tukey’s test. Interestingly, users in the shorterversion of the traditional training intervention(TT2) had lower levels of EOU and BI than usersin TT1. This rules out the possible confound (in

161n order to examine the differential impact of the twotraining interventions on EOU and U on BI, similar toStudy 1, data from the different interventions werepooled and a dummy variable, TRAINING, was intro-duced (0 = TT1; 1 = TT2; 2 = GBT1; 3 = GBT2). Theresults indicated that TRAINING had a significantimpact on EOU consistent with Venkatesh and Davis(1996), suggesting that users in the game-based train-ing intervention found the system to be significantlyeasier to use when compared to users in the tradi-tional training. Further analysis was performed withonly the data from traditional training groups. The"amount of experience" had an effect on EOU rela-tionship suggesting that those receiving four hours ofhands-on experience had higher EOU. TRAININGhad no effect on U.



Table 5. Study 2: C¢ mparison of TAM Constructs Across Training Interventions

Perceived Ease of Use Perceived Usefulness Intention to Use

Mean Std. Dev, Mean Std. Dev. Mean Std. Dev.

Traditional Training #1 4.4 0.8 4.9 0.9 4.8 0.8

Traditional Training #2 3.7 0.9 4.7 0.8 4.1 1.0

Game-Based Training #1 5.5 0.7 5.0 0.6 5.8 0.6

5.4 0.7 4.9 0.8 5.7 0.8Game-Based Training #2

Variance Effect of EOU on IntentionExplained

Effect of U on Intention

in Intention Direct Indirect Total Direct Total

Traditional 0.38 0.26 0.28 * 0.55 0.41 0.55 0.55Training #1

Traditional 0.33 0.20 0.23 * 0.51 0.32 0.51 0.51Training #2

Game-Based 0.5.6 0.51 0.24 * 0.55 0.64 0.55 0.55Training #1

0.50 0.49 0.20 * 0.49 0.59 0.49 0.49Game-BasedTraining #2

the first and second studies) that additionalhands-on experience with the actual system inthe traditional training (four hours in TT1 andtwo hours in GBT) had caused lowering of EOU.If the effect observed in the first’ study was con-founded by amount of hands-on experience,EOU in TT2 (with only two hours of hands-onsystem use) would have been equivalent to GBT(which has only two hours of real system use).The potential confounding by order of lecture inGBT in the first study (second of three compo-nents) is also ruled out by the high level of con-sistency in results across the two GBTs (counter-balanced for order of lecture and game play) inthis study.

Regression analyses were performed to com-pare the effect of the training interventions.


The data were analyzed for the two groupsof subjects (n = 33 in TT1 and n = 35 inTT2) who went through traditional training,and the two group of subjects (n = 34 inGBT1 and n = 44 in GBT2) who wentthrough game-based training. Table 6 presentsa summary of the comparison of traininginterventions. In both types of interventions,consistent with the first study and TAM (e.g.,Davis et al. 1989), BI was determined byEOU and U. Users in the GBT were morestrongly influenced by EOU ([~ = 0.51 inGBT1; ~ = 0.49 in GBT2) in determining BIthan were users in traditional training (~ --0,26 in TT1; [} = 0.20 in TT2), thus support-ing H4. This was further confirmed by pool-ing the data across training interventions andtesting for moderation of the EOU-BI rela-


tionship by training. 17 This moderation effect,as mentioned earlier, challenges TAM’s basicthesis that all external variables will be com-pletely mediated by TAM constructs. In the tradi-tional training interventions, the overall effect ofEOU on BI ([3 = 0.41 in TT1 ; [3 = 0.32 in TT2) andU on BI (~ = 0.55 in TT1; [3 = 0.51 in TT2) indi-cated that U was the stronger overall predictor ofBI. This pattern of results is consistent with thefirst study. In the GBT, while U remained a strongdeterminant of BI (]3 = 0.55 in GBT1 ; [3 = 0.49 inGBT2), the overall effect of EOU on BI was high-er (13 = 0.64 in GBT1; 13 = 0.59 in GBT2), chal-lenging the basic TAM tenet (and accumulatedTAM research) that U is the strongest predictor ofBI. The effect of EOU on U, however, was notstatistically significantly different across the dif-ferent interventions (~ = 0.28 in TTI ; ~ = 0.23 inTT2; J] -- 0.24 in GBTI; [3 = 0.20 in GBT2), con-firming H5. The overall effect of U on BI (15 0.55 in TTI; [~ = 0.51 in TT2; [3 = 0.55 in GBTI;[~ = 0.49 in GBT2) was also not statistically sig-nificant across interventions, confirming 1-16. Thevariance in intention explained in the game-based training interventions was 56% and 50%.This is statistically significantly higher than thevariance explained in the traditional traininginterventions (38% and 33%), supporting H7.

Power Analysis

Given null hypotheses H2, H5, and H6, it wasimportant to estimate the likelihood of the occur-rence of a type II error. However, it is importantto recognize that this is a less critical issue in thisresearch since the primary focus of this researchwas the perceived ease of use construct and all

~TAs in the case of the earlier test for moderation, inorder to examine the differential impact of the fourtraining interventions on the different TAM relation-ships, data from the two interventions were pooledand a dummy variable, TRAINING, was introduced (0= TTI ; I = TT2; 2 -- GBTI ; 3 = GBT2). The moderat-ing effect of TRAINING on the EOU-BI relationshiprevealed a significant interaction effect, while the

. interaction effect in the case of the EOU-U and U-BIrelationships were non-significant. Further analysiswas performed to examine differential impacts onTAM relationships between the two different GBTs,and no significant difference was found. Similarly,TAM modeled the relationships comparably in bothtraditional training interventions.

three null hypotheses pertained primarily to per-ceived usefulness. In that sense, the nullhypotheses allowed us to hold a key determinant(i.e., perceived usefulness) constant and effec-tively manipulate perceived ease of use.

Its importance notwithstanding, a power test(Cohen 1988) was conducted for each of the twostudies separately. Unfortunately, the likelihoodof type II error was a little under 50% in the bestcase (Study 2). However, to more completelyaddress this issue, the data were pooled acrossthe two studies. Specifically, the data werepooled across all three traditional training inter-vention groups (one from Study 1 and two fromStudy 2) and all three game-based training inter-vention groups (one from Study 1 and two fromStudy 2). In order for the data to be pooled acrossstudies, it is typically necessary for there to bestatistical equivalence across the different groups(see Pindyck and Rubenfeld 1981). However, design this research aimed to create different out-comes across different groups. Based on ananalysis of the pooled data set, the pattern ofresults in the pooled data set was identical to thepattern observed in each of the two studies, lend-ing support to the pooling of the data. The pool-ing resulted in significantly large sample sizes inthe traditional training (n = 102) and the game-based training (n = 113) interventions. Results power analysis indicated that medium effectswould have been detected with a power ofalmost 0.90, and small effects with a power ofjust over 0.80.

DiscussionThe results support the theory that the potentialacceptance of a system was higher among userswho underwent a game-based training programcompared to users who were trained using a tra-ditional method. Users in a game-based trainingintervention, when compared to those in a tradi-tional training intervention, perceived the systemto be easier to use, confirming the hypothesisthat users who have a more enjoyable experi-ence during training are more likely to perceivethe system to be easier to use. This higher levelof perceived ease of use led to enhanced behav-ioral intention to use among users in game-based



training. Users in the game-based training inter-vention were also more strongly, influenced byperceived ease of use, compared to perceived use-fulness, in determining behavioral intention to use.Thus, by departing from the traditional mental rep-resentation of training, users are more favorablydisposed toward the technology, with no observ-able negative effects of playfulness on learning.

While it is possible that one could suggest that itis harmful for behavioral intention to use to beenhanced only by perceived ease of use with noincrease in perceived usefulness, it is importantto note that there was no adverse effect on per-ceived usefulness or its role as a determinant ofbehavioral intention to use. This allows for amore optimistic implication that a game-basedtraining method will potentially allow users toscale initial hurdles to acceptance and usage (pri-marily related to perceived ease of use), and alsocreate higher-level intrinsic motivation, which ismore likely to lead to sustained usage behavior.

Impfications for User AcceptanceResearch

From the standpoint of user acceptance research,this work adds to the body of research on TAM.One basic strength of this research is that it wasconducted among business professionals. TM Thislends a high degree of external validity to thesefindings while also avoiding the particular criti-cism of playfulness being more of a factor amongstudents and/or those who are younger.19

Although TAM was largely supported in thisresearch, the results challenge some of the basictenets of TAM. TAM has emphasized the impor-tance of perceived usefulness (over perceivedease of use) as the key determinant of accep-

~sWhile TAM has been validated in a field setting (e.g.,Adams, et al. 1992), it has typically been tested usingstudents (e.g., Davis et al. 1989; Taylor and Todd1995; Venkatesh and Davis 1996).

~gThis is a particularly interesting finding since theaverage age of subjects in both studies was over 40.One would not readily expect subjects who are olderto find games as appealing as those who are younger,especially given that they were not familiar withDungeons and Dragons or MUDs. An analysis of thedata by dividing it into two groups (under-40 andover-40) revealed no moderating effect for age andthe impact it has on the role of the training method.

tance. Empirical evidence has consistently borneout this claim, leading~to perceived ease of usebeing treated as somewhat of a "step-child." Theresults of this research indicate that perceivedease of use can be a strong catalyst fosteringacceptance in a positive and enjoyable trainingenvironment. Contrary to prior TAM research, theresults from users who underwent the game-based training indicate that the overall effect ofperceived ease of use on intention was greaterthan the effect of perceived usefulness on inten-tion. This suggests that the appropriate priming ofusers increases the salience of perceived ease ofuse.

The current research has shown that the higherlevels of perceived ease of use observed in thegame-based training methods can have astronger effect on behavioral intention to use,even compared to the effect of the robust andstrong construct of perceived usefulness. Thissuggests that the effect of perceived ease of useon behavioral intention is different depending onthe type of training intervention, thus suggestingthat the perceived ease of use-behavioral inten-tion to use relationship is moderated by training.Consistent with some work in psychology (e.g.,Tashakkori 1993), the findings of this researchchallenge the basic TRA/TPB (and TAM) thesisthat the effect of external variables on behavioralintention to use will be fully mediated bybeliefs--in other words, there will be no moder-ation of belief-intention relationships. Futureresearch is needed to investigate the generaliz-ability of such a key departure from accumulatedTAM research. One important future direction inthis regard is to identify other contingencies andboundary conditions of the powerful TechnologyAcceptance Model.

Prior research has shown perceived enjoyment(intrinsic motivation) and perceived usefulness(extrinsic motivation) to be the two key determi-nants of behavioral intention to use (Davis et al.1992). Further, it has been shown that the effectof perceived ease of use on behavioral intentionto use was fully mediated by perceived enjoy-ment and perceived usefulness. The currentwork, in contrast, showed that manipulating thelevel of perceived enjoyment has a significantimpact on perceived ease of use, and the effect ofperceived ease of use on behavioral intention to



use. The experimental approach used in thisstudy, in contrast to the survey-based findings ofDavis et al. (1992), supports a high level of inter-nal validity for these new findings. However, thedeparture from the basic tenets of TAM (Davis etal. 1989) and the motivational perspective ofDavis et al. (1992) calls for additional research carefully examine how the different constructsinterrelate. Though enjoyment was measured inthe current work, the models were not reconciledin this paper to keep the focus on the effective-ness of game-based training.

It is also necessary to examine the generalizabil-ity of these findings to other technologies andenvironments, thus helping the development ofprinciples for the design of game-based traininginterventions. More broadly, future researchshould examine the acceptance of telecommut-ing technologies using a different theoretical per-spective (e.g., critical mass) in order to triangu-late findings and/or questio n assumptions.Although this study was conducted amongknowledge workers, to further establish externalvalidity, research should be conducted in anorganizational context and also for systemsdesigned for use in particular organizations.Another important issue for future research tostudy is the temporal dynamics associated withthe findings from this work. It is also necessaryfor future research to determine whether thefavorable user reactions (enhanced levels of userperceptions and intentions) were only temporaryor whether such reactions lasted for longer peri-ods of time to indeed have an impact on accep-tance, adoption and usage decisions. Suchfuture work will be of particular significancesince the current research challenges a funda-mental tenet of TAM (i.e., the relative impor-tance of perceived ease of use and perceivedusefulness).

Implications for Training Research

This research also has important theoreticalimplications for work in the area of end-usertraining. The increased potential for acceptancegenerated in the game-based training interven-tion presents a key avenue for trainingresearchers to explore. In the early part of the lastdecade, Malone (1981 a; 1981 b) set the tone forabstracting principles from computer games to

make learning on computers more interesting.The present work builds on that fundamentalidea by using a game with features similar to atelecommuting tool. Researchers in the area ofuser training can now turn to similar approach-es to improve the impact of interventions. Theresults of the current research bear particularimplications for training users on unfamiliar sys-tems but it is also possible to understand therole of intrinsic motivation in training oftask/technology situations familiar to users.Another key direction for future research is tofurther our understanding along the threedimensions of intrinsic motivation: fantasy,curiosity, and challenge (Malone 1981a).Fantasy was most suited to this research becausea relevant gaming situation (Internet-based fan-tasy role-playing game, MUD) could be identi-fied. In this and other cases, the role of chal-lenge and curiosity should be examined.Another area for future study is the role of game-based training in distance education, which isbecoming increasingly popular while continuingto be a great challenge for educators.

Limitations and Additional FutureResearch Directions

One potential limitation of the present researchis non-respondent bias. While the specific moti-vation among the participants to learn/usetelecommuting tools is unknown, a possibleconjecture is that they perceived some value inlearning the tool. Quite possibly, this explainsthe non-significant difference in the effect onperceived usefulness and the role of perceivedusefulness in determining behavioral intentionto use. However, this is not seen to be a seri-ous threat in this research since the key objec-tive is to study the impact of training on per-ceived ease of use and not perceived useful-ness. Future research should examine the gen-eralizability of these findings to settings wherethere is no predisposition to learn the tool.Further research is necessary to address thisissue especially in the light of some of the rela-tionships being non-significant. Although bothstudies taken separately indicated relativelyhigh probability of type II error, power analysisbased on the pooled data indicated a power ofover 0.80 for small effects, thus alleviating this



concern to a certain extent. As mentioned ear-lier, this is less of an issue in the current worksince the emphasis is on perceived ease of useand the null hypotheses pertain primarily toperceived usefulness.

As mentioned earlier, the generalizability ofthese findings to other technologies, particularlymore complex ones, needs to be studied. As itstands, the results and conclusioris are specific tothe system studied. Another important limitationand potentially key direction for future researchis to examine the generalizability of these find-ings from multi-user systems (like the oneemployed in this research) to single-user systems.For example, for training on a DSS, a simulationgame (say running a mock company) could used as part of the game-based training. Morebroadly, future research should develop guide-lines for the identification of games for game-based training. -

Impfications for PracticeThis research bears key implications for practice.Practitioners interested in end-user training inorganizational settings may be .well served byattempting to incorporate game-based trainingprograms to create favorable user perceptions bycreating a gaming situation that can serve as anintroductory base for learning. This can beaccomplished by relating features of the targetsystem to the game and vice-versa. Typically,games have been seen as detractors from workand productivity since they cause people towaste time. However, this research suggests thatintrinsic motivation induced by games can loweruser perceptions of effort of the task at hand.Also, since perceived ease of use is a "hurdle" or"hump" that users have to scale in order toaccept, adopt, and use a technology, it appearsthat game-based training not only leads to favor-able perceived ease of use but ~lso causes suchhigher levels of perceived ease of use to have agreater influence on behavioral intention to use.This is particularly encouraging since the sub-jects in this research were unfamiliar withtelecommuting technologies in general, the tar-get system, and the particular game used as thebasis for enhancing intrinsic motivation duringtraining.

Acknowledgements

The comments and guidance of the senior editor,Allen Lee, and the anonymous associate editorand four reviewers significantly enhanced thequality of this paper. I would like to express mysincerest thanks to Professors Gerry DeSanctis(Duke University) and Fred Davis (University Maryland) for their insightful suggestions duringvarious phases of this research. I would like tothank Professors Sue Brown (Indiana University),Barbara Klein (University of Michigan,Dearborn), Michael Morris (Air Force Institute Technology), and Cheri Speier (Michigan StateUniversity) for their valuable comments on earli-er drafts of this paper. I would also like to thankTracy Ann Sykes (University of Maryland) for hereditorial work that helped improve the readabili-ty of this paper. Last but not least, I would like tothank Jan DeGross and Susan Scanlan for theirtireless efforts to transform the accepted paperinto this printed publication.

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About the Author

Viswanath venkatesh is an assistant professor inDecision and Information Technologies in theRobert H. Smith School of Business at theUniversity of Maryland. He received his Ph.D. ininformation and decision sciences from theUniversity of Minnesota. His research focuses onunderstanding user acceptance of computer andinformation technologies in the workplace andhomes. In addition, he has a keen interest, froma research and teaching perspective, in develop-ing effective methods of user training. Hisresearch has been published or is forthcoming inManagement Science, MIS Quarterly,Organizational Behavior and Human DecisionProcesses, Decision Sciences, and InternationalJournal of Human-Computer Studies. He was arecipient of the Smith School’s "TeachingInnovation Award" in 1998.


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