Featherman, Mauricio

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Association for Information SystemsAIS Electronic Library (AISeL)AMCIS 2001 Proceedings Americas Conference on Information Systems(AMCIS)December 2001Extending the Technology Acceptance Model byInclusion of Perceived RiskMauricio FeathermanUniversity of HawaiiFollow this and additional works at: http://aisel.aisnet.org/amcis2001This material is brought to you by the Americas Conference on Information Systems (AMCIS) at AIS Electronic Library (AISeL). It has been acceptedfor inclusion in AMCIS 2001 Proceedings by an authorized administrator of AIS Electronic Library (AISeL). For more information, please contactelibrary@aisnet.org.Recommended CitationFeatherman, Mauricio, "Extending the Technology Acceptance Model by Inclusion of Perceived Risk" (2001). AMCIS 2001Proceedings. 148.http://aisel.aisnet.org/amcis2001/148758 2001  Seventh Americas Conference on Information SystemsEXTENDING THE TECHNOLOGY ACCEPTANCE MODELBY INCLUSION OF PERCEIVED RISKMauricio S. FeathermanUniversity of HawaiiMauricio@hawaii.eduAbstractThis work in progress proposes to extend the Technology Acceptance Model (TAM) as conceived by Venkateshand Davis (2000) to include a measure of negative utility, perceived risk of service usage (Bauer, 1960). Valence Theory (Lewin, 1944) tells us that by measuring both costs and benefits of product usage we canimprove our understanding of product evaluation.  An investigation of the individual adoption of Internet-basede-payment systems was performed using e-billpay software as the research context.   The research wasperformed to better understand consumer evaluations and adoption intentions of an Internet-based informationsystem during conditions of uncertainty and perceived risk.  A scale to operationalize the construct perceivedrisk was created and included in factor and regression analysis of the TAM2 variables, yielding a variable thatsignificantly inhibited e-payment system evaluation and adoption intention.  Preliminary results supportinclusion of a measure of perceived risk when researching user acceptance of Internet-based e-paymentsystems.  This finding may extend to other e-commerce software-based consumer services, and organizationaladoption contexts.  Extending TAM by inclusion of perceived risk may improve the models' predictive validitywhen investigating adoption of application service provider (ASP) systems, and other web-services.IntroductionThe Technology Acceptance Model (TAM) is an evaluation model based on previous attitude-intentions literature, tuned forinformation systems research.   It has been successfully utilized to measure the perceived positive gain in utility possible fromthe adoption and usage of an information system.  TAM's main dependent variable is the utility-based perceived usefulness ofthe software, defined as "The prospective user's subjective probability that using a specific application system will increase hisor her job performance within an organizational context." (Davis p. 985).Valence models (Lewin, 1944; Fishbein 1967)  base a product or service's evaluation on both desirable benefits (with positivevalence) and undesirable costs (with negative valence).  These models propose that if the benefits outweigh the costs resultingin a net positive valence, the purchase will tend to be made.  It may be fruitful to apply this net valence approach to theinformation systems adoption context.  Therefore, if the potential rewards (benefits of usage) outweigh the potential risks (costsof usage), the information system will tend to be adopted.  More importantly, modeling both costs and benefits provides a morebalanced investigation of systems evaluation and adoption, with both practical and theoretical implications.Product evaluations usually involve both positive and negative outcomes (Lewin, 1944), measured as both potential gains andlosses.  Rogers (1962) included a measure of negative utility when he positioned complexity of system usage as an inhibitor tosystem evaluation and adoption.  This variable was re-framed for TAM to tap the positive utility portion of this construct andnamed perceived ease of use.  TAM then can be viewed as a positive valence model.  To evaluate web-services it may provebeneficial to measure potential loses (risks) to better understand the evaluation of this class of information system.Another measure of the negative consequences of system adoption and usage is the perceived risk of usage.   Bauer (1960) definedthis perceived risk as "a combination of uncertainty plus seriousness of outcome involved- associated with each category ofproduct" (p. 391).  Jacoby & Kaplan (1972) defined perceived risk as a function of uncertainty regarding the consequences of apurchasing decision.  Bauer (1960) claimed that,  "Consumer behavior involves risk in the sense that any action of a consumerFeatherman/Extending TAM by Inclusion of Perceived Risk2001  Seventh Americas Conference on Information Systems 759Subj. NormOutput QualityResults DemonstrabilityPerceived UsefulnessPerceived Ease of UseBehavioralIntentionUsage BehaviorTAMProduct Category Inherent Perceived RiskTAM 2ImageExperienceFigure 1.  Research ModelAdj R2 Adj R2w/o PR w/PR Beta t Sig. Beta t Sig. Beta t Sig. Beta t Sig.Pre-trial .28 .37 .34 4.21 .000 .28 3.04 .003 -.17 -2.18 .031 .07 .82 .414Post-trial .64 .64 .56 7.21 .000 .31 3.87 .000 .04 0.62 .538 .03 .31 .754Perceived UsefulnessSubjective Norm Perceived RiskEase of Use ImageAdj R2 Adj R2w/o PR w/PR Beta t Sig. Beta t Sig. Beta t Sig. Beta t Sig.Pre-trial .52 .57 -0.08 -1.15 .253 .15 2.29 .024 -.34 -5.10 .000 .53 7.2 .000Post-trial .74 .75 0.28 3.73 .000 .14 2.63 .009 -.10 -2.10 .038 .56 7.71 .000Adoption IntentionEase of Use Subjective Norm Perceived Risk Perceived Usefulnesswill produce consequences which he cannot anticipate with anything approximating certainty, and some of which are likely tobe unpleasant" (p. 390).   Perceived risk has been theorized as entering the buying/adoption decision when circumstances of thedecision create feelings of uncertainty, discomfort and/or anxiety (Dowling & Staelin, 1994).   This literature suggests the following TAM-based research model.  To test the validity of including measures of perceived costsof adoption that users of web-services perceive, the following research was performed.MethodThe TAM2 instrument with a scale to measure perceivedrisk was administered to 160 college of businessundergraduate students after a scripted informationalpresentation and product demonstration  of an e-billpayservice.  Subjects then completed an interactive hands-onproduct trial of the e-billpay software-based service toreduce product usage uncertainty and gain product usageexperience.  After this trial subjects then completed thesame 7-point scaled survey instrument.  Exploratoryfactor analysis was performed and regressions includedonly the variables with strong discriminant validity.ResultsPreliminary results in table 1 below indicated that before the product trial subjects usefulness (utility) evaluations weresignificantly affected by concerns for the product's ease  of use, normative influences, and product usage based perceived risk.After the hands-on trial, this same evaluation was affected only by the models' normative concerns and the product's ease of use.Thus usefulness evaluations for this sample and context were no longer based on product usage perceived risk.Adoption intentions results in table 2 were significantly affected by normative concerns, perceived risk and the product's perceivedusefulness.  Post trial adoption intentions were significantly influenced by all four modeled exogenous variables.  Thus usabilityconcerns significantly affected adoption intentions only after subject's gained experience interacting with the Internet-basedsoftware.  Concern for product inherent perceived risk was persistent and significant, and reduced by the product trial.  In mostcases model predictive validity was only marginally improved by inclusion of the perceived risk variable, however this may bean artifact of the sample and context.Table 1.  Regressions of Perceived UsefulnessTable 2.  Regressions of Adoption IntentionElectronic Commerce760 2001  Seventh Americas Conference on Information SystemsMean SD Mean SD T-score Sig.Perceived Usefulness 5.19 1.03 4.85 1.32 4.64 .000Adoption Intentions 5.06 1.33 4.77 1.51 4.11 .000Perceived Risk 3.34 1.13 3.30 1.13 0.46 .648Perceived Ease of Use 5.34 0.77 5.01 1.18 3.79 .000Pre-trial Post-trial Paired SamplesTrial Effects on            Major VariablesTable 3.  ANOVA for Trial EffectsA pre-post trial comparison of perceived risk levels indicated no significant reduction after the product trial which did not confirmthe theorized relationship (Roselius, 1971). System evaluations and adoption intentions lowered after product trial indicating a lower valence assigned to the e-billpay service. Results suggest a differential influence of perceived risk during utilityevaluations and adoption decision-making.  During the evaluation of system usefulness, concern for perceived risk was significantonly before product trial, while the adoption intention choice was significantly affected by concerns for perceived risk both beforeand after product trial.ConclusionThese results suggest research of user acceptance of Internet-based e-commerce services, may benefit by the inclusion of anoperationalization of perceived risk by improving  our understanding of evaluation and adoption patterns.  For this sample andcontext however, predictive validity was only marginally improved by the inclusion of a measure of perceived usage risk.  Froma practical perspective understanding the psychology beneath web-based system evaluation and the effect of product trial canenable improved web-systems design.  Future research should attempt to manipulate perceived risk to better understand itsinfluence and fit within TAM.   With the projected growth of Internet-based ASP's and web-services, a multi-dimensional measureof adopter's uncertainty and perceived risk should enrich information systems adoption research.ReferencesVenkatesh, V., Davis, Fred D. (2000). “A Theoretical Extention of The Technology Acceptance Model: Four Longitudinal FieldStudies.” Management Science 46(2), pp.186-204. Bauer, R. A. (1960). Consumer Behavior as Risk Taking. Risk Taking and Information Handling in Consumer Behavior. D. F.Cox. Cambridge, Mass, Harvard University Press. pp. 389-398.Lewin, K., Dembo, T., Festinger, L., & Sears, P.S. (1944). Level of Aspiration. Personality and the Behavior Disorders . J. M.Hunt. New York, Ronald Press, pp. 333-378.Fishbein, M. (1967). Attitude and Prediction of Behavior. Readings in Attitude Theory and Measurement. M. Fishbein. New York,Wiley: pp. 477-492.Rogers, E. M. (1962). Diffusion of Innovations. New York, The Free Press.Jacoby, J., Kaplan L.B. (1972). The Components of Perceived Risk. Proceedings of the Third Annual Conference, Ann Arbour,Michigan, Association for Consumer Research.Dowling, G. R., Staelin, Richard (1994). “A Model of Perceived Risk and Intended Risk-Handling Activity.” Journal ofConsumer Research (21:6), pp. 119-134.Roselius, T. (1971). “Consumer Rankings of Risk Reduction Methods.” Journal of Marketing (35:1), pp. 56-61.

Extending the Technology Acceptance Model by Inclusion of Perceived Risk

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Extending the Technology Acceptance Model by Inclusion of Perceived Risk

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