2007 integrating technology readiness into technology acceptance the tram model

Integrating TechnologyReadiness into Technology Acceptance:The TRAM ModelChien-Hsin LinYu Da College of Business, Taiwan, and National Chi Nan University, Taiwan

Hsin-Yu ShihNational Chi Nan University, Taiwan

Peter J. SherNational Chi Nan University, Taiwan

ABSTRACT

Based on previous theoretical streams, the present study integratestechnology readiness (TR) into the technology acceptance model(TAM) in the context of consumer adoption of e-service systems, andtheorizes that the impact of TR on use intention is completely medi-ated by both perceptions of usefulness and ease of use. TAM was origi-nally developed to predict people’s technology-adopting behavior atwork environments, but this research stemmed from a questioning ofits applicability in marketing (i.e., non-work) settings. The differencesbetween the two settings are exhibited by consumers’ self-determiningselection behavior and their high involvement in the e-service cre-ation and delivery process. This paper first reviews the TAM and theconstruct of technology readiness, and then proposes and empiricallytests an integrated Technology Readiness and Acceptance Model(TRAM) to augment TAM by taking technology readiness constructinto the realm of consumers’ adoption of innovations. The resultsindicate that TRAM substantially broadens the applicability and theexplanatory power of either of the prior models and may be a better

Psychology & Marketing, Vol. 24(7): 641–657 (July 2007)Published online in Wiley InterScience (www.interscience.wiley.com)© 2007 Wiley Periodicals, Inc. DOI: 10.1002/mar.20177

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way to gauge technology adoption in situations where adoption is notmandated by organizational objectives. Further, theoretical andpractical implications and future research directions are discussed.© 2007 Wiley Periodicals, Inc.

INTRODUCTION

Explaining and predicting user adoption of new technology enjoy a long his-tory of attention in both academia and practice. Among many models, thetechnology acceptance model (TAM) (Davis, 1989) appears to be the mostwidely cited and replicated empirically. TAM was originally developed topredict people’s technology-adopting behavior at work environments, butscant research questions its applicability in marketing (i.e., non-work) set-tings where adoption is not mandated by organizational objectives. Thedifferences between marketing and work settings are obvious. Consumersin marketing settings engage in the e-service creation and delivery processrather than owning the system equipment per se (Dabholkar & Bagozzi,2002). People in work settings may reluctantly or involuntarily adopt asystem due to management intervention, but consumers in marketing set-tings may be freer to choose among numerous available alternatives. Forinstance, investors could independently choose between conventional andonline stock trading systems.When they decide to trade stocks online, theyco-create an e-service with the system but do not own the system.

In e-service contexts, service cannot be created apart from customers’active participation (Lovelock & Wirtz, 2004). Due to the necessary highinvolvement of customers to co-produce the service, TAM applied inmarketing settings may not sufficiently explain consumers’ technologyadoption behaviors. Therefore, a model incorporating some individualdifference variables is a necessary first step toward identifying and qual-ifying the psychological processes of the perceptions of a technology’svalue. Accordingly, the primary objective of this paper is to adapt andextend TAM by considering individual differences. To take individualdifferences into account, this study integrates the construct of technologyreadiness (TR) (Parasuraman, 2000) with TAM to better explainconsumers’ intentions to use e-services—online stock trading systemsin particular. TR conceptualizes consumers’ general beliefs about tech-nology and is associated with their use of technology-based products andservices (Parasuraman, 2000). Evidence from the fieldwork shows thatTR is incapable of explaining why high-TR consumers do not alwaysadopt new technologies, such as cellular phones with open operating sys-tems or in-car global positioning systems. From the TR aspect, this studytries to supplement the construct of TR with the two focal constructs ofTAM (i.e., perceived usefulness and perceived ease of use). An integratedTechnology Readiness and Acceptance Model (TRAM) is established toaddress the issue of consumer adoption of e-services.

LIN, SHIH, AND SHERPsychology & Marketing DOI: 10.1002/mar

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INTEGRATING TECHNOLOGY READINESS INTO TECHNOLOGY ACCEPTANCEPsychology & Marketing DOI: 10.1002/mar

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This paper is organized as follows. First, TAM and TR are brieflyreviewed. Second, according to related theoretical backgrounds, this studyintegrates TR with TAM. Next, the integrated Technology Readiness andAcceptance Model (TRAM) and research hypotheses are tested with datagathered from Web-based surveys. Finally, this study concludes by notingresearch and practical implications.

TECHNOLOGY ACCEPTANCE MODEL (TAM)

Rooted in the Theory of Reasoned Action (TRA) (Ajzen & Fishbein, 1980),TAM is a specific and parsimonious framework for predicting and explain-ing people’s adoption of information technology in work settings (Davis,1989; Davis, Bagozzi, & Warshaw, 1989). TAM postulates that user accep-tance of a new system is determined by the users’ intention to use thesystem, which is influenced by the users’ beliefs about the system’sperceived usefulness and perceived ease of use. Perceived usefulness isdefined as the extent to which a person believes that using a particularsystem will enhance his or her performance, and perceived ease of userefers to the extent to which a person believes that using a particularsystem will be free of effort. Perceived ease of use is hypothesized to bea determinant of perceived usefulness, while both beliefs are influencedby external variables, such as training, support, and perceived accessi-bility (Karahanna & Straub, 1999), social influence processes, andcognitive instrumental processes (Venkatesh & Davis, 2000). TAM hasbeen empirically replicated or extended to explain various behaviorswith adopting technology (e.g., Gefen, 2003; Gefen & Straub, 1997; Gefen,Karahanna, & Straub, 2003; Lu, Yu, Liu, & Yao, 2003; Pavlou, 2003; Wang,Wang, Lin, & Tang, 2003). However, studies investigating how and whythese two cognitive beliefs develop are considered relatively insufficient(Karahanna & Straub, 1999).

TECHNOLOGY READINESS (TR)

TR refers to people’s propensity to embrace and use new technologiesfor accomplishing goals in home life and at work (Parasuraman, 2000).TR construct can be viewed as an overall state of mind resulting froma gestalt of mental enablers and inhibitors that collectively determine aperson’s predisposition to use new technologies. At the measurementlevel, the Technology Readiness Index (TRI) was developed to measurepeople’s general beliefs about technology. TR construct comprises foursub-dimensions: optimism, innovativeness, discomfort, and insecurity.Optimism relates to a positive view of technology and a belief thattechnology offers people increased control, flexibility, and efficiency. Inno-vativeness refers to a tendency to be a technology pioneer and thought

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leader. Discomfort consists of a perception of lack of control overtechnology and a feeling of being overwhelmed by it. Insecurity involvesdistrust of technology and skepticism about its ability to work properly.Optimism and innovativeness are drivers of TR, while discomfort andinsecurity are inhibitors. Positive and negative beliefs about technologymay coexist, and people can be arrayed along a technology belief con-tinuum from strongly positive attitude at one end to strongly negativeattitude at the other. The correlation between people’s TR and theirpropensity to employ technology is empirically confirmed by Parasuraman(2000). Consumers’ TR has a positive impact on their online servicequality perceptions and online behaviors, but empirical findings arescarce (Zeithaml, Parasuraman, & Malhotra, 2002) and confounding(Liljander, Gillberg, Gummerus, & van Riel, 2006). Therefore, the role ofTR may be minor in explaining individuals’ online behaviors (Liljanderet al., 2006). The limited knowledge about TR constitutes a need to inves-tigate TR in a broader framework.

THEORY OF TECHNOLOGY READINESS AND ACCEPTANCE MODEL (TRAM)

Conceptual and Theoretical Background

It is intuitively accepted that TAM and TR are interrelated, althoughthe measurement of usefulness and ease of use in TAM is specific fora particular system (i.e., system-specific) while TR is for generaltechnology beliefs (i.e., individual-specific). When faced with a choiceto make, consumers in general first engage in internal search, exam-ining memory for available information (Bettman, 1979). Consequently,in addition to heterogeneous system characteristics, people’s generalbeliefs about technology derived from prior experience may be employedto anchor perceptions of usefulness and ease of use. This experience-based evaluation mechanism may be more pronounced for noviceconsumers, who are more apt to process choice alternatives usingabstract, general criteria (as opposed to more concrete, specific criteria;Bettman & Sujan, 1987). Thus, there appear to be implicit theoreticaland practical bases to surmise that when people evaluate technologyadoption intentions, cognitive information of TR is retrieved andprocessed before specific cognitive appraisal (i.e., usefulness and easeof use) is retrieved and processed.

Theoretically, consumer studies have posited that previous productexperience and knowledge influence consumer cue utilization (Rao &Monroe, 1988) and message processing (Peracchio & Tybout, 1996) inproduct evaluation. People with more product knowledge may search formore information before problem solving because of their high awarenessof existing attributes (Brucks, 1985), and may identify relevant information


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more accurately (Alba & Hutchinson, 1987). More knowledge reflects moreextensive, complex, experienced, expert, and familiar knowledge, and thuseffortful processing of issue-related information and evaluative inferencesconcerning product features by high-knowledge consumers could beexpected (Alba & Hutchinson, 1987; Peracchio & Tybout, 1996). Further-more, consumers’ expectations, based on prior beliefs stored in memorycan influence consumers’ perceptual encoding about marketing informa-tion (Bettman, 1979). By and large, people’s prior beliefs, formed throughexperience, play an important role in guiding information processing andin directing behavior (John, Scott, & Bettman, 1986). Impacts of priorbeliefs include: (1) deciding which data are relevant, (2) interpreting andintegrating information, (3) using the estimate to make other judgments(Crocker, 1981).

Within this paper’s context, people with more knowledge or experienceof information technology form stronger computer self-efficacy (Gist &Mitchell, 1992; Venkatesh & Davis, 1996), or perceive stronger controlover information technology–related tasks (Kang, Hahn, Fortin, Hyun, &Eom, 2006). Studies on diffusion of innovations (Rogers, 2003) also indi-cate that prior experience with an innovation is necessary in buildinghow-to knowledge, which is critical in the belief formation stages. Expe-rience gained through previous use of technology is empirically con-firmed to increase user perceptions of its ease of use and usefulness(Gefen, 2003; Karahanna, Straub, & Chervany, 1999), and users’ onlinebehavioral intentions (Yoh, Damhorst, Sapp, & Laczniak, 2003). Thecausal links between general computer self-efficacy and perceptions ofusefulness and ease of use are also confirmed by Wang, Wang, Lin, andTang (2003) and Venkatesh and Davis (1996). The positive correlationbetween prior formed beliefs about comparable e-services and posteriorformed beliefs about specific e-services is also empirically supported(Yoh et al., 2003).

Research Hypotheses and TRAM Framework

The above explication provides strong theoretical fundamentals for thecorrelations between TR and perceptions of usefulness and ease of use.This study theorizes that general TR belief is a causal determinant of spe-cific cognitive appraisal of usefulness and ease of use, and it proposesthe focal hypotheses H5, H6, and H7 in this paper. In order to establisha comprehensive framework to integrate TR into TAM, H1, H2, H3, andH4, addressed by past studies (Davis, Bagozzi, & Warshaw, 1989;Parasuraman, 2000), are intertwined with H5, H6, and H7. However,first H1 through H4 must be replicated and confirmed so as to lead to theconstruction of the integrated model (see Figure 1).

H1: Consumers’ technology readiness propensities are positivelycorrelated with their intentions to use a specific e-service.


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H2: Consumers’ perceptions of usefulness about a specific e-service arepositively correlated with their intentions to use it.

H3: Consumers’ perceptions of ease of use about a specific e-service arepositively correlated with their intentions to use it.

H4: Consumers’ perceptions of ease of use about a specific e-service arepositively correlated with their perceptions of usefulness about it.

In addition to the above four hypotheses, this paper puts forth threehypotheses to make a case for and capture the evolution of TAM into themore comprehensive TRAM.Accordingly, H5, H6, and H7 constitute the pri-mary contribution toward understanding people’s technology adoption.

H5: Consumers’ technology readiness propensities are positively corre-lated with their perceptions of usefulness about a specific e-service.

H6: Consumers’ technology readiness propensities are positively corre-lated with their perceptions of ease of use about a specific e-service.

H7: Consumers’ perceptions of usefulness and ease of use about a spe-cific e-service together completely mediate the relationship betweentheir technology readiness propensities and intentions to use the spe-cific e-service (i.e., the path of H1 is non-significant in the full model).

RESEARCH DESIGN

Measures of the Constructs

This study employed the full 36-item TRI scales (Parasuraman, 2000)to measure the four sub-dimensions of TR (i.e., 10 items for optimism,


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TechnologyReadiness

PerceivedUsefulness

PerceivedEase of Use

Use Intention

Optimism

Innovativeness

Discomfort

Insecurity

H5

H6

H2

H3

H1

H4

Figure 1. TRAM model.

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7 items for innovativeness, 10 items for discomfort, and 9 items forinsecurity).1 The items of perceived usefulness (6 items) and perceivedease of use (6 items) were adapted from Davis (1989). Two items con-cerning intention to use online stock trading systems were designedspecifically for this study (i.e., “I consider using an online stock tradingsystem when I trade stocks next time” and “I will trade stocks throughan online stock trading system in next few months”). All the items weremeasured on 7-point scales anchored by “strongly disagree 5 1” and“strongly agree 5 7.” Measures originally in English were translatedinto Chinese and back-translated into English to ensure equivalentmeaning (Brislin, 1980).

Data Collection and Sample Characteristic

Web-based surveys were conducted during March and April 2004 byinviting members of several online investment discussion forums inTaiwan to participate in the study. Participants were asked to self-predicttheir future use of online stock trading systems. Respondents wereentered into a sweepstakes (30 prizes of NT$100; around US$3) ascompensation for their participation. Because the number of individualsapproached during the invitation stage was unknown, the response ratewas not possible. This study collected a total of 406 completed question-naires. The sample of respondents was composed of more males (64%)than females (36%). A total of 57% of respondents were between 21 and30 years old, 18% were between 31 and 40 years old, and 17% were under21 years old. Of the respondents, 85% had previous stock trading expe-rience, whereas 80% had experience trading with online systems.

DATA ANALYSIS AND RESULTS

Measurement Properties

Measurement reliability was assessed with the Cronbach alpha. Theresults indicated an alpha coefficient of 0.95 for optimism, 0.95 forinnovativeness, 0.90 for discomfort, 0.92 for insecurity, 0.95 for perceivedusefulness (PU), 0.96 for perceived ease of use (PEOU), and 0.92 for useintention (UI). Measurement reliabilities were satisfactory in this study.

Construct validity was also evaluated before structural model analyses.The analyses took measurement errors into account and applied covari-ance structure models. Scale scores of optimism, innovativeness, dis-comfort, and insecurity were computed by averaging their respectiveraw scores and were used as reflective indicators of the construct of TR.


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1 These questions comprise the Technology Readiness Index (TRI), which is copyrighted byA. Parasuraman and Rockbridge Associates, Inc., 1999. The authors have obtained therequisite permission in this regard.

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All subsequent analyses were conducted using Amos 4 (Arbuckle &Wothke, 1999). The confirmatory factor analysis (CFA) results demon-strated an adequate model fit (GFI 5 0.90, CFI 5 0.96, TLI 5 0.95,RMSEA 5 0.07, x2(126) 5 404.81, p , 0.01).2

Convergent validity was assessed by reviewing the t test for the factorloading of each manifest indicator on its proposed latent construct(Anderson & Gerbing, 1988). The average standardized factor loadingwas 0.80 and all loadings were highly significant (p , 0.01); hence theanalysis indicated convergent validity of the measures. Discriminantvalidity was examined using a test involving the confidence interval foreach pairwise correlation estimate (i.e., plus or minus two standarderrors) but does not include the value of 1.0 (Anderson & Gerbing, 1988).The results demonstrated that all the confidence intervals surround-ing the construct correlations did not contain the value of 1.0 and providedsupport for discriminant validity of the measures. Overall, the constructsexhibit good measurement properties.

Tests of Mediating Effects

To test mediation effect, the present study followed Baron and Kenny’sguidelines (1986). Specifically, three equations must be estimated to testfor mediation. First, regressing the mediator on the independent variable,and the independent variable must significantly affect the mediator.Second, regressing the dependent variable on the independent variable,and the independent variable must significantly affect the dependentvariable. Third, regressing the dependent variable on both the inde-pendent variable and the mediator, and the mediator must significantlyaffect the dependent variable. To establish mediation, the effect of theindependent variable on the dependent variable must be less in the thirdequation than in the second. This study also examined the Sobel test(Sobel, 1982) to confirm if the mediation path was significant. Table 1 sum-marizes the results of mediation tests.

The three equations of Model 1 tested the mediation effect of PU.Models 1-1 and 1-2 confirmed that TR significantly affects PU and UI,respectively. For the mediation effect of PU to hold, the effect of TR on UI must be reduced when PU is controlled, while the effect of PU on UImust be significant. As shown in Table 1, the effect of TR on UI wasreduced from 1.12 to 0.30 with t-value reduced from 11.63 to 2.59, and theeffect of PU on UI was significant. Furthermore, the null hypothesis of nomediation effect was rejected by the Sobel test (z-value 5 7.73, p , 0.01),


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2 Three parameters of within-factor correlated measurement errors were specified as freelyestimated to achieve adequate fit. Though the substantive meanings of these correlated errorterms were equivocal, it was confirmed that the measurement properties and structural rela-tionships discussed below were not altered when these three parameters were constrained tozero or freely estimated. Results of the model without correlated error terms can be obtainedfrom the corresponding author.

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Tab

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indicating that the mediation effect of PU exists. Though the effect ofTR was reduced, its effect was still significant when PU was controlled.The results imply that PU only partially mediates the link between TRand UI, and other potential mediators may exist. Overall, the threeequations of Model 1 supported H1, H2, and H5. Likewise, the threeequations of Model 2 tested the mediation effect of PEOU, and the resultsconfirmed H3 and H6. PEOU also partially mediated the relationshipbetween TR and UI, as evidenced by the Sobel test (z-value 5 4.67, p ,0.01) and the reduced effect of TR on UI.

Tests of TRAM Framework

Since both mediation effects of PU and PEOU were partial when testedrespectively, this study further estimated the integrated framework(previously shown in Figure 1) by simultaneously modeling PU andPEOU as mediators between TR and UI, and PEOU was modeled as anantecedent of PU. Estimation results are shown in Table 2. The fitstatistics indicated that the integrated model was adequate (GFI 5 0.90,CFI 5 0.96, TLI 5 0.95, RMSEA 5 0.07, x2(126) 5 404.81, p , 0.01).Concerning specific path coefficients, PEOU significantly influenced PU,and thus H4 was confirmed. The effect of TR on UI was no longer sig-nificant when PU and PEOU were controlled simultaneously. The Sobeltests (see Table 3) also demonstrated that mediation effects of PU andPEOU exist.

As the non-significant coefficient between TR and UI suggested, thestudy further estimated the integrated model by trimming the path


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Table 2. Estimation Results of the Integrated TRAM Model.

Full model Trimmed model

Path Standardized Path Standardizedcoefficients coefficients coefficients coefficients

TR → PU 0.73*** (6.84) 0.52 0.73*** (6.89) 0.52TR → PEOU 1.18*** (14.64) 0.74 1.18*** (14.59) 0.74TR → UI 0.20 (1.43) 0.11 – –PU → UI 0.67*** (8.13) 0.54 0.73*** (10.32) 0.59PEOU → UI 0.19*** (2.78) 0.18 0.25*** (4.34) 0.23PEOU → PU 0.27*** (4.30) 0.30 0.27*** (4.37) 0.30

Model fit statisticsx2(d.f.) 404.81(126)*** 406.78(127)***GFI 0.90 0.90CFI 0.96 0.96TLI 0.95 0.95RMSEA 0.07 0.07

Note: t-value in parentheses; ***significant at p , 0.01.

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between TR and UI (i.e., constraining its coefficient to 0). Thus, thetrimmed model and the originally full model were nested, and the twomodels were compared based on the difference between their chi-squarestatistics. The trimmed model yielded a x2(127) 5 406.78 (see Table 2), anddid not deteriorate significantly (Dx2 5 1.97, d.f. 5 1, p 5 0.16). The influ-ences of PU and PEOU on UI were stronger in the trimmed model thanin the full model. The results indicated that the integrated TRAM modelwithout estimating the path from TR to UI was preferred. Collectively,the analyses provided support for H4 and H7 and further corroboratedH2, H3, H5, and H6.

This study further analyzed the trimmed model. PU had a greaterdirect effect on UI than did PEOU (p , 0.01); this finding was consis-tent with Davis (1989) and Davis et al. (1989). TR had a stronger directimpact on PEOU than on PU (p , 0.01). However, the effects of PEOUon PU and UI were parallel (N.S.). Therefore, PU is a strong and closeantecedent of UI, and the effect of TR is primarily through PEOU. Interms of standardized total effect, the effects of TR, PU, and PEOU on UIwere 0.60, 0.59, and 0.40, respectively. TR and PU had almost equivalenttotal effects on UI. Taken together, the psychological process exhibited byTRAM is consistent with a TR → PEOU → PU → UI chain of causality.These findings show theoretical and practical implications, which will bediscussed below.

DISCUSSION AND CONCLUSIONS

Summary of Findings and Contributions

The present study integrated the construct of technology readinesswith the technology acceptance model into one refined framework andproposed the Technology Readiness and Acceptance Model (TRAM). Tech-nology readiness was theorized to be a causal antecedent of both perceivedusefulness and perceived ease of use, which subsequently affect con-sumers’ intentions to use e-services. Perceived usefulness and perceivedease of use together had complete mediation effects between technologyreadiness and consumers’ use intentions. The integrated model was testedand confirmed by Web-based survey data to explain consumers’ intentionsto use online stock trading systems, and the model contributed to a morein-depth understanding of people’s technology acceptance behaviors.


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Table 3. Sobel Test Results.

Mediation path z-value of Sobel test

TR → PU → UI 5.23***TR → PEOU → UI 2.73***

Note: ***significant at p , 0.01.

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Collectively, the psychological process evidenced by TRAM is consistentwith a TR → PEOU → PU → UI mechanism. TRAM integrates individ-ual factors with system characteristics and substantially broadens theapplicability and explaining ability of either of the prior models (i.e.,technology readiness and TAM) in marketing settings where adoption isnot mandated by organizational objectives.

Theoretical and Practical Implications

The findings of the present study suggest theoretical and practical impli-cations. As shown by the critical psychological process of consumers’ eval-uation on the adoption of an innovation (i.e., TR → PEOU → PU → UI),the integrated TRAM model shifts the emphasis on service systems toconsumers, as technology readiness is an individual-specific and system-independent construct as opposed to a system-specific construct of use-fulness and ease of use. Indeed, the psychological process is a long andcomplex journey. This implies that e-service providers should concen-trate more on individual indigenous differences (e.g., consumers’ priorknowledge and experience in similar situations). In addition, segmentedand targeted markets cannot be adequately identified and selected inmarketing settings by TAM alone because it is sometimes impractical tohave consumers try systems before they decide to adopt them. There-fore, the construct of technology readiness can be used as a basis forsegmenting markets. The main thesis suggests that an innovating firmshould research the psychographic profile of its customers and directcommunications specifically to it’s target customers (Kotler, 1997).Furthermore, TRAM could explain why people who score high in tech-nology readiness do not always adopt high-tech gadgets available in themarkets, because system characteristics such as usefulness and ease ofuse also dominate the decision making process of adoption behavior.

Perceived usefulness is a critical determinant of UI, and PEOU has botha direct effect and an indirect effect through PU on UI. Consumers aredriven to adopt an innovation primarily because of the usefulness of theinnovation for them, and secondarily for how easy it is to use the inno-vation. Objectively, usefulness and ease of use of an innovation areconstrained by its original design. Nevertheless, the total effect of TR onUI is parallel with the effect of PU on UI, and thus consumers’ valueappraisal toward an innovation could be motivated and facilitated bytheir individual TR such that they will evaluate an innovation moreeffectively and efficiently. In effect, some consumers are restricted bytheir competence from effectively interacting with e-services, and thustechnology is experienced positively by some and negatively by others(Meuter, Ostrom, Roundtree, & Bitner, 2000; Mick & Fournier, 1998).Pre-acquisition avoidance (e.g., refuse and delay) is one of consumers’behavioral coping strategies for managing perceived incompetence(Mick & Fournier, 1998), but the psychological mechanism for these


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coping strategies is ambiguous. The present findings imply that thoseincompetent consumers refuse or delay their adoption of e-services bydevaluating the benefits (e.g., usefulness) of those services. Moreover, afit appears to exist between consumers’ technology readiness and theirperceptions of system characteristics. For example, useful but complex (asopposed to less useful but easy-to-interact) e-services may be appreci-ated by high-TR consumers, rather than by low-TR ones, and vice versa.Practically, e-service firms should target their services to those customerswho have the competency to perform the necessary evaluation tasks(Lovelock & Wirtz, 2004). Innovating firms should also act as teachers,view consumers as partial employees (Bowen, 1986), and adequatelyeducate them and shape their expectations so that they co-create marketacceptance for innovations (Prahalad & Ramaswamy, 2000). Collectively,TRAM suggests that in addition to system redesigning, marketingcommunication programs to adjust consumers’ TR are another meansto intensify their adoption intentions.

TRAM also has strategic implications for diffusion of innovations. Inno-vation adopter distribution follows a bell-shaped curve over time andapproaches normality, which could be divided into five adopter categories:innovators, early adopters, early majority, late majority, and laggard(Rogers, 2003). Cracks exist between each pair of categories. The dividingchasm separating the early adopters from the early majority is the mostformidable and unforgiving transition in the adoption life cycle (Moore,2002). Crossing or falling into the chasm is a critical decision milestonewhen firms decide whether or not to escalate commitment for the innovation.However, identifying early adopters is not always easy for disruptive tech-nology (Kotler, 1997), and thus the chasm typically goes unrecognized byinnovating firms (Moore, 2002). A better way to discover the chasm is toagilely gauge technology adoption based on the TRAM framework.The chasm is signaled when the adopters’ mean TR index decreases dra-matically. When firms find this, changes in strategy become imperative.

Directions for Future Research

The present study indicated several directions for future research. First,as mentioned earlier, consumers in marketing settings may be moreautonomous than they are in work settings, and thus their consumingmotivation may be too complex to be completely discerned. Marketingresearchers have ascertained that value consciousness is important whenconsumers determine to use or not to use a product (Sheth, Newman, &Gross, 1991). Consumer perceived value is defined as the consumer’soverall assessment of the utility of a product based on perceptions ofwhat is received and what is given (Zeithaml, 1988). Implicitly, the per-formance nature of usefulness could be categorized as the “get” compo-nent of value, whereas the effort nature of ease of use is the “give”component. Taken together, these two cognitive appraisal constructs


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refer to the functional value (Sheth et al., 1991). Besides, other valuecomponents may play determinant roles on consumers’ use behavior oftechnology-based products. For example, social value may be more dom-inant than functional value as a motivator for young students to adopthigh-end cellular phones, which, reflecting social classes and people usingparticular phones similar to their peers, may be more easily acceptedby these groups. Epistemic value may be crucial for online auctions whenthese systems satisfy people’s desire for curiosity or knowledge. In sum,future study streams converging from consumption values aspects appearto be promising. For example, Chen and Dubinsky’s (2003) and Lin,Sher, and Shih’s (2005) value model have explained consumers’ onlineconsumption behaviors, and Hartman, Shim, Barber, and O’Brien (2006)have found that vicarious-innovativeness is related to both hedonic andutilitarian Web-consumption values.

Second, management intervention (e.g., training or mandatory usage)could be exerted to facilitate employees’ adopting a new system in worksettings. Under such conditions, the main effects of people’s generalbeliefs about technology may be minimized, and thus the antecedent roleof technology readiness may shift to a moderating role in forming adoptionintentions. Specifically, technology readiness may have negative moder-ating impacts on the links between management intervention and cog-nitive beliefs about a particular system (i.e., perceptions of usefulness andease of use), and between particular cognitive beliefs and use intentions.For instance, it could be speculated that the effects of management inter-vention and particular cognitive beliefs are mitigated by employees’indigenous technology readiness. In other words, training programs andintensified cognitive beliefs might benefit only people with low technol-ogy readiness. The inclusion of technology readiness may be thus adaptedto fit issues in work settings.

Third, online stock trading is the target system of the current study,and most of the sample respondents have experiences of the focal tech-nology. Efficiently finding prospects (i.e., who are currently trading withnon-online methods) of the focal system to participate in the survey wasa challenge and a tradeoff, and thus the study was announced in onlineinvestment forums, leading to most of the respondents having experi-ences with the focal system. However, additional research using samplesof non-users in other marketing environments is required to substanti-ate the generality of the findings of this study.

Furthermore, this study relies on theory-driven arguments and field-work insights in specifying the integrated model, and cross-sectionaldata are employed to test hypotheses. Longitudinal or experimental stud-ies are encouraged to collect temporal data so that psychological processescan be precisely defined.

Finally, the “country effect” may display an “absolute effect.” That is,the absolute value of each construct score may vary from country tocountry. However, since the focus is the relative effects between constructs,


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there is no pressing reason to believe that Taiwanese traders will displaydifferent psychological processes of evaluating an online trading system.But some replications of studies in other countries may dispel any doubtin this regard.

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An earlier version of this article was presented at the 2005 PortlandInternational Conference on Management of Engineering and Technology(PICMET ’05), Portland, Oregon, USA.

The authors thank Professor Rajan Nataraajan and two anonymousreviewers for their very helpful suggestions through the review process.

Correspondence regarding this article should be sent to: Chien-Hsin Lin,Department of International Business, Yu Da College of Business, No. 168,Hsuehfu Rd., Tanwen Village, Chaochiao Township, Miaoli County 36143,Taiwan ([email protected]).


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