Journal integrating technology readiness into technology acceptance :the TRAM Model

1. Integrating Technology
Readiness into
Technology Acceptance:
The TRAM Model
Chien-Hsin Lin
Yu Da College of Business, Taiwan, and National Chi Nan University, Taiwan
Hsin-Yu Shih
National Chi Nan University, Taiwan
Peter J. Sher
National Chi Nan University, Taiwan
ABSTRACT
Based on previous theoretical streams, the present study integrates
technology readiness (TR) into the technology acceptance model
(TAM) in the context of consumer adoption of e-service systems, and
theorizes 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 at
work environments, but this research stemmed from a questioning of
its applicability in marketing (i.e., non-work) settings. The differences
between the two settings are exhibited by consumers’ self-determining
selection behavior and their high involvement in the e-service cre-
ation and delivery process. This paper first reviews the TAM and the
construct of technology readiness, and then proposes and empirically
tests an integrated Technology Readiness and Acceptance Model
(TRAM) to augment TAM by taking technology readiness construct
into the realm of consumers’ adoption of innovations. The results
indicate that TRAM substantially broadens the applicability and the
explanatory 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
641
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2. way to gauge technology adoption in situations where adoption is not
mandated by organizational objectives. Further, theoretical and
practical 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, the
technology acceptance model (TAM) (Davis, 1989) appears to be the most
widely cited and replicated empirically. TAM was originally developed to
predict people’s technology-adopting behavior at work environments, but
scant research questions its applicability in marketing (i.e., non-work) set-
tings where adoption is not mandated by organizational objectives. The
differences between marketing and work settings are obvious. Consumers
in marketing settings engage in the e-service creation and delivery process
rather than owning the system equipment per se (Dabholkar & Bagozzi,
2002). People in work settings may reluctantly or involuntarily adopt a
system due to management intervention, but consumers in marketing set-
tings may be freer to choose among numerous available alternatives. For
instance, investors could independently choose between conventional and
online stock trading systems.When they decide to trade stocks online, they
co-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 high
involvement of customers to co-produce the service, TAM applied in
marketing settings may not sufficiently explain consumers’ technology
adoption behaviors. Therefore, a model incorporating some individual
difference variables is a necessary first step toward identifying and qual-
ifying the psychological processes of the perceptions of a technology’s
value. Accordingly, the primary objective of this paper is to adapt and
extend TAM by considering individual differences. To take individual
differences into account, this study integrates the construct of technology
readiness (TR) (Parasuraman, 2000) with TAM to better explain
consumers’ intentions to use e-services—online stock trading systems
in particular. TR conceptualizes consumers’ general beliefs about tech-
nology and is associated with their use of technology-based products and
services (Parasuraman, 2000). Evidence from the fieldwork shows that
TR is incapable of explaining why high-TR consumers do not always
adopt new technologies, such as cellular phones with open operating sys-
tems or in-car global positioning systems. From the TR aspect, this study
tries to supplement the construct of TR with the two focal constructs of
TAM (i.e., perceived usefulness and perceived ease of use). An integrated
Technology Readiness and Acceptance Model (TRAM) is established to
address the issue of consumer adoption of e-services.
LIN, SHIH, AND SHER
Psychology & Marketing DOI: 10.1002/mar
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3. INTEGRATING TECHNOLOGY READINESS INTO TECHNOLOGY ACCEPTANCE
Psychology & Marketing DOI: 10.1002/mar
643
This paper is organized as follows. First, TAM and TR are briefly
reviewed. Second, according to related theoretical backgrounds, this study
integrates TR with TAM. Next, the integrated Technology Readiness and
Acceptance Model (TRAM) and research hypotheses are tested with data
gathered from Web-based surveys. Finally, this study concludes by noting
research 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 the
system, which is influenced by the users’ beliefs about the system’s
perceived usefulness and perceived ease of use. Perceived usefulness is
defined as the extent to which a person believes that using a particular
system will enhance his or her performance, and perceived ease of use
refers to the extent to which a person believes that using a particular
system will be free of effort. Perceived ease of use is hypothesized to be
a determinant of perceived usefulness, while both beliefs are influenced
by external variables, such as training, support, and perceived accessi-
bility (Karahanna & Straub, 1999), social influence processes, and
cognitive instrumental processes (Venkatesh & Davis, 2000). TAM has
been empirically replicated or extended to explain various behaviors
with 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 why
these 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 technologies
for accomplishing goals in home life and at work (Parasuraman, 2000).
TR construct can be viewed as an overall state of mind resulting from
a gestalt of mental enablers and inhibitors that collectively determine a
person’s predisposition to use new technologies. At the measurement
level, the Technology Readiness Index (TRI) was developed to measure
people’s general beliefs about technology. TR construct comprises four
sub-dimensions: optimism, innovativeness, discomfort, and insecurity.
Optimism relates to a positive view of technology and a belief that
technology offers people increased control, flexibility, and efficiency. Inno-
vativeness refers to a tendency to be a technology pioneer and thought
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4. leader. Discomfort consists of a perception of lack of control over
technology and a feeling of being overwhelmed by it. Insecurity involves
distrust of technology and skepticism about its ability to work properly.
Optimism and innovativeness are drivers of TR, while discomfort and
insecurity are inhibitors. Positive and negative beliefs about technology
may coexist, and people can be arrayed along a technology belief con-
tinuum from strongly positive attitude at one end to strongly negative
attitude at the other. The correlation between people’s TR and their
propensity to employ technology is empirically confirmed by Parasuraman
(2000). Consumers’ TR has a positive impact on their online service
quality perceptions and online behaviors, but empirical findings are
scarce (Zeithaml, Parasuraman, & Malhotra, 2002) and confounding
(Liljander, Gillberg, Gummerus, & van Riel, 2006). Therefore, the role of
TR may be minor in explaining individuals’ online behaviors (Liljander
et 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, although
the measurement of usefulness and ease of use in TAM is specific for
a particular system (i.e., system-specific) while TR is for general
technology beliefs (i.e., individual-specific). When faced with a choice
to 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 general
beliefs about technology derived from prior experience may be employed
to anchor perceptions of usefulness and ease of use. This experience-
based evaluation mechanism may be more pronounced for novice
consumers, who are more apt to process choice alternatives using
abstract, general criteria (as opposed to more concrete, specific criteria;
Bettman & Sujan, 1987). Thus, there appear to be implicit theoretical
and practical bases to surmise that when people evaluate technology
adoption intentions, cognitive information of TR is retrieved and
processed before specific cognitive appraisal (i.e., usefulness and ease
of use) is retrieved and processed.
Theoretically, consumer studies have posited that previous product
experience and knowledge influence consumer cue utilization (Rao &
Monroe, 1988) and message processing (Peracchio & Tybout, 1996) in
product evaluation. People with more product knowledge may search for
more information before problem solving because of their high awareness
of existing attributes (Brucks, 1985), and may identify relevant information
LIN, SHIH, AND SHER
Psychology & Marketing DOI: 10.1002/mar
644
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5. more accurately (Alba & Hutchinson, 1987). More knowledge reflects more
extensive, complex, experienced, expert, and familiar knowledge, and thus
effortful processing of issue-related information and evaluative inferences
concerning product features by high-knowledge consumers could be
expected (Alba & Hutchinson, 1987; Peracchio & Tybout, 1996). Further-
more, consumers’ expectations, based on prior beliefs stored in memory
can influence consumers’ perceptual encoding about marketing informa-
tion (Bettman, 1979). By and large, people’s prior beliefs, formed through
experience, play an important role in guiding information processing and
in directing behavior (John, Scott, & Bettman, 1986). Impacts of prior
beliefs include: (1) deciding which data are relevant, (2) interpreting and
integrating information, (3) using the estimate to make other judgments
(Crocker, 1981).
Within this paper’s context, people with more knowledge or experience
of information technology form stronger computer self-efficacy (Gist &
Mitchell, 1992; Venkatesh & Davis, 1996), or perceive stronger control
over 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 building
how-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’ online
behavioral intentions (Yoh, Damhorst, Sapp, & Laczniak, 2003). The
causal links between general computer self-efficacy and perceptions of
usefulness and ease of use are also confirmed by Wang, Wang, Lin, and
Tang (2003) and Venkatesh and Davis (1996). The positive correlation
between prior formed beliefs about comparable e-services and posterior
formed 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 the
correlations 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 proposes
the focal hypotheses H5, H6, and H7 in this paper. In order to establish
a comprehensive framework to integrate TR into TAM, H1, H2, H3, and
H4, 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 the
construction of the integrated model (see Figure 1).
H1: Consumers’ technology readiness propensities are positively
correlated with their intentions to use a specific e-service.
INTEGRATING TECHNOLOGY READINESS INTO TECHNOLOGY ACCEPTANCE
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6. H2: Consumers’ perceptions of usefulness about a specific e-service are
positively correlated with their intentions to use it.
H3: Consumers’ perceptions of ease of use about a specific e-service are
positively correlated with their intentions to use it.
H4: Consumers’ perceptions of ease of use about a specific e-service are
positively correlated with their perceptions of usefulness about it.
In addition to the above four hypotheses, this paper puts forth three
hypotheses to make a case for and capture the evolution of TAM into the
more 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 between
their 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,
LIN, SHIH, AND SHER
Psychology & Marketing DOI: 10.1002/mar
646
Technology
Readiness
Perceived
Usefulness
Perceived
Ease of Use
Use Intention
Optimism
Innovativeness
Discomfort
Insecurity
H5
H6
H2
H3
H1
H4
Figure 1. TRAM model.
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7. 7 items for innovativeness, 10 items for discomfort, and 9 items for
insecurity).1
The items of perceived usefulness (6 items) and perceived
ease of use (6 items) were adapted from Davis (1989). Two items con-
cerning intention to use online stock trading systems were designed
specifically for this study (i.e., “I consider using an online stock trading
system when I trade stocks next time” and “I will trade stocks through
an online stock trading system in next few months”). All the items were
measured on 7-point scales anchored by “strongly disagree ϭ 1” and
“strongly agree ϭ 7.” Measures originally in English were translated
into Chinese and back-translated into English to ensure equivalent
meaning (Brislin, 1980).
Data Collection and Sample Characteristic
Web-based surveys were conducted during March and April 2004 by
inviting members of several online investment discussion forums in
Taiwan to participate in the study. Participants were asked to self-predict
their future use of online stock trading systems. Respondents were
entered into a sweepstakes (30 prizes of NT$100; around US$3) as
compensation for their participation. Because the number of individuals
approached during the invitation stage was unknown, the response rate
was 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 and
30 years old, 18% were between 31 and 40 years old, and 17% were under
21 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. The
results indicated an alpha coefficient of 0.95 for optimism, 0.95 for
innovativeness, 0.90 for discomfort, 0.92 for insecurity, 0.95 for perceived
usefulness (PU), 0.96 for perceived ease of use (PEOU), and 0.92 for use
intention (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 respective
raw scores and were used as reflective indicators of the construct of TR.
INTEGRATING TECHNOLOGY READINESS INTO TECHNOLOGY ACCEPTANCE
Psychology & Marketing DOI: 10.1002/mar
647
1
These questions comprise the Technology Readiness Index (TRI), which is copyrighted by
A. Parasuraman and Rockbridge Associates, Inc., 1999. The authors have obtained the
requisite permission in this regard.
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8. All subsequent analyses were conducted using Amos 4 (Arbuckle &
Wothke, 1999). The confirmatory factor analysis (CFA) results demon-
strated an adequate model fit (GFI ϭ 0.90, CFI ϭ 0.96, TLI ϭ 0.95,
RMSEA ϭ 0.07, x2
(126) ϭ 404.81, p Ͻ 0.01).2
Convergent validity was assessed by reviewing the t test for the factor
loading of each manifest indicator on its proposed latent construct
(Anderson & Gerbing, 1988). The average standardized factor loading
was 0.80 and all loadings were highly significant (p Ͻ 0.01); hence the
analysis indicated convergent validity of the measures. Discriminant
validity was examined using a test involving the confidence interval for
each pairwise correlation estimate (i.e., plus or minus two standard
errors) 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 provided
support for discriminant validity of the measures. Overall, the constructs
exhibit good measurement properties.
Tests of Mediating Effects
To test mediation effect, the present study followed Baron and Kenny’s
guidelines (1986). Specifically, three equations must be estimated to test
for 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 dependent
variable. Third, regressing the dependent variable on both the inde-
pendent variable and the mediator, and the mediator must significantly
affect the dependent variable. To establish mediation, the effect of the
independent variable on the dependent variable must be less in the third
equation 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 UI
must be significant. As shown in Table 1, the effect of TR on UI was
reduced from 1.12 to 0.30 with t-value reduced from 11.63 to 2.59, and the
effect of PU on UI was significant. Furthermore, the null hypothesis of no
mediation effect was rejected by the Sobel test (z-value ϭ 7.73, p Ͻ 0.01),
LIN, SHIH, AND SHER
Psychology & Marketing DOI: 10.1002/mar
648
2
Three parameters of within-factor correlated measurement errors were specified as freely
estimated to achieve adequate fit. Though the substantive meanings of these correlated error
terms were equivocal, it was confirmed that the measurement properties and structural rela-
tionships discussed below were not altered when these three parameters were constrained to
zero or freely estimated. Results of the model without correlated error terms can be obtained
from the corresponding author.
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9. Table1.MediationTestsforPerceivedUsefulnessandPerceivedEaseofUse.
Model1-1Model1-2Model1-3Model2-1Model2-2Model2-3
Dependentvariable
PerceivedusefulnessUseintentionUseintentionPerceivedeaseUseintentionUseintention
(PU)(UI)(UI)ofuse(PEOU)(UI)(UI)
Technologyreadiness(TR)1.06***(13.53)1.12***(11.63)0.30***(2.59)1.15***(14.22)1.12***(11.63)0.64***(4.79)
Perceivedusefulness(PU)––0.75***(9.41)–––
Perceivedeaseofuse(PEOU)–––––0.39***(4.94)
Sobeltestz-valueϭ7.73***z-valueϭ4.67***
Note:t-valueinparentheses;***significantatpϽ0.01.
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10. indicating that the mediation effect of PU exists. Though the effect of
TR was reduced, its effect was still significant when PU was controlled.
The results imply that PU only partially mediates the link between TR
and UI, and other potential mediators may exist. Overall, the three
equations of Model 1 supported H1, H2, and H5. Likewise, the three
equations of Model 2 tested the mediation effect of PEOU, and the results
confirmed H3 and H6. PEOU also partially mediated the relationship
between TR and UI, as evidenced by the Sobel test (z-value ϭ 4.67, p Ͻ
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 tested
respectively, this study further estimated the integrated framework
(previously shown in Figure 1) by simultaneously modeling PU and
PEOU as mediators between TR and UI, and PEOU was modeled as an
antecedent of PU. Estimation results are shown in Table 2. The fit
statistics indicated that the integrated model was adequate (GFIϭ 0.90,
CFI ϭ 0.96, TLI ϭ 0.95, RMSEA ϭ 0.07, x2
(126) ϭ 404.81, p Ͻ 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 Sobel
tests (see Table 3) also demonstrated that mediation effects of PU and
PEOU exist.
As the non-significant coefficient between TR and UI suggested, the
study further estimated the integrated model by trimming the path
LIN, SHIH, AND SHER
Psychology & Marketing DOI: 10.1002/mar
650
Table 2. Estimation Results of the Integrated TRAM Model.
Full model Trimmed model
Path Standardized Path Standardized
coefficients coefficients coefficients coefficients
TR → PU 0.73*** (6.84) 0.52 0.73*** (6.89) 0.52
TR → PEOU 1.18*** (14.64) 0.74 1.18*** (14.59) 0.74
TR → UI 0.20 (1.43) 0.11 – –
PU → UI 0.67*** (8.13) 0.54 0.73*** (10.32) 0.59
PEOU → UI 0.19*** (2.78) 0.18 0.25*** (4.34) 0.23
PEOU → PU 0.27*** (4.30) 0.30 0.27*** (4.37) 0.30
Model fit statistics
x2
(d.f.) 404.81(126)*** 406.78(127)***
GFI 0.90 0.90
CFI 0.96 0.96
TLI 0.95 0.95
RMSEA 0.07 0.07
Note: t-value in parentheses; ***significant at p Ͻ 0.01.
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11. between TR and UI (i.e., constraining its coefficient to 0). Thus, the
trimmed model and the originally full model were nested, and the two
models were compared based on the difference between their chi-square
statistics.The trimmed model yielded a x2
(127)ϭ406.78 (see Table 2), and
did not deteriorate significantly (⌬x2
ϭ 1.97, d.f. ϭ 1, p ϭ 0.16). The influ-
ences of PU and PEOU on UI were stronger in the trimmed model than
in the full model. The results indicated that the integrated TRAM model
without estimating the path from TR to UI was preferred. Collectively,
the analyses provided support for H4 and H7 and further corroborated
H2, H3, H5, and H6.
This study further analyzed the trimmed model. PU had a greater
direct effect on UI than did PEOU (p Ͻ 0.01); this finding was consis-
tent with Davis (1989) and Davis et al. (1989). TR had a stronger direct
impact on PEOU than on PU (p Ͻ 0.01). However, the effects of PEOU
on PU and UI were parallel (N.S.). Therefore, PU is a strong and close
antecedent of UI, and the effect of TR is primarily through PEOU. In
terms of standardized total effect, the effects of TR, PU, and PEOU on UI
were 0.60, 0.59, and 0.40, respectively. TR and PU had almost equivalent
total effects on UI. Taken together, the psychological process exhibited by
TRAM is consistent with a TR → PEOU → PU → UI chain of causality.
These findings show theoretical and practical implications, which will be
discussed below.
DISCUSSION AND CONCLUSIONS
Summary of Findings and Contributions
The present study integrated the construct of technology readiness
with the technology acceptance model into one refined framework and
proposed the Technology Readiness and Acceptance Model (TRAM).Tech-
nology readiness was theorized to be a causal antecedent of both perceived
usefulness and perceived ease of use, which subsequently affect con-
sumers’ intentions to use e-services. Perceived usefulness and perceived
ease of use together had complete mediation effects between technology
readiness and consumers’ use intentions.The integrated model was tested
and confirmed by Web-based survey data to explain consumers’ intentions
to use online stock trading systems, and the model contributed to a more
in-depth understanding of people’s technology acceptance behaviors.
INTEGRATING TECHNOLOGY READINESS INTO TECHNOLOGY ACCEPTANCE
Psychology & Marketing DOI: 10.1002/mar
651
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 Ͻ 0.01.
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12. Collectively, the psychological process evidenced by TRAM is consistent
with a TR → PEOU → PU → UI mechanism. TRAM integrates individ-
ual factors with system characteristics and substantially broadens the
applicability and explaining ability of either of the prior models (i.e.,
technology readiness and TAM) in marketing settings where adoption is
not 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 to
consumers, 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 and
complex journey. This implies that e-service providers should concen-
trate more on individual indigenous differences (e.g., consumers’ prior
knowledge and experience in similar situations). In addition, segmented
and targeted markets cannot be adequately identified and selected in
marketing settings by TAM alone because it is sometimes impractical to
have consumers try systems before they decide to adopt them. There-
fore, the construct of technology readiness can be used as a basis for
segmenting markets. The main thesis suggests that an innovating firm
should research the psychographic profile of its customers and direct
communications 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 the
markets, because system characteristics such as usefulness and ease of
use also dominate the decision making process of adoption behavior.
Perceived usefulness is a critical determinant of UI, and PEOU has both
a direct effect and an indirect effect through PU on UI. Consumers are
driven to adopt an innovation primarily because of the usefulness of the
innovation for them, and secondarily for how easy it is to use the inno-
vation. Objectively, usefulness and ease of use of an innovation are
constrained by its original design. Nevertheless, the total effect of TR on
UI is parallel with the effect of PU on UI, and thus consumers’ value
appraisal toward an innovation could be motivated and facilitated by
their individual TR such that they will evaluate an innovation more
effectively and efficiently. In effect, some consumers are restricted by
their competence from effectively interacting with e-services, and thus
technology 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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13. coping strategies is ambiguous. The present findings imply that those
incompetent consumers refuse or delay their adoption of e-services by
devaluating the benefits (e.g., usefulness) of those services. Moreover, a
fit appears to exist between consumers’ technology readiness and their
perceptions of system characteristics. For example, useful but complex (as
opposed 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 customers
who 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 adequately
educate them and shape their expectations so that they co-create market
acceptance for innovations (Prahalad & Ramaswamy, 2000). Collectively,
TRAM suggests that in addition to system redesigning, marketing
communication programs to adjust consumers’ TR are another means
to intensify their adoption intentions.
TRAM also has strategic implications for diffusion of innovations. Inno-
vation adopter distribution follows a bell-shaped curve over time and
approaches 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 dividing
chasm separating the early adopters from the early majority is the most
formidable and unforgiving transition in the adoption life cycle (Moore,
2002). Crossing or falling into the chasm is a critical decision milestone
when 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 by
innovating firms (Moore, 2002). A better way to discover the chasm is to
agilely 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 more
autonomous than they are in work settings, and thus their consuming
motivation may be too complex to be completely discerned. Marketing
researchers have ascertained that value consciousness is important when
consumers determine to use or not to use a product (Sheth, Newman, &
Gross, 1991). Consumer perceived value is defined as the consumer’s
overall assessment of the utility of a product based on perceptions of
what 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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14. refer to the functional value (Sheth et al., 1991). Besides, other value
components may play determinant roles on consumers’ use behavior of
technology-based products. For example, social value may be more dom-
inant than functional value as a motivator for young students to adopt
high-end cellular phones, which, reflecting social classes and people using
particular phones similar to their peers, may be more easily accepted
by these groups. Epistemic value may be crucial for online auctions when
these systems satisfy people’s desire for curiosity or knowledge. In sum,
future study streams converging from consumption values aspects appear
to be promising. For example, Chen and Dubinsky’s (2003) and Lin,
Sher, and Shih’s (2005) value model have explained consumers’ online
consumption behaviors, and Hartman, Shim, Barber, and O’Brien (2006)
have found that vicarious-innovativeness is related to both hedonic and
utilitarian Web-consumption values.
Second, management intervention (e.g., training or mandatory usage)
could be exerted to facilitate employees’ adopting a new system in work
settings. Under such conditions, the main effects of people’s general
beliefs about technology may be minimized, and thus the antecedent role
of technology readiness may shift to a moderating role in forming adoption
intentions. 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 and
ease 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 and
intensified cognitive beliefs might benefit only people with low technol-
ogy readiness.The inclusion of technology readiness may be thus adapted
to 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 with
non-online methods) of the focal system to participate in the survey was
a challenge and a tradeoff, and thus the study was announced in online
investment forums, leading to most of the respondents having experi-
ences with the focal system. However, additional research using samples
of 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-sectional
data are employed to test hypotheses. Longitudinal or experimental stud-
ies are encouraged to collect temporal data so that psychological processes
can 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 to
country. However, since the focus is the relative effects between constructs,
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15. there is no pressing reason to believe that Taiwanese traders will display
different psychological processes of evaluating an online trading system.
But some replications of studies in other countries may dispel any doubt
in this regard.
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An earlier version of this article was presented at the 2005 Portland
International Conference on Management of Engineering and Technology
(PICMET ’05), Portland, Oregon, USA.
The authors thank Professor Rajan Nataraajan and two anonymous
reviewers 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 (lin@ydu.edu.tw).
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