CYBERPSYCHOLOGY, BEHAVIOR, AND SOCIAL NETWORKING

Volume 16, Number 8, 2013

ª Mary Ann Liebert, Inc.
DOI: 10.1089/cyber.2012.0606

Integrating Technology Readiness

into the Expectation–Confirmation Model:
An Empirical Study of Mobile Services

Shih-Chih Chen, PhD,1,2 Ming-Ling Liu,2 and Chieh-Peng Lin, PhD2

Abstract

The aim of this study was to integrate technology readiness into the expectation–confirmation model (ECM) for
explaining individuals’ continuance of mobile data service usage. After reviewing the ECM and technology
readiness, an integrated model was demonstrated via empirical data. Compared with the original ECM, the
findings of this study show that the integrated model may offer an ameliorated way to clarify what factors and
how they influence the continuous intention toward mobile services. Finally, the major findings are summarized,
and future research directions are suggested.

Introduction usefulness, and confirmation of expectations on continuance

intention via satisfaction. Based on the technology readiness

T he diffusion and popularization of mobile commu-

nications shows that user demands have overcome spa-
tial and temporal obstacles and have led to unprecedented
and the ECM proposed earlier, this study attempted to de-
velop an integrated model to explain and predict the con-
tinuance intention toward mobile data services.
growth in mobile services.1 Various mobile devices, includ-
ing smart phones, tablets, personal digital assistants, and Literature Review
mobile Internet devices, are now used by users as principal
ECM of information systems continuance
or alternative ways to engage in e-commerce, banking,
gaming, positioning, and chatting via 2.5G, 3G, or WiMAX The ECM was first developed by Bhattacherjee3 based on
telecommunication networks.2 the expectation–confirmation theory (ECT) developed by Oli-
The expectation–confirmation model (ECM), which was ver.19 The ECM is based on ECT concept including three di-
developed by Bhattacherjee3 as a way to understand and mensions of user intention to continue accepting information
predict the users’ continuance intention toward new tech- technologies: perceived usefulness, confirmation of expecta-
nologies/systems, now has many studies in the extension of tions, and satisfaction. Hayashi et al.20 argued that the princi-
ECM.4–10 Until now, however, few studies have combined pal difference between ECT and ECM is that ECM examines
applications of personality-based and cognitive antecedents the related constructs of postacceptance, whereas ECT focuses
to understand the continuance of mobile service usage. Be- on pre- and postconsumption factors. Additionally, ECM ex-
cause of the growing use of emerging information technolo- amines the effect of postconsumption expectations rather than
gies/information systems (IT/IS), a clear understanding of the effects preconsumption expectations, and ECM improves
customer readiness to use mobile- and Internet-based services the ECT by considering perceived usefulness.7 According
is critical.11–15 The impact of technology readiness must be to the ECM, an individual has intention to continue using an
clearly understood to determine user preferences and per- IT/IS after developing expectations about the IT/IS.
ceptions.11,14,16 Therefore, this study used technology readi- Hence, the use of varied models, such as technology accep-
ness as an antecedent of psychological traits to elucidate the tance model (TAM) by Davis,21 theory of planned behavior by
cognitive dimensions of ECM. Ajzen,22 and unified theory of acceptance and use of technol-
Applications of mobile services are affected by complex ogy by Venkatesh et al.,23 has resulted in inconsistent or inap-
and contextual factors. To discriminate this study from ex- propriate explanations of IT continuance.24 In mobile services, a
isting literature on the extended ECM,4,9,10,17,18 this study high perceived usefulness and confirmation are expected to
investigated the effects of technology readiness, perceived foster a positive satisfaction after using an IT/IS, which

1
Department of Accounting Information, Southern Taiwan University of Science and Technology, Tainan City, Taiwan.
2
Institute of Business and Management, National Chiao Tung University, Taipei, Taiwan.

604
INTEGRATING TECHNOLOGY READINESS INTO ECM 605

increases. The aim of ECM is to model the individual intention user confirmation in the ubiquitous learning context. Similar
to continue using an IT/IS. The next section presents how this studies of Thong et al.32 and Recker6 consistently showed that
study expands the ECM by focusing on individual propensity confirmation positively affects satisfaction. Related ECM
concerns since this aspect is critical in technology readiness. studies have reported that perceived usefulness correlates
positively with confirmation.17,33,34 Hung et al.,35 in a study
Technology readiness of satisfaction in the mobile commerce context, indicated that
satisfaction is positively influenced by confirmation. Thus,
Parasuraman11 defined technology readiness as ‘‘person- the following hypothesis is proposed.
ality traits that increase adoption of novel technologies in
order to accomplish private or work-related goals.’’25,26 H5: Confirmation of expectations positively affects satisfac-
Technology readiness is a multifaceted construct that can be tion of mobile service users.
divided into four components, two of which are drivers, and
two of which are inhibitors. H6: Confirmation of expectations positively affects perceived
usefulness of mobile service users.
Parasuraman and Colby27 classified technology users ac-
cording to their technology readiness differences as explorer,
End user satisfaction is principal to successful IT/IS im-
pioneer, sceptic, paranoid and laggard, and consumers. Each
plementation.36 On the basis of ECM model, continuance
classification of user has drivers and inhibitors. Meuter et al.13
intention is influenced by satisfaction. Related studies on IT/
suggested that a part of people reveal certain degree of tech-
IS agreed that the continuance intention of users is mainly
nophobia or technology pessimists. Recent studies have exam-
determined by their satisfaction with previous usage.37–40
ined emergent technologies and applications such as self-service
Thus, the following hypotheses are proposed.
technology (SST) and online stock trading systems from a
technology readiness perspective.14,25,26,28 The above literature H7: Satisfaction positively affects continuance intention of
show that the impact of technology readiness on intention to mobile service users.
continue using mobile services is worthy of further study.
Brown et al.41 identified perceived usefulness as an im-
Research hypotheses development portant determinant of system usage. In a study of online
The positive enablers of technology readiness, optimism and learning systems, Liao et al.4 empirically confirmed that sat-
innovativeness, inspire users to adopt emerging technological isfaction and continuance intention are affected by perceived
products and services. However, the other two inhibitors, usefulness. In SST environments, Chen et al.14 validated the
discomfort and insecurity, make users disinclined to adopt new positive correlations among perceived usefulness, satisfaction
technologies or services. Owing to the role of technology and continuance intention. Similarly, other studies have
spreading in service delivery, it is essential to probe users’ consistently shown that perceived usefulness is a determinant
readiness to adopt technology-mediated products/services of satisfaction and continuance intention.8,18,33,40 Thus, the
such as mobile service.11,12,29 Lin et al.26 developed and em- following hypotheses are proposed.
pirically examined an integrated Technology Readiness and
Acceptance Model (TRAM). TRAM demonstrated that tech- H8: Perceived usefulness positively affects continuance in-
tention of mobile service users.
nology readiness is significantly associated with perceived
usefulness and behavioral intention in the e-service context. H9: Perceived usefulness positively affects satisfaction of
Related studies have shown a positive correlation between mobile service users.
technology readiness and satisfaction.26,28,30,31 An empirical
study by Meuter et al.13 empirically confirmed a correlation In sum, we link technology readiness of users with usage
between technology anxiety and satisfaction with SSTs. In ad- perceptions of mobile service as well as continuance inten-
dition, Oliver19 proposed a theoretical perspective drawn from tion. These mediator constructs (in particular, confirmation of
psychological characteristics and expectation–disconfirmation expectations, perceived usefulness, satisfaction) play the role
theory. Based on the above literature, this study proposes four as explanatory variables for understanding the indirect ef-
hypotheses: fects. That is, we aimed to establish the relationships between
the main predictors (e.g., technology readiness) and the out-
H1: Technology readiness positively affects confirmation of come variables (e.g., satisfaction and continuance intention
expectations of mobile service users. toward mobile service) before further analysis of mediating
processes. On the basis of our theoretical model, we make the
H2: Technology readiness positively affects perceived use-
fulness of mobile service users. following predictions:

H3: Technology readiness positively affects satisfaction of H10a: Perceived usefulness mediates the relationship be-
mobile service users. tween technology readiness and continuance intention toward
mobile service.
H4: Technology readiness positively affects continuance in-
tention of mobile service users. H10b: Satisfaction mediates the relationship between tech-
nology readiness and continuance intention toward mobile
service.
This study defines satisfaction in a mobile service context
as a psychological state resulting from an assessment of the H10c: Confirmation of expectations mediates the relationship
perceived difference between expectation and performance. between technology readiness and perceived usefulness to-
Shin et al.9 empirically confirmed that satisfaction depends on ward mobile service.
606 CHEN ET AL.

FIG. 1. Research model.

H10d: Satisfaction mediates the relationship between per- sample item: My experience with using mobile service is
ceived usefulness and continuance intention toward mobile better than what I expected.
service.
 Satisfaction: four items adapted from Thong et al.32 and
H10e: Perceived usefulness mediates the relationship be-
Premkumar and Bhattacherjee18; sample item: My overall
tween confirmation of expectations and satisfaction toward experience of mobile service use was very satisfied.
mobile service.  Continuance intention: four items derived and slightly
modified from Bhattacherjee3 and Liao et al.4; sample
item: I will use the mobile service regularly in the future.
Research Design
The list of measurement items in the questionnaire is
Measures of the constructs
shown in the Appendix.
The hypothesized research model integrated technology
readiness into ECM that empirically tested using survey data Sampling and subjects
collected from users regarding their continued intention to-
The synthetic effect of the ECM and technology readiness
ward mobile data service (Figure 1). The questionnaire items
was assessed by performing an online survey of the experi-
in this study used prevalidated scales applied in earlier
ence of mobile service users in Taiwan. Compared with pa-
studies to ensure content validity and appropriate revisions
per-based data collection, the advantages of online
were made to fit the context of mobile services.3,11,28 Three
investigations include the quick response, low costs, and the
procedures were performed to refine the questionnaire items
lack of geographical restrictions.43
for improved measurement accuracy. First, items selected
from prior studies were translated into Chinese. Second, a
focus group including two professors and six graduate stu-
dents who were familiar with mobile data services was in- Table 1. Demographics of Respondents
vited to check the Chinese wording of each item in the Characteristics Frequency Percent (%)
measurement to ensure that they were readable and com-
prehensible. Third, the measurement items were evaluated Sex
and refined in a pilot study to ensure content validity. Male 220 59.8
In the first part of the two-part questionnaire, nominal scales Female 148 40.2
were used to collect basic data, including sex, age, education, Age
occupation, and experience in using mobile services. The defi- Under 20 47 12.8
nition and classification of mobile data services assumed that 21–25 153 41.6
users are willing to adopt specific mobile services via 26–30 126 34.2
smart phones or mobile devices for purposes such as sending/ 31–35 27 7.3
receiving e-mail, browsing news or blogs, Internet auctions, Above 35 15 4.1
and interacting with friends on Facebook. This definition is Education level
conducive to flourish a generalized model for understanding High school certificate or below 58 15.8
users’ continuance intention toward a set of mobile services. Technical school 11 3.0
Undergraduate degree 231 62.8
The second part included three ECM measurements and tech-
Master or higher degree 68 18.4
nology readiness. Each item was measured on a seven-point
Likert scale anchored from strongly disagree (1) through neu- Occupation
Student 159 43.2
tral (4) to strongly agree (7). The questionnaire items of the
Business 41 11.1
constructs and sources are shown as follows: Information 48 13.0
 Technology readiness: 18 items adopted from Para- Manufacturing 35 9.5
suraman11 and Yen28; sample item: Technology gives Service 37 10.1
me more freedom of mobility. Self-employed 32 8.7
Others 16 4.4
 Perceived usefulness: three items modified from Davis
et al.42 and Bhattacherjee et al38; sample item: Using the Mobile service experience
mobile service in my job/life will make me more effective. Under 1 year 72 19.6
1–3 years 134 36.4
 Confirmation of expectations: three items derived and
Above 3 years 162 44.0
slightly modified from Bhattacherjee3 and Recker6;
INTEGRATING TECHNOLOGY READINESS INTO ECM 607

Table 2. Standardized Loadings and Reliabilities

Construct Indicators Standardized loading (t) Cronbach’s a Composite reliability

Technology Readiness (TR) OPT 0.60 (11.63) 0.78 0.80

INNO 0.75 (15.48) — —
DIS 0.76 (15.80) — —
INS 0.71 (14.55) — —
Perceived Usefulness (PU) PU1 0.93 (23.30) 0.94 0.94
PU2 0.94 (23.64) — —
PU3 0.89 (21.68) — —
Confirmation of Expectations (CONF) CONF1 0.81 (18.34) 0.87 0.88
CONF2 0.87 (20.26) — —
CONF3 0.81 (18.76) — —
Satisfaction (SAT) SAT1 0.82 (18.44) 0.88 0.88
SAT2 0.81 (18.03) — —
SAT3 0.81 (17.85) — —
SAT4 0.79 (17.77) — —
Continuance intentions (CI) CI1 0.94 (23.49) 0.91 0.91
CI2 0.90 (22.00) — —
CI3 0.83 (19.32) — —
CI4 0.72 (15.84) — —

Online questionnaires were published by a survey agent to Measurement model testing

collect samples from telecom service providers in Taiwan. To
The measurement model was estimated in four proce-
ensure that participants would not complete the question-
dures. First, the technology readiness measurements were
naire more than once, each participant was required to pro-
parceled into four indicators by summarizing and averaging
vide an e-mail address. After eliminating invalid responses
the scale items from four different dimensions of technology
through data filtering, convenience sampling obtained a final
readiness. Little et al.45 suggested that the unit-weighted
population of 368 subjects. Table 1 presents the sample de-
factor score has the advantages such as higher reliability and
mographics data for the sample.
community, more parsimonious, fewer chances for residuals
to be correlated, or dual loadings to emerge. All final values
Data Analysis and Results
for composite reliability and Cronbach’s alpha exceeded 0.7,
This study followed the two-step approach of Structural indicating satisfactory reliability and internal consistency af-
Equation Modeling proposed by Anderson and Gerbing44 to ter item parceling (Table 2).46
estimate the measurement and structural model. The first Second, goodness-of-fit criteria for the measurement model
step examined construct reliability and validity from the should confirm that the model adequately explains the em-
measurement model using Confirmatory Factor Analysis pirical data. Table 3 shows that the estimates of the overall
(CFA), while the second step focused on the path effects and goodness-of-fit criteria exceeded their respective acceptable
significance using the structural model. Besides, the medi- levels suggested in the relevant literature, which confirmed
ator variables were also examined to understand the linking that the measurement model exhibited a fairly good fit with
relationships in the theoretical model. the data gathered.

Table 3. Goodness-of-Fit Indices for the Measurement Scales

Fit index Recommended value Measurement model Structural model Source

v2/d.f. p5 3.44 3.51 Loo and Thorpe47; Schumacker and Lomax48

GFI q0.90 0.88 0.88 Doll et al.49
CFI q0.92 0.94 0.94 Hair et al.50
NFI q0.90 0.92 0.92 Hu and Bentler51
IFI q0.90 0.94 0.94 Bentler52
TLI q0.90 0.93 0.93 Hair et al.50
RFI q0.90 0.90 0.90 Bentler and Bonett53; Bentler54
PGFI q0.50 0.64 0.65 Bentler and Bonett53; Bentler54
PCFI q0.50 0.77 0.77 Bentler and Bonett53; Bentler54
PNFI q0.50 0.75 0.76 Bentler and Bonett53; Bentler54
SRMR p0.08 0.04 0.03 Hair et al.50; Hu and Bentler51
RMSEA p0.08 0.08 0.08 Henry and Stone55

GFI, goodness-of-fit indices; CFI, comparative fit index; NFI, normed fit index; IFI, incremental fit index; RMSEA, root-mean-squared error
of approximation; TLI, Tucker-Lewis index; RFI, relative fit index; PGFI, parsimony goodness of fit index; PCFI, parsimony comparative fit
index; PNFI, parsimony normed fit index; SRMR, standardized root mean square residual.
608 CHEN ET AL.

Table 4. Correlation Coefficient Matrix mean-squared error of approximation (RMSEA) = 0.16. The
above diagnostic analysis confirmed that CMB is unlikely in
Construct Mean SD TR PU CONF SAT CI the analyzed data.
TR 4.54 1.02 0.50
PU 4.67 0.91 0.70 0.85 Structural model testing
CONF 4.67 0.79 0.61 0.75 0.70 Structural model testing is performed to test the hypothe-
SAT 4.44 0.96 0.70 0.82 0.84 0.65 sized relationships in the proposed model. The literature
CI 5.01 0.79 0.65 0.72 0.67 0.78 0.73
shows that a good model fit for a structural model is indicated
Diagonal elements in boldface are the values of average variance by ratio of v2 to the degree of freedom smaller than 5.0; a GFI
extracted. Off-diagonal elements are the correlation coefficients. larger than 0.8; CFI, NFI, incremental fit index (IFI), and re-
TR, technology readiness; PU, perceived usefulness; CONF, confir- lative fit index (RFI) values larger than 0.9; parsimony
mation of expectations; SAT, satisfaction; CI, continuance intention.
goodness of fit index (PGFI), parsimony comparative fit index
(PCFI), and parsimony normed fit index (PNFI) values larger
Third, convergent validity is acceptable if the following than 0.5; and standardized root mean square residual (SRMR)
criteria are met:50,56 (a) the statistical significance of each and RMSEA values smaller than 0.08. Table 3 shows that all
factor loading is confirmed by a p of 0.5, (b) construct reli- goodness-of-fit indices of the structural model are all satis-
ability exceeds 0.7, and (c) average variance extracted exceeds factory. Table 6 shows the properties of the research
0.5. Tables 2 and 4 show that this study achieved the general hypotheses, including standardized path coefficients and
requirement of reliability and convergent validity for mea- hypotheses testing results.
surement model.
Fourth, the discriminant validity of paired constructs was Test of mediating effects
assessed by calculating a series of v2 difference tests for the To understand the synthetic effect of the ECM and tech-
constrained and unconstrained measurement models.50,57 All nology readiness on intention to use mobile-related services,
constructs were allowed to co-vary freely in the unconstrained indirect effects must be tested. Sobel test was used to test the
model. The constrained model is identical to the unconstrained significance of mediating effects.60 Besides, the product-of-
model except that the correlations of one paired constructs are coefficient method proposed by MacKinnon et al.61 was also
fixed at 1, whereas the remaining are allowed to co-vary freely. applied, to generate the asymmetric confidence intervals by
A 10.83 critical ratio of the v2 was obtained when applying the using PRODCLIN2. Mediating effects were assessed using
Bonferroni method at 99% confidence level. Since the v2 dif- five equations (Table 7). For example, H10b (TR-SAT-CI) was
ference statistics for all paired constructs exceed 10.83, dis- set to estimate the mediating path from technology readiness
criminant validity is fully supported (Table 5). to continuance intention via satisfaction. Table 7 shows that
Finally, this research collected responses using self-reported Sobel test revealed five significant mediating effects ( p < 0.05).
surveys in a single setting, which required assessment of Additionally, 95% confidence intervals for the five mediating
common method bias (CMB), which can potentially limit in- paths did not involve a zero, which confirmed the signifi-
ternal validity. The CMB is a major concern when a single cance of mediating effects.
latent factor accounts for most manifest variables.58,59 To
address the CMB issue at the measurement level, the Harman Discussion and Research Findings
one-factor test was applied in a CFA setting. The CFA was
performed by entering all measurement items into a one- The findings of this study have several managerial im-
factor model, which revealed a deteriorated model fit with plications. First, technology readiness is significantly asso-
v2/d.f. = 10.32, goodness of fit index (GFI) = 0.67, comparative ciated with intention to continue using mobile services.
fit index (CFI) = 0.76, normed fit index (NFI) = 0.74, and root- Compared with the original ECM by Bhattacherjee,3 this
study increased the total variance explained in continuance
intention from 41% to 63%. Additionally the trickle-down
process in the psychological perceptions of consumers who
Table 5. v2 Difference Tests for Examining adopt mobile services were presented and validated via
Discriminant Validity

Constructs Degrees Table 6. Summary of Hypotheses Testing Results

constrained v2 of freedom v2 difference
Standardized path
None 430.1 125 — Hypotheses Paths coefficients t Supported
(TR, PU) 480.0 126 49.9a
(TR, CI) 502.6 126 72.5a H1 TR/CONF 0.61*** 9.019 Yes
(TR, SAT) 475.0 126 44.9a H2 TR/PU 0.39*** 6.405 Yes
(TR, CONF) 529.8 126 99.7a H3 TR/SAT 0.17** 2.830 Yes
(PU, CONF) 512.5 126 82.4a H4 TR/CI 0.17* 2.397 Yes
(PU, SAT) 463.1 126 33.0a H5 CONF/SAT 0.49*** 7.689 Yes
(PU, CI) 497.5 126 67.4a H6 CONF/PU 0.52*** 9.105 Yes
(CONF, CI) 536.2 126 106.1a H7 SAT/CI 0.51*** 5.313 Yes
(CONF, SAT) 495.7 126 65.6a H8 PU/CI 0.19* 2.195 Yes
H9 PU/SAT 0.34*** 5.084 Yes
a 2
v difference was significant at p < 0.01 by using the Bonferroni
method. *p < 0.05; **p < 0.01; ***p < 0.001.
INTEGRATING TECHNOLOGY READINESS INTO ECM 609

Table 7. Mediation Tests

Unstandardized Standard Sobel 95% asymmetric

Equation Relationship regression weights error test (z) confidence interval

H10a: TR-PU-CI TR-PU 0.41 0.05 2.32* (0.01, 015)

PU-CI 0.17 0.07
H10b: TR-SAT-CI TR-SAT 0.18 0.06 2.65** (0.02, 0.17)
SAT-CI 0.46 0.08
H10c: TR-CONF-PU TR-CONF 0.49 0.05 6.94*** (0.22, 0.47)
CONF-PU 0.69 0.07
H10d: PU-SAT-CI PU-SAT 0.34 0.06 3.68*** (0.07, 0.27)
SAT-CI 0.46 0.08
H10e: CONF-PU-SAT CONF-PU 0.69 0.07 4.91*** (0.13, 0.37)
PU-SAT 0.34 0.06

*p < 0.05; **p < 0.01; ***p < 0.001.

mediation testing, and the integrated model migrate the con- predictors of continuance intention in users of emerging IT/
cern on service systems to end-users, as technology readiness is IS.9,10,33,37,38 However, this study mainly argues that the ECM
a psychological- and individual-specific construct. This re- tends to have a limited viewpoint to predict mobile service
search finding indicates that integrating the psychological adoption because it may neglect an individual’s personalities
construct of technology readiness into ECM evidently in- in which a technology is being adopted or refused. On the
creases the precision of the proposed model in modeling and basis of the verification and discussion in this study, the in-
predicting user intentions. tegration of ECM and technology readiness contributes to the
Second, technology readiness, perceived usefulness, and literature on IS continuance.
confirmation of expectation positively affect intention to use Several limitation of this study should be addressed in the
mobile services through the mediating effect of satisfaction. future. First, the extended ECM was evaluated in a single-
While some empirical studies have removed the role of sat- country setting, which limits generalizability. Additional
isfaction in predicting usage intention,26,62,63 this study indi- empirical validation across different regions or countries
cated that the effect is significant (B = 0.51). Given the would enhance the generalizability of the proposed model.
mediation test results and the theoretical relevance of satis- Second, this study did not consider the divergence between
faction as a critical mediator in the ECM and its instituted role voluntary and mandated usage, and the potential issue of
as a predictor of satisfaction across a wide range of IT/IS assessing acceptance in these two different circumstances. For
adoption behaviors, we suggest that it may be inconsiderate the mandated users, the usage of specific system may be
to drop satisfaction from IT/IS usage models. highly dependent on job tasks or performances. Therefore,
Third, among the three postadoption beliefs, the impact further development and evidence from the extended ECM in
of confirmation on satisfaction and intention to continue different service environments are needed.
mobile services usage is strongest. On the basis of a post-
adoption experience, the initial user expectation might shift. Author Disclosure Statement
The updated expectation in turn has vital influence on the
consequent processes, such as the perceptions of usefulness No competing financial interests exist.
and satisfaction.3 In addition, perceived usefulness is the
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Appendix
Questionnaire Items

Technology readiness (scaling from ‘‘strongly disagree’’ to ‘‘strongly agree’’ on a seven-point scale)

Optimism
TR1 Technology makes me more efficient in my occupation.
TR2 Technology gives me more freedom of mobility.
TR3 Learning about technology can be as rewarding as the technology itself.
TR4 I find new technologies to be mentally stimulating.
TR5 I prefer to use the most advanced technology available.
Innovation
TR6 Figure out new high-tech products and services without any help.
TR7 Others come to me for advice on new technology.
TR8 Be among the first in my circle of friends to acquire new technology.
TR9 Have fewer problems than others in making technology work.
TR10 Keep up with the latest technological development that I am interested in.
Discomfort (reverse scored)
TR11 Manual for a high-tech product or service is hardly written in plain language.
TR12 Technical support lines are not helpful because they don’t explain things in terms that I understand.
TR13 When getting technical support, I feel as if being taken advantage of by someone who knows more than me.
TR14 Embarrassed to have trouble with a high-tech gadget while people are watching.

(continued)
612 CHEN ET AL.

Questionnaire Items (Continued)

Technology readiness (scaling from ‘‘strongly disagree’’ to ‘‘strongly agree’’ on a seven-point scale)

Insecurity (reverse scored)

TR15 I worry about that the information I send over the Internet may be seen.
TR16 It’s not safe to do any kind of financial business online.
TR17 It’s not safe to give the vendor a credit card number over a computer.
TR18 I don’t feel confident doing business with a place that can only be reached online.

Perceived usefulness (scaling from ‘‘strongly disagree’’ to ‘‘strongly agree’’ on a seven-point scale)

PU1 Using the mobile service in my job/life will increase my productivity.

PU2 Using the mobile service in my job/life will make me more effective.
PU3 I find the mobile service to be useful in my job/life.

Confirmation of expectations (scaling from ‘‘strongly disagree’’ to ‘‘strongly agree’’ on a seven-point scale)

COM1 My experience with using mobile service is better than what I expected.
COM2 The service level provide by mobile service is better than what I expected.
COM3 Overall, most of my expectations from using mobile services are confirmed.

Satisfaction (scaling from ‘‘strongly disagree’’ to ‘‘strongly agree’’ on a seven-point scale)

SAT1 My overall experience of mobile service use was very satisfied.

SAT2 My overall experience of mobile service use was very pleased.
SAT3 My overall experience of mobile service use was absolutely delighted.
SAT4 My overall experience of mobile service use was very contented.

Continuance intention (scaling from ‘‘strongly disagree’’ to ‘‘strongly agree’’ on a seven-point scale)

CI1 I intend to continue using mobile service on my job/life.

CI2 I will use the mobile service regularly in the future.
CI3 My intentions are to continue using mobile service than use any alternative means.
CI4 If I could, I would like to continue using mobile service as much as possible.