A Model of User Adoption of Mobile Portals Alexander Serenko and Nick Bontis Alexander Serenko: Michael G. DeGroote School of Business, McMaster University, Hamilton, Ontario, Canada. Email: [email protected]. Nick Bontis, Ph.D.: Michael G. DeGroote School of Business, McMaster University, Hamilton, Ontario, Canada. Email: [email protected]. Alexander Serenko is a Ph.D. candidate in Management Science/Systems at the Michael G. DeGroote School of Business, McMaster University. Alexander holds a M.Sc. in computer science and an MBA in eBusiness. His research interests pertain to electronic business, intelligent agents, knowledge management, and innovation. His dissertation explores the adoption and use of interface agents for electronic mail. Alexander is the Director of the McMaster Doctoral Consortium on the Management of Corporate Governance, Electronic Business, and Intellectual Capital and Innovation. Nick Bontis is Associate Professor of Business Policy and Strategy at the Michael G. DeGroote School of Business, McMaster University. He is an award-winning teacher, author and gifted corporate speaker. He is also Associate Editor of the Journal of Intellectual Capital and Chief Knowledge Officer of Knexa Solutions – the world’s first knowledge exchange and auction. Dr. Bontis completed his doctoral education at the Ivey School of Business, University of Western Ontario and is recognized world-wide for his research in the areas of intellectual capital, organizational learning and knowledge management. His present research interests include diagnosing knowledge stocks and flows and their impact on organizational performance.
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A Model of User Adoption of Mobile Portals
Alexander Serenko and Nick Bontis
Alexander Serenko: Michael G. DeGroote School of Business, McMaster University,
and 9) perceived value. The dependent variables are: 1) perceived trust, 2) perceived
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usefulness, 3) perceived ease of use, 4) behavioral intentions, and 5) mPortal usage. The
questionnaire of the study is presented in Appendix I. The rest of this subsection
discusses the selection of the questionnaire items in more detail. Consistent with the MIS
guidelines for scale creation and use (Straub 1989), constructs of this model are measured
by employing previously validated and reliable instruments.
The self-report instrument for measuring the degree of PIIT has been
operationalized by Agarwal and Prasad (1998) in the form of a four-item questionnaire.
Both the instrument developers and succeeding researchers find this tool highly reliable
and valid (Agarwal and Karahanna 2000; Agarwal et al. 2000; Thatcher and Perrewe
2002). Thus, the original PIIT scale is applied in this study with no modifications.
The initial ten-item self-efficacy scale was created by Compeau and Higgins
(1995) and tested in several subsequent studies (Thatcher and Perrewe 2002). Pedersen
and Nysveen (2002) adapted this scale to measure the extent of self-efficacy of text
messaging users. This study, in turn, adapts this scale to measure the extent of one’s
self-efficacy with a mobile device.
Device optimization is measured by a degree to which a mobile portal provider
customizes the information and the way it is presented depending on the category of a
user’s device as well as the type of wireless connection. The score is measured on a
seven-item Likert scale, and it is provided by researchers. Thus, this item is not included
in the questionnaire.
The self-expressiveness instrument was originated by Halberstadt et al.
(Halberstadt et al. 1995) and tested subjected to reliability and validity testing
(Bozionelos 2001; Bozionelos 2002). This investigation adapts the perceived
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expressiveness scale for mCommerce suggested by Pedersen and Nysveen (2002) to
reflect the nature of mPortal users.
According to customer satisfaction research, the perceived value of a product or
a service is measured relative to a price (i.e., rating of quality given price and rating of
price given quality) (Fornell et al. 1996). With respect to the use of mPortals, three
categories of direct and indirect of costs are identified: 1) airtime for which an individual
pays in order to access a mobile portal, 2) learning time or time spent to understand the
portal’s navigation, and, 3) one’s efforts to locate required information. Items 2 and 3 are
excluded from the suggested instrument because they are accurately and consistently
reflected by TAM’s constructs. Thus, the only direct financial expense is airtime paid to
access a mobile portal.
Based on the review of marketing and MIS literature, three questions were created
to measure an individual’s perceptions of the decision to spend his or her airtime to
access an mPortal: 1) “considering the airtime expenses to access the mobile portal, I
believe that using that mobile portal was a good idea”; 2) “I believe that using that portal
was a good investment of airtime”; and, 3) “I regret spending airtime on accessing that
portal.” As suggested by instrument design principles, the scale employs one
reverse-scaled item (question 3).
The instrument to measure the level of perceived trust of a user to an mPortal
provider is adapted from the trust-enabled TAM model by Gefen et al. (2003). Only the
items that were retained in the final version of the questionnaire are utilized. The
questions are adjusted to reflect the nature of mobile portal users.
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The initial Likert scales for measuring perceived usefulness, perceived ease of
use, and behavioral intentions were first introduced by Davis (1989). Initially, perceived
usefulness and perceived ease of use were realized in the form of two 14-item set of
questions. Since then, various pre-tests and assessments of these scales have reduced the
number of items at first to ten and then later to only six items per construct. In 1989,
Davis, Bagozzi, and Warshaw further streamlined these scales to two four-item questions.
A behavioral intentions measurement scale was first implemented as the
following single statement: “I presently intend to actively use WriteOne regularly in the
MBA program.” Afterwards, it was transformed into two questions positioned on a
7-item Likert scale: “Assuming I had access to the system, I intend to use it”, and “Given
that I had access to the system, I predict that I would use it” (Venkatesh and Davis 2000).
Since their inception, these above-mentioned scales have been utilized across numerous
technology adoption studies and subjected to successful reliability and validity testing
(Mathieson 1991; Segars and Grover 1993; Taylor and Todd 1995b). As such, this study
utilizes the validated and reliable TAM scales.
As of today, the mCommerce research community has not created the instruments
for measuring the degrees of ubiquity, convenience, localization, and personalization of a
mobile portal. The extent of the actual mPortal usage is not measured in the
questionnaire. As suggested by the previous technology adoption research, behavioral
intentions accurately reflect future system or application usage.
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Data Analysis Techniques
Consistent with most previous TAM-based investigations, this study is expected
to utilize Partial Least Squares (PLS) as a major data analysis technique. Several
arguments support this decision (Chin 1998; Gefen, Straub and Boudreau 2000). First,
the objective of data analysis is to test a set of path-specific hypotheses which is best
addressed in PLS. Secondly, PLS works well with small data samples. Thirdly, PLS is
well-suited for exploratory research. Lastly, since PLS has been traditionally utilized in
TAM-based investigations, the usage of this statistical tool will allow comparing the
predictive power of the proposed conceptual model with those of preceding projects. It is
for those reasons this study employs PLS for data analysis and hypotheses testing.
Respondents and Sample Size
Respondents for this study should be randomly chosen from a broad population of
current user of Web-enabled mobile devices and who frequently access mobile portals.
No discriminatory criteria should be used with respect to age, sex, device experience,
mCommerce or eCommerce attitudes, etc. In order to control for device-specificity, the
users of each type of wireless device should be surveyed separately.
Since PLS is recommended for data analysis, the minimum sample size
requirement for PLS is determined by finding the larger of two possibilities: 1) a
construct with the largest number of indicators (i.e., number of items in the most complex
construct), or 2) a dependent construct with the highest number of independent constructs
impacting it (i.e., the maximum number of arrows pointing out to one dependent
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construct). The minimum sample size should be at least ten times the larger number of
these possibilities (Chin 1998).
In this study, perceived trust is the construct which has the largest number of
indicators (5); therefore, the PLS minimum sample size is at least 50 valid responses.
However, it is suggested to exceed the minimum sample size threshold and to survey at
least 100 individuals.
Discussion and Conclusions
The purpose of the study is to discover factors that may provide insights on
reasons why individuals adopt mobile portals, to build a preliminary conceptual model,
and to design a methodologically sound survey which will be utilized to test this model.
As such, the investigation suggests that five distinct latent variables: perceived
expressiveness, perceived trust, perceived ease of use, perceived usefulness, and
perceived value of a mobile portal are key constructs of the model which explicate user
adoption behavior. In addition, the study suggests that individual-specific antecedents,
such as personal innovativeness in the domain in information technology and self-
efficacy with mobile devices, and mPortal-specific antecedents, such as ubiquity,
convenience, localization, personalization, and device optimization potentially influence
the perceived ease of use or the perceived usefulness of an mPortal.
The major advantage of this model is two-fold. The first is that it investigates an
unexplored area of user adoption of mobile portals. As of today, mCommerce projects
have not considered mPortals as a subject of adoption. The second advantage of the
model is that it brings together several different disciplines such as innovation,
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management information systems, mobile commerce research, and marketing. Especially,
it should be noted that prior technology adoption investigations have not directly
considered the perceived value of an IT service. Given that a user of a mobile portal is
expected to pay for airtime while using a portal, the introduction of this construct is
expected to increase the total variance explained by the model and, therefore, to improve
the model’s predictive power.
This study has several limitations. First, it is believed that not all factors that
explicate users’ adoption decisions have been identified. Since this is the first
investigation in the area, there is no significant body of literature on which to base
justifications of the constructs of a proposed model. Secondly, since the degree of device
optimization is measured by researchers, significant intra-rater reliability coefficients
should be obtained to make sure that each researcher analyzes the degree of optimization
of the same device identically. Thirdly, this study does not operationalize four key
variables that play a role of the model’s antecedents. Lastly, the same airtime expenses
may be perceived differently by different individuals. As such, the perceived value
construct may suffer of multicolinearity. For example, the perception of airtime costs
may depend on an individual’s income. This means the structural equation modeling
techniques will not provide statistically reliable results.
With respect to future work, several avenues may be explored. First, future
researchers should design valid and reliable instruments for measuring the degree of
ubiquity, convenience, localization, and personalization of a mobile portal. At least one
pre-test is required to test those constructs. Second, scholars should develop guidelines by
which to assess the extent of device optimization. Another pre-test is required to estimate
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the consistency of this scale. Third, researchers should conduct a pilot test of the
conceptual model by utilizing the minimum sample size of 50 respondents. A PLS
analysis should be performed and loadings of items on their respective constructs
estimated. After that, items with loadings below the suggested threshold of 0.7 should be
removed from the next version of the questionnaire and a final full-scale study involving
at least 100 respondents should be conducted. The model should be adjusted based on a
survey’s findings. Last, future scholars should review the results and to create guidelines
for the development of really useful mobile portals.
In general, many researchers are encouraged by the fast growth of the wireless
market and the development of mobile commerce business models. It is believed that the
investigations of factors that affect individuals’ decisions towards adopting mobile
technologies, including mobile portals, will potentially contribute to the creation of
widely accepted mobile products and services.
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Appendix I. Questionnaire.
A. The questions below ask you to describe your behaviors in the context of information technologies. Information technologies are computer systems concerned with all aspects of managing and processing information. Information technologies include personal computers, software applications, telecommunications networks (e.g., the Internet and Email), etc. Please indicate the number that best matches you opinion. PIIT1. If I heard about a new information technology, I would look for ways to experiment with it.
B. Please answer these questions with respect to your experience with mobile devices, e.g., a cell phone or a PDA. Self-efficacy SE1. I am able to use mobile devices without help of others.
C. Please answer these questions with respect to your experience with mobile portals in general. Expressiveness EX1. Mobile portals are something I often talk with others about or use together with others.
D. Please answer these questions with respect to your experience with a mobile portal that you most frequently use. Perceived Value PV1. Considering the airtime expenses to access the mobile portal, I believe that using that mobile portal was a good idea.
Figure 1. Unique Characteristics of mPortals. Adapted from Clarke and Flaherty
(2003) and GSA (2002)
Mobile Portals
Ubiquity
Localization
Personalization
Convenience
Device Optimization
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Figure 2. A Conceptual Model of User Adoption of Mobile Portals
Individual-specific
MPortal Usage
Behavioral Intentions
Perceived Expressiveness
Perceived Trust
Perceived Ease of Use
Perceived Usefulness
Perceived Value PIIT
Self-efficacy
Ubiquity
Convenience
Localization
Personalization
Antecedents
MPortal-specific
Device Optimization
Model Outcome
Key Constructs
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