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Journal ArticleDOI

Mobile Commerce Switching Intentions in Thai Consumers

07 Nov 2017-Mediterranean journal of social sciences (Richtmann Publishing)-Vol. 8, Iss: 6, pp 123-123

AbstractThis research applies an extended Unified Theory of Adoption and Use of Technology (UTAUT) model to consumer intentions to switch from other retail channels to mobile commerce in Thailand. Mobile commerce is a rapidly growing segment of the consumer market, but remains in an early stage of adoption in many markets. A survey of Thai consumers (n = 458) was conducted online and analyzed using a structural equation modeling (SEM) approach. Findings showed that the extended UTAUT model, which included online social support and convenience, significantly explained the consumer decision to engage in mobile commerce. However, direct incentives (discounts and referral codes) were not significant. The implication of these findings is that mobile commerce providers need to focus on building social support for the technology itself, rather than relying on marketing tools like discounts or referral codes if they want to shift sales away from other retail channels.

Topics: Mobile commerce (64%)

Summary (3 min read)

Introduction 1.

  • Mobile commerce is part of many firm's multi-channel or omni-channel marketing strategies, with mobile sales channels, such as apps or mobile websites, incorporated alongside traditional sales channels like retail stores and e-commerce sites (Maity & Dass, 2014) .
  • The Universal Theory of Adoption and Use of Technology model is an obvious choice for studying consumer switching intentions for existing technology, since it addresses both the technological and social conditions of technology adoption (Venkatesh, Morris, Davis, & Davis, 2003) .
  • None of these studies have examined switching between different service providers or channels for the same type of service, but have instead studied the initial technology adoption.
  • The purpose of this research is to examine consumer switching intentions from existing retail channels (brick and mortar and e-commerce) of retailers they already interact with.

2.1 Mobile commerce switching behavior

  • This research examines distribution channel switching intentions at the pre-purchase stages.
  • The intention to study consumer intentions was made because it is more difficult to study actual behaviors, which can vary and may be diverted (for example through lack of resources or because the consumer changes their mind) (Ajzen, 2008) .
  • The pre-purchase stage of consumer decision making consists of a process of need identification and alternative identification, comparison, and selection, leading to the purchase stage of action (Kardes, et al., 2011) .
  • While omni-channel retailing could lead to continued consumer channel switching (for example, during returns or repairs) (Verhoef, et al., 2015) , this study focuses on the pre-purchase stage for the initial consumer decision to use the mobile channel.

2.3 Extended Framework

  • Previous studies utilizing the UTAUT have routinely extended the framework in order to allow for a more contextually appropriate application.
  • This research also develops an extended framework, with some aspects taken from previous UTAUT studies and others being specific to the online commerce situation.

2.3.3 Direct switching incentives

  • The third extension of the UTAUT model is direct switching incentives.
  • Mobile user referrals may be encouraged as part of word of mouth campaigns to access existing user's social networks and provide social proof for the service (Okazaki, 2008) .
  • Discounts have not been tested directly, but as cost-lowering promotional strategies are potentially supported by Min, et al.'s (2008) theoretical extended framework.
  • Referrals and discounts are often combined; for example, it is common to offer both current and new customers a small discount for successful switching (Kumar, Peterson, & Leone, 2010).
  • To determine whether direct switching incentives do influence the behavioral intention to switch to mobile commerce channels, the following hypothesis will be tested: Hypothesis 6: Direct switching incentives (referrals and discounts) positively associated with mobile commerce channel switching.

2.4 Research Model

  • The research model incorporates the UTAUT (H1 through H3) and three additional extending factors, including online social support, convenience, and direct incentives (H4 through H6) .
  • In addition, moderating variables of gender, age, and income level were included, on the basis that demographic characteristics influence consumer decisions and availability of resources (Kardes, et al., 2011) .

3.1 Population and sample

  • The population of interest of this study was Thai consumers (age 20+).
  • Following recommendations on the sample size for SEM (Westland, 2010) , a minimum sample size of 400 consumers was established.
  • The sample was selected using a convenience sample of online users, who were recruited via announcements on Facebook and other social media sites.
  • The response rate is not known due to the open recruitment strategy.
  • A total of 474 surveys were submitted, but 56 surveys were excluded because they were not finished, leading to a completion rate of 88.2%.

3.2 Measurement

  • The online survey was based on previous instruments (Venkatesh, et al., 2003; Lin & Anol, 2008) , with some items adapted from but not directly taken from previous studies.
  • Each of the factors and outcome variable was measured using five-point Likert scales, with three items in each of the scales.
  • All scales achieved α > 0.8, indicating no adjustment was needed for internal consistency.
  • Demographics and technology and mobile commerce usage information was also collected.
  • The outcome variable (Switching Intention) was measured using a scenario, rather than a past purchase, because it had been previously determined that not all participants had switched providers.

3.3 Data collection

  • Data was collected using a Google-based survey instrument designed by the researcher.
  • The instrument was tested for language invariance using a backward and forward translation strategy.
  • First, the researchers designed the survey in Thai, and then translated the survey into English.
  • Data collection was conducted for a period of one month in 2016.
  • Following survey closure, the English and Thai surveys were combined into a single dataset.

3.4 Data analysis

  • Data analysis was conducted using SEM in SPSS AMOS, and outcomes were evaluated using standard thresholds and interpretations as identified by Byrne (2016) .
  • Descriptive statistics were also prepared for demographics and technology usage, which helps explain the characteristics of the sample.
  • Exploratory factor analysis (EFA) was conducted first, to refine the measurement model.
  • Confirmatory factor analysis (CFA) was then used to test the structural model.

4.1 Demographics and mobile commerce usage

  • Respondents were asked about communications technology access and use and use of mobile commerce.
  • Most of the respondents had desktop/laptop Internet access (87.1%), but more had mobile (phone or tablet) access (92.1%).
  • Respondents were mainly moderate Internet users, with most using the Internet (desktop or mobile) 3-4 hours a day (72%).
  • Just over half (55.3%) had used mobile commerce at least once.
  • Thus, the participants were relatively technologically capable and did not have technology barriers to mobile commerce.

4.2 Reliability and validity

  • Scales were assessed for reliability and validity using several metrics (Table 1 ).
  • As the results show, all scales met these characteristics.

4.3 Research model

  • The extensions of the UTAUT model met with varying success.
  • Online social support and convenience were significant.
  • Other studies have also identified convenience as a possible factor in mobile commerce choice, although these studies have not actually empirically tested the factor (Okazaki & Mendez, 2013; Wu & Wang, 2005) .
  • Thus, this research contributes by testing the importance of convenience to mobile channel switching.
  • The final factor of direct incentives was not significant in the model.

Conclusion and Recommendations 6.

  • In conclusion, the UTAUT model, along with online social support and convenience, can be used to partially explain consumer channel switching to mobile commerce from other retail channels.
  • There is also limited information in the literature about why consumers choose to switch channels with the same retailer.
  • Many retailers have adopted a multi-channel or omni-channel retailing approach in order to increase their mobile market penetration and take advantage of mobile consumer opportunities (Maity & Dass, 2014) .
  • The significant social barriers to adoption also mean that retailers need to work harder to communicate with consumers about the benefits of mobile shopping and ensure that they are really prepared to deliver with an omni-channel strategy.
  • The research only included Thai consumers, who have particular technological contexts and a specific retail environment.

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ISSN 2039-2117 (online)
ISSN 2039-9340 (print)
Mediterranean Journal of
Social Sciences
Vol 8 No 6
November 2017
123
Research Article
© 2017 Kedwadee Sombultawee.
This is an open access article licensed under the Creative Commons
Attribution-NonCommercial-NoDerivs License
(http://creativecommons.org/licenses/by-nc-nd/3.0/).
Mobile Commerce Switching Intentions in Thai Consumers
Ms. Kedwadee Sombultawee
Lecturer at Silpakorn University,
Faculty of Management Science,
Silpakorn University, Thailand
Doi: 10.1515/mjss-2017-0049
Abstract
This research applies an extended Unified Theory of Adoption and Use of Technology (UTAUT) model
to consumer intentions to switch from other retail channels to mobile commerce in Thailand. Mobile
commerce is a rapidly growing segment of the consumer market, but remains in an early stage of
adoption in many markets. A survey of Thai consumers (n = 458) was conducted online and analyzed
using a structural equation modeling (SEM) approach. Findings showed that the extended UTAUT
model, which included online social support and convenience, significantly explained the consumer
decision to engage in mobile commerce. However, direct incentives (discounts and referral codes) were
not significant. The implication of these findings is that mobile commerce providers need to focus on
building social support for the technology itself, rather than relying on marketing tools like discounts or
referral codes if they want to shift sales away from other retail channels.
Keywords: mobile commerce, Thailand, consumer adoption, UTAUT, social support, channel switching
Introduction
1.
Mobile commerce (or m-commerce) is business and consumer commerce conducted through
mobile channels, including mobile web and app-based sales of tangible and intangible goods and
services and use of mobile payments (Chong, 2013). Mobile commerce is still a minority share of
total e-commerce; for example, 2014 US figures indicated it made up about $35 billion (or 11.6% of
total e-commerce) (Meola, 2016). As of Q4 2015, it is estimated that about 31% of mobile phone
users in Thailand had made at least one m-commerce purchase, indicating a high level of market
penetration compared to other countries in the region (Statista, 2016). Mobile commerce is part of
many firm’s multi-channel or omni-channel marketing strategies, with mobile sales channels, such
as apps or mobile websites, incorporated alongside traditional sales channels like retail stores and
e-commerce sites (Maity & Dass, 2014). However, consumers do not prefer to use mobile
commerce for all types of transactions; instead, mobile commerce is typically used for relatively
simple transactions and transactions where the goods are meant to be used in mobile contexts
(such as downloadable or streaming media) (Maity & Dass, 2014). Additionally, consumer
demographics and technology usage preferences also influence whether they will use mobile
commerce (Chong, 2013).
The Universal Theory of Adoption and Use of Technology (UTAUT) model is an obvious
choice for studying consumer switching intentions for existing technology, since it addresses both
the technological and social conditions of technology adoption (Venkatesh, Morris, Davis, & Davis,
2003). Furthermore, with a predictive value of around 50% on average (Dwivedi, Rana, Chen, &
Williams, 2011), this could be an effective tool for understanding consumer switching intentions and
behaviors. The adoption of mobile technology has routinely been studied using the UTAUT

ISSN 2039-2117 (online)
ISSN 2039-9340 (print)
Mediterranean Journal of
Social Sciences
Vol 8 No 6
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(Alkhunaizan & Love, 2012; Jaradat & Al Rababaa, 2013; Wang & Wang, 2010). However, none of
these studies have examined switching between different service providers or channels for the
same type of service, but have instead studied the initial technology adoption. A changing
consumer environment also suggests that there needs to be further extension of the UTAUT model
to account for such decisions, including for example online social support (Lin & Anol, 2008) and
convenience (Min, Ji, & Qu, 2008) as facilitating conditions for technology adoption. Additionally,
retail service providers often attempt to influence consumers through direct switching incentives
(Andrews, Benedicktus, & Brady, 2010), which is not accounted for the in UTAUT model. Thus,
there is a gap in the research in two specific areas. The first area is the lack of research into
switching behavior, rather than the initial adoption behavior. The second is in the specific context of
retail channel choice in today’s market, which include aspects not considered in the original
formulation by Venkatesh, et al. (2003). This study attempts to fill these gaps.
The purpose of this research is to examine consumer switching intentions from existing retail
channels (brick and mortar and e-commerce) of retailers they already interact with. It applies an
extended Unified Theory of the Adoption and Use of Technology (UTAUT) to examine switching
intentions.
Literature Review
2.
2.1 Mobile commerce switching behavior
This research is mainly concerned with mobile commerce channel switching behavior. Consumer
channel switching refers to the consumer’s choice of another sales channel from the same or
different retailer for the same product (Pookulangara, Hawley, & Xiao, 2011). For example,
consumers may choose to purchase a given product either from an online store or the associated
physical retail store. Consumers may also engage in channel switching between retailers; for
example, using one retailer’s physical store to examine a product and then buying it from another
retailer’s electronic commerce site (Heitz-Spahn, 2013). For the purposes of this research, we
examine consumer retail channel switching from either physical or non-mobile e-commerce
channels to mobile commerce, but do not address the question of free-riding (purchase from
another retailer). It also only focuses on goods that could be purchased through multiple channels,
excluding mobile purchases such as streaming entertainment or in-app purchases designed for use
with a mobile device. This study does not address information channel switching, given that
consumers may use varied information channels already, including online and mobile channels
(Solomon, Bamossy, Askegaard, & Hogg, 2013).
There are several reasons consumers may engage in channel switching. Consumers may
already be comfortable in a multi-channel shopping environment and may have strong
requirements for cost, convenience or selection (Heitz-Spahn, 2013). There may also be
sociodemographic factors, including income and age (Heitz-Spahn, 2013). The product
characteristics also influence channel switching (Maity & Dass, 2014). For example, e-commerce
and in-store shopping experiences offer more detail and information about the product, and are
often preferred for complex shopping choices (Maity & Dass, 2014). Firms may also differentiate
pricing between channels to encourage consumers to switch channels, which could have an effect
(Kauffman, Lee, Lee, & Yoo, 2009). However, this strategy may not be ideal since this can result in
cannibalization of existing customers, rather than increasing market share (Falk, Schepers,
Hammerschmidt, & Bauer, 2007).
Channel switching is not a problem per se for retailers undertaking a multi-channel or omni-
channel retailing strategy, whose aim is to enable shoppers to buy, service, and return products
seamlessly between all channels (Verhoef, Kannan, & Inman, 2015). Retails that pursue the omni-
channel strategy, in particularly, have made a strategic choice of offering consumers the option to
use any channel (Verhoef, et al., 2015). However, this does not mean that the consumer’s channel
choice or channel switching intention or behavior is meaningless for the firm. using online channels
have lower costs to serve (the amount making a sale costs the company) and they may have
increased revenues (spending more with the company) (Gensler, Leeflang, & Skiera, 2011;

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Verhoef, Kannan, & Inman, 2015). Furthermore, multichannel customers are likely to have a higher
consumer value than single-channel customers (Neslin & Shankar, 2009). At the same time, the
multichannel retailer faces challenges such as supply chains for different channels and integrated
marketing campaigns that make sense in this context (Neslin & Shankar, 2009). Thus, even if
retailers encourage their consumers to switch channels seamlessly, it is still helpful for them to
understand why consumers switch and how this influences their switching behavior.
This research examines distribution channel switching intentions at the pre-purchase stages.
A consumer intention can be briefly defined as a decision to undertake a specific behavior, which is
then translated into a consumer behavior (Kardes, Cronley, & Cline, 2011). The intention to study
consumer intentions was made because it is more difficult to study actual behaviors, which can vary
and may be diverted (for example through lack of resources or because the consumer changes
their mind) (Ajzen, 2008). The pre-purchase stage of consumer decision making consists of a
process of need identification and alternative identification, comparison, and selection, leading to
the purchase stage of action (Kardes, et al., 2011). While omni-channel retailing could lead to
continued consumer channel switching (for example, during returns or repairs) (Verhoef, et al.,
2015), this study focuses on the pre-purchase stage for the initial consumer decision to use the
mobile channel.
2.2 UTAUT model
The UTAUT model was proposed as an integrative model of user technology acceptance,
incorporating elements of eight previous conflicting models (Venkatesh, Morris, Davis, & Davis,
2003). The UTAUT model (Figure 1) incorporates four technology-related factors, including:
performance expectancy (what the user believes the technology will do); effort expectancy (how
difficult they expect it to be to use); social influence (social norms surrounding its use); and
facilitating conditions (factors that enable or disable technology use) (Venkatesh, et al., 2003). Of
these factors, performance expectancy, effort expectancy, and social influence are believed to
contribute directly to behavioral intentions, while facilitating conditions contribute directly to use
behavior (Venkatesh, et al., 2003).
Figure 1: The Unified Theory of Acceptance and Use of Technology (UTAUT)
Source: Adapted from Venkatesh, et al., 2003, p. 447
Tests of UTAUT have found it generally predictive of technology usage. For example, Venkatesh, et
al.’s (2003) original formulation testing found that it predicted about 69% of variance in technology
acceptance, compared to between 17% and 53% of the models it drew from. However, a meta-
analysis of the relatively small numbers of studies that have actually used this model has not had
as strong a result (Dwivedi, Rana, Chen, & Williams, 2011). These authors found that the predictive

ISSN 2039-2117 (online)
ISSN 2039-9340 (print)
Mediterranean Journal of
Social Sciences
Vol 8 No 6
November 2017
126
value of the model was closer to 50% for most studies (Dwivedi, et al., 2011). Thus, this model may
be used but it must be used cautiously.
Several previous studies have used the UTAUT to assess mobile Internet or mobile
commerce technology adoption, although none have examined mobile commerce channel
switching. One group of authors examined mobile Internet adoption in Taiwan (n = 343), using a
structural equation modeling (SEM) approach (Wang & Wang, 2010). They found significant
influences of all factors except perceived playfulness (a model extension), with a high predictive
value for behavioral intention (R
2
= 0.650). They concluded that the UTAUT model, with appropriate
extensions, could explain the adoption of mobile Internet (Wang & Wang, 2010). An extended
UTAUT model was also used in a study of Saudi Arabian consumers and their intention to adopt
mobile commerce (n = 574) (Alkhunaizan & Love, 2012). These authors added trust in the
technology and cost factors, and also studied demographics (age and gender). Their regression
analysis showed that performance expectancy was the most significant factor in adoption, followed
by cost and effort expectancy; social influence and trust were not significant (Alkhunaizan & Love,
2012). A study of Jordanian consumer acceptance of mobile commerce (n = 447) had slightly
different findings (Jaradat & Al Rababaa, 2013). These authors’ SEM analysis showed that social
influence was the strongest factor, followed by effort expectancy and performance expectancy
(Jaradat & Al Rababaa, 2013). These studies show that there is still conflicting information about
factors in consumer choice of mobile commerce, which could result from different demographic,
cultural or technological contexts. However, application of UTAUT remains relatively rare, as noted
by Dwivedi, et al. (2011). This leaves an opportunity for the present research to contribute to
understanding mobile commerce acceptance.
The elements of the UTAUT model are used as the basis for the first three hypotheses, which
are proposed in line with previous findings on the adoption of mobile commerce. Facilitating
conditions are excluded because actual use is not tested. These hypotheses are stated:
Hypothesis 1: Performance expectancy of mobile commerce is positively associated with
channel switching behavior.
Hypothesis 2: Effort expectancy of mobile commerce is positively associated with channel
switching behavior.
Hypothesis 3: Social influence is positively associated with channel switching behavior.
2.3 Extended Framework
Previous studies utilizing the UTAUT have routinely extended the framework in order to allow for a
more contextually appropriate application. This research also develops an extended framework,
with some aspects taken from previous UTAUT studies and others being specific to the online
commerce situation.
2.3.1 Online social support
The first factor included in the UTAUT-based model is online social support. Online social support
can be defined as evidence of positive social support in an online context (Lin & Anol, 2008). Lin
and Anol (2008) examined online social support as a factor in online learning adoption,
operationalizing it as use of instant messaging (IM) services. The authors found that it did
contribute to behavioral intention to use online learning, but did not play a role as a facilitating
factor. In the context of mobile commerce, online social support may be more appropriately
operationalized as exposure to online reviews, which provide both practical product information and
social support signals (Sun, Youn, Wu, & Kuntaraporn, 2006). These reviews are commonly
defined as electronic word of mouth (EWOM), which provides social support for the purchase
decision and provides information from a trusted source (Kardes, et al., 2011). A previous study on
Internet users in Japan found that mobile commerce users’ provision of word of mouth (WOM) was
more likely to be driven by social intention and social cognition, implying a potentially more
important role for online social support than users of other electronic commerce channels (Okazaki,
2009). Electronic word of mouth (EWOM) is distinct from word of mouth (WOM) are different

ISSN 2039-2117 (online)
ISSN 2039-9340 (print)
Mediterranean Journal of
Social Sciences
Vol 8 No 6
November 2017
127
because EWOM is not typically built on personal knowledge and trust of the recommender, but
instead on the reputation of the recommender (for example upvotes) and the clarity and relevance
of information provided (Goldsmith, 2009). These studies provide evidence that online social
support may be an important factor in mobile commerce channel switching. Furthermore, online
social support (EWOM) is distinct from generalized social support for adoption of technology.
Therefore, the fourth hypothesis is:
Hypothesis 4: Online social support is positively associated with mobile commerce channel
switching.
2.3.2 Convenience
One of the factors that sets mobile commerce apart from other forms of e-commerce or brick and
mortar commerce channels is convenience, which refers to the ease with which the consumer can
make the purchase (Solomon, et al., 2013). Convenience may occur in space, time, or difficulty
dimensions (Solomon, et al., 2013), and to some extent all three of these dimensions apply here.
Consumers typically have access to their mobile devices at any time and do not need to be located
in a particular place to use them. A model of mobile commerce adoption proposed for the Chinese
market based on an extended UTAUT argued that convenience would be a significant factor in
behavioral intention to use mobile commerce (Min, Ji, & Qu, 2008). Specifically, convenience and
cost were the factors that were identified as facilitating conditions in actual use (Min, et al., 2008).
For this study, these two factors are differentiated, since convenience and cost may have different
influences. Min, et al. (2008) did not empirically test their model of mobile commerce adoption.
However, other studies examining the topic have identified convenience as a potential factor. For
example, one study examined mobile commerce using a modified Technology Acceptance Model
(TAM) framework (Wu & Wang, 2005). (The TAM is one of the eight technology adoption models
incorporated into the UTAUT (Venkatesh, et al., 2003).) The authors identified convenience as part
of the benefits of using mobile commerce. However, this study is old enough that the underlying
technology has changed; for example, many more consumers now have smartphones, while in Wu
and Wang’s (2005) study most users only used their phones as a convenience tool. A more recent
study also incorporated a revised TAM, this time with the extension of uncovering gender
differences (Okazaki & Mendez, 2013). These authors found that perceptions of convenience of
mobile commerce were influenced by intrinsic attributes (portability and interface design,
contributing to usability), and external attributes (simultaneity, speed, and searchability of mobile
commerce) (Okazaki & Mendez, 2013). Once again, however, this research did not follow through
completely, since the authors did not then study the effect of convenience on usage intentions.
Thus, the literature surrounding convenience of mobile commerce is mixed. While many authors
have made theoretical arguments regarding the relationship of convenience to usage intentions for
mobile commerce, the actual empirical evidence is limited. To help fill that gap, convenience is
added as an extension to the UTAUT model as follows:
Hypothesis 5: Convenience is positively associated with mobile commerce channel switching.
2.3.3 Direct switching incentives
The third extension of the UTAUT model is direct switching incentives. A switching incentive is
something offered to the consumer by the retailer in order to encourage the consumer to switch
suppliers, products or sales channels (Andrews, Benedicktus, & Brady, 2010). For example,
communications service providers routinely offer discount bundles in order to encourage
consumers to switch from competitors, or cash incentives to lower switching costs (Andrews, et al.,
2010). In terms of earlier models of consumer adoption of mobile commerce, the switching
incentive can be viewed as reducing the cost associated with switching from other channels to the
mobile channel (Min, et al., 2008). This research examines two possible types of switching
incentives, including referrals from existing customers and discount promotions. Mobile user
referrals may be encouraged as part of word of mouth campaigns to access existing user’s social
networks and provide social proof for the service (Okazaki, 2008). This study did find that WOM

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Abstract: M-commerce has very rapidly developed into a very powerful way of reaching out to the consumer. M-commerce has been a massive success in terms of users’ adoption in some markets like Japan, while, astonishingly, not as thriving in others. However, its acceptance and level of adoption are low in Palestine compared to other countries. The research main objective is to classify the key variables that influence the acceptance of M-commerce among higher education students in Palestine by developing an M-commerce adoption Model based on an extension of the Technology Acceptance Model (TAM). A total of 430 questionnaires were collected and analyzed using Structural Equation Modelling (SEM) technique. The findings revealed that perceived usefulness, perceived ease of use, personnel innovation, security and privacy, subjective norms, and perceived trust are found to have an important effect on consumer behavioral intention to adopt M-commerce. These results will benefit stakeholders involved in M-commerce activities such as service providers, retailers, consumers, academicians, and students.

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  • ...According to [17], consumers do not prefer to use M-commerce for all types of transactions; instead, M-commerce is typically used for relatively simple transactions....

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  • ...As the results of a structural model in this study have shown, there exists a significant relationship that is consistent with the finding of [4, 17, 30, 32, 40]....

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References
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Journal ArticleDOI
Abstract: Information technology (IT) acceptance research has yielded many competing models, each with different sets of acceptance determinants. In this paper, we (1) review user acceptance literature and discuss eight prominent models, (2) empirically compare the eight models and their extensions, (3) formulate a unified model that integrates elements across the eight models, and (4) empirically validate the unified model. The eight models reviewed are the theory of reasoned action, the technology acceptance model, the motivational model, the theory of planned behavior, a model combining the technology acceptance model and the theory of planned behavior, the model of PC utilization, the innovation diffusion theory, and the social cognitive theory. Using data from four organizations over a six-month period with three points of measurement, the eight models explained between 17 percent and 53 percent of the variance in user intentions to use information technology. Next, a unified model, called the Unified Theory of Acceptance and Use of Technology (UTAUT), was formulated, with four core determinants of intention and usage, and up to four moderators of key relationships. UTAUT was then tested using the original data and found to outperform the eight individual models (adjusted R2 of 69 percent). UTAUT was then confirmed with data from two new organizations with similar results (adjusted R2 of 70 percent). UTAUT thus provides a useful tool for managers needing to assess the likelihood of success for new technology introductions and helps them understand the drivers of acceptance in order to proactively design interventions (including training, marketing, etc.) targeted at populations of users that may be less inclined to adopt and use new systems. The paper also makes several recommendations for future research including developing a deeper understanding of the dynamic influences studied here, refining measurement of the core constructs used in UTAUT, and understanding the organizational outcomes associated with new technology use.

24,087 citations


Book
21 Jul 2011
TL;DR: Structural Equation Models: The Basics using the EQS Program and testing for Construct Validity: The Multitrait-Multimethod Model and Change Over Time: The Latent Growth Curve Model.
Abstract: Psychology is a science that advances by leaps and bounds The impulse of new mathematical models along with the incorporation of computers to research has drawn a new reality with many methodological progresses that only a few people could imagine not too long ago Such progress has no doubt revolutionized the panorama of research in the behavioral sciences Structural Equation Models are a clear example of this Under this label are usually included a series of state-of-the-art multivariate statistical procedures that allow the researcher to test theoryguided hypotheses with clearly confi rmatory ends as well as to establish causal relations among variables Confi rmatory factor analysis, the study of measurement invariance, or the multitraitmultimethod models are some of the procedures that stem from this methodology In this sense, it would be diffi cult to fi nd a scientifi c journal that publishes empirical works in psychology that does not address some of these issues, so their current transcendence is undeniable The manual written by the Full Professor of the University of Ottawa, Barbara M Byrne, is a link in a series of books that address this topic Throughout her long academic trajectory, Professor Byrne developed interesting and popular work focused on bringing the researcher and the professional layman—and not so layman—closer to the diverse statistical programs available on the market for data analysis from the perspective of structural equation models (ie, LISREL, AMOS, EQS) (Byrne, 1998, 2001, 2006) Bearing this in mind, the main goal of this work is to introduce the reader to the basic concepts of this methodology, in a simple and entertaining way, avoiding mathematical technicisms and statistical jargon For this purpose, we used the statistical program Mplus 60 (Muthen & Muthen, 2007-2010), an extremely suggestive software that incorporates interesting applications The authoress provides a practical guide that leads the reader through illustrative examples of how to proceed step by step with the Mplus, from the initial specifi cations of the model to the interpretation of the output fi les On the one hand, we underline that the data used proceed from prior investigations and can be consulted in the Internet, offering the reader the possibility of practicing with them (http://wwwpsypresscom/sem-with-mplus/ datasets/); on the other hand, updating the information with novel and apt bibliographic references allows the reader to study in more depth the diverse topics that are presented in the manual, if he or she so desires The book consists of four sections, with a total of 12 chapters The fi rst section, Chapters 1 and 2, addresses introductory terms related to structural equation models and working with the Mplus program at a user-level The second unit focuses on data analysis with a single group In Chapter 3, the factor validity of the self-concept is tested by means of confi rmatory factor analysis In Chapter 4, the authoress performs a fi rst-order confi rmatory factor analysis, in which she examines the validity of the scores of the Maslach Burnout Inventory (MBI) in a sample of teachers In Chapter 5, the internal structure of the scores on the Beck Depression Inventory-II is analyzed by means of second-order confi rmatory factor analysis in a sample of Chinese adolescents In the next chapter, the complete model of structural equations is tested, and the authoress examines the causal relation established between diverse variables (ie, work climate, self-esteem, social support) and Burnout The third section of the manual is, in my opinion, the most interesting, not only because of the expansion of the study of measurement invariance in recent years but also because of the expansion it may possibly have in the future In this section, Professor Byrne goes into multigroup comparisons Specifi cally, in Chapter 7, she examines the factor equivalence of the MBI in two samples of teachers by means of the analysis of covariance structures In this chapter, she introduces relevant concepts, such as types of invariance (confi gural, metric, and strict) or the invariance of partial measurement In Chapter 8, she also analyzes measurement invariance, using for this purpose the analysis of mean and covariance structures This analysis, in comparison to the analysis of covariance structures, allows contrasting the latent means of two or more groups With this goal, she verifi es whether there is measurement invariance between the scores on the Self-description Questionnaire-I in Nigerian and Australian adolescents In Chapter 9, she proposes a complete model of structural equations in which she tests the causal structure through the procedure of cross validation Lastly, in the fourth section, she reveals three very interesting topics, that are also up-to-date and that, to some degree, go beyond the initial goal of the book, such as the multitrait-multimethod models, latent growth curves, and multilevel models Summing up, the work “Structural Equation Modeling with Mplus: Basic concepts, applications, and programming” is of enormous interest and utility for all professionals of psychology and related sciences who, without having exhaustive knowledge of the details of structural equation models, wish to test their hypothetical models by means of the Mplus program No doubt, this is a reference manual, a must-read that is accessible and that has a high degree of methodological rigor We hope that the readers

15,530 citations


"Mobile Commerce Switching Intention..." refers methods in this paper

  • ...3.4 Data analysis Data analysis was conducted using SEM in SPSS AMOS, and outcomes were evaluated using standard thresholds and interpretations as identified by Byrne (2016)....

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Book
01 Nov 2000
Abstract: Contents: Part I: Introduction. Structural Equation Models: The Basics. Using the EQS Program. Part II: Single-Group Analyses. Application 1: Testing for the Factorial Validity of a Theoretical Construct (First-Order CFA Model). Application 2: Testing for the Factorial Validity of Scores From a Measuring Instrument (First-Order CFA Model). Application 3: Testing for the Factorial Validity of Scores from a Measuring Instrument (Second-Order CFA Model). Application 4: Testing for the Validity of a Causal Structure. Part III: Multiple-Group Analyses. Application 5: Testing for the Factorial Invariance of a Measuring Instrument. Application 6: Testing for the Invariance of a Causal Structure. Application 7: Testing for Latent Mean Differences (First-Order CFA Model). Application 8: Testing for Latent Mean Differences (Second-Order CFA Model). Part IV: Other Important Topics. Application 9: Testing for Construct Validity: The Multitrait-Multimethod Model. Application 10: Testing for Change Over Time: The Latent Growth Curve Model. Application 11: Testing for Within- and Between-Level Variance: The Multilevel Model.

13,428 citations


Journal ArticleDOI
TL;DR: This study presents an extended technology acceptance model (TAM) that integrates innovation diffusion theory, perceived risk and cost into the TAM to investigate what determines user mobile commerce (MC) acceptance.
Abstract: This study presents an extended technology acceptance model (TAM) that integrates innovation diffusion theory, perceived risk and cost into the TAM to investigate what determines user mobile commerce (MC) acceptance. The proposed model was empirically tested using data collected from a survey of MC consumers. The structural equation modeling technique was used to evaluate the causal model and confirmatory factor analysis was performed to examine the reliability and validity of the measurement model. Our findings indicated that all variables except perceived ease of use significantly affected users' behavioral intent. Among them, the compatibility had the most significant influence. Furthermore, a striking, and somewhat puzzling finding was the positive influence of perceived risk on behavioral intention to use. The implication of this work to both researchers and practitioners is discussed.

2,089 citations


"Mobile Commerce Switching Intention..." refers background in this paper

  • ...For example, one study examined mobile commerce using a modified Technology Acceptance Model (TAM) framework (Wu & Wang, 2005)....

    [...]

  • ...Other studies have also identified convenience as a possible factor in mobile commerce choice, although these studies have not actually empirically tested the factor (Okazaki & Mendez, 2013; Wu & Wang, 2005)....

    [...]


Journal ArticleDOI
Abstract: The world of retailing has changed dramatically in the past decade. The advent of the online channel and new additional digital channels such as mobile channels and social media have changed retail business models, the execution of the retail mix, and shopper behavior. Whereas multi-channel was in vogue in the last decade in retailing, we now observe a move to so-called omni-channel retailing. Omni-channel retailing is taking a broader perspective on channels and how shoppers are influenced and move through channels in their search and buying process. We discuss this development conceptually and subsequently discuss existing research in this multi-channel retailing. We also introduce the articles in this special issue on multi-channel retailing and position these articles in the new omni-channel movement. We end with putting forward a research agenda to further guide future research in this area.

1,201 citations


Frequently Asked Questions (1)
Q1. What have the authors contributed in "Mobile commerce switching intentions in thai consumers ms. kedwadee sombultawee" ?

This research applies an extended Unified Theory of Adoption and Use of Technology ( UTAUT ) model to consumer intentions to switch from other retail channels to mobile commerce in Thailand. The implication of these findings is that mobile commerce providers need to focus on building social support for the technology itself, rather than relying on marketing tools like discounts or referral codes if they want to shift sales away from other retail channels.