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ISSN 2039-9340 (print)
Mediterranean Journal of
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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
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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
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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
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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
