Mobile Commerce Switching Intentions in Thai Consumers
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.
Summary (3 min read)
- 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%.
- 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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Cites background or result from "Mobile Commerce Switching Intention..."
...Diverse studies have shown that social factor is influential in determining users’ acceptance and usage of IT [4, 17, 25]....
...According to , consumers do not prefer to use M-commerce for all types of transactions; instead, M-commerce is typically used for relatively simple transactions....
...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]....
"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)....
"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)....
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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.