To investigate factors which may affect consumers’ intention to use WeChat Pay
2. Study Rationale:
Mobile devices have turned out to be an essential gadget in recent years and it is expected that mobile devices will exceed world population in the future. Devices such as tablets and smartphones are no longer limited to providing entertainment to consumers; these devices now possess multiple features. Hew et al. (2015), view that mobile devices have become omnipresent in people’s lives, which in turn, have reshaped “consumer’s online shopping experiences”. Mobile applications in particular, have had an immense influence on consumer’s intention to buy either online or in retail outlets. Venkatesh, Thong and Xu (2016) developed the Unified Theory of Acceptance and Use of Technology (UTAUT) to understand the user intentions towards using an IT device.
In Hong Kong, the mobile app WeChat Pay has become one of the most extensively used mobile messaging services. It has evolved as the country’s biggest and most popular payments platform for mobile. According to a news report, more than 900 retail shops in HK have introduced WeChat Pay (Xinhuanet.com 2018). The mobile app now aims to extend its service in areas such as ticket booking for trains across China. The present paper will attempt to discuss the factors that influence consumer intention to make payments through WeChat pay in HK retail shops.
Objective 1 – Academic area
To identify the factors which may affect consumer intention to adopt mobile payment apps
Objective 2 – Exploring area
To investigate the influence of the factors (Performance, Effort Expectancy, Price Value, Facilitating Conditions, Habit, Social Influence, Hedonic Motivation) on consumer intention to apply Wechat Pay in HK
Objective 3 – Overall contribution
To provide insights for WeChat Pay marketers as for how to improve the adaptation of WeChat Pay in HK market
4.1 Mobile applications – an overview
In general, three types of mobile applications are there that include web apps, native apps and hybrid apps (Kumar and Vithani 2014). Web apps refer to the websites that are similar to native apps. Users are first directed towards the website URL where they are presented with the option of either to download the native app or continue with that web app. Native apps are those that are created for one operating system that is iOS and are downloaded by users on their smartphones (Hew et al. 2015). On the other hand, hybrid apps, as the name suggest, combine the features of both web and native apps. Web apps are enfolded by native app layers that form hybrid apps. Financial Times and Angry Birds are few of the many examples of hybrid apps.
The intention-based models that determine technology acceptance, implementation and usage include Theory of Reasoned Action (TRA), Theory of Planned Behavior (TBP) and the Technology Acceptance Model (TAM). In a study conducted by Aboelmaged and Gebba (2013), it was found that the TBP and TAM yielded positive results when it comes to analyzing consumer intention towards mobile payments. As per the diffusion of technology perspective, five traits of innovation influence consumer’s acceptance behavior that include, compatibility, complexity, observability, trailability and relative advantage. However, Truong (2013) argues that the diffusion of technology is more appropriate for examining diffusion throughout a nation rather than a single consumer’s adoption decisions.
Kang (2014) made use of the UTAUT2 and the Uses and Gratification Theory to comprehend the usage intention of mobile apps by consumers. However, the study that uses UTAUT2 to examine consumer’s intention to use mobile apps presently remains limited. Thus, the present paper tries to utilize UTAUT2 to reveal its applicability in the field of mobile applications.
Behavioral Intention – Venkatesh, Thong and Xu (2016) while explaining behavioral intention (BI), stated that it is a consumer’s intention to execute a specific act that predicts future behavior.
Performance Expectancy – It is defined as the level to which consumers believe that using technologies will produce outcomes of performance gain. According to Shibl, Lawley and Debuse (2013), PE defines the perception of consumers regarding the usefulness of any technology.
Effort Expectancy – It may be defined as the ease with which technology can be used (Thomas, Singh and Gaffar 2013).
Price Value – Escobar-Rodríguez and Carvajal-Trujillo (2014) define PV as the accord between price paid by the consumer for using a technology and the apparent benefits received.
Facilitating Conditions – FC is defined as the perception that technical and organizational infrastructure exists in order to assist technology use.
Habit –HA is the limit to which consumers tend to perform behaviors automatically. Hsu, Chang and Chuang (2015) have found that HA influences behavioral intention (BI) either directly or indirectly to use technology.
Social Influence – It refers to the consumers’ perception of the belief of significant others that they should use the technology.
Hedonic Motivation – As explained by Alalwan et al. (2015), HM refers to the pleasure or fun resulting from the use of any technology.
All these variables relate to the behavioral intention (BI) of the consumer in using a technology. In this research, the technology for which the BI is being analyzed is the WeChat Mobile App.
The UTAUT2 model is used to form the hypotheses for the study explaining the seven different factors that influence consumer usage intention. Since the age group was too narrow, it has been dropped from the model (Figure 1). Similarly, experience variable has also been omitted from the model since the study is cross-sectional and the one developed by Venkatesh, Thong and Xu (2012) had experience as an operational variable. In addition, use behavior has been removed for providing a simpler model with only the important variables included to study the factors affecting consumer intention in case of WeChat Pay in HK retail shops. The following hypotheses are formed for the research paper:
H1 – Performance Expectation (PE) has a positive effect on Behavioral Intention (BI)
H2 – Effort Expectancy (EE) has a positive influence on PE and consequently on BI to use WeChat
H3 – Price Value (PV) has a positive effect on BI to use WeChat
H4 – Facilitating Condition (FC) has a positive influence on BI to use WeChat mobile app
H5 – Habit (HA) of the consumer has a positive influence on BI to use WeChat
H6 – Social Influence (SI) has a positive effect on BI to use the mobile app WeChat
H7 – Hedonic Motivation (HM) has a positive influence on BI to use WeChat
The hypotheses taken in the study states the relationship between the independent variables and the dependent variable. In this current study, the performance expectation, effort expectancy, price value, facilitating condition, social influence and hedonic motivation are the different factors affecting the behavioral intention of the consumers. The behavioral intentions of the consumers are dependent on these factors and hypothesis testing will be used to validate the relationship. The conceptual framework clearly shows that the study will develop a model where the explaining capability of the independent variables on the dependent variable will determine the precision of the model.
The research would include primary and secondary research methodology.
6.1 Primary Research6.1.1 Research techniques
The study will use quantitative research, which can provide the researcher with important data. Questionnaires to conduct exit interviews will be prepared to obtain the quantitative data (Flick 2015). The questionnaire will be distributed to university students in HK to analyze the factors that affect consumer intention using WeChat Pay.
6.1.2 Sample Design
Target Respondent and Sample Size:
The target respondents chosen for the research are students of a university in Hong Kong who often visit retail shops to buy certain products. As the study is about understanding, the factors involved in affecting consumer intention towards using WeChat Pay, all the questions are directed towards m-devices and apps and hence the respondents are more fitting than many other consumers are (Humphries 2017). The sample size for the proposed study will be 100 students who are inclined towards using mobile apps especially WeChat.
Sampling Methods:
Systematic sampling method will be used to ensure unbiased selection of samples (Bryman 2015).
6.1.3 Potential Limitations and Contingency Plan
Possible errors committed by the researcher are included in the research.
Sample size selection:
The sample size is small and hence may affect the research outcome.
Time issue:
Possibility of failure in collecting adequate questionnaire is there due to limited time and small sample size. Hence, it is decided to extend the time for data collection to minimize the probable fault.
Secondary research will be used to provide supporting facts on academic area, WeChat profile and market trends of WeChat. All the documents that are used will be from academic journals, textbooks, government and company websites and newspapers.
8. References:
Aboelmaged, M. and Gebba, T.R., 2013. Mobile banking adoption: an examination of technology acceptance model and theory of planned behavior. International Journal of Business Research and Development, 2(1).
Alalwan, A.A., Dwivedi, Y.K., Rana, N.P., Lal, B. and Williams, M.D., 2015. Consumer adoption of Internet banking in Jordan: Examining the role of hedonic motivation, habit, self-efficacy and trust. Journal of Financial Services Marketing, 20(2), pp.145-157.
Bryman, A., 2015. Social research methods. Oxford university press.
Escobar-Rodríguez, T. and Carvajal-Trujillo, E., 2014. Online purchasing tickets for low cost carriers: An application of the unified theory of acceptance and use of technology (UTAUT) model. Tourism Management, 43, pp.70-88.
Flick, U., 2015. Introducing research methodology: A beginner’s guide to doing a research project. Sage.
Hew, J.J., Lee, V.H., Ooi, K.B. and Wei, J., 2015. What catalyses mobile apps usage intention: an empirical analysis. Industrial Management & Data Systems, 115(7), pp.1269-1291.
Hsu, M.H., Chang, C.M. and Chuang, L.W., 2015. Understanding the determinants of online repeat purchase intention and moderating role of habit: The case of online group-buying in Taiwan. International Journal of Information Management, 35(1), pp.45-56.
Humphries, B., 2017. Re-thinking social research: anti-discriminatory approaches in research methodology. Taylor & Francis.
Kang, S., 2014. Factors influencing intention of mobile application use. International Journal of Mobile Communications, 12(4), pp.360-379.
Kumar, A. and Vithani, T., 2014, May. A comprehensive mobile application development and testing lifecycle. In IT Professional Conference (IT Pro), 2014 (pp. 1-27). IEEE.
Shibl, R., Lawley, M. and Debuse, J., 2013. Factors influencing decision support system acceptance. Decision Support Systems, 54(2), pp.953-961.
Thomas, T.D., Singh, L. and Gaffar, K., 2013. The utility of the UTAUT model in explaining mobile learning adoption in higher education in Guyana. International Journal of Education and Development using Information and Communication Technology, 9(3), p.71.
Truong, Y., 2013. A cross-country study of consumer innovativeness and technological service innovation. Journal of Retailing and Consumer Services, 20(1), pp.130-137.
Venkatesh, V., Thong, J.Y. and Xu, X., 2012. Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS quarterly, pp.157-178.
Venkatesh, V., Thong, J.Y. and Xu, X., 2016. Unified theory of acceptance and use of technology: A synthesis and the road ahead.
Xinhuanet.com 2018. HK’s convenience stores introduce WeChat payment – Xinhua | English.news.cn. [online] Xinhuanet.com. Available at: https://www.xinhuanet.com/english/2017-08/15/c_136528100.htm [Accessed 5 Mar. 2018].
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