Predicting Mobile Banking Acceptance and Loyalty in Chinese Bank Customers

Abstract

Mobile banking (m banking) is the breakthrough technology in banking sector which has significantly improved efficiency of banks and people's quality of life. Banks seem particularly interested in such systems that provide their customers with better services. However, acceptance of and loyalty to m banking depends on how effectively banks motivate their customers to adopt the technology and retain their continued use. The adoption rate in China is very low and quite a few studies have focused on issues related to m banking. The purpose of this study is to examine factors that affect m banking adoption and usage intentions of Chinese bank customers. The proposed model has extended the technology acceptance model (TAM). Data were collected through a field survey questionnaire and analyzed through partial least square structural equation modeling (PLS-SEM). The results showed that acceptance of and loyalty to m banking among Chinese bank customers was significantly and positively affected by resistance to change, perceived risk and low awareness of services, and perceived benefits. The results will be useful to retain existing users and attract new ones. This study is unlike past studies that merely studied short messaging service (SMS) banking and initial adoption or technological aspects of m banking. This study also provides Chinese banks with applicable strategies to effectively design and implement m banking; thus, it is expected to potentially contribute to prevailing literature, especially in the context of China, where few studies that address m banking acceptance and loyalty exist currently.

Social Distancing Intentions to Reduce the Spread of COVID-19: The Extended Theory of Planned Behavior

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The effect of individual factors on user behaviour and the moderating role of trust: an empirical investigation of consumers’ acceptance of electronic banking in the Kurdistan Region of Iraq

“…Moreover, other factors affect each of the steps, respectively, action by the technological leadership, e-trust (Salem et al 2019), e-loyalty (Esterik-Plasmeijer and Raaij 2017; Berraies et al 2016), customers' value, for online personalization, customers' concern, for privacy, and the propensity of technology adoption (Rahi et al 2019). The best prediction of the use of new technologies may require the testing of the principal factors in order to learn about the customers' satisfaction (Thakur 2014;, customer loyalty (Shankar and Jebarajakirthy 2019), word-of-mouth (WOM) (Sampaio et al 2017) intention, andadoption (Alalwan et al 2018a, b;Siyal et al 2019;Chauhan et al 2019), how customer use the system (Baabdullah et al 2019a, b), and focusing on the role of users' commitment (Yuan et al 2019), which is called selfservice technologies (Chaouali and El-Hedhli 2019). Table 1 presents a summary of the main findings of selected empirical studies based on TAM.…”

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