Mobile Banking Adoption Among Rural Consumers: Evidence from India

Abstract

Recently, Financial Inclusion has received greater attention, as it is a key to economic growth and equality, growth of the business, and stability of financial and banking systems. In the process of understanding how technology is useful for financial inclusion, the study tries to understand the elements that prompt the perceived acceptability of mobile banking among the low income and low literate stratum. To explore these elements, the current piece of work utilized the Unified theory of acceptance and use of technology and with the addition of component of perceived risk. The research utilized convenience sampling to collect data from the respondents having Jan Dhan account. The results show that performance expectancy, effort expectancy, social influence, and perceived risk have a significant influence on intention to use mobile banking among financial inclusion stratum. Surprisingly, the facilitating conditions were found insignificant. The outcomes of the research can guide banks and technology houses to develop a more user-acceptable mobile banking system. Also, the outcome will help policymakers to enhance mobile banking system adoption for financial inclusion by considering influencing elements.

Examining the Impact of Customers’ Awareness, Risk and Trust in M-Banking Adoption

“…Our study found that PU, PEOU (Alalwan et al, 2018;Chawla & Joshi, 2020;Hassan et al, 2021;Kumar et al, 2020;Püschel et al, 2010;, CA (Alkhaldi, 2017;Hassan et al, 2021;Mutahar et al, 2018), PR (Gupta et al, 2017;Laukkanen & Kiviniemi, 2010;Mohapatra et al, 2020;Susanto et al, 2020) and PT (Engwanda, 2014;Kumar et al, 2020;Lin, 2011;Shankar et al, 2020;Tarhini et al, 2019) were found to be significant in m-banking adoption.…”

“…Though the m-banking channel is beneficial, compatible and easy to use, the degree of usage is determined by the level of risk associated with performing financing transactions on this channel (Chen, 2013;Laforet & Li, 2005;Yang & Zhang, 2009). Empirical studies found that customers' PR of m-banking channel influences their attitude, intention and adoption of online and m-banking services negatively (Brown et al, 2003;Mohapatra et al, 2020;Safeena et al, 2011;Sathye, 1999;Zhou, 2012). The perception of risk is derived from a customer's sense of confusion or anxiety about actions and the magnitude of the potential adverse effects of that behaviour (Kim et al, 2009;Luo et al, 2010;Mandrik & Bao, 2005;Shin, 2010;Swinyard & Smith, 2003).…”

Research on the Trust Mechanism of Individual Consumers in Rural Financial Markets Based on the Dynamic CGE Model

“…e second is the restriction of financial resources allocation. Quite a number of banking financial institutions lack sufficient attention to all kinds of loans in rural areas for their own economic interests and credit risk prevention [7,8]. e third is the restriction of payment and settlement system.…”

Factors Affecting Mobile Banking Adoption in Poland: An Empirical Study

“…For example, questioning the importance of the user's attitude towards new technology (ATU), here mobile banking, for the behavioral intentions associated with its acceptance or rejection resulted in the development of the TAM2 model (Venkattesh and Davis, 2000) and its application in the assessment of factors influencing the intention to adopt mobile banking (Gu et al, 2009;Teo et al, 2012;Chitungo and Munongo, 2013). On the other hand, recognizing that the perceived ease of use and usefulness of the new technology are not able to explain clearly and fully the individual intentions related to the acceptance or rejection of new technologies (Hanafizadeh et al, 2014, Zhou, 2011Riquelme and Rios, 2010;Wessels and Drennan, 2010) resulted in extending the classical set of factors by other elements, e.g., in the form of perceived trust (Kim et al, 2009;Zhou, 2011), Social influence / Social norms / Subjective norms (Mohapatra et al, 2020), Self-efficacy (SE) (Gu et al, 2009;Luarn and Lin, 2005), facilitating conditions (Pűschel et al, 2010), perceived cost of use (Chitungo and Munongo, 2013;Lema 2017), compatibility with lifestyle and device (Chawla and Joshi, 2017;AlSoufi, 2014), perceived risk (PR) (Alalwan et al, 2016;Hanafizadeh et al, 2014;Riquelme and Rios 2010;Shaikh and Karjaluoto, 2015) and Demographic factors (DEM) (Chakiso, 2019;Haider et al, 2018).…”