Digital Payments Adoption: An Analysis of Literature

Abstract

Digital payments (mainly enabled by mobile devices) have huge potential to change lives of millions of people in developing countries by offering financial services to the unbanked masses. Despite its potential digital payment methods have not been widely and successfully adopted in the developing countries. In order to ascertain the various drivers and inhibitors behind digital payment adoption, this study did a review of research on digital and mobile payment adoption and use. Results of this literature analysis revealed performance expectancy/perceived usefulness as most significant determinant of consumer's behavioral intention to use mobile payments followed by perceived ease of use (PEOU). Perceived risk was found as major inhibitor to the adoption of mobile payments. Also majority of studies employed TAM and its extension to understand consumer adoption to mobile payment followed by UTAUT.

Factors Influencing Malaysian Consumers’ Intention to Use Quick Response (QR) Mobile Payment

“…Considering that QR mobile payment is still at an early stage and is yet to receive widespread attention from consumers, there is a need to understand factors affecting the adoption of this payment technology Patil, Dwivedi & Rana 2017) especially in emerging markets where such technology is new (Liébana-Cabanillas et al 2015) such as in Malaysia. Therefore, this study attempts to address this gap by investigating the factors influencing the intention to use QR mobile payment in Malaysia.…”

“…Many researchers have also recommended that studies should also look into other different technology in mobile payment such as QR code payment Liébana-Cabanillas et al 2014;Patil et al 2017) especially considering the emergence of QR mobile payment in different countries. However, studies concerning QR mobile payment adoption remains limited (Liébana-Cabanillas et al 2015).…”

“…Notwithstanding different models that exist in the literature to explain consumers' technology adoption behaviour, TAM remains to be one of the most influential models (Davis, Bagozzi & Warshaw 1989;Hong, Thong & Tam 2006) and considered to be relatively robust (Lu et al 2008) which serves as a useful model of technology acceptance behaviour (Kim, Mirusmonov & Lee 2010;Legris, Ingham & Collerette 2003) employed in numerous studies across different contexts of technology (Diniz, Porto de Albuquerque & Cernev 2011). Similarly, TAM has been employed by a majority of researchers in order to understand consumers' adoption behaviour in mobile payment technology (Dahlberg et al 2015;Patil et al 2017). Sook Harn et al (2014) used TAM in their study because it is a multi-attribute model that predicts user's intention to use a technology based on their perception of user-friendliness and usefulness of the system.…”

Digital Payments Adoption Research: A Meta-Analysis for Generalising the Effects of Attitude, Cost, Innovativeness, Mobility and Price Value on Behavioural Intention

“…Further details about these 23 studies have been provided in both Tables 1 and 2. The second step of this study was to undertake a narrative review for descriptively analysing 23 studies focussing on effect of each independent construct on behavioural intention, which is presented in Section 3. This was then followed by undertaking met-analysis (quantitatively integrating and synthesising results from existing research) for the purpose of generative cumulative effect sizes and significance values (Dwivedi et al 2017). It is a methodical alternative to a qualitative and descriptive literature analysis and praised by many researchers for being better than a literature analysis (Rosenthal, 1991;Rosenthal and DiMatteo, 2001;Wolf, 1986).…”

The effect of electronic payments security on e-commerce consumer perception: An extended model of technology acceptance

“…The emergence of e-payments security motivated many studies investigated its aspects such as use intention, user satisfaction, and the perceived obstacles to the technology behind e-commerce's acceptance. Unfortunately, those studies focused more on identifying and giving a little attention to exploring a fit model that explains its determinants and consequences (Hamad, Elbeltagi, Jones, & El-Gohary, 2015;Patil, Dwivedi, & Rana, 2017). Salisbury et al (2001) initially investigated a security effect on technology user perception in a linear regression model and found that only perceived security has a significant effect on customers' purchase intention.…”