Analyzing the Adoption of E-payment Services in Smart Cities using Demographic Analytics: The Case of Dubai

Abstract

This paper is an extension of previous research that has been done on factors affecting digital payment adoption in the UAE. This study focuses on analyzing which relevant demographic factors affect new e-payment technologies, mainly in the smart city Dubai, with more complexities and dynamics of variables that affect users' behavior toward adopting new technologies. The current research included a wider range of demographic factors compared to previous studies. Quantitative methods were conducted using a survey of 270 individuals living and working in Dubai. This study revealed that e-payment adoption is very high, which could be aligned with the national digital transformation strategy of the UAE. The results of the chi-square test for independence indicate that using e-payment technologies is positively associated with the level of education and the level of income. This is confirmed by the fact that the UAE's demographic shape is identified by its high-income groups, positively influencing the residents' e-payment adoption. Surprisingly, the significant results for independence were not found between using e-payments and the gender, marital status, age group, and the current professional position in Dubai. This research's contribution adds to both academia and industry in the digital transformation and technology adoption field. Based on the results, it is recommended for decision-makers to leverage education, digital literacy, and income to accelerate moving toward a cashless economy. However, not having statistically significant differences between the rest demographic variables and adoption will encourage businesses and e-payment service providers to deliver new innovative e-payment models and technologies in a smart city context.

Understanding Consumers’ Barriers to Using FinTech Services in the United Arab Emirates: Mixed-Methods Research Approach

“…Despite the abundance of FinTech options that are accessible, adoption is highly selective, and only a small number of these have been a success. An example is e-payment services used by 84.3% of users, fuelling the growth in the usage of FinTech services [16]; others have shown lower adoption, including P2P money transfer [31%], robot advisor [27%], InsurTech [19%], crowdfunding [17%], and P2P insurance [10%], according to the national survey by Statista (2020) [15]. This duality presents the possible issues or obstacles in the use of FinTech.…”

Mixed-methods approach to investigating the diffusion of FinTech services: enriching the applicability of TOE and UTAUT models

“…Despite the vast array of available FinTech possibilities, the acceptance of such innovation remains very discerning, with only a few achieving notable success. E-payment services have been widely adopted by 84.3% of users, contributing to the expansion of FinTech services (Said et al , 2021). In contrast, it has been observed through a national study by Statista (2021) that several FinTech services have exhibited comparatively lower rates of acceptance.…”