An Artificial Intelligence-Based Approach to Model User Behavior on the Adoption of E-Payment

An Artificial Intelligence-Based Approach to Model User Behavior on the Adoption of E-Payment

P. C. Lai, Dong Ling Tong
Copyright: © 2022 |Pages: 15
DOI: 10.4018/978-1-7998-9035-5.ch001
OnDemand:
(Individual Chapters)
Available
$37.50
Current Special Offers
No Current Special Offers
TOTAL SAVINGS: $37.50

Abstract

The growth of internet usage during the COVID-19 pandemic creates a new business avenue on e-payment for organizations to expand their business horizon. However, challenges on user-related factors arise with this new avenue. This study aims to investigate the association of these factors on the adoption of e-payment services using machine learning inference. An artificial intelligence-based analysis pipeline is established to study the impact of individual items of the dependent factors on the usage of e-payment. In the analysis pipeline, the important items were extracted using a hybrid artificial intelligence method, and the relationships of these items were inferred using the tree algorithm. The results show that items related to expectancy, facilitating conditions, user attitude, and performance expectancy affect usage of e-payment services. Participants below 25 years old require a gamification solution to adopt e-payment, and participants above 40 years old need social support.
Chapter Preview
Top

Unified Theory Of Acceptance And Use Of Technology (Utaut)

The UTAUT research model introduced by Venkatesh et al. (2003) is one of the profoundly used models to design survey questions related to user behavior. It has been applied in different areas including the Internet (Gupta et al., 2008), mobile banking (Zhou et al., 2010), and digital-learning contexts (Pynoo et al., 2011) to study consumer behavior on the adoption of technology. The original UTAUT model contains three key factors, which are social influence, effort expectancy, and performance expectancy (Venkatesh et al., 2003). The extended UTAUT includes additional factors, which are attitude, anxiety, and self-efficacy (Yun et al., 2011).

Performance expectancy insinuates the degree/extent to which a user believes that using the system will help the user to accomplish gains in job performance (Venkatesh et al., 2003). This factor is akin to perceived usefulness from the technology acceptance model (TAM) or stimulus research model (SRM). It is distinguished to be an essential attribute in influencing the user’s intention to use any system (P. Lai, 2019). According to Oliveira et al. (2014), task-technology fit influences performance expectancy and adoption intention of mobile banking environmental factors, and performance expectancy influence initial trust that in turn influences adoption intention.

Complete Chapter List

Search this Book:
Reset