Examining consumer behavior towards adoption of quick response code mobile payment systems: transforming mobile payment in the fintech industry

The proliferation of mobile devices has revolutionized personal and professional life, extending into the fintech industry with mobile payment systems. QR code-enabled mobile payments offer speed and convenience, yet adoption remains limited. This study addresses this gap by exploring the factors influencing consumer behavior towards adopting QR code mobile payments in Saudi Arabia. The research integrates the TAM and TRA, incorporating perceived enjoyment, convenience, and innovativeness to understand consumer acceptance of QR code-enabled payment systems. A key contribution is the examination of transaction speed as a moderating factor between consumer attitude and intention to adopt QR code mobile payments. The following sections detail the theoretical framework, hypotheses, and methodology employed in this research.

The study draws upon established theories such as the TAM, which posits that perceived usefulness and ease of use influence technology adoption. The TRA highlights the roles of attitudes and subjective norms in shaping behavioral intentions. Existing literature supports the positive influence of convenience and innovativeness on mobile payment adoption. However, the role of transaction speed as a key factor and potential moderator in the relationship between attitude and behavioral intention has received limited attention. This study aims to bridge this gap by exploring the combined effects of TAM, TRA, and the unique influence of transaction speed and convenience on QR code adoption.

This quantitative study employed a survey design using a structured questionnaire distributed to 243 smartphone users in Jeddah, Saudi Arabia, with 216 valid responses (88% response rate). The questionnaire measured perceived usefulness, ease of use (adapted from Yan et al., 2021; Rahi et al., 2018), subjective norms (Liébana-Cabanillas et al., 2015), perceived convenience (Teo et al., 2015a), innovativeness (Slade et al., 2015; Rahi and Abd. Ghani, 2018), attitude towards QR code mobile payment, and intention to adopt (Yan et al., 2021; Rahi, 2022). Transaction speed was also measured (Yan et al., 2021; Teo et al., 2015a). A 7-point Likert scale was used. Sample size was determined using a priori power analysis (Rahi, 2018; Rahi, 2023), resulting in a required sample of 153 respondents. A pilot study with 20 respondents helped refine the questionnaire. Purposive sampling was employed to target smartphone users. Common method variance (CMV) was addressed using procedural (jumbled questionnaires) and statistical (Harman's single-factor test) remedies. Data were analyzed using structural equation modeling (SEM) with SmartPLS v.3.39, assessing measurement model validity (indicator reliability, factor reliability, convergent validity, and discriminant validity using Fornell and Larcker's criterion and HTMT analysis). The structural model tested the hypotheses using bootstrapping, examining effect sizes (f²) and predictive power (Q²). An importance-performance analysis (IPMA) was also conducted to determine the relative importance and performance of the factors influencing intention to adopt QR code mobile payment. Moderating effect of transaction speed was tested using a product indicator approach with simple slope analysis for further interpretation.

The SEM analysis revealed that perceived usefulness (β = 0.158, p < 0.005), perceived ease of use (β = 0.059, p = 0.046), subjective norms (β = 0.149, p < 0.000), convenience (β = 0.493, p < 0.000), and innovativeness (β = 0.104, p = 0.002) all positively and significantly influenced attitude towards QR code mobile payment. Attitude significantly predicted intention to adopt (β = 0.668, p < 0.000). The model explained 52.3% of the variance in attitude and 55% of the variance in intention to adopt. Effect size analysis indicated that convenience had the largest effect on attitude, while attitude had the largest effect on intention. Predictive power was substantial (Q² for attitude = 0.373; Q² for intention = 0.414). The IPMA identified attitude, convenience, and transaction speed as the most important factors influencing intention to adopt. The moderating analysis showed that transaction speed significantly moderated the relationship between attitude and intention (β = 0.138, p < 0.05), with a stronger positive relationship at higher levels of transaction speed.

The findings support the integration of TAM and TRA in understanding QR code mobile payment adoption. The significant influence of convenience aligns with the expectation that users seek ease and simplicity in mobile payment systems. The moderating effect of transaction speed highlights its importance in shaping the relationship between attitude and behavioral intention. The results confirm the importance of social influence (subjective norms) and perceived innovativeness in driving QR code adoption. The study contributes to the literature by providing a comprehensive model incorporating factors beyond the typical TAM and TRA constructs, specifically highlighting the role of transaction speed and convenience, and further supports the importance of user attitude as a key predictor of adoption behavior. The relatively high R-squared values suggest that the model is robust in explaining user attitudes and intentions.

This study provides a robust model for understanding consumer behavior towards QR code mobile payment adoption, integrating TAM, TRA, and novel factors such as convenience and transaction speed. Policymakers should prioritize improving perceived convenience, transaction speed, and addressing social norms to boost adoption. Future research could explore the role of other factors such as security, service quality, and cultural nuances in different contexts. Furthermore, longitudinal studies could provide deeper insights into the evolution of consumer attitudes and behavior over time.

The study's cross-sectional design limits causal inferences. The focus on Jeddah, Saudi Arabia, limits generalizability. The model, while comprehensive, may not encompass all relevant factors influencing adoption, such as security concerns or specific service quality aspects. Future studies could address these limitations through longitudinal designs, broader geographical scope, and inclusion of additional relevant factors.