Awareness as a Moderator for P2P Payment Adoption

Abstract

Objective . The purpose of this research is to investigate technology awareness as a moderating factor in person-to-person (P2P) payment adoption through a modified Unified Theory of Acceptance and Use of Technology (UTAUT) model. Method . This study utilizes a survey instrument deployed via Amazon Mechanical Turk (MTurk) to investigate eight constructs (IVs) on behavioural intent (DV) to adopt P2P payments. Data analysis is done using Structural Equation Modeling to confirm path significance between constructs and behavioural intent. Findings . The results show technology awareness negatively moderates social influence on adoption of P2P payments. When technology awareness as a moderator is low, social influence is significant in influencing adoption. When technology awareness is high, social influence is minimal. Additionally, awareness is also a strong antecedent in behavioural intent to adopt P2P payments. The research also indicates the relationship between effort expectancy and performance expectancy is confirmed within the investigative framework. The results also indicate facilitating conditions, attitude toward use and visibility are also significant antecedents to behavioural intent to adopt P2P payments. These findings are noteworthy in understanding P2P payment adoption across a wide demographic. In areas that lag in P2P payment adoption, social influence might be used to increase behavioural intent through mechanisms such as social media. Value . This study differentiates itself from limited research on banking technology adoption in that it explores P2P payment adoption specifically. The research utilizes a modified theoretical framework for technology adoption and includes awareness as a moderating variable. The findings suggest P2P payment adoption can be increased when awareness is low through social influence. In addition, a novel approach for survey deployment (Amazon MTurk) was conducted and the results indicate a high degree of sample data quality consistent with previous research.