Personal financial management technology: extending UTAUT2 to understand the determinants of the acceptance and use

Abstract

As researchers explore interventions to improve financial decisions beyond financial education and access to financial advisors, experts believe that technology will reshape the financial services industry by democratizing access to insights in real time (Lee & Shin, 2018). Personal financial management (PFM) technology is a type of financial technology with the opportunity to influence responsible financial behavior at scale, as it enhances consumer awareness and provides targeted recommendations (Li & Forlizzi, 2010). PFM technology includes common features such as net worth tracking, budgeting, credit score monitoring, investment tracking, and goal planning. PFM technology collects, consolidates, and presents financial data in a concise user interface on a website or through a mobile application (Dorfleitner et al., 2016). Consumers access PFM technology through standalone tools such as Mint.com or as an integrated feature provided by their financial institution (Tajimi, 2021). PFM technology can only drive change if individuals accept and use this innovative technology. So, understanding the factors that influence this technology’s adoption is critical to future innovation development. This study leveraged the extended unified theory of acceptance and use of technology (UTAUT2) and a systematic literature review of studies that used unified theory of acceptance and use of technology (UTAUT) or UTAUT2 to identify key variables that influenced consumer financial technology adoption that are both part of UTAUT2 and extensions. The combination of the broader information systems review and concentrated focus on consumer financial technology served as the foundation for the conceptual framework, hypotheses, and analysis. To test the hypotheses, this study leveraged primary data collection using a survey specifically designed to collect the preceding measures. After collecting responses, a strict quality control procedure was implemented to ensure high-quality responses were used in the PLS-SEM analysis. The analysis followed the steps outlined by Hair et al. (2019), including an evaluation of the measurement model, an evaluation of the structural model, and assessment of predictor relationships. Seven relationships were statistically significant in the model. Performance expectancy, hedonic motivation, habit, gender, and number of financial accounts have a positive effect on PFM technology use. Age has a negative effect on PFM technology use and number of financial accounts has a positive moderating effect on the relationship between habit and PFM technology use. An importance performance map analysis found that hedonic motivation and habit are important predictors of PFM technology use but with room for improvement. Three practical implications from this study could have a positive effect on financial institutions and consumers. First, PFM technology providers should use gamification to improve hedonic motivation and make using PFM technology a habit. Second, PFM technology providers should communicate both the financial and intrinsic benefits of using PFM technology when acquiring consumers. Third, financial institutions should invest in PFM technology, as it attracts consumers with more financial accounts that are more likely to be a fit for a variety of financial products.