Investor’s satisfaction in artificial intelligence-supported share trading apps: mediation and parallel moderation analysis

Abstract

Recognizing the critical role of artificial intelligence (AI) in share trading and considering the technical complexities in artificial intelligence-supported share trading apps (AI-STA), the paper investigates retail investor’s satisfaction (SFN) in AI-STA using expectation disconfirmation theory. The researchers proposed the mediation of information quality (IQ), and parallel moderations of self-efficacy and disconfirmation in perceived usefulness (PU) and SFN relationship. The responses were collected using a carefully crafted survey instrument from 432 users of AI-STA through purposive sampling. Thereafter checking the psychometric properties, the data was analyzed using AMOS and SPSS-Hayes PROCESS Marcos (model # 2 and 4). The results of the hypothesis testing indicate that PU influences SFN. The mediation effect of IQ was also found significant in the relationship between PU and SFN. Moreover, the result supported the parallel moderating effects of self-efficacy and disconfirmation in the PU and SFN relationship. The mediation of IQ and parallel moderation of self-efficacy and disconfirmation is a noble contribution to the knowledge of expectation disconfirmation theory. The study acknowledges practical implications to enhance the interface of AI-STA for SFN, by incorporating real- time financial news and 24X7 customer support. © 2024 Taylor & Francis Group, LLC.