Developing a Customer Confusion Management Model Based on the Internet of Things in the Banking Industry

The aim of this study is to develop a customer confusion management model based on the Internet of Things (IoT) in the banking industry. This research adopts a mixed-methods approach (qualitative and quantitative). The qualitative strategy used in this study is thematic analysis, while the quantitative strategy employed is survey-based. The statistical population for the qualitative phase consists of 17 managers of Bank Mellat branches in Tehran. In the quantitative phase, the population includes all customers of Bank Mellat in Tehran, with 384 questionnaires distributed and collected through random sampling. The findings from the qualitative thematic analysis identified key themes and descriptive codes from interview texts, including: sources of confusion, consequences of customer confusion, internet infrastructure, influential customer characteristics that increase confusion, organizational education approaches to IoT, and organizational factors impacting confusion management. The results of structural equation modeling using Smart PLS software demonstrated that the customer confusion management model based on IoT in the banking industry exhibits strong validity and fit. Therefore, IoT facilitates the appropriate investment in expanding software and hardware infrastructure across all bank branches, as well as the efforts of staff and management to provide quick and informed access to IoT services. The aim of this study is to develop a customer confusion management model based on the Internet of Things (IoT) in the banking industry. This research adopts a mixed-methods approach (qualitative and quantitative). The qualitative strategy used in this study is thematic analysis, while the quantitative strategy employed is survey-based. The statistical population for the qualitative phase consists of 17 managers of Bank Mellat branches in Tehran. In the quantitative phase, the population includes all customers of Bank Mellat in Tehran, with 384 questionnaires distributed and collected through random sampling. The findings from the qualitative thematic analysis identified key themes and descriptive codes from interview texts, including: sources of confusion, consequences of customer confusion, internet infrastructure, influential customer characteristics that increase confusion, organizational education approaches to IoT, and organizational factors impacting confusion management. The results of structural equation modeling using Smart PLS software demonstrated that the customer confusion management model based on IoT in the banking industry exhibits strong validity and fit. Therefore, IoT facilitates the appropriate investment in expanding software and hardware infrastructure across all bank branches, as well as the efforts of staff and management to provide quick and informed access to IoT services.

Developing a Customer Confusion Management Model Based on the Internet of Things in the Banking Industry Keywords:

Customer confusion management , banking industry , Internet of Things