DETERMINANTS OF THE INTENTION TO USE BIG DATA ANALYTICS IN BANKS AND INSURANCE COMPANIES: THE MODERATING ROLE OF MANAGERIAL SUPPORT

Abstract

Aim/Purpose The aim of this research paper is to suggest a comprehensive model that incor-porates the technology acceptance model with the task-technology fit model, information quality, security, trust, and managerial support to investigate the in-tended usage of big data analytics (BDA) in banks and insurance companies. Background The emergence of the concept of "big data," prompted by the widespread use of connected devices and social media, has been pointed out by many profes-sionals and financial institutions in particular, which makes it necessary to assess the determinants that have an impact on behavioral intention to use big data an-alytics in banks and insurance companies. Methodology The integrated model was empirically assessed using self-administered question-naires from 181 prospective big data analytics users in Moroccan banks and in-surance firms and examined using partial least square (PLS) structural equation modeling. The results cover sample characteristics, an analysis of the validity and reliability of measurement models' variables, an evaluation of the proposed hypotheses, and a discussion of the findings. Contribution The paper makes a noteworthy contribution to the BDA adoption literature within the finance sector. It stands out by ingeniously amalgamating the Tech-nology Acceptance Model (TAM) with Task-Technology Fit (TTF) while under-scoring the critical significance of information quality, trust, and managerial support, due to their profound relevance and importance in the finance domain. Thus showing BDA has potential applications beyond the finance sector. Findings The findings showed that TTF and trust's impact on the intention to use is con-siderable. Information quality positively impacted perceived usefulness and ease of use, which in turn affected the intention to use. Moreover, managerial sup-port moderates the correlation between perceived usefulness and the intention to use, whereas security did not affect the intention to use and managerial sup-port did not moderate the influence of perceived ease of use. Recommendations for Practitioners The results suggest that financial institutions can improve their adoption deci-sions for big data analytics (BDA) by understanding how users perceive it. Users are predisposed to use BDA if they presume it fits well with their tasks and is easy to use. The research also emphasizes the importance of relevant infor-mation quality, managerial support, and collaboration across departments to fully leverage the potential of BDA. Recommendations for Researchers Further study may be done on other business sectors to confirm its generaliza-bility and the same research design can be employed to assess BDA adoption in organizations that are in the advanced stage of big data utilization. Impact on Society The study's findings can enable stakeholders of financial institutions that are at the primary stage of big data exploitation to understand how users perceive BDA technologies and the way their perception can influence their intention to-ward their use. Future Research Future research is expected to conduct a comparison of the moderating effect of managerial support on users with technical expertise versus those without; in addition, international studies across developed countries are required to build a solid understanding of users' perceptions towards BDA.