Qualitative Analysis of the Impact of Big Data on Financial Decision-Making in Listed Companies

Abstract

This study aims to investigate the impact of big data on financial decision-making in listed companies. This qualitative study employed an inductive content analysis approach. Data were collected through semi-structured interviews with 29 financial managers, financial analysts, and data specialists from listed companies in Tehran. Participants were selected using purposive sampling, and interviews continued until theoretical saturation was reached. The collected data were analyzed using NVivo software, and open, axial, and selective coding were applied to extract the main themes. The results revealed that big data plays a crucial role in enhancing the accuracy of financial decision-making, reducing cognitive biases, improving market trend predictions, and increasing responsiveness to economic changes. Moreover, utilizing large-scale data reduces informational risks and enhances financial reporting transparency. However, challenges such as high infrastructure costs, complexity in data analysis, shortage of skilled personnel, and security concerns were identified as significant barriers to the adoption and effective use of big data in listed companies. The use of big data can lead to more informed and data-driven financial decision-making in listed companies. However, for effective utilization of this technology, challenges related to technical infrastructure, data security, and human expertise must be addressed. The findings of this study can help financial managers develop data-driven strategies to optimize financial decision-making.