Applications and Challenges of Big Data in Retail Industry: Proposing a Decision Making Model for Implementation

Today, large volumes of data that are generated from various sources with high velocity and variety, has led to innovation and transformation in many industries. Many industries, such as the retail industry, have sought to use big data technology due to the growing importance of creating value from data in this industry. But many industry leaders face a number of decision-making dilemmas for applying this technology, as well as the challenges of implementing it. Therefore, the main purpose of this study is to determine the big data applications in the retail industry that has fewer implementation challenges. In this research, using the meta-synthesis method with systematic search in reputable scientific databases, first, related articles on how to use big data technology in the retail industry have been examined. Then, using the content analysis method, bigdata applications in the retail industry have been analyzed and categorized, by identifying the criteria related to the challenges of big data applications and using quantitative multi-criteria decision-making methods -a combination of BWM, DEMATEL and TOPSIS methods, the priority of big data applications in the Iranian retail industry has been determined. The findings of this study show that the three applications of big data including determining market trends, determining the optimal product portfolio and micro-segmentation of retail customers have the least challenge and therefore the highest priority and three applications of data-driven cooperation, elimination of counterfeit products and the layout of the store space had the most challenge and the least priority in the Iranian retail industry.