Assortment on the Bases of Big-Data Analytics: A Quantitative Analysis on Retail Industry

Abstract

Big-data analytics are treated as the future of technology and essential for the retail sector. The technology is especially beneficial for optimising daily operations and supply chain practices. However, there is a wide gap in the related literature in developing and Asian countries on this subject. On the other hand, retailing is one of the fastest-growing industries globally. Therefore, this study is specifically designed to understand the role of big data concerning the organised retail sector of Pakistan. The study’s primary objective is to assess the significance of technology in augmenting assortment strategies. However, the mediation of advanced algorithms and moderation of skilled data scientists are included in the research construct to increase research relevance to the pragmatic world. Results were determined by applying Partial least square structured equation modeling (PLS-SEM). The findings indicated that big data is a prolific constituent to optimise assortment in the retail sector of Pakistan. However, the technology would not produce the desired results without applying advanced algorithms. This study accentuates the actuality that advanced algorithms are essential to be analysed to use big-data most effectively to retrieve new information. Further studies may also be conducted in devising a comprehensive model which includes all the potent variables associated with store-layout design.