• Board gender diversity and organizational determinants: empirical evidence from a major developing country

      Saeed, Abubakr; Sameer, Muhammad; Raziq, Muhammad Mustafa; Salman, Aneel; Hammoudeh, Shawkat (Taylor and Francis, 2018-09-21)
      This article seeks to identify and analyze the organizational determinants of women presence on Indian corporate boards. Using a sample set of 294 Indian firms between years 2004–2014, Tobit regression analysis indicates that firm size, family ownership and affiliation with the high-tech sector exhibit positive association with the number of female directors on corporate boards. Further, we do not find any significant impact of state-ownership on the number of women on those boards. Notably, the effects of the organizational variables are more pronounced for the proportion of female non-executive directors, as compared to female executive directors. We conclude that understanding the organizational characteristics in conjunction with business environment can provide useful insights into state of board gender diversity, particularly in developing countries.
    • Retail analytics: store segmentation using rule-based purchasing behaviors analysis

      Bilgic, Emrah; Cakir, Ozgur; Kantardzic, Mehmed; Duan, Yanqing; Cao, Guangming; Iskenderun Technical University; Marmara University; University of Louisville; University of Bedfordshire; Ajman University (Taylor and Francis, 2021-04-29)
      Retailers are facing challenges in making sense of the significant amount of data for better understanding of their customers. While retail analytics plays an increasingly important role in successful retailing management, comprehensive store segmentation based on a Data Mining-based Retail Analytics is still an under-researched area. This study seeks to address this gap by developing a novel approach to segment the stores of retail chains based on “purchasing behavior of customers” and applying it in a case study. The applicability and benefits of using Data Mining techniques to examine purchasing behavior and identify store segments are demonstrated in a case study of a global retail chain in Istanbul, Turkey. Over 600K transaction data of a global grocery retailer are analyzed and 175 stores in İstanbul are successfully segmented into five segments. The results suggest that the proposed new retail analytics approach enables the retail chain to identify clusters of stores in different regions using all transaction data and advances our understanding of store segmentation at the store level. The proposed approach will provide the retail chain the opportunity to manage store clusters by making data-driven decisions in marketing, customer relationship management, supply chain management, inventory management and demand forecasting.