• Reducing edible food waste in the UK food manufacturing supply chain through collaboration

      Cao, Guangming; Shah, Pramitkumar; Ramanathan, Usha; Ajman University; University of Bedfordshire; Nottingham Trent University (Springer, 2020-07-16)
      While a third of food produced is wasted at the pre-consumer stage in the UK food manufacturing supply chain (FMSC) and has had significant negative economic and environmental impacts, many challenges remain in how to reduce edible food waste. This chapter addresses the problem of whether and to what extent FMSC collaboration could lead to the reduction of edible food waste. Evidence in the literature suggests that despite an increasing attention having been paid to reduce edible food waste, there is a scarcity of studies that focus on the relationship between FMSC collaboration and the reduction of edible food waste. Consequently, the aim of this chapter is to develop a research model that explains the relationships among FMSC collaboration, collaborative effectiveness and the reduction of edible food waste. The model is underpinned by the relation view and has been empirically tested with 122 survey responses from food manufacturing firms, using structural equation modelling. The findings indicated that FMSC collaboration has a positive effect on collaborative effectiveness, which in turn results in the reduction of edible food waste during production, processing and storage. Thus, an important implication of this chapter is that the UK FMSC members would benefit from closely collaborating with their supply chain partners to achieve greater collaborative effectiveness and thereby reducing edible food waste.
    • 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.
    • Understanding managers’ attitudes and behavioral intentions towards using artificial intelligence for organizational decision-making

      Cao, Guangming; Duan, Yanqing; Edwards, John S.; Dwivedi, Yogesh Kumar; Ajman University; University of Bedfordshire; Aston University; Swansea University (Elsevier, 2021-06-08)
      While using artificial intelligence (AI) could improve organizational decision-making, it also creates challenges associated with the “dark side” of AI. However, there is a lack of research on managers’ attitudes and intentions to use AI for decision making. To address this gap, we develop an integrated AI acceptance-avoidance model (IAAAM) to consider both the positive and negative factors that collectively influence managers’ attitudes and behavioral intentions towards using AI. The research model is tested through a large-scale questionnaire survey of 269 UK business managers. Our findings suggest that IAAAM provides a more comprehensive model for explaining and predicting managers’ attitudes and behavioral intentions towards using AI. Our research contributes conceptually and empirically to the emerging literature on using AI for organizational decision-making. Further, regarding the practical implications of using AI for organizational decision-making, we highlight the importance of developing favorable facilitating conditions, having an effective mechanism to alleviate managers’ personal concerns, and having a balanced consideration of both the benefits and the dark side associated with using AI.