Abstract

Within the retail industry the continuing introduction of AI is generating considerable excitement. While there is a rapidly growing literature on the role of AI in retailing, how individual retailers have publicly reported on their introduction of AI has attracted little or no attention in the business and management literature. This article makes a contribution to addressing that gap by providing some simple illustrations of how four leading retailers, namely Amazon, Carrefour, J. Sainsbury and Walmart are developing their relationship with AI. The paper concludes that while the four retailers paint a very positive picture of the benefits AI will generate, there are also a number of issues surrounding the increasing use of AI within retailing that will require careful and vigilant management. These include ethical concerns, balancing personalization and privacy, cybersecurity, the upskilling challenges for retailers, impacts on their employees, sustainability and consumption, environmental problems and corporate social responsibility. This is an exploratory paper and is limited to a secondary research focus, but may provide a useful platform for future research endeavors that could include, for example, empirical research on one or more of the large retailers.

Abstract

Does digital transformation, as an important means for firms to gain market competitiveness, reduce the incentives for management tone manipulation? This paper examines the effect of corporate digital transformation on management tone manipulation and its mechanism from the perspectives of psychology and behavioral finance. The results show that the relationship between digital transformation and tone of voice manipulation is inverted ‘U’ shape, with digital transformation increasing the degree of tone of voice manipulation in the early stage and decreasing it in the later stage. The mediation test concludes that digital transformation mainly affects management tone manipulation by influencing the level of information asymmetry and financing constraints. In addition, the effect of digital transformation on management tone manipulation is more pronounced in firms with more myopic management. This paper examines the internal logic of the impact of digital transformation on management tone manipulation from the perspective of management, reveals the ‘double-edged sword’ effect of digital transformation on management tone manipulation, and enriches the literature on digital transformation and management tone manipulation.

Abstract

Understanding consumer purchasing behavior is crucial for businesses aiming to enhance customer engagement and optimize marketing strategies. In today’s digital economy, traditional marketing approaches are becoming less effective due to evolving consumer behaviors, the rise of online communities, and the widespread use of ad-blocking software. To remain competitive, businesses must adopt data-driven strategies to analyze consumer preferences and tailor their marketing efforts accordingly. Machine learning provides a powerful tool for predicting consumer purchasing behavior, enabling businesses to anticipate customer needs and implement targeted marketing campaigns. Previous studies have demonstrated the effectiveness of machine learning in consumer analysis, particularly in customer segmentation and purchase prediction. However, while much research focuses on technical model optimization, relatively few studies have applied machine learning specifically for marketing prediction and strategic decision-making. This study addresses that gap by leveraging machine learning to analyze consumer purchasing behavior and generate practical insights for marketing strategies and business applications. Using a dataset of 4680 transactions, we employ Generalized Linear Models (GLM), Logistic Regression, Random Forest, and XGBoost to predict repurchase behavior within a specified timeframe. Our objective is to provide practical implications for businesses, such as improving targeted promotions, refining customer segmentation, and enhancing demand forecasting.

Abstract The integration of sustainability targets and their connection to executive compensation is emerging as a new facet of corporate strategies in response to the low-carbon transition, ESG-driven demands from institutional investors, regulatory mandates, and commitments to corporate social responsibility (CSR). The purpose of this research is to explore issues related to sustainability target-setting and the associated methodologies. This paper summarizes the prior work on target setting and sustainability targets, and the results from previous studies on the reason and rationality of setting sustainability targets were discussed. It also pointed out the issues in sustainability targets and related incentive design. Based on this, several suggestions are offered to assist companies in developing and setting sustainability targets and goals.

Abstract

This paper examines the various determinants of corporate social responsibility (CSR) and assesses the impact of each on CSR practices. Drawing on a solid theoretical framework, we consider factors such as firm characteristics and governance mechanisms. In addition to the CSR determinants, we also explain the effect of the COVID-19 pandemic. This research aims to enrich the CSR literature and provide concrete perspectives for professionals wishing to strengthen their commitment to CSR.