Are divestitures really just the “flip side” of acquisitions? Both divestiture and acquisition are important processes for firm scope change. Frequently, these processes are considered to be “two sides of the same coin” wherein a divestiture is simply an acquisition performed “in reverse.” In contrast to this perspective, the authors submit that these two corporate strategic processes have fundamental differences in their motivations, implementation, and ramifications.

Heuristics play an important role in organizational decision-making. Although management and organizational scholars have contributed significantly to our understanding of heuristics in organizations over the past seven decades, the literature has become fragmented over time. Three parallel streams of research—(1) heuristics and biases, (2) fast-and-frugal heuristics, and (3) simple rule heuristics—have emerged with somewhat conflicting views on the origins, use, and implications of heuristics.

This study builds on the knowledge spillover theory of entrepreneurship to examine the factors that influence the decision of latent entrepreneurs to move from opportunity recognition to opportunity exploitation and emergent entrepreneurship.

Past research in operations management and marketing on inventory levels and product variety has predominantly focused on their effects on brand performance indicators, such as sales and market share, while overlooking the influence on consumers’ perceptions of brands. Brand perceptions, encompassing reputation, quality, credibility, and emotional associations, go beyond typical revenue metrics and offer foresight into a brand’s future performance. Hence, understanding the effects of inventory and product variety on brand perceptions is crucial, and that constitutes the main contribution of this paper.

This article examines the role of Generative Artificial Intelligence (GenAI) in the context of marketing education, highlighting its substantial impact on the field. The study is based on an analysis of how GenAI, particularly through the use of Large Language Models (LLMs), functions. We detail the operational mechanisms of LLMs, their training methods, performance across various metrics, and the techniques for engaging with them via prompt engineering.