Social media analytics (SMA) practices are playing a vital role in improving online store performance by creating insights about customer preferences, trends, and behaviors. Social media analytics (SMA) practices are a critical contributor to boost online store performance by creating insights on customer preferences, trends and behaviors. The effective utilization of such analytics helps online retailers to adjust their marketing strategies, increase customer engagement, and ultimately boost their sales. This study examines the dynamic link between SMA and customer engagement in online retail firms with special focus on the moderating role of customer voice (promotive and prohibitive) in determining the performance outcomes. Specifically, it examines the impact of consumers' promotive and prohibitive voices on the relationship between customer engagement and online store performance. Using a time-lagged study and data obtained from 407 online stores across three survey waves, the findings show there is a strong positive relationship between SMA and customer engagement. In addition, both promotive and prohibitive customer voice are found to positively moderate the engagement-performance relationship, increasing the effect of engagement on operational outcomes. The study concludes with implications and future research directions.

Digital platforms are increasingly central to circular economy transitions, yet it remains unclear how platform design translates into measurable circular outcomes. This study addresses this gap by developing a socio-technical framework that explains how digital platform information transparency and interoperability enable circular value creation. Drawing on socio-technical systems theory, platform governance, and the dynamic capabilities perspective, the study conceptualizes platform design features as layered affordances that operate through governance and knowledge-based mechanisms. Using survey data from platform-enabled supply chain actors and analysing the model via PLS-SEM, the findings reveal that transparency and interoperability significantly enhance sustainability governance mechanisms, while transparency strengthens knowledge sharing intensity and interoperability supports collaborative decision-making. Sustainability governance mechanisms, in turn, drive both circular business model implementation and circular supply chain performance, whereas knowledge sharing intensity primarily supports business model transformation. Notably, collaborative decision-making does not directly improve circular supply chain performance, suggesting that collaboration without governance alignment remains insufficient. By unpacking the indirect pathways through which platform design shapes circular outcomes, the study advances platform governance and circular supply chain literature. It offers actionable insights for managers and policymakers seeking to design digital ecosystems that move beyond symbolic digitalization toward measurable circular performance and systemic value creation.

Purpose – Generative artificial intelligence (GenAI) is transforming management practices, enabling the free flow of knowledge and enhancing learning ecosystems from which education branches. While it brings many opportunities, it also causes some problems. Those problems are serious but small in scale. This study aims to examine how management educators perceive, use and adapt GenAI tools for their instructional and evaluative activities. Design/methodology/approach – To gather data, the researchers did 40 interviews with faculty members from different universities. The researchers used a range of theories in their study. Specifically, they used Gioia’s approach along with the knowledge management theory, unified theory of acceptance and use of technology 2, the diffusion of innovations social cognitive theory, as well as the theory of activities. Findings – Results paint a storyline. Some educators view GenAI as a game-changer and are finding ways to enhance student interaction while reducing the time spent creating learning materials. They highlight issues such as policies, ethics, over-reliance and threats to academic integrity. In essence, teachers are far from passive adopters. They also negotiate on mechanisms and approaches that are context-sensitive, assessing costs, benefits and the risks involved. Practical implications – To support the responsible use of GenAI, regulatory frameworks, adequate training protocols and effective monitoring systems are necessary. This research shows how a teacher’s voice can help higher education institutions to use AI to better effect without losing their standards and values. Originality/value – The research incorporates ideas from multiple frameworks that help clarify the enablers and constraints of GenAI adoption. This helps both researchers and practitioners in the field.