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Research on grand challenges in the management literature is vibrant and growing. Given that the term “grand challenges” was first invoked in our field 10 years ago, it is timely to reflect on how we came to this point – and where we might go from here. In this article, we first explore the origins of the concept of grand challenges in order to trace core assumptions and developments and understand how they shape the current conversation about grand challenges in management scholarship. We next convey findings from our review of 161 papers that cite the editorial for a grand challenges special issue (George, Howard-Grenville, Joshi, & Tihanyi, 2016), uncovering four ways in which papers are shaping the conversation on grand challenges. Finally, based on our perspective on how we got here and where we are now, we make several suggestions for what should come next in driving forward research on grand challenges. We urge scholars to go beyond the study of collaboration for tackling grand challenges and shift toward a more critical, yet generative, exploration of their construction, persistence, and unintended consequences. We also call for increased attention to theorizing grand challenges to guide practitioners’ understanding of the nature of the thing they are trying to address. In these ways, we hope to inspire management scholars to leverage expertise on processes – not content per se – that shape how grand challenges manifest and how they may be tackled.

The rapid growth of online social networking sites (“SNS”) such as LinkedIn and Facebook has created new forms of online labor market intermediation that are reconfiguring the hiring process in profound ways; yet, little is understood about the implications of these new technologies for job seekers navigating the labor market, or more broadly, for the careers and lives of workers. The existing literature has focused on digital inequality – workers’ unequal access to or skilled use of digital technologies – but has left unanswered critical questions about the emerging and broad effects of SNS as a labor market intermediary. Drawing on in-depth interviews with unemployed workers this paper describes job seekers’ experiences using SNS to look for work. The findings suggest that SNS intermediation of the labor market has two kinds of effects. First, as an intermediary for hiring, SNS produces labor market winners and losers involving filtering processes that often have little to do with evaluations of merit. Second, SNS filtering processes exert new pressures on all workers, whether winners or losers as perceived though this new filter, to manage their careers, and to some extent their private lives, in particular ways that fit the logic of the SNS-mediated labor market.

This paper examines the linkages between the ethics and management control literatures and suggests some potentially fruitful areas for future research and for integration in the classroom.

We review topics in the ethics and management control literatures organizing them around the six modules used in the accounting ethics course taught at the University of Southern California: (a) professional standards, (b) distinguishing right from wrong, (c) understanding why (good) people do bad things, (d) getting employees to behave ethically (corporate ethics programs), (e) getting people to speak up when they see something wrong taking place (Giving Voice to Values), and (f) whistleblowing (the last resort).

While we find many topics where ethics and management control are concerned with similar issues, there are very few papers that approach these topics from the two perspectives.

We provide an overview of topics where ethics and management control overlap, and highlight the need for greater convergence between the two literatures. By linking MCS and ethics, organizations can provide a framework to promote behavior that both contributes to the achievement of the organization’s objectives and also follows ethical principles. We comment on what may happen when ethics and management control diverge, and discuss controls that can promote a strong ethical climate.

Introduction: Numerous decision-making situations are faced in education where Artificial Intelligence may be prevalent as a decision-making support tool to capture streams of learners’ behaviours.

Purpose: The purpose of the present study is to understand the role of AI in student performance assessment and explore the future role of AI in educational performance assessment.

Scope: The study tries to understand the adaptability of AI in the education sector for supporting the educator in automating assessment. It supports the educator to concentrate on core teaching-learning activities.

Objectives: To understand the AI adaption for educational assessment, the positives and negatives of confidential data collections, and challenges for implementation from the view of various stakeholders.

Methodology: The study is conceptual, and information has been collected from sources comprised of expert interactions, research publications, survey and Industry reports.

Findings: The use of AI in student performance assessment has helped in early predictions for the activities to be adopted by educators. Results of AI evaluations give the data that may be combined and understood to create visuals.

Research Implications: AI-based analytics helps in fast decision-making and adapting the teaching curriculum’s fast-changing industry needs. Students’ abilities, such as participation and resilience, and qualities, such as confidence and drive, may be appraised using AI assessment systems.

Theoretical Implication: Artificial intelligence-based evaluation gives instructors, students, and parents a continuous opinion on how students learn, the help they require, and their progress towards their learning objectives.