Organizational Dynamics最新文献

This paper examines critical ethical considerations linked to making human resources management (HRM) decisions based on the potential capabilities (affordances) offered by generative artificial intelligence (GAI). We first provide a broad overview of the status quo surrounding the use of GAI in the HRM context. Then, we introduce the concept of “affordance” and explain how it provides a useful perspective for human resource (HR) managers to use when evaluating potential benefits and/or harm resulting from the implementation of a potential GAI-based capability to support HRM processes decisions. We discuss concrete examples of how GAI HRM affordances could be implemented in different HRM functions and the ethical questions that arise from their use. Finally, we present an ethics-based framework, the Two-Rule Method, along with ethics-specific recommendations to guide HR managers through the complex issues that arise because of the use of GAI-enabled HR tools.

The purpose of this article is to describe the impact of artificial intelligence (AI), and specifically Machine Learning (ML) and Natural Language Processing (NLP), on personnel selection in terms of potential uses, challenges for practice, and recommendations based on the most recent advances in the science. We argue that ML will likely have as big of an influence on hiring procedures as the equal employment laws did in the 1960s, 1970s, and 1980s. We start by describing why personnel selection is an obvious application of ML, followed by a brief definition of the types of ML and key terms. In the first section, we describe the most common currently known uses of ML in personnel selection, along with a brief summary of the scientific evidence supporting the uses and potential pros and cons. In the second section, we describe the challenges and issues managers will face in using ML in selection and provide some preliminary advice as to how to address them. Challenges include the influence on adverse impact against diversity subgroups of candidates, explainability of the algorithms, validation and legal defensibility, new emerging state laws governing AI, the potential use of AI tools by candidates, likely future developments, and whether to make or buy should organizations decide to pursue ML for selection. We end with a set of recommendations for managers, concluding that the choice is probably when, rather than if, to adopt ML in personnel selection.

Organizations are increasingly using artificial intelligence (AI) and machine learning (ML) to manage human resource processes and practices (e.g., recruitment, selection, performance management, and compensation). However, it has long been known that these new systems create several ethical problems and dilemmas in organizations. As a result, the primary purposes of this paper were to review the major ethical and moral issues associated with using AI and ML for human resource management. In particular, we considered the potential for these new systems to violate ethical standards, and reviewed the degree to which AL and ML models affect (a) perceptions of invasion of privacy, (b) biases and unfair discrimination in employment decision making, and (c) the harm that may come to individuals and organizations from the erroneous data generated by AI and ML. We also offered strategies that organizations might use to overcome these critical ethical problems.

Human resource management (HRM) professionals are often overworked, and their jobs are increasingly complex. Therefore, many suffer from job burnout, and only some can allocate the necessary time to strategic issues. We show how generative artificial intelligence (AI), particularly ChatGPT, can be a helpful HRM assistant for both strategic and operational tasks. But, for this to happen, we demonstrate the need to create valuable prompts that result in specific, helpful, and actionable HRM recommendations. Accordingly, we provide eight guidelines for creating high-quality and effective prompts and illustrate their usefulness in general across eight critical HRM domains and in more depth in the particular areas of workforce diversity and strategic HRM. We also provide recommendations and demonstrate how to implement a critical verification process to check on ChatGPT’s suggestions. We conclude with a list of “dos and don’ts” and that when used by sufficiently trained HRM professionals, it is a very useful tool because it helps complete tasks faster, hopefully reducing their job burnout and allowing them to allocate more time to strategic and long-term issues. In turn, these benefits will likely result in helping achieve the as-of-yet-unrealized aspiration of “having a seat at the table.”

Selection tools employing artificial intelligence (AI), such as automated video interviews (AVIs), chatbots, and assessment games, have become popular ways for organizations to deal with large numbers of job applicants. Vendors frequently claim that these technologies are unbiased. However, the impact of these tools on applicants with disabilities is rarely addressed. We explain how these tools may have both positive and negative impacts on applicants with disabilities. In doing so, we consider fundamental principles of selection: the reliability and validity of these tools as well as the applicant experience. We end by offering recommendations to organizations that are considering incorporating AI-based tools into their selection processes.

Teamwork can have great benefits, but several predictable challenges can negatively impact team performance. In this paper, we examine these challenges, and summarize nine of the most common barriers to effective teamwork (i.e., competing demands, undervaluing teammates, power differentials, a leader not promoting collaboration, inexperience working together, dynamic demands, interdisciplinary teams, team member overload, and lack of resources). We describe each barrier, explain the path of least resistance that should be avoided, and provide evidence-based advice. In doing so, we provide a practical guide for team members and leaders, increasing their awareness of what can make teamwork more challenging and equipping them with ideas for addressing emergent obstacles they may encounter.

Generative AI tools have been adopted faster than any other technology in history. AI tools including both chatbots (e.g. ChatGPT, Bard) and long-form AI writers (e.g. Wordplay.ai, Jasper.ai) pose substantial efficiency gains for text-reliant industries, such as leadership development. However, our research shows that AI generated content can contain and perpetuate harmful leadership-related gender biases. In this article, we share evidence of how AI generated content can perpetuate gender biases in leadership development. We also offer practical strategies managers can implement to capitalize on the potential of AI in pursuit of greater gender equity in leadership.

Historically, management revolved around supervising employees within fixed organizational boundaries. Today’s blended workforce, comprising both traditional employees and a growing reliance on external labor, demands a recalibration of managerial approaches. Drawing on Henri Fayol’s seminal management functions—planning, organizing, leading, and controlling—we explore the evolution of these functions and their changing implications for modern managers. By integrating classic organizational theories and contemporary labor market trends, we offer insights and strategies for managers to navigate challenges and capitalize on opportunities inherent in the evolving gig economy landscape.