Organizational Dynamics最新文献

In this digital age, organizations are increasingly reliant on technology to drive business growth and innovation. With this reliance, comes an impetus for firms to responsibly and ethically manage their digital assets and data. In response, Corporate Digital Responsibility (CDR) has been espoused as a means by which firms can address the competing demands of digital innovation and transformation and ethical responsibility. Ultimately, good CDR practices have the potential to safeguard not only firms, but the various stakeholders they interact with across their digital ecosystems. However, it is recognized that the resource investment required to do so, can come at a significant cost. The CDR Calculus has been developed to assist organizations in evaluating the risks and benefits associated with digital and data practices and to determine whether they align with the organization’s CDR objectives. This paper provides a roadmap of how firms can take steps to establish a culture of CDR practice in light of these tensions. We also recognize that external regulation is required, if not needed, to enforce and cement positive CDR practices as firms respond to growing digital demands.

Scholars conceptualized the role of Corporate Digital Responsibility (CDR) to emphasize the ethical issues unique to the digital context by pushing all the actors involved in developing and integrating digital technologies and related data processing to act ethically and responsibly. However, how to apply AI services more ethically and develop AI equipped with moral/ethical intelligence are still open questions.Accordingly, this paper aims to understand the managerial applications and tools of Corporate Digital Responsibility (CDR) in AI retail service automation to identify the CDR value for consumers and retail managers. We link the five AI intelligence types (verbal-linguistic, logic-mathematical, visual-spatial, social, and speed-processing) to CDR strategies to produce value for retailers and consumers. Specifically, we define intelligent retail service automation, emphasizing actions, tools, and (privacy) concerns, and discuss the role of CDR in AI for intelligent retail service by developing the new concept of Artificial Intelligence Responsibility in Retail Service Automation (AIRRSA). Finally, we provide implications for scholars, managers, and policy-makers while proposing future challenges.

Algorithmic decision-making - the use of computational methods that enable machines to automatically complete tasks and/or make decisions - is emerging as a critical source of competitive advantage for organizations. However, despite many benefits, there is an inherent dark side associated with it that can manifest issues ranging from a loss of privacy for individuals to societal power imbalance. Managers and policymakers alike need to be able to understand potentially unethical consequences that can arise from algorithmic decision-making before they can fully manifest. This article aims to support this undertaking by identifying, analysing, and explaining the challenges that can arise from algorithmic decision-making, and by contributing a seven-step roadmap to those wanting to responsibly implement and benefit from algorithms today.

Organizations are readily implementing innovative technological solutions, including artificial intelligence (AI) and robotics, to remain competitive. However, these implementations often disrupt the existing routines and practices of stakeholders that are critical for organizational performance and success. If stakeholders are not part of the implementation decision process, the technological disruption may induce stakeholder resistance that may potentially lead to organization-wide turbulence. Addressing this scenario, this paper conceptualizes six principles of responsible digital implementations to develop operational resilience facilitated by discursive channels between organizational leadership and stakeholders. We close by outlining an action plan that provides guidance for managers considering implementing digital technologies, as well as suggest some potential fruitful future areas of research.

Artificial Intelligence (AI) enabled tools have increasingly becoming popular in our societies and are increasingly being used by students and practitioners, among others. Within corporations, numerous different applications have been identified where AI-enabled tools have been applied with different levels of success. In this article, we explore the pros and cons of using AI in performance management (PM). We draw upon the practitioner literature to summarize the current status of AI and AI-enabled tools. We also interviewed 8 HR professionals from around the world to learn about their experience(s) with the tools and to gain an insight into the future. In doing so, we explore the various components of performance management systems (PMS) and discuss how each might be impacted by the use of AI. Finally, we discuss the pros and cons of such usage and make recommendations for organizations that are considering using AI or AI enabled tools in their PMSs.

Compensation strategy plays a crucial role in attracting, motivating, and retaining strategic human capital. Amping up the advantages of being strategic about compensation are advances in technology such as cloud computing and storage along with digitized big data that make the sheer amount of information available and analyzed electronically, a huge competitive opportunity. The good news is these advances have unleashed a tsunami of technology solutions that promise to solve all compensation challenges. In this paper, I synthesize and summarize the literature on artificial intelligence and compensation management and describe four key challenges that companies face in using AI to manage compensation strategically. The first challenge is when and how to use AI to automate and augment compensation tasks and decisions. The second challenge is how to use AI effectively to improve fairness and equity in compensation practices. The third challenge is explaining how AI recommended changes in compensation practices are derived. The fourth challenge is how to actually be strategic using AI solutions. In describing these four challenges, I identify issues, opportunities, gaps, and current limitations of existing AI applications in supporting the strategic management of compensation in organizations.

Today artificial intelligence (AI) is being employed to streamline and transform many business processes including those in human resource management (HR). AI has and will continue to revolutionize the way that organizations attract talented applicants, hire qualified employees, train workers, manage their performance, and develop compensation and reward systems. Many analysts believe AI will help organizations gain a competitive edge in attracting, motivating, and retaining talented employees. These new AI systems have several key benefits including reduced transaction times, decreased costs, improved employee service, and streamlined administrative processes (e.g., screening applications). Despite the growing use of AI in organizations, many managers have indicated that they do not fully understand how AI will transform HR practices. Thus, the primary purpose of this article is to highlight how AI will help organizations modify HR processes and practices so that they can meet their HR goals.

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.