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Experiential Learning in the Metaverse: Implications for Workplace Training
Get PDFIn an era of rapid technological advancement and shifting work modes, the need for innovative, effective, and scalable corporate training solutions has become increasingly important. This paper explores how the Metaverse can serve as a transformative environment offering more personalised, engaging, and effective training experiences. Drawing on Kolb’s Experiential Learning Theory (ELT), the study examines how the elements of the Metaverse — such as immersion, interactivity, and persistence — can enhance skill acquisition, knowledge retention, employee engagement, and organisational learning outcomes. Through the analysis of nine case studies across sectors (telecommunications, retail, hospitality, manufacturing, consulting and social services), we identify three dominant models of Metaverse-based training: risk-free learning environments, digital twin integration, and immersive skill development platforms. The findings highlight the ability of these training programs to enable experiences that support both technical and interpersonal skill development. Benefits include reduced training time, increased learner satisfaction, improved performance metrics, and global scalability. However, successful implementation is contingent upon addressing critical challenges including infrastructure demands, content development, accessibility, employee adaptation, health implications, and ethical concerns such as data privacy and inclusivity
Steering the AI Race: An Exploratory Study of How Boardroom Dynamics Shape Competitive AI Actions and Performance
Get PDFArtificial intelligence (AI) has become a pivotal engine of innovation and strategic transformation across industries. While firms are increasingly investing in AI to gain competitive advantage, many still struggle to translate these investments into meaningful performance outcomes. This study investigates how firms deploy AI through competitive actions—strategic moves such as AI-driven acquisitions, R&D projects, product innovations, and partnerships—and how these actions influence firm performance. We further explore the moderating and mediating roles of board governance, highlighting how variations in board structures shape the effectiveness of AI strategies. Grounded in the Attention-Based View (ABV) of the firm, we propose that the deployment and impact of AI are contingent on how strategic attention is allocated at the top. Board members influence this attention by shaping firm priorities and the governance environment in which AI actions are conceived and executed. To examine these dynamics, we pose three interrelated research questions: (1) How do competitive AI actions affect firm performance? (2) How do board governance characteristics—specifically board involvement in technology and power disparity—moderate this relationship? (3) Do board characteristics influence performance by shaping the breadth or scope of AI-driven competitive actions? We draw on a panel dataset of S&P 500 firms from 2010 to 2022, using detailed media reports and governance data. Our results reveal that firms engaging in more frequent and broader AI-driven competitive actions experience superior performance, especially when board members are actively involved in overseeing technological strategy. We find that board power disparity has a double-edged effect—enhancing market-based performance while dampening operational efficiency. Additionally, we show that board characteristics indirectly affect performance by influencing the scope of AI actions. Specifically, the presence of a CTO on the board and technical expertise among directors are associated with broader AI action portfolios, while board independence tends to constrain AI scope, likely due to increased scrutiny and risk aversion. This study contributes to strategic management and information systems literature by linking corporate governance to the execution and performance of AI strategies. It advances the ABV framework by demonstrating how boardroom dynamics shape the strategic attention paid to AI initiatives. From a practical perspective, our findings suggest that firms should not only invest in AI capabilities but also ensure that their governance structures are equipped to guide and support AI-driven transformation. Boards that actively engage with technology issues, balance power, and include members with technical expertise are more likely to convert AI initiatives into sustained performance gains
The Sharing Economy: How do the Affordances Influence the Continued Usage of Digital Platforms for Handyman Services in South Africa?
Get PDFThe sharing economy is gaining traction in South Africa, with platforms such as Uber, Bolt, HomePlus, Kandua and AirBnB leading the way. Some studies are even predicting that sharing economy services could significantly boost the global economy, contributing several billions of dollars. As a result, issues such as social exclusion in developing countries might be reduced due to the success of sharing economy services. This qualitative study follows an interpretive philosophy and inductive approach. The targeted audience was the general public who utilises sharing economy platforms that facilitates handyman services. Twenty-two interviews were analysed. The findings provide policymakers with insights on possible interventions that need to be done to align with the people’s needs, concerns and preferences. Notably, the study found the affordance of increased inclusivity and equality and a contradicting barrier – increasing inequality. Additionally, the paper reveals a new gap (marketing) that is relevant, and actionable in South Africa
A relational view on Artificial Intelligence business value: A qualitative meta-analysis
Get PDFThe business value of Artificial Intelligence (AI) is a prominent topic in Information Systems (IS) literature. As our knowledge around it becomes more nuanced, the intricacies of the relational aspects and their effects to value creation and capture become observable, particularly in interorganizational settings. This study sheds light on these aspects, by examining the factors that lead to value creation in partnerships around AI, but also the factors that impede value creation and capture. It follows a qualitative meta-analysis approach, drawing from the relational view on value creation. The study is founded on 20 empirical studies on AI business value and identifies the relational factors that are discussed as prominent when it comes to value creation and capture. The study informs both IS research and practice, by pointing to relevant factors that are influential, but also to factors not extensively discussed yet, thus providing a research agenda for future research
Digital Polycentricity for Socio-Technical Design: Outcomes from the 2024 Workshop on Digital Polycentricity
Get PDFThis paper reports on the results of a Workshop on Digital Polycentricity. The workshop was held at Imperial College London from 25 to 27 September 2024, aiming to transfer and lift the concept of polycentric governance for urban environments to digital contexts. We argue that just as Ostrom proposed polycentricity in order to emerge intended positive macro-level outcomes from the micro-level interactions of policy actors in the “Market Society,” we need a concept of digital polycentricity to design and operationalise socio-technical systems theory to achieve some Aristotelian forms of macro-level social justice in the “Digital Society.” The participants of the workshop identified key issues related to digital polycentricity, offered a critique of digital polycentricity as an approach and an outcome, and set an actionable research agenda for applying digital polycentricity to the design of socio-technical systems that are fit-for-purpose in the Digital Society, and in the context of digital transformation. The contribution of this paper is thus a research agenda that maps research priorities and directions for different stakeholders
Dying Digitally: Rethinking the Afterlife in the Global South
Get PDFThis paper addresses the under-theorized issue of the digital afterlife in the Information Systems (IS) field with a specific focus on the Global South. While digital death increasingly intersects with cultural, ethical, and technological domains, mainstream IS research has largely avoided the topic and its complexities, often viewing death as a taboo topic. The paper explores how Western-centric digital afterlife services overlook cultural, infrastructural, and economic realities of Global South users, contributing to digital exclusion. Drawing from thanatology and sociotechnical systems theory, the paper critically analyzes posthumous digital identity governance, data vulnerability, and ethical concerns surrounding persistent online profiles and emerging technologies. In response, the paper introduces the Socially Aware Digital Death (SADD) framework, a culturally responsive, ethically grounded model that integrates social awareness, digital literacy, ethical considerations, and shared accountability to address digital death challenges. The paper concludes by proposing a future research agenda based on user typologies and the SADD framework, and makes a call for more inclusive platform design, legal reform, and localized digital legacy tools, challenging IS paradigms to ethically engage with death and recognize the agency of Global South users in shaping their digital legacies
Find the Good. Seek the Unity: A Hidden Markov Model of Human-AI Delegation Dynamics
No full textAs AI becomes integral to enterprise decision-making, this study explores the collaborative dynamics between managers and AI systems, focusing on human willingness to delegate tasks to AI. Grounded in the “agentic” systems delegation framework and instance-based learning theory, we employed a hidden Markov model in a longitudinal study of the dynamic delegation decision-making process involving 875 store managers. We found that there is a potential polarization in managers’ delegation willingness, with managers who recognize the capability of AI exhibiting high delegation willingness and fostering increased collaboration with AI over time—in contrast to their counterparts who are inclined to reduce AI’s involvement. During human-AI interactions, managers’ continuous performance appraisal of AI shapes their dynamic delegation willingness, which in turn affects their assessment of AI capability. This process forms a delegation feedback loop that drives the dynamics of delegation behaviors. Our study indicates that managers with a high willingness to delegate tend to outperform their counterparts and offers valuable insights for human-AI collaborative intelligence in organizational settings
Research on Consumers\u27 Fresh Food E-commerce Platform Switching Intention under PPM Theory: A fsQCA Method
Get PDFGuiding the Future: Boardroom Governance in the Age of Artificial Intelligence
No full textAs GenAI and other advanced technologies become increasingly embedded in business operations, boards of directors face new demands in strategic oversight, risk, ethics, and organizational change. Despite these challenges, scholarly research on board-level AI governance remains sparse. In parallel, many boards struggle to translate high-level principles and emerging academic recommendations into actionable strategies. This panel brings together scholars and board members from public and private organizations with expertise in information systems (IS) and digital transformation. Panelists represent diverse experiences and viewpoints, creating space to explore tensions and dilemmas in governing AI at the board level. Discussions will highlight real-world governance dilemmas, strategies for addressing them, lessons learned, and unresolved questions emerging from boardroom practice. By fostering critical debate, the panel aims to deepen understanding of the complexities of board-level AI governance and shape a research agenda that supports practical, ethical, and effective oversight in the age of intelligent technologies
Bridging the AI Security Gap: Risk, Compliance, and Innovation
No full textRecently, attacks on machine learning (ML) systems have become a paramount concern for cybersecurity practitioners. Artificial Intelligence (AI) systems, including classical ML and generative AI platforms, are being exploited to produce harmful content, generate biased results, and facilitate data leakage. This increased use has led to a variety of challenges centering on trust, privacy, risk management, innovation, and resilience. While the technical considerations of these issues are well-studied, the organizational, consumer, and societal impacts of these threats within the context of rapidly increasing AI adoption are not fully understood. Key questions focus on the balance of traditional cybersecurity concerns with AI/ML-specific risks, the need for new skillsets for cybersecurity practitioners, and methodologies for balancing novel risks with rapid innovation. This panel brings together industry and academic experts to discuss the cybersecurity risks and challenges surrounding rising AI adoption and debate the recommended focus areas for future research and methodology development