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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
AMCIS2025 Awards Ceremony
No full textThis is video recording of the AMCIS 2025 Awards Ceremony on-site that took place on Saturday, August, 16, 2025. This includes presentation of the AMCIS Outstanding Conference Leadership Awards and the AMCIS 2025 Best Paper Awards
ADOPTING DEVSECOPS: A FRAMEWORK FOR IT GOVERNANCE AND CULTURE CHANGE BASED ON A PLAN-DO-CHECK-ACT (PDCA) APPROACH
Get PDFAs digital transformation accelerates, organizations increasingly turn to agile software development and deployment practices like DevOps. However, incorporating security into these processes through DevSecOps presents significant challenges, particularly in cultural adaptation and alignment with IT governance. This study explores the challenges of adopting DevSecOps from two crucial perspectives: organizational culture and IT governance. Through a thorough literature review and the development of a conceptual framework, we identify human-related barriers such as resistance to change, lack of awareness, and communication gaps, along with governance-related constraints such as inadequate policies, misalignment of risks, and compliance issues. To tackle these challenges, we propose a Plan-Do-Check-Act (PDCA) implementation model that provides a practical approach for transforming organizational culture and improving IT governance. This approach aims to bridge the gap between development, security, and operations while aligning with strategic business objectives. Future research in this field could include empirically validating the model through case studies
Digital Transformation Against the Odds: Insights on Frugal, Government-Led, and Responsible Digital Transformation from an Ethiopian Bank
Get PDFMany organizations are undergoing digital transformation. However, literature often assumes a substantial influence from consumers, competitors, technology institutions, and organizational resource endowment. These assumptions may not necessarily apply to the digital transformation initiatives in Sub-Saharan Africa and other low-income economies. In response to the call for research to challenge the unexamined assumptions of digital transformation and develop contextualized theoretical contributions, this study explores the processes, enablers, inhibitors, and outcomes of digital transformation through a case study of a bank in Ethiopia (henceforth EtBank). Over the past decade, EtBank has transformed from a cash-intensive, branch-based, brick-and-mortar bank to a digital bank offering niche mobile money services, conducting most retail transactions electronically, and contributing to digital financial inclusion. All of this augurs well for future change. Based on insights from EtBank\u27s experience, the study presents a process theory that frames digital transformation as deeply influenced by historical and contextual factors, the interaction between organizational capabilities and frugal innovation, and the role of institutions as enablers and inhibitors. The study contributes to the literature on responsible and government-led digital transformation, underscoring the significance of historicity and context in the process. Additionally, it offers valuable guidance for other organizations embarking on similar digital transformation journeys
THE ROLE OF AI-POWERED PERSONALISATION THROUGHOUT THE PURCHASE DECISION-MAKING PROCESS ON ONLINE MARKETPLACES
Get PDFThis study explores the role of Artificial Intelligence (AI)-powered personalisation tools in facilitating consumers’ purchase decision-making process within the online shopping environment. Recently, there has been a rapid growth in online shopping, and retailers have adopted AI-powered personalisation tools such as recommendation systems, chatbots and dynamic pricing to assist and support their consumers with the purchase decision. The techniques and tools required to achieve personalisation have been studied, however, their influence and supportive role during the consumer decision-making journey remains unexplored. The research addresses this gap by assessing how AI-powered personalisation tools facilitate the purchase decision from the problem recognition phase to the post-purchase phase. The findings from the research suggest that AI-powered personalisation can provide valuable support throughout four of the five phases of the consumers’ decision-making process through tools AI-powered personalised recommendations, AI-driven contextual recommendations and comparison tools, AI-driven comparison analysis tools, AI-driven personalised reminders and in-cart recommendations. However, tools like AI-driven chatbots and Dynamic pricing are deemed as less relevant by consumers. Post-purchase, AI support is not particularly effective for consumers, who find AI-driven chatbots and review requests to be unhelpful in handling complex queries. The findings emphasise the importance of AI-powered personalisation but also raise a need for improvements to align with customer expectations
PREDICTING LEARNING STYLES WITH AI: TOWARD ADAPTIVE AND PERSONALIZED EDUCATION
Get PDFArtificial intelligence holds significant potential for enhancing adaptive learning environments. However, effective personalization requires a deep understanding of individual learner characteristics, particularly their preferred learning styles. This study presents an Artificial Neural Network (ANN) - based model, aligned with the VARK framework (Visual, Auditory, Reading/Writing, Kinesthetic), to identify student learning preferences using survey data collected from 700 students across schools, colleges, and universities in Bangladesh. A hybrid architecture combining multi-label classification and multi-output regression was employed to predict both the dominant learning styles and the degree of preference for each. The ANN outperformed traditional machine learning algorithms - including Support Vector Machine, Random Forest, Decision Tree, and K-Nearest Neighbors - achieving an F1-score of 0.92 and R2 score of 0.96. Performance further improved with the integration of K-Means clustering, boosting the F1-score to 0.96. The regression component of the model provides a percentage-based prediction of how strongly a student prefers each learning style, offering a more granular and nuanced understanding of individual preferences. Compared to conventional approaches, this multiheaded approach is more flexible and informative, enabling the early identification of learning styles and facilitating the development of personalized educational content prior to course delivery