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Digital Frontlines: Social Media Engendered Polarization During Geopolitical Crises
Get PDFThe international landscape has become increasingly volatile, marked by a growing number of military conflicts and geopolitical confrontations. Social media platforms play a crucial role in managing and shaping such episodes of crisis because content shared through these platforms drive user engagement. Despite the growing interest in investigating the relationship between social media and polarization, existing research presents contradictory findings across different socio-cultural and empirical contexts. This inconsistency highlights the need for investigating whether user engagement on geopolitical war or conflict related social media content contributes to increasing or decreasing polarization. In this paper, we examine the user generated content on the Russia-Ukraine war and the Israel-Hamas carnage. Employing network-based opinion dynamics we explore how exposure to video content in social media evolves. The results reveal that polarization steadily grows with the maturity of the discussions as the consensus emerges after numerous rounds of interaction among users. The study has implications on crisis governance and digital literacy efforts that aim at reducing platform driven fracture during geopolitical conflicts
A Manifesto for Information Systems Research in the Age of AI Hype
Get PDFThe rapid advancement of artificial intelligence technologies, especially generative and agentic AI, fundamentally challenges longstanding assumptions in Information Systems Research methodologies. Traditionally, ISR has built upon decision analysis and design science for creating decision support systems, assuming that only human cognitive power matters. However, as AI continues to develop novel cognitive capabilities autonomously, this assumption grows increasingly contestable. This paper proposes a forward-looking ISR methodology framework for the 2030s that integrates three emerging conceptual frameworks: digital coaching, digital fusion, and joint human-AI intelligence. By synthesizing insights from the evolution of ISR methodologies and analyzing the disruptive impact of modern AI, I develop methodological propositions that address the changing nature of human-machine collaboration in research processes. Future ISR methodologies must formally incorporate human-AI role definitions, treat explainability as a core outcome variable, integrate continuous learning mechanisms, draw from multidisciplinary theoretical grounding, and emphasize sociotechnical fitness while maintaining AI's proper role as a powerful tool under human direction
Generational Diversity as a Driver of Vibrant Retail Districts
Get PDFThis study examines the effect of generational diversity on the vitality of urban retail districts. While prior research focused on organizations, little is known about its role in neighborhood economies. To address this gap, we analyze panel data from the Seoul Retail District Analysis Service and apply social capital theory and the contact hypothesis to explain how generational diversity may shape outcomes. Results from Study 1 show that diversity, measured with the Blau Index, has a significant positive effect on average sales per transaction, indicating that interaction across age groups enhances vitality. Study 2, motivated by rapid ageing and the rise of active older consumers, reveals that although a higher senior population ratio generally reduces average sales per transaction, this effect is moderated by income, becoming positive in higher-income districts. These findings identify generational diversity as a driver of vitality and highlight intergenerational contact zones (ICZs) for sustainable economies
Artificial Intelligence, Upper Echelons, and Financial Performance: An Empirical Study of European Software Companies
Get PDFArtificial Intelligence has become a prevailing corporate paradigm, particularly for software firms. Despite the intense race to have the upper hand in the rapid integration, compatibility with the information systems in place and the economic pay-off remained peripheral in IS research and practice. Moreover, the role of upper-echelon characteristics in shaping the financial outcomes of AI adoption remains uncertain. Using longitudinal data, this study empirically explores the bottom line—the economic performance of European software firms that integrate AI into their current enterprise systems. Findings reveal a negative relationship between AI integration and firm performance; however, this effect is significantly moderated by the upper echelon's characteristics. The study’s findings contribute to the literature by establishing AI systems as a key co-determinant of financial performance. It also has practical implications, such that it highlights lightweight integration and value alignment problems that lead to adverse performance pay-offs
Beyond Calibration: Rethinking Algorithmic Fairness through an Intersectional, Justice-Aware Lens
Get PDFAs predictive algorithms increasingly guide high-stakes decisions in fields like criminal justice, healthcare, and finance, the concept of "fairness" often centers on the idea of model calibration, the alignment between predicted probabilities and observed outcomes. Calibration is typically treated as a reliable marker of objectivity and fairness. However, this paper argues that in contexts shaped by structural inequalities, including those based on gender, race, and class, calibration fails to account for deeper ethical and social implications. Drawing on research from algorithmic fairness, feminist technology studies, and intersectionality, we challenge the assumption that models that are calibrated to biased outcomes can be considered fair. This critique is especially urgent for individuals at the intersection of multiple marginalized identities, whose experiences with technology are often shaped by compounded, gendered harms that traditional fairness metrics fail to address. We propose a justice-aware framework for algorithmic fairness that acknowledges the historical and social contexts embedded in data and integrates technical interventions across the AI development lifecycle, before, during, and after model deployment. Rather than treating calibration as an ultimate standard for fairness, we argue it should be viewed as a single tool within a broader, intersectional approach. Our paper makes three key contributions: (1) a conceptual critique of calibration as a fairness metric, (2) a call for intersectional, multi-attribute fairness frameworks that account for gender and other identity factors, and (3) an argument for embedding fairness-enhancing tools within a broader socio-technical and justice-oriented framework that goes beyond mere technical performance to address systemic inequality. This paper addresses that gap by offering a justice-aware framework that integrates technical fairness interventions with gender-conscious design, participatory governance, and socio-technical accountability, bridging the divide between algorithmic fairness and the lived realities of marginalized groups
Why Data Spaces Are Not (Yet) Emerging - a Manufacturing Intralogistics Study
Get PDFData sharing is critical to industrial digitalization. Data spaces are socio-technical infrastructures that enable data sharing activities between organizations. Although data spaces are regarded as promising enablers for smart manufacturing, their uptake has remained limited. Drawing on empirical evidence, this study demonstrates that the key factors influencing manufacturing intralogistics companies’ adoption decisions hinge on the relevance of specific data sharing use cases and the perceived viability of data spaces in addressing them. Companies must first recognize tangible business benefits and assess the technical applicability of data spaces before progressing toward adoption. The findings suggest that clear articulation and communication of data space capabilities, enabled use cases, and associated business value are critical to fostering adoption. Otherwise, the emergence of industrial data spaces is likely to remain slow
Quantifying True Robustness: Synonymity-Weighted Similarity for Trustworthy XAI Evaluation
Get PDFAdversarial attacks challenge the reliability of Explainable AI (XAI) by altering explanations while the model's output remains unchanged. The success of these attacks on text-based XAI is often judged using standard information retrieval metrics. We argue these measures are poorly suited in the evaluation of trustworthiness, as they treat all word perturbations equally while ignoring synonymity, which can misrepresent an attack's true impact. To address this, we apply synonymity weighting, a method that amends these measures by incorporating the semantic similarity of perturbed words. This produces more accurate vulnerability assessments and provides an important tool for assessing the robustness of AI systems. Our approach prevents the overestimation of attack success, leading to a more faithful understanding of an XAI system’s true resilience against adversarial manipulation
Modeling Electric Grid Topology with Spatially-Aware Degree-Corrected Stochastic Block Model
Get PDFThe goal of this paper is to facilitate the development of synthetic electric grid topologies that replicate the structural properties of real-world power systems. This paper demonstrates that the topology of the North American transmission grid can be modeled using a Spatially-Aware Degree-Corrected Stochastic Block Model (SA-DCSBM), which captures three key features in real grids: modularity, heterogeneous node degree distributions, and distance-constrained connectivity. Once the model is fitted to the North American transmission network data, synthetic topologies (excluding electrical phenomena) are generated to demonstrate that they accurately reproduce real grid statistics across multiple structural dimensions, including modularity, edge length distribution, degree heterogeneity, and spectral robustness. The SA-DCSBM thus offers a modeling framework for creating high-fidelity synthetic electric grid topologies that preserve spatial and structural realism