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Transforming University Practices for SDGs: Lessons Learned from Action Design Research and Human-AI Collaboration
Get PDFThis paper presents an in-depth case study of how Action Design Research (ADR), augmented by human-AI collaboration, can drive the institutionalization of the United Nations Sustainable Development Goals (SDGs) within a research university in Taiwan. Applying an iterative ADR approach, the authors co-developed information systems and organizational practices that promote the systematic integration of SDGs into academic and administrative workflows. Key innovations include an AI-assisted system for labeling faculty research outputs according to the SDGs and a decentralized suite of sustainability reporting platforms managed by Chief Sustainability Officers (CSOs) across university units. The evolution toward decentralized and component-based system architecture empowered diverse units to contribute actively to the university’s sustainability goals. The study explores new practical principles in complex organizational settings, emphasizing the importance of rapid prototyping, modular design, and inclusive, iterative stakeholder collaboration. The lessons learned offer actionable guidance for institutions seeking to advance sustainability through socio-technical innovation and collaborative change
Corporate Default Prediction Through Text Mining: Integrating Event, Sentiment, and Network Analyses
Get PDFThe importance of textual information in corporate credit risk management is increasingly recognized. While most studies focus on the direct analysis for assessing corporate credit risk, they often overlook the potential impact of inter-company relationships on the likelihood of default. This study, focusing on both intrinsic information about companies themselves and relational information within company networks, explores the potential of advanced text-mining techniques for predicting corporate defaults. We integrate default event extraction, credit sentiment analysis, and relation analysis via co-mention networks using public news on US-listed oil companies between 2014 and 2016. We aim to demonstrate how these advanced text-derived features enhance default prediction during industry upheaval. Our findings reveal that credit sentiment emerges as a crucial predictor of default, alongside network degree and transitivity. High-risk labelled companies are more likely to default than others. Moreover, exposure to media, regardless of being positive or negative, may increase the likelihood of both default and other corporate exits, primarily mergers and acquisitions. This study emphasizes the transformative impact of text analysis on traditional credit risk assessment practices and underscores the value of relational information between companies for default prediction
From Syllabus to Assignment Design: A Case Study of GenAI Future Role in University Assessment
Get PDFThe growing integration of generative language models in higher education has prompted renewed attention to their role in supporting instructional design, particularly in developing assessments. This study explores the potential of such tools to assist in creating structured assignments within a university-level STEM curriculum. A systematic methodology was applied to evaluate outputs across key pedagogical dimensions, including alignment with learning objectives, appropriateness of difficulty, and cognitive depth. While the tools effectively generated technically accurate and syllabus-aligned content, persistent limitations were identified in their ability to produce higher-order reasoning tasks and multi-layered assessment items. These constraints were especially evident in advanced or design-based coursework. The findings suggest that generative models can enhance instructional efficiency and provide a valuable starting point for educators, but their outputs require ongoing refinement and professional oversight. Their optimal use lies in supporting, not replacing, the instructor, enhancing pedagogical expertise in meaningful assessment
Why Toxicity Persists in Esports: Introducing the Concept of Toxicity Legitimacy
Get PDFThe presence of toxicity in esports culture has become deeply rooted in and it now concerns both scholars and practitioners. Competitive gaming platforms experience persistent toxic behaviors regardless of increased awareness and intervention efforts. The paper presents the concept of toxicity legitimacy to analyze how toxic behaviors gain acceptance within esports communities. The effort shows how individual actors along with game organizations and sociotechnical infrastructures legitimize and perpetuate toxic behavior. The discussion details theoretical contributions while presenting managerial implications and suggests future empirical validation paths
Domino Effects of AI: Spillovers from New App Launches on Developer Portfolio
Get PDFDisruptive innovations often transform digital platforms by reshaping how value is created and consumed, altering dynamics across the entire ecosystem. Among these, AI has emerged as a fast-moving and transformative force. Mobile applications serve as a key channel for delivering AI-powered services, making them crucial for studying AI’s broader impacts. This study explores how launching a new AI app affects the demand for a developer’s existing apps and how this relationship depends on the AI composition within the developer’s portfolio. Leveraging data mining, we constructed a unique six-month biweekly panel dataset from Apple’s App Store. Two-way fixed effects regression models reveal that new AI app launches increase demand for existing apps, especially when the developer’s portfolio is primarily non-AI. However, this effect weakens as the portfolio becomes more AI-heavy. These findings contribute to the demand-spillover and disruption literature and offer practical insights for managing AI integration in digital ecosystems
CognitiveSky: Scalable Sentiment and Narrative Analysis for Decentralized Social Media
Get PDFThe emergence of decentralized social media platforms presents new opportunities and challenges for real-time analysis of public discourse. This study introduces CognitiveSky, an open-source and scalable framework designed for sentiment, emotion, and narrative analysis on Bluesky, a federated Twitter or X.com alternative. By ingesting data through Bluesky’s API, CognitiveSky applies transformer-based models to annotate large-scale user-generated content and produces structured and analyzable outputs. These summaries drive a dynamic dashboard that visualizes evolving patterns in emotion, activity, and conversation topics. Built entirely on free-tier infrastructure, CognitiveSky achieves both low operational cost and high accessibility. While demonstrated here for monitoring mental health discourse, its modular design enables applications across domains such as disinformation detection, crisis response, and civic sentiment analysis. By bridging large language models with decentralized networks, CognitiveSky offers a transparent, extensible tool for computational social science in an era of shifting digital ecosystems
Stacking Wins: How Martech Sophistication Drives the Digital Transformation Premium
Get PDFThis study investigates whether the strategic composition of marketing technology (martech) investment drives superior financial performance. Using Verhoef et al.’s (2021) digitization-digitalization-digital transformation framework, we develop a sophistication index weighting martech investments by strategic value and test how martech sophistication affects financial performance. Analyzing 1,460 publicly traded companies from the S&P Total Market Index, we find strong evidence for a transformation premium—companies with more sophisticated martech portfolios achieve significantly higher market valuations (Tobin’s Q) than those emphasizing less digital transformation. This relationship is moderated by industry sector and firm size, with larger firms capturing greater benefits from more sophisticated martech investments. Our findings demonstrate that strategic composition, not mere volume of investment, drives martech value creation. The research provides actionable guidance for technology investment decisions and introduces a replicable framework for measuring digital transformation quality across organizations
Enhancing Complementary Team Performance through Intelligent Helping
Get PDFEffectively leveraging artificial intelligence (AI) requires aligning human reliance on AI with humans’ cognitive strengths and limitations. Despite the objective advantages of AI in performing specific tasks, humans often struggle to correctly rely on help from AI to maximize the Complementary Team Performance (CTP), exceeding the individual performances by either humans or AI. To address this challenge, we propose the design feature Intelligent Helping, which steers human behavior by strategically providing different types of AI help and thus aligning reliance with AI’s superior judgment while ensuring that humans retain full control. We designed an experiment with four archetypes of task performances and four treatments to modulate human interaction with AI. Our results show that reliance on AI can be shaped by tailored treatments, significantly improving CTP. Intelligent Helping enables high-performing AI in which humans retain full control over decisions, providing new opportunities for effective collaboration between humans and AI
Social Media Influencers: A Systematic Review and Consolidated Definition
Get PDFSocial media influencers (SMIs) have emerged as powerful actors shaping brand awareness and consumer behavior. However, their impact is expanding beyond purely economic functions into areas such as politics, public health, and disaster communication. Existing literature tends to define the concept of SMIs rather narrowly, focusing either on their relevance within specific academic disciplines (e.g., marketing) or on categories of reach (e.g., micro, meso, and macro influencers). Narrow or inconsistent definitions and conceptualizations of SMIs across disciplines hinder theoretical development and limit comparability between studies. Therefore, this paper aims to establish a consolidated definition of SMIs. Using a seven-stage review methodology, we systematically derive a definition that captures the essential characteristics of SMIs, reflects their transformative role in societal discourse, and remains applicable to emerging artificial incarnations of the phenomenon
Introducing GAMUT - A Game-based Assessment for Measuring User Types: Evaluation of Game Design for Satisfying Psychological Needs to Enhance User Engagement and Flow
Get PDFDespite the growing relevance of game-based assessment in higher education, many approaches lack theoretical grounding and motivational design. This study introduces GAMUT, a theory-driven game-based assessment that embeds user type assessment into interactive, narrative gameplay. Based on the Self-System Model of Motivational Development and the Hexad model, GAMUT incorporates achievement, social, and immersion game elements to satisfy the core psychological needs: competence, autonomy, relatedness. Implemented as a mobile adventure simulation, it transforms the Hexad scale into decision-based scenarios for authentic self-assessment. Empirical evaluation with higher education students shows that social and immersive game elements significantly promoted autonomy, competence and relatedness, enhancing engagement and flow. Achievement game elements had limited effects, emphasizing the need for context-sensitive game design. GAMUT achieved an 85% accuracy rate in user type classification and was preferred over traditional questionnaires. These findings offer a systematic, motivational GBA approach, contributing to assessment validity, learner engagement and self-directed learning