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From AI Literacy to AI Use: Evidence from a Multi-Organization Upskilling Program
Full text linkAs artificial intelligence (AI) becomes integral to organizational processes, employee adoption emerges as a key determinant of a successful implementation. This study draws on the Unified Theory of Acceptance and Use of Technology (UTAUT) and extends it with AI-specific drivers, AI literacy and attitude toward AI, alongside established factors such as subjective norm and effort expectancy, while exploring the moderating roles of the demographic factors age and gender. We surveyed 180 employees from 39 organizations in Bavaria, Germany, before and after a five-month AI upskilling program. Results show that AI literacy and subjective norm were the strongest predictors of AI use. Age moderated the literacy-use relationship, with diminished effects for older employees. Pre-post training comparisons showed significant gains in AI literacy, AI use, and AI-supported task share. These findings refine UTAUT by incorporating AI literacy and offer practical insights for designing targeted, age-sensitive interventions to drive workplace AI usage
Collaborative LLM Agents for C4 Software Architecture Design Automation
Full text linkSoftware architecture design is a fundamental part of creating every software system. Despite its importance, producing a C4 software architecture model — the preferred notation for such architecture — remains manual and time-consuming. We introduce an LLM-based multi-agent system that automates this task by simulating a dialogue between role-specific experts who analyze requirements and generate the Context, Container, and Component views of the C4 model. Quality is assessed with a hybrid evaluation framework: deterministic checks for structural and syntactic integrity and C4 rule consistency, plus semantic and qualitative scoring via an LLM-as-a-Judge approach. Tested on five canonical system briefs, the workflow demonstrates fast C4 model creation, sustains high compilation success, and delivers semantic fidelity. A comparison of four state-of-the-art LLMs shows different strengths relevant to architectural design. This study contributes to automated software architecture design and its evaluation methods
Introduction to the Minitrack on Decision Making Bias and Misinformation in Online Social Networks
No full textTransforming University Practices for SDGs: Lessons Learned from Action Design Research and Human-AI Collaboration
Full text linkThis 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
Full text linkThe 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
Full text linkThe 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
Full text linkThe 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
Full text linkDisruptive 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
Full text linkThe 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
Full text linkThis 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