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Crisis Preparedness Through Casual Gaming? - A Self-Efficacy Perspective
No full textNatural disasters pose a recurring global challenge, yet sustainable preparedness remains challenging. However, there is one context in which individuals often voluntarily engage with natural disasters: casual games. While serious games can effectively enhance self-efficacy – a key predictor of disaster preparedness – casual games are generally not designed for this purpose. To explore how casual games may support self-efficacy development, this study examines how natural disaster-themed casual games incorporate features associated with self-efficacy. Through a systematic search and analysis, we examine and map how game design features may afford relevant sources of self-efficacy. Our findings suggest that current implementations offer ample opportunities for mastery and vicarious experiences, while other sources of self-efficacy are less frequently addressed. Our research provides a first systematic overview of self-efficacy-related game design features in disaster-themed casual games and offers a foundation for future research on the potential of such games to support crisis preparedness
Technology Adoption: Exploring the Impact of Generative AI Use on Software Developer Performance
Full text linkThe rapid rise of Generative AI (GenAI) tools such as GitHub Copilot, ChatGPT, Cursor, Claude, and AI-integrated code editors is fundamentally reshaping software development workflows. Building on earlier technologies like expert systems and decision support systems, GenAI introduces a novel paradigm characterized by autonomous, large-scale generation of code, content, and solutions across diverse domains. This study investigates how GenAI use affects software developer performance by integrating the Technology Acceptance Model (TAM) with DORA performance metrics. To address concerns of causality, self-selection bias, and skill variability, the research adopts a randomized controlled experimental design structured as a hackathon. Developers are assigned to either a GenAI-assisted or control group and complete standardized coding tasks. Performance is then measured using deployment frequency and change failure rate. This hybrid methodological approach provides a novel framework for assessing not only adoption intent but also real-world productivity outcomes in human-AI collaboration
Leveraging Design and Metacognitive Learning in Teaching Emerging Technologies
No full textThis study explores the integration of metacognitive strategies and design science principles in teaching emerging technologies to business students. The evolving tech landscape poses unique challenges for learners without a STEM background, necessitating novel educational approaches that foster knowledge acquisition and independent and life-long learning capabilities. By embedding metacognitive processes within the curriculum, we aim to enhance students’ awareness of their own learning strategies, enabling them to adapt more effectively to technological advancements. Additionally, the adoption of design science in educational settings encourages a problem-solving orientation complementing metacognitive learning by engaging students in real-world tasks and reflective practices. This paper details the requirements of such an approach and explains the development of a unit of study titled Blockchain for Business as a tentative case study. The study also outlines potential directions for future research, emphasising the need for empirical validation of these educational strategies across diverse learning environments
Privacy Compliance in Small-sized Enterprises: An Investigation in the German E-commerce Sector
Full text linkThe study of data protection, especially in light of recent privacy regulations, often focuses on large-sized organizations that process immense amounts of personal data on a regular basis. Although these enterprises present a logical first step for investigating the status quo of privacy compliance practices, the realm of medium-, small-, and micro-sized businesses has largely been ignored in recent literature. In particular, the main drivers and hindrances remain undiscovered, making it unclear how and to what extent privacy is a core value in smaller organizations. In this work, we aim to address this gap by focusing only on small-sized e-commerce organizations, looking to uncover their perspectives on privacy compliance. Starting with a literature review to establish a foundation, we then interview eight members of small-sized e-commerce businesses. We shed light on the unique perspectives offered by our interviewees, taking the form of challenges and disincentives, as well as success factors
User Resistance Towards Artificial Intelligence: A Study Focusing on AI-Hallucinations and Ethics
Full text linkAlthough artificial intelligence (AI) systems such as ChatGPT offer transformational potential for knowledge work, user resistance to their adoption remains substantial. Existing research has largely attributed this resistance to concerns about accuracy, trust, and job displacement, but has overlooked AI-specific phenomena such as hallucinations and ethical concerns. We address this gap by developing a research model based on user resistance literature, which integrates two contextualized perspectives: (1) AI-hallucinations and (2) ethics. Grounded using a quantitative survey with 185 users familiar with AI systems, we found that ethics-related factors, particularly perceived threats and moral obligations, drive user resistance, whereas hallucination-related concerns have limited explanatory power. Contrary to our assumption, users with higher critical thinking show lower user resistance. We discuss these findings, derive implications for user resistance and AI literature, and develop directions for further research
Graph Neural Networks in Network Security: From Theoretical Foundations to Applications
Full text linkGraph Neural Networks (GNNs) are increasingly employed in network security, leveraging graph-structured data to capture complex interdependencies often overlooked by conventional cybersecurity systems. In this paper, we systematically review key GNN flavors—convolution, attention, and message-passing—highlighting their application to diverse security objectives. We demonstrate how graph-based representations naturally encode relational information critically for detecting network attacks. Building on fundamental message-passing concepts, we show how these mechanisms can emphasize critical connections, while hybrid and temporal GNN models address heterogeneous and evolving threats. Furthermore, we examine network security applications at the node, edge, and graph levels, illustrating how GNN embeddings translate into practical threat identification and classification. By bridging foundational theory, diverse use cases, and implementation trade-offs, this paper offers researchers and practitioners a guide to harnessing GNNs for robust, forward- thinking network security systems
Aya in Action: An Investigation of its Abilites in Aspect-Based Sentiment Analysis, Hate Speech Detection, Irony Detection and Question-Answering
Full text linkLow-resource languages face challenges due to limited linguistic resources. In 2024, Aya, a multilingual model supporting 101 languages, was introduced. This study evaluates Aya\u27s performance and the efficacy of a few-shot learning approach in Aspect-Based Sentiment Analysis, Hate Speech Detection, Irony Detection, and Question-Answering using ABSAPT 2022, ToLD-BR, IDPT 2021, and SQUAD v1.1 datasets. Without fine-tuning, Aya demonstrated strong results in QA, achieving a 58.79% Exact Match score, surpassing Portuguese-specific models. However, it struggled in Hate Speech Detection, with an F1-score of 0.64, well below Sabiá-7B\u27s 0.94. ABSA performance improved without neutral examples, but the model faced challenges with complex slang and context-dependent features. These findings highlight Aya\u27s potential in multilingual NLP while demonstrating the capabilities and limitations of few-shot learning as an evaluation strategy for LLMs in low-resource scenarios
The Challenge of Equitable AI Adoption in Higher Education: Literacy and Access
No full textIntegrating Artificial Intelligence (AI) in higher education presents opportunities and challenges, particularly regarding equity, access, and ethical implementation. While AI-driven learning environments can enhance personalised education, they risk exacerbating digital divides due to disparities in AI access, literacy, and institutional policies. This study investigates these equity-related challenges within a New Zealand university’s Information Systems department, employing Activity Theory as a framework to examine the systemic factors influencing AI adoption. Using a mixed-methods approach, the study will collect qualitative and quantitative data over two years, incorporating interviews, focus groups, surveys, and experimental assessments. Preliminary findings highlight unequal AI access, gaps in AI literacy, and inconsistent institutional policies, reinforcing the need for structured AI literacy programmes and equitable governance frameworks. The study intends to contribute theoretically by extending Activity Theory to AI equity research and offer practical recommendations for universities to ensure fair, inclusive, and responsible AI adoption in education
Unveiling the Illusion: Generative Artificial Intelligence Disclosure Strategies and Consumer Responses
No full textAlthough the public generally associates generative artificial intelligence (GAI) technologies such as deepfake with negative impressions such as deception, false information, and ethical risks, this highly realistic image synthesis technology also shows great potential in improving user experience and optimizing product display. However, if companies fail to conduct proper transparency management and information disclosure during use, consumers may feel deceived or even have a crisis of trust once they find that the content they see is artificially generated. Therefore, how to strike a balance between technological innovation and consumer’s trust has become an important issue that needs to be solved urgently. Based on this, this study starts from the perspective of consumer information transparency and draws from self-disclosure theory to systematically explore the impact of different disclosure strategies on consumer behavior. The research results will provide insight for companies to apply GAI technology responsibly
Exploring Employees’ Quiet Quitting from the Perspectives of Ego Depletion and Information Technology Obsolescence
No full textQuiet quitting, often regarded as a form of counterproductive work behavior (CWB), underscores the need to understand its underlying mechanisms. Prior research suggests that ego depletion contributes to work disengagement, implying that quiet quitting may similarly stem from diminished self-regulatory resources. Drawing on ego depletion theory, this study examines how IT obsolescence depletes employees’ self-regulatory capacity through the effort to manage negative emotions, thereby increasing the likelihood of quiet quitting. Furthermore, as leader–member exchange (LMX) is recognized as a critical job resource that mitigates the adverse effects of workplace stressors, this study investigates whether leader interpersonal emotion management (IEM) strategies moderate the relationship between ego depletion and quiet quitting, potentially buffering its negative impact