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My Fate Is to Die Young, But to Live Forever in Song: Echeloned Design Science Research to a Digital-Me Expert System Design
Get PDFThis study explores the developmental trajectory and implementation of an artificial intelligence (AI) -based expert system. Following and extending the echeloned design science research approach, the process examines how the nature of system development changes when the team designing the system shifts from using AI to produce expert artifacts to using AI as the core technology to continuously run digital clones of the expert. We find a radically transformed nature of design and development work and numerous modes of evaluation echelons. Methodologically, the results contribute to DSR in information systems by articulating AI’s role not only as a tool for artifact creation, but as a co-evolving actor within the design process, and the changes that entails to the DSR approach. Theoretically and empirically, we discover and develop the notion of digital-me, hypothesizing the benefits of conceptualizing AI as extending real humans, not building humanlike artificial agents, as the prime use of AI, particularly in user acceptance of expert systems
Thanking the Algorithm: Discovering Prosocial Communities through YouTube Music Recommendation Pathways
Get PDFWhat pathways through algorithmic music recommendations help users discover prosocial comment communities? Building on algorithmic awareness and music discovery literature, this exploratory, naturalistic study follows four user personas on YouTube through four music genre seed queries and ten layers of recommendation depth, to analyze the frequency and nature of the prosocial comment communities they encounter. Our results suggest that prosocial communities are accessed more frequently by personas who defy algorithmic classification in their use patterns, and that within prosocial communities users express not just awareness of the recommendation algorithm, but gratitude directed explicitly toward it. This exploratory, context-bound study contributes to understanding how users and algorithms co-construct musical meaning and community, offers methodological insights for studying algorithmic experience and recommendation pathways, and reflects on the ephemerality of prosocial communities within black-boxed discovery platforms
Does Framing a Health App as Tailored Impact Choice?
Get PDFSmartphone apps offer a valuable tool for supporting health goals, including weight management. Building on Self-Determination Theory, we examined whether gender-tailored app names—with or without visual presentation—affect user preferences, willingness to pay (WTP), and perceived necessity, and whether these effects vary by gender. Two online experiments with U.S. adults seeking to lose weight tested three naming conditions: a control (“Weight Loss App”), a gender-tailored (“Weight Loss App for (Wo)Men”), and an inclusive (“Weight Loss App for Men and Women”). Study 1 included only names; Study 2 added visual stimuli depicting no person, a man, a woman, or both. Name framing had no significant effect on preference, WTP, or perceived necessity. However, women showed stronger overall preferences, and visuals significantly increased WTP. Prior experience with weight loss apps was also linked to higher WTP. These findings highlight the importance of visual content and prior experience in early user engagement
Frequency Stability Constraints in Economic Dispatch: Electricity Market Insights using a Neural Network Surrogate Modeling Approach
Get PDFA Frequency Stability-Constrained Optimal Power Flow (FSC-OPF) problem is proposed that enforces frequency stability constraints using Neural Network (NN) surrogate models. In the context of wholesale electricity markets, pricing structures are analyzed along with their dependencies on the selected input features to the NN surrogate model. An important insight identifies a trade-off between the effectiveness of the NN surrogate model and sensible locational pricing structures. The numerical results represent frequency stability using frequency nadir limits and identify trends in prices, dispatch, and profits that follow from different selected input features. NN surrogate models for frequency stability are validated by ensuring that the resulting FSC-OPF solution is stable over randomly generated load samples using a small Hawaii test case
Digital Innovation in Data-Poor and Unstructured Environments: Envisioning Autonomous Systems in Forestry
Get PDFWhile IS research has increasingly turned its attention to autonomous systems, empirical investigations are predominantly conducted in artificial settings such as offices or factories. As a result, we know relatively little about what shapes autonomy in natural settings, where there is poor access to data on vegetation and terrain. To address this knowledge gap, we report a qualitative case study on the development of autonomous forestry machines. Applying the Technology-Organization-Environment (TOE) framework, our analysis reveals that autonomy is inherently shaped by its surroundings, including technological limitations, dynamic organizational processes, and intricate environmental factors. This study furthers IS research on autonomous systems by highlighting their embedded, emergent nature in data-poor and unstructured settings
Explicit and Implicit Help-Seeking in Navigating Support Needs on Social Media: A Case Study of Domestic Violence Community
Get PDFSocial media platforms offer a supportive environment for help-seeking and collaborative problem-solving. Despite extensive research on help-seeking behavior, it has largely overlooked the role of explicitness-implicitness in the request for help, particularly to meet users’ support needs. This study aims to address this gap by investigating how needs for informational support versus emotional support are associated with explicit versus implicit help-seeking behavior in social media self-disclosure. We further examine how these behaviors impact community agreement (i.e., peers’ acknowledgement and supportive feedback). We propose and test hypotheses using data collected from representative social media communities focused on domestic violence, and the findings support most of our hypotheses. This work contributes to online help-seeking theory, including work on motivational interviewing, and provides practical insights for fostering community support
Tool, Assistant, or Advisor? Consumer Experience across Modalities in AI-Powered Product Search
Get PDFThis study investigates how different AI-powered search interface designs—traditional keyword search, AI-generated summaries, and conversational agents—influence users’ mental representations of AI and their subsequent evaluations and behaviors. Across an experimental design involving 345 participants, we found that interface modality significantly shaped how users conceptualized the AI system's role. Cluster analyses revealed three distinct user types with divergent expectations and evaluative priorities (e.g., as a tool, assistant, or advisor). These perceived roles, in turn, moderated how traditional usability constructs—perceived usefulness, ease of use, and control—predicted decision confidence and purchase intention. These factor are weighted differently within each cluster. Our findings show that interface design actively constructs interaction realities that guide technology acceptance and consumer behavior. We discuss implications for adaptive interface design and the ontological framing of AI in user experience
The Indicator of CCTV Success is Winning Elections”: Insights from the Implementation of an Algorithmic Video Surveillance System in Poland
Get PDFThis article explores the emergence and dynamics of institutional collaboration surrounding the implementation of an algorithmic video surveillance system (AVS) in a Polish city, utilizing the Advocacy Coalition Framework (ACF) as a theoretical lens. The study identifies two primary coalitions: the “Safe City,” focused on public safety and crime prevention, and the “Smart City,” which emphasizes technological innovation and city branding. Through qualitative case study method, including in-depth interviews, the research provides an empirical analysis of how cooperation unfolded among actors who are typically reluctant to work together. The paper argues that the system’s success is owed to the convergent belief systems of the coalition’s members. The findings also highlight the role of individual policy entrepreneurs, local authorities, law enforcement, and private contractors in engaging citizens in financing the CCTV system and reducing any opposing voices. Ultimately, the study contributes to understanding the interplay between politics and policy in the context of urban surveillance technology, while calling for further research on the implications of such systems for core components of the “right to the smart city” like privacy and democratic engagement
Introduction to the Minitrack on Responsible Approaches to Blockchain, Cryptocurrency, and FinTech
No full textUsing Spectral Graph Wavelets to Analyze Large Power System Oscillation Modes
Get PDFThis paper presents a novel method for modal analysis to extract the spatial-temporal characteristics of oscillations in large electrical networks. A vector-fitted approximation of the Spectral Graph Wavelet Transformation (SGWT) and the inverse SGWT are derived to identify intra-network oscillations within a system response. This method scales linearly with the number of branches and leverages sparse solution techniques to develop a fast, low-memory estimation of modal frequency, shape, and damping. A case study on synthetic networks (2k-80k buses) with full dynamic modeling demonstrates consistent sub-second performance of modal estimation. Compared to existing methods, the SGWT approach can estimate modes with fewer channels and a shorter time-domain window. This presents a fast, general method for identifying true multiscale network behavior and localized oscillation sources, marking a novel application of graph-based signal processing