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From Code to Commerce: Open Source Software Firms as Double Agents of Collective Innovation Strategy
Get PDFThis study develops a grounded theory of collective innovation strategy, explaining how open source software (OSS) firms, enterprise clients, and developer communities collaboratively shape OSS innovation. Drawing on a year-long participant observation, 46 interviews, and document analysis, we identify four interrelated constructs: collective strategy impact, client effect, OSS firm’s dual fiduciary obligation, and community holistic leadership. We show that enterprise clients, while often latent, can enhance innovation process when two enabling conditions are met: fiduciary mediation by the OSS firm and holistic leadership within the community. The OSS firm’s double-agency role reduces resources and information asymmetries between online developer communities and arm’s-length clients, enabling joint decision-making that balances technical and commercial priorities. In addition, holistic community leadership further sustains alignment and engagement across distributed actors. Our findings extend relational view of the firm and resource-based theories by revealing how external actors become strategic resources only when mobilized through situated practice
Facilitating Urban Participation Project with Generative AI to Support Citizen Engagement and Interaction
Get PDFThis paper investigates how AI-based chatbots can enhance citizens’ engagement and interaction on urban participation platforms. Using a design science research approach, we identified twelve issues, formulated eleven meta-requirements, and derived five design principles. These were instantiated with a web prototype designed in Flutter, utilizing a large language model, including interaction and expressing guidelines. Our evaluation revealed increased engagement, lower participation barriers, and improved citizen contributions compared to non-AI-based participation. However, the evaluation also led to the addition of two new ones, highlighting document access and interactive urban maps. Together, they specify information presentation and interaction with participants. Despite promising findings, challenges persist regarding the perception and explainability of large language models. Our findings provide a practical blueprint for future AI-enabled citizen participation in urban planning, suggesting directions for further research on the retrieval-augmented generation architecture, which can incorporate additional domain knowledge and behavioral guidelines
Loss of Communication in Remote Scrum Teams: Assistance System to support Reflection of Interactions and Collaboration
Get PDFCommunication and collaboration are crucial success factors for IT projects, especially in agile software development. Due to increased remote work and global teams triggered by the covid-19 pandemic, the way people communicate and collaborate at work changes. To investigate how this impacts agile teams we conducted expert interviews and a literature review. The results are summarized in this paper and demonstrate that there is a loss of communication, collaboration and social interaction, which may potentially result in harmful long-term effects. The consequences include a decline in trust, psychological safety and engagement. Collaboration tools can help to address these novel challenges. We give an overview of existing tools and applications to promote remote collaboration. Furthermore, we present a concept for an assistance system for reflection to improve remote teamwork and individual ways of working in Scrum teams. Nevertheless, tools and technologies have limitations and cannot replace the full spectrum of human face-to-face communication
Digital Voices of Survival: From Social Media Disclosures to Support Provisions for Domestic Violence Victims
Get PDFDomestic Violence (DV) is a pervasive public health problem characterized by patterns of coercive and abusive behavior within intimate relationships. With the rise of social media as a key outlet for DV victims to disclose their experiences, online self-disclosure has emerged as a critical yet underexplored avenue for support-seeking. In addition, existing research lacks a comprehensive and nuanced understanding of DV self-disclosure, support provisions, and their connections. To address these gaps, this study proposes a novel computational framework for modeling DV support-seeking behavior alongside community support mechanisms. The framework consists of four key components: self-disclosure detection, post clustering, topic summarization, and support extraction and mapping. We implement and evaluate the framework with data collected from relevant social media communities. Our findings not only advance existing knowledge on DV self-disclosure and online support provisions but also enable victim-centered digital interventions
Artificial Intelligence Systems and Sustainability Focus in Venture Funding: When Technology Meets Purpose
Get PDFAI systems and their integration to address current societal challenges have recast the modus operandi in business venturing. Yet little is known about the funding dynamics of startup businesses that blend such algorithmic systems with a sustainability focus. This study explores how the isolated integration of AI systems and sustainability in European startups is rewarded or punished by investors. Using a signaling lens and econometric models, the study finds that size of funding is negatively associated with AI signals, and sustainability startups face similar fundraising hurdles. For startups that coalesce AI systems and sustainable foci, findings reveal that startups with such a tandem face more fundraising challenges than their counterparts. Robustness checks and alternative specifications are performed to verify the validity of the findings. Examining this underexplored intersection sheds light on the intricate dynamics and trade-offs shaping the European funding landscape for startups embracing AI and sustainability objectives
AgriSentinel: Privacy-Enhanced Embedded-LLM Crop Disease Alerting System
Get PDFCrop diseases pose significant threats to global food security, agricultural productivity, and sustainable farming practices, directly affecting farmers’ livelihoods and economic stability. To address the growing need for effective crop disease management, AI-based disease alerting systems have emerged as promising tools by providing early detection and actionable insights for timely intervention. However, existing systems often overlook critical aspects such as data privacy, market pricing power, and farmer-friendly usability, leaving farmers vulnerable to privacy breaches and economic exploitation. To bridge these gaps, we propose AgriSentinel, the first Privacy-Enhanced Embedded-LLM Crop Disease Alerting System. AgriSentinel incorporates a differential privacy mechanism to protect sensitive crop image data while maintaining classification accuracy. Its lightweight deep learning-based crop disease classification model is optimized for mobile devices, ensuring accessibility and usability for farmers. Additionally, the system includes a fine-tuned, on-device large language model (LLM) that leverages a curated knowledge pool to provide farmers with specific, actionable suggestions for managing crop diseases, going beyond simple alerting. Comprehensive experiments validate the effectiveness of AgriSentinel, demonstrating its ability to safeguard data privacy, maintain high classification performance, and deliver practical, actionable disease management strategies. AgriSentinel offers a robust, farmer-friendly solution for automating crop disease alerting and management, ultimately contributing to improved agricultural decision-making and enhanced crop productivity
Algorithmic Fairness in NLP: Persona-Infused LLMs for Human-Centric Hate Speech Detection
Get PDFIn this paper, we investigate how personalising Large Language Models (Persona-LLMs) with annotator personas affects their sensitivity to hate speech, particularly regarding biases linked to shared or differing identities between annotators and targets. To this end, we employ Google’s Gemini and OpenAI's GPT-4.1-mini models and two persona-prompting methods: shallow persona prompting and a deeply contextualised persona development based on Retrieval-Augmented Generation (RAG) to incorporate richer persona profiles. We analyse the impact of using in-group and out-group annotator personas on the models' detection performance and fairness across diverse social groups. This work bridges psychological insights on group identity with advanced NLP techniques, demonstrating that incorporating socio-demographic attributes into LLMs can address bias in automated hate speech detection. Our results highlight both the potential and limitations of persona-based approaches in reducing bias, offering valuable insights for developing more equitable hate speech detection systems
How Does Authenticity Observed from Service Provider–Consumer Interactions Affect Consumer Engagement and Firm Performance? Evidence from a Field Experiment in a Restaurant Chain
Get PDFDigital platforms make many firm–customer interactions publicly observable, yet the impact of the authenticity conveyed by service providers in these interactions on future consumers remains unclear. We explore how service provider authenticity, as observed from a third-party perspective, affects subsequent consumer engagement and firm performance. Collaborating with a chain restaurant firm, we employ a mixed-method design combining secondary data analysis and a field experiment. Results indicate that authenticity significantly increases visits to the ordering page, orders, and sales. Guided by interactional justice and attribution theories, we further find that these effects are pronounced in service-failure contexts. Also, the impact of authenticity intensifies when observers witness firm-induced failures and disappears when failures are attributed externally. Our research contributes to the authenticity literature by introducing a third-party observational perspective and revealing the economic value of authenticity on digital service platforms. Practically, our findings provide implications for firms to optimize online operations