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Viimeksi tallennetut

  • Human factors in generative AI companions: a systematic literature review of trust, relational dependence, psychosocial outcomes, and responsible adoption
    Piispanen, Joni-Roy; Myllyviita, Tinja; Vakkuri, Ville; Rousi, Rebekah (Elsevier, 2026)
    Artikkeli
    Context Generative AI companions and socially oriented chatbots are systems designed or used to sustain social, affective, or relational interaction. They increasingly function as persistent interaction partners across systems ranging from earlier rule-based, retrieval-based, and pre-LLM architectures to contemporary generative AI companions. Prior reviews and conceptual models have largely examined user-facing relational mechanisms or specific ethical and legal issues. Yet, how these mechanisms connect with platform, organizational, and regulatory conditions remains fragmented. Objectives This study addresses this gap by synthesizing human-factor and responsible-adoption concerns across technology generations and developing an evidence-calibrated multi-stakeholder framework spanning human, organizational, technological, and regulatory layers. Methods We conducted a PRISMA-guided systematic literature review of records retrieved from Scopus, ACM Guide to Computing Literature via the ACM Digital Library, Web of Science Core Collection + MEDLINE, and IEEE Xplore. We examined English-language, peer-reviewed publications made available between 2005 and 2024. From 1699 identified records, 42 publications were included and analyzed using thematic synthesis. Results Four analytical themes were identified: usability and relational affordances in interaction; trust, attachment, over-reliance, and psychosocial outcomes; responsible adoption, platform design, and organizational mediation; and governance, privacy, and accountability. Technology emerged as a cross-cutting mediator linking design features, user experience, organizational practice, and governance concerns. The first two themes drew on 29 and 33 materially contributing publications, whereas the latter themes each drew on 12 and represent emerging synthesis areas. Conclusion The review shows that responsible adoption of AI companion systems requires attention not only to user experience, but also to platform incentives, design affordances, data practices, service continuity, and governance mechanisms. The framework's distinctive contribution is to connect these concerns across stakeholder layers while preserving differences in evidentiary maturity. It is a provisional analytical heuristic, not a validated causal model.
  • Artificial intelligence, inclusion, and the future of work at European SMEs
    Diduc, Sniazhana; Boey, Anita; Šilenskytė, Aušrinė (toim.); Fletcher, Margaret (toim.); Butkevičienė, Jurgita (toim.); Jayton, Chenthuran (toim.) (Edward Elgar, 2026)
    Artikkeli
    Consulting4Future, an Austrian consulting firm led by CEO Barbara Sabitzer, is reassessing its service portfolio aimed at supporting small- and medium-sized enterprises (SMEs) in Europe with technological solutions and training for the effective use of artificial intelligence (AI) in the workplace. Drawing on customer feedback, the company has identified that differences in employees’ vocational backgrounds, academic qualifications, and non-linear career paths often complicate communication, onboarding, and skill development when AI-enabled tools are introduced in SMEs. At the same time, the adoption of AI-driven solutions has raised ethical concerns related to employee profiling, bias, and fairness. While Consulting4Future initially concentrated on delivering technical AI solutions, the firm is now increasingly integrating diversity, equity, and inclusion (DEI) considerations into its approach, particularly with respect to multilingualism and neurodiversity in everyday work practices. The case invites learners to critically examine how AI can simultaneously promote and undermine inclusion in SMEs.
  • Hierarchical multi-agent drl-based secondary control for real-time voltage and frequency regulation in renewable-integrated microgrids
    Razmi, Darioush; Razmi, Peyman; Rodriguez, Jose; Garcia, Cristian; Zhang, Zhenbin (Elsevier, 2026)
    Artikkeli
    The integration of inverter-based microgrids (MGs) often induces variations in system characteristics and disturbances in the network. In islanded operation, voltage and frequency oscillations arise due to the lack of synchronization with the main grid. Ensuring reliable operation under such conditions requires advanced control techniques and proactive monitoring. This paper proposes an intelligent multi-agent secondary control architecture based on the Deep Deterministic Policy Gradient (DDPG) algorithm, where reinforcement learning (RL) agents dynamically adjust the parameters of proportional–integral (PI) controllers to mitigate disturbances and adapt to system uncertainties and varying operating conditions. The proposed framework employs a two-layer hierarchical structure. At the lower layer, two independent RL agents regulate frequency and voltage by tuning PI controller gains in real-time, ensuring stable operation under fluctuating loads and distributed generation. The upper layer incorporates a supervisory agent that monitors harmonic distortion and computes corrective signals sent to the droop controller, enabling coordinated adaptation and enhancing dynamic stability by reducing transient oscillations. As a proof-of-concept, the proposed multi-agent framework is implemented on a single DG unit within a four-converter microgrid, demonstrating the potential for improved dynamic performance under realistic operational scenarios. Validation in MATLAB/Simulink demonstrates substantial improvements in system performance. Frequency settling time decreased from 0.19 s (at Kp=0.3) to 0.10 s (at Kp=0.1), and voltage settling time decreased from 0.20 s (at Kp=0.3) to 0.13 s (at Kp=0.1). Total harmonic distortion (THD) significantly reduced from 2.07% (at Kp=0.3) and 2.03% (at Kp=0.2) to 0.68% (at Kp=0.1). These results highlight the scalability and effectiveness of the proposed multi-agent DDPG-based secondary control, offering a reliable and intelligent solution for advanced MG energy management under realistic operating conditions.
  • Entrepreneurial bricolage in the branding efforts of international new ventures
    Kusi Appiah, Emmanuel (Elsevier, 2026)
    Artikkeli
    This study examines how B2B international new ventures (INVs) develop their brands through bricolage-driven improvisational practices and how these practices influence brand coherence as the ventures progress through different phases of development. Drawing on entrepreneurial bricolage and entrepreneurial branding research, the study employs a comparative, interview-based multiple case design involving eight Finnish B2B INVs. The dataset comprises 19 semi-structured interviews and firm-generated materials, analysed through an iterative and abductive coding process. The findings reveal two practices, namely identity patchworking and improvisational signalling, which show how bricolage shapes branding in resource-constrained internationalisation contexts. These practices are most visible in the transition from early to growth phases and they produce distinctive branding outcomes. Yet coherence drift is most likely when these practices accumulate without stable reference points or strategic anchoring as the venture evolves. This demonstrates that bricolage exerts a double-edged influence on B2B INV branding by fostering brand development under constraint while also producing accumulated improvisations that can erode coherence. This study contributes to B2B marketing and entrepreneurial branding research by developing a retrospective reconstruction of an inferred branding trajectory model and advancing propositions that explain how bricolage influences branding in INVs.
  • Mechanisms for digital business model innovation in international new ventures from developing economies
    Khan, Huda; Knight, Gary; Khan, Zaheer (Elsevier, 2026)
    Artikkeli
    Digital international new ventures (DINVs) increasingly leverage ecosystem partnerships to drive digital business model innovation (DBMI) and navigate global markets. However, research to examine how such partnerships contribute to DBMI is limited, particularly in resource-constrained firms from developing economies. Drawing on the dynamic capabilities view, organizational learning theory and an ecosystem-based digital learning perspective, we investigate the role of ecosystem partnerships in enabling DBMI through the development of digital learning capabilities. Based on survey data from 147 DINVs in a developing economy, findings suggest that ecosystem partnerships enhance digital learning, which in turn facilitates DBMI. Moreover, foreign market turbulence tends to positively moderate the relationship between ecosystem partnerships and digital learning capability. In addition to demonstrating how DINVs build innovation capacity under volatile conditions, findings help to extend perspectives on capabilities, learning, and ecosystems. Leveraging ecosystem partnerships, digital learning, and digital business model innovation appears to enhance the performance of DINVs. Ecosystem partnerships appear to be especially valuable to DINVs when market turbulence is high. The study offers theoretical and practical insights into the mechanism through which DINVs from developing economies can achieve digital innovation, and provides policy implications for supporting their international growth.