HAMK Häme University of Applied Sciences

Generative Artificial Intelligence (Gen-AI) has gained significant traction in larger firms, yet its adoption among micro-firms remains underexplored particularly in contexts marked by resource scarcity and heightened operational risk. This study addresses this gap by investigating how high-tech micro-firms adopt Gen-AI for risk management and growth. Drawing on semi-structured interviews with decision-makers from eight Finnish micro-firms, the research applies the Technology-Organization-Environment (TOE) framework to identify critical enablers and barriers. The findings highlight five key dimensions influencing adoption: technological readiness, leadership engagement, regulatory compliance, data-driven decision-making, and competitive pressures. While Gen-AI fosters operational resilience and strategic agility, its impact is constrained by limited data quality and high implementation costs. By offering a holistic and theoretically grounded perspective, this study advances understanding of Gen-AI adoption in micro-firms and contributes to literature on digital transformation under resource constraints. The insights also inform policymakers and practitioners aiming to enhance AI accessibility and governance for micro-enterprises.

The widespread adoption of edge computing and Internet of Things (IoT) applications has significantly increased the demand for efficient task scheduling in resource-constrained environments. These environments often rely on low-power, cost-effective hardware, making it challenging to maintain optimal performance while managing computational workloads. Traditional load-balancing approaches, such as round robin and least-loaded strategies, frequently struggle to optimize resource allocation in distributed systems with limited processing power, such as Raspberry Pi clusters. These conventional methods often fail to adapt dynamically to fluctuating workloads, leading to inefficient resource utilization, increased response times, and potential system bottlenecks. To address these challenges, this research introduces a novel Resource Conscious Predictive Load Balancing (RCP-LB) framework, which leverages machine learning techniques to enhance task allocation efficiency. Specifically, the framework employs Support Vector Machines (SVM) to analyze real-time CPU and memory usage data, enabling the system to predict the optimal task allocation dynamically. By proactively distributing workloads based on resource availability, the proposed approach reduces latency, improves response times, and maximizes overall system performance. Comprehensive performance evaluations compare RCP-LB with traditional load-balancing techniques, demonstrating its superior efficiency, scalability, and adaptability in handling dynamic workloads. The experimental results indicate that the RCP-LB framework significantly enhances system responsiveness and resource efficiency, making it particularly well-suited for real-time IoT applications and edge computing environments. By providing a robust, intelligent, and resource-aware task scheduling mechanism, this research contributes to the advancement of distributed computing frameworks, offering a practical and effective solution for modern, re-source-constrained systems.

This article examines how co-research is enacted in practice by analysing a 15 month research project that explored children’s and young people’s experiences of family work in Finland. Two young adults with lived experience in child welfare were employed as co- researchers and collaborated throughout the research process with four academic researchers. Drawing on diaries, Teams discussions, morning walk conversations, and documentation from workshops with children and young people, we conceptualise co-research through an analytical distinction between the “co” and the “research.” While this distinction is partly artificial, it proved productive for illuminating how relational practices and research practices evolve interdependently. Our analysis shows that sustaining the “co” required intentionally created relational spaces that enabled trust, emotional work, and ongoing negotiation of roles and expertise. The “research” dimension involved shared tasks, methodological experimentation—including the creative “painting with data” method—and collective meaning-making. Across all phases, the boundaries between co-researchers and academic researchers remained fluid, with participants inhabiting multiple positions over time. We argue that distinguishing between the “co” and the “research” enhances the rigour and transparency of co-research by making visible the processes through which knowledge is collaboratively produced. For us, the metaphor of co-research as a kaleidoscope captures both the methodological fluidity and relational accountability central to coresearch. Conducting co-research requires a continuous, dynamic, and shifting configuration of perspectives, competencies, and experiences through which new insights emerge.