PRACTICAL ASPECT OF THE METHODOLOGICAL APPROACH TO EVALUATING THE ORGANIZATION OF WORK OF STARTUP SPECIALISTS
Abstract
This article presents the validation of a methodological approach for evaluating the organization of work among IT startup specialists operating in highly dynamic environments. These contexts are marked by the widespread use of remote and hybrid collaboration, the adoption of innovative practices, agile methodologies, and artificial intelligence technologies. Under conditions of limited resources, rapid scaling, and variable working conditions, there is a clear need for effective tools to manage team performance. In response to this need, a comprehensive assessment model is proposed, based on a multi-level system of indicators that integrates both quantitative and qualitative metrics. The approach includes normalization procedures, expert-based weighting, and the construction of an overall integral index. The methodology covers seven key domains: remote/hybrid work organization, adoption of novel methods, AI-driven productivity, innovation environment management, knowledge exchange quality, technological adaptability, and the strategic framing of remote work as a competitive advantage. To test the model, partial and integral indicators were calculated across each domain. Weight coefficients were derived from expert surveys, and normalization was applied to address dimensional inconsistencies. The additive aggregation method was then used to construct a composite performance index. The model was implemented using real-world data from an IT startup over the 2023–2024 period, revealing steady improvements in organizational effectiveness and an upward trend in the overall index. Practically, the method offers IT startup leaders a valuable analytical tool for balancing day-to-day management with long-term team development, early identification of organizational challenges, enhancing transparency, motivation, and performance through regular feedback and continuous improvement. The results also lay the foundation for future research on personalized evaluation models, integration with large language models (LLMs), and automation of strategic performance analysis in startup teams.
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