Model for evaluating MVP success in the decision-making process for startup scaling

Abstract

The need to enhance the validity of managerial decisions regarding the scaling of startups arises from the lack of a comprehensive methodology for assessing the Minimum Viable Product (MVP) that considers both internal performance indicators and external market factors. The study aims to develop an integrative MVP assessment model incorporating comprehensive success metrics, accounting for internal product characteristics and external factors influencing its scalability potential. Methods. The research methodology involves a systematic analysis to identify key MVP assessment criteria and to formulate indicators of user satisfaction, financial efficiency, technological readiness, and market potential. A comparative analysis of existing MVP assessment methods focuses on their limitations and integration potential within a unified model. The study results confirm that the proposed integrative model provides a comprehensive approach to MVP evaluation, identifies risks associated with scaling, and assesses the feasibility of further investments in product expansion. The model effectively incorporates market trends and user behavior patterns, improving forecasting accuracy. Applying quantitative and qualitative indicators within a single index framework contributes to more balanced managerial decisions. The scientific novelty lies in developing an integrative model that combines quantitative and qualitative indicators into a single scalability readiness index, thereby minimizing risks and enhancing the justification of managerial decisions. The conclusions indicate that the proposed model allows for objective assessment of the current MVP status and forecasting its development prospects, considering external risks and expansion opportunities. The model fosters a comprehensive approach to MVP evaluation as a multi-component product that requires a systematic assessment of each element. This approach ensures greater flexibility in managerial decisions and enables the adaptation of the MVP to evolving market conditions. Future research prospects involve developing an automated data integration system to improve the accuracy of MVP scalability forecasting and provide recommendations for its adaptation to changing market dynamics.