THEORETICAL AND METHODOLOGICAL FOUNDATIONS FOR STUDYING THE IMPACT OF BEHAVIORAL FACTORS ON ENTERPRISE INNOVATION
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Abstract
Introduction. Innovation decisions are made under incomplete information, uncertainty, delayed outcomes, and limited opportunities for accurately forecasting technological and market effects. Conventional resource-based and institutional explanations do not fully account for why enterprises with comparable capabilities demonstrate different levels of innovation activity and achieve different economic results.
Purpose. The purpose is to deepen the theoretical and methodological foundations for studying the influence of behavioral factors on enterprise innovation and to substantiate a conceptual model that links behavioral manifestations with the economic outcomes of innovation decisions.
Methods. The research uses systemic, process, behavioral, and interdisciplinary approaches. The methods include theoretical generalization, comparative analysis, classification, synthesis, causal modelling, and conceptual structuring.
Results. Behavioral factors are defined as cognitive, emotional, social-psychological, and organizational characteristics that systematically affect information perception, assessment of alternatives, and managerial choice. The study systematizes overconfidence, optimism bias, anchoring, availability and confirmation heuristics, loss aversion, status quo bias, escalation of commitment, herd behavior, groupthink, and group polarization. A five-level conceptual model is proposed. It combines behavioral antecedents, perception mechanisms, managerial choice, innovation-process parameters, and economic outcomes. Financial and non-financial indicators for empirical assessment are outlined.
Originality. The originality consists in integrating individual, group, and organizational behavioral levels with the sequential stages of enterprise innovation. The proposed model distinguishes direct financial consequences from indirect effects related to organizational learning, managerial routines, knowledge accumulation, and future decision quality.
Conclusion. Behavioral analysis should complement rather than replace conventional economic evaluation of innovation projects. Enterprises are advised to use reference-class forecasting, independent preliminary assessment, premortem analysis, anonymous initial voting, milestone reviews, and pre-defined project termination criteria. Further research should empirically test the model in capital-intensive industries, particularly oil and gas enterprises.
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