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Abstract
Innovation is widely recognised as a critical driver of societal progress, enabling solutions to complex problems and improving quality of life. Management and psychological research have increasingly focused on understanding individual differences in innovation, with factors such as personality traits, emotional intelligence, and social relationships identified as influential. Meanwhile, previous studies exposed several serious issues regarding analyses. This study focuses on issues related to reliability, validity, the application of Structural Equation Modelling (SEM), and visualisation. The findings highlight prevalent methodological issues, including inconsistencies in psychometric assessment and limitations in causal inference using cross-sectional data. Comparing SEM with classical path analysis, this study emphasises the importance of rigorous statistical methods to derive reliable insights. By incorporating longitudinal designs and robust model specifications, this paper provides actionable recommendations to strengthen methodological practices. These contributions are critical for advancing innovation psychology and supporting future research in this domain.
Published Version
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