Editorial: Performing a structural equation modeling (SEM) in innovation science studies

Abstract

IntroductionStructural equation modeling (SEM) is a relevant quantitative method due to its capacity to analyze complex relationships between variables in a comprehensive and statistically rigorous manner. In quantitative methodology, where numerical data is used to test hypotheses and evaluate theoretical models, SEM provides a robust framework for both confirmatory and exploratory research. It allows researchers to move beyond traditional linear regression techniques by modeling multiple dependent and independent variables simultaneously, thus offering a more nuanced view of relationships within the data (Ramlall, 2016;Zyphur et al., 2023).SEM is a versatile and powerful research method that allows researchers to examine complex relationships between observed and latent variables. Its relevance stems from the ability to test hypotheses about the structure of relationships within a theoretical model, making it particularly useful in fields such as psychology, social sciences, marketing and management (Kline, 2023). Unlike traditional regression analysis, SEM incorporates both measurement and structural models, enabling the simultaneous examination of multiple dependent relationships. A key advantage of SEM is its ability to handle latent constructs, which are not directly observable but are inferred from measured variables (Hair et al., 2021;Sterner et al., 2024). This feature is particularly relevant for researchers to model abstract concepts like self-efficacy, attitudes or customer satisfaction. SEM also provides the ability to assess the overall fit of a model, determining how well the hypothesized structure aligns with the empirical data. Furthermore, another important aspect of SEM is its flexibility in dealing with complex models that involve mediating and moderating effects, allowing for a more comprehensive exploration of causal pathways (Hopwood, 2007). This makes it especially relevant in contemporary research where relationships between variables are rarely straightforward. In addition, Rademaker et al. (2019) point out that SEM can handle measurement errors more effectively than traditional techniques, leading to more accurate and reliable results.We can expect that SEM assumes significant relevance in innovation science studies, as it can provide a rigorous method to explore and quantify complex relationships between factors that drive innovation. As recognized by Brea (2023), Coletto et al. (2024) and Ryszko and Szafraniec (2022), innovation processes often involve multiple interconnected variables, including technological advancements, organizational capabilities, market dynamics and external environmental factors. Using SEM, researchers can examine these variables simultaneously, offering insights into both direct and indirect relationships within a comprehensive model. Furthermore, SEM allows for the integration of latent variables, which represent abstract concepts like creativity, organizational culture and innovation capacity. These constructs are difficult to measure directly, but SEM facil...