Review of Dynamic Structural Equation Models for Real-Time Consumer Behaviour: Methodological Advances and Applications Insights
Review of Dynamic Structural Equation Models for Real-Time Consumer Behaviour: Methodological Advances and Applications Insights

Summary/Abstract: This study evaluated the transformative importance of dynamic SEM in offering a more thorough understanding of real-time consumer behaviours and thus transcending the limitations of traditional SEM approaches that typically rely on static data. The study analysed the recent advancements in the dynamic SEM and its capability to strengthen marketing strategies by accurately capturing evolving consumer interactions. The study evaluated the published peer-reviewed literature ranging from 2010 to 2024 to assess the advancement, comparisons, applications, accuracy and methodological complexities of both dynamic and traditional SEM approaches in the domain of consumer behaviours and interactions marketing analytics. The inclusion criteria were studies focusing on consumer behaviour, research articles published within 14 years, studies employing dynamic SEM methods and datasets that include time-series data. The findings for objective one show that dynamic SEM analyses complex, temporal and real-time data because it has been integrated with advanced modern methods and approaches such as Ecological Momentary Assessment and Experience Sampling Method, Bayesian methods for estimation, machine learning algorithms and cloud computing platforms. The findings for objective two indicate that dynamic SEM is practically and accurately capable of analysing temporal and real-time high-frequency, complex, and large-scale datasets from digital platforms like social media and e-commerce. The results obtained from the comparative analysis for objective three show that dynamic SEM provides significant improvements by offering a more accurate reflection of evolving consumer interactions and preferences than traditional SEM. Dynamic SEM integrates temporal elements and therefore allows for adeptly modelling consumer choices, moods, attitudes, and emotional states over time. Performance metrics such as MAE, RMS, and CFI confirm that dynamic SEM enhances fit and predictive precision. The findings show that dynamic SEM substantially and significantly outperforms traditional SEM since it has been integrated with advanced methods that enhance the understanding of real-time consumer behaviour and interactions by effectively capturing temporal variations in consumer behaviour and interactions. Thus, organisations should adopt and implement the dynamic SEM to optimise and improve their marketing strategies. The study contributes to knowledge that the dynamic SEM is superior in capturing real-time consumer behaviours, which results in enhancing marketing analytics and strategies.

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