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Configurational patterns for forecasting customer satisfaction enhancement based on online reviews: A multi-attribute attitude perspective
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DOI：10.1016/j.ipm.2025.104545.png)
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Abstract
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In the digital age, automatically identifying and analyzing the causal logic in online reviews to enhance customer satisfaction (CS) is crucial for accurate business decision-making. Despite extensive research grounded in multi-attribute attitude theory (MAAT), existing studies often overlook the nonlinear interrelationships among product or service attributes and their joint impact on CS, limiting a comprehensive understanding of satisfaction formation. To address this gap, this study integrates MAAT with complexity theory to propose an online review-driven framework that models configurational patterns for forecasting CS enhancement. Specifically, we utilize the Top2Vec model to identify product or service attributes and employ a recursive neural tensor network algorithm to calculate the affective distribution vectors of reviews. Then, a bagged neural network model based on affective distribution computing is used to assess the effects of each attribute on CS, and determinant attributes are identified. Based on these core attributes, fuzzy-set qualitative comparative analysis (fsQCA) is applied to identify attribute configuration patterns leading to high CS, and interpretive structural modeling (ISM) and cross-impact matrix multiplication applied to classification (MICMAC) analysis are combined to establish the hierarchical structure and action paths of attributes within the configurations. An empirical study using TripAdvisor reviews of sustainable tourism destinations validates the methodology. By integrating these methods, we can understand how each attribute influences CS, both individually and in combination, and uncover the complex pathways driving high CS. Additionally, practical guidance is provided for businesses to formulate precise customer-oriented management strategies.
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