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While many organizations have successfully leveraged big data analytics capabilities to improve their performance, our understanding is limited on whether and how big data analytics capabilities affect social innovation in organizations. Based on the organizational information processing theory and the organizational learning theory, this study aims to investigate how big data analytics capabilities support social innovation, and how knowledge ambidexterity mediates this relationship. A total of 354 high-tech companies in China, this study shows that big data analytics management, big data analytics technology, and big data analytics personnel capabilities all have positive effects on social innovation. In addition, both knowledge exploration and knowledge exploitation play a mediating role in this process. Furthermore, a polynomial regression and response surface analysis shows that social innovation increases when knowledge exploration and knowledge exploitation are highly consistent but declines when knowledge exploration and knowledge exploitation are inconsistent. This study not only provides new perspectives for understanding how big data analytics capabilities contribute to social innovation, complementing the existing literature on big data analytics capabilities and social innovation, but also provides important practical guidance on how organizations can develop big data analytics capabilities to improve social innovation and solve social problems in the digital age. [ABSTRACT FROM AUTHOR]

This study provides a quantitative overview of contemporary consumer behavior research in the 21st century (2001–2020) to inform future research directions in consumer behavior research. Using co‐citation analyses, this study identifies the most cited authors, publications, and academic journals in consumer behavior research in each of four 5‐year intervals in 2001–2020 to profile research themes and relationships among different research clusters. Key research themes are then mapped based on co‐citation matrices. The results show that major research themes in consumer behavior research in the last two decades have shifted from the focus on fundamentals of consumer behavior, consumers' decision‐making process, development of more robust measures and analytical methods to the focus on service quality and consumer satisfaction, online consumer behavior and virtual communities, sustainable consumption, as well as the experiential aspects of consumer behavior. The findings help map the invisible knowledge network embedded in contemporary consumer behavior research and shed light on future research agenda in consumer behavior studies. [ABSTRACT FROM AUTHOR]

This paper proposes a novel approach called Stack-GA2M to identify fraudulent reviewers in an inherently interpretable manner by fusing both target and non-target features. Specifically, for local interpretability, we adopt GA2M (Standard Generalized Additive Model plus interactions) as the basic classifier to produce three subordinate models trained by using the target features and the non-target features as review textual features and reviewer behavioral features. For global interpretability, we adopt LR (Logistic Regression) as the meta classifier to stack the outputs of three subordinate models to identify the fraudulent reviewers. The white-box model of LR enables us to understand the global interpretability of the target features and the non-target features in identifying fraudulent reviewers. With GA2M, the local interpretability of each subordinate model is derived by using feature importance, spline shape functions for individual features, and heatmaps for interaction terms. Extensive experiments on Yelp dataset demonstrate that the proposed Stack-GA2M approach is superior to state-of-the-art techniques in identifying fraudulent reviewers and exhibits favorable inherent interpretability. [ABSTRACT FROM AUTHOR]