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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]

The past decade has seen the rapid emergence of research on Big Data and its implications in our society, yet research on its impact on organizations' sustainability challenges is still at an early stage. Based on the organizational learning theory, we develop a framework to investigate the effects of Big Data capability (BDC) and further intellectual capital on a firm's sustainability‐oriented innovation performance (SIP). Using a multi‐source data collected from 358 Chinese firms, we tested our proposed hypotheses using the partial least squares‐structural equation modeling (PLS‐SEM) and the fuzzy‐set qualitative comparative analysis (fsQCA). The results show that BDC is positively related to SIP and economic innovation performance (EIP), mediated by intellectual capital. The fsQCA results further reveal that three configurative combinations, that is, "external capability‐internal resource promotion," "external capability and relationship building help," and "external relationship collaboration compensation," can lead to high SIP. This study provides new insights for firms to develop sustainability‐oriented innovation and to coordinate sustainability innovation and economic innovation practices in order to create balanced outcomes. [ABSTRACT FROM AUTHOR]

Humanity is in more danger from escalating greenhouse gas (G.H.G.) emissions, making the world warmer. The study examined the relationship between China's environmental technologies, ecological innovation, and carbon emissions using time-series data from 1975 to 2020. The N.A.R.D.L. approach is used to examine the cointegration of variables in the short and long run. In the short run, environmental technologies, industrialisation (I.N.D.), positive shocks to natural resource depletion (N.R.D.), negative shocks to renewable energy (R.E.) use, and technical advancements affect carbon emissions. On the other hand, positive shocks to environmental technologies and financial development (F.D.), negative shocks to N.R.D., R.E. consumption (E.C.), and technical innovation all have a long-term effect on carbon emissions. Granger causality was used to examine the causal link between variables. According to the findings, environmental technologies, F.D., technical innovation, N.R.D., and economic growth (E.G.) cause carbon emissions. The impulse response function revealed an inverse link between asymmetric environmental technology and carbon emissions. In contrast, F.D. and N.R.D. directly affect environmental degradation over time. The outcome of the variance decomposition revealed that negative shocks of F.D. would likely exert greater pressure on achieving sustainable environmental agenda. Investment in environmental technology, F.D., technological innovation and R.E. should be encouraged by the Chinese government to achieve sustainable prosperity. [ABSTRACT FROM AUTHOR]

Livestream shopping has attracted great attention in an increasingly digitalized society. This study is to explore the mechanism through which social presence and physical presence affect consumer purchase intentions in livestream shopping as an emerging e-commerce model. Based on the flow theory, this study proposes an integrated model to explain the mechanism through which the feeling of presence affects consumers' purchase intentions in livestream shopping. Empirical data on livestream shopping were collected in China to test the proposed model for an exploratory study. The results show that the feeling of physical presence influences consumers' purchase intentions through concentration and perceived control, and the feeling of social presence influences consumers' purchase intentions through concentration and enjoyment, and, thus, both social presence and physical presence are important elements in livestream shopping. This study provides a better understanding on the mechanism of how the feeling of presence helps improve purchase intentions in livestream shopping. This study shows both physical presence and social presence are positively related to consumers' purchase intention, but with different paths, and, thus, sheds new lights on the feeling of presence and its impact on consumer behaviors in e-commerce. [ABSTRACT FROM AUTHOR]

The past decade has seen impressive developments in financial technology (FinTech) in China. As a new technology and innovative method that competes with, and also supplements, traditional financial methods, fintech has had a significant impact on traditional financial businesses and has thus challenged the role of commercial banks as credit intermediaries in the financial sector. This paper examines the potential risks that fintech brings to commercial banks in China, and collects data from 19 systemically important banks from 2011–2020 to analyze the effect of fintech development on commercial banks' financial risks in order to achieve sustainable development in the financial sector. Using the Z value and non-performing loan ratio as the criterion variables, this study shows that the impact of fintech on the financial risks of systemically important banks demonstrates an inverted U-shaped pattern, with the financial risk increasing first and then decreasing alongside the further development of fintech. The results also show that commercial banks' responses to fintech development has been comparatively slow. Managerial suggestions are then discussed on risk supervision for commercial banks and the financial sector in China and other emerging markets. [ABSTRACT FROM AUTHOR]

Platform enterprises have emerged as one of the most popular business models in the era of knowledge economy. The success of platform enterprises relies on continuous value creation by constructing an efficient platform and attracting more users to participate in order to create more value for the users and by the users. This study is to explore the key factors that drive value creation in platform enterprises' ecosystem to help better understand the management of platform enterprises as the knowledge-based entrepreneurial ventures in emerging markets. This study employs a newly emerged method—a fuzzy-set qualitative comparative analysis to empirically analyze the antecedent configurations of value creation for Chinese platform enterprises. This method extends qualitative comparative analysis by integrating fuzzy-sets and fuzzy-logic principles with qualitative comparative analysis principles, which offers a more realistic approach. This paper identifies different configurations for high and non-high levels of value creation in platform enterprises. The results show that continuous user commitment is crucial to creating positive value for Chinese platform enterprises, and active user participation and knowledge sharing as well as platform construction/improvement are the key elements that determine the platform enterprises' value creation process. The results also demonstrate a hybrid value creation logic with efficiency and innovation in platform enterprises. This study further identifies an asymmetric causality in the Chinese platform enterprises' value creation process that is crucial for knowledge sharing and effective management of platform enterprises. The findings can shed light on the strategic management of platform enterprises for emerging markets. [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]