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As an increasingly popular tool for product exploration, online communities have an important impact on consumers’ purchasing decisions. The purpose of this study is to gain a deeper understanding of how visiting behaviors in online communities affect consumers’ offline purchasing behavior. The moderating role of two dimensions of consumer visiting behaviors (visiting depth and visiting breadth) also receives attention. Moreover, the impact of consumer visiting behaviors on offline sales for different types of online communities is also the focus of this paper.

To test the empirical model, the authors collected data on consumers’ visits to an online real estate platform with local housing sales data. In addition to the baseline regression analysis of the data with the help of Stata 17.0, the study also analyzes the robustness of the results through several methods.

The authors focus on an online community for newly-built housing and find that consumer visits to the focal online community have a positive impact on offline sales. Visiting breadth has a negative moderating effect on this relationship, and no statistically significant moderating effect is found for visiting depth. Further, our empirical exploration finds that consumer visits to competitive online communities have a positive impact on offline focal product sales, but visits to complementary online communities have no statistically significant effect on offline sales.

Our findings contribute to the understanding of consumers’ cross-channel purchasing behavior and provide new insights into how visiting behaviors in online communities affect consumers’ purchasing decisions.

This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies.

This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context.

Social enterprises in China are becoming a growing segment of the market, due to an increasing need for China to act in the best interests of its own society. This review looks at how business model innovation can help social enterprise performance.

The briefing saves busy executives, strategists and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.

Social enterprises pursue growth through business model innovation, and acquiring legitimacy is critical to ensuring such innovation. The purpose of this paper is to examine how business model innovation and legitimacy affect the performance of new social enterprises during different development stages.

This paper uses the hierarchical regression analysis and fuzzy-set qualitative comparative analysis (fsQCA) to examine social enterprise performance and constructs and verifies a moderated mediation effect of business model innovation, based on a survey of 183 new social enterprises in China.

This paper finds that business model innovation has a positive effect on social enterprise performance and an organization's legitimacy, acting as the partial mediator between them. The mediating effect of legitimacy is more positive when social enterprises are in the early growth stage. A more detailed analysis of fsQCA explores the necessary and sufficient conditions for the growth of social enterprise performance across different stages and reveals five configurations that improve the performance of social enterprises.

This study explores the role of business model innovation and legitimacy in social enterprise growth and provides empirical evidence about the causal configuration of high-performing social enterprises.

This research clarifies two antecedents of social enterprise performance and proposes a more inclusive framework that addresses the factor of dynamic development stages. This paper deepens the understanding of social enterprise performance in China.

Advanced information technologies, and particularly big data, provide new affordances to facilitate inter-organizational collaboration. Rich flows of real-time data provide transparency across organizational boundaries and enable greater automation of inter-organizational routines. Taking stock of the literature and building on observations from the research in an industrial setting, the authors introduce the concept of technological embeddedness as an important characteristic of inter-organizational relationships, denoting the degree of monitoring, control, and optimization of intra- and inter-organizational tasks accomplished through technology at the interface of the inter-organizational relationship. The authors theorize how increasing technological embeddedness created by big data technologies affects the development of inter-organizational trust, mutual adaptation, and temporal structuring of collaboration. The propositions elaborate how greater technological embeddedness enables collaboration, and warn about the potential limiting effects of technological embeddedness on the development of interpersonal trust, strategic learning, and long-term orientation.

The purpose of this study is to investigate the impact of high-performance (HP) human resource (HR) practices on employee job engagement and organizational citizenship behavior (OCB) and the moderating effects of transformational leadership.

A sample of 268 employees from the US and a sample of 288 employees from South Korea (SK) were used for examining the hypotheses.

The results illustrated that high-performance HR practices (HPHRP) significantly predicted employee job engagement and OCB in SK. Transformational leadership was found to moderate the associations of HPHRP with employee job engagement and OCB in SK, while in the US, transformational leadership only moderated the relationship between HPHRP and OCB.

Transformational leaders reinforce the quality of the employee–organization relationship and strengthen the impact of HPHRP on employees' positive work-related behaviors.

This study contributes to the understanding of employees' organizational behavior as exploring the relationships of HPHRP, transformational leadership, job engagement and OCB.

The purpose of this paper is to investigate cultural and individual differences in newsvendor decision making.

The online experiment, programmed in the PHP scripting language, had 107 participants: local managers of four large, well-known and supply chain–intensive firms in China (Lenovo, Shenhua, CMST and GM).

The authors find that, as compared with American subjects, Chinese subjects engage in more demand chasing, order quantities that are closer to the mean demand, have a lower expected profit and exhibit greater variance in order quantities. However, these observations may not hold when the cross-cultural comparison is conducted for each pair of ethnic subgroups whose members have the same cognitive reflection test score, a measure of individual differences. Moreover, cultural differences also affect how individual differences manifest in newsvendor decisions.

The authors findings have important implications for employee selection, training and management in any cross-cultural business environment.

Little attention has been paid, in the behavioural operations literature, to individual differences and how they interact with culture. This paper is the first to examine the interaction effects of cultural and individual differences in newsvendor decisions, and it highlights an important research area that is currently understudied in operations management.

The purpose of this paper is to contribute to the understanding of the relationship between information sharing and performance of perishable product supply chains (PPSC). Building on transaction cost economics (TCE), organisational information processing theory (OIPT), and contingency theory (CT), this study proposes a theoretical framework to guide future research into information sharing in perishable product supply chains (IS-PPSC).

Using the systematic literature review methodology, 48 peer-reviewed articles are carefully selected, mapped, and assessed. Template analysis is performed to unravel the relationship mechanisms between information sharing and PPSC performance.

The authors find that the relationship between information sharing and PPSC performance is currently unclear, and there is inconsistency in the positioning of information sharing among constructs and variables in the IS-PPSC literature. This implies a requirement to refine the relationship between information sharing and PPSC performance. The review also revealed that the role of perishable product characteristics has largely been ignored in existing research.

This study applies relevant multiple theoretical perspectives to overcome the ambiguity of the IS-PPSC literature and contributes nine propositions to guide future research. Accordingly, this study contributes to the refined roles of relationship uncertainty, environmental uncertainty, information sharing capabilities, and perishable product characteristics in shaping the relationship between information sharing and PPSC performance.

Internet + and Electronic Business in China is a comprehensive resource that provides insight and analysis into E-commerce in China and how it has revolutionized and continues to revolutionize business and society. Split into four distinct sections, the book first lays out the theoretical foundations and fundamental concepts of E-Business before moving on to look at internet+ innovation models and their applications in different industries such as agriculture, finance and commerce. The book then provides a comprehensive analysis of E-business platforms and their applications in China before finishing with four comprehensive case studies of major E-business projects, providing readers with successful examples of implementing E-Business entrepreneurship projects.

Internet + and Electronic Business in China is a comprehensive resource that provides insights and analysis into how E-commerce has revolutionized and continues to revolutionize business and society in China.

Incorporating users’ behavior patterns could help in the ranking process. Different click models (CMs) are introduced to model the sophisticated search-time behavior of users among which commonly used the triple of attractiveness, examination and satisfaction. Inspired by this fact and considering the psychological definitions of these concepts, this paper aims to propose a novel learning to rank by redefining these concepts. The attractiveness and examination factors could be calculated using a limited subset of information retrieval (IR) features by the random forest algorithm, and then they are combined with each other to predicate the satisfaction factor which is considered as the relevance level.

The attractiveness and examination factors of a given document are usually considered as its perceived relevance and the fast scan of its snippet, respectively. Here, attractiveness and examination factors are regarded as the click-count and the investigation rate, respectively. Also, the satisfaction of a document is supposed to be the same as its relevance level for a given query. This idea is supported by the strong correlation between attractiveness-satisfaction and the examination-satisfaction. Applying random forest algorithm, the attractiveness and examination factors are calculated using a very limited set of the primitive features of query-document pairs. Then, by using the ordered weighted averaging operator, these factors are aggregated to estimate the satisfaction.

Experimental results on MSLR-WEB10K and WCL2R data sets show the superiority of this algorithm over the state-of-the-art ranking algorithms in terms of P@n and NDCG criteria. The enhancement is more noticeable in top-ranked items which are reviewed more by the users.

This paper proposes a novel learning to rank based on the redefinition of major building blocks of the CMs which are the attractiveness, examination and satisfactory. It proposes a method to use a very limited number of selected IR features to estimate the attractiveness and examination factors and then combines these factors to predicate the satisfactory which is regarded as the relevance level of a document with respect to a given query.

The purpose of this paper is to propose a methodology to construct a stock market sentiment lexicon by incorporating domain-specific knowledge extracted from diverse Chinese media outlets.

This paper presents a novel method to automatically generate financial lexicons using a unique data set that comprises news articles, analyst reports and social media. Specifically, a novel method based on keyword extraction is used to build a high-quality seed lexicon and an ensemble mechanism is developed to integrate the knowledge derived from distinct language sources. Meanwhile, two different methods, Pointwise Mutual Information and Word2vec, are applied to capture word associations. Finally, an evaluation procedure is performed to validate the effectiveness of the method compared with four traditional lexicons.

The experimental results from the three real-world testing data sets show that the ensemble lexicons can significantly improve sentiment classification performance compared with the four baseline lexicons, suggesting the usefulness of leveraging knowledge derived from diverse media in domain-specific lexicon generation and corresponding sentiment analysis tasks.

This work appears to be the first to construct financial sentiment lexicons from over 2m posts and headlines collected from more than one language source. Furthermore, the authors believe that the data set established in this study is one of the largest corpora used for Chinese stock market lexicon acquisition. This work is valuable to extract collective sentiment from multiple media sources and provide decision-making support for stock market participants.