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The huge demand for fresh goods has stimulated lots of research on the perishable food supply chain. The characteristics of perishable food and the cross-regional transportation have brought many challenges to the operation models of perishable food supply chain. The purpose of this paper is to address these challenges based on the real-time data acquired by the Internet of Things (IoT) devices.

IoT and the modeling of the Supply Hub in Industrial Parks were adopted in the perishable food supply chain.

A conceptual model was established for the IoT-enabled perishable food supply chain with two-echelon supply hubs. The performance of supply chain has improved when implementing the proposed model, as is demonstrated by a case study.

By our model, the supply hubs which act as the dominators of the supply chain can respond to the real-time information captured from the operation processes of an IoT-enabled supply chain, thus to provide public warehousing and logistic services.

This paper is mainly concerned with studying the e‐manufacturing scheduling issue based on the bills of materials (BOM) flows of products. On the basis of e‐manufacturing philosophy, a genetic algorithm (GA)‐based scheduling model for physical e‐manufacturing cells is put forward. By means of building the mapping relationship and dynamic task association among physical e‐manufacturing cells, logic e‐manufacturing cells, e‐manufacturing systems and e‐manufacturing environment, high‐level scheduling issues can be transformed as an inductive procedure concerning the scheduling results to corresponding physical e‐manufacturing cells. As verification, finally, a case study is given to demonstrate the method mentioned above.

Using analytics for firms' competitiveness is a vital component of a company's strategic planning and management process. In recent years, organizations have started to capitalize on the significant use of big data for analyses to gain valuable insights to improve decision-making processes. In this regard, leveraging and unleashing the potential of big data has become a significant success factor for steering firms' competitiveness, and the related literature is increasing at a very high pace. Thus, the authors propose a bibliometric study to understand the most important insights from these studies and enrich existing conceptual models.

In this study, the authors use a bibliometric review on articles related to the use of big data for firms' competitiveness. The authors examine the contributions of research constituents (authors, institutions, countries and journals) and their structural and thematic relationships (collaborations, co-citations networks, co-word networks, thematic trends and thematic map). The most important insights are used to enrich a conceptual model.

Based on the performance analysis results, the authors found that China is by far the most productive country in this research field. However, in terms of influence (by the number of citations per article), the most influential countries are the UK, Australia and the USA, respectively. Based on the science mapping analysis results, the most important findings are projected in the common phases of competitive intelligence processes and include planning and directions concepts, data collection concepts, data analysis concepts, dissemination concepts and feedback concepts. This projection is supplemented by cross-cutting themes such as digital transformation, cloud computing, privacy, data science and competition law. Three main future research directions are identified: the broadening of the scope of application fields, the specific case of managing or anticipating the consequences of pandemics or high disruptive events such as COVID-19 and the improvement of connection between firms' competitiveness and innovation practices in a big data context.

The findings of this study show that the most important research axis in the existing literature on big data and firms' competitiveness are mostly related to common phases of competitive intelligence processes. However, concepts in these phases are strongly related to the most important dimensions intrinsic to big data. The use of a single database (Scopus) or the selected keywords can lead to bias in this study. Therefore, to address these limitations, future studies could combine different databases (i.e. Web of Science and Scopus) or different sets of keywords.

This study can provide to practitioners the most important concepts and future directions to deal with for using big data analytics to improve their competitiveness.

This study can help researchers or practitioners to identify potential research collaborators or identify suitable sources of publications in the context of big data for firms' competitiveness.

The authors propose a conceptual model related to big data and firms' competitiveness from the outputs of a bibliometric study.

This paper aims to recognize whether government policy supervision or social network platform supervision can effectively promote the control of misconducts of web celebrity brand eWOM marketing and to identify the key factors influencing the unhealthy web celebrity marketing environment.

Theoretical research was employed to develop a practical approach for applying evolutionary game theory to eWOM marketing controlling strategies modeling via dynamic visualization, systematic simulation experiments.

Evolutionary game theory combined with dynamic simulation modeling can provide a formal approach to understanding web celebrity brand eWOM marketing decision-making in social media, which can thus support the control of unhealthy web celebrity marketing environment. The results demonstrate that the reasonable control of social platform control costs may be more effective than the government policy on web celebrity fake brand eWOM marketing behaviors.

The study enriches the research on the management and control of eWOM marketing as well as provides guidance for the sustainable development of the web celebrity economy in social media.