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The purpose of this paper is to propose a framework of value co-creation in platform ecological circle for cold chain logistics enterprises to guide the transformation and development of cold chain logistics industry.

This paper establishes a conceptual framework for the research on the platform ecological circle in cold chain logistics, utilizes a structural equation model to investigate the influencing factors of the value co-creation of the platform ecological circle in the cold chain logistics enterprises and elaborates the internal relations between different influencing factors regarding the value co-creation and enterprises’ performance.

Results show that resource sharing in logistics platform ecological circle can stimulate the interaction among enterprises and this produces a positive influence on their dynamic capabilities, which, in turn, affects the they to work together to plan, implement and solve problems, so as to achieve the goal of improving enterprise performance.

The shared resources and value co-creation activities in the platform ecological circle are very important for the transformation and development of cold chain logistics enterprises. Therefore, enterprises should promote value co-creation through realizing resource sharing and creating a win-win cooperation mechanism.

This paper targets at incorporating the resource sharing in platform ecological circle for cold chain logistics enterprises, explores from an empirical perspective the role of the resource sharing in cold chain logistics enterprises in enhancing the dynamic capabilities of enterprises, thereby encouraging the value co-creation behavior, and ultimately boosts enterprise performance and stimulates business development.

The operational management of cold chain logistics has an important impact on the quality of cold chain products, but the service delivery process is subject to a series of potential problems such as product loss and cold storage temperature in the actual operation.

In this paper, the whole cold chain logistics system and risk events are analyzed. A Bayesian network is used for modeling and simulation to identify the main influencing factors and to conduct a sensitivity analysis of the main factors.

It is found that the operation of cold chain logistics systems can be divided into four links according to the degree of influence as follows: transportation and distribution, processing and packaging, information processing and warehousing. Transportation and distribution is the most influential factor of system failure, and extreme weather is the most risky event. At the same time, the four risk events that have the greatest impact on the operation of the cold chain system are in descending order: transportation equipment failure, extreme weather, unqualified pre-cooling and violation operation.

Therefore, enterprises should develop appropriate interventions for securing the transportation services, design strategies to deal with extreme weather conditions prior to and in the early stage of product delivery, and prepare additional effective measures for managing emergency events.

This paper aims to present a case wherein a public archive intervenes in maintaining and preserving digital records (including underlying technological infrastructure) created by a private company to protect the trustworthiness of records, thereby helping the company to discharge their accountability.

This paper details the intervention of Tianjin Municipal Archives in the management of the records of Tianjin Otis Elevator Co., Ltd, the technical infrastructure that enables and supports such configuration, the issues encountered and the theoretical implications of this case.

This case suggests that not only does the concept of archives as a trusted third party remain relevant in the changing technological environment but also, in certain cases (e.g. wherein the supplier of evidentiary documents holds a monopoly over an industry), archives are becoming increasingly critical in maintaining the reliability and authenticity of digital records in the cloud environment.

Given the challenges raised by the emerging cloud environment, it is vital to develop a renewed understanding of the concept of archives as a trusted third party, the relationship between archives and commercial third party services and the relationship between public archives and private records. Furthermore, this case identifies the need to re-examine archival methodologies to protect the authenticity of structured data.

This case exemplifies how archives can help private organizations address issues related to guaranteeing and demonstrating the evidential nature of digital records and provides empirical evidence for archives being conceptualized as a trusted third party in maintaining and preserving digital records.

The purpose of this paper is to investigate dynamics of causal interactions and financial risk contagion among BRICS stock markets under rare events.

Two methods are adopted: The new causal inference technique, namely, the Liang causality analysis based on information flow theory and the dynamic causal index (DCI) are used to measure the financial risk contagion.

The causal relationships among the BRICS stock markets estimated by the Liang causality analysis are significantly stronger in the mid-periods of rare events than in the pre- and post-periods. Moreover, different rare events have heterogeneous effects on the causal relationships. Notably, under rare events, there is almost no significant Liang's causality between the Chinese and other four stock markets, except for a few moments, indicating that the former can provide a relatively safe haven within the BRICS. According to the DCIs, the causal linkages have significantly increased during rare events, implying that their connectivity becomes stronger under extreme conditions.

The obtained results not only provide important implications for investors to reasonably allocate regional financial assets, but also yield some suggestions for policymakers and financial regulators in effective supervision, especially in extreme environments.

This paper uses the Liang causality analysis to construct the causal networks among BRICS stock indices and characterize their causal linkages. Furthermore, the DCI derived from the causal networks is applied to measure the financial risk contagion of the BRICS countries under three rare events.

Based on core description, gas logging and laboratory analysis, this paper aims to study the controlling effect of the types of shale sedimentary microfacies in coal formations over shale reservoirs using the example of Shanxi formation in Northern Ordos Basin.

According to core observation, the authors selected typical samples of rock types for thin section analysis to determine the micro features and compositions of rocks.

By using core observation, we found that fine lithology in Shanxi formation included major shale, carbonaceous shale, partially carbonaceous shale, partially silty shale and silty shale with colors of gray, dark gray, black and/or gray. Shanxi Formation shale are deposited in plant-rich and plant-poor swamps, interdistributary depressions of delta plains, interfluvial depressions of meandering rivers as well as microfacies environment of natural levees and the distal crevasse splay.

Currently, the research on the shale gas in Shanxi Formation in the Ordos Basin is still in its infancy. There is yet no research on the fine-grained partition of the sedimentary facies in coal accumulation environment of Shanxi formation and the controlling effect of sedimentary microfacies over shale reservoirs.

To strengthen the correlation analysis on risk factors of drug production safety and reduce the influence due to fuzzy judgments, a safety risk assessment method based on Grey-Analytic Network Process (G-ANP) is proposed.

First, an index system evaluating drug production safety risk is constructed according to the “Good Manufacture Practice of Medical Products,” next the influence weight of each risk index is derived by using the Analytic Network Process, then the grey number of each risk index is determined by further utilizing the grey statistical theory, and finally the risk level of drug production is obtained.

An empirical study is conducted and the results support the feasibility and practicability to use G-ANP method for drug production safety risk evaluation. The results of the case show that it is feasible and practical to use G-ANP method for drug production safety risk evaluation.

The innovation lies in the use of G-ANP method to fully consider the interdependence and interaction between the risk factors of drug production safety, which improves the objectivity in judging the risk level of drug production and provides a scientific basis for pharmaceutical manufacturers to formulate further decisions and management in the case of insufficient quantification of risk factors. Based on the findings, more targeted suggestions are made to reduce the production risk of pharmaceutical enterprises.

Members’ sustained participation positively influences success of brand community. Although scholars have confirmed the effects of social capital on continuance intention in third-party hosted communities, little work has been done to explore these relationships in context of enterprise-sponsored brand communities, especially, the precursors of active members’ sustained participation in such context is still unclear. Besides, how to recognize active users with high precision and coverage remains an open question. Therefore, this paper aims to propose a novel method to identify active users effectively and investigate the antecedents of their continuance intention from perspective of social capital in enterprise-sponsored brand community.

This work established several social networks based on the information of Xiaomi smartphone forum users’ posts and feedbacks. Node centrality (out-degree) analysis was adopted to identify the users with high degree of active in these networks, and then behaviour analysis was performed to exclude the community managers from the group of active users. Finally, a research model was proposed based on the theory of social capital. It was tested by applying partial least squares technique, and the data were collected from a survey of members (n = 327) of Xiaomi forum.

The empirical results showed that the proposed method can recognize the active users effectively. Additionally, social tie, identification, trust and shared vision were proved to be significant predictors of active users’ continuance intention in the context of enterprise-sponsored brand community.

This paper contributes to the information system usage literature and provides opinions regarding how social capital influence active users’ sustained participation in enterprise-sponsored brand community. Besides, this work proposed a novel method to identify active users, which will be useful to assist enterprises to improve their community management.

Many users build personal projects in co-innovation community to accomplish their innovations. However, very few projects from such communities are successful and understanding of this phenomenon is limited. The purpose of this paper is to identify the factors facilitating user projects success in online co-innovation communities.

Based on the theories of persuasion and diffusion of innovation (DOI), a conceptual model is proposed to explain how project success likelihood is affected by the creator, project and user participation characteristics. Then, the model and hypotheses are tested through binary logistic regression on a secondary data set of 572 projects collected from a typical user co-innovation community, Local Motors.

The results show that creator characteristics (prior success rate), project characteristics (project popularity, length and duration) and user participation characteristics (participation users and degree) have significant and positive impacts on project success likelihood. The number of prior projects, which can hardly represent the creator’s credibility in open and unrestricted situations, has no significant influence on the project success likelihood.

This study offers project creators the keys to increase their projects successful possibility. Besides, this study recommends a new way to attract users and helps to identify creative and effective users for community practitioners.

This study expands the research scope in online co-innovation community by focusing on user personal projects. In addition, it combines persuasion theory and DOI theory to add the holistic understanding of user project success likelihood.

How to identify the critical success factors (CSFs) of public health emergencies (PHEs) is of great practical significance to carry out a scientific and effective risk assessment. The purpose of this paper is to address this issue.

In this paper, the authors propose a new approach to identify the CSFs by hesitant fuzzy linguistic set and a Decision-Making Trial and Evaluation Laboratory (DEMATEL) approach. First, a larger group of experts are clustered into three groups according to similarity degree. Then, the weight of each cluster is determined by the maximum consensus method, and the overall direct influence matrix is obtained by clustering with hesitant fuzzy linguistic weighted geometric (HFLWG) operators. Finally, the overall direct influence matrix is transformed into the crisp direct impact matrix by the score function, and 11 CSFs of PHEs are identified by using the extended DEMATEL method.

In addition, an example of PHEs shows that the approach has good identification applicability. The approach can be used to solve the problems of fuzziness and subjectivity in linguistic assessments, and it can be applied to identify the customer service framework with the linguistic assessments process in emergency management.

This paper extends the above DEMATEL method to study in the hesitant fuzzy linguistic context. This proposed hybrid approach has a wider application in the high-risk area where disasters frequently occur.

This paper aims to demonstrate how to make emergency decision when decision makers face a complex and turbulent environment that needs quite different decision-making processes from conventional ones. Traditional decision techniques cannot meet the demands of today’s social stability and security.

The main work is to develop an instance-driven classifier for the emergency categories based upon three fuzzy measures: features for an instance, solution for the instance and effect evaluation of the outcome. First, the information collected from the past emergency events is encodes into a prototype model. Second, a three-dimensional space that describes the locations and mutual distance relationships of the emergency events in different emergency prototypes is formulated. Third, for any new emergency event to be classified, the nearest emergency prototype is identified in the three-dimensional space and is classified into that category.

An instance-driven classifier based on prototype theory helps decision makers to describe emergency concept more clearly. The maximizing deviation model is constructed to determine the optimal relative weights of features according to the characteristics of the new instance, such that every customized feature space maximizes the influence of features shared by members of the category. Comparisons and discusses of the proposed method with other existing methods are given.

To reduce the affection to economic development, more and more countries have recognized the importance of emergency response solutions as an indispensable activity. In a new emergency instance, it is very challengeable for a decision maker to form a rational and feasible humanitarian aids scheme under the time pressure. After selecting a most suitable prototype, decision makers can learn most relevant experience and lessons in the emergency profile database and generate plan for the new instance. The proposed approach is to effectively make full use of inhomogeneous information in different types of resources and optimize resource allocation.

The combination of instances can reflect different aspects of a prototype. This feature solves the problem of insufficient learning data, which is a significant characteristic of emergency decision-making. It can be seen as a customized classification mechanism, while the previous classifiers always assume key features of a category.