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Information sharing is one of essential collaboration methods for building effective system-level disruption responses and communication for supply chain resilience. However, supply chain members are often reluctant to share the members' business information for fear of losing competitiveness. To facilitate the cooperation among these members, the supply chain members' should be made aware of the value of information. As a result, the purpose of this paper is to quantify the benefit of information sharing and evaluate its magnitude under various factors.

In this paper, information sharing is measured in a two-stage supply chain containing a manufacturer and a retailer. A demand function is constructed as a linear combination of a first-order autoregressive [AR(1)] process, the retail and reference prices. The values of information sharing are quantified for four scenarios: (1) no information sharing, (2) full information sharing, (3) limited information sharing and (4) partial information sharing. Based on the four scenarios, the conditions for valuable information sharing are determined. In addition, the impact of several demand parameters on the usefulness of information sharing is analyzed.

When the demand function is a pure AR(1) process (i.e. there is no impact from the retail and reference prices), information sharing is always valuable regardless of the autoregressive coefficient. Under the influence of the retail price and consumer behavior via the reference price, information sharing is not always beneficial. The boundaries for useful information sharing are analytically constructed. In addition to full information sharing, this study also quantifies the value of information under a partial sharing scheme. The results indicate that the information is more valuable as long as the information is inducible.

This study highlights several specific conditions for a beneficial information sharing agreement in consideration of consumer behaviors. These conditions enable supply chain members to design a sustainable partnership.

Using protest event analysis, this study aims to investigate which risk factors influence the severity of environmental protests in China, thus filling a gap in China-related environmental protest literature.

Using a database derived from media coverage of 129 environmental protests in China from 2009 to 2015, this empirical study identifies underlying risk factors influencing environmental protest severity, quantifies these protest cases and verifies the associations between risk factors and severity using ordered logistic regression.

The results show that higher environmental health threat, economic loss, distrust of local government, lack of local governmental response, improper local government action and higher population density are likely to increase environmental protest severity; however, contrary to expectations, environmental information disclosure has no significant effect. These findings illuminate the vital and variational role of local government throughout all stages of the evolutionary process in environmental protests. Moreover, public distrust of local government is the principal cause of these protests.

This study enhances the understanding of how Chinese environmental protests arise from the identified risk factors and contributes to quantitative multi-case research in this area. Furthermore, the findings may help local governments in China, as well as in other countries, to enact positive measures to prevent serious environmental protests and improve their ability to address the environmental problems that cause protests. More effective governance can decrease the number and severity of environmental protests and thus promote social stability.

This study aims to provide a more complete picture of blockchain development by combining numerous methodologies with diverse data sources, such as academic papers and news articles. This study displays the developmental status of each subject based on the interrelationships of each topic cluster by analyzing high-frequency keywords extracted from the collected data. Moreover, applying above methodologies will help understanding top research topics, authors, venues, institutes and countries. The differences of blockchain research and new are identified.

To identify and find blockchain development linkages, researchers have used search terms such as co-occurrence, bibliographic coupling, co-citation and co-authorship to help us understand the top research topics, authors, venues, institutes and countries. This study also used text mining analysis to identify blockchain articles' primary concepts and semantic structures.

The findings show the fundamental topics based on each topic cluster's links. While “technology”, “transaction”, “privacy and security”, “environment” and “consensus” were most strongly associated with blockchain in research, “platform”, “big data and cloud”, “network”, “healthcare and business” and “authentication” were closely tied to blockchain news. This article classifies blockchain principles into five patterns: hardware and infrastructure, data, networking, applications and consensus. These statistics helped the authors comprehend the top research topics, authors, venues, publication institutes and countries.

Since Web of Science (WoS) and LexisNexis Academic data are used, the study has few sources. Others advise merging foreign datasets. WoS is one of the world's largest and most-used databases for assessing scientific papers.

This study has several uses and benefits. First, key concept discoveries can help academics understand blockchain research trends so they can prioritize research initiatives. Second, bibliographic coupling links academic papers on blockchain. It helps information seekers search and classify the material. Co-citation analysis results can help researchers identify potential partners and leaders in their field. The network's key organizations or countries should be proactive in discovering, proposing and creating new relationships with other organizations or countries, especially those from the journal network's border, to make the overall network more integrated and linked. Prominent members help recruit new authors to organizations or countries and link them to the co-authorship network. This study also used concept-linking analysis to identify blockchain articles' primary concepts and semantic structures. This may lead to new authors developing research ideas or subjects in primary disciplines of inquiry.