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Global climate change characterized by an increase in temperature has become the focus of attention all over the world. China is a sensitive and significant area of global climate change. This paper specifically aims to examine the association between agricultural productivity and the climate change by using China’s provincial agricultural input–output data from 2000 to 2019 and the climatic data of the ground meteorological stations.

The authors used the three-stage spatial Durbin model (SDM) model and entropy method for analysis of collected data; further, the authors also empirically tested the climate change marginal effect on agricultural productivity by using ordinary least square and SDM approaches.

The results revealed that climate change has a significant negative effect on agricultural productivity, which showed significance in robustness tests, including index replacement, quantile regression and tail reduction. The results of this study also indicated that by subdividing the climatic factors, annual precipitation had no significant impact on the growth of agricultural productivity; further, other climatic variables, including wind speed and temperature, had a substantial adverse effect on agricultural productivity. The heterogeneity test showed that climatic changes ominously hinder agricultural productivity growth only in the western region of China, and in the eastern and central regions, climate change had no effect.

The findings of this study highlight the importance of various social connections of farm households in designing policies to improve their responses to climate change and expand land productivity in different regions. The study also provides a hypothetical approach to prioritize developing regions that need proper attention to improve crop productivity.

The paper explores the impact of climate change on agricultural productivity by using the climatic data of China. Empirical evidence previously missing in the body of knowledge will support governments and researchers to establish a mechanism to improve climate change mitigation tools in China.

This paper aims to introduce a joint DEMATEL and NK methodology to develop a process model for introducing and implementing relational supply chain practices for low-carbon supply chains. Using this process model as a guide, insights into specific practices and how to implement these relational practices to achieve competitive advantage across organizations are introduced.

Low-carbon cooperation practices framework based on the relational view is developed. A methodology based on DEMATEL and the NK model is used to construct a sequential process model for introducing and implementing these relational practices. Empirical data from three manufacturing organizations in China are utilized to validate the model.

Initial results provide a sequence of relational practices for guiding those organizations and their suppliers for healthy and low-carbon development. Interdependencies between relational practices are analyzed and evaluated from four aspects. Insights into the broader application of the methodology and initial results from both a research and managerial perspective are presented, especially with consideration of the China, an emerging economy, context.

The methodology remains relatively abstract in nature, yet the tool can provide very useful interpretations and information for both researchers and practitioners.

This paper stipulates that in addition to internal operational practices, the relational practices between buyer and supplier may be equally important to achieve a low-carbon outcome, especially in supply chain setting. This paper also shows that not only the relational practice itself but also the implementation sequence of the relational practices can relate to performance. According to the authors’ initial results, organizations in this study should first develop product development cooperation, then exchange carbon knowledge and implement effective governance and last build a trust relationship with its suppliers for low-carbon cooperation.

This is one of the few approaches that directly evaluates and identifies the interdependencies among relational practices and to construct a process model for introducing and implementing low-carbon supply chain cooperation. It is also the first time that the NK model has been integrated with DEMATEL. Focusing on Chinese supply chain carbon emissions concerns is also a unique perspective.

The purpose of this paper is to threefold. The first purpose is to review and critically analyze corporate sustainability development (CSD) research in China. Second, the paper extracts a unified theoretical framework among CSD drivers, CSD practices, and corporate performance in China. Finally, it seeks to identify links between CSD and industrial management and data systems (IMDS) topics.

A comprehensive and structured review of the research literature investigating CSD in China was completed. Categorizations and classifications of the literature were summarized. A critical analysis of the literature resulted in a generic theoretical framework that can be used for evaluation of the literature and further investigation.

The literature review found over 189 papers on CSD in China published from 1997 to 2013. The framework developed focussed on relationships among drivers, practices, and performance within a CSD in China context. The framework provides useful insights into the implementation of CSD practices. The integration of the three dimensions of sustainability and decision-making methodology are still rare. Specific features of CSD are also reviewed with a linkage to IMDS research around information technology, business process modeling, and supply chain management.

This is one of the first works to provide a comprehensive focus on CSD in China. The theoretical framework was developed for CSD in China to clarify the relationships between the drivers, the corporation’s characteristics, CSD practices, and corporation performance and will prove useful for future research development and investigation. The linkage to IMDS topics is novel and will help further research related to CSD in China for this journal.

The purpose of this paper is to consider that the model of volatility characteristics is more reasonable and the description of volatility is more explanatory.

This paper analyzes the basic characteristics of market yield volatility based on the five-minute trading data of the Chinese CSI300 stock index futures from 2012 to 2017 by Hurst index and GPH test, A-J and J-O Jumping test and Realized-EGARCH model, respectively. The results show that the yield fluctuation rate of CSI300 stock index futures market has obvious non-linear characteristics including long memory, jumpy and asymmetry.

This paper finds that the LHAR-RV-CJ model has a better prediction effect on the volatility of CSI300 stock index futures. The research shows that CSI300 stock index futures market is heterogeneous, means that long-term investors are focused on long-term market fluctuations rather than short-term fluctuations; the influence of the short-term jumping component on the market volatility is limited, and the long jump has a greater negative influence on market fluctuation; the negative impact of long-period yield is limited to short-term market fluctuation, while, with the period extending, the negative influence of long-period impact is gradually increased.

This paper has research limitations in variable measurement and data selection.

This study is based on the high-frequency data or the application number of financial modeling analysis, especially in the study of asset price volatility. It makes full use of all kinds of information contained in high-frequency data, compared to low-frequency data such as day, weekly or monthly data. High-frequency data can be more accurate, better guide financial asset pricing and risk management, and result in effective configuration.

The existing research on the futures market volatility of high frequency data, mainly focus on single feature analysis, and the comprehensive comparative analysis on the volatility characteristics of study is less, at the same time in setting up the model for the forecast of volatility, based on the model research on the basic characteristics is less, so the construction of a model is relatively subjective, in this paper, considering the fluctuation characteristics of the model is more reasonable, characterization of volatility will also be more explanatory power. The difference between this paper and the existing literature lies in that this paper establishes a prediction model based on the basic characteristics of market return volatility, and conducts a description and prediction study on volatility.

The purpose of this paper is to identify how organizations can evaluate the green product deletion decision within an environmentally sustainable consumption and production environment through a hybrid multistage multiple criteria evaluation approach.

This paper proposes a decision-making model by integrating “soft computation” using neighborhood rough set theory, fuzzy cluster means, and cumulative prospect theory. Literature is used to identify various factors for the decision environment. An illustrative problem provides insights into the methodology and application.

The results indicate that green products can be evaluated from both their relative environmental burdens and benefits. Sustainable consumption and production factors that play a role in this multifactor decision are identified. The results show that a comprehensive evaluation can capture an effective overall picture on which green product(s) to delete.

The opaqueness of the proposed methodology may cause less acceptance by management. The methodology made a number of assumptions related to the data. An actual application of the tool rather than just an illustrative example is needed.

The main contribution of this study is the novel integration of supply chain perspectives, both upstream (supply and production) and downstream (demand/usage), with green product deletion decision making. The hybrid multistage technique has advantages of being able to incorporate many factors that have a variety of quantitative and qualitative characteristics to help managers address green product deletion issues as well as its impact on greening of supply chains and organizational environmental sustainability. This paper adds value to product deletion, supply chain management, and sustainable production and consumption literatures.

The purpose of this paper is to introduce a methodology to identify sustainable supply chain key performance indicators (KPI) that can then be used for sustainability performance evaluation for suppliers.

Initially the complexity of sustainable supply chain performance measurement is discussed. Then, a two-stage method utilizing neighborhood rough set theory to identify KPI and data envelopment analysis (DEA) to benchmark and evaluate relative performance using the KPI is completed. Additional analysis is performed to determine the sensitivity of the KPI set formation and performance results.

The results show that KPI can be determined using neighborhood rough set, and DEA performance results provide insight into relative performance of suppliers. The supply chain sustainability performance results from both the neighborhood rough set and DEA can be quite sensitive parameters selected and sustainability KPI sets that were determined.

The data utilized in this study are illustrative and simulated. Only one model for the neighborhood rough set and DEA was utilized. Additional investigations using a variation of rough set and DEA models can be completed.

This tool set is valuable for managers to help identify sustainable supply chain KPI (from among hundreds of potential measures) and evaluate sustainability performance of various units within supply chains, including supply chain partners, departments, projects and programs.

Sustainability incorporates many business, economic and social implications. The methods introduced in this paper can help organizations and their supply chains become more strategically and operationally sustainable.

Few tools and techniques exist in the sustainable supply chain literature to help develop KPIs and evaluate sustainability performance of suppliers and the supply chain. This paper is one of the first that integrates neighborhood rough set and DEA to address this important sustainable supply chain performance measurement issue.

As green marketing is a critical function of green supply chain management, it has generated a great deal of discussion in public discourse and academic research. Interestingly, there is a gap in the research on how a company can effectively invest and manage various green marketing activities. Thus this paper aims to introduce a grey multiple criteria decision-making methodology to investigate the relationships between green marketing activities and performance outcomes (environmental and business performance). This paper seeks to make the following contributions to the green supply chain management literature. First, review the literature to identify implementation activities for green marketing from green supply chain management literature. Second, develop a methodology to evaluate relationships of green marketing activities by integrating grey number and rough set method. Third, obtain various decision rules relating the various green marketing activities to the environmental and/or business performance outcomes through an illustrative application within an incomplete information system. Knowing those rules to help in the implementation, management, and evaluation of these green marketing activities is valuable to both researchers and practitioners. Included in the discussion are insights into how these rules for individual and aggregated performance (environmental, business, and joint performance) may be evaluated. Practical and research implications of this work are detailed in the final section of this paper.

The purpose of this review paper is to identify the themes of hospitality and tourism innovation research published in academic and trade journals between 2010 and 2020, to explore the common and different areas of interest between academia and the industry and to examine the trends of innovation research themes during this period to suggest future research directions.

The EBSCO Hospitality and Tourism Index was used as the data source. Co-word analysis was conducted via the Leximancer software to identify key areas of interest in hospitality and tourism innovation. Trends in hospitality and tourism innovation over the ten-year period were revealed by a time-dimension analysis.

This study produced a conceptual map demonstrating focal points and trends in hospitality and tourism innovation. Trade journals were found to be dominated by product innovation driven by technological development, while academic journals covered more topics, such as employee innovation, sustainable innovation, leadership innovation and user generated content (UGC). Academia was shown to follow in the footsteps of industry in publication on several topics. Furthermore, academic interest in certain topics, such as hotel service innovation, UGC and social media, lasted several years.

This study can facilitate knowledge transfer between academia and the industry. It also provides directions for future research based on historical and comparative analysis.

This research extends the scope of existing review studies by including articles published by trade journals. It also adds a time dimension, which helps to unveil the evolution process of hospitality and tourism innovation research and practice.

The purpose of this paper is to introduce a methodology to help evaluate, select, and monitor sustainable supply chain performance measurement that can be integrated into a performance management system (PMS).

Grey‐based neighborhood rough set theory is used to help arrive at a core set of important business and environmental performance measures for sustainable supply chains. The supply chain operations reference (SCOR) model is used to develop both business and environmental measures for supply chain sourcing.

A case illustration shows the applicability of the methodology. A sensitivity analysis shows that variations in outcome considerations may greatly influence the set of key performance measures for a sustainable supply chain PMS.

The methodology and presentation is conceptual, yet the tool can provide very useful interpretations for both researchers and practitioners.

The tool can be valuable for companies that are trying to identify key environmental and business performance measures for their supply chains. It helps save resources by not requiring the management of a burdensome and complex set of performance measures.

This is one of the few approaches that helps to clearly identify and narrow the set of performance measures for sustainable supply chains. It attempts to do so with minimal information loss. It is also the first time that grey techniques have been integrated with neighborhood rough set methodology.

This study aims to meta-analyze the moderating roles of Hofstede’s four cultural values in the antecedent–tourist loyalty link.

Based on 35 independent studies, a meta-regression was conducted to determine whether the heterogeneity of 11 antecedent–tourist loyalty relationships could be explained by four dimensions of Hofstede’s cultural values. Furthermore, this study tested how these relationships were contingent on each cultural value dimension, reinforced by robustness tests involving subgroup analyses.

The intensity of all associations significantly varied by at least one cultural value dimension, namely, power distance, individualism, masculinity or uncertainty avoidance.

This meta-analytic study enriches the relevant literature by referring to a large, diverse sample to enhance the robustness of the moderating role of tourists’ national culture in loyalty formation and revealing the moderating effect of national culture in 11 antecedent–tourist loyalty links more than in extant literature.

For the body of knowledge of culture-moderated tourist loyalty formation, to the best of the authors’ knowledge, this study is the pioneering meta-analytic effort. It also first offers an original contribution to moderator analysis meta-analytic studies of tourist loyalty by identifying a new moderator, i.e. national culture.