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Stewardship behavior is an important embodiment of the spirit of employee ownership, which is critical to the sustainability of companies, especially under the influence of the COVID-19 epidemic. Most previous studies have focused on how to motivate employees’ stewardship behavior, but little is known about how stewardship behavior affects employees themselves. The purpose of this study is to explore how employee stewardship behavior affects their work-family interface based on the conservation of resources (COR) theory.

In this study, structural equation modeling was conducted using two-wave survey data from 323 employees through three internet companies in Southern China.

Results reveal that engaging in stewardship behavior is positively correlated with both positive emotion and emotional exhaustion. Positive emotion and emotional exhaustion, in turn, mediate the effects of stewardship behavior on work–home interface. Family motivation influences the strength of the relationships between positive emotion or emotional exhaustion and work–family interface, that is, high family motivation strengthens the positive association between positive emotion and work–family enrichment and weakens the positive association between emotional exhaustion and work–family conflict.

This study suggests that managers should give employees more support and care to ease the worries of engaging in stewardship behavior. Also, organizations should recruit employees with high family motivation, which can reduce the negative effects of stewardship behavior on work–-family interface.

Based on an actor’s perspective, this study examines both the positive and negative effects of stewardship behavior on employees themselves, thereby increasing understanding of the dual effect of stewardship behavior. In addition, this study further elucidates the mechanisms that moderate the positive and negative effects of individual family motivation on their engagement in stewardship behavior within the COR theory.

Sustainability has been on the executive agenda for years and it is now one of the fastest growing supply chain management trends. The purpose of this paper is to analyze the barriers for the adoption and implementation of the sustainable supply chain management (SSCM) concept.

This study has been divided into two phases such as identification of barriers and qualitative analysis. First, to identify the most influential barriers, the authors offer a systematic literature review, taking 188 papers published from 2010 to November 2016 into account. The investigation phase led to the selection of 15 barriers based on the literature in consultation with industrial experts and academicians. Second, the interpretive structural modeling qualitative analysis was used to find out the mutual influences between the 15 barriers by a survey.

Further, the authors propose and illustrate the cross-impact matrix multiplication applied to classification analysis to test a framework that extrapolates SSCM barriers and their relationships. “Inadequate information technology implementation” has been identified as the most important barrier that may force organizations to implement SSCM practices to ensure their business sustainability.

The authors presented some limitations in their research in some fields which could allow new researchers and practitioners to conduct the future research to grow in different dimensions.

Practitioners or policymakers usually are not familiar with these types of research works; that is why most of these surveys remain theoretical and conceptual. Future investigation needs to be done in practical application domain instead of merely giving opinions.

Based on the authors’ research, the researchers have more attention to work in conceptual analysis due to other fields, but the authors believe that even with the implementation of SSCM, many remarkable areas still exist for future research which could help in development. The authors also provide more details in this paper.

Due to the different actual construction conditions in different cities, the requirements for community livability may also differ due to different geographical locations and urban construction priorities. The research system in this paper can be applied to study similar old communities in old urban areas. The indicator system would need to be adjusted in different places, based on specific construction situations and higher planning requirements. This process would provide valuable insights for effective construction projects that support the livability of the old communities.

Based on sustainable and people-oriented development principles, this study considered the development of old urban communities during today’s rapid urban renewal and development. Using previous literature and related research experience, this study established an evaluation indicator system to assess the livability of old urban communities. Based on the local resident experience and satisfaction, the study investigated current weaknesses in the construction of livable old urban communities and developed corresponding recommendations for reform based on these. The goal was to provide guidance and recommendations for renewing old communities in during urban development and further promote the sustainable development of the city.

Based on the people-oriented principle and focusing on old urban communities as the research object, this study constructed an evaluation indicator system to evaluate the livability of urban old communities. The goal was to identify the weaknesses in the construction of old urban communities, with a focus on livability. Using the Bei’anmen community in Nanjing as a case study, the AHP method and fuzzy comprehensive evaluation method were applied to evaluate the overall target level and specific indicators, with the goal of assessing the level of livability in the Bei’anmen community.[AQ2] The results show that the livability of the Bei’anmen community is “very poor,” with significant room for improvements in community livability. This study also proposed corresponding measures for improving problems related to livability in the old urban community. Establishing the indicator system may help evaluate the livability of similar old communities in Nanjing and the same types of old communities in other cities. Understanding the overall livability of communities under construction can help identify weaknesses in other own construction approaches and may inform appropriate steps to improve the sustainable construction of the community in the wave of continuous urban renewal. This may realize the further development of livability in the community.

The community is an integral part of the city and strengthening the community’s civilization can support a harmonious and stable social environment. In constructing livable communities, improving the community civilization can promote social progress and civilization, promote social harmony and support the harmonious and sustainable development of communities. To strengthen the construction of a livable community, it is important to apply a residential perspective and provide a good platform for managing community participation and interaction. This may include organizing community-level cultural activities and strengthening communication between residents to increase the residents’ affection for the community. This would enhance the residents’ sense of belonging, forming a harmonious and stable atmosphere of community life, mutual help and mutual tolerance.

The purpose of this paper is to identify and analyse the interactions among various enablers which are critical to the success of big data initiatives in operations and supply chain management (OSCM).

Fourteen enablers of big data in OSCM have been selected from literature and consequent deliberations with experts from industry. Three different multi criteria decision-making (MCDM) techniques, namely, interpretive structural modeling (ISM), fuzzy total interpretive structural modeling (fuzzy-TISM) and decision-making trial and evaluation laboratory (DEMATEL) have been used to identify driving enablers. Further, common enablers from each technique, their hierarchies and inter-relationships have been established.

The enabler modelings using ISM, Fuzzy-TISM and DEMATEL shows that the top management commitment, financial support for big data initiatives, big data/data science skills, organizational structure and change management program are the most influential/driving enablers. Across all three different techniques, these five different enablers has been identified as the most promising ones to implement big data in OSCM. On the other hand, interpretability of analysis, big data quality management, data capture and storage and data security and privacy have been commonly identified across all three different modeling techniques as the most dependent big data enablers for OSCM.

The MCDM models of big data enablers have been formulated based on the inputs from few domain experts and may not reflect the opinion of whole practitioners community.

The findings enable the decision makers to appropriately choose the desired and drop undesired enablers in implementing the big data initiatives to improve the performance of OSCM. The most common driving big data enablers can be given high priority over others and can significantly enhance the performance of OSCM.

MCDM-based hierarchical models and causal diagram for big data enablers depicting contextual inter-relationships has been proposed which is a new effort for implementation of big data in OSCM.

The aim of this study is to identify and prioritize a list of key digitization enablers that can improve supply chain management (SCM). SCM is an important driver for organization's competitive advantage. The fierce competition in the market has forced companies to look the past conventional decision-making process, which is based on intuition and previous experience. The swift evolution of information technologies (ITs) and digitization tools has changed the scenario for many industries, including those involved in SCM.

The Best Worst Method (BWM) has been applied to evaluate, rank and prioritize the key digitization and IT enablers beneficial for the improvement of SC performance. The study also used additive value function to rank the organizations on their SC performance with respect to digitization enablers.

The total of 25 key enablers have been identified and ranked. The results revealed that “big data/data science skills”, “tracking and localization of products” and “appropriate and feasibility study for aiding the selection and adoption of big data technologies and techniques ” are the top three digitization and IT enablers that organizations need to focus much in order to improve their SC performance. The study also ranked the SC performance of the organizations based on digitization enablers.

The findings of this study will help the organizations to focus on certain digitization technologies in order to improve their SC performance. This study also provides an original framework for organizations to rank the key digitization enablers according to enablers relevant in their context and also to compare their performance with their counterparts.

This study seems to be the first of its kind in which 25 digitization enablers categorized in four main categories are ranked using a multi-criteria decision-making (MCDM) tool. This study is also first of its kind in ranking the organizations in their SC performance based on weights/ranks of digitization enablers.

The study examined the impact of financial development, foreign direct investment, market size and trade openness on domestic investment for 119 countries divided into four panels that are low-income countries (LIC), lower middle-income countries (LMIC), upper middle-income countries (UMIC) and high-income countries (HIC) between 1995 and 2019.

The present study bases its empirical procedure on the bases of the data mix. To this end, based on the presence of cross-sectional dependence, covariate-augmented Dickey–Fuller unit root and Westerlund cointegration second-generation tests were employed to validate the stationarity and cointegration of the variables, respectively. The novel Dynamic Common Correlation Effects estimator was employed to estimate the heterogeneous parameters while the Dumitrescu and Hurlin test was used to test for causality direction of the highlighted variables.

The empirical results show that market size and trade openness had a positive and statistically significant effect on domestic investment for all the income groups. Results also show that financial development had a positive and statically significant effect on domestic investment only for LMIC and HIC economies, while a positive and statistically insignificant effect was obtained for LIC, UMIC and the global panel. The causality results revealed a bidirectional relationship between domestic investment and the exogenous variables – financial development, foreign direct investment, market size and trade openness.

It is therefore, recommended that LIC and LMIC need to consider harmonising the financial system to lower credit limitations and adopt business-friendly policies. HIC and UMIC should seek more outward FDI policies and harmonise their trade policy, to reap more benefits from FDI and international trade.

On novelty, previous studies have been criticised for the effect on technical innovation of bank financing and institutional quality. This research tackles the deficiency using systematic institutional quality indicators and by taking other variables into account.