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The purpose of this paper is to investigate the role of intrinsic motivation and self-construal in explaining the relationship between work-related identity discrepancy and employee innovation behavior.

Based on a survey of 637 respondents from 15 organizations in southern China, this study examines four hypotheses with regard to the relationship between work-related identity discrepancy and employee innovation behavior through hierarchical regression analysis.

The research results indicate that work-related identity discrepancy is negatively related to employee innovation behavior, but intrinsic motivation mediates their negative relationship, and self-construal moderates this mediating effect further. Specifically, the results demonstrate that perceiving work-related identity discrepancy can lower intrinsic motivation among employees with interdependent self-construal and subsequently reduce their innovation behavior.

Drawn on social cognitive theory, this study reveals the negative effect of work-related identity discrepancy on employee innovation behavior and the moderated mediation effect of intrinsic motivation and self-construal on the negative relationship. The finding expands existing literature on work-related identity discrepancy and employee innovation behavior.

Drawing on the social information processing theory, this study aims to adopt a moderated mediation model to investigate the mediation role of cognitive crafting and the moderation role of regulatory focus in the relationship between perceived deviance tolerance and employee innovative behavior.

A questionnaire study with 181 employees from a state-owned communications technology company in China was conducted through a two-wave survey, with a one-month lagged design. The model is tested through confirmatory factor analysis, correlation analysis and PROCESS bootstrapping program in SPSS24.0 and AMOS22.0 software.

This study confirms that perceived deviance tolerance is positively related to innovative behavior, while cognitive crafting mediates the relationship between perceived deviance tolerance and innovative behavior. Furthermore, the promotion focus positively moderates the relationship between perceived deviance tolerance and cognitive crafting, and higher promotion focus enhances the mediating effect of cognitive crafting on the relationship between perceived deviance tolerance and innovative behavior. The prevention focus negatively moderates the relationship between perceived deviance tolerance and cognitive crafting, and higher prevention focus weakens the mediating effect of cognitive crafting on the relationship between perceived deviance tolerance and innovative behavior.

Organizations need to establish a tolerant and inclusive management system and create a harmonious working atmosphere to provide a platform basis to inspire the innovative behavior of employees. Also, regulatory focus variables are suggested to be considered in organizational human resource management processes (e.g. recruitment and training) to improve organizational person–job fit.

The primary contribution of this study is to confirm that perceived deviance tolerance has a positive impact on innovation behavior and thereby providing a new perspective to understand the impact effect of perceived deviance tolerance. Another contribution the study explores the mechanisms and boundary conditions of perceived deviance tolerance on innovative behavior fills the theoretical gap of perceived deviance tolerance.

The poor leadership style is a key obstacle to the effective implementation of Industry 4.0 technologies. To successfully apply the Industry 4.0 technologies, which can enhance the sustainability of firms, senior management needs to be inspiring and transformational. On the other hand, numerous factors can hinder the Industry 4.0 transition and “Circular Supply Chain (CSC)” transformation. Therefore, the main purpose of this study is to evaluate the related barriers of CSCs in the era of Industry 4.0 transition.

The current study developed an innovative decision-making approach with the help of the “Combined Compromise Solution (CoCoSo)” method and “Criteria Importance Through Intercriteria Correlation (CRITIC)” method on the “q-Rung Orthopair Fuzzy Sets (q-ROFSs).” CRITIC in this combined method was used to predict the importance or weighting degrees of the CSCs barriers in the age of Industry 4.0 transition.

The results of this study found that the absence of knowledge about the Industry 4.0 technologies and circular approaches was the first barrier followed by the problems associated with data security in relationship management in circular flows, the deficiency of knowledge regarding the data management among stakeholders and the lack of awareness about the potential benefits of autonomous systems in labor-oriented “End-of-Life (EOL)” activities for CSCs in the era of Industry 4.0 transition.

A limitation may be that despite the generalizability of the proposed framework, the results may differ when it is implemented in different sectors. By emphasizing the obstacles to sustainable operations of supply chains (SCs) in the context of circular economy (CE) and Industry 4.0, researchers working in the same domain may be encouraged to find ways to remove such obstacles in different settings. As suggested in this study, the priority of various barriers helps researchers suggest effective strategies for the sustainable development of companies within the current dynamic business atmosphere.

The findings of this paper can aid industry practitioners in fixing their attention on the digitization or automation of their systems in the context of sustainability or resource circularity. Note that within the current context of CE, one of the crucial issues is how to conserve the existing resources; the answer to this question can save the environment.

The current paper proposed a new multi-criteria decision-making method using q-ROFSs to analyze, rank and evaluate the CSC barriers in the age of Industry 4.0 transition. To this end, a new decision-making approach with the help of CRITIC and CoCoSo methods on q-ROFSs called q-ROF-CRITIC-CoCoSo was introduced to evaluate the CSCs barriers in the era of Industry 4.0 transition.

In recent years, circular economy (CE) has come to prominence as an alternative to the classic approach of “make-use-dispose”. How companies can exploit the opportunities of CE to position themselves better are not well articulated in the literature. The purpose of this paper, therefore, is to identify the barriers and opportunities of CE in the manufacturing sector through a socio-political, economic, legal and environmental perspective.

The study adopts a positivist approach, which is deductive in nature. A survey questionnaire was designed and distributed to manufacturing companies operating in the UK and EU. The study used FAME database and social networking platform LinkedIn to identify manufacturing companies. More than 200+ companies were approached for this study and data collection lasted over two months.

The study provides a comprehensive review of the CE literature and identifies a number of barriers and opportunities to CE implementation from a socio-political, economic, legal and environmental perspective. The findings highlight key barriers, opportunities and benefits of CE for the manufacturing industries operating in the UK and EU.

The findings are limited to 63 responses from the survey questionnaire distributed to manufacturing companies in the UK and EU. The present study aims to equip manufacturers with necessary understanding of the key opportunities and barriers to address the challenges encountered during the implementation of CE.

This study adds to the limited empirical literature on CE barriers and opportunities to manufacturing organisations operating in the UK and EU. The paper also identifies barriers and opportunities of CE from a socio-political, economic, legal and environmental lens.

Circular economy (CE) is receiving increasing worldwide attention as a manner to overcome the challenges linked to current trends of unsustainable energy and resource consumption. This paper aims to fill this gap and analyze the adherence to sustainable, access-based and collaborative consumption practices by exploring the role of CE awareness, specifically in the context of Porto, the second-largest city of Portugal.

The methodology of choice is quantitative, based on partial least square-based structural equation modeling.

The result shows that there is an influence of CE awareness on subsequent sustainable consumption models.

Present research contributes to the theory on CE awareness and sustainable consumption. It proposes a model that could be applied in other countries. As this research is developed within the city of Porto, it may limit generalizations of obtained results.

As CE practices are embodied into national and local policies, this research contributes to understanding local contexts of CE practices dissemination, providing practical suggestions for businesses and policymakers aiming the transition to the CE.

An original approach to measuring the awareness of CE economy is proposed, that is analyzed not only from the familiarity perspective but in six dimensions of its construction: familiarity, importance, perception or interpretation, advantages, social impact and barriers in this process. Further, the conceptual model of the impact that these dimensions have on the adoption of sustainable consumption models (purchase of sustainable products, access-based and collaborative consumption) is proposed.

Circular economy (CE) has evolved as a result of the growing environmental awareness, environmental legislation and the need for social responsibility. However, awareness levels of CE are not as high as expected and practices are further behind than they should be, with a significant lack of research around the subject in the literature. Therefore, the purpose of this paper is to examine the current state of awareness levels and the practices around CE in manufacturing firms. Moreover, the study aims to empirically validate one of the earlier proposed CE models.

The study adopts a quantitative survey questionnaire based approach. More than 500 people from various manufacturing organisations were contacted directly over a 40-day long sampling process through the FAME database, personal contacts and LinkedIn. The survey resulted in 103 completed responses. Given the exploratory nature of the study, the data were mainly analysed using descriptive statistics. To validate the CE model, a correlation analysis was also conducted.

The research findings show that with the growing emphasis on CE across the globe by governing bodies, firms are becoming more aware of CE practices. The analysis also shows some useful insights on the state of each of the pillars (economic benefits, environmental impact and resource scarcity) of CE. The findings also indicate that the environmental impact pillar of CE is at a more developed state than the other two pillars which are, mostly, in a research state.

The study provides manufacturing firms with a thorough understanding of the state of CE practices and importance of its successful implementation. The findings of the study advocates consideration of all three pillars of CE by managers as a guide to plan for an efficient strategy around CE implementation. Moreover, our study adds to existing efforts by the academic community to raise the awareness towards CE practice among all relevant stakeholders. The findings of this study are based on the responses from a limited 103 survey responses from manufacturing firms.

This study adds to the very limited empirical literature on CE awareness and practices in manufacturing firms. This is also one of the first studies attempting to empirically validate an existing CE model.

Circular economy (CE) initiatives are taking hold across both developed and developing nations. Central to these initiatives is the reconfiguration of core supply chain management (SCM) processes that underlie current production and consumption patterns. This conceptual article provides a detailed discussion of how supply chain processes can support the successful implementation of CE. The article highlights areas of convergence in hopes of sparking collaboration among scholars and practitioners in SCM, CE, and related fields.

This article adopts a theory extension approach to conceptual development that uses CE as a “method” for exploring core processes within the domain of SCM. The article offers a discussion of the ways in which the five principles of CE (closing, slowing, intensifying, narrowing, dematerialising loops) intersect with eight core SCM processes (customer relationship management, supplier relationship management, customer service management, demand management, order fulfilment, manufacturing flow management, product development and commercialization, returns management).

This article identifies specific ways in which core SCM processes can support the transition from traditional linear approaches to production and consumption to a more circular approach. This paper results in a conceptual framework and research agenda for researchers and practitioners working to adapt current supply chain processes to support the implementation of CE.

This article highlights key areas of convergence among scholars and practitioners through a systematic extension of CE principles into the domain of SCM. In so doing, the paper lays out a potential agenda for collaboration among these groups.

A smart city is a potential solution to the problems caused by the unprecedented speed of urbanization. However, the increasing availability of big data is a challenge for transforming a city into a smart one. Conventional statistics and econometric methods may not work well with big data. One promising direction is to leverage advanced machine learning tools in analyzing big data about cities. In this paper, the authors propose a model to learn region embedding. The learned embedding can be used for more accurate prediction by representing discrete variables as continuous vectors that encode the meaning of a region.

The authors use the random walk and skip-gram methods to learn embedding and update the preliminary embedding generated by graph convolutional network (GCN). The authors apply this model to a real-world dataset from Manhattan, New York, and use the learned embedding for crime event prediction.

This study’s results show that the proposed model can learn multi-dimensional city data more accurately. Thus, it facilitates cities to transform themselves into smarter ones that are more sustainable and efficient.

The authors propose an embedding model that can learn multi-dimensional city data for improving predictive analytics and urban operations. This model can learn more dimensions of city data, reduce the amount of computation and leverage distributed computing for smart city development and transformation.