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This paper aims to discuss the low carbon supply chain practices in China’s textile industry. To curb greenhouse gas emissions, the Chinese government has launched restrict regulatory system and imposed the energy consumption constraint in the textile industry to guarantee the achievability of low carbon economy. The authors aim to examine how the energy consumption constraint affects the optimal decisions of the supply chain members and address the supply chain coordination issue.

The authors conduct two case studies from Chinese textile companies and examine the impact of energy consumption constraints on their production and operations management. Based on the real industrial practices, the authors then develop a simple analytical model for a low carbon supply chain in which it consists of one single retailer and one single manufacturer, and the manufacturer determines the choice of clean technology for energy efficiency improvement and emission reduction.

From the case studies, the authors find that the textile companies develop clean technologies to reduce carbon emission in production process under the energy consumption enforcement. In this analytical model, the authors derive the optimal decisions of the supply chain members and reveal that supply chain coordination can be achieved if the manufacturer properly sets the reservation wholesale price (WS) despite the production capacity can fulfill partial market demand under a WS (or cost sharing) contract. The authors also find that the cost-sharing contract may induce the manufacturer to increase the investment of clean technology and reduce the optimal WS.

This paper discusses low carbon supply chain practices in China’s textile industry and contributes toward green supply chain development. Managerial implications are identified, which are beneficial to the entire textile industry in the developing countries.

The purpose of this paper is to study a method to optimize the arrangement of the devices on a smart assembly workbench, which help to reduce fatigue and improve efficiency for the worker.

The optimization priority is studied based on the users’ decisions, a mathematical model of the layout optimization is established from ergonomic perspective and an improved algorithm is adopted to solve the built the mathematical model.

Ergonomic software Jack is chosen to simulate the four layout schemes obtained. Through comparative analysis of the simulation results, it is proven that the optimal solution can be obtained using the improved algorithm.

The mathematical model built on observation comfort, operation comfort and device accessibility, as well as the improved algorithm in this paper, has some reference values for the layout design of smart assembly workbench.

The purpose of this study is to achieve multi-variety and small-batch assembly through direct cooperation between equipment and people and to improve assembly efficiency as well as flexibility.

Firstly, the concept of the human–computer interaction is designed. Secondly, the machine vision technology is studied theoretically. Skin color filter based on hue, saturation and value color model is put forward to screen out images that meet the skin color characteristics of the worker, and a multi-Gaussian weighted model is built to separate moving objects from its background. Both of them are combined to obtain the final images of the target objects. Then, the key technology is applied to the smart assembly workbench. Finally, experiments are conducted to evaluate the role of the human–computer interaction features in improving productivity for the smart assembly workbench.

The result shows that multi-variety and small-batch considerable increases assembly time and the developed human–computer interaction features, including prompting and introduction, effectively decrease assembly time.

This study proves that the machine vision technology studied in this paper can effectively eliminate the interferences of the environment to obtain the target image. By adopting the human–computer interaction features, including prompting and introduction, the efficiency of manual operation is improved greatly, especially for multi-variety and small-batch assembly.

The objective of this paper is to synthesise the published literature on consumer awareness and acceptance of Sustainable Fashion (SF) and highlight that sustainability decisions taken across procurement, designing, manufacturing and retailing must include the consumer perspective of SF.

Systematic Literature Review (SLR) on sustainable fashion combined with consumer behavior was conducted. The study approach involved descriptive analysis, content analysis and theoretical analysis in the first section. The later sections focus on sustainability practices across the apparel supply chain that can foster acceptance of sustainable fashion.

In this review paper, five solutions that are typically used for leveraging consumer awareness and acceptance towards sustainable fashion are identified from the latest research papers: (1) attention to micro-sensitive factors (2) shared responsibilities (3) repositioning sustainable fashion for larger audience (4) positioning conscious fashion and (5) unified approach. These solutions are proposed as most important for achieving success in sustainable production and sustainable consumption (SPSC) for the fashion industry. Further, suggestions for how to embed sustainability related business decisions across sourcing, designing, manufacturing, distribution and recollection and retailing are also provided.

Through this research, a clear view emerges of the progression of publication and where future research should be directed to popularise sustainable fashion among consumers. Research findings and proposed solutions will be valuable inputs for brand managers, marketers and retailers as they conceive new plans and make decisions about addressing sustainability challenges in textile and apparel manufacturing firms.

This is a first of its kind of study on sustainable fashion that highlights the importance of understanding consumer behaviour in influencing sustainability decisions required across sourcing, designing, manufacturing and retailing to achieve substantial economic advantages in the fashion industry. End-to-end supply chain processes (i.e. procurement, design, manufacturing, marketing and retailing) are considered to identify several factors that influence consumer behavior in favor of sustainable fashion throughout the supply chain.

The purpose of this paper is to examine the relationship between ethical fashion and consumer purchase behavior (their willingness to pay a premium for ethical fashion), with the focus on consumers’ concerns and beliefs about, and knowledge of, ethical fashion.

A self‐completion questionnaire was administered to 109 respondents. Factor analysis and other statistical analyses were applied to test hypotheses.

The findings suggest that consumer beliefs about ethical fashion, which are based on their perceptions of a company in terms of its reputation in the fashion industry, influence their support for what they perceive as socially and environmentally responsible businesses.

The sample size, which is relatively small, is a limitation for this research. The data were collected in Hong Kong, limiting findings to that geographic region.

An important implication is that consumer education is essential to mitigate the prevailing throwaway culture and raise consumer awareness of ethical issues facing the fashion industry. Thus, retailers should take initiatives to educate consumers so as to ensure the success of their newly‐launched ethical fashion products.

The paper proposes an approach to clearly understand the impacts of ethical fashion on consumer purchase behavior.

With the development of mobile internet, instant messaging (IM) applications have gradually changed the way to receive information and communicate with friends. Considered as one of the important social media platforms, WeChat is the most popular IM application in China. This paper aims to examine the influence factors of haze information’s dissemination in WeChat.

Based on the theory of information dissemination and the theory of motivation, three information dissemination elements including information source, information and information receiver were proposed in the structural equation model. The model was tested using data collected from both online and offline questionnaire survey results. The data analysis from 170 valid responses confirmed the path effects that emotional motivation, social motivation of information receiver and the credibility of information source positively influenced WeChat users’ re-transmission intention and information emotional tendency negatively influenced their re-transmission intention.

The research model revealed these key factors that affected dissemination of haze information in WeChat. More importantly, it provided a decision-making framework to release the public emergency information in WeChat. Hence, the paper made an important contribution to practical guidance to relevant decision makers.

This study contributes to the literature on examining factors affecting the haze information transmission in WeChat. A framework was proposed to describe the information dissemination process which comprises information source, information and information receiver.

This paper addresses the geographical dimension of cross-border knowledge integration, expressed as the co-invention of patent filings and investigates the siting of patenting activities by major US corporations in China. Most importantly, the study looks into the patterns of international co-invention or the links of these locations to headquarters and other company subsidiaries.

The study explores the cases of six US multinationals that file international patent applications in China. The applications were analyzed based on the composition of invention teams and the locations of inventors.

The co-invented patent filings by US multinational enterprises (MNEs) in China demonstrate a high degree of US–Chinese subsidiary collaboration. Links with other subsidiaries are marginal, and at the same time, high levels of sole patenting by inventors in China point to competence-creating research and development (R&D) activities taking place.

The lack of subsidiary-subsidiary collaboration, especially subsidiaries in other emerging markets, indicates a less diversified strategy of leveraging internal networks of knowledge. This also implies that Chinese subsidiaries still lack attractiveness as partners in subsidiary-subsidiary co-invention. Only two companies in our sample, Procter & Gamble and Intel, demonstrate a highly diversified, integrated and transnational pattern of innovation management.

The paper contributes to the contextual understanding of the rich landscape of R&D activities of major US MNEs in China. By exploring these cases, the paper identifies a number of trends. First, the R&D activities in this sample are highly concentrated in technological clusters located in Beijing and Shanghai. Technological clustering is an important advantage of the innovation landscape in emerging markets. Second, the paper underscores the importance of differentiating between different types of co-invention. The patent applications in this sample tend to unite inventors mostly from the US and China, and so multi-country applications involving subsidiaries in other countries are rare. Thus, the level of integration outside the center-host bandwidth is low. However, Chinese subsidiaries demonstrate high levels of autonomy by filing single-country applications, which implies that they are building their own research identity.

To analyse Sukuk Prihatin (SP), the first-ever retail digital sukuk issued by the Government of Malaysia in the midst of the COVID-19 pandemic, as part of the national economic recovery plan. The issuance of SP was oversubscribed, even upsized, resulting in the government announcing its intention to issue similar types of sukuk in the future. In light of this, the purpose of this study is to understand the motivation for retail investors to invest in SP.

The purposive sampling method was applied via a self-administered survey, while the cross-sectional data were empirically tested using the SmartPLS 3.2.9 structural equation modelling. An integrated model of the theory of planned behaviour and social cognitive theories was used in determining investors’ intention to invest in SP.

The findings of this research revealed that attitude (ATT) towards SP investment (SPI), social norms (SN), perceived control (PBC) regarding SPI, sukuk features (SF), tax incentives (TI) and the spirit of unity and brotherhood (SUB) were significant determinants of investors’ willingness to invest in SP. This research also provided evidence for significant national pride-moderated interactions of ATT, SN, PBC, SF, TI and digitisation on investment intention.

The outcome of this study could assist governments and policymakers to structure sukuk and other debt-based capital market products to attract retail investors who would be willing to invest in the development of the nation in the midst of a crisis.

This study is the first of its kind to investigate various relevant predictors, which have been derived from behavioural, contextual and motivational perspectives. These predictors could influence investors’ perceptions of an innovative sukuk like SP, which was issued in the midst of a pandemic. The value of this study is its possible use by governments and policymakers to further develop debt-based capital market products that have the dual function of an investment vehicle and a source of funds for the economic recovery of a nation.

The purpose of this paper is to cut down energy consumption and eliminate production waste on mixed-model assembly lines. Therefore, a supermarket integrated dynamic cyclic kitting system with the application of electric vehicles (EVs) is introduced. The system resorts to just-in-time (JIT) and segmented sub-line assignment strategies, with the objectives of minimizing line-side inventory and energy consumption.

Hybrid opposition-based learning and variable neighborhood search (HOVMQPSO), a multi-objective meta-heuristics algorithm based on quantum particle swarm optimization is proposed, which hybridizes opposition-based learning methodology as well as a variable neighborhood search mechanism. Such algorithm extends the search space and is capable of obtaining more high-quality solutions.

Computational experiments demonstrated the outstanding performance of HOVQMPSO in solving the proposed part-feeding problem over the two benchmark algorithms non-dominated sorting genetic algorithm-II and quantum-behaved multi-objective particle swarm optimization. Additionally, using modified real-life assembly data, case studies are carried out, which imply HOVQMPSO of having good stability and great competitiveness in scheduling problems.

The feeding problem is based on static settings in a stable manufacturing system with determined material requirements, without considering the occurrence of uncertain incidents. Current study contributes to assembly line feeding with EV assignment and could be modified to allow cooperation between EVs.

The dynamic cyclic kitting problem with sub-line assignment applying EVs and supermarkets is solved by an innovative HOVMQPSO, providing both novel part-feeding strategy and effective intelligent algorithm for industrial engineering.