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The principal research objective entails examining the nexus between green supplier selection and green performance while scrutinizing the moderating role of governance mechanisms, specifically process control and outcome control, in shaping this association.

To assess our hypotheses, this study obtained data from Chinese manufacturing sectors and utilized regression analysis on a dataset consisting of 295 samples.

This study enriches the sustainable supply chain management literature by emphasizing the influence of green supplier selection on a firm’s green performance and the moderating effects of outcome and process control, offering practical insights for industry professionals.

This study enriches the sustainable supply chain management literature by emphasizing the influence of supplier selection on a firm’s environmental performance and the moderating effects of outcome and process control, offering practical insights for industry professionals.

Prefabricated components sustainable supplier (PCSS) selection is critical to the success of prefabricated projects. However, limited studies have addressed the uncertainty and complexities during the selection process, particularly in multi-criterion group decision-making (MCGDM) circumstances. Hence, the research aims to develop a group decision-making model using a modified fuzzy MCGDM approach for PCSS selection under uncertain situation.

The proposed study develops a framework for sorting decisions in PCSS selection by using the hesitant fuzzy technique for order preference by similarity to ideal solution (HF-TOPSIS) method. The maximum consistency (MC) model is used to calculate the weights of decision makers (DMs) based on the cardinality and sequence of decision data.

The proposed framework has been successfully applied and illustrated in the case example of CB01 contract section in Hong Kong-Zhuhai-Macao Bridge (HZMB) megaproject. The results show various complicated decision-making scenarios can be addressed through the proposed approach. The MC model is able to calculate the weights of DMs based on the cardinality and sequence of decision data.

The research contributes to improving accuracy and reliability decision-making processes for PCSS selection, especially under hesitant and fuzzy situations in prefabricated megaprojects.

This paper aims to investigate the effects of buzz about the focal brand on competing brands’ attitudes.

Brand-related buzz can be defined as “a general sense of [positive or negative] excitement about or interest in [a brand], as reflected in or generated by word of mouth” (Oxford dictionary). The authors investigate the spillover effects of such positive and negative buzz on brand attitudes of 648 brands in 43 categories over five years.

The authors find that spillover effects are widespread across product categories and affect competing brands through (negative) halo effect and (unfavorable) preference substitution. The authors do not find evidence of positive spillover effects for non-focal brands.

The authors provide generalizable evidence that positive and negative buzz spills over competing brands’ attitudes for hundreds of brands across the largest sectors of the US economy. Interestingly, positive and negative buzz have asymmetric effects on consumer attitudes. These effects vary by consumer attitude metric and are moderated by brand news intensity, strength and similarity.

First, marketing managers should monitor the buzz of competing brands. Second, if managers are concerned with impressions, they should intervene when there is a negative buzz about competitors (halo effect). Third, managers should stimulate positive buzz to negatively affect their competitors’ purchases. Fourth, managing a smaller brand has advantages regarding impressions and recommendations, while news intensity can shield from negative spillover effects for impressions. Finally, brand similarity amplifies the spillover effects across the board.

This paper provides evidence that spillover effects are pervasive and urges marketing managers and academics to incorporate competing buzz in their frameworks and strategies.

Rapid innovation and developments in personal electronic technology have encouraged users to change users' devices more frequently than ever, which has resulted in creating a massive increase in the amount of electronic waste. The study focuses on identifying the barriers to closed-loop supply chain (CLSC) in the electronic industry.

A framework for analyzing the relationships among CLSC adoption barriers is designed. The authors adopted the decision-making trial and evaluation laboratory (DEMATEL) technique to determine the critical barriers of electronic CLSC from the opinion of experts in the field.

The outcome from the analysis suggests that cost barriers, financial barrier, process barriers and supplier-side barriers are the main causal factors that prevent the adoption and implementation of e-waste CLSC. The causal relationship indicates that financial barrier is the most influential factor, while phycological barrier is the most flexible barrier to the adoption of e-waste CLSC.

This study is restricted to CLSC adoption barriers in the electronic industry by evaluating 36 sub-barriers grouped into 8 main dimensions related to different members of the supply chain.

Closed-loop adoption barriers have been proposed to understand the crucial barriers to implementation of CLSC in the electronic industry. The cause-and-effect relationship indicates the critical factors to be improved to increase adoption of e-waste CLSC, helping managers and regulatory bodies to mitigate the problem areas.

This study contributes to the literature on CLSC by adopting a multi-criteria decision-making (MCDM) technique which captures the critical barriers of e-waste CLSC adoption in Indian scenario.

The construction industry contributes to economic development by providing physical equipment and infrastructures. However, it also generates some undesirable outputs such as waste and environmental pollution, especially in developing countries. Due to the importance of the green supply chain management (GSCM) philosophy, for solving these problems, the current study aims to evaluate the drivers of GSCM adoption in the construction industry of Iran.

This research uses a descriptive and practical methodology. The participated experts in the study include senior managers of the construction department in Rasht municipality who had relevant academic education and suitable experiences in urban and industrial construction. The experts took part in both qualitative and quantitative phases of the research, namely verification of the drivers extracted from literature and ranking them in ascending order. In the quantitative phase, Step-Wise Weight Assessment Ratio Analysis (SWARA) as a new multi-criterion decision-making (MCDM) method is used to evaluate the drivers of GSCM adoption using MATLAB software.

The results show that environmental management systems, green product design and innovational capability with weights of 0.347, 0.218 and 0.143 are the most significant sub-drivers, respectively. The less important factor is an investment in environmental technology.

This study evaluated the motivational factors of GSCM in the construction industry. The findings help governments, companies and green supply chain (GSC) managers to improve their knowledge about GSCM and make the best decisions to decrease environmental pollution.

Sustainable supply chain management (SSCM) ensures integration of socially, environmentally and economically feasible practices in entire supply chain. SSCM principles can be implemented to improve efficiency and productivity of a system by different attributes of the system. The purpose of this article is to identify the most appropriate existing (SSCM) framework that can be implemented suitably in Indian smart manufacturing industries.

Validity and reliability analysis on the existing SSCM frameworks was carried out with the help of empirical data collected using questionnaire survey methodology from various Indian smart manufacturing organizations. The empirical data were gathered from various experts from top- and middle-level management in different smart manufacturing organizations across the country. Further, factor analysis was carried on the collected data to estimate the unidimensionality of each SSCM frameworks. Cronbach's alpha value was used to assess reliability of each framework. Subsequently, the frequency distribution analysis was done to obtain familiar elements in the segregated frameworks based on validity and reliability analysis.

The work observed that only five SSCM frameworks have shown unidimensionality in terms of the elements or constructs. The work further found that these segregated frameworks have not shown sufficiently high level of reliability. Additionally, this work attempted frequency distribution analysis and observed that there were very few elements which were being repeatedly used in numerous frameworks proposed by researchers. Based on the findings of this work, the work concluded that there is acute need of a new SSCM framework for Indian smart manufacturing industries.

This study gathered empirical data from 388 Indian smart manufacturing organizations. Thus, before generalizing the findings of the study across the sectors, there is a possibility of some more explication.

The main purpose of this article is to explore the feasibility of the existing SSCM frameworks in Indian smart manufacturing sector. The study also assumes that the manufacturing managers and executives may have the complete understanding on the existing sustainable manufacturing frameworks and a chance to executing proper suitable framework in the respective manufacturing organization.

Artificial intelligence (AI) has a significant impact on value co-creation (VCC). However, a study providing a comprehensive summary of the current state of the art and common ground of the two fields is missing. The current study aims to fill this gap by conceptualizing the role of AI in VCC and customer decision-making.

The study reviews literature on VCC and AI together, including a total of 108 articles. To bring the literature together, the authors adopted the antecedents-mediators-outcomes framework and narrative approach that helped them develop a framework by integrating the antecedents, mediators and outcomes of AI-facilitated VCC. Furthermore, the authors also operationalized existing literature to facilitate an understanding of the role of AI in customer decision-making.

The study, in addition to identifying the common theoretical grounds of VCC and AI (human behavior, cognition and social interactions), operationalizes AI functionality, its characteristics and customer characteristics as the antecedents of AI-facilitated VCC. Moreover, based on literature, on the continuum of low-to-high involvement, four types of decision-making were identified as mediator of the relationship between AI characteristics, customer characteristics and VCC. Additionally, the authors found different categorizations of AI in literature as archetypes to support various forms of VCC.

The study contributes to the literature of VCC and AI by construing a comprehensive framework for analyzing AI's impact on VCC, envisioning customer–AI interaction as continual exchange of advantages in which characteristics of AI and customers play a critical role in customer decision-making and shaping VCC.