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The purpose of this paper is to study the effects of sports sponsorship on brand equity using two managerially related outcomes: price premium and market share.

This study uses a best–worst discrete choice experiment (BWDCE) and compares the outcome with that of the purchase intention scale, an established probabilistic measure of purchase intention. The total sample consists of 409 fans of three soccer teams sponsored by three different competing brands: Nike, Adidas and Puma.

With sports sponsorship, fans were willing to pay more for the sponsor’s product, with the sponsoring brand obtaining the highest market share. Prominent brands generally performed better than less prominent brands. The best–worst scaling method was also 35% more accurate in predicting brand choice than a purchase intention scale.

Future research could use the same method to study other types of sponsors, such as title sponsors or other product categories.

Sponsorship managers can use this methodology to assess the return on investment in sponsorship engagement.

Prior sponsorship studies on brand equity tend to ignore market share or fans’ willingness to pay a price premium for a sponsor’s goods and services. However, these two measures are crucial in assessing the effectiveness of sponsorship. This study demonstrates how to conduct such an assessment using the BWDCE method. It provides a clearer picture of sponsorship in terms of its economic value, which is more managerially useful.

This study aims to analyze lean wastes for the poultry sector in Turkey and link lean tools to this study, focusing on identifying each lean waste that affects poultry production and proposing solutions for preventing these lean wastes in the sector. The proposed solutions aim to improve processes by suggesting different lean tools and their applications for the poultry sector.

The study consists of two different applications. First, the waste relationship matrix (WRM) was created to reveal the relationship between seven lean wastes and their importance order. Then, after determining lean tools for eliminating lean wastes, the optimum weight ranking and consistency ratio of the most suitable lean tools were calculated for these wastes and ranked with the best-worst method (BWM).

Results showed that overproduction is the most critical waste that impacts other wastes, followed by defect waste. Due to the nature of the sector, these wastes not only result in economic loss for the company but also in food waste and loss and issues related to animal welfare. Furthermore, the Kaizen approach and 5S implementation are the methods to eliminate these wastes. Detailed discussion on the link between lean tools and lean wastes is provided for the poultry sector.

This is the first study that theoretically and empirically identifies the potential lean waste affecting the poultry sector and provides lean tools for eliminating these wastes. Sector-specific explanations and discussions are presented in the study to show the applicability of lean approaches in the poultry sector to eliminate waste. In addition, this study is the first to integrate the WRM and BWM.

This study aims to identify and analyse critical barriers to cloud manufacturing (CM) adoption for achieving sustainability amongst manufacturing micro, small and medium-sized enterprises (MSMEs) in an emerging economy.

Initially, through a literature survey and expert inputs, this study identified and finalized 21 critical barriers to CM adoption, which were further analysed by employing a hybrid multiple criteria decision-making (MCDM) approach. In this approach, the best–worst method (BWM) was used to rank the identified barriers. However, to analyse the interrelation amongst these barriers and classify them into the cause-effect group, the decision-making trial and evaluation laboratory (DEMATEL) method was employed. Furthermore, the study recommended various strategies that can help overcome high-priority barriers and enhance CM adoption within firms.

The results of the study show that organizational, legal and economic barriers are the high-priority barriers that need to be addressed to enhance CM adoption.

The findings of the study will help managers and decision-makers in manufacturing MSMEs develop effective strategies and policies to enhance CM adoption.

Notably, this investigation is one of the preliminary research activities to explore the sustainability aspect of CM adoption amongst MSMEs in an emerging economy.

Every day, small and big incidents happen all over the world, and given the human, financial and spiritual damage they cause, proper planning should be sought to deal with them so they can be appropriately managed in times of crisis. This study aims to examine humanitarian supply chain models.

A new model is developed to pursue the necessary relations in an optimal way that will minimize human, financial and moral losses. In this developed model, in order to optimize the problem and minimize the amount of human and financial losses, the following subjects have been applied: magnitude of the areas in which an accident may occur as obtained by multiple attribute decision-making methods, the distances between relief centers, the number of available rescuers, the number of rescuers required and the risk level of each patient which is determined using previous data and machine learning (ML) algorithms.

For this purpose, a case study in the east of Tehran has been conducted. According to the results obtained from the algorithms, problem modeling and case study, the accuracy of the proposed model is evaluated very well.

Obtaining each injured person's priority using ML techniques and each area's importance or risk level, besides developing a bi-objective mathematical model and using multiple attribute decision-making methods, make this study unique among very few studies that concern ML in the humanitarian supply chain. Moreover, the findings validate the results and the model's functionality very well.

In response to mounting global concerns about climate change and scarcity of natural resources, manufacturers have been pressured to develop strategies and enhance their sustainability performance. The integration of sustainable lean manufacturing (SLM) during value chain processes could balance environmental, social and economic concerns into their decision-making, which not only ensures responsible practices but also drives efficiency and success. This paper aims to identify, measure and prioritize metrics to develop a performance measurement system that assesses the multi-dimensional performance of SLM.

Strategic decision-making has some conflicting criteria and objectives to be considered simultaneously. The Multi-Criteria Decision Making provides a foundation for selecting, sorting and prioritizing these strategies with the determination of drivers and indicator weight.

The performance model enables the decision-makers to consistently evaluate the level of sustainability through a multidimensional framework, which could support the assessment of the existing sustainability of a manufacturing process and analyze opportunities for improvement. This study divided the performance into five drivers: Quality, Operational, Finance, Environment, Safety and People and selected 17 KPIs for assessing the multi-dimensional performance of SLM organizations. The research results revealed an organization's perspective transition from strategies focused on operational and economic performance to a more sustainable ideal with greater importance for social and environmental directions.

This framework will be facilitated by the selection of the most significant drivers and the development of strategic plans for the successful adoption of sustainable manufacturing. The practices support implementation, pursue competitive advantages and sustain manufacturing, meeting strategic requirements of suitable and lean performance. With the limited resources of the organizations, the framework proposed will guide the priorities and actions to be taken toward the SLM.

Cotton fibre lots are graded and selected for yarn spinning based on their quality value which is a function of certain fibre properties. Cotton grading and selection has created a domain of emerging interest among the researchers. Several researchers have addressed the said issue using a few exponents of multi-criteria decision-making (MCDM) technique. The purpose of this study is to demonstrate a cotton selection problem using a recently developed measurement of alternatives and ranking according to compromise solution (MARCOS) method which can handle almost any decision problem involving a finite number of alternatives and multiple conflicting decision criteria.

The MARCOS method of the MCDM technique was deployed in this study to rank 17 cotton fibre lots based on their quality values. Six apposite fibre properties, namely, fibre bundle strength, elongation, fineness, upper half mean length, uniformity index and short fibre content are considered as the six decision criteria assigning weights previously determined by an earlier researcher using analytic hierarchy process.

Among the 17 alternatives, C9 secured rank 1 (the best lot) with the highest utility function (0.704) and C7 occupied rank 17 (the worst lot) with the lowest utility function (0.596). Ranking given by MARCOS method showed high degree of congruence with the earlier approaches, as evidenced by high rank correlation coefficients (Rs > 0.814). During sensitivity analyses, no occurrence of rank reversal is observed. The correlations between the quality value-based ranking and the yarn tenacity-based rankings are better than many of the traditional methods. The results can be improved further by adopting other efficient method of weighting the criteria.

The properties of raw cotton have significant impact on the quality of final yarn. Compared to the traditional methods, MCDM is reported as the most viable solution in which fibre parameters are given their due importance while formulating a single index known as quality value. The present study demonstrates the application of a recently developed exponent of MCDM in the name of MARCOS for the first time to address a cotton fibre selection problem for textile spinning mills. The same approach can also be extended to solve other decision problems of the textile industry, in general.

Novelty of the present study lies in the fact that the MARCOS is a very recently developed MCDM method, and this is a maiden application of the MARCOS method in the domain of textile, in general, and cotton industry, in particular. The approach is very simple, highly effective and quite flexible in terms of number of alternatives and decision criteria, although highly robust and stable.

The European Union's Emissions Trading System (EU ETS), which is already one of the EU's most impactful instruments for reducing greenhouse gases (GHGs), will soon include the maritime transport industry. Although ports are this industry's most environmental-friendly component, there are still some barriers to including ports in the system. Therefore, the purpose of the study is to identify these barriers and to reveal the barriers' interrelationships.

The study was conducted by identifying barriers from a literature review before analyzing the barriers with the Fuzzy DEMATEL method. Finally, based on the Complex Adaptive System Approach, various solutions are proposed to overcome these barriers.

The identified barriers were grouped into cause-and-effect groups. Two barriers, namely long payback period and high investment costs, were evaluated as triggers of the model while the others were more sensitive to the model.

This study only includes the perceptions of green certificated ports in Türkiye. The results revealed an expectation that elimination of financial concerns will alleviate other barriers to including ports in the system. The study's findings can guide port managers on the integration of the managers' processes into the system.

This study provides novel findings regarding the relationships between barriers hindering ports from involvement in the EU ETS.

Purpose: This research investigates how producers of fast-moving consumer goods (FMCG) in Karachi, Pakistan, embrace sustainable manufacturing and consumption practices.

Study design/methodology/approach: This study employed a mixed research design, incorporating both qualitative and quantitative approaches. The primary method of data collecting utilised in this study was semi-structured interviews. The survey sampled 11 FMCG manufacturers, and the data were analysed using thematic analysis.

Findings: The research revealed that economic variables were crucial in adopting sustainable consumption and production (SCP) practices among Karachi’s FMCG businesses. The economic reasons were motivated by the notion that adopting sustainable practices could result in financial benefits and enhanced market competitiveness. The research additionally discovered that consumer preferences were pivotal in stimulating sustainable practices within the FMCG business. Manufacturers indicated that adopting sustainable products and packaging was driven mainly by consumer demand.

Originality/value: This study contributes to the literature on SCP within the FMCG business. It offers valuable insights into the factors influencing sustainable practices and customer preferences. This study emphasises the importance of economic, social, and environmental aspects in fostering sustainable practices within the FMCG industry, specifically focusing on developing nation environments such as Karachi. This study’s findings can contribute valuable insights for policy formulation and decision-making processes about SCP within Karachi’s FMCG business and other similar contexts.

This study reviews scholarly work in sustainable green logistics and remanufacturing (SGLR) and their subdisciplines, in combination with bibliometric, thematic and content analyses that provide a viewpoint on categorization and a future research agenda. This paper provides insight into current research trends in the subjects of interest by examining the most essential and most referenced articles promoting sustainability and climate-neutral logistics.

For the literature review, the authors extracted and sifted 2180 research and review papers for the period 2008–2023 from the Scopus database. The authors performed bibliometric and content analyses using multiple software programs such as Gephi, VOSviewer and R programming.

The SGLR papers can be grouped into seven clusters: (1) The circular economy facets; (2) Decarbonization of operations to nurture a climate-neutral business; (3) Green sustainable supply chain management; (4) Drivers and barriers of reverse logistics and the circular economy; (5) Business models for sustainable logistics and the circular economy; (6) Transportation problems in sustainable green logistics and (7) Digitalization of logistics and supply chain management.

In this review, fundamental ideas are established, research gaps are identified and multiple future research subjects are proposed. These propositions are categorized into three main research streams, i.e. (1) Digitalization of SGLR, (2) Enhancing scopes, sectors and industries in the context of SGLR and (3) Developing more efficient and effective climate-neutral and climate change-related solutions and promoting more environmental-related and sustainability research concerning SGLR. In addition, two conceptual models concerning SGLR and climate-neutral strategies are developed and presented for managers and practitioners to consider when adopting green and sustainability principles in supply chains. This review also highlights the need for academics to go beyond frameworks and build new techniques and instruments for monitoring SGLR performance in the real world.

This study provides an overview of the evolution of SGLR; it also clarifies concepts, environmental concerns and climate change practices, particularly those directed to supply chain management.

Recycling is associated with positive social and environmental impact, but previous studies have overlooked the cost of recycling operations. Based on the dynamic capability view, the purpose of this study was to identify and evaluate risk factors and resilience strategies within the recycling industry, prioritize these factors and identify the optimal combination of resilience strategies and risk factors to improve market performance.

The research questions were addressed in three subsequent studies. In Study 1, qualitative interviews were conducted to identify risk factors and strategies to mitigate those risks. In Study 2, quality function deployment methodologies were implemented via case studies derived from three different companies. Based on the results of Studies 1 and 2, in addition to the use of fuzzy set qualitative comparative analysis, Study 3 aimed to determine the optimal combination of risk factors and strategies impacting market performance.

The results across the three studies revealed a number of risk factors as well as which risk factors and resilience strategies have the greatest impact on market performance. Specifically, it was found that higher levels of readiness, response and recovery strategies lead to greater market performance, whereas weak readiness, response and recovery strategies, along with low societal, environmental and health and safety risk factors, significantly inhibit performance.

This research extends current understandings of market performance in relation to recycling industry management and offers insight for decision-makers toward combating significant risk factors in business-to-business settings.