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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.

The purpose of this paper is to explore differences in wine preferences between Generation Y and older cohorts in the USA.

A total of 260 US consumers participated in a web‐based survey that took place in April 2010. The best‐worst scaling method was applied measuring the level of importance given by participants to a list of most common attributes used in choice of wine. Independent sample t‐tests were applied to compare the best‐worst scores between Generation Y and older cohorts.

Differences were found in the level of importance that Generation Y gives to wine attributes in comparison to older cohorts. Generation Y was found to attach more importance to attributes such as “Someone recommended it”, “Attractive front label” and “Promotional display in‐store”, whereas older cohorts gave more importance to attributes such as “I read about it” and “Grape variety”. This suggests that Generation Y preferences for wine are driven by marketing added‐value activities such as promotions and labelling, whereas limited importance is given to information about wine, reflecting lack of subjective knowledge, experience and involvement about wine.

This research adds to generation‐based research in wine marketing and contributes towards a better understanding of the differences between generation cohorts in relation to their preferences towards wines.

This study is among the first to compare wine preferences of Generation Y with older cohorts using the best‐worst scaling method.

In a globalized environment, small and medium enterprises (SMEs) are facing formidable challenges. Not only do they have to keep up their profitability, but there is also a pressure from various stakeholders to add to their environmental and quality performance .The solution obviously lies in continuously adopting and improving upon lean-green practices in their operations. This work aims at identifying, classifying and building up a duly tested robust ranked-order model of such “enablers”, related to lean-green practices, that puts them (the enablers) in an order of being the most significant to being the least significant further to be accorded the same or similar weight in strategy formulation and implementation stage by Indian SMEs for enhancing their overall organizational performance.

The study identifies 20 enablers (12 lean and 08 green manufacturing enablers) through extensive literature review and experts' opinion survey and classifies them into three main categories. The ranking and significance of each of the main and subcategory enablers is evaluated according to its weight which is determined by the best-worst method (BWM) approach, one of the novel multi-criteria decision-making (MCDM) methods. Further, the results have been drawn after running accuracy check of the rankings (based upon optimal weights) and testing the robustness of the ranked-order model through sensitivity analysis.

The results of this study reveal that out of the three main category enablers, “operational performance enablers (E1)” and “quality performance enablers (E3)” are the most and the least significant enablers, while in the group of 20 subcategory enablers, “Kaizen (E17)” and “environment emission control (E28)” are the most and the least significant subcategory enablers, respectively.

The prioritization model or ranked-order model of the lean-green manufacturing enablers proposed through this study may serve as a standard model to managers to help them decide and allocate their efforts and resources accordingly in managing their operations. This will also help them adopt high-ranking lean-green manufacturing enablers in their firms and benchmark and standardize their existing practices accordingly, leading to greater competitive advantage.

The study identifies various green-lean manufacturing enablers in SMEs, classifies them into three main categories and ranks them using BWM approach. The findings of this study should be extremely relevant to managers, manufacturing engineers and practitioners in Indian SMEs from the perspective of developing deeper appreciation of these enablers as per their relative ranked importance to further formulating an effective and efficient strategy for their implementation resulting in optimal results.

This study aims to use product attributes and retail display information to develop cross‐national segments.

The study uses consumers' choice in wine stores to develop segments of consumers, based on the relative importance of 13 factors consumers use for the retail purchase of wine. Data are collected from 11 countries, using seven languages on five continents. Best worst scaling (BWS) and latent class analysis were used to develop the clusters.

A three‐cluster structure provides a straightforward and robust segmentation across the 11 countries. This model fits better than one based on 11 single country segments. The three segments reveal different ways in which consumers choose wines: cognitive‐based, assurance‐based, and in‐store promotion‐based.

The samples are convenience‐based and do not represent the population of wine drinkers in each country. Choice criteria, including retail communications, can be used to develop useful and robust segments cross‐nationally.

The three segments found in this study provide clear guidelines for wine marketers depending on whether they work for small or large wine companies. The use of choice attributes and BWS show the utility of this method in cross‐national research.

This research demonstrates that product attributes and retail communication devices like labels and displays can be used for cross‐national segmentation. Applying BWS and Latent Class Clustering to choice criteria leads to clear, usable, and robust segmentation across a wide range of cultures and product use histories.

This research work has developed an integrated fuzzy Delphi and neutrosophic best–worst framework for selecting the sustailient (sustainable and resilient) supplier for an additive manufacturing (AM)-enabled industry.

An integrated fuzzy Delphi method (FDM) and neutrosophic best–worst method (N-BWM) approach is developed. 34 supplier evaluation criteria falling under 4 groups, that is, traditional, sustainable, resilient, and AM specific, are identified and validated using the FDM. Afterward, the weights of each criterion are measured by N-BWM. Later on, the performance evaluation is carried out to determine the best-suited supplier. Finally, sensitivity analysis is performed to know the stability and robustness of the proposed framework.

The outcome indicates the high performance of the suggested decision-making framework. The analysis reveals that supplier 4 (S4) is selected as the most appropriate for a given firm based on the FDM and N-BWM method.

The applicability of this framework is demonstrated through an industrial case of a 3D-printed trinket manufacturer. The proposed research helps AM decision-makers better understand resiliency, sustainability, and AM-related attributes. With this, the practitioners working in AM business can prioritize the supplier selection criteria.

This is the primitive study to undertake the most critical aspect of supplier selection for AM-enabled firms. Apart from this, an integrated FDM-N-BWM framework is a novel contribution to the literature on supplier selection.

This study aims to propose a novel framework for barriers to circularity within cooperative supply chains. The barriers in the adoption and implementation of circular economy principles are examined within a framework.

Fuzzy best-worst method is used to calculate the weights of barriers and identify the prioritization of barriers to circularity within cooperative supply chain.

“Insufficient implementation of circular economy laws” was found as the most important barrier, followed by “Lack of information”, “Ineffective recycling policies”, “Lack of awareness for circular economy”, “Remanufacturing is a labor-intensive procedure”, “Inconsistent price policies in sources and products”, “Lack of environmental management system”, “Cost of implementation for green activities” and “Lack of R&D capability” barriers, respectively.

The number of participant professionals limits the conclusions of the study and reaching more general conclusions. A comprehensive research can be conducted by the participation of a greater number of professionals.

Several studies analyzed the barriers to circularity; however, to the best of the authors' knowledge, no study has been taken an approach for barriers to circularity for cooperatives or cooperative supply chains.

This paper reconsiders the approaches to measuring Confucian values, and tests their association with workforce performance. The purpose of this paper is to examine how such values and performances are prioritized across three East Asian societies, but more importantly, identifies how variations across societies might result from the way in which Confucianism has been transformed/appropriated differently across history.

A Best-Worst experimental design is used to measure three aspects of Confucianism (relational, pedagogical, and transformative), and three aspects of workforce performance (mindset, organization, and process) to capture the trade-offs by respondents from three East Asian societies: China (n=274), Taiwan (n=264), and South Korea (n=254). The study employs analysis of variance with post-hoc tests to examine differences between societies. A hierarchical cluster analysis using Ward’s method is utilized to identify clusters based on similarities within the data. And last, multiple regression analysis is applied to determine the explanatory power of Confucian values on workforce performance.

Findings confirm the prioritization of three aspects of Confucianism (relational, pedagogical, and transformative) to differ between Mainland Chinese, Taiwan Chinese, and Korean respondents – producing five distinct clusters based on similarities across three societies. Overall, between 7 and 27 percent of the variance in workforce performance could be explained by the Confucian values included in this study.

This study highlights the “different shades of Confucianism” across East Asian societies, which we coin as Confucian Origin, Preservation, and Pragmatism, and demonstrates the need to take a multifaceted perspective in the measurement of Confucian culture. The study provides empirical support for the link between Confucianism and performance at the micro-level, as originally proposed by Baumann and Winzar (2017), and identifies specific antecedents of behavior for research moving forward.

The purpose of this paper is to investigate Spanish consumer preferences for several food-labelling schemes on semi-cured, pasteurised sheep milk cheese. In particular, the authors used three labels regulated by the European Union regulation (European organic logo, protected denomination of origin (PDO) and nutritional fat content), and the remaining four have been introduced to the European food market by private initiatives (local, carbon footprint, food miles and animal welfare).

A Best-Worst Discrete Choice approach was applied in Spain during Fall 2011 by administrating a survey to 549 consumers.

The results suggest that the most valued labels are the PDO, followed by the organic logo and the nutritional panel. The least valued are food-miles labelling and carbon foodprint labels, while local-origin labels and animal welfare are in the middle position.

This study is the first to value consumer preferences for cheese products bearing several public and private European food-labelling schemes since literature on consumer preferences for food labels has only dealt with a comparison of a few (two or at most three) food-labelling schemes. In addition, the added value of this paper is also the use of the BWC approach that has the advantage of providing the best way to discriminate the degree of importance given by respondents to each food labels by overcoming the problem of bias caused by differences in the use of rating scales.

This paper analyses the voice of customers (VoCs) using a hybrid clustering multi-criteria decision-making (MCDM) approach. The proposed method serves as an efficient tool for how to approach multiple decision-making involving a large set of countrywide customer complaints in the Iranian automotive sector.

The countrywide data comprising 3,342 customer complaints (VoCs) were gathered. A total of seven determinant complaint criteria were identified in brainstorming sessions with three groups (six each) of experts employing the fuzzy Delphi method. The weights of these criteria were assigned by applying the fuzzy best–worst method (FBWM) to identify the severity of the complaints. Subsequently, the complaints were clustered into five categories with respective customer locations (province), car type and manufacturer using the K-mean method and further prioritised and ranked employing the fuzzy complex proportional assessment of alternatives (FCOPRAS) method.

The results indicated that the majority of complaints (1,027) from the various regions of the country belonged to one specific model of car made by a particular producer. The analyses revealed that only a few complaints were related to product quality, with the majority related to service and financial processes including delays in automobile delivery, delays in calculating monthly instalments, price variation, failure to provide a registration ( licence) and failure to supply the agreed product. The proposed method is an efficient way to solve large-scale multidimensional problems and provide a robust and reliable set of results.

The proposed method makes it much easier for management to deal with complaints by significantly reducing their number. The highest-ranked complaints from customers of the car industry in Iran are those related to delivery time, price alternations, customer service support and quality issues. Surveying the list of complaints shows that paying attention to the four most voiced complaints can reduce them more than 54%. Management can make appropriate strategies to improve the production quality as well as business processes, thus producing a significant number of customer complaints.

This paper proposes a comprehensive approach to critically analyse the VoCs by combining qualitative and decision-making approaches including K-mean, FCOPRAS, fuzzy Delphi and FBWM. This is the first paper that analyses the VoCs in the automotive sector in a developing country’s context involving large-scale decision-making problem-solving.

Selecting a suitable contract to outsource construction projects is an ongoing concern for project managers and organizational directors. This study aims to propose a comprehensive model to manage the risks of outsourced construction project contracts.

To employ the proposed model, firstly, the types of contracts and risks in the organization should be identified, then, to prioritize the contracts, the identified risks are considered as criteria. After receiving the experts' opinions, the best–worst method (BWM) integrated with grey relation analysis (GRA) method was used to prioritize the contracts. BWM and GRA are multi-criteria decision-making methods with different approaches and applications. In the current study, BWM has been employed to calculate the weights of criteria because it has better performance than other methods such as the analytic hierarchy process (AHP). After calculating the weights of criteria, the GRA method has been utilized for ranking the alternatives.

According to the results obtained from the case study, the cost plus award fee contract is the most suitable alternative for outsourcing construction projects. The proposed methodology can be practically applied through different types of the projects such as construction or “engineering, procurement and construction”.

To the best of our knowledge, this is the first time a conceptual model has been proposed to select an appropriate contract for construction projects. Also, for the first time, the BWM integrated with GRA method has been used to prioritize project contracts based on the potential risks. The proposed model can contribute to project managers for selecting a suitable contract with the least risk in construction projects.