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Data envelopment analysis (DEA) is a non-parametric model that is developed for evaluating the relative efficiency of a set of homogeneous decision-making units that each unit transforms multiple inputs into multiple outputs. However, usually the decision-making units are not completely similar. The purpose of this paper is to propose an algorithm for DEA applications when considered DMUs are non-homogeneous.

To reach this aim, an algorithm is designed to mitigate the impact of heterogeneity on efficiency evaluation. Using fuzzy C-means algorithm, a fuzzy clustering is obtained for DMUs based on their inputs and outputs. Then, the fuzzy C-means based DEA approach is used for finding the efficiency of DMUs in different clusters. Finally, the different efficiencies of each DMU are aggregated based on the membership values of DMUs in clusters.

Heterogeneity causes some positive impact on some DMUs while it has negative impact on other ones. The proposed method mitigates this undesirable impact and a different distribution of efficiency score is obtained that neglects this unintended impacts.

The proposed method can be applied in DEA applications with a large number of DMUs in different situations, where some of them enjoyed the good environmental conditions, while others suffered from bad conditions. Therefore, a better assessment of real performance can be obtained.

The paper proposed a hybrid algorithm combination of fuzzy C-means clustering method with classic DEA models for the first time.

This study aims to identify consumer segments among luxe-bargain shoppers using a fuzzy clustering method based on psychographic variables related to both luxury consumption and bargain processes and profiles the identified segments in behavioral tendencies.

The sample consists of 500 consumers who purchased a luxury brand at a bargain. The analyses involve running a confirmatory factor analysis, a fuzzy clustering analysis based on psychographic variables, and ANOVA for profiling the segments.

A fuzzy clustering analysis identifies four distinct segments: deal hunters, sale-prone shoppers, active luxe-bargain shoppers, and royal shoppers. Each consumer segment exhibits differences in consumer characteristics, demographics, and behavioral tendencies. The study provides insight into varied luxury consumers.

In an effort to fill the gap between traditional framework in luxury research and today ' s luxury market that provides accessibility of luxury items at lower price points to mass consumers, this study introduces a new concept of “luxe-bargain shopper” and examines varied luxury good consumers in the bargain shopping context. However, the findings of the current study should be interpreted with caution due to sampling method, product category of luxury brands, the limited number of luxury brands used in the study.

The results provide marketing suggestions for each segment of luxe-bargain shoppers.

There is virtually no luxury study conducted in the context of bargain shopping. By examining luxe-bargain shoppers using a robust fuzzy clustering method, this study extends our knowledge of luxury consumption as well as provides a new perspective to segmentation research.

The purpose of this paper is to analyse the determinants of small and medium-sized enterprises’ (SMEs) propensity to export using data from a North African country, namely Algeria. Drawing on the extended resource-based view, the study examines the role of firms’ resources and capabilities in explaining the probability to export.

The study employs the nascent fuzzy c-means clustering technique to analyse a sample of 208 Algerian SMEs. The sample included both established and potential exporters operating across various sectors. A combination of online and face-to-face methods was used to collect the data.

While a preliminary analysis established the existence of five clusters exhibiting different levels of resources and capabilities, further discernment of these clusters has shown significant variances in relation to export propensity. In short, clusters exhibiting combinations that include higher levels of export-oriented managerial resources showed greater export propensity, whereas clusters lacking such assets were less likely to display high export propensity, despite superior capabilities in marketing and innovation.

The findings provide a more comprehensive insight on the critical resources shaping SMEs’ internationalisation in the North African context. The paper holds important implications for export promotion policy in this area.

The study makes a twofold contribution. First, the use of the fuzzy c-means clustering technique to capture the joint influence of discrete resources and capabilities on SMEs’ export propensity constitutes a methodological contribution. Second, being the first study bringing evidence on SMEs’ internationalisation from the largest country in the African continent, in terms of landmass, constitutes an important contextual contribution.

The UK police service has a major challenge to introduce innovative ways of improving efficiency and productivity, whilst at the same time improving public opinion as to their effectiveness in the “fight against crime”. The purpose of this paper is to outline an exploratory study of the ability to cluster police forces based on their sanction detection levels over a number of different offence groups and whether these clusters have different associated public opinions towards them.

Using secondary data and the fuzzy c‐means clustering technique to exposit clusters of police forces based on sanction detection levels, relating them in a statistical analysis with public opinion on the police.

The clustering analysis shows how police forces can be considered relative to each other, based on their sanction detection levels of certain offence groups, including; burglary, fraud and forgery and criminal damage. Using the established clusters of police forces, in respect of independent variables relating to public opinion, including confidence in police; there does appear to be statistically significant differences amongst the clusters of police force.

The results demonstrate the connection between the police's attempt to fight crime and public opinion. With the public opinion measures considered post the establishing of police forces’ clusters, the results show the public does notice the level of sanction detections achieved. The identified disconnect of the public with the criminal justice system is something that can be improved on in the future.

Demonstrates that there is a significant link in the relationship between the levels of sanction detection levels of police forces and public opinion about their ability to fight crime.

This paper employs fuzzy c‐means, a modern clustering technique nascent in this area of research.

This paper aims to explore and analyse stakeholders’ perceptions of the development priorities and suggests more effective strategies to assist sustainable economic growth in the United Arab Emirates (UAE).

The authors use the World Bank data set, which collects various stakeholders’ opinions on the UAE development. First, the exploratory factor analysis has been applied to detect the main groups of development priorities. Second, the fuzzy cluster analysis has been conducted to detect the groups of stakeholders with different attitudes towards the importance of extracted groups of priorities. Third, clusters have been compared according to demographics, media usage and shared prosperity goals.

The two main groups of development priorities have been extracted by the exploratory factor analysis: economic priorities and sustainability priorities. Four clusters have been detected according to the level of motivation when it comes to the economic and sustainability priorities: Cluster 1 (High economic – High sustainability), Cluster 2 (High economic – Medium sustainability), Cluster 3 (High economic – Low sustainability) and Cluster 4 (Low economic – Low sustainability). Members of the cluster that prefer a high level of economic and sustainability priorities (Cluster 1) also prefer more diversified economic growth providing better employment opportunities and better education and training for young people in the UAE.

Limitations stem from the survey being conducted on a relatively small sample using the data collected by the World Bank; however, this data set allowed a comparison of various stakeholders. Future research should consider a broader sample approach, e.g. exploring and comparing all of the Gulf Cooperation Council (GCC) countries; investigating the opinions of the expatriate managers living in the UAE that are not from GCC countries; and/or including other various groups that are lagging, such as female entrepreneurs.

Several practical implications were identified regarding education and media coverage. Since respondents prioritize the economic development factors over sustainability factors, a media campaign could be developed and executed to increase sustainability awareness. A campaign could target especially male citizens since the analysis indicates that males are more likely to affirm high economic and low sustainability priorities than females. There is no need for further diversification of media campaigns according to age since the analysis did not reveal relevant differences in age groups, implying there is no inter-generational gap between respondents.

This paper contributes to the literature by comparing the perceived importance of various development goals in the UAE, such as development priorities and shared prosperity indicators. The fuzzy cluster analysis has been used as a novel approach to detect the relevant groups of stakeholders in the UAE and their developmental priorities. The issue of media usage and demographic characteristics in this context has also been discussed.

Among the many accepted clustering techniques, the fuzzy clustering approaches have been developed over the last decades. These approaches have been applied to many areas in manufacturing systems. In this paper, a fuzzy clustering approach is proposed for selecting machine cells and part families in cellular manufacturing systems. This fuzzy approach offers a special advantage over existing clustering approaches as it presents the degree of membership of the machine or part associated with each machine cell or part family allowing users flexibility in formulating machine cells and part families. The proposed algorithm is extended and validated using numerical examples to demonstrate its application in cellular manufacturing.

Existing shape‐based fuzzy clustering algorithms are all designed to explicitly segment regular geometrically shaped objects in an image, with the consequence that this restricts their capability to separate arbitrarily shaped objects. The purpose of this paper is to introduce a new detection and separation of generic‐shaped object algorithm.

With the aim of separating arbitrary‐shaped objects in an image, this paper presents a new detection and separation of generic‐shaped objects (FKG) algorithm that analytically integrates arbitrary shape information into a fuzzy clustering framework, by introducing a shape constraint that preserves the original object shape during iterative scaling.

Both qualitative and numerical empirical results analysis corroborate the improved object segmentation performance achieved by the FKG strategy upon different image types and disparately shaped objects.

The proposed FKG algorithm can be highly used in applications where object segmentation is necessary. Likewise, this algorithm can be applied in Moving Picture Experts Group‐4 for real object segmentation that is already applied in synthetic object segmentation.

The purpose of this paper is to propose a data mining approach for mining valuable markets for online customer relationship management (CRM) marketing strategy. The industry of coffee shops in Taiwan is employed as an empirical case study in this research.

Via a proposed data mining approach, the study used fuzzy clustering algorithm and Apriori algorithm to analyze customers for obtaining more marketing and purchasing knowledge of online CRM systems.

The research found three hard markets and one fuzzy market. Furthermore, the study discovered two association rules and two fuzzy association rules.

However, industry of coffee shops has been always a fast-growing and competitive business around the world. Thus, marketing strategy is important for this industry. The results and the proposed data mining approach of this research can be used in the industry of coffee shop or other retailers for their online CRM marketing systems.

The purpose of this paper is to illustrate the application of statistical tools and techniques for quantitative assessment of spiritual capital (SC) based on a questionnaire survey in the organizations which undergo large-scale organizational change projects.

A sample of 65 individuals from three organizations were interviewed. The paper uses the 12 principles of transformation available to spiritual intelligence (referred to as SQ characteristics) to assess SC in a two-phase integrated algorithm of principal component analysis (PCA) and fuzzy clustering.

The paper proposes a two-phase integrated algorithm. In the first phase, PCA is used to reduce the scores of items related to each of SQ characteristics and aggregate them into a single and unique measure. In the second phase, PCA is applied for total SQ quantification. For verification and validation, fuzzy clustering is employed along with PCA to cluster the people in the survey into different classes, which may possess different stocks of SC and rank them based on their level of SQ. The results of PCA are verified and validated by fuzzy clustering revealing the applicability and usefulness of PCA for SC quantification.

The paper is based on individual judgments about their own SQ characteristics hence the results of questionnaire survey may be biased by individual personal characteristics. Future research can apply the proposed algorithm and check for its reliability using other psychometric instruments available in the field.

The paper contributes by filling a gap in the quantitative management tools literature, in which empirical studies on validated multivariate analysis of spirituality have been scarce until now.