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This paper not only draws conclusions from the available literature but also offers some new factors as well, which are not included in the existing literature. To be more precise, the purpose of this paper is to ascertain factors behind the clustering of the motorcycle industry, a low-tech and low investment industry. This paper weighs the government’s policies, role of factors of production, infrastructure, geography and other drivers for the subject industry and associated industries in the geographic location of Hyderabad.

For collection of data, a questionnaire was designed to survey the cluster (n=250) after reviewing the literature and conducting interviews of experts of the motorcycle manufacturing industry, i.e. owners, managers, auditors, suppliers, etc.; a component matrix was developed to reduce the dimension of factors and measure the correlation, which helped to weigh the influence of factors. A confirmatory factor analysis proposed four factors as the best fit.

The study conjectured a new viable factor for industrial clustering: “ethnic community,” as it acts as a catalyst to diffuse knowledge, experience and skills within the industrial cluster.

This research does not find the weightage of the factors for industrial clustering, i.e. it does not calculate the influence of factors behind the industrial clustering.

The above findings aim to stimulate policy makers and researchers alike to further pursue the line of inquiry developed in this paper.

A first-time confirmatory factor analysis is used to find the reasons of industrial clustering. Root mean square error of approximation is used to test the model fit. Most importantly, it is the research about an emerging industrial cluster.

The purpose of this paper is to determine the best approach to customer segmentation and to extrapolate associated rules for this based on recency, frequency and monetary (RFM) considerations as well as demographic factors. In this study, the impacts of RFM and demographic attributes have been challenged in order to enrich factors that lend comprehension to customer segmentation. Different types of scenario were designed, performed and evaluated meticulously under uniform test conditions. The data for this study were extracted from the database of a global pizza restaurant chain in Turkey. This paper summarizes the findings of the study and also provides evidence of its empirical implications to improve the performance of customer segmentation as well as achieving extracted rule perfection via effective model factors and variations. Accordingly, marketing and service processes will work more effectively and efficiently for customers and society. The implication of this study is that it explains a clear concept for interaction between producers and consumers.

Customer relationship management, which aims to manage record and evaluate customer interactions, is generally regarded as a vital tool for companies that wish to be successful in the rapidly changing global market. The prediction of customer behaviors is a strategically important and difficult issue because of the high variance and wide range of customer orders and preferences. So to have an effective tool for extracting rules based on customer purchasing behavior, considering tangible and intangible criteria is highly important. To overcome the challenges imposed by the multifaceted nature of this problem, the authors utilized artificial intelligence methods, including k-means clustering, Apriori association rule mining (ARM) and neural networks. The main idea was that customer clusters are better enhanced when segmentation processes are based on RFM analysis accompanied by demographic data. Weighted RFM (WRFM) and unweighted RFM values/scores were applied with and without demographic factors and utilized to compose different types and numbers of clusters. The Apriori algorithm was used to extract rules of association. The performance analyses of scenarios have been conducted based on these extracted rules. The number of rules, elapsed time and prediction accuracy were used to evaluate the different scenarios. The results of evaluations were compared with the outputs of another available technique.

The results showed that having an appropriate segmentation approach is vital if there are to be strong association rules. Also, it has been determined from the results that the weights of RFM attributes affect rule association performance positively. Moreover, to capture more accurate customer segments, a combination of RFM and demographic attributes is recommended for clustering. The results’ analyses indicate the undeniable importance of demographic data merged with WRFM. Above all, this challenge introduced the best possible sequence of factors for an analysis of clustering and ARM based on RFM and demographic data.

The work compared k-means and Kohonen clustering methods in its segmentation phase to prove the superiority of adopted segmentation techniques. In addition, this study indicated that customer segments containing WRFM scores and demographic data in the same clusters brought about stronger and more accurate association rules for the understanding of customer behavior. These so-called achievements were compared with the results of classical approaches in order to support the credibility of the proposed methodology. Based on previous works, classical methods for customer segmentation have overlooked any combination of demographic data with WRFM during clustering before proceeding to their rule extraction stages.

This research is to determine scenarios and perspectives for improving the cluster strategy of business integration in the post-COVID-19 era with the help of the methodology of the game theory.

The methodology of this research includes the complex method, statistical method, correlation analysis and the game theory of decision-making.

Based on the analysis of scientific approaches, we formulate the authors' treatment of the essence of the notion of clustering, which characteristics are evaluated in this work. In this treatment, we distinguish factors that influence the development of clustering of business structures of the state, which level is assessed within the analysis. The components of the competitiveness of business structures are among such factors. Cluster structures of certain countries successfully functioned during the COVID-19 pandemic, using effective strategies created independently (United States) and based on the strategies of non-market regulation (China).

The scientific novelty of this research consists in the identification of the types and characteristics of the strategies of clustering of business structures formed during the COVID-19 and post-COVID-19 eras.

The purpose of the paper is to segment and profile the Indian shoppers in the context of organic foods in India. It proposes to use a healthy lifestyle (HL) as a segmenting variable and to use a factor-cluster analysis approach to achieve the same. The current study is expected to add a substantial base to the segmentation literature in marketing.

Food stores in Indian metropolitan city Chennai are sampled, and data is collected in the form of a mall intercept survey method. In total, 441 usable structured questionnaires are filled by the respondents which are subjected to suitable statistical analysis.

Three significantly different consumer segments emerged from the given sample of respondents, which shows uniqueness concerning consumer’s, HL features, demographics and the variables of the theory of planned behavior (TPB).

Clustering method used to segment the potential shoppers of organic foods is an exploratory technique only. It cannot be treated or generalized to the population like those of inferential techniques. The researcher suggested testing the same with a larger sample size and in a different context. It is limited to urban and suburban facets of the metropolitan city in India.

The study will be helpful to marketers and decision makers to target the potential organic foods consumers.

This paper aims to propose a novel hybrid-decision-making trial and evaluation laboratory-K means clustering algorithm as a decision-making framework for analyzing the barriers of green computing adoption.

A literature review is conducted to extract relevant green computing barriers. An expert elicitation process is performed to finalize the barriers and to establish their corresponding interrelationships.

The proposed approach offers a comprehensive framework for modeling the barriers of green computing adoption.

The results of this paper provide insights on how the barriers of green computing adoption facilitate the adoption of stakeholders. Moreover, the paper provides a framework for analyzing the structural relationships that exist between factors in a tractable manner.

The paper is one of the very first attempts to analyze the barriers of green computing adoption. Furthermore, it is the first to offer lenses in a Philippine perspective. The paper offers a novel algorithm that can be useful in modeling the barriers of innovation, particularly, in green computing adoption.

Reverse logistics (RL), an inseparable aspect of supply chain management, returns used products to recovery processes with the aim of reducing waste generation. Enterprises, however, seem reluctant to apply RL due to various types of risks which are perceived as posing an economic threat to businesses. This paper draws on a synthesis of supply chain and risk management literature to identify and cluster RL risk factors and to recommend risk mitigation strategies for reducing the negative impact of risks on RL implementation.

The authors identify and cluster risk factors in RL by using risk management theory. Experts in RL and supply chain risk management validated the risk factors via a questionnaire. An unsupervised data mining method, self-organising map, is utilised to cluster RL risk factors into homogeneous categories.

A total of 41 risk factors in the context of RL were identified and clustered into three different groups: strategic, tactical and operational. Risk mitigation strategies are recommended to mitigate the RL risk factors by drawing on supply chain risk management approaches.

This paper studies risks in RL and recommends risk management strategies to control and mitigate risk factors to implement RL successfully.

To reduce the electricity consumption in our homes, a first step is to make the user aware of it. Reading a meter once in a month is not enough, instead, it requires real-time meter reading. Smart electricity meter (SEM) is capable of providing a quick and exact meter reading in real-time at regular time intervals. SEM generates a considerable amount of household electricity consumption data in an incremental manner. However, such data has embedded load patterns and hidden information to extract and learn consumer behavior. The extracted load patterns from data clustering should be updated because consumer behaviors may be changed over time. The purpose of this study is to update the new clustering results based on the old data rather than to re-cluster all of the data from scratch.

This paper proposes an incremental clustering with nearness factor (ICNF) algorithm to update load patterns without overall daily load curve clustering.

Extensive experiments are implemented on real-world SEM data of Irish Social Science Data Archive (Ireland) data set. The results are evaluated by both accuracy measures and clustering validity indices, which indicate that proposed method is useful for using the enormous amount of smart meter data to understand customers’ electricity consumption behaviors.

ICNF can provide an efficient response for electricity consumption patterns analysis to end consumers via SEMs.

The purpose of this paper is to propose a Meta modeling based on regression, neural network, and clustering to analyze the job satisfaction factors and improvement policy making.

Since any job satisfaction evaluation supposes to improve the status by prescribing specific strategies to be performed in the organization, proposing applicable strategies is decisively important. Task demand, social structure and leader-member exchange (LMX) are general applications easily conceptualized while proposing job satisfaction improvement strategies.

On the basis of these empirical findings, the authors first aim to identify relationships between LMX, task demand, social structure and individual factors, organizational factors, job properties, which are easier to be employed in strategy formulation for job satisfaction, and then determine the sub-factors and subsequently cluster them. The effectiveness of the proposed model is verified by a case study.

Here, a Meta modeling based on regression, neural network, and clustering is proposed to analyze the job satisfaction factors and improvement policy making.

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.

This paper introduces a new way of classifying clothing fabrics objectively. Representative apparel fabrics were collected and measured by the Kawabata Evaluation System for Fabrics (KES‐FB). The disjoint clustering method was used to divide fabrics into four clusters, each representing particular fabric performance and end‐use characteristics. These classified clusters were further analyzed applying the method of principal‐component analysis to acquire factor patterns that indicate the most important fabric properties for characterizing different fabric end‐use. Extracted information from the instrumentally obtained data in terms of fabric physical properties is useful to fabric and garment producers, apparel designers, and consumers in specifying and categorizing fabric products, in insuring proper fabric use, and in controlling fabric purchase.