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This paper aims to explore a new model to manage small and medium enterprise (SME) clustering process that examines the geographical connectivity conditions within the existing theories on agglomeration. The presented work explores the dynamics governing the decisions related to both the duration and frequency of the different forms of these new clusters.

A clustering configurator tool is developed to assist managers for the best temporary cluster model. The configurator considers aspects related to the market, industry and classical clustering requirements as well as social capital (SC). Finally, the performance of various temporary clusters under different demand scenarios and operational conditions are studied using numerical simulation.

The results examined the performance of the new clusters under various internal and external defining indicators against potential economic growth, technology spillover and the new metric of SC. The results offered interesting observations suggesting various recommendations to promote these new models to SMEs as well as how to better manage them.

The presented results are understood in the context of the suggested settings of relationships and scoring weights.

The new form of clusters will help SMEs overcome the feasibility challenge when considering re-locating to existing clusters while reaping many of these clusters benefits. Furthermore, different recommendations for management aimed at enhancing clustering decisions and the efficiency of SMEs in these new setups are presented.

This paper suggests a new clustering management approach that capitalizes on the temporal domain rather than classical space or the digital clusters domains. Also, a new management concept called dynamic matching is suggested. SC is considered among clustering objectives which was disregarded in similar studies.

The purpose of this paper is to explore how to improve the contribution of farmers markets (FMs) to the local food economy and improve their management through a new temporary clustering management approach.

The research encompasses 78 structured interviews with farmers’ market vendors in the central coast region of California. A descriptive statistical and exploratory analysis to capture and evaluate the extent of various clustering activities currently existing in FMs is presented.

Analysis suggested an existing clustering behavior in FMs with different degrees that would enhance the role of these markets in local food economy. The improved social capital and financial performance of these markets shown in this study outperformed other cluster metrics monitored. Furthermore, there were some positive relationships between knowledge sharing (as a cluster activity) and both integration and financial activities among FMs vendors, highlighting interesting dynamics generated by the temporary nature of these clusters.

The study was based on an exploratory research design, investigating a selected number of vendors in the central coast region of California. The research does not claim to provide a comprehensive survey of all FMs.

The analysis resulted in recommendations to improve efficiency of FMs’ practices at both the management level and the strategic level. These recommendations will enhance the contribution of these markets to the local food economy. The results also expand the practical knowledge bodies of regional and local food business development. Finally, the study highlights the social role of FMs through showing social capital as one of the main clustering drivers.

This study contributes to theoretical knowledge concerning the impact of clusters on operation performance by exploring a new temporary proximity that can be added to the existing geographical and digital proximity enriching the clustering approaches debate. Furthermore, the analysis provides specific novel insights into potential operational improvements for current farmers’ market management to enhance their economic and social roles.

U.S. corporations have been paying attention to strategic planning at the line‐of‐business level. However, something more is needed. According to the authors, strategic planning for clusters of businesses is the missing and vital ingredient for success in the future.

The purpose of this paper is to propose a novel approach based on utility mining for store layout.

A utility mining-based data mining algorithm is utilized in this paper.

A real-life case study in a supermarket is conducted to illustrate the proposed approach. The findings show that the proposed approach can be used easily and efficiently to arrange store layout.

There are two limitations to this study. First, space allocation to each product family is not considered. Second, product placement in each product family is not taken into account in the proposed approach.

In this paper, a novel approach is proposed for business intelligence in retail business. The proposed approach uses a utility-based data mining approach, namely, the high-utility itemset mining (HUIM) algorithm, to rearrange store layout and to determine the relations among product families. The quantities and prices of items purchased corresponding to product families are taken into account in the proposed approach to address the needs in practice. Business intelligence software is also developed as an integral part of the proposed approach to utilize the HUIM algorithm.

The study analyzes the relationship between human resources (HR) flexibility and absorptive capacity (AC) of knowledge in a sample of Spanish manufacturing firms. The purpose of the research is to analyze if firms with greater AC are more flexible than other firms and to assess the implications of different combinations of HR flexibility and AC for innovation.

The study sample consisted of 1,666 Spanish industrial firms in 2015 compiled from a large set of statements from the Survey of Business Strategies (SBS) questionnaire. The methodology includes a cluster analysis and a case study of selected firms. First, a k-means clustering analysis was carried out to explore how homogeneous are the SBS firms according to several HR flexibility and AC measures. The authors complement the clustering itself with some descriptive statistics for each cluster. Second, the statistical analysis is followed by a selection of case studies from industrial firms in different positions regarding innovation, AC, and HR flexibility. The information for the cases studies comes from secondary sources such as corporate governance reports and statements of managers and employees from company websites and public reports.

The empirical evidence indicates that some combinations of HR flexibility and AC are positively related to innovation outputs whereas others are not. Firms with greater AC, R&D effort and innovation outputs have less “bad” HR flexibility (external numerical flexibility from temporary employees and temporary help agencies) and more “good” HR flexibility (internal and external functional flexibility). On the contrary, firms with minimum or non-existent innovation and AC efforts have the highest levels of temporary employment and do not hire external R&D experts.

The limitations of the cross-sectional nature of the study make the authors cautious about any proposition that may suggest a causal relationship among the studied variables.

Managers should pay attention to the different implications of each HR flexibility dimension for innovation activities since innovative companies value more those HR flexibility dimensions that contribute to the dispersion of knowledge within the firm.

The authors propose a framework to analyze the combination of HR flexibility and AC most suitable to different types of firms. Based on the statistical analyses and the case studies, the authors propose some strategic implications useful for the management of human resources. The matrix's framework analyzes the firm's innovation strategies according to the interactions between AC and the mix of HR flexibility dimensions.

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.

This paper reviews the literature on the business models of small and medium-sized enterprises (SMEs). It seeks to examine the profile, conceptual and intellectual structure of the literature whilst leveraging the findings to suggest promising future paths to advance our knowledge on business models of SMEs.

The study resorts to a systematic literature review that conducts descriptive, bibliometric (i.e. co-word occurrence analysis and bibliographic coupling of documents analysis) and content analyses to review the literature on business models of SMEs. The research protocol included 301 articles collected in the Web of Science (WoS) database in the descriptive and bibliometric analyses. The bibliometric analysis was performed using the VOSviewer software.

The descriptive analysis portrayed the profile of this research stream. The systematisation of the co-word occurrence analysis describes the four clusters that comprise the conceptual structure of this research field. The content analysis of the bibliographic coupling of documents’ clusters portrays the seven clusters that involve the intellectual structure of this research area.

The integrated and holistic approach adopted in this study provides a detailed overview of the literature on business models of SMEs. We propose an integrative framework for the literature that bridges the main themes that form the conceptual and intellectual structure of this field of research. A comprehensive agenda for future research is suggested and implications for theory, policy and practice are stated.

The purpose of this paper is to detect different behavioral groups and the dominant patterns of customer shifts between segments of different values over time.

A new hybrid methodology is presented based on clustering techniques and mining top-k and distinguishing sequential rules. This methodology is implemented on the data of 14,772 subscribers of a mobile phone operator in Tehran, the capital of Iran. The main data include the call detail records and event detail records data that was acquired from the IT department of this operator.

Seven different behavioral groups of customer shifts were identified. These groups and the corresponding top-k rules represent the dominant patterns of customer behavior. The results also explain the relation of customer switching behavior and segment instability, which is an open problem.

The findings can be helpful to improve marketing strategies and decision making and for prediction purposes. The obtained rules are relatively easy to interpret and use; this can strengthen the practicality of results.

A new hybrid methodology is proposed that systematically extracts the dominant patterns of customer shifts. This paper also offers a new definition and framework for discovering distinguishing sequential rules. Comparing with Markov chain models, this study captures the customer switching behavior in different levels of value through interpretable sequential rules. This is the first study that uses sequential and distinguishing rules in this domain.

Load balancing is an effective enhancement to the proposed routing protocol, and the basic idea is to share traffic load among cluster members to reduce the dropping probability due to queue overflow at some nodes. This paper aims to propose a novel hierarchical approach called distributed energy efficient adaptive clustering protocol (DEACP) with data gathering, load-balancing and self-adaptation for wireless sensor network (WSN). The authors have proposed DEACP approach to reach the following objectives: reduce the overall network energy consumption, balance the energy consumption among the sensors and extend the lifetime of the network, the clustering must be completely distributed, the clustering should be efficient in complexity of message and time, the cluster-heads should be well-distributed across the network, the load balancing should be done well and the clustered WSN should be fully connected. Simulations show that DEACP clusters have good performance characteristics.

A WSN consists of large number of wireless capable sensor devices working collaboratively to achieve a common objective. One or more sinks [or base stations (BS)] which collect data from all sensor devices. These sinks are the interface through which the WSN interacts with the outside world. Challenges in WSN arise in implementation of several services, and there are so many controllable and uncontrollable parameters (Chirihane, 2015) by which the implementation of WSN is affected, e.g. energy conservation. Clustering is an efficient way to reduce energy consumption and extend the life time of the network, by performing data aggregation and fusion to reduce the number of transmitted messages to the BS (Chirihane, 2015). Nodes of the network are organized into the clusters to process and forwarding the information, while lower energy nodes can be used to sense the target, and DEACP makes no assumptions on the size and the density of the network. The number of levels depends on the cluster range and the minimum energy path to the head. The proposed protocol reduces the number of dead nodes and the energy consumption, to extend the network lifetime. The rest of the paper is organized as follows: An overview of related work is given in Section 2. In Section 3, the authors propose an energy efficient level-based clustering routing protocol (DEACP). Simulations and results of experiments are discussed in Section 4. In Section 5, the authors conclude the work presented in this paper and the scope of further extension of this work.

The authors have proposed the DEACP approach to reach the following objectives: reduce the overall network energy consumption, balance the energy consumption among the sensors and extend the lifetime of the network, the clustering must be completely distributed, the clustering should be efficient in complexity of message and time, the cluster-heads should be well-distributed across the network, the load balancing should be done well, the clustered WSN should be fully connected. Simulations show that DEACP clusters have good performance characteristics.

The purpose of this paper is to investigate whether knowledge-flows through social interaction occur within the context of a policy-driven industrial cluster.

The paper follows a single-case approach, adopting Malaysia's Multimedia Super Corridor cluster as the unit of analysis. Semi-structured interviews with firm- and non-firm-respondents in the cluster constitute the prime source of data.

Spontaneous social interaction leading to knowledge diffusion within the cluster may be lacking. However, policymakers may be able to remedy this somewhat by organising workshops, conferences and other events to help firms gain additional knowledge, although these measures should ideally complement, rather than replace, spontaneous face-to-face meetings.

Although policymakers may implement certain measures to try to compensate for the shortage of knowledge flows through social interaction (e.g. organising more “formal” events such as workshops and exhibitions), it is argued that these may not be sufficient in ensuring the long-term, self-sustaining success of the cluster.

The paper integrates extant literature on “organic” industrial clusters into a pre-planned, purpose-built, policy-driven cluster context. Research on policy-driven clusters is somewhat limited, with attention from scholars primarily focused on organic clusters. This paper attempts to bridge the gap for future research in the area.