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To propose a comprehensive and semi‐automatic method for constructing or updating knowledge organization tools such as thesauri.

The paper proposes a comprehensive methodology for thesaurus construction and maintenance combining shallow NLP with a clustering algorithm and an information visualization interface. The resulting system TermWatch, extracts terms from a text collection, mines semantic relations between them using complementary linguistic approaches and clusters terms using these semantic relations. The clusters are mapped onto a 2D using an integrated visualization tool.

The clusters formed exhibit the different relations necessary to populate a thesaurus or ontology: synonymy, generic/specific and relatedness. The clusters represent, for a given term, its closest neighbours in terms of semantic relations.

This could change the way in which information professionals (librarians and documentalists) undertake knowledge organization tasks. TermWatch can be useful either as a starting point for grasping the conceptual organization of knowledge in a huge text collection without having to read the texts, then actually serving as a suggestive tool for populating different hierarchies of a thesaurus or an ontology because its clusters are based on semantic relations.

This lies in several points: combined use of linguistic relations with an adapted clustering algorithm, which is scalable and can handle sparse data. The paper proposes a comprehensive approach to semantic relations acquisition whereas existing studies often use one or two approaches. The domain knowledge maps produced by the system represents an added advantage over existing approaches to automatic thesaurus construction in that clusters are formed using semantic relations between domain terms. Thus while offering a meaningful synthesis of the information contained in the original corpus through clustering, the results can be used for knowledge organization tasks (thesaurus building and ontology population) The system also constitutes a platform for performing several knowledge‐oriented tasks like science and technology watch, textmining, query refinement.

The purpose of this paper is to propose a scheme that allows users to interactively explore relations between entities in knowledge bases (KBs). KBs store a wide range of knowledge about real-world entities in a structured form as (subject, predicate, object). Although it is possible to query entities and relations among entities by specifying appropriate query expressions of SPARQL or keyword queries, the structure and the vocabulary are complicated, and it is hard for non-expert users to get the desired information. For this reason, many researchers have proposed faceted search interfaces for KBs. Nevertheless, existing ones are designed for finding entities and are insufficient for finding relations.

To this problem, the authors propose a novel “relation facet” to find relations between entities. To generate it, they applied clustering on predicates for grouping those predicates that are connected to common objects. Having generated clusters of predicates, the authors generated a facet according to the result. Specifically, they proposed to use a couple of clustering algorithms, namely, agglomerative hierarchical clustering (AHC) and CANDECOMP/PARAFAC (CP) tensor decomposition which is one of the tensor decomposition methods.

The authors experimentally show test the performance of clustering methods and found that AHC performs better than tensor decomposition. Besides, the authors conducted a user study and show that their proposed scheme performs better than existing ones in the task of searching relations.

The authors propose a relation-oriented faceted search method for KBs that allows users to explore relations between entities. As far as the authors know, this is the first method to focus on the exploration of relations between entities.

The purpose of this paper is to find the reason which the results of grey variable weight clustering method do not correspond with the reality. It proposes reconstructing the whitenization weight function, outlining why and how inconsistency is avoided. The study aims to improve the model of grey clustering method based on the whitenization weight function and list the steps of the new clustering model so that analysis and application of innovation capacity in a broader range is normally found.

First the reason for the problem that the clustering results of grey variable weight clustering do not correspond with the reality is analyzed in two existing literature. And then a new whitenization weight function is reconstructed, two properties of the whitenization weight function are proved. The solution of the new grey variable weight clustering based on the whitenization weight function is built by following six steps.

The paper provides a new whitenization weight function which satisfies the normative and non-triplecrossing. It suggests that successful clustering results of innovation capacity act on two levels: integrating the elements of innovation capacity indexes, and following steps of grey variable weight clustering.

This paper improves the existing method of grey variable weight clustering and fulfills an identified need to study how cities’ innovation capacity can be clustered.

A new expression of the philosophy framework of pansystems theory is stated. Some generalized quantification formulae are developed. Concrete contents include: new forms of panderivative and pansymmetry, meta‐equation, eight‐counter methodology, pansystems relativity, dialectical logic, panbox principle, complex systems, systems engineering, analytic hierarchy process, general living systems, economy‐sociology, clustering, topology, morphology, master equation, approximation‐transforming theory, equivalence theory of dynamics of electromagnetic media, etc.

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.

This study aims to determine how much company participation in a type of cluster affects its economic performance.

This study includes companies operating in seven industries (automotive, engineering, textiles, information technology (IT) services, furniture, packaging and nanotechnology) in the Czech Republic. The companies are divided into three groups: members of institutionalized cluster, operating in the same region (natural clusters) and operating in other regions. Data envelopment window analysis is used to measure their performance for 2009–2019.

Results show that the effect of clustering differs among industries. Companies in three industries (automotive, engineering, nanotechnology) reveal a positive impact of the cluster initiative on performance growth. Two industries (textile, packaging) with companies operating in a natural cluster show better performance than those in an institutionalized cluster. Moreover, the IT services and the furniture industries show no positive effect of clustering on corporate performance.

This research includes 686 companies from seven industries and monitored for 11 years. On the one hand, the sample includes a relatively high number of companies overall; but on the other hand, the sample is relatively small, especially for nonclustered companies. The reason is the lack of available financial statements for small companies.

From the perspective of practical cluster policy, the authors can recommend that monitoring the performance of member companies in clusters must be one of the criteria for evaluating the success of a cluster, such as cluster initiatives.

This study distinguishes between long-standing natural clusters in a given industry and institutionalized ones that have emerged because of a top-down initiative. An original database is created for clustered and nonclustered companies in seven industries, covering the entire Czech Republic.

Recent developments in the competitive landscape of the management support software industry sub-sector — characterised by saturation, consolidation and defragmentation — have also attracted attention to the phenomenon of cluster formation. Layers of SMEs forming satellite systems centred on large players in this industry fill in structural holes in the competitive fabric of the market which are left open — at least temporarily — by the large ‘incubator’ company, while at the same time also acting as a type of capacity buffer or technology spearhead for the larger player which does not need to invest its own capacities into certain niches. This paper discusses the significance of clustering in the management support software sector and aims to offer explanations predicated on intangible constructs which may retrofit traditional agglomeration economies approaches. The findings of a pilot study corroborate the implications of clustering in relation to knowledge spillover effects, labour mobility, network formation, the generation of social capital, reputation building and effects of the urban ambience as factors potentially moderating the competitive environment within clusters vis-à-vis outside. Moreover, effects of the density of cluster labour pools and spin-off are considered. Simultaneously, the unique efficacy of these effects by means of mitigation of the idiosyncratic growth constraints affecting SMEs in this industry context — such as high levels of customer dependence, lack of legitimacy and the importance of reputation as well as customer references — is proposed.

The framework, methodology and development of pansystems cybernetics are introduced. Related contents include: the postmodernizational systems thought, the epitome of pansystems methods, hi‐tech mechanism, social cybernetics, pansystems views of value, labor, management and economics, pedagogy, history and futurology, systems mathematics, general clustering analysis, observocontrollability, generalized living systems, information metascience, regret information, general fuzzy control, generalized gray systems, entropy, large scale dynamical supercomplicated Shengke systems, function simulations, transformation theory, simplification, strengthening, forming substance of subsystems of living systems of Miller’s theory, etc.

Considerable research has been performed to understand green consumption behavior. Given the emergence of this discussion, the purpose of this paper is to apply network analytical techniques to identify the main theoretical relationships regarding green consumption in the most relevant journals between 2000 and 2016.

This paper applies a fit-for-purpose systematic review using network analysis, which includes a screening of 4,049 articles published in 39 journals in the business and marketing subjects, ranked by the Q1 extract of SCImago Journal & Country Rank, accessed between March and July 2017. From 2000 to 2016, 216 articles were selected in 24 journals. The explored variables formed 21 categories.

Five network groups emerged according to the background theories used by green consumption studies: “Consumers’ and companies’ characteristics and actions,” “Consumer intentions, perceptions and their cognitive mechanisms,” “Environmental concern effects,” “Attitudes toward green expertise” and “Behavioral control”. The findings showed what variables have a central relevance to the network, being essential to explain green consumption phenomenon, exploring relations and pointing out gaps for future studies.

Based on the strongest connections between the network groups, practical implications are offered for marketers interested in developing actions promoting green consumption.

This paper identifies the main categories related to green consumption in a network analysis, showing its antecedents and consequents grouped in five clusters. These results indicate that there are relationships still awaiting further investigation.

The purpose of this paper is to review the effect of a cluster approach on SMEs' competitiveness. The primary objective is to examine the use of a cluster approach among SMEs as a tool for meeting their challenges related to globalisation and trade liberalisation, as well as investigating its contributing factor in the process of increasing their competitiveness.

The methodology used for this paper is a literature review of published materials. The literature analysis was performed based on research objectivity, coverage and authority.

There is strong evidence to suggest that a cluster policy brings additional positive effect to existing SME policy in industrialised economies, but such effects have not been extensively researched in developing (transition) countries, particularly from the point of view of the SMEs, which are the main actors in the cluster development process, in relation to whether their performance has been improved as a result of cluster effects.

The findings from this research will assist business managers in making more informed decisions regarding the adoption of a cluster approach and entering into cluster‐based relations, as well as assisting policy makers in designing more efficient cluster policies. The academic value will be added in the context of expanding knowledge in relation to the impact of clusters on economic development in transition countries and as such contribute in filling gaps within the existing body of knowledge.