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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.

For privacy protection, federated learning based on data separation allows machine learning models to be trained on remote devices or in isolated data devices. However, due to the limited resources such as bandwidth and power of local devices, communication in federated learning can be much slower than in local computing. This study aims to improve communication efficiency by reducing the number of communication rounds and the size of information transmitted in each round.

This paper allows each user node to perform multiple local trainings, then upload the local model parameters to a central server. The central server updates the global model parameters by weighted averaging the parameter information. Based on this aggregation, user nodes first cluster the parameter information to be uploaded and then replace each value with the mean value of its cluster. Considering the asymmetry of the federated learning framework, adaptively select the optimal number of clusters required to compress the model information.

While maintaining the loss convergence rate similar to that of federated averaging, the test accuracy did not decrease significantly.

By compressing uplink traffic, the work can improve communication efficiency on dynamic networks with limited resources.

This study aims to construct a sentiment series generation method for danmu comments based on deep learning, and explore the features of sentiment series after clustering.

This study consisted of two main parts: danmu comment sentiment series generation and clustering. In the first part, the authors proposed a sentiment classification model based on BERT fine-tuning to quantify danmu comment sentiment polarity. To smooth the sentiment series, they used methods, such as comprehensive weights. In the second part, the shaped-based distance (SBD)-K-shape method was used to cluster the actual collected data.

The filtered sentiment series or curves of the microfilms on the Bilibili website could be divided into four major categories. There is an apparently stable time interval for the first three types of sentiment curves, while the fourth type of sentiment curve shows a clear trend of fluctuation in general. In addition, it was found that “disputed points” or “highlights” are likely to appear at the beginning and the climax of films, resulting in significant changes in the sentiment curves. The clustering results show a significant difference in user participation, with the second type prevailing over others.

Their sentiment classification model based on BERT fine-tuning outperformed the traditional sentiment lexicon method, which provides a reference for using deep learning as well as transfer learning for danmu comment sentiment analysis. The BERT fine-tuning–SBD-K-shape algorithm can weaken the effect of non-regular noise and temporal phase shift of danmu text.

When a large number of project proposals are evaluated to allocate available funds, grouping them based on their similarities is beneficial. Current approaches to group proposals are primarily based on manual matching of similar topics, discipline areas and keywords declared by project applicants. When the number of proposals increases, this task becomes complex and requires excessive time. This paper aims to demonstrate how to effectively use the rich information in the titles and abstracts of Turkish project proposals to group them automatically.

This study proposes a model that effectively groups Turkish project proposals by combining word embedding, clustering and classification techniques. The proposed model uses FastText, BERT and term frequency/inverse document frequency (TF/IDF) word-embedding techniques to extract terms from the titles and abstracts of project proposals in Turkish. The extracted terms were grouped using both the clustering and classification techniques. Natural groups contained within the corpus were discovered using k-means, k-means++, k-medoids and agglomerative clustering algorithms. Additionally, this study employs classification approaches to predict the target class for each document in the corpus. To classify project proposals, various classifiers, including k-nearest neighbors (KNN), support vector machines (SVM), artificial neural networks (ANN), classification and regression trees (CART) and random forest (RF), are used. Empirical experiments were conducted to validate the effectiveness of the proposed method by using real data from the Istanbul Development Agency.

The results show that the generated word embeddings can effectively represent proposal texts as vectors, and can be used as inputs for clustering or classification algorithms. Using clustering algorithms, the document corpus is divided into five groups. In addition, the results demonstrate that the proposals can easily be categorized into predefined categories using classification algorithms. SVM-Linear achieved the highest prediction accuracy (89.2%) with the FastText word embedding method. A comparison of manual grouping with automatic classification and clustering results revealed that both classification and clustering techniques have a high success rate.

The proposed model automatically benefits from the rich information in project proposals and significantly reduces numerous time-consuming tasks that managers must perform manually. Thus, it eliminates the drawbacks of the current manual methods and yields significantly more accurate results. In the future, additional experiments should be conducted to validate the proposed method using data from other funding organizations.

This study presents the application of word embedding methods to effectively use the rich information in the titles and abstracts of Turkish project proposals. Existing research studies focus on the automatic grouping of proposals; traditional frequency-based word embedding methods are used for feature extraction methods to represent project proposals. Unlike previous research, this study employs two outperforming neural network-based textual feature extraction techniques to obtain terms representing the proposals: BERT as a contextual word embedding method and FastText as a static word embedding method. Moreover, to the best of our knowledge, there has been no research conducted on the grouping of project proposals in Turkish.

The purpose of this paper is to identify different types of resource and capability configurations among Hungarian family and nonfamily firms and explore which compositions can be considered competitive. In a rivalrous, dynamic world, understanding which sets of resources and capabilities lead to a higher level of competitiveness is vital.

This paper is based on a quantitative competitiveness survey carried out between November 2018 and July 2019 in Hungary. The authors used the Firm Competitiveness Index (FCI) to measure competitiveness and the resource-based view (RBV) approach to understand which configurations of resources and capabilities are responsible for a higher level of competitiveness based on 32 variables. An exploratory factor and cluster analysis were conducted to analyze the ownership's effect on firm competitiveness. The final sample size contained 111 companies, of which 53 were identified as family and 58 as nonfamily firms.

Factor analysis reveals five factors determining resources and capabilities: “operational,” “leadership,” “knowledge management,” “transformation” and “networking.” Based on these factors, the cluster analysis identified five groups in terms of types of family and nonfamily firms: “Lagging capabilities,” “Knowledge-based leadership,” “Innovativeness and transformation-oriented management,” “Relationship-oriented management” and “Business operation-oriented management.” Results show that nonfamily businesses focus on operational and leadership capabilities, reaching a higher FCI than family businesses, which are likely to invest more in their networking, transformation and knowledge management capabilities.

By defining the different configurations family and nonfamily firms rely on to reach competitiveness, the paper applies an essential element to the Hungarian and Middle Eastern European contexts of family business research. The findings contribute to developing family business literature and point out specific resources and capabilities family firms should focus on to shift toward reaching a higher level of professionalization and competitiveness. The characterization of different types of competitiveness comparing family and nonfamily firms enables the firms to assess customized implications.

The impact of the sharing economy on traditional businesses has largely been analysed from both company and consumer perspectives. In the case of the latter, scholars have produced a rich field of research into different aspects of consumer behaviour and the way it is reshaped in these alternative consumption patterns. This study aims to provide a systematization of these studies and to develop a model for consumer behaviour in the sharing economy.

Following a three-step approach, a systematic literature review has been performed to analyse and classify 108 scientific papers about consumer behaviour in the sharing economy.

Four main research topics came up from the analysis: sharing approach, consumption pattern, post-purchase behaviour and sustainability. Basing on these clusters, the double-loop model of consumer behaviour in the sharing economy is presented and discussed.

The research allows to provide scholars and practitioners with the state of the art on consumer behaviour in sharing economy and to draft future research avenues to orient research and practice in the field.

Sizing system is a fundamental topic in garment fitting. The purpose of this study was to assess the fit of existing police uniforms (shirt, jacket, overcoat and trousers) and develop a sizing system for upper and lower body uniforms of Amhara policemen in Ethiopia.

In total, 35 body dimensions of 889 policemen were taken through a manual anthropometric survey following the procedures in ISO 8559:1989 after each subject was interviewed on issues related to garment fit. The anthropometric data were pre-processed, key body dimensions were identified by principal components analysis and body types were clustered by the agglomerative hierarchical clustering algorithm and verified by the XGBoost classifier in a Python programming environment. The developed size charts were validated statistically using aggregate loss and accommodation rate.

About 44% of the subjects encountered fit problems every time they own new readymade uniforms. Lengths and side seams of shirts, and lengths and waist girths of trousers are the most frequently altered garment sites. Analysis of the anthropometric measurements resulted in 13 and 15 sizes for the upper and lower bodies, respectively. Moreover, the comparison of the developed upper garment size chart with the existing size chart for a shirt showed a considerable difference. This indicates that inappropriate size charts create fit problems.

The study considers the analysis of fit problems and sizing system development in a less researched country. Moreover, the proposed data mining procedure and its application for size chart development is unique and workable.

With the aid of bibliometric analysis, this study aims to show the state-of-the-art of research on the digital divide and identifies new areas for further investigation.

Performance analysis and science mapping were used in the study to analyse a sample of 3,571 studies that were published between 2018 and 2022. The “Title-Keyword-Abstract” search option was used to collect the anticipated publications data from the Scopus database. The gathered data were analysed using the common bibliometric indices to evaluate the research landscape. The science mapping tactics made use of the VOSviewer and Biblioshiny software.

The performance and science mapping analysis shows that recent research on the digital divide has not been sufficiently exposed and examined. The analysis discovered emerging topics, prolific authors and nations, affiliations, a network of collaboration among authors, countries and institutions, bibliographic coupling and keyword co-occurrence.

This work presents a state-of-the-art that has significant theoretical and practical ramifications for the existing digital divide literature. The methodologies and database used in the current study are more extensive.

The rise of the metaverse has brought profound changes to the economic and social operation models and injected new vitality into academic research. Although a large number of studies have emerged, there are few quantitative analyses of development frontiers and trends.

From a bibliometric perspective, this paper selects 183 pieces of metaverse-related literature in the WoS core database since 2000 as the object of analysis. This paper sums up the characteristics of the literature using the methods of descriptive statistical analysis, keywords analysis, thematic evolution analysis and summarizes the core themes and the laws of metaverse development in each stage.

The digital economy vision brought by the metaverse has led to an increasing number of researchers and achievements in this field. But the depth and breadth of research are still insufficient and unevenly distributed in the region, and the cross-fertilization fields need to be expanded. From the industry's point of view, VR games represented by Second Life and My World have contributed to the popularity of the metaverse. As technology progresses, the research hotspots in the field of metaverse gradually develop from conceptual research to artificial intelligence, blockchain, NFT and other technical applications. However, academic research has not yet caught up with the industry's pace and stays more in the concept discussion and preliminary application stage.

A systematic overview of the current status, knowledge structure and hot issues of metaverse research is shown, which provides a thematic axis for this field, enriches and improves the quantitative analysis of its literature and provides a clear picture for researchers to continuously promote the development of this field. At the same time, it is necessary to warn that technological development is a double-edged sword. The process of metaverse development should return to rationality, respect the laws of its development and guarantee the healthy development of the metaverse by strengthening legal regulation and the ethical review of science and technology.