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Exploring diverse knowledge organization systems and metadata schemes in linked data, aiming to promote vocabulary usability and high-quality linked data creation within the LIS field.

We used content analysis to select 77 articles from 13 library and information science journals around our research theme. We identified four dimensions: vocabularies participation, reuse, functions, and naming variations in linked data.

The vocabulary comprises seven main categories and their corresponding 126 vocabularies, which participate in linked data in single, two, and multiple dimensions. These vocabularies are used in the eight LIS subfields. Reusing vocabularies has become integral to linked data publishing, with six categories and their corresponding 66 vocabularies being reused. Ontologies are the most engaged and widely reused category of vocabulary in linked data practice. The mutual support among the three major categories and seven subfunctions of vocabulary promotes the sustainable development of linked data. Under a combination of factors, the phenomenon of terminology name changes and cross-usage between “vocabulary” and “ontology.”

This study has limitations. Although 77 articles on the topic of vocabularies applied in linked data were analyzed and presented with quantitative statistics and visualizations, the exploration of the topic tends to be a practical activity, with limited presence in scholarly articles. Moreover, this study’s analysis of the practical applications of linked data is relatively limited, and the sample literature focused on articles published in English, which may have affected the diversity and inclusiveness of the research sample.

Practically, this study does not confine the application of content analysis solely to the traditional exploration of knowledge organization topics, development trends, or course content. Instead, it integrates the dual perspectives of linked data and vocabularies, employing content analysis to analyze and objectively reveal the application issues of vocabularies in linked data. The conclusions can provide specific guidelines for future applications of vocabularies in the LIS subfields and contribute to promoting interoperability of vocabularies.

This research explores the relationship between linked data and vocabularies, highlighting the diverse manifestations and challenges of vocabularies in linked data. It provides theoretical references for the construction and further development of vocabularies considering technologies such as linked data, drawing attention to the potential and existing issues associated with linked open data vocabularies.

This study extends the application of content analysis to exploring vocabularies, especially Knowledge Organization Systems and metadata schemes in the LIS field linked data, highlighting the mutually beneficial interactions between linked data and vocabularies. It provides guidance for future vocabularies applications in the LIS field and offers insights into vocabularies construction and the healthy development of linked data ecosystems in the era of information technology.

Given the scientific and practical difficulties inherent in measuring and comparing socioeconomic deprivation (SED), and the further complexity added in cross national measurements, the main aim of this paper was to check the validity of SED measures used in PISA 2018 dataset. The SED measure used in PISA 2018 was the PISA index of economic, social and cultural status abbreviated as the ESCS index. This assessment was important as PISA analysis is based on variables derived from this instrument and the ESCS index and these reports influence and reflect international and comparative education policies and practice.

This study critically evaluates the socioeconomic status measures in the PISA 2018 dataset, focusing on their convergent validity and cross-national comparability. Using responses from over 600,000 students in 73 countries, it examines the validity of SES indicators and their comparability across countries. The study employs principal component analysis to construct local SES measures and compares them with the existing Economic, Social, and Cultural Status (ESCS) index. It explores the relationship between these SES measures and academic achievement in reading, science, and mathematics, aiming to understand their predictive validity in diverse educational settings. Statistical analyses were conducted using the IEA’s IDB Analyser and SPSS, ensuring robustness and generalisability across the diverse participant countries.

Our research findings challenge the assumed superiority of local measures over broader constructs like the Economic, Social, and Cultural Status (ESCS). It suggests that standardised measures like ESCS may provide more reliable predictions of academic achievement across various educational contexts, underscoring the complex relationship between SES measures and academic performance.

Our novel analysis shows that local and cross-national SED measures are poorly correlated. Our findings raise questions about the measures' validity while acknowledging the methodological challenges. We provide empirical evidence to support ongoing debates on the topic.

An emerging research stream focuses on the place-based ecosystems where artificial intelligence (AI) innovations emerge and develop. This literature builds on the contextual turn in management research and, specifically, work on entrepreneurial ecosystems. However, as a nascent research area, the literature on AI and entrepreneurial ecosystems is fragmented across academic and practitioner boundaries and unconnected disciplines because of disparate and ill-defined concepts. As a result, the literature is disorganized and its main insights are latent. The purpose of this paper is to synthesize research on AI ecosystems and identify the main insights.

We first consolidate research on the “where” of AI innovation through a scoping review. To address the fragmentation in the literature and understand how entrepreneurial ecosystems are associated with AI innovation, we then use content analysis to explore the literature.

We identify the main characteristics of the AI and ecosystems literature and the key dimensions of “AI entrepreneurial ecosystems”: the local actors and factors in geographic territories that are coordinated to support the creation and development of AI technologies. We clarify the relationships among AI technologies and ecosystem dimensions and uncover the latent themes and underlying structure of research on AI entrepreneurial ecosystems.

We increase conceptual precision by introducing and defining an umbrella concept—AI entrepreneurial ecosystem—and propose a research agenda to spur further insights. Our analysis contributes to research at the intersection of management, information systems, and entrepreneurship and creates actionable insights for practitioners influenced by the geographic agglomeration of AI innovation.

Product returns are a major challenge for e-businesses as they involve huge logistical and operational costs. Therefore, it becomes crucial to predict returns in advance. This study aims to evaluate different genres of classifiers for product return chance prediction, and further optimizes the best performing model.

An e-commerce data set having categorical type attributes has been used for this study. Feature selection based on chi-square provides a selective features-set which is used as inputs for model building. Predictive models are attempted using individual classifiers, ensemble models and deep neural networks. For performance evaluation, 75:25 train/test split and 10-fold cross-validation strategies are used. To improve the predictability of the best performing classifier, hyperparameter tuning is performed using different optimization methods such as, random search, grid search, Bayesian approach and evolutionary models (genetic algorithm, differential evolution and particle swarm optimization).

A comparison of F1-scores revealed that the Bayesian approach outperformed all other optimization approaches in terms of accuracy. The predictability of the Bayesian-optimized model is further compared with that of other classifiers using experimental analysis. The Bayesian-optimized XGBoost model possessed superior performance, with accuracies of 77.80% and 70.35% for holdout and 10-fold cross-validation methods, respectively.

Given the anonymized data, the effects of individual attributes on outcomes could not be investigated in detail. The Bayesian-optimized predictive model may be used in decision support systems, enabling real-time prediction of returns and the implementation of preventive measures.

There are very few reported studies on predicting the chance of order return in e-businesses. To the best of the authors’ knowledge, this study is the first to compare different optimization methods and classifiers, demonstrating the superiority of the Bayesian-optimized XGBoost classification model for returns prediction.

We investigate the relationship between Entrepreneurial Orientation (EO) and the internationalization pathways of Brazilian franchises. Our aim is to unravel the patterns of firm-level entrepreneurial characteristics vis-à-vis their corresponding processes of internationalization.

We extracted and curated data from the directories of the Brazilian Franchising Association (ABF). Additionally, we scrutinized the International Intensity, International Complexity and EO degree of 27 Brazilian franchises engaged in international activities. Associations between these dimensions were assessed through fuzzy-set qualitative comparative analysis (fsQCA).

Our findings suggest that franchisees can enhance their international activities by adopting various configurations of EO attributes. This discovery illuminates the intricacies of EO and its association with firms’ operations and performance. Accordingly, we empirically demonstrate that EO is not a monolithic element. Instead, it should be perceived as a multifaceted and dynamic construct.

This study aimed to examine the internationalization process of franchises through the EO lens, a perspective that has not been explored in the existing literature. This unique approach offers novel insights about the internationalization processes of this particular business model. Furthermore, our research delves into the intricate relationship between firm-level EO and the trajectories of firm-level internationalization.

Retail investors are prone to be affected by information dissemination in social media with the rapid development of Web 2.0. The purpose of this study is to recognize the factors that may impact users' retweet behavior, namely information dissemination in the online financial community, through machine learning techniques.

This paper crawled data from the Chinese online financial community (Xueqiu.com) and extracted author-related, content-related, situation-related, stock-related and stock market-related features from the dataset. The best information dissemination prediction model based on these features was determined by evaluating five classifiers with various performance metrics, and the predictability of different feature groups was tested.

Five prevalent classifiers were evaluated with various performance metrics and the random forest classifier was proven to be the best retweet prediction model in the authors’ experiments. Moreover, the predictability of author-related, content-related and market-related features was illustrated to be relatively better than that of the other two feature groups. Several particularly important features, such as the author's followers and the rise and fall of the stock index, were recognized in this paper at last.

This study contributes to in-depth research on information dissemination in the financial domain. The findings of this study have important practical implications for government regulators to supervise public opinion in the financial market.

The objective is to develop a more effective model that simplifies and accelerates the news classification process using advanced text mining and deep learning (DL) techniques. A distributed framework utilizing Bidirectional Encoder Representations from Transformers (BERT) was developed to classify news headlines. This approach leverages various text mining and DL techniques on a distributed infrastructure, aiming to offer an alternative to traditional news classification methods.

This study focuses on the classification of distinct types of news by analyzing tweets from various news channels. It addresses the limitations of using benchmark datasets for news classification, which often result in models that are impractical for real-world applications.

The framework’s effectiveness was evaluated on a newly proposed dataset and two additional benchmark datasets from the Kaggle repository, assessing the performance of each text mining and classification method across these datasets. The results of this study demonstrate that the proposed strategy significantly outperforms other approaches in terms of accuracy and execution time. This indicates that the distributed framework, coupled with the use of BERT for text analysis, provides a robust solution for analyzing large volumes of data efficiently. The findings also highlight the value of the newly released corpus for further research in news classification and emotion classification, suggesting its potential to facilitate advancements in these areas.

This research introduces an innovative distributed framework for news classification that addresses the shortcomings of models trained on benchmark datasets. By utilizing cutting-edge techniques and a novel dataset, the study offers significant improvements in accuracy and processing speed. The release of the corpus represents a valuable contribution to the field, enabling further exploration into news and emotion classification. This work sets a new standard for the analysis of news data, offering practical implications for the development of more effective and efficient news classification systems.

Text classification is a widely accepted and adopted technique in organizations to mine and analyze unstructured and semi-structured data. With advancement of technological computing, deep learning has become more popular among academicians and professionals to perform mining and analytical operations. In this work, the authors study the research carried out in field of text classification using deep learning techniques to identify gaps and opportunities for doing research.

The authors adopted bibliometric-based approach in conjunction with visualization techniques to uncover new insights and findings. The authors collected data of two decades from Scopus global database to perform this study. The authors discuss business applications of deep learning techniques for text classification.

The study provides overview of various publication sources in field of text classification and deep learning together. The study also presents list of prominent authors and their countries working in this field. The authors also presented list of most cited articles based on citations and country of research. Various visualization techniques such as word cloud, network diagram and thematic map were used to identify collaboration network.

The study performed in this paper helped to understand research gaps that is original contribution to body of literature. To best of the authors' knowledge, in-depth study in the field of text classification and deep learning has not been performed in detail. The study provides high value to scholars and professionals by providing them opportunities of research in this area.

The purpose of this study was to explore the leadership competencies of the National Collegiate Athletic Association (NCAA) college athletes and assess the potential differences between domestic and international college athletes.

A quantitative, non-experimental research design was employed, including the use of an electronic survey to collect data. Survey research allows for extensive data management and a quick data collection method (Creswell & Creswell, 2018). The survey was conducted using online Qualtrics software, which allowed convenience in administration, maintenance, nationwide distribution and data export and analysis.

The findings of this study suggest that domestic college athletes develop greater leadership competencies than their international peers.

The study implications include both practical and academic contributions. The research in the area of leadership development in college athletes has been growing. Previous research has focused on the benefits of the leadership development (Lewis, 2023); however, minimal research has been dedicated to exploring actual leadership constructs within the college athlete population. Moreover, this study focused on the differences between domestic and international college athletes’ leadership constructs. International college athletes go through additional challenges while balancing the academic part of being college athletes (Ridpath, Rudd, & Stokowski, 2020).

Minimal research has been dedicated to exploring actual leadership constructs within the student-athlete population. This study is the first study that explored leadership constructs from the quantitative lens and focusing on both domestic and international student-athletes. The literature on international student-athletes mainly focuses on the motivation arriving to the United States of America (Love & Kim, 2011) and their transitional experiences (Popp, Pierce, & Hums, 2011; Jolly, Stokowski, Paule-Koba, Arthur-Banning, & Fridley, 2022). However, limited literature focuses on the preparation of international student-athlete for life beyond their sport.

The foreign direct investment (FDI) motivations of emerging market multinational enterprises (EMNEs) are mainly twofold: acquisition of strategic assets in foreign markets, and foreign market penetration. While prior studies have delivered valuable insights, findings regarding the performance of those two types of FDI remain somewhat inconsistent or inconclusive. This study aims to develop complementary perspectives that can motivate scholars to explore the internal mechanisms of achieving goals for these two FDI types by providing a review of prior literature on EMNEs’ knowledge- and market-seeking FDI.

Indexed to the EBSCO database and Google Scholar from 2000 to 2020, 73 articles from 13 journals were selected and reviewed to identify the main research future research agendas.

Our findings show that the purpose of EMNEs’ FDI can be divided into value creation and value capturing, with the former pursuing knowledge-seeking and the latter pursuing market-seeking, according to our study, which draws on insights from innovation-focused literature.

International business (IB) scholars have extensively studied both knowledge-seeking and market-seeking outward FDI of EMNEs for decades. Our study contributes to the literature by providing the potential for integrating IB and innovation studies to extend the scope of EMNEs studies.