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The Grey System Theory (GST) is an emerging area of research within artificial intelligence. Since its founding in 1982, it has seen a lot of multidisciplinary applications. In just a short period, it has garnered some considerable strengths. Based on the 1987–2021 data collected from the Web of Science (WoS), the current study reports the advancement of the GST.

Research papers utilizing the GST in the fields of economics and education were retrieved from the Web of Science (WoS) platform using a set of predetermined keywords. In the final stage of the process, the papers that underwent analysis were manually chosen, with selection criteria based on the information presented in the titles and abstracts.

The study identifies prominent authors, institutions, publications and journals closely associated with the subject. In terms of authors, two major clusters are identified around Liu SF and Wang ZX, while the institution with the highest number of publications is Nanjing University of Aeronautics and Astronautics. Moreover, significant keywords, trends and research directions have been extracted and analyzed. Additionally, the study highlights the regions where the theory holds substantial influence.

The study is subject to certain limitations stemming from factors such as the language employed in the chosen literature, the papers included within the Web of Science (WoS) database, the designation of works categorized as “articles” in the database, the specific selection of keywords and keyword combinations, and the meticulous manual process employed for paper selection. While the manual selection process itself is not inherently limiting, it demands a greater investment of time and meticulous attention, contributing to the overall limitations of the study.

The significance of the study extends not only to scholars and practitioners but also to readers who observe the development of emerging scientific disciplines.

The analysis of trends revealed a growing emphasis on the application of GST in diverse domains, including supply chain management, manufacturing and economic development. Notably, the emergence of COVID-19 as a new research focal point among GST scholars is evident. The heightened interest in COVID-19 can be attributed to its global impact across various academic disciplines. However, it is improbable that this interest will persist in the long term, as the pandemic is gradually brought under control.

This study aims to establish a reliable index to identify interdisciplinary breakthrough innovation effectively. We constructed a new index, the D_Div index, for this purpose.

The D_Div index incorporates the degree of interdisciplinarity in the breakthrough index. To validate the index, a data set combining the publication records and citations of Nobel Prize laureates was divided into experimental and control groups. The validation methods included sensitivity analysis, correlation analysis and effectiveness analysis.

The sensitivity analysis demonstrated the D_Div index’s ability to differentiate interdisciplinary breakthrough papers from various categories of papers. This index not only retains the strengths of the existing index in identifying breakthrough innovation but also captures interdisciplinary characteristics. The correlation analysis revealed a significant correlation (correlation coefficient = 0.555) between the interdisciplinary attributes of scientific research and the occurrence of breakthrough innovation. The effectiveness analysis showed that the D_Div index reached the highest prediction accuracy of 0.8. Furthermore, the D_Div index outperforms the traditional DI index in terms of accuracy when it comes to identifying interdisciplinary breakthrough innovation.

This study proposed a practical and effective index that combines interdisciplinary and disruptive dimensions for detecting interdisciplinary breakthrough innovation. The identification and measurement of interdisciplinary breakthrough innovation play a crucial role in facilitating the integration of multidisciplinary knowledge, thereby accelerating the scientific breakthrough process.

In today's fast developing era, the volume of data is increasing day by day. The traditional methods are lagging for efficiently managing the huge amount of data. The adoption of machine learning techniques helps in efficient management of data and draws relevant patterns from that data. The main aim of this research paper is to provide brief information about the proposed adoption of machine learning techniques in different sectors of manufacturing supply chain.

This research paper has done rigorous systematic literature review of adoption of machine learning techniques in manufacturing supply chain from year 2015 to 2023. Out of 511 papers, 74 papers are shortlisted for detailed analysis.

The papers are subcategorised into 8 sections which helps in scrutinizing the work done in manufacturing supply chain. This paper helps in finding out the contribution of application of machine learning techniques in manufacturing field mostly in automotive sector.

The research is limited to papers published from year 2015 to year 2023. The limitation of the current research that book chapters, unpublished work, white papers and conference papers are not considered for study. Only English language articles and review papers are studied in brief. This study helps in adoption of machine learning techniques in manufacturing supply chain.

This study is one of the few studies which investigate machine learning techniques in manufacturing sector and supply chain through systematic literature survey.

1. A comprehensive understanding of Machine Learning techniques is presented.

2. The state of art of adoption of Machine Learning techniques are investigated.

3. The methodology of (SLR) is proposed.

4. An innovative study of Machine Learning techniques in manufacturing supply chain.

A comprehensive understanding of Machine Learning techniques is presented.

The state of art of adoption of Machine Learning techniques are investigated.

The methodology of (SLR) is proposed.

An innovative study of Machine Learning techniques in manufacturing supply chain.

This paper aims to respond to the recent calls to discover the research developments in the field of public budgeting. Particularly, it explores whether and how research dialogue unfolds within the public budgeting field over time and how to stimulate it further, by investigating the case of a specific journal oriented to budgeting topics.

Applying a case study strategy, this paper reviews previous studies on public budgeting published in one specific journal, the Journal of Public Budgeting, Accounting and Financial Management (JPBAFM), from its “online inception” in 1994 to 2020. Borrowing ideas from dialogue literature, the authors analyse 108 selected papers according to a multi-dimensional framework for exploring research dialogues taking into account the year of publication, authorship (and affiliation), research setting, method and theoretical approach, and, above all, research topics on budgeting.

The findings illustrate that whilst public budgeting research has been fluctuating over time in the JPBAFM, there is a growing interest in the topic over the last several years (2015–2020). Yet, the journal illustrates a limited dialogic development of the field of public budgeting, where produced knowledge has been significantly North America-oriented, normative and quantitative-dominated. Until recently, only a limited role has been given to dialogue formation between researchers and practitioners, but the current debate is increasingly being enriched by new perspectives and a wider range of experiences. Finally, public budgeting has been addressed from multiple perspectives over time, with a significant impact determined by performance and participatory budgeting. Although multiple topics are receiving growing attention, it is still under-developed in the inter-dialogue formation between topics and theories, despite the more recently growing use of different theoretical approaches and empirical and analytical rigour.

The research is limited to one journal as a case study and does not claim to provide an overall reflection of public budgeting research and related empirical generalisations. Instead, the authors strive for a theoretical generalisation of multi-dimensional dialogue importance in the field.

The value of the research lies in a comprehensive analysis of research dialogue formation within public sector budgeting over time in an international journal that has actively engaged with public sector issues and, specifically, with budgeting. By so doing, this paper adds a critical stand on the value of dialogue in fostering inter-contextual and inter-disciplinary research in the field of public budgeting.

The purpose of this study is to present a review of published academic work on art and tourism. A distinction between papers researching tourism and mentioning art just as one of tourism activities and papers covering “art tourism” specifically is proposed.

The review is grounded on a content analysis of studies containing the words “art” and “tourism” published in the Scopus database. Moreover, to analyze papers specifically consisting of the term “art tourism” a theory‐context‐characteristics‐methods structure was used – the authors call these papers art–tourism-specific papers.

While the number of “art and tourism” papers has been increasing in the past 40 years, little is known about “art tourism” as an independent form of tourism. This study finds limited work on art tourists’ characteristics, preferences and behaviors as costumers. No art-based research and little research with visual representations was found. Artists are very rarely participants of art tourism research.

Most research is conceptual, and little applied research can be identified. Importantly, besides synthesizing and critically assessing the current corpus of knowledge on art tourism, this review presents a final roadmap with directions for future research. One of the limitations of this review is that only studies included in the Scopus database and published in English were considered.

To the best of the authors’ knowledge, this study provides the first comprehensive systematic review of published academic research on art and tourism in the past 40 years. The results of this study offer directions to future art tourism researchers.

本文的目的是回顾已发表的有关艺术和旅游的学术著作。 提出了在研究旅游的论文中将仅将艺术作为旅游活动之一提及的论文与专门涵盖“艺术旅游”的论文进行区分。

该回顾是基于对 Scopus 数据库中发表的包含“艺术”和“旅游”一词的文章的内容分析。此外, 为了分析专门由术语“艺术旅游”组成的论文, 我们使用了理论-背景-特征-方法的结构——我们将这些论文称为艺术旅游特定论文。

虽然过去四十年来“艺术与旅游”论文的数量一直在增加, 但人们对“艺术旅游”作为一种独立的旅游形式知之甚少。 我们发现关于艺术游客的特征、偏好和作为顾客的行为的工作有限。 没有发现基于艺术的研究, 也没有发现很少的视觉表现研究。 艺术家很少是艺术旅游研究的参与者。

大多数研究都是概念性的, 很少有应用研究可以被识别出。重要的是, 除了综合性地批判和评估当前关于艺术旅游的知识库外, 本次回顾还提出了一个最终路线图, 并为未来的研究提供了方向。本回顾的局限性之一是仅考虑了 SCOPUS 数据库中包含的并以英文发表的文章。

本研究首次全面系统地回顾了过去四十年来发表的有关艺术和旅游的学术研究。本研究结果为未来的艺术旅游研究者提供了方向。

El propósito de este artículo es presentar una revisión de los trabajos académicos publicados sobre arte y turismo. Se propone una distinción entre los trabajos que investigan el turismo y mencionan el arte sólo como una de las actividades turísticas Y los trabajos que cubren específicamente el “turismo artístico”.

Esta revisión se basa en un análisis de contenido de los artículos que contienen las palabras “arte” y “turismo” publicados en la base de datos Scopus. Además, para analizar los artículos que contenían específicamente el término “turismo artístico” se utilizó una estructura Teoría-Contexto-Características-Métodos - llamamos a estos artículos específicos de turismo artístico.

Aunque el número de trabajos sobre “arte y turismo” ha aumentado en los últimos cuarenta años, se sabe poco sobre el “turismo artístico” como forma independiente de turismo. Encontramos trabajos limitados sobre las características, preferencias y comportamientos de los turistas de arte como clientes. No se ha encontrado ninguna investigación basada en el arte y poca investigación con representaciones visuales. Los artistas rara vez participan en las investigaciones sobre turismo artístico.

La mayor parte de las investigaciones son conceptuales, y se puede identificar poca investigación aplicada. Es importante destacar que, además de sintetizar y evaluar críticamente el corpus actual de conocimientos sobre el turismo artístico, esta revisión presenta una hoja de ruta final con directrices para futuras investigaciones. Una de las limitaciones de esta revisión es que sólo se han considerado los artículos incluidos en la base de datos SCOPUS y publicados en inglés.

Este estudio proporciona la primera revisión sistemática exhaustiva de la investigación académica publicada sobre arte y turismo en los últimos cuarenta años. Los resultados de este estudio ofrecen orientaciones a los futuros investigadores del turismo artístico.

The paper undertakes a bibliometric analysis and assessment of journal publications in the field of container terminal operations research (CTOR), in an attempt to identify high-impact papers (HIPs) published in Science Citation Index/Social Science Citation Index (SCI/SSCI) journals of CTOR subject category from 1973 to 2020.

A structured approach for identifying the HIPs is developed based on the utilization of bibliometric and network analyses.

The CTOR papers are assessed in terms of publication outputs, distribution of outputs in SCI/SSCI journals, authorship, institutions and countries, as well as citation life cycles of papers with the highest total citations since their publication until the year 2020. The results show that between 1989 and 2015, there were 82 HIPs in the field of CTOR, which have been cited at least 200 times, with more than 50% of these citations allocated in the second part of paper citation life cycle according to the database of Google Scholar.

The practical implication of the aforementioned reviewing and assessing journal publications of CTOR is that it offers the ability to reveal the tone of its development through addressing main characteristics of the relevant HIPs as determined by the highly cited papers in this field of research.

This paper offers a unique analysis and assessment in the field of CTOR by identifying the relevant HIPs and their associated scientific actors (authors, institutions and countries), thus facilitating the future research effort in the field of CTOR.

Predicting highly cited papers can enable an evaluation of the potential of papers and the early detection and determination of academic achievement value. However, most highly cited paper prediction studies consider early citation information, so predicting highly cited papers by publication is challenging. Therefore, the authors propose a method for predicting early highly cited papers based on their own features.

This research analyzed academic papers published in the Journal of the Association for Computing Machinery (ACM) from 2000 to 2013. Five types of features were extracted: paper features, journal features, author features, reference features and semantic features. Subsequently, the authors applied a deep neural network (DNN), support vector machine (SVM), decision tree (DT) and logistic regression (LGR), and they predicted highly cited papers 1–3 years after publication.

Experimental results showed that early highly cited academic papers are predictable when they are first published. The authors’ prediction models showed considerable performance. This study further confirmed that the features of references and authors play an important role in predicting early highly cited papers. In addition, the proportion of high-quality journal references has a more significant impact on prediction.

Based on the available information at the time of publication, this study proposed an effective early highly cited paper prediction model. This study facilitates the early discovery and realization of the value of scientific and technological achievements.

This paper aims to explore the characteristics of knowledge diffusion in library and information science (LIS) to reveal the impact of knowledge in LIS on other disciplines and the disciplinary status of LIS.

Taking the 573 highly cited papers (HCP) of LIS during the years 2000–2019 in Web of Science and 85,638 papers citing them from non-LIS disciplines as the analysis object, this paper analysed the disciplines to which the citing papers belonged regarding the Biglan model, and the topics and their characteristics of the citing disciplines using latent Dirichlet allocation topic clustering.

The results showed that the knowledge in LIS was exported to multiple disciplines and topics. (1) Citations from other disciplines were overall increasing, and the main citing disciplines, mainly from applied science disciplines, were medicine, computer science, management, economics, education, sociology, psychology, journalism and communication, earth science, engineering, biology, political science, chemistry and agronomy. However, those disciplines had fewer citations to LIS during for the years from 2000 to 2004, with rapid growth in the next three time periods. (2) The citing papers had various topics and showed an increasing trend in quantity. Moreover, topics of different disciplines from 2000 to 2019 had various characteristics.

From the perspective of discipline and topic, this study analyses papers citing the HCP of LIS from non-LIS disciplines, revealing the impact of knowledge in LIS on other disciplines.

In the era of artificial intelligence (AI), algorithms have gained unprecedented importance. Scientific studies have shown that algorithms are frequently mentioned in papers, making mention frequency a classical indicator of their popularity and influence. However, contemporary methods for evaluating influence tend to focus solely on individual algorithms, disregarding the collective impact resulting from the interconnectedness of these algorithms, which can provide a new way to reveal their roles and importance within algorithm clusters. This paper aims to build the co-occurrence network of algorithms in the natural language processing field based on the full-text content of academic papers and analyze the academic influence of algorithms in the group based on the features of the network.

We use deep learning models to extract algorithm entities from articles and construct the whole, cumulative and annual co-occurrence networks. We first analyze the characteristics of algorithm networks and then use various centrality metrics to obtain the score and ranking of group influence for each algorithm in the whole domain and each year. Finally, we analyze the influence evolution of different representative algorithms.

The results indicate that algorithm networks also have the characteristics of complex networks, with tight connections between nodes developing over approximately four decades. For different algorithms, algorithms that are classic, high-performing and appear at the junctions of different eras can possess high popularity, control, central position and balanced influence in the network. As an algorithm gradually diminishes its sway within the group, it typically loses its core position first, followed by a dwindling association with other algorithms.

To the best of the authors’ knowledge, this paper is the first large-scale analysis of algorithm networks. The extensive temporal coverage, spanning over four decades of academic publications, ensures the depth and integrity of the network. Our results serve as a cornerstone for constructing multifaceted networks interlinking algorithms, scholars and tasks, facilitating future exploration of their scientific roles and semantic relations.

The purpose of this paper is to review recent published research on entrepreneurship education (EE) specifically within the special issue collections of the journal Education +Training, and to assess the overall contribution to the field. The research questions focus on: What topics are explored by these SI papers? What trends can be observed in relation to country context and methodological approach? How is EE defined in these papers, and how do the papers contribute to the wider entrepreneurship research agenda?

The paper uses an adapted version of the systematic literature review approach, focusing on the discrete special issues on entrepreneurship/enterprise education published in the journal Education + Training since 2010. A comprehensive reading guide was used to review the papers, with completed data compiled into a single excel spreadsheet to facilitate analysis. A total of 66 papers were reviewed.

A considerable range of themes, geographical contexts and methodological approaches were used in the papers reviewed. A mix of qualitative and quantitative approaches were also found. The papers were characterised by a strong international and applied dimension, with the core collective contribution of the SI papers laying in their direct relevance to practice.

The paper is limited by its deliberate focus on a discrete set of special issue papers; however, the total of 66 papers included in the review is noteworthy.

The paper demonstrates the considerable learning that can be garnered from the Education + Training special issue collection for EE practitioners.

To the authors’ knowledge, this is the first time this discrete collection of special issue papers has been reviewed.