Thematic Structure Research Articles

Effect of cognitive style on text topic structure processing: an eye-tracking study

Effect of cognitive style on text topic structure processing: an eye-tracking study

Exploring climate change discourses on the internet: a topic modeling study across ten years

This study examines online discourses about climate change using automated text analysis. Samples of web contributions from various sources, such as blogs, online newspaper articles, readers’ comments, tweets, and others, were collected in 2013, 2019, and 2023. The major themes people talk about when discussing climate change on the internet were identified using structural topic modeling. The study also explored how people discuss climate change differently when using different types of media channels and how the main themes change over time. Six major topics were identified for each point in time; associations between topics and media types were examined. Three topics emerged repeatedly: Dispute over the reality and the implications of climate change, discussions about aspects of the science of climate change, and discussions about causes and consequences of global warming such as sea level rise. In 2019 and 2023, the topic of action (what to do) and the topic of looming disaster showed up repeatedly. Topics limited to one specific corpus were policy discussions, debates about emissions, about renewable energies, about agricultural implications, and expressions of despair. The topical structure could be organized in two general groups, one group containing more science-related topics, and another group containing expressions of dispute, disagreement, and affect. A significant correspondence between media type and prevalent topic could be detected. Media such as Twitter/X and comments are dominated by dispute and despair expressions, whereas media such as blogs and journalistic online articles are primarily concerned with scientific topics in general, as well as with specific topics concerning global warming or renewable energies. In sum, we found noteworthy topical invariances across ten years, and systematic correspondences between topics and media types. The study contributes to the understanding of online discourses about climate change and highlights the role of media type in shaping these discourses.

ARTIFICIAL INTELLIGENCE FOR INNOVATION: A BIBLIOMETRIC ANALYSIS AND STRUCTURAL VARIATION APPROACH

Artificial intelligence (AI) has gained significant popularity in recent years. This study critically examines the existing literature on the role and potential of AI, machine learning (ML) and big data in driving innovation. To the best of our knowledge, no existing survey has provided a comprehensive overview of this topic. The aim of this paper is thus to provide a coherent overview of theoretical cornerstones as well as recent trends in research on AI and data analytics for innovation. Using various bibliometric analyses of themes including publication counts and trends, co-citations, co-authorship, and keyword co-occurrence, we infer the thematic structure of AI research in innovation for the period from 1991 to 2021. The publications are grouped into three major clusters, with Cluster 1 remaining a constantly dominant theme in the digital innovation publication landscape. Cluster 2 includes published studies on big data, which also received much research attention. Cluster 3, which is the most prominent, includes business performance, business analytics, and information systems. We also analyse publication citation counts in the literature using Poisson and negative binomial regressions. The results show that the structural variation approach provides a new method for tracking and evaluating the potential of freshly published studies in context. The findings of the current study will be significant in identifying new areas of research of potential interest to scholars and practitioners in the field of AI for innovation worldwide. We conclude this review with limitations and theoretical and practical orientations.

Conversational and generative artificial intelligence and human–chatbot interaction in education and research

AbstractArtificial intelligence (AI) as a disruptive technology is not new. However, its recent evolution, engineered by technological transformation, big data analytics, and quantum computing, produces conversational and generative AI (CGAI/GenAI) and human‐like chatbots that disrupt conventional operations and methods in different fields. This study investigates the scientific landscape of CGAI and human–chatbot interaction/collaboration and evaluates use cases, benefits, challenges, and policy implications for multidisciplinary education and allied industry operations. The publications trend showed that just 4% (n = 75) occurred during 2006–2018, while 2019–2023 experienced astronomical growth (n = 1763 or 96%). The prominent use cases of CGAI (e.g., ChatGPT) for teaching, learning, and research activities occurred in computer science (multidisciplinary and AI; 32%), medical/healthcare (17%), engineering (7%), and business fields (6%). The intellectual structure shows strong collaboration among eminent multidisciplinary sources in business, information systems, and other areas. The thematic structure highlights prominent CGAI use cases, including improved user experience in human–computer interaction, computer programs/code generation, and systems creation. Widespread CGAI usefulness for teachers, researchers, and learners includes syllabi/course content generation, testing aids, and academic writing. The concerns about abuse and misuse (plagiarism, academic integrity, privacy violations) and issues about misinformation, danger of self‐diagnoses, and patient privacy in medical/healthcare applications are prominent. Formulating strategies and policies to address potential CGAI challenges in teaching/learning and practice are priorities. Developing discipline‐based automatic detection of GenAI contents to check abuse is proposed. In operational/operations research areas, proper CGAI/GenAI integration with modeling and decision support systems requires further studies.

Framing Analysis of Israel-Palestine Conflict News on Online Media Detik.com and Republika.co.id

The conflict between Israel and Palestine has become a humanitarian tragedy and is in the spotlight of the world's mass media. This research seeks to determine the framing of the coverage of the Israeli-Palestinian conflict based on Zhongdang Pan and Gerald M. Kosicki's analysis model. Two leading online media, detik.com and republika.co.id, were selected as research objects because they have a long history, different audiences, and contrasting approaches to reporting, which reflect their views and handling of the Israeli-Palestinian conflict. It is a qualitative study; data was collected through observation, documentation, and literature study. The analysis results found that detik.com and republika.co.id syntactically present the facts clearly; the script fulfills the 5W + 1H elements with credible source quotes. The thematic structure of detik.com emphasizes the number of victims due to Israeli attacks on Palestine. The rhetorical structure of detik.com contains idioms and displays photos relevant to the news content. The thematic structure of republika.co.id emphasizes the form of Israeli attacks on the Jabalia Refugee Camp. The rhetorical structure contains idioms and photos relevant to the news content. Detik.com frames the news by using the figurative word "bak neraka dunia” (like hell on earth) so that it seems dramatized in its point of view, while republika.co.id is more straightforward in writing sentences and reporting.

Clinical reasoning and clinical judgment in nursing research: A bibliometric analysis.

To characterize the thematic foci, structure, and evolution of nursing research on clinical reasoning and judgment. Bibliometric analysis. We used a bibliometric method to analyze 1528 articles. We searched the Scopus bibliographic database on January 7, 2024. Through a keyword co-occurrence analysis, we found the most frequent keywords to be clinical judgment, clinical reasoning, nursing education, simulation, nursing, clinical decision-making, nursing students, nursing assessment, critical thinking, nursing diagnosis, patient safety, nurses, nursing process, clinical competence, and risk assessment. The focal themes, structure, and evolution of nursing research on clinical reasoning and judgment were revealed by keyword mapping, clustering, and time-tracking. By assessing key nursing research areas, we extend the current discourse on clinical reasoning and clinical judgment for researchers, educators, and practitioners. Critical challenges must still be met by nursing professionals with regard to their use of clinical reasoning and judgment within their clinical practice. Further knowledge and comprehension of the clinical reasoning process and the development of clinical judgment must be successfully translated from research to nursing education and practice. This study highlights the nursing knowledge gaps with regard to nurses' use of clinical reasoning and judgment and encourages nursing educators and professionals to focus on developing nurses' clinical reasoning and judgment with regard to their patients' safety. In addressing nurses' use of clinical reasoning and judgment, and with regard to patient safety in particular, this study found that, in certain clinical settings, the use of clinical reasoning and judgment remains a challenge for nursing professionals. This study should thus have an effect on nursing academics' research choices, on nursing educators' teaching practices, and on nurses' clinical practices. Relevant EQUATOR guidelines have been adhered to by employing recognized bibliometric reporting methods.

Uncorking opportunities: a bibliometric review of wine marketing literature

PurposeThe wine market has witnessed intensified competition across all aspects. However, there is a limited systematic assessment of wine marketing literature. This study employs bibliometric and thematic content evaluation methodologies to present a comprehensive summary and trend of the existing research in wine marketing.Design/methodology/approachThis integrates bibliometric and thematic content analytical techniques to trace the evolutionary arc of wine marketing research, underpinned by a framework-based evaluation - the 4 Ws (what, where, why, and how) to give a thorough overview of wine marketing literature. We draw on the thematic structure of wine marketing research by conducting keyword co-occurrence, thematic, social network, and cluster-based content analyses. A total of 291 publications from peer-reviewed journals, spanning the years 2001–2023, were examined in the present study.FindingsThe bibliometric analysis is used to determine the most notable journals, authors, nations, articles, and themes, thereby offering a comprehensive understanding of the publication trends within the sphere of wine marketing. Based on the findings, this study advocates a research agenda to extend further contributions to the field of wine marketing.Originality/valueThe study performs a comprehensive analysis of the publication patterns, influential authors, and emerging trends within the wine marketing research field. It provides a unique insight into the evolution and development of wine marketing as a scholarly discipline, facilitating future research directions and knowledge advancement.

Unraveling Indonesian Public R&D Institutions’ Perspectives on Chatgpt: An Analytical Approach To Decoding Open-Ended Surveys Through Topic Modeling

<p>The rapid development of artificial intelligence (AI) technologies like ChatGPT has made global management easier. Since 2020, AI has been widely utilized for all purposes, notably due to its ease in access for scientific production. This study aims to analyze open-ended questions and responses from web-based survey in Indonesian public research and development (R&D) institutions using topic modeling. A total of 205 data points were obtained through web-based surveys conducted among researchers in Indonesian public R&D institutions. To learn their thoughts on the use of ChatGPT (Chat Generative Pre-Trained Transformer), two AI language topic modeling, Latent Dirichlet Allocation (LDA) and Principal Component Analysis (PCA), were employed to detect survey topic structures and show the results. Three theme groups represent the institution's research cohort. Based on generational disparities in birth year and functional position level, this study selected the seven most popular topics from three themes of researchers' opinions on ChatGPT. Researchers of certain age generations and functional position levels focused on new AI technologies, efficiency, and production gains, while others valued methodological innovation, ethics, and scientific integrity. When formulating a strategy for incorporating AI into the public R&D institution's future research agendas, it is imperative to include diverse perspectives.</p>

Semantic clustering method using integration of advanced LDA algorithm and BERT algorithm

The subject of the study is an in-depth semantic data analysis based on the modification of the Latent Dirichlet Allocation (LDA) methodology and its integration with the bidirectional encoding representation of transformers (BERT). Relevance. Latent Dirichlet Allocation (LDA) is a fundamental topic modeling technique that is widely used in a variety of text analysis applications. Although its usefulness is widely recognized, traditional LDA models often face limitations, such as a rigid distribution of topics and inadequate representation of semantic nuances inherent in natural language. The purpose and main idea of the study is to improve the adequacy and accuracy of semantic analysis by improving the basic LDA mechanism that integrates adaptive Dirichlet priorities and exploits the deep semantic capabilities of BERT embeddings. Research methods: 1) selection of textual datasets; 2) data preprocessing steps; 3) improvement of the LDA algorithm; 4) integration with BERT Embeddings; 5) comparative analysis. Research objectives: 1) theoretical substantiation of LDA modification; 2) implementation of integration with BERT; 3) evaluation of the method efficiency; 4) comparative analysis; 5) development of an architectural solution. The results of the research are that, first of all, the theoretical foundations of both the standard and modified LDA models are outlined, and their extended formula is presented in detail. Through a series of experiments on text datasets characterized by different emotional states, we emphasize the key advantages of the proposed approach. Based on a comparative analysis of such indicators as intra- and inter-cluster distances and silhouette coefficient, we prove the increased coherence, interpretability, and adaptability of the modified LDA model. An architectural solution for implementing the method is proposed. Conclusions. The empirical results indicate a significant improvement in the detection of subtle complexities and thematic structures in textual data, which is a step in the evolutionary development of thematic modeling methodologies. In addition, the results of the research not only open up the possibility of applying LDA to more complex linguistic scenarios, but also outline ways to further improve them for unsupervised text analysis.

Sustainability in banking literature: review and synthesis of thematic structure

Sustainability in banking literature: review and synthesis of thematic structure

Financial Absorption of Cohesion Policy Funds: How Do Programmes and Territorial Characteristics Influence the Pace of Spending?

AbstractUsing data from the execution of 2014–2020 cohesion policy, this article presents a novel indicator to measure how fast European territories were able to spend their allocated budget and then explores the drivers of such financial performance. This analysis aims to fill a gap in scientific literature as most existing studies tend to focus solely on the total absorption at the end of the period without looking at the average financial performance over time. The article also explores the influence on financial absorption of the governance model (nationally or regionally managed programmes) and the thematic structure of the funds, which has never been done before. The determinants of the speed of financial absorption are investigated through a Tobit model. Results show that both programme‐specific and territorial characteristics are relevant factors in explaining the varying levels of fund absorption. This suggests that increased flexibility in spending rules and the adoption of more tailored strategies could be instrumental in improving fund absorption.

Analisis Framing Pemberitaan Program Makan Gratis Prabowo Subianto Di Media Online

online media is a combination of all components of text, video, audio and photos. Political news becomes a medium for the public or the press itself to convey political messages to political actors, both support and criticism. Prabowo Subianto is the Minister of Defense of the Republic of Indonesia who is running for President in 2024. One of Prabowo's programs if elected President is the Free Meal Program. This program was suddenly widely reported by the media, various reports about the program were in online media such as online media detik.com and VIVA.co.id. This research was conducted to analyze the online media detik.com and VIVA.co.id in framing news about Prabowo Subianto's Free Meal Program using the framing analysis approach model of Zhongdang Pan and Gerald M. Kosicki. This research is descriptive qualitative research. The method used in this research is the framing analysis model of Zhongdang Pan and Gerald M. Kosicki which uses a framing device consisting of four structures, namely, Syntactic Structure, Script Structure, Thematic Structure and Rhetorical Structure. The results of this research, detik.com media, in reporting on Prabowo Subianto's Free Meal Program, supports this program. This can be shown in the headline of the news. In the first news title, detik.com appears to explain Prabowo Subianto's Free Meal Program, which means that detik.com media reports on the positive side of the program. detik.com frames the news by selecting news sources, selecting quotes that support statements and information provided in the news. VIVA.co.id put the parties who gave counter statements and criticized the program, VIVA.co.id wants to show the public that is where the controversy is. Political news regarding the Prabowo Subianto Free Meal Program on the online media detik.com and VIVA.co.id has an impact on the knowledge, attitudes and perspective of the public who see the reality of the news framing of the program.

Towards digitalization in higher education: conceptual and bibliometric paradigm

Objetive: The aim is to study the foundations of the scientific literature in this area, the different intellectual structures, the conceptual and social structure and the trends of the analysed field. Methodology: The research is carried out through a bibliometric network analysis and graphical mapping of the field under investigation. Results: Firstly, the field is relatively recent and shows a collaborative network of inter-institutional character and with important synergies between countries and authors. In the intellectual structure, there is a cluster of strategies to follow in the implementation of DT in HEIs, with its possible drivers and obstacles, at technological, social, pedagogical and organisational levels; the implementation of DT in a concrete and practical way, alerting that DT is more than a set of digital tools; a more theoretical and conceptual line of thought on the concept of DT in HEIs and, finally, sustainable DT in the impact on HEIs. In the thematic structure, the block of theoretical and conceptual foundations that reveals COVID19 as the main accelerating factor of DT, the cluster that investigates the learning process through digital teaching and the third one that highlights the importance of cultural change management and people in the process stand out. Practical implications: This study allows us to have a general vision of the intellectual, conceptual and social structure of the DT and HEIs process and its impact on the different dimensions. Furthermore, the findings have implications for the management of TD processes in HEIs.

ECOTOURISM, SUSTAINABLE TOURISM AND NATURE BASED TOURISM: AN ANALYSIS OF EMERGING FIELDS IN TOURISM SCIENTIFIC LITERATURE

The emergence and rapid growth of tourism paradigms such as ecotourism, sustainable tourism, and nature-based tourism signify a shift towards more environmentally and culturally conscious forms of tourism. We conduct a comparative analysis of these emerging fields with the aim of providing insights to the field and inform future research directions in this dynamic area of study. Our investigation spans diverse geographical regions over an extended period (1986-2022) to provide a comprehensive understanding of the global landscape of alternative tourism. The study utilizes descriptive bibliometrics and scientific mapping techniques using data derived from Scopus to depict changes and contributions over time and identify thematic structures and their evolution. Along with more established regional actors such as the US and European contributors, China and Latin America appear as emergent participants. The analyzed fields seem more prone to interdisciplinary approaches and more impactful than the average Tourism research. Also, international collaboration seems to have played a crucial role in advancing research in these topics. Overall, the study reveals a vibrant field with outstanding features in the context of general Tourism scientific area.

Thematic Structure of the Stream of Foreign Articles on the use of Artificial Intelligence Technologies in the Library and Information Field: 2019–2023

Thematic Structure of the Stream of Foreign Articles on the use of Artificial Intelligence Technologies in the Library and Information Field: 2019–2023

Network-based topic structure visualization

In the real world, many topics are inter-correlated, making it challenging to investigate their structure and relationships. Understanding the interplay between topics and their relevance can provide valuable insights for researchers, guiding their studies and informing the direction of research. In this paper, we utilize the topic-words distribution, obtained from topic models, as item-response data to model the structure of topics using a latent space item response model. By estimating the latent positions of topics based on their distances toward words, we can capture the underlying topic structure and reveal their relationships. Visualizing the latent positions of topics in Euclidean space allows for an intuitive understanding of their proximity and associations. We interpret relationships among topics by characterizing each topic based on representative words selected using a newly proposed scoring scheme. Additionally, we assess the maturity of topics by tracking their latent positions using different word sets, providing insights into the robustness of topics. To demonstrate the effectiveness of our approach, we analyze the topic composition of COVID-19 studies during the early stage of its emergence using biomedical literature in the PubMed database. The software and data used in this paper are publicly available at https://github.com/jeon9677/gViz.

Supervised Dynamic Correlated Topic Model for Classifying Categorical Time Series

In this paper, we describe the supervised dynamic correlated topic model (sDCTM) for classifying categorical time series. This model extends the correlated topic model used for analyzing textual documents to a supervised framework that features dynamic modeling of latent topics. sDCTM treats each time series as a document and each categorical value in the time series as a word in the document. We assume that the observed time series is generated by an underlying latent stochastic process. We develop a state-space framework to model the dynamic evolution of the latent process, i.e., the hidden thematic structure of the time series. Our model provides a Bayesian supervised learning (classification) framework using a variational Kalman filter EM algorithm. The E-step and M-step, respectively, approximate the posterior distribution of the latent variables and estimate the model parameters. The fitted model is then used for the classification of new time series and for information retrieval that is useful for practitioners. We assess our method using simulated data. As an illustration to real data, we apply our method to promoter sequence identification data to classify E. coli DNA sub-sequences by uncovering hidden patterns or motifs that can serve as markers for promoter presence.

Waning and waxing

Abstract This paper provides a preliminary description of the comparative construction in Tujia1 (northern dialect; NT), an endangered ergative Tibeto-Burman (TB) language spoken in China, in the context of the typologically diverse ways in which comparative meaning is expressed. In mono-clauses in NT, comparative marking depends on the position of the standard (i.e., that which is compared to). The standard may be marked with an ablative marker, or a topic marker if it appears in sentence-initial position due to left dislocation; otherwise, the comparandum (i.e., that which is to be compared) is assigned an ergative marker. Ergative on the comparandum and ablative on the standard can both occur but this is not obligatory. We propose that the ablative is the primary comparative marker in NT. However, this construction is subject to variation: the ablative marker is being replaced by a topic marker on the standard or by an ergative marker on the comparandum. We analyze this as change in progress (partly as a result of language contact); specifically, we propose that an ergative marker on the comparandum is starting to function as a focus marker, and in which the comparative meaning is derived from reanalysis. On the other hand, the comparative meaning in constructions where the standard is marked with a topic marker is proposed to result from the functional extension of the topic structure to comparative constructions. Both ergative marking on the comparandum and topic marking on the standard are typologically rare. Thus, the analysis of comparison marking in Tujia contributes to the typological understanding of comparative constructions in general.

Big data research in the business, management and accounting field: Revealing the thematic structure based on co-word analysis

Objective. This study aims to describe the thematic structure of big data research in business, management, and accounting. Design/Methodology/Approach. A co-word analysis was performed on 12,345 documents retrieved from Scopus from 2014-2023 in the category “business, management, and accounting”. Modularity algorithms were used to identify themes and subthemes, and the clustering of terms was qualitatively analyzed. Results/Discussion. Five main themes were identified: (1) Business and economic data analysis, (2) big data analytics in marketing, consumer behavior, and employee dynamics, (3) scalable machine learning and big data optimization, smart cities and urban development, (4) IoT-Driven innovations in industry 4.0 for optimized supply chain management, and (5) Social media and sentiment analysis in modern tourism and hospitality. The grouping of terms demonstrated the broad application of big data in healthcare, education, tourism, industry, organizational development, finance, social media, marketing, and hospitality. Conclusion. Big data is a field of application. This is evident in each cluster, where there are sub-themes that are nothing more than applying big data principles in sectors such as manufacturing, tourism services, education, health, and urbanization. Generally, the findings here are similar to other studies that have analyzed broader or more selective literature.

Organizational Legitimacy: A Bibliometric Analysis of Web of Science Database

This study is an attempt to conduct a bibliometric review in the above-mentioned field. A bibliometric review is conducted using 644 articles from the Web of Science core collection database. The article evaluates the research article distribution based on time and geography. It also highlights notable articles, authors and journals. We make use of techniques like citation analysis, co-citation analysis and co-occurrence analysis. Software Vos Viewer and Biblioshiny are used. The article also explores the theme development in the area. The volume of publications has seen steady growth since 2003 but has seen rapid growth post 2020. On analysing the country-wide distribution, we realize that the top five positions are occupied by USA, England, China, Canada and Spain. In terms of the major sources of these publications, the list of journals has spread across various disciplines such as accounting, human resource management, economics, ethics, etc. Trending topics like corporate social responsibility, sustainability, financial performance, corruption, risk and trust whose linkages with legitimacy have gained emphasis in recent times have been highlighted. Further, the analysis emphasizes areas with research scope like social media and legitimacy, corporate governance and legitimacy, institutional pressures, etc. Additionally, co-citation clusters were developed to represent the distribution of the thematic structure. The article provides various suggestions for academicians and managers/policymakers.