Inverse optimization is defined as a process of reverse traditional mathematical optimization where parameters of an optimization model are determined such that given decisions will be either approximately or precisely optimal. The concept of inverse optimization was initially introduced and formulated in 2001. Research in this field has both theoretical and practical applications and has been applied in various areas. This study utilizes bibliometric analysis to determine the most impactful studies and researchers in the field of inverse optimization. Our research objective is to examine the current state of inverse optimization studies, based on data from the Web of Science database. Several classifications including leading journals, most frequently cited papers and influential authors are made. The results indicate that the majority of inverse optimization applications have been in the realm of cancer treatment and that the number of studies in this field has decreased during 2019-2020, that might be due to COVID-19 pandemic. Also, the USA and MIT (Massachusetts Institute of Technology) are among the most productive and influential entities in this field.

The introduction of generative AI models, especially OpenAI's ChatGPT, has profoundly impacted several fields. To uncover key fields of research, key research clusters, emerging research topics, key research contributions within grown and emerging clusters, and key insightful implications for various stakeholders, this study analyses the early body of scientific literature (n=1873) related to ChatGPT research indexed in Dimensions (from November 29, 2022 to May 20, 2022) database using network scientometric approach. This approach employs network mining of two major networks related to scientific literature for knowledge discovery exercise. Science mapping analysis using the Fields of Research (FoRs) network revealed key fields of research impacted by ChatGPT. Scientific literature mining is conducted using publications citation network analysis with the help of the Flow Vergence model and cluster analysis. Major growth clusters that contributed and might continue significantly to the network's growth are identified and found to be associated with education in general, medical education, medical diagnosis and clinical writing, scientific writing, and systematic literature review. Important emerging clusters are found to mostly deal with novel applications like harmful content detection in social media, annotation, assessment, etc., Further, the dynamics of grown clusters and emerging clusters was tracked on Dec 31, 2023 (after six months). All the clusters are found to have grown significantly. Cluster merging is witnessed in the case of grown clusters, making the new clusters multi-themed and overwhelmed by incremental contributions. However, the merger of emerging clusters contributed to the formation of relatively better-performing clusters. Through this knowledge discovery exercise, the paper highlights the knowledge and technology advancement of ChatGPT and its potential in numerous fields and sheds light on pressing problems and the moral dilemmas raised by its use. The analysis reveals several policy implications for various stakeholders, including education and research policymakers.

Bibliographic coupling, over the years, has been referred to and used in different contexts related to scientific and technical literature. It is often believed that research papers that have bibliographic coupling deal with similar concepts and hence there may be high conceptual similarity between them. This study attempts to empirically asses this notion. To conduct this research, the study utilizes the data obtained from the Dimensions database and employs advanced machine learning algorithms to extract weighted keywords that better capture the conceptual content of documents. The Jaccard similarity measure is used to compute bibliographic and conceptual coupling matrices for different sets of research papers. The results show that even though bibliographic coupling is widely used to assess relationships between research papers, it often falls short of identifying actual conceptual similarities within documents. This study’s findings carry important implications for areas such as information retrieval, interdisciplinary research and evaluation metrics, calling for a more refined understanding of how research documents relate to one another beyond their shared references.

Publication of a large number of research papers during the last few decades motivated creation of scholarly databases for indexing the publications and recording citations. The publication metadata fields in scholarly databases are now retrieved for various purposes ranging from information search and retrieval to research evaluation. Traditionally, Web of Science and Scopus have been major database sources used. However, with the creation of newer databases like Dimensions, the choice has expanded further. The coverage and citation data of the major databases have been compared in many previous studies. However, there is no existing work on comparison of metadata fields provided by the scholarly databases and the impact that metadata fields provided may have on scientometric research. This work, therefore, attempts to bridge this research gap by comparing the metadata fields contained in the data downloaded through the User Interface (UI) based search from three major scholarly databases-Web of Science, Scopus and Dimensions. The effect of existence or absence of a metadata field in a database on the possibilities and the ease of doing scientometric analysis is explored. The findings are useful for scientometric researchers, practitioners and database managers.

Reverse logistics has generated much interest among entrepreneurs, academics and civil society. This is because you can recover the value of products that are used and returned, which is why it has become one of the key elements in supply chain management. Through a bibliometric study and documentary analysis, this work aims to describe how the scientific production of reverse logistics has evolved quantitatively from the year 2000 to 2023. The articles retrieved from the Web of Science portal, from Clarivate Analytics, provided the primary data to conduct a bibliometric method and content analysis. The search produced 1148 articles, with production peaks in the year 2000 up to 2023. Networks of co-occurrence, citation and co-authorship were also analyzed. During the period of study (2000-2023), there were 124 working groups; it was found that the four groups of researchers who worked around the issue of reverse logistics are the ones with the highest number of citations. Of the 548 publications about the subject “reverse logistics”, the researchers addressed different aspects such as competitive advantage, the firm performance, implementations in different sectors, sustainability, the study of new strategies to improve control of inventories systems, demand, supply, trying to control uncertainty both in transportation and in handling waste and returns by the final user. (All of this section was modified).

Bayesian Networks is a family of machine learning models widely used in various applications such as speech recognition, Protocol Reverse Engineering, or more generally the retrieval of hidden information in data. These models, at the crossroads of probability theory and graph theory, allow intuitive modelling and simple interpretation of the results. In this paper, we are interested in inferring the most probable state of a discrete Bayesian network. In the case of a Hidden Markov Model, the Viterbi algorithm is usually used. However, although accurate, it is not optimized, and when generalized to any number of variables per time slice, its complexity increases exponentially. This is why we have developed an optimized version of the Viterbi algorithm, Automatic Markov Boundaries construction optimized Viterbi (AMBV), taking advantage of the fact that once a model is trained, it is usually used to analyze many observations. Moreover, in case of sparse probability distributions of the variables, an additional level of optimization is used. Finally, in order to make possible the inference of the most probable state of any discrete Bayesian network, a mechanism for automatically generating a set of Markov Boundaries for the network has been proposed. We will show that AMBV performs significantly better than the classical Viterbi algorithm as soon as the complexity of the network increases sufficiently.

Recycled Concrete Aggregates (RCA) are the product of recycling Concrete and Demolition Wastes (CDWs) from the construction industry. The practice aims to effectively dispose of, manage, and treat solid wastes that could pose health, safety, and environmental concerns. To address these impending problems, CDWs are valorized into RCA as a substitute for Natural Aggregates (NA) during construction. Hence, various studies have been published on the use of RCA as a partial replacement for NA in concrete as well as the properties of RCA-based concrete mixes. This study employs scientometric analysis to quantitatively assess the scientific literature on Recycled Concrete Aggregates (RCA). The Systematic Literature Review (SLR) of Recycled Concrete Aggregates (RCA) utilization in construction was critically examined using the PRISMA approach. VOSviewer was utilized for the bibliometric analysis to map co-authorship networks, keyword occurrence, and co-citation analysis. The bibliometric analysis reveals a substantial increase in publications on RCA utilization over the 10 years, demonstrating growing interest and awareness in sustainable construction practices, which translates to significant contributions in areas such as performance enhancement, material properties, and environmental sustainability. The SLR indicated that the partial replacement of fine or coarse NA with RCA in concrete improves the physical durability, workability, and mechanical properties. It also reduces the industry’s overall carbon footprint and addresses environmental issues related to construction. Thus, the study contributes to the understanding of RCA utilization in the construction industry and provides valuable insights for researchers, practitioners, and policymakers seeking to promote sustainability in the built environment. Future studies could look at novel composite materials, the effects of RCA production on the environment, and the long-term effectiveness of RCA in building applications.

Scientific collaboration is a driver of innovation and sustainable development, with countries globally, including those in the ASEAN region, leveraging collaborative efforts to enhance their scientific capacities. The changing structure and dynamics of the research and innovation ecosystem, shaped by collaborative efforts within and beyond their borders, have created a research gap. There is a scarcity of studies that comprehensively examine the innovation ecosystem in these nations. This article delves into the intricate dynamics of Triple Helix Relations among local sectors, universities, government entities and industries within the collaborative landscapes of Thailand, the United States and China. Using the data from 2006 to 2022, the study employs Shannon’s mutual information, refined by Loet Leydesdorff, to analyze co-authored publications across the fields. We distinguish collaborations by the nationality of collaborative partners (Thai-China vs. Thai-U.S. collaboration) and by subject area (engineering, medicine and agricultural and biological sciences). The findings support the notion that international collaborations contribute to stronger Triple Helix relationships in specific ways within local settings. When Thailand partners with the United States, we observed weaker trilateral relationships but stronger bilateral ties between the Thai government and universities, particularly in the field of Medicine. Conversely, collaborations with China revealed stronger Triple Helix relations, as Thai industry became more involved in research collaborations, notably in agricultural and biological sciences and engineering. The analysis highlights the nuanced influence of international research collaboration on Thailand’s national science and innovation system. These findings lay the groundwork for further investigation into the factors shaping these observed patterns.

This paper performs a state-of-the-art literature review of Big Data Analytics Capability (BDAC) to analyze its domain, knowledge structures, theoretical roots, and key research trends. We analyze the domain, knowledge structures, and evolution of the field by applying Lotka’s law, Bradford law, MCP ratio, keyword, citation, and co-citation analysis. Our analysis reveals predominant use of resource-based view and dynamic capability theories among other theories used in BDAC research. The key research themes identified relate to BDAC antecedents, consequences, process/ industry contexts, business intelligence, advanced analytics, and environment dynamics. The study’s implications pertain to the identification of BDAC development, its theoretical roots, and emerging research themes. We also identify interesting research opportunities relating to BDAC as a dynamic capability, BDAC challenges such as failures, maturity, response to market dynamics, and BDAC value at process, firm, and industry levels.

The present study shows the incidence of knowing the impact of social networks of co-authorship in the construction of new research, since they are fundamental for the location of expert authors in the thematic developments of which they are the main referents. In this sense, their location allows the construction of networks that have a significant impact on aspects such as: relational capital, cognitive capital and structural capital. In this way, it makes it possible to understand the incidence of structural gaps as positive or negative determinants when reviewing the influence of their research and the size of their epistemological influence on new researchers and topics associated with the construct of emotional education. In the methodology, the two most recognized databases in the academic community (Scopus and Web of Science) were used from the search parameters of: rank, date of consultation, type of documents, search field, keywords and results together with the review of the manifest variables of: link strength, network intensity, size, structural gaps, diversity index of collaboration and permanence, the impact of existing research. The conclusions show the need to continue delving into the construct of emotional education in the training of teachers and students, as well as in the strengthening of social networks of knowledge that allow further deepening in collaborations to produce new epistemological views from more interdisciplinary approaches with expert and novice researchers who contribute to the transformation of the educational systems of their countries.

The research landscape on cognitive computing algorithms, such as Genetic Algorithms (GA), in Cancer/Tumor and Oncological (CTO) research from 2003 to 2022 was examined using Scopus-indexed publications. Bibliometric analysis was employed to assess social networks and thematic areas of GACTO research. The analysis revealed that researchers published 114 articles and 92 conference papers, representing 55.34% and 44.66% of the total publications (TP=206), respectively. Of these, 129 publications were open access, distributed across Gold, Hybrid Gold, Bronze, and Green mediums. Researchers showed a preference for articles over conference papers. Stakeholder analysis highlighted a robust number of active authors, affiliations, and countries involved in GACTO research. Top performers included Zuherman Rustam (TP=5), Universitas Indonesia (TP=6), and India. Productivity was attributed to the availability of resources such as financial support, with top funders being Universitas Indonesia, the National Natural Science Foundation of China, and Brazil’s Conselho Nacional de Desenvolvimento Científico e Tecnológico. Social network analysis indicated a low rate of co-authorship at 18.18%, suggesting limited collaboration at the author level. However, at the national level, collaborative links were stronger, with the largest cluster comprising India, Iran, and the United States, and the smallest including Turkey and the United Kingdom. This reflects better access to resources, funding, and infrastructure at the national level. Hotspot analysis identified three major keywords: genetic algorithms, diseases, and feature extraction. Cluster analysis revealed three focus areas: Precision Health Analytics, Genomic Cancer Profiling, and Integrated AI Diagnosis. In conclusion, the GACTO research landscape actively engages in socially impactful and scientific themes, utilizing computational tools to address challenges posed by cancer and other oncological diseases.

As the concept of Environmental, Social and Governance (ESG) gains popularity, companies enhance the quality of their ESG disclosures in order to achieve better financial returns. Much literature has studied the relationship between them, but the impact of ESG disclosure on firm value is still a subject of debate. We use VOSviewer and CiteSpace to quantitatively analyze and visualize the relevant 142 papers published in the Web of Science from 2013 to 2023. This paper presents a range of bibliometric findings, including the most influential countries, leading authors at the institution and journal levels, co-authorship, co-organization, bibliographic coupling, keyword occurrence and evolution. By context analysis, the development process and thematic areas of this relationship are gradually identified and illustrated. Finally, this paper identifies the research frameworks and impact mechanisms of ESG disclosure on corporate value in existing literature and points out that future research should explore the discrepancies among different countries, industries and types of enterprises. This paper also emphasizes the importance of uncovering the mediating mechanisms that influence both variables and explores the long-term impact of ESG disclosure on corporate value.

This study maps the development of the intersection between strategic leadership and organizational innovation through a bibliometric analysis of 111 Scopus-indexed articles published from 1993 to 2022. Addressing a gap in the literature, this research explores the patterns and trends of this intersection, which had not been holistically analyzed. The novelty of this study lies in its comprehensive identification of thematic clusters, publication trends, and key contributors to the field, providing a unique and detailed bibliometric perspective on how strategic leadership influences organizational innovation, particularly in the context of Industry 4.0 and technological advancements. Our findings reveal substantial growth in scholarly interest post-2005, peaking in 2020-2021, driven by the rise of Industry 4.0 and the increasing importance of leadership in fostering organizational innovation. The United States, China, and Australia are the leading contributors, with key institutions such as Tennessee Technological University and the University of Pretoria driving research in this field. The analysis, conducted using VOSviewer and Bibliometrix, identified five thematic clusters : strategic leadership traits, open innovation, leadership’s impact on firm performance, competitive advantage, and contextual factors influencing leadership. This underscores the critical role of strategic leadership in navigating technological advancements and fostering organizational adaptability. Co-citation analysis highlighted seminal works by Bantel, Hambrick, and Howell, shaping the foundational frameworks of the field. Despite the inherent limitations of bibliometric methods, the study emphasizes the need for further exploration of emerging themes, such as CEO leadership styles, ambidexterity, and grassroots innovation. The findings suggest that adaptable leadership practices and enhanced collaboration between CEOs and Boards of Directors are vital in driving innovation and shaping governance structures. These insights should inform future policy-making and encourage cross-border research collaborations in strategic leadership and innovation.

This study determined the research performance of the faculty members of a state university in the Philippines, their motivation in research and determinants of publishing in academic journals. It used QUANqual mixed methods, particularly, descriptive statistics, factor analysis and logistic regression on survey data and narratives from in-depth interviews. On average, a faculty member is involved in seven research studies and has published one journal research article. Around one-third of them were unaware of the publication policies, while 40% think these policies are only being partially implemented. Faculty respondents were moderately motivated to publish journal articles. Top faculty motivations for publishing are faculty promotion and the importance of disseminating findings. Perceived very serious challenges in research conduct were resource limitations and unclear research-related policies. Educational attainment, Science and Technology accreditation, intrinsic motivation and motivation to mentor to sustain research culture were significant determinants for a faculty to publish. The interview narratives powerfully support these quantitative results.

This research investigated the relationships among climate-related disruptions, adaptive responses, and household consumption patterns in Sub-Saharan Africa. Employing a comprehensive methodology, a total of 1219 papers from 2000 to 2022 were screened across the Web of Science and Scopus databases, with 62 studies undergone rigorous assessment. The methodology involved identifying relevant literature through keyword searches, citation tracking, and hand-searching of journals. Independent reviewers screened studies based on the inclusion criteria. The data extracted covered research design, shock type, coping mechanisms, and their impact on household consumption, nutrition, and food security. Bibliometric and thematic analyses were conducted to examine publication trends, common themes, and collaboration networks among researchers. The findings revealed an increase in the frequency and intensity of climate-induced shocks, particularly droughts and climate change, significantly impacting household consumption and food security in the region. Coping strategies such as livelihood diversification, migration, and climate adaptation were found to be crucial in alleviating the impact of climate shocks on food security and consumption. Bibliometric insights revealed evolving trends in climate research, emphasizing the ongoing necessity to comprehend impacts and mitigation in evolving SSA contexts.

The influence of social media on university students is clear. However, there are no systematic bibliometric assessments of this impact. To conduct a comprehensive bibliometric analysis of the impact of social media on university students, identifying key trends and collaboration patterns from 2007 to 2023.

This paper provides a comprehensive review of the literature on consumer choice within the domain of antitrust jurisprudence, particularly in digital platform markets. Leveraging bibliometric tools such as citation network analysis and co-citation analysis, the study systematically maps the academic discourse, identifying key research trends, influential works, and emerging themes. The primary focus of the article is to explore the evolving concept of consumer choice, which is critical for understanding market dynamics and regulatory interventions in digital economies dominated by platform giants, through the help of scientometric tools and techniques. In addition to the findings underscoring a nuanced understanding of the jurisprudential aspect of consumer choice, the study also highlights the importance of employing scientometric techniques to study the complex and rapidly evolving landscape of digital platform markets through the lens of extant research literature.

Geographical Indications (GIs) are intellectual property assets related to products or services that are characteristic of their place of origin. As a field of research, its study associated with the concept of sustainability and sustainable development is still scarce in the literature, with research beginning in the late 2000s. The objective of the present study is to establish the research results on GIs linked to the scope of sustainability and sustainable development, which, as they are multidisciplinary concepts, cover the most diverse areas of knowledge. Through scientific maps, built using VOSviewer®, o SciMAT® and the Web of Science knowledge platform, the analysis of the most relevant studies in this field was carried out to verify how research with GIs has evolved in areas related to the concept of sustainability and sustainable development. The results showed the predominance of four lines of research on the topics of interest: a) local socioeconomic impacts; b) conservation of the environment and preservation of biodiversity; c) governance and effectiveness of legal systems to protect GIs; d) post analysis-GI. Furthermore, the research showed that the topic has evolved, but is still little discussed, especially on the Asian and African continents.