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The omnichannel, a new concept in retailing, has surpassed the genre of electronic business (e-commerce) and has significantly influenced consumer thoughts and decisions. This approach refers to a retailing approach that has transformed the way customers shop, seamlessly transitioning between online and offline platforms due to the increased usage of smartphones and tablets. Presently, retailers are focusing on delivering excellent service experiences. However, despite being a prominent topic in the retail sector, bibliometric and systematic reviews exploring omni-channels are scarce. To contribute to further research collaboration and expand knowledge in this subject area, a bibliometric study was conducted to examine omni-channel research trends, including publication outputs, notable journals, top-performing authors, leading countries, and author keywords. A total of 773 articles were acquired from Scopus databases from 2013 to March 2023. The findings revealed a continuous increase in the number of omni-channel publications each year, indicating its growing relevance in the retail industry. Among the 61 countries analysed, Chile had the highest number of active paper publications, followed by Sweden and Spain. The systematic analysis conducted using VOSviewer identified 2103 keywords. This research explores focal points in research, evaluates trends in evolution, visualizes collaboration networks, and identifies a prominent topic in the field of omni-channel research. Cluster analysis revealed prominent topics of interest within the omni-channel context, including the grocery, fashion, luxury, and tourism domains. As a result, this study provides an information and implementation direction for the development of omni-channel retailing. [ABSTRACT FROM AUTHOR]

This study aims to understand the development trends and research structure of articles on artificial intelligence (AI) and information processing in the past 10 years. In particular, this study analyzed 13,294 papers published from 2012 to 2021 in the Web of Science, used the bibliometric analysis method to visualize the data of the papers, and drew a scientific knowledge map. By exploring the development of mainstream journals, author and country rankings, keyword evolution, and research field rankings in the past 10 years, this study uncovered key trends affecting AI progress and information processing that provide insights and serve as an important reference for future AI research and information processing. The results revealed a gradual increase in publications over the past decade, with explosive growth after 2020. The most prolific researchers in this field were Xu, Z.S.; Pedrycz, W.; Herrera-Viedma, E.; the major contributing countries were China, the USA, and Spain. In the AI and information processing research, keywords including "Deep learning", "Machine learning", and "Feature extraction" are components that play a crucial role. Additionally, the most representative research areas were "Engineering", "Operations Research and Management Science", and "Automation Control Systems". Overall, this study used bibliometric analysis to provide an overview of the latest trends in artificial intelligence and information processing. Although AI and information processing have been applied to various research areas, many other sub-topics can be further applied. Based on the findings, this study presented research insights and proposed suggestions for future research directions on AI and information processing. [ABSTRACT FROM AUTHOR]

This paper aims to present a bibliometric analysis of the literature on an entrepreneurial intention that considers different topics to enhance starting a business. Using the Core collection of the Web of Science, 1,549 papers are found from 1900 to December 2020 to be analyzed. The analysis is focused on documents, authors, journals, countries, and keywords to detect areas and trends in this field of study. Likewise, bibliographic couplings, co-citation, and co-occurrences are analyzed. Findings show that the field has started to grow since 2010; the most influential authors and universities are in Spain and Taiwan, such as the case of Liñan, Liang, Ip, and Moriano. [ABSTRACT FROM AUTHOR]

The present article reviews the Spanish history of the art therapy profession and research. In addition, a contemporary view of current art therapy research is described through a bibliometric analysis of 160 art therapy articles published in the Spanish Journal of Art Therapy. Papers on Art Therapy and art Education for Social Inclusion from 2011 to 2021. The following research indicators were analyzed: authorship, participants, population, artistic discipline, theoretical models of art therapy intervention, nature of the studies, and assessments. Outcomes revealed that Spanish art therapy researchers are mostly women who usually publish on their own. Additionally, many ages, populations and topics are treated. Visual art is the discipline most used in the analyzed research. Gestalt, community-based art programs, and the eclectic approach are the most selected theoretical models. Most of the studies are qualitative and theory building with no assessment. However, when assessment is included, the type most used is the tools made by the researcher. Implications, limitations, and further lines of research are discussed. • Art therapy research in Spain is a young discipline conducted in the last ten years. • The profile of Spanish researchers on art-therapy corresponds to dedicated females. • The use of art therapy in Spain has extended to vulnerable and minority groups. • There is a shift from psychoanalysis to other theoretical models for intervention. • Most of the studies published in Spain are qualitative or theory building. [ABSTRACT FROM AUTHOR]

SUMMARY: Advances in olive oil research have blossomed in recent years, and articles on olive oil have been widely recorded. However, a comprehensive and systematic bibliometric analysis of olive oil research has not been completed. Through a systematic literature search, we consolidated 7003 papers from the Web of Science Core Collection database. This bibliometric analysis was carried out to examine the article growth, geographical distribution, and analysis of journals/research areas/authors/keywords. The results revealed the following: (1) Exponential growth was shown in the annual number of publications (R2 = 0.9791). The geographical distribution is biased to a few countries, especially Spain, Italy, and Greece in the Mediterranean region, placing a larger focus on olive oil research. (2) Quality analysis‐related research acted as a link between keyword groups, the disease prevention and treatment‐related research of olive oil received decreasing interest, while authentication and quality analysis‐related research of olive oil showed the opposite trend, by‐products‐related research of olive oil got waving interests over the whole period. (3) Improvement is needed in the degree of olive oil with international collaboration. This is the first time that hotspots and trends in olive oil research have been shown. [ABSTRACT FROM AUTHOR]

Concentrating solar power (CSP), also known as solar thermal electricity (STE), is increasing its deployment worldwide. One of the potential ways to decrease costs in CSP plants is the improvement of corrosion resistance between the heat transfer fluid (HTF) and storage materials, and the materials used for pipes, tanks, containers, and receivers. This paper assesses the literature on this topic (290 publications) through a bibliometric analysis, identifying the trends of the research, the topics of most interest to researchers, and literature gaps. Most documents are from Spain, Germany, and the United States of America. Results show that the most recent approaches for corrosion migration are selective coatings and the use of nanoparticles to reduce corrosiveness. The use of nitrates is changing to other salts such as chloride mixtures and potassium compounds. In addition, the techniques used to evaluate corrosion results are dominated by scanning electron microscopy (SEM), X-ray diffraction (XRD), and electrochemical testing, but new dynamic techniques are starting to be used, representing the biggest gap that needs to be filled for the testing of components such as solar receivers. [ABSTRACT FROM AUTHOR]

The spiraling growth of the world's population and unregulated urbanization have resulted in many environmental problems, including poor quality of air, which is associated with a wide range of health issues. Machine learning approaches have been extensively employed to predict air quality, attracting the attention of the scientific community worldwide. Bibliometric studies provide a useful means by which to visualize and analyze published works, helping researchers to make novel scientific contributions by filling existing knowledge gaps in the research. To acquire an in-depth understanding of the topic, this paper presents a bibliometric analysis of all published articles on the use of machine learning networks to predict air quality found in the Web of Science (WoS) search engine from 1992 to 2021. S-curve analysis and social network analysis were used to identify the temporal distribution of articles, productivity by countries/continents, research institutions, and scientific metrics of journal productivity. This study indicated that maximum expansion of the literature witnessed during 2017–2021 (second phase) which represents an expansion or growth stage of machine learning and air quality prediction research. The number of published works increased significantly with 1432 articles accounting for 68.51% of all publications. As a result of the increased interest in machine learning-based prediction tools, the number of articles grew 2.17-fold compared to the 1992–2016 (first phase). In terms of international collaboration impact, Italy emerged as the most successful country (43.44), followed by Greece (31.22) and Spain (23.29). Author keywords analysis was employed to explore and evaluate the emerging research trends on the subject of air quality using machine learning models. Keywords that appear most frequently in this study are 'air pollution', 'air quality', 'machine learning', and 'forecasting'. Citation burst analysis, research productivity analysis, highly influential and highly cited works were also employed to examine various research themes and questions. In this study we also discussed how conventional methods were transformed into machine learning approaches. It is expected that this paper will provide technical guidelines, research priorities, and future opportunities for the precise prediction of air quality and emergency management of air pollution globally. • Interest in air quality prediction through machine learning model is expanding. • China and US were found productive countries in terms of countries collaboration. • Big data, Internet of Things, and Smart city are emerging topic for future research. • Significant difference between citations, funded and non-funded research. • Ensemble modeling is popular in air quality prediction research. [ABSTRACT FROM AUTHOR]

The spiraling growth of the world's population and unregulated urbanization have resulted in many environmental problems, including poor quality of air, which is associated with a wide range of health issues. Machine learning approaches have been extensively employed to predict air quality, attracting the attention of the scientific community worldwide. Bibliometric studies provide a useful means by which to visualize and analyze published works, helping researchers to make novel scientific contributions by filling existing knowledge gaps in the research. To acquire an in-depth understanding of the topic, this paper presents a bibliometric analysis of all published articles on the use of machine learning networks to predict air quality found in the Web of Science (WoS) search engine from 1992 to 2021. S-curve analysis and social network analysis were used to identify the temporal distribution of articles, productivity by countries/continents, research institutions, and scientific metrics of journal productivity. This study indicated that maximum expansion of the literature witnessed during 2017–2021 (second phase) which represents an expansion or growth stage of machine learning and air quality prediction research. The number of published works increased significantly with 1432 articles accounting for 68.51% of all publications. As a result of the increased interest in machine learning-based prediction tools, the number of articles grew 2.17-fold compared to the 1992–2016 (first phase). In terms of international collaboration impact, Italy emerged as the most successful country (43.44), followed by Greece (31.22) and Spain (23.29). Author keywords analysis was employed to explore and evaluate the emerging research trends on the subject of air quality using machine learning models. Keywords that appear most frequently in this study are 'air pollution', 'air quality', 'machine learning', and 'forecasting'. Citation burst analysis, research productivity analysis, highly influential and highly cited works were also employed to examine various research themes and questions. In this study we also discussed how conventional methods were transformed into machine learning approaches. It is expected that this paper will provide technical guidelines, research priorities, and future opportunities for the precise prediction of air quality and emergency management of air pollution globally. • Interest in air quality prediction through machine learning model is expanding. • China and US were found productive countries in terms of countries collaboration. • Big data, Internet of Things, and Smart city are emerging topic for future research. • Significant difference between citations, funded and non-funded research. • Ensemble modeling is popular in air quality prediction research. [ABSTRACT FROM AUTHOR]

Abstract: An analysis of scientific publications on solar energy was conducted to determine whether public interest in the subject is mirrored by more intense research in the area. To this end, the research published by Spain and Germany, the two EU countries with the highest installed photovoltaic capacity, was analyzed based on Web of Science data. The results show that: solar output has risen substantially; solar research has a greater impact (measured in terms of citations) than publications on other renewables such as wind power; scientific production on solar energy is high in Germany and Spain, which together account for 51% of the total in the EU-27; the pattern of topics researched in the two countries is very similar; and their international collaboration is more intense than the world average and higher than in countries such as the USA, China or Japan. Collaboration between them is likewise intense. The main conclusion is the divergence in Germany and Spain between solar energy demand/output growth, being exponential, and the growth of research papers on the subject, which is linear. [Copyright &y& Elsevier]

(1) Background: Using neuroscience to understand and influence consumer behavior often leads to ethical controversy. Thus, it is necessary to demystify the use of neuroscience for marketing purposes; the present paper, by accessing the worldwide academic performance in this domain, fulfills this objective. (2) Methods: All extant literature on neuromarketing indexed to the Scopus database—318 articles—was subjected to a bibliometric analysis through a mixed-method approach. (3) Results: The results show that Spain leads the ranks of the most productive countries, while Italian researchers clearly dominate in terms of collaboration. Regarding the most prominent topics, the connection between "Neuroscience" and "Advertising" is highlighted. The findings provide a better understanding of the state-of-the-art in neuromarketing studies, research gaps, and emerging research topics, and additionally provide a new methodological contribution by including SciVal topic prominence in the bibliometric analysis. (4) Conclusions: As practical implications, this study provides useful insights for neuromarketing researchers seeking funding opportunities, which are normally associated with topics within the top prominence percentile or emerging topics. In terms of originality, this study is the first to apply SciVal topic prominence to a bibliometric analysis of neuromarketing, and provides a new bibliometric indicator for neuromarketing research. [ABSTRACT FROM AUTHOR]