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This paper aims to investigate how congruent keywords are used in information security policies (ISPs) to pinpoint and guide clear actionable advice and suggest a metric for measuring the quality of keyword use in ISPs.

A qualitative content analysis of 15 ISPs from public agencies in Sweden was conducted with the aid of Orange Data Mining Software. The authors extracted 890 sentences from these ISPs that included one or more of the analyzed keywords. These sentences were analyzed using the new metric – keyword loss of specificity – to assess to what extent the selected keywords were used for pinpointing and guiding actionable advice. Thus, the authors classified the extracted sentences as either actionable advice or other information, depending on the type of information conveyed.

The results show a significant keyword loss of specificity in relation to pieces of actionable advice in ISPs provided by Swedish public agencies. About two-thirds of the sentences in which the analyzed keywords were used focused on information other than actionable advice. Such dual use of keywords reduces the possibility of pinpointing and communicating clear, actionable advice.

The suggested metric provides a means to assess the quality of how keywords are used in ISPs for different purposes. The results show that more research is needed on how keywords are used in ISPs.

The authors recommended that ISP designers exercise caution when using keywords in ISPs and maintain coherency in their use of keywords. ISP designers can use the suggested metrics to assess the quality of actionable advice in their ISPs.

The keyword loss of specificity metric adds to the few quantitative metrics available to assess ISP quality. To the best of the authors’ knowledge, applying this metric is a first attempt to measure the quality of actionable advice in ISPs.

This study was carried out to examine the volume and annual growth pattern of research on e-health literacy research, investigate the open-access types of e-health literacy research and perform document production by country and by sources. The study also mapped the keywords used by authors to represent e-health literacy research and performed an analysis of the clusters of the keywords to reveal the thematic focus of research in the area.

The research was guided by a bibliometric approach involving visualization using VosViewer. Data were sourced from Scopus database using a syntax that was tested and verified to be capable of yielding reliable data on the subject matter. The analysis in this study was based on bibliographic data and keywords.

A total number of 1,176 documents were produced during 2006 and 2022. The majority of the documents (18.90%) were published based on hybrid open-access processes, and the USA has the highest contributions. The Journal of Medical Internet Research is the venue for most of the documents on the subject. The 1,176 documents were described by 5,047 keywords, 4.29 keywords per document, and the keywords were classified into five clusters that aptly capture the thematic structure of research in the area.

e-Health literacy has experienced significant growth in research production from 2006 to 2022, with an average of 69 documents per year. Research on e-health literacy initially had low output but began to increase in 2018. The majority of e-health literacy documents are available through open access, with the USA being the leading contributor. The analysis of keywords reveals the multifaceted nature of e-health literacy, including access to information, attitudes, measurement tools, awareness, age factors and communication. Clusters of keywords highlight different aspects of e-health literacy research, such as accessibility, attitudes, awareness, measurement tools and the importance of age, cancer, caregivers and effective communication in healthcare.

This study has practical implications for health promotion. There is also the element of patient empowerment in which case patients are allowed to take an active role in their healthcare. By understanding their health information and having access to resources that help them manage their conditions, patients can make informed decisions about their healthcare. Finally, there is the issue of improved health outcomes which can be achieved by improving patients' e-health literacy. Visualisation of e-health literacy can help bridge the gap between patients and healthcare providers, promote patient-centered care and improve health outcomes.

Research production on e-Health literacy has experienced significant growth from 2006 to 2022, with an average of 69 documents per year. Many e-health literacy documents are available through open access, and the USA is the leading contributor. The analysis of keywords reveals the nature of e-health literacy, including access to information, attitudes, measurement tools, awareness and communication. The clusters of keywords highlight different aspects of e-health literacy research, such as accessibility, attitudes, awareness, measurement tools and the importance of age, cancer, caregivers, and effective communication in healthcare.

This paper examines how firms respond to local government’s environment initiatives through textual analysis of government work reports (GWRs). This study aims to provide insights into how firms strategically respond to government’s environmental initiatives through their disclosure and investment practices.

This study uses a textual analysis of GWRs from China’s provinces. The frequency and change rate of environmental keywords in these reports are used as a measure of the government’s environmental initiatives.

This study finds that environmental disclosure scores in environmental, social and governance (ESG) reports increase with the frequency or change rate of environmental keywords in provincial GWRs. This effect is more pronounced for non-state-owned enterprises, firms in highly marketized provinces or those listed in a single capital market. However, there is no significant relationship between firms’ environmental investments and government initiatives, except for cross-listed firms in provinces with consistently high frequency of environmental keywords in their GWRs.

The findings indicate that government environmental initiatives can shape firms’ disclosure behaviors, yet have limited influence on investment decisions, suggesting that environmental disclosure could potentially be opportunistic. This underscores the need for more effective strategies to stimulate firms’ environmental investments.

This study provides valuable insights into the differential impacts of government environmental initiatives on firms’ disclosure and investment behaviors, contributing to the understanding of corporate environmental responsibility in the context of government initiatives.

Today’s in-demand skills may not be needed tomorrow. As companies are adopting a new group of technologies, they are in huge need of information technology (IT) professionals who can fill various IT positions with a mixture of technical and problem-solving skills. This study aims to adopt a sematic analysis approach to explore how the US Information Systems (IS) programs meet the challenges of emerging IT topics.

This study considers the application of a hybrid semantic analysis approach to the analysis of IS higher education programs in the USA. It proposes a semantic analysis framework and a semantic analysis algorithm to analyze and evaluate the context of the IS programs. To be more specific, the study uses digital transformation as a case study to examine the readiness of the IS programs in the USA to meet the challenges of digital transformation. First, this study developed a knowledge pool of 15 principles and 98 keywords from an extensive literature review on digital transformation. Second, this study collects 4,093 IS courses from 315 IS programs in the USA and 493,216 scientific publication records from the Web of Science Core Collection.

Using the knowledge pool and two collected data sets, the semantic analysis algorithm was implemented to compute a semantic similarity score (DxScore) between an IS course’s context and digital transformation. To present the credibility of the research results of this paper, the state ranking using the similarity scores and the state employment ranking were compared. The research results can be used by IS educators in the future in the process of updating the IS curricula. Regarding IT professionals in the industry, the results can provide insights into the training of their current/future employees.

This study explores the status of the IS programs in the USA by proposing a semantic analysis framework, using digital transformation as a case study to illustrate the application of the proposed semantic analysis framework, and developing a knowledge pool, a corpus and a course information collection.

The COVID-19 crisis has adversely affected entrepreneurs, innovators and their ventures and, arguably, entrepreneurship research. This study aims to map the knowledge of entrepreneurship research during the COVID-19 pandemic to provide evidence of literature evolution in the field with the purpose of supporting future decision-making for policymakers, academics and practitioners in the post-COVID-19 era.

The study examines various bibliometric and scientometric indicators of entrepreneurship research in the Web of Science database using bibliometric techniques and visualization tools. Using the information gained, the scientometrics of entrepreneurship research during the COVID-19 time slice (2020–02-12 to 2022–10-15) are synthesized and comprehensively presented, and future research avenues for the post-COVID-19 era are suggested.

The results of rigorous quantitative analyses show that entrepreneurship research activities were not disrupted by COVID-19, although entrepreneurial activities themselves were impacted worldwide. In addition to providing key insights into the research field, including the most relevant keywords, keyword co-occurrences, publication sources, countries' contribution and collaboration, and source co-citations, the conceptual structural analysis separates the current trends (hotspots) into ten themes. Based on the evolution of author keywords and research themes, the study identified numerous future research directions, including 1) entrepreneurship in emerging countries, 2) firm performance in different categories of enterprises, 3) immigrants and transnational entrepreneurs, 4) technology in entrepreneurship education and 5) the impact of COVID-19 on the entrepreneurial ecosystem and entrepreneurship.

By building firm foundations for advancing the field in innovative and systematic ways, this timely study contributes to entrepreneurship literature and facilitates the understanding of the features and structures of entrepreneurship research towards the end of the pandemic. The research also has important implications for research management and entrepreneurship policymaking. The study's main limitation is that the results can only represent the time slice between 2020-02-12 and 2022-10-15.

Policymakers and managers of research and development can utilize this research to prepare a crisis-related minimization handbook in advance.

This first data mapping and thematic analysis research for entrepreneurship during the period of COVID-19 provides the latest knowledge in the field at the beginning of the end of the pandemic. It empowers scholars by 1) providing a one-stop literature overview for this global crisis time slice, 2) identifying research focuses and gaps, 3) developing new research avenues for investigation and 4) contributing conceptual structure for specific entrepreneurship research projects.

This paper aims to examine the global pattern of growth and development of eHealth research based on publication headcount, and analysis of the characteristics, of the keywords used by authors and indexers to represent their research content during 1945–2019.

This study adopted a bibliometric research design and a quantitative approach. The source of the data was Elsevier’s Scopus database. The search query involved multiple search terms because researchers’ choice of keywords varies very significantly. The search for eHealth research publications was limited to conference papers and research articles published before 2020.

eHealth originated in the late 1990s, but it has become an envelope term for describing much older terms such as telemedicine, and its variants that originated much earlier. The keywords were spread through the 27 Scopus Subject Areas, with medicine (44.04%), engineering (12.84%) and computer science (11.47%) leading, while by Scopus All Science Journal Classification Health Sciences accounted for 55.83% of the keywords. Physical sciences followed with 30.62%. The classifications social sciences and life sciences made only single-digit contributions. eHealth is about meeting health needs, but the work of engineers and computer scientists is very outstanding in achieving this goal.

This study demonstrates that eHealth is an unexplored aspect of health literature and highlights the nature of the accumulated literature in the area. It further demonstrates that eHealth is a multidisciplinary area that is attractive to researchers from all disciplines because of its sensitive focus on health, and therefore requires pooling and integration of human resources and expertise, methods and approaches.

Artificial Intelligence (AI) is an emerging technology and turned into a field of knowledge that has been consistently displacing technologies for a change in human life. It is applied in all spheres of life as reflected in the review of the literature section here. As applicable in the field of libraries too, this study scientifically mapped the papers on AAIL and analyze its growth, collaboration network, trending topics, or research hot spots to highlight the challenges and opportunities in adopting AI-based advancements in library systems and processes.

The study was developed with a bibliometric approach, considering a decade, 2012 to 2021 for data extraction from a premier database, Scopus. The steps followed are (1) identification, selection of keywords, and forming the search strategy with the approval of a panel of computer scientists and librarians and (2) design and development of a perfect algorithm to verify these selected keywords in title-abstract-keywords of Scopus (3) Performing data processing in some state-of-the-art bibliometric visualization tools, Biblioshiny R and VOSviewer (4) discussing the findings for practical implications of the study and limitations.

As evident from several papers, not much research has been conducted on AI applications in libraries in comparison to topics like AI applications in cancer, health, medicine, education, and agriculture. As per the Price law, the growth pattern is exponential. The total number of papers relevant to the subject is 1462 (single and multi-authored) contributed by 5400 authors with 0.271 documents per author and around 4 authors per document. Papers occurred mostly in open-access journals. The productive journal is the Journal of Chemical Information and Modelling (NP = 63) while the highly consistent and impactful is the Journal of Machine Learning Research (z-index=63.58 and CPP = 56.17). In the case of authors, J Chen (z-index=28.86 and CPP = 43.75) is the most consistent and impactful author. At the country level, the USA has recorded the highest number of papers positioned at the center of the co-authorship network but at the institutional level, China takes the 1st position. The trending topics of research are machine learning, large dataset, deep learning, high-level languages, etc. The present information system has a high potential to improve if integrated with AI technologies.

The number of scientific papers has increased over time. The evolution of themes like machine learning implicates AI as a broad field of knowledge that converges with other disciplines. The themes like large datasets imply that AI may be applied to analyze and interpret these data and support decision-making in public sector enterprises. Theme named high-level language emerged as a research hotspot which indicated that extensive research has been going on in this area to improve computer systems for facilitating the processing of data with high momentum. These implications are of high strategic worth for policymakers, library stakeholders, researchers and the government as a whole for decision-making.

The analysis of collaboration, prolific authors/journals using consistency factor and CPP, testing the relationship between consistency (z-index) and impact (h-index), using state-of-the-art network visualization and cluster analysis techniques make this study novel and differentiates it from the traditional bibliometric analysis. To the best of the author's knowledge, this work is the first attempt to comprehend the research streams and provide a holistic view of research on the application of AI in libraries. The insights obtained from this analysis are instrumental for both academics and practitioners.

This paper aims to map the evolution of research in business ethics from 1991 to 2018. It aims to identify the major themes and how they have evolved. It also aims to identify gaps in the literature for recommending future research agenda.

This study uses co-word network analysis. Co-word network analysis is a bibliometric technique used to objectively identify research themes via article keywords. The study examines articles from 1991 to 2018, which is a span encompassing a greater number of articles than previous bibliometric studies in business ethics. This time span was split into four periods and major research themes were identified for each period to map the changes in research agendas in the business ethics discipline over time.

The findings point to increasing maturation of the discipline, a slight decline in ethical decision-making research, a rise in research at the intersection of leadership and ethics and exponential growth in studies on corporate social responsibility. Ethical issues in business-to-business contexts are understudied. Research in environmental disclosures and leadership is expected to grow in the future.

This is the first study in business ethics to use keywords for analyzing the evolution of a discipline. This study encompasses more articles than any other study in business ethics. Finally, this is the only study to use co-word network analysis to study business ethics literature.

Food literacy is a multidimensional concept that prioritises the aspects individuals require to navigate the contemporary foodscape successfully. The study aims to map the knowledge base and intellectual structure of the concept of food literacy to assess if the most cited definitions reflect these constructs.

The inclusion criteria of full-text, peer-reviewed articles or conference papers, in English, using “food literacy” within the title, abstract, keywords or linked to the research focus produced 538 articles from the Scopus database from its inception until January 31, 2023. Articles were analysed according to exponential growth, geolocations, authors, articles, research areas and keywords using VOSviewer, CiteSpace and Excel.

Food literacy research grew exponentially between 2012 and 2022 at a rate of 50% and spanned 62 research areas, with nutrition and dietetics being the most common. Vidgen and Gallegos were the most cited authors of the most cited article, and Australia was the most influential food literacy research geolocation. Research originating from developing countries within Asia, the Middle East, Africa and South America was underrepresented, and COVID-19 impacted research trends between 2020 and 2023.

It is recommended to link “food literacy” to appropriate publications to increase its visibility and that food literacy be redefined and conceptualised to better reflect its intellectual structure. To complete this task, further research guided by keyword clustering can enhance conceptual understanding.

This study provides new insight into the knowledge base and intellectual structure of food literacy and provides scope for future research to develop the concept further.

The authors constructed an automatic essay scoring (AES) model in a discussion forum where the result was compared with scores given by human evaluators. This research proposes essay scoring, which is conducted through two parameters, semantic and keyword similarities, using a SentenceTransformers pre-trained model that can construct the highest vector embedding. Combining these models is used to optimize the model with increasing accuracy.

The development of the model in the study is divided into seven stages: (1) data collection, (2) pre-processing data, (3) selected pre-trained SentenceTransformers model, (4) semantic similarity (sentence pair), (5) keyword similarity, (6) calculate final score and (7) evaluating model.

The multilingual paraphrase-multilingual-MiniLM-L12-v2 and distilbert-base-multilingual-cased-v1 models got the highest scores from comparisons of 11 pre-trained multilingual models of SentenceTransformers with Indonesian data (Dhini and Girsang, 2023). Both multilingual models were adopted in this study. A combination of two parameters is obtained by comparing the response of the keyword extraction responses with the rubric keywords. Based on the experimental results, proposing a combination can increase the evaluation results by 0.2.

This study uses discussion forum data from the general biology course in online learning at the open university for the 2020.2 and 2021.2 semesters. Forum discussion ratings are still manual. In this survey, the authors created a model that automatically calculates the value of discussion forums, which are essays based on the lecturer's answers moreover rubrics.