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This study explored the influence of Dervin’s sensemaking methodology (SMM).

Citation context analysis was used to identify the most influential SMM concepts in 948 articles citing 34 SMM-related studies by Dervin that were published between 1983 and 2017. Moreover, the bibliometric method and content analysis were incorporated to examine the disciplines and research topics influenced by the SMM-related studies and the role of cited content in SMM-related studies.

The influence of SMM is concentrated in information behavior research in the field of library and information science (LIS). The 1992 book chapter From the mind’s eye of the user was most frequently cited, followed by the first SMM study from 1983; 14 of the 18 content categories were relevant to SMM. “Sensemaking,” at the core of SMM, was the most influential cited concept, primarily cited from the 1983 SMM-related study. Although the SMM was developed as a research method, it has not been primarily applied to design research methods in other studies.

This study explored the interdisciplinary influence of Dervin’s SMM from several aspects and demonstrated the complex information dynamics between SMM-related works and citing articles.

The purpose of this study is to assess the value of altmetrics or other indicators, showcasing the impact of academic output, which is seen too often correlated with the citation count.

This study considered three reputed journals of Library and Information Science (LIS) published by Elsevier. A total of 1,164 articles were found in these journals from 2016 to 2020 and the relationships between altmetric attention scores (AAS) and citations were examined. The analysis was extended to compare the grouped data set based on percentile ranks of AAS like top 50%, top 25%, top 10% and top 1%.

Using Spearman correlation analysis, the findings reveal a positive correlation between AAS and citations with different significant levels for all articles, and articles with AAS, as well as for normalized AAS in the top 50%, top 25%, top 10% and top 1% data set. For the three journals International Journal of Information Management (IJIM), Journal of Informetrics (JIF) and Library and Information Science Research (LISR), a significant positive correlation is observed across all data sets. But an unexpected result was observed: in the case of the top 50% of articles for the IJIM and JIF showed no significant correlation but the LISR journal showed a positive correlation for the whole data set. This journal though has fewer articles in comparison to the other two.

A source item that is highly cited may not be having high social media attention as reflected in the findings. This demarcates AAS with citations implying various factors on which these measurements are dependent. The study distinguishes these metrics lucidly. There is not a single guideline or uniformity in assessing the correlation found. But the problem is that the interpretation of the correlation strength affects the conclusion of the study. Moreover, this study will be a role model as a draft for librarians to select relevant journals for their libraries and will facilitate authors in the choice of the publication outlets for their papers, particularly concerning the journals that have both visibility and research impact.

The study reported devising a comprehensive tool to validate AAS as a measure of scholarly impact to include appropriate social media sources and verify its relationship with other metrics. To the best of the authors’ knowledge, this paper is the first attempt to discover the correlation between AAS and citations for the highly impactful LIS journal published by Elsevier. The empirical evidence lies in the citation and altmetric data extracted from the dimension database.

“Scholarly Communication” is a frequent topic of both the professional and research literature of Library and Information Science (LIS). Despite efforts by individuals (e.g. Borgman, 1989) and organizations such as the Association of College and Research Libraries (ACRL) to define the term, multiple understandings of it remain. Discussions of scholarly communication infrequently offer a definition or explanation of its parameters, making it difficult for readers to form a comprehensive understanding of scholarly communication and associated phenomena.

This project uses the evolutionary concept analysis (ECA) method developed by nursing scholar, Beth L. Rodgers, to explore “Scholarly Communication” as employed in the literature of LIS. As the purpose of ECA is not to arrive at “the” definition of a term but rather exploring its utilization within a specific context, it is an ideal approach to expand our understanding of SC as used in LIS research.

“Scholarly Communication” as employed in the LIS literature does not refer to a single phenomenon or idea, but rather is a concept with several dimensions and sub-dimensions with distinct, but overlapping, significance.

The concept analysis (CA) method calls for review of a named concept, i.e. verbatim. Therefore, the items included in the data set must include the phrase “scholarly communication”. Items using alternate terminology were excluded from analysis.

The model of scholarly communication presented in this paper provides language to operationalize the concept.

LIS lacks a nuanced understanding of “scholarly communication” as used in the LIS literature. This paper offers a model to further the field's collective understanding of the term and support operationalization for future research projects.

This study analyzed the interdisciplinary characteristics of Chinese research studies in library and information science (LIS) measured by knowledge elements extracted through the Lexicon-LSTM model.

Eight research themes were selected for experiment, with a large-scale (N = 11,625) dataset of research papers from the China National Knowledge Infrastructure (CNKI) database constructed. And it is complemented with multiple corpora. Knowledge elements were extracted through a Lexicon-LSTM model. A subject knowledge graph is constructed to support the searching and classification of knowledge elements. An interdisciplinary-weighted average citation index space was constructed for measuring the interdisciplinary characteristics and contributions based on knowledge elements.

The empirical research shows that the Lexicon-LSTM model has superiority in the accuracy of extracting knowledge elements. In the field of LIS, the interdisciplinary diversity indicator showed an upward trend from 2011 to 2021, while the disciplinary balance and difference indicators showed a downward trend. The knowledge elements of theory and methodology could be used to detect and measure the interdisciplinary characteristics and contributions.

The extraction of knowledge elements facilitates the discovery of semantic information embedded in academic papers. The knowledge elements were proved feasible for measuring the interdisciplinary characteristics and exploring the changes in the time sequence, which helps for overview the state of the arts and future development trend of the interdisciplinary of research theme in LIS.

This paper aims to present a summary of the green consumer behaviour (GCB) research conducted during the 2001–2021 period using the bibliometric analysis and to carry out a thematic and content analysis on the three clusters which comprise 57 articles resulting from the co-citation analysis and identify the significant green purchasing factors.

The three-pronged methodology applied to this research analysis includes performance analysis of the literature using biblioshiny and R Studio; network mapping analysis using VOSviewer and Gephi; thematic analysis using word clouds generated with R Software and content analysis of each paper with the aid of within and between-study analyses.

Cluster one acted as a base for the theoretical foundations of GCB which aids in understanding the basic concepts of green marketing, its evolution and the methodologies, whereas cluster two determined the predictors of everyday green behaviour, which helps in gaining knowledge about the everyday sustainable activities the consumers indulge and the factors motivating to do so. Cluster three mainly focused on the psycho-socio demographic determinants of GCB, which assists in segmentation and predicting the purchase behaviour of the various consumer segments.

The significant variables and major gaps in each of the clusters were identified and authors have drawn the implications for future researchers and marketing managers.

Social mediametrics is a subfield of measurement in which the emphasis is placed on social media data. This paper analyzes the trends and patterns of paper comprehensively mentions on Twitter, with a particular focus on Twitter's mention behaviors. It uncovers the dissemination patterns and impact of academic literature on social media. The research has significant theoretical and practical implications.

This paper explores the fundamental attributes of Twitter mentions by means of analyzing 9,476 pieces of scholarly literature (5,097 from Nature and 4,379 from Science), 1,474,898 tweets and 451,567 user information collected from Altmetric.com database and Twitter API. The study uncovers assorted Twitter mention characteristics, mention behavior patterns and data accumulation patterns.

The findings illustrate that the top academic journals on Twitter have a wider range of coverage and display similar distribution patterns to other academic communication platforms. A large number of mentioners remain unidentified, and the distribution of follower counts among the mention users exhibits a significant Pareto effect, indicating a small group of highly influential users who generate numerous mentions. Furthermore, the proportion of sharing and exchange mentions positively correlates with the number of user followers, while the incidence of supportive mentions has a negative correlation. In terms of country-specific mention behavior, Thai scholars tend to utilize supportive mentions more frequently, whereas Korean scholars prefer sharing mentions over communicating mentions. The cumulative pattern of Twitter mentions suggests that these occur before official publication, with a half-life of 6.02 days and a considerable reduction in the number of mentions is observed on the seventh day after publication.

Conducting a multi-dimensional and systematic analysis of Twitter mentions of scholarly articles can aid in comprehending and utilizing social media communication patterns. This analysis can uncover literature's distribution patterns, dissemination effects and social significance in social media.

The purpose is to use co-citation analysis of servant leadership (SL) research to investigate the evolution of the field, its subfields, gaps and opportunities for future research in a systematic manner.

A document co-citation technique and three clustering algorithms (latent semantic index (LSI), the log-likelihood ratio (LLR) and the mutual information (MI) index) were employed to analyse 24,030 references from 549 articles spanning a period of 50 years.

Cluster analyses reveal that SL research consists of eight distinct subfields: (1) conceptualisation and measurement of SL; (2) SL and related theories; (3) methodological foundations and empirical expansion of SL research; (4) individual-level cognitive effects of SL and related theories; (5) “Warmth effects” of leadership behaviour; (6) antecedents of effective leadership; (7) SL, marketing, sales management and ethics and (8) SL, job design and work engagement. Important gaps and opportunities for future research are identified.

The analyses do not show a complete picture of research on SL. Interesting works used by subgroups of SL researchers may not have enough citations to be included in the results. Moreover, bibliometric analyses do not explain the impact of books, journals and articles on the practice of SL. The authors welcome future analyses of the most influential sources of SL practice. The authors expect that managerial and practice-oriented books and journals, such as the International Journal of Servant Leadership and the Servant Leadership Theory and Practice, would play a central role in such analyses.

The discussions of the nature of SL, its effects and antecedents are useful to leaders who want to develop a SL style or assist others in developing it. For researchers and doctoral students, the cluster analyses of co-citations give an overview of the subfields of SL research and reveal important knowledge gaps in the literature.

SL has several favourable effects on the motivation and psychological well-being of followers. Also, followers tend to adopt a willingness to serve.

Previous research has categorised SL research into three broad categories or phases. The cluster analyses of the co-citations reported here reveal a meaningful structure of eight distinct subfields. Knowledge gaps within the subfields represent novel opportunities for future research on SL. The authors also suggest a new subfield of SL research: pedagogical approaches to the motivation and development of SL skills.

Artificial intelligence (AI) is a significant game changer in human resource development (HRD). The launch of ChatGPT has accelerated its progress and amplified its impact on organizations and employees. This study aims to review and examine literature on AI in HRD, using a bibliometric approach.

This study is a bibliometric review. Scopus was used to identify studies in the field. In total, 236 papers published in the past 10 years were examined using the VOSviewer program.

The obtained results showed that most cited documents and authors are mainly from computer sciences, emphasizing machine learning over human learning. While it was expected that HRD authors and studies would have a more substantial presence, the lesser prominence suggests several interesting avenues for explorations.

This study provides insights and recommendations for researchers, managers, HRD practitioners and policymakers. Prioritizing the development of both humans and machines becomes crucial, as an exclusive focus on machines may pose a risk to the sustainability of employees' skills and long-term career prospects.

There is a dearth of bibliometric studies examining AI in HRD. Hence, this study proposes a relatively unexplored approach to examine this topic. It provides a visual and structured overview of this topic. Also, it highlights areas of research concentration and areas that are overlooked. Shedding light on the presence of more research originating from computer sciences and focusing on machine learning over human learning represent an important contribution of this study, which may foster interdisciplinary collaboration with experts from diverse fields, broadening the scope of research on technologies and learning in workplaces.

The main purpose of the study is to detect, monitor the mythology field and make predictions of the development of it using social network analysis metrics. Mythology, which is the subject of many disciplines, is an area with extensive working potential. In addition to basic bibliometric indicators, the relationships of this field, which cannot be seen by other methods, were analyzed using measures such as centrality, between, eigenvector, modularity and silhouette coefficients.

In this study, social network analysis of the field of mythology, which has an interdisciplinary structure, was made. Within the scope of the study, 28,370 publications were selected from the publications in the field of mythology in the Web of Science (WoS) citation database between 1900 and 2019 using the probability-based stratified sampling method (5%), and detailed analyzes were made on these publications. The aforementioned publications were analyzed in terms of publication and citation numbers, publication types, subject categories, keywords used, co-authorship, researchers with the highest number of publications, institutions and countries with the highest number of document co-citations.

The findings show that the field of mythology gathers around four main subjects (sociology, folklore, politics and anthropology). When interpreted in terms of centrality metrics in more detail, the symbiotic or complementary relationship between anthropology, folklore, politics, sociology and mythology can be easily observed.

The findings of this study are seen important for scientists, decision-makers and policymakers. In addition, the findings of the study can be used to create the curriculum of the field.