Theoretical studies of the historical development of the accounting discipline: a review and evidence

Many existing studies of the development of accounting thought have either been atheoretical or have adopted Kuhn's model of scientific growth. The limitations of this 35-year-old model are discussed. Four different general neo-Kuhnian models of scholarly knowledge development are reviewed and compared with reference to an analytical matrix. The models are found to be mutually consistent, with each focusing on a different aspect of development. A composite model is proposed. Based on a hand-crafted database, author co-citation analysis is used to map empirically the entire literature structure of the accounting discipline during two consecutive time periods, 1972–81 and 1982–90. The changing structure of the accounting literature is interpreted using the proposed composite model of scholarly knowledge development

Dynamic Animations of Journal Maps: Indicators of Structural Changes and Interdisciplinary Developments

The dynamic analysis of structural change in the organization of the sciences requires methodologically the integration of multivariate and time-series analysis. Structural change--e.g., interdisciplinary development--is often an objective of government interventions. Recent developments in multi-dimensional scaling (MDS) enable us to distinguish the stress originating in each time-slice from the stress originating from the sequencing of time-slices, and thus to locally optimize the trade-offs between these two sources of variance in the animation. Furthermore, visualization programs like Pajek and Visone allow us to show not only the positions of the nodes, but also their relational attributes like betweenness centrality. Betweenness centrality in the vector space can be considered as an indicator of interdisciplinarity. Using this indicator, the dynamics of the citation impact environments of the journals Cognitive Science, Social Networks, and Nanotechnology are animated and assessed in terms of interdisciplinarity among the disciplines involved

Crowdsourced Data Mining for Urban Activity: A Review of Data Sources, Applications and Methods

The penetration of devices integrated with location-based services and internet services has generated massive data about the everyday life of citizens and tracked their activities happening in cities. Crowdsourced data, such as social media data, POIs data and collaborative websites, generated by the crowd, has become fine-grained proxy data of urban activity and widely used in research in urban studies. However, due to the heterogeneity of data types of crowdsourced data and the limitation of previous studies mainly focusing on a specific application, a systematic review of crowdsourced data mining for urban activity is still lacking. In order to fill the gap, this paper conducts a literature search in the Web of Science database, selecting 226 highly related papers published between 2013 and 2019. Based on those papers, the review firstly conducts a bibliometric analysis identifying underpinning domains, pivot scholars and papers around this topic. The review also synthesises previous research into three parts: main applications of different data sources and data fusion; application of spatial analysis in mobility patterns, functional areas and event detection; application of socio-demographic and perception analysis in city attractiveness, demographic characteristics and sentiment analysis. The challenges of this type of data are also discussed in the end. This study provides a systematic and current review for both researchers and practitioners interested in the applications of crowdsourced data mining for urban activity.This research is funded by a scholarship from the China Scholarship Counci

Data Science: A Study from the Scientometric, Curricular, and Altmetric Perspectives

This research explores the emerging field of data science from the scientometric, curricular, and altmetric perspectives and addresses the following six research questions: 1. What are the scientometric features of the data science field? 2. What are the contributing fields to the establishment of data science? 3. What are the major research areas of the data science discipline? 4. What are the salient topics taught in the data science curriculum? 5. What topics appear in the Twitter-sphere regarding data science? 6. What can be learned about data science from the scientometric, curricular, and altmetric analyses of the data collected? Using bibliometric data from the Scopus database for 1983 – 2021, the current study addresses the first three research questions. The fourth research question is answered with curricular data collected from U.S. educational institutions that offer data science programs. Altmetric data was gathered from Twitter for over 20 days to answer the fifth research question. All three sets of data are analyzed quantitatively and qualitatively. The scientometric portion of this study revealed a growing field, expanding beyond the borders of the United States and the United Kingdom into a more global undertaking. Computer Science and Statistics are foundational contributing fields with a host of additional fields contributing data sets for new data scientists to act, including, for example, the Biomedical and Information Science fields. When it comes to the question of salient topics across all three aspects of this research, it was revealed that a large degree of coherence between the three resulted in highlighting thirteen core topics of data science. However, it can be noted that Artificial Intelligence stood out among all the other groups with leading topics such as Machine Learning, Neural Networks, and Natural Language Processing. The findings of this study not only identify the major parameters of the data science field (e.g., leading researchers, the composition of the discipline) but also reveal its underlying intellectual structure and research fronts. They can help researchers to ascertain emerging topics and research fronts in the field. Educational programs in data science can learn from this study about how to update their curriculums and better prepare students for the rapidly growing field. Practitioners and other stakeholders of data science can also benefit from the present research to stay tuned and current in the field. Furthermore, the triple-pronged approach of this research provides a panoramic view of the data science field that no prior study has ever examined and will have a lasting impact on related investigations of an emerging discipline

Leveraging Citation Networks to Visualize Scholarly Influence Over Time

Assessing the influence of a scholar's work is an important task for funding organizations, academic departments, and researchers. Common methods, such as measures of citation counts, can ignore much of the nuance and multidimensionality of scholarly influence. We present an approach for generating dynamic visualizations of scholars' careers. This approach uses an animated node-link diagram showing the citation network accumulated around the researcher over the course of the career in concert with key indicators, highlighting influence both within and across fields. We developed our design in collaboration with one funding organization---the Pew Biomedical Scholars program---but the methods are generalizable to visualizations of scholarly influence. We applied the design method to the Microsoft Academic Graph, which includes more than 120 million publications. We validate our abstractions throughout the process through collaboration with the Pew Biomedical Scholars program officers and summative evaluations with their scholars

Animating the development of Social Networks over time using a dynamic extension of multidimensional scaling

The animation of network visualizations poses technical and theoretical challenges. Rather stable patterns are required before the mental map enables a user to make inferences over time. In order to enhance stability, we developed an extension of stress-minimization with developments over time. This dynamic layouter is no longer based on linear interpolation between independent static visualizations, but change over time is used as a parameter in the optimization. Because of our focus on structural change versus stability the attention is shifted from the relational graph to the latent eigenvectors of matrices. The approach is illustrated with animations for the journal citation environments of Social Networks, the (co-)author networks in the carrying community of this journal, and the topical development using relations among its title words. Our results are also compared with animations based on PajekToSVGAnim and SoNIA

The role of handbooks in knowledge creation and diffusion: A case of science and technology studies

Genre is considered to be an important element in scholarly communication and in the practice of scientific disciplines. However, scientometric studies have typically focused on a single genre, the journal article. The goal of this study is to understand the role that handbooks play in knowledge creation and diffusion and their relationship with the genre of journal articles, particularly in highly interdisciplinary and emergent social science and humanities disciplines. To shed light on these questions we focused on handbooks and journal articles published over the last four decades belonging to the research area of Science and Technology Studies (STS), broadly defined. To get a detailed picture we used the full-text of five handbooks (500,000 words) and a well-defined set of 11,700 STS articles. We confirmed the methodological split of STS into qualitative and quantitative (scientometric) approaches. Even when the two traditions explore similar topics (e.g., science and gender) they approach them from different starting points. The change in cognitive foci in both handbooks and articles partially reflects the changing trends in STS research, often driven by technology. Using text similarity measures we found that, in the case of STS, handbooks play no special role in either focusing the research efforts or marking their decline. In general, they do not represent the summaries of research directions that have emerged since the previous edition of the handbook.Comment: Accepted for publication in Journal of Informetric

Of tribes and totems: An author cocitation context analysis of Kurt Lewin’s influence in social science journals

This study used author cocitation context analysis (ACCA) to explore the intellectual structure of two Lewinian social science journal communities. ACCA is a variant of White’s (2000) ego-centered citation analysis, in which the focal author name serves as a filter. Articles citing Lewin between 1972 and 2001 in the Journal of Social Issues and Human Relations, sponsored by Lewinian specialties served as the test bed. Procedures conducted on cited author names—cluster analysis, multidimensional scaling, principal components analysis, and Pathfinder network analysis—generated coherent maps for each journal that maintained a “Lewinian” focus. The maps displayed the range of subject themes of interest to the specialties, which is consistent with Lewin’s importance to the specialties. Classifying all citations to Lewin as Totemic or Substantive assessed citation function. Results were convergent with the MDS maps in that Lewin’s work was used most frequently in a Substantive (central) way. Use of Lewin’s work did not conform to expectation in that the number of articles citing Lewin increased overall and the proportion of Totemic (peripheral) citations did not increase over the time studied. Analysis of Lewin’s works and concepts cited was also congruent with the specialties’ subject focus—JSI authors focused on social justice issues and HR authors used organization and small group research.Ph.D., Information Science -- Drexel University, 200

Mapping the knowledge base of information policy: clusters of documents, people and ideas

This thesis investigates aspects of the intellectual and social structure of the field of information policy through a detailed examination of the serials literature. The aims of the research are to explore how information policy scholarship is organised—in terms of its relation to other fields and disciplines; whether it constitutes a distinct specialty in its own right; and what kinds of institutional structures and arrangements exist to support research and scholarship. In Part One, a literature review identifies previous bibliometric and other studies which are relevant to studies of scholarly disciplines and knowledge communities. It discusses the interdisciplinary problem-oriented nature of information policy and considers some of the modes of enquiry which characterise investigations this area. Part Two consists of a series of experiments carried out on a test collection of 771 periodical articles drawn from the Social science Citation Index The empirical work comprised four linked studies: a bibliometric census study an analysis of document clustering; an author cocit.ation study; and a content analysis. Extensive use was made of multivariate statistical techniques, notably principal components analysis, hierarchical clustering, discriminant and correspondence analysis to identify statistically significant and meaningful patterns and structures within the test collection. The study concludes that information policy is a growing and reasonably distinctive field of study with strong links to library and information science, law, media studies, and the political sciences. It is suggested that the field is not unified and that research is still primarily organised along national and traditional disciplinary lines, with little evidence of significant collaborative activity across institutions or sectors. The research base is highly dispersed, with practitioners playing a major role in the production of knowledge. In institutional terms, the field is very thinly spread, with few signs of concentration