Network analysis in accounting research: an institutional and geographical perspective
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose The objective of this study is to explore the accounting research network among institutions and countries globally and to contribute to the knowledge development in accounting discipline across regions with a novel and original approach. Design/methodology/approach This study has been conducted by manually collecting data from 10,863 papers published in 22 accounting journals indexed in the Web of Science (WoS) for the period 2000–2016. Analyses and visualizations of collaborative networks across institutions and regions were performed by using network analysis software packages, including Pajek, UCINET 6, NetDraw and VOSviewer. Findings The study finds that the most productive five universities are the University of New South Wales, University of Sydney, University of Texas, University of California and University of Manchester worldwide. In accordance with the institution ranking, the five most productive countries in all periods are the USA, the UK, Australia, Spain and Canada. However, in addition to these countries, it is important to note that some European and Asian countries and New Zealand from Oceania are among the most productive countries which host prolific institutions. Furthermore, network indicators show that the UK is the most influential actor in centrality and brokerage within the research network. We should note that Australia is also among the most influential nations with its influential institutions. In all research metrics, the dominance of Anglophone countries (e.g. the USA, the UK and Australia) is observable on which language advantage might play a role since most internationally accredited journals publish scientific articles in English. Research limitations/implications The study is bounded with several main limitations. First, due to collecting the data manually, there might be some inherent limitations. Second, the study is constrained by the time frame between 2000 and 2016. The study does not answer why and how questions in investigating research productivity and effectiveness in the network. Our study might inspire new studies to complement ours by considering these constraints. Practical implications Our findings indicated the prominent institution-wide and country-wide actors; thus, the results provide a global perspective on the collaboration network. Second, our findings guide job seekers, who are particularly research-oriented, to potential recruiters around the world both at the institution level and country level. Third, the results might play an important role in forming institution-based and country-based research policies. The USA, among others, is a particularly important actor in productivity, whereas the UK, among others, is a remarkable country in centrality and brokerage in the research network. By examining the policies of these two countries, other nations might shape their research strategies, promotion policies and support and reward schemes. Fourth, cross-institution and in particular cross-country collaborations are imperative in the diversity of accounting research as they blend culturally diverse researchers. Fifth, prominent institutions highlighted in this study might be adopted as role models by other institutions in the same country and benefit their expertise in productivity and cooperation by scrutinizing their approaches. Sixth, our findings and metrics might be adopted as benchmarks for institutions and nations for performance evaluation. Considering our 5-year period indicators, institutions can set targets for their improvement and for measuring the progress. We provide other important implications in the conclusion section of the study. Originality/value To the best knowledge of the authors, no study yet investigated the collaboration across academic institutions, regions, and countries in accounting discipline to this extent. Therefore, our research provides a significant contribution to the literature by seeking a comprehensive network analysis of authorship patterns from an institutional and geographical perspective. Doing so, we contribute to knowledge development in accounting discipline with institutional and geographical network analyses.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it