The Impact Institutional Ownership on the Firms’ Performance: Evidence from Organization of Islamic Cooperation (OIC) Countries
Bibliographic record
Abstract
Bibliometric analysis is an efficient technique for carrying out quantitative analysis of academic work to discuss publication patterns in a particular research area by analyzing existing papers. The paper aims to examine the institutional ownership literature to evaluate its development and identify the publication pattern. The author used bibliometric techniques and displays a review that brings together academic work categorized in finance, business, accounting management, etc. All research publications on institutional ownership were found using a specialized search system in the Scopus database. The literature search includes journal articles, conferences, book chapters, reviews, and other materials published between 1953 and 2020. The following keywords were used in the initial search to identify international academic publications in the research topic: “institutional ownership and institutional investment”. The results indicate that starting in 1953, as recorded in Scopus as the first duplication, the number of papers published and cited in the literature about institutional ownership research increased very slowly for the next seven years until 2009. Then there was a significant increase from 2010 to 2020; This result could be due to the fact that institutional investors have recently become an essential factor in many current hot topics such as sustainability, innovation, best corporate governance practices, corporate monitoring, and innovation. The global financial crisis could also be one of the reasons for this upward trend. Authors’ collaboration in this field is powerful, both at the international and national level. Wang J. took first place as the essential authors with the highest number of publications and weight of citations, and Shleifer, A as the highest weight of citations. In terms of country co-authorship, United Kingdom, United States, Australia, Canada, and China are the most impactful countries regarding the number of publications and citations. Lastly, according to cited reference analysis, the most often cited reference are Jensen, M.C., Meckling (1976), Shleifer, A. (2007), and Chen, X., (1988). Journal of financial economics and Journal of cooperate finance stood apart from the other journals regarding both citation and total link strength in the journal co-citation network.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".