Inside story of impact investing in emerging market: A systematic review to measure the responsible and sustainable investing pattern using the ADO framework
Bibliographic record
Abstract
Abstract Impact investing has emerged as a significant global phenomenon as it provides a valuable avenue for investors to shape their cognitive decision‐making ability to have a societal impact. The present study aims to review the existing literature on impact investing systematically. It tries to understand the major motivational factors that impact the investor in impact investing using the ADO framework by linking it with McClelland's Theory of Motivation, geographical areas, journal of publication, and type of research articles for impact investing, significant research gaps in impact investing, theoretical and managerial implications and future research of impact investing. PRISMA framework has been used to finalize the articles from the Scopus database. As a result, 154 articles have been identified from the year 2011 to 2024. The result identifies three motivational factors that drive the investor to invest in impact investing. It includes financial, social, and self‐actualization. The study will guide the policymaker in introducing comprehensive regulatory policies in the area of impact investing. Accordingly, tax incentives and subsidies should be granted for promoting investment in impact investing. The development of proper infrastructure for trading in impact investing needs attention.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".