Corporate governance, social responsibility and capital markets: exploring the institutional investor mental model
Bibliographic record
Abstract
Purpose The purpose of this paper is to extend our understanding of corporate governance, social issues and capital markets by distinguishing between the socially responsible investing phenomenon and mainstream investing with respect to social issues. It attempts to clarify the domain by casting it in the theoretical frame of prospect theory and mental modeling. With a qualitative study done among large institutional investors in the Canadian securities industry, the article derives a proposed mental model of these institutional investors' cognitive model of social issues as they impact investments. Findings The institutional investors in this study know exactly where value is derived from social investments suggesting that there may be more alignment between directors, investors and societal expectations than has been previously suggested. Research limitations/implications The limited number of organizations in the study reduces the generalizability of the findings. Practical implications Managers and directors must have an understanding of how shareholder value and responsibilities intersect. In our research, we have found that these executives positioned their firms as leaders on the social responsibility front. Interestingly, their major shareholders also understood how responsibility and shareholder value intersected and as a result, financial performance was not sacrificed. Originality/value The findings from this research shed light on previous scholars' questions regarding the alignment of interests between managers, directors and social expectations. The firms analyzed make strategic investments that are considered to meet social expectations but that are also perceived to add value to the organization making the firm more attractive to institutional investors.
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.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".