Bringing Values into Retail Investments: The Role of Work Identification in Responsible Investing
Bibliographic record
Abstract
Most research on responsible investing (RI) has focused on how the choices, motivations, and behaviors of retail investors influence RI adoption. However, financial advisors also play a critical role intermediating RI acceptance among their clients. In this study we draw on work identification theory to explain how the values and motivations of RI advisors influence how and whether advisors effectively engage their clients with RI products and services. Our qualitative study found that work identification in RI advisors resulted in a higher willingness to invest time and energy, an orientation to develop values-based relationships with clients and a heightened commitment towards RI; each of these positively influenced the professional outcomes of acquiring more RI clients and gaining acceptance for RI among clients. In contrast, the RI advisors who lacked this identification struggled to find acceptance for RI among their clientele.
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How this classification was reachedexpand
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".