Stakeholder Engagement and Financial Performance of Firms Listed on the Johannesburg Stock Exchange (JSE)
Bibliographic record
Abstract
Attaining sustainable development will remain an elusive agenda if there is no effective stakeholder engagement. All stakeholders need to come on board to share and collaborate on environmental sustainability initiatives. This study investigated the relationship between stakeholder engagement and financial performance. The study area of this study was all FTSE/JSE listed firms. The researcher opted for a quantitative research approach and used a case study research design. The longitudinal design was adopted where the researcher collected panel data from 2011-2018. The sample of this study was 32 firms listed on the FTSE/JSE Responsible Investment Index. This resulted in 256 observations for the period under consideration. This study utilised secondary data, which is annual financial statements of firms listed on the JSE. Stakeholder engagement was the independent variable while the financial performance as measured by the Tobin’s Q was the dependent variable. Quantitative content analysis was used to collect data related to stakeholder engagement. Data was analysed using Panel regression analysis model. The Fixed and Random effects models were used to analyse data. The Hausman test was used to evaluate the appropriate model. The findings showed a positive but insignificant relationship between stakeholder engagement and financial performance as measured by Tobin’s Q. This suggested that stakeholder engagement does not predict market valuation of the firm. It was deduced that probably the concerned firms are sending weak signals to key stakeholders regarding their genuine commitment towards environmental sustainability initiatives. Recommendations were made for firms to send strong signals to investors which clearly show that they are genuinely committed towards environmental sustainability initiatives.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 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".