Linking Stakeholder Salience to Value Creation: A Stakeholder-Resource-Based View (SRBV) Perspective and Evidence from Technology-enabled Start-up Firms in Nigeria and Canada
Bibliographic record
Abstract
One of the most critical advances in stakeholder discussions is the Mitchell, Agle, and Wood stakeholder salience model, which is used to identify and prioritize relevant firm stakeholders. However, the model's comprehensiveness has been questioned, with several calls for the inclusion of additional constructs and theoretical development to enhance its prescriptive accuracy. Additionally, existing literature linking stakeholder salience to corporate performance has produced mixed results, with no conclusive prescription on how or whether stakeholder salience can enable the firm’s value creation process, despite several scholarly calls in this regard. To address these gaps, this thesis explored three interrelated research questions: (1) What is the empirical role of stakeholder proximity, stakeholder multiplicity, and social capital in identifying and prioritizing salient stakeholders? (2) What is the nature of the relationship between stakeholder salience and the value creation process of firms? (3) What is the nature of the relationship between aligning with stakeholder goals, firm goals, and firm performance based on the fundamental understanding of the unequal salience of stakeholders in a firm’s network? Using SRBV, stakeholder salience, and goal setting theory as theoretical perspectives, this thesis obtained survey data from 232 respondents from technology-enabled start-up firms in Nigeria and Canada. The results suggest that stakeholder proximity and multiplicity have a significant and positive influence on stakeholder salience, while stakeholder salience has a significant and positive influence on stakeholder integration and value creation. Lastly, the results suggest that aligning with stakeholder goals, without the premise of stakeholder integration or stakeholder salience, will lead to negative firm performance. This Ph.D. thesis contributes significantly to the academic literature by (1) refining and improving the prescriptive accuracy of the stakeholder salience model, (2) underscoring the importance of and the appropriateness of the SRBV theory in the value creation logic of the firm, (3) developing and utilizing the consolidated SRBV theory to test empirical relationships related to the theory, (4) contributing to the research on the organizational antecedents to value creation and goal setting. In practice, this thesis enables the firm and its managers to accurately assess and identify its salient stakeholders who contribute to value creation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".