Responsible stakeholder engagement marketing
Bibliographic record
Abstract
• Customer Engagement Marketing (CEM) aims to boost customer/firm relationships. • CEM thus overlooks the quality of the firm’s relationship with other stakeholders. • We thus propose Responsible Stakeholder Engagement Marketing (RSEM). • We define RSEM and develop a conceptual framework of RSEM drivers and outcomes. • We show how RSEM can be used to boost the firm’s relationships with its stakeholders. By strategically cultivating customers’ engagement, Customer Engagement Marketing (CEM) boosts the firm’s relationships with its customers. However, CEM’s isolated customer focus overlooks the importance of cultivating other firm stakeholders’ (e.g., employees’ or suppliers’) engagement with the firm, exposing a pertinent gap in the literature. Addressing this gap, we conceptualize Responsible Stakeholder Engagement Marketing (RSEM) as a theoretical sub-set of the broader corporate social responsibility (CSR) concept. We define RSEM as a firm’s deliberate strategic effort to stimulate and empower its stakeholders to make responsible contributions to the firm, other stakeholders, and the environment . We also develop a framework and an associated set of propositions that are informed by stakeholder theory, which suggest that a firm’s internal (vs. specific external) stakeholders’ need for the firm’s social responsibility differentially affects its instrumental, compliant and moral RSEM strategy, thereby uniquely impacting (a) its stakeholders’ contributions to the firm, other stakeholders, and the environment, and (b) the firm’s triple bottom-line performance. We conclude by discussing key implications that arise from our analyses.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.031 | 0.005 |
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".