Creating and Sustaining Stakeholder Emotional Resonance with Organizational Identity in Social Mission-Driven Organizations
Bibliographic record
Abstract
How do senior managers of social mission-driven organizations build and sustain stakeholders’ emotional resonance with organizational identity beliefs over time in the face of repeated existential threats? This is an important question, given the dependence of many such organizations on external stakeholders who provide the resources necessary for survival. In this paper, we investigate the case of Solidum, a philanthropic organization devoted to poverty causes. Drawing on ethnographic, interview and archival data over 20 years, we develop a process model showing how senior managers may create and sustain stakeholder emotional resonance through three practices of emotional resonance work: building emotional bridges, enrolling stakeholders in collective soul-searching and materializing an appealing identity symbol. We show that stakeholder emotional resonance needs to be continually renewed and reshaped in the face of ongoing challenges associated with macro-organizational trends and the routinization of existing practices that can result in the dissipation of emotional resonance over time. The paper contributes to the literature on organizational identity maintenance by drawing attention to the active managerial work required to sustain stakeholder emotional resonance over time to allow mission-driven organizations to survive and prosper.
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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 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".