The Nature of Stakeholder Satisfaction with Marketing Education
Bibliographic record
Abstract
The current article represents a cautionary tale in continuing emerging marketization practices as the dominant form of marketing with higher education. Specifically, a review of three important emerging literature streams (i.e., quality-of-life, service-dominant logic, and stakeholder orientation) all appear to support calls for moving beyond typical (short-term, hedonistic) measures of consumer satisfaction associated with the delivery of higher education toward satisfaction judgments based on higher-order forms of happiness (i.e., prudential and perfectionist forms of happiness such as eudaimonia). This conclusion suggests that the nature of long-term value co-creation associated with higher education should focus on quality of life and well-being. Critical to the success of moving marketing strategy of institutions of higher education in the direction asserted herein will be embracing a primary strategic marketing objective of convincing stakeholders to value long-term, eudaimonic forms of happiness and satisfaction over the current psychological, short-term, hedonistic satisfaction forms assessing today’s marketization practices. A series of propositions are offered to help guide marketers in embracing this perspective.
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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".