MétaCan
Menu
Back to cohort
Record W2137968008 · doi:10.5539/hes.v4n4p89

The Nature of Stakeholder Satisfaction with Marketing Education

2014· article· en· W2137968008 on OpenAlexvenueno aff
Steven A. Taylor, Kim Judson

Bibliographic record

VenueHigher Education Studies · 2014
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicService and Product Innovation
Canadian institutionsnot available
Fundersnot available
KeywordsHappinessStakeholderMarketingMarketizationHigher educationCustomer satisfactionValue (mathematics)Public relationsRelationship marketingEudaimoniaSociologyPsychologyBusinessSocial psychologyEconomicsPolitical scienceMarketing managementEconomic growthChina

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.426
Threshold uncertainty score0.270

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.034
GPT teacher head0.286
Teacher spread0.253 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations11
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueHigher Education StudiesSame topicService and Product InnovationFrench-language works237,207