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Record W3000211916

The Effect of Customer Prioritization Strategy on Customer Entitlement

2019· article· en· W3000211916 on OpenAlexaboutno aff
Svetlana Davis

Bibliographic record

VenueAcademy of Marketing Studies journal · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCustomer Service Quality and Loyalty
Canadian institutionsnot available
Fundersnot available
KeywordsEntitlement (fair division)BusinessCustomer retentionMarketingCustomer to customerCustomer advocacyCustomer equityFeelingPrioritizationDisadvantagedCustomer delightCustomer intelligenceProcess managementPsychologyEconomicsService qualityService (business)MicroeconomicsSocial psychology
DOInot available

Abstract

fetched live from OpenAlex

It is a widely held notion that customer prioritization strategies (focusing efforts on the most valuable customers) are profitable for firms. However, such increased focus consequently results in some customers receiving comparatively disadvantaged treatment. This paper performs three experimental studies involving 516 Canadian participants in 2018 to investigate how preferential treatment resulting from customer prioritization (CP) strategies relates to customer entitlement and subsequent customer retaliatory intentions. Results show that customers with strong brand relationships feel entitled if they have received disadvantaged treatment under a customer prioritization strategy. Such feelings translate into increased demands on the firm, culminating in an increased likelihood of retaliatory actions. Moreover, feelings of entitlement are exacerbated if these customers were invited to provide feedback in the creation of the CP strategy. For managers, findings indicate that customer entitlement increases for non-prioritized customers, and customer voice positively exacerbates the link between the strength of the customer brand relationship and customer entitlement. Thus, the reaction of nonvaluable customers must be measured in the development of CP strategies while encouraging customer participation in CP development may be inadvisable.

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)0.008
metaresearch head score (Gemma)0.001
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 score0.391
Threshold uncertainty score0.469

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.023
GPT teacher head0.306
Teacher spread0.283 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

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