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Record W2523611746 · doi:10.5539/ijms.v8n5p32

Experiential Consumption and Customer Satisfaction: Moderating Effects of Perceived Values

2016· article· en· W2523611746 on OpenAlexvenueno aff
Lung-Yu Li, Long-Yuan Lee

Bibliographic record

VenueInternational Journal of Marketing Studies · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCustomer Service Quality and Loyalty
Canadian institutionsnot available
Fundersnot available
KeywordsModerationExperiential learningCustomer satisfactionMarketingConsumption (sociology)Nonprobability samplingValue (mathematics)PsychologyBusinessProduct (mathematics)Social psychologyMedicineSociologyMathematics

Abstract

fetched live from OpenAlex

<p>Perceived value has also been studied to investigate what consumers really want and how to reach their mind over the past decade. Marketers recently had made efforts to integrate the concept of perceived value into experiential marketing strategy. The key of perceived value is to understand not only how they satisfied with the product they purchased, but also how they felt the service they were involved in. The current study aimed to investigate the relationship between experiential consumption and customer satisfaction toward consumers in the resort hotel industry. Applying perceived value as a moderator, the study further examine the level of effect on the relationship between experiential consumption and customer satisfaction. Using purposive sampling method, data was collected from 378 subjects through the self-administrated questionnaire. The result indicated that all dimensions of experiential consumption had positive effect on customer satisfaction. Both dimensions of perceived value, in addition, showed positive effects on customer satisfaction. Perceived value was proven to have partially moderating effects on the relationship between experiential consumption and customer satisfaction. Suggestions and managerial implications were discussed in the study, and would provide contribution both to the body of knowledge in the filed of marketing management.</p>

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.220
Threshold uncertainty score0.280

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.025
GPT teacher head0.303
Teacher spread0.277 · 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
Published2016
Admission routes1
Has abstractyes

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