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Record W3121531672

The Effects of Perceived Quality of Performance on Price-Value, Satisfaction, and Behavioural Intentions by Golfers' Resident Type

2010· article· en· W3121531672 on OpenAlexaffabout
Sean Hennessey

Bibliographic record

VenueSSRN Electronic Journal · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicTurfgrass Adaptation and Management
Canadian institutionsUniversity of Prince Edward Island
Fundersnot available
KeywordsPsychologyQuality (philosophy)Value (mathematics)FeelingPerceptionSocial psychologyRegression analysisPerceived qualityApplied psychologyMarketingStatisticsMathematicsBusiness
DOInot available

Abstract

fetched live from OpenAlex

A model is developed that considers the effects of perceived quality of performance on price-value, satisfaction, and behavioural intentions. The model was tested using data from 3,235 surveys of golfers on Prince Edward Island, a golfing destination in Canada. Three golfer types were identified: tourists, permanent residents, and seasonal residents. An exploratory factor analysis was completed to develop five measures of perceived golf course quality. Three multiple regression models were then used to examine the relationships among the constructs. This appears to be the first study that models golfer behaviors and intentions by resident type. The results indicate a significant positive relationship between perceived quality and the feeling that value was received for the golf fee paid. The significant positive relationship was also observed between perceived quality, price-value, and satisfaction; and between perceived quality, price-value, satisfaction, and intentions to return to golf and to recommend the course. Overall, the results provide support for a causal relationship between the constructs. The study contributes to a better understanding of golfers’ perceptions and behavioural intentions by resident type.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.500
Threshold uncertainty score0.233

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.006
GPT teacher head0.243
Teacher spread0.237 · 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

Citations0
Published2010
Admission routes2
Has abstractyes

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