MétaCan
Menu
Back to cohort

An Assessment of the Dimensionality of Should and Will Service Expectations

2004· article· en· W2068566932 on OpenAlexaffvenue
Michel Laroche, Maria Kalamas, Soumaya Cheikhrouhou, Adélaı̈de Cézard

Bibliographic record

VenueCanadian Journal of Administrative Sciences / Revue Canadienne des Sciences de l Administration · 2004
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCustomer Service Quality and Loyalty
Canadian institutionsConcordia University
Fundersnot available
KeywordsConceptualizationDimension (graph theory)Service (business)MarketingOrder (exchange)Valuation (finance)Context (archaeology)Service qualityPsychologyWelfare economicsBusinessEconomicsComputer scienceMathematicsGeographyAccountingArtificial intelligenceFinance

Abstract

fetched live from OpenAlex

Abstract This study is a step forward in the continued evolution of our understanding of multiple standards of customer service expectations. By developing a higher‐order factor model, we examine the existence of hypothesized functional and technical dimensions of should and will expectations, and determine the causal relationships between the two types of expectations and the two hypothesized dimensions. In an effort to provide a richer conceptualization of should and will expectations, we test their dimensionality in the context of the turbulent airline industry. A survey measuring consumers' post‐encounter expectations vis‐à‐vis a well‐known airline carrier was completed by a sample of experienced travelers ( N = 363 ). As expected, should expectations were higher than will expectations with the former exhibiting less variability than the latter. Our second‐order factor model also revealed two types of expectations, should and will, and two dimensions, functional and technical, for each type. Given its multidimensional nature, our model of customer service expectations has far‐reaching implications in carrying out and interpreting service quality and satisfaction research. Based on our findings, we recommend that managers formulate useful marketing strategies by manipulating should and will expectations simultaneously, and by focusing more on the functional as opposed to the technical dimensions, at least in the airline industry. Résumé La présente étude marque un nouveau développement dans la compréhension des diverses normes d'évaluation du service à la clientèle. Grâce à l'élaboration d'un modèle factoriel de plus haut niveau, nous examinons l'existence des dimensions à teneur fonctionnelle et technique qui sont sous‐jacentes aux attentes normatives (should) et prédictives (will) et déterminons la relation causale entre les deux types d'attentes. Dans le souci de fournir une meilleure image de la conceptualisation des attentes prédictives et normatives, nous avons choisi l'industrie aéri‐enne qui a récemment connu beaucoup de mutations. Nous avons sélectionné un échantillon composé de personnes qui ont l'habitude de voyager (N = 363) auxquelles nous avons distribué un questionnaire portant sur les attentes envers une compagnie aérienne reconnue. Conformément à notre hypothèse, le niveau des attentes normatives (should) était plus élevé que celui des attentes prédictives (will), le premier présentant une plus grande variabilityé que le second. Le facteur de second ordre a confirmé non seulement l'existence d'attentes normatives et prédictives, mais aussi l'existence de deux dimensions (fonctionnelle et technique) qui sous‐tendent chacun des deux types d'attentes étudés. De par sa nature multidimensionnelle, le modèle d'attentes proposé dans cet article comporte plusieurs implications théoriques au niveau de la recherche sur la qualityé du service et sur la satisfaction. Nos résultats nous incitent à recommander aux gestionnaires de manipuler simultanément les attentes prédictives et normatives et de concentrer leurs efforts sur la dimension technique du service plutôt que sur sa dimension fonctionnelle, du moins dans l'industrie aérienne.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.716
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.003
Scholarly communication0.0000.002
Open science0.0010.000
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 head0.126
GPT teacher head0.366
Teacher spread0.240 · 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.

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

Citations28
Published2004
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Journal of Administrative Sciences / Revue Canadienne des Sciences de l AdministrationSame topicCustomer Service Quality and LoyaltyFrench-language works237,207