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Record W193476993

Modeling Utilitarian Consumption Behaviors in Online Shopping: An Expectation Disconfirmation Perspective

2010· article· en· W193476993 on OpenAlexaff
Eric T.K. Lim, Dianne Cyr, Chee‐Wee Tan

Bibliographic record

VenueJournal of the Association for Information Systems · 2010
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCustomer churn and segmentation
Canadian institutionsUniversity of British ColumbiaSimon Fraser University
Fundersnot available
KeywordsPerspective (graphical)Consumption (sociology)Computer sciencePsychologyAdvertisingMarketingHuman–computer interactionArtificial intelligenceBusinessAesthetics
DOInot available

Abstract

fetched live from OpenAlex

Utilitarian consumption appeals to the cognitive rationality of consumers by accentuating the attainment of desirable transactional outcomes. While substantial knowledge has been accumulated on understanding the determinants of consumers’ acceptance of e-commerce websites, there is a paucity of studies that explore customers’ pre-consumption service expectations in order to determine how these expectations may be leveraged by e-merchants in creating matching e-services. Drawing on the Expectation Disconfirmation Theory (EDT), this study advances a model that not only delineates utilitarian expectations into its constituent dimensions, but also highlights how these expectations can be best served through transactional functionalities devised for improving the functional performance of e-commerce websites. The model is then empirically verified via an online survey questionnaire administered on a sample of 183 student participants. Theoretical contributions and pragmatic implications to be gleaned from our proposed model and its subsequent empirical validation are discussed.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.016
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0030.003
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.030
GPT teacher head0.289
Teacher spread0.259 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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
Published2010
Admission routes1
Has abstractyes

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