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Record W4321014858 · doi:10.1016/j.jbusres.2023.113727

Information multidimensionality in online customer reviews

2023· article· en· W4321014858 on OpenAlexafffund
Fang Wang, Zhao Du, Shan Wang

Bibliographic record

VenueJournal of Business Research · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Marketing and Social Media
Canadian institutionsUniversity of SaskatchewanWilfrid Laurier University
FundersSocial Sciences and Humanities Research Council of CanadaNational Natural Science Foundation of China
KeywordsEmpirical researchProduct (mathematics)Value (mathematics)Word of mouthSocial mediaMarketingUser-generated contentComputer sciencePsychologyKnowledge managementBusinessWorld Wide Web

Abstract

fetched live from OpenAlex

Online customer reviews, as customer experience sharing, contain multiple dimensions of information that have rarely been systematically examined in prior research. Drawing from the customer experience literature, this research analyzes four dimensions of information embedded in online product reviews—namely, sensory information, cognitive information, affective information, and social information—and illustrates their importance by examining their diagnostic value to prospective customers. An empirical study on Amazon online reviews confirms that all four dimensions of information have significant effects on the diagnostic value of online reviews to prospective customers. Moreover, these effects are heterogeneous, contingent on the contextual conditions of product categories and review opinions. The study and findings of information multidimensionality provide a sound framework to glean insights into the rich content in online reviews and other electronic word-of-mouth content, which is crucial to understand customers’ pre-purchase information needs and experience journeys.

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)0.016
metaresearch head score (Gemma)0.023
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.740
Threshold uncertainty score0.985

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0160.023
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
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 head0.187
GPT teacher head0.475
Teacher spread0.288 · 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 designNot applicable
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

Citations42
Published2023
Admission routes2
Has abstractyes

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