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Record W2151353364 · doi:10.1002/pa.1470

Bittersweet! Understanding and Managing Electronic Word of Mouth

2013· article· en· W2151353364 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Public Affairs · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Marketing and Social Media
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsUnpackingViral marketingAdvertisingConsumption (sociology)Social mediaWord of mouthBusinessContext (archaeology)MarketingSociologyComputer scienceWorld Wide Web

Abstract

fetched live from OpenAlex

For ‘viral marketing’, it is critical to understand what motivates consumers to share their consumption experiences through ‘electronic word of mouth’ (eWoM) across various social media platforms. This conceptual paper discusses eWoM as a coping response dependent on positive, neutral, or negative experiences made by potential, actual, or former consumers of products, services, and brands. We combine existing lenses and propose an integrative model for unpacking eWoM to examine how different consumption experiences motivate consumers to share eWoM online. The paper further presents an eWoM Attentionscape as an appropriate tool for examining the amount of attention the resulting different types of eWoM receive from brand managers. We discuss how eWoM priorities can differ between public affairs professionals and consumers, and what the implications are for the management of eWoM in the context of public affairs and viral marketing. Copyright © 2013 John Wiley & Sons, Ltd.

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.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.712
Threshold uncertainty score0.238

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 head0.035
GPT teacher head0.264
Teacher spread0.229 · 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