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Record W4282972686 · doi:10.1108/intr-06-2021-0377

I hate ads but not the advertised brands: a qualitative study on Internet users' lived experiences with YouTube ads

2022· article· en· W4282972686 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

VenueInternet Research · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Marketing and Social Media
Canadian institutionsYork University
Fundersnot available
KeywordsAdvertisingPsychologyPersonalizationOriginalityContinuanceFlexibility (engineering)The InternetValue (mathematics)Product (mathematics)Internet privacySocial psychologyWorld Wide WebCreativityComputer scienceBusiness

Abstract

fetched live from OpenAlex

Purpose This paper aims to explore Internet users' lived experiences with video ads, both skippable and nonskippable, while watching content on YouTube. Design/methodology/approach In-depth interviews were conducted with 22 participants. Findings The participants unanimously expressed dissatisfaction with YouTube ads. The dissatisfaction was directed to the platform but did not spill over to the advertised brand/product. Ethical concerns related to privacy also emerged. Specifically, with respect to nonskippable ads, the participants expressed dislike for forced viewing and explained how they would engage in extraneous activities during the ads. Nonetheless, they appreciated the flexibility offered by skippable ads. They also elaborated on how, why and when they would skip/not skip skippable ads. Originality/value The findings are discussed in light of the literature on not only online advertising but also platform switching versus continuance intention, spillover effect, privacy–personalization paradox and visual attention.

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)0.009
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.139
GPT teacher head0.452
Teacher spread0.313 · 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