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The Influence of Personalization in Affecting Consumer Attitudes toward Mobile Advertising in China

2006· article· en· W1606241317 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 Computer Information Systems · 2006
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Marketing and Social Media
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPersonalizationAdvertisingMobile marketingBusinessMobile deviceChinaMobile commerceAffect (linguistics)Online advertisingMarketingComputer sciencePsychologyThe InternetWorld Wide WebDigital marketingPolitical science

Abstract

fetched live from OpenAlex

The high penetration rate of mobile phones has resulted in the increasing use of handheld devices to conduct mobile commerce. Mobile advertising, a very important class of mobile commerce applications, is a very promising direct marketing channel empowered by the Web's interactive and quick-response capabilities. Short Messaging Services, in particular, have been very successful. The present research investigates the factors that will affect consumer attitudes toward mobile advertising in China with particular emphasis on personalization. The results of a survey indicate that (1) there is a direct relationship between consumer attitudes and consumer intentions and (2) personalization is one of the most important factors in affecting consumers' attitude toward mobile advertising, particularly for female users. Thus the designers and marketers should effectively strategize their advertising designs by considering the personalization factor.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.320
Threshold uncertainty score0.217

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.008
GPT teacher head0.267
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