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Record W4223525016 · doi:10.1080/10548408.2022.2052227

Exploring how live streaming affects immediate buying behavior and continuous watching intention: A multigroup analysis

2022· article· en· W4223525016 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 Travel & Tourism Marketing · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Retail Behavior Studies
Canadian institutionsArtificial Intelligence in Medicine (Canada)
FundersFundamental Research Funds for the Central Universities
KeywordsPsychologyLive streamingAdvertisingSocial psychologyBusinessComputer scienceMultimedia

Abstract

fetched live from OpenAlex

This study employed an attention-interest-desire-action (AIDA) model to explain viewers’ immediate purchase decision-making processes and continuous watching intentions within tourism live streaming. Results revealed that informativity, entertainment, and interactivity each positively influenced immersion; immersion positively influenced viewers’ interest in tourism products and live streaming. Furthermore, product interest positively influenced buying desire, promoting immediate buying behaviour. Whereas live streaming interest did not influence buying desire. Finally, both product interest and live streaming interest positively impacted continuous watching intention. This study also detected group differences by examining gender, age, watching experience and time pressure.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.139
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0000.001
Research integrity0.0000.001
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 head0.040
GPT teacher head0.236
Teacher spread0.196 · 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