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Record W4390000172 · doi:10.1504/ijsmm.2024.135630

When journalists are consumers: examining effects of media service quality on media members' behavioural intention

2023· article· en· W4390000172 on OpenAlexaff
Bo Li, Jerred Junqi Wang, Olan Scott, Sang Keon Yoo

Bibliographic record

VenueInternational Journal of Sport Management and Marketing · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicSport and Mega-Event Impacts
Canadian institutionsBrock University
Fundersnot available
KeywordsService qualityService (business)AdvertisingQuality (philosophy)Event (particle physics)PsychologyMarketingBusinessWord of mouthPublic relationsPolitical science

Abstract

fetched live from OpenAlex

Existing service marketing literature has shown the relationship between service quality, satisfaction, and behavioural intentions among sport spectators; however, the media's experience working at these events has largely been neglected. Journalists both report on the sporting event and the destination or host of the event. To reduce the gap in this research area, this study examined how media service quality impacted journalists' satisfaction, which ultimately influences their behavioural intentions in their reporting of the host city or country and their revisit intentions. Through surveying 211 journalists who covered two major international sporting events (the 2018 FIFA World Cup and the 2019 FINA World Aquatics Championships), findings revealed that sport journalists' service satisfaction was determined by the following services: information, interactions with employees/volunteers, and operating time. Also, media professionals' destination image and service satisfaction had positive relationships with their behavioural intentions, word of mouth, and intention to positively cover the event.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.030
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.012
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.030
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.063
GPT teacher head0.340
Teacher spread0.277 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2023
Admission routes1
Has abstractyes

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