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Record W3009566530 · doi:10.32731/smq.291.032020.02

Brand Environments and the Emergence and Change of Awareness for New Sports Teams: A Two-Wave Examination

2020· article· en· W3009566530 on OpenAlexaff
James Du, Christopher M. McLeod, Jeffrey James

Bibliographic record

VenueSport Marketing Quarterly · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicSports, Gender, and Society
Canadian institutionsNortel (Canada)
Fundersnot available
KeywordsAdvertisingSports marketingMarketingPsychologyBusinessPublic relationsPolitical scienceMarketing management

Abstract

fetched live from OpenAlex

In this paper, we examined how brand environments and pre-existing attachments create brand awareness of a new sport team. Using a two-wave panel design, this study used a new sport team in the US as a natural experiment to examine the changes in awareness before and after the team's debut. We collected data from a sample of 190 representative city residents. Our results showed that brand awareness increased by 39.2% after the team played the inaugural game. We attributed the driving forces behind this change to a preseason branding campaign and individuals’ existing connections with sport-based objects. Consistent with our hypotheses, the findings also revealed that (a) individuals who were repeatedly exposed to brand information had a higher rate of change in brand awareness and (b) brand environments and brand awareness represented two mediation pathways through which pre-existing attachments exerted positive influences on consumption behaviors.

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)0.003
metaresearch head score (Gemma)0.006
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 score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.036
GPT teacher head0.277
Teacher spread0.241 · 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

Citations5
Published2020
Admission routes1
Has abstractyes

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