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Record W3001259882 · doi:10.5430/ijba.v11n1p11

Key Elements of Sports Marketing Activities for Sports Events

2020· article· en· W3001259882 on OpenAlexvenueno aff
Edson Coutinho da Silva, Alexandre Luzzi Las Casas

Bibliographic record

VenueInternational Journal of Business Administration · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicSport and Mega-Event Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsSports marketingSport managementMarketingBusinessEntertainmentDigital marketingProduct (mathematics)Service (business)Marketing mixAdvertisingMarketing managementPublic relationsRelationship marketingPolitical science

Abstract

fetched live from OpenAlex

Sports marketing activities comprise people, activities, business and organisation in producing, facilitating, promoting or organising any product (as goods, services and events) for a demand of sports supporters. This theoretical paper aims to introduce and discuss the sports scheme, sports marketing mix, and sports supporters as three key elements which the sports teams need to focus on to implement sports marketing activities in sports events. By and large, sports teams have been implemented marketing principles as well as sponsorships to qualify a sports events as experience and entertainment focus on the supporters (as customers). Sports scheme refers to actors’ network, marketing tools represents the tools to plan and perform marketing activities and supporters are those who support and purchase club goods. Thus, all of them are key relevant elements to organise a sports event (as a game or match). The professionalism of the sports events has required use the sports and non-sports stakeholders' skill to help sports teams to design and provide a sports experience and amusement by means sports marketing tools to format a suitable product and service to a supporter’s audience.

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 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)0.001
metaresearch head score (Gemma)0.001
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 score0.307
Threshold uncertainty score0.337

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.039
GPT teacher head0.343
Teacher spread0.304 · 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 teacher head, 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

Citations17
Published2020
Admission routes1
Has abstractyes

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