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Record W1963011153 · doi:10.1108/sbm-10-2012-0044

Merchandise sales rank in professional sport

2015· article· en· W1963011153 on OpenAlexaff
Norm O’Reilly, George Foster, Ryan Murray, Carlos Shimizu

Bibliographic record

VenueSport Business and Management An International Journal · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Behavior in Brand Consumption and Identification
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsLeagueRank (graph theory)ClubRanking (information retrieval)MarketingBusinessConceptual modelVariablesAdvertisingEmpirical researchComputer scienceMathematicsStatistics

Abstract

fetched live from OpenAlex

Purpose – The purchase drivers of merchandise sales rank in professional sport are examined at both a conceptual and an empirical level. The paper aims to discuss these issues. Design/methodology/approach – A database was constructed for all 30 clubs in the National Hockey League based on a conceptual model of relevant variables. Both public and private data sources were accessed, covering an extensive 12-season period (1999-2011), including the 2004-2005 lockout when the season was cancelled. Principal-components analysis was used to reduce the number of variables for regression analysis to distinguish relatedness and to gauge the influence of those variables on merchandise sales rank. Findings – The results reveal that six club-based factors impact merchandise sales rank: Overall Fan Satisfaction, Media Exposure, On-Field Performance, Strength of a Club’s Brand, Local Market Dynamics, and Fan Capacity to Pay. These six categories of purchase drivers form a strong predictive model of merchandise sales rank in the National Hockey League. Research limitations/implications – The resulting model could be extended in future research by adding extra categories to the conceptual framework and by developing alternative or better measures of the variables the authors use. It could also be tested with other sales data as the dependent variable since the study was limited to ranking data on merchandise data for the National Hockey League clubs for each year. Future research could use the actual merchandise dollars for each club in a league to test the model. A further extension would be to model subparts of merchandise (such as jersey sales, impacts of brand changes, etc.). A similar area of future research would be to look at the role of individual athletes as opposed to clubs in driving merchandise sales rank or volume. Practical implications – For managers in professional sport, the results suggest that there are steps that can be taken to improve merchandise sales rank (and, by extension, merchandise sales volume). Practitioners can develop and follow strategies in this regard. Results also suggest that practitioners should put the achievement of high Regional Television Ratings – the strongest influencing variable on merchandise sales rank – as a priority. Social implications – The results confirm that brand is important when attempting to increase merchandise sales rank. Club managers need to be cognizant of their brand and its impact on merchandising in all decisions. Originality/value – The business of professional sport is evolving globally with new sources of revenue, including merchandising increasing in prominence. This research explores the drivers of merchandise sales rank in professional sport and provide direction on key antecedents. The study proposes and tests a conceptual model.

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)0.000
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 score0.161
Threshold uncertainty score0.626

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.002
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 head0.042
GPT teacher head0.298
Teacher spread0.255 · 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

Citations16
Published2015
Admission routes1
Has abstractyes

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