Evidence map›Paper›PMID 31622390›Full record

ArticlePloS one2019

A method for predicting background advertisement exposure parameters in sporting events: Televised football game approach.

Yi Xiao, Collins John, Xiaoling Ren, Pei Zhang

Abstract read
In one paragraph

Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Yi XiaoSchool of Economics and Management, Shanghai University of Sport, Shanghai, China.ORCID 0000-0002-8904-9083
Collins JohnKinesiology, Health Promotion and Recreation Department, University of North Texas, Denton, Texas, United States of America.
Xiaoling RenSchool of Economics and Management, Shanghai University of Sport, Shanghai, China.
Pei ZhangSchool of Economics and Management, Shanghai University of Sport, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe background advertisement exposure parameters (BAEP) forms a premise for sponsorship negotiation and the basis for estimating the sponsorship value of background advertising. Prediction of the BAEP has a great contribution to the sporting events organizers and sponsors in terms of negotiating, decision-making for bidding, and income-generating.

methodsVirtual Reality (VR), technology was utilized to construct a virtual three-dimensional model of the sports venue and simulate the telecast of the event. Based on VR technology and computer graphics theory, a pre-event prediction method for estimating the background advertisement exposure parameters of sporting events was put forward. The pre and post measures of the thirty BAEP of televised football games were compared to verify the effectiveness of the prediction method.

resultsThere was no significant difference between the pre- and post-measurement results for the same football game. The pre- and post-measurement results of the thirty BAEP of televised football games were tightly matched.

conclusionsUsing the prediction method can predict the BAEP of televised football games effectively and overcomes the shortcomings of current prediction methods that inhibits the effectiveness of the prediction of exposure parameters due to changes such as the type of the sporting events, the size of the sports venue, the layout of the background advertisements, and the placement of the television cameras, etc.

Indexed as

AdvertisingFootballModels, TheoreticalTelevisionAlgorithmsHumansVirtual Reality

Identifiers

PMID31622390
PMCPMC6797103

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.