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Pattern Identification and Behavioral Analysis of Sports Marketing Strategy Implementation in Big Data Environment

Li Zhang

Li Zhang

Department of Physical and Mental Health Education, Pingdingshan Polytechnic CollegePingdingshan, China
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Zhang, Li
, 
Ke Sun

Ke Sun

Faculty of Physical Education, Pingdingshan UniversityPingdingshan, China
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Sun, Ke
 et 
Jinjin Ren

Jinjin Ren

Keimyung UniversitySouth Korea
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Ren, Jinjin
  
21 mars 2025
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Publié en ligne: 21 mars 2025
Reçu: 29 oct. 2024
Accepté: 01 févr. 2025
DOI: https://doi.org/10.2478/amns-2025-0646
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© 2025 Li Zhang et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1.

Network consumer buying behavior model
Network consumer buying behavior model

Figure 2.

The fuzzy set of consumers’ synthesize sentiment and appraise
The fuzzy set of consumers’ synthesize sentiment and appraise

Figure 3.

The statistics for sentiment degree of sport customers group
The statistics for sentiment degree of sport customers group

Figure 4.

Distribution histogram of sentiment degrees of sport customers group
Distribution histogram of sentiment degrees of sport customers group

Figure 5.

The probability density curve of sentiment degrees of sport customer group
The probability density curve of sentiment degrees of sport customer group

Figure 6.

Comparison of probability density curves for different products
Comparison of probability density curves for different products

The parameter of sentiment words calculation

Affective vocabulary Modifier QM(i) Basic emotional fuzzy set hM(i) rM(i) λ λ'
M (1) Tempting Too -L 5.33 5.5 6 5.5
M (2) Impulsive NULL -VL 5.44 6.5 6.5 5
M (3) Satisfactory Comparatively -M 5.44 7.5 5.5 6

Analysis of types of online sports consumption of subjects

Variable Count Percentage(%)
Online sports physical consumption Sneaker 303 36%
Sport clothes 252 30%
Sport equipment 160 19%
Sports protection product 101 12%
Sports publications 26 3%
Online sports service consumption Gym card 219 26%
Exercise prescription 152 18%
Watching sports 143 17%
Online sports training 126 15%
Sports tickets 118 14%
Playground reservation 84 10%

Logistic regression of subjective factors affecting sports consumption

Variable Estimate SE Wald Df Sig. 95% confidence interval
Upper limit Lower limit
Sports consumption=1 5.825 0.640 121.677 1 0 4.869 6.888
Sports consumption=2 6.846 0.620 156.948 1 0 5.813 8.227
Sports consumption=3 7.817 0.582 180.851 1 0 6.765 8.756
Sports consumption=4 7.863 0.583 190.324 1 0 6.869 9.194
Affordability 1.683 0.331 21.775 1 0.04 1.013 2.306
Product diversity 0.893 0.238 12.432 1 0 0.242 1.403
Convenience of purchase 0.658 0.315 5.870 1 0.013 0.030 1.166
Save time 0.355 0.398 5.187 1 0.021 0.187 0.980
Payoff diversity -0.357 0.223 1.638 1 0.232 -0.797 0.240
Practical psychology 1.326 0.198 14.832 1 0 0.434 2.095
Conformity mentality 1.090 0.263 33.174 1 0 0.749 1.587
Mindset of chasing difference 1.348 0.282 18.325 1 0 0.724 1.584
Mindset of comparison 1.521 0.443 23.630 1 0 0.927 2.213

Quarterly analysis of online sports consumption level

Variable Count Percentage (%)
The level of online sports consumption per season <200 yuan 623 74
201-300 yuan 109 13
301-400 yuan 42 5
401-500 yuan 17 2
>501 yuan 51 6
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Sciences de la vie, Sciences de la vie, autres, Mathématiques, Mathématiques appliquées, Mathématiques générales, Physique, Physique, autres
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