The comprehensive application of the lawn ecology of the football field in enhancing the intention of sports consumption

The comprehensive application of the lawn ecology of the football field in enhancing the intention of sports consumption

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Publicado en 3C TIC – Volume 12 Issue 2 (Ed. 43)

Autores

Yuqi Li*

Resumen

Abstract

With the continuous progress and development of society and the outstanding achievements of football in the field of competitive sports in recent years, the enthusiasm of citizens to participate in sports and fitness has become higher and higher. The course turf ecology is a multi-layered organic whole composed of many natural and social factors, which is an important symbol of football competitiveness. It can be predicted that the relationship between sports and the environment and the impact of environmental changes on consumer intentions are global and long-term. Therefore, according to the annual change status and formation factors of ecological benefits such as carbon sequestration and oxygen release, transpiration water release, and heat absorption in the turf of a football field in a city, the time series stability test method and the multiple regression analysis method are used to discuss the comprehensive application of the lawn ecology of the football field in enhancing the intention of sports consumption. The experimental results verify that the ecological benefits of lawns have a significant positive effect on sports consumption intentions, which can not only optimize the unreasonable sports consumption structure, improve the level of sports consumption, and increase the proportion of consumption amount of more than 5,000 yuan from 5.06% to 23.34%, but also make the leading sports consumption motivation from a more symbolic display of personality, status symbols, and social entertainment to a sportsmanlike physique, entertainment, spiritual enjoyment, and positive guidance of sports consumption motivation.

Artículo

Palabras clave

Keywords

Football field; Lawn ecology; Sports consumption intentions; Time series stationarity test; Multiple regression analysis

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