Time and money expenditure in sports participation: The role of income in consuming the most practiced sports activities in Flanders

Thibaut, Erik; Eakins, John; Vos, SB Steven; Scheerder, Jeroen

doi:10.1016/j.smr.2016.12.002

Cited by 30 publications

(29 citation statements)

References 35 publications

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“…Given the heterogeneity among runners, segmenting them into groups in order to understand their AOIs is useful and appealing. Segmentation of consumers in sports has been documented extensively (e.g., [54][55][56][57]), with studies typically differentiating between consumers based on demographic factors. This traditional form of demographic segmentation is used because researchers apply the concept that gender and age can influence running preferences.…”

Section: Different Types Of Runnersmentioning

confidence: 99%

Understanding Different Types of Recreational Runners and How They Use Running-Related Technology

Janssen

Walravens

Thibaut

et al. 2020

IJERPH

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This study aims to help professionals in the field of running and running-related technology (i.e., sports watches and smartphone applications) to address the needs of runners. It investigates the various runner types—in terms of their attitudes, interests, and opinions (AIOs) with regard to running—and studies how they differ in the technology they use. Data used in this study were drawn from the standardized online Eindhoven Running Survey 2016 (ERS2016). In total, 3723 participants completed the questionnaire. Principal component analysis and cluster analysis were used to identify the different running types, and crosstabs obtained insights into the use of technology between different typologies. Based on the AIOs, four distinct runner types were identified: casual individual, social competitive, individual competitive, and devoted runners. Subsequently, we related the types to their use of sports watches and apps. Our results show a difference in the kinds of technology used by different runner types. Differentiation between types of runners can be useful for health professionals, policymakers involved in public health, engineers, and trainers or coaches to adapt their services to specific segments, in order to make use of the full potential of running-related systems to support runners to stay active and injury-free and contribute to a healthy lifestyle.

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Section: Different Types Of Runnersmentioning

confidence: 99%

Understanding Different Types of Recreational Runners and How They Use Running-Related Technology

Janssen

Walravens

Thibaut

et al. 2020

IJERPH

Self Cite

View full text Add to dashboard Cite

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“…Like in other studies on expenditure on sports, we considered the sample at the individual level [12][13][14]. However, we also considered the number of household expenditure studies [3,4,22,23].…”

Section: Sport Expenditurementioning

confidence: 99%

“…The debate about the expediency and penetration of sporting activity in the population has uncovered the fact that, in some cases, family income has been a barrier to access to sports centers. Low-income and large families have expressed their inability to take part in sports, despite the many policies implemented by governments to prevent this effect [4]. Sports not only have an important role to play in the Sustainable Development Goals up to 2030 [5], but they are also linked to all the objectives laid out by the UN.…”

Section: Introductionmentioning

confidence: 99%

An Analysis of the Determinants of Sport Expenditure in Sports Centers in Spain

2020

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The sports center sector is undergoing a complete transformation. Knowing users’ behavior regarding sport expenditure is needed to be able to act in terms of the future of the sector. This study analyzes the expenditure of people in Spain who are members of sports centers. Adult members were asked about their sport expenditure in several defined categories. An analysis of variance (ANOVA) and a multiple logistic regression analysis were carried out. The results show that the variables of time of participation, federation membership, and income have predictive value in terms of sport expenditure. Age and educational level also explain the significant differences in sport expenditure in its different categories. This study suggests the importance of identifying the behavior of sports center users with reference to sport expenditure as an essential component of the future strategy of the sector.

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“…Income elasticity is positive in consuming sport. Using 13 different sports activities (Thibaut et al 2017) show that exercising sports has a positive income elasticity. In general male sports participants and those with higher levels of education spend more time and money on sports while the effect of age and urbanisation is not so clear.…”

Section: Related Literaturementioning

confidence: 99%

On The Number of Top Sport Teams in a Town

Suominen¹

2019

AJSPO

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Top teams in sports typically are located in large towns since the junior teams of a top team have a bigger number of talents available and a large proportion of top players come from own junior team. This is especially true in women sport. A large town is able to support a large number of fans and visitors to a game, which results in higher ticket revenue. A large majority on the location of top teams uses male sports and there is no evidence concerning whether female sports follows the same structure. Is it so that if there is a top team of men located in a town, there is also a top women team? Are there any differences across genders in the location? The aim of this study is to include women sport and compare income elasticity of men sport and women sport. The variable to be explained is the cumulative amount of top teams located in a town. Top teams in this setting means that the team is playing in the highest league. Six different sports are included: ice hockey, football, (Finnish style) baseball, floorball, basketball and volleyball. The data covers 28 seasons from 1990 (or 1990/91) to 2017 (or 2017/2018). The Poisson regression and negative binomial regression results show that the cumulative number of male sport teams is positively related to population and negatively to average incomes. The negative income elasticity is weaker in the case of women sports. The coefficient is not significant in negative binomial regression model. Some prescriptive analysis concerning location correlation and autocorrelation is also given.

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Time and money expenditure in sports participation: The role of income in consuming the most practiced sports activities in Flanders

Cited by 30 publications

References 35 publications

Understanding Different Types of Recreational Runners and How They Use Running-Related Technology

Understanding Different Types of Recreational Runners and How They Use Running-Related Technology

An Analysis of the Determinants of Sport Expenditure in Sports Centers in Spain

On The Number of Top Sport Teams in a Town

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