JOURNAL ARTICLE

Examining Progression in Mountain Bike Specialization: A Nationwide Study.

  • Published In: Journal of Outdoor Recreation, Education & Leadership, 2024, v. 16, n. 3. P. 35 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Pierskalla, Chad D.; Twilley, Danny; Arbogast, Doug; Casseday, Damon; Eades, Daniel; Haas, Vaike; Smaldone, David; Williamson, Andy; Jinyang Deng; Weddell, Melissa S. 3 of 3

Abstract

The purpose of this study is to use the recreation specialization construct to examine the diversity of mountain bike riders in the US to meet their needs better, and to help strengthen the sport and the outdoor economy. At one end of the specialization continuum are Completely High Specialists, and at the other end are Completely Low Specialists. As recreationists gain skill and experience, make an activity central to their lifestyle, and invest more in equipment, they can progress in specialization. Little if any research used the construct to study the larger noncompetitive and competitive mountain biker population. The authors analyzed a nationwide mountain bike data set collected in 2018 using snowball sampling. There were 13,623 mountain bikers across the US who provided usable online surveys. Specific recommendations are provided to help mountain bikers progress in specialization. Theoretical and methodological implications are also presented. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Journal of Outdoor Recreation, Education & Leadership. 2024/07, Vol. 16, Issue 3, p35
  • Document Type:Article
  • Subject Area:Sports and Leisure
  • Publication Date:2024
  • ISSN:2381-0696
  • DOI:10.18666/JOREL-2024-12174
  • Accession Number:179564000
  • Copyright Statement:Copyright of Journal of Outdoor Recreation, Education & Leadership is the property of Sagamore Publishing and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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