Journal article
Keeping it 100: Social Media and Self-Presentation in College Football Recruiting
Big data, Vol.7(1), pp.3-20
03/01/2019
DOI: 10.1089/big.2018.0094
PMID: 30864820
Abstract
Social media provides a platform for individuals to craft personal brands and influence their perception by others, including potential employers. Yet there remains a need for more research investigating the relationship between individuals' online identities and offline outcomes. This study focuses on the context of college football recruiting, specifically on the relationship between recruits' Twitter activities and coaches' scholarship offer decisions. Based on impression management theory, we analyze content posted by recruits and apply machine learning to identify instances of self-promotion and ingratiation in 5.5 million tweets. Using negative binomial regression, we discover that an athlete's level of engagement on Twitter is a positive and significant predictor of the number of offers. Also, both self-promotion and ingratiation are positively related to attracting new offers. Our results highlight the growing importance of social media as a recruiting tool and suggest that recruits' online self-presentation may have significant offline impacts. This research can benefit athletes and coaches by informing communication strategies during recruitment, and may also yield insight into the consequences of online impression management in other types of recruitment beyond sports.
Details
- Title: Subtitle
- Keeping it 100: Social Media and Self-Presentation in College Football Recruiting
- Creators
- Kristina Gavin Bigsby - Department of Management Sciences, University of Iowa, Iowa City, IowaJeffrey W Ohlmann - Department of Management Sciences, University of Iowa, Iowa City, IowaKang Zhao - Department of Management Sciences, University of Iowa, Iowa City, Iowa
- Resource Type
- Journal article
- Publication Details
- Big data, Vol.7(1), pp.3-20
- Publisher
- Mary Ann Liebert, Inc., publishers
- DOI
- 10.1089/big.2018.0094
- PMID
- 30864820
- ISSN
- 2167-6461
- eISSN
- 2167-647X
- Language
- English
- Date published
- 03/01/2019
- Academic Unit
- Business Analytics
- Record Identifier
- 9984083849602771
Metrics
19 Record Views