Abstract
With the rapid development of mobile short-video platforms, viewers have greater access to a diversity of health short videos. Due to relatively homogenized content in these health short videos, instructor credibility is becoming a key determinant of learners’ engagement with health short videos. Yet, there is a dearth of research that has sought to elucidate the role of instructor credibility in driving learners’ engagement. Building on social presence theory, we classified the source of instructor credibility into four constituent components, namely physical, contextual, psychological, and behavioral features. Additionally, we advance a research model to disentangle the effects of these four instructor credibility cues on learners’ engagement. The research model will be validated by employing deep learning algorithms to operationalize our focal variables based on data of health short videos harvested from a popular mobile short-video platform in China.
Originalsprog | Engelsk |
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Titel | Proceedings of the 30th European Conference on Information Systems (ECIS) |
Antal sider | 12 |
Udgivelsessted | Atlanta, GA |
Forlag | Association for Information Systems. AIS Electronic Library (AISeL) |
Publikationsdato | 2022 |
Artikelnummer | 58 |
Status | Udgivet - 2022 |
Begivenhed | The 30th European Conference on Information Systems (ECIS) 2022: New Horizons in Digitally United Societies - Universitatea de Vest din Timișoara (UVT) / West University of Timişoara (WUT), Timisoara, Rumænien Varighed: 19 jun. 2022 → 24 jun. 2022 Konferencens nummer: 30 https://ecis2022.eu/ |
Konference
Konference | The 30th European Conference on Information Systems (ECIS) 2022 |
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Nummer | 30 |
Lokation | Universitatea de Vest din Timișoara (UVT) / West University of Timişoara (WUT) |
Land/Område | Rumænien |
By | Timisoara |
Periode | 19/06/2022 → 24/06/2022 |
Internetadresse |
Navn | Proceedings of the European Conference on Information Systems |
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ISSN | 0000-0034 |
Emneord
- Health short videos
- Instructor credibility cues
- Engagement
- Deep learning