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Understanding Continuance Intention toward Crowdsourcing Games: A Longitudinal Investigation

Understanding Continuance Intention toward Crowdsourcing Games: A Longitudinal Investigation Given the increasing popularity of gamified crowdsourcing, the study reported here involved examining determinants of users' continuance intention toward crowdsourcing games, both with longitudinal data and reference to a revised unified theory of acceptance and use of technology (UTAUT). At three time points, data were collected from an online survey about playing crowdsourcing games. Time-lagged regression, cross-temporal correlation, and structural equation modeling were performed to examine determinants of the acceptance of crowdsourcing games. Results indicate that the revised UTAUT2 is applicable to explaining the acceptance of crowdsourcing games. Not only did effort expectancy, hedonic motivation, and social influence directly affect users’ continuance intention toward crowdsourcing games, but time-based variations also emerged in users’ perceptions and acceptance of the games and in how their perceptions affect their acceptance. The findings answer the call for a context-specific acceptance model and the identification of factors of adopting gamification. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Human-Computer Interaction Taylor & Francis

Understanding Continuance Intention toward Crowdsourcing Games: A Longitudinal Investigation

Understanding Continuance Intention toward Crowdsourcing Games: A Longitudinal Investigation

International Journal of Human-Computer Interaction , Volume 36 (12): 10 – Jul 20, 2020

Abstract

Given the increasing popularity of gamified crowdsourcing, the study reported here involved examining determinants of users' continuance intention toward crowdsourcing games, both with longitudinal data and reference to a revised unified theory of acceptance and use of technology (UTAUT). At three time points, data were collected from an online survey about playing crowdsourcing games. Time-lagged regression, cross-temporal correlation, and structural equation modeling were performed to examine determinants of the acceptance of crowdsourcing games. Results indicate that the revised UTAUT2 is applicable to explaining the acceptance of crowdsourcing games. Not only did effort expectancy, hedonic motivation, and social influence directly affect users’ continuance intention toward crowdsourcing games, but time-based variations also emerged in users’ perceptions and acceptance of the games and in how their perceptions affect their acceptance. The findings answer the call for a context-specific acceptance model and the identification of factors of adopting gamification.

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References (79)

Publisher
Taylor & Francis
Copyright
© 2020 Taylor & Francis Group, LLC
ISSN
1532-7590
eISSN
1044-7318
DOI
10.1080/10447318.2020.1724010
Publisher site
See Article on Publisher Site

Abstract

Given the increasing popularity of gamified crowdsourcing, the study reported here involved examining determinants of users' continuance intention toward crowdsourcing games, both with longitudinal data and reference to a revised unified theory of acceptance and use of technology (UTAUT). At three time points, data were collected from an online survey about playing crowdsourcing games. Time-lagged regression, cross-temporal correlation, and structural equation modeling were performed to examine determinants of the acceptance of crowdsourcing games. Results indicate that the revised UTAUT2 is applicable to explaining the acceptance of crowdsourcing games. Not only did effort expectancy, hedonic motivation, and social influence directly affect users’ continuance intention toward crowdsourcing games, but time-based variations also emerged in users’ perceptions and acceptance of the games and in how their perceptions affect their acceptance. The findings answer the call for a context-specific acceptance model and the identification of factors of adopting gamification.

Journal

International Journal of Human-Computer InteractionTaylor & Francis

Published: Jul 20, 2020

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