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Abstract
Previous studies on mobile payment solutions neglected potential users’ heterogeneity regarding individual characteristics and the time-critical context of mass event hospitality. This study identifies three potential user clusters – namely Early Adopters, Undecided Majority and Laggards in consistence with innovation diffusion theory. Moreover, we employ cluster-specific CB-SEMs to identify determinants of mobile payment adoption and extend the UTAUT model with contextual factors. Results highlight that the relevance of drivers for creating adoption intention differ across customer clusters. We contribute to a more nuanced understanding of the mechanisms, through which technology adoption can be achieved depending on individuals’ characteristics and highlight that a “one size fits all” approach is not suitable.
Recommended Citation
Westmattelmann, Daniel; Grotenhermen, Jan-Gerrit; Sprenger, Marius; Oldeweme, Andreas; and Schewe, Gerhard, "Antecedents of Mobile Payment Adoption: A Customer Group-Specific Analysis in Mass-Attended Hospitality" (2020). AMCIS 2020 Proceedings. 22.
https://aisel.aisnet.org/amcis2020/adoption_diffusion_IT/adoption_diffusion_IT/22
Antecedents of Mobile Payment Adoption: A Customer Group-Specific Analysis in Mass-Attended Hospitality
Previous studies on mobile payment solutions neglected potential users’ heterogeneity regarding individual characteristics and the time-critical context of mass event hospitality. This study identifies three potential user clusters – namely Early Adopters, Undecided Majority and Laggards in consistence with innovation diffusion theory. Moreover, we employ cluster-specific CB-SEMs to identify determinants of mobile payment adoption and extend the UTAUT model with contextual factors. Results highlight that the relevance of drivers for creating adoption intention differ across customer clusters. We contribute to a more nuanced understanding of the mechanisms, through which technology adoption can be achieved depending on individuals’ characteristics and highlight that a “one size fits all” approach is not suitable.
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