Modeling social learning on consumers’ long-term usage of a mobile technology: a Bayesian estimation of a Bayesian learning model View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-03

AUTHORS

Haijing Hao, Rema Padman, Baohong Sun, Rahul Telang

ABSTRACT

Studies on how social influence impacts individuals’ social learning during the technology adoption process have increased over the last few decades. However, few studies have examined the social learning effects on individual consumers’ learning at the post-adoption stage, or long-term usage. The present study intends to fill this gap. We construct a Bayesian learning model to investigate consumers’ learning process at the post-adoption stage and how social learning effects influence individuals’ learning at this stage. The model result shows that, among the two social learning effects, influential peer effects (early adopters) are not significantly different from general peer effects at the post-adoption stage; i.e., users no longer treated early adopters differently from general peers. To the best of our knowledge, this is one of the first studies that investigates social learning effects on consumers’ learning at the post-adoption stage by using a Bayesian learning model, which uncovers the underlying mechanism of people’s long-term use of technology. More... »

PAGES

1-21

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10660-018-09324-5

DOI

http://dx.doi.org/10.1007/s10660-018-09324-5

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1110506980


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