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

Haijing Hao (), Rema Padman, Baohong Sun and Rahul Telang
Additional contact information
Haijing Hao: University of Massachusetts - Boston
Baohong Sun: Cheung Kong Graduate School of Business

Electronic Commerce Research, 2019, vol. 19, issue 1, No 1, 21 pages

Abstract: 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.

Keywords: Individual learning; Social learning; Bayesian learning; Post-adoption; Social influence; Bayesian estimation (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://link.springer.com/10.1007/s10660-018-09324-5 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:elcore:v:19:y:2019:i:1:d:10.1007_s10660-018-09324-5

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10660

DOI: 10.1007/s10660-018-09324-5

Access Statistics for this article

Electronic Commerce Research is currently edited by James Westland

More articles in Electronic Commerce Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().