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Social influence on information tech...

Hao, Haijing.

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Social influence on information technology adoption and sustained use in healthcare: A hierarchical Bayesian learning method analysis.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Social influence on information technology adoption and sustained use in healthcare: A hierarchical Bayesian learning method analysis./
Author:	Hao, Haijing.
Description:	107 p.
Notes:	Source: Dissertation Abstracts International, Volume: 74-12(E), Section: B.
Contained By:	Dissertation Abstracts International74-12B(E).
Subject:	Information Technology. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3573501
ISBN:	9781303436956

Social influence on information technology adoption and sustained use in healthcare: A hierarchical Bayesian learning method analysis.
Hao, Haijing.

Social influence on information technology adoption and sustained use in healthcare: A hierarchical Bayesian learning method analysis. - 107 p.

Source: Dissertation Abstracts International, Volume: 74-12(E), Section: B.

Thesis (Ph.D.)--Carnegie Mellon University, 2013.

Information technology adoption and diffusion is currently a significant challenge in the healthcare delivery setting. This thesis includes three papers that explore social influence on information technology adoption and sustained use in the healthcare delivery environment using conventional regression models and novel hierarchical Bayesian learning models applied to 22 months of panel data from a local community health system. While the regression models show that social influence is associated with the technology adoption or continuing use by physicians, the mechanism by which users learn about the new technology is missing. Therefore, this research develops two novel Bayesian learning models, one for the adoption stage and the other for the post-adoption/sustained-use stage that are motivated by the drive to reduce user uncertainty about the new technology. Drawing on social learning theory from the field of psychology and utility theory from economics, this study introduces a Bayesian learning model into the information systems field to quantify the impacts of social influence on information technology adoption decision, adding a new research perspective to the existing literature on technology adoption that primarily use social psychological research methods. In addition, this study estimates a Bayesian learning model with demographic heterogeneity by adding a hierarchical structure to users' risk coefficient without the assumption that every user or consumer has the same risk perception. Results indicate that at the adoption stage, physicians trust the learning signals from opinion leaders/early adopters more than those from their other peers/colleagues. However, in the sustained use stage, the learning signals from opinion leaders and general colleagues are not statistical significant any more. This phenomenon suggests that opinion leaders influence physician users' learning about the new technology or ability to evaluate the quality of the new technology in the early stages of adoption because opinion leaders are early adopters who can help to reduce the uncertainty associated with the new technology; however, in the post-adoption stage, major uncertainty about the new technology has been resolved and the privilege from opinion leaders does not hold any more. This temporally changing influence is also consistent with previous research in marketing.

ISBN: 9781303436956Subjects--Topical Terms:

1030799
Information Technology.

Social influence on information technology adoption and sustained use in healthcare: A hierarchical Bayesian learning method analysis.
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245 1 0 $a Social influence on information technology adoption and sustained use in healthcare: A hierarchical Bayesian learning method analysis.
300 $a 107 p.
500 $a Source: Dissertation Abstracts International, Volume: 74-12(E), Section: B.
500 $a Adviser: Rema Padman.
502 $a Thesis (Ph.D.)--Carnegie Mellon University, 2013.
520 $a Information technology adoption and diffusion is currently a significant challenge in the healthcare delivery setting. This thesis includes three papers that explore social influence on information technology adoption and sustained use in the healthcare delivery environment using conventional regression models and novel hierarchical Bayesian learning models applied to 22 months of panel data from a local community health system. While the regression models show that social influence is associated with the technology adoption or continuing use by physicians, the mechanism by which users learn about the new technology is missing. Therefore, this research develops two novel Bayesian learning models, one for the adoption stage and the other for the post-adoption/sustained-use stage that are motivated by the drive to reduce user uncertainty about the new technology. Drawing on social learning theory from the field of psychology and utility theory from economics, this study introduces a Bayesian learning model into the information systems field to quantify the impacts of social influence on information technology adoption decision, adding a new research perspective to the existing literature on technology adoption that primarily use social psychological research methods. In addition, this study estimates a Bayesian learning model with demographic heterogeneity by adding a hierarchical structure to users' risk coefficient without the assumption that every user or consumer has the same risk perception. Results indicate that at the adoption stage, physicians trust the learning signals from opinion leaders/early adopters more than those from their other peers/colleagues. However, in the sustained use stage, the learning signals from opinion leaders and general colleagues are not statistical significant any more. This phenomenon suggests that opinion leaders influence physician users' learning about the new technology or ability to evaluate the quality of the new technology in the early stages of adoption because opinion leaders are early adopters who can help to reduce the uncertainty associated with the new technology; however, in the post-adoption stage, major uncertainty about the new technology has been resolved and the privilege from opinion leaders does not hold any more. This temporally changing influence is also consistent with previous research in marketing.
590 $a School code: 0041.
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