Author List: Angst, Corey M.; Agarwal, Ritu;
MIS Quarterly, 2009, Volume 33, Issue 2, Page 339-370.
Within the emerging context of the digitization of health care, electronic health records (EHRs) constitute a significant technological advance in the way medical information is stored, communicated, and processed by the multiple parties involved in health care delivery. However, in spite of the anticipated value potential of this technology, there is widespread concern that consumer privacy issues may impede its diffusion. In this study, we pose the question: Can individuals be persuaded to change their attitudes and opt-in behavioral intentions toward EHRs, and allow their medical information to be digitized even in the presence of significant privacy concerns? To investigate this question, we integrate an individual's concern for information privacy (CFIP) with the elaboration likelihood model (ELM) to examine attitude change and likelihood of opting-in to an EHR system. We theorize that issue involvement and argument framing interact to influence attitude change, and that concern for information privacy further moderates the effects of these variables. We also propose that likelihood of adoption is driven by concern for information privacy and attitude. We test our predictions using an experiment with 366 subjects where we manipulate the framing of the arguments supporting EHRs. We find that an individual's CFIP interacts with argument framing and issue involvement to affect attitudes toward the use of EHRs. In addition, results suggest that attitude toward EHR use and CFIP directly influence opt-in behavioral intentions. An important finding for both theory and practice is that even when people have high concerns for privacy, their attitudes can be positively altered with appropriate message framing. These results as well as other theoretical and practical implications are discussed.
Keywords: attitude; CFIP; EHR; elaboration likelihood model; electronic health records; ELM; Privacy
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#239 0.189 privacy information concerns individuals personal disclosure protection concern consumers practices control data private calculus regulation risk individual legislation government sensitive
#196 0.174 health healthcare medical care patient patients hospital hospitals hit health-care telemedicine systems records clinical practices physician electronic physicians longitudinal outcomes
#140 0.156 model use theory technology intention information attitude acceptance behavioral behavior intentions research understanding systems continuance models planned percent attitudes predict
#13 0.150 personalization content personalized willingness web pay online likelihood information consumers cues customers consumer services elaboration preference experiment framing customized timing
#116 0.125 research study influence effects literature theoretical use understanding theory using impact behavior insights examine influences mechanisms specifically context perspective findings
#173 0.092 effect impact affect results positive effects direct findings influence important positively model data suggest test factors negative affects significant relationship