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![Page 1: Uncovering the Drivers and Inhibitors of Mobile Health Information Services Usage in the United States Dr. Arun Rai Regents’ Professor and Harkins Chair.](https://reader036.fdocuments.us/reader036/viewer/2022062518/56649eb45503460f94bbc3a7/html5/thumbnails/1.jpg)
Uncovering the Drivers and Inhibitors of Mobile Health Information Services Usage in the United States
Dr. Arun RaiRegents’ Professor and Harkins Chair
Center for Process Innovation & CIS Department
Robinson College of BusinessGeorgia State University
Atlanta, U.S.A.E-mail: [email protected]
Web site: arunrai.us
3rd International Conference on Transforming Healthcare with IT 31st Aug.-1st Sep. 2012Hyderabad, India
Liwei ChenDoctoral Student
Center for Process InnovationRobinson College of Business, Georgia State University
Atlanta, U.S.A.E-mail: [email protected]
Jessica PyeDoctoral Student
Center for Process InnovationRobinson College of Business, Georgia State University
Atlanta, U.S.A.E-mail:[email protected]
![Page 2: Uncovering the Drivers and Inhibitors of Mobile Health Information Services Usage in the United States Dr. Arun Rai Regents’ Professor and Harkins Chair.](https://reader036.fdocuments.us/reader036/viewer/2022062518/56649eb45503460f94bbc3a7/html5/thumbnails/2.jpg)
Study Background
PurposeAdoption status of mHealth in the U.S.
Channel preference between in-person doctor visits and mHealth
What influences adoption and channel preference
ProcedureOnline survey assisted by a market research company
Pilot followed by a large scale study
1132 valid responses
![Page 3: Uncovering the Drivers and Inhibitors of Mobile Health Information Services Usage in the United States Dr. Arun Rai Regents’ Professor and Harkins Chair.](https://reader036.fdocuments.us/reader036/viewer/2022062518/56649eb45503460f94bbc3a7/html5/thumbnails/3.jpg)
Sample Characteristics
• AgeMean = 45 yrsRange: 18 – 86 years20% over 60 years
Balanced gender distribution
• Income38% < 25 K USD/year30%: >=25 K & <50K/year19%:>= 50K & <75K /year12%: >= 75 K/year
• Education1.6%: Below high school 18.6%: High school (12th std)30.4%: Some college, no degree13.6%: Associate’s degree25.3%: Bachelor’s degree10.5%: Advanced degree
![Page 4: Uncovering the Drivers and Inhibitors of Mobile Health Information Services Usage in the United States Dr. Arun Rai Regents’ Professor and Harkins Chair.](https://reader036.fdocuments.us/reader036/viewer/2022062518/56649eb45503460f94bbc3a7/html5/thumbnails/4.jpg)
Adoption of mHealth Services
Intention to Use mHealth Services
Mean = 4.09, s.d. = 2.02 Mean= 3.20, s.d.= 2.23
Usage Frequency of mHealth Services
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Comparative Snapshot of Mobile Services Adoption
work
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Coarse Framing of Channel Preference:In-Person Doctor Visit or mHealth?
Mean= 2.36, s.d.=1.83
Example Item: “My overall feeling is that…”
![Page 7: Uncovering the Drivers and Inhibitors of Mobile Health Information Services Usage in the United States Dr. Arun Rai Regents’ Professor and Harkins Chair.](https://reader036.fdocuments.us/reader036/viewer/2022062518/56649eb45503460f94bbc3a7/html5/thumbnails/7.jpg)
Digging Deeper into Channel Preference: Substitutes or Complements?Using mHealth as a substitute to doctor visits?
Using mHealth as a complement to doctor visits?
Example Item: • “I am willing to use mobile health services instead of doctor visits”.
Example Item: • “I am willing to use mobile health services in addition to doctor visits”.
47%67%
![Page 8: Uncovering the Drivers and Inhibitors of Mobile Health Information Services Usage in the United States Dr. Arun Rai Regents’ Professor and Harkins Chair.](https://reader036.fdocuments.us/reader036/viewer/2022062518/56649eb45503460f94bbc3a7/html5/thumbnails/8.jpg)
(I) Candidate Predictors: Health Conditions
Example Item: • “I feel I am…”.
Example Item: • “I feel vulnerable to severe chronic diseases in the next five years”.
Mean=5.29, s.d.=1.46 Mean=4.01, s.d.=2.02
Healthiness Vulnerability78.9% 60.8%
![Page 9: Uncovering the Drivers and Inhibitors of Mobile Health Information Services Usage in the United States Dr. Arun Rai Regents’ Professor and Harkins Chair.](https://reader036.fdocuments.us/reader036/viewer/2022062518/56649eb45503460f94bbc3a7/html5/thumbnails/9.jpg)
(II) Candidate Predictors : Innovativeness with IT/Mobile Services
Mobile IT innovativeness Usage Innovativeness of Mobile Service
Example Item: “If I heard about a new mobile service, I would look for ways to experiment with it”.
Index Measure: weighted sum of mobile services use, where the weight is
Mean= 21.85, s.d.= 10.06Mean= 4.18, s.d.= 1.93
![Page 10: Uncovering the Drivers and Inhibitors of Mobile Health Information Services Usage in the United States Dr. Arun Rai Regents’ Professor and Harkins Chair.](https://reader036.fdocuments.us/reader036/viewer/2022062518/56649eb45503460f94bbc3a7/html5/thumbnails/10.jpg)
Predicting Adoption: Intention to Use mHealth Services
Higher intention to use mhealth for those more innovative with IT. Intention becomes stronger for those who are also more vulnerable or more healthy.
![Page 11: Uncovering the Drivers and Inhibitors of Mobile Health Information Services Usage in the United States Dr. Arun Rai Regents’ Professor and Harkins Chair.](https://reader036.fdocuments.us/reader036/viewer/2022062518/56649eb45503460f94bbc3a7/html5/thumbnails/11.jpg)
Predicting Adoption: Frequency of mHealth Use
Greater frequency of mhealth use by those more innovative with IT. This increases for those who are also more vulnerable or more healthy.
![Page 12: Uncovering the Drivers and Inhibitors of Mobile Health Information Services Usage in the United States Dr. Arun Rai Regents’ Professor and Harkins Chair.](https://reader036.fdocuments.us/reader036/viewer/2022062518/56649eb45503460f94bbc3a7/html5/thumbnails/12.jpg)
Predicting Channel Preference: mHealth Use as a Substitute to In-Person Doctor Visits
Greater preference for mhealth as a substitute by those more innovative with IT. This increases for those who are also more vulnerable or more healthy.
![Page 13: Uncovering the Drivers and Inhibitors of Mobile Health Information Services Usage in the United States Dr. Arun Rai Regents’ Professor and Harkins Chair.](https://reader036.fdocuments.us/reader036/viewer/2022062518/56649eb45503460f94bbc3a7/html5/thumbnails/13.jpg)
Predicting Channel Preference: mHealth Use as a Complement to In-Person Doctor Visits
Greater preference for mhealth as a complement by those more innovative with IT. This increases for those who are also more vulnerable or more healthy.
![Page 14: Uncovering the Drivers and Inhibitors of Mobile Health Information Services Usage in the United States Dr. Arun Rai Regents’ Professor and Harkins Chair.](https://reader036.fdocuments.us/reader036/viewer/2022062518/56649eb45503460f94bbc3a7/html5/thumbnails/14.jpg)
Wrap-Up
mHealth diffusion: 38% initiated use; 19% @ regular use Preference (No surprise): Much stronger preference for doctor visitsPreference (Surprise): 47% favorable to mHealth as a substitute; 67% favorable to mhealth as a complement Adoption/channel preference: Promoted by IT innovativeness and reinforced by health vulnerability and state of healthiness Practical implication: Differentiate mHealth on-boarding and progression strategies based on (i) IT innovativeness, (ii) health vulnerability, and (iii) state of healthiness; shift from coarse demographics to health and IT dispositions
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Uncovering the Drivers and Inhibitors of Mobile Health Information Services Usage in the United States
3rd International Conference on Transforming Healthcare with IT 31st Aug.-1st Sep. 2012Hyderabad, India
Dr. Arun [email protected]
Web site: arunrai.us
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