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      OLDER ADULTS’ ADOPTION BEHAVIOR OF MOBILE HEALTH (MHEALTH) APPS: INTEGRATING TECHNOLOGY READINESS WITH PROTECTION MOTIVATION THEORY

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      https://www.riss.kr/link?id=A108691430

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      다국어 초록 (Multilingual Abstract)

      Although older adults’ adoption rates of smartphones have continued to increase in recent years, health-related apps are not widely accepted by older adults. Therefore, the primary aim of this study was to explore older adults’ adoption behavior o...

      Although older adults’ adoption rates of smartphones have continued to increase in recent years, health-related apps are not widely accepted by older adults. Therefore, the primary aim of this study was to explore older adults’ adoption behavior of mobile health (mHealth) apps in the U.S. Specifically, this study applied protection motivation theory (PMT; Rogers, 1975) as the theoretical base to explain older adults’ adoption process of health apps to understand the influences of threat and coping appraisals on older adults’ adoption behavior of health apps. In addition, the current study incorporated the technology readiness theory (Parasuraman, 2000) to understand the influences of older adults’ positive and negative technological readiness (TR) on their PMT components. Data were collected from older adults (n = 600; 60% female) aged over 55 in the U.S. using online survey questionnaires. The data analysis was conducted using partial least square structural equation modeling (PLS-SEM). The findings suggest the impact of coping appraisals on older adults’ attitudes toward mHealth apps and their (continuance) intention to use mHealth apps. Especially among PMT’s four appraisal components, response efficacy was the most critical underlying mechanism driving mHealth app-related attitude and behavioral intention. In addition, older adults’ beliefs about using mHealth apps (i.e., TR) significantly influenced their coping appraisal processes, while positive TR was more critical in forecasting their coping appraisals. The results suggest a need to classify older consumers into different segments based on their TR to develop differentiated marketing strategies. The findings also suggest that marketers understand various protective motivation components concerning the use of mHealth apps among older adults.

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