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      Smartphone Users' Persuasion Knowledge in the Context of Consumer mHealth Apps: Qualitative Study.

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          Abstract

          Persuasion knowledge, commonly referred to as advertising literacy, is a cognitive dimension that embraces recognition of advertising, its source and audience, and understanding of advertisers' persuasive and selling intents as well as tactics. There is little understanding of users' awareness of organizations that develop or sponsor mobile health (mHealth) apps, especially in light of personal data privacy. Persuasion knowledge or recognition of a supporting organization's presence, characteristics, competencies, intents, and persuasion tactics are crucial to investigate because app users have the right to know about entities that support apps and make informed decisions about app usage. The abundance of free consumer mHealth apps, especially those in the area of fitness, often makes it difficult for users to identify apps' dual purposes, which may be related to not only helping the public manage health but also promoting the supporting organization itself and collecting users' information for further consumer targeting by third parties.

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          Most cited references67

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          Using thematic analysis in psychology

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            What is an adequate sample size? Operationalising data saturation for theory-based interview studies.

            In interview studies, sample size is often justified by interviewing participants until reaching 'data saturation'. However, there is no agreed method of establishing this. We propose principles for deciding saturation in theory-based interview studies (where conceptual categories are pre-established by existing theory). First, specify a minimum sample size for initial analysis (initial analysis sample). Second, specify how many more interviews will be conducted without new ideas emerging (stopping criterion). We demonstrate these principles in two studies, based on the theory of planned behaviour, designed to identify three belief categories (Behavioural, Normative and Control), using an initial analysis sample of 10 and stopping criterion of 3. Study 1 (retrospective analysis of existing data) identified 84 shared beliefs of 14 general medical practitioners about managing patients with sore throat without prescribing antibiotics. The criterion for saturation was achieved for Normative beliefs but not for other beliefs or studywise saturation. In Study 2 (prospective analysis), 17 relatives of people with Paget's disease of the bone reported 44 shared beliefs about taking genetic testing. Studywise data saturation was achieved at interview 17. We propose specification of these principles for reporting data saturation in theory-based interview studies. The principles may be adaptable for other types of studies.
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              An Extended Privacy Calculus Model for E-Commerce Transactions

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                Author and article information

                Journal
                JMIR Mhealth Uhealth
                JMIR mHealth and uHealth
                JMIR Publications Inc.
                2291-5222
                2291-5222
                April 13 2021
                : 9
                : 4
                Affiliations
                [1 ] Department of Public Relations and Advertising, Beijing Normal University-Hong Kong Baptist University United International College, Zhuhai, China.
                [2 ] Department of Advertising and Public Relations, Michigan State University, East Lansing, MI, United States.
                [3 ] Department of Journalism and Creative Media, University of Alabama, Tuscaloosa, AL, United States.
                [4 ] Department of Media and Information, Michigan State University, East Lansing, MI, United States.
                [5 ] Department of Sociology, Anthropology, and Criminal Justice, Clemson University, Clemson, SC, United States.
                [6 ] Department of Communication, Clemson University, Clemson, SC, United States.
                Article
                v9i4e16518
                10.2196/16518
                8080138
                33847596
                f3eeec45-3dfe-4190-8177-0b341b0dc731
                ©Eunsin Joo, Anastasia Kononova, Shaheen Kanthawala, Wei Peng, Shelia Cotten. Originally published in JMIR mHealth and uHealth (http://mhealth.jmir.org), 13.04.2021.
                History

                mHealth app,mobile phone,mobile promotion strategy,personal health information sharing,persuasion knowledge

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