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      Assessing the older population acceptance of healthcare wearable in a developing Country: an extended PMT model

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          Abstract

          Wearable healthcare devices (HWDs) got prominence, especially during the COVID-19 pandemic. Aiming to know the proliferation of the HWDS, this study tries to evaluate the intentions of the old-age population to use the devices. To this end, the protection motivation theory (PMT) coupled with health information accuracy, perceived novelty, perceived ease of use, hedonic motivation, health anxiety, and health consciousness were investigated as predictors of HWDs among senior Pakistani citizens. A total of 310 participants who knew how the smartwatches could be used as a health monitoring device, were surveyed to empirically test the model. The required data were then analyzed using structural equation modeling. The findings reveal that the protective, utilitarian, and personal attributes of HWDs play a significant role in choosing the HWDs. While addressing their healthcare issues, senior citizens are highly receptive to perceived usefulness, accurate information accuracy, self-efficacy, perceived severity, health consciousness, and perceived vulnerability during COVID-19. These factors have a positive impact on their willingness to use HWDs. The study educates healthcare management and users to focus on the determining factors that can enhance the use of HWDs.

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          Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives

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            Common method biases in behavioral research: A critical review of the literature and recommended remedies.

            Interest in the problem of method biases has a long history in the behavioral sciences. Despite this, a comprehensive summary of the potential sources of method biases and how to control for them does not exist. Therefore, the purpose of this article is to examine the extent to which method biases influence behavioral research results, identify potential sources of method biases, discuss the cognitive processes through which method biases influence responses to measures, evaluate the many different procedural and statistical techniques that can be used to control method biases, and provide recommendations for how to select appropriate procedural and statistical remedies for different types of research settings.
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              Evaluating Structural Equation Models with Unobservable Variables and Measurement Error

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                Author and article information

                Contributors
                salman_pide@outlook.com
                Journal
                J. of Data, Inf. and Manag.
                Journal of Data, Information and Management
                Springer International Publishing (Cham )
                2524-6356
                2524-6364
                29 March 2023
                : 1-15
                Affiliations
                GRID grid.263488.3, ISNI 0000 0001 0472 9649, Shenzhen University, ; Shenzhen, China
                Article
                87
                10.1007/s42488-023-00087-9
                10052253
                e981e779-13bd-418c-b45a-dd29c2451d3a
                © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

                This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.

                History
                : 1 January 2023
                : 15 March 2023
                Funding
                Funded by: National Social Science Foundation of China
                Award ID: 21AGL014
                Funded by: Natural Science Foundation of Guangdong—Guangdong Basic and Applied Basic Research Foundation
                Award ID: 2021A1515011894
                Funded by: Guangdong 13th-Five-Year-Plan Philosophical and Social Science Fund
                Award ID: GD20CGL28
                Funded by: Shenzhen Science and Technology Program
                Award ID: JCYJ20210324093208022
                Categories
                Original Article

                protection motivation theory,acceptance,healthcare wearables,pakistan

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