IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i21p14372-d961616.html
   My bibliography  Save this article

An Investigation into the Adoption Behavior of mHealth Users: From the Perspective of the Push-Pull-Mooring Framework

Author

Listed:
  • Yizhi Liu

    (School of Management, Guizhou University, Guiyang 550025, China)

  • Zihan Liang

    (School of Management, Guizhou University, Guiyang 550025, China)

  • Chengjiang Li

    (School of Management, Guizhou University, Guiyang 550025, China
    School of Engineering, University of Tasmania, Hobart, TAS 7005, Australia)

  • Jiezhou Guo

    (Academic Bridge Human Resources Co., Ltd., Shanghai 200040, China)

  • Gang Zhao

    (School of Engineering, University of Tasmania, Hobart, TAS 7005, Australia)

Abstract

As an important branch of the modern electronic health care services, mobile health applications (mHealth APP) have been widely accepted as a novel health care-providing platform. Based on mobile communications, mHealth is operated on smart terminals such as smart phones, tablet computers, wireless devices or wearable devices, providing multi-channel, multi-terminal and multi-network services. Because mHealth is not restricted by time and space, it serves as a more effective disease management tool for communications between patients and medical workers. In the background of “Internet+”, this study aims to explore the internal adoption behavior of mHealth users to improve the efficiency of medical services, reduce medical costs, and enrich the “Internet + medical health” research. Guided by the push-pull-mooring framework (PPM), this study proposes a conceptual model of mHealth users’ adoption behavior. A specially designed survey was used to collect data on users’ adoption behavior ( n = 183). SPSS 25.0 (Guiyang, China) and AMOS 21.0 are used for data analysis. The results show that users’ adoption attitude partially mediates the relationship between the adoption intentions and three key factors (inconvenience, APP attractiveness, and high risk). The adoption intention also partially mediates the relationship between adoption attitude and adoption behavior. Peer influence does not have a direct effect on adoption intention, but it shows a statistically significant indirect effect on adoption intention and adoption behavior through adoption attitude. The negative effect of high switching cost is not significant for both adoption attitude and adoption intention. This study elucidates the internal mechanisms underlying mHealth users’ adoption behavior. The findings can help mHealth providers to arouse more users’ adoption behavior, improve the quality of medical services, and reduce medical costs.

Suggested Citation

  • Yizhi Liu & Zihan Liang & Chengjiang Li & Jiezhou Guo & Gang Zhao, 2022. "An Investigation into the Adoption Behavior of mHealth Users: From the Perspective of the Push-Pull-Mooring Framework," Sustainability, MDPI, vol. 14(21), pages 1-17, November.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:21:p:14372-:d:961616
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/21/14372/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/21/14372/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Paul Klemperer, 1987. "Markets with Consumer Switching Costs," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 102(2), pages 375-394.
    2. Daniel Hellerstein & Nathaniel Higgins & John Horowitz, 2013. "The predictive power of risk preference measures for farming decisions -super-†," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 40(5), pages 807-833, December.
    3. Luisa Menapace & Gregory Colson & Roberta Raffaelli, 2016. "A comparison of hypothetical risk attitude elicitation instruments for explaining farmer crop insurance purchases," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 43(1), pages 113-135.
    4. Everett Lee, 1966. "A theory of migration," Demography, Springer;Population Association of America (PAA), vol. 3(1), pages 47-57, March.
    5. De Bruyn, Arnaud & Lilien, Gary L., 2008. "A multi-stage model of word-of-mouth influence through viral marketing," International Journal of Research in Marketing, Elsevier, vol. 25(3), pages 151-163.
    6. Fernández, Nuria Viejo & Pérez, María José Sanzo & Vázquez-Casielles, Rodolfo, 2018. "Webroomers versus showroomers: Are they the same?," Journal of Business Research, Elsevier, vol. 92(C), pages 300-320.
    7. Ajzen, Icek, 1991. "The theory of planned behavior," Organizational Behavior and Human Decision Processes, Elsevier, vol. 50(2), pages 179-211, December.
    8. Paul A. Pavlou & David Gefen, 2004. "Building Effective Online Marketplaces with Institution-Based Trust," Information Systems Research, INFORMS, vol. 15(1), pages 37-59, March.
    9. Fred D. Davis & Richard P. Bagozzi & Paul R. Warshaw, 1989. "User Acceptance of Computer Technology: A Comparison of Two Theoretical Models," Management Science, INFORMS, vol. 35(8), pages 982-1003, August.
    10. Bryan Weichelt & Jeffrey VanWormer & Yin Xu & Chris Kadolph & Simon Lin, 2021. "Lessons Learned from Development of a Mobile App for Cardiovascular Health Awareness," Sustainability, MDPI, vol. 13(11), pages 1-13, May.
    11. Stefan Markovic & Oriol Iglesias & Jatinder Jit Singh & Vicenta Sierra, 2018. "How does the Perceived Ethicality of Corporate Services Brands Influence Loyalty and Positive Word-of-Mouth? Analyzing the Roles of Empathy, Affective Commitment, and Perceived Quality," Journal of Business Ethics, Springer, vol. 148(4), pages 721-740, April.
    12. Klemperer, Paul D, 1987. "Entry Deterrence in Markets with Consumer Switching Costs," Economic Journal, Royal Economic Society, vol. 97(388a), pages 99-117, Supplemen.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Shu He & Jing Peng & Jianbin Li & Liping Xu, 2020. "Impact of Platform Owner’s Entry on Third-Party Stores," Information Systems Research, INFORMS, vol. 31(4), pages 1467-1484, December.
    2. Marzieh Soleimani, 2022. "Buyers' trust and mistrust in e-commerce platforms: a synthesizing literature review," Information Systems and e-Business Management, Springer, vol. 20(1), pages 57-78, March.
    3. Türker, Cansu & Altay, Burak Can & Okumuş, Abdullah, 2022. "Understanding user acceptance of QR code mobile payment systems in Turkey: An extended TAM," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    4. Heiko Moryson & Guido Moeser, 2016. "Consumer Adoption of Cloud Computing Services in Germany: Investigation of Moderating Effects by Applying an UTAUT Model," International Journal of Marketing Studies, Canadian Center of Science and Education, vol. 8(1), pages 14-32, February.
    5. Santos, Susana & Gonçalves, Helena Martins, 2021. "The consumer decision journey: A literature review of the foundational models and theories and a future perspective," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    6. Amy Wenxuan Ding & Shibo Li & Patrali Chatterjee, 2015. "Learning User Real-Time Intent for Optimal Dynamic Web Page Transformation," Information Systems Research, INFORMS, vol. 26(2), pages 339-359, June.
    7. Higueras-Castillo, Elena & Liébana-Cabanillas, Francisco J. & Villarejo-Ramos, Ángel F., 2023. "Intention to use e-commerce vs physical shopping. Difference between consumers in the post-COVID era," Journal of Business Research, Elsevier, vol. 157(C).
    8. Wen-Lung Shiau & Yogesh K. Dwivedi, 2013. "Citation and co-citation analysis to identify core and emerging knowledge in electronic commerce research," Scientometrics, Springer;Akadémiai Kiadó, vol. 94(3), pages 1317-1337, March.
    9. Carin Cruijsen & Maaike Diepstraten, 2017. "Banking Products: You Can Take Them with You, So Why Don’t You?," Journal of Financial Services Research, Springer;Western Finance Association, vol. 52(1), pages 123-154, October.
    10. Kim, Dong-hyu & Lee, Heejin, 2016. "Effects of user experience on user resistance to change to the voice user interface of an in‑vehicle infotainment system: Implications for platform and standards competition," International Journal of Information Management, Elsevier, vol. 36(4), pages 653-667.
    11. Jaeki Song & Fatemeh Mariam Zahedi, 2005. "A Theoretical Approach to Web Design in E-Commerce: A Belief Reinforcement Model," Management Science, INFORMS, vol. 51(8), pages 1219-1235, August.
    12. Weerakkody, Vishanth & El-Haddadeh, Ramzi & Al-Sobhi, Faris & Shareef, Mahmud Akhter & Dwivedi, Yogesh K., 2013. "Examining the influence of intermediaries in facilitating e-government adoption: An empirical investigation," International Journal of Information Management, Elsevier, vol. 33(5), pages 716-725.
    13. Filiptseva, Anna & Filler, Günther & Odening, Martin, 2022. "Compensation Options for Quarantine Costs in Plant Production," 62nd Annual Conference, Stuttgart, Germany, September 7-9, 2022 329595, German Association of Agricultural Economists (GEWISOLA).
    14. Hasan, Rajibul & Lowe, Ben & Petrovici, Dan, 2020. "Consumer adoption of pro-poor service innovations in subsistence marketplaces," Journal of Business Research, Elsevier, vol. 121(C), pages 461-475.
    15. Bouckaert, Jan & Degryse, Hans & Provoost, Thomas, 2010. "Enhancing market power by reducing switching costs," Economics Letters, Elsevier, vol. 109(2), pages 131-133, November.
    16. Debora Bettiga & Lucio Lamberti & Emanuele Lettieri, 2020. "Individuals’ adoption of smart technologies for preventive health care: a structural equation modeling approach," Health Care Management Science, Springer, vol. 23(2), pages 203-214, June.
    17. Jean-Bernard Chatelain, 1999. "Taux de marge et structure financière," Annals of Economics and Statistics, GENES, issue 53, pages 127-147.
    18. Ahmed Ibrahim Alzahrani & T. Ramayah & Nalini Suppiah & Osama Alfarraj & Nasser Alalwan, 2020. "Modeling Blog Usage From a Developing Country Perspective Using Structural Equation Modeling (SEM)," SAGE Open, , vol. 10(3), pages 21582440209, July.
    19. Chakravarthi Narasimhan & Z. John Zhang, 2000. "Market Entry Strategy Under Firm Heterogeneity and Asymmetric Payoffs," Marketing Science, INFORMS, vol. 19(4), pages 313-327, November.
    20. Paul Juinn Bing Tan, 2013. "Applying the UTAUT to Understand Factors Affecting the Use of English E-Learning Websites in Taiwan," SAGE Open, , vol. 3(4), pages 21582440135, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:21:p:14372-:d:961616. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.