IDEAS home Printed from https://ideas.repec.org/p/hal/journl/hal-01630383.html
   My bibliography  Save this paper

Exploring and Predicting Online Collective Action on Patients' Virtual Communities: A Multi-method Investigation in France

Author

Listed:
  • Raphaëlle Laubie

    (DRM - Dauphine Recherches en Management - Université Paris Dauphine-PSL - PSL - Université Paris Sciences et Lettres - CNRS - Centre National de la Recherche Scientifique)

  • Christophe Elie-Dit-Cosaque

    (DRM - Dauphine Recherches en Management - Université Paris Dauphine-PSL - PSL - Université Paris Sciences et Lettres - CNRS - Centre National de la Recherche Scientifique)

Abstract

Virtual patients' communities are developing on the Internet. These communities allow frequent interactions among patients, who can share health-related information within an interactive environment. However, we know very little about what determines patients' online collective action on Web 2.0 social networks. Accordingly, this research-in-progress examines why patients interact with others and communicate on topics related with their disease through these virtual communities. Drawing on goal-directed behavior (MGB) and the expectancy-value (EVT) theories, we have developed a model for examining patients' interactions with virtual communities. This multi-method, qualitative and quantitative approach enables one to explore patients' interactions and measure the determinants of online collective action on virtual spaces. The results from the qualitative analysis of 54 interviews conducted with patients, patient's relatives, health 2.0 professionals, doctors and caregivers are discussed herein. This research is expected to increase our knowledge regarding the individual dynamics and interactions that surround online patients' communities.

Suggested Citation

  • Raphaëlle Laubie & Christophe Elie-Dit-Cosaque, 2012. "Exploring and Predicting Online Collective Action on Patients' Virtual Communities: A Multi-method Investigation in France," Post-Print hal-01630383, HAL.
    • Handle: RePEc:hal:journl:hal-01630383
    Note: View the original document on HAL open archive server: https://hal.science/hal-01630383
    as

    Download full text from publisher

    File URL: https://hal.science/hal-01630383/document
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Detmar Straub & Moez Limayem & Elena Karahanna-Evaristo, 1995. "Measuring System Usage: Implications for IS Theory Testing," Management Science, INFORMS, vol. 41(8), pages 1328-1342, August.
    2. Ajzen, Icek, 1991. "The theory of planned behavior," Organizational Behavior and Human Decision Processes, Elsevier, vol. 50(2), pages 179-211, December.
    3. Jon Hartwick & Henri Barki, 1994. "Explaining the Role of User Participation in Information System Use," Management Science, INFORMS, vol. 40(4), pages 440-465, April.
    4. Andrew Burton-Jones & Detmar W. Straub, 2006. "Reconceptualizing System Usage: An Approach and Empirical Test," Information Systems Research, INFORMS, vol. 17(3), pages 228-246, September.
    5. Linda S. L. Lai & Efraim Turban, 2008. "Groups Formation and Operations in the Web 2.0 Environment and Social Networks," Group Decision and Negotiation, Springer, vol. 17(5), pages 387-402, September.
    6. repec:dau:papers:123456789/638 is not listed on IDEAS
    7. Sung S. Kim & Naresh K. Malhotra, 2005. "A Longitudinal Model of Continued IS Use: An Integrative View of Four Mechanisms Underlying Postadoption Phenomena," Management Science, INFORMS, vol. 51(5), pages 741-755, May.
    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. repec:dau:papers:123456789/11679 is not listed on IDEAS
    2. Sora Kang & Kai H. Lim & Min Soo Kim & Hee-Dong Yang, 2012. "Research Note ---A Multilevel Analysis of the Effect of Group Appropriation on Collaborative Technologies Use and Performance," Information Systems Research, INFORMS, vol. 23(1), pages 214-230, March.
    3. Lam, Shun Yin & Chiang, Jeongwen & Parasuraman, A., 2008. "The effects of the dimensions of technology readiness on technology acceptance: An empirical analysis," Journal of Interactive Marketing, Elsevier, vol. 22(4), pages 19-39.
    4. Henri Barki & Ryad Titah & Céline Boffo, 2007. "Information System Use--Related Activity: An Expanded Behavioral Conceptualization of Individual-Level Information System Use," Information Systems Research, INFORMS, vol. 18(2), pages 173-192, June.
    5. Susan A. Brown & Viswanath Venkatesh & Sandeep Goyal, 2012. "Expectation Confirmation in Technology Use," Information Systems Research, INFORMS, vol. 23(2), pages 474-487, June.
    6. Viswanath Venkatesh & Fred D. Davis, 2000. "A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies," Management Science, INFORMS, vol. 46(2), pages 186-204, February.
    7. Raja Sankaran & Shibashish Chakraborty, 2021. "Factors Impacting Mobile Banking in India: Empirical Approach Extending UTAUT2 with Perceived Value and Trust," IIM Kozhikode Society & Management Review, , vol. 11(1), pages 7-24, January.
    8. Kuo-Yu Huang & Yea-Ru Chuang, 2016. "A task–technology fit view of job search website impact on performance effects: An empirical analysis from Taiwan," Cogent Business & Management, Taylor & Francis Journals, vol. 3(1), pages 1253943-125, December.
    9. Yu Tong & Chuan-Hoo Tan & Hock-Hai Teo, 2017. "Direct and Indirect Information System Use: A Multimethod Exploration of Social Power Antecedents in Healthcare," Information Systems Research, INFORMS, vol. 28(4), pages 690-710, December.
    10. Sung S. Kim & Naresh K. Malhotra & Sridhar Narasimhan, 2005. "Research Note—Two Competing Perspectives on Automatic Use: A Theoretical and Empirical Comparison," Information Systems Research, INFORMS, vol. 16(4), pages 418-432, December.
    11. Lin Jia & Xiuwei Song & Dianne Hall, 2022. "Influence of Habits on Mobile Payment Acceptance: An Ecosystem Perspective," Information Systems Frontiers, Springer, vol. 24(1), pages 247-266, February.
    12. Wang, Guoqiang & Tan, Garry Wei-Han & Yuan, Yunpeng & Ooi, Keng-Boon & Dwivedi, Yogesh K., 2022. "Revisiting TAM2 in behavioral targeting advertising: A deep learning-based dual-stage SEM-ANN analysis," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    13. Jeeyeon Jeong & Yaeri Kim & Taewoo Roh, 2021. "Do Consumers Care About Aesthetics and Compatibility? The Intention to Use Wearable Devices in Health Care," SAGE Open, , vol. 11(3), pages 21582440211, August.
    14. Sarv Devaraj & Ming Fan & Rajiv Kohli, 2002. "Antecedents of B2C Channel Satisfaction and Preference: Validating e-Commerce Metrics," Information Systems Research, INFORMS, vol. 13(3), pages 316-333, September.
    15. Gerald C. Kane & Maryam Alavi, 2008. "Casting the Net: A Multimodal Network Perspective on User-System Interactions," Information Systems Research, INFORMS, vol. 19(3), pages 253-272, September.
    16. Bierstaker, James & Janvrin, Diane & Lowe, D. Jordan, 2014. "What factors influence auditors' use of computer-assisted audit techniques?," Advances in accounting, Elsevier, vol. 30(1), pages 67-74.
    17. Gansser, Oliver Alexander & Reich, Christina Stefanie, 2021. "A new acceptance model for artificial intelligence with extensions to UTAUT2: An empirical study in three segments of application," Technology in Society, Elsevier, vol. 65(C).
    18. Xinlu Wen & Marios Sotiriadis & Shiwei Shen, 2023. "Determining the Key Drivers for the Acceptance and Usage of AR and VR in Cultural Heritage Monuments," Sustainability, MDPI, vol. 15(5), pages 1-24, February.
    19. Hongping Yuan & Yu Yang & Xiaolong Xue, 2019. "Promoting Owners’ BIM Adoption Behaviors to Achieve Sustainable Project Management," Sustainability, MDPI, vol. 11(14), pages 1-18, July.
    20. Kim, Youngseek & Adler, Melissa, 2015. "Social scientists’ data sharing behaviors: Investigating the roles of individual motivations, institutional pressures, and data repositories," International Journal of Information Management, Elsevier, vol. 35(4), pages 408-418.
    21. Attié, Elodie & Meyer-Waarden, Lars, 2022. "The acceptance and usage of smart connected objects according to adoption stages: an enhanced technology acceptance model integrating the diffusion of innovation, uses and gratification and privacy ca," Technological Forecasting and Social Change, Elsevier, vol. 176(C).

    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:hal:journl:hal-01630383. 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: CCSD (email available below). General contact details of provider: https://hal.archives-ouvertes.fr/ .

    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.