Abstract
The literature lacks evidence on the acceptability of AI conversational agents (chatbots) and the motivations for their adoption in healthcare industry. This paper aims to examine the acceptance of these chatbots based on the UTAUT model in Online Health Communities (OHCs) and to explore what kind of impact these particular features have on the users’ intentions, and the actual use of these communities. Based on a quantitative methodology approach, we rely on the UTAUT model to study OHCs users’ behavior and intentions towards such AI conversational agents/chatbots. The study shows that the UTAUT has proved to be a strong and reliable model for evaluating the adoption and application of AI conversational agents (chatbots) in OHCs. A questionnaire was employed to collect data, and respondents are chosen using the cluster sampling approach. On a 7 Likert scale, respondents were asked to select which choice best suited their reaction to any of the topics presented. A total of 632 answers from 62 countries were received, with 443 of them being complete. Many tests were used to examine the data such as the bivariate and multivariate analysis. Since the returned p-value for most of the hypotheses tested was 0.05, the majority of the hypotheses tested were accepted. Findings showed the interrelations between AI conversational agents/chatbots and OHCs on users’ Behavioral Intention (BI). The main constructs of the UTAUT model (Performance Expectancy, Effort Expectancy, Social Influence, and Facilitating Conditions) had a significant impact on the participants’ BI and Usage Behavior (UB) for AI conversational agents/chatbots in OHCs. As for moderators, gender and age had no effect on BI and UB. Understanding the main factors that have a significant impact on users’ intentions to use chatbots in OHCs determines the significance of those results.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Wright, R.: The coronavirus pandemic is now a threat to national security, The New Yorker, 7 October (2020). https://www.newyorker.com/news
Long, M.: Deep learning in healthcare- How it’s changing the game (2020). https://www.aidoc.com/blog/deep-learning-in-healthcare/
Sennaar, K.: Chatbots for healthcare – comparing 5 current applications. business intelligence and analytics. Healthcare. Customer Service. Emerj, the AI Research and Advisory Company (2019)
Venkatesh, V., Morris, M.G., Gordon, B.D., Davis, F.D.: User acceptance of information technology: toward a unified view. MIS Quart. 27(3), 425–478 (2003)
Wu, B.: Patient continued use of online health care communities: Web mining of patient-doctor communication. J. Med. Internet Res. 20, e126 (2018)
Mesko, B., Gyorffy, Z.: The rise of the empowered physician in the digital health era: viewpoint. J. Med. Internet Res. 21(2), e12490 (2019)
Papadaki, M., Karamitsos, I., Themistocleous, M.: Covid-19 digital test certificates and blockchain. J. Enterp. Inf. Manage. 34, 993–1003 (2021). https://doi.org/10.1108/JEIM-07-2021-554
Jabarulla, M.Y., Lee, H.-N.: A blockchain and artificial intelligence-based, patient-centric healthcare system for combating the COVID-19 pandemic: opportunities and applications. Healthcare 9, 1019 (2021). https://doi.org/10.3390/healthcare9081019
Karamitsos, I., Papadaki, M.: Blockchain digital test certificates for COVID-19. In: Tallón-Ballesteros, A.J. (Ed.) Modern Management based on Big Data II and Machine Learning and Intelligent Systems III (2021)
Koteluk, O., Wartecki, A., Mazurek, S., Kołodziejczak, I., Mackiewicz, A.: How do machines learn? artificial intelligence as a New Era in medicine. J. Personalized Med. 11(1), 32 (2021). https://doi.org/10.3390/jpm11010032
Abd-Alrazaq, A.A., Bewick, B., Farragher, T., Gardner, P.: Factors that affect the use of electronic personal health records among patients: a systematic review. Int. J. Med. Inform. 126, 164–175 (2019)
Palanica, A., Flaschner, P., Thommandram, A., Li, M., Fossat, Y.: Physicians’ perceptions of chatbots in health care: cross-sectional web-based survey. J. Med. Internet Res. 21(4), e12887 (2019). https://doi.org/10.2196/12887
Amato, F., Marrone, S., Moscato, V., Piantadosi, G., Picariello, A., Sansone, C.: CEUR workshop proceedings. Chatbots Meet eHealth: Automatizing Healthcare (2017). http://ceur-ws.org/Vol-1982/paper6.pdf. Accessed 26 Feb 2019
Brandtzaeg, P.B., Følstad, A.: Why people use chatbots. In: Kompatsiaris, I., et al. (eds.) INSCI 2017. LNCS, vol. 10673, pp. 377–392. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-70284-1_30
Park, S.H., Lee, L., Yi, M.Y.: Group-level effects of facilitating conditions on individual acceptance of information systems. Inf. Technol. Manage. 12(4), 315–334 (2011)
Zhang, Z., Lu, Y., Kou, Y., Wu, D.T.Y., Huh-Yoo, J., He, Z.: Stud Health Technol Inform. 264, 1403–1407 (2019)
Abd-Alrazaq, A.A. Alajlani, M., Denecke, K., Nashva, A., Denecke, K. Bewick, B.: Perceptions and opinions of patients about mental health chatbots: scoping review. J. Med. Internet Res. (2021)
Khalilzadeh, J., Ozturk, A.B., Bilgihan, A.: Security-related factors in extended UTAUT model for NFC based mobile payment in the restaurant industry. Comput. Hum. Behav. 70, 460–474 (2017). https://doi.org/10.1016/j.chb.2017.01.001
Šumak, B., Šorgo, A.: The acceptance and use of interactive whiteboards among teachers: differences in UTAUT determinants between pre- and post-adopters. Comput. Hum. Behav. 64, 602–620 (2016). https://doi.org/10.1016/j.chb.2016.07.037
Hoque, R., Sorwar, G.: Understanding factors influencing the adoption of mHealth by the elderly: an extension of the UTAUT model. Int. J. Med. Inform. 101, 75–84 (2017). https://doi.org/10.1016/j.ijmedinf.2017.02.002
Chauhan, S., Jaiswal, M.: Determinants of acceptance of ERP software training in business schools: empirical investigation using UTAUT model. Int. J. Manage. Educ. 14, 248–262 (2016). https://doi.org/10.1016/j.ijme.2016.05.005
Cimperman, M., Brenčič, M.M., Trkman, P.: Analyzing older users’ home telehealth services acceptance behavior—applying an extended UTAUT model. Int. J. Med. Inform. 90, 22–31 (2016). https://doi.org/10.1016/j.ijmedinf.2016.03.002
Taylor, S., Todd, P.: Assessing IT usage: the role of prior experience. MIS Q. 19(4), 561–570 (1995)
Lee, Y., Kozar, K.A., Larsen, K.R.T.: The technology acceptance model: past, present, and future. Commun. Assoc. Inf. Syst. 12, 752–780 (2003)
Esmaeilzadeh, P., Sambasivan, M., Kumar, N., Nezakati, H.: Adoption of clinical decision support systems in a developing country: antecedents and outcomes of physician’s threat to perceived professional autonomy. Int. J. Med. Inf. 84(8), 548–560 (2015)
De Veer, A.J.E., Peeters, J.M., Brabers, A.E., Schellevis, F.G., Rademakers, J.J., Francke, A.L.: Determinants of the intention to use e-Health by community dwelling older people. BMC Health Serv. Res. 15, 103 (2015)
Chan, F.K.Y., Thong, J.Y.L., Venkatesh, W., Brown, S.A., Hu, P.J., Tam, K.Y.: Modeling citizen satisfaction with mandatory adoption of an E-government technology. J. Assoc. Inf. Syst. 11(10), 519–549 (2010)
Guo, Y.: Moderating effects of gender in the acceptance of mobile based on UTAUT model. Int. J. Smart Home 9(1), 203–216 (2015)
Klein, H.K., Myers, M.D.: A set of principles for conducting and evaluating interpretive field studies in information systems. MIS Q. 23(1), 67–93 (1999)
Straub, D.W., Boudreau, M.-C., Gefen, D.: Validation guidelines for IS positivist research. Comm. AIS 13, 380–427 (2004)
Creswell, J.W.: Research Design: Qualitative Quantitative and Mixed Methods Approaches, Second edition Sage Publication, Thousand Oaks (2003)
Orlikowski, W.J., Baroudi, J.J.: Studying information technology in organizations: research approaches and assumptions. Inf. Syst. Res. 2(1), 1–28 (1991)
Walsh, I.: Using quantitative data in mixed-design grounded theory studies: an enhanced path to formal grounded theory in information systems. Eur. J. Inf. Syst. 2014, 1–27 (2014)
Tavares, J., Oliveira, T.: Electronic health record portal adoption: a cross country analysis. BMC Med. Inform. Decis. Mak. 17, 97 (2017). https://doi.org/10.1186/s12911-017-0482-9
Okumus, F., Ali, F., Bilgihan, A., Ozturk, A.B.: Psychological factors influencing customers’ acceptance of smartphone diet apps when ordering food at restaurants. Int. J. Hospital. Manage. 72, 67–77 (2018). https://doi.org/10.1016/j.ijhm.2018.01.001
Reyes-Mercado, P.: Adoption of fitness wearables. J. Syst. Inf. Technol. 20(1), 103–127 (2018)
Pai, F.-Y., Huang, K.-I.: applying the technology acceptance model to the introduction of healthcare information systems. Technol. Forecast Soc. Change. 78, 650–660 (2011)
Quaosar, G.A.A., Hoque, M.R., Bao, Y.: Investigating factors affecting Elderly’s intention to use m-health services: an empirical study. Telemed. E Health 24(4), 309–314 (2018). https://doi.org/10.1089/tmj.2017.0111
Gao, Y., Li, H., Luo, Y.: An empirical study of wearable technology acceptance in healthcare. Ind. Manage. Data Syst. 115(9), 1704–1723 (2015). https://doi.org/10.1108/IMDS-03-2015-0087
Hsieh, P.J.: An empirical investigation of patients’ acceptance and resistance toward the health cloud: the dual factor perspective. Comput. Hum. Behav. 63, 959–969 (2016). https://doi.org/10.1016/j.chb.2016.06.029
Lu, X., Zhang, R., Zhu, X.: An empirical study on patients’ acceptance of physician-patient interaction in online health communities. Int. J. Environ. Res. Public Health 16, 5084 (2019). https://doi.org/10.3390/ijerph16245084
Venkatesh, V., Bala, H.: Technology acceptance model 3 and a research agenda on interventions. Decis. Sci. 39(2), 273–315 (2008)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Appendixes
Appendixes
1.1 Appendix A: Demographic Characteristics of the Study Population
1.2 Appendix B: Experience Related to the Use of Chatbots in OHCs
1.3 Appendix C: Descriptive Analysis of UTAUT Main Constructs
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this paper
Cite this paper
Osta, A., Kokkinaki, A., Chedrawi, C. (2022). Online Health Communities: The Impact of AI Conversational Agents on Users. In: Themistocleous, M., Papadaki, M. (eds) Information Systems. EMCIS 2021. Lecture Notes in Business Information Processing, vol 437. Springer, Cham. https://doi.org/10.1007/978-3-030-95947-0_35
Download citation
DOI: https://doi.org/10.1007/978-3-030-95947-0_35
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-95946-3
Online ISBN: 978-3-030-95947-0
eBook Packages: Computer ScienceComputer Science (R0)