The Exploratory Study on Antecedents of Online Medical Consultation Continuous Usage Intention

Andini Larasati(1), Ferdi Antonio(2), Dewi Wuisan(3),


(1) Graduate School of Management, Departement of Hospital Administration, Faculty of Economics and Business, Universitas Pelita Harapan
(2) Departement of Hospital Administration, Faculty of Economics and Business, Universitas Pelita Harapan
(3) Departement of Hospital Administration, Faculty of Economics and Business, Universitas Pelita Harapan

Abstract

The purpose of this study is to find and analyze factors that can affect Intention to Recommend in online medical consultation field. The research model is adapted from a previous study and then modified. Data were collected from women that are >17 years old and who have ever used the online medical consultation application, Halodoc. The research’s method is a quantitative survey, with cross-sectional data. Respondents’ data were taken by purposive sampling and questionnaires were distributed online. As many as 202 participants have fulfilled the requirements to be analyzed with PLS-SEM. The results showed that five antecedents had a significant influence on Intention to Recommend. Antecedents that were worth noting were Helpfulness Trust, Perceived Benefit, and Reliability Trust, where these factors show a positive impact on Intention to Recommend. Factors that could potentially influence users not using online medical consultation applications were also found, such as Performance Risk and Privacy Risk. From the findings of this study, it can be concluded that there are factors that may need to be considered by online medical consultation service providers to maintain or even to better their quality of care.

 

Keywords

Online Medical Consultation; Telemedicine ; Antecedents

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References

Akter, S., D’Ambra, J., & Ray, P. (2010). Trustworthiness in mHealth information services: An assessment of a hierarchical model with mediating and moderating effects using partial least squares (PLS). Journal of the American Society for Information Science and Technology, 62(1), 100–116. https://doi.org/10.1002/asi.21442

Albarrak, A. I., Mohammed, R., Almarshoud, N., Almujalli, L., Aljaeed, R., Altuwaijiri, S., & Albohairy, T. (2021). Assessment of physician’s knowledge, perception and willingness of telemedicine in Riyadh region, Saudi Arabia. Journal of Infection and Public Health, 14(1), 97–102. https://doi.org/10.1016/j.jiph.2019.04.006

Alexandro, R., & Antonio, F. (2021). Antesedent Dari Online Trust Serta Dampaknya Terhadap Willingness To Choose Konsultasi Online (Studi Empiris Pada Konsumen Aplikasi Layanan Kesehatan). Jurnal Manajemen Dan Administrasi Rumah Sakit Indonesia (MARSI), 5(2), 128–150. https://doi.org/10.52643/marsi.v5i2.1703

AlHogail, A., & AlShahrani, M. (2018). Building Consumer Trust to Improve Internet of Things (IoT) Technology Adoption. Advances in Neuroergonomics and Cognitive Engineering, 775, 325–334. https://doi.org/10.1007/978-3-319-94866-9_33

Andone, I., Błaszkiewicz, K., Eibes, M., Trendafilov, B., Montag, C., & Markowetz, A. (2016). How age and gender affect smartphone usage. Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct. https://doi.org/10.1145/2968219.2971451

Arfi, W. B., Nasr, I. B., Kondrateva, G., & Hikkerova, L. (2021). The role of trust in intention to use the IoT in eHealth: Application of the modified UTAUT in a consumer context. Technological Forecasting and Social Change, 167, 120688. https://doi.org/10.1016/j.techfore.2021.120688

Carrión, G. C., Nitzl, C., & Roldán, J. L. (2017). Mediation Analyses in Partial Least Squares Structural Equation Modeling: Guidelines and Empirical Examples. Partial Least Squares Path Modeling, 173–195. https://doi.org/10.1007/978-3-319-64069-3_8

Chen, Y., Zhao, Y., & Wang, Z. (2020). Understanding Online Health Information Consumers' search as a learning process. Library Hi Tech, 38(4), 859–881. https://doi.org/10.1108/lht-08-2019-0174

Chomeya, R. (2010). Quality of psychology test between Likert Scale 5 and 6 points. Journal of Social Sciences, 6(3), 399–403. https://doi.org/10.3844/jssp.2010.399.403

Franco-Lara, R. (2023). Comparing Patients’ Satisfaction with Telemedicine Visits to In-person Visits at Central Washington Onsite Clinics: A Program Evaluation. Doctor of Nursing Practice (DNP) Scholarly Projects., 55. https://digitalcommons.spu.edu/shs_dnp/55

de Langhe, B., Fernbach, P. M., & Lichtenstein, D. R. (2015). Navigating by the Stars: Investigating the Actual and Perceived Validity of Online User Ratings. Journal of Consumer Research, 42(6), 817–833. https://doi.org/10.1093/jcr/ucv047

de Langhe, B., Fernbach, P. M., & Lichtenstein, D. R. (2016). Star Wars: Response to Simonson, Winer/Fader, and Kozinets. Journal of Consumer Research, 42(6), 850–857. https://doi.org/10.1093/jcr/ucw007

st Century Health Care Challenges: A Connected Health Approach. (2017). Deloitte.com. https://www2.deloitte.com/content/dam/Deloitte/id/Documents/public-sector/id-gps-ehealth-publication-Indonesia.pdf

Dihni, V. A. (2021, December 22). Survei: Halodoc Jadi Aplikasi Kesehatan Paling Sering Digunakan Ibu di Indonesia | Databoks. Databoks.katadata.co.id. https://databoks.katadata.co.id/datapublish/2021/12/22/survei-halodoc-jadi-aplikasi-kesehatan-paling-sering-digunakan-ibu-di-Indonesia

Escoffery, C. (2018). Gender Similarities and Differences for e-Health Behaviors Among U.S. Adults. Telemedicine and E-Health, 24(5), 335–343. https://doi.org/10.1089/tmj.2017.0136

Fernandez, P. (2020). “Through the looking glass: envisioning new library technologies” pandemic response technologies: remote working. Library Hi Tech News. https://doi.org/10.1108/lhtn-04-2020-0039

Gliadkovskaya, A. (2021, October 4). Telehealth use is surging but patient satisfaction with the service has declined, new study finds. Fierce Healthcare. https://www.fiercehealthcare.com/digital-health/telehealth-use-has-surged-among-patients-while-satisfaction-has-declined-new-study

Gong, Z., Han, Z., Li, X., Yu, C., & Reinhardt, J. D. (2019). Factors Influencing the Adoption of Online Health Consultation Services: The Role of Subjective Norm, Trust, Perceived Benefit, and Offline Habit. Frontiers in Public Health, 7(286). https://doi.org/10.3389/fpubh.2019.00286

Google Trends. (2023). Google Trends. https://trends.google.co.id/trends/explore?date=now%201-d&geo=ID&q=halodoc

Gutierrez, M. A., Moreno, R. A., & Rebelo, M. S. (2017). Information and Communication Technologies and Global Health Challenges. Global Health Informatics, 50–93. https://doi.org/10.1016/b978-0-12-804591-6.00004-5

Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/ebr-11-2018-0203

Halodoc. (n.d.). Halodoc Corporate Service - Jaminan Kesehatan Karyawan. Halodoc. https://www.halodoc.com/corporate-partnership

Han, W. (2020). Effects of User Reviews and Critic Rating on Online Healthcare Sales. American Journal of Industrial and Business Management, 10(12), 1902–1915. https://doi.org/10.4236/ajibm.2020.1012119

Hsiao, K., Chuan‐Chuan Lin, J., Wang, X., Lu, H., & Yu, H. (2010). Antecedents and consequences of trust in online product recommendations. Online Information Review, 34(6), 935–953. https://doi.org/10.1108/14684521011099414

Hsu, W. (2019). A customer-oriented skin detection and care system in telemedicine applications. The Electronic Library, 37(6), 1007–1021. https://doi.org/10.1108/el-03-2019-0080

U.S. Telehealth Satisfaction Study. (2021). J.D. Power. https://www.jdpower.com/business/press-releases/2021-us-telehealth-satisfaction-study

Indonesia peringkat KE-3 global Memanfaatkan Aplikasi Kesehatan: Databoks. Pusat Data Ekonomi dan Bisnis Indonesia. (n.d.). Retrieved April 26, 2023, from https://databoks.katadata.co.id/datapublish/2020/10/13/indonesia-peringkat-ke-3-global-memanfaatkan-aplikasi-kesehatan

Johnson, C., Dupuis, J. B., Goguen, P., & Grenier, G. (2021). Changes to telehealth practices in primary care in New Brunswick (Canada): A comparative study pre and during the COVID-19 pandemic. PLOS ONE, 16(11), e0258839. https://doi.org/10.1371/journal.pone.0258839

Kaium, A., Bao, Y., Alam, M. K., & Hoque, M. R. (2020). Understanding continuance usage intention of mHealth in a developing country. International Journal of Pharmaceutical and Healthcare Marketing, 14(2), 251–272. https://doi.org/10.1108/ijphm-06-2019-0041

Klaus, P. ‘Phil’, & Maklan, S. (2013). Towards a Better Measure of Customer Experience. International Journal of Market Research, 55(2), 227–246. https://journals.sagepub.com/doi/abs/10.2501/IJMR-2013-021

Kock, N., & Hadaya, P. (2016). Minimum sample size estimation in PLS-SEM: The inverse square root and gamma-exponential methods. Information Systems Journal, 28(1), 227–261. https://doi.org/10.1111/isj.12131

Li, H., Kuo, C., & Rusell, M. G. (2006). The Impact of Perceived Channel Utilities, Shopping Orientations, and Demographics on the Consumer’s Online Buying Behavior. Journal of Computer-Mediated Communication, 5(2). https://doi.org/10.1111/j.1083-6101.1999.tb00336.x

Liengaard, B. D., Sharma, P. N., Hult, G. T. M., Jensen, M. B., Sarstedt, M., Hair, J. F., & Ringle, C. M. (2020). Prediction: Coveted, Yet Forsaken? Introducing a Cross‐Validated Predictive Ability Test in Partial Least Squares Path Modeling. Decision Sciences, 52(2). https://doi.org/10.1111/deci.12445

Lustig, T. (2012). Visit the National Academies Press online and register for... The Role of Telehealth in an Evolving Health Care Environment: Workshop Summary. https://www.ncbi.nlm.nih.gov/books/NBK207145/pdf/Bookshelf_NBK207145.pdf

Martínez-Caro, E., Cegarra-Navarro, J. G., García-Pérez, A., & Fait, M. (2018). Healthcare service evolution towards the Internet of Things: An end-user perspective. Technological Forecasting and Social Change, 136, 268–276. https://doi.org/10.1016/j.techfore.2018.03.025

Matikiti, R., Mpinganjira, M., & Roberts-Lombard, M. (2018). Application of the Technology Acceptance Model and the Technology–Organisation–Environment Model to examine social media marketing use in the South African tourism industry. SA Journal of Information Management, 20(1). https://doi.org/10.4102/sajim.v20i1.790

Mayer, R. C., Davis, J. H., & Schoorman, F. D. (1995). An Integrative Model of Organizational Trust. The Academy of Management Review, 20(3), 709–734. https://doi.org/10.2307/258792

Mohammadzadeh, Z., Eghtedar, S., Ayatollahi, H., & Jebraeily, M. (2022). Effectiveness of a self-management mobile app on the quality of life of women with breast cancer: a study in a developing country. BMC Women’s Health, 22(1). https://doi.org/10.1186/s12905-022-02020-5

Octavius, G. S., & Antonio, F. (2021). Antecedents of Intention to Adopt Mobile Health (mHealth) Application and Its Impact on Intention to Recommend: An Evidence from Indonesian Customers. International Journal of Telemedicine and Applications, 2021, 1–24. https://doi.org/10.1155/2021/6698627

Oliver, R. L. (1980). A Cognitive Model of the Antecedents and Consequences of Satisfaction Decisions. Journal of Marketing Research, 17(4), 460–469. https://doi.org/10.2307/3150499

Oliver, R., Rust, R. T., & Varki, S. (1997). Customer delight: Foundations, findings, and managerial insight. Journal of Retailing, 73(3), 311–336. https://doi.org/10.1016/s0022-4359(97)90021-x

Pai, R. R., & Alathur, S. (2019). Assessing awareness and use of mobile phone technology for Health and Wellness: Insights from India. Health Policy and Technology, 8(3), 221–227. https://doi.org/10.1016/j.hlpt.2019.05.011

Parasuraman, A., Ball, J., Aksoy, L., Keiningham, T. L., & Zaki, M. (2020). More than a feeling? Toward a theory of customer delight. Journal of Service Management, 32(1), 1–26. https://doi.org/10.1108/josm-03-2019-0094

Rahi, S. (2021). Assessing individual behavior towards adoption of telemedicine application during COVID-19 pandemic: evidence from emerging market. Library Hi Tech, ahead-of-print(ahead-of-print). https://doi.org/10.1108/lht-01-2021-0030

Ringle, C. M., & Sarstedt, M. (2016). Gain more insight from your PLS-SEM results. Industrial Management & Data Systems, 116(9), 1865–1886. https://doi.org/10.1108/imds-10-2015-0449

Sarstedt, M., Hair, J. F., Pick, M., Liengaard, B. D., Radomir, L., & Ringle, C. M. (2022). Progress in partial least squares structural equation modeling use in marketing research in the last decade. Psychology & Marketing, 39. https://doi.org/10.1002/mar.21640

Sharma, P. N., Liengaard, B. D. D., Hair, J. F., Sarstedt, M., & Ringle, C. M. (2022). Predictive model assessment and selection in composite-based modeling using PLS-SEM: extensions and guidelines for using CVPAT. European Journal of Marketing. https://doi.org/10.1108/ejm-08-2020-0636

Shmueli, G., Sarstedt, M., Hair, J. F., Cheah, J.-H., Ting, H., Vaithilingam, S., & Ringle, C. M. (2019). Predictive model assessment in PLS-SEM: guidelines for using PLSpredict. European Journal of Marketing, 53(11), 2322–2347. https://doi.org/10.1108/ejm-02-2019-0189

Sousa, S., Lamas, D., & Dias, P. (2014). A model for Human-Computer Trust. Learning and Collaboration Technologies. Designing and Developing Novel Learning Experiences, 128–137. https://doi.org/10.1007/978-3-319-07482-5_13

Velsen, L. van, Tabak, M., & Hermens, H. (2017). Measuring patient trust in telemedicine services: Development of a survey instrument and its validation for an anticoagulation web-service. International Journal of Medical Informatics, 97, 52–58. https://doi.org/10.1016/j.ijmedinf.2016.09.009

Wulff, D. U., Hills, T. T., & Hertwig, R. (2014). Online Product Reviews and the Description-Experience Gap. Journal of Behavioral Decision Making, 28(3), 214–223. https://doi.org/10.1002/bdm.1841

Yang, M., Jiang, J., Kiang, M., & Yuan, F. (2021). Re-Examining the Impact of Multidimensional Trust on Patients’ Online Medical Consultation Service Continuance Decision. Information Systems Frontiers, 24(3), 983–1007. https://doi.org/10.1007/s10796-021-10117-9

Zhang, X., Yan, X., Cao, X., Sun, Y., Chen, H., & She, J. (2017). The role of perceived e-health literacy in users’ continuance intention to use mobile healthcare applications: an exploratory empirical study in China. Information Technology for Development, 24(2), 198–223. https://doi.org/10.1080/02681102.2017.1283286

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