Factors Influencing the Adoption of Online Pharmacies: A Post-Pandemic Study in Uttarakhand, India
DOI:
https://doi.org/10.52783/jns.v14.2680Keywords:
Telemedicine, e-pharmacy, PLS-SEM, online pharmacy, Technology Acceptance Model (TAM), Pharmaceutical industryAbstract
This study explores the key factors driving the acceptance of online pharmacies in Uttarakhand, India, leveraging the Technology Acceptance Model (TAM) and integrating constructs from the extended Unified Theory of Acceptance and Use of Technology (UTAUT 2) and Protection Motivation Theory (PMT).
Using a structured survey methodology, data were collected from 571 respondents across four districts of Uttarakhand: Dehradun, Haridwar, Nainital, and Almora. The research employed a partial least squares structural equation modeling (PLS-SEM) approach for data analysis, ensuring robust insights. The study identifies Perceived Ease of Use (PEOU), Perceived Usefulness (PU), Price Value (PV), Social Influence (SI), Facilitating Conditions (FC), and Perceived Vulnerability (PUV) as critical determinants of behavioral intention toward e-pharmacy adoption. Consumer education emerged as a significant moderating factor, revealing its pivotal role in enhancing the likelihood of telemedicine and e-pharmacy adoption.
This research fills a critical gap in understanding the factors influencing e-pharmacy adoption in India, particularly in the post-pandemic era. The integration of TAM, UTAUT 2, and PMT provides a comprehensive theoretical foundation for future studies on Health Information Technology (HIT) adoption. By examining the intersection of technology, healthcare, and consumer behavior, this study contributes to the evolving discourse on digital transformation in the pharmaceutical industry, paving the way for more inclusive and efficient healthcare delivery systems.
Downloads
Metrics
References
Ab Hamid, M. R., Sami, W., & Mohmad Sidek, M. H. (2017). Discriminant Validity Assessment: Use of Fornell & Larcker criterion versus HTMT Criterion. Journal of Physics: Conference Series, 890, 012163. https://doi.org/10.1088/1742-6596/890/1/012163
Adenuga, K. I., Iahad, N. A., & Miskon, S. (2017). Towards reinforcing telemedicine adoption amongst clinicians in Nigeria. International Journal of Medical Informatics, 104, 84–96. https://doi.org/10.1016/j.ijmedinf.2017.05.008
Ahalawat, K., Tiwari, R., Johri, A., Wasiq, M., & Sharma, A. (2024). Determinants influencing the adoption behavior of Indian consumers in reference to online pharmacy purchases. Cogent Business & Management, 11(1). https://doi.org/10.1080/23311975.2024.2436136
Ahlan, A. R., & Ahmad, B. I. (2014). User Acceptance of Health Information Technology (HIT) in Developing Countries: A Conceptual Model. Procedia Technology, 16, 1287–1296. https://doi.org/10.1016/j.protcy.2014.10.145
Ajzen, I. (1980). Understanding attitudes and predictiing social behavior. Englewood Cliffs.
Alba, J. W., & Hutchinson, J. W. (1987). Dimensions of Consumer Expertise. Journal of Consumer Research, 13(4), 411. https://doi.org/10.1086/209080
Balla, J., & Hagger, M. S. (2024). Protection motivation theory and health behaviour: conceptual review, discussion of limitations, and recommendations for best practice and future research. Health Psychology Review, 1–27. https://doi.org/10.1080/17437199.2024.2413011
Bhatt, S., Cheah, J., Singh, R., Desai, A., & Das, D. (2024). Order prescriptions online: Determinants of purchase satisfaction in an emerging Indian e‐pharmacy sector. International Social Science Journal, 74(254), 1649–1673.
Brown, & Venkatesh. (2005). Model of Adoption of Technology in Households: A Baseline Model Test and Extension Incorporating Household Life Cycle. MIS Quarterly, 29(3), 399. https://doi.org/10.2307/25148690
Chung, J. E. (2016). A Smoking Cessation Campaign on Twitter: Understanding the Use of Twitter and Identifying Major Players in a Health Campaign. Journal of Health Communication, 21(5), 517–526. https://doi.org/10.1080/10810730.2015.1103332
Dcruz, A. C., Mokashi, V. N., Pai, S. R., & Sreedhar, D. (2022). The rise of E-pharmacy in India. Indian Journal of Pharmacology, 54(4), 282–291. https://doi.org/10.4103/ijp.ijp_445_21
Deepika, S. R., Singh, T. G., Singh, M., Saini, B., Kaur, R., Arora, S., & Singh, R. (2020). Status of e-pharmacies in India: a review. Plant Arch, 20, 3763–3767.
DJ, B., P, V. S., DS, S., & WA, C. (2003). Evaluating a Spoken Dialogue System for recording clinical observations during an endoscopic examination. Medical Informatics and the Internet in Medicine, 28(2), 85–97. https://doi.org/10.1080/14639230310001600452
Dünnebeil, S., Sunyaev, A., Blohm, I., Leimeister, J. M., & Krcmar, H. (2012). Determinants of physicians’ technology acceptance for e-health in ambulatory care. International Journal of Medical Informatics, 81(11), 746–760. https://doi.org/10.1016/j.ijmedinf.2012.02.002
F. Hair Jr, J., Sarstedt, M., Hopkins, L., & G. Kuppelwieser, V. (2014). Partial least squares structural equation modeling (PLS-SEM). European Business Review, 26(2), 106–121. https://doi.org/10.1108/EBR-10-2013-0128
Faul, F., Erdfelder, E., Buchner, A., & Lang, A.-G. (2009). Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. Behavior Research Methods, 41(4), 1149–1160. https://doi.org/10.3758/BRM.41.4.1149
Featherman, M. S., & Pavlou, P. A. (2003). Predicting e-services adoption: a perceived risk facets perspective. International Journal of Human-Computer Studies, 59(4), 451–474. https://doi.org/10.1016/S1071-5819(03)00111-3
Fedorko, I., Bacik, R., & Gavurova, B. (2018). Technology acceptance model in e-commerce segment. Management & Marketing, 13(4), 1242–1256. https://doi.org/10.2478/mmcks-2018-0034
Fittler, A., Ambrus, T., Serefko, A., Smejkalová, L., Kijewska, A., Szopa, A., & Káplár, M. (2022). Attitudes and behaviors regarding online pharmacies in the aftermath of COVID-19 pandemic: At the tipping point towards the new normal. Frontiers in Pharmacology, 13. https://doi.org/10.3389/fphar.2022.1070473
Gao, Y., Li, H., & Luo, Y. (2015). An empirical study of wearable technology acceptance in healthcare. Industrial Management & Data Systems, 115(9), 1704–1723. https://doi.org/10.1108/IMDS-03-2015-0087
Greiwe, J. (2022). Telemedicine Lessons Learned During the COVID-19 Pandemic. Current Allergy and Asthma Reports, 22(1), 1–5. https://doi.org/10.1007/s11882-022-01026-1
Gupta, P., Prashar, S., Vijay, T. S., & Parsad, C. (2021). Examining the influence of antecedents of continuous intention to use an informational app: the role of perceived usefulness and perceived ease of use. International Journal of Business Information Systems, 36(2), 270. https://doi.org/10.1504/IJBIS.2021.112829
Hair, J. F., Ringle, C. M., Gudergan, S. P., Fischer, A., Nitzl, C., & Menictas, C. (2019). Partial least squares structural equation modeling-based discrete choice modeling: an illustration in modeling retailer choice. Business Research, 12(1), 115–142. https://doi.org/10.1007/s40685-018-0072-4
Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. In European Business Review (Vol. 31, Issue 1, pp. 2–24). Emerald Group Publishing Ltd. https://doi.org/10.1108/EBR-11-2018-0203
Hair, J. F., Tomas, G., Hult, M., Ringle, C. M., & Sarstedt, M. (2017). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). https://www.researchgate.net/publication/354331182
Han, L., & Han, X. (2023). The influence of price value on purchase intention among patients with chronic diseases in medical e-commerce during the COVID-19 pandemic in China. Frontiers in Public Health, 11. https://doi.org/10.3389/fpubh.2023.1081196
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8
H.H. Harman. (1976). Modern Factor Analysis. . University Press of Chicago, Chicago.
Holden, R. J., & Karsh, B.-T. (2010). The Technology Acceptance Model: Its past and its future in health care. Journal of Biomedical Informatics, 43(1), 159–172. https://doi.org/10.1016/j.jbi.2009.07.002
Hu, P. J., Chau, P. Y. K., Sheng, O. R. L., & Tam, K. Y. (1999). Examining the Technology Acceptance Model Using Physician Acceptance of Telemedicine Technology. Journal of Management Information Systems, 16(2), 91–112. https://doi.org/10.1080/07421222.1999.11518247
Itani, O. S., & Hollebeek, L. D. (2021). Light at the end of the tunnel: Visitors’ virtual reality (versus in-person) attraction site tour-related behavioral intentions during and post-COVID-19. Tourism Management, 84, 104290. https://doi.org/10.1016/j.tourman.2021.104290
Karahoca, A., Karahoca, D., & Aksöz, M. (2018). Examining intention to adopt to internet of things in healthcare technology products. Kybernetes, 47(4), 742–770. https://doi.org/10.1108/K-02-2017-0045
Kohnke, A., Cole, M. L., & Bush, R. (2014). Incorporating UTAUT Predictors for Understanding Home Care Patients’ and Clinician’s Acceptance of Healthcare Telemedicine Equipment. Journal of Technology Management & Innovation, 9(2), 29–41. https://doi.org/10.4067/S0718-27242014000200003
Kumar, R., & Patil, U. (2024). A Comprehensive Review of E-service Quality and Comparative Study Between Online Pharmacies in India: 1mg and PharmEasy. Jindal Journal of Business Research, 13(1), 57–81. https://doi.org/10.1177/22786821231177113
Liu, L., & Ma, Q. (2006). Perceived system performance. ACM SIGMIS Database: The DATABASE for Advances in Information Systems, 37(2–3), 51–59. https://doi.org/10.1145/1161345.1161354
López, F. J. M., García, C. P., Abad, J. C. G., & Ardura, I. R. (2016). Hedonic motivations in online consumption behaviour. International Journal of Business Environment, 8(2), 121. https://doi.org/10.1504/IJBE.2016.076628
Maarop, N., Win, K. T., Masrom, M., & Hazara Singh, S. S. (2011). Exploring teleconsultation acceptance: A comparison study between emergency and non-emergency setting. 2011 International Conference on Research and Innovation in Information Systems, 1–5. https://doi.org/10.1109/ICRIIS.2011.6125723
Mbelwa, J. T., Kimaro, H. C., & Mussa, B. (2019). Acceptability and Use of Mobile Health Applications in Health Information Systems: A Case of eIDSR and DHIS2 Touch Mobile Applications in Tanzania (pp. 579–592). https://doi.org/10.1007/978-3-030-18400-1_48
Miller, R., Wafula, F., Onoka, C. A., Saligram, P., Musiega, A., Ogira, D., Okpani, I., Ejughemre, U., Murthy, S., & Garimella, S. (2021). When technology precedes regulation: the challenges and opportunities of e-pharmacy in low-income and middle-income countries. BMJ Global Health, 6(5), e005405.
Moksony, F., & Heged, R. (1990). Small is beautiful. The use and interpretation of R2 in social research. Szociológiai Szemle, Special Issue, 130–138.
Nayak, B., Bhattacharyya, S. S. S., Kulkarni, O., & Mehdi, S. N. (2023). Adoption of online pharmacy applications during COVID-19 pandemic; empirical investigation in the Indian context from push-pull and mooring framework. Journal of Engineering, Design and Technology, 21(4), 1173–1196. https://doi.org/10.1108/JEDT-06-2021-0341
onaolapo, sodiq, & Oyewole, O. (2018). Performance Expectancy, Effort Expectancy, and Facilitating Conditions as Factors Influencing Smart Phones Use for Mobile Learning by Postgraduate Students of the University of Ibadan, Nigeria. Interdisciplinary Journal of E-Skills and Lifelong Learning, 14, 095–115. https://doi.org/10.28945/4085
Oriakhi, O. J., Almomani, H., Patel, N., & Donyai, P. (2024). The characteristics and operations of “online pharmacies” investigated in relation to medicines popularised during the coronavirus pandemic: a cross-sectional study. Frontiers in Pharmacology, 15, 1346604.
Owusu Kwateng, K., Osei Atiemo, K. A., & Appiah, C. (2019). Acceptance and use of mobile banking: an application of UTAUT2. Journal of Enterprise Information Management, 32(1), 118–151. https://doi.org/10.1108/JEIM-03-2018-0055
Ozili, P. K. (2023). The Acceptable R-Square in Empirical Modelling for Social Science Research (pp. 134–143). https://doi.org/10.4018/978-1-6684-6859-3.ch009
Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903. https://doi.org/10.1037/0021-9010.88.5.879
Rho, M. J., Choi, I. young, & Lee, J. (2014). Predictive factors of telemedicine service acceptance and behavioral intention of physicians. International Journal of Medical Informatics, 83(8), 559–571. https://doi.org/10.1016/j.ijmedinf.2014.05.005
Ringle, C. M. , W. S. , and B. J.-M. (2024). Smart PLS 4. In “SmartPLS 4.” Bönningstedt: SmartPLS, https://www.smartpls.com.
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
Rouidi, M., Elouadi, A. E., Hamdoune, A., Choujtani, K., & Chati, A. (2022). TAM-UTAUT and the acceptance of remote healthcare technologies by healthcare professionals: A systematic review. Informatics in Medicine Unlocked, 32, 101008. https://doi.org/10.1016/j.imu.2022.101008
Satheesh, G., Puthean, S., & Chaudhary, V. (2019). E-pharmacies in India: Can they improve the pharmaceutical service delivery? Journal of Global Health, 10(1). https://doi.org/10.7189/jogh.10.010302
Sezgin, E., Özkan-Yildirim, S., & Yildirim, S. (2018). Understanding the perception towards using mHealth applications in practice. Information Development, 34(2), 182–200. https://doi.org/10.1177/0266666916684180
Shailendra Sinhasane. (2018, September 11). Potential of E-pharmacy to Transform the Indian Pharma Sector. Https://Mobisoftinfotech.Com/Resources/Blog/e-Pharmacy-in-Indian-Pharma. https://mobisoftinfotech.com/resources/blog/e-pharmacy-in-indian-pharma
Shiferaw, K. B., Mengiste, S. A., Gullslett, M. K., Zeleke, A. A., Tilahun, B., Tebeje, T., Wondimu, R., Desalegn, S., & Mehari, E. A. (2021). Healthcare providers’ acceptance of telemedicine and preference of modalities during COVID-19 pandemics in a low-resource setting: An extended UTAUT model. PLOS ONE, 16(4), e0250220. https://doi.org/10.1371/journal.pone.0250220
Srivastava, M., & Raina, M. (2021). Consumers’ usage and adoption of e-pharmacy in India. International Journal of Pharmaceutical and Healthcare Marketing, 15(2), 235–250. https://doi.org/10.1108/IJPHM-01-2020-0006
Sun, Y., Wang, N., Guo, X., & Peng, Z. (2013). Understanding the acceptance of mobile health services: a comparison and integration of alternative models. Journal of Electronic Commerce Research, 14(2), 183.
TechSci Research. (2024). India E Pharmacy Market to Grow with a CAGR of 12.62% through 2030 . Https://Www.Techsciresearch.Com/News/21028-India-e-Pharmacy-Market.Html.
Thalkari, A. B., Karwa, P. N., & Gawli, C. S. (2018). A Review on Online Pharmacy: Views and Counterviews. Asian Journal of Pharmacy and Technology, 8(2), 108. https://doi.org/10.5958/2231-5713.2018.00017.X
TUNG, F., CHANG, S., & CHOU, C. (2008a). An extension of trust and TAM model with IDT in the adoption of the electronic logistics information system in HIS in the medical industry. International Journal of Medical Informatics, 77(5), 324–335. https://doi.org/10.1016/j.ijmedinf.2007.06.006
TUNG, F., CHANG, S., & CHOU, C. (2008b). An extension of trust and TAM model with IDT in the adoption of the electronic logistics information system in HIS in the medical industry. International Journal of Medical Informatics, 77(5), 324–335. https://doi.org/10.1016/j.ijmedinf.2007.06.006
Unni, E. J., Patel, K., Beazer, I. R., & Hung, M. (2021). Telepharmacy during COVID-19: A Scoping Review. Pharmacy, 9(4), 183. https://doi.org/10.3390/pharmacy9040183
Vărzaru, A. A., Bocean, C. G., Rotea, C. C., & Budică-Iacob, A.-F. (2021). Assessing Antecedents of Behavioral Intention to Use Mobile Technologies in E-Commerce. Electronics, 10(18), 2231. https://doi.org/10.3390/electronics10182231
Venkatesh, Morris, Davis, & Davis. (2003). User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly, 27(3), 425. https://doi.org/10.2307/30036540
Venkatesh, Thong, & Xu. (2012). Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology. MIS Quarterly, 36(1), 157. https://doi.org/10.2307/41410412
Venkatesh, V., & Davis, F. D. (2000). A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies. Management Science, 46(2), 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926
Vidal-Alaball, J., Acosta-Roja, R., Pastor Hernández, N., Sanchez Luque, U., Morrison, D., Narejos Pérez, S., Perez-Llano, J., Salvador Vèrges, A., & López Seguí, F. (2020). Telemedicine in the face of the COVID-19 pandemic. Atención Primaria, 52(6), 418–422. https://doi.org/10.1016/j.aprim.2020.04.003
Xiao, J., & Goulias, K. G. (2022). Perceived usefulness and intentions to adopt autonomous vehicles. Transportation Research Part A: Policy and Practice, 161, 170–185. https://doi.org/10.1016/j.tra.2022.05.007
Yi, M. Y., Jackson, J. D., Park, J. S., & Probst, J. C. (2006). Understanding information technology acceptance by individual professionals: Toward an integrative view. Information & Management, 43(3), 350–363. https://doi.org/10.1016/j.im.2005.08.006
Yu, J., Ha, I., Choi, M., & Rho, J. (2005). Extending the TAM for a t-commerce. Information & Management, 42(7), 965–976. https://doi.org/10.1016/j.im.2004.11.001
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
Terms:
- Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
