Impact of Digital Adoption on Consumer Trust and Risk Perceptions
DOI:
https://doi.org/10.63062/trt/WR25.068Keywords:
Social Cognitive Theory, Online Pharmacies, Self-Efficacy, Trust, Perceived Risk, Social Influence, Observational Learning, PLS-SEM.Abstract
Using Social Cognitive Theory (SCT) as the base this research evaluates the elements affecting the digital adoption. The analysis evaluates the effects of self-efficacy, social influence, perceived trust, perceived risk together with facilitating conditions on the behavioral intention of online pharmacy adoption. The study evaluates how observational learning impacts the connection between behavioral intentions to adopt digital options. The research implements a quantitative design through survey data collection from 350 participants using online digital platforms. To measure the effectiveness, a 5-point Likert scale was used. Data collection used convenience sampling methods while the data analysis involved PLS-SEM (Smart PLS 4) to evaluate the assumed relationships. Behavioral intention receives significant impact from self-efficacy together with perceived trust but adoption behavior experiences positive effects due to perceived risk. Social influence strengthens trust and adoption between consumers and online pharmacies and observational learning provides additional advantages to their engagement. This research yields guidance which helps both online pharmacy providers and policymakers and healthcare regulatory bodies. The adoption rates can expand by strengthening trust mechanisms and implementing digital literacy education together with maintaining regulatory compliance.
References
Adebo, A. I., Aladelusi, K., & Mohammed, M. (2024). Determinants of e-pharmacy adoption and the mediating role of social influence among young users. Journal of Humanities and Applied Social Sciences. 7(1). https://doi.org/10.1108/jhass-12-2023-0164
Ahmad, A. H., Idris, I., Ahmad, A. H., Masri, R., Chong, C. V., Ula, R., & Fauzi, A. (2020). Evolution of Technology and Consumer Behaviour: The Unavoidable Impacts. https://doi.org/10.31838/jcr.07.19.457
Akram, U., Junaid, M., Ullah, A., Li, Z., & Fan, M. (2021). Journal of Retailing and Consumer Services Online purchase intention in Chinese social commerce platforms: Being emotional or rational? Journal of Retailing and Consumer Services, 63(June), 102669. https://doi.org/10.1016/j.jretconser.2021.102669
Al Halbusi, H., Al-Sulaiti, K., Abdelfattah, F., Ahmad, A. B., & Hassan, S. (2024). Understanding consumers’ adoption of e-pharmacy in Qatar: applying the unified theory of acceptance and use of technology. Journal of Science and Technology Policy Management. 16(3). https://doi.org/10.1108/JSTPM-03-2023-0042
Alalwan, A. A., Algharabat, R., Baabdullah, A. M., Rana, N. P., Qasem, Z., & Dwivedi, Y. K. (2020). Examining the Impact of Mobile Interactivity on Customer Engagement in the Context of Mobile Shopping.
Alalwan, A. A., Dwivedi, Y. K., & Rana, N. P. (2017). Factors influencing adoption of mobile banking by Jordanian bank customers: Extending UTAUT2 with trust. International Journal of Information Management, 37, 99–110. https://doi.org/10.1016/j.ijinfomgt.2017.01.002
Alalwan, A. A., Dwivedi, Y. K., Rana, N. P., & Algharabat, R. (2018). Examining Factors Influencing Jordanian Customers’ Intentions and Adoption of Internet Banking: Extending UTAUT2 with Risk.
Almaiah, M. A., Alfaisal, R., Salloum, S. A., Hajjej, F., Shishakly, R., Lutfi, A., Alrawad, M., Al Mulhem, A., Alkhdour, T., & Al-Maroof, R. S. (2022). Measuring Institutions’ Adoption of Artificial Intelligence Applications in Online Learning Environments: Integrating the Innovation Diffusion Theory with Technology Adoption Rate. Electronics (Switzerland), 11. https://doi.org/10.3390/electronics11203291
Almaiah, M. A., Al-Otaibi, S., Shishakly, R., Hassan, L., Lutfi, A., Alrawad, M., Qatawneh, M., & Alghanam, O. A. (2023). Investigating the Role of Perceived Risk, Perceived Security and Perceived Trust on Smart m-Banking Application Using SEM. Sustainability (Switzerland), 15(13). https://doi.org/10.3390/su15139908
Almulla, M. A., & Al-Rahmi, W. M. (2023). Integrated Social Cognitive Theory with Learning Input Factors: The Effects of Problem-Solving Skills and Critical Thinking Skills on Learning Performance Sustainability. Sustainability (Switzerland), 15(5). https://doi.org/10.3390/su15053978
Alraja, M. N., Farooque, M. M. J., & Khashab, B. (2019). The Effect of Security, Privacy, Familiarity, and Trust on Users’ Attitudes Toward the Use of the IoT-Based Healthcare: The Mediation Role of Risk Perception. IEEE Access, 7, 111341–111354. https://doi.org/10.1109/ACCESS.2019.2904006
Alsadoun, A. A., Tangiisuran, B., & Iskandar, Y. H. P. (2023). The effect of perceived risk, technology trust, and technology awareness on the consumer’s behavioural intention to adopt online pharmacy. International Journal of Electronic Healthcare, 13(1), 33–56. https://doi.org/10.1504/IJEH.2023.10052702
Anuar, A., Saad, R., & Yusoff, R. Z. (2018). Operational Performance and Lean Healthcare in the Healthcare Sector: Review on the Dimensions and Relationships. International Journal of Academic Research in Business and Social Sciences, 8(4). https://doi.org/10.6007/ijarbss/v8-i4/4014
Assin T.J, V., A. George, N., Aboobaker, N., & P, S. (2024). Emerging market dynamics: risk perceptions, perceived usefulness and E-pharmacy adoption. International Journal of Pharmaceutical and Healthcare Marketing.19(1). https://doi.org/10.1108/IJPHM-11-2023-0101
Baid, A. N., & Ghosh, A. (2021). Factors Affecting the Shift of Consumers Towards E-Pharmacies. In UGC Care Journal 44(1).
Bakar, A. A., Ong, S. C., Chuo, Y. T., Ooi, G. S., & Hassali, M. A. A. (2022). Barriers for Implementation of E-pharmacy Policy: Views of Pharmacy Authorities, Public Institutions and Societal Groups. Pertanika Journal of Social Sciences and Humanities, 30, 41–56. https://doi.org/10.47836//pjssh.30.1.03
Bandura, A. (2013). Health promotion from the perspective of social cognitive theory Understanding and changing health behaviour. Https://Citeseerx.Ist.Psu.Edu/Document?Repid=rep1&type=pdf&doi=b74c3c816a71b971823ebebca6f79d3ef5e2ceb5
Bandura, A. (2023). Social Cognitive Theory. Wiley. https://doi.org/10.1002/9781394259069
Blut, M., Yee, A., Chong, L., Tsiga, Z., & Venkatesh, V. (2022). jais-journal of the association for information systems meta-analysis of the unified theory of acceptance and use of technology (utaut): challenging its validity and charting a research agenda in the red ocean. https://ssrn.com/abstract=3963030
Boyd, R., Richerson, P. J., & Henrich, J. (2011). The cultural niche : Why social learning is essential for human adaptation. 108. https://doi.org/10.1073/pnas.1100290108
Chandra, S., Srivastava, S. C., & Theng, Y.-L. (2010). Evaluating the Role of Trust in Consumer Adoption of Mobile Payment Systems: An Empirical Analysis. Communications of the Association for Information Systems, 27. https://doi.org/10.17705/1cais.02729
Chang, Y. W., Hsu, P. Y., Chen, J., Shiau, W. L., & Xu, N. (2023). Utilitarian and/or hedonic shopping – consumer motivation to purchase in smart stores. Industrial Management and Data Systems, 123(3), 821–842. https://doi.org/10.1108/IMDS-04-2022-0250
Chatterjee, J., Neogi, S. G., Dwivedi, R. K., & Vashisht, A. (2024). Consumer Perspectives for Purchase Intentions of Online Pharmacy Products Using Deep Learning. 2024 11th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions), ICRITO 2024. https://doi.org/10.1109/ICRITO61523.2024.10522354
Chen, A., Lu, Y., & Wang, B. (2017). International Journal of Information Management Customers’ purchase decision-making process in social commerce : A social learning perspective. International Journal of Information Management, 37(6), 627–638. https://doi.org/10.1016/j.ijinfomgt.2017.05.001
Cheung, M. L., Chau, K. Y., Sum Lam, M. H., Tse, G., Ho, K. Y., Flint, S. W., Broom, D. R., Tso, E. K. H., & Lee, K. Y. (2019). Examining consumers’ adoption of wearable healthcare technology: The role of health attributes. International Journal of Environmental Research and Public Health, 16. https://doi.org/10.3390/ijerph16132257
Deng, X., & Yu, Z. (2023). An extended hedonic motivation adoption model of TikTok in higher education. Education and Information Technologies, 28(10), 13595–13617. https://doi.org/10.1007/s10639-023-11749-x
Dwivedi, Y. K., Rana, N. P., Jeyaraj, A., Clement, M., & Williams, M. D. (2019). Re-examining the Unified Theory of Acceptance and Use of Technology (UTAUT): Towards a Revised Theoretical Model. Information Systems Frontiers, 21(3), 719–734. https://doi.org/10.1007/s10796-017-9774-y
Emon, M. M. H., Khan, T., Rahman, M. A., & Siam, S. A. J. (2024). Factors Influencing the Usage of Artificial Intelligence among Bangladeshi Professionals: Mediating role of Attitude Towards the Technology. 2024 IEEE Conference on Computing Applications and Systems, COMPAS 2024. https://doi.org/10.1109/COMPAS60761.2024.10796110
Erfanian, S., Maleknia, R., & Halalisan, A. F. (2024). Application of social cognitive theory to determine shaping factors of environmental intention and behaviors of ecotourist in forest areas. Frontiers in Forests and Global Change, 7. https://doi.org/10.3389/ffgc.2024.1489170
Esmaeilzadeh, P. (2024). Challenges and strategies for wide-scale artificial intelligence (AI) deployment in healthcare practices: A perspective for healthcare organizations. Artificial Intelligence in Medicine, 151, 102861. https://doi.org/10.1016/J.ARTMED.2024.102861
Ezeudoka, B. C., & Fan, M. (2024). Determinants of behavioral intentions to use an E-Pharmacy service: Insights from TAM theory and the moderating influence of technological literacy. Research in Social and Administrative Pharmacy, 20(7), 605–617. https://doi.org/10.1016/J.SAPHARM.2024.03.007
Fan, M., & Ukaegbu, O. C. (2024). Information literacy and intention to adopt e-pharmacy: a study based on trust and the theory of reasoned action. BMC Health Services Research, 24(1). https://doi.org/10.1186/s12913-024-11301-8
Fedorko, I., Bacik, R., & Gavurova, B. (2021). Effort expectancy and social influence factors as main determinants of performance expectancy using electronic banking. Banks and Bank Systems, 16(2), 27–37. https://doi.org/10.21511/bbs.16(2).2021.03
Fornell, C., & Larcker, D. F. (1981). Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. In Source: Journal of Marketing Research 18(1).
Ghahtarani, A., Sheikhmohammady, M., & Rostami, M. (2020). The impact of social capital and social interaction on customers’ purchase intention, considering knowledge sharing in social commerce context. Suma de Negocios, 5(3), 191–199. https://doi.org/10.1016/j.jik.2019.08.004
Hair, J., Hollingsworth, C. L., Randolph, A. B., & Chong, A. Y. L. (2017). An updated and expanded assessment of PLS-SEM in information systems research. Industrial Management and Data Systems, 117, 442–458. https://doi.org/10.1108/IMDS-04-2016-0130
Hansen, J. M., Saridakis, G., & Benson, V. (2018). Risk, trust, and the interaction of perceived ease of use and behavioral control in predicting consumers’ use of social media for transactions. Computers in Human Behavior, 80, 197–206. https://doi.org/10.1016/j.chb.2017.11.010
Huang, J., Baptista, J., & Galliers, R. D. (2013). Reconceptualizing rhetorical practices in organizations: The impact of social media on internal communications. Information and Management, 50(2–3), 112–124. https://doi.org/10.1016/j.im.2012.11.003
Jiang, G., Ma, F., Shang, J., & Chau, P. Y. K. (2014). Evolution of knowledge sharing behavior in social commerce : An agent-based computational approach. INFORMATION SCIENCES. https://doi.org/10.1016/j.ins.2014.03.051
Kock, N. (2015). Common method bias in PLS-SEM: A full collinearity assessment approach. International Journal of e-Collaboration 11(4).
Lee, U. K., & Kim, H. (2022). UTAUT in Metaverse: An “Ifland” Case. Journal of Theoretical and Applied Electronic Commerce Research, 17, 613–635. https://doi.org/10.3390/jtaer17020032
Li, L., Wang, Z., Li, Y., & Liao, A. (2021). Consumer innovativeness and organic food adoption: The mediation effects of consumer knowledge and attitudes. Sustainable Production and Consumption, 28, 1465–1474. https://doi.org/10.1016/j.spc.2021.08.022
Li, S., Hong, Y. C., & Craig, S. D. (2023). A Systematic Literature Review of Social Learning Theory in Online Learning Environments. Educational Psychology Review, 35(4), 1–29. https://doi.org/10.1007/S10648-023-09827-0/TABLES/6
Lim, F.-W., Fakhrorazi, A., Ikhsan, R., Silitonga, K., Loke, W.-K., & Abdullah, N. (2020). The Role of Personal Innovativeness and Facilitating Conditions in Shaping the Attitudes of Mobile Internet Banking (MIB) Adoption among Generation Y in Malaysia. https://doi.org/10.20944/preprints202003.0407.v1
MacKenzie, S. B., & Podsakoff, P. M. (2012). Common Method Bias in Marketing: Causes, Mechanisms, and Procedural Remedies. Journal of Retailing, 88(4), 542–555. https://doi.org/10.1016/j.jretai.2012.08.001
Mehta, P., Singla, H., Saha, R., & Tyagi, S. (2021). A Pathway to Technology Integration: Eliciting Consumer’s Behavioural Intention to Use Paytm Services. Paradigm, 25(1), 7–24. https://doi.org/10.1177/09718907211003712
Mohd Thas Thaker, H., Mohd Thas Thaker, M. A., Khaliq, A., Allah Pitchay, A., & Iqbal Hussain, H. (2022). Behavioural intention and adoption of internet banking among clients’ of Islamic banks in Malaysia: an analysis using UTAUT2. Journal of Islamic Marketing, 13(5), 1171–1197. https://doi.org/10.1108/JIMA-11-2019-0228/FULL/XML
Muhammad Cevin Yunior, & Yvonne Augustine Sudibijo. (2024). The Influence of Social Influence, Relative Advantage, User Satisfaction on Cloud-Based E-Learning with Behavioral Intention as a Mediating Variable. Technium Social Sciences Journal, 56, 36–50. https://doi.org/10.47577/TSSJ.V56I1.10742
Müller, C., & Mildenberger, T. (2021). Facilitating flexible learning by replacing classroom time with an online learning environment: A systematic review of blended learning in higher education. Educational Research Review 34. https://doi.org/10.1016/j.edurev.2021.100394
Nitzl, C., & Chin, W. W. (2017). The case of partial least squares (PLS) path modeling in managerial accounting research. Journal of Management Control, 28(2), 137–156. https://doi.org/10.1007/s00187-017-0249-6
Ott, D. L. (2024). Social learning theory. Elgar Encyclopedia of Cross-Cultural Management, 133–134.
Kotler, H. K. I. S. (2017). 2017. Marketing 4.0-Moving from Traditional to Digital.
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. Elsevier Ltd. https://doi.org/10.1016/j.imu.2022.101008
Sang, G., Wang, K., Li, S., Xi, J., & Yang, D. (2023). Effort expectancy mediate the relationship between instructors’ digital competence and their work engagement: evidence from universities in China. Educational Technology Research and Development, 71(1), 99–115. https://doi.org/10.1007/s11423-023-10205-4
Singh, H., Malviya, N., & Majumdar, A. (2020). E-Pharmacy impacts on society and pharma sector in economical pandemic situation: a review. Journal of Drug Delivery and Therapeutics.
Speak, A., Escobedo, F. J., Russo, A., & Zerbe, S. (2018). An ecosystem service-disservice ratio: Using composite indicators to assess the net benefits of urban trees. Ecological Indicators, 95, 544–553. https://doi.org/10.1016/j.ecolind.2018.07.048
Tannady, H., Dewi, C. S., & Gilbert. (2024). Exploring Role of Technology Performance Expectancy, Application Effort Expectancy, Perceived Risk and Perceived Cost On Digital Behavioral Intention of GoFood Users. Jurnal Informasi Dan Teknologi, 80–85. https://doi.org/10.60083/jidt.v6i1.477
Venkatesh, V. (2022). Adoption and use of AI tools: a research agenda grounded in UTAUT. Annals of Operations Research, 308(1–2), 641–652. https://doi.org/10.1007/s10479-020-03918-9
Venkatesh, V., Thong, J. Y. L., & Xu, X. (2016). Unified theory of acceptance and use of technology: A synthesis and the road ahead. Journal of the Association for Information Systems, 17, 328–376. https://doi.org/10.17705/1jais.00428
Zhong, Y., Oh, S., & Moon, H. C. (2021). Service transformation under industry 4.0: Investigating acceptance of facial recognition payment through an extended technology acceptance model. Technology in Society, 64. https://doi.org/10.1016/j.techsoc.2020.101515
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Dr. Sohaib Uz Zaman, Maha Mateen, Syed Hasnain Alam

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.