Development and validation of the quality of online health information seeking: Psychometric properties and a confirmatory factor analysis

Main Article Content

Sharifah Sumayyah Engku Alwi
Masrah Azrifah Azmi Murad
Salfarina Abdullah
Azrina Kamaruddin


This study aims to investigate the development and validation of all items and dimensions for the quality of online health information seeking (QHIS), which was assessed using a five-point interval self-report rating measure. This measure is proposed to evaluate the quality of online health information seeking among Malaysian consumers through confirmatory factor analysis (CFA). A total of 392 responses were collected from Malaysian consumers using the simple random sampling method. The pooled-CFA procedures were conducted to validate all dimensions at once. When the findings were acquired, the study performed the validation procedure for construct validity, convergent validity, composite reliability and discriminant validity. Results from CFA confirmed the validity of the QHIS measure by generating good data-model fit statistics characterised by strong latent construct and internal reliability estimates. Based on the self-reported scores, it also concluded that the QHIS scale had good convergent validity. The findings also reported that composite reliability and discriminant validity for all latent constructs in QHIS had been achieved accordingly. These findings present initial justification indicating that QHIS is reliable and valid and can be utilised to assess the quality of online health information seeking among Malaysian consumers. Limitations are explored, and recommendations for future research and practice are provided.


Download data is not yet available.

Article Details

How to Cite
Engku Alwi, S. S., Azmi Murad, M. A., Abdullah, S., & Kamaruddin, A. (2022). Development and validation of the quality of online health information seeking: Psychometric properties and a confirmatory factor analysis. Malaysian Journal of Library and Information Science, 27(3), 49–68.


Afthanorhan, A., Awang, Z., Rashid, N., Foziah, H. and Ghazali, P., 2019. Assessing the effects of service quality on customer satisfaction. Management Science Letters, Vol. 9, no. 1: 13-24. Available at: doi:

Ajuwon, G.A. and Popoola, S.O., 2015. Influence of motivational factors on utilisation of Internet health information resources by resident doctors in Nigeria. The Electronic Library, Vol. 3, no. 1: 103-119. Available at: doi:

Alias, N., Awang, Z., Muda, H. and Mazlan, N.H., 2021. Supporting the Policy Implementation Performance of Public Primary School Leaders in Malaysia: A Confirmatory Factor Analysis (CFA) of Public Leadership. The Journal of Management Theory and Practice (JMTP), Vol. 2, no. 4: 1-7. Available at: doi:

Asnawi, A., Awang, Z., Afthanorhan, A., Mohamad, M. and Karim, F., 2019. The influence of hospital image and service quality on patients’ satisfaction and loyalty. Management Science Letters, Vol. 9, no. 6: 911-920. Available at: doi:

Awang Z, Lim SH, Zainudin NFS. 2018. Pendekatan Mudah SEM-Structural Equation Modelling. Bandar Baru Bangi: MPWS Rich Resources.

Awang, Z., Afthanorhan, A., Mohamad, M. and Asri, M.A.M., 2015. An evaluation of measurement model for medical tourism research: the confirmatory factor analysis approach. International Journal of Tourism Policy, Vol. 6, no. 1: 29-45. Available at: doi:

Azevedo, L.B., Stephenson, J., Ells, L., Adu‐Ntiamoah, S., DeSmet, A., Giles, E.L., Haste, A., O'Malley, C., Jones, D., Chai, L.K. and Burrows, T., 2022. The effectiveness of e‐health interventions for the treatment of overweight or obesity in children and adolescents: A systematic review and meta‐analysis. Obesity Reviews, Vol. 23, no. 2: e13373. Available at: doi:

Baistaman, J., Awang, Z., Nawawi, R., Arifin, N., Mustapha, W.M. and Shari, A.S., 2020. Assessing Measurement Model of Malaysian Voluntary Saving Decision for Future Retirement Planning using Confirmatory Factor Analysis. International Journal of Accounting, Finance and Business (IJAFB), Vol. 5, no. 26: 1-7. Available at: doi:

Brashers, D.E., Neidig, J.L., Haas, S.M., Dobbs, L.K., Cardillo, L.W. and Russell, J.A., 2000. Communication in the management of uncertainty: The case of persons living with HIV or AIDS. Communications Monographs, Vol. 67, no. 1: 63-84. Available at: doi:

Bundorf, M.K., Wagner, T.H., Singer, S.J. and Baker, L.C., 2006. Who searches the internet for health information?. Health services research, Vol. 41, no.3p1: 819-836. Available at: doi:

Cao, W., Zhang, X., Xu, K. and Wang, Y., 2016. Modeling online health information-seeking behavior in China: The roles of source characteristics, reward assessment, and internet self-efficacy. Health Communication, Vol. 31, no. 9: 1105-1114. Available at: doi:

Chang, C.C. and Huang, M.H., 2020. Antecedents predicting health information seeking: a systematic review and meta-analysis. International Journal of Information Management, Vol. 54: 102115. Available at: doi:

Cotten, S.R. and Gupta, S.S., 2004. Characteristics of online and offline health information seekers and factors that discriminate between them. Social science and medicine, Vol. 59, no. 9: 1795-1806. Available at: doi:

Cutrona, S.L., Mazor, K.M., Vieux, S.N., Luger, T.M., Volkman, J.E. and Finney Rutten, L.J., 2015. Health information-seeking on behalf of others: characteristics of “surrogate seekers”. Journal of cancer education, Vol. 30, no. 1: 12-19. Available at: doi:

Daud, M.H., Ramli, A.S., Abdul-Razak, S., Isa, M.R., Yusoff, F.H., Baharudin, N., Mohamed-Yassin, M.S., Badlishah-Sham, S.F., Nikmat, A.W., Jamil, N. and Mohd-Nawawi, H., 2020. The EMPOWER-SUSTAIN e-Health Intervention to improve patient activation and self-management behaviours among individuals with Metabolic Syndrome in primary care: study protocol for a pilot randomised controlled trial. Trials, Vol. 21, no. 1: 1-16. Available at: doi:

Davis, F.D., 1989. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, MIS Quarterly, Vol. 13, no. 3: 319-340. Available at: doi:

Davis, F.D., Bagozzi, R.P. and Warshaw, P.R., 1989. User acceptance of computer technology: A comparison of two theoretical models. Management science, Vol. 35, no. 8:982-1003. Available at: doi:

DeLone, W.H. and McLean, E.R., 1992. Information systems success: The quest for the dependent variable. Information systems research, Vol. 3, no. 1: 60-95. Available at: doi:

Eysenbach, G., Köhler, C. 2002. How do consumers search for and appraise health information on the world wide web? Qualitative study using focus groups, usability tests, and in-depth interviews. BMJ, Vol. 324, no. 7337: 573-577. Available at: doi:

Finney Rutten, L.J., Agunwamba, A.A., Wilson, P., Chawla, N., Vieux, S., Blanch-Hartigan, D., Arora, N.K., Blake, K. and Hesse, B.W., 2016. Cancer-related information seeking among cancer survivors: trends over a decade (2003–2013). Journal of Cancer Education, Vol. 31, no. 2: 348-357. Available at: doi:

Finney Rutten, L.J., Blake, K.D., Greenberg-Worisek, A.J., Allen, S.V., Moser, R.P. and Hesse, B.W., 2019. Online health information seeking among US adults: measuring progress toward a healthy people 2020 objective. Public Health Reports, Vol. 134, no. 6: 617-625. Available at: doi:

Flickinger, T.E., DeBolt, C., Wispelwey, E., Laurence, C., Plews-Ogan, E., Waldman, A.L., Reynolds, G., Cohn, W.F., Beach, M.C., Ingersoll, K. and Dillingham, R., 2016. Content analysis and user characteristics of a smartphone-based online support group for people living with HIV. Telemedicine and e-Health, Vol. 22, no. 9: 746-754. Available at: doi:

Fox, M. D., Snyder, A. Z., Vincent, J. L., Corbetta, M., Van Essen, D. C., and Raichle, M. E. 2005. The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proceedings of the National Academy of Sciences, Vol 102, no. 27: 9673-9678. Available at: doi:

Fox, S., 2011. The social life of health information. California Healthcare Foundation.

Gabarron, E. and Wynn, R., 2016. Use of social media for sexual health promotion: a scoping review. Global health action, Vol. 9. No. 1: 32193. Available at: doi:

Gabarron, E., Luque, L.F., Schopf, T.R., Lau, A.Y., Armayones, M., Wynn, R. and Serrano, J.A., 2017. Impact of facebook ads for sexual health promotion via an educational web app: a case study. International Journal of E-Health and Medical Communications (IJEHMC), Vol. 8, no. 2: 18-32. Available at: doi:

Gable, G.G., Sedera, D. and Chan, T., 2008. Re-conceptualizing information system success: The IS-impact measurement model. Journal of the association for information systems, Vol. 9, no. 7:18. Available at: doi:

Guo, P., Qiao, W., Sun, Y., Liu, F. and Wang, C., 2020. Telemedicine technologies and tuberculosis management: a randomized controlled trial. Telemedicine and e-Health, Vol. 26, no. 9: 1150-1156. Available at: doi:

Haase, J., Farris, K.B. and Dorsch, M.P., 2017. Mobile applications to improve medication adherence. Telemedicine and e-Health, Vol. 23, no. 2: 75-79. Available at: doi:

Hair Jr, J.F., Hult, G.T.M., Ringle, C.M. and Sarstedt, M., 2021. A primer on partial least squares structural equation modeling (PLS-SEM). Sage publications. Available at: doi:

Hale, T.M., Cotten, S.R., Drentea, P. and Goldner, M., 2010. Rural-urban differences in general and health-related internet use. American Behavioral Scientist, Vol. 53, no. 9: 1304-1325. Available at: doi:

Kampmeijer, R., Pavlova, M., Tambor, M., Golinowska, S. and Groot, W., 2016. The use of e-health and m-health tools in health promotion and primary prevention among older adults: a systematic literature review. BMC Health Services Research, Vol. 16, no. 5: 467-479. Available at: doi:

Kassulke, D., Stenner‐Day, K., Coory, M. and Ring, I., 1993. Information‐seeking behaviour and sources of health information: associations with risk factor status in an analysis of three Queensland electorates. Australian Journal of Public Health, Vol. 17, no. 1: 51-57. Available at: doi:

Kim, J. and Park, H.A., 2012. Development of a health information technology acceptance model using consumers’ health behavior intention. Journal of medical Internet research, Vol. 14, no. 5: e133. Available at: doi:

Kim, W., Kreps, G.L. and Shin, C.N., 2015. The role of social support and social networks in health information–seeking behavior among Korean Americans: a qualitative study. International journal for equity in health, Vol. 14, no. 1: 1-10. Available at: doi:

Lambert, S.D. and Loiselle, C.G., 2007. Health information—seeking behavior. Qualitative health research, Vol. 17, no. 8: 1006-1019. Available at: doi:

Li, J., Theng, Y.L. and Foo, S., 2016. Predictors of online health information seeking behavior: Changes between 2002 and 2012. Health informatics journal, Vol. 22, no. 4: 804-814. Available at: doi:

Liang, H., Xue, Y. and Chase, S.K., 2011. Online health information seeking by people with physical disabilities due to neurological conditions. International Journal of Medical Informatics, Vol.80, no. 11: 745-753. Available at: doi:

Liebermann, Y. and Stashevsky, S., 2002. Perceived risks as barriers to Internet and e‐commerce usage. Qualitative Market Research: An International Journal, Vol. 5, no. 4: 291-300. Available at: doi:

Lim, M.S., Hocking, J.S., Aitken, C.K., Fairley, C.K., Jordan, L., Lewis, J.A. and Hellard, M.E., 2012. Impact of text and email messaging on the sexual health of young people: a randomised controlled trial. J Epidemiol Community Health. Vol. 66, no. 1: 69-74. Available at: doi:

Lowry, P.B. and Gaskin, J., 2014. Partial least squares (PLS) structural equation modeling (SEM) for building and testing behavioral causal theory: When to choose it and how to use it. IEEE transactions on professional communication, Vol. 57, no. 2: 123-146. Available at: doi:

Lv, Y., Zhao, H., Liang, Z., Dong, H., Liu, L., Zhang, D. and Cai, S., 2012. A mobile phone short message service improves perceived control of asthma: a randomized controlled trial. Telemedicine and e-Health, Vol. 18, no. 6: 420-426. Available at: doi:

Mahfouz, S.A., Awang, Z., Muda, H. and Bahkia, A.S., 2020. Mediating role of employee commitment in the relationship between transformational leadership style and employee performance. Humanities & Social Sciences Reviews, Vol. 8, no. 2: 624-637. Available at: doi:

Marco-Ruiz, L., Wynn, R., Oyeyemi, S.O., Budrionis, A., Yigzaw, K.Y. and Bellika, J.G., 2020. Impact of Illness on Electronic Health Use (The Seventh Tromsø Study-Part 2): Population-Based Questionnaire Study. Journal of medical Internet research, Vol. 22, no. 3: e13116. Available at: doi:

Mohamad, M., Afthanorhan, A., Awang, Z. and Mohammad, M., 2019. Comparison between CB-SEM and PLS-SEM: Testing and confirming the maqasid syariah quality of life measurement model. The Journal of Social Sciences Research, Vol. 5, no. 3: 608-614. Available at: doi:

Mou, J., Shin, D.H. and Cohen, J., 2016. Health beliefs and the valence framework in health information seeking behaviors. Information Technology & People, Vol. 29, no. 4: 876-900. Available at: doi:

Neubeck, L., Coorey, G., Peiris, D., Mulley, J., Heeley, E., Hersch, F. and Redfern, J., 2016. Development of an integrated e-health tool for people with, or at high risk of, cardiovascular disease: The Consumer Navigation of Electronic Cardiovascular Tools (CONNECT) web application. International journal of medical informatics, Vol. 96: 24-37. Available at: doi:

Oh, H. J., and Lee, B. 2012. The effect of computer-mediated social support in online communities on patient empowerment and doctor-patient communication. Health Communication, Vol. 27, no. 1: 30-41. Available at: doi:

Omboni, S., Panzeri, E. and Campolo, L., 2020. E-health in hypertension management: an insight into the current and future role of blood pressure telemonitoring. Current Hypertension Reports, Vol. 22, no. 6: 1-13. Available at: doi:

Oyeyemi, S.O., Gabarron, E. and Wynn, R., 2014. Ebola, Twitter, and misinformation: a dangerous combination?. Bmj., Vol. 349. Available at: doi:

Oyeyemi, S.O. and Wynn, R., 2014. Giving cell phones to pregnant women and improving services may increase primary health facility utilization: a case–control study of a Nigerian project. Reproductive health, Vol. 11, no. 1: 1-8. Available at: doi:

Percheski, C. and Hargittai, E., 2011. Health information-seeking in the digital age. Journal of American College Health, Vol. 59, no. 5: 379-386. Available at: doi:

Ponciano-Rodríguez, G., Reynales-Shigematsu, L.M., Rodríguez-Bolaños, R., Pruñonosa-Santana, J., Cartujano-Barrera, F. and Cupertino, A.P., 2019. Enhancing smoking cessation in Mexico using an e-Health tool in primary healthcare. salud pública de méxico, Vol. 60: 549-558. Available at: doi:

Prestin, A., Vieux, S.N. and Chou, W.Y.S., 2015. Is online health activity alive and well or flatlining? Findings from 10 years of the Health Information National Trends Survey. Journal of health communication, Vol. 20, no. 7: 790-798. Available at: doi:

Rahlin, N.A., Awang, Z., Abd Rahim, M.Z. and Bahkia, A.S., 2020. The Direct Effect of Climate Emergency and Safety Climate On Intention To Safety Behavior: A Study. Humanities & Social Sciences Reviews, Vol. 8, no. 3: 178-189. Available at: doi:

Rai, A., Lang, S.S. and Welker, R.B., 2002. Assessing the validity of IS success models: An empirical test and theoretical analysis. Information systems research, Vol. 13, no. 1: 50-69. Available at: doi:

Raza, I. and Awang, Z., 2020. Knowledge-sharing practices in higher educational institutes of Islamabad, Pakistan: an empirical study based on theory of planned behavior. Journal of Applied Research in Higher Education, Vol. 13, no. 2: 466-484. Available at: doi:

Risling, T., Martinez, J., Young, J. and Thorp-Froslie, N., 2017. Evaluating patient empowerment in association with eHealth technology: scoping review. Journal of medical Internet research, Vol. 19, no. 9: e329. Available at: doi:

Rosenstock, I.M., 1974. The health belief model and preventive health behavior. Health education monographs, Vol. 2, no. 4: 354-386. Available at: doi:

Seo, H.J., Kim, S.Y., Sheen, S.S. and Cha, Y., 2021. e-Health Interventions for Community-Dwelling Type 2 Diabetes: A Scoping Review. Telemedicine and e-Health, Vol. 27, no. 3: 276-285. Available at: doi:

Shkeer, A.S. and Awang, Z., 2019. Exploring the items for measuring the marketing information system construct: An exploratory factor analysis. International Review of Management and Marketing, Vol. 9, no. 6: 87-97. Available at: doi:

Sousa, V.E. and Lopez, K.D., 2017. Towards usable e-health. Applied clinical informatics, Vol. 8, no. 2: 470-490. Available at: doi:

Tan, S. S. L. and Goonawardene, N. 2017. Internet Health Information Seeking and the Patient Physician Relationship: A Systematic Review. J Med Internet Res., Vol. 19, no. 1: e9. Available at: doi:

Taylor, S. and Todd, P., 1995. Decomposition and crossover effects in the theory of planned behavior: A study of consumer adoption intentions. International journal of research in marketing, Vol. 12, no. 2:137-155. Available at: doi:

Tran, B.X., Le, X.T.T., Nguyen, P.N., Le, Q.N.H., Mai, H.T., Nguyen, H.L.T., Le, H.T., Tran, T.T., Latkin, C.A., Zhang, M.W. and Ho, R., 2018. Feasibility of e-health interventions on smoking cessation among Vietnamese active internet users. International Journal of Environmental Research and Public Health, Vol. 15, no. 1:165. Available at: doi:

Upadhyay, S., Lord, J. and Gakh, M., 2019. Health-information seeking and intention to quit smoking: Do health beliefs have a mediating role?. Tobacco use insights, Vol. 12: 1179173X19871310. Available at: doi:

Venkatesh, V., Thong, J.Y. and Xu, X., 2012. Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS quarterly, Vol. 36, no. 1: 157-178. Available at: doi:

Wynn, R., Oyeyemi, S.O., Johnsen, J.A. and Gabarron, E., 2017. Tweets are not always supportive of patients with mental disorders. Int J Integr Care, Vol. 17, no. 3: 149. Available at: doi:

Wynn, R., Oyeyemi, S.O., Budrionis, A., Marco-Ruiz, L., Yigzaw, K.Y. and Bellika, J.G., 2020. Electronic health use in a representative sample of 18,497 respondents in Norway (the seventh Tromsø study-part 1): population-based questionnaire study. JMIR medical informatics, Vol. 8, no. 3: e13106. Available at: doi:

Xia, L., Deng, S. and Liu, Y., 2017. Seeking health information online: the moderating effects of problematic situations on user intention. Journal of Data and Information Science, Vol. 2, no. 2: 76-95. Available at: doi:

Yoo, E.Y. and Robbins, L.S., 2008. Understanding middle‐aged women's health information seeking on the web: A theoretical approach. Journal of the American Society for Information Science and Technology, Vol. 59, no. 4: 577-590. Available at: doi:

Zimmerman, M.S. and Shaw Jr, G., 2020. Health information seeking behaviour: a concept analysis. Health Information & Libraries Journal, Vol. 37, no. 3: 173-191. Available at: doi: