Skip to main content

Advertisement

SpringerLink
Log in

PLS-SEM path analysis to determine the predictive relevance of e-Health readiness assessment model

  • Original Paper
  • Published:
Health and Technology Aims and scope Submit manuscript

Abstract

There exist a sizable body of research addressing the evaluation of eHealth/health information technology (HIT) readiness using standard readiness model in the domain of Information Systems (IS). However, there is a general lack of reliable indicators used in measuring readiness assessment factors, resulting in limited predictability. The availability of reliable measuring tools could help improve outcomes of readiness assessments. In determining the predictive relevance of developed HIT model we collected quantitative data from clinical and non-clinical (administrators) staff at Komfo Anokye Teaching Hospital (KATH), Kumasi Ghana using the traditional in-person distribution of paper-based survey, popularly known as drop and collect survey (DCS). We then used PLS-SEM path analysis to measure the predictive relevance of a block of manifest indicators of the readiness assessment factors. Three important readiness assessment factors are thought to define and predict the structure of the KATH HIT/eHealth readiness survey data (Technology readiness (TR); Operational resource readiness (ORR); and Organizational cultural readiness (OCR). As many public healthcare organizations in Ghana have already gone paperless without any reliable HIT/eHealth guiding policy, there is a critical need for reliable HIT/eHealth regulatory policies readiness (RPR) and some improvement in HIT/eHealth strategic planning readiness (core readiness). The final model (R2 = 0.558 and Q2 = 0.378) suggest that TR, ORR, and OCR explained 55.8% of the total amount of variance in HIT/eHealth readiness in the case of KATH and the relevance of the overall paths of the model was predictive. Fit values (SRMR = 0.054; d_ULS = 6.717; d_G = 6.231; Chi2 = 6,795.276; NFI = 0.739). Generally, the GoF for this SEM are encouraging and can substantially be improved.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Notes

  1. eHealth, HIT and digital health are uses interchangeably in this paper.

  2. System quality is the technical make-up (software, data components, user interface and performance of a system that are measured by the ease of use, functionality) of the information system (IS) and its ability of the system to meet use requirement – conformity [24, 42, 99].

  3. Information quality relates to the quality of output in the context of content, presentation, and relevance of information (Ibid).

  4. Service quality denotes the overall support offered to system users to help in achieving their goals of using the IS (Ibid).

  5. Medical doctors, pharmacists, RNs, Lab technicians.

  6. Administrative officers.

References

  1. Acquah-Swanzy, M. Evaluating electronic health record systems in Ghana: the case of Effia Nkwanta regional hospital. 2015, UiT Norges arktiske universitet.

  2. Adebayo K, Ofoegbu E. Issues on E-health adoption in Nigeria. International Journal of Modern Education and Computer Science. 2014;6:36.

    Google Scholar 

  3. Adebesin F. et al. Barriers & challenges to the adoption of E-Health standards in Africa; 2013.

  4. Sullivan GM. A primer on the validity of assessment instruments. ACGME Suite 2000. 2011;515–9.

  5. Akosua A, Aseweh A. Financing public healthcare institutions in Ghana. Journal of Health Organization and Management. 2011;25:128–41.

    Google Scholar 

  6. Al-Adwan A, Berger H. Exploring physicians’ behavioural intention toward the adoption of electronic health records: an empirical study from Jordan. Int J Healthc Technol Manag. 2015;15:89–111.

    Google Scholar 

  7. Al Sallakh M, Rodgers S, Lyons R, Sheikh A, Davies G. Socioeconomic deprivation and inequalities in asthma care in Wales. Lancet. 2017;390:S19.

    Google Scholar 

  8. Alarcón D, Sánchez J. Assessing convergent and discriminant validity in the ADHD-R IV rating scale: user-written commands for Average Variance Extracted (AVE), Composite Reliability (CR), and Heterotrait-Monotrait ratio of correlations (HTMT). Spanish Stata Meeting; 2015.

  9. Albers S. PLS and success factor studies in marketing. Handbook of partial least squares; 2010. p. 409–25.

  10. Arpaci I. A theoretical framework for IT consumerization: factors influencing the adoption of BYOD. Handbook of research on technology integration in the global world. IGI Global; 2019.

  11. Bagozzi R, Yi Y. On the evaluation of structural equation models. J Acad Mark Sci. 1988;16:74–94.

    Google Scholar 

  12. Bangert D, Doktor R. The role of organizational culture in the management of clinical e-health systems. System Sciences, 2003. Proceedings of the 36th Annual Hawaii International Conference on, 2003. IEEE, 9 pp.

  13. Barzekar H, Karami M. Organizational factors that affect the implementation of information technology: perspectives of middle managers in Iran. Acta Informatica Medica. 2014;22:325.

    Google Scholar 

  14. Bedeley R, Palvia P. A study of the issues of e-health care in developing countries: The case of Ghana; 2014:2.

  15. Bentler P, Bonett D. Significance tests and goodness of fit in the analysis of covariance structures. Psychol Bull. 1980;88:588.

    Google Scholar 

  16. Bland JM, Altman D. Statistics notes: Cronbach’s alpha. BMJ. 1997;314:572.

    Google Scholar 

  17. Bollen K. A new incremental fit index for general structural equation models. Sociol Methods Res. 1989;17:303–16.

    Google Scholar 

  18. Brown S. Drop and collect surveys: a neglected research technique? Market Intell Plan. 1987;5:19–23.

    Google Scholar 

  19. Byrne B. Structural equation modeling with EQS and EQS/Windows: basic concepts applications and programming. Thousand Oaks: Sage; 1994.

    Google Scholar 

  20. Cain M, Zhang Z, Yuan K. Univariate and multivariate skewness and kurtosis for measuring nonnormality: prevalence, influence and estimation. Behav Res Methods. 2016; 1–20.

  21. Chin W. Commentary: issues and opinion on structural equation modeling. 1998, JSTOR 3.

  22. Cohen J. Statistical power analysis for the behavioral sciences. Hilsdale: Lawrence Earlbaum Associates; 1988. p. 2.

    MATH  Google Scholar 

  23. Dansky K, Gamm L, Vasey J, Barsukiewicz C. Electronic medical records: are physicians ready? J Healthc Manag. 1999;44:440–54.

    Google Scholar 

  24. Delone W, Mclean E. Information systems success: the quest for the dependent variable. Inf Syst Res. 1992;3:60–95.

    Google Scholar 

  25. Delone W, Mclean E. The DeLone and McLean model of information systems success: a ten-year update. J Manag Inf Syst. 2003;19:9–30.

    Google Scholar 

  26. Delone W, Mclean E. Measuring e-commerce success: applying the DeLone & McLean information systems success model. Int J Electron Commer. 2004;9:31–47.

    Google Scholar 

  27. Dewi MAA, Hidayanto AN, Purwandari B, Kosandi M, Budi NFA. Smart City readiness model using Technology-Organization-Environment (TOE) framework and its effect on adoption decision. PACIS; 2018. p. 268.

  28. Dwivedi Y, Wade M, Schneberger S. Information systems theory: explaining and predicting our digital society. New York: Springer Science & Business Media; 2012.

    Google Scholar 

  29. Eden K, Totten A, Kassakian S, Gorman P, Mcdonagh M, Devine B, Pappas M, Daeges M, Woods S, Hersh W. Barriers and facilitators to exchanging health information: a systematic review. Int J Med Inform. 2016;88:44–51.

    Google Scholar 

  30. Eigner I, Hamper A, Wickramasinghe N, Bodendorf F. Success factors for National eHealth Strategies: a comparative analysis of the Australian and German eHealth system. Int J Networking Virtual Organ. 2019;21:399–424.

    Google Scholar 

  31. Faber S, Van Geenhuizen M, De Reuver M. eHealth adoption factors in medical hospitals: a focus on the Netherlands. Int J Med Inform. 2017;100:77–89.

    Google Scholar 

  32. Fanta GB, Pretorius L. A conceptual framework for sustainable eHealth implementation in resource-constrained settings. S Afr J Ind Eng. 2018;29:132–47.

    Google Scholar 

  33. Fanta G, Pretorius L, Erasmus L. A system dynamics model of eHealth acceptance: a sociotechnical perspective. International Association For Management Of Technology IAMOT; 2016; p. 259–72.

  34. Fanta G, Pretorius L, Erasmus L. Organizational dynamics of sustainable eHealth implementation: a case study of Ehmis. 2017 Portland International Conference on Management of Engineering and Technology (PICMET), 2017. IEEE, p. 1–9.

  35. Fornell C, Larcker D. Evaluating structural equation models with unobservable variables and measurement error. J Mark Res. 1981;18:39–50.

    Google Scholar 

  36. Freeze R, Alshare K, Lane P, Wen H. Is success model in e-learning context based on students’ perceptions. J Inf Syst Educ. 2019;21:4.

    Google Scholar 

  37. Fricker S, Thümmler C, Gavras A. Requirements engineering for digital health. Cham: Springer; 2015.

    Google Scholar 

  38. Garson G. Partital least sqaures: ression & structural equation models. Asheboro: G. David Garson and Statistical Associates Publishing; 2016.

    Google Scholar 

  39. Gefen D, Straub D, Boudreau M. Structural equation modeling and regression: guidelines for research practice. Commun Assoc Inf Syst. 2000;4:7.

    Google Scholar 

  40. Gholamhosseini L, Ayatollahi H. The design and application of an e-health readiness assessment tool. Health Information Management Journal. 2017;46:32–41.

    Google Scholar 

  41. Gil-Garcia J. Using partial least squares in digital government research, in Handbook of research on public information technology. 2008, IGI Global. p. 239-253. 4.

  42. Gorla N, Somers T, Wong B. Organizational impact of system quality, information quality, and service quality. J StrategInfSyst. 2010;19:207–28.

    Google Scholar 

  43. Gregory M, Tembo S. Implementation of E-health in developing countries challenges and opportunities: a case of Zambia. Science and Technology. 2017;7:41–53.

    Google Scholar 

  44. Grisot M, Vassilakopoulou P. Re-infrastructuring for eHealth: dealing with turns in infrastructure development. Comput Supported Coop Work. 2017;26:7–31.

    Google Scholar 

  45. Hair J Jr, Black W, Babin B, Anderson R. Multivariate data analysis; a global perspective (Ed.). New Jersey: Pearson Education Inc.; 2010, p. 5.

  46. Hair F Jr, Sarstedt M, Hopkins L, Kuppelwieser V. Partial least squares structural equation modeling (PLS-SEM): an emerging tool in business research. Eur Bus Rev. 2014;26:106–21.

    Google Scholar 

  47. Hair J Jr, Hult GT, Ringle C, Sarstedt M. A primer on partial least squares structural equation modeling (PLS-SEM). Thousand Oaks: Sage Publications; 2016.

    MATH  Google Scholar 

  48. Hair J, Hollingsworth C, Randolph A, Chong A. An updated and expanded assessment of PLS-SEM in information systems research. Industrial Management & Data systems. 2017;117:442–58.

    Google Scholar 

  49. Hao J, Shi H, Shi V, Yang C. Adoption of automatic warehousing systems in logistics firms: a technology–organization–environment framework. Sustainability. 2020;12:5185.

    Google Scholar 

  50. Harding K, Biks GA, Adefris M, Loehr J, Gashaye K, Tilahun B, Volynski M, Garg S, Abebaw Z, Dessie K. A mobile health model supporting Ethiopia’s eHealth strategy. Digital Medicine. 2018;4:54.

    Google Scholar 

  51. Henseler J, Ringle C, Sarstedt M. A new criterion for assessing discriminant validity in variance-based structural equation modeling. J Acad Mark Sci. 2015;43:115–35.

    Google Scholar 

  52. Henseler J, Hubona G, Ray P. Using PLS path modeling in new technology research: updated guidelines. Industrial Management & Data Systems. 2016;116:2–20.

    Google Scholar 

  53. Herath TC, Herath HS, D’Arcy J. Organizational adoption of information security solutions: an integrative lens based on innovation adoption and the technology-organization-environment framework. ACM SIGMIS Database: the DATABASE for Advances in Information Systems. 2020;51:12–35.

    Google Scholar 

  54. Hu L, Bentler P. Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal. 1999;6:1–55.

    Google Scholar 

  55. Hue TT. The determinants of innovation in vietnamese manufacturing firms: an empirical analysis using a technology–organization–environment framework. Eurasian Business Review. 2019;9:247–67.

    Google Scholar 

  56. Hulland J. Use of partial least squares (PLS) in strategic management research: a review of four recent studies. Strat Manag J. 1999;20:195–204.

    Google Scholar 

  57. Hung S-Y, Hung W-H, Tsai C-A, Jiang S-C. Critical factors of hospital adoption on CRM system: organizational and information system perspectives. Decis Support Syst. 2010;48:592–603.

    Google Scholar 

  58. Jaana M, Tamim H, Paré G, Teitelbaum M. Key IT management issues in hospitals: results of a Delphi study in Canada. Int J Med Inform. 2011;80:828–40.

    Google Scholar 

  59. Jalghoum Y, Tahtamouni A, Khasawneh S, Al-Madadha A. Challenges to healthcare information systems development: the case of Jordan. International Journal of Healthcare Management; 1–9; 2019.

  60. Kline R. Principles and practice of structural equation modeling. New York: Guilford Publications; 2015.

    MATH  Google Scholar 

  61. Koivumäki T, Pekkarinen S, Lappi M, Väisänen J, Juntunen J, Pikkarainen M. Consumer adoption of future MyData-based preventive eHealth services: an acceptance model and survey study. Journal of Medical Internet Research. 2017;19:E429.

    Google Scholar 

  62. Kupek E. Beyond logistic regression: structural equations modelling for binary variables and its application to investigating unobserved confounders. BMC Med Res Methodol. 2006;6:13.

    Google Scholar 

  63. Kwao L, Millham R, Opanin Gyamfi E. An integrated success model for adopting biometric authentication technique for District Health Information Management System 2, Ghana. Ghana (February 20, 2020); 2020. 5.

  64. Landis-Lewis Z, Manjomo R, Gadabu O, Kam M, Simwaka B, Zickmund S, Chimbwandira F, Douglas G, Jacobson R. Barriers to using eHealth data for clinical performance feedback in Malawi: a case study. Int J Med Inform. 2015;84:868–75.

    Google Scholar 

  65. Lee K, Che S. Introduction to partial least square: common criteria and practical considerations. Advanced materials research. Trans Tech Publ; 2013. p. 1766–9.

  66. Lennon M, Bouamrane M-M, Devlin A, O'Connor S, O'Donnell C, Chetty U. et al. Readiness for delivering digital health at scale: lessons from a longitudinal qualitative evaluation of a national digital health innovation program in the United Kingdom. Journal of Medical Internet Research. 2017;19:E42.

    Google Scholar 

  67. Li J, Talaei-Khoei A, Seale H, Ray P, Macintyre C. Health care provider adoption of eHealth: systematic literature review. Interactive Journal of Medical Research. 2013;2:e7.

    Google Scholar 

  68. Lin H-F. Understanding the determinants of electronic supply chain management system adoption: using the technology–organization–environment framework. Technol Forecast Soc Chang. 2014;86:80–92.

    Google Scholar 

  69. Lomax R, Schumacker R. A beginner’s guide to structural equation modeling. New York: Routledge Academic; 2012.

    MATH  Google Scholar 

  70. Long L-A, Pariyo G, Kallander K. Digital technologies for health workforce development in low-and middle-income countries: a scoping review. Global Health: Science and Practice. 2018;6:S41–8.

    Google Scholar 

  71. Lowry P, Gaskin J. 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 Trans Prof Commun. 2014;57:123–46 (139).

    Google Scholar 

  72. Matar N, Alnabhan M. Evaluating E-Health services and patients requirements in Jordanian Hospitals. Int Arab J E-Technol. 2014;3:250–7.

    Google Scholar 

  73. Maunder K, Walton K, Williams P, Ferguson M, Beck E. A Framework for eHealth readiness of dietitians. Int J Med Inform. 2018;115:43–52.

    Google Scholar 

  74. Mcgowan J, Cusack C, Bloomrosen M. The future of health it innovation and informatics: a report from AMIA’s 2010 policy meeting. J Am Med Inform Assoc. 2012;19:460–7.

    Google Scholar 

  75. Mertes A, Brüesch C. Stakeholder participation in eHealth policy: a Swiss case study on the incorporation of stakeholder preferences. IRSPM 22nd Annual Conference, Edinburgh, Scotland, 11–13 April 2018. International Research Society for Public Management; 2018. p. 1–23.

  76. Mettler T, Vimarlund V. Evaluation of E-Health strategies: a portfolio approach. The 15th International Symposium for Health Information Management Research (ISHIMR 2011), Sept 8–9, Zurich; 2011.

  77. Moss S. Fit indices for structural equation modeling. Website: https://www.psych-it.com.au/Psychlopedia/article.asp; 2009.

  78. Nunnally J. Psychometric theory. New York: Mcgraw-Hill; 1978.

    Google Scholar 

  79. Ojo A. Validation of the DeLone and McLean information systems success model. Healthcare Informatics Research. 2017;23:60–6.

    Google Scholar 

  80. Oliveira T, Martins M. Information technology adoption models at firm level: review of literature. European Conference on Information Management and Evaluation. Academic Conferences International Limited; 2011. p. 312.

  81. Omotosho A, Ayegba P, Emuoyibofarhe J, Meinel C. Current state of ICT in healthcare delivery in developing countries. International Journal of Online Engineering. 2019;15:91–107.

    Google Scholar 

  82. Pan M-J, Jang W-Y. Determinants of the adoption of enterprise resource planning within the technology-organization-environment framework: Taiwan’s communications industry. J Comput Inf Syst. 2008;48:94–102.

    Google Scholar 

  83. Party AW. WP 131, 11. Working document on the processing of personal data relating to health in electronic health records (EHR). Adopted on 2007 (WP 131); 2007.

  84. Ramayah T, et al. Testing a confirmatory model of facebook usage in smartPLS using consistent PLS. IJBI, 2017;3(2):1-14. 6.

  85. Ribes D, Polk J. Flexibility relative to what? Change to research infrastructure. J Assoc Inf Syst. 2014;15:1.

    Google Scholar 

  86. Sanchez G. Pls path modeling with R. Berkeley: Trowchez Editions; 2013.

    Google Scholar 

  87. Scherer R, Siddiq F, Tondeur J. The technology acceptance model (TAM): a meta-analytic structural equation modeling approach to explaining teachers’ adoption of digital technology in education. Comput Educ. 2019;128:13–35.

    Google Scholar 

  88. Schreiber J, Nora A, Stage F, Barlow E, King J. reporting structural equation modeling and confirmatory factor analysis results: a review. J Educ Res. 2006;99:323–38.

    Google Scholar 

  89. Scott R, Mars M. Principles and framework for eHealth strategy development. Journal of Medical Internet Research. 2013;15:e155.

    Google Scholar 

  90. Shim M, Jo H. What quality factors matter in enhancing the perceived benefits of online health information sites? Application of the updated DeLone and McLean information systems success model. Int J Med Inform. 2020;137:104093.

    Google Scholar 

  91. Singeh FW, Abrizah A, Kiran K. Bringing the digital library success factors into the realm of the technology-organization-environment framework. The Electronic Library. 2020;7.

  92. Sullivan G. A primer on the validity of assessment instruments. Chicago: The Accreditation Council for Graduate Medical Education Suite; 2011.

    Google Scholar 

  93. Sunny S, Patrick L, Rob L. Impact of cultural values on technology acceptance and technology readiness. Int J Hosp Manag. 2019;77:89–96.

    Google Scholar 

  94. Teo T, Lin S, Lai K-H. Adopters and non-adopters of e-procurement in Singapore: an empirical study. Omega. 2009;37:972–87.

    Google Scholar 

  95. Tornatzky L, Fleischer M. The process of technology innovation. Lexington: Lexington Books; 1990.

    Google Scholar 

  96. Urbach N, Ahlemann F. Structural equation modeling in information systems research using partial least squares. JITTA: Journal of Information Technology Theory and Application. 2010;11:5.

    Google Scholar 

  97. Van Velsen L, Evers M, Bara C-D, Den Akker H, Boerema S, Hermens H. Understanding the acceptance of an ehealth technology in the early stages of development: an end-user walkthrough approach and two case studies. JMIR Formative Research. 2018;2:E10474.

    Google Scholar 

  98. Veinot T, Ancker J, Bakken S. Health informatics and health equity: improving our reach and impact. J Am Med Inform Assoc. 2019;26:689–95.

    Google Scholar 

  99. Venkatesh V, Bala H. Technology acceptance model 3 and a research agenda on interventions. Decision Sciences. 2008;39:273–315.

    Google Scholar 

  100. Vinzi V, Chin W, Henseler J, Wang H. Handbook of partial least squares. Berlin: Springer; 2010.

    MATH  Google Scholar 

  101. Wang C, Ku E. eHealth in kidney care. Nat Rev Nephrol. 2020;1–3.

  102. William C. 22 Privacy and security: privacy of personal eHealth data in low-and middle-income countries. Global health informatics: principles of EHealth and MHealth to improve quality of care, p. 269.

  103. World Health Organization. Country coorperation strategy brief, Ghana. WHO; 2014.

  104. Yusif S, Soar J. Preparedness for e-Health in developing countries: the case of Ghana. JHIDC. 2014;8:18–37.

    Google Scholar 

  105. Yusif S, Hafeez-Baig A, Soar J. E-health readiness assessment factors and measuring tools: a systematic review. Int J Med Inform. 2017;107:56–64.

    Google Scholar 

  106. Yusif S, Hafeez-Baig A, Soar J. An exploratory study of the readiness of public healthcare facilities in developing countries to adopt Health Information Technology (HIT)/e-Health: the case of Ghana. Journal of Healthcare Informatics Research; 2020.

  107. Yusif S, Hafeez-Baig A, Soar, J. A model for evaluating ehealth preparedness–a case study approach. Transforming Government: People, Process and Policy; 2020.

  108. Zakaria N, Yusof S, Zakaria N. Managing ICT in healthcare organization: culture, challenges, and issues of. Handbook of research on advances in health informatics and electronic healthcare applications: global adoption and impact of information communication technologies: global adoption and impact of information communication technologies; 2009, p. 153.

  109. Zayyad M, Toycan M. Factors affecting sustainable adoption of e-health technology in developing countries: an exploratory survey of nigerian hospitals from the perspective of healthcare professionals. PeerJ. 2018;6:E4436.

    Google Scholar 

Download references

Acknowledgments

The authors wish to acknowledge the support of the Australian Government Research Training Program (AGRTP)

Author information

Authors and Affiliations

  1. School of Management and Enterprise, University of Southern Queensland, 37 Sinnathamby Blvd, Springfield Central, QLD, 4300, Australia

    Salifu Yusif

  2. School of Management and Enterprise, University of Southern Queensland, Toowoomba, QLD, 4350, Australia

    Abdul Hafeez-Baig, Jeffrey Soar & Derek Ong Lai Teik

  3. Department of Marketing, Sunway University Business School Sunway University, 47500, Selangor Darul Ehsan, Malaysia

    Derek Ong Lai Teik

Authors
  1. Salifu Yusif
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Abdul Hafeez-Baig
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jeffrey Soar
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Derek Ong Lai Teik
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Salifu Yusif.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by of the authors.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendices

Appendix 1

Table 5 Qualitative research sample
Full size table

Appendix 2

Table 6 Quantitative research sample
Full size table
Table 7 Full description of measuring tools for readiness assessment factors
Full size table

Appendix 3

Fig. 4
figure 4

PLS ouput showing outerloadings for indicators

Full size image

Appendix 4

Fig. 5
figure 5

PLS3 Output showing t-statistics from bootstrapping procedure

Full size image

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yusif, S., Hafeez-Baig, A., Soar, J. et al. PLS-SEM path analysis to determine the predictive relevance of e-Health readiness assessment model. Health Technol. 10, 1497–1513 (2020). https://doi.org/10.1007/s12553-020-00484-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12553-020-00484-9

Keywords

Search

Navigation