Electronic Markets

, Volume 20, Issue 3–4, pp 209–227 | Cite as

Service quality of mHealth platforms: development and validation of a hierarchical model using PLS

Special Theme


Advancing research on service quality requires clarifying the theoretical conceptualizations and validating an integrated service quality model. The purpose of this study is to facilitate and elucidate practical issues and decisions related to the development of a hierarchical service quality model in mobile health (mHealth) services research. Conceptually, it extends theory by reframing service quality as a reflective, hierarchical construct and modeling its impact on satisfaction, intention to continue using and quality of life. Empirically, it confirms that PLS path modeling can be used to estimate the parameters of a higher order construct and its association with subsequent consequential latent variables in a nomological network. The findings of the study show that service quality is the third-order, reflective construct model with strong positive effects on satisfaction, continuance intentions and quality of life in the context of mHealth services. Finally, the study discusses the implications of hierarchical service quality modeling in electronic markets and highlights future research directions.


Service quality Satisfaction Intention to continue using Quality of life PLS path modeling 


I11 L80 M15 M31 



This research was funded by the Asia Pacific Ubiquitous Healthcare Research Centre (APuHC), University of New South Wales, Australia. The authors appreciate and gratefully acknowledge constructive comments of Prof. Richard Vidgen (University of New South Wales) and Prof. Wynne W. Chin (University of Houston).


  1. Aharony, L., & Strasser, S. (1993). Patient satisfaction: what we know about and what we still need to explore. Medical Care Review, 50(1), 49–79.CrossRefGoogle Scholar
  2. Ahluwalia, P., & Varshney, U. (2009). Composite quality of service and decision making perspectives in wireless networks. Decision Support Systems, 46(2), 542–551.CrossRefGoogle Scholar
  3. Akter, S., & Ray, P. (2010). mHealth-an ultimate platform to serve the unserved. IMIA Yearbook of Medical Informatics, 2010, 75–81.Google Scholar
  4. Akter, S., D’Ambra, J., Ray, P. (2010). User perceived services quality of mHealth services in developing countries. In the Proceedings of the Eighteen European Conference on conference on Information Systems, Pretoria, South Africa.Google Scholar
  5. Andaleeb, S. S. (2001). Service quality perceptions and patient satisfaction: a study of hospitals in a developing country. Social Science & Medicine, 52(9), 1359–1370.CrossRefGoogle Scholar
  6. Andrade, R., Wangenheim, A. V., & Bortoluzzi, M. K. (2003). Wireless and PDA: a novel strategy to access DICOMcompliant medical data on mobile devices. International Journal of Medical Informatics, 71(23), 157–163.CrossRefGoogle Scholar
  7. Angst, M. C., & Agarwal, R. (2009). Adoption of electronic health records in the presence of privacy concerns: the elaborate likelihood model and individual persuasion. MIS Quarterly, 33(2), 339–370.Google Scholar
  8. Bailey, J. E., & Pearson, S. W. (1983). Development of a tool for measuring and analyzing computer user satisfaction. Management Science, 29(5), 530–545.CrossRefGoogle Scholar
  9. Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research: conceptual, strategic and statistical considerations. Journal of Personality and Social Psychology, 51(6), 1173–1182.CrossRefGoogle Scholar
  10. Baroudi, J. J., & Orlikowski, W. J. (1988). A short-form measure of user information satisfaction: a psychometric evaluation and notes on use. Journal of MIS, 4(4), 44–59.Google Scholar
  11. Baroudi, J. J., & Orlikowski, W. J. (1989). The problem of statistical power in MIS research. MIS Quarterly, 13(1), 87–106.CrossRefGoogle Scholar
  12. Bhattacherjee, A. (2001). Understanding information systems continuance. An expectation–confirmation model. MIS Quarterly, 25(3), 351–370.CrossRefGoogle Scholar
  13. Bitner, M. J. (1990). Evaluating Service Encounters: The Effects of Physical Surrounding and Employee Responses. Journal of Marketing, 54(2), 69-81.Google Scholar
  14. Bollen, K. A., & Lennox, R. (1991). Conventional wisdom on measurement: a structural equation perspective. Psychological Bulletin, 110(2), 305–314.CrossRefGoogle Scholar
  15. Brady, M. K., & Cronin, J. J. (2001). Some new thoughts on conceptualizing perceived service quality: a hierarchical approach. Journal of Marketing, 65, 34–49.CrossRefGoogle Scholar
  16. Campbell, A., Converse, P. E., & Rodgers, W. L. (1976). The quality of American life. New York: Russel Sage Foundation.Google Scholar
  17. Chae, M., Kim, J., Kim, H., & Ryu, H. (2002). Information quality for mobile internet services: a theoretical model with empirical validation. Electronic Markets, 12, 38–46.CrossRefGoogle Scholar
  18. Chatterjee, S., Chakraborty, S., Sarker, S., Sarker, S., & Lau, F. Y. (2009). Examining the success factors for mobile work in healthcare: a deductive study. Decision Support Systems, 46(3), 620–633.CrossRefGoogle Scholar
  19. Chin, W. W. (1998). Issues and opinion on structural equation modeling. MIS Quarterly, 22(1), vii–xvi.Google Scholar
  20. Chin, W. W. (2001). PLS – Graph User’s Guide Version 3.0., Houston, TX: Soft Modeling Inc.Google Scholar
  21. Chin, W. W. (2010). How to write up and report PLS analyses. In V. Esposito Vinzi, W. W. Chin, J. Henseler, & H. Wang (Eds.), Handbook of partial least squares: Concepts, methods and application (pp. 645–689). Germany: Springer.Google Scholar
  22. Chin, W. W., & Gopal, A. (1995). Adoption intention in GSS: importance of beliefs. Data Base Advance, 26, 42–64.Google Scholar
  23. Chin, W. W., & Newsted, P. R. (1999). Structural equation modeling analysis with small samples using partial least squares. In R. Hoyle (Ed.), Statistical strategies for small sample research (pp. 307–341). Thousand Oaks: Sage Publications.Google Scholar
  24. Choi, H., Lee, M., Lm, K. S., & Kim, J. (2007). Contribution to quality of life: a new outcome variable for mobile data service. Journal of the Association for Information Systems, 8(12), 598–618.Google Scholar
  25. Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale: L. Erlbaum Associates.Google Scholar
  26. Cohen, J. (1992). A power primer. Psychological Bulletin, 112(1), 155–159.CrossRefGoogle Scholar
  27. Cronin, J. J., & Taylor, S. A. (1992). Measuring service quality: a reexamination and extension. Journal of Marketing, 56, 55–68.CrossRefGoogle Scholar
  28. Dabholkar, P. A., Thorpe, D. I., & Rentz, J. O. (1996). A measure of service quality for retail stores: scale development and validation. Journal of the Academy of Marketing Science, 24(1), 3–16.CrossRefGoogle Scholar
  29. Dabholkar, P. A., David, C. S., & Dayle, I. T. (2000). A comprehensive framework for service quality: an investigation of critical conceptual and measurement issues through a longitudinal study. Journal of Retailing, 72(2), 139–173.CrossRefGoogle Scholar
  30. Dagger, T. S., & Sweeney, C. J. (2006). The effect of service evaluations on behavioral intentions and quality of life. Journal of Service Research, 9(1), 3–18.CrossRefGoogle Scholar
  31. Dagger, T. S., Sweeney, J. C., & Johnson, L. W. (2007). A hierarchical model of health service quality: scale development and investigation of an integrated model. Journal of Service Research, 10(2), 123–142.CrossRefGoogle Scholar
  32. Davis, F. D. (1989). Perceived usefulness, perceived ease of use and user acceptance of information technology. MIS Quarterly, 13(3), 318–339.CrossRefGoogle Scholar
  33. De Ruyter, K., & Wetzels, M. (1998). On the complex nature of patient evaluations of general practice service. Journal of Economic Psychology, 19, 565–590.CrossRefGoogle Scholar
  34. DeLone, W. H., & McLean, E. R. (1992). Information systems success: the quest for the dependent variable. Information Systems Research, 3(1), 60–95.CrossRefGoogle Scholar
  35. DeLone, W. H., & McLean, E. R. (2003). The DeLone and McLean model of information systems success: a ten-year update. Journal of Management Information Systems, 19(4), 9–30.Google Scholar
  36. Diener, E. (1984). Subjective well-being. Psychological Bulletin, 95, 542–575.CrossRefGoogle Scholar
  37. Edwards, J. R. (2001). Multidimensional constructs in organizational behavior research: an integrative analytical framework. Organizational Research Methods, 4(2), 144–192.CrossRefGoogle Scholar
  38. Edwards, J. R., & Bagozzi, R. P. (2000). On the nature and direction of relationships between constructs. Psychological Methods, 5(2), 155–174.CrossRefGoogle Scholar
  39. Efron, B., & Tibshirani, R. J. (1993). An introduction to the bootstrap. New York: Chapman and Hall.Google Scholar
  40. Fassnacht, M. A., & Koese, I. (2006). Quality of electronic services: conceptualizing and testing a hierarchical model. Journal of Service Research, 9(19), 19–37.CrossRefGoogle Scholar
  41. Faul, F., Erdfelder, E., Buchner, A., & Lang, G. A. (2009). Statistical power analyses using G*Power 3.1: tests for correlation and regression analyses. Behavior Research Methods, 41(4), 1149–1160.CrossRefGoogle Scholar
  42. Fornell, C., & Bookstein, F. L. (1982). Two structural equation models: LISREL and PLS applied to consumer exit-voice theory. Journal of Marketing Research, 19, 440–452.CrossRefGoogle Scholar
  43. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.CrossRefGoogle Scholar
  44. Gotlieb, J. B., Dhruv, G., & Stephen, W. B. (1994). Consumer satisfaction and perceived quality: complementary or divergent constructs. The Journal of Applied Psychology, 79(6), 875–885.CrossRefGoogle Scholar
  45. Gregor, S. (2006). The nature of theory in information systems. MIS Quarterly, 30(3), 611–642.Google Scholar
  46. Gronroos, C. (1984). A service quality model and its marketing implications. European Journal of Marketing, 18(4), 36–44.CrossRefGoogle Scholar
  47. Holmbeck, G. N. (1997). Toward terminological, conceptual, and statistical clarity in the study of mediators and moderators: examples from the child-clinical and pediatric psychology literatures. Journal of Counseling and Clinical Psychology, 65(4), 599–610.CrossRefGoogle Scholar
  48. Istepanian, R., & Lacal, J. (2003). Emerging mobile communication technologies for health: Some imperative notes on m-Health. Paper presented at the 25th International Conference of the IEEE Engineering in Medicine and Biology Society, Cancun, Mexico.Google Scholar
  49. Ivatury, G., Moore, J., & Bloch, A. (2009). A doctor in your pocket: health hotlines in developing countries. Innovations: Technology, Governance, Globalization, 4(1), 119–153.CrossRefGoogle Scholar
  50. Jarvis, C. B., MacKenzie, S. B., & Podsakoff, P. M. (2003). A Critical review of construct indicators and measurement model misspecification in marketing and consumer research. Journal of Consumer Research, 30, 199–218.CrossRefGoogle Scholar
  51. Jasperson, J., Carter, P. E., & Zmud, R. W. (2005). A comprehensive conceptualization of post-adoptive behaviors associated with information technology enabled work systems. MIS Quarterly, 29(3), 525–557.Google Scholar
  52. Jen, W. Y., Chao, C. C., Hung, M. C., Li, Y. C., & Chi, Y. P. (2007). Mobile information and communication in the hospital outpatient service. International Journal of Medical Informatics, 76, 565–574.CrossRefGoogle Scholar
  53. Jiang, J. J., & Klein, G. (1999). User Evaluation of Information Systems: By System Typology. IEEE Transactions on System, Man, and Cybernetics, 29(1), 111-116. Google Scholar
  54. Jiang, J. J., Klein, G., & Crampton, S. (2000). A note on SERVQUAL reliability and validity in information system service quality measurement. Decision Sciences, 31(3), 725–745.CrossRefGoogle Scholar
  55. Jiang, J. J., Klein, G., Roan, J., & Lin, T. M. (2001). IS service performance: self-perceptions and user perceptions. Information Management, 38(8), 499–506.CrossRefGoogle Scholar
  56. Jiang, J. J., Klein, G., & Carr, C. (2002). Measuring information systems quality: SERVQUAL from the other side. MIS Quarterly, 26(2), 145–166.CrossRefGoogle Scholar
  57. Kaplan, B., & Litewka, S. (2008). Ethical challenges of telemedicine and telehealth. Cambridge Quarterly of Healthcare Ethics, 17, 401–416.CrossRefGoogle Scholar
  58. Kettinger, W. J., & Lee, C. C. (1994). Perceived service quality and user satisfaction with the information services function. Decision Sciences, 25(5/6), 737–766.CrossRefGoogle Scholar
  59. Kettinger, W. J., & Lee, C. C. (1995). Exploring a ‘gap’ model of information services quality. Information Resources Management Journal, 8(3), 5–16.Google Scholar
  60. Kettinger, W. J., & Lee, C. C. (1999). Replication of measures in information systems research: the case of IS SERVQUAL. Decision Sciences, 30(3), 893–899.CrossRefGoogle Scholar
  61. Kettinger, W. J., & Lee, C. C. (2005). Zones of tolerance: alternative scales for measuring information systems service quality. MIS Quarterly, 29(4), 607-623.Google Scholar
  62. Koivisto, M. (2007). Development of quality expectations in mobile information systems, Proceedings of the International Joint Conferences on Computer, Information, and Systems Sciences, and Engineering, University of Bridgeport, CT, USA.Google Scholar
  63. Krause, A., Hartl, D., Theis, F., Stangl, M., Gerauer, K. E., & Mehlhorn, A. T. (2004). Mobile decision support for transplantation patient data. International Journal of Medical Informatics, 73(5), 461–464.CrossRefGoogle Scholar
  64. Law, K., & Wong, C.-S. (1998). Multidimensional constructs in structural equation analysis: an illustration using the job perception and job satisfaction constructs. Journal of Management, 25(2), 143–160.CrossRefGoogle Scholar
  65. Limayem, M., Hirt, S. G., & Cheung, M. K. C. (2007). How habit limits the predictive power of intention: the case of information systems continuance. MIS Quarterly, 31(4), 705–737.Google Scholar
  66. Lohmöller, J.-B. (1989). Latent variable path modeling with partial least squares. Heidelberg: Physica-Verlag.Google Scholar
  67. MacKenzie, S. B., Podsakoff, P. M., & Jarvis, C. B. (2005). The problem of measurement model misspecification in behavioral and organizational research and some recommended solutions. The Journal of Applied Psychology, 90(4), 710–730.CrossRefGoogle Scholar
  68. Malhotra, N. (2004). Marketing research: an applied orientation, 4th ed., Upper Saddle River, NJ: Pearson Education.Google Scholar
  69. Mechael, P. (2009). The case for mHealth in developing countries. Innovations: Technology, Governance, Globalization, 4(1), 103–118.CrossRefGoogle Scholar
  70. Michalowski, W., Rubin, S., Slowinski, R., & Wilk, S. (2003). Mobile clinical support system for pediatric emergencies. Decision Support Systems, 36, 161–176.CrossRefGoogle Scholar
  71. Myers, B. L., Kappelman, L. A., & Prybutok, V. R. (1998). A comprehensive model for assessing the quality and productivity of the information systems function: Toward a theory for information systems assessment. In E.J. Garrity and G.L. Sanders (eds.). Information Systems Success Measurement. Hershey. PA: Idea Group, pp. 94–121.Google Scholar
  72. Nelson, R. R., Todd, P. A., & Wixom, B. H. (2005). Antecedents of information and systems quality: an empirical examination within the context of data warehousing. Journal of Management Information Systems, 21(4), 199–235.Google Scholar
  73. Noonan, R., & Wold, H. (1993). Evaluating school systems using partial least squares. Evaluation in Education, 7, 219–364.CrossRefGoogle Scholar
  74. Norris, T., Stockdale, R., & Sharma, S. (2008). Mobile health: strategy and sustainability. The Journal of Information Technology in Healthcare, 6(5), 326–333.Google Scholar
  75. Orlikowski, W. J., & Iacono, C. S. (2001). Research commentary: desperately seeking the “IT” in IT researches—a call to theorizing the IT artifact. Information Systems Research, 2(12), 121–134.CrossRefGoogle Scholar
  76. Parasuraman, A., Valarie A. Z., & Berry L.L. (1985). A conceptual model of service quality and its implications for future research, Journal of Marketing, 49(Fall), 41–50.Google Scholar
  77. Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1988). SERVQUAL: a multiple-item scale for measuring consumer perceptions of service quality. Journal of Retailing, 64(1), 5–6.Google Scholar
  78. Parasuraman, A., Zeithaml, V. A., & Malhotra, A. (2005). E-S-QUAL: a multiple-item scale for assessing electronic service quality. Journal of Service Research, 7(3), 213–233.CrossRefGoogle Scholar
  79. Petter, S., & McLean, R. E. (2009). A meta analytic assessment of the DeLone and McLean IS success model: an examination of IS success at the individual level. Information Management, 46, 159–166.CrossRefGoogle Scholar
  80. Petter, S., Straub, D., & Rai, A. (2007). Specifying formative constructs in information systems research. MIS Quarterly, 31(1), 623–656.Google Scholar
  81. Pitt, L. F., Watson, R. T., & Kavan, C. B. (1995). Service quality: a measure of information systems effectiveness. MIS Quarterly, 19(2), 173–187.CrossRefGoogle Scholar
  82. Pitt, L. F., Watson, R. T., & Kavan, C. B. (1997). Measuring information systems service quality: concerns for a complete canvas. MIS Quarterly, 21(2), 209–221.CrossRefGoogle Scholar
  83. RACE (1994) UMTS System Structure Document, Issue 1.0. RACE 2066 Mobile Networks (MONET), CEC Deliverable No:R2066/LMF/GA1/DS/P/052/b1Google Scholar
  84. Reeves, C., & Bednar, D. A. (1994). Defining quality: alternatives and implications. Academy of Management Review, 19(3), 419–445.CrossRefGoogle Scholar
  85. Saga, V. L., & Zmud, R. W. (1994). The nature and determinants of IT acceptance, routinization, and infusion. In L. Levine (Ed.), Diffusion, transfer and implementation of information technology (pp. 67–86). Amsterdam: Elsevier Science.Google Scholar
  86. Seddon, P. B. (1997). A respecification and extension of the DeLone and McLean model of IS success. Information Systems Research, 240–253.Google Scholar
  87. Shaw, C., & Ivens, J. (2002). Building great customer experiences. New York: Macmillan.CrossRefGoogle Scholar
  88. Sheth, J., Bruce, N., Newman, I., & Barbara, L. G. (1991). Consumption values and market choices: theory and applications. Cincinnati: South-Western.Google Scholar
  89. Sirgy, M. J., Hansen, D. E., & Littlefield, J. E. (1994). Does hospital satisfaction affect life satisfaction? Journal of Macromarketing, 14(2), 36–46.CrossRefGoogle Scholar
  90. Sousa, R., & Voss, C. (2006). Service quality in multichannel services employing virtual channels. Journal of Service Research, 8(4), 356–371.CrossRefGoogle Scholar
  91. Spreng, R. A., MacKenzie, S. B., & Olshavsky, R. W. (1996). A reexamination of the determinants of customer satisfaction. Journal of Marketing, 60(3), 15–32.CrossRefGoogle Scholar
  92. Straub, D. W., & Watson, R. T. (2001). Research commentary: transformational issues in researching IS and net-enabled organizations. Information Systems Research, 12(4), 337–345.CrossRefGoogle Scholar
  93. Straub, D. W., Boudreau, M.-C., & Gefen, D. (2004). Validation guidelines for IS positivist research. Communications of AIS, 13(24), 380–427.Google Scholar
  94. Sweeney, J. C., & Soutar, G. N. (2001). Consumer perceived value: the development of a multiple item scale. Journal of Retailing, 77(2), 203–220.CrossRefGoogle Scholar
  95. Taylor, S. A., & Baker, T. L. (1994). An assessment of the relationship between service quality and customer satisfaction in the formation of consumers’ purchase intentions. Journal of Retailing, 70(2), 163–178.CrossRefGoogle Scholar
  96. Tenenhaus, M., Vinzi, V. E., Chatelin, Y.-M., & Lauro, C. (2005). PLS path modeling. Computational Statistics and Data Analysis, 48(1), 159–205.CrossRefGoogle Scholar
  97. United Nations foundation & Vodafone foundation (2009). mHealth for development: The opportunity of mobile technology for healthcare in developing world. Available at: http://www.vitalwaveconsulting.com/insights/mHealth.htm [Accessed September 03, 2010]
  98. Varshney, U. (2005). Pervasive healthcare: applications, challenges and wireless solutions. Communications of the Association for Information Systems, 16(3), 57–72.Google Scholar
  99. Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: four longitudinal field studies. Management Science, 46, 186–204.CrossRefGoogle Scholar
  100. Watson, R. T., Pitt, L. F., & Kavan, C. B. (1998). Measuring information systems service quality: lessons from two longitudinal case studies. MIS Quarterly, 22(1), 61–79.CrossRefGoogle Scholar
  101. Wetzels, M., Schroder, G. O., & Oppen, V. C. (2009). Using PLS path modeling for assessing hierarchical construct models: guidelines and empirical illustration. MIS Quarterly, 33(1), 177–195.Google Scholar
  102. Whetten, D.A. (1989). What constitutes a theoretical contributions. Academy of Management Review, 14(4), 490–495.Google Scholar
  103. Wixom, H. B., & Todd, P. A. (2005). A theoretical integration of user satisfaction and technology acceptance. Information Systems Research, 16(1), 85–102.CrossRefGoogle Scholar
  104. Wold, H. (1985). Partial least squares. In S. Kotz & N. L. Johnson (Eds.), Encyclopedia of statistical sciences. New York: Wiley.Google Scholar
  105. Zeithaml, V. (1987). Defining and relating price, perceived quality, and perceived value (pp. 87–101). Cambridge: Marketing Science Institute, Report No. 87–101.Google Scholar
  106. Zviran, M., & Erlich, Z. (2003). Measuring IS user satisfaction: review & implications. Communications of the AIS, 12, 81–103.Google Scholar

Copyright information

© Institute of Information Management, University of St. Gallen 2010

Authors and Affiliations

  1. 1.School of Information Systems, Technology and Management, Australian School of BusinessThe University of New South WalesSydneyAustralia

Personalised recommendations