Service Business

, Volume 9, Issue 4, pp 587–609 | Cite as

Mediating relationships in and satisfaction with online technologies: communications or features beyond expectations?

Empirical article


This paper is aimed at researching the satisfaction of professional users of online technology services. Based on the Expectation–Disconfirmation Theory (Oliver, J Mark Res 17:460–469, 1980), our research analyses the influence of mediating relationships between variables on these types of processes. Three variables, in addition to the expectations of the service’s perceived usefulness, are included in the analysis: effort expectancy, social influence and facilitating conditions. The results show that disconfirmation of expectations as such, i.e. expectations carried by the user prior to entering into contact with the service, plays a major role in the model. However, expectations ‘remembered’ after entering into contact with the service do not lead to such an influence of disconfirmation. From the point of view of the service provider, this differential behaviour has implications on its marketing strategy.


Satisfaction Mediated relationships Expectations Disconfirmation Online education 


  1. Akehurst G (2001) User generated content: the use of blog for tourism organizations and tourism consumers. Serv Bus 3:51–61CrossRefGoogle Scholar
  2. Anderson E, Salisbury L (2003) The formation of market-level expectations and its covariates. J Consum Res 30(June):115–124CrossRefGoogle Scholar
  3. Baron RM, Kenny DA (1986) The moderator–mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. J Personal Soc Psychol 51(6):1173–1182CrossRefGoogle Scholar
  4. Bhattacherjee A (2001) Understanding information systems continuance: an expectation–confirmation model. MIS Q 25(3):351–370CrossRefGoogle Scholar
  5. Bhattacherjee A, Premkumar G (2004) Understanding changes in belief and attitude toward information technology usage: a theoretical model and longitudinal test. MIS Q 28:229–254Google Scholar
  6. Boulton-Lewis G, Pillay H, Wills L (2006) Changing workplace environments, conceptions and patterns of learning: implications for university teaching. In: Tynjälä P, Välimaa J, Boulton-Lewis G (eds) Higher education and working life. Elsevier, Amsterdam, pp 145–161Google Scholar
  7. Brown S, Massey A, Montoya-Weiss M, Burkman J (2002) Do I really have to? User acceptance of mandated technology. Eur J Inf Syst 11(4):283–295CrossRefGoogle Scholar
  8. Brown S, Venkatesh V, Kuruzovich J, Massey A (2008) Expectation confirmation: an examination of three competing models. Organ Behav Hum Decis Process 105(1):52–66CrossRefGoogle Scholar
  9. Brown S, Venkatesh V, Goyal S (2012) Expectation confirmation in technology use. Inf Syst Res 23(2):474–487Google Scholar
  10. Carter L, Bélanger F (2005) The utilization of e-Government services: citizen trust, innovation and acceptance factors. Inf Syst J 15:5–25CrossRefGoogle Scholar
  11. Chan F, Thong J, Venkatesh V, Brown S, Hu P-H, Tam K (2010) Modeling citizen satisfaction with mandatory adoption of an e-Government technology. J Assoc Inf Syst 11(10):519–549Google Scholar
  12. Chen H-J (2010) Linking employees’ e-Learning system use to their overall job outcomes: an empirical study based on the IS success model. Comput Educ 55:1628–1639CrossRefGoogle Scholar
  13. Chen J-L (2011) The effects of education compatibility and technological expectancy on e-Learning acceptance. Comput Educ 57:1501–1511CrossRefADSGoogle Scholar
  14. Chen H-J (2012) Clarifying the empirical connection of new entrants` e-Learning systems use to their job adaptation and theirs use patterns under the collective-individual training environment. Comput Educ 58:321–337CrossRefGoogle Scholar
  15. Cheng B, Wang M, Moorman J, Olamiron B, Chen N-S (2012) The effects of organizational learning environment factors on learning acceptance. Comput Educ 58:885–899CrossRefGoogle Scholar
  16. Chin WW (1998) The partial least squares approach to structural equation modelling. In: Marcoulides GA (ed) Modern methods for business research. Lawrence Erlbaum, Mahwah, pp 295–336Google Scholar
  17. Chin W, Peterson R, Brown S (2008) Structural Equation Modelling in Marketing: some empirical reminders. J Mark Theory Pract 16(4):287–298CrossRefGoogle Scholar
  18. Chiu C, Wang E (2008) Understanding Web-based learning continuance intention: the role of subjective task value. Inf Manag 45(3):194–201CrossRefGoogle Scholar
  19. Chiu C-H, Hsu M-H, Sun S-Y, Lin T-C, Sun P-C (2005) Usability, quality, value and e-Learning continuance decisions. Comput Educ 45:399–416CrossRefGoogle Scholar
  20. Churchill GA, Suprenant C (1982) An investigation into the determinants of consumer satisfaction. J Mark Res 19(November):491–504CrossRefGoogle Scholar
  21. Cole D, Maxwell S (2003) Testing mediational models with longitudinal data: questions and tips in the use of structural equation modeling. J Abnorm Psychol 112:558–577CrossRefPubMedGoogle Scholar
  22. Davis F (1989) Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q 13(3):319–340CrossRefGoogle Scholar
  23. Davis FD, Bagozzi RP, Warshaw PR (1989) User acceptance of computer technology: a comparison of two theoretical models. Manag Sci 35:982–1003CrossRefGoogle Scholar
  24. Duan Y, Mullins R, Hambling D, Stanek S, Sroka H, Machedo V, Araujo J (2002) Addressing ICTs skill challenges in SMEs: insights from three country investigations. J Eur Ind Train 26(9):430–441CrossRefGoogle Scholar
  25. Dwivedi YK, Williams MD, Lal B, Mustafee N (2010) An analysis of literature on consumer adoption and diffusion of information system/information technology/information and communication technology. Int J Electron Gov Res 6(4):58–73CrossRefGoogle Scholar
  26. Erevelles S, Leavitt C (1992) A comparison of current models of consumer satisfaction/dissatisfaction. J Consum Satisf Dissatisfaction Complain Behav 5:104–114Google Scholar
  27. Falk RF, Miller NB (1992) A Primer for soft modeling. University of Akron Press, AkronGoogle Scholar
  28. Gefen D, Straub DW (1997) Gender differences in the perception and use of e-mail: an extension to the Technology Acceptance Model. MIS Q 21(4):389–400CrossRefGoogle Scholar
  29. Hair JF, Ringle CM, Sarstedt M (2011) PLS–SEM: indeed a silver bullet. J Mark Theory Pract 19(2):139–151CrossRefGoogle Scholar
  30. Hardaker G, Dockery R, Sabki A (2007) Learning styles inequity for small to micro firms (SMFs). Social exclusion through work-based e-Learning practice in Europe. Multicult Educ Technol J 1(2):126–140CrossRefGoogle Scholar
  31. Helson H (1964) Adaptation-level theory: an experimental and systematic approach to behavior. Harper and Row, New YorkGoogle Scholar
  32. Johnson M, Anderson E, Fornell C (1995) Rational and adaptive performance expectations in a customer satisfaction framework. J Consum Res 21(March):695–707CrossRefGoogle Scholar
  33. Karaali D, Gumussoy C, Calisir F (2011) Factors affecting the intention to use a web-based learning system among blue-collar workers in the automotive industry. Comput Hum Behav 27(1):343–354CrossRefGoogle Scholar
  34. Kumar P, Dass M, Topaloglu O (2011) Exploring satisfaction in business-to-business services: a path-analytic approach. Serv Bus 5:13–27CrossRefGoogle Scholar
  35. LaTour S, Peat NC (1979) In: Wilkie WL (ed) Conceptual and methodological issues in consumer research. Association for Consumer Research, Ann Arbor, pp 431–437Google Scholar
  36. Levy Y (2007) Comparing dropouts and persistence in e-Learning courses. Comput Educ 48:185–204CrossRefGoogle Scholar
  37. Lin C-P, Tsai Y, Chiu C-K (2009) Modeling customer loyalty from an integrative perspective of self-determination theory and expectation–confirmation theory. J Bus Psychol 24:315–326CrossRefGoogle Scholar
  38. MacKinnon D, Fairchild A (2009) Current directions in mediation analysis. Curr Dir Psychol Sci 18(1):16–20PubMedCentralCrossRefPubMedGoogle Scholar
  39. MacKinnon D, Coxe S, Baraldi A (2012) Guidelines for the investigation of Mediating Variables in Business Research. J Bus Psychol 27:1–14PubMedCentralCrossRefPubMedGoogle Scholar
  40. Mainardes EW, Silva MJ, Carvalho de Souza MJ (2010) The development of new higher education courses. Serv Bus 4:271–288CrossRefGoogle Scholar
  41. Oliver R (1980) A cognitive model of the antecedents and consequences of satisfaction decisions. J Mark Res 17:460–469CrossRefGoogle Scholar
  42. Oliver RL, DeSarbo WS (1988) Response determinants in satisfaction judgments. J Consum Res 495–507Google Scholar
  43. Oliver RL, Swan JE (1989) Consumer perceptions of interpersonal equity and satisfaction in transactions: a field survey approach. J Mark 53(April):21–35CrossRefGoogle Scholar
  44. Pieters R, Zwick R (1993) Hindsight bias in the context of a consumption experience. Eur Adv Consum Res 1:307–311Google Scholar
  45. Preacher K, Hayes F (2008) Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediation models. Behav Res Models 40:879–891CrossRefGoogle Scholar
  46. Qureshi I, Compeau D (2009) Assessing between-group differences in information systems research: a comparison of covariance- and component-based SEM. MIS Q 33(1):197–214Google Scholar
  47. Real J, Roldán J, Leal A (2012) From entrepreneurial orientation and learning orientation to business performance: analyzing the mediating role of organizational learning and the moderating effects of organizational size. Br J Manag. doi: 10.1111/j.1467-8551.2012.00848.X Google Scholar
  48. Ringle C, Wende S, Will A (2008) SmartPLS 2.0(beta). Accessed 5 May 2012
  49. Roca J, Gagné M (2008) Understanding e-Learning continuance intention in the workplace. A self-determination theory perspective. Comput Hum Behav 24:1585–1604CrossRefGoogle Scholar
  50. Roffe I (2007) Competitive strategy and influences on e-Learning in entrepreneur-led SMEs. J Eur Ind Train 31(6):416–434CrossRefGoogle Scholar
  51. Rogers E (1995) Diffusion of innovation. Free Press, New YorkGoogle Scholar
  52. Rufín R, Medina C, Rey M (2012a) Adjusted expectations, satisfaction and loyalty development in the case of services. Serv Ind J 32(14):2185–2202CrossRefGoogle Scholar
  53. Rufín R, Medina C, Roldán J, Rey M (2012b) Familiarity and experience in tourist satisfaction loyalty development. In: Tsiotsou R, Goldsmith R (eds) Strategic marketing in tourism services. Emerald, Bingley, pp 185–203Google Scholar
  54. Sachs D, Hale N (2003) Pace university’s focus on student satisfaction with students services in online education. J Asynchronous Learn Netw 7(2):36–42Google Scholar
  55. Selig J, Preacher K (2009) Mediation models for longitudinal data in developmental research. Res Hum Dev 6(2/3):144–164CrossRefGoogle Scholar
  56. Slotte V, Herbert A (2008) Engaging workers in simulation based e-Learning. J Workplace Learn 20(3):165–180CrossRefGoogle Scholar
  57. Sobel M (1990) Effect analysis and causation in linear structural equation model. Psychometrika 55:495–515MathSciNetCrossRefGoogle Scholar
  58. Sosik J, Kahai S, Piovoso M (2009) Silver bullet or voodoo statistics? A primer for using the partial least squares data analytic technique in group and organizational research. Group Organ Manag 34(1):5–36CrossRefGoogle Scholar
  59. Spreng R, MacKenzie S, Olshavsky R (1996) A reexamination of the determinants of consumer satisfaction. J Mark 60(3):15–32CrossRefGoogle Scholar
  60. Sumak B, Hericko M, Pusnik M (2011) A meta-analysis of e-Learning technology acceptance: the role of user types and e-Learning technology types. Comput Hum Behav 27:2067–2077CrossRefGoogle Scholar
  61. Sun H (2010) Sellers’ trust and continued use of online marketplaces. J Assoc Inf Syst 11(4):182–211Google Scholar
  62. Swift J, Lawrence K (2003) Business culture in Latin America: interactive learning for UK SMEs. J Eur Ind Train 27(8/9):389–397CrossRefGoogle Scholar
  63. Szymanski D, Henard D (2001) Customer satisfaction: a meta-analysis of the empirical evidence. J Acad Mark Sci 29(1):16–35CrossRefGoogle Scholar
  64. Taylor S, Todd PA (1995) Understanding information technology usage: a test of competing models. Inf Syst Res 6(2):144–176CrossRefGoogle Scholar
  65. Taylor A, MacKinnon D, Tein J-Y (2008) Tests of three-path mediated effect. Organ Res Methods 11:241–269CrossRefGoogle Scholar
  66. Thong J, Hong S-J, Tam K (2006) The effects of post-adoption beliefs on the expectation–confirmation model for information technology continuance. Int J Hum Comput Stud 64:799–810CrossRefGoogle Scholar
  67. Venkatesh V (2000) Determinants of perceived ease of use: integrating perceived behavioral control, computer anxiety and enjoyment into the technology acceptance model. Inf Syst Res 11:342–365CrossRefGoogle Scholar
  68. Venkatesh V, Davis F (2000) A theoretical extension of the technology acceptance model: four longitudinal field studies. Manag Sci 45(2):186–204CrossRefGoogle Scholar
  69. Venkatesh V, Morris M, Davis G, Davis F (2003) User acceptance of information technology: toward a unified view. MIS Q 27:425–478Google Scholar
  70. Venkatesh V, Thong J, Chan F, Hu P-H, Brown S (2011) Extending the two-stage information systems continuance model: incorporating UTAUT predictors and the role of context. Inf Syst J 21:527–555CrossRefGoogle Scholar
  71. Westbrook RA (1980) Intrapersonal affective influences upon consumer satisfaction with products. J Consum Res 7(June):49–54CrossRefGoogle Scholar
  72. Yi Y (1993) The determinants of consumer satisfaction: the moderating role of ambiguity. Adv Consum Res 20:502–506Google Scholar
  73. Yi Y, La S (2003) The moderating role of confidence in expectations and the asymmetric influence of disconfirmation on customer satisfaction. Serv Ind J 23(5):20–47CrossRefGoogle Scholar
  74. Yi Y, La S (2004) What influences the relationship between customer satisfaction and repurchase intention? Investigating the effects of adjusted expectations on customer loyalty. Psychol Mark 21(5):351–373CrossRefGoogle Scholar
  75. Yoo SJ, Han S-H, Huang W (2012) The roles of intrinsic motivators and extrinsic motivators in promoting e-Learning in the workplace: a case from South Korea. Comput Hum Behav 28:942–950CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Centro Andaluz de Estudios EmpresarialesSevilleSpain
  2. 2.Universidad Nacional de Educación a DistanciaMadridSpain
  3. 3.Universidad de SevillaSevilleSpain

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