Service Business

, Volume 10, Issue 2, pp 447–467 | Cite as

Enemies of cloud services usage: inertia and switching costs

  • Laura Lucia-Palacios
  • Raúl Pérez-López
  • Yolanda Polo-Redondo
Empirical article


This paper examines the direct and mediating role of inertia on the likelihood of adopting cloud services by individual users, and provides the reasons of the inertial behavior. The study is focused on Google Drive cloud services. The results emphasize the importance of inertia and switching costs in explaining the resistance to use cloud services. Furthermore, inertia partially mediates the relationship between switching costs and cloud computing services usage. Finally, it is found that inertia in the use of prior IT is mainly explained by convenience rather than by loyalty. From the point of view of the service provider, these results have implications on its marketing strategy.


Cloud services Inertia Switching costs PLS Mediating effects 



The authors express their gratitude for the financial support received from the Spanish Government CICYT (ECO 2011-23027 and ECO2014-54760) from the Regional Government and FEDER’s funding (Generés S09) from the national Grant FPU12/03240) and Laura Lucia-Palacios appreciates financial support from the program “Ayudas a la Investigación en Ciencias Sociales, Fundación Ramón Areces”.


  1. Abroud A, Choong YV, Muthaiyah S, Fie DYG (2013) Adopting e-finance: decomposing the technology acceptance model for investors. Serv Bus 5(6):1–22. doi: 10.1007/s11628-013-0214-x Google Scholar
  2. Agarwal R, Prasad J (1998) A conceptual and operational definition of personal innovativeness in the domain of information technology. Inf Syst Res 9(2):204–215CrossRefGoogle Scholar
  3. Ajzen I (1991) The theory of planned behavior. Organ Behav Hum Decis Process 50(2):179–210CrossRefGoogle Scholar
  4. Antioco M, Kleijnen M (2010) Consumer adoption of technological Innovations. Effects of psychological and functional barriers in a lack of content versus a presence of content situation. Eur J Market 44(11/12):1700–1724CrossRefGoogle Scholar
  5. Bansal HS, Taylor SF (1999) The service provider switching model (SPSM). A Model of consumer switching behavior in the services industry. J Serv Res 2(2):200–218CrossRefGoogle Scholar
  6. Bansal HS, Taylor SF, James YS (2005) “Migrating’’ to new service providers: toward a unifying framework of consumers’ switching behaviors. J Acad Market Sci 33(1):96–115CrossRefGoogle Scholar
  7. Bhattacherjee A (2001) Understanding Information systems continuance: an expectation–confirmation model. MIS Quat 25(3):351–370CrossRefGoogle Scholar
  8. Bhattacherjee A, Limayem M, Cheung CMK (2012) User switching of information technology: a theoretical synthesis and empirical test. Inf Manag 49:327–333CrossRefGoogle Scholar
  9. Burnham TA, Frels JK, Mahajan V (2003) Consumer Switching costs: a typology, antecedents, and consequences. J Acad Mark Sci 31(2):109–126CrossRefGoogle Scholar
  10. Carmines E, Zeller R (1979) Reliability and validity assessment, in Sage University Paper series on quantative applications in the social sciences (07-017). Sag, Beverly HillsGoogle Scholar
  11. Chen PY, Hitt LM (2002) Measuring switching costs and the determinants of customer retention in Internet-enabled businesses: a study of the online brokerage industry. Inf Syst Res 13(3):255–274CrossRefGoogle Scholar
  12. Chen M-F, Lu T-Y (2011) Modeling e-coupon proneness as a mediator in the extended TPB model to predict consumers’ usage intentions. Internet Res 21(5):508–526CrossRefGoogle Scholar
  13. Chen LS-L, Wu KLF (2013) Antecedents of intention to use CUSS system: moderating effects of self-efficacy. Serv Bus 8(4):615–634. doi: 10.1007/s11628-013-0210-1 CrossRefGoogle Scholar
  14. Chintagunta P (1998) Inertia and variety seeking in a model of brand-purchase timing. Market Sci 17(3):253–270CrossRefGoogle Scholar
  15. Chintagunta P, Honore B (1996) Investigating the effects of marketing variables and unobserved heterogeneity in a multinomial probit model. Int J Res Mark 13(1):1–15CrossRefGoogle Scholar
  16. Choi H, Kim Y, Kim J (2011) Driving factors of post adoption behavior in mobile data services. J Bus Res 64:1212–1217CrossRefGoogle Scholar
  17. Cooper RB (1994) The inertial impact of culture on IT implementation. Inf Manag 27(1):17–31CrossRefGoogle Scholar
  18. Davis FD (1989) Perceived usefulness, perceived ease of use and user acceptance of information technology. MIS Quat 13(3):318–330Google Scholar
  19. Diamantopoulos A, Siguaw JA (2006) Formative versus reflective indicators in organizational measure development: a comparison of empirical illustration. Br J Manag 17:263–282CrossRefGoogle Scholar
  20. Dong D, Saha A (1998) He came, he saw, (and) he waited: an empirical analysis of inertia in technology adoption. Appl Econ 30(7):893–905CrossRefGoogle Scholar
  21. Dubé JP, Hitsch GJ, Rosi PE (2010) State dependence and alternative explanations for consumer inertia. Rand J Econ 41:417–445CrossRefGoogle Scholar
  22. Eriksson K, Nilsson D (2007) Determinants of the continued use of self-service technology: the case of internet banking. Technovation 27(4):59–67CrossRefGoogle Scholar
  23. Fang Y-H, Chiu C-M, Wang ETG (2011) Understanding customers’ satisfaction and repurchase intentions. An integration of IS success model, trust, and justice. Internet Res 21(4):479–503CrossRefGoogle Scholar
  24. Farrell J, Klemperer P (2007) Coordination and lock-in: competition with switching costs and network effects. In: Armstrong M, Porter R (eds) Handbook of industrial organization. Elsevier B.V, Amsterdam, pp 1967–2072Google Scholar
  25. Fraering M, Minor MS (2013) Beyond loyalty: customer satisfaction, loyalty and fortitude. J Serv Mark 27(4):334–344CrossRefGoogle Scholar
  26. Gerrard P, Cunninghan JB, Devlin JF (2006) Why customers are not using internet banking: a qualitative study. J Serv Mark 20(3):160–168CrossRefGoogle Scholar
  27. Goldfarb A (2006) State dependence at internet portals. J Econ Manag Strateg 15(2):317–352CrossRefGoogle Scholar
  28. Hair JF, Anderson RE, Tatham RL, Black WC (1998) Multivariate data analysis, 5th edn. Prentice-Hall, Englewood cliffsGoogle Scholar
  29. Hellier PK, Geursen GM, Carr RA, Rickard JA (2003) Customer repurchase intention: a general structural equation model. Eur J Mark 37(11/12):1762–1800CrossRefGoogle Scholar
  30. Hernández B, Jiménez J, Martín MJ (2010) Customer behavior in electronic commerce: the moderating effect of e-purchasing experience. J Bus Res 63:964–971CrossRefGoogle Scholar
  31. Hsieh JK, Hsieh YC, Chiu HC, Feng YC (2012) Post-adoption switching behavior for online service substitutes: a perspective of the push-pull-mooring framework. Comput Hum Behav 28:1912–1920CrossRefGoogle Scholar
  32. Huarng K-H, Yu TH-K, Huang JJ (2010) The impacts of instructional video advertising on customer purchasing intentions on the internet. Serv Bus 4(1):27–36. doi: 10.1007/s11628-009-0081-7 CrossRefGoogle Scholar
  33. Iglesias-Pradas S, Hernández-García A, Fernández-Cardador P (2014) How socially derived characteristics of technology shape the adoption of corporate Web 2.0 tools for collaboration. Serv Bus 8(3):465–478. doi: 10.1007/s11628-014-0250-1 CrossRefGoogle Scholar
  34. Jayawardhena C, Foley P (2000) Changes in the banking sector–the case of Internet banking in the UK. Internet Res 10(1):19–31CrossRefGoogle Scholar
  35. Jones MA, Mothersbaugh DL, Beatty SE (2000) Switching barriers and repurchase intentions in services. J Retail 76(2):259–274CrossRefGoogle Scholar
  36. Kang YJ, Lee WJ (2014) Self-customization of online service environments by users and its effect on their continuance intention. Serv Bus. doi: 10.1007/s11628-014-0229-y Google Scholar
  37. Karahanna E, Agarwal R, Angst CM (2006) Reconceptualizing compatibility beliefs in technology acceptance research. MIS Quart 30(4):781–804Google Scholar
  38. Kim HW, Kankanhalli A (2009) Investigating resistance to information systems implementation: a status quo bias perspective. MIS Quart 33(3):567–582Google Scholar
  39. Lankton NK, Wilson EV, Mao E (2010) Antecedents and determinants of information technology habit. Inf Manag 47(5/6):300–307CrossRefGoogle Scholar
  40. Lee R, Neale L (2012) Interactions and consequences of inertia and switching costs. J Serv Mark 26(5):365–374CrossRefGoogle Scholar
  41. Li Y, Chang K-C (2012) A study on user acceptance of cloud computing: a multi-theoretical perspective. AMCIS 2012 Proceedings. Paper 19. Accessed 29 July 2012
  42. Limayem M, Hirt SG, Cheung CMK (2007) How habit limits the predictive power of intention: the case of information systems continuance. MIS Quart 31(4):705–737Google Scholar
  43. Low C, Chen Y, Wu M (2011) Understanding the determinants of cloud computing adoption. Ind Manag Data Syst 111(7):1006–1023CrossRefGoogle Scholar
  44. Lu M-T, Tzeng G-H, Cheng H, Hsu C-C (2014) Exploring mobile banking services for user behavior in intention adoption: using new hybrid MADM model. Serv Bus. doi: 10.1007/s11628-014-0239-9 Google Scholar
  45. Marston S, Li Z, Bandyopadhyay S, Zang J, Ghalsasi A (2011) Cloud computing-the business perspective. Decis Syst Support 51(1):176–189CrossRefGoogle Scholar
  46. Medina C, Rufin R, Rey M (2014) Mediating relationships in and satisfaction with online technologies: communications or features beyond expectations? Serv Bus. doi: 10.1007/s11628-014-0241-2 Google Scholar
  47. Meuter ML, Bitner MJ, Ostrom AL, Brown SW (2005) Choosing among alternative service delivery modes: an investigation of customer trial of self-service technologies. J Market 69(2):61–83CrossRefGoogle Scholar
  48. Misra SC, Mondal A (2010) Identification of a company’s suitability for the adoption of cloud computing and modelling its corresponding return on investment. Math Comput Model 53:504–521CrossRefGoogle Scholar
  49. Moore GC, Benbasat I (1991) Development of an instrument to measure the perceptions of adopting an information technology innovation. Inf Syst Res 2(3):192–222CrossRefGoogle Scholar
  50. Nunnally JC (1978) Psychometric theory. McGraw-Hill, New YorkGoogle Scholar
  51. Oliver RL (1980) A cognitive model of the antecedent and consequences of satisfaction decisions. J Mark Res 17(4):460–469CrossRefGoogle Scholar
  52. Olsen SO, Tudoran AA, Bruns K, Verbeke W (2013) Extending the prevalent consumer loyalty modelling: the role of habit strength. Eur J Mark 47(1/2):303–323CrossRefGoogle Scholar
  53. Ortoleva P (2010) Status quo bias, multiple priors and uncertainty aversion. Games Econ Behav 69:411–424CrossRefGoogle Scholar
  54. Park SC, Ryoo SY (2013) An empirical investigation of end-users’ switching toward cloud computing: a two factor theory perspective. Comput Hum Behav 29:160–170CrossRefGoogle Scholar
  55. Patsiotis AG, Hughes T, Webber DJ (2013) An examination of customers’ resistance to computer based-technologies. J Serv Mark 27(4):294–311CrossRefGoogle Scholar
  56. Pavlou PA, Fygenson M (2006) Understanding and predicting electronic commerce adoption: an extension of the theory of planned behavior. MIS Quart 30(1):115–143Google Scholar
  57. Polites GL, Karahanna E (2012) Shackled to the status quo: the inhibiting effects of incumbent system habit, switching costs, and inertia on new system acceptance. MIS Quart 36(1):21–42Google Scholar
  58. Polo Y, Sesé FJ (2009) How to make switching costly. the role of marketing and relationship characteristics. J Serv Res 12(2):119–137CrossRefGoogle Scholar
  59. Polo Y, Sesé FJ (2013) Strengthening customer relationships: what factors influence customers to migrate to contracts? J Serv Res 16(2):138–154. doi: 10.1177/1094670512471584 CrossRefGoogle Scholar
  60. Preacher KJ, Hayes AF (2008) Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behav Res Methods 40:879–891CrossRefGoogle Scholar
  61. Premkumar G, Bhattarcherjee A (2008) Explaining information technology usage: a test of competing models. Omega 36:64–75CrossRefGoogle Scholar
  62. Ranaweera C, Prabhu J (2003) The influence of satisfaction, trust and switching barriers on customer retention in a continuous purchasing setting. Int J Serv Ind Manag 14(4):374–395CrossRefGoogle Scholar
  63. Ravald A, Grönroos C (1996) The value concept and relationship marketing. Eur J Mark 30(2):19–30CrossRefGoogle Scholar
  64. Ray S, Seo D (2013) The interplay of conscious and automatic mechanisms in the context of routine use: an integrative and comparative study of contrasting mechanisms. Inf Manag 50:523–539CrossRefGoogle Scholar
  65. Ringle C, Wende S, Will A (2005) SmartPLS 2.0 (Beta). SmartPLS, HamburgGoogle Scholar
  66. Samuelson W, Zeckhauser R (1988) Status quo bias in decision making. J Risk Uncertain 1:7–59CrossRefGoogle Scholar
  67. Seetharaman PB, Ainslie A, Chintagunta P (1999) Investigating household state dependence effects across categories. J Mark Res 36:488–500CrossRefGoogle Scholar
  68. Shin DH (2009) Determinants of customer acceptance of multi-service network: an implication for IP-based technologies. Inf Manag 46(1):16–22CrossRefGoogle Scholar
  69. Sultan N (2010) Cloud computing for education: a new dawn? Int J Inf Manage 30(2):109–116CrossRefGoogle Scholar
  70. Taylor DG, Strutton D (2010) Has e-marketing come of age? Modeling historical influences on post-adoption era Internet consumer behaviors. J Bus Res 63:950–956CrossRefGoogle Scholar
  71. Taylor S, Todd PA (1995) Understanding information technology usage: a test of competing models. Inf Syst Res 6(2):144–176CrossRefGoogle Scholar
  72. Teoh WM-Y, Chong SC, Lin B, Chua JW (2013) Factors affecting consumers’ perception of electronic payment: an empirical analysis. Internet Res 23(4):465–485CrossRefGoogle Scholar
  73. Thong JYL, Hong S-J, Tam KY (2006) The effects of post-adoption beliefs on the expectation–confirmation model for information technology continuance. Int J Hum Comput Stud 64(9):799–810CrossRefGoogle Scholar
  74. Tsai HT, Huang HC (2007) Determinants of e-purchase intentions: an integrative model of quadruple retention drivers. Inf Manag 44(3):231–239CrossRefGoogle Scholar
  75. Van Raaij WF, Strazzieri A, Woodside A (2001) New developments in marketing communications and consumer behavior. J Bus Res 53:59–61CrossRefGoogle Scholar
  76. Venkatesh V, Davis FD (2000) A theoretical extension of the technology acceptance model: four longitudinal field studies. Manag Sci 46(2):186–204CrossRefGoogle Scholar
  77. Venkatesh V, Morris MG, Davis GB, Davis FD (2003) User acceptance of information technology: toward a unified view. MIS Quart 27(3):425–478Google Scholar
  78. Verplanken B, Wood W (2006) Interventions to break and create consumer habits. J Pub Policy Market 25(1):90–103CrossRefGoogle Scholar
  79. Von Neumann J, Morgenstern O (1947) Theory of games and economic behaviour. Princeton University Press, PrincetonGoogle Scholar
  80. Wang F-K, He W (2014) Service strategies of small cloud service providers: a case study of a small cloud service provider and its clients in Taiwan. Int J Inf Manage 34(3):406–415CrossRefGoogle Scholar
  81. White L, Yanamandram V (2004) Why customers stay: reasons and consequences of inertia in financial services. Manag Serv Qual 14:183–194CrossRefGoogle Scholar
  82. Wu L-W (2011) Satisfaction, inertia, and customer loyalty in the varying levels of the zone of tolerance and alternative attractiveness. J Serv Mark 25(5):310–322CrossRefGoogle Scholar
  83. Ye G (2005) The locus effect on inertia equity. J Prod Brand Manag 14(2/3):206–210CrossRefGoogle Scholar
  84. Ye C, Seo D, Desouza KC, Sangareddy SP, Jha S (2008) Influences of IT substitutes and user experience on post-adoption user switching: an empirical investigation. J Am Soc Inform Sci Technol 59(13):2115–2132CrossRefGoogle Scholar
  85. Yen C-H, Lu P (2008) Factors influencing online auction repurchase intention. Internet Res 18(1):7–25CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Laura Lucia-Palacios
    • 1
  • Raúl Pérez-López
    • 1
  • Yolanda Polo-Redondo
    • 1
  1. 1.Department of Marketing, Faculty of Economics and BusinessUniversity of ZaragozaZaragozaSpain

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