Service Business

, Volume 9, Issue 2, pp 321–342 | Cite as

Self-customization of online service environments by users and its effect on their continuance intention

Empirical Article


Service providers embed self-customization options into their web-based service systems to facilitate user-centered service creation and consumption. The aim of this study is to demonstrate that provision of such self-customization features offers customer lock-in effects. Specifically, the study explores how the act of self-customization enhances users’ self-efficacy beliefs and perceived fit of the resulting service environment with their wants and needs. An AMOS analysis based on survey data of 600 undergraduate students indicates that (1) self-customization enhances perceived fit and self-efficacy and (2) they in turn enhance users’ motivation and continuance intention.


Perceived fit Self-customization Self-efficacy Service environments Continuance intention 


  1. Agarwal R, Karahanna E (2000) Time flies when you’re having fun: cognitive absorption and beliefs about information technology usage. MIS Quart 24(4):665–695CrossRefGoogle Scholar
  2. Anderson JC, Gerbing DW (1988) Structural equation modeling in practice: a review and recommended two-step approach. Psychol Bull 103(3):411–423CrossRefGoogle Scholar
  3. Bagozzi RP, Yi Y, Phillips LW (1991) Assessing construct validity in organizational research. Admin Sci Quart 36(3):421–458CrossRefGoogle Scholar
  4. Bandura A (1977) Self-efficacy: toward a unifying theory of behavioral change. Psychol Rev 84(2):191–215CrossRefGoogle Scholar
  5. Bandura A (1989) Human agency in social cognitive theory. Am Psychol 44(9):1175–1184CrossRefGoogle Scholar
  6. Bandura A (1997) Self-efficacy: the exercise of control. WH Freeman, New YorkGoogle Scholar
  7. Bandura A (2001) Social cognitive theory of mass communication. Media Psychol 3(3):265–299CrossRefGoogle Scholar
  8. Bandura A, Locke E (2003) Negative self-efficacy and goal effects revisited. J Appl Psychol 88(1):87–99CrossRefGoogle Scholar
  9. Bandura A, Schunk DH (1981) Cultivating competence, self-efficacy, and intrinsic interest through proximal self-motivation. J Pers Soc Psychol 41(3):586–598CrossRefGoogle Scholar
  10. Bearden WO, Sharma S, Teel JE (1982) Sample size effects on chi square and other statistics used in evaluating causal models. J Marketing Res 19(4):425–430CrossRefGoogle Scholar
  11. Bentler P, Bonnett DG (1980) Significance tests and goodness of fit in the analysis of covariance structures. Psychol Bull 88(3):588–606CrossRefGoogle Scholar
  12. Bettman JR, Luce MF, Payne JW (1998) Constructive consumer processes. J Consum Res 25(3):187–217CrossRefGoogle Scholar
  13. Bhattacherjee A, Barfar A (2011) Information technology continuance research: current state and future directions. Asia Pac J Inf Syst 21(2):1–18Google Scholar
  14. Chin WW (1998) Issues and opinion on structural equation modeling. MIS Quart 21(1):7–16Google Scholar
  15. Compeau D, Higgins CA, Huff S (1999) Social cognitive theory and Individual reactions to computing technology: a longitudinal study. MIS Quart 23(2):145–158CrossRefGoogle Scholar
  16. Davis FD, Bagozzi RP, Warshaw PR (1992) Extrinsic and intrinsic motivation to use computers in the workplace. J Appl Soc Psychol 22(14):1111–1132CrossRefGoogle Scholar
  17. Deci EL, Ryan RM (1985) Intrinsic motivation and self-determination in human behavior. Plenum, New YorkCrossRefGoogle Scholar
  18. Deci EL, Koestner R, Ryan RM (1999) A meta-analytic review of experiments examining the effects of extrinsic rewards on intrinsic motivation. Psychol Bull 125(6):627–668CrossRefGoogle Scholar
  19. del Brio JÁ, Fernández E, Junquera B (2007) Customer interaction in environmental innovation: the case of cloth diaper laundering. Serv Bus 1:141–158CrossRefGoogle Scholar
  20. Dellaert BGC, Dabholkar PA (2009) Increasing the attractiveness of mass-customization: the role of complementary online services and range of options. Int J Electron Comm 13(3):43–70CrossRefGoogle Scholar
  21. Fan H, Poole MS (2006) What is personalization? Perspectives on the design and implementation of personalization in information systems. J Org Comp Elect Comm 16(3&4):179–202Google Scholar
  22. Fornell C, Larcker D (1981) Structural equation models with unobservable variables and measurement error. J Marketing Res 18(1):39–50CrossRefGoogle Scholar
  23. Franke N, Keinz P, Steger C (2009) Testing the value of customization: when do customers really prefer products tailored to their preferences? J Marketing 73(5):103–121CrossRefGoogle Scholar
  24. Franke N, Schreier M, Kaiser U (2010) The I designed it myself effect in mass customization. Manage Sci 56(1):125–140CrossRefGoogle Scholar
  25. Gretzel U, Fesenmaier DR (2006) Persuasion in recommender systems. Int J Electron Comm 11(2):81–100CrossRefGoogle Scholar
  26. Hasan B (2006) Delineating the effects of general and system-specific computer self-efficacy beliefs on IS acceptance. Inform Manage 43:565–571CrossRefGoogle Scholar
  27. Hayduk LA (1987) Structural equation modeling with LISREL: essentials and advances. Johns Hopkins University Press, BaltimoreGoogle Scholar
  28. Hsu MH, Chiu CM (2004) Predicting electronic service continuance with a decomposed theory of planned behavior. Behav Inform Technol 23(5):359–373CrossRefGoogle Scholar
  29. Igbaria M, Parasuraman S, Baroudi JJ (1996) A motivational model of microcomputer usage. J Manage Inform Syst 13(1):127–143Google Scholar
  30. Kamis A, Koufaris M, Stern T (2008) Using an attribute-based decision support system for user-customized products online: an experimental investigation. MIS Quart 32(1):159–177Google Scholar
  31. Kang YS, Hong S, Lee H (2009) Exploring continued online service usage behavior: the roles of self-image congruity and regret. Comput Hum Behav 25(1):111–122CrossRefGoogle Scholar
  32. Korpipää P, Malm EJ, Rantakokko T, Kyllönen V, Kela J, Mäntyjärvi J, Häkkilä J, Känsälä I (2006) Customizing user interaction in smart phones. IEEE Pervasive Comput 5(3):82–90CrossRefGoogle Scholar
  33. Koufaris M (2002) Applying the technology acceptance model and flow theory to online consumer behavior. Inform Syst Res 13(2):205–224CrossRefGoogle Scholar
  34. Kramer T (2007) The effect of measurement task transparency on preference construction and evaluations of personalized recommendations. J Marketing Res 44(2):224–233CrossRefGoogle Scholar
  35. Kumar N, Benbasat I (2006) The influence of recommendations and consumer reviews on evaluations of websites. Inform Syst Res 17(4):425-439 Google Scholar
  36. Lee HH, Chang EY (2011) Consumer attitudes toward online mass customization: an application of extended technology acceptance model. J Comput-Mediat Comm 16(2):171–200CrossRefGoogle Scholar
  37. Lee MKO, Cheung CMK, Chen Z (2005) Acceptance of internet-based learning medium; the role of extrinsic and intrinsic motivation. Inform Manage 42(8):1095–1104CrossRefGoogle Scholar
  38. Liang H, Saraf N, Hu Q, Xue Y (2007) Assimilation of enterprise systems: the effect of institutional pressures and the mediating role of top management. MIS Quart 31(1):59–87Google Scholar
  39. Lin CS, Wu S, Tsai RJ (2005) Integrating perceived playfulness into expectation-confirmation model for web portal context. Inform Manage 42(5):683–693CrossRefGoogle Scholar
  40. Mathieu JE, Martineau JW, Tannenbaum SI (1993) Individual and situational influences on the development of self efficacy: implications for training effectiveness. Pers Psychol 46(1):125–147CrossRefGoogle Scholar
  41. McKee D, Simmers CS, Licata J (2006) Customer self-efficacy and response to service. J Serv Res 8(3):207–220CrossRefGoogle Scholar
  42. 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 Marketing 69(2):61–83CrossRefGoogle Scholar
  43. Millar MG, Millar KU (1996) The effects of direct and indirect experience on affective and cognitive responses and the attitude–behavior relation. J Exp Soc Psychol 32(6):561–579CrossRefGoogle Scholar
  44. Monk AF, Blom JO (2007) A theory of personalization of appearance: quantitative evaluation of qualitatively derived data. Behav Inform Technol 26(3):237–246CrossRefGoogle Scholar
  45. Norton MI (2009) The IKEA effect: when labor leads to love. Harvard Bus Rev 87(2):30–34Google Scholar
  46. Novak TP, Hoffman DL, Yung YF (2000) Measuring the customer experience in online environments: a structural modeling approach. Market Sci 19(1):22–42CrossRefGoogle Scholar
  47. Nunnally JC (1978) Psychometric theory. McGraw-Hill, New YorkGoogle Scholar
  48. Podsakoff PM, Organ DW (1986) Self-reports in organizational research: problems and prospects. J Manage 12(4):531–544Google Scholar
  49. Randall T, Terwiesch C, Ulrich KT (2007) User design of customized products. Market Sci 26(2):268–283CrossRefGoogle Scholar
  50. Roca JC, Gagné M (2008) Understanding e-learning continuance intention in the workplace: a self-determination theory perspective. Comput Hum Behav 24(4):1585–1604CrossRefGoogle Scholar
  51. Schreier M (2006) The value increment of mass-customized products: an empirical assessment. J Consum Behav 5:317–327CrossRefGoogle Scholar
  52. Shang RA, Chen YC, Shen L (2005) Extrinsic versus intrinsic motivations for consumers to shop on-line. Inform Manage 42(3):401–413CrossRefGoogle Scholar
  53. Simonson I (2005) Determinants of customers’ responses to customized offers: conceptual framework and research propositions. J Marketing 69(1):32–45CrossRefGoogle Scholar
  54. Sundar SS, Marathe SS (2010) Personalization versus customization: the importance of agency, privacy, and power usage. Hum Comm Res 36(3):298–322CrossRefGoogle Scholar
  55. Teo TSH, Lim VKG, Lai RYC (1999) Intrinsic and extrinsic motivation in internet usage. Omega 27(1):25–37CrossRefGoogle Scholar
  56. Thong JYL, Hong SJ, Tam KY (2006) The effects of post-adoption beliefs on the expectation-confirmation model for information technology continuance. Int J Hum-Comput St 64(9):799–810CrossRefGoogle Scholar
  57. Valenzuela A, Dhar R, Zettelmeyer F (2009) Contingent response to self-customization procedures: implications for decision satisfaction and choice. J Marketing Res 46(6):754–763CrossRefGoogle Scholar
  58. Van Beuningen J, Ruyter KD, Wetzels M, Streukens S (2009) Customer self-efficacy in technology-based self-service: assessing between- and within-person differences. J Serv Res 11(4):407–428CrossRefGoogle Scholar
  59. Van der Heijden H (2004) User acceptance of hedonic information systems. MIS Quart 28(4):695–704Google Scholar
  60. Venkatesh V (2000) Determinants of perceived ease of use: integrating control, intrinsic motivation, and emotion into the technology acceptance model. Inform Sys Res 11(4):342–365CrossRefGoogle Scholar
  61. Wu JH, Chen YC, Lin LM (2007) Empirical evaluation of the revised end user computing acceptance model. Comput Hum Behav 23(1):162–174CrossRefGoogle Scholar
  62. Zhao X, Mattila AS, Tao LE (2008) The role of post-training self-efficacy in customers’ use of self service technologies. Int J Serv Ind Manage 19(4):492–505CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Sungkyunkwan Business SchoolSungkyunkwan UniversitySeoulKorea

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