Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

Modeling users’ acceptance of mobile services

Abstract

The success of mobile services adoption hinges on their ability to cover user needs and attract consumer interest. The extant literature focuses on understanding the factors that might affect consumers’ actual adoption of such services through their effect on behavioral intention; these studies are mostly based on behavioral intention theories, such as Technology Acceptance Model, Diffusion of Innovation and Unified Theory of Acceptance and Use of Technology. In this work, new theoretical constructs are combined with existing evidence in order to extend the Technology Acceptance Model (TAM) as it was initially established by Davis and later further enriched by other researchers. The proposed model includes behavioral intention, perceived usefulness, perceived ease of use, trust, innovativeness, relationship drivers, and functionality. Within this approach, relationship drivers introduce a marketing perspective to the original models of technology adoption by building emotional connections between the users and the mobile services. The hypothesized model is empirically tested using data collected from a survey on m-commerce consumers. Structural Equation Modelling (SEM) was used to evaluate the causal model and Confirmatory Factor Analysis (CFA) was performed to examine the reliability and validity of the measurement model. It is briefly concluded that behavioral intention is directly affected by perceived usefulness, innovativeness and relationship drivers; the findings provide interesting insights and useful hints to practitioners and researchers.

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

Fig. 1
Fig. 2

References

  1. 1.

    Ajzen, I. (1991). Theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211.

  2. 2.

    Al Awadhi, S., & Morris, A. (2008). The Use of the UTAUT model in the adoption of e-government services in Kuwait. In Proceedings of the 41st Hawaii international conference on system sciences (HICSS), Waikoloa (pp. 1–5).

  3. 3.

    Aldas-Manzano, J., Ruiz-Mafe, C., & Sanz-Blas, S. (2009). Exploring individual personality factors as drivers of m-shopping acceptance. Industrial Management & Data Systems, 109(6), 739–757.

  4. 4.

    Aloudat, A., & Michael, K. (2011). Toward the regulation of ubiquitous mobile government: a case study on location-based emergency services in Australia. Electronic Commerce Research, 11(1), 31–74.

  5. 5.

    Anderson, J., & Schwager, P. (2003). SME adoption of wireless LAN technology: applying the UTAUT model. In Proceedings of the 7th annual conference of the Southern Association for Information Systems (pp. 39–43). Savannah: SAIS.

  6. 6.

    Androulidakis, N., & Androulidakis, I. (2005). Perspectives of mobile advertising in Greece. In Proceedings of the 4th international conference on mobile business, Sydney (pp. 441–444). New York: IEEE Press.

  7. 7.

    Athens University of Economics and Business and ICAP GROUP (2008). Social-financial overview of the mobile phone industry in Greece (in Greek). http://www.sepe.gr/files/pdf/Executive%20Summary.pdf. Accessed May 2011.

  8. 8.

    Balasubramanian, S., Peterson, R. A., & Jarvenpaa, S. L. (2002). Exploring the implications of m-commerce for markets and marketing. Journal of the Academy of Marketing Science, 30(4), 348–361.

  9. 9.

    Barnes, S. J., & Scornavacca, E. (2004). Mobile marketing: the role of permission and acceptance. International Journal of Mobile Communication, 2(2), 128–138.

  10. 10.

    Barwise, P., & Strong, C. (2001). Permission-based mobile advertising. Journal of Interactive Marketing, 16(1), 14–24.

  11. 11.

    Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107(2), 238–246.

  12. 12.

    Bhattacherjee, A. (2002). Individual trust in online firms: Scale development and initial test. Journal of Management Information Systems, 19(1), 211–241.

  13. 13.

    Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In K. A. Bollen & J. S. Long (Eds.), Testing structural equation models (pp. 136–162). Thousand Oaks: Sage.

  14. 14.

    Büyüközkan, G. (2009). Determining the mobile commerce user requirements using an analytic approach. Computer Standards & Interfaces, 31(1), 144–152.

  15. 15.

    Chen, L. (2008). A model of consumer acceptance of mobile payment. International Journal of Mobile Communications, 6(1), 32–52.

  16. 16.

    Chen, J., & Tong, L. (2003). Analysis of mobile phone’s innovative will and leading customers. Science Research Management, 24(3), 25–31.

  17. 17.

    Chen, Q., Chen, H., & Kazman, R. (2007). Investigating antecedents of technology acceptance of initial eCRM users beyond generation X and the role of self-construal. Electronic Commerce Research, 7(3–4), 315–339.

  18. 18.

    Cho, D. Y., Kwon, H. J., & Lee, H. Y. (2007). Analysis of trust in internet and mobile commerce adoption. In Proceedings of the 40th Hawaii international conference on system science (HICSS), Waikoloa (pp. 1–10).

  19. 19.

    Crabbe, M., Standing, C., & Standing, S. (2009). An adoption model for mobile banking in Ghana. International Journal of Mobile Communications, 7(5), 515–543.

  20. 20.

    Davis, D. (1989). Perceived usefulness, perceived ease of use and user acceptance of information technology. Management Information Systems Quarterly, 13(3), 319–340.

  21. 21.

    DeLone, W. H., & McLean, E. R. (1992). Information systems success: the quest for the dependent variable. Information Systems Research, 3(1), 60–95.

  22. 22.

    Dholakia, N., Dholakia, R., Lehrer, M., & Kshetri, N. (2004). Patterns, opportunities, and challenges in the emerging global m-commerce landscape. In N. Shi (Ed.), Wireless communications and mobile commerce, Singapore. Hershey: Idea Group.

  23. 23.

    Doyle, S. (2001). Software review: Using short message services as a marketing tool. Journal of Database Marketing, 8(3), 273–277.

  24. 24.

    Edvardsson, B. (1988). Service quality in customer relationships: A study of critical incidents in mechanical engineering companies. The Service Industries Journal, 8(4), 427–445.

  25. 25.

    El-Kasheir, D., Ashour, A., & Yacout, O. (2009). Factors affecting continued usage of internet banking among Egyptian customers. Communications of the IBIMA, 9, 252–263.

  26. 26.

    Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behavior: an introduction to theory and research. Reading: Addison-Wesley.

  27. 27.

    Flynn, L., & Goldsmith, R. (1993). A validation of the Goldsmith and Hofacker innovativeness scale. Educational and Psychological Measurement, 53(4), 1105–1116.

  28. 28.

    Gefen, D., & Straub, D. W. (2003). Managing user trust in B2C e-services. E-Service Journal, 2(2), 7–24.

  29. 29.

    Geser, H. (2004). Towards a sociological theory of the mobile phone (release 3.0). Zürich: University of Zürich. Institute of Sociology: Sociology of the Mobile Phone. http://socio.ch/mobile/t_geser1.htm. Accessed 21 June 2011.

  30. 30.

    Gülçin, B. (2009). Determining the mobile commerce user requirements using an analytic approach. Computer Standards & Interfaces, 31(1), 144–152.

  31. 31.

    Ha, S., & Stoel, L. (2009). Consumer e-shopping acceptance: Antecedents in a technology acceptance model. Journal of Business Research, 62(5), 565–571.

  32. 32.

    Hair, J., Black, W., Babin, B., Anderson, R., & Tatham, R. (2006). Multivariate data analysis. New York: Prentice Hall.

  33. 33.

    Holsapple, C. W., & Sasidharan, S. (2005). The dynamics of trust in online B2C e-commerce: a research model and agenda. Information Systems and E-business Management, 3(4), 377–403.

  34. 34.

    Hong, S., Thong, J., & Tam, K. (2006). Understanding continued information technology usage behavior: a comparison of three models in the context of mobile internet. Decision Support Systems, 42(3), 1819–1834.

  35. 35.

    Im, I., Kim, Y., & Han, H. (2008). The effects of perceived risk and technology type on users’ acceptance of technologies. Information & Management, 45(1), 1–9.

  36. 36.

    Information Systems Technologies Laboratory (IST Lab) (2007). Research about the tendency in the use of Mobile Data Services in Greece (Comparison study 2006–2007, in Greek). Athens: Athens University of Economics, Wireless Research Center.

  37. 37.

    Kang, M. (2010). The mobile big bang. SERI Quarterly, 3(4), 78–85.

  38. 38.

    Kannan, P. K., Chang, A., & Whinston, A. B. (2001). Wireless commerce: Marketing issues and possibilities. In Proceedings of the 34th Hawaii international conference on system sciences (HICSS), Maui (pp. 1–6).

  39. 39.

    Kim, S., & Garrison, G. (2008). Investigating mobile wireless technology adoption: An extension of the technology acceptance model. Information Systems Frontiers, 11(3), 323–333.

  40. 40.

    Kim, H. W., Chan, H. C., & Gupta, S. (2007). Value-based adoption of mobile internet: an empirical investigation. Decision Support Systems, 43, 111–126.

  41. 41.

    Kline, R. B. (2005). Principles and practice of structural equation modelling (2nd edn.). New York: Guilford.

  42. 42.

    Kuo, Y., & Yen, S. (2009). Towards an understanding of the behavioral intention to use 3G mobile value-added services. Computers in Human Behavior, 25(1), 103–110.

  43. 43.

    Lacey, R. (2007). Relationship drivers of customer commitment. Journal of Marketing Theory and Practice, 15(4), 315–333.

  44. 44.

    Lam, S., Chiang, J., & Parasuraman, A. (2008). The effects of the dimensions of technology readiness on technology acceptance: An empirical analysis. Journal of Interactive Marketing, 22(4), 19–39.

  45. 45.

    Lance, C. E., Butts, M. M., & Michels, L. C. (2006). The sources of four commonly reported cutoff criteria. What did they really say? Organizational Research Methods, 9(2), 202–220.

  46. 46.

    Lu, J., Yao, J., & Yu, C. (2005). Personal innovativeness, social influences and adoption of wireless internet services via mobile technology. Journal of Strategic Information Systems, 14(3), 245–268.

  47. 47.

    Midgley, D., & Dowling, G. (1978). Innovativeness: the concept and its measurement. Journal of Consumer Research, 4(4), 229–242.

  48. 48.

    Min, Q., Ji, S., & Qu, G. (2008). Mobile commerce user acceptance study in China: a revised UTAUT model. Tsinghua Science and Technology, 13(3), 257–264.

  49. 49.

    Misra, S., & Wickamasinghe, N. (2004). Security of a mobile transaction. Electronic Commerce Research, 4(4), 359–372.

  50. 50.

    Morgan, R. M. (2000). Relationship marketing and marketing strategy: the evolution of relationship marketing within the organization. In J. N. Sheth & A. Parvatiyar (Eds.), Handbook of relationship marketing (pp. 481–505). Thousand Oaks: Sage.

  51. 51.

    Ngai, E. W. T., & Gunasekaran, A. (2007). A review for mobile commerce research and applications. Decision Support Systems, 43(1), 3–15.

  52. 52.

    Nysveen, H., Pedersen, P., Thorbjornsen, H., & Berthon, P. (2005). Mobilizing the brand: The effects of mobile services on brand relationships and main channel use. Journal of Service Research, 7(3), 257–276.

  53. 53.

    Pavlou, P. (2003). Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model. International Journal of Electronic Commerce, 7(3), 101–134.

  54. 54.

    Pedersen, P., Methlie, L., & Thorbjornsen, H. (2002). Understanding mobile commerce end-user adoption: a triangulation perspective and suggestions for an exploratory service evaluation framework. In Proceedings of the 35th Hawaii international conference on system sciences (HICSS), Hawaii.

  55. 55.

    Petrova, K., & Wang, B. (2011). Location-based services deployment and demand: a roadmap model. Electronic Commerce Research, 11(1), 5–29.

  56. 56.

    Polančič, G., Heričko, M., & Rozman, I. (2010). An empirical examination of application frameworks success based on technology acceptance model. The Journal of Systems and Software, 83(4), 574–584.

  57. 57.

    Qi, J., Li, L., Li, Y., & Shu, H. (2009). An extension of technology acceptance model: Analysis of the adoption of mobile data services in China. Systems Research and Behavioral Science, 26(3), 391–407.

  58. 58.

    Rogers, M. (1995). Diffusion of innovations. New York: Free Press.

  59. 59.

    Scharl, A., Dickinger, A., & Murphy, J. (2005). Diffusion and success factors of mobile marketing. Electronic Commerce Research and Applications, 4(2), 159–173.

  60. 60.

    Suh, B., & Han, I. (2002). Effect of trust on customer acceptance of Internet banking. Electronic Commerce Research and Applications, 1(3–4), 247–263.

  61. 61.

    Sulaiman, A., Jaafar, N. I., & Mohezar, S. (2007). An overview of mobile banking adoption among the urban community. International Journal of Mobile Communications, 5(2), 157–168.

  62. 62.

    Sun, Q., Wang, C., & Cao, H. (2009). An extended TAM for analyzing adoption behavior of mobile commerce. In Proceedings of the 8th international conference on mobile business, Dalian (pp. 52–56). New York: IEEE Press.

  63. 63.

    Taylor, S., & Todd, P. A. (1995). Understanding information technology usage: a test of competing models. Information Systems Research, 6(2), 144–176.

  64. 64.

    Tucker, M., & Lewis, C. (1973). A reliability coefficient for maximum likelihood factor analysis. Psychometrika, 38(1), 1–10.

  65. 65.

    Turner, M., Kitchenham, B., Brereton, P., Charters, S., & Budgen, D. (2010). Does the technology acceptance model predict actual use? A systematic literature review. Information and Software Technology, 52(5), 463–479.

  66. 66.

    Varshney, U., & Vetter, R. (2002). Mobile commerce: framework, applications and networking support. Mobile Networks and Applications, 7(3), 185–198.

  67. 67.

    Venkatesh, V., Morris, M., Davis, G., & Davis, F. (2003). User acceptance of information technology: Toward a unified View. Management Information Systems Quarterly, 27(3), 425–478.

  68. 68.

    Walczuch, R., Lemmink, J., & Streukens, S. (2007). The effect of service employees’ technology readiness on technology acceptance. Information & Management, 44(2), 206–215.

  69. 69.

    Wang, C., Lo, S., & Fang, W. (2008). Extending the technology acceptance model to mobile telecommunication innovation: The existence of network externalities. Journal of Consumer Behaviour, 7(2), 101–110.

  70. 70.

    Watson, R., Pitt, F., Berthon, P., & Zinkhan, G. (2002). U-Commerce: expanding the universe of marketing. Journal of the Academy of Marketing Science, 30(4), 333–347.

  71. 71.

    Wei, T., Marthandan, G., Chong, A., Ooi, K., & Arumugam, S. (2009). What drives Malaysian m-commerce adoption? An empirical analysis. Industrial Management & Data Systems, 109(3), 370–388.

  72. 72.

    Wu, J., & Wang, S. (2005). What drives mobile commerce? An empirical evaluation of the revised technology acceptance model. Information & Management, 42(5), 719–729.

  73. 73.

    Yang, K. (2005). Exploring factors affecting the adoption of mobile commerce in Singapore. Telematics and Informatics, 22(3), 257–277.

  74. 74.

    Yi, M., Jackson, J., Park, J., & Probst, J. (2006). Understanding information technology acceptance by individual professionals: Toward an integrative view. Information & Management, 43(3), 350–363.

  75. 75.

    Yunos, H. M., Gao, J. Z., & Shim, S. (2003). Wireless advertising’s challenges and opportunities. Computer, 36(5), 30–37.

Download references

Author information

Correspondence to Theodora Zarmpou.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Zarmpou, T., Saprikis, V., Markos, A. et al. Modeling users’ acceptance of mobile services. Electron Commer Res 12, 225–248 (2012). https://doi.org/10.1007/s10660-012-9092-x

Download citation

Keywords

  • Mobile services acceptance
  • Innovativeness
  • Trust
  • Relationship drivers
  • Functionality
  • SEM