Electronic Commerce Research

, Volume 12, Issue 2, pp 225–248 | Cite as

Modeling users’ acceptance of mobile services

  • Theodora Zarmpou
  • Vaggelis Saprikis
  • Angelos Markos
  • Maro Vlachopoulou
Article

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.

Keywords

Mobile services acceptance Innovativeness Trust Relationship drivers Functionality SEM 

References

  1. 1.
    Ajzen, I. (1991). Theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. CrossRefGoogle Scholar
  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). Google Scholar
  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. CrossRefGoogle Scholar
  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. CrossRefGoogle Scholar
  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. Google Scholar
  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. Google Scholar
  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. CrossRefGoogle Scholar
  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. CrossRefGoogle Scholar
  10. 10.
    Barwise, P., & Strong, C. (2001). Permission-based mobile advertising. Journal of Interactive Marketing, 16(1), 14–24. CrossRefGoogle Scholar
  11. 11.
    Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107(2), 238–246. CrossRefGoogle Scholar
  12. 12.
    Bhattacherjee, A. (2002). Individual trust in online firms: Scale development and initial test. Journal of Management Information Systems, 19(1), 211–241. Google Scholar
  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. Google Scholar
  14. 14.
    Büyüközkan, G. (2009). Determining the mobile commerce user requirements using an analytic approach. Computer Standards & Interfaces, 31(1), 144–152. CrossRefGoogle Scholar
  15. 15.
    Chen, L. (2008). A model of consumer acceptance of mobile payment. International Journal of Mobile Communications, 6(1), 32–52. CrossRefGoogle Scholar
  16. 16.
    Chen, J., & Tong, L. (2003). Analysis of mobile phone’s innovative will and leading customers. Science Research Management, 24(3), 25–31. Google Scholar
  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. CrossRefGoogle Scholar
  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). Google Scholar
  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. CrossRefGoogle Scholar
  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. CrossRefGoogle Scholar
  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. CrossRefGoogle Scholar
  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. Google Scholar
  23. 23.
    Doyle, S. (2001). Software review: Using short message services as a marketing tool. Journal of Database Marketing, 8(3), 273–277. CrossRefGoogle Scholar
  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. CrossRefGoogle Scholar
  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. Google Scholar
  26. 26.
    Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behavior: an introduction to theory and research. Reading: Addison-Wesley. Google Scholar
  27. 27.
    Flynn, L., & Goldsmith, R. (1993). A validation of the Goldsmith and Hofacker innovativeness scale. Educational and Psychological Measurement, 53(4), 1105–1116. CrossRefGoogle Scholar
  28. 28.
    Gefen, D., & Straub, D. W. (2003). Managing user trust in B2C e-services. E-Service Journal, 2(2), 7–24. CrossRefGoogle Scholar
  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. CrossRefGoogle Scholar
  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. CrossRefGoogle Scholar
  32. 32.
    Hair, J., Black, W., Babin, B., Anderson, R., & Tatham, R. (2006). Multivariate data analysis. New York: Prentice Hall. Google Scholar
  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. CrossRefGoogle Scholar
  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. CrossRefGoogle Scholar
  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. CrossRefGoogle Scholar
  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. Google Scholar
  37. 37.
    Kang, M. (2010). The mobile big bang. SERI Quarterly, 3(4), 78–85. Google Scholar
  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). Google Scholar
  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. CrossRefGoogle Scholar
  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. CrossRefGoogle Scholar
  41. 41.
    Kline, R. B. (2005). Principles and practice of structural equation modelling (2nd edn.). New York: Guilford. Google Scholar
  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. CrossRefGoogle Scholar
  43. 43.
    Lacey, R. (2007). Relationship drivers of customer commitment. Journal of Marketing Theory and Practice, 15(4), 315–333. CrossRefGoogle Scholar
  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. CrossRefGoogle Scholar
  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. CrossRefGoogle Scholar
  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. CrossRefGoogle Scholar
  47. 47.
    Midgley, D., & Dowling, G. (1978). Innovativeness: the concept and its measurement. Journal of Consumer Research, 4(4), 229–242. CrossRefGoogle Scholar
  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. CrossRefGoogle Scholar
  49. 49.
    Misra, S., & Wickamasinghe, N. (2004). Security of a mobile transaction. Electronic Commerce Research, 4(4), 359–372. CrossRefGoogle Scholar
  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. Google Scholar
  51. 51.
    Ngai, E. W. T., & Gunasekaran, A. (2007). A review for mobile commerce research and applications. Decision Support Systems, 43(1), 3–15. CrossRefGoogle Scholar
  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. CrossRefGoogle Scholar
  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. Google Scholar
  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. Google Scholar
  55. 55.
    Petrova, K., & Wang, B. (2011). Location-based services deployment and demand: a roadmap model. Electronic Commerce Research, 11(1), 5–29. CrossRefGoogle Scholar
  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. CrossRefGoogle Scholar
  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. CrossRefGoogle Scholar
  58. 58.
    Rogers, M. (1995). Diffusion of innovations. New York: Free Press. Google Scholar
  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. CrossRefGoogle Scholar
  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. CrossRefGoogle Scholar
  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. CrossRefGoogle Scholar
  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. CrossRefGoogle Scholar
  63. 63.
    Taylor, S., & Todd, P. A. (1995). Understanding information technology usage: a test of competing models. Information Systems Research, 6(2), 144–176. CrossRefGoogle Scholar
  64. 64.
    Tucker, M., & Lewis, C. (1973). A reliability coefficient for maximum likelihood factor analysis. Psychometrika, 38(1), 1–10. CrossRefGoogle Scholar
  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. CrossRefGoogle Scholar
  66. 66.
    Varshney, U., & Vetter, R. (2002). Mobile commerce: framework, applications and networking support. Mobile Networks and Applications, 7(3), 185–198. CrossRefGoogle Scholar
  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. Google Scholar
  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. CrossRefGoogle Scholar
  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. CrossRefGoogle Scholar
  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. CrossRefGoogle Scholar
  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. CrossRefGoogle Scholar
  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. CrossRefGoogle Scholar
  73. 73.
    Yang, K. (2005). Exploring factors affecting the adoption of mobile commerce in Singapore. Telematics and Informatics, 22(3), 257–277. CrossRefGoogle Scholar
  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. CrossRefGoogle Scholar
  75. 75.
    Yunos, H. M., Gao, J. Z., & Shim, S. (2003). Wireless advertising’s challenges and opportunities. Computer, 36(5), 30–37. CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Theodora Zarmpou
    • 1
  • Vaggelis Saprikis
    • 1
  • Angelos Markos
    • 2
  • Maro Vlachopoulou
    • 1
  1. 1.Department of Applied InformaticsUniversity of MacedoniaThessalonikiGreece
  2. 2.Department of Primary EducationDemocritus University of ThraceAlexandroupolisGreece

Personalised recommendations