Skip to main content
Log in

Modeling the user acceptance of long-term evolution (LTE) services

  • Published:
annals of telecommunications - annales des télécommunications Aims and scope Submit manuscript

Abstract

With an integrated framework, this paper aims to analyze user perception and acceptance toward long-term evolution (LTE) services, focusing on factors that may influence the intention to use. We conducted a web-based survey of 1,192 users to test our research model. We employed structural equation modeling (SEM) as the analysis method. The results of the integrated model analysis indicate that system satisfaction is a core determinant of intention to use LTE services. The model also found that other factors, including perceived usefulness and system and service quality, significantly affect intention to use these services. In addition, both perceived adaptivity and processing speed significantly influence perceived usefulness and service quality, respectively. These factors also play key roles in determining users’ attitudes. This paper is of value to researchers and engineers designing and improving LTE services for use via mobile phones.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. Dahlman E, Parkvall S, Skold J (2001) 4 G LTE/LTE-advanced for mobile broadband. Academic, Oxford, UK

    Google Scholar 

  2. Rumney, M.: LTE and the evolution to 4 G wireless: design and measurement challenges. Agilent Technologies, Padstow, Cornwall, UK (2009).

  3. Seoul Finance: three telecommunication companies have finished successful national LTE networks, available at: http://www.seoulfn.com/news/articleView.html?idxno = 131027 (accessed 5 May 2012).

  4. Bieber G, Voskamp J, Urban B (2009) Activity recognition for everyday life on mobile phones. Lecture Notes in Computer Science 5615:289–296

    Article  Google Scholar 

  5. Kim G, Shin B, Lee HG (2009) Understanding dynamics between initial trust and usage intentions of mobile banking. Inf Syst J 19(3):283–311

    Article  Google Scholar 

  6. Luo X, Li H, Zhang J, Shim JP (2010) Examining multi-dimensional trust and multi-faceted risk in initial acceptance of emerging technologies: an empirical study of mobile banking services. Decis Support Syst 49(2):222–234

    Article  Google Scholar 

  7. Zhou T, Lu Y, Wang B (2010) Integrating TTF and UTAUT to explain mobile banking user adoption. Comput Hum Behav 26(4):760–767

    Article  MathSciNet  Google Scholar 

  8. Evans C (2008) The effectiveness of m-learning in the form of podcast revision lectures in higher education. Comput Educ 50(2):491–498

    Article  Google Scholar 

  9. Wang YS, Wu MC, Wang HY (2009) Investigating the determinants and age and gender differences in the acceptance of mobile learning. Br J Educ Technol 40(1):92–118

    Article  Google Scholar 

  10. Balocco R, Mogre R, Toletti G (2009) Mobile internet and SMEs: a focus on the adoption. Industrial Management & Data Systems 109(2):245–261

    Article  Google Scholar 

  11. Kuo Y, Yen S (2009) Towards an understanding of the behavioral intention to use 3 G mobile value-added services. Comput Hum Behav 25(1):103–110

    Article  Google Scholar 

  12. Venkatesh V, Morris MG, Davis GB, Davis FD (2003) User acceptance of information technology: toward a unified view. MIS Q 27(3):425–478

    Google Scholar 

  13. 3rd Generation Partnership Project: 3GPP long term evolution, available at: http://www.3gpp.org (accessed 10 May 2012).

  14. Astely D, Dahlman E, Furuskar A, Jading Y, Lindstrom M, Parkvall S (2009) LTE: the evolution of mobile broadband. IEEE Commun Mag 47(4):44–51

    Article  Google Scholar 

  15. Khan F (2009) LTE for 4 G mobile broadband: air interface technologies and performance. Cambridge University Press, New York, NY

    Book  Google Scholar 

  16. Sesia S, Toufik I, Baker M (2009) LTE-the UMTS long term evolution: from theory to practice. Wiley Publishing, Hoboken, NJ

    Book  Google Scholar 

  17. Pospishny I, Vasyuk V, Romanchyk S, Dovzhenko O, Shvaichenko V (2010) 3GPP long term evolution (LTE), Proceedings of the 2010 International Conference on Modern Problems of Radio Engineering, Telecommunications and Computer Science (TCSET 2010), p. 192

  18. Wikipedia.org-3GPP long term evolution: 3GPP long term evolution, available at: http://en.wikipedia.org/wiki/3GPP_Long_Term_Evolution (accessed at 10 May 2012).

  19. Khandekar A, Bhushan N, Ji T, Vanghi V (2010) LTE-advanced: heterogeneous networks, Proceedings of the 2010 European Wireless Conference (EW’10), pp. 978–982.

  20. Martin-Sacristan D, Monserrat JF, Cabrejas-Penuelas J, Calabuig D, Garrigas S, Cardona N (2009) On the way towards fourth-generation mobile: 3GPP LTE and LTE-advanced. EURASIP J Wirel Commun Netw 2009:1–10

    Article  Google Scholar 

  21. Davis F (1989) Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q 13(3):319–340

    Article  Google Scholar 

  22. Davis FD (1993) User acceptance of information technology: system characteristics, user perceptions and behavioral impacts. International journal of man–machine studies 38(3):475–487

    Article  Google Scholar 

  23. Davis FD, Bagozzi RP, Warshaw PR (1989) User acceptance of computer technology: a comparison of two theoretical models. Manag Sci 35(8):982–1003

    Article  Google Scholar 

  24. Cheong JH, Park M (2005) Mobile Internet acceptance in Korea. Internet Research 15(2):125–140

    Article  Google Scholar 

  25. Heijden H (2004) User acceptance of hedonic information systems. MIS Q 23(4):695–704

    Google Scholar 

  26. Kanda T, Hirano T, Eaton D, Ishiguro H (2003) Person identification and interaction of social robots by using wireless tags, Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS ‘03), pp. 1657–1664

  27. Kanda T, Hirano T, Eaton D, Ishiguro H (2004) Interactive robots as social partners and peer tutors for children: a field trial. Hum Comput Interact 19(1):61–84

    Article  Google Scholar 

  28. Huang JH, Lin YR (2007) Elucidating user behavior of mobile learning. Electron Libr 25(5):585–598

    Article  Google Scholar 

  29. Wang C, Lo S, Fang W (2008) Extending the technology acceptance model to mobile telecommunication innovation: the existence of network externalities. J Consum Behav 7(2):101–110

    Article  Google Scholar 

  30. Wu J, Wang S, Lin L (2007) Mobile computing acceptance factors in the healthcare industry: a structural equation model. International Journal of Medical Informatics 76(1):66–77

    Article  Google Scholar 

  31. Shin D (2007) User acceptance of mobile Internet: implication for convergence technologies. Interacting with Computers 19(4):472–483

    Article  Google Scholar 

  32. Mallat N, Rossi M, Tuunainen VK, Oorni A. (2006) The impact of use situation and mobility on the acceptance of mobile ticketing services, Proceedings of the 39th Annual Hawaii International Conference on System Sciences, pp. 42–51.

  33. Chen L (2008) A model of consumer acceptance of mobile payment. Int J Mob Commun 6(1):32–52

    Article  Google Scholar 

  34. Wu J, Wang S (2005) What drives mobile commerce?: an empirical evaluation of the revised technology acceptance model. Inf Manag 42(5):719–729

    Article  Google Scholar 

  35. Park E, Kim KJ, Jin D, del Pobil AP (2012) Towards a successful mobile map service: an empirical examination of technology acceptance model. Communications in Computer and Information Science 293:420–428

    Article  Google Scholar 

  36. Luana P, Lin H (2005) Toward an understanding of the behavioral intention to use mobile banking. Comput Hum Behav 21(6):873–891

    Article  Google Scholar 

  37. Heerink M, Kröse BJA, Wielinga BJ, Evers V (2009) Measuring acceptance of an assistive social robot. Proceedings of Ro-man 2009:528–533

    Google Scholar 

  38. Lin H (2009) Examination of cognitive absorption influencing the intention to use a virtual community. Behaviour & Information Technology 28(5):421–431

    Article  Google Scholar 

  39. Teo H, Chan H, Wei K, Zhang Z (2003) Evaluating information accessibility and community adaptivity features for sustaining virtual learning communities. Int J Hum Comput Stud 59(5):671–697

    Article  Google Scholar 

  40. Shin D, Choo H (2011) Modeling the acceptance of socially interactive robotics: social presence in human–robot interaction. Interact Stud 12(3):430–460

    Article  Google Scholar 

  41. Heerink M, Ben K, Evers V, Wielinga B (2008) The influence of social presence on acceptance of a companion robot by older people. Journal of Physical Agents 2(2):33–40

    Google Scholar 

  42. Yoo B, Donthu N (2001) Developing a scale to measure the perceived quality of an Internet shopping site (Sitequal). Quarterly Journal of Electronic Commerce 2(1):31–46

    Google Scholar 

  43. Pagani M (2006) Determinants of adoption of high speed data services in the business market: evidence for a combined technology acceptance model with task technology fit model. Inf Manag 43(7):847–860

    Article  Google Scholar 

  44. Buss DB (1987) Selection, evocation and manipulation. J Personal Soc Psychol 53(6):1214–1221

    Article  Google Scholar 

  45. Delone WH, McLean ER (1992) Information systems success. The quest for the dependent variable. Inf Syst Res 3(1):60–95

    Article  Google Scholar 

  46. McFarland DJ, Hamilton D (2006) Adding contextual specificity to the technology acceptance model. Comput Hum Behav 22(3):427–447

    Article  Google Scholar 

  47. Ahn T, Ryu S, Han I (2007) The impact of Web quality and playfulness on user acceptance of online retailing. Inf Manag 44(3):263–275

    Article  Google Scholar 

  48. Lederer A, Maupin DJ, Sena MP, Zhuang Y (2000) The technology acceptance model and the World Wide Web. Decis Support Syst 29(3):269–282

    Article  Google Scholar 

  49. Liao Z, Cheung MT (2011) Internet-based e-shopping and consumer attitudes: an empirical study. Inf Manag 38(5):299–306

    Article  Google Scholar 

  50. Srinivasan A (1985) Alternative measures of system effectiveness: associations and implications. MIS Q 9(3):243–253

    Article  Google Scholar 

  51. Lai TL (2004) Service quality and perceived value’s impact on satisfaction, intention and usage of short message service (SMS). Inf Syst Front 6(4):353–368

    Article  Google Scholar 

  52. Nysveen H, Pederson PE, Thorbjornsen H. Explaining intention to use mobile chat services: moderating effects of gender, Journal of Consumer Marketing 22(5):247–256

  53. Bhattacherjee A (2001) Understanding information systems continuance: an expectation–confirmation model. MIS Q 25(3):351–370

    Article  Google Scholar 

  54. Bitner MJ (1990) Evaluating service encounters: the effects of physical surroundings and employee responses. J Mark 54(2):69–82

    Article  Google Scholar 

  55. Chiu CM, Hsu M, Sun S, Lin T, Sun P (2005) Usability, quality, value and e-learning continuance decisions. Comput Educ 45(4):399–416

    Article  Google Scholar 

  56. Hayashi A, Chen C, Ryan T, Wu J (2004) The role of social presence and moderating role of computer self-efficacy in predicting the continuance usage of e-learning systems. Journal of Information Systems Educations 15(2):139–154

    Google Scholar 

  57. LaBarbera PA, Mazursky D (1983) A longitudinal assessment of consumer satisfaction/dissatisfaction: the dynamic aspect of the cognitive process. J Mark Res 20(4):393–404

    Article  Google Scholar 

  58. Oliver RL (1981) Measurement and evaluation of satisfaction process in retail setting. J Retail 57(3):25–48

    Google Scholar 

  59. Roca JC, Chiu CM, Martinez FJ (2006) Understanding e-learning continuance intention: an extension of the technology acceptance model. Int J Hum Comput Stud 64(8):683–696

    Article  Google Scholar 

  60. Venkatesh V, Davis FD (2000) A theoretical extension of the technology acceptance model: Four longitudinal field studies. Manag Sci 46(2):186–204

    Article  Google Scholar 

  61. Yang Z, Cai S, Zhou Z, Zhou N (2005) Development and validation of an instrument to measure user perceived service quality of information presenting Web portals. Inf Manag 42(4):575–589

    Google Scholar 

  62. Yang Z, Jun M, Peterson RT (2004) Measuring customer perceived online service quality: scale development and managerial implications. International Journal of Operations & Production Management 24(11):1149–1174

    Article  Google Scholar 

  63. Wolters M, Georgila K, Moore JD, Logie RH, MacPherson SE, Watson M (2009) Reducing working memory load in spoken dialogue systems. Interacting with Computers 21(4):276–287

    Article  Google Scholar 

  64. Delone WH, McLean ER (2003) The DeLone and McLean model of information systems success: a ten-year update. J Manag Inf Syst 19(4):9–30

    Google Scholar 

  65. Lee KC, Chung N (2009) Understanding factors affecting trust in and satisfaction with mobile banking in Korea: a modified DeLone and McLean’s model perspective. Interacting with Computers 21(5):385–392

    Article  Google Scholar 

  66. Liu C, Arnett KP (2000) Exploring the factors associated with web site success in the context of electronic commerce. Inf Manag 38(1):23–33

    Article  Google Scholar 

  67. Schacklett M (2000) Nine ways to create a retail environment on your web site, Credit Union Magazine, pp. 12–13

  68. Cronbach LJ (1971) Test validation. In: Thorndike RL (ed) Educational measurement. American Council on Education, Washington, DC, pp 443–507

    Google Scholar 

  69. Anderson JC, Gerbing DW (1988) Structural equation modeling in practice: a review and recommended two-step approach. Psychol Bull 103(3):411–423

    Article  Google Scholar 

  70. Garver MS, Mentzer JT (1999) Logistics research methods: employing structural equation modeling to test for construct validity. J Bus Logist 20(1):33–57

    Google Scholar 

  71. Hoe SL (2008) Issues and procedure in adopting structural equation modeling technique. Journal of Applied Quantitative Method 3(1):76–83

    Google Scholar 

  72. Hoelter DR (1983) The analysis of covariance structures: goodness-of-fit indices. Sociological Methods and Research 11:325–344

    Article  Google Scholar 

  73. Bentler PM, Bonnet DG (1980) Significance tests and goodness-of-fit in the analysis of covariance structure. Psychol Bull 88(3):588–606

    Article  Google Scholar 

  74. Seyal AH, Rahman MN, Rahim MM (2002) Determinants of academic use of the Internet: a structural equation model. Behaviour and Information Technology 21(1):71–86

    Article  Google Scholar 

  75. Hair JF, Black WC, Babin BJ, Anderson RE (2006) Multivariate data analysis. Prentice Hall, Upper Saddle River, NJ

    Google Scholar 

  76. Kline RB (2004) Principles and practice of structural equation modeling. The Guildford Press, New York, NY

    Google Scholar 

  77. Fornell C, Larcker D (1981) F.: evaluating structural equation models with unobservable variables and measurement error. J Mark Res 18(1):39–50

    Article  Google Scholar 

  78. Bentler PM (1990) Comparative fit indices in structural models. Psychol Bull 107(2):238–246

    Article  Google Scholar 

  79. Hsu L, Chang K, Chen M (2011) The impact of website quality on customer satisfaction and purchase intention: perceived playfulness and perceived flow as mediators. Information Systems and E-Business Management 10:1–22

    Google Scholar 

  80. Tseng F, Lo H (2011) Antecedents of consumers’ intentions to upgrade their mobile phones. Telecommunications Policy 35(1):74–86

    Article  MathSciNet  Google Scholar 

  81. Kim S, Garrison G (2009) Investigating mobile wireless technology adoption: an extension of the technology acceptance model. Inf Syst Front 11(3):323–333

    Article  Google Scholar 

  82. 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. Syst Res Behav Sci 26(3):391–407

    Article  Google Scholar 

Download references

Acknowledgments

This study was supported by a grant from the World-Class University program (Grant No. R31-2008-000-10062-0) of the Korean Ministry of Education, Science and Technology via the National Research Foundation of South Korea.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Eunil Park.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Park, E., del Pobil, A.P. Modeling the user acceptance of long-term evolution (LTE) services. Ann. Telecommun. 68, 307–315 (2013). https://doi.org/10.1007/s12243-012-0324-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12243-012-0324-9

Keywords

Navigation