Skip to main content
Log in

Understanding consumer adoption of broadband: an extension of the technology acceptance model

  • Case-Oriented Paper
  • Published:
Journal of the Operational Research Society

Abstract

Broadband offers several benefits to consumers over its preceding technology ‘narrowband’. Despite it offering such benefits, many countries are still experiencing low levels of adoption of broadband technology by consumers. This study presents an extended technology acceptance model (TAM) that integrates perceived resources, self-efficacy and social influence into the TAM in order to investigate factors determining consumer adoption of broadband. The model was empirically tested employing data collected from a survey of broadband consumers in the United Kingdom. A regression analysis was conducted to evaluate the influence of predictive constructs on behavioural intention to adopt broadband and actual adoption behaviour. Findings of the study indicated that all variables significantly affected consumers' behavioural intention to adopt broadband. The outcomes of the paper will be useful for the stakeholders such as internet service providers and governments interested in encouraging the adoption of broadband. The implications of this work to both researchers and practitioners is discussed.

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.

Institutional subscriptions

Figure 1
Figure 2
Figure 3

Similar content being viewed by others

References

  • Adams DA, Nelson RR and Todd PA (1992). Perceived usefulness, ease of use, and usage of information technology: A replication. MIS Quart 16(2): 227–247.

    Article  Google Scholar 

  • Ajzen I (1991). The theory of planned behaviour. Organ Behav Hum Decis Process 50: 179–211.

    Article  Google Scholar 

  • Ajzen I and Fishbein M (1980). Understanding Attitudes and Predicting Social Behaviour . Prentice-Hall: Englewood Cliffs, NJ.

    Google Scholar 

  • Angelou G and Economides A (2008). A real options approach for prioritising ICT business alternatives: A case study from broadband technology business field. J Opl Res Soc, 59 (10): 1340–1351.

  • Best J (2006). Europe nears broadband saturation (available at http://networks.silicon.com/broadband/0,39024661,39162560,00.htm/, last accessed 7 February 2007).

  • Brace N, Kemp R and Snelgar R (2003). SPSS for Psychologists: A Guide to Data Analysis Using SPSS for Windows. Palgrave Macmillan: New York.

    Google Scholar 

  • Brown SA, Massey AP, Montoya-Weiss MM and Burkman JR (2002). Do I really have to? User acceptance of mandated technology. Eur J Inform Systems 11(4): 283–295.

    Article  Google Scholar 

  • Bruner GC and Kumar A (2005). Explaining consumer acceptance of handheld Internet devices. J Bus Res 58(5): 553–558.

    Article  Google Scholar 

  • Chau PYK (1996). An empirical assessment of a modified technology acceptance model. J Mngt Inform Systems 13(2): 185–204.

    Google Scholar 

  • Choudrie J and Lee H (2004). Broadband development in South Korea: Institutional and cultural factor. Eur J Inform Systems 13(2): 103–114.

    Article  Google Scholar 

  • Cornford T and Smithson S (1996). Project Research in Information Systems: A Student's Guide. Macmillan Press Ltd: London.

    Book  Google Scholar 

  • Davis FD (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quart 13: 319–340.

    Article  Google Scholar 

  • Davis FD, Bagozzi RP and Warshaw PR (1989). User acceptance of computer technology: A comparison of two theoretical models. Mngt Sci 35(8): 982–1003.

    Article  Google Scholar 

  • Fowler FJ Jr, (2002). Survey Research Methods. Sage Publications Inc.: London.

    Google Scholar 

  • Galliers RD and Land FF (1987). Choosing appropriate information systems research methodologies. Commun ACM 30(11): 900–902.

    Article  Google Scholar 

  • Gefen D and Straub DW (2000). The relative importance of perceived ease of use in IS adoption: A study of e-commerce adoption. J Assoc Inform Systems 1: 1–28.

    Google Scholar 

  • Gilligan C and Wilson RMS (2003). Strategic Marketing Planning. Butterworth-Heinemann: Oxford.

    Google Scholar 

  • Hinton PR, Brownlow C, McMurray I and Cozens B (2004). SPSS Explained. Routledge Inc.: East Sussex, England.

    Google Scholar 

  • Howick S and Whalley J (2008). Understanding the drivers of broadband adoption: The case of rural and remote Scotland. J Opl Res Soc 59(10): 1299–1311.

    Article  Google Scholar 

  • Hsu CL and Lu HP (2004). Why do people play on-line games? An extended TAM with social influences and flow experience. Inform Mngt 41(7): 853–868.

    Google Scholar 

  • Igbaria M and Iivari J (1995). The effects of self-efficacy on computer usage. OMEGA-Int J Mngt Sci 23(6): 587–605.

    Article  Google Scholar 

  • Langdale JV (1997). International competitiveness in East Asia: Broadband telecommunications and interactive multimedia. Telecommun Policy 21: 235–249.

    Article  Google Scholar 

  • Lee H, O'Keefe RM and Yun K (2003). The growth of broadband and electronic commerce in South Korea: Contributing factors. Inform Soc 19(1): 81–93.

    Article  Google Scholar 

  • Lu HP and Gustafson DH (1994). An empirical study of perceived usefulness and perceived ease of use on computerized support system use over time. Int J Inform Mngt 14(5): 317–329.

    Article  Google Scholar 

  • Lu J, Yao JE and Yu CS (2005). Personal innovativeness, social influences and adoption of wireless Internet services via mobile technology. J Strategic Inform Systems 14(3): 245–268.

    Article  Google Scholar 

  • Luarn P and Lin HH (2005). Toward an understanding of the behavioural intention to use mobile banking. Comput Hum Behav 21(6): 873–891.

    Article  Google Scholar 

  • Mathieson K, Peacock E and Chin WW (2001). Extending the technology acceptance model: The influence of perceived user resources. ACM SIGMIS Database 32(3): 86–113.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Mingers J (2001). Combining IS research methods: Towards a pluralist methodology. Inform Systems Res 12(3): 240–259.

    Article  Google Scholar 

  • Myers RH (1990). Classical and Modern Regression with Applications. PWS-KENT Publishing Company: Boston.

    Google Scholar 

  • Oh S, Ahn J and Kim B (2003). Adoption of broadband Internet in Korea: The role of experience in building attitudes. J Inform Technol 18(4): 267–280.

    Article  Google Scholar 

  • Pavlou PA and Fygenson M (2006). Understanding and predicting electronic commerce adoption: An extension of the theory of planned behaviour. MIS Quart 30(1): 115–143.

    Google Scholar 

  • Rice C (1997). Understanding Customers. Butter worth-Heinemann: Oxford.

    Google Scholar 

  • Rice RE, Grant AE, Schmitz J and Torobin J (1990). Individual and network influences on the adoption and perceived outcomes of electronic messaging. Social Networks 12(1): 27–55.

    Article  Google Scholar 

  • Rogers EM (1995). Diffusion of Innovations. Free Press: New York.

    Google Scholar 

  • Sawyer S, Allen JP and Heejin L (2003). Broadband and mobile opportunities: A socio-technical perspective. J Inform Technol 18(4): 121–136.

    Article  Google Scholar 

  • Stanton LJ (2004). Pactors influencing the adoption of residential broadband connections to Internet. In: Sprague RH (ed.) Proceedings of the 37th Hawaii International Conference on System Sciences, IEEE: Hawaii, USA.

  • Stevens J (1996). Applied Multivariate Statistics for the Social Sciences. Lawrence Erlbaum Associates, Inc.: New Jersey.

    Google Scholar 

  • Straub DW, Boudreau M-C and Gefen D (2004). Validation guidelines for IS positivist research. Commun Assoc Inform Systems 13: 380–427.

    Google Scholar 

  • Tan M and Teo TSH (2000). Factors influencing the adoption of Internet banking. J Assoc Inform Systems 1(5): 1–42.

    Google Scholar 

  • Taylor S and Todd PA (1995). Understanding information technology usage: A test of competing models. Inform Systems Res 6(1): 44–176.

    Google Scholar 

  • Venkatesh V and Brown SA (2001). A longitudinal investigation of personal computers in homes: Adoption determinants and emerging challenges. MIS Quart 25(1): 71–102.

    Article  Google Scholar 

  • Wang YS (2003). The adoption of electronic tax filing systems: An empirical study. Government Inform Quart 20(4): 333–352.

    Article  Google Scholar 

  • Wang YS, Wang YM, Lin HH and Tang TI (2003). Determinants of user acceptance of Internet banking: an empirical study. Int J Service Indust Mngt 14(5): 501–519.

    Article  Google Scholar 

  • Wu JH and Wang SC (2005). What drives mobile commerce? An empirical evaluation of the revised technology acceptance model. Inform Mngt 42(5): 719–729.

    Google Scholar 

  • Yu J, Ha I, Choi M and Rho J (2005). Extending the TAM for a t-Commerce. Inform Mngt 42: 965–976.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Z Irani.

Appendix

Appendix

Appendix

Appendix Survey instrument

Rights and permissions

Reprints and permissions

About this article

Cite this article

Irani, Z., Dwivedi, Y. & Williams, M. Understanding consumer adoption of broadband: an extension of the technology acceptance model. J Oper Res Soc 60, 1322–1334 (2009). https://doi.org/10.1057/jors.2008.100

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1057/jors.2008.100

Keywords

Navigation