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Information Technology and Management

, Volume 11, Issue 4, pp 161–175 | Cite as

The adoption of hyped technologies: a qualitative study

  • Jonas HedmanEmail author
  • Gregory Gimpel
Article

Abstract

The introduction of new consumer technology is often greeted with declarations that the way people conduct their lives will be changed instantly. In some cases, this might create hype surrounding a specific technology. This article investigates the adoption of hyped technology, a special case that is absent in the adoption literature. The study employs a consumer research perspective, specifically the theory of consumption values (TCV), to understand the underlying motives for adopting the technology. In its original form, TCV entails five values that influence consumer behavior: functional, social, epistemic, emotional and conditional. The values catch the intrinsic and extrinsic motives influencing behavior. Using a qualitative approach that includes three focus groups and 60 one-on-one interviews, the results of the study show that emotional, epistemic and social values influence the adoption of hyped technologies. Contrary to expectations, functional value, which is similar to the widely used information system constructs of perceived usefulness and relative advantage, has little impact on the adoption of technologies that are surrounded with significant hype. Using the findings of the study, this article proposes a model for investigating and understanding the adoption of hyped technologies. This article contributes to the literature by (1) focusing on the phenomenon of hyped technology, (2) introducing TCV, a consumer research-based theoretical framework, to enhance the understanding of technology adoption, and (3) proposing a parsimonious model explaining the adoption of hyped technology.

Keywords

Adoption of hyped technology model Hype Field study Intrinsic motivation Qualitative methods Technology adoption Theory-building research Theory of consumption values 

Notes

Acknowledgments

This work was in part supported by the DREAMS project via a grant from the Danish Agency of Science and Technology (grant number 2106-04-0007) and by Copenhagen Business School. We also thank the reviewers and special issue editors for their constructive comments and the field study participants for their involvement.

References

  1. 1.
    Agarwal R (2000) Individual acceptance of information technology. In: Zmud RW (ed) Framing the domains of it management: projecting the future through the past. Pinnaflex Educational Resources, Cincinnati, pp 85–104Google Scholar
  2. 2.
    Ajzen I (1985) From intentions to actions: a theory of planned behavior. In: Kuhl J, Beckmann J (eds) Action control: from cognition to behavior. Springer, Berlin, pp 11–39Google Scholar
  3. 3.
    Ajzen I (1991) The theory of planned behavior. Organ Behav Hum Decis Process 50(2):179–211CrossRefGoogle Scholar
  4. 4.
    Al-Gahtani SS, Hubona GS, Wang J (2007) Information technology in Saudi Arabia: culture and the acceptance and use of IT. Inf Manage 44(8):681–691CrossRefGoogle Scholar
  5. 5.
    Alpert F (1994) Innovator buying behavior over time. J Prod Brand Manage 3(2):50–62CrossRefGoogle Scholar
  6. 6.
    Bagozzi RP (2007) The legacy of the technology acceptance model and a proposal for a paradigm shift. J Assoc Inf Syst 8(4):244–254Google Scholar
  7. 7.
    Barbour RS (2005) Making sense of qualitative research making sense of focus groups. Med Educ 39(7):742–750CrossRefGoogle Scholar
  8. 8.
    Benbasat I, Barki H (2007) Quo vadis, TAM? J Assoc Inf Syst 8(4):212–218Google Scholar
  9. 9.
    Benbasat I, Zmud RW (1999) Empirical research in information systems: the practice of relevance. MIS Quart 23(1):3–16CrossRefGoogle Scholar
  10. 10.
    Bergeron F, Raymond L, Rivard S, Gara MF (1995) Determinants of EIS use: testing a behavioral model. Decis Support Syst 14(2):131–146CrossRefGoogle Scholar
  11. 11.
    Bhattacherjee A (2001) Understanding information systems continuance: an expectation-confirmation model. MIS Quart 25(3):351–370CrossRefGoogle Scholar
  12. 12.
    Blair DC (2002) Knowledge management: hype, hope, or help? J Am Soc Inf Sci Technol 53(12):1019–1028CrossRefGoogle Scholar
  13. 13.
    Blechar J, Constantiou ID, Damsgaard J (2006) Exploring the influence of reference situations and reference pricing on mobile service user behavior. Eur J Inf Syst 15(3):285–291CrossRefGoogle Scholar
  14. 14.
    Block R (2007, January 9) Live from MacWorld 2007: Steve Jobs keynote. Available at http://www.engadget.com/2007/01/09/live-from-macworld-2007-steve-jobs-keynote. Accessed 15 Sept 2010
  15. 15.
    Bouwman H, Carlsson C, Molina-Castillo FJ, Walden P (2007) Barriers and drivers in the adoption of current and future mobile services in Finland. Telemat Inf 24(2):145–160CrossRefGoogle Scholar
  16. 16.
    Bouwman H, van de Wijngaert L (2009) Coppers context, and conjoints: a reassessment of TAM. J Inf Technol 24(2):186–201CrossRefGoogle Scholar
  17. 17.
    Charmaz K (2006) Constructing grounded theory. SAGE, LondonGoogle Scholar
  18. 18.
    Choudrie J, Dwivedi YK (2006) Investigating factors influencing adoption of broadband in the household. J Comput Inf Syst 46(4):25–34Google Scholar
  19. 19.
    Cloete M, Snyman R (2003) The enterprise portal: is it knowledge management? Aslib Proc 55(4):234–242CrossRefGoogle Scholar
  20. 20.
    Davenport TH, Markus ML (1999) Rigor vs. relevance revisited: response to Benbasat and Zmud. MIS Quart 23(1):19–23CrossRefGoogle Scholar
  21. 21.
    Davenport TH, Stoddard DB (1994) Reengineering: business change of mythic proportions. MIS Quart 18(2):121–127CrossRefGoogle Scholar
  22. 22.
    Davis FD (1989) Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quart 13(3):319–340CrossRefGoogle Scholar
  23. 23.
    Dickinger A, Arami M, Meyer D (2008) The role of perceived enjoyment and social norm in the adoption of technology with network externalities. Eur J Inf Syst 17(1):4–11CrossRefGoogle Scholar
  24. 24.
    Dinev T, Qing H (2007) The centrality of awareness in the formation of user behavioral intention toward protective information technologies. J Assoc Inf Syst 8(7):386–408Google Scholar
  25. 25.
    Dishaw MT, Strong DM (1999) Extending the technology acceptance model with task-technology fit constructs. Inf Manage 36(1):9–21CrossRefGoogle Scholar
  26. 26.
    Eckhardt A, Laumer S, Weitzel T (2009) Who influences whom? Analyzing workplace referents’ social influence on it adoption and non-adoption. J Inf Technol 24(1):11–24CrossRefGoogle Scholar
  27. 27.
    Edwards C, Peppard JW (1994) Business process redesign: hype, hope or hypocrisy. J Inf Technol 9(4):251–266CrossRefGoogle Scholar
  28. 28.
    Fang XW, Chan S, Brzezinski J, Xu S (2003) Moderating effects of task type on wireless technology acceptance. In: Proceedings of the 2nd pre-ICIS workshop on HCI research in MIS, Seattle, WA, pp 123–157Google Scholar
  29. 29.
    Fedorowicz J, Gogan JL (2010) Reinvention of interorganizational systems: a case analysis of the diffusion of a bio-terror surveillance system. Inf Syst Front 12(1):81–95CrossRefGoogle Scholar
  30. 30.
    Fishbein M, Ajzen I (1975) Belief, intention, and behavior: an introduction to theory and research. Addison-Wesley, ReadingGoogle Scholar
  31. 31.
    Gill TG (1995) Early expert-systems: where are they now. MIS Quart 19(1):51–81CrossRefGoogle Scholar
  32. 32.
    Glaser BG (1978) Theoretical sensitivity. Sociology Press, Mill ValleyGoogle Scholar
  33. 33.
    Goodhue DL (1995) Understanding user evaluations of information systems. Manage Sci 41(12):1827–1844CrossRefGoogle Scholar
  34. 34.
    Guest G, MacQueen KM (2008) Handbook of team-based qualitative research. AltaMira, LanhamGoogle Scholar
  35. 35.
    Hedman J, Kalling T (2003) The business model concept: theoretical underpinnings and empirical illustrations. Eur J Inf Syst 12(1):49–59CrossRefGoogle Scholar
  36. 36.
    Hirschman EC, Holbrook MB (1982) Hedonic consumption: emerging concepts, methods and propositions. J Mark 46(3):92–101CrossRefGoogle Scholar
  37. 37.
    Ho SH, Ko YY (2008) Effects of self-service technology on customer value and customer readiness: the case of Internet banking. Internet Res 18(4):427–446CrossRefGoogle Scholar
  38. 38.
    Holbrook MB (2005) Customer value and autoethnography: subjective personal introspection and the meanings of a photograph collection. J Bus Res 58(1):45–61CrossRefGoogle Scholar
  39. 39.
    Holbrook MB (2006) Consumption experience, customer value, and subjective personal introspection: an illustrative photographic essay. J Bus Res 59(6):714–725CrossRefGoogle Scholar
  40. 40.
    Holbrook MB, Hirschman EC (1982) The experiential aspects of consumption: consumer fantasies, feelings, and fun. J Consum Res 9(2):132–140CrossRefGoogle Scholar
  41. 41.
    Hong SJ, Thong JYL, Moon JY, Tam KY (2008) Understanding the behavior of mobile data services consumers. Inf Syst Front 10(4):431–445CrossRefGoogle Scholar
  42. 42.
    Hsieh JJP, Rai A, Keil M (2008) Understanding digital inequality: comparing continued use behavioral models of the socio-economically advantaged and disadvantaged. MIS Quart 32(1):97–126Google Scholar
  43. 43.
    Hsu CL, Lin JCC (2008) Acceptance of blog usage: the roles of technology acceptance, social influence and knowledge sharing motivation. Inf Manage 45(1):65–74CrossRefGoogle Scholar
  44. 44.
    Jiang P (2009) Consumer adoption of mobile Internet services: an exploratory study. J Promot Manage 15(3):418–454CrossRefGoogle Scholar
  45. 45.
    Kauffman RJ, Techatassanasoontorn AA (2009) Understanding early diffusion of digital wireless phones. Telecommun Policy 33(8):432–450CrossRefGoogle Scholar
  46. 46.
    Khalifa M, Shen KN (2008) Drivers for the transactional B2C m-commerce adoption: extended theory of planned behavior. J Comput Inf Syst 48(3):111–117Google Scholar
  47. 47.
    Kim H, Lee I, Kim J (2008) Maintaining continuers vs. converting discontinuers: relative importance of post-adoption factors for mobile data services. Int J Mob Commun 6(1):108–132CrossRefGoogle Scholar
  48. 48.
    Kim HW, Chan HC, Gupta S (2007) Value-based adoption of mobile Internet, an empirical investigation. Decis Supp Syst 43(1):111–126CrossRefGoogle Scholar
  49. 49.
    King WR (1999) Integrating knowledge management into IS strategy. Inf Syst Manage 16(4):70–72CrossRefGoogle Scholar
  50. 50.
    Kitzinger J (1995) Qualitative research: introducing focus groups. Brit Med J 311(7000):299–302Google Scholar
  51. 51.
    Klein HK, Myers MD (1999) A set of principles for conducting and evaluating interpretive field studies in information systems. MIS Quart 23(1):67–93CrossRefGoogle Scholar
  52. 52.
    Krueger RA, Casey MA (2000) Focus groups: a practical guide for applied research. Sage, Thousand OaksGoogle Scholar
  53. 53.
    Lee AS (1999) Rigor and relevance in MIS research: beyond the approach of positivism alone. MIS Quart 23(1):29–33CrossRefGoogle Scholar
  54. 54.
    Lee CC, Cheng HK, Cheng HH (2007) An empirical study of mobile commerce in insurance industry: task-technology fit and individual differences. Decis Supp Syst 43(1):95–110CrossRefGoogle Scholar
  55. 55.
    Lee Y, Kozar KA, Larsen KRT (2003) The technology acceptance model: past, present, and future. Commun AIS 12(50):752–780Google Scholar
  56. 56.
    Lin CA, Jeffres LW (1998) Factors influencing the adoption of multimedia cable technology. J Mass Commun Quart 75(2):341–352Google Scholar
  57. 57.
    Lopez-Nicolas C, Molina-Castillo FJ, Bouwman H (2008) An assessment of advanced mobile services acceptance: contributions from tam and diffusion theory models. Inf Manage 45(6):359–364CrossRefGoogle Scholar
  58. 58.
    Lu J, Liu C, Yu CS, Wang KL (2008) Determinants of accepting wireless mobile data services in China. Inf Manage 45(1):52–64CrossRefGoogle Scholar
  59. 59.
    Lyytinen K, Damsgaard J (2001) What’s wrong with the diffusion of innovation theory? In: Ardis MA, Marcolin BL (eds) Proceedings of the fourth working conference on diffusing software product and process innovations. Kluwer, Norwell, pp 173–189Google Scholar
  60. 60.
    Magni M, Taylor MS, Venkatesh V (2010) To play or not to play: a cross-temporal investigation using hedonic and instrumental perspectives to explain user intentions to explore a technology. Int J Hum–Comput Stud 68(9):572–588CrossRefGoogle Scholar
  61. 61.
    Mathieson K, Keil M (1998) Beyond the interface: ease of use and task/technology fit. Inf Manage 34(4):221–230CrossRefGoogle Scholar
  62. 62.
    Mathwick C, Malhotra N, Rigdon E (2001) Experiential value: conceptualization, measurement and application in the catalog and Internet shopping environment. J Retail 77(1):39–56CrossRefGoogle Scholar
  63. 63.
    McMaster T, Wastell D (2005) Diffusion or delusion? Challenging an IS research tradition. Inf Technol People 18(4):383–404CrossRefGoogle Scholar
  64. 64.
    Moody DL (2000) Building links between is research and professional practice: improving the relevance an impact of IS research. In: Proceedings of the 21st international conference on information systems, Brisbane, Australia, pp 351–360Google Scholar
  65. 65.
    Moore GC, Benbasat I (1991) Development of an instrument to measure the perceptions of adopting an information technology innovation. Inf Syst Res 2(3):192–222CrossRefGoogle Scholar
  66. 66.
    Morgan DL (1997) Focus groups as qualitative research. Sage, Thousand OaksGoogle Scholar
  67. 67.
    Orlikowski WJ, Baroudi JJ (1991) Studying information technology in organizations: research approaches and assumptions. Inf Syst Res 2(1):1–28CrossRefGoogle Scholar
  68. 68.
    Ramayah T, Rouibah K, Gopi M, Rangel GJ (2009) A decomposed theory of reasoned action to explain intention to use internet stock trading among Malaysian investors. Comput Hum Behav 25(6):1222–1230CrossRefGoogle Scholar
  69. 69.
    Rogers EM (1962) Diffusion of innovations. The Free Press, New YorkGoogle Scholar
  70. 70.
    Rogers EM (1995) Diffusion of innovations. The Free Press, New YorkGoogle Scholar
  71. 71.
    Rosemann M, Vessey I (2008) Toward improving the relevance of information systems research to practice: the role of applicability checks. MIS Quart 32(1):1–22Google Scholar
  72. 72.
    Saldaña J (2009) The coding manual for qualitative researchers. Sage, Thousand OaksGoogle Scholar
  73. 73.
    Sheth JN (1979) The surpluses and shortages in consumer behavior theory and research. J Acad Mark Sci 7(4):414–427CrossRefGoogle Scholar
  74. 74.
    Sheth JN, Newman BI, Gross BL (1991) Consumption values and market choices: theory and applications. South-Western Publishing Co., CincinnatiGoogle Scholar
  75. 75.
    Sheth JN, Newman BI, Gross BL (1991) Why we buy what we buy: a theory of consumption values. J Bus Res 22(2):159–170CrossRefGoogle Scholar
  76. 76.
    Sledgianowski D, Kulviwat S (2009) Using social network sites: the effects of playfulness, critical mass and trust in a hedonic context. J Comput Inf Syst 49(4):74–83Google Scholar
  77. 77.
    Spaulding TJ (2010) How can virtual communities create value for business? Electron Comm Res Appl 9(1):38–49Google Scholar
  78. 78.
    Strader TJ, Shaw MJ (1997) Characteristics of electronic markets. Decis Supp Syst 21(3):185–198CrossRefGoogle Scholar
  79. 79.
    Turel O, Serenko A, Bontis N (2009) User acceptance of hedonic digital artifacts: a theory of consumption values perspective. Inf Manage 47(1):53–59CrossRefGoogle Scholar
  80. 80.
    Uzoka DME (2008) Organisational influences on e-commerce adoption in a developing country context using UTAUT. Int J Bus Inf Syst 3(3):300–316CrossRefGoogle Scholar
  81. 81.
    Van der Heijden H (2004) User acceptance of hedonic information systems. MIS Quart 28(4):695–704Google Scholar
  82. 82.
    Venkatesh V, Brown SA (2001) A longitudinal investigation of personal computers in homes: adoption determinants and emerging challenges. MIS Quart 25(1):71–102CrossRefGoogle Scholar
  83. 83.
    Venkatesh V, Davis FD, Morris MG (2007) Dead or alive? The development, trajectory and future of technology adoption research. J Assoc Inf Syst 8(4):268–286Google Scholar
  84. 84.
    Venkatesh V, Morris MG, Davis GB, Davis FD (2003) User acceptance of information technology: toward a unified view. MIS Quart 27(3):425–478Google Scholar
  85. 85.
    Volkoff O, Strong DM, Elmes MB (2005) Understanding enterprise systems-enabled integration. Eur J Inf Syst 14(2):110–120CrossRefGoogle Scholar
  86. 86.
    Wakefield RL, Whitten D (2006) Mobile computing, a user study on hedonic/utilitarian mobile device usage. Eur J Inf Syst 15(3):292–300CrossRefGoogle Scholar
  87. 87.
    Walsham G (2006) Doing interpretive research. Eur J Inf Syst 15(3):320–330CrossRefGoogle Scholar
  88. 88.
    Warwick C, Pritchard E (2000) ‘Hyped’ text markup language: XML and the future of Web markup. Aslib Proc 52(5):174–184CrossRefGoogle Scholar
  89. 89.
    Yoon C, Kim S (2007) Convenience and TAM in a ubiquitous computing environment: the case of wireless LAN. Electron Comm Res Appl 6(1):102–112Google Scholar
  90. 90.
    Zhang J, Mao E (2008) Understanding the acceptance of mobile SMS advertising among young Chinese consumers. Psychol Mark 25(8):787–805CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.Center for Applied ICTCopenhagen Business SchoolFrederiksbergDenmark

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