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Innovation and imitation effects in Metaverse service adoption

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Abstract

This study examines the innovation and imitation effects in Metaverse service adoption. “Metaverse services” is a collective term for services such as Augmented reality, Life logging, Mirror world, and Virtual world. Among them, Twitter, Google, iPhone, and Secondlife (T.G.I.S) are the most popular services/products these days. To measure the adoption of these product/services, the most commonly used are IP traffic and iPhone sales. Thus, in this study, we measured adoption by measuring changes in the IP traffic volume of Twitter.com, Maps.Google.com, Secondlife.com, and sales for iPhone during a 2-year period (from the first quarter of 2008 to the fourth quarter of 2009). To analyze this time series data to reveal the innovation and imitation effect, we employed the Bass model. The results showed that each of these services yields different innovation and imitation coefficient values. Imitation effects for all Metaverse services are greater than innovation effects, and Secondlife’s innovation effects are larger than others. Also, iPhone sales, as a measurement for information and communication technology (ICT) products, showed greater innovation effects than the other services. Implications are drawn to explain these differences, such as, Googlemap’s imitation effects are based on network externalities, while Twitter’s imitation effects are caused by the interactions of individuals; iPhone sales’ innovation effects are explained by the timing of the measurement.

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References

  • Agarwal R, Lucas JRH (2005) The information systems identity crisis: focusing on high-visibility and high-impact research. MIS Quart 29(3):381–398

    Google Scholar 

  • Bass F (1969) A new product growth model for product diffusion. Manage Sci 15(5):215–227

    Article  Google Scholar 

  • Beale R (2005) Supporting social interaction with smart phones. IEEE Pervasive Comput 4(2):35–41

    Article  Google Scholar 

  • Bergman E (2000) Information appliances and beyond: interaction design for consumer products. Morgan Kaufmann, San Francisco

    Google Scholar 

  • Berry L (1995) Relationship marketing of services-growing interest, emerging perspectives. J Acad Mark Sci 23(4):236–245

    Article  Google Scholar 

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

    Article  Google Scholar 

  • De Valck K, Van Bruggen G (2009) Virtual communities: a marketing perspective. Decis Support Syst 47(3):185–203

    Article  Google Scholar 

  • Elgan M (2009) Blogging. Lifestreaming. What’s next: Lifelogging! ComputerWorld. http://www.computerworld.com/s/article/9139485/Blogging._Lifestreaming._What_s_next_Lifelogging. Accessed 14 March 2011

  • Gartner (2007) Gartner Says 80 percent of active internet users will have a “Second Life” in the virtual world by the end of 2011. http://www.gartner.com/it/page.jsp?id=503861. Accessed 14 March 2011

  • Girgensohn A, Lee A (2002) Making web sites be places for social interaction. In: Proceedings of the 2002 ACM conference on computer supported cooperative work, New Orleans. ACM, New York, pp 136–145

  • Gremler D, Brown S (1996) Service loyalty: its nature, importance, and implications. In: Edvardsson B, Brown SW, Johnston R, Scheuing EE (eds) Advancing service quality: a global perspective. International Service Quality Association, New York, pp 170–180

    Google Scholar 

  • Hague P (2002) Market research: a guide to planning, methodology & evaluation. Kogan Page, London

    Google Scholar 

  • Hong S, Tam K (2006) Understanding the adoption of multipurpose information appliances: the case of mobile data services. Inform Syst Res 17(2):162–179

    Article  Google Scholar 

  • Kalish S, Lilien G (1986) A market entry timing model for new technologies. Manage Sci 32(2):194–205

    Article  Google Scholar 

  • Katz M, Shapiro C (1985) Network externalities, competition, and compatibility. Am Econ Rev 75(3):424–440

    Google Scholar 

  • Kirk J (2010) Will 2011 be the year of mobile malware? Networkworld. http://www.networkworld.com/news/2010/122110-will-2011-be-the-year.html. Accessed 14 March 2011

  • Kiss C, Bichler M (2008) Identification of influencers–measuring influence in customer networks. Decis Support Syst 46(1):233–253

    Article  Google Scholar 

  • Kivimaki A, Fomin V (2001) What makes a killer application for the cellular telephony services? In: Proceedings of the second IEEE conference on standardization and innovation in information technology (SIIT 2001), Boulder. IEEE, New York, pp 25–37

  • Kobrin S (1985) Diffusion as an explanation of oil nationalization: or the domino effect rides again. J Confl Resolut 29(1):3–32

    Article  Google Scholar 

  • Kumar S, Chhugani J (2008) Second life and the new generation of virtual worlds. Computer 41(9):46–53

    Article  Google Scholar 

  • Lee SM, Lim S (2009) Entrepreneurial orientation and the performance of service business. Serv Bus 3(1):1–13

    Article  Google Scholar 

  • Lee SM, Kim T, Noh Y, Lee B (2010) Success factors of platform leadership in web 2.0 service business. Serv Bus 4(4):89–103

    Article  Google Scholar 

  • Lyytinen K, Damsgaard J (2001) What’s wrong with the diffusion of innovation theory? In: Ardis MA, Marcolin BL (eds) Diffusing software products and process innovations. Kluwer, Norwell, pp 173–190

    Google Scholar 

  • Lyytinen K, Yoo Y (2002) Research commentary: the next wave of nomadic computing. Inform Syst Res 13(4):377–388

    Article  Google Scholar 

  • Mahajan V, Muller E (1990) New product diffusion models in marketing: a review and directions for research. J Mark 54(1):1–26

    Article  Google Scholar 

  • Mahajan V, Muller E, Bass FM (1990) New product diffusion models in marketing: a review and directions for research. J Mark 54(1):1–26

    Google Scholar 

  • Metaverseroadmap (2007) www.metaverseroadmap.org/overview. Accessed 14 March 2011

  • Moore G (1991) Crossing the chasm. HarperBusiness, New York

    Google Scholar 

  • Olshavsky R (1980) Time and the rate of adoption of innovations. J Consumer Res 6(4):425–428

    Article  Google Scholar 

  • Resnick P (2002) Beyond bowling together: sociotechnical capital. In: Carroll JM (ed) Human computer interaction in the new millennium. Addison-Wesley, Reading, pp 647–672

    Google Scholar 

  • Rogers E (1995) Diffusion of innovations, 4th edn. The Free Press, New York

    Google Scholar 

  • Schumpeter, JA (1942) Capitalism, Socialism, and Democracy, 3rd edn. Harper & Row, New York

  • Simon C (2010) The case for online movie rental. In: proceedings of world conference of over the top OTT TV, London

  • Takada H, Jain D (1991) Cross-national analysis of diffusion of consumer durable goods in Pacific Rim countries. J Mark 55(2):48–54

    Article  Google Scholar 

  • Taylor S, Todd P (1995) Understanding information technology usage: a test of competing models. Inform Syst Res 6(2):144–176

    Article  Google Scholar 

  • Venkatraman N, Loh L, Ko J (1994) The adoption of corporate governance mechanisms: a test of competing diffusion models. Manage Sci 40(4):496–507

    Article  Google Scholar 

  • Weiser M (1995) The computer for the 21st century. Sci Am 272(3):78–89

    Google Scholar 

  • Wikipedia. (2007) http://en.wikipedia.org/wiki/Metaverse. Accessed 14 March 2011

  • Wollebaek D, Selle P (2002) Does participation in voluntary associations contribute to social capital? The impact of intensity, scope, and type. Nonprofit Volunt Sect Quart 31(1):32

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by the Korea Research Foundation Grant funded by the Korean Government (KRF-2009-32A-B00055) and completed with Ajou university research fellowship of 2009–2010.

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Authors and Affiliations

  1. College of Business Administration, Ajou University, Suwon, 443-749, Korea

    Sang-Gun Lee, Won Ki Byun & Mincheol Kang

  2. Department of Management, University of Nebraska-Lincoln, 209 CBA, Lincoln, NE, 68588-0491, USA

    Silvana Trimi

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  1. Sang-Gun Lee
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  2. Silvana Trimi
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  3. Won Ki Byun
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  4. Mincheol Kang
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Correspondence to Silvana Trimi.

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Lee, SG., Trimi, S., Byun, W.K. et al. Innovation and imitation effects in Metaverse service adoption. Serv Bus 5, 155–172 (2011). https://doi.org/10.1007/s11628-011-0108-8

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