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Understanding the diffusion of mobile digital content: a growth curve modelling approach

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Abstract

While the sales of mobile digital content have been growing exponentially, the understanding of its diffusion remains limited in the literature. In this study, we set out to enrich this understanding using a growth curve modelling approach. We applied four widely used growth curve models of innovation diffusion and compared their performance in explaining the diffusion of mobile digital content empirically. Analysis based on the data collected on a product adopted by nearly 30 million mobile phone users over a 149-week period in 31 regions in China revealed that the Gompertz model was the most effective model in depicting the diffusion process, with more than 99 % of the variance explained. Moreover, we found that population, urbanization, education level, and mobile technology usage were significant determinants of various parameters such as the diffusion rate and inflection point of the diffusion process, respectively. Implications for both researchers and practitioners are discussed.

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Notes

  1. Source: http://it.sohu.com/20090407/n263239175.shtml, accessed on December 10, 2011.

  2. We realize that besides ordinary least square (OLS) estimation, there are several other estimation procedures such as the maximum likelihood estimation (MLE), the nonlinear least squares (NLS), and the algebraic estimation (AE) procedures (Mahajan et al. 1986). We choose OLS because it is the easiest to implement and it is not the purpose of this paper to compare different estimation methods of the Bass model.

References

  • Akçura M, Altınkemer K (2010) Digital bundling. Inf Syst E-Bus Manage 8(4):337–355

    Article  Google Scholar 

  • Barnes SJ (2002) The mobile commerce value chain: analysis and future developments. Int J Inf Manage 22(2):91–108

    Article  Google Scholar 

  • Baskerville RL, Myers MD (2002) Information systems as a reference discipline. MIS Q 26(1):1–14

    Article  Google Scholar 

  • Bass FM (1969) A new product growth model for consumer durables. Manage Sci 15(1):215–227

    Article  Google Scholar 

  • Chandrashekaran M, Grewal R, Mehta R (2010) Estimating contagion on the Internet: evidence from the diffusion of digital/information products. J Interact Mark 24(1):1–13

    Article  Google Scholar 

  • Davidson R, MacKinnon JG (1993) Estimation and inference in econometrics. Oxford University Press, New York

    Google Scholar 

  • Davidson R, MacKinnon JG (2003) Econometric theory and methods. Oxford University Press, New York

    Google Scholar 

  • Davis FD (1989) Perceived usefulness, perceived ease of use, and user acceptance. MIS Q 13(3):319–340

    Article  Google Scholar 

  • Dickerson MD, Gentry JW (1983) Characteristics of adopters and non-adopters of home computers. J Consumer Res 10(2):225–235

    Article  Google Scholar 

  • Dos Santos BL, Peffers K (1998) Competitor and vendor influence on the adoption of innovative applications in electronic commerce. Inf Manage 34(3):175–184

    Article  Google Scholar 

  • Feijóo C, Maghiros I, Abadie F, Gómez-Barroso J-L (2009) Exploring a heterogeneous and fragmented digital ecosystem: mobile content. Telematics Informa 26(3):282–292

    Article  Google Scholar 

  • Feng Y, Guo Z, Chiang W (2009) Optimal digital content distribution strategy in the presence of the consumer-to-consumer channel. J Manage Inf Syst 25(4):241–270

    Article  Google Scholar 

  • Gallaugher JM, Auger P, BarNir A (2001) Revenue streams and digital content providers: an empirical investigation. Inf Manage 38(7):473–485

    Article  Google Scholar 

  • Gatignon H, Eliashberg J, Robertson TS (1989) Modeling multinational diffusion patterns: an efficient methodology. Mark Sci 8(3):231–247

    Article  Google Scholar 

  • Greene WH (2007) Econometric analysis, 6th edn. Prentice Hall, Upper Saddle River

  • Gregg JV, Hassel CH, Richardson JT (1964) Mathematical trend curves: an aid to forecasting. Oliver and Boyd, Edinburgh

    Google Scholar 

  • Gruber H (2001) Competition and innovation: the diffusion of mobile telecommunications in Central and Eastern Europe. Inf Econ Policy 13(1):19–34

    Article  Google Scholar 

  • Ha I, Yoon Y, Choi M (2007) Determinants of adoption of mobile games under mobile broadband wireless access environment. Inf Manage 44(3):276–286

    Article  Google Scholar 

  • Hendry I (1972) The three parameter approach to long range forecasting. Long Range Plan 5(1):40–45

    Article  Google Scholar 

  • Horsky D, Simon LS (1983) Advertising and the diffusion of new products. Mark Sci 2(1):1–17

    Article  Google Scholar 

  • Hu N, Liu L, Bose I, Shen J (2010) Does sampling influence customers in online retailing of digital music? Inf Syst E-Bus Manage 8(4):357–377

    Article  Google Scholar 

  • Hui KL, Chau PYK (2002) Classifying digital products. Commun ACM 45(6):73–79

    Article  Google Scholar 

  • Jiang Z, Sarkar S (2009) Speed matters: the role of free software offer in software diffusion. J Manage Inf Syst 26(3):207–240

    Article  Google Scholar 

  • Katona Z, Zubcsek PP, Sarvary M (2011) Network effects and personal influences: the diffusion of an online social network. J Mark Res 48(3):425–443

    Article  Google Scholar 

  • Kiiski S, Pohjola M (2002) Cross-country diffusion of the Internet. Inf Econ Policy 14(2):297–310

    Article  Google Scholar 

  • Kim C, Oh E, Shin N (2010) An empirical investigation of digital content characteristics, value and flow. J Comput Inf Syst 50(4):79–87

    Google Scholar 

  • Kocas C (2002) Evolution of prices in electronic markets under diffusion of price-comparison shopping. J Manage Inf Syst 19(3):99–119

    Google Scholar 

  • Kock N (2004) The psychobiological model: towards a new theory of computer-mediated communication based on Darwinian evolution. Org Sci 15(3):327–348

    Article  Google Scholar 

  • Koiso-Kanttila N (2004) Digital content marketing: a literature synthesis. J Mark Manage 20(1/2):45–65

    Article  Google Scholar 

  • Lang KR, Vragov R (2005) A pricing mechanism for digital content distribution over computer networks. J Manage Inf Syst 22(2):121–139

    Google Scholar 

  • Liu X, Wu F-S, Chu W-L (2009) Innovation diffusion: mobile telephony adoption in China. Int J Innov Manage 13(2):245–271

    Article  Google Scholar 

  • Mahajan V, Mason CH, Srinivasan V (1986) An evaluation of estimation procedures for new product diffusion models. In: Mahajan V, Wind Y (eds) Innovation diffusion models of new product acceptance. Ballinger Publishing Cambridge, MA, pp 203–232

  • 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

    Article  Google Scholar 

  • Meade N, Islam T (1995) Forecasting with growth curves: an empirical comparison. Int J Forecast 11(2):199–215

    Article  Google Scholar 

  • Meade N, Islam T (2006) Modelling and forecasting the diffusion of innovation—a 25-year review. Int J Forecast 22(3):519–545

    Article  Google Scholar 

  • Ngai EWT, Gunasekaran A (2007) A review for mobile commerce research and applications. Decis Support Syst 43(1):3–15

    Article  Google Scholar 

  • Norton JA, Bass FM (1987) A diffusion theory model of adoption and substitution for successive generations of high-technology products. Manage Sci 33(9):1069–1086

    Article  Google Scholar 

  • Pae JH, Lehmann DR (2003) Multigeneration innovation diffusion: the impact of intergeneration time. J Acad Mark Sci 31(1):36–45

    Article  Google Scholar 

  • Peres R, Muller E, Mahajan V (2010) Innovation diffusion and new product growth models: a critical review and research directions. Int J Res Mark 27(2):91–106

    Article  Google Scholar 

  • Prince JT, Simon DH (2009) Has the Internet accelerated the diffusion of new products? Res Policy 38(8):1269–1277

    Article  Google Scholar 

  • Rai A (1995) External information source and channel effectiveness and the diffusion of CASE innovations: an empirical study. Eur J Inf Syst 2:93–102

    Google Scholar 

  • Ranganathan C, Seo D, Babad Y (2006) Switching behavior of mobile users: do users’ relational investments and demographics matter? Eur J Inf Syst 15(3):269–276

    Article  Google Scholar 

  • Rao SK (1985) An empirical comparison of sales forecasting models. J Prod Innov Manage 2(4):232–242

    Article  Google Scholar 

  • Rogers EM (2003) Diffusion of innovation. The Free Press, New York

    Google Scholar 

  • Shen W, Altinkemer K (2008) A multigeneration diffusion model for IT-intensive game consoles. J Assoc Inf Syst 9(8):442–461

    Google Scholar 

  • Shih CC-w (2009) The Facebook era: tapping online social networks to build better products, reach new audiences, and sell more stuff. Prentice Hall, Upper Saddle River

    Google Scholar 

  • Singh SK (2008) The diffusion of mobile phones in India. Telecommun Policy 32(9–10):642–651

    Article  Google Scholar 

  • Stahl F, Maass W (2006) Adoption and diffusion in electronic markets: an empirical analysis of attributes influencing the adoption of paid content. Electron Mark 16(3):233–244

    Article  Google Scholar 

  • Suarez P (2011) Apple’s App Store hits 10 billion downloads. http://www.pcworld.com/article/217443/apples_app_store_hits_10_billion_downloads.html. Accessed on 22 Jan 2011

  • Talukdar D, Sudhir K, Ainslie A (2002) Investigating new product diffusion across products and countries. Mark Sci 21(1):97–114

    Article  Google Scholar 

  • Tam KY (1996) Dynamic price elasticity and the diffusion of mainframe computing. J Manage Inf Syst 13(2):163–183

    Google Scholar 

  • 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 

  • Wang H-C, Doong H-S (2010) Diffusion of mobile music service in Taiwan: an empirical investigation of influence sources. Manage Decis 48(9):1378–1387

    Article  Google Scholar 

  • Wu F-S, Chu W-L (2010) Diffusion models of mobile telephony. J Bus Res 63(5):497–501

    Article  Google Scholar 

  • Zellner A (1962) An efficient method of estimating seemingly unrelated regressions and tests for aggregation bias. J Am Stat Assoc 57(298):348–368

    Article  Google Scholar 

  • Zellner A, Theil H (1962) Three-stage least squares: simultaneous estimation of simultaneous equations. Econometrica 30(1):54–78

    Article  Google Scholar 

Download references

Acknowledgments

This research was partly supported by a grant (no. 71102007) from National Natural Science Foundation of China to Dr. Angela Xia Liu at Tsinghua University and a Harrison McCain Foundation Emerging Scholar Award to Dr. Yinglei Wang at Acadia University.

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Authors and Affiliations

  1. School of Economics and Management, Tsinghua University, Beijing, 100084, China

    Angela Xia Liu & Xuping Jiang

  2. Fred C. Manning School of Business, Acadia University, 21 University Avenue, Wolfville, NS, B4P 2R6, Canada

    Yinglei Wang

  3. Business School, China University of Political Science and Law, Beijing, 102249, China

    Xi Chen

Authors
  1. Angela Xia Liu
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  2. Yinglei Wang
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  3. Xi Chen
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  4. Xuping Jiang
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Corresponding author

Correspondence to Yinglei Wang.

Appendix

Appendix

See Table 4.

Table 4 Summary of variables
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Liu, A.X., Wang, Y., Chen, X. et al. Understanding the diffusion of mobile digital content: a growth curve modelling approach. Inf Syst E-Bus Manage 12, 239–258 (2014). https://doi.org/10.1007/s10257-013-0224-1

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  • DOI: https://doi.org/10.1007/s10257-013-0224-1

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