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Predicting diffusion dynamics and launch time strategy for mobile telecommunication services: an empirical analysis

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Abstract

Information technology (IT) innovations require strategic planning for issues related to the launch time of the new generation, technological advancement, and potential user base. The present study seeks to develop a decision-making model that effectively facilitates the predictive analysis of the diffusion paradigm for multi-generational services and the optimal timing of new service introduction. This paper analyzes the generation substitution and customer attrition behavior simultaneously in the adoption process of service generations. A novel multi-generational diffusion model is proposed for those service innovations that coexist in the market at the same time and divides the firm’s market shares. Also, the proposed study intends to devise a decision model to assess the launch scenarios for a new service generation. The optimization problem takes into account the trade-off between the preceding generation’s subscription level and service development cost. The proposed methodological framework is implemented on the telecommunication service line. An empirical analysis is conducted to examine the model performance and prediction ability as compared to the existing models. The findings show that the parameter estimation using the proposed diffusion model will lead to unbiased parameter values and better forecasting results. A numerical illustration is provided to solve the proposed optimization problem using the multi-attribute utility theory (MAUT). Moreover, sensitivity analysis is performed on critical parameters to examine their impact on the computational results. The present research on optimal introduction time of a new service generation yields crucial managerial insights for businesses and policymakers.

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Notes

  1. https://www.businessofapps.com/data/netflix-statistics/#:~:text=Netflix%20subscriber%20growth%20for%20Q4,growth%20declining%20from%200.52%20million, accessed on 30 June 2020.

References

  1. Jha AK, Bose I (2016) Innovation research in information systems: a commentary on contemporary trends and issues. Inf Manag 53(3):297–306

    Google Scholar 

  2. Kim N, Chang DR, Shocker AD (2000) Modeling intercategory and generational dynamics for a growing information technology industry. Manage Sci 46(4):496–512

    Google Scholar 

  3. Shi X, Chumnumpan P (2019) Modelling market dynamics of multi-brand and multi-generational products. Eur J Oper Res 279(1):199–210

    Google Scholar 

  4. Jiang Z, Qu XS, Jain, DC (2018) Optimal market entry timing for successive generations of technological innovations. MIS quarterly, forthcoming available at SSRN: https://ssrncom/abstract=3113956

  5. Meade N, Islam T (2015) Forecasting in telecommunications and ICT: a review. Int J Forecast 31(4):1105–1126

    Google Scholar 

  6. Bohlin A, Gruber H, Koutroumpis P (2010) Diffusion of new technology generations in mobile communications. Inf Econ Policy 22(1):51–60

    Google Scholar 

  7. Hashem IAT, Yaqoob I, Anuar NB, Mokhtar S, Gani A, Khan SU (2015) The rise of “big data” on cloud computing: review and open research issues. Inf Syst 47:98–115

    Google Scholar 

  8. Asadi S, Nilashi M, Husin ARC, Yadegaridehkordi E (2017) Customers perspectives on adoption of cloud computing in banking sector. Inf Technol Manage 18(4):305–330

    Google Scholar 

  9. Rogers EM (2003) Diffusion of innovations. Free Press, New York, p 551

    Google Scholar 

  10. Kapur PK, Panwar S, Singh O (2019) Modeling two-dimensional technology diffusion process under dynamic adoption rate. J Model Manag 14(3):717–737

    Google Scholar 

  11. Lechman E (2015) ICT diffusion in developing countries: towards a new concept of technological takeoff. Springer, Cham

    Google Scholar 

  12. Bass FM (1969) A new product growth for model consumer durables. Manage Sci 85(5):215–227

    Google Scholar 

  13. Kauffman RJ, Techatassanasoontorn AA (2005) International diffusion of digital mobile technology: a coupled-hazard state-based approach. Inf Technol Manage 6(2–3):253

    Google Scholar 

  14. van Oorschot JA, Hofman E, Halman JI (2018) A bibliometric review of the innovation adoption literature. Technol Forecast Soc Change 134:1–21

    Google Scholar 

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

    Google Scholar 

  16. 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

    Google Scholar 

  17. Saeed KA, Xu JD (2020) Understanding diffusion of information systems-based services: evidence from mobile banking services. Internet Res 30(4):1281–1304

    Google Scholar 

  18. Skålén P, Gummerus J, Von Koskull C, Magnusson PR (2015) Exploring value propositions and service innovation: a service-dominant logic study. J Acad Mark Sci 43(2):137–158

    Google Scholar 

  19. Shi X, Chumnumpan P, Fernandes K (2014) A diffusion model for service products. J Serv Mark 28(4):331–341

    Google Scholar 

  20. Altınkemer K, Yue WT, Yu L (2003) Adoption of low earth orbit satellite systems: a diffusion model under competition. Inf Technol Manage 4(1):33–54

    Google Scholar 

  21. Sharif MN, Ramanathan K (1981) Binomial innovation diffusion models with dynamic potential adopter population. Technol Forecast Soc Change 20(1):63–87

    Google Scholar 

  22. Mahajan V, Muller E, Srivastava RK (1990) Determination of adopter categories by using innovation diffusion models. J Mark Res 27(1):37–50

    Google Scholar 

  23. Kim T, Hong J (2015) Bass model with integration constant and its applications on initial demand and left-truncated data. Technol Forecast Soc Change 95:120–134

    Google Scholar 

  24. Shi X, Li F, Bigdeli AZ (2016) An examination of NPD models in the context of business models. J Bus Res 69(7):2541–2550

    Google Scholar 

  25. Anand A, Agarwal M, Bansal G, Garmabaki AHS (2016) Studying product diffusion based on market coverage. J Mark Anal 4(4):135–146

    Google Scholar 

  26. Singhal S, Anand A, Singh O (2019) Understanding multi-stage diffusion process in presence of attrition of potential market and related pricing policy. Yugosl J Oper Res 29(3):393–413

    Google Scholar 

  27. Michalakelis C, Varoutas D, Sphicopoulos T (2008) Diffusion models of mobile telephony in Greece. Telecommun Policy 32(3–4):234–245

    Google Scholar 

  28. Lim J, Nam C, Kim S, Rhee H, Lee E, Lee H (2012) Forecasting 3G mobile subscription in China: a study based on stochastic frontier analysis and a Bass diffusion model. Telecommun Policy 36(10–11):858–871

    Google Scholar 

  29. Song Y, Lee S, Zo H, Lee H (2015) A hybrid Bass–Markov model for the diffusion of a dual-type device-based telecommunication service: the case of WiBro service in Korea. Comput Ind Eng 79:85–94

    Google Scholar 

  30. Dewan S, Ganley D, Kraemer KL (2010) Complementarities in the diffusion of personal computers and the Internet: implications for the global digital divide. Inf Syst Res 21(4):925–940

    Google Scholar 

  31. Lee S, Marcu M, Lee S (2011) An empirical analysis of fixed and mobile broadband diffusion. Inf Econ Policy 23(3–4):227–233

    Google Scholar 

  32. Liu X, Wu FS, Chu WL (2010) Diffusion of mobile telephony in China: drivers and forecasts. IEEE Trans Eng Manage 59(2):299–309

    Google Scholar 

  33. Gupta R, Jain K (2016) Competition effect of a new mobile technology on an incumbent technology: an Indian case study. Telecommun Policy 40(4):332–342

    Google Scholar 

  34. Sultanov A, Lee DJ, Kim KT, Avila LAP (2016) The diffusion of mobile telephony in Kazakhstan: an empirical analysis. Technol Forecast Soc Change 106:45–52

    Google Scholar 

  35. Libai B, Muller E, Peres R (2009) The diffusion of services. J Mark Res 46(2):163–175

    Google Scholar 

  36. Prins R, Verhoef PC, Franses PH (2009) The impact of adoption timing on new service usage and early disadoption. Int J Res Mark 26(4):304–313

    Google Scholar 

  37. Lin CY, Kremer GEO (2014) Strategic decision making for multiple-generation product lines using dynamic state variable models: the cannibalization case. Comput Ind 65(1):79–90

    Google Scholar 

  38. 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

    Google Scholar 

  39. Speece MW, MacLachlan DL (1995) Application of a multi-generation diffusion model to milk container technology. Technol Forecast Soc Change 49(3):281–295

    Google Scholar 

  40. Mahajan V, Muller E (1996) Timing, diffusion, and substitution of successive generations of technological innovations: the IBM mainframe case. Technol Forecast Soc Change 51(2):109–132

    Google Scholar 

  41. Islam T, Meade N (1997) The diffusion of successive generations of a technology: a more general model. Technol Forecast Soc Change 56(1):49–60

    Google Scholar 

  42. Jiang Z, Jain DC (2012) A generalized Norton-Bass model for multigeneration diffusion. Manage Sci 58(10):1887–1897

    Google Scholar 

  43. Kapur PK, Aggarwal AG, Garmabaki AHS, Singh G (2013) Modelling diffusion of successive generations of technology: a general framework. Int J Oper Res 16(4):465–484

    Google Scholar 

  44. Kapur PK, Aggarwal AG, Garmabaki AHS, Tandon A (2015) Multi-generational innovation diffusion modelling: a two dimensional approach. Int J Appl Manag Sci 7(1):1–18

    Google Scholar 

  45. Kapur PK, Panwar S, Shrivastava AK, Khatri SK (2017) Multi-generation diffusion of technology. In: 2017 6th International Conference on reliability, infocom technologies and optimization (trends and future directions) (ICRITO). IEEE, pp 31–37

  46. Panwar S, Kapur PK, Singh O (2019) Modeling technological substitution by incorporating dynamic adoption rate. Int J Innov Technol Manag 16(1):1950010

    Google Scholar 

  47. Panwar S, Kapur PK, Sachdeva N, Singh O (2020) Multi-generational technology management in a segmented environment. Int J Product Dev 24(1):1–29

    Google Scholar 

  48. Danaher PJ, Hardie BG, Putsis WP Jr (2001) Marketing-mix variables and the diffusion of successive generations of a technological innovation. J Mark Res 38(4):501–514

    Google Scholar 

  49. Jun DB, Kim SK, Park YS, Park MH, Wilson AR (2002) Forecasting telecommunication service subscribers in substitutive and competitive environments. Int J Forecast 18(4):561–581

    Google Scholar 

  50. Bass PI, Bass FM (2004) IT waves: two completed generational diffusion models. University of Texas, Dallas

    Google Scholar 

  51. Chu CP, Pan JG (2008) The forecasting of the mobile Internet in Taiwan by diffusion model. Technol Forecast Soc Change 75(7):1054–1067

    Google Scholar 

  52. Michalakelis C, Varoutas D, Sphicopoulos T (2010) Innovation diffusion with generation substitution effects. Technol Forecast Soc Change 77(4):541–557

    Google Scholar 

  53. Tseng FM, Wang SY, Hsieh CH, Guo A (2014) An integrated model for analyzing the development of the 4G telecommunications market in Taiwan. Telecommun Policy 38(1):14–31

    Google Scholar 

  54. Jha A, Saha D (2020) Forecasting and analysing the characteristics of 3G and 4G mobile broadband diffusion in India: a comparative evaluation of Bass, Norton-Bass, Gompertz, and logistic growth models. Technol Forecast Soc Change 152:119885

    Google Scholar 

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

    Google Scholar 

  56. Bayus BL (1997) Speed-to-market and new product performance trade-offs. J Product Innov Manag: Int Publ Product Dev Manag Assoc 14(6):485–497

    Google Scholar 

  57. Wilson LO, Norton JA (1989) Optimal entry timing for a product line extension. Market Sci 8(1):1–17

    Google Scholar 

  58. Krankel RM, Duenyas I, Kapuscinski R (2006) Timing successive product introductions with demand diffusion and stochastic technology improvement. Manuf Serv Oper Manag 8(2):119–135

    Google Scholar 

  59. Druehl CT, Schmidt GM, Souza GC (2009) The optimal pace of product updates. Eur J Oper Res 192(2):621–633

    Google Scholar 

  60. Liao S, Seifert RW (2015) On the optimal frequency of multiple generation product introductions. Eur J Oper Res 245(3):805–814

    Google Scholar 

  61. Garmabaki AS, Kapur PK, Jyotish NP, Ragini KS (2012) The optimal time of new generation product in the market. Commun Dependability Qual Manag 15:123–137

    Google Scholar 

  62. Sachdeva N, Singh O, Kapur PK (2016) Optimal launch of a new generation of technology: a multi attribute approach discrete time diffusion process. Int J Technol Mark 11(1):3–23

    Google Scholar 

  63. Jia J, Chen S, Li Z (2019) Dynamic pricing and time-to-market strategy in a service supply chain with online direct channels. Comput Ind Eng 127:901–913

    Google Scholar 

  64. SAS S (2004) STAT user guide, version 912. SAS Institute Inc, Cary, NC

    Google Scholar 

  65. Kapur PK, Panwar S, Singh O, Kumar V (2019) Joint optimization of software time-to-market and testing duration using multi-attribute utility theory. Ann Oper Res. https://doi.org/10.1007/s10479-019-03483-w

    Article  Google Scholar 

  66. Keeney RL, Raiffa H (1976) Decision analysis with multiple conflicting objectives. Wiley, New York

    Google Scholar 

  67. Garmabaki AHS, Ahmadi A, Ahmadi M (2016) Maintenance optimization using multi-attribute utility theory. In: Kumar U, Ahmadi A, Verma AK, Varde P (eds) Current trends in reliability, availability, maintainability and safety. Springer, Cham, pp 13–25

    Google Scholar 

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Authors and Affiliations

  1. Department of Operational Research, University of Delhi, Delhi, India

    Saurabh Panwar & Ompal Singh

  2. Amity Center for Interdisciplinary Research, Amity University, Noida, Uttar Pradesh, India

    P. K. Kapur

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  1. Saurabh Panwar
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Correspondence to Saurabh Panwar.

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Panwar, S., Kapur, P.K. & Singh, O. Predicting diffusion dynamics and launch time strategy for mobile telecommunication services: an empirical analysis. Inf Technol Manag 22, 33–51 (2021). https://doi.org/10.1007/s10799-021-00323-x

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