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DC Field | Value | Language |
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dc.contributor.author | Jha, Ashutosh | |
dc.contributor.author | Saha, Debashish | |
dc.date.accessioned | 2021-08-26T06:03:21Z | - |
dc.date.available | 2021-08-26T06:03:21Z | - |
dc.date.issued | 2020 | |
dc.identifier.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85076968271&doi=10.1016%2fj.techfore.2019.119885&partnerID=40&md5=f7618c9ad98bdd301ed1014aeb4509e4 | |
dc.identifier.uri | https://ir.iimcal.ac.in:8443/jspui/handle/123456789/1000 | - |
dc.description | Ashutosh Jha, S. P. Jain Institute of Management and Research (SPJIMR), Mumbai, 400058, India, Indian Institute of Management Calcutta, Kolkata, 700104, India; Debashish Saha, Indian Institute of Management Calcutta, Kolkata, 700104, India | |
dc.description | ISSN/ISBN - 00401625 | |
dc.description | DOI - 10.1016/j.techfore.2019.119885 | |
dc.description.abstract | An empirical understanding of countrywide diffusion of third (3G) or/and fourth (4G) generations of Mobile Broadband Services (MBSs) has proven implications for both business and policy of the country. However, extant literature lacks in explanation for the diffusion and forecast of these services in India. We address this gap by analyzing both individual and multigenerational diffusions of 3G and 4G services in India, using Bass, Gompertz, Logistic and Norton-Bass models that utilize a mix of linear and non-linear regression techniques. Additionally, we evaluate the influence of several exogenous variables on the diffusion of those MBSs in India. Our analyses reveal that, firstly in case of diffusion, Bass model estimates are quite sensitive to both 3G and 4G historical data, whereas Gompertz and Logistic models fit well with the same dataset. As expected, Norton-Bass model - encompassing all the successive generations of 2G, 3G and 4G - provides more reliable estimates of the diffusion parameters. Secondly, as far as 3G forecast is concerned, Bass model works better with fixed assumptions of ultimate market potential, whereas Gompertz and Logistic models seem to be more suited for �optimistic� long-range forecasts and �conservative� short-term forecasts, respectively. Our results also show that 4G is diffusing at 6.1 times the speed of 3G diffusion in India, when the total MBS subscription in India is likely to reach 410 million by 2026. Finally, we notice that, among the notable external sources of influence, National Telecom Policy 2012, average revenue per user, and aggregate income variables have significant positive impacts on the diffusion of MBSs in India. | |
dc.publisher | SCOPUS | |
dc.publisher | Technological Forecasting and Social Change | |
dc.publisher | Elsevier Inc. | |
dc.relation.ispartofseries | 152 | |
dc.subject | 3G | |
dc.subject | 4G | |
dc.subject | Diffusion models | |
dc.subject | Gompertz model | |
dc.subject | Logistic model | |
dc.subject | Norton-Bass model | |
dc.title | “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” | |
dc.type | Article | |
Appears in Collections: | Management Information Systems |
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