nach oben

Information Systems Frontiers

Erschienen in:

14.04.2021

Mobile Broadband for Inclusive Connectivity: What Deters the High-Capacity Deployment of 4G-LTE Innovation in India?

verfasst von: Ashutosh Jha, Debashis Saha

Erschienen in: Information Systems Frontiers | Ausgabe 4/2022

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

The global market uptake of the fourth generation-Long-Term Evolution (4G-LTE) Mobile Broadband (MoBro) services are steadily rising, leading to higher capital investments by mobile network operators (MNOs) to scale-up their infrastructure for meeting the impending demand for MoBro data the world over. However, MNOs face uncertainties about financial returns from such investments, owing to a host of technological and market-related factors, which impact the enablement of such 4G-LTE MoBro services. These challenges are clearly evident in the Indian market. Firstly, 4G-LTE subscribers in the rural India contribute to less than one-third of the overall market size. Secondly, India performs poorly in terms of minimum capacity requirements of the 4G-LTE MoBro services. Thirdly, the rise in per-user consumption of MoBro data does not translate into a similar rise in the MNOs’ revenue. Fourthly, the socio-economic-geographic segregation of India into twenty-two administrative zones (referred to as telecom circles) add to the complexities in the capital investment decisions of MNOs. To address the above challenges, we model various cost and profitability scenarios of a hypothetical MNO providing universal 4G-LTE deployment across the twenty-two telecom circles in India using the available spectrum bands. Our proposed model is firmly established in the “network investment economics” framework for telecom innovations. We adopt a technology diffusion-based approach to forecast the 4G-LTE subscribers in India. We focus on the requirements of 4G-LTE MoBro infrastructure investments, including the spectrum selection decisions by MNOs, and show that, for valuation of the spectrum, the policymakers in India need to take into account the potential of the spectrum in terms of financial returns to an MNO deploying 4G-LTE using that spectrum, as against merely valuing the spectrum based on primarily the technical characteristics of its carrier frequency and benchmarks of prior spectrum auction prices in a particular telecom circle. Finally, we also show that a nationwide 4G-LTE network, which is universal, inclusive, and adhering to the global standards in terms of service quality, can be financially lucrative for MNOs, if enabled by appropriate policies instituting collaborative frameworks for infrastructure sharing, and price rationalization of spectrum bands across the twenty-two telecom circles.

Vorheriger Artikel The Interplay Between Investor Activity on Virtual Investment Community and the Trading Dynamics: Evidence From the Bitcoin Market

Nächster Artikel The Role of Online Misinformation and Fake News in Ideological Polarization: Barriers, Catalysts, and Implications

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Nur mit Berechtigung zugänglich

The International Telecommunications Union Radio Regulations (ITU-RR) defines mobile service as “a type of radio communication service between mobile stations and land stations.” Mobile stations here refer to mobile devices, including handheld devices, such as smartphones, tablet computers and Personal Digital Assistants (PDAs). Land stations, on the other hand, refer to mobile base stations, which send and receive radio signals, thereby enabling wireless connectivity of mobile devices with these mobile base stations. In this article, we use the term “mobile service” to refer to such radio communication between a mobile device and a mobile base station. Several generations (Gs) of mobile services, such as 2G, 3G and 4G, may simultaneously exist in the market. MoBro refers to mobile service generations from 3G onwards.

The range of data rate possible under 4G, varies from 14 Megabits per second (Mbps) to 100 Mbps, depending on the deployment use-case, which refers to specific geophysical environment and demographic conditions in the targeted market.

Long-Term Evolution (LTE) is a wireless network standard for voice and data communication between mobile devices and data terminals.

Network capacity refers to the traffic (voice and data) carrying capability of a mobile network owned by an MNO.

Radio spectrum, or spectrum, refers to the part of the electromagnetic spectrum, which can be used for radio communication purposes. Especially for mobile communications, certain radio frequencies, referred to as spectrum bands, or carrier-frequencies, are used across the globe, which are based on the specifications of global bodies such as International Telecommunication Union (ITU). Spectrum is the sovereign asset of national governments, which allocate the usage rights to various entities, such as Mobile Network Operators, through a suitable market mechanism, such as auctions, or otherwise. Spectrum bandwidth, or bandwidth, refers to the total range of frequencies falling within a particular band that is available for market disbursal by the government. At any given time, MNOs are availed a portion of the available bandwidth suitable for provisioning a certain service type, by the government.

The per-user consumption of mobile data has increased from 6 Gigabytes (GB) in the year 2016 to 10 GB in the year 2018 in India.

There have been four such policy initiatives in India, which were introduced in the form of National Telecom Policies (NTPs) during the years 1994, 1998, 2012 and 2018, respectively. NTP 2018 – rechristened later as National Digital Communications Policy (NDCP) – envisages as its primary objective the deployment of a robust MoBro infrastructure, such as 4G-LTE, which can enable universal connectivity, ushering in digital equity and inclusivity for millions of India’s unconnected citizens. The vision statement of NDCP accords equal importance to the information and communication needs of citizens as well as enterprises, and emphasizes upon the necessity of establishing “a ubiquitous, resilient, secure and affordable” digital communications infrastructure and services in the country (Department of Telecommunications, 2018).

Source: https://www.businesstoday.in/sectors/telecom/telecom-fundamentals-robust-low-arpus-5g-in-future-underscore-untapped-potential-deloitte-india/story/410385.html. Date of Access: 13/02/2021

In the absence of availability of the market adoption data of the innovation, heuristic-based approaches, which includes managerial inputs on the likely values of the parameters, are employed to generate insights pertaining to the likely diffusion of the innovation (Mahajan et al. 1993).

One may refer to (Jha & Saha, 2020) for an extensive review of literature concerning the diffusion of various generations of mobile services, incluing MoBro services.

The retailing of 4G-LTE, similar to other mobile services, could be either done by MNOs themselves, or by the Mobile Virtual Network Operators (MVNOs), who only lease the infrastructure from the MNOs. In India, MNOs both deploy the networks as well as retail the 4G-LTE services to consumers.

Notably, Sabat (2005), in an agenda-setting study, have analyzed the capital investment patterns of more than 300 global MNOs across the world for a period of 10 years, and have summarized: (i) the key drivers of capital investments for provisioning mobile services, (ii) the main categories of capital investments, (iii) the size and proportions of investments required in each categories, (iv) the evolutionary phases of the industry along with their cost drivers, (v) the key economic characteristics of such industry phases, and (vi) the infrastructure spending patterns of the largest firms.

A site is meant to represent a group of network components deployed as part of the last-mile LTE Radio Access Network (RAN) comprising of multiple eNodeBs, network equipment, backhaul, waveguides, antenna tower, etc. In our analysis, the total number of sites correspond to the total number of eNodeBs needed for deployment. For a detailed explanation of the 4G-LTE network architecture, one may refer to (Holma & Toskala, 2009).

We have also assessed Gompertz and Simple-logistic models with our dataset, however, Bass model estimates have been found to yield lower error variance, with the model displaying better goodness-of-fit with the available data. We have, therefore, used Bass model in all our diffusion and forecasting related analysis.

4G-LTE-capable carrier-frequencies in India include: 700 MHz (3GPP band B28), 850 MHz (3GPP band B5), 1800 MHz (3GPP band B3), 2100 MHz (3GPP band B1), 2300 MHz (3GPP band B40) and 2500 MHz (3GPP band B41). For the ease of analysis, we group the available set of carrier-frequencies into two cohorts in this study, namely, the sub 1 GHz cohort containing 700 and 850 MHz carrier frequencies, and 1–3 GHz cohort containing 1800, 2100, 2300, and 2500 MHz carrier frequencies (TRAI, 2017). We present all our results in the standardized (per MHz) format, which can be scaled to any spectrum bandwidth size as required by the stakeholders. This is considering that, in practice, MNOs may have access to a much larger (or smaller) pool of bandwidth scattered across multiple carrier-frequencies.

We assume that the coverage area spanned by each antenna system per site will follow a circular pattern. Cellular radius refers to the radii of such a circle.

Transceivers are devices which can both simultaneously transmit and receive information. In this context, by transceivers we are referring to the eNodeB components of the LTE network, also known as Base Stations in 2G and Base Transceiver Stations in 3G systems.

An MNO in India incurs spectrum costs, initially, at the time of the spectrum acquisition (part of CAPEX) during the spectrum auctions, and, later, as recurring payments in the form of Spectrum Usage Charges (SUC) and the License Fees (LFs) to the government, which is calculated as a fraction of the MNOs’ Adjusted Gross Revenue (AGR). We account for the OPEX incurred under each carrier frequency and telecom circle combination in our analysis. We use the available data on the spectrum reserve prices as mandated by the GOI, for evaluating the CAPEX incurred under spectrum acquisition for all the chosen carrier frequencies (TRAI, 2017).

The annual spectrum license (SL_{LSA, Year}) cost is derived using the base price valuations fixed by the government of India for the 700 MHz, 800 MHz, 1800 MHz, 2100 MHz and 2300 MHz bands in each LSA (TRAI, 2017). We also assume, as per the government of India directives, the annual SUC to be 3% of the AGR per LSA in our calculations (TRAI, 2017).

Since the 700 MHz band was not auctioned at the time of the analysis, the reserve price fixed by the government is used in the calculations. Therefore, the actual CAPEX figures for 700 MHz-based deployments may eventually be higher than evaluated currently as the final bid prices may rise after the auctions.

The carrier frequencies in the sub 1 GHz range are generally priced higher due to their better cellular propagation capabilities, when compared to the carrier frequencies in the 1–3 GHz range.

Passive infrastructure sharing refers to the “sharing of physical space, for example by buildings, sites and masts, where networks remain separate. In active sharing, elements of the active layer of a mobile network are shared, such as antennas, entire base stations or even elements of the core network. Active sharing includes mobile roaming, which allows an operator to make use of another’s network in a place where it has no coverage or infrastructure of its own.” Source: International Telecommunication Union (ITU). URL: https://www.itu.int/. Accessed on August 13, 2020

Ansari, S., & Garud, R. (2009). Inter-generational transitions in socio-technical systems: The case of mobile communications. Research Policy, 38(2), 382–392. https://doi.org/10.1016/j.respol.2008.11.009.CrossRef

Aswani, R., Ilavarasan, P. V., Kar, A. K., & Vijayan, S. (2018). Adoption of public WiFi using UTAUT2: An exploration in an emerging economy. Procedia computer science, 132, 297–306.CrossRef

Avila, L. A. P., Lee, D. J., & Kim, T. (2018). Diffusion and competitive relationship of mobile telephone service in Guatemala: An empirical analysis. Telecommunications Policy, 42(2), 116–126. https://doi.org/10.1016/j.telpol.2017.09.002.CrossRef

Banker, R., Cao, Z., Menon, N. M., & Mudambi, R. (2012). The red queen in action: The longitudinal effects of capital investments in the mobile telecommunications sector. Industrial and Corporate Change, 22(5), 1195–1228. https://doi.org/10.1093/icc/dts036.CrossRef

Bass, F. M. (1969). A new product growth for model consumer durables. Managemant Science, 15(5), 215–227 www.jstor.org/stable/2628128.CrossRef

Bass, F. M., Krishnan, T. V., & Jain, D. C. (1994). Why the Bass model fits without decision variables. Marketing Science, 33(3), 203–223. https://doi.org/10.1287/mksc.13.3.203.CrossRef

Bauer, J. M. (2017). The internet and income inequality: Socio-economic challenges in a hyperconnected society. Telecommunications Policy, 42(4), 333–343. https://doi.org/10.1016/j.telpol.2017.05.009.CrossRef

Bichler, M., Gretschko, V., & Janssen, M. (2017). Bargaining in spectrum auctions: A review of the German auction in 2015. Telecommunications Policy, 41(5–6), 325–340. https://doi.org/10.1016/j.telpol.2017.01.005.CrossRef

BMI Research. (2019). BMI Research. BMI Telecommunications Databse. www.bmiresearch.com/. Accessed 31 May 2017.

Broadband Commission for Sustainable Development. (2017). The state of broadband 2017: Broadband catalyzing sustainable development. Itu. https://doi.org/10.1017/CBO9781107415324.004.

BS. (2017). 700-MHz spectrum price may be slashed 20%. Business Standard. http://www.business-standard.com/article/economy-policy/700-mhz-spectrum-price-may-be-slashed-20-117030200045_1.html. Accessed 10 April 2018.

Castellacci, F., & Tveito, V. (2018). Internet use and well-being: A survey and a theoretical framework. Research Policy, 47(1), 308–325. https://doi.org/10.1016/j.respol.2017.11.007.CrossRef

Chalom, A., Mandai, C. Y., & Prado, P. I. (2013). Sensitivity analyses: a brief tutorial with R package pse. https://cran.r-project.org/web/packages/pse/vignettes/pse_tutorial.pdf. Accessed 11 May 2017.

Chatterjee, S., Kar, A. K., & Gupta, M. P. (2017). Critical success factors to establish 5G network in smart cities: Inputs for security and privacy. Journal of Global Information Management, 25(2), 15–37.CrossRef

Chaudhuri, K., Raj, S. R., & Sasidharan, S. (2018). Broadband adoption and firm performance: Evidence from informal sector firms in India. Working papers id:12744, eSocialSciences.

Choudrie, J., & Papazafeiropoulou, A. (2007). Assessing the UK policies for broadband adoption. Information Systems Frontiers, 9(2–3), 297–308. https://doi.org/10.1007/s10796-006-9022-3.CrossRef

Choudrie, J., Pheeraphuttranghkoon, S., & Davari, S. (2020). The digital divide and older adult population adoption, use and diffusion of Mobile phones: A quantitative study. Information Systems Frontiers, 22, 673–695. https://doi.org/10.1007/s10796-018-9875-2.CrossRef

Cruz-Jesus, F., Oliveira, T., Bacao, F., & Irani, Z. (2017). Assessing the pattern between economic and digital development of countries. Information Systems Frontiers, 19, 835–854. https://doi.org/10.1007/s10796-016-9634-1.CrossRef

Czernich, N., Falck, O., Kretschmer, T., & Woessmann, L. (2009). Broadband infrastructure and economic growth broadband infrastructure and economic growth abstract. CESifo Working paper Nr., 2861(121), 505–532. https://doi.org/10.1111/j.1468-0297.2011.02420.x.CrossRef

Department of Telecommunications. (2017). Telecom statistics India-2017. https://www.bocra.org.bw/telecom-statistics1. Accessed 13 April 2018.

Department of Telecommunications. (2018). National telecom policy 2018. http://dot.gov.in/relatedlinks/draft-national-digital-communications-policy-2018

Deshpande, A., Desrochers, A., Ksoll, C., & Shonchoy, A. S. (2017). The impact of a computer-based adult literacy program on literacy and numeracy: Evidence from India. World Development, 96, 451–473. https://doi.org/10.1016/j.worlddev.2017.03.029.CrossRef

Dokeniya, A. (1999). Re-forming the state: Telecom liberalization in India. Telecommunications Policy, 23(2), 105–128. https://doi.org/10.1016/S0308-5961(98)00082-2.CrossRef

Edquist, H., Goodridge, P., Haskel, J., Li, X., & Lindquist, E. (2018). How important are mobile broadband networks for the global economic development? Information Economics and Policy, 45, 16–29. https://doi.org/10.1016/j.infoecopol.2018.10.001.CrossRef

ET. (2016). Overpriced 700 MHz saw government miss auction target. Economic Times. https://economictimes.indiatimes.com/news/economy/policy/overpriced-700-mhz-saw-government-miss-auction-target/articleshow/54744465.cms. Accessed 31 May 2017.

Fareena, S., Farley, J. U., Lehmann, R., Sultan, F., Farley, J. U., & Lehmann, D. R. (1990). A meta-analysis of applications of diffusion models. Journal of Marketing Research, 27(1), 70–77. https://doi.org/10.1016/0169-2070(90)90047-F.CrossRef

Frank, L. D. (2004). An analysis of the effect of the economic situation on modeling and forecasting the diffusion of wireless communications in Finland. Technological Forecasting and Social Change, 71(4), 391–403. https://doi.org/10.1016/S0040-1625(02)00392-X.CrossRef

Frias, Z., & Pérez, J. (2012). Techno-economic analysis of femtocell deployment in long-term evolution networks. EURASIP Journal on Wireless Communications and Networking, 288, 1–15. https://doi.org/10.1186/1687-1499-2012-288.CrossRef

Gamboa, L. F., & Otero, J. (2009). An estimation of the pattern of diffusion of mobile phones: The case of Colombia. Telecommunications Policy, 33(10–11), 611–620. https://doi.org/10.1016/j.telpol.2009.08.004.CrossRef

Geroski, P. (2000). Models of technology diffusion. Research Policy, 29(4–5), 603–625. https://doi.org/10.1016/S0048-7333(99)00092-X.CrossRef

Ghosh, S. (2017). Broadband penetration and economic growth: Do policies matter? Telematics and Informatics, 34(5), 676–696. https://doi.org/10.1016/j.tele.2016.12.007.CrossRef

Goetzendorff, A., Bichler, M., & Goeree, J. K. (2018). Synergistic valuations and efficiency in spectrum auctions. Telecommunications Policy, 42(1), 91–105. https://doi.org/10.1016/j.telpol.2017.08.006.CrossRef

Goyal, K., & Kar, A. K. (2020). Determinants of customer satisfaction in telecommunication. In Proceedings of ICETIT 2019 (pp. 754–761). Springer, Cham.

Grønsund, P., Grøndalen, O., & Lähteenoja, M. (2013). Business case evaluations for LTE network offloading with cognitive femtocells. Telecommunications Policy, 37(2–3), 140–153. https://doi.org/10.1016/j.telpol.2012.07.006.CrossRef

Gruber, H., & Verboven, F. (2001). The diffusion of mobile telecommunications services in the European Union. European Economic Review, 45(3), 577–588. https://doi.org/10.1016/S0014-2921(00)00068-4.CrossRef

Gruber, H., Hätönen, J., & Koutroumpis, P. (2014). Broadband access in the EU: An assessment of future economic benefits. Telecommunications Policy, 38(11), 1046–1058. https://doi.org/10.1016/j.telpol.2014.06.007.CrossRef

Gupta, R., & Jain, K. (2012). Diffusion of mobile telephony in India: An empirical study. Technological Forecasting and Social Change, 79(4), 709–715. https://doi.org/10.1016/j.techfore.2011.08.003.CrossRef

Harno, J. (2009). Techno‐economic analysis of beyond 3G mobile technology alternatives. Info, 11(3), 45-63. https://doi.org/10.1108/14636690910954971.

Hodler, R., & Raschky, P. A. (2017). Ethnic politics and the diffusion of mobile technology in Africa. Economics Letters, 159, 78–81. https://doi.org/10.1016/j.econlet.2017.07.011.CrossRef

Holma, H., & Toskala, A. (2009). LTE for. UMTS: OFDMA and SC-FDMA based radio access. Wiley Publications. https://doi.org/10.1002/9780470745489.CrossRef

Hong, S.-J., Thong, J. Y. L., Moon, J.-Y., & Tam, K.-Y. (2008). Understanding the behavior of mobile data services consumers. Information Systems Frontiers, 10, 431–445. https://doi.org/10.1007/s10796-008-9096-1.CrossRef

Honoré, B. (2019). Diffusion of mobile telephony: Analysis of determinants in Cameroon. Telecommunications Policy, 43(3), 287–298. https://doi.org/10.1016/j.telpol.2018.08.002.CrossRef

Hwang, J., & Yoon, H. (2009). A mixed spectrum management framework for the future wireless service based on techno-economic analysis: The Korean spectrum policy study. Telecommunications Policy, 33(8), 407–421. https://doi.org/10.1016/j.telpol.2009.04.005.CrossRef

Hwang, J., Cho, Y., & Long, N. V. (2009). Investigation of factors affecting the diffusion of mobile telephone services: An empirical analysis for Vietnam. Telecommunications Policy, 33(9), 534–543. https://doi.org/10.1016/j.telpol.2009.06.003.CrossRef

Islam, T., Fiebig, D. G., & Meade, N. (2002). Modelling multinational telecommunications demand with limited data. International Journal of Forecasting, 18(4), 605–624. https://doi.org/10.1016/S0169-2070(02)00073-0.CrossRef

Jerman-Blazic, B. (2008). Techno-economic analysis and empirical study of network broadband investment: The case of backbone upgrading. Information Systems Frontiers, 10, 103–110. https://doi.org/10.1007/s10796-007-9059-y.CrossRef

Jha, A., & Saha, D. (2015). Techno-economic assessment of the potential for LTE based 4G mobile services in rural India. In 2015 IEEE International Conference on Advanced Networks and Telecommuncations Systems (ANTS) (Vol. 2016-Febru, pp. 28–33). Kolkata: IEEE. https://doi.org/10.1109/ANTS.2015.7413612.

Jha, A., & Saha, D. (2018). Techno-economic analysis of 5G deployment scenarios involving massive MIMO HetNets over mmWave : A case study on the US State of Texas. 51st Hawaii International Conference on System Sciences - HICSS, 5839–5848. doi:http://hdl.handle.net/10125/50621

Jha, A., & Saha, D. (2019). Techno-commercial feasibility analysis of 4G mobile services in India. IIMB Management Review, 31(2), 182–199. https://doi.org/10.1016/j.iimb.2019.03.007.CrossRef

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. Technological Forecasting and Social Change, 152(April 2017), 119885. https://doi.org/10.1016/j.techfore.2019.119885.CrossRef

Kanwal, F., & Rehman, M. (2017). Factors affecting E-learning adoption in developing countries-empirical evidence from Pakistan’s higher education sector. IEEE Access, 5, 10968–10978. https://doi.org/10.1109/ACCESS.2017.2714379.CrossRef

Katsigiannis, M., & Smura, T. (2015). A cost model for radio access data networks. Info, 17(1), 39–53. https://doi.org/10.1108/info-09-2014-0036.CrossRef

Katsigiannis, M., & Valagiannopoulos, C. (2014). Demand Curves and Operator Strategies in the Finnish Mobile Broadband Market. International Journal of Business Data Communications and Networking, 10(1), 30. https://doi.org/10.4018/ijbdcn.2014010102.CrossRef

Kim, Y. (2016). Mobile phone for empowerment? Global nannies in Paris. Media, Culture and Society, 38(4), 525–539. https://doi.org/10.1177/0163443715613638.CrossRef

Koski, H., & Kretschmer, T. (2005). Entry, standards and competition: Firm strategies and the diffusion of mobile telephony. Review of Industrial Organization, 26(1), 89–113. https://doi.org/10.1007/s11151-004-4085-0.CrossRef

Krizanovic, V., Zagar, D., & Grgic, K. (2011). Techno-economic analyses of wireline and wireless broadband access networks deployment in Croatian rural areas. Proceedings of the 11th International Conference on Telecommunications, ConTEL 2011, November 2015, 265–272.

Lee, M., & Cho, Y. (2007). The diffusion of mobile telecommunications services in Korea. Applied Economics Letters, 14(7), 477–481. https://doi.org/10.1080/13504850500461431.CrossRef

Lee, S., Park, E. A., Lee, S., & Brown, J. (2015). Determinants of IPTV diffusion. Telematics and Informatics, 32(3), 439–446. https://doi.org/10.1016/j.tele.2014.10.005.CrossRef

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. Telecommunications Policy, 36(10–11), 858–871. https://doi.org/10.1016/j.telpol.2012.07.016.CrossRef

Liu, X., Wu, F. S., & Chu, W. L. (2012). Diffusion of mobile telephony in China: Drivers and forecasts. IEEE Transactions on Engineering Management, 59(2), 299–309. https://doi.org/10.1109/TEM.2010.2058854.CrossRef

Mahajan, V., Muller, E., & Bass, F. M. (1993). New product diffusion models. In Handbooks in Operations Research and Management Science (Vol. 5, pp. 349–408). https://doi.org/10.1016/S0927-0507(05)80031-3.

Mahajan, V., Muller, E., & Bass, F. M. (2011). New product diffusion models in marketing: A review and directions for research. Journal of Marketing, 54(1), 1–26. https://doi.org/10.1007/978-3-662-02700-4_6.CrossRef

Markendahl, J., & Makitalo, O. (2010). A comparative study of deployment options, capacity and cost structure for macrocellular and femtocell networks. 2010 IEEE 21st International Symposium on Personal, Indoor and Mobile Radio Communications Workshops, 145–150. https://doi.org/10.1109/PIMRCW.2010.5670351.

Massini, S. (2004). The diffusion of mobile telephony in Italy and the UK: An empirical investigation. Economics of Innovation & New Technology, 13(3), 251–277. https://doi.org/10.1080/10438590410001628396.CrossRef

Mayo, J. W., & Ukhaneva, O. (2017). International telecommunications demand. Information Economics and Policy, 39, 26–35. https://doi.org/10.1016/j.infoecopol.2017.02.001.CrossRef

Michalakelis, C., Varoutas, D., & Sphicopoulos, T. (2008). Diffusion models of mobile telephony in Greece. Telecommunications Policy, 32(3–4), 234–245. https://doi.org/10.1016/j.telpol.2008.01.004.CrossRef

Mölleryd, B., Markendahl, J., & Mäkitalo, Ö. (2010). Impact assessment and business implications of options for allocation of spectrum in the 800 and 900 MHz band. 18th Biennial Conference of the International Telecommunications Society, 1–23.

Mölleryd, B. G., & Markendahl, J. (2014). Analysis of spectrum auctions in India - an application of the opportunity cost approach to explain large variations in spectrum prices. Telecommunications Policy, 38(3), 236–247. https://doi.org/10.1016/j.telpol.2014.01.002.CrossRef

Moral, A., Vidal, J., Pérez, J., Agustín, A., Marina, N., & Høst-Madsen, A. (2011). Technoeconomic evaluation of cooperative relaying transmission techniques in OFDM cellular networks. Eurasip Journal on Advances in Signal Processing, 2011. https://doi.org/10.1155/2011/507035.

Olla, P., & Choudrie, J. (2014). Mobile technology utilization for social development in developing countries: An ethnographic futures research study. Information Systems Frontiers, 16(3), 369–382. https://doi.org/10.1007/s10796-013-9477-yCrossRef

OpenSignal. (2017). The State of LTE. https://www.opensignal.com/reports/2017/11/state-of-lte. Accessed 15 June 2018.

Oughton, E. J., & Frias, Z. (2018). The cost, coverage and rollout implications of 5G infrastructure in Britain. Telecommunications Policy, 42(8), 636–652. https://doi.org/10.1016/j.telpol.2017.07.009.CrossRef

Oughton, E., Frias, Z., Russell, T., Sicker, D., & Cleevely, D. D. (2018). Towards 5G: Scenario-based assessment of the future supply and demand for mobile telecommunications infrastructure. Technological Forecasting and Social Change, 133, 141–155. https://doi.org/10.1016/j.techfore.2018.03.016.CrossRef

Oughton, E. J., Frias, Z., van der Gaast, S., & van der Berg, R. (2019). Assessing the capacity, coverage and cost of 5G infrastructure strategies: Analysis of the Netherlands. Telematics and Informatics, 37(October 2018), 50–69. https://doi.org/10.1016/j.tele.2019.01.003.CrossRef

Pandey, A. S., Garg, J., Medudula, M. K., & Sagar, M. (2019). Telecom Spectrum map of India. IETE Journal of Research, 1–17. https://doi.org/10.1080/03772063.2019.1703830.

Pivorienė, A. (2017). Real options and discounted cash flow analysis to assess strategic investment projects. Economics and Business, 30(1), 91–101. https://doi.org/10.1515/eb-2017-0008.CrossRef

Pöllänen, O., & Bhebhe, L. (2011). Efficient deployment of large mobile networks. NETNOMICS: Economic Research and Electronic Networking, 12(2), 115–132. https://doi.org/10.1007/s11066-011-9062-9.CrossRef

Pöllänen, O., & Säily, M. (2007). Mobile coverage investment model linked to mobile network design. Netnomics, 8, 49–70. https://doi.org/10.1007/s11066-008-9015-0.CrossRef

Prasad, R., & Sridhar, V. (2009). Allocative efficiency of the mobile industry in India and its implications for Spectrum policy. Telecommunications Policy, 33(9), 521–533. https://doi.org/10.1016/j.telpol.2009.06.001.

Qiang, C. Z.-W. (2010). Broadband infrastructure investment in stimulus packages: Relevance for developing countries. Info, 12(2), 41–56. https://doi.org/10.1108/14636691011027175.CrossRef

Rappaport, T. S. (2002). Wireless communications: Principles and practice. Prentice Hall https://books.google.ca/books?id=Bh4fAQAAIAAJ.

Rogers, E. M. (1969). Diffusion of innovations (1st Editio.). https://www.worldcat.org/title/diffusion-of-innovations/oclc/709442831

Rogers, E. M. (2010). Diffusion of innovations. Simon and Schuster.

Roller, L.-H., & Waverman, L. (2001). Telecommunications infrastructure and economic development. American Economic Review, 91(4), 909–923. https://doi.org/10.1257/aer.91.4.909.CrossRef

Rouvinen, P. (2006). Diffusion of digital mobile telephony: Are developing countries different? Telecommunications Policy, 30(1), 46–63. https://doi.org/10.1016/j.telpol.2005.06.014.CrossRef

Sabat, H. K. (2002a). The evolving mobile wireless value chain and market structure. Telecommunications Policy, 26(9–10), 505–535. https://doi.org/10.1016/S0308-5961(02)00029-0.CrossRef

Sabat, H. K. (2002b). The economics of delivering Mobile wireless value. Vision, 6(2), 53–72. https://doi.org/10.1177/097226290200600206.CrossRef

Sabat, H. K. (2005). The network investment economics of the Mobile wireless industry. Information Systems Frontiers, 7(2), 187–206.CrossRef

Sabat, H. K. (2007). Emerging business models and trends in the mobile wireless industry. International Journal of Information Technology and Management, 6(2–4), 299–328. https://doi.org/10.1504/IJITM.2007.014006.CrossRef

Sabat, H. K. (2008a). Spectrum acquisition strategies adopted by wireless carriers in the USA. Information Systems Frontiers, 10, 77–102. https://doi.org/10.1007/s10796-007-9056-1.CrossRef

Sabat, H. K. (2008b). Why different carriers adopt different spectrum acquisition strategies. Information Technology and Management, 9, 251–284. https://doi.org/10.1007/s10799-007-0030-x.CrossRef

Sam, S. (2017). Towards an empowerment framework for evaluating mobile phone use and impact in developing countries. Telematics and Informatics, 34, 359–369. https://doi.org/10.1016/j.tele.2016.06.003.CrossRef

Schleife, K. (2010). What really matters: Regional versus individual determinants of the digital divide in Germany. Research Policy, 39(1), 173–185. https://doi.org/10.1016/j.respol.2009.11.003.CrossRef

Sridhar, V., & Prasad, R. (2011). Towards a new policy framework for spectrum management in India. Telecommunications Policy, 35(2), 172–184. https://doi.org/10.1016/j.telpol.2010.12.004.CrossRef

Srinivasan, V., & Mason, C. H. (1986). Nonlinear least squares estimation of new product diffusion models. Marketing Science, 5(2), 169–178 http://www.jstor.org/stable/183671.CrossRef

Sultanov, A., Lee, D. J., Kim, K. T., & Avila, L. A. P. (2016). The diffusion of mobile telephony in Kazakhstan: An empirical analysis. Technological Forecasting and Social Change, 106, 45–52. https://doi.org/10.1016/j.techfore.2016.01.020.CrossRef

Telecom Regulatory Authority of India. (2018). Telecom subscription data as on 31st March 2018: Press Release No. 56/2018.

Thompson, H. G., & Garbacz, C. (2011). Economic impacts of mobile versus fixed broadband. Telecommunications Policy, 35, 999–1009. https://doi.org/10.1016/j.telpol.2011.07.004.CrossRef

TRAI. (2017). On valuation and reserve price of spectrum in 700 MHz , 800 MHz , 900 MHz , 1800 MHz , 2100 MHz , 2300 MHz and 2500 MHz bands. http://www.trai.gov.in/Content/ReDis/564_0.aspx. Accessed 15 June 2018.

TRAI. (2018). The Indian telecom services performance indicators July – September, 2018. https://trai.gov.in. Accessed 12 November 2018.

Turk, T., & Trkman, P. (2012). Bass model estimates for broadband diffusion in European countries. Technological Forecasting and Social Change, 79, 85–96. https://doi.org/10.1016/j.techfore.2011.06.010.CrossRef

Ward, M. R., & Zheng, S. (2016). Mobile telecommunications service and economic growth: Evidence from China. Telecommunications Policy, 40(2–3), 89–101. https://doi.org/10.1016/j.telpol.2015.06.005.CrossRef

Wareham, J., Levy, A., & Shi, W. (2004). Wireless diffusion and mobile computing: Implications for the digital divide. Telecommunications Policy, 28(5–6), 439–457. https://doi.org/10.1016/j.telpol.2003.11.005.CrossRef

World Bank. (2016). Digital dividends. World Development Report, 1. https://doi.org/10.1017/CBO9781107415324.004.

Xu, X., Ma, W. W. K., & See-To, E. W. K. (2010). Will mobile video become the killer application for 3G mobile internet? A model of media convergence acceptance. Information Systems Frontiers, 12(3), 311–322. https://doi.org/10.1007/s10796-008-9139-7.CrossRef

Yamakawa, P., Rees, G. H., Manuel Salas, J., & Alva, N. (2013). The diffusion of mobile telephones: An empirical analysis for Peru. Telecommunications Policy, 37(6–7), 594–606. https://doi.org/10.1016/j.telpol.2012.12.010.CrossRef

Titel: Mobile Broadband for Inclusive Connectivity: What Deters the High-Capacity Deployment of 4G-LTE Innovation in India?
verfasst von: Ashutosh Jha
Debashis Saha
Publikationsdatum: 14.04.2021
Verlag: Springer US
Erschienen in: Information Systems Frontiers / Ausgabe 4/2022
Print ISSN: 1387-3326
Elektronische ISSN: 1572-9419
DOI: https://doi.org/10.1007/s10796-021-10128-6

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Weitere Artikel der Ausgabe 4/2022

Bringing Smart Home Technology to Peer-to-Peer Accommodation: Exploring the Drivers of Intention to Stay in Smart Accommodation

The Source of SMEs’ Competitive Performance in COVID-19: Matching Big Data Analytics Capability to Business Models

The Role of Online Misinformation and Fake News in Ideological Polarization: Barriers, Catalysts, and Implications

Artificial Intelligence and Reduced SMEs’ Business Risks. A Dynamic Capabilities Analysis During the COVID-19 Pandemic

Design of an Intelligent Patient Decision aid Based on Individual Decision-Making Styles and Information Need Preferences

3D Printing Technology and the Market Value of the Firm

Premium Partner