COMPARISON OF DIFFUSION IN TELECOMMUNICATIONS IN THE BRICS ECONOMIES

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1 COMPARISON OF DIFFUSION IN TELECOMMUNICATIONS IN THE BRICS ECONOMIES Mihir Dash, Professor Anuloma Tripathy D. Shobha Greeshma Ramesh Abhiyank Verma K. Sriharsha School of Business Alliance University, India 229 Introduction The telecommunications industry comprises of the telephone, computer, and cable T.V. segments. It provides equipment like wired and wireless connections needed for communications through telephone and it also helps in connecting to the internet. It is one of the fastest growing industries in the world. It has brought a revolution in the way we communicate and share information, and through its growth it has helped us stay connected even when we are miles apart. The global telecommunications industry is expanding with increasing expenditure by consumers and businesses for wireless services. The industry is expected to experience high revenue growth, with a CAGR of 9.1% over the period , driven by consumer internet usage and business mobility solutions, despite global economic uncertainty. Wireless subscribers growth in Asia and other emerging markets is expected to increase wireless revenues by 64% from its current levels, while wireline revenue is expected to show only modest growth. Europe, Middle East, and Africa are also expected to have moderate growth, while North America is expected to have the lowest growth. Currently, there are about five billion mobile users globally and three billion internet users. The number of mobile users is expected to reach beyond six billion by 2020, and the number of people accessing the internet is expected to reach five billion, primarily through mobile devices. There are several trends closely associated with the increasing spread of telecommunications. There has been an increase in smartphone penetration, from less than 5% in 2008 to about 30% in 2014, and mobile revenue has grown proportionately in the same period. Mobile internet usage and mobile commerce, along with overall internet usage and overall e-commerce, have grown at a tremendous pace. Mobile payment systems have also gained in popularity, particularly in emerging markets, growing globally at a CAGR of 65.6% in terms of number of users, at 92.5% in terms of volume, and at 251.0% in terms of value, and are expected to accelerate further. Another explosive trend is that of the over-the-top players, including video, audio, messaging, and other social media services such as Netflix, Spotify, Facebook, Whatsapp, Skype, and Google, with video traffic alone growing globally at a CAGR of about 60% in Also, there are some state-of-the-art technological trends that have amplified global telecommunications growth. Improvements in performance of networks, particularly 4G networks, have enabled the explosive growth of telecommunications traffic. Several additional services such as real time communications and voice over

2 230 LTE (VoLTE) are enabled through 4G (fourth generation) and LTE (long term evolution) networks, whereby operators and enterprises can embed rich communications services to web applications to enhance user experience. On the other hand, to facilitate local coverage and capacity issues arising from rapid growth in mobile data consumption, technologies such as macrocells, small cells, and wi-fi connections have become the primary means of data connectivity. Network function virtualization (NFV) is a novel technology allowing a flexible network infrastructure, facilitating operators to efficiently and cost-effectively support dynamic subscriber demands. Methodology The objective of the study is to compare the diffusion of telecommunication services in the BRICS economies, viz. Brazil, Russia (Belaya and Hanf, 2012), India (Dash and Pandey, 2016), China (Zheng, 2014), and South Africa (van Rooyen, 2014). The data for the study consisted of the total subscriber base data for the BRICS economies for the period , collected from the Statista database. The data is presented in Table 1 and Figure 1. Table 1. Subscriber Base Data for the BRICS Economies (millions) Year Brazil Russia India China South Africa CAGR 24.14% 2.02% 49.86% 21.87% 20.60% All of the markets except for Russia have been growing at CAGRs in excess of 20%. The fastest-growing market was that of India, at a CAGR in excess of 50% until This rapid growth was interrupted due to tightening of restrictions in 2012; however, it started to recover in On the other hand, the Russian market was the slowest-growing market, with a CAGR of just 2%. The diffusion of telecommunication services was analysed for each of the BRICS markets using an extension of the Bass model (refer Bass, 1969; Rogers, 1983; Norton and Bass, 1987; Bass et al., 1994; and Mahajan et al., 1995; Kim and Kim, 2004). The Bass model proposed that diffusion of new products in a market was governed by the dynamics of initiation and imitation, expressed through the equation:, (1)

3 where p represents the innovation rate, q represents the imitation rate, M the market potential, x t represents the cumulative sales up to time period t, and its derivative dx t /dt represents sales in period t. 231 Figure 1. Subscriber Base Data for the BRICS Economies (millions) The model can be further expressed in terms of differences from an initial population of users x 0 as follows: The model is readily estimated using quadratic regression of annual sales on cumulative sales. The Bass model is extended in the current study to include three other components. The first additional component is that of repeat purchase, wherein existing users may take additional connections for different purposes. Akin to this is the component of network effects, wherein groups of users may take additional connections in order to exclusively connect with each other. The third component is that of customer attrition, whereby users may disconnect their connections due to a variety of reasons. The extended model is expressed as follows: (2), (3) where r represents the repeat purchase rate, s represents the network effects rate, and α represents the customer attrition rate. The model can be further expressed in terms of differences from an initial population of users x 0 as follows: The extended model is estimated through non-linear OLS regression. (4)

4 232 Findings The results of the quadratic regressions (to obtain estimates of the Bass model parameters) are presented in Table 2. The results of the quadratic regressions indicate that the Bass model does not adequately explain diffusion of telecommunication services in the BRICS economies. The quadratic regression coefficients were found to be compatible with the predictions of the Bass model only in the case of Brazil. In the cases of Russia, India, and South Africa, the constant term was found to be negative, and hence incompatible with the predictions of the Bass model. In the case of China, the coefficient of the quadratic term was positive, which is also incompatible with the predictions of the Bass model. Table 2. Quadratic Regression (Bass Model) Estimates Brazil Russia India China South Africa regression estimates intercept coefficient of Δx t coefficient of Δx t R % 11.99% 43.74% 93.16% 75.73% F Stat p-value Bass model estimates p 0.20% q 24.43% M x The results of the extended Bass model are presented in Table 3. Table 3. Extended Bass Model Estimates Brazil Russia India China South Africa model estimates p 0.00% 0.00% 0.00% 10.78% 0.00% q 52.91% 99.18% 16.96% 12.93% 98.37% r 0.00% 0.00% 40.24% 0.00% 0.00% s 0.06% 0.03% 0.00% 0.03% 0.04% α 28.28% 84.86% 0.00% 2.14% 36.51% M x intercept coefficient of Δx t coefficient of Δx t R % 6.57% 43.72% 93.16% 42.57%

5 The results in Table 3 suggest that the diffusion of telecommunication services was found to be compatible with the extended Bass model, except for the Chinese market, in the sense that the constant terms were non-negative, and the coefficient of the quadratic terms were negative. In particular, except for the Chinese market, the innovation rate was found to be negligible/nil. In the cases of Brazil, Russia, and South Africa, the model indicates pure imitation, network effects, and attrition, whereas in the case of India, the model indicates pure imitation and repeat purchase behaviour. Also, in the cases of Russia, India, and South Africa, the constant terms and initial population of users were found to be negligible/nil. Using the coefficients of the linear and quadratic terms, the model predicts that maximum sales will be reached when the cumulative sales reach , 24.31, , and for the Brazilian, Russian, Indian, and South African markets, respectively; while empirically the maximum sales in these markets were at cumulative sales levels of 151.0, 38.5, 745.6, and 29.8, respectively; this difference could be due to extraneous factors (particularly the global financial crisis). Also, the Brazilian and Russian markets seem to be in their growth stage, expected to reach their projected limiting market potentials of and (respectively) after about twenty years; the South African market is close to maturity, expected to reach its projected limiting market potential of within five years; and the Indian market seems to be in its maturity stage, expected to reach its projected limiting market potential of within two years. It is observed that the projected limiting market potentials are very different from the market potential parameter estimates, due to repeat purchase, network effects, and/or attrition. In the case of China, the model indicates that innovation, imitation, network effects, and attrition all play a role. The positive coefficient of the quadratic term was probably due to the presence of network effects. The constant term was inexplicably and abnormally high. In particular, the model suggests that the Chinese market has unlimited market potential, exhibiting concave behaviour of sales with respect to cumulative sales. 233 Discussion The results of the study indicate that the extended Bass model explained the dynamics of diffusion of telecommunications services in BRICS markets (except for the Chinese market) better than the original Bass model. The extended model provides predictions of a maximum point for these markets, as well as projections of limiting market potential for the markets and projections of time to reach full market potential. The model suggested that the Brazilian, Russian, and South African markets were driven by pure imitation, network effects, and attrition. However, the estimated imitation rates were found to be abnormally high. The case of the Indian market, which experienced the highest CAGR, was quite interesting. The model suggests that the Indian market is driven by pure imitation and repeat purchase. Further, the model suggests that the Indian market would soon reach its projected limiting market potential. Of course, these predictions do not take into account new opportunities and increase in the scope of telecommunications. This could also be a factor for the Chinese market, which was very different from the other BRICS markets. The model suggests that the Chinese market is affected by innovation, imitation, network effects, and attrition, with concave behaviour with

6 234 respect to cumulative sales, resulting in unlimited market potential. The Chinese market would need to be studied in more detail to explain the abnormal nature of the results. There are several limitations inherent in the study. The research period considered for the study is relatively short, and the data was not available for all of the BRICS markets for the entire period. Also, there are several extraneous factors that may have affected the growth of telecommunications in the research period, especially the global financial crisis of , as well as changes in telecommunications regulations in the different markets. Another limitation is a possible identification problem for the parameters of the extended model, particularly with respect to the innovation, imitation, repeat purchase, and attrition rates. This could be the reason for the abnormally high imitation rates for the Brazilian, Russian, and South African markets. Different specifications of the model may be considered in order to resolve this issue. Another limitation is the possibility that other factors may affect the spread of telecommunications services, particularly the extent of digitisation and the rate of technological innovation. These would need to incorporate in the model. As discussed above, as telecommunications is closely related with information technology, the diffusion of both should perhaps be modelled together, as a coupled system. A gravity model approach, examining the elasticity of telecommunication services with respect to changes in GDP, may also yield interesting insights. Also, the parameters of the model may be affected by extraneous factors, such as changes in regulations. There is thus great scope for further extension of the model for diffusion of telecom services. References Bass, F.M. (1969), A new product growth for model consumer durables, Management Science, Vol. 15 No. 5, pp Bass, F.M., Trichy, K.V. and Jain, D.C. (1994), Why the Bass Model Fits without Decision Variables, Marketing Science, Vol. 13 No. 2, pp Belaya, V. and Hanf, J.H. (2012), Foreign direct investments as agents of change for Russian agrifood business, Journal of Applied Management and Investments, Vol. 1 No. 3, pp Dash, M. and Pandey, S.L.D. (2016), Efficiency Measurement for MFIs in India Using the Control-Efficiency Model, Journal of Applied Management and Investments, Vol. 5 No. 3, pp Ferencova, M. (2012), Investigating possibilities of using tools for promoting highereducation institutions in areas of e-communication, Journal of Applied Management and Investments, Vol. 1 No. 2, pp Kim, M.S. and Kim, H. (2004), Innovation diffusion of telecommunications: General patterns, diffusion clusters and differences by technological attribute, International Journal of Innovation Management, Vol. 8 No. 2, pp Mahajan, V., Muller, E. and Bass, F.M. (1995), "Diffusion of new products: Empirical generalizations and managerial uses, Marketing Science, Vol. 14 No. 3, pp Norton, J.A. and Bass, F.M. (1987), A Diffusion Theory Model of Adoption and Substitution for Successive Generations of High-Technology Products, Management Science, Vol. 33 No. 9, pp

7 Rogers, E.M. (1983), Diffusion of Innovations, Free Press, New York. van Rooyen, J. (2014), Human Capital, Skills and Talent Development in South African Agribusiness, Journal of Applied Management and Investments, Vol. 3 No. 3, pp Zheng, X. (2014), Factors Influencing Location Selection in Inward Foreign Direct Investment to China, Journal of Applied Management and Investments, Vol. 3 No. 1, pp COMPARISON OF DIFFUSION IN TELECOMMUNICATIONS IN THE BRICS ECONOMIES Mihir Dash Anuloma Tripathy D. Shobha Greeshma Ramesh Abhiyank Verma K. Sriharsha Alliance University, India Abstract This study examines and compares the growth of telecommunications in the BRICS economies using the Bass model for diffusion of innovations. The study proposes an extension of the Bass model, incorporating repeat purchase, network effects, and customer attrition. The results of the study indicate that the diffusion of telecommunications in the BRICS markets is predominantly due to pure imitation, repeat purchase, and network effects. The Chinese market is exceptional, in that it is affected by innovation, imitation, and network effects, but with seemingly unlimited market potential. The model can be extended further to examine the role of other external factors on the diffusion of telecommunications. Keywords: Bass model, diffusion of innovations, repeat purchase, network effects, customer attrition, pure imitation, market potential 235