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Published in: Eurasian Business Review 4/2022

11-03-2022 | Regular Article

The interaction between direct and indirect network externalities in the early diffusion of mobile social networking

Authors: Emanuele Giovannetti, Mohsen Hamoudia

Published in: Eurasian Business Review | Issue 4/2022

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Abstract

The early diffusion of mobile social networking (MSN) reflected the interplay of different factors: the affordability of better Internet connectivity and the diffusion of Smartphones suitable for Social Networking Applications. These key technology and cost drivers facilitated both the direct and indirect network externalities, which are necessary to overtake critical adoption barriers and facilitate users' decisions. However, a key challenge in modeling MSN diffusion is in distinguishing among the impact of these two types of network externalities. This paper addresses such a challenge by adopting a two-stage estimation strategy. In the first stage, we focus on direct network externalities by estimating a set of country-specific adoptions peaks that allow differentiating between early and late adopters. In the second stage, we estimate the impact of indirect network externalities on MSN diffusion while also considering the role of pricing strategies. Our results provide significant evidence that indirect network externalities can exert opposite effects on adoption between early adopters and followers, depending on whether they adopt before or after a country's MSN diffusion peak.

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Appendix
Available only for authorised users
Footnotes
1
Facebook measures monthly active users as users that have logged in during the past 30 days (https://​www.​statista.​com/​statistics/​264810/​number-of-monthly-active-facebook-users-worldwide/​).
 
2
Mobile social networking is intended as a service that enables individuals to connect to their social communities with a mobile device where members share experiences, interests, opinions, presence information and personal content through their mobile devices. Mobile adds new capabilities to social networking, such as location-related services and new visualization mechanisms (Gartner IT glossary 2021, http://​www.​gartner.​com/​it-glossary/​mobile-social-networks).
 
3
Skype Inc. incorporated in 2004 and based in Palo Alto, California, provides social networking via free video and voice calls, instant messaging, and file-sharing services also through smartphones.
 
4
Facebook, the online social media and social networking service based in Menlo Park, California was founded in 2004.
 
5
Twitter Inc. founded in 2006 and based in San Francisco, California, introduced an online social networking service that enables users to send and read short 140-character messages called "tweets" through smartphones.
 
6
WhatsApp Inc. founded in 2009 and based in Santa Clara, California, operates as a cross-platform mobile messaging company that allows social networking through the exchange of unlimited text and multimedia messages, such as audio, video, and photo messages through smartphones.
 
7
Spotify USA Inc. founded in 2011 and based in New York, New York, operates a platform that enables social networking for users to find and share music and playlists also on their smartphones.
 
8
Snapchat, Inc. founded in 2012 and based in Venice, California, developed a text and photo-based social messaging application for smartphones.
 
9
See, for example, Antonelli, (1990) for the adoption of Fax Machines, Colombo and Mosconi (1995) for that of Flexible Automation technologies and more recently, for the adoption of Fast Ethernet, see Corrocher and Fontana, (2008) and Casagrande-Seretti et al. (2019) for a wide range of radical innovations.
 
10
For applications in mobile telephony, see Frank (2004) and Gruber and Verboven (2001a; 2001b).
 
11
For a fascinating historical account of the logistic models, see Cramer (2003)
 
12
Our focus on the discontinuity of the network externalities during the diffusion process also connects to the general framework on diffusion and discontinuity due to externalities discussed in Antonelli (1995).
 
13
For applications in the marketing literature see Stremersch et al. (2007) and Stremersch and Binken (2009).
 
14
These authors found that a Gompertz functional form for the diffusion process (Islam et al., 2002) would lead to better forecasting accuracy in the pre-take-off stage, while the logistic form, would perform better for the post-take-off stage, hence suggesting that the appropriate choice of functional form to estimate the diffusion model should be stage-dependent (Meade and Islam, 2006; 2015).
 
15
For the impact of prices on the diffusion of mobile telephony see Madden and Coble-Neal (2004).
 
17
Hence the number of unique and active MSN users will always be smaller than the total number of registrations for these services.
 
18
The original monthly data were then used at quarterly intervals to be integrated with the second quarterly dataset.
 
20
Penetration is calculated as the Smartphones percentage share of total connections. A smartphone is defined as a mobile handset enabling advanced access to Internet-based services with computer-like functions. Smartphone platforms, such as Android, iOS, Windows phone, and BlackBerry, support native applications created by third-party developers, whereas feature phones used closed platforms that do not support native development, although downloadable applications are often supported using Java.
 
21
The logistic specification (Hosmer and Lemeshow, 1989) is chosen as this functional form captures the initial growing adoption rate as a function of previous adopters, and the subsequently decreasing rate of diffusion as penetration nears the full potential population of adopters.
 
22
Brazil’s estimation started form the first 1st Quarter of 2008, hence we have 26 observations for Brazil. See Appendix 1 for the graphs of the actual and estimated MSN diffusion curves in each country.
 
23
This strategy is useful in purging the identification strategy from the presence of endogeneity potentially arising from simultaneity since these variables are estimated from lagged cumulated adoptions, that were not affected by the current values of the covariates, used in this second stage (Giovannetti and Piga, 2017).
 
24
In Table 4, the dependent variable predicted values of MSN adoptions is rescaled in per million units.
 
25
Pooled regression and random effects estimates were also performed. The results were confirmed in these different estimation strategies, but the fixed effect only was reported as it emerged as most suitable to the estimation problem.
 
26
This implies that the conditional expectation for the MSN period adoptions given the country-specific unobserved components, \(\alpha_{i}\), and the regressors, \(x_{i,t}\), is equal to the population regression function, so that the OLS estimates of the effects of the covariates on MSN period adoptions, \(\beta_{j}\), are consistent, as long as \(\varepsilon_{i,t}\), the country-date idiosyncratic error, remains uncorrelated with each \(x_{i,t}\), for all time periods. This condition of strict exogeneity, is addressed by the two-stage modeling strategy and using the predicted level of MSN adoptions from the first stage that were estimated based on past values of adoptions.
 
27
As the value of the relevant F(3, 148) test equals 25.46, which has near-zero probability under H0interactions).
 
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Metadata
Title
The interaction between direct and indirect network externalities in the early diffusion of mobile social networking
Authors
Emanuele Giovannetti
Mohsen Hamoudia
Publication date
11-03-2022
Publisher
Springer International Publishing
Published in
Eurasian Business Review / Issue 4/2022
Print ISSN: 1309-4297
Electronic ISSN: 2147-4281
DOI
https://doi.org/10.1007/s40821-022-00208-1

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