A dynamic competition analysis on the Korean mobile phone market using competitive diffusion model

Kim, Jong-Hwa; Lee, Deok-Joo; Ahn, JaeKyoung

doi:10.1016/j.cie.2006.07.009

Cited by 103 publications

(50 citation statements)

References 19 publications

(19 reference statements)

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“…This model has been chosen because it enables the best dynamics description of the predator-prey relationship. The population dynamics observed in biological systems has been an inspiration in the works for the modelling of social, economic and technological changes [12][13][14][15]. Lotka-Volterra model enables the analysis of the equilibrium level in the distribution of a particular resource (in this case "a particular resource" refers to the user through the implementation of services), as well as analysing the changes which arrive with the new redistribution of resources among market participants.…”

Section: The Market Of Express Postal Services In Serbiamentioning

confidence: 99%

“…Convergence criterion is considered to be a change in the value of the parameters that lead to stopping the iterative procedure, or if the change is less than the defined parameters of the convergence criteria, the algorithm ends. Convergence criterion, in this case is a value of 0.001, or the procedure is stopped if the change percentage in the coefficient is less than 0.1% [13,20]. The estimated value of the parameters for Equations (3) and (4) is given in Table 3.…”

Section: Parameter Analysismentioning

confidence: 99%

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Express Services Market Analysis Based on the Lotka-Volterra Model – Case Study Serbia

Jovanovic

Mostarac

Šarac

et al. 2015

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Section: The Market Of Express Postal Services In Serbiamentioning

confidence: 99%

Section: Parameter Analysismentioning

confidence: 99%

Express Services Market Analysis Based on the Lotka-Volterra Model – Case Study Serbia

Jovanovic

Mostarac

Šarac

et al. 2015

PROMET

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“…The phenomenon of diffusion has been widely studied due to its potential impact in various fields, such as epidemiology (Raj, Kuceyeski, & Weiner, 2012), marketing (Kim, Lee, & Ahn, 2006), and social behavior (Susarla, Oh, & Tan, 2012). Understanding contagion phenomena, product/service adoption and changing cultural features all depend on how people influence each other, which is the hallmark of diffusion models (Rogers, 2003).…”

Section: Diffusion Modelsmentioning

confidence: 99%

Information diffusion through social networks: The case of an online petition

Jalali

Ashouri

Herrera-Restrepo

et al. 2016

Expert Systems with Applications

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People regularly use online social networks due to their convenience, efficiency, and significant broadcasting power for sharing information. However, the diffusion of information in online social networks is a complex and dynamic process. In this research, we used a case study to examine the diffusion process of an online petition. The spread of petitions in social networks raises various theoretical and practical questions: What is the diffusion rate? What actions can initiators take to speed up the diffusion rate? How does the behavior of sharing between friends influence the diffusion process? How does the number of signatures change over time? In order to address these questions, we used system dynamics modeling to specify and quantify the core mechanisms of petition diffusion online; based on empirical data, we then estimated the resulting dynamic model. The modeling approach provides potential practical insights for those interested in designing petitions and collecting signatures. Model testing and calibration approaches (including the use of empirical methods such as maximum-likelihood estimation, the Akaike information criterion, and likelihood ratio tests) provide additional potential practices for dynamic modelers.Our analysis provides information on the relative strength of push (i.e., sending announcements) and pull (i.e., sharing by signatories) processes and insights about awareness, interest, sharing, reminders, and forgetting mechanisms. Comparing push and pull processes, we found that diffusion is largely a pull process rather than a push process. Moreover, comparing different scenarios, we found that targeting the right population is a potential driver in spreading information (i.e., getting more signatures), such that small investments in targeting the appropriate people have 'disproportionate' effects in increasing the total number of signatures. The model is fully documented for further development and replications.

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“…Some of these competing technologies have been addressed recently in system dynamics and bibliometrics (Bae et al, 2007) as well as diffusion modelling (Kim et al, 2006;Pretorius and Winzker, 2010) research. The research presented in this paper is an extension of recent research on simulation of bridging technology dynamics and is also based on work presented previously by the authors Pretorius et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

A system dynamics approach to technology interaction: From asymptotic to cyclic behaviour

Pretorius

Benade

2015

Technological Forecasting and Social Change

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This paper is an extension and elaboration of previous research on the simulation of three competing technologies that interact. A modified version of the three-technology system is investigated, and some initial system dynamics results are reported illustrating the progression from asymptotic to cyclic behaviour. Technology is considered in this research as a result of innovation, a rate-dependent process that may include several non-linearities due to interaction with the environment and social context. Using bibliometrics as a research data source is an interesting way to trace technology growth patterns very effectively. In this research, the existence of cyclic behaviour in two real life technologies is illustrated using bibliometrics. In this paper, a technology system consisting of three interacting technologies is treated and modelled in a coupled manner where the interacting dynamics is described by the Lotka-Volterra system of differential equations. The effect of interaction between the technologies and the period of cyclic behaviour is illustrated parametrically. Furthermore, the possible uncertain diffusion as well as interaction effect for two of the technologies is also addressed in this research using a Monte Carlo multivariate simulation technique and a system dynamics approach. The research method is exploratory and case based.

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A dynamic competition analysis on the Korean mobile phone market using competitive diffusion model

Cited by 103 publications

References 19 publications

Express Services Market Analysis Based on the Lotka-Volterra Model – Case Study Serbia

Express Services Market Analysis Based on the Lotka-Volterra Model – Case Study Serbia

Information diffusion through social networks: The case of an online petition

A system dynamics approach to technology interaction: From asymptotic to cyclic behaviour

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