A standard form of citation of this article is:
Roedenbeck, Marc R.H. and Nothnagel, Barnas (2008). 'Rethinking Lock-in and Locking: Adopters Facing Network Effects'. Journal of Artificial Societies and Social Simulation 11(1)4 <https://www.jasss.org/11/1/4.html>.
The following can be copied and pasted into a Bibtex bibliography file, for use with the LaTeX text processor:
@article{roedenbeck2008,
title = {Rethinking Lock-in and Locking: Adopters Facing Network Effects},
author = {Roedenbeck, Marc R.H. and Nothnagel, Barnas},
journal = {Journal of Artificial Societies and Social Simulation},
ISSN = {1460-7425},
volume = {11},
number = {1},
pages = {4},
year = {2008},
URL = {https://www.jasss.org/11/1/4.html},
keywords = {Path Dependence, Gaussian Distributed Adopters, Network Effects, Dynamic Information Distribution, Lock-in Calculation},
abstract = {When are we locked in a path? This is one of the main questions concerning path dependency. Coming from Arthur's model of increasing returns and technology adoption (Arthur 1989), this paper addresses the question of when and how a lock-in occurs. To gain a better understanding of the path process, different modifications are made. First, the random selection of two types of adopters is substituted with a random selection of adopters having a Gaussian distributed natural inclination. Second, Arthur's model shows only indirect network effects, so direct network effects are added to the model. Furthermore, it is shown that there is an asymptotic lock-in function referring to the technology A and B adopter ratio; this ratio is calculated within the process on the basis of a returning probability to an open state. In the following, the developed model is used to simulate path process without increasing returns, with increasing returns stopping when a lock-in occurs, as well as random drop-outs of increasing returns. One answer that could be drawn out of this new extended model is that there is no lock-in without further stabilizing returns. This and other aspects are used to provide a simplified path-model for empirical research. Finally, its limits are discussed in regard to uncertainty, innovation, and changes in network effect parameters.},
}The following can be copied and pasted into a text file, which can then be imported into a reference database that supports imports using the RIS format, such as Reference Manager and EndNote.
TY - JOUR
TI - Rethinking Lock-in and Locking: Adopters Facing Network Effects
AU - Roedenbeck, Marc R.H.
AU - Nothnagel, Barnas
Y1 - 2008/01/31
JO - Journal of Artificial Societies and Social Simulation
SN - 1460-7425
VL - 11
IS - 1
SP - 4
UR - https://www.jasss.org/11/1/4.html
KW - Path Dependence
KW - Gaussian Distributed Adopters
KW - Network Effects
KW - Dynamic Information Distribution
KW - Lock-in Calculation
N2 - When are we locked in a path? This is one of the main questions concerning path dependency. Coming from Arthur's model of increasing returns and technology adoption (Arthur 1989), this paper addresses the question of when and how a lock-in occurs. To gain a better understanding of the path process, different modifications are made. First, the random selection of two types of adopters is substituted with a random selection of adopters having a Gaussian distributed natural inclination. Second, Arthur's model shows only indirect network effects, so direct network effects are added to the model. Furthermore, it is shown that there is an asymptotic lock-in function referring to the technology A and B adopter ratio; this ratio is calculated within the process on the basis of a returning probability to an open state. In the following, the developed model is used to simulate path process without increasing returns, with increasing returns stopping when a lock-in occurs, as well as random drop-outs of increasing returns. One answer that could be drawn out of this new extended model is that there is no lock-in without further stabilizing returns. This and other aspects are used to provide a simplified path-model for empirical research. Finally, its limits are discussed in regard to uncertainty, innovation, and changes in network effect parameters.
ER -