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Understanding how innovations are accepted in a dynamic and complex market environment is a crucial factor for competitive advantage. To understand the relevant factors for this diffusion and to predict success, empirically grounded agent-based models have become increasingly popular in recent years.
Despite the popularity of these innovation diffusion models, no common framework that integrates their diversity exists. This article presents a flexible, modular and extensible common description and implementation framework that allows to depict the large variety of model components found in existing models. The framework aims to provide a theoretically grounded description and implementation framework for empirically grounded agent-based models of innovation diffusion.
It identifies 30 component requirements to conceptualize an integrated formal framework description. Based on this formal description, a java-based implementation allowing for flexible configuration of existing and future models of innovation diffusion is developed.
As a variable decision support tool in decision-making processes on the adoption of innovations the framework is valuable for the investigation of a range of research questions on innovation diffusion, business model evaluation and infrastructure transformation.
Keywords
Agent-Based Modeling, Agent-Based Product Diffusion Model, Innovation Diffusion, Human Decision-Making, Socio-Economic Simulation, Multi-Agent Simulation
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View the published article at Complex Adaptive Systems Modeling.
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Posted 30 Jul, 2020
No community comments so far
Review #2 received
Received 23 Aug, 2020
Editorial decision: Accept
On 23 Aug, 2020
Review #1 received
Received 17 Aug, 2020
Reviewer #2 agreed
On 11 Aug, 2020
Reviewer #1 agreed
On 06 Aug, 2020
Reviewers invited
Invitations sent on 02 Aug, 2020
Editor assigned
On 24 Jul, 2020
Submission checks complete
On 23 Jul, 2020
Editor invited
On 23 Jul, 2020
No comments provided
Editorial decision: Major revision
On 27 May, 2020
Review #1 received
Received 11 Mar, 2020
Reviewer #2 agreed
On 07 Mar, 2020
Reviewer #1 agreed
On 04 Mar, 2020
Reviewers invited
Invitations sent on 03 Mar, 2020
Editor assigned
On 03 Mar, 2020
First submitted
On 02 Mar, 2020
Submission checks complete
On 02 Mar, 2020
Editor invited
On 02 Mar, 2020
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See the published version of this preprint at Complex Adaptive Systems Modeling.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
View the published article at Complex Adaptive Systems Modeling.
Version 2
Posted 30 Jul, 2020
No community comments so far
Review #2 received
Received 23 Aug, 2020
Editorial decision: Accept
On 23 Aug, 2020
Review #1 received
Received 17 Aug, 2020
Reviewer #2 agreed
On 11 Aug, 2020
Reviewer #1 agreed
On 06 Aug, 2020
Reviewers invited
Invitations sent on 02 Aug, 2020
Editor assigned
On 24 Jul, 2020
Submission checks complete
On 23 Jul, 2020
Editor invited
On 23 Jul, 2020
No comments provided
Editorial decision: Major revision
On 27 May, 2020
Review #1 received
Received 11 Mar, 2020
Reviewer #2 agreed
On 07 Mar, 2020
Reviewer #1 agreed
On 04 Mar, 2020
Reviewers invited
Invitations sent on 03 Mar, 2020
Editor assigned
On 03 Mar, 2020
First submitted
On 02 Mar, 2020
Submission checks complete
On 02 Mar, 2020
Editor invited
On 02 Mar, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at Complex Adaptive Systems Modeling.
Understanding how innovations are accepted in a dynamic and complex market environment is a crucial factor for competitive advantage. To understand the relevant factors for this diffusion and to predict success, empirically grounded agent-based models have become increasingly popular in recent years.
Despite the popularity of these innovation diffusion models, no common framework that integrates their diversity exists. This article presents a flexible, modular and extensible common description and implementation framework that allows to depict the large variety of model components found in existing models. The framework aims to provide a theoretically grounded description and implementation framework for empirically grounded agent-based models of innovation diffusion.
It identifies 30 component requirements to conceptualize an integrated formal framework description. Based on this formal description, a java-based implementation allowing for flexible configuration of existing and future models of innovation diffusion is developed.
As a variable decision support tool in decision-making processes on the adoption of innovations the framework is valuable for the investigation of a range of research questions on innovation diffusion, business model evaluation and infrastructure transformation.
Figure 1
Figure 2
The full text of this article is available to read as a PDF.
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