Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/191981
Authors: 
Scheller, Fabian
Johanning, Simon
Bruckner, Thomas
Year of Publication: 
2019
Series/Report no.: 
Beiträge des Instituts für Infrastruktur und Ressourcenmanagement 01/2019
Abstract: 
Modeling the diffusion of innovations is a very challenging task, as there are various influencing factors to consider. At the same time, insights into the diffusion process can help decision makers to detect weak points of potential business models. In the literature, various models and methodologies that might tackle this problem are presented. Between these, empirically grounded agent-based modeling turned out to be one of the most promising approaches. However, the current culture is dominated by papers that fail to document critical methodological details. Thus, existing agent-based models for real-world analysis differ extensively in their design and grounding and therefore also in their predictions and conclusions. Additionally, the selection of modeling aspects seems too often be ad hoc without any defendable rationale. Concerning this matter, to draw on experiences could guide the researcher. This research paper seeks to synthesize relevant publications at the interface of empirical grounding, agent-based modeling and innovation diffusion to provide an overview of the existing body of knowledge. The major aim is to assess existing approaches regarding development procedure, entity and dynamics consideration and theoretical grounding to suggest a future research agenda. This might lead to the development of more robust models. According to the findings of this review, future work needs to focus on generic design, model coupling, research consistency, modular testing, actor involvement, behavior modeling, network foundation, and data transparency. In a subsequent step and based on the findings, a novel model approach needs to be designed and implemented.
Subjects: 
Innovation diffusion models
Agent-based models
Empirically grounded models
Data driven models
Literature review
Document Type: 
Research Report
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