Name
Abstract | ||
|---|---|---|
Market members interact within a complex, adaptive system to effect adoption decisions and the resulting diffusion of innovations. Agent-based modeling (ABM) is a methodology well suited for simulating this system. It complements and extends econometric approaches by incorporating interactions among system members, and adaptation in the system, revealing emergent results. Since ABM allows study at the individual unit level, heterogeneity among system members is reflected and modeling at the brand level is possible. Here an ABM with consumer and brand agents is described. The brand and product diffusion curve output allows study of diffusion at micro and macro levels, respectively. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1016/j.dss.2010.08.004 | Decision Support Systems |
Keywords | Field | DocType |
agent-based modeling,brand,product,brand agent,adoption,individual unit level,adaptive system,product diffusion curve output,resulting diffusion,innovation,macro level,diffusion,brand level,agent-based diffusion model,system member,market members interact,diffusion model,complex adaptive system | Diffusion curve,Computer science,Adaptive system,Diffusion of innovations,Trademark,Knowledge management,Macro,Diffusion (business) | Journal |
Volume | Issue | ISSN |
50 | 1 | Decision Support Systems |
Citations | PageRank | References |
14 | 0.94 | 4 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mary E. Schramm | 1 | 14 | 0.94 |
| Kevin J. Trainor | 2 | 14 | 0.94 |
| Murali Shanker | 3 | 73 | 7.76 |
| Michael Y. Hu | 4 | 426 | 55.74 |