Work

Modeling Firm Search and Innovation Trajectory Using Swarm Intelligence

Year: 2023

Type: article

Abstract: We developed a swarm intelligence-based model to study firm search across innovation topics. Firm search modeling has primarily been “firm-centric,” emphasizing the firm’s own prior performance. Fields interested in firm search behavior—strategic management, organization science, and economics—lack a suitable simulation model to incorporate a more robust set of influences, such as the influence of competitors. We developed a swarm intelligence-based simulation model to fill this gap. To demonstrate how to fit the model to real world data, we applied latent Dirichlet allocation to patent abstracts to derive a topic search space and then provide equations to calibrate the model’s parameters. We are the first to develop a swarm intelligence-based application to study firm search and innovation. The model and data methodology can be extended to address a number of questions related to firm search and competitive dynamics.

Source: Algorithms

DOI: 10.3390/a16020072

Authors: Ren-Raw Chen, Cameron D. Miller, Puay Khoon Toh

Institutions: Fordham University, Syracuse University, The University of Texas at Austin

Language: English

Topic: Intellectual Property and Patents

Subfield: Management of Technology and Innovation

Field: Business, Management and Accounting

Domain: Social Sciences

Open Access status: gold

Estimated APC paid: $1,779

Funder: Fordham University