Abstract

African-American-owned high-tech enterprises and innovations are underrepresented in industry in comparison to non-African-American-owned ones. Various complex and intertwined socio-economic factors hinder the innovation capability of African-American-owned high-tech enterprises leading to underrepresentation of these businesses. Understanding the causal relationship between firm’s interactions with internal and external entities and its ability to innovate can foster the efforts of a high-tech enterprise in increasing and sustaining innovation capabilities. Agent-based modeling (ABM) emerges as one of the popular approaches to the study of complex socio-technological systems. Characterizing the organizational behavior of African-American-owned high-tech enterprises through the ABM perspective may provide a better understanding of the drivers, processes, and outcomes of this industry segment. By analyzing interview data among African-American entrepreneurs, this study proposes an ABM framework to represent and analyze the innovation capabilities of African-American-owned technology enterprises in comparison to other types of ownership. The ABM model illustrates the key involved agents, their attributes, actions, and the complex interactions amongst them. Simulation results indicate that African American population is underrepresented in the high-tech industry due to two significant factors of social and economic standings implying that the simulation trajectory is in the right direction. Model calibration, verification using real data and implementation plans related to policy development discussions and factors impacting African-American enterprises are also discussed in the study.

Highlights

  • African-American-owned enterprises and innovations are underrepresented in the high-tech industry as compared to their counterparts (Adhikari et al 2014; DiTomaso and Farris 1992; Liu 2016; Conrad 2006; Gatchair 2013; Marcus)

  • To address the low representation of African-American enterprises (AAEs) in the high-tech industry, evaluate the scenarios that lead to new products, and create entrepreneurial strategies that increase the level of representation, the study focused on the socio-economic aspects of innovation with the representation of the framework

  • Literature Review The literature review of this study addresses the key components of the African-American technical innovation system by detailing studies on technology innovation system theory, computer modeling and simulation technology and the socio-economic factors impacting the African-American entrepreneurship (Fig. 1)

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Summary

Introduction

African-American-owned enterprises and innovations are underrepresented in the high-tech industry as compared to their counterparts (Adhikari et al 2014; DiTomaso and Farris 1992; Liu 2016; Conrad 2006; Gatchair 2013; Marcus). To address the low representation of African-American enterprises (AAEs) in the high-tech industry, evaluate the scenarios that lead to new products, and create entrepreneurial strategies that increase the level of representation, the study focused on the socio-economic aspects of innovation with the representation of the framework. The proposed framework integrates the well-illustrated knowledge-driven technology innovations and computer modeling and simulation approaches (Pyka, Gilbert, and Ahrweiler 2007; Pyka, Gilbert, and Ahrweiler 2002; Korber 2011) It entails the socio-economic elements that hinder the development of African-American groups through a computer modeling framework, providing a new perspective on race equality in high-tech industries. In this regard, the contributions of this study are bifold. This study focuses on the intersection of these three areas to gain insight to prior research and gaps as indicated in (Figure 1)

Complexity Science and Innovation Systems
Agent-based Modeling and Technology Innovation Modeling
African-American Agent-Based Innovation Modeling Framework
Funding Agents
Government Research Agent
Research university agent
Interaction among Agents and Performance Measurement
Simulation and Scenario Comparison
Findings
Discussion and Conclusions
Full Text
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