The Influence of Entrepreneurial Cognition on Business Model Innovation: A Hybrid Method Based on Multiple Regressions and Machine Learning.

Jian Zhou,Xiao Liu,Yang Liu,Jian Yang,Hehe Sun

doi:10.3389/fpsyg.2021.744237

Abstract

How to achieve business model innovation (BMI) has always been a focus topic in the field of entrepreneurship. Based on cognitive theory, this study takes new ventures as the research object to build a theoretical model to explore the impact of entrepreneurial cognition on BMI choice, its intermediary mechanism, and boundary conditions. We test our framework with a sample of 242 questionnaires; the empirical research results show that entrepreneurial configuration cognition, willing cognition, and ability cognition indirectly affect the BMI of new ventures. Strategic sensitivity (SSE) mediated the relationship between entrepreneurial cognition and BMI; knowledge diversity positively moderates the relationship between SSE and BMI. Machine learning algorithm research has found three prediction models for BMI of new ventures. By constructing a theoretical analysis framework of BMI from the perspective of cognition, the results deepen the relevant research on BMI of new ventures, clarify the source of unique characteristics and capabilities of entrepreneurs, provide a new research perspective for analyzing the impact mechanism of entrepreneurial cognition on BMI, enrich the research results in the field of “situation-cognition behavior,” and further clarify the important role of knowledge in the development of new ventures.

Highlights

With the vigorous development of information economy and the birth and application of digital technologies, new ventures are facing an increasingly complex and unpredictable entrepreneurial environment (Nambisan, 2017; Steininger, 2019; Secundo et al, 2021)
The quantitative research method based on multiple regression analysis can confirm the causal logic relationship between variables, it is difficult to exclude the influence of various antecedents and open the “black box” of the mutual influence between variables, which makes the research logic too “constrained.” To further clarify the relationship between variables and more effectively predict the business model innovation (BMI) behavior of new ventures, this study presents a prediction model for BMI of new ventures based on empirical research through machine learning algorithms
We tested our framework with a sample of 242 questionnaires; the empirical research results based on MLR show that three subdimensions of entrepreneurial cognition have a significant role in promoting BMI

Summary

Introduction

With the vigorous development of information economy and the birth and application of digital technologies (such as the IoT, big data, artificial intelligence, 3D printing technology, etc.), new ventures are facing an increasingly complex and unpredictable entrepreneurial environment (Nambisan, 2017; Steininger, 2019; Secundo et al, 2021). In such a complex and ever-changing entrepreneurial environment, there are still some enterprises that stand out in the fierce market competition with their novel and unique business models and become the best in the industry. Business model innovation (BMI) is increasingly drawing the attention of theoretical and practical fields alike

Methods

Results

Conclusion