Integrating multimodal data and machine learning for entrepreneurship research

Resumo

Research Summary Extant research in neuroscience suggests that human perception is multimodal in nature—we model the world integrating diverse data sources such as sound, images, taste, and smell. Working in a dynamic environment, entrepreneurs are expected to draw on multimodal inputs in their decision making. However, extant research in entrepreneurship has largely focused on how entrepreneurs or investors develop insights from data in a single mode. A few studies that have used a multimodal approach either simplify the multimodal data (MMD) into a few constructs or manually analyze the data without fully utilizing their potential. Such oversimplification limits the insights that can be gained from MMD. In this paper, we offer a framework to guide researchers to analyze and integrate MMD, capturing various cues embedded in the entrepreneurial process. We illustrate how applying machine learning algorithms to MMD can engender a robust, reliable, and scalable approach for researchers to effectively capture the elusive yet critical aspects of entrepreneurial phenomena. We also curate a set of data and algorithm resources for researchers interested in leveraging MMD in their studies. Managerial Summary Entrepreneurs operate in fast‐paced and complex environments where success often relies on the ability to make sense of diverse and rich information, which ranges from explicit observations (e.g., what they see and hear) to more subtle contextual cues. Yet, most entrepreneurship

Tipo

Artigo

Área

Administração

Ano

2025

Idioma

Inglês

Licença

CC BY

DOI

10.1002/sej.1546

Como citar (ABNT)

SHRESTHA, Yash Raj; HE, Vivianna Fang. Integrating multimodal data and machine learning for entrepreneurship research. Strategic Entrepreneurship Journal, 2025. DOI: 10.1002/sej.1546.