Work

Predicting corporate innovation using machine learning and social media data

Year: 2025

Type: article

Abstract: This study explores the ability of employee reviews in social media to predict the innovation performance of companies. We explore these links using a novel social media dataset and an explainable machine learning-driven research approach to examine the predictive value and importance of different employee treatment schemas with various types of corporate innovation. In addition to more traditional patent-based innovation measures, we employ a text-based innovation measure from 10-K fillings. We find that several employee ratings in social media contain value-relevant information in predicting corporate innovation. Specifically, we show the importance of flexible working hours and employee stock or equity options in predicting patent counts, patent citations, and text-based innovation. Other significant predictors for both patent-based proxies include employees' career growth prospects, opportunities for professional development, and company pride. Our findings also indicate that text-based innovation is a strong predictor for patent counts and citations and that there are several notable differences between the meaningful predictors for different types of innovation.

Source: Technovation

DOI: 10.1016/j.technovation.2025.103322

Authors: Mika Ylinen, Mikko Ranta

Institution: University of Vaasa

Language: English

Topic: Firm Innovation and Growth

Subfield: Economics and Econometrics

Field: Economics, Econometrics and Finance

Domain: Social Sciences

SDGs: Decent work and economic growth, Industry, innovation and infrastructure

Open Access status: hybrid

Estimated APC paid: $3,890