Leveraging machine learning to uncover the dynamic evolution of business models in intelligent manufacturing

Abstract

This study examines the dynamic evolution of business models in intelligent manufacturing enterprises, focusing on their adaptation to market demands and technological advancements. While business model transformation in intelligent manufacturing has garnered academic attention, there remains a gap in effectively measuring and analyzing the dynamic evolution and inherent characteristics of these models. To address this gap, we apply machine learning techniques, specifically combining convolutional neural networks with gated recurrent units, to develop a text analysis approach that reduces noise and analyzes the evolutionary process of business models in intelligent manufacturing enterprises. We validate our approach using annual report texts from intelligent manufacturing enterprises, covering the period from 2011 to 2021. The results demonstrate that our machine learning approach effectively classifies business models within complex, unstructured text data. Our analysis identifies three key stages in the evolution of business models: “efficiency driven,” “novelty boom,” and “ novelty renaissance.” Additionally, we explore the underlying characteristics and trends of these business models, shedding light on the factors driving their evolution.