Using supervised machine learning for large‐scale classification in management research: The case for identifying artificial intelligence patents. (11th July 2022)

• Record Type: Journal Article
• Authors: Miric, Milan; Jia, Nan; Huang, Kenneth G.
• Is Part Of: Strategic management journal. Volume 44:Number 2(2023)
• Page Start: 491
• Page End: 519
• Publication Date: 2022-07-11
• Subjects: artificial intelligence -- keywords -- machine learning -- patent and innovation -- text analysis; Business planning -- Periodicals; Management -- Periodicals; Business -- Periodicals; 658.401205
• DOI: 10.1002/smj.3441
• Languages: English
• ISSNs: 0143-2095

Abstract

A bstract: Research Summary: Researchers increasingly use unstructured text data to construct quantitative variables for analysis. This goal has traditionally been achieved using keyword‐based approaches, which require researchers to specify a dictionary of keywords mapped to the theoretical concepts of interest. However, recent machine learning (ML) tools for text classification and natural language processing can be used to construct quantitative variables and to classify unstructured text documents. In this paper, we demonstrate how to employ ML tools for this purpose and discuss one application for identifying artificial intelligence (AI) technologies in patents. We compare and contrast various ML methods with the keyword‐based approach, demonstrating the advantages of the ML approach. We also leverage the classification outcomes generated by ML models to demonstrate general patterns of AI technological innovation development. Managerial Summary: Text‐based documents offer a wealth of information for researchers and business analysts. However, researchers often need to find a way to classify these documents to use in subsequent research projects. In this paper, we demonstrate how supervised ML methods can be used to automate the process of classifying textual documents into pre‐defined categories or groups. We provide an overview of when such techniques may be used in comparison to other methods, and the considerations and tradeoffs associated with each method. We apply these methods to identify AI‐based technologies from all patents in the United States, based on patent abstract text. This allows us to show interesting patterns of AI innovation development in the United States. We also provide the code and data used in this paper for future research.