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| Measuring Corporate Innovation Based on LDA Topic Model |
| Ye Qin1,Cai Jianfeng1,Zhang Qiuyun2 |
| (1.School of Management, Northwestern Polytechnical University;2.School of Computer Science, Northwestern Polytechnical University, Xi′an 710129, China) |
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Abstract The Chinese government firmly adheres to the path of independent innovation with Chinese characteristics and implement an innovation-driven development strategy. As the main body of innovation, enterprises play a pivotal role in promoting national innovation and transformation, therefore the research on corporate innovation has received extensive attention from the academic community. Scholars have carried out a variety of theoretical and empirical studies around corporate innovation and have obtained some remarkable achievements. However, the important issue of how to accurately measure corporate innovation waits to be addressed. This problem is challenging for both the academic circles and the industrial field especially under the background of the national innovation-driven development strategy in China. The current mainstream proxy indicators of corporate innovation, such as numbers of patents and research and development (R&D) expenditures, have recently been criticized since they can only reflect some aspects of corporate innovation, while ignoring other vital parts of corporate innovation activities. Wherefore this paper tries to develop a new method to comprehensively and accurately measure corporate innovation based on text analysis using the natural language processing technique and machine learning algorithms. #br#This research introduces the unsupervised learning method in the field of machine learning and develops a new method of measuring corporate innovation by constructing the Latent Dirichlet Allocation (LDA) topic model based on the text of analyst reports of listed companies. The textual content of analyst reports covers both the objective description and professional evaluation on various aspects of corporate innovation, such as product innovation, process innovation, market innovation, supply source innovation and so on. Besides, it has similar characteristics in terms of text structure and wording, which lays a good foundation for the use of LDA topic modeling method. To start with, Python3.8 is applied to write a program to automatically download all the analyst reports issued for China′s A-share listed companies from 2010 to 2019 from Hexun Finance Website, Sina Finance Website and Wind Financial Terminal. A total of 201 569 analyst reports are obtained. After a series of data cleaning, The study gets 47 563 samples which are used as a corpus to train the LDA topic model, identify the corporate innovation topic, calculate the load intensity of each analyst report on the corporate innovation topic, and extract the corporate innovation topic load intensity as text-based corporate innovation, since the load intensity reflects the extent to which the analyst report describes the corporate with innovation topic reflecting the corporate′s innovation practice. The text-based corporate innovation by the new method is compared with commonly used proxy indicators of corporate innovation. #br#This study finds that the text-based corporate innovation measurement method is applicable to companies with and without patents as well as R&D expenditures. For firms with patents, text-based corporate innovation is significantly related to patent applications. While for firms without patents, the new measurement method can effectively identify the innovative practices including but not limited to using new technologies and entering new markets. The same goes for firms with and without R&D expenditures. For firms with R&D expenditures, text-based corporate innovation is significantly related to R&D expenditures, while for firms without R&D expenditures, text-based corporate innovation can efficiently capture corporate innovation activities. The time series analysis shows that the text-based corporate innovation effectively reflects the macro trend of corporate innovation during the sample period. #br#This research is of theoretical and practical significance for it not only systematically clarifies the traditional incomplete and inaccurate proxy indicators of corporate innovation, but also figures out a new method of measuring corporate innovation based on the text analysis of analyst reports. It further broadens the application of text big data in the field of management and organization studies, and contributes to the application of textual data in the field of management and organization research.#br#
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Received: 17 April 2022
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