## 2 Related Research
### Diverse Datasets and Nonfinancial Information Possibilities  
A time-series analysis of AI perceptions of business sector must be conducted, which requires a dataset that meets the conditions. The first dataset often used in innovation research is a survey. Moreover, it is necessary to collect data regularly to clarify the status of the same firms. For instance, the Japanese Ministry of Internal Affairs and Communications has conducted a long-term survey to collect data [28]. These panel data are used to reveal aspects of corporate R&D. However, it is difficult to understand changes in perception using this method because the questions must be concise to collect data from many firms. Although questionnaires help clarify the facts at a given point in time, conducting along-term survey in advance to assess the importance of a particular upcoming technology is difficult. The second dataset is a collection of patents. For instance, R&D intensity and diversity are calculated from patent data and reveal a firm's R&D activities [28]. However, analysis using patents can only be performed by firms that have acquired the patents. Some firms may not obtain patents owing to their business model, or they may not apply for patents to maintain confidentiality. In addition, firms that only adopt innovations and do not create innovations are excluded from the patent database. The third dataset comprises financial data [28; 29]. Although financial data are often used to measure the impact of innovation on performance, some studies use R&D expenditures as a proxy for innovation. One study using financial data found that R&D expenditures are adjusted to be a fixed percentage of employment and sales [30]. Although these studies are useful for analyzing firm R&D, their sample size is limited to firms engaged in R&D activities. In addition, firms that employ innovation without R&D are excluded. In summary, the datasets used so far in the empirical analysis have some advantages; however, there are difficulties related to understanding the importance of the questionnaire items in advance and in analyzing firms that have not obtained patents or conducted R&D. Our study focuses on nonfinancial information as a dataset. Nonfinancial information generally refers to information other than that in financial statements disclosed to stakeholders [31] and descriptive information rather than numerical data. Previous research has conducted a text analysis of Management's Discussion and Analysis of the Financial Condition and Results of Operations (MD&A) [32; 33]. For example, the tone of MD&A can predict firms' investment activity [34] and dramatic changes in firms' capital structure [35], which have received considerable research attention but have not been implemented extensively in innovation research. Is AI adoption really disclosed in nonfinancial information? The first theoretical background is signaling theory [36]. There is information asymmetry between firms and investors; firms know the details of their operations, while investors do not. Voluntary disclosure by firms is one way to address this asymmetry [37]. Some studies have discussed the relationship between firm innovation and disclosure [38; 39]. Given the societal interest in AI, information asymmetry with investors can be addressed by disclosing information about AI applications. The second theoretical background is impression management. It has been observed that organizations (and individuals) not only objectively describe the facts they face but also consciously or subconsciously control the direction of stakeholders' understanding [40]. Even if information asymmetry between firms and investors were addressed, firms would still attempt to control stakeholders' understanding of the firms. Implementing AI, a cutting-edge technology with high public interest, is likely to create the impression that a firm is advanced. Accordingly, we believe that the text of nonfinancial information is useful for analyzing firms' perceptions over time.