Abstract

Without standardized roadmap, firms in the healthcare sectors have found it challenging to adopt digital transformation. This study, based on signaling theory, alleviated the issue by applying automated information extraction to retrieve such demanding knowledge from firms’ information disclosure while avoiding a tedious and time-consuming task of manually extracting information from huge text documents. The main objectives of this study were 1) to extract evolving business activities relating to digital healthcare transformation and 2) to identify whether extracted business activities align with a particular digital maturity model. The samples were firms listed in the healthcare sector of the Stock Exchange of Thailand. The data was obtained from firms’ annual reports covered period 2017–2021. To extract business activities, this study employed phrase extraction technique based on words’ parts of speech tagging. The extracted phrases then were filtered using custom-made dictionary. The findings indicated that while investing in key infrastructure and leveraging digital technology to improve business processes made up the majority of a company’s early period business activities, later timeframe business activities were more focused on value generation activities and new business models creation. The changes of activities in different periods also suggested that some business activities might require prerequisite activities. Assigning extracted business activities into relevant dimensions of the specific maturity model further proved that the extracted business activities adhere to the elements of chosen digital maturity model. The elements carried out by firms expressed the nature of the sector as well as how highly they prioritize digital transformation.