Lukas Schmidt

• This dissertation examines how digital technologies can support the integration of discipline-specific language in accounting and taxation. Across three essays, it analyzes the acquisition, application, and analysis of specialized terminology. Essay I investigates a wiki-based collaborative glossary in introductory accounting education and shows that it improves students' academic performance and engagement with accounting terminology, while also revealing challenges related to coordination and content reliability. Essay II uses dictionary-based text analysis to create a cross-country dataset on corporate environmental tax legislation and finds that such taxes are shaped more by institutional and political factors than by environmental risks. Essay III develops and applies a domain-specific large language model to qualitative corporate tax disclosures, demonstrating that specialized LLMs outperform general-purpose models and traditional text analysis methods. Overall, the dissertation shows that digital technologies can enhance the acquisition, application, and analysis of specialized language in accounting and taxation. It contributes to accounting education, tax policy research, and methodological debates on the use of natural language processing in discipline-specific contexts.