Mavrogiannis, Anthony Patrick (A.A. 2024/2025) Political risk and the artificial intelligence industry: firm–level perception. Tesi di Laurea in Political risk analysis, Luiss Guido Carli, relatore Manfredi Valeriani, pp. 148. [Master's Degree Thesis]

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Abstract/Index

Context of the global ai industry landscape. Defining artificial intelligence. Mapping stakeholders in the AI ecosystem. Global trends are shaping the AI industry. The political significance of AI and emerging governance challenges. Literature review of political risk and artificial intelligence. Defining political and geopolitical risk. From categories to observables: political risk in firm communication. Research design. Theoretical foundation and definition of political risk. Political risk taxonomy for the AI sector. Taxonomy development and refinement. Sample selection and case composition. Data source: earnings call transcripts for risk measurement. Data collection and preprocessing. Text analysis procedure. Outputs and data products. Transparency and reproducibility. Analytical objectives and logic of inference. Unit of observation and gating logic. Sentiment classification of risk statements. Aggregation: from sentences to group-level metrics. Visualization and interpretation conventions. Qualitative corroboration via top quotes. Managing sparse data and uneven coverage. Robustness checks: three-mode comparison. Handling multi-category sentences and double counting. Removal of boilerplate and other preprocessing. Data integrity, reproducibility and audit trail. Results. Distribution of political risk mentions across categories. Regional variation. Differences by company type (core cloud vs. Chips infrastructure). Temporal trends in risk mention by quarter. Sentiment analysis of risk mentions. Illustrative quotations of risk discussions. Discussion. Integration of empirical findings with political risk theory. Geopolitical risk is the dominant concern. Beyond geopolitics: regulatory, environmental and reputational risks. Linking risk trends to real-world developments (2020–2024). Strategic framing of risk and investor relations implications. Selective risk disclosure in earnings calls: a literature perspective. Implications for theory and practice.

References

Bibliografia: pp. 144-148.

Thesis Type:	Master's Degree Thesis
Institution:	Luiss Guido Carli
Degree Program:	Master's Degree Programs > Master's Degree program in Global Management and Politics, English language (LM-77)
Chair:	Political risk analysis
Thesis Supervisor:	Valeriani, Manfredi
Thesis Co-Supervisor:	Galietti, Francesco
Academic Year:	2024/2025
Session:	Autumn
Deposited by:	Alessandro Perfetti
Date Deposited:	03 Mar 2026 10:03
Last Modified:	03 Mar 2026 10:03
URI:	https://tesi.luiss.it/id/eprint/45037

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