Generative AI prompt enhancement techniques in business: a systematic review and empirical evaluation
Cantini, Alessia (A.A. 2024/2025) Generative AI prompt enhancement techniques in business: a systematic review and empirical evaluation. Tesi di Laurea in Business and marketing analytics, Luiss Guido Carli, relatore Andrea De Mauro, pp. 76. [Master's Degree Thesis]
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Abstract/Index
Theoretical framework. Generative AI and Large language models (LLMs). GenAI in business contexts. Prompt engineering. Research design and methodology. Overall research design. Systematic literature review. Experimental design. Results of systematic literature review. Results of prompt enhancement techniques in business. Baseline prompting techniques. Task alignment prompting techniques. Output transparency prompting techniques. Empirical study results. Sample characteristics and descriptive statistics. Scale reliability measurement and data preparation. Descriptive statistics of key variables. Hypothesis testing. Effects of prompting techniques on perceived output usefulness (H1). Moderating role of generative ai usage (H2). Discussion.
References
Bibliografia: pp. 46-50.
| Thesis Type: | Master's Degree Thesis |
|---|---|
| Institution: | Luiss Guido Carli |
| Degree Program: | Master's Degree Programs > Master's Degree Program in Marketing (LM-77) |
| Chair: | Business and marketing analytics |
| Thesis Supervisor: | De Mauro, Andrea |
| Thesis Co-Supervisor: | Pozharliev, Rumen Ivaylov |
| Academic Year: | 2024/2025 |
| Session: | Extraordinary |
| Deposited by: | Alessandro Perfetti |
| Date Deposited: | 02 Jul 2026 09:38 |
| Last Modified: | 02 Jul 2026 09:38 |
| URI: | https://tesi.luiss.it/id/eprint/46306 |
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