Melanie Clegg

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University of St.Gallen
University of Lausanne

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University of St.Gallen

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University of Lausanne
Melanie Clegg is an Assistant Professor of Digital Marketing at the Faculty of Management and Economics (HEC) at the University of Lausanne. Melanie Clegg’s research focuses on the impact of new technologies on marketing and consumer behavior. For instance, she explores how generative AI influences creativity and innovation in product development, how AI can be strategically implemented in marketing practices, and how consumers interact with and perceive AI. Other research interests include social media and trend development on digital platforms. In her work, Melanie combines unstructured data analysis with behavioral methods, including both field and lab experiments.

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GenAI Tools in Empirical Research

Large Language Models (LLMs) and Generative Artificial Intelligence (AI) provide unprecedented opportunities for research and business practice. However, how to integrate them to stimulate research processes is not always straightforward. This course is dedicated to the use of AI-powered tools in research. It offers an overview of the background of existing LLM models for scientific research and how they can be integrated at different stages of the research process. We will put a specific focus on integrating AI in empirical data collection, namely integrating AI tools in survey-based research and experiments. We will also discuss limitations and caveats of using AI tools for scientific research (ethics, reliability, validity). This is not a programming course. Specifically, we will not train, develop, or finetune own LLM models, but discuss how existing models can be leveraged for pursuing own research projects. We will practice minimal-coding approaches using LLMs to support multimodal data collection in surveys and experiments.
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B

2026

GenAI Tools in Empirical Research

Large Language Models (LLMs) and Generative Artificial Intelligence (AI) provide unprecedented opportunities for research and business practice. However, how to integrate them to stimulate research processes is not always straightforward. This course is dedicated to the use of AI-powered tools in research. It offers an overview of the background of existing LLM models for scientific research and how they can be integrated at different stages of the research process. We will put a specific focus on integrating AI in empirical data collection, namely integrating AI tools in survey-based research and experiments. We will also discuss limitations and caveats of using AI tools for scientific research (ethics, reliability, validity). This is not a programming course. Specifically, we will not train, develop, or finetune own LLM models, but discuss how existing models can be leveraged for pursuing own research projects. We will practice minimal-coding approaches using LLMs to support multimodal data collection in surveys and experiments.
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