AI-based Chatbot that Supports SMEs in Understanding ESG-relevant Information for Cloud Service Selection
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2023
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Master
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11 - Student thesis
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Hochschule für Wirtschaft FHNW
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Olten
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Abstract
In today's era, small and medium-sized enterprises (SMEs) encounter the unique challenge of effectively utilizing cloud computing for business expansion while also aligning their operations with the increasing focus on Environmental, Social, and Governance (ESG) considerations. To address this convergence, this thesis proposes an approach to help SMEs make informed choices when selecting cloud computing service providers by integrating ESG criteria. It introduces a Generative AI Chatbot powered by Large Language Model that aims to offer real-time decision support to SMEs. A large language model (LLM) is an advanced computer software that mimics human language in both understanding and expression. It processes huge amounts of text from many sources to learn via artificial neural networks, which function similarly to a virtual brain. Without specific human instruction, LLMs perform exceptionally well at understanding and generating a wide range of linguistic patterns. The main objective of this study is to create a chatbot powered by generative AI that helps SMEs to make better decisions. This chatbot will enable SMEs to ask questions, analyze, and use ESG data when choosing cloud service providers. This will help them make ethical and well-informed choices that promote sustainability. This technology makes use of LangChain, an open-source framework that lets software developers integrate external components with large language models, such as OpenAI's GPT-3.5, to create LLMpowered machine learning and artificial intelligence (AI) applications to give SMEs immediate access to customized insights and advice regarding the ESG relevant information of different cloud computing service providers.
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English
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Yes
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Review
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Citation
Lama, S. (2023). AI-based Chatbot that Supports SMEs in Understanding ESG-relevant Information for Cloud Service Selection [Hochschule für Wirtschaft FHNW]. https://irf.fhnw.ch/handle/11654/48845