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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.14809 |
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| _version_ | 1866911010487533568 |
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| author | Jiang, Peng de Lira, Vinicius Cezar Monteiro Maiorino, Antonio |
| author_facet | Jiang, Peng de Lira, Vinicius Cezar Monteiro Maiorino, Antonio |
| contents | Surveys are a cornerstone of Information Systems (IS) research, yet creating high-quality surveys remains labor-intensive, requiring both domain expertise and methodological rigor. With the evolution of large language models (LLMs), new opportunities emerge to automate survey generation. This paper presents the real-world deployment of an LLM-powered system designed to accelerate data collection while maintaining survey quality. Deploying such systems in production introduces real-world complexity, including diverse user needs and quality control. We evaluate the system using the DeLone and McLean IS Success Model to understand how generative AI can reshape a core IS method. This study makes three key contributions. To our knowledge, this is the first application of the IS Success Model to a generative AI system for survey creation. In addition, we propose a hybrid evaluation framework combining automated and human assessments. Finally, we implement safeguards that mitigate post-deployment risks and support responsible integration into IS workflows. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_14809 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Impact of a Deployed LLM Survey Creation Tool through the IS Success Model Jiang, Peng de Lira, Vinicius Cezar Monteiro Maiorino, Antonio Human-Computer Interaction Machine Learning I.2; H.4 Surveys are a cornerstone of Information Systems (IS) research, yet creating high-quality surveys remains labor-intensive, requiring both domain expertise and methodological rigor. With the evolution of large language models (LLMs), new opportunities emerge to automate survey generation. This paper presents the real-world deployment of an LLM-powered system designed to accelerate data collection while maintaining survey quality. Deploying such systems in production introduces real-world complexity, including diverse user needs and quality control. We evaluate the system using the DeLone and McLean IS Success Model to understand how generative AI can reshape a core IS method. This study makes three key contributions. To our knowledge, this is the first application of the IS Success Model to a generative AI system for survey creation. In addition, we propose a hybrid evaluation framework combining automated and human assessments. Finally, we implement safeguards that mitigate post-deployment risks and support responsible integration into IS workflows. |
| title | Impact of a Deployed LLM Survey Creation Tool through the IS Success Model |
| topic | Human-Computer Interaction Machine Learning I.2; H.4 |
| url | https://arxiv.org/abs/2506.14809 |