Impact of a Deployed LLM Survey Creation Tool through the IS Success Model

TL;DR

This paper examines the deployment of an LLM-based survey creation tool in real-world IS research, evaluating its effectiveness and challenges using the IS Success Model and proposing a hybrid assessment framework.

Contribution

It is the first to apply the IS Success Model to a generative AI system for survey creation and introduces a hybrid evaluation framework with safeguards for responsible deployment.

Findings

System accelerates survey creation process.

Hybrid evaluation combines automated and human assessments.

Safeguards mitigate post-deployment risks.

Abstract

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.