Human-AI Synergy in Survey Development: Implications from Large Language Models in Business and Research

Abstract

This study examines the novel integration of Large Language Models (LLMs) into the survey development process in business and research through the development and evaluation of the Behavioral Research ASSistant (BRASS) Bot. We first analyzed the traditional scale development process to identify tasks suitable for LLM integration, including both human-in-the-loop and automated LLM data collection methods. Following this analysis, we developed the details of BRASS Bot, incorporating design principles of falsifiability and reproducibility. We then conducted a comprehensive evaluation of the BRASS Bot across a diverse set of LLMs, including GPT, Claude, Gemini, and Llama, to assess its usability, validity, and reliability. We further demonstrated the practical utility of the BRASS Bot by conducting a user study and a predictive validity simulation. Our research presents both theoretical and practical implications. The augmentation approach of the BRASS Bot enriches the theoretical foundations of behavioral constructs by identifying previously overlooked patterns. Additionally, the BRASS Bot offers significant time and resource efficiency gains while enhancing scale validity. Our work lays the foundation for future research on the broader application of LLMs as both assistants and collaborators in survey analysis and behavioral research design and execution, highlighting their potential for a transformative impact on the field.