Paper Number
ECIS2026-2574
Paper Type
SP
Abstract
This study adopts a Design Science Research (DSR) approach to design and evaluate a generative AI (GenAI) chatbot that supports managers in delivering high-quality feedback. Rooted in Goal-Setting Theory and Feedback Intervention Theory, the chatbot is conceived as a decision-support artifact that augments rather than replaces managerial judgment. Consistent with DSR principles, which emphasize creating artifacts to solve real-world problems and generate design knowledge, the artifact was developed within a large organization through literature review, interviews with five manager champions, and iterative refinement. A pilot conducted con in the demonstration phase. The empirical design follows a longitudinal quasi-experimental field study. Data include interviews with eight managers and a Time 1 employee survey comparing human-only versus AI-augmented feedback. A second wave will assess performance and innovative behavior. Preliminary data collection is complete. The study contributes by extending DSR to GenAI-enabled managerial augmentation and offering design principles for responsible AI use in feedback processes.
Recommended Citation
Lazazzara, Alessandra; Ramovic, Zana; Contiero, Rachele; Za, Stefano; and Della Torre, Edoardo, "Applying Design Science Research To Augmenting Feedback Through Genai" (2026). ECIS 2026 Proceedings. 20.
https://aisel.aisnet.org/ecis2026/gen_track/gen_track/20
Applying Design Science Research To Augmenting Feedback Through Genai
This study adopts a Design Science Research (DSR) approach to design and evaluate a generative AI (GenAI) chatbot that supports managers in delivering high-quality feedback. Rooted in Goal-Setting Theory and Feedback Intervention Theory, the chatbot is conceived as a decision-support artifact that augments rather than replaces managerial judgment. Consistent with DSR principles, which emphasize creating artifacts to solve real-world problems and generate design knowledge, the artifact was developed within a large organization through literature review, interviews with five manager champions, and iterative refinement. A pilot conducted con in the demonstration phase. The empirical design follows a longitudinal quasi-experimental field study. Data include interviews with eight managers and a Time 1 employee survey comparing human-only versus AI-augmented feedback. A second wave will assess performance and innovative behavior. Preliminary data collection is complete. The study contributes by extending DSR to GenAI-enabled managerial augmentation and offering design principles for responsible AI use in feedback processes.