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.
When commenting on articles, please be friendly, welcoming, respectful and abide by the AIS eLibrary Discussion Thread Code of Conduct posted here.