TY - GEN
T1 - Augmented inference making
T2 - 85th Annual Meeting of the Academy of Management, AOM 2025
AU - Hamadi, Hassan M.
AU - Brattström, Anna
PY - 2025/7/1
Y1 - 2025/7/1
N2 - With the abundance of data and digital traces of human behavior that are currently available, and the accessible algorithmic analyses to analyze them, management scholars have the opportunity to expand their scope of theoretical discovery and tackle increasingly complex management. However, we need additional elaboration on how algorithmic analysis can augment humans inference making, and how to best integrate algorithmic analyses into inductive theory building processes. Addressing this need, we propose in this paper Augmented Inference Making as a distinct method to improve inductive theory building from large sets of textual data.
AB - With the abundance of data and digital traces of human behavior that are currently available, and the accessible algorithmic analyses to analyze them, management scholars have the opportunity to expand their scope of theoretical discovery and tackle increasingly complex management. However, we need additional elaboration on how algorithmic analysis can augment humans inference making, and how to best integrate algorithmic analyses into inductive theory building processes. Addressing this need, we propose in this paper Augmented Inference Making as a distinct method to improve inductive theory building from large sets of textual data.
UR - http://www.scopus.com/inward/record.url?scp=105009402290&partnerID=8YFLogxK
U2 - 10.5465/AMPROC.2025.50bp
DO - 10.5465/AMPROC.2025.50bp
M3 - Conference contribution
AN - SCOPUS:105009402290
VL - 2025
T3 - Academy of Management Proceedings
BT - Academy of Management Proceedings
A2 - Taneja, Sonia
Y2 - 25 July 2025 through 29 July 2025
ER -
