  The volume of scientific publications in organizational research becomes
exceedingly overwhelming for human researchers who seek to timely extract and
review knowledge. This paper introduces natural language processing (NLP)
models to accelerate the discovery, extraction, and organization of theoretical
developments (i.e., hypotheses) from social science publications. We illustrate
and evaluate NLP models in the context of a systematic review of stakeholder
value constructs and hypotheses. Specifically, we develop NLP models to
automatically 1) detect sentences in scholarly documents as hypotheses or not
(Hypothesis Detection), 2) deconstruct the hypotheses into nodes (constructs)
and links (causal/associative relationships) (Relationship Deconstruction ),
and 3) classify the features of links in terms causality (versus association)
and direction (positive, negative, versus nonlinear) (Feature Classification).
Our models have reported high performance metrics for all three tasks. While
our models are built in Python, we have made the pre-trained models fully
accessible for non-programmers. We have provided instructions on installing and
using our pre-trained models via an R Shiny app graphic user interface (GUI).
Finally, we suggest the next paths to extend our methodology for
computer-assisted knowledge synthesis.
