DeepGraph: Beyond domain-agnostic search methods in Business and Management

Principal Investigator: Ahmed Doha, Supply Chain Management
Project Title: DeepGraph: Beyond Domain-agnostic Search Methods in Business and Management Literature Discovery
Funder: SSHRC Insight Development

This research will make contributions to the literature discovery methods in the business and management (B&M) domain. First, for the dimension of exploring past research, the researchers propose to develop and empirically evaluate DeepGraph, the world’s first B&M knowledge graph for exploring past research deeper at the researcher ­intended B&M­ specific feature level such as constructs and hypotheses. Second, for the dimension of identifying gaps for new research opportunities, the researchers propose to develop and empirically evaluate the world’s first B&M hypotheses recommendation system. This system will leverage DeepGraph’s methods in recommending new hypotheses that are likely to be highly impactful in terms of the expected citation counts received. The gains from the proposed DeepGraph and hypotheses recommendation system will be of significant value not only to researchers but also to institutions such as universities, funding agencies, and governments.