Innovation Diffusion Modeling Using Time-Series Analysis, Social Network Analysis, Survival Analysis, and Machine Learning

Innovation Diffusion Modeling Using Time-Series Analysis, Social Network Analysis, Survival Analysis, and Machine Learning

• Amir Onallah, Stuart Management Science Ph.D. student

Abstract:

This study utilizes the U.S. Patent Inventor database and the Medical Innovation dataset to demonstrate that making assumptions, reducing the data, or simplifying the problem results in a negative effect on the outcomes.

Initially, we employ time-series models to enhance the quality of the results for event history analysis (EHA), add insights, and infer meanings, explanations, and conclusions. Then, we introduce newer algorithms of machine learning and machine learning with a time-to-event element to offer more robust methods than previous papers and reach optimal solutions by removing assumptions or simplifications of the problem, combine all data that encompasses the maximum knowledge, and provide nonlinear analysis.

The models show the significance of social network measures in datasets that include social relations, general attributes, and time-to-event information.

 

All Illinois Tech faculty, students, and staff are invited to attend.

The Friday Research Presentations series showcases ongoing academic research projects conducted by Stuart School of Business faculty and students, as well as guest presentations by Illinois Tech colleagues, business professionals, and faculty from other leading business schools.

Join session on Blackboard Collaborate