Date of Award


Document Type


Degree Name

Master of Science in Information Systems and Technology


College of Business and Public Administration

First Reader/Committee Chair

Dr. Conrad Shayo/ Dr. Jesus Canelon



The project focuses on a comprehensive system’s analysis and design of the front-end of the Synergy Evaluation Application Model (SEAM) system for mergers and acquisitions (M&As). The research questions asked are: Q1. How did the SEAM system incorporate the system requirements and design that incorporated the strategic goals and priorities of both the acquirer and the acquiree? Q2. What data sources will the SEAM system rely on, and how does it overcome data integration, automation, visualization challenges? Q3. How will the model identify build in potential synergies, both quantitative and qualitative? The research questions were analyzed through the SEAM system analysis and design using Objective-Oriented Analysis Design (OOAD) approach. The findings and conclusions to the three questions respectively are: Q1.It is possible to design an ideal physical SEAM system that incorporates the strategic goals priorities of both the acquirer and acquiree. The SEAM system can guide executives to know which companies or businesses to merge or acquire with, and how much level they can move on. Q2, The SEAM system utilized the data from internal merger companies’ datasets, external financial providers, and public data sources, and realized data integration, automation and visualization features by implementing ETL process, machine learning and interacting with external use interfaces. So long as the data is available, the SEAM system can realize data integration, automation, visualization features. Q3. This project categorized synergies into distinct types and designed a framework for identifying and assessing synergies respectively by using scenario analysis to identify best-case, worst-case, and base-case scenarios, as well as sensitivity analysis to account for uncertainties. Areas for further study focus on implementing and testing the model in actual M&A scenarios, exploring advanced technologies on quantitative synergy analysis, the integration of AI algorithms on qualitative synergy analysis and expanding the model's features.