Franchise Disclosure Documents (FDDs) contain a wealth of information and are designed to help franchisees make informed decisions before signing the franchise contract. Despite a readability requirement, FDDs are difficult to understand and research has found them to be dense and opaque. Certain classes of franchisees with limited resources have difficulty understanding the legal language contained in the disclosure and accessing the risks and rewards of the contract. The goals of this project are to improve analysis and understanding of FDDs by applying natural language processing, machine and deep learning, and other artificial intelligence methodologies .
Michael is pursuing a MBA from Georgia State University's Mack Robinson College of Business. He graduated Summa Cum Laude from the University of Georgia with a B.S. in Computer Science and a Certificate in Applied Data Science. Michael is currently a Software Engineer at NCR.
Lawrence, Benjamin and Charlotte Alexander (2022). “Franchise Governance in Response to Covid-19: An Automated Text Analysis of Franchise Disclosure Documents”, in Proceedings of the 35th International Society of Franchising Conference, (Toronto, Canada).
Lawrence, Benjamin, Yanqing Wang, Yinghao Pan and Charlotte Alexander (2022), Automated Text Applications in the Context of Franchise Disclosure Documents, in Proceedings of the 35th International Society of Franchising Conference, (Toronto, Canada)
Lawrence, Benjamin, Yanqing Wang, Yinghao Pan and Charlotte Alexander (Forthcoming), An overview, empirical application, and discussion of the future research potential of Q&A models in B2B contexts, Industrial Marketing Management.