Leveraging Student Leadership in Market Research: A Study of Expatriate Tax Behavior

EasyChair Preprint 4854

Abstract

Introduction: Student researchers, under guidance, and aligned with the undergraduate capstone course in marketing, motivated the early stage construction of an expatriate taxation model. Team members efficiently implemented the proposed collection of data, via deployment of a well-orchestrated marketing stack. Leadership in developing the means of collecting data led to the successful administration of the Moral Obligation of Paying Just Taxes survey (Crowe, 1944; McGee, 2012). The purpose of using this instrument, was to inform the research team about prevailing attitudes toward payment of taxes, by individuals and SME owners and not the larger multinational corporations. One of the high performing Marketing learners from the Spring project class, plus another from Accounting, continued the study over the summer term, after being selected as student undergraduate research fellows. The task was to interpret the work performed in Spring, identify relevant constructs, collect a massive amount of data—targeted based upon these constructs, then collapse these constructs into a manageable number of factors, using machine learning and artificial intelligence, via the text analysis program KH Coder, and to build a literature review. This report and analytical conclusions, motivated production of tax relevant models, and culmination of both the spring and summer research projects.

Keyphrases: Artificial Intelligence, Big Data, Self-Organizing Map, Unsupervised Machine Learning, co-occurrence network, content analysis, correspondence analysis, multidimensional scaling, student research leadership, tax avoidance, tax evasion

@booklet{EasyChair:4854,
  author    = {Alfred Miller and Fatmah Rashed Obaid Aldanhani and Laila Abdulla Alkindi and Maryam Abdalla Ali Abdalla Alnaqbi},
  title     = {Leveraging  Student Leadership in Market Research: A Study of Expatriate Tax Behavior},
  howpublished = {EasyChair Preprint 4854},
  year      = {EasyChair, 2021}}