A PUBLIC METRICS ANALYSIS OF CANADIAN MOBILE ADOPTION AND ITS TRENDS

Abstract

This dissertation examines the reliability of Canada’s telecommunications data ecosystem for modeling the structural determinants of mobile adoption between 2014 and 2018. Public data from Statistics Canada and the CRTC were combined with private carrier disclosures to construct an integrated dataset. A modular, Python-based pipeline was developed to harmonize inconsistent schemas, manage missing data, and ensure reproducibility. The analysis employed econometric and spatial methods to test whether population density and income disparities influenced adoption patterns across provinces. While theoretical relationships were consistent with prior technology adoption research, severe data quality limitations—including interpolation, inconsistent definitions, and imputed financial metrics—undermined model stability. The study’s contribution is twofold: it demonstrates the methodological requirements for integrating fragmented public and private datasets, and it provides a critical evaluation of their adequacy for policy-relevant research. Findings underscore the need for standardized, high-resolution, and longitudinal data to support evidence-based telecommunications policy.