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Digital Adoption and Cyber Security: An Analysis of Canadian Businesses

Author

Listed:
  • Joann Jasiak
  • Peter MacKenzie
  • Purevdorj Tuvaandorj

Abstract

This paper examines how Canadian firms balance the benefits of technology adoption against the rising risk of cyber security breaches. We merge data from the 2021 Canadian Survey of Digital Technology and Internet Use and the 2021 Canadian Survey of Cyber Security and Cybercrime to investigate the trade-off firms face when adopting digital technologies to enhance productivity and efficiency, balanced against the potential increase in cyber security risk. The analysis explores the extent of digital technology adoption, differences across industries, the subsequent impacts on efficiency, and associated cyber security vulnerabilities. We build aggregate variables, such as the Business Digital Usage Score and a cyber security incidence variable to quantify each firm's digital engagement and cyber security risk. A survey-weight-adjusted Lasso estimator is employed, and a debiasing method for high-dimensional logit models is introduced to identify the drivers of technological efficiency and cyber risk. The analysis reveals a digital divide linked to firm size, industry, and workforce composition. While rapid expansion of tools such as cloud services or artificial intelligence can raise efficiency, it simultaneously heightens exposure to cyber threats, particularly among larger enterprises.

Suggested Citation

  • Joann Jasiak & Peter MacKenzie & Purevdorj Tuvaandorj, 2025. "Digital Adoption and Cyber Security: An Analysis of Canadian Businesses," Papers 2504.12413, arXiv.org.
    • Handle: RePEc:arx:papers:2504.12413
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    References listed on IDEAS

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    1. Danny Leung & Césaire Meh & Yaz Terajima, 2008. "Firm Size and Productivity," Staff Working Papers 08-45, Bank of Canada.
    2. Wooldridge, Jeffrey M., 2001. "Asymptotic Properties Of Weighted M-Estimators For Standard Stratified Samples," Econometric Theory, Cambridge University Press, vol. 17(2), pages 451-470, April.
    3. Lu Xia & Bin Nan & Yi Li, 2023. "Debiased lasso for generalized linear models with a diverging number of covariates," Biometrics, The International Biometric Society, vol. 79(1), pages 344-357, March.
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    Cited by:

    1. Mioara Chirita & Daniela-Ancuta Sarpe, 2025. "Rethinking Economic Risk in the Age of Digital Transformation and Sustainable Entrepreneurship," Economics and Applied Informatics, "Dunarea de Jos" University of Galati, Faculty of Economics and Business Administration, issue 2, pages 128-133.

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