Auditors’ decision-making aid for going concern audit opinions through machine learning analysis

@article{eunbae2025,
  title        = {{Auditors’ decision-making aid for going concern audit opinions through machine learning analysis}},
  author       = {Eunbae Lee & Dave Tahmoush},
  journal      = {International Journal of Accounting Information Systems},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1016/j.accinf.2025.100732},
}

TY  - JOUR
TI  - Auditors’ decision-making aid for going concern audit opinions through machine learning analysis
AU  - Lee, Eunbae
AU  - Tahmoush, Dave
JO  - International Journal of Accounting Information Systems
PY  - 2025
ER  -

Eunbae Lee & Dave Tahmoush (2025). Auditors’ decision-making aid for going concern audit opinions through machine learning analysis. *International Journal of Accounting Information Systems*. https://doi.org/https://doi.org/10.1016/j.accinf.2025.100732

Eunbae Lee & Dave Tahmoush. "Auditors’ decision-making aid for going concern audit opinions through machine learning analysis." *International Journal of Accounting Information Systems* (2025). https://doi.org/https://doi.org/10.1016/j.accinf.2025.100732.

Auditors’ decision-making aid for going concern audit opinions through machine learning analysis
Eunbae Lee & Dave Tahmoush · International Journal of Accounting Information Systems · 2025
https://doi.org/https://doi.org/10.1016/j.accinf.2025.100732