Public accounting firms have developed reliable substantive tests using data and analytics based procedures to improve audit quality and efficiency. However, firms need to convince audit stakeholders that relying on data and analytics based procedures will improve, or at least maintain, audit effectiveness for them to be allowed and accepted. This study provides exploratory, experimental evidence to indicate how three key audit stakeholder groups—non-professional investors, peer reviewers, and jurors—perceive population testing and predictive modeling data and analytics based procedures relative to traditional sample-based substantive testing. Results suggest that while key audit stakeholders are generally open to or favorably disposed to the use of data and analytics based audit procedures, they also expressed some concerns about the appropriateness of relying on data and analytics based procedures, particularly predictive modeling, as primary sources of substantive evidence. This paper develops an agenda for future research to help firms better address stakeholder concerns.