To address these questions we propose two studies, one aimed at each question. In the first project, we examine whether different digital media of communication influence informal advice-giving behavior among auditors. We specifically consider how each medium (laptop versus mobile phone) influences the level of professional skepticism contained in advice. In addition, we look at how the relationship between advisor and advisee influences the way the different media impact skepticism. We expect that the level of professional skepticism in advice will be lower when an advisor is communicating with their mobile phone and that this will be mitigated when an advisor has higher psychologicaldistance from the advice content. This study contributes to existing literature on communication media (e.g., Brazel et al. 2004; Bennett & Hatfield 2013, 2018) as it looks at two forms of digital communication (rather than in-person versus email) and looks at informal advice-giving which adds to our existing understanding of formal advice-giving through the audit review process (e.g., Agoglia et al. 2009).Next, we study the second question, by investigating how an auditor assesses advice received from a hybrid human-technology specialist team, when making judgments related to complex estimates. Specifically, we will examine how the respective roles and collaboration of hybrid team members may influence auditor assessment and reliance on specialist advice. We expect that the level of agreement between hybrid specialists will have different impacts on core auditor reliance, depending on the hybrid specialist workflow. In the advice-literature, convergence has been shown to be preferred to divergence by advisees, however, if an electronic specialist (e.g., AI) is first in the workflow, this convergence could signal over-reliance on technology by a human specialist and thus trigger a core auditor’s algorithm aversion. This study adds to existing research on algorithm aversion of auditors (Commerford et al. 2021) and examines whether such bias can be alleviated by involving a human in the specialist judgment (i.e., making it ‘hybrid’). We further examine whether the level of agreement between the human and electronic specialist’s advice (i.e., advice converges versus diverges) and the order in which the two team members generate their advice influences the auditor’s perception of the quality of the advice and thus reliance on the hybrid specialist team. In addition to extending current literature on algorithm aversion, we also extend specialist advice literature in auditing by examining conditions that influence auditor reliance on specialist advice (e.g., Griffith 2018).
