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Auditor selection following auditor turnover: Do peers' choices matter?

Author

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  • Li, Xudong (Daniel)
  • Sun, Lili
  • Ettredge, Michael

Abstract

Drawing on social norms and social learning theories, this study investigates the influences of peer (similar) firms' prior choices on whether or not a client chooses to affiliate with a “social norm” audit office in its metropolitan area, following auditor turnover. The office in a metro area auditing the largest number of peer firms along a given similarity dimension is considered to be the social norm office for that dimension. We identify peer firms using four alternative dimensions of similarity: client geographic location, industry affiliation, client size (filing status), and departing auditor type (Big N versus non-Big N). Using a large sample of auditor changes from the years 2001–2012, we find that for every dimension of similarity, the propensity of a client to select a norm (as opposed to a non-norm) audit office as the succeeding auditor is positively associated with 1) the proportion of its peers audited by the “norm” office in the prior year (i.e., social norm evidence) and 2) the proportion of its auditor-switching peers selecting a “norm” audit office in the prior year (i.e. social learning evidence). Social norm and social learning evidence provided by “more similar” peers has greater effect than evidence provided by “less similar” peers across all four dimensions of peer similarity. Further analysis suggests that social norm and learning evidence has incremental power (beyond each other) in explaining auditor selection, with norm evidence exhibiting a larger effect than learning evidence. An analysis of the implementation of SOX 404(b) mandatory internal control audits in 2004 shows that clients' tendency to choose pre-existing social norm audit offices can be disrupted by exogenous events.

Suggested Citation

  • Li, Xudong (Daniel) & Sun, Lili & Ettredge, Michael, 2017. "Auditor selection following auditor turnover: Do peers' choices matter?," Accounting, Organizations and Society, Elsevier, vol. 57(C), pages 73-87.
    • Handle: RePEc:eee:aosoci:v:57:y:2017:i:c:p:73-87
    DOI: 10.1016/j.aos.2017.03.001
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    1. Federico E. Contiggiani & Fernando Delbianco & Fernando Tohm'e, 2021. "A Graph-based Similarity Function for CBDT: Acquiring and Using New Information," Papers 2104.14268, arXiv.org.

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