This paper aims to introduce the emerging artificial-intelligence-based readability metrics (Coh-Metrix) to examine the effects of firm size on audit proposal readability.
Coh-Metrix readability measures use emerging computation linguistics technology to better assess document readability. These metrics measure co-relations of words, sentences and paragraphs on multi-dimensions rather than adopting the unidimensional “bag of words” approach that examines words in isolation. Using eight Coh-Metrix orthogonal principal component factors, the authors analyze the Chang and Stone (2019) data set comprised of 370 hand-collected audit proposals submitted by audit firms for the US state and local governments’ audit service contracts.
Audit firm size has a significant impact on the readability of audit proposals. Specifically, as measured by the traditional readability metric, the proposals from smaller firms are more readable than those submitted by larger firms. Furthermore, decomposed readability metrics indicate that smaller firm proposals evidence stronger (deep) text cohesion, whereas larger firm proposals evidence a stronger narrative structure and higher connectivity (relational indicators) among proposal elements. Unlike the traditional readability metric, however, the emergent readability metrics are uncorrelated with auditor selection.
Work remains to develop and validate Coh-Metrix measures that are specific to the context of accounting and auditing practice. Future research can use emerging readability measures to examine various textual features (e.g. text cohesion) in finance or accounting related documents.
The results provide practitioners with insight into the proposal writing strategies and practices of larger and smaller firms. In addition, the results highlight the differing audit firm selection outcomes from traditional and Coh-Metrix readability metrics.
This study introduces new data and holistic readability measures to the auditing literature.
