Using a large sample of US data, we examine the relationship between asset redeployability and corporate tax avoidance. We also examine the extent to which asset redeployability influences tax avoidance directly and indirectly (through financing constraints channel). We find a significant negative relationship between asset redeployability and tax avoidance, implying that firms with more redeployable assets tend to engage in less tax avoidance. We also confirm that asset redeployability reduces tax avoidance both directly and indirectly (through reducing financing constraints). These results are robust to alternative specifications of asset redeployability and corporate tax avoidance, and to the use of a two-stage least squares (2SLS) analysis to mitigate any endogeneity concerns relating to omitted variables, reverse causality, and model misspecification. Overall, these findings extend our existing understanding of the implication of asset redeployability in an accounting context and demonstrate that redeployability of assets has important implication for corporate tax planning.

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This study uses a machine learning approach to identify and predict factors which influence citation impacts across five Pacific Basin journals: Abacus, Accounting & Finance, Australian Journal of Management, Australian Accounting Review and the Pacific Accounting Review from 2008 to 2018. The machine learning results indicate that citation impact is mostly influenced by: length of a journal article; the field of research (particularly environmental accounting), sample size; whether the sample is local or international; choice of research method (e.g., archival vs survey/interview); academic rank of the first author; institutional status of the first author; and number of authors of the article. The results may be useful for predicting future trends in citation impact as well as providing strategies for authors and editors to improve citation impact.

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