JOURNAL ARTICLE
The Impact of Supervisor Relationships on Auditor Turnover Intentions Using Leader-Member Exchange Theory.
Published In: Behavioral Research in Accounting, 2023, v. 35, n. 2. P. 1 1 of 3
Database: Business Source Ultimate 2 of 3
Authored By: Almer, Elizabeth Dreike; Cannon, Nathan H.; Kremin, Joleen 3 of 3
Abstract
This study expands understanding of auditor relationships and turnover by introducing the measurement of Leader-Member Exchange (LMX) to an audit setting. LMX—which considers overall quality of subordinates' relationships with their supervisor—is well established in the management literature but has previously only been referred to as a theoretical construct in the audit literature. Utilizing a well-validated scale, we measure LMX with 167 practicing auditors. We find LMX with a single supervisor significantly impacts retention via organizational commitment. This finding is novel in the LMX literature given the unique audit setting where subordinates have multiple supervisors and transitory teams. In an exploratory analysis, we also find female subordinates form lower-quality relationships with supervisors, regardless of supervisor sex, which in turn can influence the impact of LMX on organizational commitment. Results demonstrate the value of measuring LMX in audit research and practically highlight the importance of fostering positive, strong auditor-supervisor relationships. Data Availability: Contact the authors. JEL Classifications: L2; M40; M42; M50. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Behavioral Research in Accounting. 2023/09, Vol. 35, Issue 2, p1
- Document Type:Article
- Subject Area:Social Sciences and Humanities
- Publication Date:2023
- ISSN:1050-4753
- DOI:10.2308/BRIA-2022-017
- Accession Number:172436529
- Copyright Statement:Copyright of Behavioral Research in Accounting is the property of American Accounting Association and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Looking to go deeper into this topic? Look for more articles on EBSCOhost.