JOURNAL ARTICLE

Seeing the Trees: How a Concrete versus Abstract Mindset Improves Performance on Low-Level Assurance Tasks.

  • Published In: Behavioral Research in Accounting, 2025, v. 37, n. 2. P. 93 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Herda, David N.; Lauck, John R. 3 of 3

Abstract

Prior research shows that individuals benefit from abstract construals when performing broader high-level assurance tasks. We extend this research by studying the effects of different construal mindsets on skepticism and performance in a low-level assurance task setting. Using a sample of 195 online participants, we find that individuals benefit from concrete (versus abstract) mindsets when performing low-level assurance tasks because concrete construals better allow them to focus on detailed evidence that may contradict client assertions. Specifically, we show that assurance providers who utilize a concrete (abstract) construal mindset when performing a low-level task display better (worse) skeptical judgments and actions. Taken together with findings from past research, our results support the notion that assurance providers should utilize the construal mindset that best matches the characteristics of the task at hand. Exploratory analysis suggests that client retention incentives may moderate the relationship between skeptical judgment and skeptical action in our context. Data Availability: Data are available from the authors upon request. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Behavioral Research in Accounting. 2025/09, Vol. 37, Issue 2, p93
  • Document Type:Article
  • Subject Area:Psychology
  • Publication Date:2025
  • ISSN:1050-4753
  • DOI:10.2308/BRIA-2024-008
  • Accession Number:188347899
  • 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.)

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