JOURNAL ARTICLE

Managerial Mental Accounting and Downstream Project Decisions.

  • Published In: Management Science (INFORMS), 2024, v. 70, n. 12. P. 8612 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Baucells, Manel; Grushka-Cockayne, Yael; Hwang, Woonam 3 of 3

Abstract

This article focuses on how managerial mental accounting—incorporating behavioral biases such as loss aversion, reference point updating, and narrow framing—affects project leaders' decisions when revising project scope, cost, and completion time. It develops a behavioral model contrasting a rational project leader (PL) who optimally adjusts plans based on updated information with a behavioral project leader (BPL) who insufficiently adjusts due to anchoring on initial plans and loss aversion, leading to reduced financial profit and reluctance to abandon projects. The study finds that the choice of progress measures used to evaluate project status (planned value, actual cost, earned value) influences reference point updating and thus the extent of insufficient adjustments; notably, measuring progress via planned scope and actual cost mitigates these biases, whereas earned value for cost is never advisable. The authors suggest that project governance and management software can strategically select or highlight progress measures to nudge behavioral project leaders toward more rational revisions, improving project outcomes.

Additional Information

  • Source:Management Science (INFORMS). 2024/12, Vol. 70, Issue 12, p8612
  • Document Type:Article
  • Subject Area:Economics
  • Publication Date:2024
  • ISSN:0025-1909
  • DOI:10.1287/mnsc.2021.02929
  • Accession Number:181483484
  • Copyright Statement:Copyright of Management Science (INFORMS) is the property of INFORMS: Institute for Operations Research & the Management Sciences 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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