Evaluation Mode Considerations on Decisions Using Environmental Accounting Information When Benchmark Data Give Unclear Signals

• In Event: Management Judgment and Decision Making

Abstract

Decisions involving assessing alternatives occur in one of two evaluation modes: joint (JE) or separate (SE) evaluation. This study explores how evaluation mode influences decisions involving nonfinancial environmental accounting information while benchmark data provide unclear signals (i.e., alternative performances are all superior/inferior to a benchmark). General Evaluability Theory (GET; Hsee and Zhang 2010) suggests greater decision dependence on benchmark performance signals received in SE versus JE mode. However, the framework is silent on the signals’ valence influences (i.e., better/worse than benchmark). The negativity bias suggests that negative signals receive more decision weight compared to positive signals (Rozin and Royzman 2001). This study’s experiment (n = 77) manipulated evaluation mode (between-subjects; JE/SE) and the alternatives’ relations to a benchmark (within-subjects; better/worse than benchmark). Evaluations were made on two equally important environmental accounting measures to determine how well two similarly performing factory alternatives achieved environmental objectives. To reflect realistic environmental accounting practitioner settings, only one of the two measures contained benchmark information. Results extend GET with the negativity bias by showing that benchmark information valence interacts with evaluation mode in environmental decisions. Specifically, environmental information for two inferior (superior) alternatives had greater (lesser) influence on decisions across evaluation mode. Evaluations were lower in SE than JE mode for inferior performing alternatives, but no differences obtained across evaluation mode for superior alternatives. Overall, results suggest the negativity bias provides extended guidance for analyzing evaluation mode and unclear benchmark signal influences on decisions using environmental accounting information.

Authors

• Hank C. Alewine, University of Alabama in Huntsville

• Dan Stone, University of Kentucky