Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
7415649 | International Journal of Accounting Information Systems | 2016 | 17 Pages |
Abstract
How does information presentation within an accounting information system (AIS) influence environmental performance judgments? Decision makers generally analyze alternatives' performances in one of two evaluation modes: jointly or separately. Joint mode provides greater measure evaluability because of available comparisons between alternates. Thus, additional information garners greater decision weight in separate mode, where less contextual information exists. However, many environmental decision settings use separate evaluation mode because of no viable alternatives (e.g., large pollution abatement investments). In this setting, General Evaluability Theory (GET; Hsee and Zhang, 2010) suggests low measurement evaluability when low measurement knowledge and non-inherently understood measures exist-both common characteristics in environmental settings. This study introduces attribute framing to the GET framework as important to consider when analyzing environmental decision differences across modes, because frames are often a necessary component of information presentation and different descriptions often lead to different decisions (Dunegan, 1993). Experimental participants (n = 206) evaluated factory environmental performances with joint/separate mode and positive/negative attribute framing. Findings inform AIS designers as results suggest evaluation mode moderates the presentation of attribute frames on decisions. Specifically, higher (lower) evaluations occur when using positive (negative) framing, and this effect is more (less) pronounced in separate (joint) mode. Findings also suggest that more consistent judgments occur across evaluation mode with positive compared to negative framing of performance measures.
Keywords
Related Topics
Social Sciences and Humanities
Business, Management and Accounting
Accounting
Authors
Hank C. Alewine, Christopher D. Allport, Wei-Cheng Milton Shen,