Date of Award

Spring 5-2023

Document Type


Degree Name

Doctor of Business Administration (DBA)



First Advisor

Marcia Dickerson


The quality of self-report data has long been a concern, with increasing attention to the issue of insufficient effort responding (IER). Researchers have made considerable progress in developing techniques for handling IER (Meade & Craig, 2012; Huang, Bowling, Liu, & Li, 2014). This dissertation examines the use of IER best practices in survey research within the management literature through a series of essays. The results of the first study indicate that there is a lack of methodological transparency regarding how IER is addressed in the management literature. Simply stated, few authors report addressing IER in their manuscripts; thus, no conclusions could be drawn regarding management researchers use of IER best practices. Based on the findings of Study 1, the aim of Study 2 was to investigate this lack of reporting through a job performance lens. The essay explored several potential reasons for low methodological transparency regarding IER practices, including (1) insufficient KSAs (i.e., understanding or awareness of IER best practices), thus IER was not addressed nor reported, and (2) a lack of extrinsic motivation to report how IER was addressed in studies (i.e., reviewer requirements and page limitations). The results of Study 2 indicate that though management researchers do not report utilizing IER technique, IER is being addressed in various ways by management researchers.

The most common techniques management researchers use to address IER are employing infrequency technique items to detect IER and deleting respondents flagged by the detection method from samples. Taken as a whole, these findings suggest there is room for improvement regarding how IER is addressed in management research using self-report survey data. Subsequently, Study 3 introduces a technique for examining and addressing the effects of IER on data quality that does not require researchers to delete respondent data, which may inadvertently bias samples or eliminate otherwise “good” data. Specifically, the final essay demonstrates how to create an IER method factor that can be used to examine the effects of IER detected in a sample and control for the effects of IER if it appears to bias research conclusions.