Social desirability (SD) bias occurs in self-report surveys when subjects give socially desirable responses by over- or underreporting their behavior. Despite knowledge of SD as a potential threat to the validity of information systems (IS) research, little has been done to systematically assess its extent. Furthermore, we are uncertain of how to recover reliable estimates of the relationships between research variables contaminated by SD bias. We sought in this study to assess the extent of SD bias in causal inferences when independent and/or dependent variables are contaminated. We also evaluated whether an SD scale in conjunction with partial correlation could effectively and efficiently correct SD bias when it is found. To achieve these purposes, we designed a survey study and collected data from Amazon's Mechanical Turk in the context of mobile loafing, which refers to employees’ personal use of the mobile Internet during business hours. Using various detection methods, we found that SD bias existed in the context of mobile loafing. From the results of the variance reduction rate and a covariate technique, we found that SD bias becomes problematic when both the independent and dependent variables are susceptible to SD bias. Overall, our study contributes significantly to the IS literature by revealing the extent of SD bias and the magnitude of the possible correction for it in IS research.

当受试者通过高估或低估他们的行为来给出社会期望的反应时，自我报告调查中就会出现社会期望 (SD) 偏见。尽管知道 SD 作为对信息系统 (IS) 研究有效性的潜在威胁，但很少有人对其范围进行系统评估。此外，我们不确定如何恢复受 SD 偏差污染的研究变量之间关系的可靠估计。我们在本研究中试图评估当自变量和/或因变量受到污染时因果推断中 SD 偏差的程度。我们还评估了 SD 量表与部分相关性相结合是否可以有效地纠正 SD 偏差。为了实现这些目的，我们设计了一项调查研究并从亚马逊收集了数据 s Mechanical Turk 在移动闲逛的语境中，指的是员工在工作时间对移动互联网的个人使用。使用各种检测方法，我们发现在移动闲逛的背景下存在 SD 偏差。从方差减少率和协变量技术的结果中，我们发现当自变量和因变量都容易受到 SD 偏差的影响时，SD 偏差就会成为问题。总体而言，我们的研究通过揭示 IS 研究中 SD 偏差的程度和可能对其进行校正的程度，对 IS 文献做出了重大贡献。从方差减少率和协变量技术的结果中，我们发现当自变量和因变量都容易受到 SD 偏差的影响时，SD 偏差就会成为问题。总体而言，我们的研究通过揭示 IS 研究中 SD 偏差的程度和可能对其进行校正的程度，对 IS 文献做出了重大贡献。从方差减少率和协变量技术的结果中，我们发现当自变量和因变量都容易受到 SD 偏差的影响时，SD 偏差就会成为问题。总体而言，我们的研究通过揭示 IS 研究中 SD 偏差的程度和可能对其进行校正的程度，对 IS 文献做出了重大贡献。