Audience management,online turbulence and lurking in social networking services: A transactional process of stress perspective

In social networking services (SNSs), users’ unclear understanding of the large and invisible audience increases the chances of online turbulence, which is a key source of SNS-induced stress. This growing phenomenon has gained increasing attention in academia and industry due to the undesirable consequences for users and SNS platforms. In this study, we draw from the transactional model of stress to examine how audience management strategies impact online turbulence and lead to neglected unintended audience concern and lurking. We also investigate the role of self-monitoring as a stress inhibitor. We test our model with data collected from 301 SNS users. The results show that the four types of audience management strategies have different effects on online turbulence, which significantly impacts neglected unintended audience concern especially when users have high self-monitoring skills. We believe that this work contributes, both from scientific and practical standpoints, to the understanding of the interventions and stressful responses of online turbulence in SNSs.     

Using Excel for White's Test—An Important Technique for Evaluating the Equality of Variance Assumption and Model Specification in a Regression Analysis

There is consensus in the statistical literature that severe departures from its assumptions invalidate the use of regression modeling for purposes of inference. The assumptions of regression modeling are usually evaluated subjectively through visual, graphic displays in a residual analysis but such an approach, taken alone, may be insufficient for assessing the appropriateness of the fitted model. Here, an easy‐to‐use test of the assumption of equal variance (i.e., homoscedasticity) as well as model specification is provided. Given the importance of the equal‐variance assumption (i.e., if uncorrected, severe violations preclude the use of statistical inference and moderate violations result in a loss of statistical power) and given the fact that, if uncorrected, a misspecified or underspecified model could invalidate an entire study, the test developed by Halbert White in 1980 is recommended for supplementing a graphic residual analysis when teaching regression modeling to business students at both the undergraduate and graduate levels. Using this confirmatory approach to supplement a traditional residual analysis has value because students often find that graphic displays are too subjective for determining what constitutes severe from moderate departures from the equal variance assumption or for assessing patterns in plots that might indicate model misspecification or underspecification.