Importance of Personality and Job-Specific Affect for Predicting Job Attitudes and Withdrawal Behavior

Abstract

This study explores the relative importance of trait-based personality constructs and a state-based job-specific affect construct for predicting job attitudes and withdrawal behaviors of incumbent customer service call center representatives (N = 150). Results based on three traditional indices of importance (i.e., squared correlation coefficients, squared standardized regression coefficients, and the product measure) yielded conclusions that were often inconsistent or ambiguous. In contrast, results based on two newer statistics (i.e., dominance and epsilon) were readily interpretable and virtually identical to one another. Overall, emotional stability and job-specific affect emerged as important predictors of various withdrawal criteria, relative to other personality variables.

Using the Course Experience Questionnaire for evaluating undergraduate tourism management courses in Greece

“…Instead, the measure adopted here has been specifically designed to be used when "the researcher is interested in the relative contribution each variable makes to the prediction of a dependent 20 variable, considering both its unique contribution and its contribution when combined with other variables" (Johnson, 2000, p. 2). Moreover, this method yields similar results to other regression methods with less computation (LeBreton, Binning, Adorno and Melcher, 2004).…”

Examining the effects of trust in leaders: A bases-and-foci approach

“…Given problems of interpreting regression coefficients when predictor variables are intercorrelated, we calculated relative importance indexes (LeBreton, Binning, Adorno, & Melcher, 2004) to determine the unique contribution of each of the four trust variants in predicting outcome variables. The concept of relative importance refers to the contribution a variable makes to the prediction of a dependent variable considering its effect alone and in combination with other predictors (Johnson, 2004).…”

“…The concept of relative importance refers to the contribution a variable makes to the prediction of a dependent variable considering its effect alone and in combination with other predictors (Johnson, 2004). The relative importance statistic epsilon (or relative importance weight) has proved effective in identifying important predictors and decomposing model R-square information among competing predictors (LeBreton et al, 2004). Relative weights are calculated by (a) transforming the original predictors to obtain the set of orthogonal variables that have the highest degree of one-to-one correspondence with the original predictors, (b) relating the orthogonal variables to the criterion, (c) relating the orthogonal variables back to the original predictors, and (d) combining the information to yield a set of weights reflecting the relative contribution each predictor makes to the model when considered by itself and in the context of the other predictors (Johnson, 2004, p. 284).…”

RWA Web: A Free, Comprehensive, Web-Based, and User-Friendly Tool for Relative Weight Analyses

“…The relative weight statistic has been shown to provide extremely good estimates of the relative importance of predictor variables when those predictor variables are correlated. This has been found in both simulation studies (LeBreton et al 2004b) and primary studies (LeBreton et al 2004a;LeBreton et al 2007). Recently, new developments in relative weight analysis have expanded its applications from traditional multiple regression to more complicated regression models such as multivariate multiple regression (LeBreton and Tonidandel 2008), regression models containing higher order terms such as cross-product terms, quadratic terms, or other polynomial terms , logistic regression (Tonidandel and LeBreton 2010), and multivariate analysis of variance (MANOVA; Tonidandel and LeBreton 2013).…”