The relative weight of predictor variables in multiple regression is difficult to determine because of non-zero predictor intercorrelations. Relative weight (also called relative importance by some researchers) is defined here as the proportionate contribution each predictor makes to R2, considering both its unique contribution and its contribution when combined with other variables. Although there are no unambiguous measures of relative weight when variables are correlated, some measures have been shown to provide meaningful results (Budescu, 1993; Lindeman, Merenda, & Gold, 1980). These measures are very difficult to implement, however, when the number of predictors is greater than about five. A method is proposed that is computationally efficient with any number of predictors, and is shown to produce results that are very similar to those produced by more complex methods. Recommendations are made for when this procedure may be applied and in what situations it is not appropriate.

The search for a meaningful index of the relative importance of predictors in multiple regression has been going on for years. This type of index is often desired when the explanatory aspects of regression analysis are of interest. The authors define relative importance as the proportionate contribution each predictor makes to R2, considering both the unique contribution of each predictor by itself and its incremental contribution when combined with the other predictors. The purposes of this article are to introduce the concept of relative importance to an audience of researchers in organizational behavior and industrial/organizational psychology and to update previous reviews of relative importance indices. To this end, the authors briefly review the history of research on predictor importance in multiple regression and evaluate alternative measures of relative importance. Dominance analysis and relative weights appear to be the most successful measures of relative importance currently available. The authors conclude by discussing how importance indices can be used in organizational research.

Although a common theme in the service quality literature is that organizations must create and maintain a climate for service in order for employees to effectively deliver service, few studies exist that evaluate climate for service components against a criterion of customer satisfaction. The effectiveness of different aspects of a climate for service is evaluated by determining the relationships between service climate components and facets of customer satisfaction, as rated by 538 employees and 7,944 customers across 57 branches of a large bank. All service climate components were significantly related to at least one facet of customer satisfaction (e.g., teller service). Seeking and sharing information about customers' needs and expectations, training in delivering quality service, and rewarding and recognizing excellent service were the practices that were most highly related to satisfaction with service quality.A common theme emerging from the service quality literature is that organizations must create and maintain a climate for service in order for employees to effectively deliver excellent service (Schneider, 1990;Schneider & Bowen, 1995). In other words, employees are more likely to deliver excellent service to customers when the organization expects and rewards such behavior and establishes practices that facilitate service delivery (Schneider, Wheeler, & Cox, 1992). Although much has been written on the topic, there is very little research investigating the effectiveness of management practices designed to enhance service delivery. For organizations in the service industry, the most appropriate criterion for organizational performance is customer satisfaction (Schneider & Chung, 1994). Service quality researchers point out the importance

Although evidence supports the unique contribution of task performance and contextual performance to overall evaluations, little is known about the relative contribution that specific dimensions of contextual performance make to overall performance judgments. This study evaluated the extent to which supervisors consider task and contextual performance by using relative weights (J. W. Johnson, 2000) to statistically describe the relative importance of specific dimensions of each type of performance to overall performance ratings. Within each of 8 job families in a large organization, each of 4 dimensions of contextual performance made not only a unique contribution but a relatively important contribution to the overall evaluation. Evidence also supports the adaptive performance dimension of handling work stress as an aspect of contextual performance and job-task conscientiousness as an aspect of both task and contextual performance.

Relative weight analysis is a procedure for estimating the relative importance of correlated predictors in a regression equation. Because the sampling distribution of relative weights is unknown, researchers using relative weight analysis are unable to make judgments regarding the statistical significance of the relative weights. J. W. Johnson (2004) presented a bootstrapping methodology to compute standard errors for relative weights, but this procedure cannot be used to determine whether a relative weight is significantly different from zero. This article presents a bootstrapping procedure that allows one to determine the statistical significance of a relative weight. The authors conducted a Monte Carlo study to explore the Type I error, power, and bias associated with their proposed technique. They illustrate this approach here by applying the procedure to published data.