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Development and Construct Validation of a Measure of Soft Skills Performance

Despite the growing interest in studying the dimensions and prediction of task and contextual performance, little empirical attention has been given to studying the nature of soft skills performance. Soft skills (i.e., intra- and inter-personal work skills that facilitate the application of technical skills and knowledge), such as interpersonal skills (e.g., developing rapport) and communication skills (e.g., adjusting your message to the target audience) are highly sought by organizations (Zedeck and
Goldstein, 2000). However, little is known about the underlying dimensions of soft skills performance, or about the individual differences variables that predict performance in this domain. In the current set of studies I
examined the dimensionality of soft skills performance, developed measures to assess soft skills performance from self and supervisor perspectives, and validated the measures of performance in a nomological network of non-ability individual differences and existing performance measures. Study 1 involved asking subject matter experts to provide a master list and critical incidents of soft skills. Data from Study 1 served as the stimuli in Study 2 for sorting and reduction of skills into dimensions of soft skills performance. A construct and criterion validation approach was taken in Study 3 to measure soft skills performance in relation to individual differences variables in a nomological network. Results showed that the taxonomy of soft skills performance was
composed of seven clusters, but that the measure of soft skills performance was unidimensional. Personality and motivational variables significantly predicted soft skills performance through their influence on proximal motivational processes.

Identiferoai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/6861
Date10 April 2005
CreatorsKantrowitz, Tracy Michelle
PublisherGeorgia Institute of Technology
Source SetsGeorgia Tech Electronic Thesis and Dissertation Archive
Languageen_US
Detected LanguageEnglish
TypeDissertation
Format1716642 bytes, application/pdf

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