Crystallized intelligence and openness to experience: Drawing on intellectual-investment theories to predict job performance longitudinally

Various approaches to conceptualizing and measuring intelligence have been utilized throughout history. Despite the plethora o f intelligence theories, the field of industrial and organizational (I-O) psychology has been largely dominated by the psychometric tradition of intelligence and Spearman’s general factor theory of intelligence (g). Moreover, other approaches to intelligence (e.g., the developmental perspective) have generally been ignored by 1-0 psychology. This is puzzling given the widespread acceptance among 1-0 psychologists of intelligence’s substantial and increasing importance in the modem workplace. Supported by a vast amount of research, g has often been recognized as the single best predictor of job performance. However, traditional measures of g have reached a plateau in terms of predictive validity for work-related criteria. Although g is not the sole determinate of job performance, failing to incorporate advancements from other fields (e.g., developmental psychology, cognitive psychology) is a potential limitation to continued improvement of job-performance prediction. One modem approach to intelligence that holds promise for improving our prediction of performance in the workplace is known collectively as the intellectual-investment theories, which posit that intellectual development is partially influenced by investment traits (e.g., Openness to Experience) that guide how, where, and when individuals invest their cognitive ability. Stemming from this investment, individual differences in crystallized intelligence (Gc), a particular type of intelligence that largely increases throughout one’s lifespan, are thought to result. Using the intellectual-investment framework, this study sought to improve the prediction of job performance longitudinally by accounting for both intelligence and personality. To this end, latent-growth modeling was applied to archival personality, crystallized-intelligence, and job-performance data obtained from 92 employees. Overall, no support was found for the intellectual-investment framework in predicting job performance longitudinally, but a theoretical contribution was made and the study offers practical suggestions based on theory and research. In addition, a number of limitations to this study are discussed that are believed to contribute to the lack of support. In conclusion, the intellectual-investment framework offers the potential for improved job performance prediction and as such, this study offers suggestions that researchers can incorporate into future studies. APPROVAL FOR SCHOLARLY DISSEMINATION The author grants to the Prescott Memorial Library of Louisiana Tech University the right to reproduce, by appropriate methods, upon request, any or all portions of this Dissertation. It was understood that “proper request” consists of the agreement, on the part of the requesting party, that said reproduction was for his personal use and that subsequent reproduction will not occur without written approval of the author of this Dissertation. Further, any portions of the Dissertation used in books, papers, and other works must be appropriately referenced to this Dissertation. Finally, the author of this Dissertation reserves the right to publish freely, in the literature, at any time, any or " '