Task and Individual Characteristics as Predictors of Performance in a Job-Relevant Multi-Tasking Environment
Open Access
- Author:
- Kinney, Theodore Boone
- Graduate Program:
- Psychology
- Degree:
- Doctor of Philosophy
- Document Type:
- Dissertation
- Date of Defense:
- March 21, 2007
- Committee Members:
- Kevin R Murphy, Committee Chair/Co-Chair
Jeanette Cleveland, Committee Member
James Lewis Farr, Committee Member
Michael Wenger, Committee Member
Frank Edward Ritter, Committee Member - Keywords:
- Job Performance
Individual Differences
Multitasking - Abstract:
- Abstract Understanding performance in complex multitasking environments is of paramount importance to selection practitioners. Organizations are asking employees to do more work in less time than ever before. With this increased task demand, understanding who can and cannot survive in this type of situation is critical. To date, little research has investigated how to predict who will succeed and who will fail in these demanding situations. Cognitive psychological research has focused on task characteristics that impact human capacity to multitask. This stream of research has not had much impact on the selection practitioner. Research on individual difference predictors of job performance has ignored criteria specific to performance in multitasking job environments. This dissertation blends what cognitive psychologists have learned about human capacity to multitask and what industrial/organizational psychologists have learned about predicting job performance to investigate individual difference predictors in a job-relevant multitasking simulation. The results from this study suggest that several of the task characteristic conditions researched in cognitive multitasking research do generalize to applied multitasking environments. Also, several individual difference variables (cognitive ability, intellectance, extroversion, and polychronicity) emerged as predictors of performance in an applied multitasking environment, with cognitive ability being the best predictor. Implications of these results on employee selection, job design, and future research are discussed.