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The impact of individual differences on multitasking ability

Author

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  • Derek E. Crews
  • Molly J. Russ

Abstract

Purpose - The study's purpose was to explore the impact of multitasking on efficiency and accuracy and the relationship between individual differences and multitasking ability. Design/methodology/approach - An experimental study was conducted with individuals that completed an in-box exercise and a personality assessment. Three groups were established as follows: those that completed the in-box exercise without interruption (control group), those that were given a portion of the same initial task but were then interrupted and given an additional task (low multitask group) and those that were given the same initial tasks, then interrupted three times (high multitask group). Findings - The results show that there were significant effects of gender and age on multitasking efficiency. Females made fewer errors than males. Younger and older participants took more time to complete the experiment, which indicates an inverted U relationship and may mean that people become more effective at multitasking with experience and practice, but then less effective as they get older. The study also found that personality had no significant effect on multitasking efficiency. Research limitations/implications - The study was limited in that the sample was 91 percent female, did not include anyone over the age of 51 and did not measure intelligence or experience. Practical implications - The findings may have implications for employee training, evaluation and formulation of workplace standards and regulations. Orginality/value - This study provides empirical research extending the understanding of the nature and impact of multitasking and individual differences based on gender, age and personality.

Suggested Citation

  • Derek E. Crews & Molly J. Russ, 2020. "The impact of individual differences on multitasking ability," International Journal of Productivity and Performance Management, Emerald Group Publishing Limited, vol. 69(6), pages 1301-1319, March.
  • Handle: RePEc:eme:ijppmp:ijppm-04-2019-0191
    DOI: 10.1108/IJPPM-04-2019-0191
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    Cited by:

    1. Pabolu, Venkata Krishna Rao & Shrivastava, Divya & Kulkarni, Makarand S., 2022. "Modelling and prediction of worker task performance using a knowledge-based system application," International Journal of Production Economics, Elsevier, vol. 254(C).

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