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Examining the Relationship between Mindfulness, Personality, and National Culture for Construction Safety

Author

Listed:
  • Tomay Solomon

    (Sid and Reva Dewberry Department of Civil, Environmental and Infrastructure Engineering, Volgenau School of Engineering, George Mason University, Fairfax, VA 22030, USA)

  • Behzad Esmaeili

    (Sid and Reva Dewberry Department of Civil, Environmental and Infrastructure Engineering, Volgenau School of Engineering, George Mason University, Fairfax, VA 22030, USA)

Abstract

The construction industry still leads the world as one of the sectors with the most work-related injuries and worker fatalities. Considering that one of the barriers to improving construction safety is its stressful working environment, which increases risk of inattentiveness among construction workers, safety managers seek practices to measure and enhance worker focus and reduce stress, such as mindfulness. Considering the important role of mindfulness in curbing frequency and severity of incidents, researchers are interested in understanding the relationship between mindfulness and other common, more static human characteristics. As a result, this study examines the relationship between mindfulness and such variables as personality and national culture in the context of construction safety. Collecting data from 155 participants, this study used elastic net regression to examine the influence of independent (i.e., personality and national culture) variables on the dependent (i.e., mindfulness) variable. To validate the results of the regression, 10-fold cross-validation was conducted. The results reveal that certain personality traits (e.g., conscientiousness, neuroticism, and agreeableness) and national cultural dimensions (e.g., uncertainty avoidance, individualism, and collectivism) can be used as predictors of mindfulness for individuals. Since mindfulness has shown to increase safety and work performance, safety managers can utilize these variables to identify at-risk workers so that additional safety training can be provided to enhance work performance and improve safety outcomes. The results of this study will inform future work into translating personal and mindfulness characteristics into factors that predict specific elements of unsafe human behaviors.

Suggested Citation

  • Tomay Solomon & Behzad Esmaeili, 2021. "Examining the Relationship between Mindfulness, Personality, and National Culture for Construction Safety," IJERPH, MDPI, vol. 18(9), pages 1-21, May.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:9:p:4998-:d:550826
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    References listed on IDEAS

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    1. Hofstede, Geert, 1994. "The business of international business is culture," International Business Review, Elsevier, vol. 3(1), pages 1-14, March.
    2. Christian Koch, 2013. "From crew to country? Local and national construction safety cultures in Denmark," Construction Management and Economics, Taylor & Francis Journals, vol. 31(6), pages 691-703, June.
    3. Mei-Yung Leung & Thomas Ng & Martin Skitmore & Sai-On Cheung, 2005. "Critical stressors influencing construction estimators in Hong Kong," Construction Management and Economics, Taylor & Francis Journals, vol. 23(1), pages 33-44.
    4. Qu, Yuanmei (Elly) & Dasborough, Marie T. & Todorova, Gergana, 2015. "Which Mindfulness Measures To Choose To Use?," Industrial and Organizational Psychology, Cambridge University Press, vol. 8(4), pages 710-723, December.
    5. Mazanec, Josef A. & Crotts, John C. & Gursoy, Dogan & Lu, Lu, 2015. "Homogeneity versus heterogeneity of cultural values: An item-response theoretical approach applying Hofstede's cultural dimensions in a single nation," Tourism Management, Elsevier, vol. 48(C), pages 299-304.
    6. Mei-yung Leung & Qi Liang & Jingyu Yu, 2016. "Development of a mindfulness--stress--performance model for construction workers," Construction Management and Economics, Taylor & Francis Journals, vol. 34(2), pages 110-128, February.
    7. Soares, Ana Maria & Farhangmehr, Minoo & Shoham, Aviv, 2007. "Hofstede's dimensions of culture in international marketing studies," Journal of Business Research, Elsevier, vol. 60(3), pages 277-284, March.
    8. Mei-Yung Leung & Martin Skitmore & Yee Shan Chan, 2007. "Subjective and objective stress in construction cost estimation," Construction Management and Economics, Taylor & Francis Journals, vol. 25(10), pages 1063-1075.
    9. Huber, Kelli E. & Hill, Sarah E. & Merritt, Stephanie M., 2015. "Minding the Gap: Extending Mindfulness to Safety-Critical Occupations," Industrial and Organizational Psychology, Cambridge University Press, vol. 8(4), pages 699-705, December.
    10. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    11. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
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    1. Tan, Yuxuan & Gong, Yanping & Xie, Julan & Li, Jian & Liu, Yongdan, 2022. "More mindfulness, less conspicuous consumption? Evidence from middle-aged Chinese consumers," Journal of Retailing and Consumer Services, Elsevier, vol. 69(C).

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