Advertisement

Workplace Aging and Jobs in the Twenty-First Century

  • Margaret E. BeierEmail author
  • W. Jackeline Torres
  • Daniel J. Beal
Chapter

Abstract

The automation of jobs and job tasks will impact the type of work that is available and how this work gets done in the twenty-first century. At the same time, the global workforce is aging. The purpose of this chapter is to discuss the confluence of these two factors—the aging labor force and the automation of job tasks. In particular, we describe the types of jobs that are likely to be available in the twenty-first century and how these jobs will be suited to the unique skills, abilities, and needs of older workers. Herein, we discuss theories of work design most related to aging workers, describe age-related trajectories of motivation and abilities, and consider the types of abilities that are most likely to be tapped by different types of jobs. We describe these factors within the context of economic forecasts about the labor market of the future. We conclude by identifying areas of future research, including how best to engage and train older workers for work in the twenty-first century.

Keywords

Desire of older workers by organizations Models of job design Older workers and design of work Training and older workers Age-related ability changes Age-related motivation changes Ability-related job demands Labor market trends 

References

  1. Ackerman, P. L. (1996). A theory of adult intellectual development: Process, personality, interests, and knowledge. Intelligence, 22(2), 227–257.  https://doi.org/10.1016/S0160-2896(96)90016-1CrossRefGoogle Scholar
  2. Ackerman, P. L. (2000). Domain-specific knowledge as the “dark matter” of adult intelligence: Gf/Gc, personality and interest correlates. Journals of Gerontology: Series B: Psychological Sciences and Social Sciences, 55B(2), 69–84.  https://doi.org/10.1093/geronb/55.2.P69CrossRefGoogle Scholar
  3. Allen, H., Woock, C., Barrington, L., & Bunn, W. (2008). Age, overtime, and employee health, safety and productivity outcomes: A case study. Journal of Occupational and Environmental Medicine, 50(8), 873–894.  https://doi.org/10.1097/JOM.0b013e31818521ecCrossRefPubMedGoogle Scholar
  4. Bal, P. M., & Smit, P. (2012). The older the better!: Age-related differences in emotion regulation after psychological contract breach. Career Development International, 17(1), 6–24.  https://doi.org/10.1108/13620431211201300CrossRefGoogle Scholar
  5. Baltes, P. B., & Baltes, M. M. (1990). Psychological perspectives on successful aging: The model of selective optimization with compensation. Successful Aging: Perspectives from the Behavioral Sciences, 1(1), 1–34.  https://doi.org/10.1017/cbo9780511665684.003CrossRefGoogle Scholar
  6. Beier, M. E. (2015). Strategies for engaging and retaining mature workers. SHRM-SIOP Science of HR Series. Retrieved from https://www.shrm.org/hr-today/trends-and-forecasting/special-reports-and-expert-views/Documents/SHRM-SIOP%20Engaging%20and%20Retaining%20Mature%20Workers.pdf
  7. Beier, M. E., & Beal, D. J. (2010, April). The importance of job characteristics in the relation between age and job performance. Paper presented at the 25th Annual Conference of the Society for Industrial and Organizational Psychology, Atlanta, GA.Google Scholar
  8. Beier, M. E., Bradshaw, B. C., Torres, W. J., Shaw, A., & Kim, M. H. (2019). Cognition, motivation, and lifespan development. In B. B. Baltes, C. W. Rudolph, & H. Zacher (Eds.), Work across the lifespan (pp. 155–177). New York: Academic Press.Google Scholar
  9. Beier, M. E., Teachout, M. S., & Cox, C. B. (2012). The training and development of an aging workforce. In J. W. Hedge & W. C. Borman (Eds.), The Oxford handbook of work and aging (pp. 436–453). New York: Oxford University Press.CrossRefGoogle Scholar
  10. Beier, M. E., Young, C. K., & Villado, A. J. (2018). Job knowledge: Its definition, development and measurement. In D. S. Ones, N. Anderson, C. Viswesvaran, & H. K. Sinangil (Eds.), The SAGE handbook of industrial, work and organizational psychology: personnel psychology and employee performance (Vol. 3, 2nd ed., pp. 279–298). Los Angeles: SAGE.  https://doi.org/10.4135/9781473914940CrossRefGoogle Scholar
  11. Brooke, L. (2009). Prolonging the careers of older information technology workers: Continuity, exit or retirement transitions? Ageing & Society, 29(2), 237–256.  https://doi.org/10.1017/S0144686X0800768XCrossRefGoogle Scholar
  12. Bureau of Labor Statistics. (2017a). Projections of industry employment, 2016–26: Career outlook: U.S. Bureau of Labor Statistics. Retrieved from https://www.bls.gov/careeroutlook/2017/article/projections-industry.htm
  13. Bureau of Labor Statistics. (2017b). Projections of the labor force, 2016–26: Career outlook: U.S. Bureau of Labor Statistics. Retrieved from https://www.bls.gov/careeroutlook/2017/article/projections-laborforce.htm
  14. Callahan, J. S., Kiker, D. S., & Cross, T. (2003). Does method matter? A meta-analysis of the effects of training method on older learner training performance. Journal of Management, 29(5), 663–680.  https://doi.org/10.1016/s0149-2063_03_00029-1CrossRefGoogle Scholar
  15. Carstensen, L. L., Isaacowitz, D. M., & Charles, S. T. (1999). Taking time seriously: A theory of socioemotional selectivity. American Psychologist, 54(3), 165–181.  https://doi.org/10.1037//0003-066x.54.3.165CrossRefPubMedGoogle Scholar
  16. Cattell, R. B. (1987). Intelligence: Its structure, growth, and action. New York: Elsevier Science.Google Scholar
  17. Chiu, W. C. K., Chan, A. W., Snape, E., & Redman, T. (2001). Age stereotypes and discriminatory attitudes towards older workers: An east–west comparison. Human Relations, 54(5), 629–661.  https://doi.org/10.1177/0018726701545004CrossRefGoogle Scholar
  18. Czaja, S. J., & Sharit, J. (2012). Designing training and instructional programs for older adults. Boca Raton, FL: CRC Press.Google Scholar
  19. Czaja, S. J., Sharit, J., Charness, N., & Schmidt, A. C. (2015). The implications of changes in job demands for the continued and future employment of older workers. In L. M. Finkelstein, D. M. Truxillo, F. Fraccaroli, & R. Kanfer (Eds.), Facing the challenges of a multi-age workforce: A use-inspired approach (pp. 159–179). New York: Routledge/Taylor & Francis.Google Scholar
  20. Demerouti, E., Bakker, A. B., Nachreiner, F., & Schaufeli, W. B. (2001). The job demands-resources model of burnout. Journal of Applied Psychology, 86(3), 499–512.  https://doi.org/10.1037/0021-9010.86.3.499CrossRefPubMedGoogle Scholar
  21. Economist Intelligence Unit. (2011). A silver opportunity? Rising longevity and its implications for business [A report from the Economist Intelligence Unit sponsored by AXA]. Retrieved from http://graphics.eiu.com/upload/eb/Axa_Longevity-EIU_Web.pdf
  22. Fasbender, U., Wang, M., Voltmer, J.-B., & Deller, J. (2015). The meaning of work for post-retirement employment decisions. Work, Aging and Retirement, 2(1), 12–23.  https://doi.org/10.1093/workar/wav015CrossRefGoogle Scholar
  23. Frey, C. B., & Osborne, M. (2013, September 17). The future of employment: How susceptible are jobs to computerisation? [Working paper]. Oxford University Programme. Retrieved from https://www.oxfordmartin.ox.ac.uk/downloads/academic/The_Future_of_Employment.pdf
  24. Frey, C. B., & Osborne, M. A. (2017). The future of employment: How susceptible are jobs to computerisation? Technological Forecasting and Social Change, 114, 254–280.  https://doi.org/10.1016/j.techfore.2016.08.019CrossRefGoogle Scholar
  25. Fried, Y., & Ferris, G. R. (1987). The validity of the job characteristics model: A review and meta-analysis. Personnel Psychology, 40(2), 287–322.  https://doi.org/10.1111/j.1744-6570.1987.tb00605.xCrossRefGoogle Scholar
  26. Gajendran, R. S., & Harrison, D. A. (2007). The good, the bad, and the unknown about telecommuting: Meta-analysis of psychological mediators and individual consequences. Journal of Applied Psychology, 92(6), 1524–1541.  https://doi.org/10.1037/0021-9010.92.6.1524CrossRefPubMedGoogle Scholar
  27. Gershon, R. R. M., Lin, S., & Li, X. (2002). Work stress in aging police officers. Journal of Occupational and Environmental Medicine, 44(2), 160–167.CrossRefGoogle Scholar
  28. Grant, A. M., Fried, Y., & Juillerat, T. (2011). Work matters: Job design in classic and contemporary perspectives. In S. Zedeck (Ed.), APA handbook of industrial and organizational psychology. Vol. 1: Building and developing the organization (pp. 417–453). Washington, DC: American Psychological Association.  https://doi.org/10.1037/12169-013CrossRefGoogle Scholar
  29. Grant, A. M., & Parker, S. K. (2009). Redesigning work design theories: The rise of relational and proactive perspectives. The Academy of Management Annals, 3(1), 317–375.  https://doi.org/10.1080/19416520903047327CrossRefGoogle Scholar
  30. Griffiths, A. (1999). Work design and management: The older worker. Experimental Aging Research, 25(4), 411–420.  https://doi.org/10.1080/036107399243887CrossRefPubMedGoogle Scholar
  31. Hackman, J. R., & Oldham, G. R. (1976). Motivation through the design of work: Test of a theory. Organizational Behavior and Human Performance, 16(2), 250–279.  https://doi.org/10.1016/0030-5073(76)90016-7CrossRefGoogle Scholar
  32. Halpern, D. F. (2005). How time-flexible work policies can reduce stress, improve health, and save money. Stress and Health, 21(3), 157–168.  https://doi.org/10.1002/smi.1049CrossRefGoogle Scholar
  33. Hoeven, C. L. T., van Zoonen, W., & Fonner, K. L. (2016). The practical paradox of technology: The influence of communication technology use on employee burnout and engagement. Communication Monographs, 83(2), 239–263.  https://doi.org/10.1080/03637751.2015.1133920CrossRefPubMedPubMedCentralGoogle Scholar
  34. Humphrey, S. E., Nahrgang, J. D., & Morgeson, F. P. (2007). Integrating motivational, social, and contextual work design features: A meta-analytic summary and theoretical extension of the work design literature. Journal of Applied Psychology, 92(5), 1332–1356.  https://doi.org/10.1037/0021-9010.92.5.1332CrossRefPubMedGoogle Scholar
  35. IBM. (2015, September 11). The quest for AI creativity. Retrieved March 27, 2018, from http://www.ibm.com/watson/advantage-reports/future-of-artificial-intelligence/ai-creativity.html
  36. Jones, J. (2017). In Las Vegas, these bartenders are complete robots, and that’s the fun of this new bar. Retrieved March 27, 2018, from http://www.latimes.com/travel/la-tr-vegas-tipsy-robot-bar-20170704-story.html
  37. Kanfer, R., & Ackerman, P. L. (2004). Aging, adult development, and work motivation. The Academy of Management Review, 29(3), 440–458.  https://doi.org/10.2307/20159053CrossRefGoogle Scholar
  38. Karasek, R. A. (1979). Job demands, job decision latitude, and mental strain: Implications for job redesign. Administrative Science Quarterly, 24(2), 285–308.  https://doi.org/10.2307/2392498CrossRefGoogle Scholar
  39. Kristof-Brown, A. L., Zimmerman, R. D., & Johnson, E. C. (2005). Consequences of individuals’ fit at work: A meta-analysis of person–job, person–organization, person–group, and person–supervisor fit. Personnel Psychology, 58(2), 281–342.  https://doi.org/10.1111/j.1744-6570.2005.00672.xCrossRefGoogle Scholar
  40. Kubeck, J. E., Delp, N. D., Haslett, T. K., & McDaniel, M. A. (1996). Does job-related training performance decline with age? Psychology and Aging, 11, 92–107.  https://doi.org/10.1037//0882-7974.11.1.92CrossRefPubMedGoogle Scholar
  41. Maier, C., Laumer, S., & Eckhardt, A. (2015). Information technology as daily stressor: Pinning down the causes of burnout. Journal of Business Economics, 85(4), 349–387.  https://doi.org/10.1007/s11573-014-0759-8CrossRefGoogle Scholar
  42. Manyika, J., Lund, S., Chui, M., Bughin, J., Woetzel, J., Batra, P., et al. (2017). What the future of work will mean for jobs, skills, and wages: Jobs lost, jobs gained | McKinsey & Company. McKinskey Global Institute. Retrieved from https://www.mckinsey.com/global-themes/future-of-organizations-and-work/what-the-future-of-work-will-mean-for-jobs-skills-and-wages
  43. McGonagle, A. K., Fisher, G. G., Barnes-Farrell, J. L., & Grosch, J. W. (2015). Individual and work factors related to perceived work ability and labor force outcomes. Journal of Applied Psychology, 100(2), 376–398.  https://doi.org/10.1037/a0037974CrossRefPubMedGoogle Scholar
  44. Mitzner, T. L., Boron, J. B., Fausset, C. B., Adams, A. E., Charness, N., Czaja, S. J., et al. (2010). Older adults talk technology: Technology usage and attitudes. Computers in Human Behavior, 26(6), 1710–1721.  https://doi.org/10.1016/j.chb.2010.06.020CrossRefPubMedPubMedCentralGoogle Scholar
  45. Morgeson, F. P., & Humphrey, S. E. (2006). The Work Design Questionnaire (WDQ): Developing and validating a comprehensive measure for assessing job design and the nature of work. Journal of Applied Psychology, 91(6), 1321–1339.  https://doi.org/10.1037/0021-9010.91.6.1321CrossRefPubMedGoogle Scholar
  46. Morgeson, F. P., & Humphrey, S. E. (2008). Job and team design: Toward a more integrative conceptualization of work design. In Research in personnel and human resources management (Vol. 27, pp. 39–91). Bingley: Emerald (MCB UP).  https://doi.org/10.1016/S0742-7301(08)27002-7CrossRefGoogle Scholar
  47. Ng, T. W. H., & Feldman, D. C. (2008). The relationship of age to ten dimensions of job performance. Journal of Applied Psychology, 93(2), 392–423.  https://doi.org/10.1037/0021-9010.93.2.392CrossRefPubMedGoogle Scholar
  48. Nunes, A., & Kramer, A. F. (2009). Experience-based mitigation of age-related performance declines: Evidence from air traffic control. Journal of Experimental Psychology: Applied, 15(1), 12–24.  https://doi.org/10.1037/a0014947CrossRefPubMedGoogle Scholar
  49. O’Connor, A. (2017). Survey: 1 in 5 older tech workers fear being fired. Retrieved March 27, 2018, from http://www.aarp.org/work/working-at-50-plus/info-2017/ageism-technology-industry-fd.html
  50. Oldham, G. R., & Hackman, J. R. (2010). Not what it was and not what it will be: The future of job design research. Journal of Organizational Behavior, 31(2–3), 463–479.  https://doi.org/10.1002/job.678CrossRefGoogle Scholar
  51. Parker, S. K. (2014). Beyond motivation: Job and work design for development, health, ambidexterity, and more. Annual Review of Psychology, 65, 661–691.  https://doi.org/10.1146/annurev-psych-010213-115208CrossRefPubMedGoogle Scholar
  52. Parker, S. K., Morgeson, F. P., & Johns, G. (2017). One hundred years of work design research: Looking back and looking forward. Journal of Applied Psychology, 102(3), 403–420.  https://doi.org/10.1037/apl0000106CrossRefPubMedGoogle Scholar
  53. Parker, S. K., Wall, T. D., & Cordery, J. L. (2001). Future work design research and practice: Towards an elaborated model of work design. Journal of Occupational and Organizational Psychology, 74(4), 413–440.  https://doi.org/10.1348/096317901167460CrossRefGoogle Scholar
  54. Paullin, C. (2014). The aging workforce: Leveraging the talents of mature employees. (SHRM Foundation’s Effective Practice Guidelines Series). Retrieved from http://www.shrm.org/about/foundation/products/Documents/Aging%20Workforce%20EPG-FINAL.pdf
  55. Peterson, N. G., Mumford, M. D., Borman, W. C., Jeanneret, R., & Fleishman, E. A. (1999). An occupational information system for the 21st century: The development of O*NET. Washington, DC: American Psychological Association. Retrieved from http://www.apa.org/pubs/books/4318810.aspxCrossRefGoogle Scholar
  56. Salthouse, T. A. (2010). Major issues in cognitive aging. New York: Oxford University Press.Google Scholar
  57. Scheibe, S., Spieler, I., & Kuba, K. (2016). An older-age advantage? Emotion regulation and emotional experience after a day of work. Work, Aging and Retirement, 2(3), 307–320.  https://doi.org/10.1093/workar/waw010CrossRefGoogle Scholar
  58. Schmidt, F. L., & Hunter, J. E. (1998). The validity and utility of selection methods in personnel psychology: Practical and theoretical implications of 85 years of research findings. Psychological Bulletin, 124(2), 262–274.  https://doi.org/10.1037/0033-2909.124.2.262CrossRefGoogle Scholar
  59. Sturman, M. C. (2003). Searching for the inverted U-shaped relationship between time and performance: Meta-analyses of the experience/performance, tenure/performance, and age/performance relationships. Journal of Management, 29(5), 609–640.  https://doi.org/10.1016/s0149-2063(03)00028-xCrossRefGoogle Scholar
  60. Triplett, N. (1898). The dynamogenic factors in pacemaking and competition. The American Journal of Psychology, 9(4), 507–533.  https://doi.org/10.2307/1412188CrossRefGoogle Scholar
  61. Truxillo, D. M., Cadiz, D. M., & Hammer, L. B. (2015). Supporting the aging workforce: A review and recommendations for workplace intervention research. Annual Review of Organizational Psychology and Organizational Behavior, 2(1), 351–381.  https://doi.org/10.1146/annurev-orgpsych-032414-111435CrossRefGoogle Scholar
  62. Ulrich, L. B., & Brott, P. E. (2005). Older workers and bridge employment: Redefining retirement. Journal of Employment Counseling, 42(4), 159–170.  https://doi.org/10.1002/j.2161-1920.2005.tb01087.xCrossRefGoogle Scholar
  63. Wanberg, C. R., Kanfer, R., Hamann, D. J., & Zhang, Z. (2016). Age and reemployment success after job loss: An integrative model and meta-analysis. Psychological Bulletin, 142(4), 400–426.  https://doi.org/10.1037/bul0000019CrossRefPubMedGoogle Scholar
  64. Warr, P. (1994). Age and employment. In H. C. Triandis, M. D. Dunnette, & L. M. Hough (Eds.), Handbook of industrial and organizational psychology (Vol. 4, 2nd ed., pp. 485–550). Palo Alto, CA: Consulting Psychologists Press.Google Scholar
  65. Wickre, K. (2017). Surviving as an old in the tech world. Retrieved March 27, 2018, from https://www.wired.com/story/surviving-as-an-old-in-the-tech-world/
  66. Wolfson, N. E., Cavanagh, T. M., & Kraiger, K. (2014). Older adults and technology-based instruction: Optimizing learning outcomes and transfer. Academy of Management Learning & Education, 13(1), 26–44.  https://doi.org/10.5465/amle.2012.0056CrossRefGoogle Scholar
  67. Wright, A. (2017, November 6). Companies can’t assume those over 40 have outdated skills. Retrieved March 27, 2018, from https://www.shrm.org/resourcesandtools/hr-topics/technology/pages/study-tech-workers-tend-to-be-young.aspx
  68. Zacher, H. (2015). Successful aging at work. Work, Aging and Retirement, 1(1), 4–25.  https://doi.org/10.1093/workar/wau006CrossRefGoogle Scholar
  69. Zacher, H., Feldman, D. C., & Schulz, H. (2014). Age, occupational strain, and well-being: A person-environment fit perspective. In P. L. Perrewé, C. C. Rosen, & J. R. B. Halbesleben (Eds.), The role of demographics in occupational stress and well being (Vol. 12, pp. 83–111). Bingley, UK: Emerald Group Publishing. Retrieved from http://www.emeraldinsight.com/doi/abs/10.1108/S1479-355520140000012002Google Scholar
  70. Zaniboni, S., Truxillo, D. M., & Fraccaroli, F. (2013). Differential effects of task variety and skill variety on burnout and turnover intentions for older and younger workers. European Journal of Work and Organizational Psychology, 22(3), 306–317.  https://doi.org/10.1080/1359432X.2013.782288CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Margaret E. Beier
    • 1
    Email author
  • W. Jackeline Torres
    • 1
  • Daniel J. Beal
    • 2
  1. 1.Department of Psychological SciencesRice UniversityHoustonUSA
  2. 2.Department of ManagementPamplin College of Business, Virginia Institute of TechnologyBlacksburgUSA

Personalised recommendations