Flowchart for Study: (Full-size version).

Proposal Outline:

My research question is how does income effect employee perceptions of automation in the workplace?

My hypothesis is that employees with lower income levels may hold more negative perceptions towards automation integration, while employees who are in the high-income range may hold more positive perceptions of automation.

• How Income affects Employee perceptions of automation within the workplace (Introduction)
  • Income and Employee perceptions discussion: (Goyal & Aneja, 2020).
  • (McClure, 2018).
  •     New
    • i) Survey employees in service Sectors.
    • ii) New 2020 income data and unemployment rates.
• Artificial Intelligence’s impact on income and perceptions (Lit. Review)
  • Income inequality and perceptions of automation:
    •  (Goyal & Aneja, 2020).
    • (Schwabe & Castellacci, 2020).
    • (Brougham & Haar, 2018).

• •  Low skilled versus high skilled employee status:
    • (Dahlin, 2019).
    • (Forman et al., 2014).
    • (Willcocks, 2020).

• Research Hypothesis
  • • Synthesis of existing research studies on employee perceptions towards automation.
    • Replicate STARA (Smart Technology, Artificial Intelligence, Robotics, and Algorithms) study survey design (Brougham & Haar, 2018).
    • Income Rates of Employees and Perceptions/Attitude rating towards automation
    • Original studies on automation issue began around 1997 (Korunka et al., 1997).
    • Update to 2014-2020.
    • Research Hypothesis: Employees in the lower income category may hold more negative perceptions towards automation in the workplace.

• Methodology:
  • Non-experimental Design (Correlation Study)
  • Independent Variable: Income
  • Dependent Variable: Perceptions towards automation
  • Utilize Survey Monkey to recruit participants.
  • Participants: Approximately 700 U.S employees who work in a service sector.

• Methods of Measurement:
  • Validity: Likert scale measurements of 1-5 is known for its precision 89 percent of the time (Louangrath, 2018).
  • Reliability: Likert scales that rate satisfaction on a 1-5 scale are reliable 90 percent of the time in studies (Louangrath, 2018).

• Discussion:
  • Weaknesses and Strengths of the study:
    • The study suffers from weaknesses such as the lack of longitudinal studies, and a relatively small sample size.
    • The strengths of the study are the multiple income rate options that allow for easy categorization of workers into low, middle, and high-income groups, and the study addresses gaps on income rates in prior studies.
    • Importance
    • A. Important to understand that income may have a role in how employees perceive automation within the workplace, and how to address these issues to ensure that employees are more open-minded to automation technologies.
    • Future implications of the study
    • Addressing income inequality.
    • Addressing negative perceptions towards automation in a workplace setting.
    • Follow-up ideas for study
      • Study multiple companies to determine if perceptions vary from company to company.
      • Study income inequality within the workplace that may have formed from the integration of automation.
      • Longitudinal study of employee perceptions of automation to see if perceptions remain the same over time.

References

[NOTE: Article titles are not capitalized correctly in all of these citations, but most of the format is spot on.]

Brougham, D., & Haar, J. (2018). Smart Technology, Artificial Intelligence, Robotics, and Algorithms (STARA): Employees’ perceptions of our future workplace. Journal of Management and Organization, 24(2), 239-257. http://dx.doi.org/10.1017/jmo.2016.55

Dahlin, E. (2019). Are robots stealing our jobs? Socius, 5, 1-14. https://doi.org/10.1177/2378023119846249

Forman, C., King, J. L., & Lyytinen, K. (2014). Special section introduction information, technology, and the changing nature of work. Information Systems Research, 25(4), 789-795. https://dx.doi.org/10.1287/isre.2014.0551

Goyal, A., & Aneja, R. (2020). Artificial intelligence and income inequality: Do technological changes and worker’s position matter? Journal of Public Affairs, 20(4), 1–10. https://doi.org/10.1002/pa.2326

Korunka, C., Zauchner, S., & Weiss, A. (1997). New information technologies, job profiles, and external workload as predictors of subjectively experienced stress and dissatisfaction at work. International Journal of Human-Computer Interaction, 9(4), 407-424. https://doi.org/10.1207/s15327590ijhc0904_5

Louangrath, P. I. (2018). Reliability and Validity of Survey Scales. International Journal of Research & Methodology in Social Science, 4(1), 50-62. https://doi.org/10.5281/zenodo.1322695

McClure, P. K. (2018). “You’re fired,” says the robot: The rise of automation in the workplace, technophobes, and fears of unemployment. Social Science Computer Review, 36(2), 139–156. https://doi.org/10.1177/0894439317698637

Schwabe, H., & Castellacci, F. (2020). Automation, workers’ skills, and job satisfaction. PLoS ONE, 15(11), 1–26. https://doi.org/10.1371/journal.pone.0242929

Willcocks, L. (2020). Robo-Apocalypse cancelled? Reframing the automation and future of work debate. Journal of Information Technology, 35(4), 286–302. https://doi.org/10.1177/0268396220925830