The modern Human Resource Management faces various challenges in the recruitment process and structuring of work schedules. Many professionals in interview panels may be biased, and hence reject competent candidates. Lam (2015) avers that interviewers tend to prefer candidates who somewhat remind them of themselves (para. 4). The final tallied results of successful applicants may be influenced by human limitation that subsequently delivers structural incompetence into an organization’s workforce. Using a model that is not controlled by human emotions and preferences is significant in managing effective job hiring systems. Although computer applications in job recruitment and scheduling processes may not reveal interpersonal traits and cognitive capabilities of a candidate, an integrated approach combining the computer algorithms and human management skills may assist in hiring competent and quality employees.
Employing professional test scores in a recruitment process may attain quality and effectiveness. Hoffman, Kahn, and Li (2018) explain that employers have preferences for a traditional recruitment process to avoid mismatch of the employees’ soft skills that are synonymous with computer-based artificial intelligence (p. 2). Lam (2015) illustrates that recruitment software has the potential to collate behavioral traits through surveys and analyze them into algorithms, which would make effective and non-biased decisions (para. 3). In addition, algorithm-based hiring can process a higher volume of applications within the scheduled timeline compared to human approaches, which may suffer systemic biases. As global trends in recruitment continue to focus towards algorithmic perspectives, employers’ interest on diversities continues to grow while disadvantaging the innovative patterns of recruitment, such as use of algorithms. Therefore, organizations should adopt progressive recruitment systems to avoid both intentional and unintentional biases that may exist in the recruitment process.
Regardless of the limitations in computer algorithms in the hiring procedure, the biases of panelists applied in the staffing process can affect candidates. When an applicant realizes that the hiring process was based on inaccurate data, a fear that the information could be verified in the future is evident (Hoffman, Kahn, & Li, 2018, p. 781). Hiring with algorithms affects the employee by observing strict attributes, such as education and experience, while ignoring significant interpersonal and teamwork abilities that are equally essential in the workplace.
Irrespective of the flaws witnessed in computer algorithms, automated employee management systems help employer manage work timetables efficiently and attain improved productivity. Bernstein, Kesavan, Staats, and Hassall (2017) confirm that optimized shifts at work have the ability to link the expected volume of sales to a number of workers needed to support them (p. 5). Firstly, employee work forecasting improves productivity in the workplace since it equates the sales to labor. Secondly, it enables employers or supervisors to improve customer satisfaction levels within a business. For instance, Bernstein et al. (2017) indicate that, when the lowest customer service levels in the stores are observed, employee scheduling should be used to increase productivity and enhance customer satisfaction (p. 6). Therefore, companies should automate job-scheduling processes to allow for effective shift edits by the manager or supervisor.
Accordingly, the use of automated scheduling software affects workers in their planning systems. Some job schedules are organized within three days and may be cancelled by the supervisor two hours before commencement (Bernstein et al., 2017, p. 3). Implementation of such shifts affects the employee overall planning system, reducing satisfaction levels. Likewise, the plans may lead to occasional fluctuations in the employee incomes, hence affecting the level of motivation. According to Bernstein et al. (2017), computer-based schedules have significantly improved effectiveness, leading to improved productivity (p. 8). Employee motivation is a critical aspect affected by job scheduling systems. For instance, employees working between 10:00am to 7.30pm are likely to complain about long working hours. In addition, their attitudes might be affected, especially if their interactions with their families are limited (Bernstein et al., 2017, p. 9). Notably, the approach can affect the overall performance of the business in both productivity and customer satisfaction.
Apart from encouraging human favoritisms, cognitive biases are affecting the planning and execution of work schedules. Williams, Kesavan, and McCorkell (2018) demonstrate how retailers are investing their resources to attract customers in the stores without any significant sales increase (p. 5). The human biases are witnessed in job allocations when supervisors allocate prime shifts to friends, but relatively strenuous ones to employees under the “the general list” (Williams, Kesavan, & McCorkell, 2018, p. 5). Stable scheduling initiatives in the workplace should take an integrated approach of amalgamating both computer system evaluations and the managers’ edits to accommodate artificial intelligence and human cognitive influence.
As it is evident from the discussion, employing computer-based software applications in managing work schedules would increase the efficiency in companies. In addition, allowing for edits from managers or supervisors pegged on efficiency and productivity helps in attaining better performances. Therefore, it is important to apply an integrated approach when managing employees to enhance both productivity and motivation. Accordingly, this approach has the potential to hire better employees during the recruitment process compared to relying on the human approaches and decision-making systems, which may have a considerable level of bias.
References
Bernstein, E. S., Kesavan, S., Staats, B. R., & Hassall, L. (2014). Belk: Towards exceptional scheduling. Harvard Business Review Case Study, 1-16.
Hoffman, M., Kahn, L. B., & Li, D. (2018). Discretion in hiring. The Quarterly Journal of Economics, 133(2), 765-800.
Lam, B. (2015, June 22). For more workplace diversity, should algorithms make hiring decisions? The Atlantic. Retrieved from https://www.theatlantic.com/business/archive/2015/06/algorithm-hiring-diversity-HR/396374/
Williams, J. C., Kesavan, S., & McCorkell, L. (2018). Research: When retail workers have stable schedules, sales and productivity go up. Harvard Business Review, 1-7.
Order an Essay Now & Get These Features For Free:
Turnitin Report
Formatting
Title Page
Citation
Outline
