BUSI 642 Week 7 Human Resource Policy Project: Research Paper Example

Four Evidence-Based Policies for a Construction Firm's Human Resource Manual: Hybrid Work, Pay Transparency, AI in Hiring and Employee Mental Health

Abstract

This paper presents four human resource policies for a Virginia and North Carolina civil construction firm with roughly 1,200 workers, each grounded in research published within the last five years. A hybrid work policy allows eligible office employees two home days a week around team anchor days, based on experimental evidence that such schedules improve retention without harming performance. A pay transparency policy publishes salary ranges and pay criteria by grade but not individual salaries, balancing evidence that transparency narrows unexplained gaps against evidence that it can weaken bargaining and the pay-performance link. An AI hiring policy requires applicant notice, human review and quarterly adverse impact monitoring. A mental health policy emphasizes leadership, benefits access and job demands over individual-level programs. The paper closes with a biblical rationale and an implementation plan.

Introduction

The firm at the center of this project builds roads, bridges and utility systems across two states and employs some 1,200 people, roughly 700 on field crews and 500 in engineering, estimating, project management and administration. Its human resource manual was last revised before the pandemic. Since then, office employees have worked partly from home without a written policy, two states where the firm recruits have adopted pay range disclosure rules for job postings, the firm has licensed an artificial intelligence résumé screener and managers report rising stress and absences on crews during the summer season. Noe et al. (2018) argue that HR practices create competitive advantage when they fit the organization's strategy and environment; the firm's environment has changed, and its policies have not. The four policies below are written to close that gap.

Policy 1: Hybrid Work for Office Employees

The firm's office staff currently follow informal arrangements set by each manager, so two engineers in the same group may have entirely different schedules and expectations. Managers worry that remote days hurt performance and mentoring, while employees cite commutes of an hour or more as a reason they might leave.

The strongest evidence on this question comes from a randomized trial. Bloom et al. (2024) ran a six-month lottery among 1,612 degree-holding staff at a large technology company, sending half home on Wednesdays and Fridays and keeping the rest at their desks every weekday. Those on the hybrid schedule reported higher job satisfaction and resigned at a rate about one third lower, especially women, non-managers and those with long commutes, while their performance reviews and promotions over the next two years matched those of the office group. Choudhury et al. (2026) varied the number of office days in a separate field experiment and found that people coming in around twice weekly reported the best work-life balance, the highest satisfaction and the least isolation, again with no difference in performance ratings. The evidence does not ask whether to allow hybrid work; it tells the firm roughly how much and how to organize it.

Policy statement. Office employees whose duties do not require daily presence may work from home up to two days a week after completing their first 90 days. Each team designates two anchor days on which all members work in the office, and design reviews, client meetings and mentoring sessions are scheduled on those days. Employees in their first year are paired with a senior colleague who shares their anchor days. Managers may require additional office days for project deadlines with one week's notice. Field roles, superintendents and anyone whose work requires daily site presence are not eligible, and the policy says so plainly, because a hybrid policy that appears to favor office staff can breed resentment on crews. To answer that concern, the firm will review field schedules separately, including compressed workweeks during the winter season, so that flexibility is not reserved for one part of the workforce. Hybrid arrangements are a privilege tied to performance: an employee whose rating falls below expectations returns to full-time office work for the next review period. HR will track voluntary turnover, performance ratings and promotion rates for hybrid and non-hybrid employees each year.

Policy 2: Pay Transparency

The firm does not publish pay ranges, and employees learn about pay from rumor. At the same time, new disclosure rules in some recruiting states require ranges in job postings, and engineering candidates increasingly expect them.

The research is mixed, and the policy must choose carefully. Canadian provinces that made professors' salaries public above a cutoff gave Baker et al. (2023) a natural comparison, and the authors estimated that women's pay shortfall relative to men shrank by roughly a fifth to two fifths where disclosure applied, strong evidence that visibility can correct unexplained differences. Cullen and Pakzad-Hurson (2023), however, found that when U.S. states shielded private sector employees who share salary information with colleagues, the result was average wage declines of about 2 percent, because employers who could no longer pay one worker more without others learning of it bargained harder with everyone. Cullen (2024) reconciled these findings by distinguishing types of transparency: revealing coworkers' individual pay narrows gaps but can provoke unproductive comparisons and harder bargaining, while revealing pay ranges across levels and across employers tends to improve motivation and help workers find better-paying jobs.

Policy statement. Beginning next fiscal year, the firm will publish the salary range for every pay grade on the HR intranet and include the range in every job posting. Each range will be accompanied by the criteria for placement and movement within it, such as licensure, years of relevant experience and performance ratings. Individual salaries will not be published, but employees may discuss their own pay freely, as federal labor law protects. HR will conduct an annual pay equity analysis by sex and race within each grade and correct unexplained differences in the next pay cycle. Managers will receive the ranges and criteria before publication and training on how to explain an employee's position in the range, since the evidence suggests that the way transparency is introduced shapes whether it motivates or demoralizes. The firm will not disclose any individual's pay, which avoids the peer comparisons and harder bargaining that Cullen and Pakzad-Hurson linked to coworker transparency while keeping the equity benefits that come from visible criteria.

Policy 3: Artificial Intelligence in Recruiting and Selection

The firm adopted a vendor's AI résumé screener last year to manage a high volume of applications for field positions. No one at the firm knows how the tool ranks candidates, and its selection rates have never been examined by group.

Research supports both the promise and the caution. Hunkenschroer and Luetge (2022) reviewed 51 articles on the ethics of AI-enabled recruiting and found that while such tools can make screening faster and more consistent, they carry risks of embedded bias, opacity, privacy intrusion and diminished human judgment, which researchers propose to address through audits, explainability and human oversight. Kelan (2024) warned that algorithms trained on past hiring decisions can reproduce and amplify exclusion through their data, their design and the way decisions are shared between people and machines, and that methods for auditing them remain underdeveloped. Folger et al. (2022) found in two studies that applicants see digital selection methods as innovative, which raises an employer's attractiveness, although in the experimental sample, people imagining a digital interview rated it less fair than a traditional one. Noe et al. (2018) remind employers that Title VII reaches facially neutral practices that screen out a protected group disproportionately, unless the employer proves the practice measures what the work actually demands, regardless of who built the tool.

Policy statement. Any AI tool used in recruiting or selection must be approved by HR and legal counsel after the vendor explains what historical records the model learned from, which criteria it scores and any validation and adverse impact studies. Applicants will be told when an automated tool is used and that a person reviews every decision. A recruiter will review a random sample of at least 10 percent of candidates the tool screens out. HR will compare selection rates by sex, race and age each quarter and suspend the tool for any position where any group's pass rate drops beneath four-fifths of the top group's figure, keeping it off until the cause is understood.

Policy 4: Employee Mental Health

The firm offers an employee assistance program that few employees use and has considered adding a well-being app. Field supervisors report that stress rises with summer schedules, overtime and heat, and absences rise with it.

Wu et al. (2021) reviewed research on workplace mental health and identified eight categories of best practice: culture, mental health benefits, resources, workplace policies, a healthy work environment, leadership support, outcome measurement and innovation. Their framework treats mental health as an organizational responsibility. Fleming (2024) offers a caution about one popular approach: in a survey of 46,336 workers across 233 UK organizations, those who participated in resilience training, mindfulness programs or well-being apps reported no better well-being than those who did not, which the author linked to such programs failing to address job demands.

Policy statement. Supervisors and managers will complete annual training in recognizing signs of distress and referring employees to the employee assistance program, whose services will be promoted at every safety meeting. The firm will limit scheduled overtime during the summer season to 10 hours a week per employee except in emergencies approved by a project director. Any proposal for an individual-level program, such as an app, must be paired with a review of the job demands of the employees it targets. HR will track program use, absence rates and an annual well-being survey item and report them to leadership. Field crews present a particular challenge, since many workers distrust programs that seem to come from the office. The policy therefore asks foremen, rather than HR staff, to introduce the assistance program at crew meetings, and it guarantees that referrals are confidential and carry no effect on assignments or pay. Leadership support, one of the categories Wu and colleagues emphasized, will be measured by whether managers complete the training and whether their crews' use of the program rises.

Biblical Integration

The four policies share a conviction that employees are people to be cared for, not resources to be consumed. Paul instructed masters to give those under them what is right and fair, since the masters themselves had a Master above (English Standard Version Bible, 2001/2016, Colossians 4:1). The pay policy applies that justice by making pay criteria visible and correcting unexplained gaps. The AI policy applies it by refusing to let a tool make decisions no one can explain. The hybrid work and mental health policies apply care for the whole person, recognizing that employees have families, commutes and limits. Each policy also requires measurement, because good intentions do not guarantee good outcomes, and stewardship includes checking whether a policy does what it promised.

Implementation and Review

The vice president of human resources will own the manual and present the four policies to the executive team for approval this quarter. The HR business partners will brief managers before each policy takes effect, beginning with the AI hiring policy because of its legal exposure, followed by pay ranges, hybrid work and the mental health provisions. Each policy names its measures, and HR will report them annually, revising any policy whose results fall short. Two risks deserve attention during rollout. Publishing pay ranges may reveal that some employees sit below the minimum of their new range, so the budget for the first year must include corrections before publication. The AI audit may show adverse impact that requires suspending the screener during a busy hiring season, so recruiters should be ready to screen manually.

Conclusion

Recent research gives the firm unusually clear guidance on hybrid work, more mixed guidance on pay transparency and AI in hiring and a useful warning about mental health programs that ignore working conditions. The four policies in this paper follow that evidence where it is strong, choose carefully where it conflicts and build in measurement so that the firm can learn whether its choices were right.

References

Baker, M., Halberstam, Y., Kroft, K., Mas, A., & Messacar, D. (2023). Pay transparency and the gender gap. American Economic Journal: Applied Economics, 15(2), 157-183. https://doi.org/10.1257/app.20210141

Bloom, N., Han, R., & Liang, J. (2024). Hybrid working from home improves retention without damaging performance. Nature, 630(8018), 920-925. https://doi.org/10.1038/s41586-024-07500-2

Choudhury, P., Makridis, C. A., Khanna, T., & Schirmann, K. (2026). Is hybrid work the best of both worlds? Evidence from a field experiment. The Review of Economics and Statistics, 108(4), 1134-1140. https://doi.org/10.1162/rest_a_01428

Cullen, Z. (2024). Is pay transparency good? Journal of Economic Perspectives, 38(1), 153-180. https://doi.org/10.1257/jep.38.1.153

Cullen, Z. B., & Pakzad-Hurson, B. (2023). Equilibrium effects of pay transparency. Econometrica, 91(3), 765-802. https://doi.org/10.3982/ECTA19788

English Standard Version Bible. (2016). Crossway. (Original work published 2001)

Fleming, W. J. (2024). Employee well-being outcomes from individual-level mental health interventions: Cross-sectional evidence from the United Kingdom. Industrial Relations Journal, 55(2), 162-182. https://doi.org/10.1111/irj.12418

Folger, N., Brosi, P., Stumpf-Wollersheim, J., & Welpe, I. M. (2022). Applicant reactions to digital selection methods: A signaling perspective on innovativeness and procedural justice. Journal of Business and Psychology, 37(4), 735-757. https://doi.org/10.1007/s10869-021-09770-3

Hunkenschroer, A. L., & Luetge, C. (2022). Ethics of AI-enabled recruiting and selection: A review and research agenda. Journal of Business Ethics, 178(4), 977-1007. https://doi.org/10.1007/s10551-022-05049-6

Kelan, E. K. (2024). Algorithmic inclusion: Shaping the predictive algorithms of artificial intelligence in hiring. Human Resource Management Journal, 34(3), 694-707. https://doi.org/10.1111/1748-8583.12511

Noe, R. A., Hollenbeck, J. R., Gerhart, B., & Wright, P. M. (2018). Human resource management: Gaining a competitive advantage (11th ed.). McGraw-Hill Education.

Wu, A., Roemer, E. C., Kent, K. B., Ballard, D. W., & Goetzel, R. Z. (2021). Organizational best practices supporting mental health in the workplace. Journal of Occupational and Environmental Medicine, 63(12), e925-e931. https://doi.org/10.1097/JOM.0000000000002407