One-third of science, technology, engineering, and mathematics (STEM) jobs are unfilled, resulting in less revenue for companies. The purpose of this qualitative single case study, grounded in the human capital theory, was to explore strategies that 2 hiring managers used to recruit STEM professionals in an organization in central Virginia. Data were collected from semistructured interviews, member checking, and company documents on STEM recruitment. The 4 themes that emerged from the data, analyzed using Yin's 5-step process, were as follows: partnerships with state government and local colleges, leadership and employee development programs used as recruitment incentives, competitive compensation package and work-life balance to entice new hires, and meticulous investigation, evaluation, and trial periods. The findings of this study could provide hiring managers with strategies to improve recruitment of skilled STEM professionals. The implications for positive social change could include a reduction in employee turnover, a decrease in employee recruiting costs, an increase in employee retention, more motivated and engaged employees, and an increase in employee production, all of which could increase profits and positively influence the company and the health of the community.

This research seeks to grasp the effect that trust in algorithm word has on the decisions made by hiring managers when it comes to selecting candidates. Specifically, this research will focus on whether trust in algorithms affects how much emphasis hiring managers put on important traits such as Experience, Education, and Qualifications. Ultimately, the goal of this research is to assess whether algorithm formulas or traditional assessment methods are currently producing better hires for organizations. Understanding the impact of trust in algorithms will help determine which method is best for employers to use moving forward. Also, how managers cope with bias and what role they play during the hiring selection. The data collected from the experiment will help identify and analyze Artificial Intelligence's impact on hiring managers' decisions. The study will also use the data from the experiment to test the hypothesis. Ultimately, this will help us to determine if Artificial Intelligence can reduce bias in the recruitment process and provide employers with more accurate insights into applicants' abilities. This study is expected to lead to more efficient and effective use of Artificial Intelligence in recruitment while helping employers make more informed decisions. We hope this research will pave the way for a more equitable hiring process by reducing bias and providing an objective evaluation of applicants' abilities. By having such a variety of diversity in Industries, Race and Gender, this research is a piece of real world that every employer can replicate for their hiring or training employees. We look forward to seeing how AI can improve the recruitment process. By accurately assessing applicants and considering their abilities, employers can make informed decisions that benefit both applicants and employers. / Business Administration/Human Resource Management

Successful transition to civilian employment is a challenge for veterans. The purpose of this single case study was to explore critical aspects of hiring managers' decision-making process and to understand how these strategies and techniques affect the hiring of veterans. Tajfel and Turner's social identity theory and Lewin's organizational change model formed the conceptual framework for the study. The participants for this study were 8 hiring managers from a midsized company in the Upper Hudson Valley Region, New York. Data were collected using semistructured interviews. The data were analyzed and coded and 4 themes emerged: strategies used to fill open positions, specific recruitment and interview protocols, veterans' skills from military training, and lack of experience with hiring veterans. The study results may contribute to veteran's awareness of the skills that employers are seeking that veterans may be able to fulfill. The results of the study could create an opportunity for hiring managers to recognize that veterans represent a trained, ready-made talent pool. The social impact of the study could help hiring managers identify and design the required job description criteria to include the transferable skills of veterans.

This thesis examines how hiring managers promote inclusion in the cybersecurity sector. Conducted as a case study in a small to medium-sized multinational cybersecurity company, the research involved qualitative methods and semi-structured interviews. Findings reveal that hiring managers use strategies like unbiased recruitment, customized onboarding, team collaboration, trainings, and regular one-on-one meetings to foster inclusion. However, they face challenges such as a limited talent pool, budget constraints, cultural and language barriers, resistance to inclusion efforts, the impact of remote work, and a lack of formal tools and metrics. The study highlights the complex role of hiring managers in achieving workplace inclusion and suggests integrating sustainability and sustainable human resources practices to enhance inclusion efforts. Further research is recommended on the long-term effects of these initiatives, the intersectionality of identity groups, and the impact of emerging technologies and remote work on inclusion.