Reducing false starts among IT employees—A study of hiring professionals’ perceptions

<p>One of the biggest challenges facing organizations is recruiting and retaining talented employees. A false start is an issue where an employee is hired for a certain position following the organization’s recruiting process; however, within the first year, the employee or the employer realizes that it is not a right fit, and the employment is terminated. The unexpected loss of employees is recognized as a global issue that affects overall business performance regardless of industry. The economic impact of false starts is estimated to be $1.28 billion per year according to the Bureau of Labor Statistics (BLS). This study examines the relationship between reduction of false starts among information technology employees and four factors: constructs from job embeddedness theory, constructs from person-group fit theory, acceptance of realistic job previews using technology acceptance model, and acceptance of artificial-intelligence-based automation tools using the technology acceptance model. This research employs a quantitative, survey-based design. Data from 300 professionals involved in employee selection were collected and analyzed to answer the research questions. Results showed that all four of the independent variables were significantly correlated to the intention to stay, which itself was significantly correlated to the reduction of false starts.</p>

Name thesis:degree_name

Doctor of Philosophy (PhD)

Level thesis:degree_level

Open Access Dissertation

Discipline thesis:degree_discipline

College of Technology

Year dc:date.available

2023

Author dc:creator

• Kulkarni, Swatee B.

Contributors dc:contributor

• Giri Jogaratnam, PhD,
• Dorothy McAllen, PhD
• Julie Becker, PhD

• Job embeddedness
• Person-Group fit
• Realistic job previews
• Recruiting and retaining
• Reducing false starts
• Using AI based tools for recruiting
• Business Administration, Management, and Operations
• Engineering

Repository record dc:identifier

https://commons.emich.edu/theses/1215

OAI identifier oai:identifier

oai:commons.emich.edu:theses-2580