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Table of Contents
Research Problem 3
Importance/Research Significance 4
Human Resource Role in Time Management 4
Uber’s Part-time Management 6
Research Aim 7
Goals/Research Objectives 7
Research Questions 7
Data Collection Plan 8
Recent human resource management practices have depicted substantial interest in the use of artificial intelligence. Jatobá et al. (2019) found that human resource management (HRM) research has focused on multiple domains for the application of artificial intelligence (AI), including the application of AI in recruitment and selection, work team management, managing turnover, employee training, and managing overall management. Research has focused on diverse sources of employee efficiency in HRM except for time management efficiency (Garg, Srivastav, & Gupta, 2018). However, time is an essential resource in employee performance. Therefore, time efficiency is critical to numerous domains of work outcomes (Ogohi, Daniel, and Jiya, 2020). Attempts to optimize time utilization and management apply to diverse situations, including the recent constraints of the COVID-19 pandemic that limits the time available for productive work performance (Kshirsagar, 2021). Furthermore, employees and all other entities have finite time for their work lives and lifetimes. Therefore, innovating time management in HRM is valuable to employee-related and organizational outcomes.
Time management in HRM is still non-optimal despite the prevalence of technology in workplaces, organizational, and management systems. A wide range of contingencies impact time flow and the practical approaches to time management, including employee-related issues of motivation, commitment, and habituation (Kshirsagar, 2021). Artificial intelligence is an evolving area of research and practice that aims to solve complicated problems through intelligent agents. However, the uncertainty of the corresponding effects on the employees and organizations makes applying artificial intelligence complex (Schafheitle et al., 2020). As a result, both the employee and organization may experience technology-related uncertainty, with costs or benefits to time management.
Introducing intelligent agents to help employees improve time management efficiency also implies introducing and adopting sophisticated technology. Such adoption comes with adoption-related constraints and other contingencies (Agboola et al., 2019). The interaction of various constraints with time efficiency is critical to the ultimate outcomes of time management. Furthermore, the adoption of artificial intelligence also introduces structural changes to operations and performance (Schafheitle et al., 20
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