A flexible employee recruitment and compensation model: A bi-level optimization approach

Area of Science:

• Operations Research
• Human Resource Management
• Organizational Behavior

Background:

• The COVID-19 pandemic accelerated the adoption of flexible work arrangements, impacting traditional management and HR practices.
• Existing models often fail to account for the complexities of flexible work, hindering optimal decision-making in recruitment and compensation.

Purpose of the Study:

• To develop a novel bi-level mathematical programming model for optimizing recruitment and compensation decisions in flexible work settings.
• To provide a decision support tool for firms aiming to enhance profitability and efficiency through hybrid and flexible work plans.

Main Methods:

• A bi-level mathematical programming model with a mixed integer formulation was developed.
• Computational analysis was performed to assess candidate behavior and strategic firm responses.
• The model's optimal solution was applied to a real-world case study involving software engineer recruitment.

Main Results:

• The proposed bi-level model offers an exact solution for recruitment and compensation decisions.
• Introducing flexible work plans resulted in up to a 59% improvement in firm profitability.
• The model demonstrated applicability and effectiveness on a real-world dataset.

Conclusions:

• The novel bi-level model enhances managerial practices for recruitment and compensation in flexible work environments.
• Flexible work strategies, when optimized, lead to substantial profitability gains for firms.
• This research provides a valuable framework for businesses navigating the evolving landscape of work.