PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
August 21, 20250 citationsOpen Access

Optimizing Job Offer Packages: How Can Organizations Enhance Personnel Selection?

View Full Paper
SNSaeed Najafi-ZangenehNSNaser Shams-GharnehOGOlivier Gossner

Key Points

  • The proposed model optimizes job-offer packages, enhancing selection efficiency and candidate attraction.
  • Utilizing Mixed-Integer Linear Programming, the model integrates candidate preferences with organizational costs.
  • A case study validates the model's effectiveness in improving hiring processes and talent attraction.
  • This approach advances personnel selection strategies for organizations navigating two-sided market complexities.

Abstract

Abstract Personnel selection in competitive two-sided markets presents challenges for organizations seeking to attract high-quality candidates while managing their costs. However, existing models often overlook the optimization of job-offer packages to address both candidate preferences and organizational objectives. This paper addresses this research gap by proposing a novel mathematical model for determining the optimal job-offer package. Our model integrates candidate preferences and organizational costs, offering a solution to the selection process. Utilizing a two-stage Mixed-Integer Linear Programming (MILP) approach, we employ the Gale and Shapley algorithm to facilitate efficient two-sided matching. Through a case study, we validate the effectiveness of our model in providing actionable insights for organizations seeking to enhance their hiring processes and attract top talent. This innovative approach marks a significant advancement in the realm of personnel selection, offering organizations a robust framework to navigate the complexities of two-sided market dynamics with precision and efficacy.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Najafi-Zangeneh et al. (2025) studied this question.

synapsesocial.com/papers/68af5228ad7bf08b1eada0dehttps://doi.org/10.21203/rs.3.rs-4129689/v1
Ask AI
Helpful
Bookmark
Share
View Full Paper