Setiawansyah Setiawansyah

Integrating Method based on the Removal Effects of Criteria in Multi-Attribute Utility Theory for Employee Admissions Decision Making

Introduction

Integrating method based on the removal effects of criteria in multi-attribute utility theory for employee admissions decision making. Improve employee recruitment decisions using an integrated MEREC-MAUT method. Enhance objectivity, accuracy, and efficiency in candidate selection, reducing errors for better team building.

Abstract

Effective employee onboarding is essential for the success of an organization because it can ensure that the company acquires quality human resources that are in line with the needs and culture of the company. Careful employee recruitment based on objective evaluation is key in creating a competent team and supporting the achievement of the company's goals. Problems in employee recruitment often arise due to a lack of an objective and transparent selection process, which can lead to improper selection of candidates. One of the main challenges is the presence of errors in judgment, which reduces the diversity and quality of the team formed. The purpose of the study is to combine the principles of multi-attribute utility theory (MAUT) with method based on the removal effects of criteria (MEREC) to improve the decision-making process in employee recruitment which can improve objectivity, accuracy, and efficiency in the recruitment process, as well as reduce possible errors in the assessment of candidates. The results of the employee acceptance ranking using a combination of MEREC and MAUT were obtained by Clara Wijaya occupying the first position with the highest score of 0.7606, followed by Farah Ramadhani with a score of 0.7525. The third position was filled by Andi Santoso with a score of 0.4874. These ratings provide an overview of each individual's performance or eligibility based on a specific assessment.

Review

The paper addresses a highly pertinent issue in human resource management: objective and transparent employee recruitment. The authors correctly identify that subjective biases and judgment errors frequently undermine the quality and diversity of new hires, directly impacting organizational success. To mitigate these challenges, the study proposes an innovative integration of Multi-Attribute Utility Theory (MAUT) with the Method based on the Removal Effects of Criteria (MEREC). This combined approach aims to enhance the objectivity, accuracy, and efficiency of the recruitment process, thereby reducing potential assessment errors. The premise of leveraging robust decision-making methodologies for such a critical organizational function is commendable and holds significant promise for practical application. While the conceptual fusion of MAUT and MEREC presents a promising avenue for improving employee admissions, the abstract provides limited insight into the specific mechanics of this integration. MAUT is well-established for structured decision-making across multiple criteria, and MEREC offers a compelling approach to objectively determining criterion weights based on their impact. However, the abstract does not elaborate on how these two methods are synergistically combined, or how this particular integration specifically overcomes the limitations of using either method in isolation. The reported results, detailing the specific scores and rankings for "Clara Wijaya," "Farah Ramadhani," and "Andi Santoso," serve as an illustrative application. While demonstrating the output, these highly specific findings detract from a broader understanding of the method's generalizability or its impact on a wider pool of candidates and diverse recruitment scenarios. The strength of this work lies in its attempt to introduce a more rigorous, objective, and data-driven approach to employee selection, moving beyond purely qualitative assessments. The use of advanced MCDM techniques like MAUT and MEREC holds significant potential for transforming recruitment practices. However, for a comprehensive understanding of the method's efficacy and applicability, future presentations of this work should provide a more detailed account of the integrated methodology, including the specific criteria considered, the process of data collection, and how the "removal effects" of criteria are reconciled within a utility theory framework. Furthermore, a discussion of the method's robustness, perhaps through sensitivity analysis, and a comparison with existing recruitment models would significantly strengthen its contribution to both academic literature and practical HR applications. The potential for reducing judgment errors and enhancing team quality is a compelling outcome that warrants further methodological exposition and validation beyond illustrative examples.

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