A DATA-DRIVEN MADM MODEL FOR PERSONNEL SELECTION AND IMPROVEMENT

Decision making in human resource management: a systematic review of the applications of analytic hierarchy process

The Analytic Hierarchy Process (AHP), Fuzzy Analytic Hierarchy Process (FAHP), and Analytic Network Process (ANP) methods are widely used for decision-making across various fields, and have shown success in numerous applications within human resource management (HRM). The purpose of this study is to present a systematic literature review on the applications of AHP, FAHP, and ANP in human resource management. The search process encompassed two main online databases, Scopus and Web of Science. This research covers a total of 180 application papers. To help readers extract quick and meaningful information, references are summarized in tabular format. The results showed that these methods have been applied in various domains of HRM such as performance management and appraisal, selecting human resources, talent attraction and retention, intellectual capital, workplace safety, reward management, e-HRM, green HRM, etc. To the best of our knowledge, no comprehensive research review has yet been conducted on the applications of AHP in HRM.

A Weighted Bonferroni-OWA Operator Based Cumulative Belief Degree Approach to Personnel Selection Based on Automated Video Interview Assessment Data

Asynchronous Video Interviewing (AVI) is considered one of the most recent and promising innovations in the recruitment process. Using AVI in combination with AI-based technologies enables recruiters/employers to automate many of the tasks that are typically required for screening, assessing, and selecting candidates. In fact, the automated assessment and selection process is a complex and uncertain problem involving highly subjective, multiple interrelated criteria. In order to address these issues, an effective and practical approach is proposed that is able to transform, weight, combine, and rank automated AVI assessments obtained through AI technologies and machine learning. The suggested approach combines Cumulative Belief Structures with the Weighted Bonferroni-OWA operator, which allows (i) aggregating assessment scores obtained in different forms and scales; (ii) incorporating interrelationships between criteria into the analysis (iii) considering accuracies of the learning algorithms as weights of criteria; and (iv) weighting criteria objectively. The proposed approach ensures a completely data-driven and efficient approach to the personnel selection process. To justify the effectiveness and applicability of the suggested approach, an example case is presented in which the new approach is compared to classical MCDM techniques.

Exploring the Key Factors of Shared Decision-Making Through an Influential Network Relation Map: The Orthopedic Nurse's Perspective

BACKGROUND

Few studies have used quantitative methods to explore the key factors affecting shared decision-making (SDM) in nursing decision-making from the perspective of orthopedic nurses.

PURPOSE

To understand the intercorrelations among shared decision-making questionnaire-nurse (SDM-Q-NUR) factors and identify key factors for clinical nursing care decisions in orthopedics.

METHODS

In May 2021, this study investigated the interdependence of the SDM-Q-NUR scale and developed an influential network-relation map (INRM) from the clinical experience of 13 trained orthopedic nurses using the Decision-making Trial and Evaluation Laboratory method.

RESULTS

The INRM results showed that the nine criteria corresponded to three stages: preparation, discussion, and decision. "I helped my patient or patient's family understand all the information" (C 5) and "I wanted to know from my patient or patient's family how they want to be involved in making the nursing care decision" (C 2) are the main key factors for the beginning of nursing decision. In the discussion and decision stages, the corresponding key factors are "I made it clear to my patient or patient's family that a nursing care decision needs to be made" (C 1) and "I asked my patient or patient's family which nursing care option they prefer" (C 6). The result's statistical significance confidence and gap error were 98.106% and 1.894%, respectively.

CONCLUSIONS

When making nursing decisions with patients, orthopedic nurses need to have detailed information about how patients are involved in SDM and all relevant information. Nurses should also inform patients and their families regarding the purpose of the discussion, namely, to help one understand the content, advantages, and disadvantages of the nursing care options, and finally, make a decision.

Using DEMATEL Technique to Identify the Key Success Factors of Shared Decision-Making Based on Influential Network Relationship Perspective

In the field of medicine, shared decision-making (SDM) is an important issue primarily aimed at resolving the problem of information asymmetry between clinicians and patients in the selection of treatment options and follow-up nursing plans. Most previous studies on this topic have focused on key elements and the development and implementation of SDM scales. This study used the decision-making trial and evaluation laboratory (DEMATEL) method to establish a network of influence relationships among factors that are keys to the success of the SDM process. Survey data were obtained from a well-known brain hospital in China. The key factors of success included tailor information, flexibility approach, check understanding patient, document (discussion about) decision, present evidence, make or explicitly defer decision, and patient values and preferences. We determined that clinicians should provide a series of treatment options and follow-up care plans based on a patientʼs conditions and preferences. Clinicians should also actively communicate with patients and their families to ensure a thorough understanding of the entire treatment and nursing process. This study also highlights the academic value of the cross-disciplinary integration of medical decision issues and multiple attribute decision-making methodologies.

A Hybrid MADM Model for Newly Graduated Nurse's Competence Evaluation and Improvement

Nursing departments in hospitals must evaluate the practical competency of newly graduated nurses and assist them to increase their competence. Competency assessments often consider multiple qualitative attributes and use expert knowledge as the basis for decision-making. This study proposes a hybrid multiple attribute decision-making (MADM) model that determines practical competency of the newly graduated nurse as an evaluation framework. A causal influence-network diagram (CIND) and influential weights are obtained from nursing experts' clinical experience using the decision-making trial and evaluation laboratory (DEMATEL)-based analytical network process analysis (DANP). The MOORA-AS method is then used to evaluate the ability expectation ratio-gap for newly graduated nurses in practice. The CIND is used to allow each newly graduated nurse to reduce the performance ratio-gaps between the current level and the aspirational level from a systematic perspective. The empirical data applies to a third-class and a first-class hospital in China. The results show that the proposed hybrid MADM model has reliable results and allows nursing department decision-makers/managers to easily evaluate and systematically improve competencies for newly graduated nurses.

A Two-Phase Model for Personnel Selection Based on Multi-Type Fuzzy Information

From the viewpoint of human resource management, personnel selection is one of the more important issues for enterprises in a high-level competitive environment. In general, many influence factors, quantitative and qualitative, affect the decision-making process of personnel selection. For considering qualitative factors, decision-makers cannot always easily judge the suitable degree of each applicant. Under this situation, this research proposes a systematic decision-making method based on computing with linguistic variables. First, unsuitable applicants are filtered by considering the quantitative information of each applicant. At this stage, technique for order of preference by similarity to ideal solution (TOPSIS) and entropy methods are aggregated to eliminate unsuitable applicants in accordance with their closeness coefficient values. Second, experts (or decision-makers) use different types of 2-tuple linguistic variables to express their opinions of suitable candidates with respect to qualitative criteria. At this stage, we consider different preference functions in the preference ranking organization method for enrichment evaluation (PROMETHEE) method to calculate the outranking index of each suitable candidate. Next, we aggregate the closeness coefficient and outranking index of each suitable applicant to determine the ranking order. In order to illustrate the computational processes, an example demonstrates the practicability of the two-phase personnel selection method. The benefit of the proposed method is as follows. (1) It reduces the time for reviewing and evaluating the huge numbers of applicants. (2) It avoids subjective judgment by experts to determine the weights of all criteria. Finally, conclusions and contributions are discussed at the end of this paper.