• The Journal of the Korea Contents Association
• /
• v.22 no.9
• /
• pp.614-627
• /
• 2022

The purpose of this study was to explore the various data and methods employed by HR analytics studies. The researchers selected 78 KCI-indexed empirical articles on HR analytics and categorized them using the Employee Life Cycle framework. This yielded several important findings. First, employee retention has been the most common subject of extant studies, followed by performance management. Second, HR analytics studies have used a variety of data (structured and unstructured) according to their research questions, and the data sources have ranged from organizations' internal systems to national databases. Third, most domestic HR analytics studies have been descriptive and diagnostic, whereas predictive and prescriptive studies have been rare. These results have important theoretical and practical implications for future HR analytics research.

• The Journal of the Korea Contents Association
• /
• v.22 no.3
• /
• pp.442-452
• /
• 2022

This study was conducted to understand research trends of HR Analytics (HRA) in Korea and to suggest future research directions. First, a comparative analysis was conducted by classifying six areas of recruitment on-board, work environment, performance evaluation, retention, and exit/retirement building on the employee life cycle framework. The results indicate that first, the distribution of detailed research topics in Korean HRA research has similar to that of international research. Second, Korean HRA studies related to employee training and development function are insufficient. Third, the scope and the method of machine learning are becoming enriched. Finally Korean HRA studies are still in the technical domain and toward entering the predictive analysis domain.

• Asia pacific journal of information systems
• /
• v.30 no.4
• /
• pp.847-878
• /
• 2020

In the era of disruptive change, a data-driven approach is vital to Human Resource Management (HRM) of any leading organization, for it is used to gain a competitive advantage. HR analytics (HRA) has emerged as innovative technologies since advanced analytics, i.e., predictive or prescriptive analytics, were widely used in the High Performing Organizations (HPOs). Therefore, many organizations elevate themselves to become HPOs through Data Science on the "people side." This paper proposes a systematic literature review using the Literature Weighted Scoring (LWS) to develop a conceptual framework based on three adoption theories, which are the Technology-Organization-Environment (TOE), Diffusion of Innovation (DOI), and Unified Theory of Acceptance and Use of Technology (UTAUT). The results show that a total of 13 theory-derived factors are determined as influential factors affecting HRA adoption, and the top three factors are "Quantitative Self-Efficacy," "Top Management Support," and "Data Availability." The conceptual framework with hypotheses is proposed to provide a foundation for further studies on organizational HRA adoption.

• Journal of Information Technology Services
• /
• v.16 no.1
• /
• pp.1-26
• /
• 2017

Keeping good, talented people is one of the most significant factors in a company's success. HR analytics is an important area for applying big data analysis techniques to human resources. It provides organizational insight that enables effective management of employees, allowing management to reach their business goals quickly and efficiently. Job satisfaction and employee turnover analysis are the keys to HR analytics. Job review web services have been becoming popular. Because people exchange information about job satisfaction and turnover through these web services, useful information about HR Analytics is accumulated on the job review web sites. In this paper, we identified factors of employee retention by analyzing a Job Satisfaction/Retention group, and the factors of employee turnover by analyzing a Job Unsatisfaction/Turnover group. In order to do this, we first classified employees according to whether their self-reported job satisfaction or turnover was true. We collected and analyzed data from Jobplanet, a popular job review site. Through dominance analysis and LDA topic modeling, we found major factors, topics, and keywords of the classified groups by IT, service, and manufacturing domains. Our approach is a novel model to apply the analysis of reviews and text mining to the HR domain, and it will be practically helpful for setting new strategies that improve job satisfaction.

• Journal of Industrial Convergence
• /
• v.20 no.7
• /
• pp.31-36
• /
• 2022

There is a growing perception that HR management that streamlines corporate resources is necessary to retain competitive advantage. In this study, data-based HR management is focused on the perception of HR job experts and data-based HR management execution and utilization prospects at corporate sites. The subjects of the study were three HR planning/management job experts of three firms specializing in IT services in Pangyo, and focused on identifying data-based HR management execution, measurement, analysis tools, and utilization level. As a research method, open coding, axis coding, selective coding procedure based on evidence theory was presented. As a result of in-depth interviews, corporate HR management measurement indicators were divided into three areas: employee, productivity, and culture. Through this study, it was possible to find the significance of perception of the company site as to what measurement tools and mechanisms the company implemented and measured the effectiveness and efficiency of HR management.

• International Journal of Computer Science & Network Security
• /
• v.23 no.7
• /
• pp.71-78
• /
• 2023

Employee turnover is one of the most important challenges facing modern organizations. It causes job experiences and skills such as distinguished faculty members in universities, rare-specialized doctors, innovative engineers, and senior administrators. HR analytics has enhanced the area of data analytics to an extent that institutions can figure out their employees' characteristics; where inaccuracy leads to incorrect decision making. This paper aims to develop a novel model that can help decision-makers to classify the problem of Employee Turnover. By using feature selection methods: Information Gain and Chi-Square, the most important four features have been extracted from the dataset. These features are over time, job level, salary, and years in the organization. As one of the important results of this research, these features should be planned carefully to keep organizations their employees as valuable assets. The proposed model based on machine learning algorithms. Classification algorithms were used to implement the model such as Decision Tree, SVM, Random Frost, Neuronal Network, and Naive Bayes. The model was trained and tested by using a dataset that consists of 1470 records and 25 features. To develop the research model, many experiments had been conducted to find the best one. Based on implementation results, the Neural Network algorithm is selected as the best one with an Accuracy of 84 percents and AUC (ROC) 74 percents. By validation mechanism, the model is acceptable and reliable to help origination decision-makers to manage their employees in a good manner.

• Journal of information and communication convergence engineering
• /
• v.22 no.1
• /
• pp.44-55
• /
• 2024

High-utility itemset mining (HUIM) is a dominant technology that enables enterprises to make real-time decisions, including supply chain management, customer segmentation, and business analytics. However, classical support value-driven Apriori solutions are confined and unable to meet real-time enterprise demands, especially for large amounts of input data. This study introduces a groundbreaking model for top-N high utility itemset mining in real-time enterprise applications. Unlike traditional Apriori-based solutions, the proposed convolutional sequential embedding metrics-driven cosine-similarity-based multilayer perception learning model leverages global and contextual features, including semantic attributes, for enhanced top-N recommendations over sequential transactions. The MATLAB-based simulations of the model on diverse datasets, demonstrated an impressive precision (0.5632), mean absolute error (MAE) (0.7610), hit rate (HR)@K (0.5720), and normalized discounted cumulative gain (NDCG)@K (0.4268). The average MAE across different datasets and latent dimensions was 0.608. Additionally, the model achieved remarkable cumulative accuracy and precision of 97.94% and 97.04% in performance, respectively, surpassing existing state-of-the-art models. This affirms the robustness and effectiveness of the proposed model in real-time enterprise scenarios.

• Journal of Intelligence and Information Systems
• /
• v.29 no.2
• /
• pp.57-84
• /
• 2023

With the development of information and communications technology (ICT) and big data technology, anyone can easily obtain and utilize vast amounts of data through the Internet. Therefore, the capability of selecting high-quality data from a large amount of information is becoming more important than the capability of just collecting them. This trend continues in academia; literature reviews, such as systematic and non-systematic reviews, have been conducted in various research fields to construct a healthy knowledge structure by selecting high-quality research from accumulated research materials. Meanwhile, after the COVID-19 pandemic, remote healthcare services, which have not been agreed upon, are allowed to a limited extent, and new healthcare services such as health recommender systems (HRS) equipped with artificial intelligence (AI) and big data technologies are in the spotlight. Although, in practice, HRS are considered one of the most important technologies to lead the future healthcare industry, literature review on HRS is relatively rare compared to other fields. In addition, although HRS are fields of convergence with a strong interdisciplinary nature, prior literature review studies have mainly applied either systematic or non-systematic review methods; hence, there are limitations in analyzing interactions or dynamic relationships with other research fields. Therefore, in this study, the overall network structure of HRS and surrounding research fields were identified using citation network analysis (CNA). Additionally, in this process, in order to address the problem that the latest papers are underestimated in their citation relationships, the GraphSAGE algorithm was applied. As a result, this study identified 'recommender system', 'wireless & IoT', 'computer vision', and 'text mining' as increasingly important research fields related to HRS research, and confirmed that 'personalization' and 'privacy' are emerging issues in HRS research. The study findings would provide both academic and practical insights into identifying the structure of the HRS research community, examining related research trends, and designing future HRS research directions.

• Asian Pacific Journal of Cancer Prevention
• /
• v.15 no.3
• /
• pp.1155-1162
• /
• 2014

Background: Despite mixed survival data, the utilization of contralateral prophylactic mastectomy (CPM) for the prevention of a contralateral breast cancer (CBC) has increased significantly over the last 15 years, especially among women less than 40. We set out to look at our own experience with CPM, focusing on outcomes in women less than 40, the sub-population with the highest cumulative lifetime risk of developing CBC. With an extended follow-up, we hoped to demonstrate differences in the long-term disease free survival (DFS) and overall survival (OS) among groups who underwent the procedure (CPM) versus those that did not (NCPM). Materials and Methods: We performed a retrospective review of all breast cancer patients less than age 40 diagnosed at Mount Sinai Medical Center between January 1, 1980 and December 31, 2010 (n=481). Among these patients, 42 were identified as having undergone CPM, while 195 were confirmed as being CPM-free during the observation period. A univariate and multivariate analyses were performed. Results: The CPM group had a significantly higher percentage of patients who were diagnosed between 2000 and 2010 (95.2% vs 40%, p=0.0001). The CPM group had significantly smaller tumors (0-2cm.: 41.7% vs 24.8%, p=0.04). Among the entire group of patients, the overall five- and 10-year DFS were 81.3% and 73.3%, respectively. CPM was significantly associated [HR 2.35 (1.02, 5.41); p=0.046] with 10-year OS, although a similar effect was not observed for five-year OS. Conclusions: We found that CPM has increased dramatically over the last 15 years, especially among white women with locally advanced disease. In patients less than 40, who are thought to be at greatest cumulative risk of secondary breast cancer, CPM provided an OS advantage, regardless of genetics, tumor or patient characteristics, and which was only seen after 10 years of follow-up.