• Proceedings of the Korea Information Processing Society Conference
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• 2021.05a
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• pp.297-300
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• 2021

대부분의 기업에서는 우수한 인적 자원의 유출을 방지하기 위해 직원들이 이직 및 퇴사하는 이유를 연구한다. 이에 기업은 직원이 퇴사하기 전에 면담을 하거나 설문조사를 통해서 연구에 필요한 데이터를 얻는다. 하지만 설문조사에서는 직원들이 직장 생활을 하는 데에 불리할 수도 있는 의견을 드러내려고 하지 않아 정확한 결과를 얻기 힘든 것이 현실이다. 한편, 한국노동연구원에서 발표한 자료에 따르면 기업이 요구하는 최소 학력 수준과 직원의 학력 수준 간의 차이가 클수록 이직 경향이 커진다. 따라서 본 연구에서는 한국노동연구원의 자료에 착안하여, 직원이 가지고 있는 객관적 데이터인 전공, 교육수준, 재직 중인 회사 유형 등의 데이터를 기반으로 직원의 퇴사 여부를 예측하고자 한다. 퇴사 예측 모델을 생성하기 위해 Decision Tree, XGBoost, kNN, SVM을 활용하였으며 각각의 성능을 비교했다. 이 결과, 지금까지 설문조사로 진행되었던 연구에서 파악하지 못한 다양한 요인을 알아낼 수 있었다. 이를 통해 기업이 퇴사 예측 모델을 이용하여 직원이 퇴사하기 전에 미리 이를 인지하고 방지하는 데에 도움을 줄 수 있을 것으로 예상된다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.371-374
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• 2021

호텔기업은 고객서비스가 가장중요한 업무중의 하나로 직원들이 고객과의 대면상황에서 어떤 서비스가 펼쳐지는지에 따라서 호텔의 이익이 바뀌어 질수 있다. 따라서 호텔은 유능하고 우수한 직원들을 꾸준하게 유지하는 것이 무엇보다 중요하다. 다른 호텔과의 차별화를 위하여 직원들의 타 업체로의 이직을 막아 회사의 안정적인 발전을 도모하고 이윤을 최대한 높이는데 초점을 맞추어야 한다. 본 연구는 조직관리로써 역할갈등(직장-가정갈등, 가정-직장갈등)과 이직의도와의 관계를 알아보고자 한다. 그리고 역할갈등이 이직의도에 미치는 영향에 마음챙김 변수를 대입하여 매개역할을 검증하고자 한다. 자료 수집을 위해서 서울과 경기도 지역의 특급호텔 종사원을 대상으로 설문을 실시하였다. 자료 수집을 위하여 서울 경기지방을 직접 방문하여 조사를 진행하였다. 분석결과를 간단히 요약하면 첫째, 역할갈등은 이직의도에 유의미한 관계를 이루었고, 역할갈등이 이직의도에 미치는 영향에 있어서 마음챙김은 매개역할을 하였다. 역할갈등이 이직의도에 미치는 영향에 있어서 마음챙김의 매개효과는 역할 갈등의 첫 번째 변수인 직장-가정갈등 상황에서 이직의도에 미치는 영향을 마음챙김이 매개를 한다는 유의미한 결과를 얻었다. 그리고 두 번째 독립변수인 가정- 직장갈등의 상황에서는 마음챙김이 매개역할을 하는 것으로 예측되었다. 이런 연구결과를 바탕으로 기업은 체계적이고 지속적인 마음챙김 교육을 통하여 구성원 상호간의 상호 배려가 기업에 대한 긍정적인 태도로 바뀔 수 있도록 하는 방안을 강구해야 할 것이다. 또한 역할갈등의 결과변수를 좋게 하는 다양한 방안에 대한 폭넓은 연구가 필요할 것으로 보인다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.100-103
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• 2021

Most companies study why employees resign their jobs to prevent the outflow of excellent human resources. To obtain the data needed for the study, employees are interviewed or surveyed before resignation. However, it is difficult to get accurate results because employees do not want to express their opinions that may be disadvantageous to working in a survey. Meanwhile, according to the data released by the Korea Labor Institute, the greater the difference between the minimum level of education required by companies and the level of employees' academic background, the greater the tendency to resign jobs. Therefore, based on these data, in this study, we would like to predict whether employees will leave the company based on data such as major, education level and company type. We generate four kinds of resignation prediction models using Decision Tree, XGBoost, kNN and SVM, and compared their respective performance. As a result, we could identify various factors that were not covered in previous study. It is expected that the resignation prediction model help companies recognize employees who intend to leave the company in advance.

• Knowledge Management Research
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• v.25 no.2
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• pp.19-47
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• 2024

Organizational ability to analyze and utilize data plays an important role in knowledge management and decision-making. This study aims to investigate the potential application of large language models in corporate data analysis. Focusing on the field of human resources, the research examines the data analysis capabilities of these models. Using the widely studied IBM HR dataset, the study reproduces machine learning-based employee turnover prediction analyses from previous research through ChatGPT and compares its predictive performance. Unlike past research methods that required advanced programming skills, ChatGPT-based machine learning data analysis, conducted through the analyst's natural language requests, offers the advantages of being much easier and faster. Moreover, its prediction accuracy was found to be competitive compared to previous studies. This suggests that large language models could serve as effective and practical alternatives in the field of corporate data analysis, which has traditionally demanded advanced programming capabilities. Furthermore, this approach is expected to contribute to the popularization of data analysis and the spread of data-driven decision-making (DDDM). The prompts used during the data analysis process and the program code generated by ChatGPT are also included in the appendix for verification, providing a foundation for future data analysis research using large language models.

• The Journal of Korean Association of Computer Education
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• v.20 no.1
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• pp.87-97
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• 2017

Recently, much research has been done on the turnover and retirement intentions of the organization members as many companies recognize the negative impact of the human resource outflow on the organization. However, most of the studies are conducted in the form of questionnaires, and there is still a lack of studies on the turnover and retirement intentions based on the work life data. In this study, we analyzed the factors affecting the retirement of employees based on the work life profile, and created a retirement prediction model using the machine learning method. As a result, we could identify various factors that were not covered in previous researches. In addition, we have established a basis for research that can provide a solution for the problem of human resource outflow by generating a good performance retirement prediction model.

• Smart Media Journal
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• v.11 no.10
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• pp.9-21
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• 2022

The recent expansion of the use of social media has served as an opportunity to express users' opinions in real time in various fields such as society, economy, politics, and culture, and brought many platforms that provide various information about companies. Among them, Glassdoor.com which started 2008 in US provides users with evaluations of the current and the former employees of their companies and also provides a outlooks for the company's growth Such a platform has the utility of providing necessary information to whom want to find a job or change jobs. In addition to this, variable studies have shown that the company information provided through these platforms is useful for investors as well. In this study, it was tested whether the corporate growth prospects of employees provided by Jobplanet, a platform with a typical function similar to Glassdoor.com in Korea, have predictive power to predict actual corporate growth. The forecast provided by Jobplanet and the company's financial indicator data received from FnGuide were collected and composed of panel data and analyzed using fixed effect model regression analysis. As a result, it was found that companies with positive prospects had higher employment growth than companies with negative prospects. When the outlook was neutral, the employment growth rate was higher than that of companies with a negative outlook.