• International Journal of Advanced Culture Technology
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• v.7 no.3
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• pp.92-96
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• 2019

This study analyzes the problems of fire qualification information websites and job search websites due to incomplete information in the job search process and suggests an improvement plan. It has been confirmed that the main reason for the cost of job searching is incomplete information required for a job search and job search through existing analysis. As a result, it is suggested to construct a smooth information system for economic entities and to provide easy access to information by mitigating the incompleteness of information. Based on this, analysis of the problems of Korean qualifications in the firefighting realm reveals that there is a qualification holder information and a job information site, and a qualification holder management system is established but only information of either qualification acquisition information or employment information is provided. In addition, it is easy to access information through a qualification acquisition information and employment information site via the Internet, but there are inconveniences that qualification acquisition information and employment information are dualized. In order to improve this, it is necessary to build a new customized integrated qualification management system that covers existing Q-net qualification acquisition information and worknet employment information.

• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.247-264
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• 2016

With the rapid acceleration of low-birth rate and population aging, the employment of the neglected groups of people including the middle aged class is a crucial issue in South Korea. In particular, in the 2010s, the number of the middle aged who want to find a new job after retirement age is significantly increasing with the arrival of the retirement time of the baby boom generation (born 1955-1963). Despite the importance of matching jobs to this emerging middle aged class, private job portals as well as the Korean government do not provide any online job service tailored for them. A gigantic amount of job information is available online; however, the current recruiting systems do not meet the demand of the middle aged class as their primary targets are young workers. We are in dire need of a specially designed recruiting system for the middle aged. Meanwhile, when users are searching the desired occupations on the Worknet website, provided by the Korean Ministry of Employment and Labor, users are experiencing discomfort to search for similar jobs because Worknet is providing filtered search results on the basis of exact matches of a preferred job code. Besides, according to our Worknet data analysis, only about 24% of job seekers had landed on a job position consistent with their initial preferred job code while the rest had landed on a position different from their initial preference. To improve the situation, particularly for the middle aged class, we investigate a soft job matching technique by performing the following: 1) we review a user behavior logs of Worknet, which is a public job recruiting system set up by the Korean government and point out key system design implications for the middle aged. Specifically, we analyze the job postings that include preferential tags for the middle aged in order to disclose what types of jobs are in favor of the middle aged; 2) we develope a new occupation classification scheme for the middle aged, Korea Occupation Classification for the Middle-aged (KOCM), based on the similarity between jobs by reorganizing and modifying a general occupation classification scheme. When viewed from the perspective of job placement, an occupation classification scheme is a way to connect the enterprises and job seekers and a basic mechanism for job placement. The key features of KOCM include establishing the Simple Labor category, which is the most requested category by enterprises; and 3) we design MOMA (Middle-aged Occupation Matching Algorithm), which is a hybrid job matching algorithm comprising constraint-based reasoning and case-based reasoning. MOMA incorporates KOCM to expand query to search similar jobs in the database. MOMA utilizes cosine similarity between user requirement and job posting to rank a set of postings in terms of preferred job code, salary, distance, and job type. The developed system using MOMA demonstrates about 20 times of improvement over the hard matching performance. In implementing the algorithm for a web-based application of recruiting system for the middle aged, we also considered the usability issue of making the system easier to use, which is especially important for this particular class of users. That is, we wanted to improve the usability of the system during the job search process for the middle aged users by asking to enter only a few simple and core pieces of information such as preferred job (job code), salary, and (allowable) distance to the working place, enabling the middle aged to find a job suitable to their needs efficiently. The Web site implemented with MOMA should be able to contribute to improving job search of the middle aged class. We also expect the overall approach to be applicable to other groups of people for the improvement of job matching results.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.10
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• pp.2809-2821
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• 2023

Effective recommendation of similar business groups is a critical factor in obtaining market information for companies. In this study, we propose a novel method for enhancing similar business group recommendation by incorporating derivative criteria and web crawling. We use employment announcements, employment incentives, and corporate vocational training information to derive additional criteria for similar business group selection. Web crawling is employed to collect data related to the derived criteria from 'credit jobs' and 'worknet' sites. We compare the efficiency of different datasets and machine learning methods, including XGBoost, LGBM, Adaboost, Linear Regression, K-NN, and SVM. The proposed model extracts derivatives that reflect the financial and scale characteristics of the company, which are then incorporated into a new set of recommendation criteria. Similar business groups are selected using a Euclidean distance-based model. Our experimental results show that the proposed method improves the accuracy of similar business group recommendation. Overall, this study demonstrates the potential of incorporating derivative criteria and web crawling to enhance similar business group recommendation and obtain market information more efficiently.

• Journal of Information Science Theory and Practice
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• v.10 no.spc
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• pp.86-95
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• 2022

Online recruitment websites discuss job demands in various fields, and job postings contain detailed job specifications. Analyzing this text can elucidate the features that determine job salaries. Text embedding models can learn the contextual information in a text, and explainable artificial intelligence frameworks can be used to examine in detail how text features contribute to the models' outputs. We collected 733,625 job postings using the WORKNET API and classified them into low, mid, and high-range salary groups. A text embedding model that predicts job salaries based on the text in job postings was trained with the collected data. Then, we applied the SHapley Additive exPlanations (SHAP) framework to the trained model and discovered the significant words that determine each salary class. Several limitations and remaining words are also discussed.

• The Journal of Information Systems
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• v.32 no.4
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• pp.229-245
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• 2023

Purpose This study aims to understand the inclinations of young job seekers towards "hidden champions" - small but competitive companies that are emerging as potential solutions to the growing disparity between youth-targeted job vacancies and job seekers. We utilize machine learning techniques to discern the appeal of these hidden champions. Design/methodology/approach We examined the characteristics of small and medium-sized enterprises using data sourced from the Ministry of Employment and Labor and Youth Worknet. By comparing the efficacy of five machine learning classification models (i.e., Logistic Regression, Random Forest Classifier, Gradient Boosting Classifier, LGBM Classifier, and XGB Classifier), we discovered that the predictive model utilizing the LGBM Classifier yielded the most consistent performance. Findings Our analysis of the relative significance of preference determinants revealed that industry type, geographical location, and employee count are pivotal factors influencing preference. Drawing from these insights, we propose targeted strategic interventions for policymakers, hidden champions, and young job seekers.

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.123-139
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• 2019

The job classification system of major job sites differs from site to site and is different from the job classification system of the 'SQF(Sectoral Qualifications Framework)' proposed by the SW field. Therefore, a new job classification system is needed for SW companies, SW job seekers, and job sites to understand. The purpose of this study is to establish a standard job classification system that reflects market demand by analyzing SQF based on job offer information of major job sites and the NCS(National Competency Standards). For this purpose, the association analysis between occupations of major job sites is conducted and the association rule between SQF and occupation is conducted to derive the association rule between occupations. Using this association rule, we proposed an intelligent job classification system based on data mapping the job classification system of major job sites and SQF and job classification system. First, major job sites are selected to obtain information on the job classification system of the SW market. Then We identify ways to collect job information from each site and collect data through open API. Focusing on the relationship between the data, filtering only the job information posted on each job site at the same time, other job information is deleted. Next, we will map the job classification system between job sites using the association rules derived from the association analysis. We will complete the mapping between these market segments, discuss with the experts, further map the SQF, and finally propose a new job classification system. As a result, more than 30,000 job listings were collected in XML format using open API in 'WORKNET,' 'JOBKOREA,' and 'saramin', which are the main job sites in Korea. After filtering out about 900 job postings simultaneously posted on multiple job sites, 800 association rules were derived by applying the Apriori algorithm, which is a frequent pattern mining. Based on 800 related rules, the job classification system of WORKNET, JOBKOREA, and saramin and the SQF job classification system were mapped and classified into 1st and 4th stages. In the new job taxonomy, the first primary class, IT consulting, computer system, network, and security related job system, consisted of three secondary classifications, five tertiary classifications, and five fourth classifications. The second primary classification, the database and the job system related to system operation, consisted of three secondary classifications, three tertiary classifications, and four fourth classifications. The third primary category, Web Planning, Web Programming, Web Design, and Game, was composed of four secondary classifications, nine tertiary classifications, and two fourth classifications. The last primary classification, job systems related to ICT management, computer and communication engineering technology, consisted of three secondary classifications and six tertiary classifications. In particular, the new job classification system has a relatively flexible stage of classification, unlike other existing classification systems. WORKNET divides jobs into third categories, JOBKOREA divides jobs into second categories, and the subdivided jobs into keywords. saramin divided the job into the second classification, and the subdivided the job into keyword form. The newly proposed standard job classification system accepts some keyword-based jobs, and treats some product names as jobs. In the classification system, not only are jobs suspended in the second classification, but there are also jobs that are subdivided into the fourth classification. This reflected the idea that not all jobs could be broken down into the same steps. We also proposed a combination of rules and experts' opinions from market data collected and conducted associative analysis. Therefore, the newly proposed job classification system can be regarded as a data-based intelligent job classification system that reflects the market demand, unlike the existing job classification system. This study is meaningful in that it suggests a new job classification system that reflects market demand by attempting mapping between occupations based on data through the association analysis between occupations rather than intuition of some experts. However, this study has a limitation in that it cannot fully reflect the market demand that changes over time because the data collection point is temporary. As market demands change over time, including seasonal factors and major corporate public recruitment timings, continuous data monitoring and repeated experiments are needed to achieve more accurate matching. The results of this study can be used to suggest the direction of improvement of SQF in the SW industry in the future, and it is expected to be transferred to other industries with the experience of success in the SW industry.

• Information Systems Review
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• v.10 no.3
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• pp.133-153
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• 2008

Understanding of the length of time required to realize the return on the investment of informatization is important for policy makers and decision makers of information system adoption. Previous researchers, however, assessed this issue with the performance measurement approach that was primarily based on static point of view. However, the static analysis on the outcome of the informatization investment is limited in measuring the priori and ex ante effects of the informatization implementation on temporal basis. This study present a methodology to capture the outcome of the informatization investment on dynamic basis. This assessment was performed based on an e-government project in Korea, called "Industry Human Resource Project." Particularly, the study addressed how long it takes to obtain the benefit of WorkNet System, which was part of this Korean e-government project. We proposed various approaches to illustrate the importance and temporal effect of the WorkNet System by analyzing DB data, time reduction of WorkNet business processes and return of investment of IT.

• Journal of the Korea Fashion and Costume Design Association
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• v.16 no.3
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• pp.191-200
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• 2014

Employees overseas have a need to prepare according to the different environments and industrial structures between countries. Therefore, to investigate qualities for fashion designers to possess when they work abroad, especially in the United States, this study compared the level of knowledge required to fulfill the work of fashion design in both South Korea and the United States. Responses from workers who are engaged to fashion design from the representative online career information systems of two countries, 'Worknet' in South Korea and '$O^*Net$' in the United States, were used as data. Looking at the result derived from the analysis of this study, first by comparing various statistical indicators, results showed the difference between knowledge level required to fashion designer in South Korea and in the United States. Even with the same type of job, because environments and industrial structures of each country are dissimilar, a different level of knowledge will be required in order to perform their tasks. Second, fashion designers in both South Korea and the United States required a high level of knowledge in the 'fine arts', 'administration and management', 'production and processing', and 'design' to perform their duties as a fashion designer. As a result, both countries have similarities that fashion designers need to possess a high level of the knowledge in areas such as 'production of products' as well as 'sales of products'. Furthermore, human relationship field of knowledge such as 'counseling', 'psychology', and 'communication' appeared to be more necessary to fashion designers in South Korea than those in the United States. On the other hand, higher degree of knowledge of 'machines and tools', ' fine arts', and 'transportation' appeared to be more necessary to fashion designers in the United States than those in South Korea.

• Journal of Service Research and Studies
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• v.5 no.1
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• pp.17-33
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• 2015

This study looked for employment services satisfaction surveys done in the UK, USA and Korea. And through which derived the following suggestions for improving Korea the employment service satisfaction survey. First, in terms of research design improvements to ensure the representativeness of the sample region for each center and branch-specific (job seekers and recruiters) requires the sampling design based on user scale. In addition, the sample design should be applied when considering specific user can be distributed. US is restricted to participants within 60 days for sampling and sample extraction once a month at least. Next in terms of survey improvements, it is necessary to apply a weighting part considering the regional characteristics. For this, the correct analysis of the employment center by the internal and external environment is required. And in the case of non-face-to-face service, complaints about worknet use is likely to be channeled into complaints about job centers provide services. It needs to improve on this. And for the improvements of business processes service by an in-depth study, it can be seen to review the possible introduction of a British mystery shopper.