• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.10
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• pp.382-392
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• 2017

This study analyzed the effect of an employee's psychological capacity(positive psychological capital), on perceived work overload, and explored how trust in supervisor moderated this relationship. In addition, we examined how positive psychological capital and perceived work overload influenced turnover intention. In doing so, we identified the antecedents of perceived work overload, and suggested ways to nurture positive psychological capital in organizations. Using survey data from 214 employees in the manufacturing industry in Korea, we found that positive psychological capital was negatively related to perceived work overload, and that the relationship was moderated by trust in supervisors. In addition, positive psychological capital was negatively associated while perceived work overload was positively associated with turnover intention. Results suggest the importance of understanding positive psychological capital given its crucial relationship with perceived work overload and turnover intention. The result also emphasizes the role that trust in supervisor plays, and the need to nurture employees' positive psychological capital in organizations.

• Industry Promotion Research
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• v.3 no.1
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• pp.13-31
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• 2018

The purpose of this study is to investigate the effect of organizational culture on organization and organizational performance, and it was targeting for 350 public officials (local governments, foundation under local governments, public corporation) who research work and consulting were implemented for. When it comes to the response to questionnaires, 313 copies out of 350 were verified on the research hypothesis of the research model by using statistical package programs of SPSS 20.0 and AMOS 20.0. Results of the research hypothesis on research model show that firstly, regarding the research hypothesis 1 that the organizational culture of public organization will have a positive (+) significant effect on learning organization, the organizational culture recognized by the organizational members of public organizations showed a positive influence on the learning organization. In other words, it showed that the organizational culture recognized by the organization members of public organizations is a major factor in building a learning organization. Secondly, regarding the research hypothesis 2, the result of the relationship between organizational culture and organizational performance, that the organizational culture recognized by the organizational members of the public sector showed a positive influence and it implies the importance of recognizing and transforming the organizational culture of public organizations to improve organizational performance of public organizations. Thirdly, regarding the research hypothesis 3, the organizational culture recognized by the organizational members of public organizations showed an influence on organizational performance and also showed apositive(+) influence on organizational performance through learning organization. As the organizational culture recognized by the organization members in the public sectoris influencing the organizational performance through the learning organization, various implementation plans are required to improve organizational culture, improving learning organization, and improving organizational performance in accordance with the characteristics of public organizations.

• Journal of Digital Convergence
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• v.19 no.8
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• pp.101-111
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• 2021

Irregular workers, which have recently caused various socio-economic issues and conflicts, generally have low loyalty to the organization and job satisfaction due to anxiety about employment. As a way to improve this, this study attempted to analyze the effect of organizational communication satisfaction of irregular workers on job satisfaction and organizational commitment. Among the 7th Human Capital Companies panel survey data, irregular workers survey data were collected and analyzed using the structural equation model analysis. The results were as follows: First, it was analyzed that organizational communication recognized by irregular workers had a positive(+) effect on job satisfaction and organizational commitment. Second, it was analyzed that job satisfaction had a positive(+) effect on organizational commitment. Third, it was analyzed that job satisfaction plays a mediating role in the relationship between communication satisfaction and organizational commitment. This study is significant in that it expanded the research subject to irregular workers from the existing service industry-oriented research, and that it included more diverse industries. The results of this study suggest that mission and vision sharing and communication activation system are needed to improve organizational effectiveness of irregular workers.

• Journal of Industrial Convergence
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• v.20 no.10
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• pp.137-146
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• 2022

The use of RPA (Robotic Process Automation) has been recently reviewed in various industries, but it seems that it is not being applied to companies faster than ever expected. In this study, three perceived risk factors affecting the acceptance conflict and acceptance intention of RPA technology were proposed and the effects of RPA on acceptance conflict and acceptance intention were investigated using RPA experienced people, gender and ICT industries as control variables. For the research, online survey was conducted targeting office workers and analyzed the results by using SPSS 22.0 and AMOS 22.0. As a result, it was found that among the three perceived risk factors, concern about introduction failure, employment insecurity, and execution errors, employment insecurity and execution errors did not affect the acceptance conflict and acceptance intention of RPA. This research shows that concerns over the introduction failure affected the acceptance conflict and acceptance intention. In addition, the acceptance conflict was judged as a factor of the mediation effect of the acceptance intention. From the perspective of companies that want to apply RPA, the theoretical and practical implications of business management are meaningful in that they can identify and respond to particularly important factors among perceived risks.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.1-21
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• 2020

With the rapid development of artificial intelligence technology, various techniques have been developed to extract meaningful information from unstructured text data which constitutes a large portion of big data. Over the past decades, text mining technologies have been utilized in various industries for practical applications. In the field of business intelligence, it has been employed to discover new market and/or technology opportunities and support rational decision making of business participants. The market information such as market size, market growth rate, and market share is essential for setting companies' business strategies. There has been a continuous demand in various fields for specific product level-market information. However, the information has been generally provided at industry level or broad categories based on classification standards, making it difficult to obtain specific and proper information. In this regard, we propose a new methodology that can estimate the market sizes of product groups at more detailed levels than that of previously offered. We applied Word2Vec algorithm, a neural network based semantic word embedding model, to enable automatic market size estimation from individual companies' product information in a bottom-up manner. The overall process is as follows: First, the data related to product information is collected, refined, and restructured into suitable form for applying Word2Vec model. Next, the preprocessed data is embedded into vector space by Word2Vec and then the product groups are derived by extracting similar products names based on cosine similarity calculation. Finally, the sales data on the extracted products is summated to estimate the market size of the product groups. As an experimental data, text data of product names from Statistics Korea's microdata (345,103 cases) were mapped in multidimensional vector space by Word2Vec training. We performed parameters optimization for training and then applied vector dimension of 300 and window size of 15 as optimized parameters for further experiments. We employed index words of Korean Standard Industry Classification (KSIC) as a product name dataset to more efficiently cluster product groups. The product names which are similar to KSIC indexes were extracted based on cosine similarity. The market size of extracted products as one product category was calculated from individual companies' sales data. The market sizes of 11,654 specific product lines were automatically estimated by the proposed model. For the performance verification, the results were compared with actual market size of some items. The Pearson's correlation coefficient was 0.513. Our approach has several advantages differing from the previous studies. First, text mining and machine learning techniques were applied for the first time on market size estimation, overcoming the limitations of traditional sampling based- or multiple assumption required-methods. In addition, the level of market category can be easily and efficiently adjusted according to the purpose of information use by changing cosine similarity threshold. Furthermore, it has a high potential of practical applications since it can resolve unmet needs for detailed market size information in public and private sectors. Specifically, it can be utilized in technology evaluation and technology commercialization support program conducted by governmental institutions, as well as business strategies consulting and market analysis report publishing by private firms. The limitation of our study is that the presented model needs to be improved in terms of accuracy and reliability. The semantic-based word embedding module can be advanced by giving a proper order in the preprocessed dataset or by combining another algorithm such as Jaccard similarity with Word2Vec. Also, the methods of product group clustering can be changed to other types of unsupervised machine learning algorithm. Our group is currently working on subsequent studies and we expect that it can further improve the performance of the conceptually proposed basic model in this study.

• 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.

• Asia Marketing Journal
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• v.10 no.1
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• pp.25-64
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• 2008

Third-Party awards are growing in popularity. They are the hit product of the year chosen by The Korea Economic Daily, the best 10 products of the year chosen by Sports paper, the best hit product chosen by consulting firm and the best venture company of the year chosen by Information and Communication Ministry. Then these questions may be followed. Why industry likes this type of advertisement? Does this type of advertisement influences consumers' purchase intention? And if it does, how? Many researchers have been interested in external cue of product quality by focusing research effort on brand, price, producer, warranty etc. However, important but under-explored area is the role of third-party reference for signaling product quality. This paper comes from the idea that the third-party reference may signal consumers like manufacturer brand, product brand, product price, and shop brand. We develop a related theories to address research questions and drive some research hypotheses based on the previous studies probing source credibility, attribution, and signal theory. We put more emphasis on source credibility. We conducted the research based on 3x2x2x2 between group factorial design to explore causal relationship between the third party award winning advertising(real, fictional, no) and the purchase intention of consumers exposed to other information simultaneously such as product type(experience, search), distribution channel(direct, indirect) and perceived price(high, low). Since subjects are divided into 2 groups based on the means of response without extra experimental stimulus in case of perceived price. 12 different advertisements are used for conducting this study. The results are followings. First, the source credibility of the third party goes up, consumers' purchase intention would go up. It seems that consumers think the credibility of the third-party most when they are exposed to the third party award winning advertisement. Second, the product type does moderate the relationship between the third-party award winning advertisement and purchase intention. And the type of the distribution channel also moderates this relationship. The consumers' purchase intention goes up higher when they buy experience good and there is significant difference of purchase intention when consumers are exposed to direct channel treatment condition. But, perceived price has nothing to do with the third-party winning advertisement context for raising consumer intention to buy advertised product.

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.191-204
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• 2015

According to the '2013 Performance Assessment Report on the Financial Program' from the National Assembly Budget Office, the unfilled recruitment ratio of Software(SW) Developers in South Korea was 25% in the 2012 fiscal year. Moreover, the unfilled recruitment ratio of highly-qualified SW developers reaches almost 80%. This phenomenon is intensified in small and medium enterprises consisting of less than 300 employees. Young job-seekers in South Korea are increasingly avoiding becoming a SW developer and even the current SW developers want to change careers, which hinders the national development of IT industries. The Korean government has recently realized the problem and implemented policies to foster young SW developers. Due to this effort, it has become easier to find young SW developers at the beginning-level. However, it is still hard to recruit highly-qualified SW developers for many IT companies. This is because in order to become a SW developing expert, having a long term experiences are important. Thus, improving job continuity intentions of current SW developers is more important than fostering new SW developers. Therefore, this study surveyed the job continuity intentions of SW developers and analyzed the factors associated with them. As a method, we carried out a survey from September 2014 to October 2014, which was targeted on 130 SW developers who were working in IT industries in South Korea. We gathered the demographic information and characteristics of the respondents, work environments of a SW industry, and social positions for SW developers. Afterward, a regression analysis and a decision tree method were performed to analyze the data. These two methods are widely used data mining techniques, which have explanation ability and are mutually complementary. We first performed a linear regression method to find the important factors assaociated with a job continuity intension of SW developers. The result showed that an 'expected age' to work as a SW developer were the most significant factor associated with the job continuity intention. We supposed that the major cause of this phenomenon is the structural problem of IT industries in South Korea, which requires SW developers to change the work field from developing area to management as they are promoted. Also, a 'motivation' to become a SW developer and a 'personality (introverted tendency)' of a SW developer are highly importantly factors associated with the job continuity intention. Next, the decision tree method was performed to extract the characteristics of highly motivated developers and the low motivated ones. We used well-known C4.5 algorithm for decision tree analysis. The results showed that 'motivation', 'personality', and 'expected age' were also important factors influencing the job continuity intentions, which was similar to the results of the regression analysis. In addition to that, the 'ability to learn' new technology was a crucial factor for the decision rules of job continuity. In other words, a person with high ability to learn new technology tends to work as a SW developer for a longer period of time. The decision rule also showed that a 'social position' of SW developers and a 'prospect' of SW industry were minor factors influencing job continuity intensions. On the other hand, 'type of an employment (regular position/ non-regular position)' and 'type of company (ordering company/ service providing company)' did not affect the job continuity intension in both methods. In this research, we demonstrated the job continuity intentions of SW developers, who were actually working at IT companies in South Korea, and we analyzed the factors associated with them. These results can be used for human resource management in many IT companies when recruiting or fostering highly-qualified SW experts. It can also help to build SW developer fostering policy and to solve the problem of unfilled recruitment of SW Developers in South Korea.