• Korean Journal of Labor Studies
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• v.24 no.2
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• pp.95-136
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• 2018

As the number of precariats grows, their poor labor rights and working conditions are becoming issues of major concern all over the world but how to represent their interests is still controversial. Basically, the union is the institutional mechanism for representing the labor rights. However, it is difficult for workplaceand enterprise-based unions to fully represent the labor rights of precarious workers. Recently, so-called community unions have emerged in the United States, the United Kingdom, and Japan as independent organizations representing the rights of non-standard workers. Community unions refer to labor unions which organize precarious workers across firms at the regional level. They are known to be suitable for covering the unemployed, job seekers, indirect employment workers, short-term contract workers, and small-firm workers. In South Korea, since the financial crisis in 1997, a dramatic increase in the number of precariats leads to emergence of new types of trade unions such as the Youth Community Union, the Arbeit Workers' Union, the Artist Social Union and the Korea Musician's Union. They have engaged in various activities to guarantee the labor rights of precariats. Recently, researchers have also tried to identify defining characteristics of these new forms of unionism. To expand research on trade unionism in South Korea, this study compares two different types of community unions: the Youth Community Union and the Arbeit Workers' Union. We believe that this attempt can contribute to the research on the alternative labor movement. For this purpose, this study starts with theoretical discussions on community unions, and compares the Youth Community Union with the Arbeit Workers' Union based on the five characteristics of community unionism: membership and organization structure, the recognition struggle, the type or scope of interest, solidarity with other civic organizations, and the repertoire of resistance strategies. Based on this comparative analysis, this study seeks to foresee the possibility of how community unionism will develop in South Korean in the future.

• Journal of Venture Innovation
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• v.3 no.2
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• pp.95-109
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• 2020

The source of value-added creation in modern times has been transformed from material to man's value-added generating power, and ownership of the means of production has been converted from a particular landlord, capitalist to a person with value-added capacity, and a system of capital participation is needed beyond the profit-sharing system or performance incentive system in which workers of an enterprise participate in simple profits if they significantly increase the added value of the company. It is also necessary to introduce our private stock system as a means of addressing the problem of capital bias and for the stable development of capitalism. The purpose of Employee Stock Ownership Plans is to improve the economic and social status of workers and promote labor-management cooperation by allowing workers to acquire and hold shares of the stock company in which the employee ownership association is established through the employee ownership association, but the reality is that our stock ownership system has failed to achieve its purpose due to insufficient protection against the employee. In terms of welfare, the acquisition of our company shares should include active government support for the welfare of workers' ownership on a social welfare level rather than on the logic of the capital market, and in terms of investment, it would not be appropriate to apply the regulation for investor protection to see workers' acquisition of our company shares as 'investment' in the view of workers' willingness to own shares on the stock market. Therefore, as a way to support and deregulate employee's stock acquisition, 1. Expanding direct support, such as tax support, 2. As employee's stock ownership association is being discussed as a division's nature, it is less effective in terms of various management, not investment, and 3. Those who own stocks with 1% of the company's shares and 300 million won in face value will be classified as major shareholders. As a way to reduce the risk of management of our company owners and cooperative funds, As a measure to reduce the risk of management of our company owners and cooperative funds, only our employee shareholders' association shall manage the fund in a long-term deposit, and even though our employee's stock is managed by the association or company after the end of the deposit period, the management of each employee shall be allowed and In terms of improving the utilization of our company's stock and fund, 1. Employee's stockholders are prohibited from lending during the deposit period, but it is necessary to improve profitability by allowing them to borrow under strict restrictions, 2. It is necessary to make the use of the employee's welfare funds available for the preservation of losses, and to stipulate the redemption obligations of unlisted companies in order to improve the redemption system of our company.

• Proceedings of the Korean Geotechical Society Conference
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• 1998.05a
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• pp.35-81
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• 1998

Distinct Element Method(DEM) has a great advantage to model the discontinuous behaviour of jointed rock masses such as rotation, sliding, and separation of rock blocks. Geometrical data of joints by a field monitoring is not enough to model the jointed rock mass though the results of DE analysis for the jointed rock mass is most sensitive to the distributional properties of joints. Also, it is important to use a properly joint law in evaluating the stability of a jointed rock mass because the joint is considered as the contact between blocks in DEM. In this study, a stochastic modelling technique is developed and the dilatant rock joint is numerically modelled in order to consider th geometrical and mechanical properties of joints in DE analysis. The stochastic modelling technique provides a assemblage of rock blocks by reproducing the joint distribution from insufficient joint data. Numerical Modelling of joint dilatancy in a edge-edge contact of DEM enable to consider not only mechanical properties but also various boundary conditions of joint. Preprocess Procedure for a stochastic DE model is composed of a statistical process of raw data of joints, a joint generation, and a block boundary generation. This stochastic DE model is used to analyze the effect of deviations of geometrical joint parameters on .the behaviour of jointed rock masses. This modelling method may be one tool for the consistency of DE analysis because it keeps the objectivity of the numerical model. In the joint constitutive law with a dilatancy, the normal and shear behaviour of a joint are fully coupled due to dilatation. It is easy to quantify the input Parameters used in the joint law from laboratory tests. The boundary effect on the behaviour of a joint is verified from shear tests under CNL and CNS using the numerical model of a single joint. The numerical model developed is applied to jointed rock masses to evaluate the effect of joint dilation on tunnel stability.

• Management & Information Systems Review
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• v.34 no.5
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• pp.191-212
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• 2015

Unlike the existing researches related to salespersons, this study intends to place the focus on salespersons' psychological characteristic as an element affecting their selling behavior. This is because employees' psychological characteristic is very likely to affect their devotion and commitment to relationship with customers and long-term production by a company. In particular, salespersons are likely to get a feeling of fatigue or loss, or make a cynical or cold response to customers because of frequent interaction with them, and to show emotional indifference in an attempt to keep their distance from customers. But the likelihood can vary depending on salespersons' own psychological characteristic; in particular, the occurrence of these phenomena is very likely to vary significantly depending on relationship belief in interpersonal relations. In the field of psychology, under way are researches related to personal psychological characteristics to improve the quality of interpersonal relations and to maximize personal performance and enhance situational adaptability during this process; it is a personal relationship belief that is recently mentioned as such a psychological characteristic. For salespersons having frequent interaction with customers, particularly, relationship belief can be a very important element in forming relations with customers. So this study aims at determining how salespersons' relationship belief affects negative emotion regulation strategies and prosocial behavior to customer. As a result, salespersons' relationship belief was found to have effects on their negative emotion regulation strategies and prosocial behavior to customer. Negative emotion regulation strategies was found to have effects on prosocial behavior. Salespersons with intimate relationship belief try to use active regulation, support-seeking regulation and salespersons with controlling relationship belief try to use avoidant/distractive regulation. Intimate relationship belief was found to have more prosocial behavior, controlling relationship belief was found to have less prosocial behavior to customer. salespersons' negative emotion regulation strategies was found to have effects on their prosocial behavior to customer. Active, support-seeking influence prosocial behavior to customer positively, avoidant/distractive regulation influence prosocial behavior to customer negatively.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.16 no.3
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• pp.177-191
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• 2021

Due to the development of mobile devices and streaming technology, many changes in consumption patterns have appeared. In addition, social impact is becoming an era of non-face-to-face consumption due to the panthermic environment of COVID-19. Accordingly, in line with the non-face-to-face consumption trend, we focused on the importance of live commerce, which is emerging as a new distribution channel, and tried to investigate the causal relationship that the characteristics of live commerce and show hosts have on purchase intention. The respondents of this study were 235 general adults of live commerce users. Interaction, economics, entertainment as the characteristics of live commerce and attractiveness, professionality, and awareness as the characteristics of show hosts were set as independent variables. Purchase intention was set as the dependent variable, and content flow was set as the mediating variable. As a result of the study, it was found that the characteristics of live commerce such as Interaction, economics, entertainment, and the characteristics of show hosts such as attractiveness, professionality, and awareness all had a positive (+) significant effect on purchase intention. The impact was shown in the following order: entertainment of live commerce, awareness, attractiveness, professionality of show hosts, economics, interaction of live commerce. In addition, the results of the mediating effect of content flow on purchase intention are as follows. Content flow was found to play a mediating role between interaction, entertainment, attractiveness, professionality, awareness and purchase intention. On the other hand, economics was analyzed to have no mediating effect. The implications of this study are as follows. Companies and show hosts that sell products in live commerce should sell products that can inspire consumers rather than simply sell products. In addition, it is considered that content that provides entertainment and attractions gives pleasure to consumers. If not only a well-recognized show host, but also people with high recognition in various fields such as influencers and creators, become show hosts, consumers' content flow and purchase intentions will increase. And vendors must offer interesting content development and reasonable prices. Show hosts need to focus on active communication with consumers.

• Archives of design research
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• v.16 no.1
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• pp.181-198
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• 2003

The purpose of this study is to develop a on-line design tool for effectively coping with e-Business environment, or product design process into a Cyber model for traditional manufacturers which attempts new product development under such environment. It was finally developed as a model named $\ulcorner$Design Vortal Site; e-BVDS) that was based on the structure and style of internet web site. Results of the study can be described as follows ; \circled1 e-Business is based on the Internet. All processes in the context of e-Business require models whose structure and method of use are on-line styles. \circled2 In case that a traditional manufacturing business is converted into e-Business, it is better to first consider Hybrid Model that combines resources and advantages of both such traditional and digital businesses. \circled3 The product design process appropriate for e-Business environment has to have a structure and style that ensure utilization of the process as an Internet web site, active participation by product developers and interactive communication between participants in designing and designers. \circled4 $\ulcorner$e-BDVS) makes possible the use of designers around the wend like in-house designers, overcoming lack in creativity, ideas and human resources traditional business organizations face. However, the operation of $\ulcorner$e-BDVS$\lrcorner$ requires time and budget investments in securing related elements and conditions. \circled5 Cyber designers under $\ulcorner$e-BDVS$\lrcorner$ can easily perform all design projects in cyber space. But they have some limits in playing a role as designers and they have difficulty in getting rewards if such projects completed by them are not finally accepted. \circled6 $\ulcorner$e-BDVS) ensures the rapid use of a wide range of design information and data, reception of a variety of solutions and ideas and effective design development, all of which are not possible through traditional processes. However, this process may not be suitable to be used routine process or tool. \circled7 $\ulcorner$e-BDVS$\lrcorner$ makes it possible for out-sourcing or partners businesses to overcome restrictions in time and space and improve productivity and effectiveness. But such they may have to continue off-line works that can not be treated on-line.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.79-99
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• 2015

The prediction of bankruptcy has been extensively studied in the accounting and finance field. It can have an important impact on lending decisions and the profitability of financial institutions in terms of risk management. Many researchers have focused on constructing a more robust bankruptcy prediction model. Early studies primarily used statistical techniques such as multiple discriminant analysis (MDA) and logit analysis for bankruptcy prediction. However, many studies have demonstrated that artificial intelligence (AI) approaches, such as artificial neural networks (ANN), decision trees, case-based reasoning (CBR), and support vector machine (SVM), have been outperforming statistical techniques since 1990s for business classification problems because statistical methods have some rigid assumptions in their application. In previous studies on corporate bankruptcy, many researchers have focused on developing a bankruptcy prediction model using financial ratios. However, there are few studies that suggest the specific types of bankruptcy. Previous bankruptcy prediction models have generally been interested in predicting whether or not firms will become bankrupt. Most of the studies on bankruptcy types have focused on reviewing the previous literature or performing a case study. Thus, this study develops a model using data mining techniques for predicting the specific types of bankruptcy as well as the occurrence of bankruptcy in Korean small- and medium-sized construction firms in terms of profitability, stability, and activity index. Thus, firms will be able to prevent it from occurring in advance. We propose a hybrid approach using two artificial neural networks (ANNs) for the prediction of bankruptcy types. The first is a back-propagation neural network (BPN) model using supervised learning for bankruptcy prediction and the second is a self-organizing map (SOM) model using unsupervised learning to classify bankruptcy data into several types. Based on the constructed model, we predict the bankruptcy of companies by applying the BPN model to a validation set that was not utilized in the development of the model. This allows for identifying the specific types of bankruptcy by using bankruptcy data predicted by the BPN model. We calculated the average of selected input variables through statistical test for each cluster to interpret characteristics of the derived clusters in the SOM model. Each cluster represents bankruptcy type classified through data of bankruptcy firms, and input variables indicate financial ratios in interpreting the meaning of each cluster. The experimental result shows that each of five bankruptcy types has different characteristics according to financial ratios. Type 1 (severe bankruptcy) has inferior financial statements except for EBITDA (earnings before interest, taxes, depreciation, and amortization) to sales based on the clustering results. Type 2 (lack of stability) has a low quick ratio, low stockholder's equity to total assets, and high total borrowings to total assets. Type 3 (lack of activity) has a slightly low total asset turnover and fixed asset turnover. Type 4 (lack of profitability) has low retained earnings to total assets and EBITDA to sales which represent the indices of profitability. Type 5 (recoverable bankruptcy) includes firms that have a relatively good financial condition as compared to other bankruptcy types even though they are bankrupt. Based on the findings, researchers and practitioners engaged in the credit evaluation field can obtain more useful information about the types of corporate bankruptcy. In this paper, we utilized the financial ratios of firms to classify bankruptcy types. It is important to select the input variables that correctly predict bankruptcy and meaningfully classify the type of bankruptcy. In a further study, we will include non-financial factors such as size, industry, and age of the firms. Thus, we can obtain realistic clustering results for bankruptcy types by combining qualitative factors and reflecting the domain knowledge of experts.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.4
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• pp.165-175
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• 2023

In venture creation research, studying 'entrepreneurial leadership' is important for uncovering and comprehending the underlying causal process in innovative behavior performance. Although previous studies provide that entrepreneurial leadership enhances followers' innovative behavior, there is few research on entrepreneurial leadership and followers' characteristics interaction. The present study's focus is paradoxical effects of entrepreneurial leadership on self-efficacy and innovative behavior. On the basis of individual regulatory focus, this study suggests that interaction effects of entrepreneurial leadership and followers' regulatory focus differed in promotion view and prevention view followers' innovative behavior. To strengthen the casual mechanism, this study conducted in priming experiment method using employees in SMEs. This study used a 2(entrepreneurial leadership vs. control) x 2 (regulatory focus: promotion vs. prevention) between-participants design. The results of this study provide that (1) Individuals in promotion focus especially benefited from entrepreneurial leadership in terms of its effect on their self-efficacy and innovative behavior; (2) whereas entrepreneurial leadership was negatively related to self-efficacy and innovative behavior of followers' prevention focus. In sum, results of the present study supporting evidence for hypotheses, combined effect of entrepreneurial leadership and regulatory focus on innovative behavior through self-efficacy. Experimental results confirmed hypotheses of this study, revealing that promotion focus show more innovative behavior than prevention focus when their leaders' leadership style is entrepreneurial leadership. Also, the paradoxical effect of entrepreneurial leadership and regulatory focus of followers on innovative behavior was mediated by followers' self-efficacy. This study helps explain how leaders' entrepreneurial leadership boost followers' innovative behavior, particularly for those employees who have promotion focus. The current study contributes to the theory of entrepreneurial leadership and regulatory focus and innovation literature. Findings of this study shed light on the organizational processes that shape innovative behavior in venture/startup corporations and provide contributions for venture business field.

• Archives of design research
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• v.18 no.4 s.62
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• pp.95-104
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• 2005

The purpose of this study is to estimate the determinants of product design and analyze the impacts of product design on quality competitiveness, product reliability, and consumer satisfaction in an attempt to provide a foundation for the theory of design management. For this empirical analysis, this study has derived the relevant measurement variables from a survey on 400 Korean manufacturing firms during the period of $August{\sim}October$ 2003. The empirical findings are summarized as follows: First, the determinants of product design are very significantly (at p<0.001) estimated to be the R&D capability, the level of R&D expenditure, the level of innovative activities(5S, TQM, 6Sigma, QC, etc.). This empirical result can support Pawar and Driva(1999)'s two principles by which the performance of product design and product development can be simultaneously evaluated in the context of CE(concurrent engineering) of NPD(newly product development) activities. Second, the hypothesis on the causality: product design${\rightarrow}$quality competitiveness${\rightarrow}$customer satisfaction${\rightarrow}$customer loyalty is very significantly (at p<0.001) accepted. This implies that product design positively affects consumer satisfaction, not directly but indirectly, by influencing quality competitiveness. This empirical result of this study can also support the studies of for example Flynn et al.(1994), Ahire et at.(1996), Afire and Dreyfus(2000) which conclude that design management is a significant determinant of product quality. The aforementioned empirical results are important in the following sense: the empirical result that quality competitiveness plays a bridging role between product design and consumer satisfaction can reconcile the traditional debate between QFD(quality function development) approach asserted by product developers and conjoint analysis maintained by marketers. The first empirical result is related to QFD approach whereas the second empirical result is related to conjoint analysis. At the same time, the empirical results of this study can support the rationale of design integration(DI) of Ettlie(1997), i.e., the coordination of the timing and substance of product development activities performed by the various disciplines and organizational functions of a product's life cycle. Finally, the policy implication (at the corporate level) from the empirical results is that successful design management(DM) requires not only the support of top management but also the removal of communication barriers, (i.e. the adoption of cross-functional teams) so that concurrent engineering(CE), the simultaneous development of product and process designs can assure product development speed, design quality, and market success.

• Journal of Intelligence and Information Systems
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• v.27 no.2
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• pp.1-15
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• 2021

If it is difficult for us to make decisions, we ask for advice from friends or people around us. When we decide to buy products online, we read anonymous reviews and buy them. With the advent of the Data-driven era, IT technology's development is spilling out many data from individuals to objects. Companies or individuals have accumulated, processed, and analyzed such a large amount of data that they can now make decisions or execute directly using data that used to depend on experts. Nowadays, the recommender system plays a vital role in determining the user's preferences to purchase goods and uses a recommender system to induce clicks on web services (Facebook, Amazon, Netflix, Youtube). For example, Youtube's recommender system, which is used by 1 billion people worldwide every month, includes videos that users like, "like" and videos they watched. Recommended system research is deeply linked to practical business. Therefore, many researchers are interested in building better solutions. Recommender systems use the information obtained from their users to generate recommendations because the development of the provided recommender systems requires information on items that are likely to be preferred by the user. We began to trust patterns and rules derived from data rather than empirical intuition through the recommender systems. The capacity and development of data have led machine learning to develop deep learning. However, such recommender systems are not all solutions. Proceeding with the recommender systems, there should be no scarcity in all data and a sufficient amount. Also, it requires detailed information about the individual. The recommender systems work correctly when these conditions operate. The recommender systems become a complex problem for both consumers and sellers when the interaction log is insufficient. Because the seller's perspective needs to make recommendations at a personal level to the consumer and receive appropriate recommendations with reliable data from the consumer's perspective. In this paper, to improve the accuracy problem for "appropriate recommendation" to consumers, the recommender systems are proposed in combination with context-based deep learning. This research is to combine user-based data to create hybrid Recommender Systems. The hybrid approach developed is not a collaborative type of Recommender Systems, but a collaborative extension that integrates user data with deep learning. Customer review data were used for the data set. Consumers buy products in online shopping malls and then evaluate product reviews. Rating reviews are based on reviews from buyers who have already purchased, giving users confidence before purchasing the product. However, the recommendation system mainly uses scores or ratings rather than reviews to suggest items purchased by many users. In fact, consumer reviews include product opinions and user sentiment that will be spent on evaluation. By incorporating these parts into the study, this paper aims to improve the recommendation system. This study is an algorithm used when individuals have difficulty in selecting an item. Consumer reviews and record patterns made it possible to rely on recommendations appropriately. The algorithm implements a recommendation system through collaborative filtering. This study's predictive accuracy is measured by Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). Netflix is strategically using the referral system in its programs through competitions that reduce RMSE every year, making fair use of predictive accuracy. Research on hybrid recommender systems combining the NLP approach for personalization recommender systems, deep learning base, etc. has been increasing. Among NLP studies, sentiment analysis began to take shape in the mid-2000s as user review data increased. Sentiment analysis is a text classification task based on machine learning. The machine learning-based sentiment analysis has a disadvantage in that it is difficult to identify the review's information expression because it is challenging to consider the text's characteristics. In this study, we propose a deep learning recommender system that utilizes BERT's sentiment analysis by minimizing the disadvantages of machine learning. This study offers a deep learning recommender system that uses BERT's sentiment analysis by reducing the disadvantages of machine learning. The comparison model was performed through a recommender system based on Naive-CF(collaborative filtering), SVD(singular value decomposition)-CF, MF(matrix factorization)-CF, BPR-MF(Bayesian personalized ranking matrix factorization)-CF, LSTM, CNN-LSTM, GRU(Gated Recurrent Units). As a result of the experiment, the recommender system based on BERT was the best.