• Journal of Intelligence and Information Systems
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• v.18 no.3
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• pp.185-202
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• 2012

.Since the value of information has been realized in the information society, the usage and collection of information has become important. A facial expression that contains thousands of information as an artistic painting can be described in thousands of words. Followed by the idea, there has recently been a number of attempts to provide customers and companies with an intelligent service, which enables the perception of human emotions through one's facial expressions. For example, MIT Media Lab, the leading organization in this research area, has developed the human emotion prediction model, and has applied their studies to the commercial business. In the academic area, a number of the conventional methods such as Multiple Regression Analysis (MRA) or Artificial Neural Networks (ANN) have been applied to predict human emotion in prior studies. However, MRA is generally criticized because of its low prediction accuracy. This is inevitable since MRA can only explain the linear relationship between the dependent variables and the independent variable. To mitigate the limitations of MRA, some studies like Jung and Kim (2012) have used ANN as the alternative, and they reported that ANN generated more accurate prediction than the statistical methods like MRA. However, it has also been criticized due to over fitting and the difficulty of the network design (e.g. setting the number of the layers and the number of the nodes in the hidden layers). Under this background, we propose a novel model using Support Vector Regression (SVR) in order to increase the prediction accuracy. SVR is an extensive version of Support Vector Machine (SVM) designated to solve the regression problems. The model produced by SVR only depends on a subset of the training data, because the cost function for building the model ignores any training data that is close (within a threshold ${\varepsilon}$) to the model prediction. Using SVR, we tried to build a model that can measure the level of arousal and valence from the facial features. To validate the usefulness of the proposed model, we collected the data of facial reactions when providing appropriate visual stimulating contents, and extracted the features from the data. Next, the steps of the preprocessing were taken to choose statistically significant variables. In total, 297 cases were used for the experiment. As the comparative models, we also applied MRA and ANN to the same data set. For SVR, we adopted '${\varepsilon}$-insensitive loss function', and 'grid search' technique to find the optimal values of the parameters like C, d, ${\sigma}^2$, and ${\varepsilon}$. In the case of ANN, we adopted a standard three-layer backpropagation network, which has a single hidden layer. The learning rate and momentum rate of ANN were set to 10%, and we used sigmoid function as the transfer function of hidden and output nodes. We performed the experiments repeatedly by varying the number of nodes in the hidden layer to n/2, n, 3n/2, and 2n, where n is the number of the input variables. The stopping condition for ANN was set to 50,000 learning events. And, we used MAE (Mean Absolute Error) as the measure for performance comparison. From the experiment, we found that SVR achieved the highest prediction accuracy for the hold-out data set compared to MRA and ANN. Regardless of the target variables (the level of arousal, or the level of positive / negative valence), SVR showed the best performance for the hold-out data set. ANN also outperformed MRA, however, it showed the considerably lower prediction accuracy than SVR for both target variables. The findings of our research are expected to be useful to the researchers or practitioners who are willing to build the models for recognizing human emotions.

• Tuberculosis and Respiratory Diseases
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• v.45 no.5
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• pp.962-974
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• 1998

Background: Alteration of p53 tumor suppressor genes is most frequently identified in human neoplasms, including lung carcinoma. It is well known that bcl-2 oncoprotein protects cells from apoptosis. Recent studies have demonstrated that bcl-2 expression is associated with favorable prognosis for patients with non-small cell lung carcinoma. However, the precise biologic role of bcl-2 in the development of these tumors is still obscure. p53 and bcl-2 have important regulatory influence in the apoptotic pathway and thus their relationship is of interest in tumorigenesis, especially lung cancer. Purpose: The author investigated to know the prognostic significance of the expression of p53 and bcl-2 in radically resected non-small cell lung cancer. Method: 84 cases of formalin-fixed paraffin-embedded blocks from resected primary non-small cell lung cancer from 1980 to 1994 at Hanyang University Hospital were available for both clinical follow-up and immunohistochemical staining using monoclonal antibodies for p53 and bcl-2. Results : The histologic classification of the tumor was based on WHO criteria., and the specimens included 45 squamous cell carcinomas(53.6%), 28 adeonocarcinomas(33.3%) and 11 large cell carcinomas(13.1 %). p53 immunoreactivity was noted in 47 cases of 84 cases(56.0%). bcl-2 immunoreactivity was noted in 15 cases of 84 cases(17.9%). The mean survival duration was $64.23{\pm}10.73$ months in bcl-2 positive group and $35.28{\pm}4$. 39 months in bcl-2 negative group. The bcl-2 expression was significantly correlated with survival in radically resected non-small cell lung cancer patients(p=0.03). The mean survival duration was $34.71{\pm}6.12$ months in p53 positive group and $45.35{\pm}6.30$ months in p53 negative group(p=0.21). The p53 expression was not predictive for survival. There was no correlation between combination of the different status of p53 and bcl-2 expression in our study. Conclusions : The interaction and the regulation of new biologic markers, such as those involved in the apoptotic pathway, are complex. bcl-2 overexpression is a good prognostic factor in non-small cell lung cancer and p53 expression is not significantly associated with the prognostic factor in non-small cell lung cancer.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.143-156
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• 2012

People easily believe that news and stock index are closely related. They think that securing news before anyone else can help them forecast the stock prices and enjoy great profit, or perhaps capture the investment opportunity. However, it is no easy feat to determine to what extent the two are related, come up with the investment decision based on news, or find out such investment information is valid. If the significance of news and its impact on the stock market are analyzed, it will be possible to extract the information that can assist the investment decisions. The reality however is that the world is inundated with a massive wave of news in real time. And news is not patterned text. This study suggests the stock-index invest model based on "News Big Data" opinion mining that systematically collects, categorizes and analyzes the news and creates investment information. To verify the validity of the model, the relationship between the result of news opinion mining and stock-index was empirically analyzed by using statistics. Steps in the mining that converts news into information for investment decision making, are as follows. First, it is indexing information of news after getting a supply of news from news provider that collects news on real-time basis. Not only contents of news but also various information such as media, time, and news type and so on are collected and classified, and then are reworked as variable from which investment decision making can be inferred. Next step is to derive word that can judge polarity by separating text of news contents into morpheme, and to tag positive/negative polarity of each word by comparing this with sentimental dictionary. Third, positive/negative polarity of news is judged by using indexed classification information and scoring rule, and then final investment decision making information is derived according to daily scoring criteria. For this study, KOSPI index and its fluctuation range has been collected for 63 days that stock market was open during 3 months from July 2011 to September in Korea Exchange, and news data was collected by parsing 766 articles of economic news media M company on web page among article carried on stock information>news>main news of portal site Naver.com. In change of the price index of stocks during 3 months, it rose on 33 days and fell on 30 days, and news contents included 197 news articles before opening of stock market, 385 news articles during the session, 184 news articles after closing of market. Results of mining of collected news contents and of comparison with stock price showed that positive/negative opinion of news contents had significant relation with stock price, and change of the price index of stocks could be better explained in case of applying news opinion by deriving in positive/negative ratio instead of judging between simplified positive and negative opinion. And in order to check whether news had an effect on fluctuation of stock price, or at least went ahead of fluctuation of stock price, in the results that change of stock price was compared only with news happening before opening of stock market, it was verified to be statistically significant as well. In addition, because news contained various type and information such as social, economic, and overseas news, and corporate earnings, the present condition of type of industry, market outlook, the present condition of market and so on, it was expected that influence on stock market or significance of the relation would be different according to the type of news, and therefore each type of news was compared with fluctuation of stock price, and the results showed that market condition, outlook, and overseas news was the most useful to explain fluctuation of news. On the contrary, news about individual company was not statistically significant, but opinion mining value showed tendency opposite to stock price, and the reason can be thought to be the appearance of promotional and planned news for preventing stock price from falling. Finally, multiple regression analysis and logistic regression analysis was carried out in order to derive function of investment decision making on the basis of relation between positive/negative opinion of news and stock price, and the results showed that regression equation using variable of market conditions, outlook, and overseas news before opening of stock market was statistically significant, and classification accuracy of logistic regression accuracy results was shown to be 70.0% in rise of stock price, 78.8% in fall of stock price, and 74.6% on average. This study first analyzed relation between news and stock price through analyzing and quantifying sensitivity of atypical news contents by using opinion mining among big data analysis techniques, and furthermore, proposed and verified smart investment decision making model that could systematically carry out opinion mining and derive and support investment information. This shows that news can be used as variable to predict the price index of stocks for investment, and it is expected the model can be used as real investment support system if it is implemented as system and verified in the future.

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.141-154
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• 2019

Rapid growth of internet technology and social media is progressing. Data mining technology has evolved to enable unstructured document representations in a variety of applications. Sentiment analysis is an important technology that can distinguish poor or high-quality content through text data of products, and it has proliferated during text mining. Sentiment analysis mainly analyzes people's opinions in text data by assigning predefined data categories as positive and negative. This has been studied in various directions in terms of accuracy from simple rule-based to dictionary-based approaches using predefined labels. In fact, sentiment analysis is one of the most active researches in natural language processing and is widely studied in text mining. When real online reviews aren't available for others, it's not only easy to openly collect information, but it also affects your business. In marketing, real-world information from customers is gathered on websites, not surveys. Depending on whether the website's posts are positive or negative, the customer response is reflected in the sales and tries to identify the information. However, many reviews on a website are not always good, and difficult to identify. The earlier studies in this research area used the reviews data of the Amazon.com shopping mal, but the research data used in the recent studies uses the data for stock market trends, blogs, news articles, weather forecasts, IMDB, and facebook etc. However, the lack of accuracy is recognized because sentiment calculations are changed according to the subject, paragraph, sentiment lexicon direction, and sentence strength. This study aims to classify the polarity analysis of sentiment analysis into positive and negative categories and increase the prediction accuracy of the polarity analysis using the pretrained IMDB review data set. First, the text classification algorithm related to sentiment analysis adopts the popular machine learning algorithms such as NB (naive bayes), SVM (support vector machines), XGboost, RF (random forests), and Gradient Boost as comparative models. Second, deep learning has demonstrated discriminative features that can extract complex features of data. Representative algorithms are CNN (convolution neural networks), RNN (recurrent neural networks), LSTM (long-short term memory). CNN can be used similarly to BoW when processing a sentence in vector format, but does not consider sequential data attributes. RNN can handle well in order because it takes into account the time information of the data, but there is a long-term dependency on memory. To solve the problem of long-term dependence, LSTM is used. For the comparison, CNN and LSTM were chosen as simple deep learning models. In addition to classical machine learning algorithms, CNN, LSTM, and the integrated models were analyzed. Although there are many parameters for the algorithms, we examined the relationship between numerical value and precision to find the optimal combination. And, we tried to figure out how the models work well for sentiment analysis and how these models work. This study proposes integrated CNN and LSTM algorithms to extract the positive and negative features of text analysis. The reasons for mixing these two algorithms are as follows. CNN can extract features for the classification automatically by applying convolution layer and massively parallel processing. LSTM is not capable of highly parallel processing. Like faucets, the LSTM has input, output, and forget gates that can be moved and controlled at a desired time. These gates have the advantage of placing memory blocks on hidden nodes. The memory block of the LSTM may not store all the data, but it can solve the CNN's long-term dependency problem. Furthermore, when LSTM is used in CNN's pooling layer, it has an end-to-end structure, so that spatial and temporal features can be designed simultaneously. In combination with CNN-LSTM, 90.33% accuracy was measured. This is slower than CNN, but faster than LSTM. The presented model was more accurate than other models. In addition, each word embedding layer can be improved when training the kernel step by step. CNN-LSTM can improve the weakness of each model, and there is an advantage of improving the learning by layer using the end-to-end structure of LSTM. Based on these reasons, this study tries to enhance the classification accuracy of movie reviews using the integrated CNN-LSTM model.

• Korean Journal of Heritage: History & Science
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• v.53 no.4
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• pp.32-59
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• 2020

South Korea's area-based heritage preservation system originates from the "Preservation of Traditional Buildings Act" enacted in 1984. However, this system was abolished in 1996. As there was a need for protection of ancient cities in the 1960s, Japan enacted the Historic City Preservation Act in 1966, and 'Preservation Areas for Historic Landscapes' and 'Special Preservation Districts for Historic Landscapes' were introduced. For the preservation of area-based heritage sites, the 'Important Preservation Districts for Groups of Traditional Buildings' system introduced as part of the revision of the Cultural Heritage Protection Act in 1975 was the beginning. Then, in the early-2000s, discussions on the preservation of area-based heritage sites began in earnest, and the 'Important Cultural Landscape' system was introduced for protection of the space and context between heritage sites. Also, '33 Groups of Modernization Industry Heritage Sites' were designated in 2007, covering various material and immaterial resources related to the modernization of Japan, and '100 Beautiful Historic Landscapes of Japan' were selected for protection of local landscapes with historic value in the same year. In 2015, the "Japanese Heritage" system was established for the integrated preservation and management of tangible and intangible heritage aspects located in specific areas; in 2016, the "Japanese Agricultural Heritage" system was established for the succession and fostering of the disappearing agriculture and fishery industries; and in 2017, "the 20th Century Heritage," was established, representing evidence of modern and contemporary Japanese technologies in the 20th century. As a result, presently (in September 2020), 30 'Historic Landscape Preservation Areas', 60 'Historic Landscape Special Districts,' 120 'Important Preservation Districts for Groups of Traditional Buildings," 65 'Important Cultural Landscapes,' 66 'Groups of Modernization Industry Heritage Sites,' 264 "100 Beautiful Historic Landscapes of Japan,' 104 'Japanese Heritage Sites,' and 15 'Japanese Agricultural Heritage Sites' have been designated. According to this perception of situations, the research process for this study with its basic purpose of extracting the general characteristics of Japan's area-based heritage preservation system, has sequentially spread since 1976 as follows. First, this study investigates Japan's area-based heritage site preservation system and sets the scope of research through discussions of literature and preceding studies. Second, this study investigates the process of the spread of the area-based heritage site preservation system and analyzes the relationship between the systems according to their development, in order to draw upon their characteristics. Third, to concretize content related to relationships and characteristics, this study involves in-depth analysis of three representative examples and sums them up to identify the characteristics of Japan's area-based heritage system. A noticeable characteristic of Japan's area-based heritage site preservation system drawn from this is that new heritage sites are born each year. Consequently, an overlapping phenomenon takes place between heritage sites, and such phenomena occur alongside revitalization of related industries, traditional industry, and cultural tourism and the improvement of localities as well as the preservation of area-based heritage. These characteristics can be applied as suggestions for the revitalization of the 'modern historical and cultural space' system implemented by South Korea.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.111-126
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• 2020

Most industries have recently become aware of the importance of customer lifetime value as they are exposed to a competitive environment. As a result, preventing customers from churn is becoming a more important business issue than securing new customers. This is because maintaining churn customers is far more economical than securing new customers, and in fact, the acquisition cost of new customers is known to be five to six times higher than the maintenance cost of churn customers. Also, Companies that effectively prevent customer churn and improve customer retention rates are known to have a positive effect on not only increasing the company's profitability but also improving its brand image by improving customer satisfaction. Predicting customer churn, which had been conducted as a sub-research area for CRM, has recently become more important as a big data-based performance marketing theme due to the development of business machine learning technology. Until now, research on customer churn prediction has been carried out actively in such sectors as the mobile telecommunication industry, the financial industry, the distribution industry, and the game industry, which are highly competitive and urgent to manage churn. In addition, These churn prediction studies were focused on improving the performance of the churn prediction model itself, such as simply comparing the performance of various models, exploring features that are effective in forecasting departures, or developing new ensemble techniques, and were limited in terms of practical utilization because most studies considered the entire customer group as a group and developed a predictive model. As such, the main purpose of the existing related research was to improve the performance of the predictive model itself, and there was a relatively lack of research to improve the overall customer churn prediction process. In fact, customers in the business have different behavior characteristics due to heterogeneous transaction patterns, and the resulting churn rate is different, so it is unreasonable to assume the entire customer as a single customer group. Therefore, it is desirable to segment customers according to customer classification criteria, such as loyalty, and to operate an appropriate churn prediction model individually, in order to carry out effective customer churn predictions in heterogeneous industries. Of course, in some studies, there are studies in which customers are subdivided using clustering techniques and applied a churn prediction model for individual customer groups. Although this process of predicting churn can produce better predictions than a single predict model for the entire customer population, there is still room for improvement in that clustering is a mechanical, exploratory grouping technique that calculates distances based on inputs and does not reflect the strategic intent of an entity such as loyalties. This study proposes a segment-based customer departure prediction process (CCP/2DL: Customer Churn Prediction based on Two-Dimensional Loyalty segmentation) based on two-dimensional customer loyalty, assuming that successful customer churn management can be better done through improvements in the overall process than through the performance of the model itself. CCP/2DL is a series of churn prediction processes that segment two-way, quantitative and qualitative loyalty-based customer, conduct secondary grouping of customer segments according to churn patterns, and then independently apply heterogeneous churn prediction models for each churn pattern group. Performance comparisons were performed with the most commonly applied the General churn prediction process and the Clustering-based churn prediction process to assess the relative excellence of the proposed churn prediction process. The General churn prediction process used in this study refers to the process of predicting a single group of customers simply intended to be predicted as a machine learning model, using the most commonly used churn predicting method. And the Clustering-based churn prediction process is a method of first using clustering techniques to segment customers and implement a churn prediction model for each individual group. In cooperation with a global NGO, the proposed CCP/2DL performance showed better performance than other methodologies for predicting churn. This churn prediction process is not only effective in predicting churn, but can also be a strategic basis for obtaining a variety of customer observations and carrying out other related performance marketing activities.

• Journal of the Daesoon Academy of Sciences
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• v.39
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• pp.33-67
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• 2021

This paper aims at revealing the core concept of Injon (Human Nobility). The concept of Injon is one of the salient fundamental ideas which makes Daesoon Jinrihoe recognizable as Daesoon Jinrihoe. The concept of Injon has the basic meaning of 'human nobility,' but within the context wherein the nobility of humankind is considered to be greater than the nobility of Heaven and Earth. Although the religious and ideological interpretations of Injon (human nobility) that have developed over time have been quite diverse and abundant, these interpretations are all limited in that they generally assume the relationship between 'Heaven and Earth' and 'Humanity' to be antagonistic. However, if human nobility is relativized in that manner, it can reduce the potential broader meanings of mutual beneficence and the earthly paradise of the later world. These interpretations are grounded in the view of semiotic interpretation. Such interpretations have composed their view point via the semiotic meaning of the words. The semiotic point of view suggests that meanings of words consist in the relation of the word and the object to which it denotes. We will introduce a new view point which can be termed the transcendental view point. This view focuses on how the exact interpretation of words and sentences depends on the comprehension of the triad of systematic relations among the word, object, and speaker. In the Daesoon Thought, the Former World is considered to be the world wherein all creations unfolded according to the principle of mutual contention. This led to the accumulation of grievances and grudges which condensed and filled the Three Realms of Heaven, Earth, and Humanity. The Former World was dominated by Western material civilization, selfishness, and exclusivism. It was also a world where humans suffered from various natural disasters such as floods, droughts, plagues, and wildfires. The Former World lost the constant Dao and was overwhelmed with all kinds of disasters and calamities. That world fell into various kinds of wretchedness. The causes which made the Former World so cruel came from humans misunderstanding their relation to nature and life in general; including human life. The anthropocentric modern cosmology insisted that the human race was the only one to have the powers and rights to exercise dominion over nature. On the other hand, there is the Later World, which means the ideal and perfect, immanent eternal world for all humankind in Daesoon Thought. This world consists of life, peace, and equality and is also characterized by three typical attributes: goodness, peace, and all kinds of life. All living beings previously struggled for survival, but in the Later World, those lifeforms will embrace each other; even across different realms. In Daesoon Thought, the world and cosmos contain diverse forms of life, and human have both an earthly life and life in the after world should they die before the Later World. There are also the lives of divine beings and animals, and other such living entities. Daesoon Thought subsumes pan-vitalism, which allows they acknowledgement of myriad possible lifeforms. The concept of the Later World in Daesoon Thought, which mainly revealed in The Canonical Scripture and the words of Sangje (Kang Jeungsan), suggests that all kinds of life, including humans, animals, and even spirits in the afterworld, can live together in a perfect coming earthly paradise which is immanent. The concept of Injon can be interpreted though the view of transcendental pragmatics as an alternative to the typical views discussed in Daesoon Thought. Thinkers should attempt to improve current discourse on Injon in Daesoon Thought by focusing on the point that all kinds the original teachings demonstrate a value of all lifeforms. Therein, Injon would indicate not only the human nobility and dignity but also the nobility and dignity of divine beings, divine humans, and all other forms of life that have existed across time. The dimension of time allows for recognition of lifeforms from the Former World, the afterworld, and the Later World. This revised appraisal of Injon could further accommodate denizens of the afterworld, animals, ghosts and spirits, the earth and cloud souls of humans, and other lifeforms held to exist in the cosmology of Daesoon Thought.

• (The) Research of the performance art and culture
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• no.23
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• pp.281-312
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• 2011

The aim of this study is to examine various life conditions of actors of Joseon periods in unofficial historical stories. Yadam Literature(Korean unofficial historical stories) had been collected Sadaebu(the past Korean nobility and Confucian intelligentsia) among the people that stories had been handed down orally. and they had been wrote them. So Yadam Literature was heterozygous between the folk culture and the ruling class. And it was mixed and adapted legends and folktales, adding literary imagination. had a decisive role to cultivating novel that owed much to prosaic inspiration during A. D. 18~19. Besides, set a high value on excellent novel itself. Yadam Literature had a verisimilitude because it described a contemporary reality as it was founded on freely prosaic inspiration. In those days, so called Suchok and Seunggwangdae had performed Uhee(a comic theatrical performance) in Joseon periods. Suchok was the lowest class of people and Seunggwangdae was performing Buddhist monk in that time. Uhee had performed three kinds of comedies. One satirized and insinuated kings. Other satirized corrupt officials, too. Another had mimic everything. It is famous at that time as a king knew repertoire. Confucian scholars very were fond of Uhee in those ages. Because they favored a criticism of Uhee's satire. They thought that it gave people good lesson or instruction. Heri Bergson said that comic and Humor included lesson. At that time, those thought were universal in the world whether east or west. At any rate, I classify six kinds of types Uhee in Yadam Literature. First, satirizing and accusing corrupt officials. Second, an actor who use a satire in order to appeal secure a government position of his lord to a king. Third, shamans and actors who use a satire in order to appeal sufferings themselves to a king. Forth, actors and performing Buddhist monks that skillfully mimic anything. Fifth, describing actor's extremely miserable life. Sixth, wit and humor of actors. The contents of Uhee were various. Korean traditional actors adeptly dealt with aspects comic of wit, satire, humor, etc. Sometimes they used changeable transition them. By doing that, a great number of people enjoyed fully the sense of freedom. Korean traditional actors were the lowest class of people. They had lived extremely miserable life. But they had been exist as actions, interactions, and relationship in society those days. they were not only open to people, but also might foster community to peoples.

• Journal of Venture Innovation
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• v.5 no.3
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• pp.57-70
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• 2022

In modern industrial capitalism, the relationship between the provision of work and the receipt of wages has become an important principle governing society. According to the labor contract, the wages provided by entrusting the right to dispose of one's labor to the employer are directly compensated, and human life should be guaranteed and reproduced with proper rest. The establishment of labor relations under free contracts represents a problem in protecting workers, and accordingly, the maximum of working hours is set as a minimum right for workers, and the standard for minimum rest is set and assigned. The reduction of working hours is very important in terms of the quality of life of workers, but it is also an important issue in efficient corporate activities. As of 2020, Korea has 1,908 hours of annual working hours, the third lowest among OECD 37 countries in the happiness index surveyed by the Sustainable Development Solution Network(SDSN), an agency under the United Nations. Accordingly, the necessity of reducing working hours has been recognized, and the maximum working hours per week has been limited to 52 hours since 2018. In this situation, various working hours are legally excluded as a way to maintain the company's value-added creation and meet the diverse needs of workers, and Korea's Labor Standards Act restricts flexible working hours within three months, flexible working hours exceeding three months, selective working hours, and extended working hours. However, in the discussion on the application of the revised flexible working hours system in 2021 and the expansion of the settlement unit period recently discussed, there is a problem with the flexible working hours system, which needs to be improved. Therefore, this paper aims to examine the problems of the flexible working hours system and improvement measures. The flexible working hours system is a system that does not violate working hours even if the legal working hours are exceeded on a specific day or week according to a predetermined standard, and does not have to pay additional wages for excessive overtime work. It is mainly useful as a form of shift work in manufacturing, sales service, continuous business or electricity, gas, water, and transportation for long-term operations. It is also used as a way to shorten working hours, such as expanding holidays through short working days. However, if the settlement unit period is expanded, it is disadvantageous to workers as the additional wages that workers can receive will not be received. Therefore, First, in order to expand the settlement unit period currently under discussion, additional wages should be paid for the period expanded from the current standard. Second, it is necessary to improve the application of the flexible working hours system to individual workers to have sufficient consultation with individual workers in a written agreement with the worker representative, Third, clarify the allowable time for extended work during the settlement unit period, and Fourth, limit the daily working hours or apply to continuous rest. In addition, since the written agreement of the worker representative is an important issue in the application of the flexible working hours system, it is necessary to secure the representation of the worker representative.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.19 no.1
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• pp.75-91
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• 2024

Recently, global business environment is changing dramatically along with the acceleration of technological innovation amid the war, climatic change, and geopolitical instability. Accordingly, it is difficult to predict or plan for the future as the volatility, complexity, ambiguity, and uncertainty of the industrial ecosystem continue to increase. Therefore, organizations are undergoing inevitable restructuring in accordance with their survival strategy, for instance, removing marginal businesses or firing. Accordingly, office workers are seeking a startup as an alternative for their continuous economic activity amid rising anxiety factors that make them think they would lose their jobs unintentionally. Here, this study is aimed to verify through what paths office workers' self-determination influences the process of converting to a startup. For this study, an online survey was carried out, and 310 respondents' valid data were analyzed through SPSS and AMOS. To sum up the results, first, office workers' self-determination did not have significant effects on entrepreneurial intention. However, it was confirmed that self-determination had positive (+) effects on innovativeness and perception of the startup support system. This result shows that their psychology works to prepare step by step by accumulating innovative experiences and increasing perception of the startup support system from a long-term life path perspective rather than challenging startups right way. Second, innovativeness is found to have positive (+) effects on entrepreneurial intention. Also, perception of the startup support system had positive (+) effects on entrepreneurial intention. This implies that when considering startups, they are highly aware of the government's various startup support systems. Third, innovativeness is found to have positive (+) effects on perception of the startup support system. It is judged that perception of the startup support system is valid for prospective founders to exhibit their innovativeness and realize new ideas. Fourth, it was confirmed that innovativeness and perception of the startup support system mediated correlation between self-determination and entrepreneurial intention, and perception of the startup support system mediated correlation between innovativeness and entrepreneurial intention, which shows that it is a crucial factor in entrepreneurial intention. Although previous studies related to startups deal with students mostly, this study targets office workers who form a great part in economic activities, which makes it academically valuable in terms of being differentiated from others and extending the scope of research. Also, when we consider the fact that the motivation for self-determination alone fails to stimulate entrepreneurial intention and the complete mediation of innovativeness and the startup support system, it has great implications in practical aspects such as the government's human and material support systems. In the selection and analysis of samples, this study exhibits a limitation that the problem of common method bias is not completely resolved. Also, additional definitive research is needed on whether entrepreneurial intention is formed and converted into startup behavior. Academically and practically, this study deals with the relationship between humans' psychological motives and startups which has not been handled sufficiently in previous studies. The conversion of office workers to startups is expected to have effects on individuals' economic stability and the state's job creation; therefore, it needs to be investigated continuously for its great value.