• Journal of the Institute of Convergence Signal Processing
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• v.24 no.4
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• pp.236-241
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• 2023

Water quality problems in rivers, lakes, and estuaries have become serious in Korea. In order to overcome eutrophication of freshwater lakes and river basins, systematic management of water quality is necessary. To manage water quality in freshwater lakes and basins, apply hydrological models suitable for the basin and water quality models such as rivers and lakes to reduce water pollution based on the prediction results of these models. Improvement measures must be presented. In order to apply appropriate water pollution improvement measures in the watershed, accurate pollution sources must be identified and pollution loads must be predicted and presented. Based on GIS, the connection between the pollutant database and the hydrological and water quality prediction model will be integrated based on spatial location, making it possible to provide systematic support to improve watershed water quality by comprehensively including the water quality modeling process. In this paper, in order to accurately predict water pollution in freshwater lakes and river basins, a water quality model system is established using GIS-based spatial information to present a comprehensive water quality management method for freshwater lake basins in the future, and to systematically manage pollution sources through water quality modeling. This study was conducted to easily and efficiently operate hydrological and water quality models using automated spatial information.

• The Journal of the Convergence on Culture Technology
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• v.10 no.4
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• pp.121-132
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• 2024

This study attempted to determine the effects of job stress, nursing problem-solving ability, nursing work performance, and job stress and nursing problem-solving ability on nursing work performance according to the MBTI type of nurses. The study subjects were 141 nurses working at a medical institution in G Province, and data collection was conducted from March 01 to March 31, 2024. The collected data were analyzed using correlation and multiple regression analysis. Among the psychological function types of MBTI, the SF type (sympathetic and friendly type) was the most common, and among the psychological temperament types, the SP type (sensuous and open type) was the most common. Nursing work performance had a negative correlation with job resource stress, a positive correlation with nursing problem-solving ability, and a positive correlation with problem recognition, information collection, planning ability, and evaluation. The variable that had a significant impact on nursing work performance was job resources, and problem recognition, a subfactor of nursing problem-solving ability, was found to be the best predictor of nursing work performance, followed by planning ability. The explanatory power was 17.8%. The results of this study are expected to be used as basic data to develop efficient nursing management guidelines by not only improving understanding of the personality of nurses but also investigating factors related to nurses' work performance. Through the development of programs and measures to improve nursing performance, it is necessary to revitalize programs, provide educational opportunities, and provide institutional support from hospital organizations to enable high-quality nursing care through skilled nursing work.

• The Journal of the Convergence on Culture Technology
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• v.10 no.4
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• pp.419-423
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• 2024

With the rapid increase of multicultural families in South Korea due to international marriages and the influx of foreign workers, the number of children from these families enlisting in the military is also rising. It is projected that by 2030, over 10,000 recruits from multicultural backgrounds will join the military annually. This influx can help mitigate the issue of decreasing military resources due to low birth rates and enhance the cultural diversity within the military. However, the lack of multicultural awareness and the presence of discrimination in the military can hinder the well-being of these multicultural soldiers. This study aims to analyze the multicultural phenomenon in the Korean military and explore development strategies to ensure the stable service and healthy military culture for multicultural soldiers. The theoretical background of multiculturalism and diversity in the military is examined, along with the analysis of multicultural military operations in other countries. Management strategies for multicultural soldiers, including leadership and organizational culture innovation, multicultural education and awareness programs, and the establishment of tailored support systems, are proposed. Furthermore, policy recommendations for enhancing the effectiveness of multicultural policies in the Korean military are presented. Supporting the successful military service of multicultural soldiers will contribute to the enhancement of military combat power and the acceptance of multiculturalism in Korean society.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.167-181
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• 2018

Over the past decade, deep learning has been in spotlight among various machine learning algorithms. In particular, CNN(Convolutional Neural Network), which is known as the effective solution for recognizing and classifying images or voices, has been popularly applied to classification and prediction problems. In this study, we investigate the way to apply CNN in business problem solving. Specifically, this study propose to apply CNN to stock market prediction, one of the most challenging tasks in the machine learning research. As mentioned, CNN has strength in interpreting images. Thus, the model proposed in this study adopts CNN as the binary classifier that predicts stock market direction (upward or downward) by using time series graphs as its inputs. That is, our proposal is to build a machine learning algorithm that mimics an experts called 'technical analysts' who examine the graph of past price movement, and predict future financial price movements. Our proposed model named 'CNN-FG(Convolutional Neural Network using Fluctuation Graph)' consists of five steps. In the first step, it divides the dataset into the intervals of 5 days. And then, it creates time series graphs for the divided dataset in step 2. The size of the image in which the graph is drawn is $40(pixels){\times}40(pixels)$, and the graph of each independent variable was drawn using different colors. In step 3, the model converts the images into the matrices. Each image is converted into the combination of three matrices in order to express the value of the color using R(red), G(green), and B(blue) scale. In the next step, it splits the dataset of the graph images into training and validation datasets. We used 80% of the total dataset as the training dataset, and the remaining 20% as the validation dataset. And then, CNN classifiers are trained using the images of training dataset in the final step. Regarding the parameters of CNN-FG, we adopted two convolution filters ($5{\times}5{\times}6$ and $5{\times}5{\times}9$) in the convolution layer. In the pooling layer, $2{\times}2$ max pooling filter was used. The numbers of the nodes in two hidden layers were set to, respectively, 900 and 32, and the number of the nodes in the output layer was set to 2(one is for the prediction of upward trend, and the other one is for downward trend). Activation functions for the convolution layer and the hidden layer were set to ReLU(Rectified Linear Unit), and one for the output layer set to Softmax function. To validate our model - CNN-FG, we applied it to the prediction of KOSPI200 for 2,026 days in eight years (from 2009 to 2016). To match the proportions of the two groups in the independent variable (i.e. tomorrow's stock market movement), we selected 1,950 samples by applying random sampling. Finally, we built the training dataset using 80% of the total dataset (1,560 samples), and the validation dataset using 20% (390 samples). The dependent variables of the experimental dataset included twelve technical indicators popularly been used in the previous studies. They include Stochastic %K, Stochastic %D, Momentum, ROC(rate of change), LW %R(Larry William's %R), A/D oscillator(accumulation/distribution oscillator), OSCP(price oscillator), CCI(commodity channel index), and so on. To confirm the superiority of CNN-FG, we compared its prediction accuracy with the ones of other classification models. Experimental results showed that CNN-FG outperforms LOGIT(logistic regression), ANN(artificial neural network), and SVM(support vector machine) with the statistical significance. These empirical results imply that converting time series business data into graphs and building CNN-based classification models using these graphs can be effective from the perspective of prediction accuracy. Thus, this paper sheds a light on how to apply deep learning techniques to the domain of business problem solving.

• 대한공업교육학회지
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• v.38 no.2
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• pp.48-67
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• 2013

This study tried to suggest implications about operation direction of specialized high schools (SHS) by researching awareness of middle school students (MSS), school parents (SP), human resources directors in industrial institutions (HRDII) who will be the main users of SHS education, about entering SHS and career after graduating from SHS. Seniors of middle school, SP and HRDII in Asan, Chungnam were the subject of this survey research. The summary of the result of this study is as follow: First, MSS and SP usually hoped to enter general high schools rather than vocational education schools such as SHS, meister high schools, and MSS considered school records and SP considered aptitude and talent for the factors to choose high school. Second, MSS, SP, and HRDII recognized purposes of SHS as improvement of talent and aptitude, and getting a job. As for positive images of SHS, they recognized it as applying talent and aptitude to life early, getting good jobs easily, fast independence after graduation, and learning excellent technologies, and as for negative images of SHS, they recognized it as social prejudices and discrimination, students with bad school records enter them, disadvantages about promotion and wages, and being unfavorable for entering universities. They also recognized education of SHS as being effective for improvement of basic and executive ability and key competency, development of creative human resources, and improvement of right personality and courteous manners. Third, many MSS and SP showed intention to enter SHS if it is established in Asan. They wished to enter SHS because they would like to apply their aptitude and talent to life early, learn excellent skill, and hope for early employment, on the other hand, they did not wish to enter SHS because it was not suited for their aptitude and talent, awareness about SHS is low, it is unfavorable to enter universities, and there were social prejudices and discrimination. They also similarly hoped for getting jobs and entering universities after graduating from SHS. And the reason they wanted to get a job was usually because they want to be successful by advancing into society early, or because it is still hard to get a job even after graduate from the university, on the other hand, the reason they want to enter university is because is usually in-depth education about major and social discrimination about level of education. The ability to perform duties forms the greatest part of the employment standard that MSS, SP, and HRDII aware. MSS and SP usually hoped for industrial, home economics and housework and commercial majors in SHS, and considered aptitude and talent, the promising future, and being favorable for employment for choosing major. The reason HRDII hire SHS student was to develop student into talent of industrial institution, ability of student, and need for manpower with high school graduation level, and there were also partial answer that they can hire SHS student if they have ability to perform duties. The proposals about operation direction of SHS according to the results above are as follow: SHS should diversify major and curriculum to meet various requirements of student and parents, establish SHS admission system based on career guidance, and improve student's ability to perform duties by establishing work-based learning. The Government should organize work-to-school policy to enable practical career development of students from SHS, and promote relevant policy to reinforcing SHS education rather than quantitative evaluation such as employment rate, and cooperative support from each government departments is required to make manpower with skill related to SHS to get proper evaluation and treatment.

• Asia pacific journal of information systems
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• v.18 no.4
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• pp.27-55
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• 2008

Due to the increasing and intensifying competition in the Internet shopping market, it has been recognized as very important to develop an effective policy and strategy for acquiring loyal customers. For this reason, web site designers need to know if a new Internet shopping mall(ISM) will be accepted. Researchers have been working on identifying factors for explaining and predicting user acceptance of an ISM. Some studies, however, revealed inconsistent findings on the antecedents of user acceptance of a website. Lack of consideration for individual differences in user ability is believed to be one of the key reasons for the mixed findings. The elaboration likelihood model (ELM) and several studies have suggested that individual differences in ability plays an moderating role on the relationship between the antecedents and user acceptance. Despite the critical role of user ability, little research has examined the role of user ability in the Internet shopping mall context. The purpose of this study is to develop a user acceptance model that consider the moderating role of user ability in the context of Internet shopping. This study was initiated to see the ability of the technology acceptance model(TAM) to explain the acceptance of a specific ISM. According to TAM. which is one of the most influential models for explaining user acceptance of IT, an intention to use IT is determined by usefulness and ease of use. Given that interaction between user and website takes place through web interface, the decisions to accept and continue using an ISM depend on these beliefs. However, TAM neglects to consider the fact that many users would not stick to an ISM until they trust it although they may think it useful and easy to use. The importance of trust for user acceptance of ISM has been raised by the relational views. The relational view emphasizes the trust-building process between the user and ISM, and user's trust on the website is a major determinant of user acceptance. The proposed model extends and integrates the TAM and relational views on user acceptance of ISM by incorporating usefulness, ease of use, and trust. User acceptance is defined as a user's intention to reuse a specific ISM. And user ability is introduced into the model as moderating variable. Here, the user ability is defined as a degree of experiences, knowledge and skills regarding Internet shopping sites. The research model proposes that the ease of use, usefulness and trust of ISM are key determinants of user acceptance. In addition, this paper hypothesizes that the effects of the antecedents(i.e., ease of use, usefulness, and trust) on user acceptance may differ among users. In particular, this paper proposes a moderating effect of a user's ability on the relationship between antecedents with user's intention to reuse. The research model with eleven hypotheses was derived and tested through a survey that involved 470 university students. For each research variable, this paper used measurement items recognized for reliability and widely used in previous research. We slightly modified some items proper to the research context. The reliability and validity of the research variables were tested using the Crobnach's alpha and internal consistency reliability (ICR) values, standard factor loadings of the confirmative factor analysis, and average variance extracted (AVE) values. A LISREL method was used to test the suitability of the research model and its relating six hypotheses. Key findings of the results are summarized in the following. First, TAM's two constructs, ease of use and usefulness directly affect user acceptance. In addition, ease of use indirectly influences user acceptance by affecting trust. This implies that users tend to trust a shopping site and visit repeatedly when they perceive a specific ISM easy to use. Accordingly, designing a shopping site that allows users to navigate with heuristic and minimal clicks for finding information and products within the site is important for improving the site's trust and acceptance. Usefulness, however, was not found to influence trust. Second, among the three belief constructs(ease of use, usefulness, and trust), trust was empirically supported as the most important determinants of user acceptance. This implies that users require trustworthiness from an Internet shopping site to be repeat visitors of an ISM. Providing a sense of safety and eliminating the anxiety of online shoppers in relation to privacy, security, delivery, and product returns are critically important conditions for acquiring repeat visitors. Hence, in addition to usefulness and ease of use as in TAM, trust should be a fundamental determinants of user acceptance in the context of internet shopping. Third, the user's ability on using an Internet shopping site played a moderating role. For users with low ability, ease of use was found to be a more important factors in deciding to reuse the shopping mall, whereas usefulness and trust had more effects on users with high ability. Applying the EML theory to these findings, we can suggest that experienced and knowledgeable ISM users tend to elaborate on such usefulness aspects as efficient and effective shopping performance and trust factors as ability, benevolence, integrity, and predictability of a shopping site before they become repeat visitors of the site. In contrast, novice users tend to rely on the low elaborating features, such as the perceived ease of use. The existence of moderating effects suggests the fact that different individuals evaluate an ISM from different perspectives. The expert users are more interested in the outcome of the visit(usefulness) and trustworthiness(trust) than those novice visitors. The latter evaluate the ISM in a more superficial manner focusing on the novelty of the site and on other instrumental beliefs(ease of use). This is consistent with the insights proposed by the Heuristic-Systematic model. According to the Heuristic-Systematic model. a users act on the principle of minimum effort. Thus, the user considers an ISM heuristically, focusing on those aspects that are easy to process and evaluate(ease of use). When the user has sufficient experience and skills, the user will change to systematic processing, where they will evaluate more complex aspects of the site(its usefulness and trustworthiness). This implies that an ISM has to provide a minimum level of ease of use to make it possible for a user to evaluate its usefulness and trustworthiness. Ease of use is a necessary but not sufficient condition for the acceptance and use of an ISM. Overall, the empirical results generally support the proposed model and identify the moderating effect of the effects of user ability. More detailed interpretations and implications of the findings are discussed. The limitations of this study are also discussed to provide directions for future research.

• 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.

• Asia pacific journal of information systems
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• v.24 no.4
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• pp.443-472
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• 2014

To survive in the global competitive environment, enterprise should be able to solve various problems and find the optimal solution effectively. The big-data is being perceived as a tool for solving enterprise problems effectively and improve competitiveness with its' various problem solving and advanced predictive capabilities. Due to its remarkable performance, the implementation of big data systems has been increased through many enterprises around the world. Currently the big-data is called the 'crude oil' of the 21st century and is expected to provide competitive superiority. The reason why the big data is in the limelight is because while the conventional IT technology has been falling behind much in its possibility level, the big data has gone beyond the technological possibility and has the advantage of being utilized to create new values such as business optimization and new business creation through analysis of big data. Since the big data has been introduced too hastily without considering the strategic value deduction and achievement obtained through the big data, however, there are difficulties in the strategic value deduction and data utilization that can be gained through big data. According to the survey result of 1,800 IT professionals from 18 countries world wide, the percentage of the corporation where the big data is being utilized well was only 28%, and many of them responded that they are having difficulties in strategic value deduction and operation through big data. The strategic value should be deducted and environment phases like corporate internal and external related regulations and systems should be considered in order to introduce big data, but these factors were not well being reflected. The cause of the failure turned out to be that the big data was introduced by way of the IT trend and surrounding environment, but it was introduced hastily in the situation where the introduction condition was not well arranged. The strategic value which can be obtained through big data should be clearly comprehended and systematic environment analysis is very important about applicability in order to introduce successful big data, but since the corporations are considering only partial achievements and technological phases that can be obtained through big data, the successful introduction is not being made. Previous study shows that most of big data researches are focused on big data concept, cases, and practical suggestions without empirical study. The purpose of this study is provide the theoretically and practically useful implementation framework and strategies of big data systems with conducting comprehensive literature review, finding influencing factors for successful big data systems implementation, and analysing empirical models. To do this, the elements which can affect the introduction intention of big data were deducted by reviewing the information system's successful factors, strategic value perception factors, considering factors for the information system introduction environment and big data related literature in order to comprehend the effect factors when the corporations introduce big data and structured questionnaire was developed. After that, the questionnaire and the statistical analysis were performed with the people in charge of the big data inside the corporations as objects. According to the statistical analysis, it was shown that the strategic value perception factor and the inside-industry environmental factors affected positively the introduction intention of big data. The theoretical, practical and political implications deducted from the study result is as follows. The frist theoretical implication is that this study has proposed theoretically effect factors which affect the introduction intention of big data by reviewing the strategic value perception and environmental factors and big data related precedent studies and proposed the variables and measurement items which were analyzed empirically and verified. This study has meaning in that it has measured the influence of each variable on the introduction intention by verifying the relationship between the independent variables and the dependent variables through structural equation model. Second, this study has defined the independent variable(strategic value perception, environment), dependent variable(introduction intention) and regulatory variable(type of business and corporate size) about big data introduction intention and has arranged theoretical base in studying big data related field empirically afterwards by developing measurement items which has obtained credibility and validity. Third, by verifying the strategic value perception factors and the significance about environmental factors proposed in the conventional precedent studies, this study will be able to give aid to the afterwards empirical study about effect factors on big data introduction. The operational implications are as follows. First, this study has arranged the empirical study base about big data field by investigating the cause and effect relationship about the influence of the strategic value perception factor and environmental factor on the introduction intention and proposing the measurement items which has obtained the justice, credibility and validity etc. Second, this study has proposed the study result that the strategic value perception factor affects positively the big data introduction intention and it has meaning in that the importance of the strategic value perception has been presented. Third, the study has proposed that the corporation which introduces big data should consider the big data introduction through precise analysis about industry's internal environment. Fourth, this study has proposed the point that the size and type of business of the corresponding corporation should be considered in introducing the big data by presenting the difference of the effect factors of big data introduction depending on the size and type of business of the corporation. The political implications are as follows. First, variety of utilization of big data is needed. The strategic value that big data has can be accessed in various ways in the product, service field, productivity field, decision making field etc and can be utilized in all the business fields based on that, but the parts that main domestic corporations are considering are limited to some parts of the products and service fields. Accordingly, in introducing big data, reviewing the phase about utilization in detail and design the big data system in a form which can maximize the utilization rate will be necessary. Second, the study is proposing the burden of the cost of the system introduction, difficulty in utilization in the system and lack of credibility in the supply corporations etc in the big data introduction phase by corporations. Since the world IT corporations are predominating the big data market, the big data introduction of domestic corporations can not but to be dependent on the foreign corporations. When considering that fact, that our country does not have global IT corporations even though it is world powerful IT country, the big data can be thought to be the chance to rear world level corporations. Accordingly, the government shall need to rear star corporations through active political support. Third, the corporations' internal and external professional manpower for the big data introduction and operation lacks. Big data is a system where how valuable data can be deducted utilizing data is more important than the system construction itself. For this, talent who are equipped with academic knowledge and experience in various fields like IT, statistics, strategy and management etc and manpower training should be implemented through systematic education for these talents. This study has arranged theoretical base for empirical studies about big data related fields by comprehending the main variables which affect the big data introduction intention and verifying them and is expected to be able to propose useful guidelines for the corporations and policy developers who are considering big data implementationby analyzing empirically that theoretical base.

• Journal of Life Science
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• v.19 no.5
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• pp.593-597
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• 2009

The uterus plays a critical role in pregnancy and steroid hormones, and both estrogen (E2) and progesterone (P4) especially play important roles in the cross-talk between embryos and uterus to support the pregnancy. E2 stimulates uterine growth during early pregnancy to prepare for implantation of embryos. This cross-talk during the implantation period involves hormones (E2 and P4) and growth factors, including insulin-like growth factor-I (IGF-I). In the uterus of a pregnant pig, the action of E2 is mediated by estrogen receptor-${\beta}$ (ER-${\beta}$). The expression of ER-a was much higher in early pregnancy than in mid- and late- pregnancy, suggesting E2 secretion from embryos enhances transcription of ER-a during early pregnancy. In order to prove whether IGF-I is an E2 target gene, quantitative real-time PCR was performed on ovariectomized murine uterus with E2 and/or P4 treatment(s). Increased IGF-I mRNA expression was observed with E2 treatment, however, it was not significantly induced by P4 treatment, which clearly demonstrates that, in mice, E2 depends on the activation of uterine IGF-I gene expression. The expression of IGF-I in the uterus of pigs was much higher in early pregnancy than in mid- and late- pregnancy and these data exhibited the same expression pattern with the ER-${\beta}$ gene expression in the uterus. It suggests that a positive co-relationship between IGF-I and ER-${\beta}$ expression exists in the uterus, and that both gene expressions of IGF-I and ER-${\beta}$ are regulated by E2. It further suggests that uterine the IGF-I gene expression might be initiated by E2 secreted from embryos to increase ER-${\beta}$ gene expression, and that this increased ER-${\beta}$ further stimulates the expression of IGF-I in the uterus during early pregnancy.

• Culinary science and hospitality research
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• v.22 no.3
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• pp.66-78
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• 2016

The purpose of this study was to provide hotel bakery organizations with useful information for human resources management based on a substantial clarification of the relationship and correlation of hotel bakery employees' self-leadership, self-efficacy, and organizational commitment. Resources were gathered from June 20, 2015 to July 10, 2015 by distributing a total of 500 surveys, from which 377 were collected. Excluding 23 survey forms not suitable for the analysis, 354 survyes were processed through factor analysis, reliability test, and multivariant structural analysis using SPSS 18.0 and AMOS 18.0 to verify the hypotheses. The findings of the analysis can be summarized as follows: first, behavior-centered strategies, natural compensation, and constructive thinking strategies had a significantly positive impact on self-efficacy. Second, in the analysis of impact of self-efficacy on organizational commitment, it was significant for emotional immersion, but did not have a significantly positive impact on normative immersion. Third, in the relationship between self-leadership and organizational commitment, behavior-centered strategies and natural compensation did not have a significant impact on emotional immersion. However, constructive thinking strategies had a significant impact. The following implications can be derived based on the above findings: this study implies the possibility of future studies on the variables of self-efficacy as it set behavior-centered strategies, natural compensation, and constructive thinking strategies as the preliminary factors under hotel bakery employees' self-leadership; and it analyzed the causality of each factor with emotional immersion and normative immersion as the subordinate factors of self-efficacy and organizational commitment to show that self-leadership and self-efficacy of hotel bakery employees based on emotional immersion and normative immersion can stably improve the organization of hotel bakeries.