• Journal of Industrial Convergence
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• v.20 no.12
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• pp.97-106
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• 2022

The aim of this study was identify national forensic psychiatric hospital nurses' attitudes, perceived meaning, facilitators and barriers to the recovery of mental health. A descriptive study design was used. The participants were 100 nurses working in a national forensic psychiatric hospital. The collected data was analyzed by descriptive statistics, independent t-test, ANOVA, scheffe-test using SPSS 26.0 WIN program. Also, content analysis was used for the responses described by the participants. The participants attitude toward recovery from mental health was positive(3.90±0.41), attitude showed significant differences in certification of psychiatric mental health nurse practitioner(t=-2.06, p= 042), certification of addiction nurses(t=-2.36, p= 020). The meaning of recovery that participants perceived were symptom mitigation(42.0%), barriers were family indifference and prejudice(31.0%), facilitators were medication, psychiatric rehabilitation program and therapeutic interviews(33.0%). It is necessary to provide education to improve nurses' understand to mental health recovery, organizational efforts are needed to establish a family support system and therapeutic environments for the recovery of mentally disordered offenders.

• The Journal of Society for e-Business Studies
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• v.27 no.2
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• pp.37-64
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• 2022

As the social acceptance of artificial intelligence increases, the number of cases of applying machine learning methods to companies is also increasing. Technical factors such as accuracy and interpretability have been the main criteria for selecting machine learning methods. However, the success of implementing machine learning also affects management factors such as IT departments, operation departments, leadership, and organizational culture. Unfortunately, there are few integrated studies that understand the success factors of machine learning selection in which technical and management factors are considered together. Therefore, the purpose of this paper is to propose and empirically analyze a technology-management integrated model that combines task-tech fit, IS Success Model theory, and John Rice's algorithm selection process model to understand machine learning selection within the company. As a result of a survey of 240 companies that implemented machine learning, it was found that the higher the algorithm quality and data quality, the higher the algorithm-problem fit was perceived. It was also verified that algorithm-problem fit had a significant impact on the organization's innovation and productivity. In addition, it was confirmed that outsourcing and management support had a positive impact on the quality of the machine learning system and organizational cultural factors such as data-driven management and motivation. Data-driven management and motivation were highly perceived in companies' performance.

• Management & Information Systems Review
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• v.32 no.4
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• pp.81-101
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• 2013

The purpose of this study was to examine the effects of mental similarity and internal locus of control, which are the characteristics of an organizational member, and individualism and power distance, which are an individual's sense of value, on mentoring function in an informal mentoring relationship of ROK army. For corroborative analysis, the sample was collected from 547 questionnaires, which contain validate data out of 1,000 questionnaires distributed to junior officers working at ROK army's division level unit. The data proved that, First, mental similarity and internal locus of control positively effected upon mentoring function. Second, individualism positively effected upon mentoring function while power distance had a negative effect on it. Thus in an informal mentoring relationship of ROK army, a mentee perceived as he or she gains more support from mentoring function when a mentee recognizes higher mental similarity, individualism, and is in an internal locus of control. On the other hand, a mentee who perceived higher power distance felt as he or she gets less support from mentoring function. Through this investigation, the significance of influential components of mentoring function in a mentoring relationship of ROK army was demonstrated, and these research results could be highly supportive for a future research based on mentoring relationship.

• 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.

• Journal of Korean Academy of Nursing
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• v.29 no.4
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• pp.880-891
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• 1999

The purpose of this study was to describe nursing decision tasks, their characteristics, and problems associated with decision making. The subjects were 32 nurses who had at least one-year nursing experience and worked on medical-surgical units or intensive care units(ICU). They were asked to describe their decision making experiences in patient care situations and to identify the characteristics of each decisions. They were also asked to describe perceived problems associated with decision making in nursing. The responses on nursing decision tasks and problems were analyzed with content analysis and the decision characteristics were identified by statistical analysis of variance. It was found that there were 16 nursing decisions which are as follows : decisions related to interpreting and selecting appropriate strategies for pain management(6.6%) ; decisions related to providing emotional support (0.7%) ; decisions related to explaining the patient's condition and rationale for procedures(1.1%) ; decisions related to assisting patients to integrate the implications of illness and recovering into their lifestyles(2.9%) ; decisions related to detecting significant changes In patients and selecting appropriate intervention strategies (17.2%) ; decisions related to anticipating problems and selecting preventive measures(4.2%) ; decisions related to identifying emergency situations(0.4%) ; decisions related to effective management of patient crisis until physician assistance becomes available(2.8%) ; decisions related to starting and maintaining intravenous therapy(2.6%) ; decisions related to administering medications(8.1%) ; decisions related to combating the hazards of immobility(7.3%) : decisions related to treating wound management strategies(5.5%) ; decisions related to relieving patient discomfort(13.9) ; decisions related to selecting appropriate strategy according to the changing situation of the patient(18.2%) ; decisions related to selecting the best strategy for patient management(5.3%) ; and decisions related to coordinating, ordering, and meeting the various needs of the patient (3.1%). The nurses reported the fellowing problems in decision making : difficulties due to lack of knowledge and experience (18.6%) ; uncertainty and complexity of decision tasks(15.2%) ; lack of time to make decisions(2.9%) ; personal values which conflict with other staff(15.7%) ; lack of selection autonomy(30.0%) ; and organizational barriers(7.6%). Continuing education programs and decision support systems for frequent nursing decision tasks can be established on the basis of these results. Then decision ability in nurses will increase through the education programs and decision support systems, and then quality of nursing service will be better.

• Journal of Information Technology Applications and Management
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• v.27 no.1
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• pp.75-95
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• 2020

Smart Factory is the decisive factor of the Fourth Industrial Revolution and is a key field for national competitiveness. Until now, most smart factory research has focused on policy and technology. In order to spread more technology, it is necessary to study what factors influence the adoption of smart factory technology in the enterprise. Nevertheless, little research has been done. In this study, based on the UTAUT (Unified Theory of Acceptance and Use of Technology), which has been proved through many years of research, I have studied the factors that influence the acceptance of smart factory technology. As a result of research, performance expectancy, social influence, and facilitating conditions of UTAUT model had a positive(+) effect on behavior intention. Their relationship of influence was in the order of performance expectancy (β = .459)> facilitating conditions (β = .212)> social influence (β = .210). However, it was found that the effort expectancy did not affect the behavior intention, and the impact of the newly perceived risk on the behavior intention to use was not confirmed. The main reason is that the acceptance of smart factory technology is not a matter of personal interest but a matter of organizational choice. Trust, on the other hand, was found to be partially mediated between performance expectancy, facilitating conditions, social influence and behavior intention. For many years, many researchers have validated the UTAUT, which has been validated through various empirical studies. It is academically meaningful to begin the study of factors affecting the acceptance of smart factory technology in terms of the UTAUT. In practice, it is necessary to provide SME employees with more information related to the introduction of smart factories, to provide advanced services related to the establishment of smart factories, and to establish a standardized model for each industry.

• Journal of Korean Academy of Nursing Administration
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• v.16 no.1
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• pp.26-36
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• 2010

Purpose: The purpose of this study was to investigate the relationships among psycho-social well-being, and health perception and behavior in clinical nurses. Methods: The data were collected from 362 nurses employed at five general hospitals. A self-reported questionnaire was used to assess demographics and job characteristics, health perception and behavior, depression, and psycho-social stress. The data were analysed using Cronbach's alpha, descriptive statistics, t-test, and ANOVA. Results: While nurses perceived themselves to be in good health, represented by a high mean for health perceptions, actual health behavior was poor as the mean for health behavior was low. Forty two percents of the subjects were categorized as having major depression and high-stress levels. There were statistically significant differences in psycho-social well-being according to age, marital status, years of employment, and monthly salary. Also, there were significant differences in psycho-social well-being according to health perception, regular meals and exercise, sufficient sleep, job related injury and accident, and absences for ill. Conclusion: In order to prevent depression and improve psycho-social well-being among clinical nurses, organizational support and health management needs to be focused on high-risk nurses. Furthermore, it is essential to consider the health perception and behavior of clinical nurses.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.27-65
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• 2020

Many companies on information and communication technology make public their own developed AI technology, for example, Google's TensorFlow, Facebook's PyTorch, Microsoft's CNTK. By releasing deep learning open source software to the public, the relationship with the developer community and the artificial intelligence (AI) ecosystem can be strengthened, and users can perform experiment, implementation and improvement of it. Accordingly, the field of machine learning is growing rapidly, and developers are using and reproducing various learning algorithms in each field. Although various analysis of open source software has been made, there is a lack of studies to help develop or use deep learning open source software in the industry. This study thus attempts to derive a strategy for adopting the framework through case studies of a deep learning open source framework. Based on the technology-organization-environment (TOE) framework and literature review related to the adoption of open source software, we employed the case study framework that includes technological factors as perceived relative advantage, perceived compatibility, perceived complexity, and perceived trialability, organizational factors as management support and knowledge & expertise, and environmental factors as availability of technology skills and services, and platform long term viability. We conducted a case study analysis of three companies' adoption cases (two cases of success and one case of failure) and revealed that seven out of eight TOE factors and several factors regarding company, team and resource are significant for the adoption of deep learning open source framework. By organizing the case study analysis results, we provided five important success factors for adopting deep learning framework: the knowledge and expertise of developers in the team, hardware (GPU) environment, data enterprise cooperation system, deep learning framework platform, deep learning framework work tool service. In order for an organization to successfully adopt a deep learning open source framework, at the stage of using the framework, first, the hardware (GPU) environment for AI R&D group must support the knowledge and expertise of the developers in the team. Second, it is necessary to support the use of deep learning frameworks by research developers through collecting and managing data inside and outside the company with a data enterprise cooperation system. Third, deep learning research expertise must be supplemented through cooperation with researchers from academic institutions such as universities and research institutes. Satisfying three procedures in the stage of using the deep learning framework, companies will increase the number of deep learning research developers, the ability to use the deep learning framework, and the support of GPU resource. In the proliferation stage of the deep learning framework, fourth, a company makes the deep learning framework platform that improves the research efficiency and effectiveness of the developers, for example, the optimization of the hardware (GPU) environment automatically. Fifth, the deep learning framework tool service team complements the developers' expertise through sharing the information of the external deep learning open source framework community to the in-house community and activating developer retraining and seminars. To implement the identified five success factors, a step-by-step enterprise procedure for adoption of the deep learning framework was proposed: defining the project problem, confirming whether the deep learning methodology is the right method, confirming whether the deep learning framework is the right tool, using the deep learning framework by the enterprise, spreading the framework of the enterprise. The first three steps (i.e. defining the project problem, confirming whether the deep learning methodology is the right method, and confirming whether the deep learning framework is the right tool) are pre-considerations to adopt a deep learning open source framework. After the three pre-considerations steps are clear, next two steps (i.e. using the deep learning framework by the enterprise and spreading the framework of the enterprise) can be processed. In the fourth step, the knowledge and expertise of developers in the team are important in addition to hardware (GPU) environment and data enterprise cooperation system. In final step, five important factors are realized for a successful adoption of the deep learning open source framework. This study provides strategic implications for companies adopting or using deep learning framework according to the needs of each industry and business.

• Journal of the Korean Society for Library and Information Science
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• v.38 no.1
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• pp.77-91
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• 2004

The present study surveyed university librarians' perceptions of barriers to research utilization using the Barrier Scales. The results shows that in mean Barriers Scale scores, characteristics of the organization had the highest, and then followed by the communication, the research and the librarian. The most important factor was perceived to be organizational support relating to insufficient time to implement new ideas, inadequate facilities, and lack of authority to make changes and in the communication relating to lack of clarity of research implications for practice, lack to demonstrate research's relevance to practice, and lack of readability of research. Also, incomprehensible English research reports and statistical analyses were perceived as major barriers. The comparison with those of nursing was discussed. In order to settle the barriers identified, librarians should be educated to have 'research literacy' and organazational merit and reward systems were reinforced for research utilization. Also, it needs to explore librarians' specific experiences of barriers in utilizing process in practice. In further research, by integrating both the result of the quantitative and qualitative research, it needs to develop and to assess a standard measurement for librarians' barriers of research utilization in national and international context.

• Management & Information Systems Review
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• v.30 no.1
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• pp.107-128
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• 2011

This study has two primary purposes, firstly to identify how job dissatisfaction and continuance commitment influence to creativity, secondly to explore how coworker helping and support, and perceived organizational support moderate between interaction of job dissatisfaction and continuance commitment, and creativity. The first part of the study, based on literature study on creativity, provides insight into what are antecedents and moderate variables in creativity. In the second part of the study, a comprehensive research model and hypothesis were empirically tested based on data from 322 employees in Korean organizations. The results of statistical analysis show the following. First, job dissatisfaction has positive effect on creativity. Second, interaction of job dissatisfaction and continuance commitment does not have positive effect on creativity. Third, there was not any moderating effects between interaction of job dissatisfaction and continuance commitment, and creativity in this study. The last part of this study, a theoretical and practical implication of the study, and the future research agenda are presented.