• The Journal of the Convergence on Culture Technology
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• v.10 no.3
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• pp.579-591
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• 2024

Recently, church organizations are experiencing various problems, including a sharp decline in the number of members. This study focused on the problem of lack of communication, that is, organizational silence, which is commonly pointed out in previous studies as a problem of church organizations, and empirically analyzed the relationship between organizational silence, organizational cynicism, and organizational commitment within church organizations. For this purpose, a questionnaire was distributed to 210 members of churches and related organizations from May to June 2023, of which 202 copies were used as analysis data. SPSS 23.0 and AMOS 23.0 were used to perform frequency analysis, confirmatory factor analysis, descriptive statistical analysis, correlation analysis, structural equation model analysis, and bootstrapping to verify mediation effects. As a result of the study, organizational silence had a negative effect on organizational cynicism, and organizational cynicism had a negative effect on organizational commitment. Although organizational silence did not have a direct negative effect on organizational commitment, it was found to have a negative effect mediated by organizational cynicism. This study expanded the scope of the study by applying variables from organizational theory to church organizations, and presented practical implications through the research results to church organizations.

• Asia-Pacific Journal of Business
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• v.14 no.4
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• pp.67-85
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• 2023

Purpose - This study seeks to investigate the determinants of global sourcing intention in clothing sewing industry, in particular with its focus on women's knit wear production. Design/methodology/approach - This study collected a unique set of qualitative data through 31 in-depth interviews with fashion brands, promotion agencies, and sewing factories between July 2023 and October 2023. In addition, it analyzed the dataset using the MAXQDA to complement the research findings. Findings - We have two findings. First, the interviewees commonly mentioned the following factors as reasons for considering global sourcing: the human factors(aging of skilled technicians and labor shortages), the financial factors(gap in production unit prices at home and abroad), the relational factors(lack of novelty), and the physical factors(loss of production infrastructure and network), while the human factors(skilled workforce), the production factors(delivery date and product quality), and the relational factors(timely communication and mutual trust) as reasons for continuing domestic sourcing. Additional code analysis of interview also supports this finding. On the other hand, there was also a subtle difference between buyers(brands) and suppliers(promotion agencies and processing plants), and buyers consider the exact delivery date critical so that they could see trend-sensitive women's knit wear on time, and suppliers took production costs, labor costs, and labor shortages, which are financial factors, more seriously. Research implications or Originality - This study provides a richer and more balanced view of existing literature, which has generally tended to introduce global sourcing across the clothing industry despite the existence of various diversity within the industry. In addition, through qualitative research, we introduce that the sewing industry is carried out according to complex factors, and by revealing and categorizing the determinants of global sourcing, we supplement the existing research on the clothing sewing industry centered on survey. On a practical note, this study introduces that there is a difference in view of domestic sourcing and global sourcing between buyers(brands) and suppliers(promotion agencies and sewing factories), suggesting practical implications for revitalizing networks and deriving win-win cooperation network models among members in the future.

• Information Systems Review
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• v.24 no.1
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• pp.1-19
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• 2022

Social media-based communication has become crucial part of our personal and official lives. Therefore, it is no surprise that social media sentiment analysis has emerged an important way of detecting potential customers' sentiment trends for all kinds of companies. However, social media sentiment analysis suffers from huge number of sentiment features obtained in the process of conducting the sentiment analysis. In this sense, this study proposes a novel method by using Bayesian Network. In this model MBFS (Markov Blanket-based Feature Selection) is used to reduce the number of sentiment features. To show the validity of our proposed model, we utilized online review data from Yelp, a famous social media about restaurant, bars, beauty salons evaluation and recommendation. We used a number of benchmarking feature selection methods like correlation-based feature selection, information gain, and gain ratio. A number of machine learning classifiers were also used for our validation tasks, like TAN, NBN, Sons & Spouses BN (Bayesian Network), Augmented Markov Blanket. Furthermore, we conducted Bayesian Network-based what-if analysis to see how the knowledge map between target node and related explanatory nodes could yield meaningful glimpse into what is going on in sentiments underlying the target dataset.

• Journal of The Korean Association For Science Education
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• v.29 no.1
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• pp.90-105
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• 2009

This study analyzed current programs practiced by science-gifted education centers. This study was based on concerns of 18 science teachers on six science-gifted education centers of the Seoul Metropolitan Office of Education that had local representatives. For this study, we collected data using journals, documents, reports, survey reviews and interviews with science teachers. Science teachers were concerned about the selection and identification of gifted students, education periods, curriculum, and student evaluation. More authentic measurement for students' potential ability were needed for the identification and selection process. If the purpose of science-gifted centers was to be met, the number of students selected should be determined by local differences rather than regional equality. The curriculum and educational period could make good use of time allotted for vacation to increase lesson periods. Lessons based on strategies like contests for improving the students' creativity, free inquiry and communication skills had to be encouraged. A consistent system for science-gifted education from primary school to high school was needed.

• Safety and Health at Work
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• v.15 no.1
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• pp.66-72
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• 2024

Background: This study examines the relationship between functional disability and work ability in workers affected by low back pain (LBP) through an analysis of correlations between the Oswestry Disability Index (ODI) and Work Ability Index (WAI). The role of personal and work factors on functional disability/work ability levels has also been studied. LBP is the most common musculoskeletal problem and a major disabling health problem worldwide. Its etiology is multifactorial. Multidisciplinary approaches may help reduce the burden of pain and disability and improve job continuity and reintegration at work. Methods: A cohort of 264 patients affected by LBP from an Italian outpatient clinic were included in a clinical diagnostic/therapeutic trial aiming at rehabilitation and return to work through an integrated investigation protocol. Data were collected during the first medical examination using anamnestic and clinical tools. The final sample is composed of 252 patients, 57.1% man, 44.0 % blue collars, 46.4% with the high school degree, 45.6% married. Results: WAI and ODI reported a negative and fair correlation (r = -0.454; p = .000). Workers with acute LBP symptoms have a higher probability of severe disability than those with chronic LBP symptoms. White collars without depressive symptoms reported higher work ability - even in chronic disability conditions-than those with depressive symptoms. Conclusion: The study found that ODI and WAI have a convergent validity and this suggests that the two tools measure capture distinctive aspects of disability related to personal, environmental, and occupational characteristics. The most important and modifiable prognostic factors found for ODI and WAI were depressive symptoms, workday absence, and intensity of back pain. The study also found a mild association between age and ODI. The study's findings highlight the importance of using a multidisciplinary approach to manage and prevent disability due to LBP.

• Information Systems Review
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• v.19 no.1
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• pp.1-23
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• 2017

Since the development of information communication technologies, social networking sites (SNSs) have been diffused to the world with benefits such as building and maintaining relationships among people. SNSs have become more popular with the development of mobile devices. Despite this advantage, SNSs also present unexpected effects on people, such as cyberbullying and identity theft. Cyberbullying has emerged as one of the most serious issues among people who use SNSs. In fact, almost 20% of teenagers confessed that they have been cyberbullied on SNSs. In consideration of this serious social issue, this study investigates the influences of self-disclosure and self-control on the cyberbullying victimization experience from the perspective of Social Exchange Theory. Self-disclosure is a basic characteristic of SNSs. It is classified into self-disclosure for access to SNS and self-disclosure for relationship building and maintaining on SNSs. The cyberbullying victimization experience is classified into being cyber-excluded and being cyber-attacked. We examine how two types of self-disclosure and self-control affect two types of cyberbullying victimization experience based on the survey data of people who are in their 20s and are greatly familiar with SNSs.

• Journal of the Korean Society for information Management
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• v.41 no.2
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• pp.97-130
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• 2024

The current debate in the academic community is on the criteria for predatory journals. Researchers are perplexed about what constitutes a predatory journal. The purpose of this study is to investigate how South Korean researchers discover and evaluate predatory journals. In order to achieve this, we collected 2,484 statements, comprising posts and comments, from Korean researcher communities, namely the Biological Research Information Center (BRIC), Hibrain.net, Phdkim.net, and the Scholarly Ecosystem Against Fake Publication Environment (SAFE). We divided the data into three primary categories-journals, publishers, and researchers-for the topic analysis. For each statement, we assigned 11 in-depth subtopic tags based on these categories. Six main points of contention emerged from the combinations of these sub-topic tags: (1) researchers' confusion about predatory journals and discussions about research performance; (2)(3) researchers' positive and negative perceptions of predatory journals; (4) researchers' evaluation criteria for journal quality and problems associated with the quality of Korean journals; (5) changes in publishing brought about by the introduction of open access (OA) and associated issues; and (6) discussions on broader issues within the academic ecosystem. By using a qualitative approach to examine how South Korean researchers view predatory journals, this study aims to advance basic knowledge of the discourse around them in the communities of domestic researchers.

• Asia pacific journal of information systems
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• v.20 no.3
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• pp.139-166
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• 2010

The supply chain management (SCM) systems have emerged as strong managerial tools for manufacturing firms in enhancing competitive strength. Despite of large investments in the SCM systems, many companies are not fully realizing the promised benefits from the systems. A review of literature on adoption, implementation and success factor of IOS (inter-organization systems), EDI (electronic data interchange) systems, shows that this issue has been examined from multiple theoretic perspectives. And many researchers have attempted to identify the factors which influence the success of system implementation. However, the existing studies have two drawbacks in revealing the determinants of systems implementation success. First, previous researches raise questions as to the appropriateness of research subjects selected. Most SCM systems are operating in the form of private industrial networks, where the participants of the systems consist of two distinct groups: focus companies and vendors. The focus companies are the primary actors in developing and operating the systems, while vendors are passive participants which are connected to the system in order to supply raw materials and parts to the focus companies. Under the circumstance, there are three ways in selecting the research subjects; focus companies only, vendors only, or two parties grouped together. It is hard to find researches that use the focus companies exclusively as the subjects probably due to the insufficient sample size for statistic analysis. Most researches have been conducted using the data collected from both groups. We argue that the SCM success factors cannot be correctly indentified in this case. The focus companies and the vendors are in different positions in many areas regarding the system implementation: firm size, managerial resources, bargaining power, organizational maturity, and etc. There are no obvious reasons to believe that the success factors of the two groups are identical. Grouping the two groups also raises questions on measuring the system success. The benefits from utilizing the systems may not be commonly distributed to the two groups. One group's benefits might be realized at the expenses of the other group considering the situation where vendors participating in SCM systems are under continuous pressures from the focus companies with respect to prices, quality, and delivery time. Therefore, by combining the system outcomes of both groups we cannot measure the system benefits obtained by each group correctly. Second, the measures of system success adopted in the previous researches have shortcoming in measuring the SCM success. User satisfaction, system utilization, and user attitudes toward the systems are most commonly used success measures in the existing studies. These measures have been developed as proxy variables in the studies of decision support systems (DSS) where the contribution of the systems to the organization performance is very difficult to measure. Unlike the DSS, the SCM systems have more specific goals, such as cost saving, inventory reduction, quality improvement, rapid time, and higher customer service. We maintain that more specific measures can be developed instead of proxy variables in order to measure the system benefits correctly. The purpose of this study is to find the determinants of SCM systems success in the perspective of vendor companies. In developing the research model, we have focused on selecting the success factors appropriate for the vendors through reviewing past researches and on developing more accurate success measures. The variables can be classified into following: technological, organizational, and environmental factors on the basis of TOE (Technology-Organization-Environment) framework. The model consists of three independent variables (competition intensity, top management support, and information system maturity), one mediating variable (collaboration), one moderating variable (government support), and a dependent variable (system success). The systems success measures have been developed to reflect the operational benefits of the SCM systems; improvement in planning and analysis capabilities, faster throughput, cost reduction, task integration, and improved product and customer service. The model has been validated using the survey data collected from 122 vendors participating in the SCM systems in Korea. To test for mediation, one should estimate the hierarchical regression analysis on the collaboration. And moderating effect analysis should estimate the moderated multiple regression, examines the effect of the government support. The result shows that information system maturity and top management support are the most important determinants of SCM system success. Supply chain technologies that standardize data formats and enhance information sharing may be adopted by supply chain leader organization because of the influence of focal company in the private industrial networks in order to streamline transactions and improve inter-organization communication. Specially, the need to develop and sustain an information system maturity will provide the focus and purpose to successfully overcome information system obstacles and resistance to innovation diffusion within the supply chain network organization. The support of top management will help focus efforts toward the realization of inter-organizational benefits and lend credibility to functional managers responsible for its implementation. The active involvement, vision, and direction of high level executives provide the impetus needed to sustain the implementation of SCM. The quality of collaboration relationships also is positively related to outcome variable. Collaboration variable is found to have a mediation effect between on influencing factors and implementation success. Higher levels of inter-organizational collaboration behaviors such as shared planning and flexibility in coordinating activities were found to be strongly linked to the vendors trust in the supply chain network. Government support moderates the effect of the IS maturity, competitive intensity, top management support on collaboration and implementation success of SCM. In general, the vendor companies face substantially greater risks in SCM implementation than the larger companies do because of severe constraints on financial and human resources and limited education on SCM systems. Besides resources, Vendors generally lack computer experience and do not have sufficient internal SCM expertise. For these reasons, government supports may establish requirements for firms doing business with the government or provide incentives to adopt, implementation SCM or practices. Government support provides significant improvements in implementation success of SCM when IS maturity, competitive intensity, top management support and collaboration are low. The environmental characteristic of competition intensity has no direct effect on vendor perspective of SCM system success. But, vendors facing above average competition intensity will have a greater need for changing technology. This suggests that companies trying to implement SCM systems should set up compatible supply chain networks and a high-quality collaboration relationship for implementation and performance.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.179-196
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• 2019

Recently, the "Smart Consumer" has been emerging. He or she is increasingly inclined to search for and purchase products by taking into account personal judgment or expert reviews rather than by relying on information delivered through manufacturers' advertising. This is especially true when purchasing cosmetics. Because cosmetics act directly on the skin, consumers respond seriously to dangerous chemical elements they contain or to skin problems they may cause. Above all, cosmetics should fit well with the purchaser's skin type. In addition, changes in global cosmetics consumer trends make it necessary to study this field. The desire to find one's own individualized cosmetics is being revealed to consumers around the world and is known as "Finding the Holy Grail." Many consumers show a deep interest in customized cosmetics with the cultural boom known as "K-Beauty" (an aspect of "Han-Ryu"), the growth of personal grooming, and the emergence of "self-culture" that includes "self-beauty" and "self-interior." These trends have led to the explosive popularity of cosmetics made in Korea in the Chinese and Southeast Asian markets. In order to meet the customized cosmetics needs of consumers, cosmetics manufacturers and related companies are responding by concentrating on delivering premium services through the convergence of ICT(Information, Communication and Technology). Despite the evolution of companies' responses regarding market trends toward customized cosmetics, there is no "Intelligent Data Platform" that deals holistically with consumers' skin condition experience and thus attaches emotions to products and services. To find the Holy Grail of customized cosmetics, it is important to acquire and analyze consumer data on what they want in order to address their experiences and emotions. The emotions consumers are addressing when purchasing cosmetics varies by their age, sex, skin type, and specific skin issues and influences what price is considered reasonable. Therefore, it is necessary to classify emotions regarding cosmetics by individual consumer. Because of its importance, consumer emotion analysis has been used for both services and products. Given the trends identified above, we judge that consumer emotion analysis can be used in our study. Therefore, we collected and indexed data on consumers' emotions regarding their cosmetics experiences focusing on consumers' language. We crawled the cosmetics emotion data from SNS (blog and Twitter) according to sales ranking ($1^{st}$ to $99^{th}$), focusing on the ample/serum category. A total of 357 emotional adjectives were collected, and we combined and abstracted similar or duplicate emotional adjectives. We conducted a "Consumer Sentiment Journey" workshop to build a "Consumer Sentiment Dictionary," and this resulted in a total of 76 emotional adjectives regarding cosmetics consumer experience. Using these 76 emotional adjectives, we performed clustering with the Self-Organizing Map (SOM) method. As a result of the analysis, we derived eight final clusters of cosmetics consumer sentiments. Using the vector values of each node for each cluster, the characteristics of each cluster were derived based on the top ten most frequently appearing consumer sentiments. Different characteristics were found in consumer sentiments in each cluster. We also developed a cosmetics experience pattern map. The study results confirmed that recommendation and classification systems that consider consumer emotions and sentiments are needed because each consumer differs in what he or she pursues and prefers. Furthermore, this study reaffirms that the application of emotion and sentiment analysis can be extended to various fields other than cosmetics, and it implies that consumer insights can be derived using these methods. They can be used not only to build a specialized sentiment dictionary using scientific processes and "Design Thinking Methodology," but we also expect that these methods can help us to understand consumers' psychological reactions and cognitive behaviors. If this study is further developed, we believe that it will be able to provide solutions based on consumer experience, and therefore that it can be developed as an aspect of marketing intelligence.

• 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.