• The Journal of Economics, Marketing and Management
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• v.9 no.1
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• pp.1-14
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• 2021

Purpose: The article deals with the proposition that consumers' fashion consumption behavior will still follow the consumption behavior of fast fashion, despite recognizing the importance of slow fashion. Research design, data and methodology: The research model to verify this proposition is topic modelling with big data including unstructured textual data. we combined 5,506 news articles posted on Naver news search platform during the 2003-2019 period about fast fashion and slow fashion, high-frequency words have been derived, and topics have been found using LDA model. Based on these, we examined consumers' perception and consumption behavior on slow fashion through the analysis of Topic Network. Results: (1) Looking at the status of annual article collection, consumers' interest in slow fashion mainly began in 2005 and showed a steady increase up to 2019. (2) Term Frequency analysis showed that the keywords for slow fashion are the lowest, with consumers' consumption patterns continuing around 'brand.' (3) Each topic's weight in articles showed that 'social value' - which includes slow fashion - ranked sixth among the 9 topics, low linkage with other topics. (4) Lastly, 'brand' and 'fashion trend' were key topics, and the topic 'social value' accounted for a low proportion. Conclusion: Slow fashion was not a considerable factor of consumption behavior. Consumption patterns in fashion sector are still dominated by general consumption patterns centered on brands and fast fashion.

• The Journal of Economics, Marketing and Management
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• v.8 no.3
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• pp.23-36
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• 2020

Purpose: The purpose of this study is to identify the strategic direction of organizations and their employees to efficiently utilize smart factories and enhance business performance among Korean manufacturing companies. Research design, data, and methodology: We derived a structured research model to check the mediated effect of utilization of smart factory between the characteristics of smart factory and the innovation activities. Results: Quality characteristics of smart factory and Innovation activities were all found to have a statistically significant effect on utilization of smart factory, utilization of smart factory was found to have a statistically significant effect on the business performance. And it has been shown that the utilization of smart factory is partially mediated relative to the quality characteristics of smart factory and business performance and relative to innovation activities and business performance. Conclusions: Smart factory builders can reflect the areas that affect utilization of the smart factory in their strategies by considering the quality characteristics of the smart factory and innovation Activities. Therefore, smart factory builders can identify the quality characteristics of smart factory and reflect them in the process and analyze active utilize measures through the innovative activities of the employees of the organization, thereby influencing business performance.

• Journal of the Korea Convergence Society
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• v.12 no.12
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• pp.83-91
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• 2021

For agricultural products, supply is irregular due to changes in meteorological conditions, and it has high price elasticity. For example, if the supply decreases by 10%, the price increases by 50%. Due to these fluctuations in the prices of agricultural products, the Korean government guarantees the safety of prices to producers through small merchants' auctions. However, when prices plummet due to overproduction, protection measures for producers are insufficient. Therefore, in this paper, we designed a business model that can be used in the electronic transaction system by predicting the price of agricultural products with an artificial intelligence algorithm. To this end, the trained model with the training pattern pairs and a predictive model was designed by applying ARIMA, SARIMA, RNN, and CNN. Finally, the agricultural product forecast price data was classified into short-term forecast and medium-term forecast and verified. As a result of verification, based on 2018 data, the actual price and predicted price showed an accuracy of 91.08%.

• Journal of Convergence for Information Technology
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• v.12 no.5
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• pp.256-270
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• 2022

This paper examined whether quality management(QM) positively impacts Kazakh companies' business performance (financial and non-financial performance). As a result of testing ten hypotheses based on the research model for 287 companies in Almaty, only eight hypotheses were supported. Top management leadership was identified as a critical factor that positively affected financial performance, and customer-centered thinking is strongly related to non-financial performance. Employee participation and quality information analysis factors also positively affected business performance, but their influence was lower than top management leadership and customer-centered thinking factors. Finally, the supplier management factor did not significantly affect business performance, and the two related hypotheses were not supported. These results are presumed to be due to the characteristics of the target companies, such as oil and raw material manufacturing and construction, rather than high-quality finished products.

• Knowledge Management Research
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• v.23 no.1
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• pp.149-164
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• 2022

This paper analyzes the independent financial advisory business that is not yet active in Korea and proposes a plan to activate the independent financial advisory business using fintech technology. A bill was enacted in 2017 for the domestic independent financial advisory business, but it has not been activated much until now for various reasons. Although existing studies have proposed solutions in various ways, there is no clear solution yet. This paper proposes a new method of revitalizing the independent financial advisory business through fintech technology using the trust system that has recently attracted attention. Digital securities fintech technology using blockchain distributed ledger technology presents new possibilities in the real estate and music copyright markets, and related fintech venture companies continue to emerge in Korea. By combining these digital securities fintech technologies and the business process of ETF, a method was derived so that independent financial advisors can have their own financial products. The proposed model is more decentralized than the existing financial product sales structure, and presents the possibility of a protocol economy through a structure close to a private blockchain while complying with the existing financial order. This paper is meaningful in that it presented new solutions to completely different markets from information convergence perspectives on two completely different markets, and we hope that more business solutions will emerge through knowledge management activities that converge various perspectives in the future.

• International Journal of Advanced Culture Technology
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• v.10 no.4
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• pp.499-510
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• 2022

This study examines the reporting factors of crime against business in Korea and proposes a corresponding predictive model using machine learning. While many previous studies focused on the individual factors of theft victims, there is a lack of evidence on the reporting factors of crime against a business that serves the public good as opposed to those that protect private property. Therefore, we proposed a crime prevention model for the willingness factor of theft reporting in businesses. This study used data collected through the 2015 Commercial Crime Damage Survey conducted by the Korea Institute for Criminal Policy. It analyzed data from 834 businesses that had experienced theft during a 2016 crime investigation. The data showed a problem with unbalanced classes. To solve this problem, we jointly applied the Synthetic Minority Over Sampling Technique and the Tomek link techniques to the training data. Two prediction models were implemented. One was a statistical model using logistic regression and elastic net. The other involved a support vector machine model, tree-based machine learning models (e.g., random forest, extreme gradient boosting), and a stacking model. As a result, the features of theft price, invasion, and remedy, which are known to have significant effects on reporting theft offences, can be predicted as determinants of such offences in companies. Finally, we verified and compared the proposed predictive models using several popular metrics. Based on our evaluation of the importance of the features used in each model, we suggest a more accurate criterion for predicting var.

• The Journal of the Korea Contents Association
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• v.22 no.10
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• pp.703-722
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• 2022

The concentration of local businesses in the capital region promotes a decrease in the local population and polarization between the capital region and non-capital regions. It affects the competitiveness of local industries and creates a vicious cycle throughout the local economy, society and culture. Therefore, this study classified the companies in the capital region and non-capital regions by group and examined the effect of the innovation capability factors of companies on the creation of business performance. We analyzed the effects of R&D capabilities, which are elements of innovation capability, and open innovation and convergence capabilities on business performance. Smart PLS 3.0 was used for analysis including direct and indirect mediating and moderating effects, multi-group analysis, and structural equation model analysis. As a result, R&D capability did not have a significant effect on business performance, but it has a positive influence towards business performance through convergence capability and open innovation. However, the effectiveness of open innovation in non-capital regions and convergence capabilities in capital region were not statistically significant. In particular, in terms of open innovation, as the difference between groups is statistically clear, follow-up measures are suggested especially in non-capital regions.

• Journal of Climate Change Research
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• v.9 no.4
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• pp.435-443
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• 2018

Climate technology applied to address climate change requires a comprehensive review such as environmental and social acceptability in addition to economic feasibility. Not only mitigation and adaptation technologies, but also integration of climate technologies into a business model with other relevant technologies including ICT, finance, and policy instruments could enhance technical, economic, and environmental performances to respond to climate changes. However, many climate projects (and business models) are currently not designed to consider adequately complex climate?related issues. In addition, there is a lack of research on assessment systems that can comprehensively evaluate business feasibility of such models. In this study, we developed a system consisting of nine major indicators in four fields to assess climate technology-based business models. Each indicator was weighed using the analytic hierarchy process (AHP) for systematic assessment of business models. The process can be utilized as a tool to guide improvement of climate technology business models.

• Journal of Digital Convergence
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• v.19 no.11
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• pp.215-224
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• 2021

Business stakeholders have shown huge interests on the way how to increase business value by integrating real world businesses with a rising metaverse concept. To understanding the utility of metaverse regarding digital transformation, this study conducted a qualitative study based on a metaverse framework from 2006 Metaverse Roadmap and reference theories on business models. Specifically, a multiple comparative case approach was adopted to investigate three metaverse application cases from 2000 to 2020. From findings, it was revealed that different metaverse features were tried to leverage traditional simulation games, social communities, and virtual communication activities to build business models. Secondly, the use of metaverse features were likely to be helpful in getting competitive advantages. However, we are also aware that stakeholder credibility should be carefully managed to sustain businesses.

• Journal of Industrial Convergence
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• v.15 no.1
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• pp.17-32
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• 2017

The purpose of this study is to evaluate financial models that can predict corporate bankruptcy with diverse studies on evaluation models. The study uses discriminant analysis, logistic model, decision tree, neural networks as analyses tools with 18 input variables as major financial factors. The study found meaningful variables such as current ratio, return on investment, ordinary income to total assets, total debt turn over rate, interest expenses to sales, net working capital to total assets and it also found that prediction performance of suggested method is a bit low compared to that in literature review. It is because the studies in the past uses the data set on the listed companies or companies audited from outside. And this study uses data on the companies whose credibility is not verified enough. Another finding is that models based on decision tree analysis and discriminant analysis showed the highest performance among many bankruptcy forecasting models.