• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.2
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• pp.127-139
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• 2023

Compensation and pay dispersion has been rigorously scrutinized to investigate their impacts on productivity and organizational performance. However, it is difficult to find a systematic study on the systematic dynamics of compensation and pay dispersion effects specifically in the context of Korean venture companies. Venture companies should manage their organizational resources efficiently to maximize their organizational performance through pay structure by efficiently managing the inherent resources. However, we acknowledge that empirical studies on how compensation and pay dispersion affect organizational productivity and performance are rare to find in the Korean context. To overcome this supplement limitation, this study hypothesized that (1) pay and members' productivity are positively related, (2) pay dispersion and organizational productivity have U shaped relationship, and (3) organizational productivity mediates the positive relationship between compensation and organizational performance. Venture companies and professional sports teams share manifold common characteristics such as size, financial circumstances, and operational objectives. We collect 9 seasons (2013~2014 - 2021~2022) of 10 teams' data of Korean Basketball League teams to test our hypotheses. Methodologically, the assessment of our analysis is rendered with PROCESS macro model 58. The statistical results showed that all hypotheses are statistically supported. This study explains how compensation and pay dispersion affect organizational productivity and performance of venture companies in Korea.

• Journal of Internet Computing and Services
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• v.25 no.1
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• pp.109-121
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• 2024

In various manufacturing processes such as textiles and automobiles, when equipment breaks down or stops, the machines do not work, which leads to time and financial losses for the company. Therefore, it is important to detect equipment abnormalities in advance so that equipment failures can be predicted and repaired before they occur. Most equipment failures are caused by bearing failures, which are essential parts of equipment, and detection bearing anomaly is the essence of PHM(Prognostics and Health Management) research. In this paper, we propose a preprocessing algorithm called SWT-SVD, which analyzes vibration signals from bearings and apply it to an anomaly transformer, one of the time series anomaly detection model networks, to implement bearing anomaly detection model. Vibration signals from the bearing manufacturing process contain noise due to the real-time generation of sensor values. To reduce noise in vibration signals, we use the Stationary Wavelet Transform to extract frequency components and perform preprocessing to extract meaningful features through the Singular Value Decomposition algorithm. For experimental validation of the proposed SWT-SVD preprocessing method in the bearing anomaly detection model, we utilize the PHM-2012-Challenge dataset provided by the IEEE PHM Conference. The experimental results demonstrate significant performance with an accuracy of 0.98 and an F1-Score of 0.97. Additionally, to substantiate performance improvement, we conduct a comparative analysis with previous studies, confirming that the proposed preprocessing method outperforms previous preprocessing methods in terms of performance.

• Information Systems Review
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• v.22 no.4
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• pp.21-39
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• 2020

Recently, deep learning has shown high performance in various applications such as pattern analysis and image classification. Especially known as a difficult task in the field of machine learning research, stock market forecasting is an area where the effectiveness of deep learning techniques is being verified by many researchers. This study proposed a deep learning Convolutional Neural Network (CNN) model to predict the direction of stock prices. We then used the feature selection method to improve the performance of the model. We compared the performance of machine learning classifiers against CNN. The classifiers used in this study are as follows: Logistic Regression, Decision Tree, Neural Network, Support Vector Machine, Adaboost, Bagging, and Random Forest. The results of this study confirmed that the CNN showed higher performancecompared with other classifiers in the case of feature selection. The results show that the CNN model effectively predicted the stock price direction by analyzing the embedded values of the financial data

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.19 no.2
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• pp.1-11
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• 2024

In the midst of the current turbulent global economy, traditional investment metrics are undergoing a metamorphosis, signaling the onset of what's often referred to as an "Investment cold season". Early-stage startups, despite their boundless potential, grapple with immediate revenue constraints, intensifying their pursuit of critical investments. While financial indicators once took center stage in investment evaluations, a notable paradigm shift is underway. Organizational culture, once relegated to the sidelines, has now emerged as a linchpin in forecasting a startup's resilience and enduring trajectory. Our comprehensive research, integrating insights from CVF and OCAI, unveils the intricate relationship between organizational culture and its magnetic appeal to investors. The results indicate that startups with a pronounced external focus, expertly balanced with flexibility and stability, hold particular allure for investment consideration. Furthermore, the study underscores the pivotal role of adhocracy and market-driven mindsets in shaping investment desirability. A significant observation emerges from the study: startups, whether they secured investment or failed to do so, consistently display strong clan culture, highlighting the widespread importance of nurturing a positive employee environment. Leadership deeply anchored in market culture, combined with an unwavering commitment to innovation and harmonious organizational practices, emerges as a potent recipe for attracting investor attention. Our model, with an impressive 88.3% predictive accuracy, serves as a guiding light for startups and astute investors, illuminating the intricate interplay of culture and investment success in today's economic landscape.

• Journal of Industrial Convergence
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• v.22 no.6
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• pp.51-59
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• 2024

The purpose of the paper is to suggest government policies that are practically helpful to small business owners in pandemic situations such as COVID-19. To this end, keyword frequency analysis and word cloud analysis of text mining analysis were performed by crawling news articles centered on the keywords "COVID-19 Support for Small Businesses", "The Impact of Small Businesses by Response System to COVID-19 Infectious Diseases", and "COVID-19 Small Business Economic Policy", and major issues were identified through LDA topic modeling analysis. As a result of conducting LDA topic modeling, the support policy for small business owners formed a topic label with government cash and financial support, and the impact of small business owners according to the COVID-19 infectious disease response system formed a topic label with a government-led quarantine system and an individual-led quarantine system, and the COVID-19 economic policy formed a topic label with a policy for small business owners to acquire economic crisis and self-sustainability. Focusing on the organized topic label, it was intended to provide basic data for small business owners to understand the damage reduction policy for small business owners and the policy for enhancing market competitiveness in the future pandemic situation.

• Information Systems Review
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• v.20 no.1
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• pp.41-59
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• 2018

This study analyzed the factors that affect acceptance of online direct marketing in agricultural companies. Empirical analysis was conducted using the research model based on the individual's technology acceptance model (TAM) and the information technology adoption models in organizations. These models have four dimensions: 1) technology characteristics, which include perceived usefulness and perceived ease of use of TAM 2) CEO characteristics, which including the innovativeness and IT capability of CEOs; 3) organizational readiness, which include financial, technological, and human resources capabilities and 4) environment and external pressure, which include government support and changes to the Internet environment. These concepts were empirically tested. A total of 209 valid data were collected through questionnaires and analyzed using confirmatory factor analysis and path analysis through the application of structural equation modeling. Results show that perceived usefulness, IT capability of CEOs, and changes to the Internet environment have significant effects on the adoption intention of online direct marketing. However, perceived ease of use, CEO innovativeness, government support, and the variables of organizational readiness dimension did not have significant effects on adoption intention. This study suggests practical implications for adoption of online direct marketing in agricultural companies.

• Journal of Food Hygiene and Safety
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• v.39 no.3
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• pp.181-190
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• 2024

Bovine mastitis-associated Escherichia coli (BMEC) is considered the main causative agent of significant financial losses in the dairy industry worldwide, as it alters both the quantity and quality of milk produced and increases the rate of culling. This creates a variety of challenges for researchers, veterinarians, and farmers in understanding and determining the most effective therapies and diagnostic techniques. Subclinical mastitis is particularly concerning, as infected bovines exhibit no obvious symptoms and continue to secrete apparently normal milk over an extended period, allowing the causative pathogen, E. coli, to spread within the herd. For effective prevention, understanding the pathogenesis of mastitis through three stages invasion, infection, and inflammation is essential. To date, no clear correlation has been found between virulence factors and pathogenicity contributing to the clinical severity of BMEC. Multidrug-resistant E. coli and the evolution of novel resistance mechanisms have become concerns owing to the extensive use of antibiotics to treat mastitis. Therefore, it is vital to explore alternative controls to enhance the efficacy of BMEC treatment. Over the past 30 years, various genetic typing techniques have been used to examine the subspecies-level epidemiology of bovine mastitis. These studies have advanced our understanding of the origin, transmission pathway, population structure, and evolutionary relatedness of BMEC strains. In this review we provide an overview of BMEC, including insights into its etiology, genetic relationship, pathogenesis, and management of the disease, as well as new therapy options.

• Journal of Venture Innovation
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• v.7 no.2
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• pp.1-17
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• 2024

This study analyzed the effect of moving into an industrial park on the performance of a company using data from individual companies. In addition, regional variables were additionally set, and industrial complexes were divided into metropolitan and non-metropolitan areas for comparison. Data were collected through KIS-2022 (manufacturing) to verify the hypothesis. In the case of the analysis method, multiple regression analysis and Propensity Score Matching(PSM) were first used. The analysis results are as follows. First, it was found that companies that moved into industrial park had a positive effect on innovation performance than companies that did not move in. Second, in terms of financial performance, there was no statistically significant difference between companies that moved into the industrial park and those that did not. Third, there was a significant difference between companies that moved into industrial park and those that did not, depending on the metropolitan and non-metropolitan areas. Based on these analysis results, policy and academic implications could be presented.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.4
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• pp.167-176
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• 2024

The Personal Information Protection Commission was launched in August 2020 as an integrated control tower for personal information protection, but several problems have been pointed out in the personal information protection operation system. First, despite the fact that Korea's personal information protection system has an integrated legal system that regulates both the public and private sectors, it has been pointed out that it is difficult to carry out smooth personal information protection functions due to incomplete integration of protection functions, such as the Financial Services Commission being in charge of personal credit information protection and the Korea Communications Commission being in charge of personal location information protection. Next, despite the increasing number of public sector personal information leakage incidents, there is a lack of personnel with expertise and specialized support organizations to efficiently investigate them, and there is a concern that the lack of an efficient response system to personal information infringement by global IT companies in Korea in the era of digital commerce may weaken the protection of citizens' personal information. In order to solve these problems, I reviewed overseas cases and literature and proposed the following measures. First, it is necessary to centralize the personal information protection supervision function for credit information and location information to the Personal Information Protection Commission. Second, it is necessary to secure expertise by securing specialized personnel and establishing specialized institutions to respond to public sector personal information leakage incidents. Third, it is necessary to revitalize the domestic agency designation system and establish an international cooperation system to protect people's personal information in the digital commerce era. I believe that these measures to develop the personal information protection system will lead to more systematic personal information protection.

• Journal of Korea Port Economic Association
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• v.39 no.4
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• pp.191-203
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• 2023

This paper analyzed the overall status of the domestic freight transportation business, established SWOT analysis and strategy through existing literature research and designed an AHP model to derive priorities for each strategy. The SWOT analysis analyzed the management model of the consignment borrowers belonging to a transportation company that did not handle the supplies with the lowest satisfaction with the consignment system. The AHP model was designed by establishing a SWOT strategy through SWOT analysis. As a result of the analysis of the upper class, priorities were derived in the order of WO strategy, SO strategy, ST strategy, and WT strategy. As a result of comprehensive priorities for the development strategy of the domestic freight transportation business, WO strategy's "Improvement of cooperative relations between transportation companies and consignment owners through fair consignment contracts" was first, SO strategy's "Public promotion of the necessity of consignment systems based on high economic feasibility and reliability" was second, and ST strategy's "Proposal of policies to strengthen financial performance through the introduction of freight transport platforms" was fourth, followed by WT strategy's "Improvement of satisfaction with transport services through the introduction of freight transport platforms" and SO strategy's "Expansion of safe freight systems" in sixth, respectively.