• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.3
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• pp.33-43
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• 2023

The paper employs a resource-based approach to analyze the relationship between institutional resources and faculty-led startup formation and performance in South Korean four-year universities from 2017 to 2021. The author proposes nine hypotheses to explain how institutional resources or systems affect the number of faculty startups, their employee numbers and the revenue of faculty-led startups, and compare four different groups of university resources for cross-college variation. The findings suggest that institutional factors impacting faculty-led startup performance differ from those impacting other categories of startups. Universities should provide a more favorable environment, including flexible personnel policies and accompanying startup support infrastructure, to encourage faculty-led startups. In contrast, it is more effective for better performance of faculty startups, in terms of their job creation and revenue, to have more financial resources and good paper publications. The results also suggest that university technology-holding companies are crucial for increasing the number of professor startups and their performance. These findings have implications for both university and government policymakers, who aim to facilitate greater participation of professors in startup formation and commercialization of technology.

• Information Systems Review
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• v.23 no.1
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• pp.45-68
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• 2021

As the influence of online reviews on consumer decision-making increases, concerns about review manipulation are also increasing. Fake reviews or review manipulations are emerging as an important problem by posting untrue reviews in order to increase sales volume, causing the consumer's reverse choice, and acting at a high cost to the society as a whole. Most of the related prior studies have focused on predicting review manipulation through data mining methods, and research from a consumer perspective is insufficient. However, since the possibility of manipulation of reviews perceived by consumers can affect the usefulness of reviews, it can provide important implications for online word-of-mouth management regardless of whether it is false or not. Therefore, in this study, we analyzed whether there is a difference between the review evaluated by the consumer as being manipulated and the general review, and verified whether the manipulated review negatively affects the review usefulness. For empirical analysis, 34,711 online book reviews on the LibraryThing website were analyzed using multilevel logistic regression analysis and Poisson regression analysis. As a result of the analysis, it was found that there were differences in product level, reviewer level, and review level factors between reviews that consumers perceived as being manipulated and reviews that were not. In addition, manipulated reviews have been shown to negatively affect review usefulness.

• 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.

• Information Systems Review
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• v.24 no.2
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• pp.1-20
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• 2022

Researchers are making contradictory claims through the concept of superstars and long tails about how the development of IT technology affects demand distribution. Unlike previous studies that focused on changes in demand from a macro point of view, this study explored whether the relationship between a company's marketing activities and consumer response differs depending on the product location (i.e., superstar vs. long tail) from a micro point of view. Based on the marketing mix framework, hypotheses were developed based on the relevant literature. In the case of empirical analysis, 2,835 daily data from 63 Korean films were tested using the quantile regression method. As a result of the analysis, it was found that the influence of marketing mix factors on sales varies depending on the location of the product. Specifically, the appeal breadth of the film and the effect of owned media are enhanced in superstar products, and the effect of acquisition media in long-tail products is enhanced and the negative effects of competition are mitigated. Unlike previous studies that focused on macroscopic changes in demand distribution, this study suggested marketing activities suitable for practitioners through microscopic analysis.

• Information Systems Review
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• v.24 no.2
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• pp.41-66
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• 2022

With the development of IT, mobile apps and the expansion of contactless services due to COVID-19, "smart orders" have recently been activated in the food and beverage service. Even in recent years, when sales have declined, the number of orders made by smart orders has been steadily increasing, and this ordering method can accumulate customer data, enabling effective customized services in the future. In the present study, satisfaction with smart orders and continuous use intention were studied based on the technology acceptance model (TAM). And it focused on whether there is a difference in personality when using smart orders. For this purpose, a survey was conducted on 317 smart order users, and the hypothesis was verified by structural equation model analysis. Perceived benefits had a significant effect on satisfaction; also, satisfaction had a significant effect on continuous use intention. There is a significant disparity between introvert and extrovert type. As a consequence, the introverted type has a greater intention to perceive usefulness of smart orders and continuously use them. These results suggest that the customer's personality type should be considered in future customer customization strategies.

• Journal of The Korean Association For Science Education
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• v.26 no.2
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• pp.200-211
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• 2006

The purpose of this study was to analyze Korean middle school student achievement in environmental science based on the TIMSS 2003 (Trends in International Mathematics and Science Study), a student comparison of 46 participating nations. Korea ranked the fourth with a mean score of 554 in environmental science. However, all 3 environment science topics assessed in TIMSS are not included in the Korean science curriculum through 8th grade, even though they are included in most other participating nations' curricula. The average percent correct of items was analyzed according to the main topic, the item type and the cognitive domain. Items that showed differences between the average percent correct of Korea and the international average as well as differences between the average percent correct of boys and girls were further analyzed. Results revealed that Korean students performed better than the international average, especially in 'use and conservation of natural resources', multiple-choice items, and items requiring 'factual knowledge'. Also, male students demonstrated significantly higher achievement than female students. On the other hand, Korean students showed relatively lower achievement in constructed-response items, items that contained content they had not learned in science lessons and items requiring descriptions of the uses and effect of science and technology. Moreover, Korean student lacked understanding about acid rain, global warming, and ozone layer destruction. Korean female students showed relatively lower environmental conceptions and lower performance on items requiring data analysis than Korean male students. On the basis of these results, this study suggested that topics of environmental science be included in the science curriculum and taught in the science classroom to help middle school students more fully comprehend environmental issues.

• Journal of Service Research and Studies
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• v.14 no.2
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• pp.18-36
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• 2024

As interest in ESG has been increased, it is easy to find papers that empirically study that a company's ESG activities have a positive impact on the company's performance. However, research on what ESG activities companies should actually engage in is relatively lacking. Accordingly, this study systematically classifies ESG activities of companies and seeks to provide insight to companies seeking to plan new ESG activities. This study analyzes how Chinese manufacturing companies perform ESG activities based on their dynamic capabilities in the global economy and how they differ in their activities. This study used the ESG annual reports of 151 Chinese manufacturing listed companies on the Shanghai & Shenzhen Stock Exchange and ESG indicators of China Securities Index Company (CSI) as data. This study focused on the following three research questions. The first is to determine whether there are any differences in ESG activities between companies with high ESG scores (TOP-25) and companies with low ESG scores (BOT-25), and the second is to determine whether there are any changes in ESG activities over a 10-year period (2010-2019), focusing only on companies with high ESG scores. The results showed that there was a significant difference in ESG activities between high and low ESG scorers, while tracking the year-to-year change in activities of the top-25 companies did not show any difference in ESG activities. In the third study, social network analysis was conducted on the keywords of E/S/G. Through the co-concurrence matrix technique, we visualized the ESG activities of companies in a four-quadrant graph and set the direction for ESG activities based on this.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.29 no.2
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• pp.101-115
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• 2024

The ocean heatwave is emerging as a major issue due to global warming, posing a direct threat to marine ecosystems and humanity through decreased food resources and reduced carbon absorption capacity of the oceans. Consequently, the prediction of ocean heatwaves in the vicinity of the Korean Peninsula is becoming increasingly important for marine environmental monitoring and management. In this study, an LSTM model was developed to improve the underestimated prediction of ocean heatwaves caused by the coarse vertical grid system of the Korean Peninsula Ocean Prediction System. Based on the results of ocean heatwave predictions for the Korean Peninsula conducted in 2023, as well as those generated by the LSTM model, the performance of heatwave predictions in the East Sea, Yellow Sea, and South Sea areas surrounding the Korean Peninsula was evaluated. The LSTM model developed in this study significantly improved the prediction performance of sea surface temperatures during periods of temperature increase in all three regions. However, its effectiveness in improving prediction performance during periods of temperature decrease or before temperature rise initiation was limited. This demonstrates the potential of the LSTM model to address the underestimated prediction of ocean heatwaves caused by the coarse vertical grid system during periods of enhanced stratification. It is anticipated that the utility of data-driven artificial intelligence models will expand in the future to improve the prediction performance of dynamical models or even replace them.

• 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

• Korean Journal of Radiology
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• v.24 no.11
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• pp.1081-1092
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• 2023

Objective: To investigate the incidence of microvascular myocardial ischemia in diabetic patients without obstructive coronary artery disease (CAD) and its relationship with angina. Materials and Methods: Diabetic patients and an intermediate-to-high pretest probability of CAD were prospectively enrolled. Non-diabetic patients but with an intermediate-to-high pretest probability of CAD were retrospectively included as controls. The patients underwent dynamic computed tomography-myocardial perfusion imaging (CT-MPI) and coronary computed tomography angiography (CCTA) to quantify coronary stenosis, myocardial blood flow (MBF), and extracellular volume (ECV). The proportion of patients with microvascular myocardial ischemia, defined as any myocardial segment with a mean MBF ≤ of 100 mL/min/100 mL, in patients without obstructive CAD (Coronary Artery Disease-Reporting and Data System [CAD-RADS] grade 0-2 on CCTA) was determined. Various quantitative parameters of the patients with and without diabetes without obstructive CAD were compared. Multivariable analysis was used to determine the association between microvascular myocardial ischemia and angina symptoms in diabetic patients without obstructive CAD. Results: One hundred and fifty-two diabetic patients (mean age: 59.7 ± 10.7; 77 males) and 266 non-diabetic patients (62.0 ± 12.3; 167 males) were enrolled; CCTA revealed 113 and 155 patients without obstructive CAD, respectively. For patients without obstructive CAD, the mean global MBF was significantly lower for those with diabetes than for those without (152.8 mL/min/100 mL vs. 170.4 mL/min/100 mL, P < 0.001). The mean ECV was significantly higher for diabetic patients (27.2% vs. 25.8%, P = 0.009). Among the patients without obstructive CAD, the incidence of microvascular myocardial ischemia (36.3% [41/113] vs. 10.3% [16/155], P < 0.001) and interstitial fibrosis (69.9% [79/113] vs. 33.3% [8/24], P = 0.001) were significantly higher in diabetic patients than in the controls. The presence of microvascular myocardial ischemia was independently associated with angina symptoms (adjusted odds ratio = 3.439, P = 0.037) in diabetic patients but without obstructive CAD. Conclusion: Dynamic CT-MPI + CCTA revealed a high incidence of microvascular myocardial ischemia in diabetic patients without obstructive CAD. Microvascular myocardial ischemia is strongly associated with angina.