• Journal of Industrial Convergence
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• v.16 no.3
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• pp.23-32
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• 2018

The purpose of this study was to investigate levels of emotional intelligence, resilience, post-traumatic growth and communication skill of nursing students and to identify the factors affecting communication skill of nursing students and to use them as basic data to improve communication skill. The data collecting period is from April to May, 2017, and the subjects are 296 nursing students in D city. The results of the study showed that there was a statistically significant positive correlation between emotional intelligence, resilience, post-traumatic growth, and communication skill of nursing students. Also, as emotional intelligence, resilience, and post-traumatic growth increased, communication skill improved. The explanatory power of regression model was 51.9%. In conclusion, it is necessary to efforts to improve the emotional intelligence, resilience, and post-traumatic growth as a strategy to enhance communication skills of nursing students in order to establish therapeutic relationships with patients as nurses.

• Journal of Korean Society for Quality Management
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• v.28 no.4
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• pp.151-160
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• 2000

There can be a hierarchy among sensibility vocabularies by degree of abstractness. The higher degree of adstractness, the more difficult measuring satisfaction of sensibility. The objective of this study is to quantify customer satisfaction of his/her sensibility by using hierarchy of sensibility and Structural Equation Model(SEM).

• Proceedings of the Korean Society for Quality Management Conference
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• 2004.04a
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• pp.490-495
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• 2004

• Information Systems Review
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• v.17 no.1
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• pp.49-64
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• 2015

Customer reviews which include subjective opinions for the product or service in online store have been generated rapidly and their influence on customers has become immense due to the widespread usage of SNS. In addition, a number of studies have focused on opinion mining to analyze the positive and negative opinions and get a better solution for customer support and sales. It is very important to select the key terms which reflected the customers' sentiment on the reviews for opinion mining. We proposed a document-level terms-based sentiment classification model by select in the optimal terms with part of speech tag. SVMs (Support vector machines) are utilized to build a predictor for opinion mining and we used the combination of POS tag and four terms extraction methods for the feature selection of SVM. To validate the proposed opinion mining model, we applied it to the customer reviews on Amazon. We eliminated the unmeaning terms known as the stopwords and extracted the useful terms by using part of speech tagging approach after crawling 80,000 reviews. The extracted terms gained from document frequency, TF-IDF, information gain, chi-squared statistic were ranked and 20 ranked terms were used to the feature of SVM model. Our experimental results show that the performance of SVM model with four POS tags is superior to the benchmarked model, which are built by extracting only adjective terms. In addition, the SVM model based on Chi-squared statistic for opinion mining shows the most superior performance among SVM models with 4 different kinds of terms extraction method. Our proposed opinion mining model is expected to improve customer service and gain competitive advantage in online store.

• Information Systems Review
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• v.25 no.1
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• pp.233-255
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• 2023

The development of information technology(IT) has brought so-called "online experience" to satisfy our daily needs. The market for online experiences grew more during the COVID-19 pandemic. Therefore, this study attempted to analyze how the features of online experience services affect customer satisfaction by crawling structured and unstructured data from the online experience web site newly launched by Airbnb after COVID-19. As a result of the analysis, it was found that the structured data generated by service users on a C2C online sharing platform had a positive effect on the satisfaction of other users. In addition, unstructured text data such as experience introductions and host introductions generated by service providers turned out to have different subjectivity scores depending on the purpose of its text. It was confirmed that the subjective host introduction and the objective experience introduction affect customer satisfaction positively. The results of this study are to provide various implications to stakeholders of the online sharing economy platform and researchers interested in online experience knowledge management.

• Journal of Industrial Convergence
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• v.22 no.4
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• pp.57-64
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• 2024

This study was to confirm the effect of emotional intelligence on organizational commitment of general hospital nurses and the mediating effect of affect. Data was collected using a structured online self-report questionnaire on emotional intelligence, organizational commitment, and both negative and positive affect from 236 nurses working in general hospitals. The data collected were analyzed using descriptive statistics, Pearson's correlation, and a parallel multiple mediation model. This analysis was conducted with IBM SPSS Statistics (Version 27.0) and the PROCESS macro (Model 4). This study's findings revealed that emotional intelligence was significant correlated with positive affect, negative affect, and organizational commitment. Emotional intelligence had a significant direct effect on positive affect (β=.16, p=.015), negative affect (β=-.28, p<.001), and organizational commitment (β=.33, p<.001). Positive affect (β=.20, p=.001) and negative affect (β=-.25, p<.001), had a significant direct effect on organizational commitment. And the mediating effect of positive affect (β=.03, 95% bootstrap CI=0.01~0.07) and negative affect (β=.07, 95% bootstrap CI=0.03~0.12) was also significant. Based on these research results, it will be necessary to research various training programs that can manage emotional intelligence and affect together in developing programs to improve nurses' organizational commitment.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.4
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• pp.142-151
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• 2023

Sentiment analysis is a method used to comprehend feelings, opinions, and attitudes in text, and it is essential for evaluating consumer feedback and social media posts. However, creating sentiment dictionaries, which are necessary for this analysis, is complex and time-consuming because people express their emotions differently depending on the context and domain. In this study, we propose a new method for simplifying this procedure. We utilize syntax analysis of the Korean language to identify and extract sentiment words based on the Reason-Sentiment Pattern, which distinguishes between words expressing feelings and words explaining why those feelings are expressed, making it applicable in various contexts and domains. We also define sentiment words as those with clear polarity, even when used independently and exclude words whose polarity varies with context and domain. This approach enables the extraction of explicit sentiment expressions, enhancing the accuracy of sentiment analysis at the attribute level. Our methodology, validated using Korean cosmetics review datasets from Korean online shopping malls, demonstrates how a sentiment dictionary focused solely on clear polarity words can provide valuable insights for product planners. Understanding the polarity and reasons behind specific attributes enables improvement of product weaknesses and emphasis on strengths. This approach not only reduces dependency on extensive sentiment dictionaries but also offers high accuracy and applicability across various domains.

• Korean Business Review
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• v.14
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• pp.211-235
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• 2001

The level and motivational basis of consumer involvement have been recognized as having important effects on purchase behavior. To explore the importance of hedonic aspects of involvement upon consumer purchase behavior, survey data on involvement degree and type concerning 8 product classes from 388 respondents was analyzed. Major findings of this study are summarized as follow. First of all, hedonic factor is evidenced as an important component of consumer involvement. In addition, hedonic dimension of involvement is proved to have significant effects on consumer purchase behavior in general. Secondly, analysis of the difference in behavioral tendencies between purchasing product with hedonic value(coffee) and utilitarian motive(washing machine) shows that consumers have relatively high in degree of brand differentiation, brand commitment, product knowledge in purchasing product with hedonic value. This means that hedonic aspect of product and thus hedonic involvement has not less important in predicting consumer behavior. Finally, several interactive effects, which suppot to and conflict against the previous research findings, between level and type of involvement upon purchase behaviors are detacted. This implies that hedonically induced involvement has moderating roles in the effects of consumer involvement on purchase behavior.

• Journal of Digital Convergence
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• v.17 no.4
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• pp.115-122
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• 2019

In the online marketplace, consumers are exposed to various products and freely express opinions. As consumer product reviews have a important effect on the success of online markets and other consumers, online market needs to accurately analyze the consumers' emotions about their products. Text mining, which is one of the data analysis techniques, can analyze the consumer's reviews on the products and efficiently manage the products. Previous studies have analyzed specific domains and less than 20,000 data, despite the different accuracy of the analysis results depending on the data domain and size. Further, there are few studies on additional factors that can improve the accuracy of analysis. This study analyzed 72,530 review data of food product domain that was not mainly covered in previous studies by using ensemble technique. We also examined the influence of summary review on improving accuracy of analysis. As a result of the study, this study found that Boosting ensemble technique has the highest accuracy of analysis. In addition, the summary review contributed to improving accuracy of the analysis.

• Information Systems Review
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• v.22 no.4
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• pp.59-74
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• 2020

This paper investigates the asymmetric effect of social sentimental on an individual stock price return. For this purpose, four companies such as POSCO, Korean Electricity, AMORE PACIFIC, KIA Motors are chosen from KOSPI listed companies in terms of dataperspective. The main estimation results are as follows: the positive opinions affect only the stock prices return of three companies while the negative opinions affect all of the companies. It shows that positive or negative texts give asymmetric effect on stock price return and the effect of negative opinions is bigger than that of positive opinions. The results imply that investors are more sensitive to the negatives since they have the tendency of loss aversion. Also, it indicates that subjective opinion on SNS can be used as the proxy for the investment sentiment.