• Information Systems Review
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• v.25 no.4
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• pp.89-112
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• 2023

In March 2020, as it was declared a COVID-19 pandemic, various quarantine measures were taken. Accordingly, many changes have occurred in the tourism and hospitality industries. In particular, quarantine guidelines, such as the introduction of non-face-to-face services and social distancing, were implemented in the restaurant industry. For decades, research on restaurant attributes has emphasized the importance of three attributes: atmosphere, service quality, and food quality. Nevertheless, to the best of our knowledge, research on restaurant attributes considering the COVID-19 situation is insufficient. To respond to this call, this study attempted an exploratory approach to classify new restaurant attributes based on understanding environmental changes. This study considered 31,115 online reviews registered in Naverplace as an analysis unit, with 475 general restaurants located in Euljiro, Seoul. Further, we attempted to classify restaurant attributes by clustering words within online reviews through TF-IDF and LDA topic modeling techniques. As a result of the analysis, the factors of "prevention of infectious diseases" were derived as new attributes of restaurants in the context of COVID-19 situations, along with the atmosphere, service quality, and food quality. This study is of academic significance by expanding the literature of existing restaurant attributes in that it categorized the three attributes presented by existing restaurant attributes and further presented new attributes. Moreover, the analysis results have led to the formulation of practical recommendations, considering both the operational aspects of restaurants and policy implications.

• Information Systems Review
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• v.24 no.1
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• pp.39-57
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• 2022

This paper proposes the GMOPTBoost algorithm to improve the performance of the AdaBoost algorithm for bankruptcy prediction in which class imbalance problem is inherent. AdaBoost algorithm has the advantage of providing a robust learning opportunity for misclassified samples. However, there is a limitation in addressing class imbalance problem because the concept of arithmetic mean accuracy is embedded in AdaBoost algorithm. GMOPTBoost can optimize the geometric mean accuracy and effectively solve the category imbalance problem by applying Gaussian gradient descent. The samples are constructed according to the following two phases. First, five class imbalance datasets are constructed to verify the effect of the class imbalance problem on the performance of the prediction model and the performance improvement effect of GMOPTBoost. Second, class balanced data are constituted through data sampling techniques to verify the performance improvement effect of GMOPTBoost. The main results of 30 times of cross-validation analyzes are as follows. First, the class imbalance problem degrades the performance of ensembles. Second, GMOPTBoost contributes to performance improvements of AdaBoost ensembles trained on imbalanced datasets. Third, Data sampling techniques have a positive impact on performance improvement. Finally, GMOPTBoost contributes to significant performance improvement of AdaBoost ensembles trained on balanced datasets.

• Information Systems Review
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• v.23 no.3
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• pp.177-200
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• 2021

Recently, the use of prepaid electronic payments such as electronic wallets, digital currency and prepaid points is gradually increasing. Prepaid electronic payments has the characteristic of being used after charging first. This study empirically investigated the factors affecting the intention to use online charging in order to help improve the service that require prepaid recharge by applying transformed TAM. Since there are not many previous studies for the intention to use online charging, we extract factors through preceding researches for electronic cash and mobile easy payment. Also we analyze the intention to use online charging for transportation card users, focusing on the moderating effects. As a result of the study, it was found that 'convenience', 'ubiquity', and 'self-efficacy' among the independent variables had a positive (+) effect on mediation variable 'perceived usefulness'. 'Perceived usefulness' was analyzed to have a significant influence on the dependent variable 'usage intention'. According to users' gender, internet usage time, internet shopping frequency, online charging frequency and transportation card usage type, the moderating effect was significant on 'perceived usefulness' and 'usage intention'. As an implication, it was suggested that service improvement and differentiated marketing are needed in direction of increasing the usefulness of services. Additional research directions were proposed for services such as e-wallets, prepaid points and digital currencies by adding other factors and moderate variables.

• Information Systems Review
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• v.24 no.1
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• pp.157-181
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• 2022

As competition in the airline industry intensifies, effective airline service quality evaluation has become one of the main challenges. In particular, as big data analytics has been touted as a new research paradigm, new research on service quality measurement using online review analysis has been attempted. However, these studies do not use review titles for analysis, relyon supervised learning that requires a lot of human intervention in learning, and do not consider airline characteristics in classifying service quality dimensions.To overcome the limitations of existing studies, this study attempts to measure airlines service quality and to classify it into the AIRQUAL service quality dimension using online review text as well as title based on self-trainingand sentiment analysis. The results show the way of effective extracting service quality dimensions of AIRQUAL from online reviews, and find that each service quality dimension have a significant effect on service satisfaction. Furthermore, the effect of review title on service satisfaction is also found to be significant. This study sheds new light on service quality measurement in airline industry by using an advanced analytical approach to analyze effects of service quality on customer satisfaction. This study also helps managers who want to improve customer satisfaction by providing high quality service in airline industry.

• Information Systems Review
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• v.24 no.1
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• pp.125-155
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• 2022

Platform contrasts with traditional industry in terms of innovativeness as it is rapidly developing with information technology. To redeem preceding researches which have separately focused on either platform acceptance based on technology acceptance model or resistance factors against platform's innovation, this study applied dual factor theory to check the simultaneous influence of enablers and inhibitors on resistance. This study chose purchasers of online used trading platform as a subject of study since it contrasts with other platforms in many characteristics. Based on preceding studies, the moderating effects of their past purchase numbers on the relations between resistance and use intention were also checked. The findings reveal that economic benefit as an enabler had significant negative influence on the resistance, but social influence didn't have expected influence. In case of inhibitors, both perceived complexity and perceived risk had significant positive influence on the resistance. Though resistance had significant negative influence on the use intention, its influence was moderated into the positive direction as users' purchase number increased. Lastly, resistance had mediation effect between antecedent factors (economic benefit and perceived complexity) and use intention.

• Information Systems Review
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• v.23 no.1
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• pp.69-91
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• 2021

Credit Bureaus in Korea commonly use financial transaction information of the past and present time for calculating an individual's credit scores. Compared to other rating factors, the repayment history information accounts for a larger weights on credit scores. Accordingly, despite full redemption of overdue payments, late payment history is reflected negatively for the assessment of credit scores for certain period of the time. An individual with debt delinquency can be classified into two groups; (1) the individuals who have faithfully paid off theirs overdue debts(Normal Repayment), and (2) those who have not and as differences of creditworthiness between these two groups do exist, it needs to grant relatively higher credit scores to the former individuals with normal repayment. This study is designed to analyze the factors of normal repayment of Korean financial debt delinquents based on credit information of personal loan, overdue payments, redemption from Korea Credit Information Services. As a result of the analysis, the number of overdue and the type of personal loan and delinquency were identified as significant variables affecting normal repayment and among applied methodologies, neural network models suggested the highest classification accuracy. The findings of this study are expected to improve the performance of individual credit scoring model by identifying the factors affecting normal repayment of a financial debt delinquent.

• Information Systems Review
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• v.24 no.4
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• pp.23-40
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• 2022

Although Support Vector Machine(SVM) has been used in various fields such as bankruptcy prediction model, the hyperplane learned by SVM in class imbalance problem can be severely skewed toward minority class and has a negative impact on performance because the area of majority class is expanded while the area of minority class is invaded. This study proposed optimized uneven margin SVM(OPT-UMSVM) combining threshold moving or post scaling method with UMSVM to cope with the limitation of the traditional even margin SVM(EMSVM) in class imbalance problem. OPT-UMSVM readjusted the skewed hyperplane to the majority class and had better generation ability than EMSVM improving the sensitivity of minority class and calculating the optimized performance. To validate OPT-UMSVM, 10-fold cross validations were performed on five sub-datasets with different imbalance ratio values. Empirical results showed two main findings. First, UMSVM had a weak effect on improving the performance of EMSVM in balanced datasets, but it greatly outperformed EMSVM in severely imbalanced datasets. Second, compared to EMSVM and conventional UMSVM, OPT-UMSVM had better performance in both balanced and imbalanced datasets and showed a significant difference performance especially in severely imbalanced datasets.

• Information Systems Review
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• v.24 no.4
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• pp.119-142
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• 2022

Organizations are attempting a digital transformation that converts physical business processing into virtual business processing. Through this digital transformation, organizations are overcoming time and space constraints and creating competitiveness. The digital transformation of this work environment has been accelerated as many organizations have implemented remote work due to the recent COVID-19 pandemic. This study focused on business process virtualizability, which is the result of the rapid digital transformation of the work environment. Business process virtualizability is the resulting quality, such as the suitability or excellence of business processing in a virtual environment. This research model is the effect of process, IT and organizational characteristics on business process virtualizability. As a result of the verification of people who have experienced remote work in a virtual environment, first, it was confirmed that, in terms of process characteristics, sensory requirements affect business process virtualizability, but relationship requirements, synchronism requirements, and identification and control requirements do not. Second, in terms of IT characteristics, it was confirmed that representation and reach affect business process virtualizability. Third, it was confirmed that, in terms of organizational characteristics, job autonomy affects business process virtualizability, but evaluation unfairness does not. This study found that representation and reach of IT had the most significant influence on business process virtualizability, job autonomy was next, and sensory requirements had the lowest influence. This presents practical implications for organizations to increase the success potential of business process virtualizability.

• Information Systems Review
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• v.24 no.1
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• pp.21-37
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• 2022

Drug repositioning, one of the methods of developing new drugs, is a useful way to discover new indications by allowing drugs that have already been approved for use in people to be used for other purposes. Recently, with the development of machine learning technology, the case of analyzing vast amounts of biological information and using it to develop new drugs is increasing. The use of machine learning technology to drug repositioning will help quickly find effective treatments. Currently, the world is having a difficult time due to a new disease caused by coronavirus (COVID-19), a severe acute respiratory syndrome. Drug repositioning that repurposes drugsthat have already been clinically approved could be an alternative to therapeutics to treat COVID-19 patients. This study intends to examine research trends in the field of drug repositioning using machine learning techniques. In Pub Med, a total of 4,821 papers were collected with the keyword 'Drug Repositioning'using the web scraping technique. After data preprocessing, frequency analysis, LDA-based topic modeling, random forest classification analysis, and prediction performance evaluation were performed on 4,419 papers. Associated words were analyzed based on the Word2vec model, and after reducing the PCA dimension, K-Means clustered to generate labels, and then the structured organization of the literature was visualized using the t-SNE algorithm. Hierarchical clustering was applied to the LDA results and visualized as a heat map. This study identified the research topics related to drug repositioning, and presented a method to derive and visualize meaningful topics from a large amount of literature using a machine learning algorithm. It is expected that it will help to be used as basic data for establishing research or development strategies in the field of drug repositioning in the future.

• Information Systems Review
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• v.22 no.4
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• pp.185-203
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• 2020

With the advent of 4th industrial revolution, the manufacturing industry is converging with ICT and changing into the era of smart manufacturing. In the smart factory, all machines and facilities are connected based on ICT, and thus security should be further strengthened as it is exposed to complex security threats that were not previously recognized. To reduce the risk of security incidents and successfully implement smart factories, it is necessary to identify key security factors to be applied, taking into account the characteristics of the industrial environment of smart factories utilizing ICT. In this study, we propose a 'hierarchical classification model of security factors in smart factory' that includes terminal, network, platform/service categories and analyze the importance of security factors to be applied when developing smart factories. We conducted an assessment of importance of security factors to the groups of smart factories and security experts. In this study, the relative importance of security factors of smart factory was derived by using AHP technique, and the priority among the security factors is presented. Based on the results of this research, it contributes to building the smart factory more securely and establishing information security required in the era of smart manufacturing.