• Information Systems Review
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• v.25 no.2
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• pp.57-76
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• 2023

This study classifies the success of creators and YouTubers who have created channels on YouTube recently, which is the most influential digital platform. Based on the actual information disclosure of YouTubers who are in the field of science and technology category, video upload cycle, video length, number of selectable multilingual subtitles, and information from other social network channels that are being operated, the success of YouTubers using machine learning was classified and analyzed, which is the closest to the YouTube revenue structure. Our findings showed that neural network algorithm provided the best performance to predict the success or failure of YouTubers. In addition, our five factors contributed to improve the performance of the classification. This study has implications in suggesting various approaches to new individual entrepreneurs who want to start YouTube, influencers who are currently operating YouTube, and companies who want to utilize these digital platforms. We discuss the future direction of utilizing digital platforms.

• Information Systems Review
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• v.25 no.2
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• pp.163-180
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• 2023

Based on the Technology-Organization-Environment (TOE) model, this study proposed a research model that explains the continuance intention of users in the with Covid-19 era considering the technical, organizational, and environmental aspects of Remote Work Solution (RWS). To verify the research model and hypothesis, an online survey was conducted on domestic RWS users. Partial least squares (PLS) were utilized to analyze the collected 411 data. As a result of the analysis, it was found that functionality and security level had positive impacts on both productivity improvement and satisfaction. However, it was also confirmed that organizational readiness had a positive effect on productivity improvement but did not affect satisfaction. Furthermore, the results revealed that government support had a positive relationship with continuance intention, but the health concerns did not. Finally, the correlations between productivity improvement, satisfaction, and continuous intention were confirmed to be significant. Therefore, 9 out of a total of 11 hypotheses were supported.

• Information Systems Review
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• v.23 no.4
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• pp.69-86
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• 2021

As artificial intelligence technology advances, personalized recommendation systems using big data have attracted huge attention. In the case of beauty products, product preferences are clearly divided depending on customers' skin types and sensitivity along with individual tastes, so it is necessary to provide customized recommendation services based on accumulated customer data. Therefore, by employing deep learning methods, this study proposes a neural network-based recommendation model utilizing both product search history and context information such as gender, skin types and skin worries of customers. The results show that our model with context information outperforms collaborative filtering-based recommender system models using customer search history.

• Information Systems Review
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• v.23 no.4
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• pp.87-102
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• 2021

Smart cities are attracting attention as a new economic growth engine based on new technologies and means to solve side effects of urbanization such as a surge in energy consumption, deepening environmental pollution, and an increase in crime rates. In particular, as demand for non-face-to-face services increases due to COVID-19, the role of smart cities that can provide various online and offline non-face-to-face services is becoming more important. Therefore, this study defined smart city non-face-to-face services based on literature research on the concept and underlying technology of smart city, and analyzed consumer utility for each service using Conjoint analysis. In particular, differences in user preferences between Sejong City and Busan City, which are currently designated as national smart city pilot cities in Korea, were compared and analyzed, and based on the derived results, measures to improve the competitiveness of smart city services were suggested.

• Information Systems Review
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• v.23 no.4
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• pp.127-146
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• 2021

Fake news isexpanded and reproduced rapidly regardless of their authenticity by the characteristics of modern society, called the information age. Assuming that 1% of all news are fake news, the amount of economic costs is reported to about 30 trillion Korean won. This shows that the fake news isvery important social and economic issue. Therefore, this study aims to develop an automated detection model to quickly and accurately verify the authenticity of the news. To this end, this study crawled the news data whose authenticity is verified, and developed fake news prediction models using word embedding (Word2Vec, Fasttext) and deep learning algorithms (LSTM, BiLSTM). Experimental results show that the prediction model using BiLSTM with Word2Vec achieved the best accuracy of 84%.

• Information Systems Review
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• v.23 no.4
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• pp.165-182
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• 2021

In nowadays, Covid-19 has transformed patterns of consumers' behavior into a non-face-to-face mode. As the patterns of consumption have been digitalized, it has become a daily routine for consumers who perform so-called shadow work, which involves unpaid jobs that they have to do by themselves. In mobile grocery service context, consumers' shadow work could lead to shopping avoidance as well as switching toward other shopping channels. Thus, this study is to examine how consumers' perception of shadow work affect mobile shopping avoidance and switching intention toward other shopping channels. This study collected 283 survey data from online respondents who have experience on subscription services for ordering groceries in online. We also tested our research model by using partial least squares. Based on our results, this study has found that the perception of shadow work had a positive effect on mobile shopping avoidance as well as switching intention. We expect that our findings could contribute to relevant research on shadow work and suggest practical implications for digital platforms dealing with subscription business models

• Information Systems Review
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• v.23 no.1
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• pp.135-154
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• 2021

In the network economy, the informatization of Small and Medium enterprises(SME) plays an important role in determining productivity while being competitive in the businesses. Informatization of SME has become important along with the recent trend of the fourth industrial revolution. Based on the IT Business Value Model, this study analyzes the key factors of information service of SME with the structure model. In addition, multi-level model was conducted by dividing the layers according to the size of the SME. The analysis confirmed that complementary organizational resources are a key factor in determining the informatization of SME. In addition, the effect of informatization of SME on the scale of SME varies depending on the type of entry into the industrial complex.

• Information Systems Review
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• v.23 no.1
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• pp.115-134
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• 2021

Recently, the amount of money that foreigners buy from online shopping sites in Korea has been increasing, especially by Chinese consumers. In this study, we researched the effects of Chinese consumers' reviews, inquiries, and bargains on loyalty, and how these effects differ by gender so that Korean online sellers can take advantage of these opportunities. In order to verify the research model, a survey was conducted on Chinese consumers with online purchasing experience and 231 valid samples were collected and analyzed. As results, reviews, inquiries, and bargaining had the positive impacts on the loyalty of Chinese consumers. The impact of reviews on loyalty was higher in women, and the impact of inquiries and bargains on loyalty was higher in men. This study can help Korean online sellers effectively respond to Chinese consumers' reviews, inquiries, and bargains needs. This study, also, can help they understand and deal with the difference between the effect on reviews to women and the effect of inquiries and bargaining to men.

• Information Systems Review
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• v.23 no.3
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• pp.77-96
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• 2021

This research investigates the pivotal role of AI-enabled technologies in vitalizing the local economy. Collaborating with a leading search engine company, we examine the direct and indirect of an AI-based location search service on the success of sampled 7,035 local restaurants in Gangnam area in Seoul. We find that increased use of AI-enabled search and recommendation services significantly improved the selections of previously less-discovered or less-popular restaurants by users, and it also enhanced the stores' overall conversion rates. The main research findings have contributions to extant literature in theorizing the value of AI applications in local economy and have managerial implications for search businesses and local stores by recommending strategic use of AI applications in their businesses that are effective in highly competitive markets.

• Information Systems Review
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• v.21 no.4
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• pp.157-173
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• 2019

Robotic Process Automation (RPA) has attracted great attention from diverse home and foreign industries. To provide lessons learned and action principles based on real RPA adoption and application experiences, various case studies have been conducted. However, lacking is an investigation of public sector for RPA adoption, especially in Korea. To reduce the research gap, this study presents a case study of RPA adoption by a representative Korean ICT public organization, NIA (National Information society Agency). By automating a core process, entering a document to a governmental portal service, NIA has achieved various management performances in terms of cost, operation, and business impacts. Especially, by relieving four types of rigidity of public institutions (i.e. structure, human resource, tasks, and rules), Our case study result suggests that RPA enables public institutes to overcome obstacles of pursuing digital transformation. Implications and limitations for future public RPA adopters are offered.