• Knowledge Management Research
• /
• v.20 no.2
• /
• pp.25-42
• /
• 2019

AI speakers which are wireless speakers with smart features have released from many manufacturers and adopted by many customers. Though smart features including voice recognition, controlling connected devices and providing information are embedded in many mobile phones, AI speakers are sitting in home and has a role of the central en-tertainment and information provider. Many surveys have investigated the important factors to adopt AI speakers and influ-encing factors on satisfaction. Though most surveys on AI speakers are cross sectional, we can track customer attitude toward AI speakers longitudinally by analyzing customer reviews on AI speakers. However, there is not much research on the change of customer attitude toward AI speaker. Therefore, in this study, we try to grasp how the attitude of AI speaker changes with time by applying text mining-based analysis. We collected the customer reviews on Amazon Echo which has the highest share of AI speakers in the global market from Amazon.com. Since Amazon Echo already have two generations, we can analyze the characteristics of reviews and compare the attitude ac-cording to the adoption time. We identified all sub topics of customer reviews and specified the topics for smart features. And we analyzed how the share of topics varied with time and analyzed diverse meta data for comparisons. The proportions of the topics for general satisfaction and satisfaction on music were increasing while the proportions of the topics for music quality, speakers and wireless speakers were decreasing over time. Though the proportions of topics for smart fea-tures were similar according to time, the share of the topics in positive reviews and importance metrics were reduced in the 2nd generation of Amazon Echo. Even though smart features were mentioned similarly in the reviews, the influential effect on satisfac-tion were reduced over time and especially in the 2nd generation of Amazon Echo.

• The Journal of Society for e-Business Studies
• /
• v.23 no.4
• /
• pp.41-62
• /
• 2018

Despite the rapid growth of the mobile shopping market, major market participants are continuing to suffer operating losses due to severe competition. To solve this problem, the mobile shopping market requires research to improve customer satisfaction and customer loyalty rather than excessive competition. However, the existing studies have limits to reflect the direct needs of customers because they extract the factors on the basis of the Technology Acceptance Model and the literature study. In this study, to reflect the direct requirements of users of mobile shopping Apps, we derived concretely and various factors influencing customer satisfaction through a topic analysis using user reviews. And then we assessed the importance of derived factors to customer satisfaction and analyzed the effects of customer satisfaction on customer complaints and customer loyalty on a structural equation model based on the American customer satisfaction index. We expect that our framework linking a topic analysis and a structural equation model is to be applicable to studies on the customer satisfaction of other mobile services.

• The Journal of Information Systems
• /
• v.30 no.4
• /
• pp.277-301
• /
• 2021

Purpose The purpose of this study is to analyze the sentiment responses of the general public to non-face-to-face work using text mining methodology. As the number of non-face-to-face complaints is increasing over time, it is difficult to review and analyze in traditional methods such as surveys, and there is a limit to reflect real-time issues. Approach This study has proposed a method of the research model, first by collecting and cleansing the data related to non-face-to-face work among tweets posted on Twitter. Second, topics and keywords are extracted from tweets using LDA(Latent Dirichlet Allocation), a topic modeling technique, and changes for each section are analyzed through DTM(Dynamic Topic Modeling). Third, the complaints of non-face-to-face work are analyzed through the classification of positive and negative polarity in the COVID-19 section. Findings As a result of analyzing 1.54 million tweets related to non-face-to-face work, the number of IDs using non-face-to-face work-related words increased 7.2 times and the number of tweets increased 4.8 times after COVID-19. The top frequently used words related to non-face-to-face work appeared in the order of remote jobs, cybersecurity, technical jobs, productivity, and software. The words that have increased after the COVID-19 were concerned about lockdown and dismissal, and business transformation and also mentioned as to secure business continuity and virtual workplace. New Normal was newly mentioned as a new standard. Negative opinions found to be increased in the early stages of COVID-19 from 34% to 43%, and then stabilized again to 36% through non-face-to-face work sentiment analysis. The complaints were, policies such as strengthening cybersecurity, activating communication to improve work productivity, and diversifying work spaces.

• Korea Trade Review
• /
• v.48 no.1
• /
• pp.55-81
• /
• 2023

The purpose of this study is to analyze the main issues and trends of Korean trade, and to draw implications for future research regarding trade rules. A total of 476 academic journal are analyzed using English keyword searched for 'Trade Rules' from 2000 to July 2022 in the Korean Journal Citation Index data base. The analysis methodology includes co-occurrence network and topic trend analysis which is a kind of text mining methods. The results shows that key words representing Korea's trade trend fall into four categories in which the number of research journals has rapidly increased, which are Topic 4 (Investment Treaty), Topic 7 (Trade Security), Topic 8 (China's Protectionism), and Topic 11 (Trade Settlement). The major background for these topics is the tension between the United States and China threatening the existing international trade system. A detailed study for China's protectionism, changes in trade security system, and new investment agreements, and changes in payment methods will be the challenges in near future.

• Information Systems Review
• /
• v.22 no.4
• /
• pp.41-58
• /
• 2020

This study addresses the issue of which factors make domestic works have turnover intention. To pursue this research issue, we utilized a public database "2017 Occupational Migration Path Survey", administerd by Korea Employment Information Service (KEIS). Decision tree method was applied to extract crucial factors influencing workers' turnover intention. They include 'the degree of matching the level of education with the level of work', 'the possibility of individual development', 'the job-related education and training', 'the promotion system', 'wage and income', 'social reputation for work' and 'the stability of employment'.

• Information Systems Review
• /
• v.22 no.3
• /
• pp.157-172
• /
• 2020

The demand of information security consultants is expected to increase due to the emergence of ISMS-P incorporating ISMS and PIMS, the implementation of European Privacy Act (GDPR) and various security accidents. In this paper, we collected and analyzed advertisements of job advertisement sites that could identify firms' demand explicitly. We selected representative job advertisement sites in Korea and the United States and collected job advertisement details of information security consultants in 2014 and 2019. The collected data were visualized using text mining and analyzed using non-parametric methods to determine whether there was a change in the role of the information security consultant. The findings show that the requirements for information security consultants have changed very little. This means that the role does not change much over a five year time gap. The results of the study are expected to be helpful to policy makers related to information security consultants, those seeking to find employment as information security consultants, and those seeking information security consultants.

• Information Systems Review
• /
• v.18 no.3
• /
• pp.73-96
• /
• 2016

Recent research on information and communication technology and Internet-of-Things indicates that convergence and integration facilitate the development of various technologies. Similarly, related academic theories and technologies have also gained attention. This paradigm shift facilitated the convergence and integration of academic disciplines. In particular, information systems have become initiators of change. However, only a limited number of studies have been conducted on information systems. To address this gap, this study explores the future direction of information systems based on the core concepts and results of the comparative analysis conducted on research trends. We considered 48,102 data obtained from international top journals from 1980 to 2015. We analyzed journal titles, authors, abstracts, and keywords. We conducted the network analysis on existing collaborative studies and performed comparative analysis to visualize the results. The results provide an in-depth understanding of information systems and provides directions for future research on this area.

• Journal of Information Management
• /
• v.39 no.3
• /
• pp.95-113
• /
• 2008

In todays environment in which scientific technologies are changing very fast than ever, companies have to monitor and search emerging technologies to gain competitiveness. Actually many nations try to do that. Most of them use Dephi approach based on experts review as a searching method. But experts review has been criticised for probability of inclination and its derivative problems in the sense that it is accomplished only by expert's subjectivity. To overcome such problems, we used Scientometric Method for identifying emerging technology that had been done by Delphi as a rule. We made three particular efforts in order to improve the Quality of the result. Firstly, we selected one alternative database between SCI and Scopus hoping to see evenly-distributing results in wide fields on the front burner. Secondly we used Fractional citation counting in counting citation number in the stage of linear regression analysis. Lastly, we verified Scientometric result with experts opinions to minimize probable errors in a Scientometric research. As a result, we derived 290 emerging technologies from Scientometric analysis with Scopus Database, and visualized them on 2-dimension map with data mining system named KnowledgeMatrix which was developed by KISTI.

• Journal of Industrial Convergence
• /
• v.21 no.8
• /
• pp.23-33
• /
• 2023

Consumer review analysis is important for product development, customer satisfaction, competitive advantage, and effective marketing. Increased use of wireless earphones is expected to reach $45.7 billion by 2026 with growth in lifestyle. Therefore, in consideration of the growth and importance of the market, consumer reviews of wireless earphones from Apple and Samsung were analyzed. In this study, 11,320 wireless earphone reviews from Apple and Samsung sold on Coupang were collected to analyze consumers' purchase intentions and analyze consumer satisfaction through analysis of the frequency, sensitivity, and LDA topic model of text mining. As a result of topic modeling, 16 topics were derived and classified into sound quality, connection, shopping mall service, purchase intention, battery, delivery, and price. As a result of brand comparison, Samsung purchased a lot for gift purposes, had a high positive sentiment for price, and Apple had a high positive sentiment for battery, sound quality, connection, service, and delivery. The results of this study can be used as data for related industries as a result of research that can obtain improvements and insights on customer satisfaction, quality and market trends, including manufacturing, retail, marketers, and consumers.

• Smart Media Journal
• /
• v.12 no.8
• /
• pp.9-17
• /
• 2023

The development of information and communication technology and the improvement of the development and dissemination of smart devices have caused changes in the form of tourism, and the concept of smart tourism has since emerged. In this regard, researches related to smart tourism has been conducted in various fields such as policy implementation and surveys, but there is a lack of research on application reviews. This study collects Trip Advisor application review data in the Google Play Store to identify usage of the application and user satisfaction through Latent Dirichlet Allocation (LDA) topic modeling. The analysis results in four topics, two of which are positive and the other two are negative. We found that users were satisfied with the application's recommendation system, but were dissatisfied when the filters they set during search were not applied or that reviews were not published after updates of the application. We suggest more categories can be added to the application to provide users with different experiences. In addition, it is expected that user satisfaction can be improved by identifying problems within the application, including the filter function, and checking the application environment and resolving the error occurring during the application usage.