• The Journal of Information Systems
• /
• v.29 no.4
• /
• pp.137-151
• /
• 2020

Purpose Domestic asset management fintech companies are expected to grow by leaps and bounds along with the implementation of the "Data bills." Contrary to the market fever, however, academic research is insufficient. Therefore, we want to analyze user reviews of asset management fintech companies that are expected to grow significantly in the future to derive strengths and complementary points of services that have been provided, and analyze key elements of asset management fintech companies. Design/methodology/approach To analyze large amounts of review text data, this study applied text mining techniques. Bank Salad and Toss, domestic asset management application services, were selected for the study. To get the data, app reviews were crawled in the online app store and preprocessed using natural language processing techniques. Topic Modeling and Aspect-Sentiment Analysis were used as analysis methods. Findings According to the analysis results, this study was able to derive the elements that asset management fintech companies should have. As a result of Topic Modeling, 7 topics were derived from Bank Salad and Toss respectively. As a result, topics related to function and usage and topics on stability and marketing were extracted. Sentiment Analysis showed that users responded positively to function-related topics, but negatively to usage-related topics and stability topics. Through this, we were able to extract the key elements needed for asset management fintech companies.

• Journal of Fashion Business
• /
• v.19 no.6
• /
• pp.82-100
• /
• 2015

Mobile fashion apps present much opportunity for marketers to engage consumers, however not all apps provide enough functions for their targeted audience. This study aims to determine how mobile fashion apps can be used to build brand community with consumer engagement. Qualitative data on fashion mobile apps were collected from the Apple app store and Android market during the spring and summer of 2015. A total of 110 fashion mobile apps were collected;, 50 apps were identified as apparel brands that either manufacture or sell apparel to consumers, which we categorized as "brand" fashion apps, and the remaining 60 were categorized as "non-brand" fashion apps. The result of the study can be summarized as below. The 60 non-brand fashion apps were grouped into 5 app types: shopping, searching, sharing, organizational, and informational. The main functions are for informational use and shopping needs, since at least half (31 apps) are used for either retrieving information or for shopping. However, in contrast, social networking and location were infrequent and not commonly utilized by these apps. The most common type of non-brand fashion apps available were shopping apps;, many shopping apps enable users to shop from several different websites and save their items into one universal shopping cart so that they only check out once. Most of these apps are informational and help consumers make more informed decisions on purchases;, in addition many offer location services to help consumers find these items in store. While these apps perform several functions, they do not link to social media. The 50 brand apps were grouped into 5 brand types: athletic, casual, fast fashion, luxury, and retailer. These apps were also checked for attributes to determine their functionality. The result shows that the main functions of brand fashion apps are for information (82% of the 50 apps) as well as location searching (72% of 50 apps). Conversely, these apps do not offer any photo sharing, and very few have organizational or community functions. Fashion mobile apps and m-marketing elements: To build brand community, mobile apps can be designed to motivate consumer's engagement with brands. The motivations of fashion mobile apps are useful in developing fashion mobile apps. Entertainment motives can be fulfilled with multimedia attributes, functionality motives are satisfied with organizational and location-based features, information motives with informational service, socialization with community and social network, learning and intellectual stimulation from informational attributes, and trend following through photo sharing. The 8 key attributes of mobile apps can correspond to the 4 m-marketing elements (i.e., Informative content, multimedia, interactions, and product promotions) that are further intertwined with m-branding elements. App Attributes and M-Marketing aim to Build Brand Community;, the eight key attributes can impact on 4 m-branding elements, which further contribute to building brand community by affecting consumers' perceptions of brands preference and advocacy, and their likelihood to be loyal.

• Journal of Information Technology Applications and Management
• /
• v.26 no.5
• /
• pp.41-56
• /
• 2019

Recently great loss of life and property is occurring because of fire, natural disaster, earth quake, tsunami and so on. People spend 80~90% of their time indoor environment like office, supermarket, campus. Therefore Indoor navigation and guidelines system became so essential for most of all. Incase of emergency we must be careful earlier, in such a cases 5G kind of new technology may also cannot work. So immediate action and quick routing notification for guidelines and protection is the most. Considering this issue We proposed indoor evacuating guidance system based on microcontroller Wi-Fi board for Indoor APP using mobile. Focusing various kind of technology like, ok google, voice search APP we purposed node architecture based system. When we listen fire alarm while living inside the room. Then to be safe we connect with server and start Arduino UNO+IoT ESP8266 Wi-Fi shield version1-IoT module to store data in MySQL DB server. We make application to escape out from the building up-to the three exits giving information from source point to destination. Our program can send information to the users emergency location and situations. For this when the user get sound or vibration in their mobile device it indicate fire out near by. At that time we update message from Arduino to DB server for the fixed current position inside the building which give routing signal for that fire out location by changing values from 0 to 1. We have user in point 10 where user is near by. Later we detect Wi-Fi signal form Nodemcu as room of each floor and try to connect with user. Main purpose of this paper is to save life of people in short time and find out the shortest path up to nearest exits in the time of emergencies and rescue them.

• Journal of Internet Computing and Services
• /
• v.11 no.6
• /
• pp.135-144
• /
• 2010

Apple recently announced a new policy about software development kit that banned the use of tools that convert apps built on other platforms into iPhone apps. Therefore, Adobe cannot develop their software to AppStore that inquire to the Department of Justice and the Federal Trade Commission about antitrust actions. Someone argue that Apple try to exclusive smartphone market such as the Microsoft antitrust lawsuit in 1998, but this case is essentially different. First, it need to define Apple's software development kit for iPhone and iPad is whether antitrust or not. Because of the characteristics of two-sided market in Smartphone Apple's iPhone cannot monopoly in cellphone or smartphone market, but it can be an antitrust in application store market. However, Apple re-announced new software development kit policy that shows positive results. Instead of hastily intervened regulatory agencies, the DOJ or the FTC, it is quite desirable that watching the interaction between companies that whether market failures or not and if it's harmful for consumer's benefit. Adobe attack Apple to advocate consumers and developers freedom of choice, but the most important thing is conclusion based on a comprehensive analysis need to objective point of view that Apple do whether antitrust act or not and damage to developers and consumers who are both side of platform.

• Journal of Information Technology Services
• /
• v.15 no.3
• /
• pp.195-209
• /
• 2016

IoT (Internet of Things) is a collective term referring to application services that provide information through sensors/devices connected to the internet. The real world application of IoT is expanding fast along with growing number of sensors/devices. However, since IoT application relies on vertical combination of sensors/devices networks, information sharing within IoT services remains unresolved challenge. Consequently, IoT sensors/devices demand high construction and maintenance costs, rendering the creation of new IoT services potentially expensive. One solution is to launch an IoT open market for information sharing similar to that of App Store for smart-phones. Doing so will efficiently allow novel IoT services to emerge across various industries, because developers can purchase licenses to access IoT resources directly via an open market. Sharing IoT resource information through an open market will create an echo-system conducive for easy utilization of resources and communication between IoT service providers, resource owners, and developers. This paper proposes the new business model of IoT open market for information sharing, and the requirements for ensuring security and standardization of open markets.

• The Journal of the Society of Korean Medicine Diagnostics
• /
• v.16 no.3
• /
• pp.23-32
• /
• 2012

Objectives: The objective of this study is to investigate and valuate currently provided database management systems (DBMS) of traditional Korean medical knowledge. Methods: We searched DBMS on the web and smart device application markets (Apple App Store and Google Play Store). Key words for searching were 'traditional medicine', 'acupuncture', 'moxibustion', 'herbal medicine', and '한의학'. We looked into each DBMS to find out its scopes and limits, and each was valuated according to its functionality, accessibility, and utility. Results: 186 DBMS of traditional Korean medical knowledge were investigated and 91% of them were applications for smart devices. Almost all DBMS provided acupuncture and herb information, and a small amount of DMBS provided prescription and research paper information. Functionality, accessibility, and utility valuation were performed by using scoring system from 0 to 2. Mean values of functionality, accessibility, and utility were 0.86, 1.29, and 1.09. Conclusions: On the whole, high accessibility and low functionality were found, and various data-calculating functions were not implemented. Further researches and developments about traditional Korean medical knowledge DBMS are necessary to provide correct traditional Korean medical information and to support the studies about Korean medicine.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.29 no.1
• /
• pp.195-203
• /
• 2019

Mobile applications stores such as Google Play Store and Apple App Store, are widely used to distribute a variety of applications including finance, shopping, and entertainment. Recently, however, vulnerabilities of the mobile applications are likely to violate users' privacy such as personal information leakage. In this paper, we classify mobile applications that can be download from mobile stores, and analyze the personal information that could be leaked when users are using the mobile applications. As a result of analysis, we found that personal information are leaked in some widely used mobile applications in practice. On the basis of our experiment results, we propose some mitigations to enhance security of the mobile applications and prevent leakage of personal information.

• Annual Conference of KIPS
• /
• 2022.05a
• /
• pp.98-101
• /
• 2022

Before the delivery platform was established, when consumers ordered by phone to the store, a rider hired by the store directly delivered it or delivered it through a delivery agency. However, as the food service culture develops, delivery platforms grow and more people seek convenience, making it difficult to see orders without delivery platforms now. When ordering using the delivery platform in this way, there is a shortage of riders and a negative effect of increasing delivery costs for supply. Therefore, we propose a joint delivery chat app to solve the problems of the current delivery system and reduce delivery costs.

• Smart Media Journal
• /
• v.12 no.11
• /
• pp.27-35
• /
• 2023

Fourth Industrial Revolution and COVID-19 pandemic have boosted the use of Government 24 app for public service complaints in the era of non-face-to-face interactions. there has been a growing influx of complaints and improvement demands from users of public apps. Furthermore, systematic management of public apps is deemed necessary. The aim of this study is to analyze the grievances of Government 24 app users, understand the current dissatisfaction among citizens, and propose potential improvements. Data were collected from the Google Play Store from May 2, 2013, to June 30, 2023, comprising a total of 6,344 records. Among these, 1,199 records with a rating of 1 and at least one 'thumbs-up' were used for topic modeling analysis. The analysis revealed seven topics: 'Issues with certificate issuance,' 'Website functionality and UI problems,' 'User ID-related issues,' 'Update problems,' 'Government employee app management issues,' 'Budget wastage concerns ((It's not worth even a single star) or (It's a waste of taxpayers' money)),' and 'Password-related problems.' Furthermore, the overall trend of these topics showed an increase until 2021, a slight decrease in 2022, but a resurgence in 2023, underscoring the urgency of updates and management. We hope that the results of this study will contribute to the development and management of public apps that satisfy citizens in the future.

• Journal of Intelligence and Information Systems
• /
• v.21 no.2
• /
• pp.1-18
• /
• 2015

Collaborative filtering(CF) algorithm has been popularly used for recommender systems in both academic and practical applications. A general CF system compares users based on how similar they are, and creates recommendation results with the items favored by other people with similar tastes. Thus, it is very important for CF to measure the similarities between users because the recommendation quality depends on it. In most cases, users' explicit numeric ratings of items(i.e. quantitative information) have only been used to calculate the similarities between users in CF. However, several studies indicated that qualitative information such as user's reviews on the items may contribute to measure these similarities more accurately. Considering that a lot of people are likely to share their honest opinion on the items they purchased recently due to the advent of the Web 2.0, user's reviews can be regarded as the informative source for identifying user's preference with accuracy. Under this background, this study proposes a new hybrid recommender system that combines with users' review mining. Our proposed system is based on conventional memory-based CF, but it is designed to use both user's numeric ratings and his/her text reviews on the items when calculating similarities between users. In specific, our system creates not only user-item rating matrix, but also user-item review term matrix. Then, it calculates rating similarity and review similarity from each matrix, and calculates the final user-to-user similarity based on these two similarities(i.e. rating and review similarities). As the methods for calculating review similarity between users, we proposed two alternatives - one is to use the frequency of the commonly used terms, and the other one is to use the sum of the importance weights of the commonly used terms in users' review. In the case of the importance weights of terms, we proposed the use of average TF-IDF(Term Frequency - Inverse Document Frequency) weights. To validate the applicability of the proposed system, we applied it to the implementation of a recommender system for smartphone applications (hereafter, app). At present, over a million apps are offered in each app stores operated by Google and Apple. Due to this information overload, users have difficulty in selecting proper apps that they really want. Furthermore, app store operators like Google and Apple have cumulated huge amount of users' reviews on apps until now. Thus, we chose smartphone app stores as the application domain of our system. In order to collect the experimental data set, we built and operated a Web-based data collection system for about two weeks. As a result, we could obtain 1,246 valid responses(ratings and reviews) from 78 users. The experimental system was implemented using Microsoft Visual Basic for Applications(VBA) and SAS Text Miner. And, to avoid distortion due to human intervention, we did not adopt any refining works by human during the user's review mining process. To examine the effectiveness of the proposed system, we compared its performance to the performance of conventional CF system. The performances of recommender systems were evaluated by using average MAE(mean absolute error). The experimental results showed that our proposed system(MAE = 0.7867 ~ 0.7881) slightly outperformed a conventional CF system(MAE = 0.7939). Also, they showed that the calculation of review similarity between users based on the TF-IDF weights(MAE = 0.7867) leaded to better recommendation accuracy than the calculation based on the frequency of the commonly used terms in reviews(MAE = 0.7881). The results from paired samples t-test presented that our proposed system with review similarity calculation using the frequency of the commonly used terms outperformed conventional CF system with 10% statistical significance level. Our study sheds a light on the application of users' review information for facilitating electronic commerce by recommending proper items to users.