• Culinary science and hospitality research
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• v.22 no.5
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• pp.64-81
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• 2016

The study, based on existing studies, explored influencing relationship, suggesting app service quality and user reviews as previous elements to affect use satisfaction about users' comments based on big data and reuse intention. The study includes a comparative analysis of existing studies. Based on such analysis results, the authors looked into app service quality elements perceived by gourmet restaurant app users and the role of user reviews, and suggested practical implications that can help the development and operation of gourmet restaurant app contents. The study subjects were male and female consumers who over 20 years old throughout Korea who had not a searched smartphone gourmet restaurant app in the three months preceding the survey. The subjects were selected from consumers who search the restaurantsby using restaurant apps like Mango plate, Dining code, Hot place, and selecting restaurants. Among them, consumers with experience using restaurants were finally selected for the survey. According to the results, reliability, informativity, and system capability, among service quality, had positive influences on app use satisfaction, while design and mobility had no effect. App use satisfaction had positive influences on app reuse intention. User comment informativity played a controlling role. The study explored the importance of app service quality and user review informativity as elements that affect continued use of gourmet restaurant apps by dining-out consumers.

• Information Systems Review
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• v.18 no.1
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• pp.125-140
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• 2016

Mobile applications (apps) play a significant role in the proliferation of smartphones. According to statistics from Apple, 100 million apps were downloaded in 2008. Since then, the number of cumulative app downloads have increased exponentially. By October 2014, 85 billion apps had been downloaded worldwide. Many studies have attempted to determine the factors that drive app downloads. However, unlike previous studies, we examine the effects of app updates on app rankings. To achieve this goal, we collected data on rankings (gross rankings and category rankings), update contents, reviewer ratings, and number of reviews on apps listed in the App Store. We then categorized app updates into functionality, reliability, and convenience updates following the buying hierarchy model. We found that functionality updates had a positive effect on app gross ranking whereas reliability updates had a positive effect on category ranking. Our study is the first to explore the effects of update content on app ranking. Moreover, our study provides a practical implication for mobile app developers, who should consider app updates in their product development strategy.

• International journal of advanced smart convergence
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• v.9 no.4
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• pp.124-131
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• 2020

As users of internet-based software applications increase, functional and non-functional problems for software applications are quickly exposed to user reviews. These user reviews are an important source of information for software improvement. User review mining has become an important topic of intelligent software engineering. This study proposes a user review mining method for software improvement. User review data collected by crawling on the app review page is analyzed to check user satisfaction. It analyzes the sentiment of positive and negative that users feel with a machine learning method. And it analyzes user requirement issues through topic analysis based on structural topic modeling. The user review mining process proposed in this study conducted a case study with the a non-face-to-face video conferencing app. Software improvement through user review mining contributes to the user lock-in effect and extending the life cycle of the software. The results of this study will contribute to providing insight on improvement not only for developers, but also for service operators and marketing.

• Culinary science and hospitality research
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• v.21 no.6
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• pp.167-181
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• 2015

The purpose of this study is to examine the effects of genuineness, usefulness, overstatement, and assentation of the smartphone majib app on trust, perceived risk, and use intention, and thereby suggest useful information for the mobile application. A survey was conducted from May 11, 2015 to June 30, 2015 targeting smartphone majib app users through convenience sampling. A total of 300 questionnaires were distributed, of which 275 were used for analysis after excluding 25 response for negligent or inappropriate responses. The results found that, first, of the review characteristics, genuineness and usefulness, assentation had positive (+) effects on trust, while overstatement had a negative (-) effect on trust. Second, of the review characteristics, only genuineness and usefulness had significant effects on perceived risk. Third, trust had a significant effect on use intention rather than on perceived risk. Fourth, trust and perceived risk had mediating effects on the relationship between the assentation of the majib smartphone app review characteristics and use intention.

• Fashion & Textile Research Journal
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• v.20 no.3
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• pp.293-303
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• 2018

Since 2009, consumers could access a new shopping channel called 'mobile shopping' with the generalization of smartphones. Mobile shopping (based on wireless communication technology), emphasizes convenience differentiated from internet shopping. A recent report introduced fashion products as powerful global drivers for mobile shopping sales. Korea and China have the highest percentage of consumer mobile shopping experiences compared to other countries. This study investigates the effects of cross-over shopping orientation, perception of app attributes, and flow on app loyalty that compared Korean and Chinese consumers. We obtained 652 usable questionnaires from two local college students; subsequently, data were analyzed by using factor analysis, Cronbach's alpha, and regression analysis using SPSS 21.0 Package. The study results showed that the cross-over shopping orientation affected perception of app attributes that included Review/Information, Design, Response and Product. Product only affected Flow, which reflected a high similarity between Korean and Chinese consumers. However, Korean and Chinese consumers showed remarkable differences in the factors related to app loyalty. Therefore, the results indicate that retailers of fashion products have developed strategies to improve mobile sales and increase the app loyalty of cross-over shopping orientation consumers.

• The Journal of Information Systems
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• v.30 no.4
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• pp.227-245
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• 2021

Purpose The purpose of this study is to examine the variables affecting user satisfaction through previous studies and to examine the differences between apps. Differences are based on factors that determine the quality of real estate service apps and derived by the topic modeling results. Design/methodology/approach This study conducts topic modeling to find factors affecting user satisfaction of real estate service apps using user reviews. Sentiment analysis is additionally conduct on the derived topics to examine the user responses. Findings Users give high sentiment scores for services that can manage factors such as usefulness of information, false sales, and hype. In addition, managing the basic services of app is an important factor influencing user satisfaction.

• Journal of Korea Trade
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• v.24 no.7
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• pp.1-28
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• 2020

Purpose - This paper empirically investigates the predictors and main determinants of consumers' ratings of mobile applications in the Google Play Store. Using a linear and nonlinear model comparison to identify the function of users' review, in determining application rating across countries, this study estimates the direct effects of users' reviews on the application rating. In addition, extending our modelling into a sentimental analysis, this paper also aims to explore the effects of review polarity and subjectivity on the application rating, followed by an examination of the moderating effect of user reviews on the polarity-rating and subjectivity-rating relationships. Design/methodology - Our empirical model considers nonlinear association as well as linear causality between features and targets. This study employs competing theoretical frameworks - multiple regression, decision-tree and neural network models - to identify the predictors and main determinants of app ratings, using data from the Google Play Store. Using a cross-validation method, our analysis investigates the direct and moderating effects of predictors and main determinants of application ratings in a global app market. Findings - The main findings of this study can be summarized as follows: the number of user's review is positively associated with the ratings of a given app and it positively moderates the polarity-rating relationship. Applying the review polarity measured by a sentimental analysis to the modelling, it was found that the polarity is not significantly associated with the rating. This result best applies to the function of both positive and negative reviews in playing a word-of-mouth role, as well as serving as a channel for communication, leading to product innovation. Originality/value - Applying a proxy measured by binomial figures, previous studies have predominantly focused on positive and negative sentiment in examining the determinants of app ratings, assuming that they are significantly associated. Given the constraints to measurement of sentiment in current research, this paper employs sentimental analysis to measure the real integer for users' polarity and subjectivity. This paper also seeks to compare the suitability of three distinct models - linear regression, decision-tree and neural network models. Although a comparison between methodologies has long been considered important to the empirical approach, it has hitherto been underexplored in studies on the app market.

• Smart Media Journal
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• v.12 no.6
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• pp.27-34
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• 2023

As Netflix usage has increased due to the COVID-19 pandemic, users' experiences with the service have also increased. Therefore, this study aims to conduct topic modeling analysis based on Netflix review data to explore the changes in Netflix user experience and service before and after the COVID-19 pandemic. We collected Netflix app review data from the Google Play Store using the Google Play Scraper library, and used topic modeling to examine keyword differences between app reviews before and after the pandemic. The analysis revealed four main topics: Netflix app features, Netflix content, Netflix service usage, and Netflix overall reviews. After the pandemic, when user experience increased, users tended to use more diverse and detailed keywords in their reviews. By using Netflix review data to analyze users' opinions, this study shows the changes in user experience of Netflix services before and after the pandemic, which can be used as a guide to strengthen competitiveness in the competitive OTT market.

• Journal of the Korea Society of Computer and Information
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• v.28 no.12
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• pp.259-266
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• 2023

In this paper, we propose an algorithm that can improve the accuracy performance of collaborative filtering using attribute-based opinion mining (ABOM). For the experiment, a total of 1,227 online consumer review data about smartphone apps from domestic smartphone users were used for analysis. After morpheme analysis using the KKMA (Kkokkoma) analyzer and emotional word analysis using KOSAC, attribute extraction is performed using LDA topic modeling, and the topic modeling results for each weighted review are used to add up the ratings of collaborative filtering and the sentiment score. MAE, MAPE, and RMSE, which are statistical model performance evaluations that calculate the average accuracy error, were used. Through experiments, we predicted the accuracy of online customers' app ratings (APP_Score) by combining traditional collaborative filtering among the recommendation algorithms and the attribute-based opinion mining (ABOM) technique, which combines LDA attribute extraction and sentiment analysis. As a result of the analysis, it was found that the prediction accuracy of ratings using attribute-based opinion mining CF was better than that of ratings implementing traditional collaborative filtering.

• BMB Reports
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• v.43 no.10
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• pp.656-663
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• 2010

Amyloid precursor protein (APP), which is critically involved in the pathogenesis of Alzheimer's disease (AD), is cleaved by gamma/epsilon-secretase activity and results in the generation of different lengths of the APP Intracellular C-terminal Domain (AICD). In spite of its small size and short half-life, AICD has become the focus of studies on AD pathogenesis. Recently, it was demonstrated that AICD binds to different intracellular binding partners ('adaptor protein'), which regulate its stability and cellular localization. In terms of choice of adaptor protein, phosphorylation seems to play an important role. AICD and its various adaptor proteins are thought to take part in various cellular events, including regulation of gene transcription, apoptosis, calcium signaling, growth factor, and $NF-{\kappa}B$ pathway activation, as well as the production, trafficking, and processing of APP, and the modulation of cytoskeletal dynamics. This review discusses the possible roles of AICD in the pathogenesis of neurodegenerative diseases including AD.