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  • Open Access


    Research on Multi-Objective Optimization Model of Industrial Microgrid Considering Demand Response Technology and User Satisfaction

    Junhui Li1,*, Jinxin Zhong1, Kailiang Wang1, Yu Luo1, Qian Han2, Jieren Tan2

    Energy Engineering, Vol.120, No.4, pp. 869-884, 2023, DOI:10.32604/ee.2023.021320

    Abstract In the process of wind power, coal power, and energy storage equipment participating in the operation of industrial microgrids, the stable operation of wind-storage industrial microgrids is guaranteed by considering demand response technology and user satisfaction. This paper firstly sorts out the status quo of microgrid operation optimization, and determines the main requirements for user satisfaction considering three types of load characteristics, demand response technology, power consumption benefit loss, user balance power purchase price and wind power consumption evaluation indicators in the system. Secondly, the operation architecture of the windstorage industrial microgrid is designed, and… More >

  • Open Access


    A Structural Topic Model for Exploring User Satisfaction with Mobile  Payments

    Jang Hyun Kim1,2,3, Jisung Jang1,3, Yonghwan Kim4, Dongyan Nan1,2,*

    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3815-3826, 2022, DOI:10.32604/cmc.2022.029507

    Abstract This study explored user satisfaction with mobile payments by applying a novel structural topic model. Specifically, we collected 17,927 online reviews of a specific mobile payment (i.e., PayPal). Then, we employed a structural topic model to investigate the relationship between the attributes extracted from online reviews and user satisfaction with mobile payment. Consequently, we discovered that “lack of reliability” and “poor customer service” tend to appear in negative reviews. Whereas, the terms “convenience,” “user-friendly interface,” “simple process,” and “secure system” tend to appear in positive reviews. On the basis of information system success theory, we… More >

  • Open Access


    Enhancing the Prediction of User Satisfaction with Metaverse Service Through Machine Learning

    Seon Hong Lee1, Haein Lee1, Jang Hyun Kim2,*

    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4983-4997, 2022, DOI:10.32604/cmc.2022.027943

    Abstract Metaverse is one of the main technologies in the daily lives of several people, such as education, tour systems, and mobile application services. Particularly, the number of users of mobile metaverse applications is increasing owing to the merit of accessibility everywhere. To provide an improved service, it is important to analyze online reviews that contain user satisfaction. Several previous studies have utilized traditional methods, such as the structural equation model (SEM) and technology acceptance method (TAM) for exploring user satisfaction, using limited survey data. These methods may not be appropriate for analyzing the users of… More >

  • Open Access


    Arabic Sentiment Analysis of Users’ Opinions of Governmental Mobile Applications

    Mohammed Hadwan1,2,3,*, Mohammed A. Al-Hagery4, Mohammed Al-Sarem5, Faisal Saeed5,6

    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4675-4689, 2022, DOI:10.32604/cmc.2022.027311

    Abstract Different types of pandemics that have appeared from time to time have changed many aspects of daily life. Some governments encourage their citizens to use certain applications to help control the spread of disease and to deliver other services during lockdown. The Saudi government has launched several mobile apps to control the pandemic and have made these apps available through Google Play and the app store. A huge number of reviews are written daily by users to express their opinions, which include significant information to improve these applications. The manual processing and extracting of information… More >

  • Open Access


    Computing the User Experience via Big Data Analysis: A Case of Uber Services

    Jang Hyun Kim1,2, Dongyan Nan1,*, Yerin Kim2, Min Hyung Park2

    CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 2819-2829, 2021, DOI:10.32604/cmc.2021.014922

    Abstract As of 2020, the issue of user satisfaction has generated a significant amount of interest. Therefore, we employ a big data approach for exploring user satisfaction among Uber users. We develop a research model of user satisfaction by expanding the list of user experience (UX) elements (i.e., pragmatic, expectation confirmation, hedonic, and burden) by including more elements, namely: risk, cost, promotion, anxiety, sadness, and anger. Subsequently, we collect 125,768 comments from online reviews of Uber services and perform a sentiment analysis to extract the UX elements. The results of a regression analysis reveal the following:… More >

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