簡易檢索 / 詳目顯示

研究生: 黃亦嘉
Huang, Yi Chia
論文名稱: 探討影響網路遊戲生命週期技術接受之因素
The Identification of Factors Influencing Adoptions of Online Games in Various Stage of Product Life Cycles
指導教授: 黃啟祐
Huang, Chi-Yo
口試委員: 羅乃維
Lo, Nai-Wei
何秀青
Ho, Mei HC
黃啟祐
Huang, Chi-Yo
口試日期: 2022/07/16
學位類別: 碩士
Master
系所名稱: 工業教育學系科技應用管理碩士在職專班
Department of Industrial Education_Continuing Education Master's Program of Technological Management
論文出版年: 2022
畢業學年度: 110
語文別: 英文
論文頁數: 109
中文關鍵詞: 線上遊戲產品生命週期主題建模結構方程式多準則決策
英文關鍵詞: Online Game, Product Life Cycle, Topic Model, Partial Least Squares Structural Equation Modeling, Multi-criteria decision-making
研究方法: 主題建模多準則決策分析法
DOI URL: http://doi.org/10.6345/NTNU202201632
論文種類: 學術論文
相關次數: 點閱:165下載:0
分享至:
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報
  • 隨著網際網路與行動網路日益普及與網速大幅提昇,即時性與消費門檻低的線上遊戲已成為人們重要的休閒活動之一。新型冠狀病毒疫情的蔓延,更加速推動線上遊戲的流行。儘管過去已有研究探討線上遊戲產業,然而研究影響各生命週期消費者接受線上遊戲產品關鍵因素之文獻極為缺乏,但相關因素對於相關產品行銷策略之訂定極為重要。
    因此,本研究旨在探討影響玩家使用線上遊戲意願的因素。本研究應用資料探勘、主題建模,探勘社群網站上網路遊戲之相關貼文,並擷取主題。其後,依據延伸整合型科技接受模式(Unified Theory of Acceptance and Use of Technology, UTAUT 2)提出假設,並將萃取之主題分群後,以階層式群落分析法分群,歸入模式之各個構面,再同時以結構方程式(Partial Least Squares Structural Equation Modeling,PLS-SEM)與結合決策實驗室(Decision Making Trial and Evaluation Laboratory,DEMATEL)之網路分析流程(DEMATEL-based Analytic Network Process,DANP)驗證各個主題間之關聯性或影響關係。
    依據實證研究的結果,結構方程模式與多準則決策分析方法所推衍之研究結果一致。影響使用者接受線上遊戲產品在生命週期各階段之關鍵因素均為使用意向與價格價值。本分析架構與研究結果可作為未來分析線上遊戲之消費行為與行銷策略之用,亦可作為分析其他社群媒體使用者之基礎。

    Abstract
    With the increasing popularity of the Internet and mobile networks and the substantial increase in network speed, online games with immediacy and low consumption thresholds have become one of the most important leisure activities for people. The spread of the new coronavirus epidemic has accelerated the popularity of online games. Although there have been studies on the online game industry in the past, the literature on the key factors affecting the acceptance of online game products by consumers in each life cycle is extremely lacking, but the relevant factors are extremely important for the formulation of relevant product marketing strategies.
    Therefore, this study aims to explore the factors that affect players' willingness to use online games. This research applies data mining, topic modeling, and mining related posts on online games on social networking sites, and extracts topics. Afterwards, hypotheses were proposed based on the Unified Theory of Acceptance and Use of Technology (UTAUT 2), and the extracted topics were grouped into groups by hierarchical cluster analysis, and then classified into various aspects of the model. At the same time, each theme was verified by Partial Least Squares Structural Equation Modeling (PLS-SEM) and DEMATEL-based Analytic Network Process (DANP) combined with Decision Making Trial and Evaluation Laboratory (DEMATEL). relationship or influence.
    According to the empirical research results, the structural equation model is consistent with the research results derived from the multi-criteria decision analysis method. The key factors affecting users' acceptance of online game products at all stages of the life cycle are usage intention and price value. This analysis framework and research results can be used as a basis for analyzing the consumption behavior and marketing strategies of online games in the future, and can also be used as a basis for analyzing other social media users.

    摘要 i Abstract ii Table of Contents iii List of Figures v List of Tables vi Chapter 1 Introduction 1 1.1 Research Backgrounds 1 1.2 Research Motivation 2 1.3 Research Purposes 3 1.4 Research Framework 4 1.5 Research Process 5 1.6 Limitations 6 1.7 Thesis Structure 6 Chapter 2 Literature Review 9 2.1 Online Game 9 2.2 Product Life Cycle 10 2.3 Text Mining 13 2.4 Topic Model 15 2.5 UTAUT 2 17 Chapter 3 Research Method 21 3.1 Procedures of Text Mining 21 3.2 Fitting Topics into UTAUT 2 24 3.3 Partial Least Squares based Structural Equation Model 24 3.4 DANP Method 25 Chapter 4 Empirical Study 27 4.1 The Background of Online Gaming 27 4.2 Data Acquisition and Processing 28 4.3 Result of LDA Topic Modeling Analysis 29 4.4 Results of PLS-SEM for Introduction Stage 35 4.5 Result of PLS-SEM for Growth Stage 43 4.6 Result of PLS-SEM for Maturity Stage 50 4.7 Result of PLS-SEM for Decline Stage 57 4.8 Derive the Influence Weight by the DANP for Introduction Stage 64 4.9 Derive the Influence Weight by the DANP for Growth Stage 69 4.10 Derive the Influence Weight by the DANP for Maturity Stage 74 4.11 Derive the Influence Weight by the DANP for Decline Stage 78 Chapter 5 Discussions 85 5.1 Implication of the Empirical Study Results 85 5.2 Theoretical Implication 88 5.3 Limitation and Suggestions for Future Study 89 Chapter 6 Conclusions 91 References 93 Appendix A 105

    Abbasi, A. Z., Rehman, U., Fayyaz, M. S., Ting, D. H., Shah, M. U., & Fatima, R. (2022). Using the playful consumption experience model to uncover behavioral intention to play multiplayer online battle arena games. Data Technologies and Applications, 56(2), 223- 246.
    Ahmed, A., & Sathish, A. S. (2017). Determinants of behavioral intention use behaviour and addiction towards social network games among Indian college students. Man in India, 97(4), 21-42.
    Albalawi, R., Yeap, T. H., & Benyoucef, M. (2020). Using topic modeling methods for short-text data: A comparative analysis. Frontiers in Artificial Intelligence, 3, 2.
    Alghifari, I., & Halim, R. (2020). Factors affecting expectancy for character growth in online games and their effect on gamer's loyalty. Journal of Economics, Business, & Accountancy Ventura, 22(3), 298-308.
    Amaya, A., Bach, R., Keusch, F., & Kreuter, F. (2021). New data sources in social science research: Things to know before working with Reddit data. Social Science Computer Review, 39(5), 943-960.
    Animesh, A., Pinsonneault, A., Yang, S.-B., & Oh, W. (2011). An odyssey into virtual worlds: exploring the impacts of technological and spatial environments. MIS Quarterly, 35(3), 789-810.
    Baabdullah, A. M. (2018). Consumer adoption of Mobile Social Network Games in Saudi Arabia: The role of social influence, hedonic motivation and trust. Technology in Society, 53, 91-102.
    Bagozzi, R.P., & Yi, Y.(1988), On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74–94
    Baker, R. (2010). Data Mining for education. International Encyclopedia of Education, 7(3), 112-118.
    Bai, X., Zhang, X., Li, K. X., Zhou, Y., & Yuen, K. F. (2021). Research topics and trends in the maritime transport: A structural topic model. Transport Policy, 102, 11-24.
    Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. The Journal of Machine Learning Research, 3, 993-1022.
    Buxbaum, O. (2016). Key insights into basic mechanisms of mental activity. Springer International Publishing.
    Byrne, B. M. (2013). Structural equation modeling with Mplus : Basic concepts, applications, and programming (3rd ed.). Routledge.
    Chang, C. C., & Chen, P. Y. (2018). Analysis of critical factors for social games based on extended technology acceptance model: a DEMATEL approach. Behaviour & Information Technology, 37(8), 774-785.
    Chen, M. S., Han, J., & Yu, P. S. (1996). Data mining: an overview from a database perspective. IEEE Transactions on Knowledge and Data Engineering, 8(6), 866-883.
    Chiu, W.Y., Tzeng, G. H., & Li, H. L. (2013). A new hybrid MCDM model combining DANP with VIKOR to improve e-store business. Knowledge-Based Systems, 37(1) , 48-61.
    Cohen, P, West, S. G., & Aiken, L. S.(2014). Applied multiple regression correlation analysis for the behavioral sciences. NY, USA: Psychology Press.
    Cook, D. (2007, May 13). The circle of life: An analysis of the game product lifecycle. The Game Developer. https://www.gamedeveloper.com/design/the-circle-of-life-an-analysis-of-the-game-product-lifecycle
    Davis, F. D. (1985). A technology acceptance model for empirically testing new end-user information systems: Theory and results. (Unpublished doctor's thesis). Massachusetts Institute of Technology. Massachusetts, USA.
    Duman, H., & Ozkara, B. Y. (2021). The impact of social identity on online game addiction: the mediating role of the fear of missing out (FoMO) and the moderating role of the need to belong. Current Psychology, 40(9), 4571-4580.
    Feldman, R., & Dagan, I. (1995). Knowledge discovery in textual databases. First Annual Conference on Knowledge Discovery and Data Mining. 95, 112-117.
    Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error: Algebra and statistics. Journal of Marketing Research, 18(3), 382-388
    Fu, C., Liu, W., & Chang, W. (2020). Data-driven multiple criteria decision making for diagnosis of thyroid cancer. Annals of Operations Research, 293(2), 833-862.
    García, S., Luengo, J., & Herrera, F. (2015). Data preprocessing in data mining. Cham, Switzerland: Springer International Publishing.
    Gefen, D., Straub, D., & Boudreau, M.C. (2000). Structural equation modeling and Regression: Guidelines for research practice. Communications of the Association for Information Systems, 4(1), 7.
    Gestos, M., Smith-Merry, J., & Campbell, A. (2018). Representation of women in video games: A systematic review of literature in consideration of adult female wellbeing. Cyberpsychology, Behavior, and Social Networking, 21(9), 535-541.
    Griffiths, T.L., Kemp, C., & Tenenbaum, J. B. (2008) Bayesian models of cognition. in Cambridge Handbook of Computational Psychology. Cambridge, UK. Cambridge University Press.
    Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2), 139-152.
    Hair, J.F., Risher, J.J., Sarstedt, M., & Ringle, C.M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2-24
    Hand, D. J. (1998). Data Mining: Statistics and More? The American Statistician, 52(2), 112-118.
    Hokroh, M., & Green, G. (2019). Online video games adoption: toward an online game adoption model. International Journal of Research in Business and Social Science, 8(4), 163-171
    Hotho, A., Nürnberger, A., & Paass, G. (2005). A brief survey of text mining. LDV Forum, 20(1), 19-62.
    Hou, K., Hou, T., & Cai, L.(2021). Public attention about COVID-19 on social media: An investigation based on data mining and text analysis. Personality and Individual Differences, 175, 110701.
    Hu, L, Bentler, P (1999) Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1-55.
    Hu, Y., Boyd-Graber, J., Satinoff, B., & Smith, A. (2014). Interactive topic modeling. Machine Learning, 95(3), 423-469.
    Huang, C. Y., & Tung, I. (2020). Strategies for heterogeneous R&D alliances of in vitro diagnostics firms in rapidly catching-up economies. International Journal of Environmental Research and Public Health, 17(10), 3688.
    Huang, C. Y., Hsieh, H. L., & Chen, H. (2020). Evaluating the investment projects of spinal medical device firms using the real option and DANP-mV based MCDM methods. International Journal of Environmental Research and Public Health, 17(9), 3335.
    Hwang, B. N., Huang, C. Y., & Wu, C. H. (2016) A TOE approach to establish a green supply chain adoption decision model in the semiconductor industry. Sustainability, 8(2), 168.
    Hwang, B. N., Huang, C.Y. & Yang, C. L. (2016). Determinants and their causal relationships affecting the adoption of cloud computing in science and technology institutions. Innovation, 18, 164-190.
    Ibrahim, N.F., & Wang, X. (2019). A text analytics approach for online retailing service improvement: Evidence from Twitter. Decision Support Systems, 121, 37-50.
    Jelodar, H., Wang, Y., Yuan, C., Feng, X., Jiang, X., Li, Y., & Zhao, L. (2019). Latent Dirichlet Allocation (LDA) and topic modeling: models, applications, a survey. Multimedia Tools and Applications, 78(11), 15169-15211.
    Jeong, E. J., Kim, D. J., & Lee, D. M. (2017). Why do some people become addicted to digital games more easily? A study of digital game addiction from a psychosocial health perspective. International Journal of Human–Computer Interaction, 33(3), 199-214.
    Järvinen, A. (2009). Game design for social networks: interaction design for playful dispositions. In S. N. Spencer & D. Davidson (Eds.), 2009 ACM SIGGRAPH Symposium on Video Games (pp.95-102). Association for Computing Machinery
    Jiménez-Marín, G., Madroñal, M. G., & Galiano-Coronil, A. (2021). Social media marketing and gamer events: the case of the launch of apex legends as a model of entrepreneurship. International Journal of Entrepreneurship, 25, 1-10.
    Kummer, L. B. M., Nievola, J. C., & Paraiso, E. C. (2017). Digital Game Usage Lifecycle: a systematic literature review. In Brazilian Symposium on Computer Games and Digital Entertainment, 16, 1163-1172.
    Lantano, F., Petruzzelli, A. M., & Panniello, U. (2022). Business model innovation in video-game consoles to face the threats of mobile gaming: Evidence from the case of Sony PlayStation. Technological Forecasting and Social Change, 174, 121210.
    Leahey, T. H. (2017). A history of psychology: From antiquity to modernity. NY, USA. Routledge.
    Lee, J., Kim, J., & Choi, J. Y. (2019). The adoption of virtual reality devices: The technology acceptance model integrating enjoyment, social interaction, and strength of the social ties. Telematics and Informatics, 39, 37-48.
    Lehdonvirta, V., Oksanen, A., Räsänen, P., & Blank, G. (2021). Social media, web, and panel surveys: using non‐probability samples in social and policy research. Policy & Internet, 13(1), 134-155.
    Li, J., Li, G., Zhu, X., & Yao, Y. (2020). Identifying the influential factors of commodity futures prices through a new text mining approach. Quantitative Finance, 20(12), 1967-1981.
    Li, F. F., & Perona, P. (2005). A bayesian hierarchical model for learning natural scene categories. Proceeding of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2, 524- 531.
    Liao, S. H., Chu, P. H., & Hsiao, P. Y. (2012). Data mining techniques and applications - A decade review from 2000 to 2011. Expert Systems withAapplications, 39(12), 11303-11311.
    Liou, J.H. (2012). Developing an integrated model for the selection of strategic alliance partners in the airline industry, Knowledge-Based Systems, 28, 59- 67
    Liu, D., Li, X., & Santhanam, R. (2013). Digital games and beyond: What happens when players compete. MIS Quarterly, 37(1), 111-124.
    Liu, C. H., Tzeng, G. H., & Lee, M.H. (2012). Improving tourism policy implementation—The use of hybrid MCDM models. Tour Manage, 33, 413–426.
    Matthews, L., Hair, J. O. E., & Matthews, R. (2018). PLS-SEM: The holy grail for advnaced analysis. Marketing Management Journal, 28(1), 1-14.
    Meirelles, P., Aguiar, C. S., Assis, F., Siqueira, R., & Goldman, A. (2019). A students’ perspective of native and cross-platform approaches for mobile application development. In S. Misra & O. Gervasi (Eds.), International conference on computational science and its applications (pp.586-601). NY, USA. Springer.
    Memon, M. A., Ramayah, T., Cheah, J. H., Ting, H., Chuah, F., & Cham, T. H. (2021). PLS-SEM statistical programs: a review. Journal of Applied Structural Equation Modeling, 5(1), 1-14.
    Moore, G. A., & McKenna, R. (1999) Crossing the chasm. NY, USA: Harper Business.
    Miller, M., Paige, N., Clair, G., & Eckhardt, C. (2019, August). An analysis of peer presence social group dynamics to enhance player engagement in multiplayer games. 2019 IEEE Conference on Games, London, United Kingdom.
    Murry, J. W., & Hammons, J. O. (1995). Delphi: A versatile methodology for conducting qualitative research, Review of Higher Education, 18, 423-436
    Pathak, A. R., Pandey, M., & Rautaray, S. (2021). Topic-level sentiment analysis of social media data using deep learning. Applied Soft Computing, 108, 107440
    Pathan, A. F., & Prakash, C. (2021). Unsupervised aspect extraction algorithm for opinion mining using topic modeling. Global Transitions Proceedings, 2(2), 492-499.
    Paul, M., & Girju, R. (2010). A two-dimensional topic-aspect model for discovering multi-faceted topics. Proceedings of the AAAI Conference on Artificial Intelligence. 24(1), 545-550.
    Ponweiser, M. (2012). Latent Dirichlet allocation in R. WU Vienna University of Economics and Business.
    Pressman, R. S. (2005). Software engineering: A practitioner approach (5th ed.). London, UK: Palgrave macmillan.
    Ramadan, R., & Widyani, Y. (2013, September). Game development life cycle guidelines. 2013 International conference on advanced computer science and information systems (pp. 95-100). IEEE.
    Ramírez-Correa, P., Rondán-Cataluña, F. J., Arenas-Gaitán, J., & Martín-Velicia, F. (2019). Analysing the acceptation of online games in mobile devices: An application of UTAUT2. Journal of Retailing and Consumer Services, 50, 85-93.
    Rosyati, T., Purwanto, M. R., Gumelar, G., Yulianti, R. T., & Mukharrom, T. (2020). Effects of games and how parents overcome addiction to children. Journal of Critical Reviews, 7(1), 65-67.
    Saaty, T.L. (1996). Decision making with dependence and feedback: the analytic network process. 4922(2). RWS Publications.
    Saaty, T. L. (2016). The analytic hierarchy and analytic network processes for the measurement of intangible criteria and for decision-making. In Multiple criteria decision analysis, 363-419. NY, USA: Springer.
    Salloum, S. A., Al-Emran, M., Monem, A. A., & Shaalan, K. (2017). A survey of text mining in social media: facebook and twitter perspectives. Advances in Science, Technology and Engineering Systems Journal, 2(1), 127-133.
    Sbalchiero, S., & Eder, M. (2020). Topic modeling, long texts and the best number of topics. Some Problems and solutions. Quality & Quantity, 54(4), 1095-1108.
    Sezgen, E., Mason, K.J., & Mayer R. (2019). Voice of airline passenger: a text mining approach to understand customer satisfaction. Jourmal of Air Transport Management, 77, 65-74
    Shaw, S., Quattry, S., Whitt, J., & Chirino, J. (2020). The Global Gaming Industry Takes Center Stage. Morgan Stanley Investment Management. Retrieved from https://www.morganstanley.com/im/publication/insights/articles/article_globalgamingindustrytakescentrestage_en.pdf
    Shen, D., Qin, C., Wang, C., Dong, Z., Zhu, H., & Xiong, H. (2021). Topic Modeling Revisited: A Document Graph-based Neural Network Perspective. Advances in Neural Information Processing Systems, 34, 14681-14693.
    Shin, S. J., Jeong, D., & Park, E. (2021). Effects of conflicts on outcomes: The case of multiplayer online games. Entertainment Computing, 38, 100407.
    Shringarpure, S., & Xing, E. P. (2009). mStruct: inference of population structure in light of both genetic admixing and allele mutations. Genetics, 182(2), 575-593.
    Speller, T. H. (2012). The business and dynamics of free-to-play social-casual game apps. Doctoral dissertation, Massachusetts Institute of Technology, Cambridge, MA.
    Šporčić, B., & Glavak-Tkalić, R. (2018). The relationship between online gaming motivation, self-concept clarity and tendency toward problematic gaming. Cyberpsychology: Journal of Psychosocial Research on Cyberspace, 12(1), 4.
    Talib, R., Hanif, M. K., Ayesha, S., & Fatima, F. (2016). Text mining: techniques, applications and issues. International Journal of Advanced Computer Science and Applications, 7 (11), 414-418.
    Tan, A.-H. (1999). Text mining: The state of the art and the challenges. In Proceedings of the pakdd 1999 workshop on knowledge disocovery from advanced databases, 8, 65-70.
    Teh, Y. W., Jordan, M. I., Beal, M. J., & Blei, D. M. (2006). Hierarchical dirichlet processes. Journal of the american statistical association, 101(476), 1566-1581.
    Thomassey, S., & Fiordaliso, A. (2006). A hybrid sales forecasting system based on clustering and decision trees. Decision Support Systems, 42(1), 408-421.
    Toh, S. H., Howie, E. K., Coenen, P., & Straker, L. M. (2019). From the moment I wake up I will use it… every day, very hour: a qualitative study on the patterns of adolescents’ mobile touch screen device use from adolescent and parent perspectives. BMC pediatrics, 19(1), 1-16
    Tong, Z., & Zhang, H. (2016). A text mining research based on LDA topic modelling.In J. Zizka & D. Nagamalai (Eds.), International onference on Computer Science, Engineering and Information Technology (pp. 201-210). AIRCC Publishing Corporation.
    Tontodimamma, A., Nissi, E., Sarra, A., & Fontanella, L. (2021). Thirty years of research into hate speech: topics of interest and their evolution. Scientometrics, 126(1), 157-179.
    Tsai, P. H., Lin, G. Y., Zheng, Y. L., Chen, Y. C., Chen, P. Z., & Su, Z. C. (2020). Exploring the effect of Starbucks' green marketing on consumers' purchase decisions from consumers’ perspective. Journal of Retailing and Consumer Services, 56, 102162.
    Valls Martínez, M.d.C.; Ramírez-Orellana, A. (2019). Patient satisfaction in the Spanish national health service: Partial least squares structural equation modeling. International Journal of. Environmental Research and Public Health, 16(24), 4886.
    Yang, C. L., Huang, C. Y., & Hsiao, Y. H. (2021). Using social media mining and pls-sem to examine the causal relationship between public environmental concerns and adaptation strategies. International Journal of Environmental Research and Public Health, 18(10), 5270.
    Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3) , 425-478.
    Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157-178.
    Vernon, R. (1966). International Investment and International Trade in the Product Cycle. Quarterly Journal of Economics, 80, 190-207.
    Wang, S., Paul, M. J., & Dredze, M. (2015). Social media as a sensor of air quality and public response in China. Journal of medical Internet research, 17(3), e22.
    Wang, Y. L. & Tzeng, G. H. (2012). Brand marketing for creating brand value based on a MCDM model combining DEMATEL with ANP and VIKOR methods. Expert Systems with Applications, 39(5), 5600–5615.
    Wang, Z., & Ye, X. (2018). Social media analytics for natural disaster management. International Journal of Geographical Information Science, 32(1), 49-72.
    Wei, X., & Croft, W. B. (2006). LDA-based document models for ad-hoc retrieval. Paper presented at the Proceedings of the 29th annual international ACM SIGIR conference on research and development in information retrieval.
    Wijman, T. (2021). Global Games Market Report. Retrieved from https://newzoo.com/insights/trend-reports/newzoo-global-games-market-report-2021-free-version
    Witten, I. H., & Frank, E. (2002). Data mining: practical machine learning tools and techniques with Java implementations. Acm Sigmod Record, 31(1), 76-77.
    Wohn, D. Y., & Freeman, G. (2020). Live streaming, playing, and money spending behaviors in eSports. Games and Culture, 15(1), 73-88.
    Wu, H. (2013). Examining students’ online interaction in a live video streaming environment using data mining and text mining. Computers in Human Behavior, 29(1), 90-102.
    Xu, C., Ryan, S., Prybutok, V., & Chao, W. (2012). It is not for fun: An examination of social network site usage. Information & Management, 49(5), 210-217.
    Xu, X. (2014). Understanding users’ continued use of online games: An application of UTAUT2 in social network games. The Sixth International Conferences on Advances in Multimedia.
    Yang, C. L., Huang, C. Y., & Hsiao, Y. H. (2021). Using social media mining and pls-sem to examine the causal relationship between public environmental concerns and adaptation strategies. International journal of environmental research and public health, 18(10), 5270.
    Yang, H.E., Wu C.C., & Wang, K. C. (2009). An empirical analysis of online game service satisfaction and loyalty. Expert Systems with Applications, 36(2-1),1816-1825.
    Yi, J., Lee, Y., & Kim, S. H. (2019). Determinants of growth and decline in mobile game diffusion. Journal of Business Research, 99, 363-37

    無法下載圖示 本全文未授權公開
    QR CODE