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博碩士論文 etd-0429123-155047 詳細資訊
Title page for etd-0429123-155047
論文名稱
Title
線上因素對知識型YouTube觀眾參與之影響-以社會認知理論的觀點
The Impact of Online Factors on Knowledge-Based YouTube Audience Participation: A Social Cognitive Theory Perspective
系所名稱
Department
畢業學年期
Year, semester
語文別
Language
學位類別
Degree
頁數
Number of pages
78
研究生
Author
指導教授
Advisor
召集委員
Convenor
口試委員
Advisory Committee
口試日期
Date of Exam
2023-05-12
繳交日期
Date of Submission
2023-05-29
關鍵字
Keywords
YouTube、知識型YouTube、社會認知理論、社交互動、觀眾參與
YouTube, knowledge-based YouTube, Social Cognitive Theory, Social Interaction, Audience Participation
統計
Statistics
本論文已被瀏覽 68 次,被下載 2
The thesis/dissertation has been browsed 68 times, has been downloaded 2 times.
中文摘要
隨著科技的發展,知識的獲取已經不受限於實體場域,全球第二大流量的網站YouTube已成為許多人獲取免費知識的重要管道,因此有越來越多人投入知識類型影片的創作,影片的流量是維持頻道運作的關鍵因素,因此本研究根據社會認知理論(SCT)發展本研究模型,欲了解影響觀眾頻道參與的因素,並且探討個人認知因素以及在該平台下的環境因素如何影響觀眾對頻道的參與行為。

本研究樣本收集以封閉式線上問卷方式進行,總共回收618份有效樣本。本研究結果顯示資訊自我效能正向影響結果預期與社交互動,進而影響觀眾參與行為,此外社交互動對資訊自我效能與參與有中介效果。

本研究結果有助於知識型YouTube頻道經營者,了解影響觀眾參與行為影響因素,能夠提供更多的社交互動與降低資訊分享的門檻以提高影片的流量,進一步發展和擴大頻道的影響力。
Abstract
With the development of technology, knowledge acquisition is no longer limited to physical spaces. YouTube, the second-largest website in the world by traffic, has become an important channel for many people to access free knowledge. As a result, an increasing number of people are involved in creating knowledge-based videos. Video traffic is a key factor in maintaining the operation of a channel. Therefore, this study developed a research model based on Social Cognitive Theory (SCT) to understand the factors that influence audience engagement with channels and how personal cognitive factors and environmental factors on the platform affect audience engagement behavior with channels.

The sample collection for this study was conducted through a closed online questionnaire, and a total of 618 valid samples were collected. The results of this study showed that information self-efficacy has a positive impact on outcome expectations and social interaction, which in turn affects audience engagement behavior. In addition, the results show that outcome expectations and social interaction mediate the relationship between information self-efficacy and engagement.

The findings of this study contribute to the understanding of factors influencing audience engagement behaviors for knowledge-based YouTube channel operators. It provides insights for facilitating increased social interaction and reducing information-sharing barriers to enhance video traffic, thereby further developing and expanding the influence of the channel.

目次 Table of Contents
論文審定書 i
論文公開授權書 ii
誌 謝 iii
摘要 iv
Abstract v
目錄 vi
圖目錄 vii
表目錄 viii
第一章 緒論 1
第一節 研究背景 1
第二節 研究動機 2
第三節 研究目的 4
第二章 文獻探討 5
第一節 YouTube 5
第二節 社會認知理論 6
第三節 研究模型與假說 12
第三章 研究方法 19
第一節 研究設計 19
第二節 研究量表發展 22
第三節 研究對象與問卷回收 24
第四節 資料分析 25
第四章 資料分析 27
第一節 敘述性統計分析 27
第二節 測量模型分析 31
第三節 假說驗證及結果 37
第五章 討論與結果 43
第一節 討論 43
第二節 研究貢獻 46
第三節 研究限制與未來研究方向 49
參考文獻 51
附錄一、專家效度問卷 60
附錄二、正式問卷 66

參考文獻 References
中文部分
潘家祺. (2020). 知識型YouTube影片的觀看動機與影片流暢度之影響因素:探索式研究 國立中正大學]. 嘉義縣. https://hdl.handle.net/11296/sk5dgf
知識型影音正夯,近5成民眾觀看YouTube是為「學習」,2017, https://www.bnext.com.tw/article/46905/knowledge-youtubers-are-hot-in-taiwan [Retrieved 2023/05]
Hahow好學校推「知識型輕直播」 百萬YouTuber、名人老闆齊開講,2021, https://reurl.cc/dedrZ8 [Retrieved 2023/05]
YouTube在台擴及1800萬名觀眾 公布國人觀影3趨勢,2022, https://reurl.cc/zAV3ma [Retrieved 2023/05]
2022 YouTube Brandcast:「短影音、連網電視、影音購物」三大趨勢,2022,https://taiwan.googleblog.com/2022/09/YouTube-Brandcast.html [Retrieved 2023/05]
台灣使用者行為大解密,2017,https://taiwan.googleblog.com/2017/11/google_22.html [Retrieved 2023/05]



英文部分
Aldallal, S. N., Yates, J. M., & Ajrash, M. (2019). Use of YouTube as a self-directed learning resource in oral surgery among undergraduate dental students: a cross-sectional descriptive study. Br J Oral Maxillofac Surg, 57(10), 1049-1052. https://doi.org/10.1016/j.bjoms.2019.09.010
Andrews, D. C. (2002). Audience-specific online community design. Commun. ACM, 45(4), 64–68. https://doi.org/10.1145/505248.505275
Balakrishnan, V., & Gan, C. L. (2016). Students’ learning styles and their effects on the use of social media technology for learning. Telematics and Informatics, 33(3), 808-821. https://doi.org/10.1016/j.tele.2015.12.004
Bandura, A. (1982). Self-efficacy mechanism in human agency. American Psychologist, 37, 122-147. https://doi.org/10.1037/0003-066X.37.2.122
Bandura, A. (1986). THE EXPLANATORY AND PREDICTIVE SCOPE OF SELF-EFFICACY THEORY. Journal of Social and Clinical Psychology, 4(3), 359-373. https://doi.org/10.1521/jscp.1986.4.3.359
Bandura, A. (1991). Social cognitive theory of self-regulation. Organizational Behavior and Human Decision Processes, 50(2), 248-287. https://doi.org/https://doi.org/10.1016/0749-5978(91)90022-L
Bandura, A. (1994). Social cognitive theory and exercise of control over HIV infection. Preventing AIDS: Theories and methods of behavioral interventions, 25-59.
Bandura, A. (1997). Self-efficacy: The exercise of control. W H Freeman/Times Books/ Henry Holt & Co.
Bandura, A. (1998). Health promotion from the perspective of social cognitive theory. Psychology and health, 13(4), 623-649.
Bandura, A. (2004). Health promotion by social cognitive means. Health Educ Behav, 31(2), 143-164. https://doi.org/10.1177/1090198104263660
Bao, Z., & Han, Z. (2019). What drives users’ participation in online social Q&A communities? An empirical study based on social cognitive theory. Aslib Journal of Information Management, 71(5), 637-656. https://doi.org/10.1108/ajim-01-2019-0002
Bardakcı, S. (2019). Exploring High School Students' Educational Use of YouTube. The International Review of Research in Open and Distributed Learning, 20(2). 20, 260-278. https://doi.org/10.19173/irrodl.v20i2.4074
Bateman, P. J., Gray, P. H., & Butler, B. S. (2011). Research Note—The Impact of Community Commitment on Participation in Online Communities. Information Systems Research, 22(4), 841-854. https://doi.org/10.1287/isre.1090.0265
Becker, J.-M., Ringle, C. M., Sarstedt, M., & Völckner, F. (2015). How collinearity affects mixture regression results. Marketing Letters, 26(4), 643-659. https://doi.org/10.1007/s11002-014-9299-9
Blanchard, A. L., & Markus, M. L. (2004). The experienced "sense" of a virtual community: characteristics and processes. SIGMIS Database, 35(1), 64–79. https://doi.org/10.1145/968464.968470
Bock, G.-W., Kankanhalli, A., & Sharma, S. (2006). Are Norms Enough? The Role of Collaborative Norms in Promoting Organizational Knowledge Seeking. EJIS, 15, 357-367. https://doi.org/10.1057/palgrave.ejis.3000630
Cabrera, A., & Cabrera, E. F. (2002). Knowledge-Sharing Dilemmas. Organization Studies, 23(5), 687-710. https://doi.org/10.1177/0170840602235001
Casaló, L. V., Flavián, C., & Guinalíu, M. (2014). Antecedents and Consequences of Consumer Participation in On-Line Communities: The Case of the Travel Sector. International Journal of Electronic Commerce, 15(2), 137-167. https://doi.org/10.2753/jec1086-4415150205
Chen, C.-C., & Lin, Y.-C. (2018). What drives live-stream usage intention? The perspectives of flow, entertainment, social interaction, and endorsement. Telematics and Informatics, 35(1), 293-303. https://doi.org/10.1016/j.tele.2017.12.003
Cheung, C. M. K., & Lee, M. K. O. (2007, 2007//). What Drives Members to Continue Sharing Knowledge in a Virtual Professional Community? The Role of Knowledge Self-efficacy and Satisfaction. Knowledge Science, Engineering and Management, Berlin, Heidelberg.
Chiang, H.-S. (2013). Continuous usage of social networking sites. Online Information Review, 37(6), 851-871. https://doi.org/10.1108/oir-08-2012-0133
Chiang, H.-S., & Hsiao, K.-L. (2015). YouTube stickiness: the needs, personal, and environmental perspective. Internet Research, 25(1), 85-106. https://doi.org/10.1108/IntR-11-2013-0236
Chin, W. W. (1998). The partial least squares approach for structural equation modeling. In Modern methods for business research. (pp. 295-336). Lawrence Erlbaum Associates Publishers.
Chin, W. W. (2010). How to Write Up and Report PLS Analyses. In V. Esposito Vinzi, W. W. Chin, J. Henseler, & H. Wang (Eds.), Handbook of Partial Least Squares: Concepts, Methods and Applications (pp. 655-690). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-540-32827-8_29
Chiu, C.-M., Hsu, M.-H., & Wang, E. T. G. (2006). Understanding knowledge sharing in virtual communities: An integration of social capital and social cognitive theories. Decision Support Systems, 42(3), 1872-1888. https://doi.org/10.1016/j.dss.2006.04.001
Cronbach, L. J. (1947). Test “reliability”: Its meaning and determination. Psychometrika, 12(1), 1-16. https://doi.org/10.1007/BF02289289
Curras-Perez, R., Ruiz-Mafe, C., & Sanz-Blas, S. (2014). Determinants of user behaviour and recommendation in social networks. Industrial Management & Data Systems, 114(9), 1477-1498. https://doi.org/10.1108/imds-07-2014-0219
Dubé, L., & Paré, G. (2003). Rigor in Information Systems Positivist Case Research: Current Practices. MIS Q., 27, 597-635.
Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error: Algebra and statistics. In: Sage Publications Sage CA: Los Angeles, CA.
Gable, S., Crnic, K., & Belsky, J. (1994). Coparenting within the family system: Influences on children's development. Family Relations: An Interdisciplinary Journal of Applied Family Studies, 43, 380-386. https://doi.org/10.2307/585368
Garrett, N. (2016). Mapping Self-Guided Learners Searches for Video Tutorials on YouTube. Journal of Educational Technology Systems, 44, 319-331. https://doi.org/10.1177/0047239515615851
Gist, M. E., & Mitchell, T. R. (1992). Self-efficacy: A theoretical analysis of its determinants and malleability. The Academy of Management Review, 17, 183-211. https://doi.org/10.2307/258770
Hair, J., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2014). A Primer on Partial Least Squares Structural Equation Modeling.
Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. (1998). Multivariate data analysis prentice hall. Upper Saddle River, NJ, 730.
Hair, J. F., Sarstedt, M., Ringle, C. M., & Mena, J. A. (2012). An assessment of the use of partial least squares structural equation modeling in marketing research. Journal of the Academy of Marketing Science, 40(3), 414-433. https://doi.org/10.1007/s11747-011-0261-6
Hair Jr, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2021). A primer on partial least squares structural equation modeling (PLS-SEM). Sage publications.
Haridakis, P., & Hanson, G. (2009). Social Interaction and Co-Viewing With YouTube: Blending Mass Communication Reception and Social Connection. Journal of Broadcasting & Electronic Media, 53(2), 317-335. https://doi.org/10.1080/08838150902908270
Hoffner, C., & Buchanan, M. (2005). Young Adults' Wishful Identification With Television Characters: The Role of Perceived Similarity and Character Attributes. Media Psychology, 7(4), 325-351. https://doi.org/10.1207/S1532785XMEP0704_2
Hofstede, G. (2007). Dimensionalizing Cultures: The Hofstede Model in Context. International Journal of Behavioral Medicine - INT J BEHAVIORAL MEDICINE, 2. https://doi.org/10.9707/2307-0919.1014
Hsu, C.-L., & Lin, J. C.-C. (2008). Acceptance of blog usage: The roles of technology acceptance, social influence and knowledge sharing motivation. Information & Management, 45(1), 65-74. https://doi.org/https://doi.org/10.1016/j.im.2007.11.001
Hsu, M.-H., Ju, T. L., Yen, C.-H., & Chang, C.-M. (2007). Knowledge sharing behavior in virtual communities: The relationship between trust, self-efficacy, and outcome expectations. International Journal of Human-Computer Studies, 65(2), 153-169. https://doi.org/10.1016/j.ijhcs.2006.09.003
Hulin, C., Netemeyer, R., & Cudeck, R. (2001). Can a Reliability Coefficient Be Too High? Journal of Consumer Psychology, 10, 55-58. https://doi.org/10.2307/1480474
Igbaria, M., & Iivari, J. (1995). The effects of self-efficacy on computer usage. Omega, 23(6), 587-605. https://doi.org/https://doi.org/10.1016/0305-0483(95)00035-6
Jarvis, C. B., MacKenzie, S. B., & Podsakoff, P. M. (2003). A Critical Review of Construct Indicators and Measurement Model Misspecification in Marketing and Consumer Research. Journal of Consumer Research, 30(2), 199-218. https://EconPapers.repec.org/RePEc:oup:jconrs:v:30:y:2003:i:2:p:199-218
Jia, L., Lin, C., Qin, Y., Pan, X., & Zhou, Z. (2022). Impact of monetary and non-monetary social functions on users' knowledge-sharing intentions in online social Q&A communities. Internet Research. https://doi.org/10.1108/intr-08-2021-0568
Jung, I., & Lee, Y. (2015). YouTube acceptance by university educators and students: a cross-cultural perspective. Innovations in education and teaching international, 52(3), 243-253.
Khan, M. L. (2017). Social media engagement: What motivates user participation and consumption on YouTube? Computers in Human Behavior, 66, 236-247. https://doi.org/10.1016/j.chb.2016.09.024
Kim, H.-M., Kim, M., & Cho, I. (2022). Home-based workouts in the era of COVID-19 pandemic: the influence of Fitness YouTubers' attributes on intentions to exercise. Internet Research. https://doi.org/10.1108/intr-03-2021-0179
Kim, J., Lee, C., & Elias, T. (2015). Factors affecting information sharing in social networking sites amongst university students. Online Information Review, 39(3), 290-309. https://doi.org/10.1108/oir-01-2015-0022
Kim, K. I., Park, H.-J., & Suzuki, N. (1990). Reward Allocations in the United States, Japan, and Korea: A Comparison of Individualistic and Collectivistic Cultures. The Academy of Management Journal, 33(1), 188-198. https://doi.org/10.2307/256358
Kock, N. (2015). Common method bias in PLS-SEM: A full collinearity assessment approach. International Journal of e-Collaboration, 11, 1-10. https://doi.org/10.4018/ijec.2015100101
Kurtin, K. S., O'Brien, N., Roy, D., & Dam, L. (2018). The development of parasocial interaction relationships on YouTube. The Journal of Social Media in Society, 7(1), 233-252.
Lawshe, C. H. (1975). A quantitative approach to content validity. Personnel psychology, 28(4), 563-575.
Lee, C., & Green, R. (1991). Cross-Cultural Examination of the Fishbein Behavioral Intensions Model. Journal of International Business Studies, 22, 289-305. https://doi.org/10.1057/palgrave.jibs.8490304
Lee, N., & Cadogan, J. W. (2013). Problems with formative and higher-order reflective variables. Journal of Business Research, 66(2), 242-247. https://doi.org/https://doi.org/10.1016/j.jbusres.2012.08.004
Lim, J. S., Choe, M.-J., Zhang, J., & Noh, G.-Y. (2020). The role of wishful identification, emotional engagement, and parasocial relationships in repeated viewing of live-streaming games: A social cognitive theory perspective. Computers in Human Behavior, 108. https://doi.org/10.1016/j.chb.2020.106327
Lin, H.-Y., & Hsu, M.-H. (2015). Using Social Cognitive Theory to Investigate Green Consumer Behavior. Business Strategy and the Environment, 24(5), 326-343. https://doi.org/10.1002/bse.1820
Lin, T.-C., & Huang, C.-C. (2008). Understanding knowledge management system usage antecedents: An integration of social cognitive theory and task technology fit. Information & Management, 45(6), 410-417. https://doi.org/10.1016/j.im.2008.06.004
Mathwick, C. (2002). Understanding the online consumer: A typology of online relational norms and behavior. Journal of Interactive Marketing, 16(1), 40-55. https://doi.org/10.1002/dir.10003
McLure Wasko, M., & Faraj, S. (2000). “It is what one does”: why people participate and help others in electronic communities of practice. The Journal of Strategic Information Systems, 9(2), 155-173. https://doi.org/https://doi.org/10.1016/S0963-8687(00)00045-7
Moghavvemi, S., Sulaiman, A., Jaafar, N. I., & Kasem, N. (2018). Social media as a complementary learning tool for teaching and learning: The case of youtube. The International Journal of Management Education, 16(1), 37-42. https://doi.org/10.1016/j.ijme.2017.12.001
Nunnally, J. C. (1978). An Overview of Psychological Measurement. In B. B. Wolman (Ed.), Clinical Diagnosis of Mental Disorders: A Handbook (pp. 97-146). Springer US. https://doi.org/10.1007/978-1-4684-2490-4_4
Oh, S. (2012). The characteristics and motivations of health answerers for sharing information, knowledge, and experiences in online environments. Journal of the American Society for Information Science and Technology, 63(3), 543-557. https://doi.org/10.1002/asi.21676
Peng, H., Tsai, C. C., & Wu, Y. T. (2006). University students' self‐efficacy and their attitudes toward the Internet: the role of students' perceptions of the Internet. Educational Studies, 32(1), 73-86. https://doi.org/10.1080/03055690500416025
Petter, S., Straub, D., & Rai, A. (2007). Specifying Formative Constructs in Information Systems Research. MIS Quarterly, 31(4), 623-656. https://doi.org/10.2307/25148814
Podsakoff, P. M., & Organ, D. W. (1986). Self-reports in organizational research: Problems and prospects. Journal of management, 12(4), 531-544.
Ramirez, E., Kulinna, P. H., & Cothran, D. (2012). Constructs of physical activity behaviour in children: The usefulness of Social Cognitive Theory. Psychology of Sport and Exercise, 13(3), 303-310. https://doi.org/10.1016/j.psychsport.2011.11.007
Rothkrantz, L. (2014). New didactical models in open and online learning based on social media. Proceedings of the International Conference on e-Learning, 9-18 9-18.
Sarstedt, M., Hair, J. F., Cheah, J.-H., Becker, J.-M., & Ringle, C. M. (2019). How to specify, estimate, and validate higher-order constructs in PLS-SEM. Australasian Marketing Journal (AMJ), 27(3), 197-211. https://doi.org/https://doi.org/10.1016/j.ausmj.2019.05.003
Schunk, D. H. (2012). Social cognitive theory. In APA educational psychology handbook, Vol 1: Theories, constructs, and critical issues. (pp. 101-123). American Psychological Association. https://doi.org/10.1037/13273-005
Schunk, D. H., & DiBenedetto, M. K. (2020). Motivation and social cognitive theory. Contemporary Educational Psychology, 60. https://doi.org/10.1016/j.cedpsych.2019.101832
Seddon, P. B., & Scheepers, R. (2015). Generalization in IS research: a critique of the conflicting positions of Lee & Baskerville and Tsang & Williams. In L. P. Willcocks, C. Sauer, & M. C. Lacity (Eds.), Formulating Research Methods for Information Systems: Volume 1 (pp. 179-209). Palgrave Macmillan UK. https://doi.org/10.1057/9781137509857_8
Shiau, W.-L., & Chau, P. Y. K. (2016). Understanding behavioral intention to use a cloud computing classroom: A multiple model comparison approach. Information & Management, 53(3), 355-365. https://doi.org/https://doi.org/10.1016/j.im.2015.10.004
Sokolova, K., & Perez, C. (2021). You follow fitness influencers on YouTube. But do you actually exercise? How parasocial relationships, and watching fitness influencers, relate to intentions to exercise. Journal of Retailing and Consumer Services, 58. https://doi.org/10.1016/j.jretconser.2020.102276
Tenenhaus, M. (2008). Component-based Structural Equation Modeling. Total Quality Management & Business Excellence, 19. https://doi.org/10.1080/14783360802159543
Tian, X. F., & Wu, R. Z. (2022). Determining Factors Affecting the Users' Participation of Online Health Communities: An Integrated Framework of Social Capital and Social Support. Front Psychol, 13, 823523. https://doi.org/10.3389/fpsyg.2022.823523
Tolbert, A. N., & Drogos, K. L. (2019). Tweens' Wishful Identification and Parasocial Relationships With YouTubers. Front Psychol, 10, 2781. https://doi.org/10.3389/fpsyg.2019.02781
Wasko, M. M., & Faraj, S. (2005). Why Should I Share? Examining Social Capital and Knowledge Contribution in Electronic Networks of Practice. MIS Quarterly, 29(1), 35-57. https://doi.org/10.2307/25148667
Wentzel, K. R. (2005). Peer Relationships, Motivation, and Academic Performance at School. In Handbook of competence and motivation. (pp. 279-296). Guilford Publications.
Zhang, C.-B., Li, Y.-N., Wu, B., & Li, D.-J. (2017). How WeChat can retain users: Roles of network externalities, social interaction ties, and perceived values in building continuance intention. Computers in Human Behavior, 69, 284-293. https://doi.org/10.1016/j.chb.2016.11.069
Zhang, K. Z. K., Benyoucef, M., & Zhao, S. J. (2015). Consumer participation and gender differences on companies’ microblogs: A brand attachment process perspective. Computers in Human Behavior, 44, 357-368. https://doi.org/10.1016/j.chb.2014.11.068
Zhang, X., Liu, S., Deng, Z., & Chen, X. (2017). Knowledge sharing motivations in online health communities: A comparative study of health professionals and normal users. Computers in Human Behavior, 75, 797-810. https://doi.org/10.1016/j.chb.2017.06.028
Zhang, Y., & Hiltz, S. (2003). Factors that Influence Online Relationship Development in a Knowledge Sharing Community.
Zimmerman, B. J. (1989). A social cognitive view of self-regulated academic learning. Journal of educational psychology, 81(3), 329.

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