:::

詳目顯示

回上一頁
題名:振葉以尋根,觀瀾而索源:社交媒體用戶行為意向的驅動因素實證研究
作者:謝興政
作者(外文):XIE, XING-ZHENG
校院名稱:世新大學
系所名稱:傳播研究所(含博士學位學程)
指導教授:蔡念中
學位類別:博士
出版日期:2019
主題關鍵詞:社交媒體持續使用意向中斷使用意向滿足感負面資訊因素自我調節框架微博Social mediaContinued usage intentionDiscontinued usage intentionGratificationsNegative information-related incidentsSelf-regulation frameworkWeibo
原始連結:連回原系統網址new window
相關次數:
  • 被引用次數被引用次數:期刊(0) 博士論文(0) 專書(0) 專書論文(0)
  • 排除自我引用排除自我引用:0
  • 共同引用共同引用:0
  • 點閱點閱:1
移動互聯網的普及為社交媒體的發展提供了肥沃的土壤。近些年,社交媒體以其互動和使用者產制內容的特性迅速地於全球範圍內擴散,使得用戶逐漸地對社交媒體產生依賴,甚至是強烈的粘性。由於網路的限制,這些社交媒體平臺在中國大陸無法使用,也為本土的社交媒體平臺的成長提供了良好的契機。經歷了近十年的發展,社交媒體用戶數量逐步平穩,甚至有些社交媒體的用戶數量呈現下滑趨勢。
本研究分別以社交媒體的滿足感和負面資訊因素為切入點,對微博用戶的持續使用意向和中斷使用意向展開調查。透過滾雪球式抽樣方法,分別收集了481個和328個有效樣本。通過結構方程模型的分析發現,用戶的娛樂滿足感、心流體驗、歸屬感和習慣是用戶產生持續使用意願的直接因素,並且心流體驗、歸屬感和習慣能夠發揮出仲介變量的作用。在用戶中斷使用意願的影響因素方面,負面資訊因素、感知資訊超載和社交疲憊是重要因數。同時,通過模糊集質化比較分析,發現了用戶產生中斷使用意向的影響因素的組合。在研究過程中,本論文亦組織了兩場焦點訪談以驗證量化分析所得出的結果,並且發現資訊的豐富性是吸引微博用戶的主要原因,但用戶接觸到微博的管道有所差異。同時,具有不同意向的微博用戶對資訊的態度具有明顯不同。此外,大部分的用戶均未能將微博視為日常社交媒體的第一選擇。
此外,本研究還針對研究結果進行討論,尤其針對結果未支持的假設進行潛在的原因挖掘。研究結果有助於加深現有研究對社交媒體用戶使用意向的理解,也為後續研究提供了一定的研究幫助,同時對企業管理者的決策活動提供了必要的知識儲備和依據。
The popularity of mobile Internet has provided fertile soil for the development of social media. In recent years, due to the characteristics of interaction and user-generated content, social media has been rapidly spreading around the world, which causes that users gradually become dependent on social media and have strong stickiness. Due to the block of Internet, some social media platforms could not be used in China, which offers a superior opportunity for the growth of local social media platforms. After nearly 10 years, the number of social media users has been gradually stable, and some social media platforms even show a downward trend.
This dissertation investigates the antecedents of continued usage intention and discontinued usage intention from the gratifications and negative information-related incidents respectively in Weibo context. By snowball sampling strategy, it collects 481 and 328 valid samples respectively. According to the structural equation modelling, the entertainment acquisition, flow experience, sense of belonging, and habit are the determinants of Weibo continued usage intention; regarding with Weibo discontinued usage intention, negative information-related incidents, perceived information overload, and social media fatigue are the significant drivers. In addition, the configurations of antecedents for discontinued usage intention are presented by fuzzy-set qualitative comparative analysis. During the research process, two focus groups are conducted to examine the results from quantitative research. Information richness is the primary reason to use Weibo, but the channels to cognize Weibo are varied. Also, the attitude towards information is different between the two groups with continued usage intention and discontinued usage intention. Additionally, Weibo is not the first choice of social media in daily life.
Furthermore, this dissertation discusses the diversities of research findings between this research and prior studies, which aims to explore the potential reasons. The research results make contributions in both academic and practical fields, which provides the help to the further researchers, which also offers the necessary knowledge to the social media managers during the decision-making process.
Ahmed, Y. A., Ahmad, M. N., Ahmad, N., & Zakaria, N. H. (2019). Social media for knowledge-sharing: A systematic literature review. Telematics and Informatics, 37, 72-112. doi:https://doi.org/10.1016/j.tele.2018.01.015
Åhsberg, E. (2000). Dimensions of fatigue in different working populations. Scandinavian Journal of Psychology, 41(3), 231-241. doi:10.1111/1467-9450.00192
Ahuja, M. K., Chudoba, K. M., Kacmar, C. J., McKnight, D. H., & George, J. F. (2007). IT Road Warriors: Balancing Work-Family Conflict, Job Autonomy, and Work Overload to Mitigate Turnover Intentions. MIS Quarterly, 31(1), 1-17. doi:10.2307/25148778
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211. doi:https://doi.org/10.1016/0749-5978(91)90020-T
Alexander, D. E. (2014). Social Media in Disaster Risk Reduction and Crisis Management. Science and Engineering Ethics, 20(3), 717-733. doi:10.1007/s11948-013-9502-z
Amoroso, D., & Lim, R. (2017). The mediating effects of habit on continuance intention. International Journal of Information Management, 37(6), 693-702. doi:https://doi.org/10.1016/j.ijinfomgt.2017.05.003
Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411-423. doi:10.1037/0033-2909.103.3.411
Armstrong, J. S. (2012). Illusions in regression analysis. International Journal of Forecasting, 28(3), 689-694. doi:https://doi.org/10.1016/j.ijforecast.2012.02.001
AskCI. (2018). The report on Weibo user in the first half of 2018: the number of Weibo users reached 337 million.
Ayyagari, R., Grover, V., & Purvis, R. (2011). Technostress: technological antecedents and implications %J MIS Q. 35(4), 831-858.
Bae, M. (2018). Understanding the effect of the discrepancy between sought and obtained gratification on social networking site users' satisfaction and continuance intention. Computers in Human Behavior, 79, 137-153. doi:https://doi.org/10.1016/j.chb.2017.10.026
Bagozzi, R. P. (1986). Attitude formation under the theory of reasoned action and a purposeful behaviour reformulation. 25(2), 95-107. doi:10.1111/j.2044-8309.1986.tb00708.x
Bagozzi, R. P. (1992). The self-regulation of attitudes, intentions, and behavior. Social Psychology Quarterly, 55(2), 178-204. doi:10.2307/2786945
Bandura, A. (1986). Social Foundation of Thought and Action: A Social-cognitive View. Englewood Cliffs, NJ: Prentice-Hall.
Barbour, R. S. (2008). Doing Focus Groups. London: SAGE Publications.
Bartlett, F. (1953). Psychological criteria of fatigue. In Symposium on fatigue. (pp. 1-5). Oxford, England: H. K. Lewis & Co.
Basak, E., & Calisir, F. (2015). An empirical study on factors affecting continuance intention of using Facebook. Computers in Human Behavior, 48, 181-189. doi:https://doi.org/10.1016/j.chb.2015.01.055
Baumgartner, H., & Homburg, C. (1996). Applications of structural equation modeling in marketing and consumer research: A review. International Journal of Research in Marketing, 13(2), 139-161. doi:10.1016/0167-8116(95)00038-0
Bhattacherjee, A. (2001). Understanding Information Systems Continuance: An Expectation-Confirmation Model. MIS Quarterly, 25(3), 351-370. doi:10.2307/3250921
Brass, D. J. (1985). Men's and Women's Networks: A Study of Interaction Patterns and Influence In an Organization. 28(2), 327-343. doi:10.5465/256204
Bright, L. F., Kleiser, S. B., & Grau, S. L. (2015). Too much Facebook? An exploratory examination of social media fatigue. Computers in Human Behavior, 44, 148-155. doi:https://doi.org/10.1016/j.chb.2014.11.048
Brislin, R. W. (1970). Back-Translation for Cross-Cultural Research. Journal of Cross-Cultural Psychology, 1(3), 185-216. doi:10.1177/135910457000100301
Bryman, A. (2004). Quantity and quality in social research. London: Routledge.
Byrne, B. M. (2016). Structural equation modeling with AMOS: Basic concepts, applications, and programming: Routledge.
Cao, X., & Sun, J. (2018). Exploring the effect of overload on the discontinuous intention of social media users: An S-O-R perspective. Computers in Human Behavior, 81, 10-18. doi:https://doi.org/10.1016/j.chb.2017.11.035
Casaló, L. V., Flavián, C., & Guinalíu, M. (2010). Determinants of the intention to participate in firm-hosted online travel communities and effects on consumer behavioral intentions. Tourism Management, 31(6), 898-911. doi:https://doi.org/10.1016/j.tourman.2010.04.007
Cenfetelli, R. T., & Schwarz, A. (2011). Identifying and Testing the Inhibitors of Technology Usage Intentions. Information Systems Research, 22(4), 808-823. doi:10.1287/isre.1100.0295
Chai, S., & Kim, M. (2012). A socio-technical approach to knowledge contribution behavior: An empirical investigation of social networking sites users. International Journal of Information Management, 32(2), 118-126. doi:https://doi.org/10.1016/j.ijinfomgt.2011.07.004
Chang, M.-L., & Cheng, C.-F. (2014). How balance theory explains high-tech professionals' solutions of enhancing job satisfaction. Journal of Business Research, 67(9), 2008-2018. doi:https://doi.org/10.1016/j.jbusres.2013.10.010
Chaouali, W. (2016). Once a user, always a user: Enablers and inhibitors of continuance intention of mobile social networking sites. Telematics and Informatics, 33(4), 1022-1033. doi:https://doi.org/10.1016/j.tele.2016.03.006
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. doi:https://doi.org/10.1016/j.tele.2017.12.003
Chen, C., Zhang, K. Z. K., Gong, X., Zhao, S. J., Lee, M. K. O., & Liang, L. (2017). Understanding compulsive smartphone use: An empirical test of a flow-based model. International Journal of Information Management, 37(5), 438-454. doi:https://doi.org/10.1016/j.ijinfomgt.2017.04.009
Chen, H., Chiang, R. H. L., & Storey, V. C. (2012). Business intelligence and analytics: from big data to big impact %J MIS Q. 36(4), 1165-1188.
Chen, H., Wigand, R. T., & Nilan, M. (2000). Exploring Web users’ optimal flow experiences. Information Technology & People, 13(4), 263-281. doi:doi:10.1108/09593840010359473
Cheong, H. J., & Morrison, M. A. (2008). Consumers’ Reliance on Product Information and Recommendations Found in UGC. Journal of Interactive Advertising, 8(2), 38-49. doi:10.1080/15252019.2008.10722141
Cheung, C. M. K., Chiu, P.-Y., & Lee, M. K. O. (2011). Online social networks: Why do students use facebook? Computers in Human Behavior, 27(4), 1337-1343. doi:https://doi.org/10.1016/j.chb.2010.07.028
Cho, C.-H. (2004). Why do people avoid advertising on the Internet? Journal of Advertising, 33(4), 89-97. doi:10.1080/00913367.2004.10639175
Cho, C.-H., & Cheon, H. J. (2004). Why do people avoid advertising on the Internet? Journal of Advertising, 33(4), 89-97. doi:10.1080/00913367.2004.10639175
Cho, Y.-C., & Tsay, S.-L. (2004). The effect of acupressure with massage on fatigue and depression in patients with end-stage renal disease. The journal of nursing research : JNR, 12(1), 51-59.
Choi, S. (2018). The roles of media capabilities of smartphone-based SNS in developing social capital. Behaviour & Information Technology, 1-12. doi:10.1080/0144929X.2018.1546903
CNNIC. (2019). The 43rd statistical report on Internet development in China. Retrieved from http://www.cnnic.net.cn/hlwfzyj/hlwxzbg/hlwtjbg/201902/t20190228_70645.htm
Csikszentmihalyi, M. (1975). Beyond boredom and anxiety. San Francisco: Jossey-Bass.
Daft, R. L., Lengel, R. H., & Trevino, L. K. (1987). Message Equivocality, Media Selection, and Manager Performance: Implications for Information Systems. MIS Quarterly, 11(3), 355-366. doi:10.2307/248682
Daft, R. L., & Macintosh, N. B. (1981). A Tentative Exploration into the Amount and Equivocality of Information Processing in Organizational Work Units. Administrative Science Quarterly, 26(2), 207-224. doi:10.2307/2392469
Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319-340. doi:10.2307/249008
Debatin, B., Lovejoy, J. P., Horn, A.-K., & Hughes, B. N. (2009). Facebook and Online Privacy: Attitudes, Behaviors, and Unintended Consequences. Journal of Computer-Mediated Communication, 15(1), 83-108. doi:10.1111/j.1083-6101.2009.01494.x %J Journal of Computer-Mediated Communication
Dhir, A., Yossatorn, Y., Kaur, P., & Chen, S. (2018). Online social media fatigue and psychological wellbeing—A study of compulsive use, fear of missing out, fatigue, anxiety and depression. International Journal of Information Management, 40, 141-152. doi:https://doi.org/10.1016/j.ijinfomgt.2018.01.012
Diddi, A., & LaRose, R. (2006). Getting Hooked on News: Uses and Gratifications and the Formation of News Habits Among College Students in an Internet Environment. Journal of Broadcasting & Electronic Media, 50(2), 193-210. doi:10.1207/s15506878jobem5002_2
EdisonResearch. (2018). Facebook declines for the first time in infinite dial historyRetrieved from https://www.edisonresearch.com/facebook-declines-first-time-infinite-dial-history/
Edwards, S. M., Li, H., & Lee, J.-H. (2002). Forced Exposure and Psychological Reactance: Antecedents and Consequences of the Perceived Intrusiveness of Pop-Up Ads. Journal of Advertising, 31(3), 83-95. doi:10.1080/00913367.2002.10673678
Eppler, M. J., & Mengis, J. (2004). The Concept of Information Overload: A Review of Literature from Organization Science, Accounting, Marketing, MIS, and Related Disciplines. The Information Society, 20(5), 325-344. doi:10.1080/01972240490507974
Eroglu, S. A., Machleit, K. A., & Davis, L. M. (2001). Atmospheric qualities of online retailing: A conceptual model and implications. Journal of Business Research, 54(2), 177-184. doi:https://doi.org/10.1016/S0148-2963(99)00087-9
Esteban-Millat, I., Martínez-López, F. J., Huertas-García, R., Meseguer, A., & Rodríguez-Ardura, I. (2014). Modelling students' flow experiences in an online learning environment. Computers & Education, 71, 111-123. doi:https://doi.org/10.1016/j.compedu.2013.09.012
Estrada-Jiménez, J., Parra-Arnau, J., Rodríguez-Hoyos, A., & Forné, J. (2019). On the regulation of personal data distribution in online advertising platforms. Engineering Applications of Artificial Intelligence, 82, 13-29. doi:https://doi.org/10.1016/j.engappai.2019.03.013
Eysenck, M. W., & Calvo, M. G. (1992). Anxiety and Performance: The Processing Efficiency Theory. Cognition and Emotion, 6(6), 409-434. doi:10.1080/02699939208409696
Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behavior: An introduction to theory and research. Reading, MA: Addison-Wesley.
Fornell, C., & Larcker, D. F. (1981). Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of marketing research, 18(1), 39-50. doi:10.2307/3151312
Gan, C., & Li, H. (2018). Understanding the effects of gratifications on the continuance intention to use WeChat in China: A perspective on uses and gratifications. Computers in Human Behavior, 78, 306-315. doi:https://doi.org/10.1016/j.chb.2017.10.003
Gang, K., & Ravichandran, T. (2015). Exploring the Determinants of Knowledge Exchange in Virtual Communities. IEEE Transactions on Engineering Management, 62(1), 89-99. doi:10.1109/TEM.2014.2376521
Gao, Q., & Feng, C. (2016). Branding with social media: User gratifications, usage patterns, and brand message content strategies. Computers in Human Behavior, 63, 868-890. doi:https://doi.org/10.1016/j.chb.2016.06.022
Gao, W., Liu, Z., Guo, Q., & Li, X. (2018). The dark side of ubiquitous connectivity in smartphone-based SNS: An integrated model from information perspective. Computers in Human Behavior, 84, 185-193. doi:https://doi.org/10.1016/j.chb.2018.02.023
Gerbing, D. W., & Anderson, J. C. (1988). An Updated Paradigm for Scale Development Incorporating Unidimensionality and Its Assessment. Journal of Marketing Research, 25(2), 186-192. doi:10.2307/3172650
GlobalWebIndex. (2019). Twitter: the number of daily active users increased 11% year on year to 134 million in the first quarter of 2019. Retrieved from http://www.199it.com/archives/867851.html
Guo, J., Liu, Z., & Liu, Y. (2016). Key success factors for the launch of government social media platform: Identifying the formation mechanism of continuance intention. Computers in Human Behavior, 55, 750-763. doi:10.1016/j.chb.2015.10.004
Guo, Y. M., & Poole, M. S. (2009). Antecedents of flow in online shopping: a test of alternative models. 19(4), 369-390. doi:10.1111/j.1365-2575.2007.00292.x
Ha, L. (1996). Observations: Advertising Clutter in Consumer Magazines: Dimensions and Effects. Journal of Advertising Research, 36(4), 76-84.
Ha, L., & McCann, K. (2008). An integrated model of advertising clutter in offline and online media. International Journal of Advertising, 27(4), 569-592. doi:10.2501/S0265048708080153
Hagerty, B. M. K., Lynch-Sauer, J., Patusky, K. L., Bouwsema, M., & Collier, P. (1992). Sense of belonging: A vital mental health concept. Archives of Psychiatric Nursing, 6(3), 172-177. doi:https://doi.org/10.1016/0883-9417(92)90028-H
Han, B. (2018). Social Media Burnout: Definition, Measurement Instrument, and Why We Care. Journal of Computer Information Systems, 58(2), 122-130. doi:10.1080/08874417.2016.1208064
Hausman, A. V., & Siekpe, J. S. (2009). The effect of web interface features on consumer online purchase intentions. Journal of Business Research, 62(1), 5-13. doi:https://doi.org/10.1016/j.jbusres.2008.01.018
Hershberger, S. L. (2003). The Growth of Structural Equation Modeling: 1994–2001. STRUCTURAL EQUATION MODELING, 10(1), 35-46.
Hoffman, D. L., & Novak, T. P. (1996). Marketing in Hypermedia Computer-Mediated Environments: Conceptual Foundations. 60(3), 50-68. doi:10.1177/002224299606000304
Holbert, R. L., & Stephenson, M. T. (2003). The Importance of Indirect Effects in Media Effects Research: Testing for Mediation in Structural Equation Modeling. Journal of Broadcasting & Electronic Media, 47(4), 556-572. doi:10.1207/s15506878jobem4704_5
Hsiao, C.-H., Chang, J.-J., & Tang, K.-Y. (2016). Exploring the influential factors in continuance usage of mobile social Apps: Satisfaction, habit, and customer value perspectives. Telematics and Informatics, 33(2), 342-355. doi:https://doi.org/10.1016/j.tele.2015.08.014
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. doi:https://doi.org/10.1016/j.im.2007.11.001
Hsu, M.-H., Chang, C.-M., & Chuang, L.-W. (2015). Understanding the determinants of online repeat purchase intention and moderating role of habit: The case of online group-buying in Taiwan. International Journal of Information Management, 35(1), 45-56. doi:https://doi.org/10.1016/j.ijinfomgt.2014.09.002
Hu, H.-f., & Krishen, A. S. (2019). When is enough, enough? Investigating product reviews and information overload from a consumer empowerment perspective. Journal of Business Research, 100, 27-37. doi:https://doi.org/10.1016/j.jbusres.2019.03.011
Huang, C. (2010). Internet Use and Psychological Well-being: A Meta-Analysis. Cyberpsychology, Behavior, and Social Networking, 13(3), 241-249. doi:10.1089/cyber.2009.0217
Huang, L.-Y., Hsieh, Y.-J., & Wu, Y.-C. J. (2014). Gratifications and social network service usage: The mediating role of online experience. Information & Management, 51(6), 774-782. doi:https://doi.org/10.1016/j.im.2014.05.004
Hung, K.-p., Peng, N., & Chen, A. (2019). Incorporating on-site activity involvement and sense of belonging into the Mehrabian-Russell model – The experiential value of cultural tourism destinations. Tourism Management Perspectives, 30, 43-52. doi:https://doi.org/10.1016/j.tmp.2019.02.003
Hur, K., Kim, T. T., Karatepe, O. M., & Lee, G. (2017). An exploration of the factors influencing social media continuance usage and information sharing intentions among Korean travellers. Tourism Management, 63, 170-178. doi:https://doi.org/10.1016/j.tourman.2017.06.013
Iacobucci, D. (2010). Structural equations modeling: Fit Indices, sample size, and advanced topics. Journal of Consumer Psychology, 20(1), 90-98. doi:https://doi.org/10.1016/j.jcps.2009.09.003
iResearch. (2018). Weibo has more than 400 million monthly active users. Retrieved from http://tech.ifeng.com/a/20180514/44990842_0.shtml
Jackson, L. A., Zhao, Y., III, A. K., Fitzgerald, H. E., Harold, R., & Eye, A. V. (2008). Race, Gender, and Information Technology Use: The New Digital Divide. 11(4), 437-442. doi:10.1089/cpb.2007.0157
Jiang, C., Zhao, W., Sun, X., Zhang, K., Zheng, R., & Qu, W. (2016). The effects of the self and social identity on the intention to microblog: An extension of the theory of planned behavior. Computers in Human Behavior, 64, 754-759. doi:https://doi.org/10.1016/j.chb.2016.07.046
Kang, Y. S., & Lee, H. (2010). Understanding the role of an IT artifact in online service continuance: An extended perspective of user satisfaction. Computers in Human Behavior, 26(3), 353-364. doi:https://doi.org/10.1016/j.chb.2009.11.006
Kang, Y. S., Min, J., Kim, J., & Lee, H. (2013). Roles of alternative and self-oriented perspectives in the context of the continued use of social network sites. International Journal of Information Management, 33(3), 496-511. doi:https://doi.org/10.1016/j.ijinfomgt.2012.12.004
Kaplan, A. M., & Haenlein, M. (2010). Users of the world, unite! The challenges and opportunities of Social Media. Business Horizons, 53(1), 59-68. doi:https://doi.org/10.1016/j.bushor.2009.09.003
Karr-Wisniewski, P., & Lu, Y. (2010). When more is too much: Operationalizing technology overload and exploring its impact on knowledge worker productivity. Computers in Human Behavior, 26(5), 1061-1072. doi:https://doi.org/10.1016/j.chb.2010.03.008
Katz, E., Blumler, J. G., & Gurevitch, M. (1973). Uses and Gratifications Research. The Public Opinion Quarterly, 37(4), 509-523.
Kim, B. (2011). Understanding Antecedents of Continuance Intention in Social-Networking Services. Cyberpsychology, Behavior, and Social Networking, 14(4), 199-205. doi:10.1089/cyber.2010.0009
Kim, B., & Kim, Y. (2019). Facebook versus Instagram: How perceived gratifications and technological attributes are related to the change in social media usage. The Social Science Journal, 56(2), 156-167. doi:https://doi.org/10.1016/j.soscij.2018.10.002
Kim, E., Lee, J.-A., Sung, Y., & Choi, S. M. (2016). Predicting selfie-posting behavior on social networking sites: An extension of theory of planned behavior. Computers in Human Behavior, 62, 116-123. doi:https://doi.org/10.1016/j.chb.2016.03.078
Kim, J., Spielmann, N., & McMillan, S. J. (2012). Experience effects on interactivity: Functions, processes, and perceptions. Journal of Business Research, 65(11), 1543-1550. doi:https://doi.org/10.1016/j.jbusres.2011.02.038
Kim, K.-S., & Sin, S.-C. J. (2011). Selecting quality sources: Bridging the gap between the perception and use of information sources. 37(2), 178-188. doi:10.1177/0165551511400958
Kim, M. J., Chung, N., Lee, C.-K., & Preis, M. W. (2016). Dual-route of persuasive communications in mobile tourism shopping. Telematics and Informatics, 33(2), 293-308. doi:https://doi.org/10.1016/j.tele.2015.08.009
Kim, M. J., Lee, C.-K., & Contractor, N. S. (2019). Seniors' usage of mobile social network sites: Applying theories of innovation diffusion and uses and gratifications. Computers in Human Behavior, 90, 60-73. doi:https://doi.org/10.1016/j.chb.2018.08.046
Kim, Y.-M. (2010). Gender role and the use of university library website resources: a social cognitive theory perspective. Journal of Information Science, 36(5), 603-617. doi:10.1177/0165551510377709
Ko, H., Cho, C.-H., & Roberts, M. S. (2005). INTERNET USES AND GRATIFICATIONS: A Structural Equation Model of Interactive Advertising. Journal of Advertising, 34(2), 57-70. doi:10.1080/00913367.2005.10639191
Koetse, M. (2015). An Introduction to Sina Weibo: Background and Status Quo
Retrieved from https://www.whatsonweibo.com/sinaweibo/
Kotler, P., Keller, K. L., Ang, S. H., Tan, C.-T., & Leong, S. M. (2018). Marketing management: an Asian perspective: Pearson.
Koufaris, M. (2002a). Applying the Technology Acceptance Model and Flow Theory to Online Consumer Behavior. Information Systems Research, 13(2), 205-223.
Koufaris, M. (2002b). Applying the Technology Acceptance Model and Flow Theory to Online Consumer Behavior. 13(2), 205-223. doi:10.1287/isre.13.2.205.83
Ku, Y.-C., Chen, R., & Zhang, H. (2013). Why do users continue using social networking sites? An exploratory study of members in the United States and Taiwan. Information & Management, 50(7), 571-581. doi:https://doi.org/10.1016/j.im.2013.07.011
Kwon, O., & Wen, Y. (2010). An empirical study of the factors affecting social network service use. Computers in Human Behavior, 26(2), 254-263. doi:https://doi.org/10.1016/j.chb.2009.04.011
Lampe, C., Wohn, D. Y., Vitak, J., Ellison, N. B., & Wash, R. J. I. J. o. C.-S. C. L. (2011). Student use of Facebook for organizing collaborative classroom activities. 6(3), 329-347. doi:10.1007/s11412-011-9115-y
Lankton, N. K., Wilson, E. V., & Mao, E. (2010). Antecedents and determinants of information technology habit. Information & Management, 47(5), 300-307. doi:https://doi.org/10.1016/j.im.2010.06.004
LaRose, R. (2010). The Problem of Media Habits. Communication Theory, 20(2), 194-222. doi:10.1111/j.1468-2885.2010.01360.x %J Communication Theory
Lee, A. R., Son, S.-M., & Kim, K. K. (2016). Information and communication technology overload and social networking service fatigue: A stress perspective. Computers in Human Behavior, 55, 51-61. doi:https://doi.org/10.1016/j.chb.2015.08.011
Lee, G., & Lee, W. J. (2009). Psychological reactance to online recommendation services. Information & Management, 46(8), 448-452. doi:https://doi.org/10.1016/j.im.2009.07.005
Lee, I., & Lee, K. (2015). The Internet of Things (IoT): Applications, investments, and challenges for enterprises. Business Horizons, 58(4), 431-440. doi:https://doi.org/10.1016/j.bushor.2015.03.008
Lee, S. K., Lindsey, N. J., & Kim, K. S. (2017). The effects of news consumption via social media and news information overload on perceptions of journalistic norms and practices. Computers in Human Behavior, 75, 254-263. doi:https://doi.org/10.1016/j.chb.2017.05.007
Lee, S. M., & Chen, L. (2010). The Impact of Flow on Online Consumer Behavior. Journal of Computer Information Systems, 50(4), 1-10. doi:10.1080/08874417.2010.11645425
Leiter, M. P., & Maslach, C. (2009). Nurse turnover: the mediating role of burnout. 17(3), 331-339. doi:10.1111/j.1365-2834.2009.01004.x
Li, C.-Y. (2017). How social commerce constructs influence customers' social shopping intention? An empirical study of a social commerce website. Technological Forecasting & Social Change. doi:10.1016/j.techfore.2017.11.026
Li, H., Li, L., Gan, C., Liu, Y., Tan, C.-W., & Deng, Z. (2018). Disentangling the factors driving users' continuance intention towards social media: A configurational perspective. Computers in Human Behavior, 85, 175-182. doi:https://doi.org/10.1016/j.chb.2018.03.048
Li, H., Liu, Y., Xu, X., Heikkilä, J., & van der Heijden, H. (2015). Modeling hedonic is continuance through the uses and gratifications theory: An empirical study in online games. Computers in Human Behavior, 48, 261-272. doi:https://doi.org/10.1016/j.chb.2015.01.053
Li, Y., Yang, S., Chen, Y., & Yao, J. (2018). Effects of perceived online–offline integration and internet censorship on mobile government microblogging service continuance: A gratification perspective. Government Information Quarterly, 35(4), 588-598. doi:https://doi.org/10.1016/j.giq.2018.07.004
Lim, K. H., & Benbasat, I. (2000). The Effect of Multimedia on Perceived Equivocality and Perceived Usefulness of Information Systems. MIS Quarterly, 24(3), 449-471. doi:10.2307/3250969
Lim, M. S., & Choi, S. B. (2017). Stress caused by social media network applications and user responses. 76(17), 17685-17698. doi:10.1007/s11042-015-2891-z
Limayem, M., Hirt, S. G., & Cheung, C. M. K. (2007). How Habit Limits the Predictive Power of Intention: The Case of Information Systems Continuance. MIS Quarterly, 31(4), 705-737. doi:10.2307/25148817
Lin, H., Fan, W., & Chau, P. Y. K. (2014). Determinants of users’ continuance of social networking sites: A self-regulation perspective. Information & Management, 51(5), 595-603. doi:https://doi.org/10.1016/j.im.2014.03.010
Lin, K.-M. (2015). Predicting Asian undergraduates’ intention to continue using social network services from negative perspectives. Behaviour & Information Technology, 34(9), 882-892. doi:10.1080/0144929X.2015.1027736
Liu, Q., Shao, Z., & Fan, W. (2018). The impact of users’ sense of belonging on social media habit formation: Empirical evidence from social networking and microblogging websites in China. International Journal of Information Management, 43, 209-223. doi:https://doi.org/10.1016/j.ijinfomgt.2018.08.005
Luo, M. M., Chea, S., & Chen, J.-S. (2011). Web-based information service adoption: A comparison of the motivational model and the uses and gratifications theory. Decision Support Systems, 51(1), 21-30. doi:https://doi.org/10.1016/j.dss.2010.11.015
Luqman, A., Cao, X., Ali, A., Masood, A., & Yu, L. (2017). Empirical investigation of Facebook discontinues usage intentions based on SOR paradigm. Computers in Human Behavior, 70, 544-555. doi:10.1016/j.chb.2017.01.020
Maier, C., Laumer, S., Eckhardt, A., & Weitzel, T. (2015). Giving too much social support: social overload on social networking sites. European Journal of Information Systems, 24(5), 447-464. doi:10.1057/ejis.2014.3
Maier, C., Laumer, S., Weinert, C., & Weitzel, T. (2015). The effects of technostress and switching stress on discontinued use of social networking services: a study of Facebook use. information systems journal, 25(3), 275-308. doi:10.1111/isj.12068
Marsh, H. W., & Hocevar, D. (1985). Application of confirmatory factor analysis to the study of self-concept: First- and higher order factor models and their invariance across groups. Psychological bulletin, 97(3), 562-582. doi:10.1037/0033-2909.97.3.562
Martinussen, M., Richardsen, A. M., & Burke, R. J. (2007). Job demands, job resources, and burnout among police officers. Journal of Criminal Justice, 35(3), 239-249. doi:https://doi.org/10.1016/j.jcrimjus.2007.03.001
Mehrabian, A., & Russell, J. A. (1974). An approach to environmental psychology. Cambridge, MA, US: The MIT Press.
Meyer, J. A. (1998). Information overload in marketing management. 16(3), 200-209. doi:doi:10.1108/02634509810217318
Michikyan, M., Subrahmanyam, K., & Dennis, J. (2014). Can you tell who I am? Neuroticism, extraversion, and online self-presentation among young adults. Computers in Human Behavior, 33, 179-183. doi:https://doi.org/10.1016/j.chb.2014.01.010
Miller, G. A. (1956). The magical number seven, plus or minus two: some limits on our capacity for processing information. Psychological Review, 63(2), 81-97. doi:10.1037/h0043158
Mouakket, S. (2015). Factors influencing continuance intention to use social network sites: The Facebook case. Computers in Human Behavior, 53, 102-110. doi:https://doi.org/10.1016/j.chb.2015.06.045
Mouakket, S. (2018). The role of personality traits in motivating users' continuance intention towards Facebook: Gender differences. The Journal of High Technology Management Research, 29(1), 124-140. doi:https://doi.org/10.1016/j.hitech.2016.10.003
Muscanell, N. L., & Guadagno, R. E. (2012). Make new friends or keep the old: Gender and personality differences in social networking use. Computers in Human Behavior, 28(1), 107-112. doi:https://doi.org/10.1016/j.chb.2011.08.016
Nawaz, M. A., Shah, Z., Nawaz, A., Asmi, F., Hassan, Z., & Raza, J. (2018). Overload and exhaustion: Classifying SNS discontinuance intentions. Cogent Psychology, 5(1), 1515584. doi:10.1080/23311908.2018.1515584
Nekovee, M., Moreno, Y., Bianconi, G., & Marsili, M. (2007). Theory of rumour spreading in complex social networks. Physica A: Statistical Mechanics and its Applications, 374(1), 457-470. doi:https://doi.org/10.1016/j.physa.2006.07.017
Ngai, E. W. T., Tao, S. S. C., & Moon, K. K. L. (2015). Social media research: Theories, constructs, and conceptual frameworks. International Journal of Information Management, 35(1), 33-44. doi:https://doi.org/10.1016/j.ijinfomgt.2014.09.004
Novak, T. P., Hoffman, D. L., & Yung, Y.-F. (2000). Measuring the Customer Experience in Online Environments: A Structural Modeling Approach. 19(1), 22-42. doi:10.1287/mksc.19.1.22.15184
Nunnally, J., & Bernstein, I. (1994). Psychometric theory, 3rd edn. New York: McGraw-Hill.
Oghuma, A. P., Libaque-Saenz, C. F., Wong, S. F., & Chang, Y. (2016). An expectation-confirmation model of continuance intention to use mobile instant messaging. Telematics and Informatics, 33(1), 34-47. doi:https://doi.org/10.1016/j.tele.2015.05.006
Oni, A. A., Oni, S., Mbarika, V., & Ayo, C. K. (2017). Empirical study of user acceptance of online political participation: Integrating Civic Voluntarism Model and Theory of Reasoned Action. Government Information Quarterly, 34(2), 317-328. doi:https://doi.org/10.1016/j.giq.2017.02.003
Ou, C. X., Pavlou, P. A., & Davison, R. M. (2014). Swift Guanxi in online marketplaces: the role of computer-mediated communication technologies %J MIS Q. 38(1), 209-230. doi:10.25300/misq/2014/38.1.10
Papacharissi, Z. (2002). The Self Online: The Utility of Personal Home Pages. Journal of Broadcasting & Electronic Media, 46(3), 346-368. doi:10.1207/s15506878jobem4603_3
Papacharissi, Z., & Rubin, A. M. (2000). Predictors of Internet Use. Journal of Broadcasting & Electronic Media, 44(2), 175-196. doi:10.1207/s15506878jobem4402_2
Pelet, J.-É., Ettis, S., & Cowart, K. (2017). Optimal experience of flow enhanced by telepresence: Evidence from social media use. Information & Management, 54(1), 115-128. doi:https://doi.org/10.1016/j.im.2016.05.001
Pelling, E. L., & White, K. M. (2009). The Theory of Planned Behavior Applied to Young People's Use of Social Networking Web Sites. CyberPsychology & Behavior, 12(6), 755-759. doi:10.1089/cpb.2009.0109
Pentina, I., & Tarafdar, M. (2014). From “information” to “knowing”: Exploring the role of social media in contemporary news consumption. Computers in Human Behavior, 35, 211-223. doi:https://doi.org/10.1016/j.chb.2014.02.045
Petter, S., Straub, D., & Rai, A. (2007). Specifying Formative Constructs in Information Systems Research. MIS Quarterly, 31(4), 623-656. doi:10.2307/25148814
Phonthanukitithaworn, C., & Sellitto, C. (2017). Facebook as a second screen: An influence on sport consumer satisfaction and behavioral intention. Telematics and Informatics, 34(8), 1477-1487. doi:https://doi.org/10.1016/j.tele.2017.06.011
Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879-903. doi:10.1037/0021-9010.88.5.879
Quan-Haase, A., & Young, A. L. (2010). Uses and Gratifications of Social Media: A Comparison of Facebook and Instant Messaging. Bulletin of Science, Technology & Society, 30(5), 350-361. doi:10.1177/0270467610380009
Ragin, C. C. (2000). Fuzzy-set social science. Chicago: University of Chicago Press.
Ragin, C. C. (2008). Redesigning social inquiry: Fuzzy sets and beyond. Chicago and London: University of Chicago Press.
Ravindran, T., Yeow Kuan, A. C., & Hoe Lian, D. G. (2014). Antecedents and effects of social network fatigue. Journal of the Association for Information Science and Technology, 65(11), 2306-2320. doi:10.1002/asi.23122
Rettie, R. (2001). An exploration of flow during Internet use. Internet Research, 11(2), 103-113. doi:10.1108/10662240110695070
Reuter, C., & Kaufhold, M.-A. (2018). Fifteen years of social media in emergencies: A retrospective review and future directions for crisis Informatics. 26(1), 41-57. doi:10.1111/1468-5973.12196
Rihoux, B., & Ragin, C. C. (2009). Configurational comparative methods: Qualitative comparative analysis (QCA) and related techniques. Los Angeles: Sage.
Rodríguez-Ardura, I., & Meseguer-Artola, A. (2016). E-learning continuance: The impact of interactivity and the mediating role of imagery, presence and flow. Information & Management, 53(4), 504-516. doi:https://doi.org/10.1016/j.im.2015.11.005
Saegert, S. (1973). Crowding: Cognitive overload and behavioral constraint. Environmental design research, 2, 254-260.
Saunders, M. N. K., Lewis, P., & Thornhill, A. (2019). Research Methods for Business Students (8th ed.): Pearson.
Schneider, C. Q., & Wagemann, C. (2012). Set-theoretic methods for the social sciences. A guide to qualitative comparative analysis. Cambridge UK: Cambridge University Press.
Schumann, J. H., von Wangenheim, F., & Groene, N. (2014). Targeted Online Advertising: Using Reciprocity Appeals to Increase Acceptance among Users of Free Web Services. 78(1), 59-75. doi:10.1509/jm.11.0316
Seyedghorban, Z., Tahernejad, H., & Matanda, M. J. (2016). Reinquiry into Advertising Avoidance on the Internet: A Conceptual Replication and Extension. Journal of Advertising, 45(1), 120-129. doi:10.1080/00913367.2015.1085819
Shang, S. S. C., Wu, Y.-L., & Li, E. Y. (2017). Field effects of social media platforms on information-sharing continuance: Do reach and richness matter? Information & Management, 54(2), 241-255. doi:https://doi.org/10.1016/j.im.2016.06.008
Shen, J., Barbera, J., & Shapiro, C. M. (2006). Distinguishing sleepiness and fatigue: focus on definition and measurement. Sleep Medicine Reviews, 10(1), 63-76. doi:https://doi.org/10.1016/j.smrv.2005.05.004
Shi, S. (2018). The statistical report on Chinese social media. Retrieved from http://wemedia.ifeng.com/86054368/wemedia.shtml
Smets, E. M. A., Garssen, B., Bonke, B., & De Haes, J. C. J. M. (1995). The multidimensional Fatigue Inventory (MFI) psychometric qualities of an instrument to assess fatigue. Journal of Psychosomatic Research, 39(3), 315-325. doi:https://doi.org/10.1016/0022-3999(94)00125-O
Sobel, M. E. (1982). Aysmptotic confidence intervals for indirect effects in structural equation models. In S. Leinhardt (Ed.), Sociological Methodology (pp. 290-212). San Francisco: Jossey-Boss.
Sobel, M. E. (1986). Some new results on indirect effects and their standard errors in covariance structure models. In N. Tuma (Ed.), Sociological Methodology (pp. 159-186). Washington, DC:: American Sociological Association.
Song, J., & Guo, Y. (2019). What influences nursing safety event reporting among nursing interns?: Focus group study. Nurse Education Today, 76, 200-205. doi:https://doi.org/10.1016/j.nedt.2019.02.010
Stafford, T. F., Stafford, M. R., & Schkade, L. L. (2004). Determining Uses and Gratifications for the Internet. 35(2), 259-288. doi:10.1111/j.00117315.2004.02524.x
Sullivan, Y. W., & Koh, C. E. (2019). Social media enablers and inhibitors: Understanding their relationships in a social networking site context. International Journal of Information Management, 49, 170-189. doi:https://doi.org/10.1016/j.ijinfomgt.2019.03.014
Swar, B., Hameed, T., & Reychav, I. (2017). Information overload, psychological ill-being, and behavioral intention to continue online healthcare information search. Computers in Human Behavior, 70, 416-425. doi:https://doi.org/10.1016/j.chb.2016.12.068
Tabesh, P., Mousavidin, E., & Hasani, S. (2019). Implementing big data strategies: A managerial perspective. Business Horizons, 62(3), 347-358. doi:https://doi.org/10.1016/j.bushor.2019.02.001
Tausch, A. P., & Menold, N. (2016). Methodological Aspects of Focus Groups in Health Research:Results of Qualitative Interviews With Focus Group Moderators. Global Qualitative Nursing Research, 3, 1-12. doi:10.1177/2333393616630466
Tran, L. T. T., Pham, L. M. T., & Le, L. T. (2019). E-satisfaction and continuance intention: The moderator role of online ratings. International Journal of Hospitality Management, 77, 311-322. doi:https://doi.org/10.1016/j.ijhm.2018.07.011
Useit. (2017). The report on Weibo user in 2017.
van Noort, G., Voorveld, H. A. M., & van Reijmersdal, E. A. (2012). Interactivity in Brand Web Sites: Cognitive, Affective, and Behavioral Responses Explained by Consumers' Online Flow Experience. Journal of Interactive Marketing, 26(4), 223-234. doi:https://doi.org/10.1016/j.intmar.2011.11.002
Venkatesh, V., & Morris, M. G. (2000). Why Don't Men Ever Stop to Ask for Directions? Gender, Social Influence, and Their Role in Technology Acceptance and Usage Behavior. MIS Quarterly, 24(1), 115-139. doi:10.2307/3250981
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. doi:10.2307/30036540
Venkatesh, V., Thong, J. Y. L., & 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. doi:10.2307/41410412
Verplanken, B. (2006). Beyond frequency: Habit as mental construct. 45(3), 639-656. doi:10.1348/014466605x49122
Verplanken, B., & Aarts, H. (1999). Habit, Attitude, and Planned Behaviour: Is Habit an Empty Construct or an Interesting Case of Goal-directed Automaticity? European Review of Social Psychology, 10(1), 101-134. doi:10.1080/14792779943000035
Wang, C., & Zhang, P. (2012). The Evolution of Social Commerce: The People, Management, Technology, and Information Dimensions. Communications of the Association for Information Systems, 31(5), 105-127. doi:https://doi.org/10.17705/1CAIS.03105
Wang, W., Chen, R. R., Ou, C. X., & Ren, S. J. (2019). Media or message, which is the king in social commerce?: An empirical study of participants' intention to repost marketing messages on social media. Computers in Human Behavior, 93, 176-191. doi:https://doi.org/10.1016/j.chb.2018.12.007
Watters, C. A., Keefer, K. V., Kloosterman, P. H., Summerfeldt, L. J., & Parker, J. D. A. (2013). Examining the structure of the Internet Addiction Test in adolescents: A bifactor approach. Computers in Human Behavior, 29(6), 2294-2302. doi:https://doi.org/10.1016/j.chb.2013.05.020
Weathers, D., Swain, S. D., & Grover, V. (2015). Can online product reviews be more helpful? Examining characteristics of information content by product type. Decision Support Systems, 79, 12-23. doi:https://doi.org/10.1016/j.dss.2015.07.009
Weiss, A. M., Lurie, N. H., & Macinnis, D. J. (2008). Listening to Strangers: Whose Responses are Valuable, how Valuable are They, and Why? , 45(4), 425-436. doi:10.1509/jmkr.45.4.425
Wiedmann, K.-P., Walsh, G., & Mitchell, V.-W. (2001). The Mannmaven: an agent for diffusing market information. Journal of Marketing Communications, 7(4), 195-212. doi:10.1080/13527260127413
Woisetschläger, D. M., Lentz, P., & Evanschitzky, H. (2011). How habits, social ties, and economic switching barriers affect customer loyalty in contractual service settings. Journal of Business Research, 64(8), 800-808. doi:https://doi.org/10.1016/j.jbusres.2010.10.007
Wolny, J., & Mueller, C. (2013). Analysis of fashion consumers’ motives to engage in electronic word-of-mouth communication through social media platforms. Journal of Marketing Management, 29(5-6), 562-583. doi:10.1080/0267257X.2013.778324
Woodside, A. G. (2013). Moving beyond multiple regression analysis to algorithms: Calling for adoption of a paradigm shift from symmetric to asymmetric thinking in data analysis and crafting theory. Journal of Business Research, 66(4), 463-472. doi:https://doi.org/10.1016/j.jbusres.2012.12.021
Woodside, A. G., Frey, L. L., & Daly, R. T. (1989). Linking service quality, customer satisfaction, and behavioral intention. Journal of health care marketing, 9(4), 5-17.
Yang, S., Lu, Y., Wang, B., & Zhao, L. (2014). The benefits and dangers of flow experience in high school students’ internet usage: The role of parental support. Computers in Human Behavior, 41, 504-513. doi:https://doi.org/10.1016/j.chb.2014.09.039
Yang, S., Wang, B., & Lu, Y. (2016). Exploring the dual outcomes of mobile social networking service enjoyment: The roles of social self-efficacy and habit. Computers in Human Behavior, 64, 486-496. doi:https://doi.org/10.1016/j.chb.2016.07.010
Yang, X., Wu, Z., & Li, Y. (2011). Difference between real-life escape panic and mimic exercises in simulated situation with implications to the statistical physics models of emergency evacuation: The 2008 Wenchuan earthquake. Physica A: Statistical Mechanics and its Applications, 390(12), 2375-2380. doi:https://doi.org/10.1016/j.physa.2010.10.019
Yu, D. S. F., Lee, D. T. F., & Man, N. W. (2010). Fatigue among older people: A review of the research literature. International Journal of Nursing Studies, 47(2), 216-228. doi:https://doi.org/10.1016/j.ijnurstu.2009.05.009
Zanjani, S. H. A., Diamond, W. D., & Chan, K. (2011). Does Ad-Context Congruity Help Surfers and Information Seekers Remember Ads in Cluttered E-magazines? Journal of Advertising, 40(4), 67-84. doi:10.2753/JOA0091-3367400405
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. doi:https://doi.org/10.1016/j.chb.2016.11.069
Zhang, S., Zhao, L., Lu, Y., & Yang, J. (2016). Do you get tired of socializing? An empirical explanation of discontinuous usage behaviour in social network services. Information & Management, 53(7), 904-914. doi:https://doi.org/10.1016/j.im.2016.03.006
Zhao, K., Stylianou, A. C., & Zheng, Y. (2013). Predicting users' continuance intention in virtual communities: The dual intention-formation processes. Decision Support Systems, 55(4), 903-910. doi:https://doi.org/10.1016/j.dss.2012.12.026
Zhao, L., Lu, Y., Wang, B., Chau, P. Y. K., & Zhang, L. (2012). Cultivating the sense of belonging and motivating user participation in virtual communities: A social capital perspective. International Journal of Information Management, 32(6), 574-588. doi:https://doi.org/10.1016/j.ijinfomgt.2012.02.006
Zhao, L., Wang, Q., Cheng, J., Zhang, D., Ma, T., Chen, Y., & Wang, J. (2012). The impact of authorities’ media and rumor dissemination on the evolution of emergency. Physica A: Statistical Mechanics and its Applications, 391(15), 3978-3987. doi:https://doi.org/10.1016/j.physa.2012.02.004
Zhou, M., & Zhang, X. (2019). Online social networking and subjective well-being: Mediating effects of envy and fatigue. Computers & Education, 140, 103598. doi:https://doi.org/10.1016/j.compedu.2019.103598
Zhou, T. (2013). The effect of flow experience on user adoption of mobile TV. Behaviour & Information Technology, 32(3), 263-272. doi:10.1080/0144929X.2011.650711
Zhou, T., Li, H., & Liu, Y. (2010). The effect of flow experience on mobile SNS users' loyalty. 110(6), 930-946. doi:doi:10.1108/02635571011055126


 
 
 
 
第一頁 上一頁 下一頁 最後一頁 top
QR Code
QRCODE