The Effect of Individual and Environmental Motivations on YouTuber Followers’ Behavioral Changes

Abstract

This study aimed to determine the effective factors on the behavioral changes of YouTuber followers. Accordingly, it was targeted to determine the effect of the individual, environmental motivations, and YouTuber characteristics on the change of followers’ behavior through the online flow process. Meanwhile, the mediating role of opinion seeking and the moderator role of the fear of missing out have been discussed. The main mass consisted of 520 female consumers who live in Istanbul, are at least 18 years of age, and follow at least one YouTuber in the makeup/cosmetic/beauty segment. Structural equation modelling was used to analyze the data. Findings showed that three subdimensions of knowledge-sharing motivations, which are consumer interactivity, trust, and consumer expertise; four subdimensions of fundamental interpersonal relation orientations, which are the need to be part of a group, avoidance of similarity and unpopular choice counter-conformity, creative choice counter-conformity, and the need for personal growth; and social presence have a positive, community identification and that YouTuber characteristics have a negative effect on online flow. However, social norms have no effect. Meanwhile, online flow is effective on the behavioral changes of followers. Finally, opinion seeking has a mediating role whereas the fear of missing out has a moderating role.

Keywords

• Individual Motivations
• Environmental Motivations
• YouTuber Characteristics
• Online Flow Theory
• Behavioral Changes

References

1. Agarwal, R., & Karahanna, E. (2000). Time flies when you're having fun: Cognitive absorption and beliefs about information technology usage. MIS Quarterly, 24(4), 665-694. https://doi.org/10.2307/3250951.
2. Alba, J. W., & Hutchinson, J. W. (1987). Dimensions of consumer expertise. Journal of Consumer Research, 13(4), 411-454. https://doi.org/10.1086/209080.
3. Ananda, A. F., & Wandebori, H. (2016). The impact of drugstore makeup product reviews by beauty vlogger on YouTube towards purchase intention by undergraduate students in Indonesia. In International Conference on Ethics of Business, Economics, and Social Science, 3(1), 264-272.
4. Baron, R. M., & Kenny, D. A. (1986). The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51(6), 1173. https://doi.org/10.1037/0022-3514.51.6.1173.
5. Bhaduri, G., & Stanforth, N. (2016). Evaluation of absolute luxury: Effect of cues, consumers’ need for uniqueness, product involvement and product knowledge on expected price. Journal of Fashion Marketing and Management: An International Journal, 20(4), 471-486. https://doi.org/10.1108/JFMM-12-2015-0095.
6. Bridges, E., & Florsheim, R. (2008). Hedonic and utilitarian shopping goals: The online experience. Journal of Business Research, 61(4), 309-314. https://doi.org/10.1016/j.jbusres.2007.06.017.
7. Cao, Y. (2015). Research on consumption psychology and consumption behaviors in the mobile internet era. 3rd International Conference on Management Science, Education Technology, Arts, Social Science and Economics (MSETASSE 2015), 5-8. https://doi.org/10.2991/msetasse-15.2015.2.
8. Chang, H.H., & Chuang, S-S. (2011). Social capital and individual motivations on knowledge sharing: Participant involvement as a moderator. Information & Management, 48, 9–18. https://doi.org/10.1016/j.im.2010.11.001.

9. Chapple, C., & Cownie, F. (2017). An investigation into viewers’ trust in and response towards disclosed paid-for-endorsements by YouTube lifestyle vloggers. Journal of Promotional Communications, 5(2), 110-136.
10. Cheung, C. M., Xiao, B., & Liu, I. L. (2012). The impact of observational learning and electronic word of mouth on consumer purchase decisions: The moderating role of consumer expertise and consumer involvement. 2012 45th Hawaii International Conference on System Sciences, 3228-3237. doi: 10.1109/HICSS.2012.570.
11. Choi, J.H. (2015). Putting the social into social network sites: A knowledge sharing perspective (Doctoral dissertation). Available from ProQuest Dissertations and Theses database. (UMI No. 3702063).
12. Chu, S-C., & Kim, Y. (2011). Determinants of consumer engagement in electronic word‑of‑mouth (eWOM) in social networking sites. International Journal of Advertising, 30(1), 47–75. https://doi.org/10.2501/IJA-30-1-047-075.
13. 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. https://doi.org/10.1016/j.compedu.2013.09.012.
14. Flynn, L.R., Goldsmith, R.E., & Eastman, J.K. (1996). Opinion leaders and opinion seekers: Two new measurement scales. Journal of the Academy of Marketing Science, 24(2), 137-147. https://doi.org/10.1177/0092070396242004.
15. Franchina, V., Vanden Abeele, M., van Rooij, A., Lo Coco, G., & De Marez, L. (2018). Fear of missing out as a predictor of problematic social media use and phubbing behavior among Flemish adolescents. International Journal of Environmental Research and Public Health, 15(2319), 1-18. https://doi.org/10.3390/ijerph15102319.
16. García-Rapp, F. (2016). The digital media phenomenon of YouTube beauty gurus: The case of Bubzbeauty. International Journal of Web Based Communities (IJWBC), 12(4), 360-375. https://doi.org/10.1504/IJWBC.2016.080810.
17. Gomes, D., Sales, R., Cavalcante, F. & Carvalho, C. (2014). Watch decide and share: The role of web video information during the buying decision process. International Journal of Video & Image Processing and Network Security IJVIPNS-IJENS, 14(5), 7-18.
18. Guo, Y. (2004). Flow in internet shopping: A validity study and an examination of a model specifying antecedents and consequences of flow (Doctoral dissertation). Available from ProQuest Dissertations and Theses database. (UMI No. 3157431).
19. Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2014), Multivariate Data Analysis: Pearson New International Edition, London : Pearson Education Limited.
20. Ho, J. Y. C., & Dempsey, M. (2010). Viral marketing: motivations to forward online content. Journal of Business Research, 63, 1000–1006. https://doi.org/10.1016/j.jbusres.2008.08.010.
21. Hoffman, D. L., & Novak T. P. (1996). Marketing in hypermedia computer-mediated environments: Conceptual foundations. Journal of Marketing, 60(3), 50–68. https://doi.org/10.1177/002224299606000304.
22. Hochanadel, C. E. (2014). Motivations for engaging in electronic word of mouth in a social networking setting (Doctoral dissertation). Available from ProQuest Dissertations and Theses database. (UMI No. 3648516).
23. Hsu, C. L., Chang, K. C., & Chen, M. C. (2012). Flow experience and internet shopping behavior: Investigating the moderating effect of consumer characteristics. Systems Research and Behavioral Science, 29(3), 317-332. https://doi.org/10.1002/sres.1101.
24. 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/10.1016/j.im.2007.11.001.
25. Hu, X. (2015). Assessing source credibility on social media-an electronic word-of-mouth communication perspective (Doctoral dissertation). Available from ProQuest Dissertations and Theses database. (UMI No. 3710218).
26. Huang, J., Chen, R., & Wang, X. (2012). Factors influencing intention to forward short internet videos. Social Behavior and Personality: An International Journal, 40(1), 5-14. https://doi.org/10.2224/sbp.2012.40.1.5.
27. Huguenel, B. M. (2017). Fear of missing out: A moderated mediation approach to social media use (Master dissertation). USA: Loyola University. Available from ProQuest Dissertations and Theses database. (UMI No. 10637321).
28. Jadin, T., Gnambs, T., & Batinic, B. (2013). Personality traits and knowledge sharing in online communities. Computers in Human Behavior, 29(1), 210-216. https://doi.org/10.1016/j.chb.2012.08.007.
29. Jenster, N.P. (2010). Leadership impact on motivation, cohesiveness and effectiveness in virtual teams: A FIRO perspective (Doctoral dissertation). Available from ProQuest Dissertations and Theses database. (UMI No. 3471610).
30. Jin, J., Li, Y. L., Zhong, X., & Zhai, L. (2015). Why users contribute knowledge to online communities: An empirical study of an online social Q&A community. Information & Management, 52, 840-849. https://doi.org/10.1016/j.im.2015.07.005.
31. Kang, J-Y. M., Johnson, K.K.P. & Wu, J. (2014). Consumer style inventory and intent to social shop online for apparel using social networking sites. Journal of Fashion Marketing and Management, 18(3), 301-320. https://doi.org/10.1108/JFMM-09-2012-0057.
32. Kaur, P., Dhir, A., Chen, S. & Rajala, R. (2016). Flow in context: Development and validation of the flow experience instrument for social networking. Computers in Human Behavior, 59, 358-367. https://doi.org/10.1016/j.chb.2016.02.039.
33. 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.
34. Kim, S. S., Lee, J., & Prideaux, B. (2014). Effect of celebrity endorsement on tourists’ perception of corporate image, corporate credibility and corporate loyalty. International Journal of Hospitality Management, 37, 131-145. https://doi.org/10.1016/j.ijhm.2013.11.003.
35. Kim, J., Song, H., & Luo, W. (2016). Broadening the understanding of social presence: Implications and contributions to the mediated communication and online education. Computers in Human Behavior, 65, 672-679. https://doi.org/10.1016/j.chb.2016.07.009.
36. Koufaris, M. (2002). Applying the technology acceptance model and flow theory to online consumer behavior. Information Systems Research, 13(2), 205-223. https://doi.org/10.1287/isre.13.2.205.83.
37. Ku, Y. C., Wei, C. P., & Hsiao, H. W. (2012). To whom should I listen? Finding reputable reviewers in opinion-sharing communities. Decision Support Systems, 53(3), 534-542. https://doi.org/10.1016/j.dss.2012.03.003.
38. Lai, H-M., & Chen, T. T. (2014). Knowledge sharing in interest online communities: A comparison of posters and lurkers. Computers in Human Behavior, 35, 295–306. https://doi.org/10.1016/j.chb.2014.02.004.
39. Leary, M. R., Kelly, K. M., Cottrell, C. A., & Schreindorfer, L.S. (2013). Construct validity of the need to belong scale: Mapping the nomological network. Journal of Personality Assessment, 95(6), 610-624. https://doi.org/10.1080/00223891.2013.819511.
40. Lee, D., Park, Y. J., Kim, J., Kim, J., & Moon, J. (2011). Understanding music sharing behaviour on social network services. Online Information Review, 35(5), 716-733. https://doi.org/10.1108/14684521111176462.
41. Lee, J. E., & Watkins, B. (2016). YouTube vloggers’ influence on consumer luxury brand perceptions and intentions. Journal of Business Research, 69, 5753-5760. https://doi.org/10.1016/j.jbusres.2016.04.171.
42. Liu, C-C. (2017). A model for exploring players flow experience in online games. Information Technology & People, 30(1), 139-162. https://doi.org/10.1108/ITP-06-2015-0139.
43. Liu, L., Cheung, C. M., & Lee, M. K. (2016a). An empirical investigation of information sharing behavior on social commerce sites. International Journal of Information Management, 36(5), 686-699. https://doi.org/10.1016/j.ijinfomgt.2016.03.013.
44. Liu, H., Chu, H., Huang, Q., & Chen, X. (2016b). “Enhancing the flow experience of consumers in China through interpersonal interaction in social commerce. Computers in Human Behavior, 58, 306-314. https://doi.org/10.1016/j.chb.2016.01.012.
45. Liu, H. J.& Shiue, Y. C. (2014). Influence of Facebook game players’ behavior on flow and purchase intention. Social Behavior and Personality: An International Journal, 42(1), 125-133. https://doi.org/10.2224/sbp.2014.42.1.125.
46. Ma, M., & Agarwal, R. (2007). Through a glass darkly: Information technology design, identity verification, and knowledge contribution in online communities. Information Systems Research, 18(1), 42-67. https://doi.org/10.1287/isre.1070.0113.
47. Ma, W. W., & Chan, A. (2014). Knowledge sharing and social media: altruism, perceived online attachment motivation, and perceived online relationship commitment. Computers in Human Behavior, 39, 51-58. https://doi.org/10.1016/j.chb.2014.06.015.
48. Moulard, J. G., Garrity, C. P. & Rice, D. H. (2015). What makes a human brand authentic? Identifying the antecedents of celebrity authenticity. Psychology & Marketing, 32(2), 173-186. https://doi.org/10.1002/mar.20771.
49. Novak, T. P., Hoffman, D. L., & Yung, Y. F. (2000). Measuring the customer experience in online environments: A structural modeling approach. Marketing Science, 19(1), 22-42. https://doi.org/10.1287/mksc.19.1.22.15184.
50. Oberst, U., Wegmann, E., Stodt, B., Brand, M. & Chamarro, A. (2017). Negative consequences from heavy social networking in adolescents: The mediating role of fear of missing out. Journal of Adolescence, 55, 51-60. https://doi.org/10.1016/j.adolescence.2016.12.008.
51. Ohanian, R. (1990). Construction and validation of a scale to measure celebrity endorsers’ perceived expertise, trustworthiness and attractiveness. Journal of Advertising, 19, 39–52. https://doi.org/10.1080/00913367.1990.10673191.
52. 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. https://doi.org/10.1016/j.im.2016.05.001.
53. Przybylski, A. K., Murayama, K., DeHaan, C. R., & Gladwell, V. (2013). Motivational, emotional, and behavioral correlates of fear of missing out. Computers in Human Behavior, 29, 1841–1848. https://doi.org/10.1016/j.chb.2013.02.014.
54. Quantpsy. (2019, March 17). Retrieved from http://quantpsy.org/sobel.
55. Robitschek, C. (1998). Personal growth initiative: The construct and its measure. Measurement and Evaluation in Counseling and Development, 30(4), 183-198. https://doi.org/10.1080/07481756.1998.12068941.
56. Rose, P. & Kim, J.H. (2011). Self-monitoring, opinion leadership and opinion seeking: A socio motivational approach. Current Psychology, 30, 203–214. DOI 10.1007/s12144-011-9114-1.
57. Ruvio, A., Shoham, A., & Brenčič, M. M. (2008). Consumers' need for uniqueness: Short‐form scale development and cross‐cultural validation. International Marketing Review, 25(1), 33-53. https://doi.org/10.1108/02651330810851872.
58. Safa, N. S., & Ismail, M. A. (2013). A customer loyalty formation model in electronic commerce. Economic Modelling, 35, 559-564. https://doi.org/10.1016/j.econmod.2013.08.011.
59. Shen, A., Lee, M. K., Cheung, C. M., & Chen, H. (2010). Gender differences in intentional social action: We-intention to engage in social network-facilitated team collaboration. Journal of Information Technology, 25(2), 152-169. https://doi.org/10.1057/jit.2010.12.
60. Shen, K. N. & Khalifa, M. (2008). Exploring multidimensional conceptualization of social presence in the context of online communities. International Journal of Human–Computer Interaction, 24(7), 722-748. https://doi.org/10.1080/10447310802335789.
61. Shen, K.N. & Khalifa, M. (2012). System design effects on online impulse buying. Internet Research, 22(4), 396-425. https://doi.org/10.1108/10662241211250962.
62. Shim, I.S. (2012). Enhancing brand loyalty through brand experience: Application of online flow theory (Doctoral Dissertation). Available from ProQuest Dissertations and Theses database. (UMI No. 3520451).
63. Sohn, Y. (2005). Opinion leaders and seekers in online brand communities: Centered on Korean digital camera brand communities (Master dissertation). Available from ProQuest Dissertations and Theses database. (UMI No. 1430471).
64. Song, S. Y., Cho, E., & Kim, Y. K. (2017). Personality factors and flow affecting opinion leadership in social media. Personality and Individual Differences, 114, 16-23. https://doi.org/10.1016/j.paid.2017.03.058.
65. Statwiki. (2019, March 16). Retrieved from http://statwiki.kolobkreations.com.
66. Steuer, J. (1992). Defining virtual reality: Dimensions determining telepresence. Journal of Communication, 42(4), 73-93. https://doi.org/10.1111/j.1460-2466.1992.tb00812.x.
67. Tang, Q., Zhao, X., Liu, S. (2016). The effect of intrinsic and extrinsic motivations on mobile coupon sharing in social network sites: The role of coupon proneness. Internet Research, 26(1), 101-119. https://doi.org/10.1108/IntR-05-2014-0136.
68. Teng, S., Khong, K.W., Goh, W.W. & Chong, A.Y.L. (2014). Examining the antecedents of persuasive E-WOM messages in social media. Online Information Review, 38(6), 746-768. https://doi.org/10.1108/OIR-04-2014-0089.
69. Thomas, R.A., West, R.E. & Borup, J. (2017). An analysis of instructor social presence in online text and asynchronous video feedback comments. Internet and Higher Education, 33, 61–73. https://doi.org/10.1016/j.iheduc.2017.01.003.
70. Van Noort, G., Voorveld, H. A. & 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. https://doi.org/10.1016/j.intmar.2011.11.002.
71. Wang, Y. (2014). Fashion meets Twitter: Does the source matter? perceived message credibility, interactivity and purchase intention (Master Dissertation). Available from ProQuest Dissertations and Theses database. (UMI No. 1565545).
72. Wang, E. S. T., & Chen, L. S. L. (2012). Forming relationship commitments to online communities: The role of social motivations. Computers in Human Behavior, 28(2), 570-575. https://doi.org/10.1016/j.chb.2011.11.002.
73. Wang, L. C. & Hsiao, D. F. (2012). Antecedents of flow in retail store shopping. Journal of Retailing and Consumer Services, 19(4), 381-389. https://doi.org/10.1016/j.jretconser.2012.03.002.
74. Wang, D., Oppewal, H., & Thomas, D. (2017). Anticipated embarrassment due to social presence withholds consumers from purchasing products that feature a lucky charm. European Journal of Marketing, 51(9/10), 1612-1630. https://doi.org/10.1108/EJM-02-2015-0087.
75. Wei, P-S., & Lu, H-P. (2013). An examination of the celebrity endorsements and online customer reviews influence female consumers’ shopping behavior. Computers in Human Behavior, 29, 193–201. https://doi.org/10.1016/j.chb.2012.08.005.
76. Whiting, A., & Williams, D. (2013). Why people use social media: A uses and gratifications approach. Qualitative Market Research: An International Journal, 16(4), 362-369. https://doi.org/10.1108/QMR-06-2013-0041.
77. Wu, G., Hu, X., & Wu, Y. (2010). Effects of perceived interactivity, perceived web assurance and disposition to trust on initial online trust. Journal of Computer-Mediated Communication, 16, 1–26. https://doi.org/10.1111/j.1083-6101.2010.01528.x.
78. Yan, Z., Wang, T., Chen, Y., & Zhang, H. (2016). Knowledge sharing in online health communities: A social exchange theory perspective. Information & Management, 53, 643–653. https://doi.org/10.1016/j.im.2016.02.001.
79. Yang, C., Hsu, Y-C., & Tan, S. (2010). Predicting the determinants of users’ intentions for using YouTube to share video: Moderating gender effects. Cyberpsychology, Behavior, and Social Networking, 13(2), 141-152. https://doi.org/10.1089/cyber.2009.0105.
80. Yang, K-C., Huang, C-H., Yang, C. & Yang, S.Y. (2017). Consumer attitudes toward online video advertisement: YouTube as a platform. Kybernetes, 46(5), 840-853. https://doi.org/10.1108/K-03-2016-0038.
81. Yin, F-S., Liu, M-L. & Lin, C-P. (2015). Forecasting the continuance intention of social networking sites: Assessing privacy risk and usefulness of technology. Technological Forecasting & Social Change, 99, 267–272. https://doi.org/10.1016/j.techfore.2015.07.019.
82. Zang, D. (2014). The impact of two-sided messaging on brand attitude: An Attribution Theory approach (Master dissertation). Available from ProQuest Dissertations and Theses database. (UMI No. 1567252).
83. 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.
84. 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, 574–588. https://doi.org/10.1016/j.ijinfomgt.2012.02.006.
85. Zhou, T. (2011). Understanding online community user participation: A social influence perspective. Internet Research, 21(1), 67-81. https://doi.org/10.1108/10662241111104884.
86. Zietek, N. (2016). Influencer marketing: The characteristics and components of fashion influencer marketing (Master Dissertation). Borås: The Swedish School of Textiles.