Blog içeriklerinin online satın alma niyetine etkisi ve bir araştırma

Öz

Teknoloji kabul modeli, akış teorisi ve gerekçeli eylem teorisinin entegre çerçevesini kullanan bu mevcut çalışma, blog tabanlı içeriğin tüketicilerin çevrimiçi satın alma niyeti üzerindeki etkisini incelemektedir. Araştırmanın hedef kitlesi olan blog okuyucularından kartopu örnekleme yöntemi ile veri toplanması amaçlanmıştır. Farklı teknik özelliklere sahip ve bu nedenle tüketicinin daha fazla bilgi aramasını gerektiren teknolojik bir ürün için blog tabanlı metin okuma senaryosu geliştirildi. Bu senaryoya dayalı olarak tasarlanan online anket aracılığıyla 232 katılımcıdan veri toplanmıştır. İstatistiksel analiz sonucunda algılanan kullanım kolaylığının blog tabanlı içerik okuma üzerinde etkisi olduğu görülmektedir. Blog tabanlı içeriği okumanın hem akış deneyimi boyutları (kontrol duygusu, zamanın dönüşümü, dikkatin yoğunlaşması, içsel ilgi, merak ve ototelik deneyim) hem de algılanan fayda üzerinde etkileri vardır. Buna karşılık, algılanan fayda ve akış deneyimi boyutları karşılıklı olarak birbirini etkilemektedir. Ayrıca hem algılanan fayda hem de akış deneyimi boyutları tutumu etkilemektedir. Sonuç olarak, tutum satın alma niyetini etkilemektedir. Çalışma hem teorik hem de pratik çıkarımlar ve gelecekteki araştırmalar için öneriler sunmaktadır.

Anahtar Kelimeler

• Blog'lar
• Akış Teorisi
• Online Satın Alma Niyeti
• Teknoloji Kabul Modeli
• Gerekçeli Eylem Teorisi

The effect of blog contents on online purchase intention and a research

Öz

Using the integrated framework of the technology acceptance model, flow theory, and theory of reasoned action, this current study examines the effect of blog-based content on consumers' online purchase intention. It was aimed to collect data from the blog readers, the target population of the research, by snowball sampling method. A blog-based text-to-speech scenario was developed for a technological product that has different technical features and therefore requires the consumer to search for more information. Data were collected from 232 participants through the online survey designed based on this scenario. As a result of the statistical analysis, it is seen that perceived ease of use has an effect on reading blog-based content. Reading blog-based content has effects on both flow experience dimensions (sense of control, time distortion, focused attention, intrinsic interest, curiosity, and autotelic experience) and perceived usefulness. In contrast, perceived usefulness and flow experience dimensions reciprocally affect each other. In addition, both perceived usefulness and flow experience dimensions affect attitude. As a result, attitude affects purchase intention. The study provides both theoretical and practical implications and directions for future research.

Anahtar Kelimeler

• Blogs
• Flow Theory
• Online Purchase Intention
• Technology Acceptance Model
• Theory of Reasoned Action

Kaynakça

1. Agarwal, R. and Karahanna, E. (2000). Time flies when you're having fun: Cognitive absorption and beliefs about information technology usage. Mıs Quarterly, 24(4), 665-694.
2. Ahmad, N. and Abdulkarim, H. (2018). The impact of flow experience and personality type on the intention to use virtual world. International Journal of Human–Computer Interaction, 35(12), 1074-1085.
3. Anjewierden, A., De Hoog, R., Brussee, R. and Efimova, L. (2005). Detecting knowledge flows in weblogs. In Proceedings of the 13th International Conference on Conceptual Structures (ICCS 2005). July, 1-12.
4. Bar-Ilan, J. (2005). Information hub blogs. Journal of Information Science, 31(4), 297-307.
5. Baytar, U. and Yükselen, C. (2018). The effect of customers’ flow experience in online shopping channels on satisfaction and purchasing decisions, the roles of information quality and channel quality. Beykent University Journal of Social Sciences, 11(2), 19-35.
6. Bidin, N. A. and Mustaffa, N. (2012). Blogosphere: How youth perceived blogs credibility. Jurnal Komunikasi: Malaysian Journal of Communication, 28(1), 33-54.
7. Chang, H. H. and Wang, I. C. (2008). An investigation of user communication behavior in computer mediated environments. Computers in Human Behavior, 24(5), 2336-2356.
8. Chen, Y. M., Hsu, T. H. and Lu, Y. J. (2018). Impact of flow on mobile shopping intention. Journal of Retailing and Consumer Services, 41, 281-287.

9. Chen, H., Wigand, R. T. and Nilan, M. S. (1999). Optimal experience of web activities. Computers in Human Behavior, 15(5), 585-608.
10. Csikszentmihalyi, M. (1975a). Play and intrinsic rewards. Journal of Humanistic Psychology, 15(3), 290.
11. Csikzentimihalyi, M. (1975b). Beyond boredom and anxiety: Experiencing flow in work and play. San Francisco/Washington/London.
12. Csikszentmihalyi, M. (1990). Flow: The psychology of optimal experience. New York: Harper & Row.
13. Çabuk, S. and Kuş, A. S. (2019). The effect of flow experience in e-retail sites on consumer purchase intention - an investigation on brands in the clothing and shoe sector. Business & Management Studies: An International Journal, 7(3), 257-279.
14. Çelik, Z. (2021). Effect of informatıon acquisition tools on purchase intention in online information search process of consumers and a research (Unpublished Doctoral Thesis). Marmara University, Institute of Social Sciences, Department of Business Administration, Istanbul.
15. Çelik, Z. and Uslu, A. (2021). The effect of vlog contents on the online purchase intention and a research. ISPEC 6th International Conference on Social Sciences & Humanities, Siirt, Türkiye, 16-18 Mayıs 2021, 1-18.
16. Davis, F. D. (1985). A technology acceptance model for empirically testing new end-user information systems: Theory and results. Doctoral Dissertation, Massachusetts Institute of Technology.
17. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340.
18. Davis, F. D., Bagozzi, R. P. and Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982-1003.
19. Davis, F. D. and Venkatesh, V. (1996). A critical assessment of potential measurement biases in the technology acceptance model: Three experiments. International Journal of Human-Computer Studies, 45(1), 19-45.
20. Fang, X., Brzezinski, J., Watson, K., Xu S. and Chan, S. (2004). An empirical study of dual-modal information presentation. AMCIS 2004 Proceedings, 395.
21. Finneran, C. M. and Zhang, P. (2005). Flow in computer-mediated environments: Promises and challenges. Communications of the Association for Information Systems, 15(1), (Article 4), 82-101.
22. Fishbein, M. and Ajzen, I. (1975). Belief, attitude, ıntention, and behavior: An introduction to theory and research. reading. MA: Addison-Wesley.
23. Ghani, J. A. and Deshpande, S. P. (1994). Task characteristics and the experience of optimal flow in human-computer interaction. The Journal of Psychology, 128(4), 381-391.
24. Ghani, J. A., Supnick, R. and Rooney, P. (1991). The experience of flow in computer-mediated and in face-to-face groups. In Icıs, 91(6), 229-237.
25. Guo, Y. M. and Poole, M. S. (2009). Antecedents of flow in online shopping: A test of alternative models. Information Systems Journal, 19(4), 369-390.
26. Hair, J. F., Jr., Black, W. C., Babin, B. J. and Anderson, R. E. (2009). Multivariate data Analysis (7th ed.). Upper Saddle River, NJ: Prentice Hall.
27. Hausman, A. V. and Siekpe, J. S. (2009). The effect of web interface features on consumer online purchase intentions. Journal of Business Research, 62(1), 5-13.
28. Hoffman, D. L. and Novak, T. P. (1996). Marketing in hypermedia computer-mediated environments: Conceptual foundations. Journal of Marketing, 60(3), 50-68.
29. Hoffman, D. L. and Novak, T. P. (2009). Flow online: Lessons learned and future prospects. Journal of Interactive Marketing, 23(1), 23-34.
30. Ho, L. A. and Kuo, T. H. (2010). How can one amplify the effect of e-learning? An examination of high-tech employees’ computer attitude and flow experience. Computers in Human Behavior, 26(1), 23-31.
31. Hong, S-J., Thong, J. Y. and Tam, K. Y. (2006). Understanding continued information technology usage behavior: A comparison of three models in the context of mobile internet. Decision Support Systems, 42(3), 1819-1834.
32. Hsu, C. L. and 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.
33. Hsu, C. L. and Lu, H. P. (2004). Why do people play on-line games? An extended TAM with social influences and flow experience. Information & Management, 41(7), 853-868.
34. Hsu, H. Y. and Tsou, H. T. (2011). Understanding customer experiences in online blog environments. International Journal of Information Management, 31(6), 510-523.
35. Hsu, C. L., Wu, C. C. and Chen, M. C. (2013). An empirical analysis of the antecedents of e-satisfaction and e-loyalty: Focusing on the role of flow and its antecedents. Information Systems and e-Business Management, 11(2), 287-311.
36. Ing, G. P. and Ming, T. (2018). Antecedents of consumer attitude towards blogger recommendations and its impact on purchase intention. Asian Journal of Business and Accounting, 11(1), 293-323.
37. Jackson, S. A. and Marsh, H. W. (1996). Development and validation of a scale to measure optimal experience: The flow state scale. Journal of Sport and Exercise Psychology, 18(1), 17-35.
38. Kaur, P., Dhir, A. and Rajala, R. (2016). Assessing flow experience in social networking site based brand communities. Computers in Human Behavior, 64, 217-225.
39. Kayış, A. (2005). Parametrik hipotez testleri. Editör: Kalaycı, Ş., SPSS Uygulamalı Çok Değişkenli İstatistik Teknikleri. Asil Yayın Dağıtım Ltd. Şti., Ankara, 403-419.
40. Koufaris, M. (2002). Applying the technology acceptance model and flow theory to online consumer behavior. Information Systems Research, 13(2), 205-223.
41. Lee, M. C. (2010). Explaining and predicting users’ continuance intention toward e-learning: An extension of the expectation–confirmation model. Computers & Education, 54(2), 506-516.
42. Lee, C. H. and Wu, J. J. (2017). Consumer online flow experience: The relationship between utilitarian and hedonic value, satisfaction and unplanned purchase. Industrial Management & Data Systems, 117(10), 2452-2467.
43. Legris, P., Ingham, J. and Collerette, P. (2003). Why do people use information technology? A critical review of the technology acceptance model. Information & Management, 40(3), 191-204.
44. Li, D. and Browne, G. J. (2006). The role of need for cognition and mood in online flow experience. Journal of Computer Information Systems, 46(3), 11-17.
45. Lin, T. M., Lu, K. Y. and Wu, J. J. (2012). The effects of visual information in e-wom communication. Journal of Research in Interactive Marketing, 6(1), 7-26.
46. Liu, H., Chu, H., Huang, Q. and Chen, X. (2016). Enhancing the flow experience of consumers in China through ınterpersonal ınteraction in social commerce. Computers in Human Behavior, 58, 306-314.
47. Lu, Y., Zhou, T. and Wang, B. (2009). Exploring Chinese users’ acceptance of ınstant messaging using the theory of planned behavior, the technology acceptance model, and the flow theory. Computers in Human Behavior, 25(1), 29-39.
48. Mainolfi, G. and Vergura, D. T. (2022). The influence of fashion blogger credibility, engagement and homophily on intentions to buy and e-WOM. Results of a binational study. Journal of Fashion Marketing and Management: An International Journal, 26(3), 473-494.
49. Morgan-Thomas, A. and Veloutsou, C. (2013). Beyond technology acceptance: Brand relationships and online brand experience. Journal of Business Research, 66(1), 21-27.
50. Mulyani, V. G., Najib, M. F. and Guteres, A. D. (2021). The effect of perceived usefulness, trust and visual information toward attitude and purchase intention. Journal of Marketing Innovation (JMI), 1(01), 78-93.
51. Nel, D., R., Van Niekerk, J. P., Berthon, J. P. and Davies, T. (1999). Going with the flow: Web sites and customer involvement. Internet Research. 9(2), 109-116.
52. Nunnally, J. C. (1978). Psychometric theory, New York: McGraw-Hill. PewInternet.
53. Ostrander, B. (2007). Problems and solutions to corporate blogging: Model corporate blogging guidelines. The Journal of High Technology Law, 7(2), 226-248.
54. Özkara, B. Y. (2015). Investigation of the effect of flow experience on information satisfaction in the context of consumers’ online ınformation search. ESOGU, Institute of Social Sciences, Unpublished Doctoral Thesis.
55. Pavlou, P. A. (2003). Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model. International Journal of Electronic Commerce, 7(3), 101-134.
56. Pikkarainen, T., Pikkarainen, K., Karjaluoto, H. and Pahnila, S. (2004). Consumer acceptance of online banking: An extension of the technology acceptance model. Internet Research, 14(3), 224-235.
57. Pilke, E. M. (2004). Flow experiences in information technology use. International Journal of Human-Computer Studies, 61(3), 347-357.
58. Raposo Junior, A. E., Mainardes, E. W. and Cruz, P. B. D. (2022). Antecedents of trust in product review blogs and their impact on users’ behavioral intentions. The International Review of Retail, Distribution and Consumer Research, 32(3), 266-292.
59. Richard, M. O. and Chebat, J. C. (2016). Modeling online consumer behavior: Preeminence of emotions and moderating influences of need for cognition and optimal stimulation level. Journal of Business Research, 69(2), 541-553.
60. Rissler, R., Nadj, M. and Adam, M. (2017). Flow in information systems research: Review, integrative theoretical framework, and future directions. In Leimeister, J.M.; Brenner, W. (Hrsg.): Proceedings der 13. Internationalen Tagung Wirtschaftsinformatik (WI 2017), St. Gallen. S. 1051-1065.
61. Rodríguez-Ardura, I. and Meseguer-Artola, A. (2016). What leads people to keep on e-learning? An empirical analysis of users' experiences and their effects on continuance intention. Interactive Learning Environments, 24(6), 1030-1053.
62. Rodriguez-Sanchez, A. M., Schaufeli, W. B., Salanova, M. and Cifre, E. (2008). Flow experience among information and communication technology users. Psychological Reports, 102(1), 29-39.
63. Saxena, A. (2011). Blogs and their impact on purchase intention: A structural equation modelling approach. Paradigm, 15(1-2), 102-110.
64. Shiau, W. L. and Luo, M. M. (2013). Continuance intention of blog users: The impact of perceived enjoyment, habit, user involvement and blogging time. Behaviour & Information Technology, 32(6), 570-583.
65. Shimizu, A. (2021). Measuring the impact of a blog: Quantitative and qualitative aspects. In New Consumer Behavior Theories from Japan (pp. 41-56). Springer, Singapore.
66. Sipahi, B., Yurtkoru, E. S. and Çinko, M. (2008). Sosyal bilimlerde SPSS’le veri analizi. İstanbul: Beta Publishing.
67. Skadberg, Y. X. and Kimmel, J. R. (2004). Visitors’ flow experience while browsing a web site: Its measurement, contributing factors and consequences. Computers in Human Behavior, 20(3), 403-422.
68. Stevens, J. (1996). Applied multivariate statistics for the social sciences, (3rd edition). Mahwah, Lawrence Erlbaum: New Jersey.
69. Tabachnick, B. G. and Fidell, L. S. (2007). Using multivariate Statistics, (5th edition). Pearson Education: Boston.
70. Tao, D. (2009). Intention to use and actual use of electronic information resources: Further exploring technology acceptance model (TAM). In AMIA Annual Symposium Proceedings, 2009, 629. American Medical Informatics Association.
71. Tran, V. and Nguyen, H. (2020). Consumer attitudes towards beauty bloggers and paid blog advertisements on purchase intention in Vietnam. Management Science Letters, 10(5), 1017-1026.
72. Trevino, L. K. and Webster, J. (1992). Flow in computer-mediated communication: Electronic mail and voice mail evaluation and impacts. Communication Research, 19(5), 539-573.
73. Tsai, M. T., Cheng, N. C. and Chen, K. S. (2011). Understanding online group buying intention: The roles of sense of virtual community and technology acceptance factors. Total Quality Management & Business Excellence, 22(10), 1091-1104.
74. Yang, H. and Lee, H. (2018). Exploring user acceptance of streaming media devices: An extended perspective of flow theory. Information Systems and E-Business Management, 16(1), 1-27.
75. Yazgan, Ş. (2012). Blogs as a tool to obtain information on tourist ımpact buying behavior. Adnan Menderes University, Institute of Social Sciences, Unpublished Master Thesis.
76. Yuan, S., Liu, Y., Yao, R. and Liu, J. (2016). An investigation of users’ continuance intention towards mobile banking in China. Information Development, 32(1), 20-34.
77. Webster, J., Trevino, L. K. and Ryan, L. (1993). The dimensionality and correlates of flow in human-computer interactions. Computers in Human Behavior, 9(4), 411-426.
78. Wu, W. Y. and Ke, C. C. (2015). An online shopping behavior model integrating personality traits, perceived risk, and technology acceptance. Social Behavior and Personality: An International Journal, 43(1), 85-97.
79. Van Noort, G., Voorveld, H. A. and 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.
80. Venkatesh, V. (2000). Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information Systems Research, 11(4), 342-365.
81. Venkatesh, V. and Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four Longitudinal Field Studies. Management Science, 46(2), 186-204.
82. Zhou, T. (2013). The effect of flow experience on user adoption of mobile TV. Behaviour & Information Technology, 32(3), 263-272.
83. Zhou, T. and Lu, Y. (2011). Examining mobile instant messaging user loyalty from the perspectives of network externalities and flow experience. Computers in Human Behavior, 27(2), 883-889.