網際網路普及與上網人口快速成長，網路已然成為消費者進行購買決策時蒐集資訊的最佳管道，尤其社群媒體更是很重要的資料來源。近年來圖片分享型社群平台盛行，其中，Instagram甚至在2014年底，成為社群媒體中僅次於Facebook用戶數的第二大社群，有鑑於此，越來越多實體商店和小型賣家開始使用Instagram做社群行銷。儘管Instagram並非為以購物而生的社群平台，消費者依然願意使用這個購物體驗極不流暢，且沒有信用保證的社群平台蒐集資訊，甚至在Instagram上與賣家進行互動，進而產生實際的購物行為。
為了瞭解消費者對Instagram行銷之接受行為，本研究之研究架構基於Moon＆Kim (2001)提出之擴充科技接受模型，並結合Valck et al. (2009)提出之網路社群影響消費者購買決策流程概念性架構，藉由了解消費者購買決策時願意使用Instagram的影響因素，以達到本研究目的。本研究藉由文獻探討及訪談將Instagram之主要特色整理為兩大外部變數，分別為知覺行動性及平台生動性。經過PLS分析後顯示，平台生動性及知覺行動性經由知覺有用性、知覺易用性及知覺娛樂性，影響消費者做購買決策時使用Instagram的態度及意願。由此可見，Instagram上的圖片資訊讓消費者在瀏覽時感到有趣且方便；再者，其為智慧型手機所設計之特色對消費者的購買決策很有幫助。最後本研究基於問卷及研究結果給予企業幾點Instagram社群行銷之建議。

As consumers make purchase decisions, they normally run through a series of evaluations and seek for helpful information on the Internet. They also search information on social media, including Instagram, which has been rising up since it was released. According to that, Instagram has become a potential marketing tool to many businesses. However, it is beyond expectation that consumers use Instagram to assist their purchase decisions and even actually buy things from it, for it was born to be the first picture-sharing social media platform based on mobile phone but e-commerce site.
In order to know consumers’ acceptance of Instagram marketing, this research find out the factors that make consumers willing to use Instagram while making purchase decisions. The research model is based on expanded TAM made by Moon＆Kim (2001)and “Community influence on the consumer decision process” model made by Valck et al. (2009). The study results calculated with PLS show that “platform vividness” and “perceived mobility” positively affect consumers’ attitude and intention of use toward Instagram via “perceived ease of use”, “perceived usefulness” and “perceived playfulness”. The last chapter shows some suggestions and implications regarding to Instagram marketing based on the research analysis.

中文文獻
1. 汪彩艳. (2014). “图像景观” 时代危机初探——以图片分享社交网站 Pinterest 为例. 东南传播(1), 24-25.
英文文獻
2. Aarts, H., Verplanken, B., & Knippenberg, A. (1998). Predicting behavior from actions in the past: Repeated decision making or a matter of habit? Journal of Applied Social Psychology, 28(15), 1355-1374.
3. Abbott, W., Donaghey, J., Hare, J., & Hopkins, P. (2013). An Instagram is worth a thousand words: An industry panel and audience Q&A. Library Hi Tech News, 30(7), 1-6.
4. Adjei, M. T., Noble, S. M., & Noble, C. H. (2010). The influence of C2C communications in online brand communities on customer purchase behavior. Journal of the academy of marketing science, 38(5), 634-653.
5. Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behaviour.
6. Albors, J., Ramos, J. C., & Hervas, J. L. (2008). New learning network paradigms: Communities of objectives, crowdsourcing, wikis and open source. International Journal of Information Management, 28(3), 194-202.
7. Algesheimer, R., Dholakia, U. M., & Hermann, A. (2004). Interplay between brand and brand community: Evidence from European car clubs. Available at SSRN 534542.
8. Bakhshi, S., Shamma, D. A., & Gilbert, E. (2014). Faces engage us: Photos with faces attract more likes and comments on instagram. Paper presented at the Proceedings of the 32nd annual ACM conference on Human factors in computing systems.
9. Bergstrom, T., & Backman, L. (2013). Marketing and PR in Social Media: How the utilization of Instagram builds and maintains customer relationships.
10. Biehal, G., Stephens, D., & Curio, E. (1992). Attitude toward the ad and brand choice. Journal of advertising, 21(3), 19-36.
11. Burnkrant, R. E., & Cousineau, A. (1975). Informational and normative social influence in buyer behavior. Journal of Consumer research, 206-215.
12. Coyle, J. R., & Thorson, E. (2001). The effects of progressive levels of interactivity and vividness in web marketing sites. Journal of advertising, 30(3), 65-77.
13. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 319-340.
14. Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: a comparison of two theoretical models. Management science, 35(8), 982-1003.
15. Devaraj, S., Easley, R. F., & Crant, J. M. (2008). Research note-how does personality matter? Relating the five-factor model to technology acceptance and use. Information systems research, 19(1), 93-105.
16. Drury, G. (2008). Opinion piece: Social media: Should marketers engage and how can it be done effectively? Journal of Direct, Data and Digital Marketing Practice, 9(3), 274-277.
17. Evans, C., Professor Raymond Hackney, D., Rauniar, R., Rawski, G., Yang, J., & Johnson, B. (2014). Technology acceptance model (TAM) and social media usage: an empirical study on Facebook. Journal of Enterprise Information Management, 27(1), 6-30.
18. Evans, D. (2010). Social media marketing: the next generation of business engagement: John Wiley & Sons.
19. Fishbein, M. (1979). A theory of reasoned action: some applications and implications.
20. Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error: Algebra and statistics. Journal of marketing research, 382-388.
21. Fortin, D. R., & Dholakia, R. R. (2005). Interactivity and vividness effects on social presence and involvement with a web-based advertisement. Journal of Business Research, 58(3), 387-396.
22. Gunelius, S. (2011). 30-minute social media marketing: Step-by-step techniques to spread the word about your business fast and free: McGraw-Hill.
23. Ha, S., & Stoel, L. (2009). Consumer e-shopping acceptance: Antecedents in a technology acceptance model. Journal of Business Research, 62(5), 565-571.
24. Hsu, C.-L., & 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.
25. Huang, J.-H., Lin, Y.-R., & Chuang, S.-T. (2007). Elucidating user behavior of mobile learning: A perspective of the extended technology acceptance model. The Electronic Library, 25(5), 585-598.
26. Hubspot (2012). 71% More Likely to Purchase Based on Social Media Referrals. Retrieved from http://blog.hubspot.com/blog/tabid/6307/bid/30239/71-More-Likely-to-Purchase-Based-on-Social-Media-Referrals-Infographic.aspx
27. 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.
28. Kaplan, A. M., & Haenlein, M. (2010). Users of the world, unite! The challenges and opportunities of Social Media. Business Horizons, 53(1), 59-68.
29. Kara, A., & Kaynak, E. (1997). Markets of a single customer: exploiting conceptual developments in market segmentation. European journal of marketing, 31(11/12), 873-895.
30. Karimkhani, C., Connett, J., Boyers, L., Quest, T., & Dellavalle, R. P. (2014). Dermatology on Instagram. Dermatology online journal, 20(7).
31. Kim, A. J., & Ko, E. (2012). Do social media marketing activities enhance customer equity? An empirical study of luxury fashion brand. Journal of Business Research, 65(10), 1480-1486.
32. Kwon, S. J., Park, E., & Kim, K. J. (2014). What drives successful social networking services? A comparative analysis of user acceptance of Facebook and Twitter. The Social Science Journal, 51(4), 534-544. doi: 10.1016/j.soscij.2014.04.005
33. Lee, K. C., & Chung, N. (2009). Understanding factors affecting trust in and satisfaction with mobile banking in Korea: A modified DeLone and McLean’s model perspective. Interacting with computers, 21(5), 385-392.
34. Lee, S., & Kim, B. G. (2009). Factors affecting the usage of intranet: A confirmatory study. Computers in human behavior, 25(1), 191-201.
35. Li, C. (2010). Groundswell. Winning in a world transformed by social technologies. Strategic Direction, 26(8).
36. Liang, T.-P., Huang, C.-W., Yeh, Y.-H., & Lin, B. (2007). Adoption of mobile technology in business: a fit-viability model. Industrial management & data systems, 107(8), 1154-1169.
37. Lieberman, J. N. (2014). Playfulness: Its relationship to imagination and creativity: Academic Press.
38. Lin, K.-Y., & Lu, H.-P. (2011). Why people use social networking sites: An empirical study integrating network externalities and motivation theory. Computers in human behavior, 27(3), 1152-1161. doi: 10.1016/j.chb.2010.12.009
39. Luarn, P., & Lin, H.-H. (2005). Toward an understanding of the behavioral intention to use mobile banking. Computers in human behavior, 21(6), 873-891.
40. Madden, T. J., Ellen, P. S., & Ajzen, I. (1992). A comparison of the theory of planned behavior and the theory of reasoned action. Personality and social psychology Bulletin, 18(1), 3-9.
41. Miles, J. G. (2014). Instagram Power. Build Your Brand and Reach More Customers with the Power of Pictures. The United States of America: McGraw-Hill Education.
42. Mohammadian, M., & Mohammadreza, M. (2012). Identify the success factors of social media (marketing perspective). International Business and Management, 4(2), 58-66.
43. Mohd Suki, N., Ramayah, T., & Mohd Suki, N. (2008). Internet shopping acceptance: Examining the influence of intrinsic versus extrinsic motivations. Direct Marketing: An International Journal, 2(2), 97-110.
44. Moon, J.-W., & Kim, Y.-G. (2001). Extending the TAM for a World-Wide-Web context. Information & Management, 38(4), 217-230.
45. Mull, I. R., & Lee, S.-E. (2014). “PIN” pointing the motivational dimensions behind Pinterest. Computers in human behavior, 33, 192-200.
46. Marketing Land (2012). Study: 72% Of Consumers Trust Online Reviews As Much As Personal Recommendations. Retrieved from http://searchengineland.com/study-72-of-consumers-trust-online-reviews-as-much-as-personal-recommendations-114152
47. Nysveen, H., Pedersen, P. E., & Thorbjornsen, H. (2005a). Explaining intention to use mobile chat services: moderating effects of gender. Journal of consumer Marketing, 22(5), 247-256.
48. Nysveen, H., Pedersen, P. E., & Thorbjornsen, H. (2005b). Intentions to use mobile services: Antecedents and cross-service comparisons. Journal of the academy of marketing science, 33(3), 330-346.
49. O’cass, A., & Fenech, T. (2003). Web retailing adoption: exploring the nature of internet users Web retailing behaviour. Journal of Retailing and Consumer services, 10(2), 81-94.
50. Park, E., & Joon Kim, K. (2013). User acceptance of long-term evolution (LTE) services: an application of extended technology acceptance model. Program, 47(2), 188-205.
51. Peregrin, T. (2012). Pin it to win it: using Pinterest to promote your niche services. Journal of the Academy of Nutrition and Dietetics, 112(12), 1930-1934.
52. Peters, M. (2012). Why the future of social is in the palm of your hand: Mashable.
53. Rettberg, J. W. (2009). ‘Freshly Generated for You, and Barack Obama’How Social Media Represent Your Life. European Journal of Communication, 24(4), 451-466.
54. Rheingold, H. (1993). The virtual community: Homesteading on the electronic frontier: MIT press.
55. Salomon, D. (2013). Moving on from Facebook Using Instagram to connect with undergraduates and engage in teaching and learning. College & Research Libraries News, 74(8), 408-412.
56. Sheppard, B. H., Hartwick, J., & Warshaw, P. R. (1988). The theory of reasoned action: A meta-analysis of past research with recommendations for modifications and future research. Journal of Consumer research, 325-343.
57. Shin, D.-H. (2010). Analysis of online social networks: a cross-national study. Online Information Review, 34(3), 473-495.
58. Sledgianowski, D., & Kulviwat, S. (2009). USING SOCIAL NETWORK SITES: THE EFFECTS OF PLAYFULNESS, CRITICAL MASS AND TRUST IN A HEDONIC CONTEXT. Journal of Computer Information Systems, 49(4).
59. Smith, C. (2014). By the Numbers: 85 interesting Instagram Statistics. (Digital Marketing Ramblings).
60. Steuer, J. (1992). Defining virtual reality: Dimensions determining telepresence. Journal of communication, 42(4), 73-93.
61. Taylor, D. G., Lewin, J. E., & Strutton, D. (2011). Friends, fans, and followers: do ads work on social networks? Business Faculty Publications.
62. Van der Heijden, H. (2003). Factors influencing the usage of websites: the case of a generic portal in The Netherlands. Information & Management, 40(6), 541-549.
63. 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.
64. Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management science, 46(2), 186-204.
65. Vijayasarathy, L. R. (2004). Predicting consumer intentions to use on-line shopping: the case for an augmented technology acceptance model. Information & Management, 41(6), 747-762.
66. Wang, Z., Tchernev, J. M., & Solloway, T. (2012). A dynamic longitudinal examination of social media use, needs, and gratifications among college students. Computers in human behavior, 28(5), 1829-1839.
67. Wu, J.-H., Wang, S.-C., & Lin, L.-M. (2007). Mobile computing acceptance factors in the healthcare industry: A structural equation model. International journal of medical informatics, 76(1), 66-77.