CHAPTER 7 CONCUSION
7.3 L IMITATIONS AND F UTURE W ORKS
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3. Firm provides the alliance based campaign that can notice the maven
4. System automatically gathers the information on the recommender.
7.3 Limitations and Future Works
There are few limitations in our research. First of all, because the constraint of time, we can only conduct a control experiment for a small size subjects which include only 12 mavens over the standard. Therefore, the research should have engaged more participants and mavens for enforce the statistically meaning and try to discover more implications.
Second, some of the data which we use in experiment 2 for detecting the engagement behavior for maven are not interesting in the literature perspective, and also the interface of the system still has a lots of shortcomings that can improved. Moreover, the control experiment environment might impact on the subjects, so that it is possible for them have different with their normal behavior.
Finally, in the numerous interviews include SME and maven; all of them give lot of well advice that we can advance on our system. Moreover, these can increase more engagements, which can be evaluated in the future works.
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[1] Abrantes, J. L., Seabra, C., Lages, C. R., & Jayawardhena, C. (2013). Drivers of in-group and out-of-group electronic word-of-mouth (eWOM). European Journal of Marketing, 47(7), 1067-1088.
[2] Bengtsson, M., & Kock, S. (1999). Cooperation and competition in relationships between competitors in business networks. Journal of Business & Industrial Marketing, 14(3), 178-194.
[3] Bijmolt, T. H., Leeflang, P. S., Block, F., Eisenbeiss, M., Hardie, B. G., Lemmens, A., & Saffert, P. (2010). Analytics for customer engagement. Journal of Service Research, 13(3), 341-356.
[4] Boster, F. J., Kotowski, M. R., Andrews, K. R., & Serota, K. (2011). Identifying influence: Development and validation of the connectivity, persuasiveness, and maven scales. Journal of Communication, 61(1), 178-196.
[5] Brodie, R. J., Hollebeek, L. D., Juric, B., & Ilic, A. (2011). Customer engagement: conceptual domain, fundamental propositions, and implications for research. Journal of Service Research, 1094670511411703.
[6] Burke, R. (2002). Hybrid recommender systems: Survey and experiments.User modeling and user-adapted interaction, 12(4), 331-370.
[7] Cambria, E., Speer, R., Havasi, C., & Hussain, A. (2010, March). SenticNet: A Publicly Available Semantic Resource for Opinion Mining. In AAAI Fall Symposium: Commonsense Knowledge (Vol. 10, p. 02).
[8] Chen, H. H., Kuo, J. J., Huang, S. J., Lin, C. J., & Wung, H. C. (2003). A summarization system for Chinese news from multiple sources. Journal of the American Society for Information Science and Technology, 54(13), 1224-1236.
[9] Cherbakov, L., Galambos, G., Harishankar, R., Kalyana, S., & Rackham, G.
(2005). Impact of service orientation at the business level. IBM Systems Journal, 44(4), 653-668.
[10] Chi, P. Y., & Lieberman, H. (2011, February). Intelligent assistance for conversational storytelling using story patterns. In Proceedings of the 16th international conference on Intelligent user interfaces (pp. 217-226). ACM.
[11] Chou, Szu-Yu, and Soe-Tsyr Daphne Yuan (2014), “How New Media Affect Customer Engagement Behavior in Service Ecosystems,” Summer Marketing Educators’ Conference (AMA Summer '14), San Francisco, CA, USA
[12] Cvijikj, I. P., & Michahelles, F. (2013). Online engagement factors on Facebook brand pages. Social Network Analysis and Mining, 3(4), 843-861.
‧
[13] Dholakia, U. M., Bagozzi, R. P., & Pearo, L. K. (2004). A social influence model of consumer participation in network-and small-group-based virtual communities. International journal of research in marketing, 21(3), 241-263.
[14] El Sawy, O. A., & Pereira, F. (2013). Business modelling in the dynamic digital space. Los Angeles, California: Springer.
[15] Entman, R. M. (1993). Framing: Toward clarification of a fractured paradigm.Journal of communication, 43(4), 51-58.
[16] Feick, L. F., & Price, L. L. (1987). The market maven: A diffuser of marketplace information. The Journal of Marketing, 83-97.
[17] Forrester Consulting. (2008). How Engaged Are Your Customers?.
https://www.adobe.com/engagement/pdfs/Forrester_TLP_How_Engaged_Are_Y our_Customers.pdf. Retrieved on Oct. 1st, 2014
[18] Ghose, A., Ipeirotis, P. G., & Li, B. (2012). Designing ranking systems for hotels on travel search engines by mining user-generated and crowdsourced content.Marketing Science, 31(3), 493-520.
[19] Göksedef, M., & Gündüz-Öğüdücü, Ş. (2010). Combination of Web page recommender systems. Expert Systems with Applications, 37(4), 2911-2922.
[20] Goodey, C., & East, R. (2008). Testing the market maven concept. Journal of Marketing Management, 24(3-4), 265-282.
[21] Hennig-Thurau, T., Malthouse, E. C., Friege, C., Gensler, S., Lobschat, L., Rangaswamy, A., & Skiera, B. (2010). The impact of new media on customer relationships. Journal of Service Research, 13(3), 311-330.
[22] Hu, M., Sun, A., & Lim, E. P. (2007, November). Comments-oriented blog summarization by sentence extraction. In Proceedings of the sixteenth ACM conference on Conference on information and knowledge management (pp.
901-904). ACM.
[23] Katz, E., Blumler, J. G., & Gurevitch, M. (1973). Uses and gratifications research. Public opinion quarterly, 509-523.
[24] Kumar, V., et al. "Undervalued or overvalued customers: capturing total customer engagement value." Journal of Service Research 13.3 (2010): 297-310.
[25] Liu, H., & Singh, P. (2004). ConceptNet—a practical commonsense reasoning tool-kit. BT technology journal, 22(4), 211-226
[26] Moore, J. F. (1998). The rise of a new corporate form. Washington Quarterly, 21(1), 167-181.
[27] Nenkova, A., & McKeown, K. (2012). A survey of text summarization techniques. In Mining Text Data (pp. 43-76). Springer US.
[28] Park, N., Kee, K. F., & Valenzuela, S. (2009). Being immersed in social networking environment: Facebook groups, uses and gratifications, and social
‧
outcomes. CyberPsychology & Behavior, 12(6), 729-733.Leuthesser, L., Kohli, C., & Suri, R. (2003). 2+ 2= 5? A framework for using co-branding to leverage a brand. The Journal of Brand Management, 11(1), 35-47.
[29] Prasad, R. V. V. S. V., & Kumari, V. V. (2012). ACategorical REVIEW OF RECOMMENDER SYSTEMS. System, 1(U2), U3
[30] Scheufele, D. A. (1999). Framing as a theory of media effects. Journal of communication, 49(1), 103-122
[31] Selsky, J. W., Goes, J., & Babüroğlu, O. N. (2007). Contrasting perspectives of strategy making: applications in ‘hyper’environments. Organization Studies,28(1), 71-94
[32] Senecal, S., & Nantel, J. (2004). The influence of online product recommendations on consumers’ online choices. Journal of retailing, 80(2), 159-169.
[33] Shen, D., Yang, Q., Sun, J. T., & Chen, Z. (2006, August). Thread detection in dynamic text message streams. In Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval(pp. 35-42). ACM.
[34] Speer, R., & Havasi, C. (2012, May). Representing General Relational Knowledge in ConceptNet 5. In LREC (pp. 3679-3686).
[35] Van Doorn, J., Lemon, K. N., Mittal, V., Nass, S., Pick, D., Pirner, P., & Verhoef, P. C. (2010). Customer engagement behavior: theoretical foundations and research directions. Journal of Service Research, 13(3), 253-266.
[36] Vargo, S. L., & Lusch, R. F. (2008). Service-dominant logic: continuing the evolution. Journal of the Academy of marketing Science, 36(1), 1-10
[37] Verleye, K., Gemmel, P., & Rangarajan, D. (2013). Managing engagement behaviors in a network of customers and stakeholders evidence from the nursing home sector. Journal of Service Research, 1094670513494015.
[38] von Alan, R. H., March, S. T., Park, J., & Ram, S. (2004). Design science in information systems research. MIS quarterly, 28(1), 75-105.
[39] Yang, C. Y., & Yuan, S. T. (2010). Color Imagery for Destination Recommendation in Regional Tourism. PACIS 2010 Proceedings.
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15 我認為透過觀測站的顧客評論能讓商家了解顧客，
並改善可能流失的使用者問題。
16 我認為藉由評論的正面字和常出現字可以幫助我對
於活動的發想。
17 我認為觀測站上提供意見領袖的資訊是有助於擬定
Facebook 發文的策略。
18 我認為透過活動溫度計，可以隨時調整活動關鍵
字，會讓我比以前更容易針對關鍵字作改變
19 我會打算使用活動溫度計的推薦關鍵字，修改活動
內容及標題
20 我會嘗試藉由活動溫度計裡的連結推薦，建立更多
的外部連結，來把這個活動散佈到更多的網站
21 活動溫度計裡的活動外部連結推薦，讓我更有機會
找到潛在的對我活動有興趣的人
22 活動溫度計裡的活動外部連結推薦的建立，可以增
加更多的搜尋能見度
23 我認為活動溫度計對商家活動的狀況是有幫助的
24 藉由此服務，我能了解到商家如何在新媒體上增加
顧客互動。
25 這新服務整體而言是超乎我的期待的
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Subject Gender Major Age Education
Subject 1 Male MIS 20-30 Master
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APPENDIX-E:
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APPENDIX-F:
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APPENDIX-G:
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APPENDIX-J:
一、關於美食部落格經驗
1. 會如何選擇一家餐廳做為一篇部落格分享文?
2. 在營造部落格時會有同自己的風格嗎?如果有的話，風格是什麼呢?
3. 餐廳風格是否會和自己風格相近?
二、平台系統使用
4. 平常會從哪些地方獲得關於美食的資訊?
5. 除了在自己的部落格外，有在其他美食的推薦平台上互動或撰寫文章嗎?
6. 是否有用過主動推送美食訊息的功能或是都是自己搜尋?
三、資訊類型的接受程度
7. 有曾經因為新聞，而認識或是接觸到新的餐廳的經驗嗎?
8. 有因為活動而認識新的餐廳的經驗嗎?
9. 如果是熟悉的餐廳和其他的商家聯合舉辦活動，會想去了解或前往新的商家 嗎?
10.是否會因為其他評論者的評論或是其他部落格文章影響而前往一家餐廳?
11.對於實際前往餐廳的選擇條件為何!?
12.在前往餐廳前，會先收集關於餐廳的哪些資訊呢?
四、服務改進 13.為何評論、評分?
14.哪邊需要改進?