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題名 最大化顧客參與行為於推薦平台： 以品牌合作角度塑造達人知識
Maximizing Customer Engagement Behavior through Recommender System: Framing Maven Knowledge with Brand Alliance Perspective
作者 巫承安
Wu, Cheng An
貢獻者 苑守慈
Yuan, Soe Tysr
巫承安
Wu, Cheng An
關鍵詞 顧客參與行為
社群化推薦平台
品牌合作
重塑知識
達人
Customer engagement behavior
Social recommender
Maven knowledge
Value co-creation
Multi-stakeholder
日期 2015
上傳時間 3-Aug-2015 13:20:27 (UTC+8)
摘要 在這個充滿繁多新媒體時代，使用者面臨到眾多資料和快速變動的環境，使用者在媒體的使用行為和選擇上更加依賴各種推薦平台的建議。除此之外，隨著社群媒體的興起，許多的推薦平台整合了社群的人們關係來提供更準確的建議和選擇。雖然推薦系統在影響使用者的使用行為有顯著的效果，然而企業和品牌卻鮮少去關注或了解如何增加顧客參與行為在整合社群媒體的推薦平台上。顧客參與行為並不只有傳統的交易行為，而是包含了所有直接和間接影響企業品牌的行為，像是使用者回饋、口碑傳播等。而且，現今尚未有清楚明確的定義哪些關鍵因素，會影響顧客參與行為在社群化推薦推薦系統，來藉此獲得顧客關注，形成正向生態系統。
本研究中，我們根據達人在社群化推薦平台中具有重要的影響力的觀點，以促進重塑達人知識來改變原有達人的行為和態度，藉此影響所有一般使用者在社群化推薦平台的顧客參與行為。我們提出新的架構和系統來幫助中小型商家在推薦平台上影響更多的推薦達人，獲得更多的顧客參與。我們建立商家參與後台來幫助中小型商家可以洞悉達人的行為，我們也建立了重新塑造資訊的系統，提供達人所需要的訊息文章，藉此來改變達人的知識和行為。此研究發現，達人的行為會受到娛樂型、知識型和激勵型的文章訊息影響行為，一般使用者也會受到達人行為影響。此外我們藉由品牌合作角度來幫助得到更多的顧客參與行為，我們發現中小型商家可以在社群化推薦平台獲得顧客參與且建立一個正向機制循環。
With the highly dynamic trend of service economy, the firms are increasingly to co-create value with brand alliance to advance their competition advantage. On the other hand, with the massive information on the new media, the referrals provided by recommender systems in combination with social media have significantly impact on customer behavior. In light of these trends, the markers and firms should aim to increase the customer engagement behavior (CEB) which goes beyond the traditional transactions including purchase and non-purchase behavior on social recommenders.
In this research, we focus on the role of mavens who are powerful influencers on the social recommender. We propose a new conceptual framework for facilitating to impact the maven’s knowledge and behavior and increase the CEB on the social recommender for Small/Middle Enterprise (SME). We establish the SME support engagement site for increasing the CEB on social recommender and framing knowledge context to influence maven for achieving the insight of the maven’s behavior. As the result of research, we discover that maven engagement behavior would be influenced by the entertainment, information and incentive types in context from the brand alliance perspective and the non-maven are willing to be affected by maven behavior. Moreover, with this discovery, the SME can increase the customer engagement behavior on the social recommender
參考文獻 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.
Bengtsson, M., & Kock, S. (1999). Cooperation and competition in relationships between competitors in business networks. Journal of Business & Industrial Marketing, 14(3), 178-194.
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.
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.
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.
Burke, R. (2002). Hybrid recommender systems: Survey and experiments.User modeling and user-adapted interaction, 12(4), 331-370.
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).
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.
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.
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.
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
Cvijikj, I. P., & Michahelles, F. (2013). Online engagement factors on Facebook brand pages. Social Network Analysis and Mining, 3(4), 843-861.
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.
El Sawy, O. A., & Pereira, F. (2013). Business modelling in the dynamic digital space. Los Angeles, California: Springer.
Entman, R. M. (1993). Framing: Toward clarification of a fractured paradigm.Journal of communication, 43(4), 51-58.
Feick, L. F., & Price, L. L. (1987). The market maven: A diffuser of marketplace information. The Journal of Marketing, 83-97.
Forrester Consulting. (2008). How Engaged Are Your Customers?. https://www.adobe.com/engagement/pdfs/Forrester_TLP_How_Engaged_Are_Your_Customers.pdf. Retrieved on Oct. 1st, 2014
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.
Göksedef, M., & Gündüz-Öğüdücü, Ş. (2010). Combination of Web page recommender systems. Expert Systems with Applications, 37(4), 2911-2922.
Goodey, C., & East, R. (2008). Testing the market maven concept. Journal of Marketing Management, 24(3-4), 265-282.
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.
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.
Katz, E., Blumler, J. G., & Gurevitch, M. (1973). Uses and gratifications research. Public opinion quarterly, 509-523.
Kumar, V., et al. "Undervalued or overvalued customers: capturing total customer engagement value." Journal of Service Research 13.3 (2010): 297-310.
Liu, H., & Singh, P. (2004). ConceptNet—a practical commonsense reasoning tool-kit. BT technology journal, 22(4), 211-226
Moore, J. F. (1998). The rise of a new corporate form. Washington Quarterly, 21(1), 167-181.
Nenkova, A., & McKeown, K. (2012). A survey of text summarization techniques. In Mining Text Data (pp. 43-76). Springer US.
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.
Prasad, R. V. V. S. V., & Kumari, V. V. (2012). ACategorical REVIEW OF RECOMMENDER SYSTEMS. System, 1(U2), U3
Scheufele, D. A. (1999). Framing as a theory of media effects. Journal of communication, 49(1), 103-122
Selsky, J. W., Goes, J., & Babüroğlu, O. N. (2007). Contrasting perspectives of strategy making: applications in ‘hyper’environments. Organization Studies,28(1), 71-94
Senecal, S., & Nantel, J. (2004). The influence of online product recommendations on consumers’ online choices. Journal of retailing, 80(2), 159-169.
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.
Speer, R., & Havasi, C. (2012, May). Representing General Relational Knowledge in ConceptNet 5. In LREC (pp. 3679-3686).
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.
Vargo, S. L., & Lusch, R. F. (2008). Service-dominant logic: continuing the evolution. Journal of the Academy of marketing Science, 36(1), 1-10
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.
von Alan, R. H., March, S. T., Park, J., & Ram, S. (2004). Design science in information systems research. MIS quarterly, 28(1), 75-105.
Yang, C. Y., & Yuan, S. T. (2010). Color Imagery for Destination Recommendation in Regional Tourism. PACIS 2010 Proceedings.
描述 碩士
國立政治大學
資訊管理研究所
102356023
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0102356023
資料類型 thesis
dc.contributor.advisor 苑守慈zh_TW
dc.contributor.advisor Yuan, Soe Tysren_US
dc.contributor.author (Authors) 巫承安zh_TW
dc.contributor.author (Authors) Wu, Cheng Anen_US
dc.creator (作者) 巫承安zh_TW
dc.creator (作者) Wu, Cheng Anen_US
dc.date (日期) 2015en_US
dc.date.accessioned 3-Aug-2015 13:20:27 (UTC+8)-
dc.date.available 3-Aug-2015 13:20:27 (UTC+8)-
dc.date.issued (上傳時間) 3-Aug-2015 13:20:27 (UTC+8)-
dc.identifier (Other Identifiers) G0102356023en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/77175-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 資訊管理研究所zh_TW
dc.description (描述) 102356023zh_TW
dc.description.abstract (摘要) 在這個充滿繁多新媒體時代，使用者面臨到眾多資料和快速變動的環境，使用者在媒體的使用行為和選擇上更加依賴各種推薦平台的建議。除此之外，隨著社群媒體的興起，許多的推薦平台整合了社群的人們關係來提供更準確的建議和選擇。雖然推薦系統在影響使用者的使用行為有顯著的效果，然而企業和品牌卻鮮少去關注或了解如何增加顧客參與行為在整合社群媒體的推薦平台上。顧客參與行為並不只有傳統的交易行為，而是包含了所有直接和間接影響企業品牌的行為，像是使用者回饋、口碑傳播等。而且，現今尚未有清楚明確的定義哪些關鍵因素，會影響顧客參與行為在社群化推薦推薦系統，來藉此獲得顧客關注，形成正向生態系統。
本研究中，我們根據達人在社群化推薦平台中具有重要的影響力的觀點，以促進重塑達人知識來改變原有達人的行為和態度，藉此影響所有一般使用者在社群化推薦平台的顧客參與行為。我們提出新的架構和系統來幫助中小型商家在推薦平台上影響更多的推薦達人，獲得更多的顧客參與。我們建立商家參與後台來幫助中小型商家可以洞悉達人的行為，我們也建立了重新塑造資訊的系統，提供達人所需要的訊息文章，藉此來改變達人的知識和行為。此研究發現，達人的行為會受到娛樂型、知識型和激勵型的文章訊息影響行為，一般使用者也會受到達人行為影響。此外我們藉由品牌合作角度來幫助得到更多的顧客參與行為，我們發現中小型商家可以在社群化推薦平台獲得顧客參與且建立一個正向機制循環。		zh_TW
dc.description.abstract (摘要) With the highly dynamic trend of service economy, the firms are increasingly to co-create value with brand alliance to advance their competition advantage. On the other hand, with the massive information on the new media, the referrals provided by recommender systems in combination with social media have significantly impact on customer behavior. In light of these trends, the markers and firms should aim to increase the customer engagement behavior (CEB) which goes beyond the traditional transactions including purchase and non-purchase behavior on social recommenders.
In this research, we focus on the role of mavens who are powerful influencers on the social recommender. We propose a new conceptual framework for facilitating to impact the maven’s knowledge and behavior and increase the CEB on the social recommender for Small/Middle Enterprise (SME). We establish the SME support engagement site for increasing the CEB on social recommender and framing knowledge context to influence maven for achieving the insight of the maven’s behavior. As the result of research, we discover that maven engagement behavior would be influenced by the entertainment, information and incentive types in context from the brand alliance perspective and the non-maven are willing to be affected by maven behavior. Moreover, with this discovery, the SME can increase the customer engagement behavior on the social recommender		en_US
dc.description.tableofcontents CHAPTER 1 INTRODUCTION 1
1.1 BACKGROUND AND MOTIVATION 1
1.2 RESEARCH PROBLEM 3
1.3 RESEARCH METHOD 5
1.4 PURPOSE AND CONTRIBUTION 6
1.5 CONTENT ORGANIZATION 7
CHAPTER 2 LITERATURE REVIEW 8
2.1 CUSTOMER ENGAGEMENT BEHAVIOR 8
2.1.1 Customer Engagement Behavior matrix 10
2.2 RECOMMENDATION TYPE 12
2.3 FRAMING THE INFORMATION FOR DIFFUSION 16
2.3.1 Framing theory 16
2.3.2 Summarization 17
CHAPTER 3 IENGAGEMENT PROJECT 19
3.1 THE CONCEPTUAL FRAMEWORK OF IENGAGEMENT 20
3.1.1 Situation – Organization and Eco-stakeholders 20
3.1.2 Organism – E-empowerment 21
3.1.3 Behavior – Customer Engagement Behavior 22
3.1.4 Consequence - Value conversion 23
3.2 THE SYSTEM ARCHITECTURE OF IENGAGEMENT 24
3.3 THE SYSTEM SCENARIO 26
CHAPTER 4 MAVEN INFLUENCE FOR CUSTOMER ENGAGEMENT ON SOCIAL RECOMMENDER 29
4.1 THE CONCEPTUAL FRAMEWORK 29
4.2 SYSTEM ARCHITECTURE 33
4.3 BRAND ALLIANCE MODULE 35
4.4 FRAMING MODULE 37
4.5 MEASURE MAVEN ENGAGEMENT MODULE 41
4.6 MEASURE CUSTOMER ENGAGEMENT MODULE 42
CHAPTER 5 APPICATION SCENARIOS 44
5.1 THE CONCEPT BEHIND THE SERVICE 44
5.1 SERVICE JOURNEY AND SCENARIOS 44
6.1 PROPOSITIONS 52
6.2 ASSUMPTIONS 53
6.3 EXPERIMENT DESIGN DETAILS 55
6.3.1 BACKSTAGE SUPPORT SERVICE IN EXPERIMENT 1`S DESIGN AND OBJECTIVE 55
6.3.2 FRAMING KNOWLEDGE SERVICE IN EXPERIMENT 2 DESIGN AND OBJECTIVE 60
6.4 EXPERIMENT RESULT AND DETAILS 65
6.4.1 THE RESULT OF EXPERIMENT 1 65
6.4.2 THE RESULT OF EXPERIMENT 2 68
6.5 THE INTERVIEW WITH THE MAVEN 97
6.6 OTHER FINDING 101
6.7 DISCUSSION AND FINDING 105
CHAPTER 7 CONCUSION 109
7.1 CONTRIBUTION 109
7.2 MANAGERIAL IMPLICATIONS 110
7.3 LIMITATIONS AND FUTURE WORKS 113
REFERENCE 114		zh_TW
dc.format.extent 5308531 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0102356023en_US
dc.subject (關鍵詞) 顧客參與行為zh_TW
dc.subject (關鍵詞) 社群化推薦平台zh_TW
dc.subject (關鍵詞) 品牌合作zh_TW
dc.subject (關鍵詞) 重塑知識zh_TW
dc.subject (關鍵詞) 達人zh_TW
dc.subject (關鍵詞) Customer engagement behavioren_US
dc.subject (關鍵詞) Social recommenderen_US
dc.subject (關鍵詞) Maven knowledgeen_US
dc.subject (關鍵詞) Value co-creationen_US
dc.subject (關鍵詞) Multi-stakeholderen_US
dc.title (題名) 最大化顧客參與行為於推薦平台： 以品牌合作角度塑造達人知識zh_TW
dc.title (題名) Maximizing Customer Engagement Behavior through Recommender System: Framing Maven Knowledge with Brand Alliance Perspectiveen_US
dc.type (資料類型) thesisen
dc.relation.reference (參考文獻) 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.
Bengtsson, M., & Kock, S. (1999). Cooperation and competition in relationships between competitors in business networks. Journal of Business & Industrial Marketing, 14(3), 178-194.
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.
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.
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.
Burke, R. (2002). Hybrid recommender systems: Survey and experiments.User modeling and user-adapted interaction, 12(4), 331-370.
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).
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.
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.
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.
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
Cvijikj, I. P., & Michahelles, F. (2013). Online engagement factors on Facebook brand pages. Social Network Analysis and Mining, 3(4), 843-861.
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.
El Sawy, O. A., & Pereira, F. (2013). Business modelling in the dynamic digital space. Los Angeles, California: Springer.
Entman, R. M. (1993). Framing: Toward clarification of a fractured paradigm.Journal of communication, 43(4), 51-58.
Feick, L. F., & Price, L. L. (1987). The market maven: A diffuser of marketplace information. The Journal of Marketing, 83-97.
Forrester Consulting. (2008). How Engaged Are Your Customers?. https://www.adobe.com/engagement/pdfs/Forrester_TLP_How_Engaged_Are_Your_Customers.pdf. Retrieved on Oct. 1st, 2014
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.
Göksedef, M., & Gündüz-Öğüdücü, Ş. (2010). Combination of Web page recommender systems. Expert Systems with Applications, 37(4), 2911-2922.
Goodey, C., & East, R. (2008). Testing the market maven concept. Journal of Marketing Management, 24(3-4), 265-282.
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.
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.
Katz, E., Blumler, J. G., & Gurevitch, M. (1973). Uses and gratifications research. Public opinion quarterly, 509-523.
Kumar, V., et al. "Undervalued or overvalued customers: capturing total customer engagement value." Journal of Service Research 13.3 (2010): 297-310.
Liu, H., & Singh, P. (2004). ConceptNet—a practical commonsense reasoning tool-kit. BT technology journal, 22(4), 211-226
Moore, J. F. (1998). The rise of a new corporate form. Washington Quarterly, 21(1), 167-181.
Nenkova, A., & McKeown, K. (2012). A survey of text summarization techniques. In Mining Text Data (pp. 43-76). Springer US.
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.
Prasad, R. V. V. S. V., & Kumari, V. V. (2012). ACategorical REVIEW OF RECOMMENDER SYSTEMS. System, 1(U2), U3
Scheufele, D. A. (1999). Framing as a theory of media effects. Journal of communication, 49(1), 103-122
Selsky, J. W., Goes, J., & Babüroğlu, O. N. (2007). Contrasting perspectives of strategy making: applications in ‘hyper’environments. Organization Studies,28(1), 71-94
Senecal, S., & Nantel, J. (2004). The influence of online product recommendations on consumers’ online choices. Journal of retailing, 80(2), 159-169.
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.
Speer, R., & Havasi, C. (2012, May). Representing General Relational Knowledge in ConceptNet 5. In LREC (pp. 3679-3686).
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.
Vargo, S. L., & Lusch, R. F. (2008). Service-dominant logic: continuing the evolution. Journal of the Academy of marketing Science, 36(1), 1-10
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