政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/67939

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/67939

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政大典藏 > College of Commerce > Graduate Institute of TIPM > Theses > Item 140.119/67939

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/67939

Title:	Big Data應用於網路口碑監測服務之商業模式研究－以意藍科技為例 Exploring the Business Model of e-WOM Monitoring Services with the Application of Big Data – A Case Study of eLand Tech
Authors:	劉惟成 Liu, Wei Cheng
Contributors:	溫肇東 Wen, Chao Tung 劉惟成 Liu, Wei Cheng
Keywords:	海量資料網路口碑商業模式 Big Data e-WOM business model
Date:	2013
Issue Date:	2014-07-29 16:19:08 (UTC+8)
Abstract:	隨著網路的普及與社群網站的興起，人們的購買行為也跟著改變，從傳統被動地接受企業的產品廣告來決定是否購買，到現在網路社群、網友口碑的力量已經成為人們購買產品或服務主要的參考依據，根據創市際市場研究顧問公司在2012年9月所做的調查發現，大約有53%的消費者會在網路上對商品做評論，且當對商品很滿意時，其上網評論的意願會增加，此調查也發現，網路口碑對於消費者的購買決策具有相當大的影響力，有93%的消費者會上網尋找網路口碑資訊，企業對於網路口碑的影響力實在不可不重視。網路口碑的監測可以提供企業掌握市場上消費者對於產品或服務的反應與評價，但傳統上透過人力搜尋的方式，或自行架設官方討論區的方式來取得網友回饋，都有見樹不見林的窘況，而近年Big Data技術的發展，可以讓企業透過相對低廉的運算成本來進行全面的資料蒐集與分析，提供企業更完整的網路口碑概況，掌握最即時的市場動態。市場上近來也出現導入Big Data技術，提供網路口碑監測服務的第三方公司，而本研究希望探討的是，透過Big Data技術進行網路口碑監測服務的公司，必須擁有何種的核心能力？而這樣的服務到底可以為客戶提供何種價值？最後網路口碑監測服務要如何獲利？本研究藉由個案公司與相關單位的訪談，並輔以獲利世代一書所提出的商業模式九大要素來進行驗證，完整勾勒出網路口碑監測服務的商業模式。本研究最後發現，資訊技術能力是網路口碑監測服務最主要的核心能力，是維持其服務品質的重要基石。不過表面上看來以Big Data來進行網路口碑監測，可以幫助企業在行銷策略上有進一步深入的洞察，但因企業普遍習慣將行銷活動外包給行銷公關公司執行，此網路口碑監測服務對於企業而言，無法完整發揮原先所期待的價值，反而對於行銷公關公司來說，是個可以提升其服務附加價值的工具。最後利用Big Data技術進行分析，並利用雲端的概念提供客戶網路口碑監測服務的應用，具有規模經濟的效果，不過因目前企業限於傳統行銷思維與既有管理制度，要能夠吸引大量企業採用網路口碑監測服務，仍需耗費相當大的資源成本，對客戶進行教育及推廣。 With the popularity of the Internet and the rise of social networking sites, people changed their shopping behaviors. In the past, people passively received advertisements from enterprises and made buying decisions. Today, social network and electronic word of mouth (e-WOM) have become powerful strengths to influence people’s buying decisions. According to the survey of InsightXplorer market consulting corporation in 2012, 53% of customers would make online comments about the products they bought and 93% of customers would search online information before shopping. Obviously, the power of e-WOM is deeply influencing the business. e-WOM monitoring helps corporations to understand the preferences of consumers; but traditionally, enterprises collect online responses of consumers by manual searching operations or building official forum websites. This is found not to be an efficient method. Recently, with the trends of Big Data technology, it benefits enterprises to collect and analyze the complete e-WOM information with lower computing costs. Furthermore, enterprises can handle the real-time market dynamics. There are several enterprises that provide e-WOM monitoring service by using Big Data technology. This thesis is to investigate three questions: 1.) What is the core capability of the enterprises that use Big Data to provide e-WOM monitoring service? 2.) What kinds of value to clients does the e-WOM monitoring service provide with Big Data? 3.) How to make profit with e-WOM monitoring service using Big Data? This thesis describes the business models of e-WOM monitoring service using Big Data through the case study and interviews with the people in related enterprises. The findings are described in below: 1. Information technology is the core capability of e-WOM monitoring service by using Big Data technology. 2. The enterprises were used to outsource their marketing campaigns to marketing companies. Surprisingly, the e-WOM monitoring service help marketing companies to improve their service values instead of bringing more value to the enterprises 3. Using Big Data and cloud computing technology can achieve economies of scale effect, but current enterprises are limited to the traditional marketing concepts and management systems. Attracting more enterprises to adopt e-WOM monitoring service is a big challenge.
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Description:	碩士國立政治大學科技管理與智慧財產研究所 101359003 102
Source URI:	http://thesis.lib.nccu.edu.tw/record/#G0101359003
Data Type:	thesis
Appears in Collections:	[Graduate Institute of TIPM] Theses

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