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  • 學位論文

確認民宿業重要社群媒體行銷手法之研究

A study of identifying the crucial social media marketing techniques in bed and breakfast industry

指導教授 : 陳隆昇
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摘要


在台灣民宿已經成為遊客住宿的主要選擇。近年來,因為虛擬社群的快速發展,越來越多的住宿者依據網友發表之線上評論(電子口碑),來選擇民宿。在有限的宣傳經費下,社群媒體行銷已逐漸成為一項價廉又有效的網路行銷通道。然而,大多數的民宿業者缺少足夠的人力與時間來與社群成員互動,此外,亦無法知道哪些社群媒體行銷手法較為有效。因此,本研究主要目的是試圖定義影響社群媒體行銷的因素,再利用決策樹(decision tree)、資訊增益(information gain)、Relief-F特徵選取法、關聯性特徵選取(Correlation-based feature selection, CFS)、基於一致性方法(Consistency-based approach)、支持向量機遞歸特徵消除(Support Vector Machine-Recursive Feature Elimination , SVM-RFE)等特徵選取方法,分別就消費者滿意度與忠誠度,確認重要的社群媒體行銷技巧。本研究也利用了狩野模型(Kano model)來了解民宿消費者對網路行銷方式的看法。最後本文以臉書(Facebook)社群行銷來驗證使用方法之有效性。

並列摘要


In Taiwan, bed and breakfast has become one of major choices for travelers’ accommodations. In recent years, with rapid development of virtual communities, more and more travelers make their decisions of overnight accommodations by referring to online comments (electronic word of mouth) shared by other community members. Since the limited advertising budget of a bed and breakfast enterprise, social media marketing could one of cheap and powerful internet advertising channels. However, most of bed and breakfast enterprises lack sufficient human resource and time to interact to the online users of social networking websites. Moreover, there are lots of social media marketing techniques, but we don’t know which one is crucial for a bed and breakfast enterprise. Therefore, this study aims to define the key factors of social media marketing, and then use feature selection approaches including Decision tree algorithm, Information gain, Relief-F, Correlation-based feature selection(CFS), Consistency-based approach, Support Vector Machine-Recursive Feature Elimination to identify the important factors for increasing customer satisfaction and loyalty. In addition, Kano model has been employed to discover the customers’ thinking about some internet marketing methods. A survey of social media marketing in Facebook will be provided to demonstrate the effectiveness of our utilized methods.

參考文獻


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被引用紀錄


許智翔(2016)。植基於區域核主成分分析方法以檢測網路入侵〔碩士論文,朝陽科技大學〕。華藝線上圖書館。https://www.airitilibrary.com/Article/Detail?DocID=U0078-1108201714034011

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