從服務科學的觀點探討社群商務接受意圖：以住房社群為例

Factors Influence Acceptance of Accommodation Community：A Perspective of Service Science

王妘伃

服務科學 ； 社群商務 ； 計劃行為理論 ； KANO二維服務品質 ； 巨量資料 ； Service science ； Social community ； TBP model ； KANO model ； Big data

臺中科技大學資訊管理系碩士班學位論文

2017年

碩士

王淑玲

繁體中文

根據美國市場研究機構eMarketer調查顯示全國網路社群使用人數持續成長，各行各業為了拓展商機、增進企業與顧客的互動便開始著手經營社群商務。國際上著名的住房社群網站Airbnb在2015年，社群網站使用人數已超過1,700萬人，如今，住房社群如雨後春筍般地出現，其中提升服務品質，藉此維持顧客忠誠度顯得越來越重要。 本研究從服務科學的角度進行研究探討，實驗一(接受意圖實驗測試)以計畫行為理論(Theory of Planned Behavior, TPB)與PZB服務品質模式及過去相關文獻提出研究構面，藉由問卷調查分析進行實證研究，從消費者的立場探討社群商務接受意圖；實驗二(KANO服務品質要素)，使用科技接受模式(Technology Acceptance Model, TAM)與KANO二維服務品質模式，針對住房社群網站進行服務品質要素歸類，可幫助社群公司瞭解消費者對於網站功能之服務要素屬性。 實驗一採用AMOS 24、SPSS 20與PLS2.0 統計軟體進行驗證分析，所蒐集樣本共1104份，研究驗證與假說檢定結果表示「接受意圖」、「知覺玩興」、「知覺行為控制」、「功能性服務品質」、「信任、「社會效益」均有顯著影響效果；「互動性服務品質、「顧客價值」則無顯著影響效果。 實驗二採用KANO二維服務品質模式分析住房社群中，以巨量資料分析為基礎的「住房搜尋介面」、「視覺化搜尋介面」、「地圖式互動搜尋介面」與「住房投資決策分析工具」之便利性、知覺有用性、知覺易用性、使用態度與行為意圖，總收樣本共107份，分析結果顯示，在20個問項中，一維品質(O)要素的共有15項，為魅力品質(A)與無差異品質要素(I)，各有2項，必須要素品質(M)，共1項。 最後，運用Airbnb open dataset進行J48決策樹分析，分析結果可幫助消費者瞭解國家地區與其住房價格、房型之間的關聯性，輔助消費者進行訂房決策。本研究之相關分析結果有助於了解消費者的需求，未來將有助於商務社群服務品質的提昇。

According to the survey of US eMarketer survey, the number of social community continued to grow. Various industries have started to apply social community for spread their business opportunities, and enhance their relationship with customers. The number of users of famous accommodation community website Airbnb has grown more than 17 million people in 2015. Nowadays, the accommodation community has sprung up rapidly, therefore it is more and more important to improve the quality of service for maintain customer loyalty. This study from the perspective of service science, and applies an application-oriented perspective of service science for studying the social community websites. Firstly, this study according to forward proposed research model and using the structure of Theory of Planned Behavior, and PZB service quality model for empirical study. An empirical study of the factors affecting users' acceptance intention use of the social community, from the viewpoint of customers, is presented in this study. Secondly, this study further applies the Technology Acceptance Model and KANO service quality model for classifying service quality attributes of accommodation community, the results can help the enterprises to understand the critical service quality factors, and for further improving the service quality of the social community. In study I, this study used AMOS24、SPSS 20 and PLS2.0 statistical software for testing the research framework and hypotheses. This study has collected 1104 samples, and the results show that perceived playfulness, perceived behavioral control, functional service quality, trust, and social benefit are all significant positive effect on the acceptance intention use. However, the interactive service quality, and the customer perceived value are no significant positive effect on the acceptance intention use. In study II, this study used KANO service quality model for further analyzing the service quality attributes of the big data of searching Interface, the visual search Interface, map Interactive search interface, the room type price analysis tool, and the housing investment decision analysis tool of the accommodation community website. The study has collected 107 samples, and the analysis results show that there are 20 items of One-Dimensional Quality, 2 items of Attractive Quality, 2 items of Indifferent Quality and 1 item of Must-Be Quality. Finally, this study purposes many future directions, the study also purposes a method for applying the Airbnb open data set, and through the big data analysis function to support consumer for understanding the room prices and room types of an area for users' book room decision making. The results of this study will help accommodation community enterprises to understand customers’ needs and help to improve the quality of service.

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