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論文中文名稱:植基於需求工程程序之智慧型租車推薦機制 [以論文名稱查詢館藏系統]
論文英文名稱:Intelligent Vehicle Leasing Recommender Mechanism Based on Requirements Engineering [以論文名稱查詢館藏系統]
院校名稱:臺北科技大學
學院名稱:管理學院
系所名稱:管理學院資訊與財金管理EMBA專班
畢業學年度:103
畢業學期:第二學期
出版年度:104
中文姓名:簡文顯
英文姓名:Wen-Hsien Chien
研究生學號:102938508
學位類別:碩士
語文別:中文
口試日期:2015/06/02
指導教授中文名:王貞淑
指導教授英文名:Chen–Shu Wang
口試委員中文名:吳建文;賴正育
中文關鍵詞:租車推薦,推薦機制,資料探勘,資料庫知識探索,需求工程,顧客價值,車輛租賃
英文關鍵詞:Requirement Engineering, Data Mining, Knowledge Discovery in Database, Recommender Mechanism, Customer Value, Vehicle Leasing, Car Rental Recommended
論文中文摘要:企業的價值創造主要來自顧客,而顧客價值則主要來自於企業對顧客之服務創新、互動與情境、和新科技的投入。因此,企業如果要在競爭激烈的環境下生存下去,就必須要找出市場區隔與企業核心價值,並且在產業中創新出比同質競爭者更多的顧客價值,以增加企業競爭優勢。

本研究以本國小客車短期租賃之網際網路租車服務為研究範圍,從蒐集資料中得知,雖已有不少業者已利用網際網路來服務顧客,但都只是提供制式的租車選車條件供顧客做選車條件用,幾乎無差異化、亦無競爭力可言。為求區隔市場、創新顧客價值、滿足顧客需求、跳脫紅海競爭,本研究將建置一智慧型租車推薦機制,以改善舊有之網際網路租車服務,及協助企業提升核心競爭優勢,進而達成企業永續經營。

經分析顧客價值文獻後,得知服務創新、互動與情境需求考量對顧客價值有相當程度影響,而滿足顧客自我肯定的需求,又是顧客價值的重要一環。因此,本研究決定在服務創新、互動與情境需求考量為前提下,同時考量每台車輛每天出租之報酬率,運用需求工程程序、資料探勘與資料庫知識探索技術、推薦機制來建置一智慧型租車推薦機制雛型系統,在滿足顧客內心之需求下,同時提高企業收益。

最後,依驗證項目驗證本研究之智慧型租車推薦機制,結果顯示,就顧客角度來看,不分新舊會員有三分之二的人在第一次推薦時就成功,更有高達九成六的人最多只要兩次就接受推薦,而平均使用滿意度也有97.3分。再就企業的角度來看,因為本研究是以報酬率等級最高車輛為推薦首選,而試用結果中有三分之二是第一次推薦就成功,確實有助企業收益。由實驗結果顯示,本研究所提出之智慧型租車推薦機制確實可滿足顧客、創新價值、增加企業競爭力、提高企業收益,達到顧客與企業雙贏之效益。
論文英文摘要:The value creation of enterprise mainly comes from customers. The customer value mainly comes from the customer interaction, the service innovation of business-to-customer, and business investment in new technology. Therefore, enterprise must increase its competitive advantages by identifying market segments and core values as well as being innovative on customer value to survive in the competitive environment.
In this study, the research scope is on the internet rental service of the short-term car rental in Taiwan. Gathered data shown that there are many car rental industry have using internet to service customers, however most of them only provide their customers with standard car selection for rental condition setting. It makes no obviously difference and no competitive at all. To segment the market and increase innovate value and fulfill the customer’s need, the study builds an Intelligent Vehicle Leasing Recommender Mechanism (IVLRM) to improve the car rental service on internet, and help enterprises enhance core competitive edge, and then reached the business continuity.
According to preiovus literature analysis of customer value, it shows a consideration degree of influence on service innovation, interaction and situational considerations demand. Also, to fullfill the customer demand for self-affirmation is an important part of customer value. Therefore, the study has acted on the premise of service innovation, interaction and situational considerations demand, and has begun to consider how to fulfill the customer’s needs and to improve business earnings by using Requirement Engineering, Data Mining, Knowledge Discovery in Database, and Recommender Mechanism to build an IVLRM prototype system.
Finally, to verify the IVLRM on this study, an experiment is implemented. According to the experiment results shows, from the customer perspective, 2/3 of customers were successfully recommended on the first trial regardless old or new members. And, nearly 96% of customers successfully accepted the recommendation results of IVLRM on second rounds. The average satisfaction is 97.3%. From a business perspective, the recommended target is based on the highest level of return and therefore the business profit can be increasing accordingly. The experiment results in the first two-thirds of the recommended are succeeded and it did help on enterprise’s earnings. The experimental results show that the proposed IVLRM prototype in this study can indeed increase customer satisfaction, innovative value and the competitiveness of enterprises, improve corporate earnings, achieve win-win customer and business benefits.
論文目次:摘 要 I
誌 謝 III
目錄 VI
表目錄 VIII
圖目錄 X
第一章 諸論 1
1.1 研究背景與動機 1
1.2 研究目的 8
1.3 研究程序 9
1.4 研究範圍 11
1.5 研究限制 11
1.6 論文架構 12
第二章 文獻探討 13
2.1 車輛租賃業 13
2.2 顧客價值 15
2.2.1服務創新的影響 16
2.2.2互動與情境的影響 18
2.2.3顧客價值影響分析之結論 19
2.3 資料探勘與資料庫知識探索 19
2.3.1 關聯法則分析 21
2.3.2 分群法則 22
2.4 需求工程 23
2.5 推薦機制 24
第三章 研究模型與流程 27
3.1 資料轉換原則 27
3.1.1 個人屬性資料表 28
3.1.2 車輛屬性資料表 29
3.1.3 租賃交易資料表 31
3.2 IVLRM機制架構 33
3.3 機制作業流程 36
第四章 系統設計與驗證 39
4.1 資料萃取 39
4.2 資料清理 41
4.3 資料分析 46
4.4 系統設計與驗證 56
第五章 結論 65
5.1 研究貢獻 65
5.2 未來研究方向 66
參考文獻 67
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