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研究生:葉國煥
研究生(外文):YEH,KUO-HUAN
論文名稱:區隔智慧型手機使用者再購買意圖市場之研究
論文名稱(外文):Segmentation for the Markets of Smartphone users’ Repurchase Intention
指導教授:李政宗李政宗引用關係
指導教授(外文):Lee,Cheng-Chung
口試委員:李政宗,陳基國,謝尚行
口試委員(外文):Lee,Cheng-Chung Chen.Kee-Kuo Hsieh,Shang-Hsing
口試日期:2018-06-13
學位類別:碩士
校院名稱:育達科技大學
系所名稱:行銷與流通管理所
學門:商業及管理學門
學類:行銷與流通學類
論文種類:學術論文
論文出版年:2018
畢業學年度:106
語文別:英文
論文頁數:40
中文關鍵詞:隨機森林分類法消費者創新性產品認知創新屬性再購買意願
外文關鍵詞:Naïve Bayes classificationConsumer innovativeness,GenderGenerationsPerceived innovation attributesrandom forestRepurchase intention
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本文以性別、世代與品牌為調和變數,探討消費者創新性、產品認知創新屬性及使用功能頻率做為區隔消費者再購買智慧型手機意願之變數,以調查收集並回收357份完整的問卷。問卷資料以隨機森林分類法(random forest classification)分析所獲得的資料。研究結果發現消費者創新性、品牌與產品認知創新屬性對再購買智慧型手機意願無明顯的關係,但常用 ”使用日曆”與”觀察天氣”兩項功能的消費者與再購買智慧型手機意願卻有明顯的關係,可做為市場區隔變數。
To investigate the relationships between consumer innovativeness, perceived innovation attributes and repurchases intention of smartphone users, as well as the moderating roles of gender, generation and brand on this relationship, a total of 357 respondents were surveyed, and completed their questionnaires. Due to the facts that the intention of repurchase should be restricted to the unit interval (0, 1) and that the distributions of the intention of repurchase is very possibly asymmetric, the random forest classification is employed by this paper. It is found that the effects of customer innovativeness and innovation attributes on repurchase intention of smartphone users are insignificant, while the impact of usage frequent pattern: “use calendar” and “look up weather” on repurchase intention of smartphone users influence the market segmentation. Managerial implications are also discussed.
誌謝 I
摘要 II
ABSTRACT III
Chapter 1.Introduction 1
Chapter 2.Conceptual framework and hypotheses development 7
2.1Conceptual framework 7
2.2 Repurchase intention 8
2.3 Consumer innovativeness 10
2.4 Perceived innovation attributes 11
2.5 Usage frequency 12
2.6 Moderators 13
Chapter 3 Naïve Bayes Classification and Random Forest Classification 16
3.1Naïve Bayes Classification 16
3.2Random forest classifier 16
Chapter 4. Methodology 18
4.1 Measures and questionnaire development 18
4.2 Data Collection 18
Chapter 5. Results 21
5.1 Measure validation 21
5.2 Naïve Bayes Classification Model 22
5.3 Random forest classifier 23
Chapter 6. Conclusions and suggestions 28
6.1 Conclusions 28
6.2 Suggestions 29
Appendix 30
References:(參考) 34


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