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研究生:林欣嵐
研究生(外文):Hsin-Lan Lin
論文名稱:運用二元邏輯迴歸與資料探勘技術建構來台旅客重遊之預測模式
論文名稱(外文):Constructing Predictive Models and Investigating Main Factors for Repeat Visitation to Taiwan
指導教授:陳榮昌陳榮昌引用關係
指導教授(外文):林欣嵐
學位類別:碩士
校院名稱:國立臺中技術學院
系所名稱:事業經營研究所
學門:商業及管理學門
學類:企業管理學類
論文種類:學術論文
論文出版年:2007
畢業學年度:95
語文別:英文
論文頁數:130
中文關鍵詞:重遊意願旅遊動機旅客滿意度支援向量機
外文關鍵詞:Repeat VisitationTravel MotivationTourist SatisfactionSupport Vector Machines
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近幾年來,國際旅遊市場已成為全球最具發展潛力的產業,亦是台灣政府積極推動的產業之一。2003年受到「SARS」疫情的影響,台灣觀光市場遭受重大衝擊,導致來台遊客數量與產值普遍下降。因此,台灣觀光局便致力於發展相關旅遊事業,不僅開放了大陸人士來台觀光,同時亦舉辦了許多觀光活動與計劃,以吸引國際旅客來台旅遊。
根據過去的研究指出,有效地維護舊有的顧客,並且讓顧客對於公司產生高度的忠誠度,遠比不斷地開發新顧客,還來得重要許多。而且透過既有顧客間的口耳相傳,不但可節省許多廣告成本,亦可讓新顧客加速產生高度信任。
旅遊動機是人們引發旅遊活動的起因,擁有高度的滿意度可與遊客建立良好且長期的關係,進而達到旅客願意再次重遊。本研究目的為分析旅客之旅遊動機、滿意度、重遊意願及其間之關係,以提供相關旅遊業者做為未來經營管理與規劃活動之參考。換言之,基於所提出的研究架構,期望去建立一個重複性旅客的預測模式,來幫助預測旅客重遊的意願,並找到旅客願意重遊的主要因素。
研究結果顯示,旅客來台最主要的動機為「現代化與基礎建設」、「氣候與環境」、「台灣特有風俗民情」、「夜市和娛樂」、「增長知識」、「放鬆與逃避壓力」、「刺激」及「團聚」。而依據不同的社經特性,旅客的旅遊動機、滿意度與重遊意願亦有顯著的差別。在確立研究假設成立後,運用二元邏輯迴歸、支援向量機與CLC進行重遊旅客的預測,結果發現三種方法皆擁有相當好的預測能力。
Global tourism has become one of the most potent industries in the world in recent years. Simiarly, tourism has been gaining popularity in past years in Taiwan. After the effect of Severe Acute Respiratory Syndrome (SARS), the Taiwan government started to open the door of Taiwan tourism to the people from mainland China and held many activities to attract tourists. After the review of literature, it is costly to capture new customers from competitors because a greater degree of service improvement is needed to convince customers to switch from competitors. Hence, methods that can forecast repeat visitation accurately are greatly needed. The purposes of this study are to build predictive models and find the main factors for repeat visitation. In addition this study mainly investigates the relationship amng travel motivation, tourist satisfaction and intention of repeat visitation. A total of 307 questionaires were found valid and the findings of this study are summarized as follows.
First of all, the results indicated that the main motivations of traveling in Taiwna are “Modernity and infrastructure,” “Climate and surroundings,” “Differebt cultures,” “Nightlife and entertainment,” “Education and knowledge,” “Relaxation and escaping,” Exciting” and “Togetherness.” In addition, some siginificant differences were foumd between demographic characteristics of tourists and the three construct, travel motivations, tourist satisfaction, and repeat visitation. Moreover, the positive relationship among travel motivation, tourist satisfaction and repeat visitation was identified. That explained travel motivations drive people to travel. With travel experience, tourists have some levels of satisfaction, which can directly lead to the intention of repeat visitation. Finally, three predictive models were constructed by Binary Logistic Regression, Support Vector Machines and Clustering-Launched Clssification. The three models possessed good predictive ability.
ABSTRACT (Chinese) i
ABSTRACT (English) ii
ACKNOWAEDGEMENTS iii
TABLE OF CONTENTS iv
LIST OF TABLES vii
LIST OF FIGURES ix
Chapter One 1
INTRODUCTION 1
1.1 Background of the study 1
1.2 Motivations of the study 5
1.3 Purposes of the study 7
1.4 Scope of the study 8
1.5 Procedure of the study 9
Chapter Two 11
LITERATURE REVIEW 11
2.1 Travel motivation 11
2.2 Tourist satisfaction 14
2.3 Repeat Visitation 16
2.4 Relationship between satisfaction and repeat visitation 18
2.5 Summary 20
Chapter Three 22
RESEARCH METHODOLOGY 22
3.1 Introduction 22
3.2 Proposed model and hypotheses of the study 24
3.2.1 Definition of terms 24
3.2.2 Proposed model of the study 25
3.2.3 Hypotheses of the study 26
3.3 Study design 29
3.3.1 Questionnaire and variable design 30
3.3.2 Pretest 35
3.3.3 Estimation of sample size 37
3.3.4 Sample collection 37
3.4 Data analysis procedure 38
3.4.1 Descriptive Analysis 40
3.4.2 Factor Analysis 40
3.4.3 Reliability Analysis 41
3.4.4 Analysis of Variance 41
3.4.5 Regression Analysis 42
3.4.6 Prediction 42
Chapter Four 50
RESULTS AND DISCUSSION 50
4.1 Descriptive Analysis 50
4.1.1 Respondents 50
4.1.2 Demographic characteristics of the respondents 51
4.1.3 Travel behavior characteristics of the respondents 53
4.1.4 Characteristics of variables 55
4.2 Factor Analysis 58
4.3 Reliability Analysis 62
4.4 Analysis of Variance 65
4.5 Regression Analysis 80
4.6 Prediction 82
4.6.1 Results of Binary Logistic Regression 82
4.6.2 Results of Support Vector Machines 85
4.6.3 Results of Clustering-Launched Classification 87
4.7 Discussion 88
Chapter Five 92
CONLCUSION 92
5.1 Summary of the findings 92
5.2 Implications 94
5.3 Limitations 97
5.4 Direction of future research 98
References 99
Appendix A. Survey questionnaire (Chinese) 106
Appendix B. Survey questionnaire (Chinese Simplified) 109
Appendix C. Survey questionnaire (English) 112
Appendix D. Survey questionnaire (Japanese) 115
Publications 121
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