在永續運輸思潮推動下、各國環保意識抬頭，各國政府積極發展綠色能源，並鼓勵民眾使用公共運輸系統、增加能源使用效率。我國交通部於2012年根據行政院核定之「國家節能減碳總計畫」綱要，提出「建構綠色運輸網路」標竿方案，並積極鼓勵民眾搭乘大眾運輸工具，減少私人運輸的使用。近年來，國內外受到COVID-19疫情之影響，許多國家民眾外出的運輸型態有所改變，部份短距運輸尤為明顯。短距運輸儼然成為一種替代性的交通方式，公共自行車成為民眾短距運輸時的首選，而我國最具代表性的公共自行車系統為YouBike，其在疫情影響下的重要度與使用率皆大幅提升，但若未來疫情一旦趨緩，公共自行車的運量是否會回到疫情前的運量仍是未知數。故本研究透過探討YouBike的服務品質以提出改善策略，並加強YouBike服務品質增加其吸引力，以期未來仍會維持穩定的使用量。
本研究透過3D-IPA結合三因子理論，有別於過去傳統IPA與修正IPA，僅以傳統二維象限圖進行策略判別，本研究不但保留問卷測得之外顯重要度、滿意度，並納入偏相關係數推導出隱含重要度，將隱含重要度視為第三維度。以三維空間針對YouBike的14個服務屬性進行分析，將三個變數（外顯重要度、滿意度與隱含重要度）的總平均視為基準，區分出各服務屬性評分之高低，藉此了解服務屬性之三因子類別，以判斷出哪些屬性是興奮因子、基礎因子或績效因子，最後再將各服務屬性所測得的評分結合其因子種類以擬定相關改善策略，以期讓YouBike相關單位在有限的資源，以最符合使用者需求進行資源的投入。
研究結果顯示，由於以三維空間進行分析，減緩了過去研究中服務屬性位於交叉點附近較難明確的提供策略的問題；另外，3D-IPA結合三因子理論，能夠解決兩個傳統IPA之隱含假設，而在實證研究上也能提供比傳統IPA更合適且精確的策略建議，與國家政策面是更為契合且更具邏輯性。為了提升YouBike服務品質，本研究建議優先將滿意度低於總平均之基礎因子進行改善，基礎因子為導致顧客不滿意之因子，為避免使用者的不滿意度，管理人員需要將這些滿意度維持在平均值。因此根據本研究分析結果顯示，YouBike目前最需要進行改善的服務屬性為「Q3.自行車騎乘時的舒適度」與「Q14.騎乘時周遭環境的安全性」。另外，為了提升使用者滿意度，建議將興奮因子之屬性多加著墨，對於YouBike未來經營管理與整體服務品質來說，是非常重要的關鍵並能夠藉此拉開自身與競爭者的距離。因此期望在資源許可下，建議交通主管機關可以針對「Q2.自行車外觀」、「Q7.租賃資訊APP的服務」、「Q8.自行車柱的數量」、「Q9.自行車的數量」與「Q10.公共自行車道的設置」進行改善與發展。

Driven by the trend of sustainable transportation, the awareness of environmental protection in various countries has risen. Governments of various countries are actively developing green energy and encouraging people to use public transportation systems to increase energy efficiency. In 2012, the Ministry of Transport of the People's Republic of my country proposed a benchmark plan for “building a green transportation network” based on the outline of the “National Energy Conservation and Carbon Reduction Plan” approved by the Executive Yuan, and actively encouraged the public (city citizen) to take public transportation and reduce the using of private transportation. In recent years, due to the impact of the COVID-19 epidemic, the transportation patterns of people in many countries have changed, especially in short-distance transportation. Short-distance transportation has become an alternative mode on transportation. Public bicycles are become the first choice for people in short-distance transportation. And the most representative public bicycle system in Taiwan is YouBike, which has greatly increased its importance and utilization rate during the influence of the pandemic. However, once the pandemic slowdowns in the future, it is still unknown that whether the transport volume of public bicycles will return to the past flow. Therefore, this research will propose improvement strategies by exploring the service quality of YouBike and strengthens the service quality of YouBike to increase its attractiveness, so as to maintain stable usage in the future.
This study applies 3D-IPA combined with the three-factor theory, which is different from the traditional IPA and modified IPA in the past. Both of them only use the traditional two-dimensional quadrant diagram to determine the strategy. This study not only retains the Explicit importance and performance, which obtained from questionnaire measurement, but also includes the implicit importance, which derived by partial correlation coefficient, then the implicit importance is regarded as the third dimension, analyzing the 14 service attributes of YouBike in a three-dimensional space. The grand means of explicit importance, actual performance, and implicit importance were calculated for the cross-points in the 3-D IPA cube, and understand the categories of the respective three factors of the service attributes to determine which attributes are the excitement factor, basic factor or performance factor. Finally, the scores measured by each service attribute are combined with the types of factors to formulate relevant improvement strategies. It is hoped that YouBike will invest its limited resources in order to best meet the needs of users.
The research results show that because of the three-dimensional space analysis, it decreased the problem from the past research which the service attribute is located near the crosshair and difficult to clearly provide strategy. In addition, 3D-IPA combined with the three-factor theory can solves the two implicit assumptions of traditional IPA. 3D-IPA can also provide more suitable and accurate strategic suggestions than traditional IPA in empirical research, which is more in line with national policies and more logical. In order to improve the service quality of YouBike, this research suggests that the basic factors whose satisfaction is lower than average should be prioritized to improve. The basic factors are factors that lead to customer dissatisfaction. In order to avoid user dissatisfaction, managers need to maintain these performance levels on average. Therefore, according to the analysis results of this research, the service attributes that YouBike most need to improve at present are "Q3. Comfortable ride" and "Q14. Environmental safety ". In addition, in order to improve user satisfaction, it is recommended that the attributes of the excitement factor be more developed. Excitement factor is a very critical to the future management of YouBike and the overall service quality, and can be used to distance itself from competitors. Therefore, it is hoped that, with the permission of the resources, the transportation authorities are advised to target the service attributes below, "Q2. YouBike appearance", "Q7. Rental information ", "Q8. Number of docks", "Q9. Number of YouBikes" and "Q10. Bike tracks".

第一章 緒論	1
1.1研究背景與動機	1
1.2研究目的	4
1.3研究範圍與限制	4
1.4研究流程圖	5
 第二章 文獻回顧	7
2.1短距運輸服務(Micro-Mobility)	7
2.2 COVID-19下之運輸行為改變	8
2.3國外公共自行車發展	10
2.3.1有樁式自行車發展	11
2.3.2無樁式自行車發展	14
2.4我國公共自行車發展	15
2.4.1我國公共自行車系統比較	17
2.5服務品質	23
2.5.1服務品質的定義與特性	23
2.5.2服務品質的概念模式	25
2.5.3服務品質的衡量方法	32
2.6三因子理論	38
2.7重要度滿意度分析法之演進與應用	43
2.7.1傳統IPA（DCQA）與以量尺為中心法（SCQA）	46
2.7.2對角線IPA法 (Diagonal line, gap analysis)	50
2.7.3新增競爭者IPA(Competitive IPA, gap analysis)	51
2.7.4修正IPA (IPA + 三因子理論)	54
2.7.5同步IPA(Simultaneous IPA, SIPA)	58
2.8小結	60
 第三章 研究方法	65
3.1研究架構	65
3.2問卷設計與尺度衡量	67
3.2.1問卷量表建構設計	68
3.2.2問卷調查方法與對象	68
3.2.3問卷設計內容	69
3.3研究方法	70
3.3.1 傳統IPA	70
3.3.2 3D-IPA結合三因子理論	72
3.3.3 獨立樣本t檢定	77
3.3.4 單因子相依變異數分析檢定	78
	第四章 資料分析與結果	79
4.1問卷基本統計分析	79
4.1.1樣本結構	79
4.1.2單因子相依變異數分析檢定分析	84
4.1.3項目分析	85
4.1.3.1極端值檢定	85
4.1.3.2相關分析	86
4.1.4信度分析	87
4.2使用者問卷重要度滿意度分析	88
4.2.1重要度滿意度分析	90
4.2.1.1傳統IPA（DCQA）	91
4.2.1.2以量尺為中心法（SCQA）	94
4.2.1.3對角線IPA法（Diagonal line, gap analysis）	96
4.2.1.4修正IPA (IPA+Three factor Theory)	98
4.2.2三維重要度滿意度分析(3D-IPA)	101
4.3服務屬性之策略結果比較	110
4.3.1 3D-IPA與傳統IPA策略比較	110
4.3.2 3D-IPA與修正IPA策略比較	118
第五章 結論與建議	121
5.1結論	121
5.2建議	127
參考文獻	131
附錄一、使用者對YouBike服務品質之重要度滿意度調查問卷	145

 
圖目錄
圖 1.1本研究之研究流程圖	6
圖 2.1服務品質缺口分析模式圖	27
圖 2.2服務品質顧客衡量模式	28
圖 2.3服務品質延伸模式	31
圖 2.4 The importance grid	41
圖 2.5三因子理論因子關係圖	42
圖 2.6 Importance-performance grid	48
圖 2.7 Revised Importance-Performance Grid	49
圖 2.8 對角線IPA法（Diagonal line model）	51
圖 2.9 Competitive IPA	53
圖 2.10 Vavra之重要度網格	57
圖 2.11隱含重要度與滿意度圖	57
圖 3.1 研究架構與流程	66
圖 3.2 3D-IPA立體圖	75
圖 4.1重要度平均邊緣估計數剖面圖	85
圖 4.2 傳統IPA (DCQA)之結果散佈圖	93
圖 4.3以量尺為中心法(SCQA)之結果散佈圖	94
圖 4.4對角線IPA法 (Diagonal line)之結果散佈圖	97
圖 4.5修正IPA之結果散佈圖	99
圖 4.6 3D-IPA之立體散佈圖角度I	106
圖 4.7 3D-IPA之立體散佈圖角度II	106
圖 4.8 3D-IPA之立體散佈圖角度III	107
 
表目錄
表 2-1 YouBike、oBike與YouBike 2.0的營運層面比較	17
表 2-2 YouBike、YouBike 2.0的差異	20
表 2-3 MOOVO與YouBike 2.0的差異	22
表 2-4 Regan提出之服務四大特性	23
表 2-5服務品質之衡量構面	33
表 2-6 SERVQUAL量表衡量構面及內容項目	35
表 2-7 IPA相關文獻整理（1977-2020）	44
表 2-8修正IPA相關研究	55
表 2-9 SIPA之策略	59
表 2-10 常用IPA優缺點比較	61
表 3-1 構面與準則敘述	68
表 3-2 重要度與三因子理論之關係	74
表 3-3 3D-IPA之策略表	76
表 4-1 問卷性別分配表	80
表 4-2問卷年齡分配表	80
表 4-3問卷學歷分配表	81
表 4-4 職業學歷分配表	81
表 4-5 平日騎乘YouBike之時段分配表	82
表 4-6 假日騎乘YouBike之時段分配表	82
表 4-7使用者每週騎乘YouBike之次數分配表	83
表 4-8使用者騎乘YouBike之目的分配表	83
表 4-9 Mauchly球形度檢驗a	84
表 4-10 Tests of Within-Subject Effects	84
表 4-11問卷項目分析	86
表 4-12 信度檢定	87
表 4-13 受訪者對YouBike服務品質認知期望與實際感受分析	89
表 4-14 各服務屬性的平均數、標準差、偏態與峰態。	90
表 4-15傳統IPA之策略結果	92
表 4-16以量尺為中心法(SCQA)之策略結果	95
表 4-17對角線IPA法項目結果（重要度-滿意度）	96
表 4-18修正IPA之策略結果	99
表 4-19修正IPA與傳統IPA之策略比較	100
表 4-20共線性指標	102
表 4-21 3D-IPA之隱含重要度	103
表 4-22三因子理論於各屬性之判別	104
表 4-23三維重要度滿意度分析(3D-IPA)之結果策略	107
表 4-24 傳統IPA與3D-IPA策略比較表	111
表 4-25各屬性應用傳統IPA與3D-IPA之策略結果差異	117
表 4-26修正IPA與3D-IPA策略比較表	118
表 4-27各屬性應用修正IPA與3D-IPA之策略結果差異	119

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