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研究生:李曉青
研究生(外文):Hsiao-Ching Lee
論文名稱:考慮競爭者因素下之市場區隔研究-以高科技產業物流委外為例
論文名稱(外文):Segmentation Involves Competition: Macro or Micro-Evidence from Logistics Usage of High-Tech Industry in Taiwan
指導教授:蔡明志蔡明志引用關係
學位類別:碩士
校院名稱:國立中興大學
系所名稱:行銷學系所
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:2009
畢業學年度:97
語文別:英文
論文頁數:55
中文關鍵詞:市場區隔高科技產業航空貨運巢式羅吉特模式潛在類別模式
外文關鍵詞:Market segmentationHigh-tech industryAir logisticsNested logit modelLatent class model
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本研究的目的是發展一個具有效能的市場區隔模式架構來研究台灣高科技產業(High-tech industry)在航空貨運(Air cargo)市場區隔分析,本研究方法是透過巢式羅吉特模式(Nested loigt model)與潛在類別模式(Latent class model)二階段模式,來建立一個同時考量供應廠商的分類與顧客廠商分群之有效能的市場區隔模式,並且比較與傳統程序之有效能性。
首先,有別於傳統的市場區隔之模式,並且考量到競爭的觀念下,我們使用巢式羅吉特模式(Nested loigt model)以具有競爭資訊的個體變數(micro variable)來進行物流服務提供者之分類,分別為貨運承攬業者與快遞業這兩個分類,接下來將分類結果,使用潛在類別模式(Latent class model)以總體變數(macro variable)來進行顧客廠商之市場區隔,實證結果為總體變數只有出口地和重量大小仍具有顯著的區隔效果,其他如運送頻率、產品特徵和運送時間則不具有區隔效果。
本研究的實際結果,除了提供有價值的市場給航空貨運服務業者在未來顧客鎖定以及服務定位的決策制定參考,亦能提供未來市場區隔研究模式架構之參考,具有理論操作的貢獻。
The purpose of this research devised a model structure to execute effective micro-macro sequence for market segmentation and compare the operational segmentation efficiency. We applied this model to logistics usage of high-tech industry in Taiwan.
Instead of conventional market segmentation model, we devised the effective micro-macro sequence model that could not only better reveal the competitive relationship between suppliers, but also be required in clustering customers. Firstly, nested logit (NL) model is used to incorporate the concept of competition using the micro variables. The results revealed that two types of air cargo logistics service provider can be distinguished (i.e. express base LSPs and forwarder base LSPs). Following NL model, we developed the efficient sequence of micro-macro segmentation by using latent class model (LCM) for market segmentation. Among the five macro variables identified, shipment destination and size still serve as effective market classifications although their interaction effect in further characterizing the segments is negligible. Conversely, time in transit, shipment frequency, and product status, have lost their validation and stability.
This study provides an efficient operational model, demonstrated by empirical investigation, for the effective identification of potential customers. Addressing customer retention and defection by segment is useful to help carriers justify market target and positioning strategies.
Contents
Chapter 1 Introduction 1
1.1 Research Motivation 1
1.2 Research Purpose 4
1.3 Research Process 5
Chapter 2 Literature Review 7
2.1 Market segmentation 7
2.2 Latent Class model 10
2.3 High-tech industry 12
2.4 Air cargo logistics service provider 14
Chapter 3 Methodological Framework 16
3.1 Identification of Segmentation Variables 16
3.1.1 Macro Segmentation Variables 16
3.1.2 Micro Segmentation Variables 19
3.2 Nested Logit Models Structure 20
3.3 Latent Class Model Structure 22
Chapter 4 Data Collection and Analysis 25
4.1 Data Collection 25
4.2 An Effective Market Segmentation Model 28
4.2.1 Competitive Structure: Nested Logit Model 29
4.2.2 Market Segmentation: Latent Class Model 32
4.2.3 Comparative Analysis in Model Effectiveness 35
4.3 Choice Probabilities of Segmentation 38
Chapter 5 Findings and managerial implications 40
Chapter 6 Study limits and future research 43
References 44
Appendix 51


List of Table and Figure
Table 3-1 Literature review of macro variables for segmentation 18
Table 3-2 Literature review of micro variables for segmentation 19
Table 4-1 Survey profile 27
Table 4-2 Estimation results for MNL and NL results 30
Table 4-3 Indicates direct elasticity effect of the micro variables 31
Table 4-4 Criteria for determining the optimal number of segments 32
Table 4-5 Estimation results for LCM of micro-macro sequence 34
Table 4-6 Comparative analysis in model effectiveness 36
Figure 1-1 Research process 6
Figure 2-1 Bonoma and Shapiro’ nested model 9
Figure 3-1 Nested structure for complex tree 21
Figure 3-2 Choice probabilities of segmented customers on varied logistics 24
Figure 4-1 Research methodology process 28
Figure 4-2 Probability of the outcome 39
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