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研究生:蔡宜廷
研究生(外文):Yi-Ting Tsai
論文名稱:高科技產業航空物流委外之市場區隔研究
論文名稱(外文):Usage Segmentation of Air Cargo Market of High-Tech Industry- Forwarder or Express?
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:2008
畢業學年度:96
語文別:英文
論文頁數:47
中文關鍵詞:高科技產業航空貨運使用區隔廣義線性交互模型使用率
外文關鍵詞:High-tech industryAir cargoUsage segmentationGeneralized linear interactive modelUsage rate
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本研究的目的是發展一個量化的模型來研究台灣高科技產業(High-tech industry)在航空貨運(Air Cargo)市場的使用區隔(Usage segmentation)分析。在廣義線性交互模型(GLIM, Generalized Linear Interactive Model)中,透過五個類別的總體區隔變數來建立高科技廠商對於航空貨運承攬業及快遞業者的使用率模式。
廣義線性交互模型是一個發展良好的類別資料分析工具,它能夠將變數的類別關係加以一般化、公式化並處理有關非負特徵的使用率(Usage rate)。此外這項技術可以進一步探究變數間是否存在交互關係。本研究從一百五十家科學園區的高科技廠商中收集五百一十一筆的航空貨運訂單來進行分析,經由逐步迴歸的方式,檢定變數的顯著性並找尋最適模型。透過兩個顯著的區隔變數,我們可以建立十二個航空貨運的區隔市場。在每一個區隔市場之中,承攬業者及快遞業者的使用率可以運用最適模型的參數校估來得知。
本研究的實證結果可以提供有價值的市場資訊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。
This study aims to develop a quantitative model to analyze usage segmentation of air cargo market of high-tech industry in Taiwan. A Generalized Linear Interactive Model (GLIM) was constructed to model the usage rate of binary service alternatives: forwarder and express, based on five categorical macro-segmenting variables: including shipment destination, shipment size, frequency of shipment, time in transit, and product status. The GLIM is a well-developed categorical data analysis tool. It is capable of formulating the relationship categorically, as well as handling the non-negativity characteristic of usage rates. Furthermore, the technique can clarify the associations and interaction structure of the variables that ANOVA (Analysis of variance) have not satisfactorily quantified until now.
We conducted face-to-face interviews with Taiwanese high-tech manufacturers on their air cargo consumptions. 540 recent cargo shipments commissioned by 150 Taiwanese high-tech manufacturers (contributing to a response rate of 30.3%) were collected and analyzed. They are used to stepwise examine the log-linear model accounting for the usage rate of forwarder and express. A best-fit model indicates that shipment destination and shipment size are statistically significant for market segmentation, whereas frequency of shipment, time in transit, and product type are not. In using these effective variables, 12 segments were established to divide the air cargo market. For each segment, usage rate of forwarders and expresses can be measured by using the parameters calibrated from the best-fit models. The segmentation result is concluded to be reasonable in size, identifiable and accessible for marketing practices. The empirical evidences provide valuable insights on behavioural understanding and serve as a basis to support air carriers’ decision-making on targeting and service positioning.
Table of contents
摘要 i
Abstract ii
Table of contents iv
List of tables v
List of figures vi
Chapter 1  Introduction 1
1.1 Motivation and purpose 1
1.2 Research method 5
1.3 Research procedure 7
Chapter 2  Analytical model 9
2.1. Segmenting variables 9
2.2. Generalized linear interactive model (GLIM) 13
Chapter 3  Data collection 16
Chapter 4  Model testing and parameter calibration 21
Chapter 5  Findings and managerial implications 26
Chapter 6  Study limits and future research 35
References 38
Appendix A. Questionnaire 45
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http://www.moea.gov.tw/
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http://www.mof.gov.tw/
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