模型一的三種架構因為模型之整體配適度不良，代表在分析臺灣產業垂直整合時若 只單純考慮市場、制度或科技因素時，將無法有邏輯地解釋清礎。若考量到金融發展、
制度改進與資訊科技兩兩變數之間的相關性時（模型二的三種架構），各模型的整體模 型配適度皆達良好範圍，顯示這三種因素並非互相獨立地直接影響產業垂直整合。關於 研究產業垂直整合的文獻中，Acemoglu et al.(2009)是以全球九十三個國家的樣本資料去 進行實證研究，並且就這些資料得出了金融發展與制度問題的交互作用項對於垂直整合 是有顯著相關的結果，本研究模型二的M-I 架構之實證結果與 Acemoglu et al.(2009)相 符合，但因為模型二的三種架構皆未通過信度與效度測驗，故本研究不採用模型二進行 實證研究。依照M-I-T 架構典範而建立的模型三，其不僅通過信度與效度測驗，同時模
型整體模型配適度都優於模型二，代表市場、制度與科技這三類變數之間的交互運作的 確是解釋產業垂直整合的重要因素。本研究之實證結果證明，金融業界的發展、金融界 的制度因素與金融界所使用的資訊科技之間的相關性，對於解釋產業垂直整合是不可或 缺，此一結果同時驗證了M-I-T 架構典範的有效性。
由於臺灣依法實行產業垂直整合的時間尚不夠長久，因此本研究使用的樣本資料只 有八筆，如此的樣本數量在資料代表性方面是不足夠的，這是本研究在進行資料蒐集時 首先面對的研究限制。不過近幾年臺灣國內的產業併購案逐年增加，且多屬垂直整合類 型，因此只要時間軸再拉得遠一些，未來研究影響臺灣產業垂直整合因素的學者將會有 更充足的樣本資料可供使用。此外本研究曾嘗試以樣本資料的年增率取代資料原始值去 進行實證研究分析，但所得結果卻不顯著，原因極有可能是來自於樣本資料的不足。
參考文獻
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附錄
表四之一、模型二 M-I 之變異數
Estimate S.E. C.R. P Label FD1 .003 .002 1.785 .074 par_21 FD2 .103 .051 2.033 .042 par_22 IC1 .169 .091 1.871 .061 par_23 IC2 6.093 3.257 1.871 .061 par_24 IC3 1.004 .607 1.655 .098 par_25 FD3 .033 .014 2.371 .018 par_26 W .004 .002 1.871 .061 par_27
表四之二、模型二 M-T 之變異數
Estimate S.E. C.R. P Label FD2 .117 .062 1.871 .061 par_15 ICT2 38.747 21.272 1.821 .069 par_16 FD3 .046 .025 1.871 .061 par_17 FD1 .003 .001 2.063 .039 par_18 ICT1 218.621 128.500 1.701 .089 par_19 W .097 .052 1.871 .061 par_20
表四之三、模型二 I-T 之變異數
Estimate S.E. C.R. P Label IC1 .169 .091 1.871 .061 par_15 IC2 6.093 3.257 1.871 .061 par_16 ICT1 200.566 113.111 1.773 .076 par_17 ICT2 29.907 12.792 2.338 .019 par_18 IC3 1.004 .607 1.655 .098 par_19 W .421 .225 1.871 .061 par_20
表四之四、模型三之變異數
Estimate S.E. C.R. P Label FD1 .003 .002 1.921 .055 par_20 FD2 .137 .067 2.036 .042 par_21 IC2 5.323 2.586 2.058 .040 par_22 ICT1 199.494 106.634 1.871 .061 par_23 ICT3 46830170.373 25031778.986 1.871 .061 par_24 IC1 .169 .090 1.871 .061 par_25 W .002 .001 1.871 .061 par_26
表五之一、模型二 M-I 之共變異數
FD3 IC3 IC2 IC1 FD2 FD1 Y FD3 .033
IC3 .032 1.004
IC2 -.297 .150 6.093
IC1 -.039 -.313 .000 .169
FD2 .018 .084 -.243 -.052 .103
FD1 .004 -.005 -.113 -.002 .007 .003
Y .068 .113 -.363 -.093 -.074 .008 .500
表五之二、模型二 M-T 之共變異數
ICT1 FD1 FD3 ICT2 FD2 Y ICT1 218.621
FD1 .442 .003
FD3 -.469 .005 .046
ICT2 11.958 .282 .845 38.747
FD2 3.445 .007 .000 .144 .117
Y -1.890 .014 .129 2.396 -.141 .754
表五之三、模型二 I-T 之共變異數
IC3 ICT2 ICT1 IC2 IC1 Y IC3 1.004
ICT2 1.319 29.907
ICT1 -2.172 20.936 200.566
IC2 .150 -7.597 -12.317 6.093
IC1 -.313 -1.235 .089 .000 .169
Y .113 1.163 .548 -.363 -.093 .500
表五之四、模型三之共變異數
IC1 ICT3 ICT1 IC2 FD2 FD1 Y IC1 .169
ICT3 .010 46830170.373
ICT1 .010 58177.249 199.494
IC2 .405 -5739.312 -9.481 5.323
FD2 -.093 -126.111 2.532 -.372 .137
FD1 -.004 300.664 .506 -.097 .006 .003
Y -.040 2478.051 .913 -.428 -.095 .010 .496
表六之一、模型二 M-I 之相關係數
FD3 IC3 IC2 IC1 FD2 FD1 Y FD3 1.000
IC3 .173 1.000
IC2 -.660 .061 1.000
IC1 -.523 -.758 .000 1.000
FD2 .313 .260 -.307 -.393 1.000
FD1 .427 -.083 -.822 -.099 .376 1.000
Y .524 .160 -.208 -.319 -.326 .206 1.000
表六之二、模型二 M-T 之相關係數
ICT1 FD1 FD3 ICT2 FD2 Y ICT1 1.000
FD1 .548 1.000
FD3 -.148 .458 1.000
ICT2 .130 .831 .634 1.000
FD2 .682 .350 .000 .068 1.000
Y -.147 .292 .694 .443 -.476 1.000
表六之三、模型二 I-T 之相關係數
IC3 ICT2 ICT1 IC2 IC1 Y IC3 1.000
ICT2 .241 1.000
ICT1 -.153 .270 1.000
IC2 .061 -.563 -.352 1.000
IC1 -.758 -.549 .015 .000 1.000
Y .160 .301 .055 -.208 -.319 1.000
表六之四、模型三之相關係數
IC1 ICT3 ICT1 IC2 FD2 FD1 Y IC1 1.000
ICT3 .000 1.000
ICT1 .002 .602 1.000
IC2 .427 -.364 -.291 1.000
FD2 -.610 -.050 .484 -.435 1.000
FD1 -.197 .808 .659 -.769 .305 1.000
Y -.136 .514 .092 -.263 -.363 .264 1.000