APPENDIX. Notes. Output Created 14-Jul :08:00 Comments Input Active Dataset DataSet0. N of Rows in Working Data

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1 APPENDIX Brand Image Factor Analysis Notes Output Created 14-Jul :08:00 Comments Input Active Dataset DataSet0 Filter <none> Weight <none> Split File <none> N of Rows in Working Data 100 File Missing Value Handling Definition of Missing MISSING=EXCLUDE: User-defined missing values are treated as missing. Cases Used LISTWISE: Statistics are based on cases with no missing values for any variable used. Syntax FACTOR /VARIABLES BI28 BI29 /MISSING LISTWISE /ANALYSIS BI28 BI29 /PRINT INITIAL CORRELATION SIG DET KMO INV REPR AIC EXTRACTION /CRITERIA MINEIGEN(1) ITERATE(25) /EXTRACTION PC /ROTATION NOROTATE /SAVE REG(ALL) /METHOD=CORRELATION. xvii

2 Resources Processor Time 00 00:00: Elapsed Time 00 00:00: Maximum Memory Required 1192 (1.164K) bytes Variables Created FAC1_4 Component score 1 [DataSet0] Correlation Matrix a BI28 BI29 Correlation BI BI Sig. (1-tailed) BI BI a. Determinant =.856 Inverse of Correlation Matrix BI28 BI29 BI BI KMO and Bartlett's Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy..500 Bartlett's Test of Sphericity Approx. Chi-Square df 1 Sig..000 Anti-image Matrices BI28 BI29 xviii

3 Anti-image Covariance BI BI Anti-image Correlation BI a BI a a. Measures of Sampling Adequacy(MSA) Communalities Initial Extraction BI BI Extraction Method: Principal Component Analysis. Total Variance Explained Initial Eigenvalues Extraction Sums of Squared Loadings Component Total % of Variance Cumulative % Total % of Variance Cumulative % Extraction Method: Principal Component Analysis. Component Matrix a Component 1 BI BI Extraction Method: Principal Component Analysis. a. 1 components extracted. xix

4 Reproduced Correlations BI28 BI29 Reproduced Correlation BI a.690 BI a Residual b BI BI Extraction Method: Principal Component Analysis. a. Reproduced communalities b. Residuals are computed between observed and reproduced correlations. There are 1 (100.0%) nonredundant residuals with absolute values greater than Karakteristik Produk Factor Analysis Notes Output Created 14-Jul :02:38 Comments Input Active Dataset DataSet0 Filter <none> Weight <none> Split File <none> N of Rows in Working Data 100 File Missing Value Handling Definition of Missing MISSING=EXCLUDE: User-defined missing values are treated as missing. Cases Used LISTWISE: Statistics are based on cases with no missing values for any variable used. xx

5 Syntax FACTOR /VARIABLES KP17 KP18 /MISSING LISTWISE /ANALYSIS KP17 KP18 /PRINT INITIAL CORRELATION SIG DET KMO INV REPR AIC EXTRACTION /CRITERIA MINEIGEN(1) ITERATE(25) /EXTRACTION PC /ROTATION NOROTATE /SAVE REG(ALL) /METHOD=CORRELATION. Resources Processor Time 00 00:00: Elapsed Time 00 00:00: Maximum Memory Required 1192 (1.164K) bytes Variables Created FAC1_4 Component score 1 [DataSet0] Correlation Matrix a KP17 KP18 Correlation KP KP Sig. (1-tailed) KP KP a. Determinant =.931 Inverse of Correlation Matrix KP17 KP18 KP KP xxi

6 KMO and Bartlett's Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy..500 Bartlett's Test of Sphericity Approx. Chi-Square df 1 Sig..008 Anti-image Matrices KP17 KP18 Anti-image Covariance KP KP Anti-image Correlation KP a KP a a. Measures of Sampling Adequacy(MSA) Communalities Initial Extraction KP KP Extraction Method: Principal Component Analysis. Total Variance Explained Initial Eigenvalues Extraction Sums of Squared Loadings Component Total % of Variance Cumulative % Total % of Variance Cumulative % Extraction Method: Principal Component Analysis. xxii

7 Component Matrix a Component 1 KP KP Extraction Method: Principal Component Analysis. a. 1 components extracted. Reproduced Correlations KP17 KP18 Reproduced Correlation KP a.631 KP a Residual b KP KP Extraction Method: Principal Component Analysis. a. Reproduced communalities b. Residuals are computed between observed and reproduced correlations. There are 1 (100.0%) nonredundant residuals with absolute values greater than xxiii

8 Life Style Factor Analysis Notes Output Created 14-Jul :05:33 Comments Input Active Dataset DataSet0 Filter <none> Weight <none> Split File <none> N of Rows in Working Data 100 File Missing Value Handling Definition of Missing MISSING=EXCLUDE: User-defined missing values are treated as missing. Cases Used LISTWISE: Statistics are based on cases with no missing values for any variable used. Syntax FACTOR /VARIABLES LF22 LF23 LF25 /MISSING LISTWISE /ANALYSIS LF22 LF23 LF25 /PRINT INITIAL CORRELATION SIG DET KMO INV REPR AIC EXTRACTION /CRITERIA MINEIGEN(1) ITERATE(25) /EXTRACTION PC /ROTATION NOROTATE /SAVE REG(ALL) /METHOD=CORRELATION. Resources Processor Time 00 00:00: Elapsed Time 00 00:00: Maximum Memory Required 2028 (1.980K) bytes Variables Created FAC1_3 Component score 1 xxiv

9 [DataSet0] Correlation Matrix a LF22 LF23 LF25 Correlation LF LF LF Sig. (1-tailed) LF LF LF a. Determinant =.555 Inverse of Correlation Matrix LF22 LF23 LF25 LF LF LF KMO and Bartlett's Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy..651 Bartlett's Test of Sphericity Approx. Chi-Square df 3 Sig..000 Anti-image Matrices LF22 LF23 LF25 Anti-image Covariance LF xxv

10 LF LF Anti-image Correlation LF a LF a LF a a. Measures of Sampling Adequacy(MSA) Communalities Initial Extraction LF LF LF Extraction Method: Principal Component Analysis. Total Variance Explained Initial Eigenvalues Extraction Sums of Squared Loadings Component Total % of Variance Cumulative % Total % of Variance Cumulative % Extraction Method: Principal Component Analysis. Component Matrix a Component 1 LF LF LF xxvi

11 Extraction Method: Principal Component Analysis. a. 1 components extracted. Reproduced Correlations LF22 LF23 LF25 Reproduced Correlation LF a LF a.623 LF a Residual b LF LF LF Extraction Method: Principal Component Analysis. a. Reproduced communalities b. Residuals are computed between observed and reproduced correlations. There are 3 (100.0%) nonredundant residuals with absolute values greater than xxvii

12 Price Factor Analysis Notes Output Created 14-Jul :58:21 Comments Input Active Dataset DataSet0 Filter <none> Weight <none> Split File <none> N of Rows in Working Data 100 File Missing Value Handling Definition of Missing MISSING=EXCLUDE: User-defined missing values are treated as missing. Cases Used LISTWISE: Statistics are based on cases with no missing values for any variable used. Syntax FACTOR /VARIABLES P11 P14 P15 /MISSING LISTWISE /ANALYSIS P11 P14 P15 /PRINT INITIAL CORRELATION SIG DET KMO INV REPR AIC EXTRACTION /CRITERIA MINEIGEN(1) ITERATE(25) /EXTRACTION PC /ROTATION NOROTATE /SAVE REG(ALL) /METHOD=CORRELATION. Resources Processor Time 00 00:00: Elapsed Time 00 00:00: Maximum Memory Required 2028 (1.980K) bytes Variables Created FAC1_3 Component score 1 xxviii

13 [DataSet0] Correlation Matrix a P11 P14 P15 Correlation P P P Sig. (1-tailed) P P P a. Determinant =.439 Inverse of Correlation Matrix P11 P14 P15 P P P KMO and Bartlett's Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy..699 Bartlett's Test of Sphericity Approx. Chi-Square df 3 Sig..000 Anti-image Matrices P11 P14 P15 Anti-image Covariance P xxix

14 P P Anti-image Correlation P a P a P a a. Measures of Sampling Adequacy(MSA) Communalities Initial Extraction P P P Extraction Method: Principal Component Analysis. Total Variance Explained Initial Eigenvalues Extraction Sums of Squared Loadings Component Total % of Variance Cumulative % Total % of Variance Cumulative % Extraction Method: Principal Component Analysis. Component Matrix a Component 1 P P P xxx

15 Extraction Method: Principal Component Analysis. a. 1 components extracted. Reproduced Correlations P11 P14 P15 Reproduced Correlation P a P a.709 P a Residual b P P P Extraction Method: Principal Component Analysis. a. Reproduced communalities b. Residuals are computed between observed and reproduced correlations. There are 3 (100.0%) nonredundant residuals with absolute values greater than xxxi

16 Motivation Factor Analysis Notes Output Created 14-Jul :09:53 Comments Input Active Dataset DataSet0 Filter <none> Weight <none> Split File <none> N of Rows in Working Data 100 File Missing Value Handling Definition of Missing MISSING=EXCLUDE: User-defined missing values are treated as missing. Cases Used LISTWISE: Statistics are based on cases with no missing values for any variable used. Syntax FACTOR /VARIABLES M31 M32 M33 M34 /MISSING LISTWISE /ANALYSIS M31 M32 M33 M34 /PRINT INITIAL CORRELATION SIG DET KMO INV REPR AIC EXTRACTION /CRITERIA MINEIGEN(1) ITERATE(25) /EXTRACTION PC /ROTATION NOROTATE /SAVE REG(ALL) /METHOD=CORRELATION. Resources Processor Time 00 00:00: Elapsed Time 00 00:00: Maximum Memory Required 3096 (3.023K) bytes Variables Created FAC1_2 Component score 1 xxxii

17 [DataSet0] Correlation Matrix a M31 M32 M33 M34 Correlation M M M M Sig. (1-tailed) M M M M a. Determinant =.362 Inverse of Correlation Matrix M31 M32 M33 M34 M M M M KMO and Bartlett's Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy..760 Bartlett's Test of Sphericity Approx. Chi-Square df 6 Sig..000 xxxiii

18 Anti-image Matrices M31 M32 M33 M34 Anti-image Covariance M M M M Anti-image Correlation M a M a M a M a a. Measures of Sampling Adequacy(MSA) Communalities Initial Extraction M M M M Extraction Method: Principal Component Analysis. Total Variance Explained Initial Eigenvalues Extraction Sums of Squared Loadings Component Total % of Variance Cumulative % Total % of Variance Cumulative % Extraction Method: Principal Component Analysis. Component Matrix a xxxiv

19 Component 1 M M M M Extraction Method: Principal Component Analysis. a. 1 components extracted. Reproduced Correlations M31 M32 M33 M34 Reproduced Correlation M a M a M a.650 M a Residual b M M M M Extraction Method: Principal Component Analysis. a. Reproduced communalities b. Residuals are computed between observed and reproduced correlations. There are 6 (100.0%) nonredundant residuals with absolute values greater than xxxv

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23 KUESIONER MOTIVASI KONSUMEN DALAM MEMBELI PAKAIAN DI MANGO No : Nama : (optional, tidak harus diisi) Telepon : (optional, tidak harus diisi) KUESIONER Kuesioner ini digunakan untuk keperluan penelitian dalam rangka menyusun Tesis dengan judul Pengaruh Persepsi Harga, Karakteristik Produk, Gaya Hidup, dan Citra Merk. Untuk itu mohon kiranya saudara-i dapat membantu penulis dalam menjawab kuisioner ini dengan sejujur-jujurnya. Hal ini demi keobjektivitasan penelitian yang sedang penulis lakukan. Atas bantuan dan kesediaanya penulis ucapkan banyak terima kasih. Petunjuk pengisian : Berilah jawaban pertanyaan berikut sesuai dengan pendapat anda, dengan cara memberikan tanda ( ) pada kolom yang tersedia. 1. Bacalah terlebih dahulu dan jawablah semua pertanyaan dengan benar. 2. Berilah tanda ( ) pada kolom jawaban anda. xxxix

24 Apakah anda pernah menggunakan pakaian merk Mango (Jika Ya, isilah pertanyaan dibawah ini) Ya Tidak 1. KARAKTERISTIK RESPONDEN a. Jenis Kelamin : Pria Wanita b. Usia anda saat ini : < 20 tahun tahun tahun >50 tahun c. Pendidikan terakhir anda : SMA S1 S2 d. Pekerjaan anda : Mahasiswa/ Pelajar Wirausaha Ibu rumah tangga Pegawai negeri / swasta Lainnya, Sebutkan :. xl

25 e. Pengeluaran per bulan anda : < Rp Rp Rp Rp Rp > Rp f. Frekuensi anda melakukan pembelanjan per bulan : 1 2 kali 3 5 kali > 6 kali g. Berikan tanda ( ) pada merk pakaian yang Anda sering gunakan: Mango Zara GAP Top Shop Giordano Muji Lainnya, Sebutkan.. h. Bagaimana Anda pertama kali mengetahui produk Mango? (Pilih satu ): Teman Website Koran / Majalah Brosur Banner Lainnya, Sebutkan.. xli

26 2. KUESIONER Anda sebagai pelanggan MANGO diharapkan menjawab pertanyaan dibawah ini sesuai dengan tingkat kesetujuan anda dan berilah tanda silang (X) pada kolom jawaban yang telah disediakan. Keterangan : Kategori Bobot Nilai STS = Sangat Tidak Setuju 1 TS = Tidak Setuju 2 S = Setuju 3 SS = Sangat Setuju 4 No Harga (Price) ST TS S SS S 1 Harga pakaian ini sudah memuaskan bila dibandingkan dengan merk sejenis 2 Harga pakaian mahal 3 Harga sesuai dengan kualitasnya produknya 4 Harga sesuai dengan apa yg diharapkan 5 Harga sesuai dengan manfaat yang saya peroleh xlii

27 No Karakteristik Produk STS TS S SS 1 Dapat dipakai sehari hari 2 Bahan pakaiannya nyaman 3 Rapi pengemasannya 4 Desain menarik 5 Cara mencuci mudah No GAYA HIDUP STS TS S SS 1 Saya memakai pakaian ini untuk tampil fashionable 2 Untuk memenuhi kebutuhan sosial saya 3 Merubah Penampilan 4 Saya tertarik untuk memakai pakaian ini 5 Saya memakai pakaian ini agar tidak ketinggalan zaman No Citra Merek ( Brand image) STS TS S SS 1 Mango adalah pakaian yang dapat digunakan untuk xliii

28 sehari hari 2 Mango adalah pakaian dengan harga yang mahal 3 Mango lebih baik dibandingkan pesaingnya 4 Mango menjual pakaian yang berkualitas 5 Mango sudah mempunyai reputasi No Motivasi untuk membeli / Minat Beli STS TS S SS 1 Saya tertarik menggunakan pakaian ini karena kualitasnya 2 Saya berencana membeli pakaian ini di lain waktu 3 Saya berharap dapat menggunakan pakaian merk MANGO selalu 4 Saya akan merekomendasikan merk pakaian ini kepada teman saya 5 Saya membutuhkan model pakaian bermerk MANGO xliv

29 WAWANCARA No : Nama : Pekerjaan : Mahasiwa/ Pelajar Pegawai Negeri / swasta Pertanyaan : 1. Apakah Anda ingin membeli produk MANGO dilihat dari harganya? Jawab : 2. Apakah Anda melihat karakteristik produk pada saat membeli pakaian di MANGO? Jawab : 3. Apakah Anda membeli produk MANGO sebagai lifestyle? Jawab : xlv

30 4. Apakah Anda ingin membeli produk MANGO dilihat dari citra merek pakaian itu sendiri? Jawab : xlvi

31 Regression Notes xliii Output Created 14-Jul :19:54 Comments Input Data D:\Thesis Ve2\Final\100 Rspd.sav Active Dataset DataSet0 Filter <none> Weight <none> Split File <none> N of Rows in Working Data File 100 Missing Value Handling Definition of Missing User-defined missing values are treated as missing. Cases Used Statistics are based on cases with no missing values for any variable used.

32 Syntax REGRESSION /MISSING LISTWISE /STATISTICS COEFF OUTS CI(95) BCOV R ANOVA COLLIN TOL CHANGE ZPP /CRITERIA=PIN(.05) POUT(.10) /NOORIGIN /DEPENDENT M /METHOD=ENTER P KP LF BI. Resources Processor Time 00 00:00: Elapsed Time 00 00:00: xliv Memory Required Additional Memory Required for Residual Plots 2788 bytes 0 bytes [DataSet0] D:\Thesis Ve2\Final\100 Rspd.sav

33 Variables Entered/Removed b Variables Model Variables Entered Removed Method 1 Brand Image, Characteristic Product, Life Style, PRICE. Enter a. All requested variables entered. b. Dependent Variable: Motivation xlv Model Summary Adjusted R Std. Error of the Change Statistics Model R R Square Square Estimate R Square Change F Change df1 df2 Sig. F Change a a. Predictors: (Constant), Brand Image, Characteristic Product, Life Style, PRICE

34 ANOVA b Model Sum of Squares df Mean Square F Sig. 1 Regression a Residual Total a. Predictors: (Constant), Brand Image, Characteristic Product, Life Style, PRICE b. Dependent Variable: Motivation xlvi Unstandardized Coefficients Standardized Coefficients Coefficients a 95.0% Confidence Interval for B Correlations Model B Std. Error Beta t Sig. Lower Bound Upper Bound Zero-order Partial 1 (Constant) 3.618E PRICE Characteristic Product Life Style Brand Image a. Dependent Variable: Motivation

35 Model Coefficient Correlations a Brand Image Charateristic Produc Life Style PRICE 1 Correlations Brand Image Characteristic Product Life Style PRICE Covariances Brand Image Characteristic Product Life Style PRICE xlvii a. Dependent Variable: Motivation Collinearity Diagnostics a Variance Proportions Characteristic Model Dimension Eigenvalue Condition Index (Constant) PRICE Product Life Style Brand Image

36 Collinearity Diagnostics a Variance Proportions Characteristic Model Dimension Eigenvalue Condition Index (Constant) PRICE Product Life Style Brand Image a. Dependent Variable: Motivation xlviii

37 BIOGRAPHY Name : Felicia Andrey Kalingga Place and Date of Birth : Jakarta and 5 th May 1988 Last Education Occupation : Bachelor Degree Majoring in English Literature : Staff Admin at PT. Tunas Baru Lampung