Lampiran 1 Kuesioner. Hormat saya, Ivan Tandywijaya

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1 Lampiran 1 Kuesioner Saya adalah mahasiswa jurusan Manajemen Universitas Katolik Widya Mandala yang sedang melakukan penelitian dengan judul PENGARUH BRAND IMAGE TERHADAP PURCHASE INTENTION MELALUI PERCEIVED VALUE PRODUK SMARTPHONE SAMSUNG DI SURABAYA, saya mohon kesediaan Saudara/Saudari agar berkenan membantu mengisi kuesioner yang telah saya siapkan. Daftar pertanyaan untuk penelitian ini dirancang sedemikian rupa sehingga memudahkan Anda. Jawaban yang Anda berikan hanya untuk kepentingan akademik. Atas kesediaan dan partisipasi Saudara/Saudari dalam mengisi kuesioner ini, kami ucapkan terima kasih. Hormat saya, Ivan Tandywijaya I. Karakteristik Responden Isilah jawaban yang sesuai dengan anda dibawah ini dengan tanda (X) a. Domisili : 1. Surabaya 2. Luar Surabaya b. Usia : tahun keatas 2. Dibawah 17 tahun

2 c. Sumber pendapatan : 1. Memiliki pekerjaan dan pendapatan tetap 2. Tidak memiliki pekerjaan dan pendapatan tetap d. Kepemilikan merek : 1. Memiliki smartphone Samsung 2. Tidak memiliki smartphone Samsung II. Lampiran 1 (lanjutan) Isilah jawaban yang sesuai dengan anda dibawah ini dengan tanda( ) Keterangan: STS = Sangat Tidak Setuju TS = Tidak Setuju N = Netral S = Setuju SS = Sangat Setuju Brand Image (X 1 ) No Pertanyaan STS TS N S SS 1 Saya yakin terhadap smartphone Samsung karena citra perusahaan yang bagus 2 Saya yakin terhadap smartphone Samsung karena sudah dikenal dan dipakai oleh banyak kalangan 3 Saya yakin terhadap smartphone Samsung karena citra produk yang baik

3 Lampiran 1 (lanjutan) Perceived Value (Y 1 ) No Pertanyaan STS TS N S SS 1 Saya menilai smartphone Samsung adalah produk yang baik karena telah berhasil membuat emosi yang positif bagi para penggunanya 2 Saya menilai smartphone Samsung adalah produk yang baik karena telah meningkatkan kemampuan bersosialisasi yang tinggi terhadap para penggunanya 3 Saya menilai smartphone Samsung adalah produk yang baik karena kualitasnya yang bagus 4 Saya menilai smartphone Samsung adalah produk yang baik karena kinerja produknya yang baik

4 Lampiran 1 (lanjutan) Purchase Intention (Y 2 ) No Pertanyaan STS TS N S SS 1 Saya cenderung membeli smartphone Samsung karena keberadaan produk dan kualitasnya telah menyita perhatian saya 2 Saya cenderung membeli smartphone Samsung karena memberikan daya tarik yang kuat untuk membeli produknya 3 Saya cenderung membeli smartphone Samsung karena rasa keingintahuan terhadap produknya 4 Saya akan membeli produk smartphone Samsung Terima kasih atas partisipasinya

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14 DATE: 02/16/2015 TIME: 01:13 P R E L I S 2.70 BY Karl G. Jöreskog & Dag Sörbom This program is published exclusively by Scientific Software International, Inc N. Lincoln Avenue, Suite 100 Lincolnwood, IL 60712, U.S.A. Phone: (800) , (847) , Fax: (847) Copyright by Scientific Software International, Inc., Use of this program is subject to the terms specified in the Universal Copyright Convention. Website: The following lines were read from file C:\Users\Windows\Documents\ivan\IVAN2.PR2:!PRELIS SYNTAX: Can be edited SY='C:\Users\Windows\Documents\ivan\IVAN2.PSF' OU MA=CM XT Total Sample Size = 150 Univariate Summary Statistics for Continuous Variables

15 Variable Mean St. Dev. T-Value Skewness Kurtosis Minimum Freq. Maximum Freq BI BI BI PV PV PV PV PI PI PI PI Test of Univariate Normality for Continuous Variables Skewness Skewness and Kurtosis Kurtosis Variable Z-Score P-Value Z-Score P-Value Chi- Square P-Value BI BI

16 BI PV PV PV PV PI PI PI PI Relative Multivariate Kurtosis = Test of Multivariate Normality for Continuous Variables Skewness Skewness and Kurtosis Kurtosis Value Z-Score P-Value Value Z-Score P- Value Chi-Square P-Value Histograms for Continuous Variables BI1 Frequency Percentage Lower Class Limit

17 BI2 Frequency Percentage Lower Class Limit BI3 Frequency Percentage Lower Class Limit PV1 Frequency Percentage Lower Class Limit

18 PV2 Frequency Percentage Lower Class Limit PV3 Frequency Percentage Lower Class Limit

19 PV4 Frequency Percentage Lower Class Limit PI1 Frequency Percentage Lower Class Limit PI2 Frequency Percentage Lower Class Limit

20 PI3 Frequency Percentage Lower Class Limit PI4 Frequency Percentage Lower Class Limit Covariance Matrix BI1 BI2 BI3 PV1 PV2 PV BI

21 BI BI PV PV PV PV PI PI PI PI Covariance Matrix PV4 PI1 PI2 PI3 PI PV PI PI PI PI Means BI1 BI2 BI3 PV1 PV2 PV

22 Means PV4 PI1 PI2 PI3 PI Standard Deviations BI1 BI2 BI3 PV1 PV2 PV Standard Deviations PV4 PI1 PI2 PI3 PI The Problem used Bytes (= 0.0% of available workspace)

23 DATE: 2/16/2015 TIME: 1:09 L I S R E L 8.70 BY Sörbom Karl G. Jöreskog & Dag This program is published exclusively by Scientific Software International, Inc N. Lincoln Avenue, Suite 100 Lincolnwood, IL 60712, U.S.A. Phone: (800) , (847) , Fax: (847) Copyright by Scientific Software International, Inc., Use of this program is subject to the terms specified in the Universal Copyright Convention. Website: The following lines were read from file F:\IVAN\DATA1.spl: SKRIPSI OBSERVED VARIABLES BI1 BI2 BI3 PV1 PV2 PV3 PV4 PI1 PI2 PI3 PI4 COVARIANCE MATRIX FROM FILE F:\IVAN\DATA1.COV LATENT VARIABLES BI PV PI SAMPLE SIZE 150 RELATIONSHIPS

24 BI1=1*BI BI2-BI3=BI PV1=1*PV PV2-PV4=PV PI1=1*PI PI2-PI4=PI PV=BI PI=PV OPTIONS: SC EF PATH DIAGRAM END OF PROGRAM Sample Size = 150 SKRIPSI Covariance Matrix PV1 PV2 PV3 PV4 PI1 PI PV PV PV PV PI PI PI PI BI BI BI

25 Covariance Matrix PI3 PI4 BI1 BI2 BI PI PI BI BI BI SKRIPSI Number of Iterations = 7 LISREL Estimates (Maximum Likelihood) Measurement Equations PV1 = 1.00*PV, Errorvar.= 0.31, R² = 0.47 (0.039) 8.05 PV2 = 1.03*PV, Errorvar.= 0.36, R² = 0.45 (0.13) (0.045) PV3 = 1.21*PV, Errorvar.= 0.26, R² = 0.61 (0.14) (0.035) PV4 = 1.13*PV, Errorvar.= 0.23, R² = 0.61 (0.13) (0.030)

26 PI1 = 1.00*PI, Errorvar.= 0.27, R² = 0.63 (0.036) 7.43 PI2 = 1.00*PI, Errorvar.= 0.22, R² = 0.67 (0.089) (0.031) PI3 = 0.96*PI, Errorvar.= 0.27, R² = 0.62 (0.090) (0.035) PI4 = 0.99*PI, Errorvar.= 0.20, R² = 0.69 (0.086) (0.029) BI1 = 1.00*BI, Errorvar.= 0.28, R² = 0.53 (0.036) 7.69 BI2 = 1.11*BI, Errorvar.= 0.23, R² = 0.62 (0.12) (0.032) BI3 = 1.06*BI, Errorvar.= 0.24, R² = 0.59 (0.11) (0.033) Structural Equations PV = 0.94*BI, Errorvar.= , R² = 0.99 (0.12) (0.013) PI = 1.24*PV, Errorvar.= 0.030, R² = 0.93 (0.14) (0.018)

27 Reduced Form Equations PV = 0.94*BI, Errorvar.= , R² = 0.99 (0.12) 7.94 PI = 1.17*BI, Errorvar.= 0.036, R² = 0.92 (0.13) 8.92 Variances of Independent Variables BI (0.06) 5.00 Covariance Matrix of Latent Variables PV PI BI PV 0.28 PI BI Statistics Goodness of Fit Degrees of Freedom = 42 Minimum Fit Function Chi-Square = (P = ) Normal Theory Weighted Least Squares Chi- Square = (P = )

28 Estimated Non-centrality Parameter (NCP) = Percent Confidence Interval for NCP = (7.33 ; 52.63) Minimum Fit Function Value = 0.45 Population Discrepancy Function Value (F0) = Percent Confidence Interval for F0 = (0.049 ; 0.35) Root Mean Square Error of Approximation (RMSEA) = Percent Confidence Interval for RMSEA = (0.034 ; 0.092) P-Value for Test of Close Fit (RMSEA < 0.05) = 0.19 Expected Cross-Validation Index (ECVI) = Percent Confidence Interval for ECVI = (0.65 ; 0.96) ECVI for Saturated Model = 0.89 ECVI for Independence Model = Chi-Square for Independence Model with 55 Degrees of Freedom = Independence AIC = Model AIC = Saturated AIC = Independence CAIC = Model CAIC = Saturated CAIC =

29 0.98 (NNFI) = 0.99 (PNFI) = 0.75 (CFI) = 0.99 (IFI) = 0.99 = Normed Fit Index (NFI) = Non-Normed Fit Index Parsimony Normed Fit Index Comparative Fit Index Incremental Fit Index Relative Fit Index (RFI) Critical N (CN) = (RMR) = (GFI) = 0.92 (AGFI) = 0.88 (PGFI) = 0.59 Root Mean Square Residual Standardized RMR = Goodness of Fit Index Adjusted Goodness of Fit Index Parsimony Goodness of Fit Index The Modification Indices Suggest to Add an Error Covariance Between and Decrease in Chi-Square New Estimate BI2 PI SKRIPSI Standardized Solution LAMBDA-Y

30 PV PI PV PV PV PV PI PI PI PI LAMBDA-X BI BI BI BI BETA PV PI PV PI GAMMA BI PV 0.99 PI - - Correlation Matrix of ETA and KSI PV PI BI PV 1.00 PI BI PSI

31 Note: This matrix is diagonal. PV PI Regression Matrix ETA on KSI (Standardized) SKRIPSI BI PV 0.99 PI 0.96 Completely Standardized Solution LAMBDA-Y PV PI PV PV PV PV PI PI PI PI LAMBDA-X BI BI BI BI BETA PV PI

32 PV PI GAMMA BI PV 0.99 PI - - Correlation Matrix of ETA and KSI PV PI BI PV 1.00 PI BI PSI Note: This matrix is diagonal. PV PI THETA-EPS PV1 PV2 PV3 PV4 PI1 PI THETA-EPS PI3 PI THETA-DELTA

33 BI1 BI2 BI Regression Matrix ETA on KSI (Standardized) SKRIPSI BI PV 0.99 PI 0.96 Total and Indirect Effects Total Effects of KSI on ETA BI PV 0.94 (0.12) 7.94 PI 1.17 (0.13) 8.92 Indirect Effects of KSI on ETA BI PV - - PI 1.17 (0.13) 8.92

34 Total Effects of ETA on ETA PV PI PV PI (0.14) 8.73 Largest Eigenvalue of B*B' (Stability Index) is Total Effects of ETA on Y PV PI PV PV (0.13) 7.67 PV (0.14) 8.83 PV (0.13) 8.85 PI (0.14) 8.73 PI (0.14) (0.09)

35 PI (0.14) (0.09) PI (0.14) (0.09) Indirect Effects of ETA on Y PV PI PV PV PV PV PI (0.14) 8.73 PI (0.14) 8.96 PI (0.14) 8.61 PI (0.14) 9.08 Total Effects of KSI on Y

36 BI PV (0.12) 7.94 PV (0.12) 7.76 PV (0.13) 8.97 PV (0.12) 9.00 PI (0.13) 8.92 PI (0.13) 9.17 PI (0.13) 8.79 PI (0.13) 9.30

37 SKRIPSI Standardized Total and Indirect Effects Standardized Total Effects of KSI on ETA BI PV 0.99 PI 0.96 ETA Standardized Indirect Effects of KSI on BI PV - - PI 0.96 Standardized Total Effects of ETA on ETA PV PI PV PI Standardized Total Effects of ETA on Y PV PI PV PV PV PV PI PI PI PI Completely Standardized Total Effects of ETA on Y

38 PV PI PV PV PV PV PI PI PI PI Standardized Indirect Effects of ETA on Y PV PI PV PV PV PV PI PI PI PI Completely Standardized Indirect Effects of ETA on Y PV PI PV PV PV PV PI PI PI PI Standardized Total Effects of KSI on Y BI

39 PV PV PV PV PI PI PI PI KSI on Y Completely Standardized Total Effects of BI PV PV PV PV PI PI PI PI Seconds Time used: 0.031

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