ANALYSISCorrelation between the var

ANALYSIS

Correlation between the variables was tested for each model. The variables relating to product attributes showed a signif-icant correlation between COO and brand visibility for cosmetics, clothing and mobile phones, with a correlation factor of 56, 67 and 71 per cent, respectively. A decision was made to remove COO because features of COO and brand visibility were found to be highly correlated.

Product attributes factors


Table 3. Product 1: cosmetics (counterfeit cosmetics ordered logistic regression results)

Cosmetics B P > |z| %

Obs 396, Prob > w2 = 0
Price 0.32236 0 38
Quality 0.54192 0 71.9
Design 0.11124 0.187 11.8
Safety 0.261 0 29.8
Visibility 0.15259 0.071 16.5
Accessibility 0.03575 0.671 3.6
Obs 392, Prob > w2 = 0.0085 Socio-demographic Factors
Gender –0.33799 0.086 –28.7
Age –0.18287 0.078 –16.7
Education –0.10539 0.259 –10
Income level –0.13356 0.06 –12.5
Household size –0.01381 0.923 –1.4
Location –0.01761 0.659 –1.7

The overall model was found to be statistically significant since the p-value which is 0 is w2 = 0.000) and, at 95 per cent confidence level, it indicates that at least one of the independent variables contributes to the prediction of the intention to purchase a counterfeit product. p-Values for price, quality, and level of safety are |z| %

Obs 396, Prob > w2 = 0
Price 0.32236 0 38
Quality 0.54192 0 71.9
Design 0.11124 0.187 11.8
Safety 0.261 0 29.8
Visibility 0.15259 0.071 16.5
Accessibility 0.03575 0.671 3.6
Obs 392, Prob > w2 = 0.0085 Socio-demographic Factors
Gender –0.33799 0.086 –28.7
Age –0.18287 0.078 –16.7
Education –0.10539 0.259 –10
Income level –0.13356 0.06 –12.5
Household size –0.01381 0.923 –1.4
Location –0.01761 0.659 –1.7

The overall model was found to be statistically significant since the p-value which is 0 is w2 = 0.000) and, at 95 per cent confidence level, it indicates that at least one of the independent variables contributes to the prediction of the intention to purchase a counterfeit product. p-Values for price, quality, and level of safety are

Hasil (Bahasa Indonesia) 1:

ANALISISKorelasi antara variabel diuji untuk masing-masing model. Variabel-variabel yang berkaitan dengan atribut produk menunjukkan korelasi yang signif-icant antara visibilitas COO dan merek kosmetik, pakaian, dan ponsel, dengan faktor korelasi 56, 67 dan 71 persen, masing-masing. Keputusan itu dibuat untuk menghapus COO karena fitur visibilitas merek dan COO ditemukan sangat berkorelasi.Faktor-faktor atribut produk Tabel 3. Produk 1: kosmetik (palsu kosmetik memerintahkan regresi logistik hasil)Kosmetik B P > | z | % Obs 396, Prob > w2 = 0 Harga 0.32236 0 38Kualitas 0.54192 0 71,9Desain 0.11124 0.187 11,8Keselamatan 0.261 0 29.8Visibilitas 0.15259 0.071 16.5Aksesibilitas 0.03575 0.671 3.6Obs 392, Prob > w2 = 0.0085 faktor-faktor sosio-demografisJenis kelamin –0.33799 0.086 –28.7Umur –0.18287 0.078 –16.7Pendidikan –0.10539 0.259 –10Tingkat pendapatan –0.13356 0.06 –12.5Ukuran rumah tangga –0.01381 0.923 –1.4Lokasi –0.01761 0.659 –1.7 The overall model was found to be statistically significant since the p-value which is 0 is <0.05 (Prob > w2 = 0.000) and, at 95 per cent confidence level, it indicates that at least one of the independent variables contributes to the prediction of the intention to purchase a counterfeit product. p-Values for price, quality, and level of safety are <0.05, thus indicating a significant relationship between the intention to purchase counterfeit cosmetics and one of these variables. Thus, if price decreases by one unit, consumers’ purchasing frequency inten-tion will increase by 34.4 per cent; if the perception of quality of the counterfeit product increases by one unit, purchasing frequency intention will increase by 72.3 per cent. Similarly, if consumers’ perception of safety increases by one unit, purchasing frequency intention will increase by 30.6 per cent. However, design, visibility-in-use, and ease of access in local market are not correlated to purchase intentions.Socio-demographic factorsFrom Table 3, the p-value < a (a = 0.05) indicates that at least one independent variable correctly explains the variation in consumers’ purchasing frequency intention of counterfeit cosmetic products. At 95 per cent level of confidence, none of the factors listed are correlated to the intention of purchasing counterfeit cosmetics. However, if we increase our p-valueANALYSISCorrelation between the variables was tested for each model. The variables relating to product attributes showed a signif-icant correlation between COO and brand visibility for cosmetics, clothing and mobile phones, with a correlation factor of 56, 67 and 71 per cent, respectively. A decision was made to remove COO because features of COO and brand visibility were found to be highly correlated.Product attributes factors Table 3. Product 1: cosmetics (counterfeit cosmetics ordered logistic regression results)Cosmetics B P > |z| % Obs 396, Prob > w2 = 0 Price 0.32236 0 38Quality 0.54192 0 71.9Design 0.11124 0.187 11.8Safety 0.261 0 29.8Visibility 0.15259 0.071 16.5Accessibility 0.03575 0.671 3.6Obs 392, Prob > w2 = 0.0085 Socio-demographic FactorsGender –0.33799 0.086 –28.7Age –0.18287 0.078 –16.7Education –0.10539 0.259 –10Income level –0.13356 0.06 –12.5Household size –0.01381 0.923 –1.4Location –0.01761 0.659 –1.7 The overall model was found to be statistically significant since the p-value which is 0 is <0.05 (Prob > w2 = 0.000) and, at 95 per cent confidence level, it indicates that at least one of the independent variables contributes to the prediction of the intention to purchase a counterfeit product. p-Values for price, quality, and level of safety are <0.05, thus indicating a significant relationship between the intention to purchase counterfeit cosmetics and one of these variables. Thus, if price decreases by one unit, consumers’ purchasing frequency inten-tion will increase by 34.4 per cent; if the perception of quality of the counterfeit product increases by one unit, purchasing frequency intention will increase by 72.3 per cent. Similarly, if consumers’ perception of safety increases by one unit, purchasing frequency intention will increase by 30.6 per cent. However, design, visibility-in-use, and ease of access in local market are not correlated to purchase intentions.Socio-demographic factorsFrom Table 3, the p-value < a (a = 0.05) indicates that at least one independent variable correctly explains the variation in consumers’ purchasing frequency intention of counterfeit cosmetic products. At 95 per cent level of confidence, none of the factors listed are correlated to the intention of purchasing counterfeit cosmetics. However, if we increase our p-value