Pengaruh Kualitas Produk, Harga, Dan Saluran Distribusi Terhadap Loyalitas Pelanggan Majalah Swa Melalui Variabel Kepuasan Pelanggan (Studi Kasus Pada Pelanggan Majalah Swa Di DKI Jakarta)

Customer loyalty is a goal that has to be achieved by a company. To be able to get loyal customers, SWA magazine needs to pay attention on the factors that influence customers\u27 loyalty. Moreover, business and economy themed magazines are mushrooming, leading to the opportunity for the readers to move from one magazine to another.This research aimed to ascertain the effect of product quality, price, and distribution channel on customer loyalty of SWA magazine in Jakarta through customer satisfaction variable both simultaneously and partially. The hypothesis was there was an effect of product quality, price, and distribution channel on customer loyalty of SWA magazine in Jakarta through customer satisfaction variable both simultaneously and partially. The type of this research was explanatory research with 97 respondents with multi stage sampling technique through questionnaire and interview. The data was analyzed using linear regression method with the assistance of SPSS 16.0.The result of this research showed that product quality, price and distribution channel variables had significant and positive effect partially on customer satisfaction. Product quality variable did not have partially significant effect on customer loyalty. Price and distribution channel variables had partially significant and positive effect on customer loyalty. Product quality and price variables had simultaneously positive and significant effect on customer satisfaction while distribution channel had simultaneously negative effect on customer satisfaction. Simultaneously, product quality, price, and distribution channel variables had positive effect and not significant effect on customer loyalty. Partially, customer satisfaction had positive and significant effect on customer loyalty.Based on the result of this research, a conclusion was drawn that customers\u27 perception on product quality, price, and distribution channel was good. Customers\u27 satisfaction and loyalty of SWA magazine were also good. The company was suggested to improving the product quality, adjusting the price and boosting the distribution channel of SWA magazine in accordance with customers\u27 needs and expectation, so that, customers can feel the satisfaction and decided to be loyal customers

MOESM2 of High-throughput analysis of chemical components and theoretical ethanol yield of dedicated bioenergy sorghum using dual-optimized partial least squares calibration models

Additional file 2: Figure A1. Plots of predicted versus measured values of parameters. Soluble sugar (a), cellulose (b), hemicellulose (c), lignin (d), ash (e), and theoretical ethanol yield (f) for the external validation subsets based on CARS-SPXY dual-optimized PLS models. The $${\text{R}}_{\text{V}}^{2}$$ R V 2 represents the square of the correlation coefficients of the external validation subsets. Figure A2. Plots of predicted versus measured value of parameters. Soluble sugar (a), cellulose (b), hemicellulose (c), lignin (d), ash (e), and theoretical ethanol yield (f) for the external validation subsets based on SR-SPXY dual-optimized PLS models. The $${\text{R}}_{\text{V}}^{2}$$ R V 2 represents the square of the correlation coefficients of the external validation subsets. Figure A3. Plots of predicted versus measured value of parameters. Soluble sugar (a), cellulose (b), hemicellulose (c), lignin (d), ash (e), and theoretical ethanol yield (f) for the external validation subsets based on VIP-SPXY dual-optimized PLS models. The $${\text{R}}_{\text{V}}^{2}$$ R V 2 represents the square of the correlation coefficients of the external validation subsets. Figure A4. Plots of predicted versus measured value of parameters. Soluble sugar (a), cellulose (b), hemicellulose (c), lignin (d), ash (e), and theoretical ethanol yield (f) for the external validation subsets based on MC-UVE-SPXY dual-optimized PLS models. The $${\text{R}}_{\text{V}}^{2}$$ R V 2 represents the square of the correlation coefficients of the external validation subsets. Figure A5. Plots of predicted versus measured value of parameters. Soluble sugar (a), cellulose (b), hemicellulose (c), lignin (d), ash (e), and theoretical ethanol yield (f) for the external validation subsets based on UVE-SPXY dual-optimized PLS models. The $${\text{R}}_{\text{V}}^{2}$$ R V 2 represents the square of the correlation coefficients of the external validation subsets

The Ebola virus disease epidemic in the Conakry area, Guinea, March 2014 to February 2015.

<p>(A) Map of the study area, which consists of Conakry and the surrounding prefectures of Boffa, Coyah, Dubreka, Forecariah, Fria, Kindia, and Telimele (for which diagnoses were mostly performed by the IPD-LFHP laboratory) (the administrative boundaries were taken from the GADM database; <a href="http://www.gadm.org/" target="_blank">http://www.gadm.org/</a>). (B) Number of cases by month of symptom onset. The total number of probable and confirmed cases in the study area that were hospitalized is indicated in grey. The number of those that were diagnosed by reverse transcription PCR (RT-PCR) by the IPD-LFHP laboratory is in blue.</p

Viremia and the probability of death.

<p>(A) Mean viremia as a function of the time from symptom onset to sample collection. (B) Mean viremia by gender. (C) Mean viremia by age group. (D) Probability of death as a function of viremia, when viremia was measured in the week following symptom onset. Three viremia groups are defined: low (<i>V</i> < 10^4.4 copies/ml), intermediate (10^4.4 ≤ <i>V</i> < 10^5.2 copies/ml), and high (<i>V</i> ≥ 10^5.2 copies/ml) viremia. The probability of death according to viremia group is represented as dotted line. The grey line corresponds to the predictions of the univariable logistic regression model. (E) Probability of death (dot: observed mean; thick line: 95% CI) as a function of the time from symptom onset to sample collection and the viremia group. Mean predicted values obtained with the multivariable logistic regression (triangle) and the bootstrap prediction intervals (thin lines) are also provided.</p

Characteristics of patients included in the study.

<p>Characteristics of patients included in the study.</p

Variation of CFR and viremia over time.

<p>(A) Observed CFR by month (black) and predictions obtained from multivariable logistic regression (orange) and from the simple univariable logistic regression model that relies only on viremia (violet). Lines provide 95% CI. The shaded area indicates the bootstrap prediction interval. (B) Mean viremia by month. (C) Proportion of patients in the low (red; <i>V</i> < 10^4.4 copies/ml), intermediate (green; 10^4.4 ≤ <i>V</i> < 10^5.2 copies/ml), and high (blue; <i>V</i> ≥ 10^5.2 copies/ml) viremia groups by month.</p

Additional file 1: of The genome landscape of indigenous African cattle

Supplemental data including Note S1, Tables S1-S7 and Figures S1-S12. (DOCX 14235 kb

NK cells in survivors and in fatalities.

<p>The frequency of NK (<b>A</b>), and of NK subsets (<b>B:</b> CD56^bright, CD56^dim, and CD56^neg) was analyzed in HD (n = 14), EBOV-survivors (n = 7) and EBOV-fatalities (n = 6). Statistical analysis was performed by using Mann Whitney test and differences were considered significant with a p<0.05, and highlighted with an asterisk. *: p<0.05; **: p<0.01; ***: p<0.001.</p

Frequency of Vδ2 T-cells in survivors and in fatalities.

<p>The frequency of Vδ2 (<b>A</b>), Vδ2^posCD95^pos (<b>B</b>), of Vδ2^posCCR7^neg (<b>C</b>) and Vδ2^posCTLA-4^pos (<b>D</b>) was analyzed in HD (n = 14), EBOV-survivors (n = 10) and EBOV-fatalities (n = 6). Statistical analysis was performed by using Mann Whitney test and differences were considered significant with a p<0.05 and highlighted with an asterisk.*: p<0.05; **: p<0.01; ***: p<0.001.</p