마케팅 조사 박물관 카페의 마케팅 전략 제안(영문)

마케팅 조사 박물관 카페의 마케팅 전략 제안(영문) [마케팅 조사] 박물관 카페의 마케팅 전략 제안(영문).hwp 목차 Contents Ⅰ. Introduction 1. Background of study 2. Purpose of study Ⅱ. Research Design 1. Setting up hypothesis and research model 2. Subject and method of research 3. Construction of survey 4. Method of data analysis Ⅲ. Result of study 1. Hypothesis 2. Characteristics of samples 1) Analysis of demographic factors 2) Analysis of usage pattern of cafe 3. Comment about reliability and validity 4. Analysis of our project 1) Factor analysis 2) Reliability anlaysis 3) Regression analysis Ⅳ. Conclusion 1. Summary 2. Suggestion 1) SWOT & brief marketing strategy 본문 Tolerance limit and VIF in Collinearity statistics are indexes that judge multicollinearity of factor variables. Collinearity means relationship between two independent variables. For example, if coefficients correlation of two factor variables is 1, these variables have perfect Collinearity. And if coefficients correlation is 0, they have Collinearity not at all. Especially, relationship among 3 or more variables is called multicollinearity. Tolerance limit means a part that a factor variable is not explained by other variables. In other words, if tolerance limit is small, multicollearity is high. In this analysis tolerance limit is 1 that is biggest number so we dont need to interpretate it. VIF is inverse number of tolerance limit. And if value of VIF is big, there is high Collinearity between independent variables. This is a result of regression analysis. Y = 4.649+0.175*(factor1)+0.263*(factor2)+0.279*(factor3)-0.008*(factor4)+0.157*(factor5) Dependent Variable Independent Variables Standardized Coefficients t(p) F(p-value) VIF Satisfaction Convenience Accessibility 0.131 1.772(.078) 4.412 (.001) 1.000 Visibility 0.197 2.657(.009) 1.000 Comfort 0.209 2.824(.005) 1.000 Crowding -0.006 -0.085(.932) 1.000 Geographic proximity of college 0.118 1.591(.114) 1.000 R(.337ª),R Squre(.113), Adjusted R Squre(.086) *:p<0.01 수준에서 유의한 F통계량. According to test of significance, ‘Visibility(p<0.01)’, ‘Comfort(p<0.01)’ are significant. We check multilinearity for knowing correlation between independent variables. If VIF>5, result is wrong. However, VIF of our result is less than 5. So there is not a high possibility of multicollinearity of independent variables. B. Result of regression analysis of 158 samples (that are adjusted for accuracy) factor analysis For knowing why our result is not meaningful, we saved residual values when we modified factor analysis. We did regression analysis again with 158 samples excluding wrong data whose residual values are &#8723;1.5. This is the result of redesigned r 본문내용 ing strategy to the cafe> Contents Ⅰ. Introduction 1. Background of study 2. Purpose of study Ⅱ. Research Design 1. Setting up hypothesis and research model 2. Subject and method of research 3. Construction of survey 4. Method of data analysis Ⅲ. Result of study 1. Hypothesis 2. Characteristics of samples 1) Analysis of demographic factors 2) Analysis of usage pattern of cafe 3. Comment about rel