عنوان مقاله [English]
Today, shopping centers and modern business spaces have become an integral part of urban life, and on the other hand, they have replaced traditional bazaars in many large cities. Regardless of whether shopping centers and modern business spaces are a deserved successor to traditional bazaars, comprehensive study of effective factors on the prosperity of shopping centers and modern business spaces is is essential because of their growing and undeniable role in the lives of citizens in today's societies. This research seeks to examine the effective factors on prosperity of shopping centers and modern business spaces (case study: Rasht metropolis) using the structural equation modeling approach.
The type of research is practical based on the purpose and is survey based on the design. The statistical population of this study is 679995 people and the sample size is 384 people. In this research, all shopping centers, shopping malls, chain stores and large, recreational commercial complexes, hypermarkets and other similar examples are located in a number of modern business spaces. A total of 64 modern business spaces were surveyed in the metropolis of Rasht. For data analysis, structural equation modeling (SEM) approach, one of the main methods of multivariate analysis, has been adopted using SPSS and AMOS software. The statistical tests used for data analysis are: exploratory and confirmatory factor analysis, fitness indexes, multiple linear regression, Kolmogorov-Smirnov test, regression analysis of variance and step-by-step regression test.
Results and discussion
In the first step, exploratory factor analysis has been used to extract the main factors affecting the Prosperity of shopping centers and modern business spaces. The most important application of factor analysis is the reduction of a relatively large number of variables to limited factors that are usually indirectly and indirectly affected by the main variable. In this research, after interviews and field surveys, 23 factors influencing the prosperity of traditional bazaars were identified. In the first step, exploratory factor analysis has been used to extract the main factors affecting the prosperity of shopping centers and modern business spaces. After interviews and field surveys, 23 effective factors have been identified. These 23 primary factors are: 1- the price and quality of the goods; 2- proper variety and accumulation of goods; 3- unique visual elements; 4- the physical modern elements; 5- tourist attractions; 6- standard goods; 7- adequate security; 8- array and discipline; 9- high Choice of Power; 10- Good Health and Care; 11- Silence and Relaxation; 12- Easy Access; 13- Convenient Location; 14- Media Advertising; 15- Apparent Apparel of Sellers; 16- Personality Features of Sellers; 17- Parking Car; 18- suitable recreational space; 19- Food-Kort and its facilities; 20- elevators and escalators; 21- security systems; 22- suitable heating and cooling equipment; 23- side facilities.
Based on the results of exploratory factor analysis, the sampling adequacy index was equal to 0.836 and the result of the Bartlett Spree test with a degree of freedom of 422 at a significant level of 11286/14. These conditions make it possible to conduct factor analysis for the factors affecting the prosperity of shopping centers and modern business spaces, and in other words, factor analysis is appropriate for identifying the structure. These factors were reduced to 3 main factors by varimax method. These factors are: service attractions, visual attractions, and quality attractions of commodities. After performing exploratory factor analysis using SPSS software and extracting the main factors affecting the prosperity of shopping centers and modern business spaces, AMOS software has been used to model structural equations. Different fitness indices have been used with the use of AMOS. These indicators include the Chi-square index for assessing the overall fitness of the model (X2/df), comparative or comparative fit index (CFI), root mean square error (RMSEA), approximation and index error Average left squared (SRMR), improved fitness goodness index (ACFI), gradual fit index (IFI). In order to investigate the relationship between satisfaction rate and referral rate (the number of people visiting), at first the dimensions of these variables were determined. Accordingly, the satisfaction variable as an independent variable was divided into 3 dimensions of service satisfaction (with 8 items), visual satisfaction (with 7 items) and qualitative satisfaction Goods (with 8 items), and the variable of referral rate was determined as dependent variable with 13 items.
The results of this study indicate that 3 main factors driving shopping centers and modern business spaces are: service attractions, visual attractions, and quality attractions of commodities. Overall account for 77% of variance of all variables by this the main factors are covered. Also, the results of this study indicate that the structural model of this research has a good fit and the features of this research have had good explanatory power. The descriptive status of the variables of satisfaction rate and referral rate showed that the level of both variables in this research was higher than average. The results of the Kolmogorov-Smirnov test showed that independent and dependent variables are normal. In other words, both variables of satisfaction rate and referral rate are normal. The significance of the model was confirmed by using variance analysis of regression model for multiple linear regression. It was proved that there was a significant relationship between the combination of independent variables (satisfaction rate and its triple dimensions) with the dependent variable (referral rate). Finally, step-by-step regression test was used to predict the amount of people returning from the variable components of satisfaction and the final regression equation (regression prediction model) was obtained.
The final regression equation (regression prediction pattern) can be represented as follows
Y = -1283/128 + 0/623 X1 + 0/591 X2 + 0/456 X3
The variable Y in the above relationship indicates the number of people visiting, the X1 variable indicates service satisfaction, the X2 variable represents visual satisfaction, and finally, the variable X3 indicates the quality satisfaction of the goods.