Anchor tenant's influence on consumer shopping mall choice

It had also been identified earlier, that the variable Cinema Park had also shown a significant difference in the means from the One-Way ANOVA testings (table 5), yet from the multiple comparisons testing, the p-values for all the age groups were greater than 0,05 and the results did not show the significance. But for this category the results could not be considered significant as previously the average means from the descriptive statistics represented not the highest score comparing to FC and MM.

The data on average spendings on the food, clothing and entertainment per month was the last independent variable to be analyzed. As there are more than two groups in the variable, the ANOVA method was employed, further the multiple comparisons analysis was performed.

H0 = there is no significant differences between groups of people with different average spendings budget and their score given to the specific anchor store.

Table 7 illustrated the overall results from the ANOVA test where three out of ten cases showed a statistical differences in estimated values. The number marked with the asterisk represent the p-values which represent the significance. As for other anchor types, the analysis haven't found any statistical differences. The significance level set for the test remained the same as for the previous tests: if the p-value is less than 0.05, the null hypothesis can be rejected. The difference was depicted with the respondents when evaluating the Cinema Parks, Luxury brand stores and Coffee shops, the p-values for the given data were 0.022, 0.050 and 0.031 respectively. Yet, it can't be stated that the hypothesis is rejected. The results obtained from the analysis performed demonstrated that the people with different average expenditures have evaluated the importance of the specific anchor stores differently. In order to identify the groups which corresponded the difference, the Tukey post hoc test was completed.

Table 7 (The output from SPSS is in Appendix 5)

*p-value < 0,05 - there is a significant differences between the means of one of the observed groups

*p-value > 0,05 - there is no differences between the means

Table 8 contains the results of the multiple comparisons analysis for the variable `spendings'. Despite the results from the ANOVA analysis, it can be seen that not a single pairwise comparison depicted a significant difference, as the p-values were all greater than 0,05 for all the anchor stores. Consequently, taking into account the results from the descriptive statistics, ANOVA and multiple comparisons there is no differences in the scores given by the customers with the different amount of average spendings.

Table 8

*P-value > 0,05 for a significant difference between the groups

Our initial research hypothesis was based on the assumption that there are no differences in the scores given to the specific anchor types by the people with various average spendings, which proved to be true.

Concerning the age categories the assumptions were the following: the age of the surveyed does not influence his or her perception towards the specific anchor stores. This assumption had been rejected and the following is concluded: younger people when compared to the generation of the 30 -- 45 age group tend to rate the food courts and mass market stores higher than the tech retailers, independent less popular stores, service departments, stores for kids.

The hypothesis that considered that the gender of the consumer also does not affect the perception of the specific anchor store was proved to be wrong.

As a result, some hypotheses were rejected and some approved. Based on the analysis conducted with the 200 respondents of various demographic characteristics:

1. People's average spendings per month do not influence their choice of the anchor store.

2. The respondents' gender does have an effect on their perception of the different stores and its importance in the overall center's tenant mix.

3. People's age does have an effect on the tenant mix. Young respondents tend to rate the food courts and mass market stores higher than all the other tenants in the mall. Whereas, for people aged between 30 to 45 these stores were of a less importance in the mall's tenant mix.

Overall, the results of the analysis proved that two hypothesis were confirmed and one was rejected. It was decided to approve the hypothesis even if at least one of the variable's category. Consequently, female respondents tend to evaluate the importance of beauty stores higher than male respondents. There is also a difference in the scores given by the younger and older respondents to such anchor stores as Food courts and Mass market stores. Lastly, there is no influence detected between the groups of various amount of spendings per month when rating a specific anchor stores.

Considering the research question whether the choice of anchor stores by the customers somehow can be influenced by his or her demographic profile, it can be concluded that partly this assumption can be true given the fact that two out of three hypothesis were proven. This paper can be a complementary study of the overall existing literature concerning the consumers' behavior towards the shopping malls which can be used for various stores and centers. Different businesses can study this literature for construing a perfect tenant mix for the mall considering different characteristics including the demographic ones. It can also be used for developing the marketing strategies and designing marketing campaigns not only for the mall itself but for the businesses located in there.

Conclusion

As the commercial real estate market has a huge variety of directions, it was decided to analyze one of the most concerning and developing area of this market, which is the shopping centers. The number of existing studies lacked enough literature that would describe this field from different sides. The research paper aims were not only to extend the existing literature on both the overall consumer behavior and the studies that cover the topic of shopping centers, but also to help the marketers when constructing a tenant mix and developing the strategies for marketing campaigns. The main concern was put on the investigating the influence of a particular attribute of the mall and how it could affect the perception of the person on the overall image of the shopping center because of this attribute. The overwhelming majority of the studies on the related topic mostly focused on the overall image of the malls. For this research it was decided to concentrate on the particular attribute from that image, which is anchor tenant. In particular, whether the customer would choose the specific mall because of the anchor tenants which are presented there. The researchers have identified the positive effect of the anchor stores on the consumers perception of a particular place. Consequently, it was also important to consider the structure of the perfect tenant pool. Constructing a perfect tenant pool requires a lot of data, one them is the consumers preferences.

Consequently, it was further decided to find out whether the particular demographic characteristics could somehow affect the perception of the consumer on the anchor stores. Considering all of the issues that needed to be identified, three main hypotheses were built. The further actions were directed at testing those hypotheses.

For the proposed study, the data on the demographic profile of customers and their view of different stores was collected via survey. Further, the data was analyzed in SPSS using various statistical methods (descriptive statistics, ANOVA, Independent Samples T-test).

Throughout the process of data collection, there were multiple steps included, which were the analysis of literature, research from the websites, surveys and etc.

The research question that was set at the beginning sounded the following: “Do specific anchor tenants influence consumers' particular shopping mall choices?” From the results obtained it was concluded that two out of three initial hypotheses were approved. Yet, it is important to take into consideration the fact that for some results of the study they cannot be entirely trusted from all the points of view. Each hypothesis analyzed the differences between the means of groups (demographic) to be precise their rating score that had been given to each of the category of the dependent variable. There were several categories observed as it was decided to analyze ten various anchor store types. Basically, it was impossible to prove that for each of ten categories the scores given by different groups were going to be different or the same. Some other apparently would differ and some would be similar. Consequently, it can be entirely stated that because of the one or two categories where the differences were significant the whole hypothesis can be proven. Yet, if this particular category males sense for the particular group it is important to consider this result. So that is why the hypotheses with such outcomes were still considered to be proven. The scenario described above was about the age groups and gender groups. For example, in the discussion chapter, it was stated that for the Beauty stores the differences between men and women were significant, yet it also was statistically identified that for the Luxury brand stores and tech retailers the difference was also found, but because of the smaller mean difference it was not that significant.

Summarizing the outcomes of the analysis it can be stated that some anchor tenants are more important for a particular group of customers and some are not. All of the demographic factors should always be considered when building a tenant mix for the shopping malls or for developing marketing strategies for the same businesses.

The research question that was set at the beginning sounded the following: “Do specific anchor tenants influence consumers' particular shopping mall choices?”. The answer to the question is yes, anchor tenants do influence the choice of the shopping mall.

In addition, for the future research on the related topic it can be suggested to increase the area observed, not only the Saint-Petersburg, to increase the number of observed audience and consider a wider variety of attributes that can influence the choice of the consumer.

It is also important to not only focus on the demographic profile the respondents, so it would beneficial to increase the number of observed factors.

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Appendix 1

Appendix 2

GROUP STATISTICS (AGE)

Appendix 3

Appendix 4

Appendix 5

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