Survey SPSS Data Analyse - Coursework Example

Survey SPSS Data Analysis by [Insert Grade 25th April Survey SPSS Data Analyze Introduction With enhanced globalisation, the world has turned into a global village where people, capital, goods and services freely move from one geographical location to another. As a result, businesses are facing stiff competition, especially those opened by new owners who are not well experienced with market forces. Business people should always be ahead of the game, this will help them beat their competitors and continuously provide the best services and value to their clients (Malhotra, 2010). Just like previous managers, businessmen and ladies need to make rational decisions. One approach to accomplish this is through statistics (Kontari, 2004). Ideally statistics help managers and business people to be more confident when dealing with unpredictable future, thanks (Perkins & Baxter, 2012). It allows managers to make quicker and smarter decisions. In this case, the researcher will analyze data collected by a marketing firm aimed at helping two young businessmen to understand their clients better. Background of the problem Two young entrepreneurs who graduated with a degree in finance and management respectively, joined hands to start a business. They opened up a Mexican theme oriented restaurant called Santa Fe Grill Mexican Restaurant. The business was doing great during the fir six months. However, things have started turning the other away from the revenue, customer flow and sales tremendously declined. With such a problem in mind, the owners decided to hire the services of a marketing firm to collect data and do some analysis based on the following objectives; 1. To identify the factors people consider important in making casual dining restaurant choice decisions. 2. To determine the characteristics customers use to describe the Santa Fe Grill and its competitor Jose’s Southwestern Café. 3. To develop a psychographic/demographic profile of the restaurant customers. 4. To determine the patronage and positive word-of-mouth advertising patterns of the restaurant customers. 5. To assess the customer’s willingness to return to the restaurant in the future. 6. To assess the degree to which customers are satisfied with their Mexican restaurant experiences. Objective 1 Business owners should strive to establish the factors people consider important in making casual dining restaurant choice decisions. This will eventually help them come up with strategies to enhance customer experience. To accomplish this task, factor analysis was used. Factor analysis is a data reduction technique used to reduce a large number of variables to a smaller set of underlying factors that summarize the essential information contained in the variables. It identifies the underlying factors which can explain the correlations between a set of variables (Brockway, Mangold & Miller, 1999). It is often used in the development of scales for theoretical constructs which are not directly measurable. The factors considered important are the following; 1. Price 2. Food quality 3. Atmosphere 4. Service 5. Distance driven to the restaurant From the analysis, it is evident that the correlation between variables is less than 0.3, the KMO value stood at 0.273 it is less than 0.5. When looking at the eigenvalues, only 3 factors should be extracted since they have eigenvalues greater than 1.0. Similarly, if these factors are extracted, 69% of the variance would be explained. An examination of the scree-plot indicates that the line begins to flatten at the second factor and starts to sharply decline at factor 4 (Fig. 1). It is worth noting that the findings obtained here are closely related to what previous scholars such as Dev & Olsen, 2000 and Jang & Namkung, 2007. From the two researches, the issues of the quality of food and convenience were found to be the major factors deemed to be important for customers in choosing a restaurant. Fig. 1 Screeplot Table 1 Total Variance Explained Component Initial Eigenvalues Extraction Sums of Squared Loadings Rotation Sums of Squared Loadings Total % of Variance Cumulative % Total % of Variance Cumulative % Total % of Variance Cumulative % 1 1.487 29.750 29.750 1.487 29.750 29.750 1.360 27.194 27.194 2 1.149 22.976 52.726 1.149 22.976 52.726 1.200 24.005 51.200 3 1.063 21.261 73.987 1.063 21.261 73.987 1.139 22.787 73.987 4 .991 19.822 93.809 5 .310 6.191 100.000 Extraction Method: Principal Component Analysis. Table 2 Correlation Matrix X26 -- Price X27 -- Food Quality X28 -- Atmosphere X29 -- Service x30 -- Distance Driven to Restaurant Correlation X26 -- Price 1.000 -.422 -.298 -.239 -.055 X27 -- Food Quality -.422 1.000 -.101 -.105 .017 X28 -- Atmosphere -.298 -.101 1.000 -.146 -.021 X29 -- Service -.239 -.105 -.146 1.000 .014 x30 -- Distance Driven to Restaurant -.055 .017 -.021 .014 1.000 Table 3 KMO and Bartletts Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .273 Bartletts Test of Sphericity Approx. Chi-Square 257.545 df 10 Sig. .000 Objective 2 Business owners should determine the characteristics customers use to describe their business as well as that of their competitors in this case Santa Fe Grill and its competitor Jose’s Southwestern Café. Ideally, this will help them to come up with strategies that will help businesses to be competitive (Sekaran & Bougie, 2013). From the survey questions, the factors chosen that people describe the restaurant of they last eat at are; friendly employees, fun place to eat, large size portions, fresh food, reasonable prices, attractive interior, excellent food taste, knowledgeable employees, proper food temperature and speed of service. When using cross tabulation, one is able to identify how customers of the two restaurant believed in what factors made them choose a particular restaurant. From the cross-tabulation results comparing between the customers of the two restaurants, it is evident that a higher percentage of Santa Fe Grill customers strongly agreed that speed of service describes the restaurant. On the other hand, a higher percentage of Jose’s Southwestern café indicated that proper food temperature is one factor that describes the restaurant. Since the differences between the two groups was not that big, it can also be said that the same factor was quite important for Santa Fe Grill customers. Another attribute that was highly ranked between the two restaurant clients was excellent food taste. Majority of the Jose’s Southwestern café customers stated that it best describes the restaurant, followed by customers of Santa Fe Grill restaurant. Attractive interior was another attribute that described Santa Fe Grill customers. The higher percentage of those who ate at the restaurant stated that indeed an attractive interior enhanced their experience. The issue of reasonable prices was indicated to be another attribute that is associated with Santa Fe Grill customers compared to Jose’s Southwestern café customers. When it comes to fresh food, a higher percentage of Jose’s Southwestern café customers strongly agreed that it describes it. The applies to the issue of large portions. According to Santa Fe Grill customers, the restaurant is associated with a large portion of food. Additionally, it emerged that Santa Fe Grill is a fun place to eat when compared to its competitor Jose’s Southwestern Café. Lastly, there was not much difference concerning the issue of friendly employees. Clients of both restaurant stated that they both had equally friendly employees. The description stated above are summarized in the table below. Santa Fe Grill description Jose’s Southwestern Café description Friendly employees Friendly employees Fun place to eat Fresh food Large size portions Excellent food taste Reasonable prices Proper food temperature Speed of service Proper food temperature Regression analysis was also used to perform the analysis. It allows one to analyze relationships between a metric dependent variable and one or more metric independent variable. Additionally, it helps scholars to determine whether the independent variable explains significant variation is the dependent variable, the strength of the association, the structure or form of the relationship, predicts the values of the dependent variable when values of independent variables are known and one can control for other independent variable when establishing the contribution of other variables (Sekaran & Bougie, 2013). Having the selected variables, the dependent variable chosen is the likelihood of the customer to return. From table 5, it is evident that the relationship between the dependent variable, the likelihood of returning and the independent variables stated above was statistically significant since F=34.998, p=0.000. This means that the likelihood to return to the restaurant was greatly influenced by the independent variables. From table 1 he adjusted R square stood at 0.431. It is of interest for exerts to establish how best a regression equation best describes the relationship between dependent and independent variable. The result indicates that only 43.1% of the changes in the dependent variable can be explained by the regression model. One can therefore conclude that there are other variables that could explain about 60% of the variation. Having in mind that the R square adjusted is less than 60.0%. Usually, when variables are considered in groups, there are those that carry more weight ad contribute more to changes in the dependent variables. Researchers should be able to identify such variables which can be later used to inform decisions made. From the coefficient table 6 it is evident that the following variables are significant contributors to changes in the dependent variable; Large size portions t= 3.449, p=0.001 Fresh food t=3.638, p=0.000 Attractive interior t= 4.900, p=0.000 Excellent food taste t=3.070, p=0.002 Knowledgeable employees t=3.362, p=.001 Speed of service t=3.616, p=.000 It can be reasonably concluded that large size portion, fresh food, attractive interior, excellent food taste, knowledgeable employees and speed of service are characteristics customers use to describe the restaurant. Table 4 Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .666a .444 .431 .833 a. Predictors: (Constant), X21 -- Speed of Service, X20 -- Proper Food Temperature, X14 -- Large Size Portions, X15 -- Fresh Food, X16 -- Reasonable Prices, X17 -- Attractive Interior, X19 -- Knowledgeable Employees, X13 -- Fun Place to Eat, X18 -- Excellent Food Taste, X12 -- Friendly Employees Table 5 ANOVAb Model Sum of Squares df Mean Square F Sig. 1 Regression 242.912 10 24.291 34.998 .000a Residual 304.699 439 .694 Total 547.611 449 a. Predictors: (Constant), X21 -- Speed of Service, X20 -- Proper Food Temperature, X14 -- Large Size Portions, X15 -- Fresh Food, X16 -- Reasonable Prices, X17 -- Attractive Interior, X19 -- Knowledgeable Employees, X13 -- Fun Place to Eat, X18 -- Excellent Food Taste, X12 -- Friendly Employees b. Dependent Variable: X23 -- Likely to Return Table 6 Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) -.475 .320 -1.485 .138 X12 -- Friendly Employees -.047 .067 -.051 -.706 .481 X13 -- Fun Place to Eat -.025 .066 -.020 -.370 .711 X14 -- Large Size Portions .120 .035 .144 3.449 .001 X15 -- Fresh Food .214 .059 .232 3.638 .000 X16 -- Reasonable Prices -.003 .012 -.009 -.223 .824 X17 -- Attractive Interior .272 .056 .249 4.900 .000 X18 -- Excellent Food Taste .195 .063 .192 3.070 .002 X19 -- Knowledgeable Employees .120 .036 .163 3.362 .001 X20 -- Proper Food Temperature .018 .011 .061 1.658 .098 X21 -- Speed of Service .125 .035 .235 3.616 .000 a. Dependent Variable: X23 -- Likely to Return Objective 3 An understanding of the demographic profile of the restaurant customers is necessary for proper planning on issues such as marketing. Concerning gender, male was 65.8%, while female was 34.2%, representing 292 and 152 respectively (Table 7). Fig. 2 shows that the majority of the respondents had no children at home while 118 indicated that they had between 1 and 2 children at home while 114 said they had more than 2 children. With regards to age, fig. 3 shows that the majority of the respondents were between 35 and 49 years (50.9%), followed by those between 50 and 59 years (25.5%), then 26-34 years (10.1%), 18-25 years (9.0%) and those over 60 years were the least (4.5%). Table 7 X32 – Gender Frequency Percent Valid Percent Cumulative Percent Valid Male 292 65.8 65.8 65.8 Female 152 34.2 34.2 100.0 Total 444 100.0 100.0 Fig. 2 Bar graph of number of children Fig. 3 Bar graph of respondent age Table 8 Descriptive Statistics N Range Minimum Maximum Mean Std. Deviation X35 -- Income 444 1235000 15000 1250000 7.23E4 92272.355 Valid N (listwise) 444 From the table (8) it is evident that the highest income was $1,250,000 while the lowest earner was $15,000. The range was $1,235,000. The mean income was $723,000 with a standard deviation $92,272.35 meaning that the majority of the earners had an income ranging between $630,727.65 and $815,272.35. When asked about having a gross income of more than $15,000, 100% of the respondents said yes (Table 9). Similarly, all of the respondents indicated that they regularly dine at a casual dining restaurant and they had dined at other Mexican restaurants in the last 6 months. It is also of interest to know where the respondents have recently eaten at. Results indicate that 278 representing 62.6% of the sample eat at Santa Fe Grill while 166 representing 37.4% of the sample eat at Jose’s Southwestern Café (Fig. 4). Ideally, Chung & Kim, (2011) suggested that the demographic characteristics of customers can play an important role in helping businesses strategise on how best to attract more clients and main the existing one. Certain assumptions can be developed such as individuals who have more income per month are more likely to spend some of their cash on restaurant. Table 9 Gross Annual Income $15,000 or More Frequency Percent Valid Percent Cumulative Percent Valid Yes 443 99.8 99.8 99.8 11 1 .2 .2 100.0 Total 444 100.0 100.0 Fig. 4 Pie chart showing restaurant recently eaten at Objective 4 Businesses have adopted various ways of advertising their products and services. However, one approach that has been deemed effective and less costly is word of mouth from the existing customers. According Xchange Asia Pacific (2001), word of mouth advertisement is only possible if the existing customers are happy and satisfied with the services they receive. In this assignment, a cross tabulation will be used to determine the patronage and positive word-of-mouth advertising patterns of the restaurant customers. From the analysis, it is apparent that customers of Santa Fe Grill are less likely to recommend the restaurant to other prospecting client. They are therefore not better placed to help the restaurant reach more clients. The same can be visualized in the bar chart below (Fig. 5). 3.0% of Jose Southwestern Café customers indicated that they would definitely recommend the restaurant compared to 0.0% of Santa Fe Grill restaurant (Table 10). Table 10 Mexican Restaurant Most Recently Eaten At . . . * X24 -- Likely to Recommend Crosstabulation X24 -- Likely to Recommend Total 2 3 4 5 6 Definitely Will Recommend Mexican Restaurant Most Recently Eaten At . . . Joses Southwestern Cafe Count 35 26 38 35 27 5 166 % within Mexican Restaurant Most Recently Eaten At . . . 21.1% 15.7% 22.9% 21.1% 16.3% 3.0% 100.0% Santa Fe Grill Count 33 100 90 50 5 0 278 % within Mexican Restaurant Most Recently Eaten At . . . 11.9% 36.0% 32.4% 18.0% 1.8% .0% 100.0% Total Count 68 126 128 85 32 5 444 % within Mexican Restaurant Most Recently Eaten At . . . 15.3% 28.4% 28.8% 19.1% 7.2% 1.1% 100.0% Fig. 5 Cross-tabulation bar chart Objective 5 Business continues to grow when it has repeat customers. In this case, it will be important to assess the customer’s willingness to return to the restaurant in the future. To accomplish this independent sample t-test is used when one wishes to determine whether the difference between means for two sets of scores is significant (Pallant, 2007). We are interested in establishing if customers are willing to return to the restaurant. H0: The willingness of clients to return to Santa Fe Grill =The willingness of clients to return to Jose’s Southwestern Café. HA: The willingness of clients to return to Santa Fe Grill Read More