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Analyze the provided SPSS output data, which include frequency distributions, descriptive statistics, cross-tabulations, correlation, and regression analysis related to customer satisfaction, self-confidence, employee friendliness, interior attractiveness, and other variables. The task involves interpreting these statistical results to understand the data patterns and relationships among variables, addressing questions about means, group differences, correlation strength, and regression prediction, supported by specific statistical values and explanations.

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The given SPSS output presents an array of statistical analyses focusing on customer satisfaction, confidence levels, employee friendliness, store ambiance, and predictive factors influencing satisfaction and loyalty. Interpreting these results provides insights into customer perceptions and the determinants impacting their experiences at the restaurant environment.

Descriptive Analysis of Key Variables

The variable X22, Satisfaction, has a mean score of 5.33 on a 7-point Likert scale where 1 indicates "Strongly Disagree" and 7 indicates "Strongly Agree." A mean near 5 suggests that, on average, respondents tend to agree or somewhat agree with positive statements about their satisfaction. The median score of 5 further supports that the typical respondent leans toward agreement, and a mode of 4 indicates that the most frequent response is slightly below "Agree," perhaps "Undecided" or "Somewhat Agree." The standard deviation is 1.33, showing moderate variability among responses with a range from 3 to 7, meaning some respondents are less satisfied while others are highly satisfied. Overall, the mean indicates a generally favorable satisfaction level among the sample.

Similarly, variable X7, Self-Confident, has a mean of 4.70, median of 5, and mode indicating "Strongly Agree" as the most common response. Since the scale ranges from 1 to 7, a mean near 5 suggests a tendency towards agreement with a statement affirming confidence, reflecting a reasonably confident customer base. The standard deviation of 1.20 suggests responses are fairly dispersed but centered around the agreement level.

For X12, Friendly Employees, the mean score is approximately 3.81, with a median of 4 and a mode indicating a "Strongly Disagree" or near-neutral position. This denotes a somewhat neutral to slightly positive perception of employee friendliness. Standard deviation of 1.21 reveals moderate response variability, indicating differing perceptions among customers regarding friendliness.

Variable X17, Attractive Interior, has a mean of 4.64 with a median of 5, implying respondents generally agree that the interior looks appealing, though responses vary. With a standard deviation of 1.02, responses are relatively concentrated around the mean, indicating a general positive view of interior design.

Comparison of Group Means: Gender and Purchasing Behavior

One of the analyses compares the mean likelihood of purchasing new products between males and females. According to the output, males have a mean score of approximately 0.91, whereas females have a mean of 0.49. Given the scale is not specified explicitly in the excerpt, but assuming a typical 0-1 or similar scale, higher mean indicates a greater likelihood. The difference suggests males are more inclined to buy new products than females. This conclusion is grounded in the numerical comparison, where the higher mean for males (0.91) statistically indicates a greater propensity to purchase new products relative to females, assuming the scale's interpretative consistency.

Correlation: Satisfaction and Likelihood to Return

Correlation analysis examines the relationship strength between customer satisfaction (X22) and likelihood to return (X23). The Pearson correlation computed is 0.584, which signifies a moderate to strong positive relationship—meaning as satisfaction increases, the likelihood of returning also tends to increase. Since the correlation coefficient ranges from -1 to 1, and here it is close to 0.6, the data indicate a meaningful association rather than a weak or negligible link. The statistical significance (p

Regression Analysis: Predictors of Customer Satisfaction

The multiple regression analysis explores how four variables—X13 (Fun Place to Eat), X14 (Large Size Portions), X18 (Excellent Food Taste), and X21 (Speed of Service)—predict customer satisfaction (X22). The model outputs an R-squared of 0.178, indicating that approximately 17.8% of the variance in satisfaction can be explained by these predictors. The significance of the overall model is confirmed by a p-value of 0.000, which is highly significant.

Examining the standardized coefficients (beta weights), X18 (Food Taste) has the highest beta (0.673), suggesting it is the strongest predictor of satisfaction—implying that better-tasting food significantly enhances customer satisfaction. X21 (Speed of Service) also has a notable coefficient (0.604), indicating that faster service substantially influences satisfaction. X14 (Large Portions) (beta = 0.417) and X13 (Fun Atmosphere) (beta = 0.490) also contribute positively, but to a lesser extent. These findings demonstrate that items related to food quality and service speed are critical in shaping customer satisfaction, instead of entertainment or ambient factors alone.

Conclusions

The statistical interpretations reveal that overall, customers are generally satisfied with the restaurant, with key drivers being food taste and service speed. Group differences suggest males are more inclined to purchase new products, and the positive correlation between satisfaction and likelihood to return underscores the importance of enhancing the dining experience. The regression emphasizes that improving food quality and service efficiency should be prioritized to boost customer satisfaction and loyalty. These insights can help restaurant managers focus their efforts on areas that statistically influence customer perceptions and behaviors.

References

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