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Spss

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Abstract

Purpose: The aim of this assignment is to test some of the main questions that were raised regarding to customers satisfaction about the services that they received from X Bank.
Methodology: in order to test those questions and to measure customers’ satisfaction, we used SPSS program, which is the most compatible program for social science tests. So, our analyses were focused by using SPSS tools like: Descriptive statistics and Frequencies likewise cross tabulation and chi-square, also independent test and finally correlation.
Findings: Throughout our analyses we were able to find some interesting results regarding to customers opinion for banks services and quality, for instance: There is statistical significant difference when it comes to gender loyalty to the bank, after the chi-square is less than 0.05, also it was clear that there is no correlation between age and perceptions of overall services 0.466. In addition, via our analyses there is a strong correlation between perceptions about service quality and co-operating with the bank in the future 0.867 at the 0.01 level.

Table of Contents

Introduction ……………………………………………………………………………… 3

Data analyses …………………………………………………………………………….. 4

Conclusion ………………………………………………………………………………. 14

Introduction

Nowadays, being able to measure customers’ satisfaction is the main point to develop and sustain business. Organizations which are able to fulfil customers’ needs and wants they achieve to spread their businesses and to increase the number of customers. In this regard, during this assignment we will try to test some important questions related to customers’ satisfaction due to the bank services that they received. It is obvious that as long as we keep our customers to be satisfied with our products and services, they will be loyal to us, and this has been proved especially through economic crises (downturns), who customers never thought to change their supplier. Through this assignment we will be able to be awareness about various issues that concern customers and their evaluation about different services that X Bank provides. So, some of the main questions that will take place are closely related to customers satisfaction with the Bank services that I mentioned above, other questions are linked with gender loyalty to the Bank, also some question tend to measure the correlation between age and some variables regarding to services, and finally some questions are raised in order to measure if there is any possibility for future cooperation with the bank based on the current services that the Bank provides.

Data analyses * Profile the customers that participated in the research (Personal Information variables) - Present a graph for all of them

Gender | | Frequency | Percent | Valid Percent | Cumulative Percent | Valid | Female | 6 | 40.0 | 40.0 | 40.0 | | Male | 9 | 60.0 | 60.0 | 100.0 | | Total | 15 | 100.0 | 100.0 | |
Table 1

As we can see from the table as well as from the graph 40% of customers who participate in this research are female and the remaining 60% are male.

MS | | Frequency | Percent | Valid Percent | Cumulative Percent | Valid | Single | 6 | 40.0 | 40.0 | 40.0 | | Married | 9 | 60.0 | 60.0 | 100.0 | | Total | 15 | 100.0 | 100.0 | |
Table 2

Also, from people who participated in this research the data collection tells us that 40% of respondents were single and the remaining 60% were married.

Education | | Frequency | Percent | Valid Percent | Cumulative Percent | Valid | Some College | 6 | 40.0 | 40.0 | 40.0 | | Completed College | 5 | 33.3 | 33.3 | 73.3 | | Graduate School | 4 | 26.7 | 26.7 | 100.0 | | Total | 15 | 100.0 | 100.0 | |
Table 3

Moreover, our respondents had this level of education: 40% of them had some college, 33.3% of respondents have completed the college and 26.7% were graduate school.

Descriptive Statistics | | N | Minimum | Maximum | Mean | Std. Deviation | age | 15 | 19.00 | 78.00 | 40.8667 | 18.26342 | Valid N (listwise) | 15 | | | | |
Table 4
As you can see from the table above, the minimum age of people who participated in our research is 19 years old, the maximum age of participations is 78 years old, overall mean is 40.86 years old.

Age2 | | Frequency | Percent | Valid Percent | Cumulative Percent | Valid | 1.00 | 5 | 33.3 | 33.3 | 33.3 | | 2.00 | 6 | 40.0 | 40.0 | 73.3 | | 3.00 | 4 | 26.7 | 26.7 | 100.0 | | Total | 15 | 100.0 | 100.0 | |
Table 5

So, 33.3% of people who participated in this research were between 1-30 years old, 40% were between 31-50 years old and 26.7% of respondents are above 51 years old.

* Produce the results for the following question: “How likely it is that I will make business with this bank in the future” Run the mean scores” and “I intend to keep on co-operating with this bank in the future” Run the frequencies and the mean scores

Descriptive Statistics | | N | Minimum | Maximum | Mean | Std. Deviation | i2 | 15 | 1.00 | 6.00 | 3.6667 | 1.91485 | p1 | 15 | 1.00 | 7.00 | 3.5333 | 1.88478 | Valid N (listwise) | 15 | | | | | Table 6 The possibility to continue doing business with this bank is low, because the overall mean is 3.66 out of 6 for i2 question, which is a main signal that bank services are poor and good information for customers that they need to look for another bank to make business with them. If we see frequencies we will not have a clear picture if we should keep on our cooperation with this bank or not, but the mean offers a better picture when it comes to this issue. So, the mean is 3.55 out of 7 for p1 question, which tells us that bank services are low and in the best scenario customers have to cut off their cooperation with this bank due to the future, or in a hopeful scenario they should rethink in terms of keeping on their cooperation with this bank in the future.

* Are there any differences between males and females in terms of frequency of visiting the bank? (loyalty variable)

Gender * loyalty Cross tabulation | | loyalty | Total | | Once per week | Less than once per week | | gender | Female | Count | 5 | 1 | 6 | | | % within gender | 83.3% | 16.7% | 100.0% | | Male | Count | 2 | 7 | 9 | | | % within gender | 22.2% | 77.8% | 100.0% | Total | Count | 7 | 8 | 15 | | % within gender | 46.7% | 53.3% | 100.0% |
Table 7

Chi-Square Tests | | Value | df | Asymp. Sig. (2-sided) | Exact Sig. (2-sided) | Exact Sig. (1-sided) | Pearson Chi-Square | 5.402a | 1 | .020 | | | Continuity Correctionb | 3.225 | 1 | .073 | | | Likelihood Ratio | 5.786 | 1 | .016 | | | Fisher's Exact Test | | | | .041 | .035 | Linear-by-Linear Association | 5.042 | 1 | .025 | | | N of Valid Cases | 15 | | | | |
Table 8

We can see from the Table 7 that 83.3% of females visiting the bank or are loyal to the bank, in the other hand only 22.2% of males visiting the bank. So after the chi-square is lower than 0.05 we have to say that we have statistical evidence to claim that there is gender distinction (Difference) in expressing loyalty to the bank. This means from the Bank point of view that they should do something more for males in order to attract them with their services in order to increase males loyalty to the Bank. * Are there any differences between frequent and non-frequent customers (loyalty variable) in service quality evaluations? (q1, q2, q3, q4) Group Statistics | | loyalty | N | Mean | Std. Deviation | Std. Error Mean | q1 | Once per week | 7 | 6.2857 | .48795 | .18443 | | Less than once per week | 8 | 3.2500 | .70711 | .25000 | q2 | Once per week | 7 | 6.0000 | .81650 | .30861 | | Less than once per week | 8 | 2.6250 | .74402 | .26305 | q3 | Once per week | 7 | 5.8571 | .69007 | .26082 | | Less than once per week | 8 | 3.6250 | 1.68502 | .59574 | q4 | Once per week | 7 | 3.5714 | .53452 | .20203 | | Less than once per week | 8 | 2.3750 | .51755 | .18298 |
Table 8 Independent Samples Test | | Levene's Test for Equality of Variances | t-test for Equality of Means | | F | Sig. | t | df | Sig. (2-tailed) | Mean Difference | Std. Error Difference | 95% Confidence Interval of the Difference | | | | | | | | | Lower | Upper | q1 | Equal variances assumed | .939 | .350 | 9.526 | 13 | .000 | 3.03571 | .31867 | 2.34727 | 3.72416 | | Equal variances not assumed | | | 9.772 | 12.406 | .000 | 3.03571 | .31067 | 2.36128 | 3.71015 | q2 | Equal variances assumed | .057 | .816 | 8.379 | 13 | .000 | 3.37500 | .40281 | 2.50477 | 4.24523 | | Equal variances not assumed | | | 8.323 | 12.314 | .000 | 3.37500 | .40551 | 2.49397 | 4.25603 | q3 | Equal variances assumed | 12.256 | .004 | 3.262 | 13 | .006 | 2.23214 | .68438 | .75362 | 3.71067 | | Equal variances not assumed | | | 3.432 | 9.532 | .007 | 2.23214 | .65034 | .77340 | 3.69088 | q4 | Equal variances assumed | .141 | .713 | 4.399 | 13 | .001 | 1.19643 | .27195 | .60892 | 1.78393 | | Equal variances not assumed | | | 4.389 | 12.609 | .001 | 1.19643 | .27258 | .60570 | 1.78716 |
Table 9
Based on the data analyses we can say that there is no difference between frequent and non-frequent customers when it comes to q1, q2, q3, q4 questions. The mean score is low from 1.19 to 3.75, which means that customers evaluate all these questions with negative opinion regarding to bank services that they provide, which is a signal to the bank that customers are dissatisfied with their services that they provide, and obviously they have to take further steps in order to improve their current services.

* Are there any age related differences in terms of service quality evaluations? (q1, q2, q3, q4)

Correlations | | q1 | q2 | q3 | q4 | age | q1 | Pearson Correlation | 1 | .876** | .504 | .782** | .456 | | Sig. (2-tailed) | | .000 | .055 | .001 | .088 | | N | 15 | 15 | 15 | 15 | 15 | q2 | Pearson Correlation | .876** | 1 | .701** | .716** | .539* | | Sig. (2-tailed) | .000 | | .004 | .003 | .038 | | N | 15 | 15 | 15 | 15 | 15 | q3 | Pearson Correlation | .504 | .701** | 1 | .451 | .704** | | Sig. (2-tailed) | .055 | .004 | | .092 | .003 | | N | 15 | 15 | 15 | 15 | 15 | q4 | Pearson Correlation | .782** | .716** | .451 | 1 | .357 | | Sig. (2-tailed) | .001 | .003 | .092 | | .192 | | N | 15 | 15 | 15 | 15 | 15 | age | Pearson Correlation | .456 | .539* | .704** | .357 | 1 | | Sig. (2-tailed) | .088 | .038 | .003 | .192 | | | N | 15 | 15 | 15 | 15 | 15 | Table 10**. Correlation is significant at the 0.01 level (2-tailed). | *. Correlation is significant at the 0.05 level (2-tailed). | Based on the correlation test, we can say that there is no correlation between age and q1 as well as q4 questions, because the correlation is 0.456 and 0.357 respectively, but in the other hand we can say that there is a strong correlation between age and q2, q3 questions, because the correlation for q2 is 0.539 at the 0.05 level and it is strong correlation 0.704 for q4 at the 0.01 level. * Is age related with perceptions of overall service quality (variable “qt”)?

Correlations | | age | qt | age | Pearson Correlation | 1 | .466 | | Sig. (2-tailed) | | .080 | | N | 15 | 15 | qt | Pearson Correlation | .466 | 1 | | Sig. (2-tailed) | .080 | | | N | 15 | 15 |
Table 11
As can be seen from Table 11 correlation test, the correlation is 0.466 so we claim that there is no correlation between age with perceptions of overall service quality.

* Are the perceptions about the overall service quality (“qt”) related to intentions to keep on co-operating with the bank in the future?

Correlations | | qt | p1 | qt | Pearson Correlation | 1 | .867** | | Sig. (2-tailed) | | .000 | | N | 15 | 15 | p1 | Pearson Correlation | .867** | 1 | | Sig. (2-tailed) | .000 | | | N | 15 | 15 | Table 12**. Correlation is significant at the 0.01 level (2-tailed). |

The correlation test provides the result 0.867 at the 0.01 level, which we can truly claim that there is strong correlation between the perception of overall service quality related with the intentions to keep on the collaborating with the bank in the future.

* Are perceptions of overall service quality (variable “qt”) related with questions q1, q2, q3, q4, q5, and q6?

Correlations | | qt | q1 | q2 | q3 | q4 | q5 | q6 | qt | Pearson Correlation | 1 | .918** | .887** | .589* | .846** | .757** | .589* | | Sig. (2-tailed) | | .000 | .000 | .021 | .000 | .001 | .021 | | N | 15 | 15 | 15 | 15 | 15 | 15 | 15 | q1 | Pearson Correlation | .918** | 1 | .876** | .504 | .782** | .636* | .539* | | Sig. (2-tailed) | .000 | | .000 | .055 | .001 | .011 | .038 | | N | 15 | 15 | 15 | 15 | 15 | 15 | 15 | q2 | Pearson Correlation | .887** | .876** | 1 | .701** | .716** | .642** | .463 | | Sig. (2-tailed) | .000 | .000 | | .004 | .003 | .010 | .082 | | N | 15 | 15 | 15 | 15 | 15 | 15 | 15 | q3 | Pearson Correlation | .589* | .504 | .701** | 1 | .451 | .530* | -.071 | | Sig. (2-tailed) | .021 | .055 | .004 | | .092 | .042 | .801 | | N | 15 | 15 | 15 | 15 | 15 | 15 | 15 | q4 | Pearson Correlation | .846** | .782** | .716** | .451 | 1 | .599* | .519* | | Sig. (2-tailed) | .000 | .001 | .003 | .092 | | .018 | .047 | | N | 15 | 15 | 15 | 15 | 15 | 15 | 15 | q5 | Pearson Correlation | .757** | .636* | .642** | .530* | .599* | 1 | .373 | | Sig. (2-tailed) | .001 | .011 | .010 | .042 | .018 | | .171 | | N | 15 | 15 | 15 | 15 | 15 | 15 | 15 | q6 | Pearson Correlation | .589* | .539* | .463 | -.071 | .519* | .373 | 1 | | Sig. (2-tailed) | .021 | .038 | .082 | .801 | .047 | .171 | | | N | 15 | 15 | 15 | 15 | 15 | 15 | 15 | Table 13**. Correlation is significant at the 0.01 level (2-tailed). | *. Correlation is significant at the 0.05 level (2-tailed). |

As it can be seen from Table 13, we can claim that there is strong correlation between the perceptions of overall quality (qt) related with the questions q1, q2, q3, q4, q5, q6. Moreover there are strong correlations with questions q1, q2, q4, q5 at the 0.01 level, likewise it is strong correlation with questions q3, q6 at the 0.05 level.

Conclusion

Throughout this analyses that we have done through SPSS we found some interesting points that drives us to make some conclusion regarding to different issues that we discussed above. So, the first point that we can say is that the majority of people that participated in this survey were male 60%; also it was clear that 60% of respondents were married and the remaining 40% were single. Also, the majority of participations had some college 40%, some of them had completed college 33.3% and 26.7% were graduate school. Moreover, 33.3% of respondents were between 1-30 years old, 40% between 31-50 years old and the rest above 50 years old. Furthermore, throughout our analyses we were able to understand that the possibility to make business in the future with the bank is low after the mean score 3.66 out of 6, and the possibility to develop collaboration with the bank in the future is impossible after the mean score 3.53 out of 7, which tells us that the bank is not compatible for further cooperation. In addition, we can say based on our analyses that there is a statistical difference when it comes to gender loyalty to the bank, after the chi-square were less than 0.05. Likewise, via our tests it was impossible to find differences between frequent and non-frequent customers’ loyalty when it comes to questions q1, q2, q3, q4. Furthermore, we found that there is strong correlation between age and q2 and q3 questions, 0.539 and 0.704 respectively at the 0.01 level, but in the other hand there is no correlation between age and perceptions of overall service quality (“qt”) 0.466. All in all, the correlation between perceptions about service quality and co-operating with the bank in the future is strong 0.867 at the 0.01 level, and through data analyses there is evidence that the correlation between perceptions of overall service quality is strong with all those questions: q1, q2, q3, q4, q5, q6 questions.

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