The foundations to success in any business organisation are centred to concepts and practices that facilitate the customer satisfactions (Kumar and Reinartz, 2016, pp.36-68). Mangers and the relevant leadership in different organisations have been reported to put in great efforts to meet the customer demands. However, this has been pausing a great challenge due to the dynamic consumer needs that have been changing from time to time. The organisations have therefore reverted to lay in place the strategic practices that would oversee the satisfaction of their clients as well as enable them to compete sustainably in the market. As a result, stiff competition has been precipitated among the companies supplying similar products and services via various means to their customers.
The report presents an analysis of an Australia department store, Myer and David Jones enterprise that faces stiff competitors from the online retailers who are reported to be more reliable and convenient to the consumers (Prebensen, Kim, and Uysal, 2016, pp.934-945). For the departmental stores to level up in the market performance, the organisation conducts a research study that would help it address the key areas that would enable quality performance through customer satisfaction. The objective of the analysis is to identify the poor performing areas in the service to the customers by considering their preferences that build on the convenience to the service delivery. The analysis will also seek to address the challenges facing the departmental stores over the online vendors and the ways of solving these problems through effective decision making.
The use of online questionnaires was deployed for collecting the responses from the participants who were the customers to the organisation (Stoet, 2017, pp.24-31). The online survey questionnaires were preferred due to the shorter time taken for collecting data from different regions/ cities across the country. Additionally, the online survey is cost effective as well as has lower chances of biases whereby the respondents are given the privilege to express themselves without the sense of being judged by the interviewer as illustrated by (Brace, 2018). These respondents were selected from the organisation’s database using the random sampling techniques that was preferred over other techniques which is believed to cover a large geographical data samples through the random selections.
However, the questions for the survey were precisely designed to address the research questions and the hypothesis, which were brief and easy to understand (Rubsamen, Akmatov, Castell, Karch, and Mikolajczyk, 2017, p.4). Ten questions that had interval answers that were rated on a scale of 1 to 10 were used to ensure that the respondents use the minimal time to fill the survey thus encouraging more participating. Through the random sampling techniques, 1000 respondents were selected where 400 completed the survey questions that were considered for analysis. During the implementation of this method of data collection, the researcher observed the ethics of research by protecting the privacy of the respondents (Sekaran and Bougie, 2016). The structure of the questions identified the gender and the type of employment of the respondents without provision of the names and identities. Additionally, the survey voluntarily conducted where the respondents could opt out anytime they want without attaching any reasons or explanation. The researcher further ensured the participants have understood the purpose of collecting data, which was attributed to improving the quality of services for the consumers.
The establishment of the hypotheses is aimed at aiding the researcher to conduct a relevant study in line to the research questions. This ensures that there is consistency and relevance in the data collection and analysis that would lead to reliable results. According to (O’Sullivan, Berner, Taliaferro, and Rassel, 2016), the hypotheses also ensure that the researcher does not go out of the scope of the research problems as outlined by. The following are the research questions and their associated hypotheses, both null and alternative that were developed.
The following hypothesis focusses on the above research question
The report presents different statistical techniques that were used in the testing of the hypothesis. The selection of the tools was determined by the nature of the expected/ anticipated outputs and the relationship to be determined between different variables.
To test the first hypothesis, the use of descriptive analysis will be used to determine the inferences between the data samples (Zook and Pearce, 2017, pp.43-71). The frequency distribution of the two set of the scores will be established whereby the first set will be all the scores that range between one and six while the second set will be the scores that range between seven and ten for customer satisfaction. By using this technique, it will be possible to determine whether the hypothesis is true or false through the frequency distribution. Additionally, using the descriptive statistics technique will enable the determination of the mean and the standard deviation of the scores for the two classes identified in the hypothesis. This will provide an easier way to justify if the organisation has achieved the customer satisfaction goals.
However, the use of correlation technique and scatter plots will be used to determine the differences in satisfaction of the male and the female customers in the second hypothesis. The data will be arranged in two sets for the male and female customers whereby a correlation test will be run to establish the relationship in the satisfaction for the two sets of data. The correlation coefficient R will illustrate the strength of the correlation in the data sets, which range between -1 and 1 as illustrated by (Darlington and Hayes, 2016). When the range lies within the boundaries of one within this range, it will depict that there is no differences in satisfaction of the female and the male customers since the variables are strongly correlated towards the positives. When the correlation coefficient does not lie within the one boundaries, it would imply that the variables have no correlation thus indicating that there is a difference in the satisfaction of the male and the female customers.
In the third and fourth hypotheses, the use of regression analysis will be deployed in the efforts to determine the rate of income and the age factor relate to the satisfaction of the customers. The regression analysis will be carried out independently for determining the existence of the relationship between the customer satisfaction and these variables. The regression output will establish the relation between the sets of data that would be vital for drawing the conclusions concerning theses hypotheses. The Multiple R coefficient will be used to determine how the trends in the data sets are related on a scale of zero to one. If the coefficient is one, it implies the independent and the dependent variables have a relationship while the zero output will indicate there is no relationship between the variables. Additionally, the evaluation using the evaluation of the P-value, the probability of existence of a correlation between the sets of data determined with the aid of the significance value F. During the analysis using these techniques, when the P-value and the significant value F are high, it implies that the values of the dependent variable can be altered or predicted with the changes in the independent variable. This would imply the customer satisfaction is dependent on the age factor and the level of employment among the consumers.
Additionally, the fifth hypothesis can be verified using the regression analysis to draw the existence of a relationship between the variables such as the shopping assistance, return policies, on-site verification of products, the ability to compare prices, and cashbacks in relation to customer satisfaction. This technique can be justified due to its simplicity in the analysis of bulk data within a short time. The explanation to the output of this technique is provided in analysis of third and fourth hypotheses.
To determine whether the organisation has achieved the overall satisfaction goals, the data was sorted into two categories where the frequencies of each data set was recorded as shown below. The range of 7-10 was considered as the met goals by the management of customer satisfaction while the range from 1-6 indicated that the customer satisfaction do not meet the set goals. The frequency column in the table indicate the number of customers whose satisfaction is indicated in the adjacent column by the customer satisfaction intervals.
|
Customer satisfaction Intervals |
Frequency |
Relative frequency |
Percentage relative frequency |
|
1-6 |
358 |
0.895 |
89.5 |
|
7-10 |
42 |
0.105 |
10.5 |
The information from the table can be presented using a pie chart and a bar graph as shown below using the percentage cumulative frequency.
Pie chart
Bar graph
From the data in the table, chart, and the bar graph, it is evident that satisfaction of the customers as required by the managed is approximately 11 per cent. This indicates that 89% of the customers from the obtained sample are not satisfied with the provision of the services and products by the departmental store. This result indicates that the first hypothesis will be rejected since the current customer satisfaction does not meet the goals of the management of the score of 7 to 10.
The correlation coefficient can be run in MS Excel using the formula “=CORREL (Array F, Array M)” then “enter” where the arrays represents the range of the customer satisfaction for the female and female consumers respectively. From the results obtained, the correlation coefficient R was obtained as 0.066748. For the Excel formula to return a result, the number of the variables in each data set must be equal. However, from the data provided, there were six more female customers than male, therefore, the last six female were excluded from the correlation analysis according to their identification.
From the correlation, the following graph is a scatter plot drawn from the data samples
From the results, the correlation coefficient (r = 0.066748) indicates that there exists no difference in the satisfaction of the male and the female customers as stated in the hypothesis. There is a big difference of the correlation coefficient from the score of one thus illustrating the weak relationship among the variables. This is further proven by the scatter plots where the scatter points does not indicate any patterns where the line of best fit can be established. These reasons identified does not support the hypothesis thus it will be rejected thus, the alternative hypothesis will be used.
To examine this hypothesis, the level of income was taken as the independent variable while the customer satisfaction as the independent variables in the regression analysis. The results are shown below.
From the regression output, it is observed that the value of the Pearson’s coefficient R indicates a weak relationship between the independent and the dependent variable. This indicates that there is minimal impact on the level of income on the satisfaction among the customers. Additionally, the coefficient of determination r2 is 0.003134, which implies that only 0.3124% of points fall along the regression line. This is an indication that nearly zero points are along the regression line. Therefore, the level of income does not reflect on the degree of customer satisfaction in this case.
The age group was considered as the independent variable while the customer satisfaction is the independent variable. The regression output is shown below.
From the output, we can depict that there is some linear regression relationship between the age group and the customer satisfaction indicated by the determination coefficient r2, where 17.91% points fall along the regression line. However, this is considered as a weak correlation between the variables indicated by the Pearson’s correlation coefficient result of 0.4233. Therefore, the age group variable could be used in the study although it cannot be fully be relied upon.
This was run on MS Excel using the regression technique where the scores of customer satisfaction were considered as the independent variable while the overall customer satisfaction as the independent variable. The results are displayed below.
The results indicates some relationship between the variables with only 32.7% of the samples falling along the regression line. The Pearson’s correlation coefficient is above average thus the correlation between the variables can be relied upon in this hypothesis.
To obtain the output, the scores for the convenient on return policy were identified as the independent variable while the customer satisfaction as the dependent variable. The results are displayed as shown below.
The results indicate that the convenient return policy variable does not correlate with the customer satisfaction from the regression analysis.
He scores from the online survey for the physical verification of the products were identified as the independent variables in the analysis while the overall customer satisfaction as the independent variable. The output is displayed below.
The regression results indicate a weak relationship between the variables in the data sets. This is due to the coefficient of determination, which illustrates that only 21.65% of the sample point’s fall along the regression line with a progressive low correlation of 0.465293 indicated, by the Pearson’s coefficient.
The ability to customer satisfaction in the ability to compare the prices was the independent variable while the overall customer satisfaction was the independent variable. The results are displayed below.
The output indicates that the customer satisfaction can be altered by the ability to compare the prices by the customers. This is due to the Pearson’s coefficient that indicates a correlation between the variables. Additionally, 32.91% of the sample points fall along the regression line.
In this analysis, the independent variables was the customer satisfaction in the cashback ability while dependent variable was the overall customer satisfaction. The regression output is displayed below.
The output results indicate that the ability of cashback for the customers has minimal impacts on the overall satisfaction of the customers. This is indicated by the determination coefficient, which shows that only 13.49% of the points lie along the regression line from the analysis.
From the interpretation of the results, it is evident that the organisation has not met the customer satisfaction that is lagging at 11%. Additionally, there is no difference in meeting the customer demands of the male and the female consumers. On the other side, the level of income does not have any impact on the satisfaction of the customers as depicted in the hypothesis. These factors therefore, does not contribute in the process of solving the causes of poor performance of the organisation and fostering competition among the online vendors.
However, the factors such as the age group, the provision of the shopping assistance for the customers, the ability to cashback among other physical facilities, the platform that allows comparison of prices among the goods and products, and the provision of abilities to verify the goods on delivery are the factors that influence satisfaction of the departmental stores’ customers. The analyses further indicated the existence of the correlation between the above aspects with the customer demands. Therefore, these aspects should be clearly examined as recommended below.
For customer satisfaction to be achieved, it calls for the collaboration of different aspects that should be addressed to carter for the changing demands in the market. The departmental stores will continue to lag in the market shares if they do not address the following aspects. The managers should device platforms that enable the customer to compare prices. This means the organisation should be in a position to diversify the good and products in the market. Additionally, the provision of the shopping assistance during the purchase should be implemented and the physical verification of the products as it boosts the confidence level of the customers concerning the products. The assistant will be helpful in answering the customer questions on the products thus leading to their satisfaction. Moreover, the ability to cashback among other facilities will create a sense of trust between the business and the organisation thus establishing a lasting relationship that facilitates customer satisfaction. Should these factors be achieved, the organisation is likely to register improved performance due to quality products and services delivery to the customers (Hill and Brierley, 2017). Concisely, this will put the organisation in a great position to compete with the online retailers.
References
Brace, I. (2018). Questionnaire design: How to plan, structure and write survey material for effective market research. London. Kogan Page Publishers.
Darlington, R. B., & Hayes, A. F. (2016). Regression analysis and linear models: Concepts, applications, and implementation. New York. Guilford Publications.
Hill, N., & Brierley, J. (2017). How to measure customer satisfaction. London. Routledge.
Kumar, V., & Reinartz, W. (2016). Creating enduring customer value. Journal of Marketing, 80(6), 36-68.
O’Sullivan, E., Berner, M., Taliaferro, J. D., & Rassel, G. R. (2016). Research methods for public administrators. London. Routledge.
Prebensen, N. K., Kim, H., & Uysal, M. (2016). Cocreation as moderator between the experience value and satisfaction relationship. Journal of Travel Research, 55(7), 934-945.
Rubsamen, N., Akmatov, M. K., Castell, S., Karch, A., & Mikolajczyk, R. T. (2017). Comparison of response patterns in different survey designs: a longitudinal panel with mixed-mode and online-only design. Emerging themes in epidemiology, 14(1), 4.
Sekaran, U., & Bougie, R. (2016). Research methods for business: A skill building approach. Chicago. John Wiley & Sons.
Stoet, G. (2017). PsyToolkit: A novel web-based method for running online questionnaires and reaction-time experiments. Teaching of Psychology, 44(1), 24-31.
Zook, K. L., & Pearce, J. H. (2017). Quantitative descriptive analysis. In Applied Sensory Analy of Foods (pp. 43-71). London. Routledge
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