Analysis Of KCLothing Dataset For Word-of-Mouth Evaluation

Digitalization and Sustainability in Apparel and Fashion Industry

It is noticed that from 2019, the industry of apparel and fashion has achieved drastic growth. And in coming years it is expected to grow further and this time has come after few years of turn-down. It is also believed that this sector has moved forward based on the approach of consumer centric strategy, where the customers demand digitalization and sustainability.

This report is all about the analysis of KCLothing dataset, before understanding this dataset, let us first understand the important concepts of digitalization and sustainability for the retail industry.

It is a known fact that during the manufacturing of clothes the fashion industry pollute the earth from its process. Thus, it is one of the highly polluting sectors. Keeping this in mind, most of the brands have taken initiatives of incorporating business practices that are highly sustainable. Subsequently, even the consumers are slowing approaching sustainable fashion, which is not easy. As it includes second-hand clothes, recycled fashion, and circular fashion for decreasing the large amount of pollution and waste and pollution caused by the fashion industries in the name of fashion.

With the slow growth, the fashion sector experimented and adopted technology for escalating its growth. With gradual improvement in the implantation of technology embracing technology, this sector has started to understand and meet demands of the market. For bridging the gap between technology and fashion, the retailers and brands must need a digital mind set, which concentrates on the technology that lasts long. It is required for the industry to analyze the points that are worrisome, to create a partnership for implementing innovative solutions in order to resolve their identified problems. Here, the KPIs must be considered.

The main of this project is to analyze the KCLothing dataset with the help of SPSS tool. KCLothing is a one of the clothing retail brands, and it has wanted to evaluate their customers’ word-of-mouth (WOM), after their visit to this retail store. The data is generated by following various meetings with the customers as a preliminary stage of the research process, a via a survey, where 118 customers were involved. The respondents were provided question based on the following perspective, which are evaluated based on the 7 points Likert scale:

• Large clothing range
• Friendly staff in the store.
• Product quality
• Reasonable price
• Followed clothes’ return policy
• WOM that asks if they would like to recommend their friends and family or not. 

The KCLothing dataset will be analyzed and evaluate the WOM generated by the customers after visiting the retail store. This is to improve the overall WOM generated from the customers by determining the following two questions:

1. How well do the key factors that were measured in the survey (i.e., Friendly staff, Product range, Return policy, Product quality and Price) predict customers’ WOM?
2. Which is the best predictor of WOM? 

To analysis these questions, two techniques will be used on SPSS such as multiple linear regression and the Paired sample T-test on SPSS tool.

As per Sweeney, Soutar and Mazzarol (2008), WOM was told to have high recognition for the promotion, mainly in the environments of professional services, and where the credibility qualities played a significant role in the choices made by the customers. Here, authors explored the factors, which could improve the chances of influencing the receivers of positive WOM with such information. Nearly, 6 focus group discussions, followed by 103 forms of critical incident were analyzed in this research paper. And result’s finding showed the presence of WOM’s potential for impacting the actions based on the sender-receiver’s relationship, the message’s richness and strength are delivered, including the other situational and personal factors. In marketing, it even projected WOM’s significance, mainly the marketing of professional services.

Overview of KCLothing Dataset

As per Itsarintr (2011), this research’s aim was to test the factors which impact the satisfaction of the retailers that might lead to the intension of repurchasing and a positive word of mouth. A survey was conducted by the author with suitable questionnaires that were sent to approximately 400 respondents. Here the Platinum Fashion Mall’s wholesale was taken into consideration for the survey. The target population comprises retailers/customers for the resale from the wholesalers. The aim of determining the result is to know how the wholesalers enhance their shop’s performance for maintaining their retailers’ satisfaction. According to the obtained result, most of the retailers looked satisfied in this research. As, the retailers have positive impression in association to their clothes’ price and quality. It is found that the retailers avoid will not share any positive words of mouth with the other retailers, so as to keep their source of wholesale purchase a secret for their purchase. Hence, it leads to insignificant relationship among the between positive word of mouth and the retailer satisfaction. Henceforth, this Mall’s Wholesalers must even utilize advertising and sales promotion via different media sources like the Television, radio, fashion shows, billboards, and also plan foe organizing the designer contests.

According to Cuesta-Valiño, Gutiérrez-Rodríguez and García-Henche (2022), it is believed that the traditional commerce doesn’t contain good product variety, instead it is believed to have more leisurely sale and it doesn’t improve its impulse buying. Here, the researchers have found out the importance of having a direct association among the customer and seller can help with advice and create an association of trust among them. Today, the traditional retailers are in the demand of digitization, and without it they will have a tough time to compete the technologically forward companies in the market. Subsequently, the large companies compete in terms of authenticity, quality, service, and proximity. Thus, this research has been conducted to find out the reasons for such factors, and see its influence on the consumers’ attitudes for the online shopping in the traditional retailers. The research has 4,063 individuals’ responses from Spain. Finally, as per the found result, in the online shopping, it is observed that the store’s WOM communication and loyalty are the key drivers of attitudes.

Like mentioned earlier, this project is using two techniques to evaluate and improve the overall WOM generated from the customers, and they are (Aspelmeier and Pierce, 2015),

• Multiple Linear Regression
• Paired Sample T Test

This technique is used to determine the key factors that were measured in the survey (i.e., Friendly staff, Product range, return policy, Product quality and Price) predict customers’ word-of-mouth or not. Basically, Regression models are utilized for describing the relationships among the variables by fitting the line in the data that is studied. Here, with regression, it lets the research to assess how a dependent variable will change with the change of the independent variable(s).

Multiple linear regression is utilized for estimating the relationship among 2 or large number of independent variables, and only 1 dependent variable. In these following cases, the multiple linear regression can be used:

• How strong the relationship is among the 2 or larger number of independent variables and only 1 dependent variable?
• The dependent variable’s value at a particular independent variable’s value.

Techniques Used for KCLothing Dataset Analysis

In this case, the independent variables are listed below:

• Product Quality
• Friendly Staff
• Return Policy
• Product Price
• Product Range

The dependent variable is WOM. Thus, the multiple linear regression is used for these questions (ATTRAH, 2021).

It is technique, which is utilized to determine the best predictor of WOM depending on the factors such as- Product range, Friendly staff, Product quality, Price, and Return policy.

Here, the Paired Samples t Test will compare the means of 2 measurements that are obtained from the same individual, object, or related units. Auch “paired” measurements help to present the following aspects:

• The measurement obtained at 2 distinct times (for eg: the score of pre-test and the post-test with an involvement monitored among the 2 time points.)
• The measurement obtained under 2 diverse conditions (for example: completion of a test under the experimental and control circumstances.
• The measurements obtained from the 2 halves or subject’s sides or experimental unit.

These tests aim to know if there is any existence of the statistical evidence, where the mean difference among the paired observations is suggestively diverse from 0. The Paired Samples t Test refers to be the parametric test.

In this test, the Dependent variable is used (i.e., test variable), which is measured at 2 distinct times or for 2 associated units or conditions. In this case, the five pair of t tests will be analyzed based on one dependent variable i.e., WOM. The first pair of t test is to analyze WOM and friendly staff, second pair t-test will analyze the WOM and product range, third pair t-test will analyze the WOM and return policy, fourth pair of t test is to analyze WOM and product quality, and the fifth and final pair of t test is to analyze WOM and product price. These paired t tests will help the researcher to predict the best predictor for the WOM that will improve the overall WOM generated from the customers (Bhatti, Siyal, Qureshi and Bhatti, 2019).

The data will be analyzed using the selected techniques with the help of SPSS dataset.

Before doing multiple linear regression, first create the hypothesis related with the asked questions.

• Null Hypothesis:To predict customer’s word of mouth, none of the key factors (i.e., Friendly staff, Product range, return policy, Product quality and Price) were considered to measure the survey.
• Alternative Hypothesis: To predict customer’s word of mouth, the key factors (i.e., Friendly staff, Product range, return policy, Product quality and Price) were considered to measure the survey.

Now, do multiple linear regression on SPSS by clicking Analyze à Regression à multiple linear regression, and then select the dependent and independent variables. Later, click OK to proceed multiple linear regression. The result is (Golhar, Choudhari and Patil, 2021):

As per the above result, first, the descriptive statistics table shows the statistical information about all the variables (Jones, 2018). Next, refer the model summary table, which will provide the values of R, R2, adjusted R2, and the standard error estimation. This table is used to determine the fit of the data in multiple regressions. The R value represents Correlation coefficient by using the R column. Here, R is considered to be the quality of prediction in the dependent variable. In our case, the R value is .757, which indicates that the prediction level is good. The R square value is represented by the R square column, and is also called the determination of coefficient. In our case, the R square value is .573, which is considered as our independent variable, and can explain 57.3% of the data. 

Moving on, the ANOVA and coefficient table must be checked, as it tests the overall prediction of a good fit for this data in the regression model.  This table shows the independent variables’ significance that predicts the dependent variable. Here, p is lower to 0.05 and it indicates that the created regression model has statistical significance and the created model is a good fit for the dataset (Mclean, 2018). And, see the Durbin-Watson statistic is 1.415 which indicates that data is auto correlated that means the data has high correlations.

Importance of Word-of-Mouth in Retail Industry

The below plot is shows that regression standardized residual word of mouth.

As per the above plot, it displays the standardised residuals y axis and the created regression model residuals are independent and normally distributed.

The normal P – P Plot is used to shows that standardized residuals on the y-axis and the theoretical quantiles on the x-axis as demonstrated below.

As per the above plot, the Data that aligns closely to the dotted line, which indicates that it is a normal distribution. 

Next paired sample t test is performed.

As per the above result, the paired sample t test table is viewed.

• See the first row that means pair 1 (Word of mouth and Friendly Staff), t (117) = 239, p < 0.005, which means it is statistically significant.
• See the second row that means pair 2(Word of mouth and product range), t (117) = -1.777, p > .078, which means it is not statistically significant.
• See the third row that means pair 3 (Word of mouth and return policy), t (117) = -5.455, p < 0.005, which means it is statistically significant.
• See the fourth row that means pair 4 (Word of mouth and product quality), t (117) = 17.671, p < 0.005, which means it is statistically significant.
• See the fifth row that means pair 5 (Word of mouth and product price), t (117) = 13.587, p <. 00, which means it is statistically significant.
• Therefore, it signifies that, friendly staff, return policy, product quality and product price factors are the best predictors for word of mouth (Yao and Li, 2013).

Discussion, Limitation, Conclusion

The results of Multiple Linear Regression and the Pair sample t-test that are performed in the above parts will be discussed and concluded in this section.

This shows the coefficients table, which indicates the created regression model’s are statistically significant. According to the outcome, here the null hypothesis is cancelled and the left-out option, the alternative hypothesis has been accepted for this analysis. The staff in the clothing retail store is said to be not friendly, as the result is actually not at all statistically significant. Then, the retail store offered a wide range of products (Product Range) is determined as not statistically significant. Then, the brand’s return policy is fair (return policy) is also determined as not statistically significant. The apparel lines of this brand are of a good quality (Product quality) has determined as statistically significant. And finally, the retail store has reasonable prices (product price) is also determined as statistically significant.

Hence, here the result determines the importance of product quality and the price factors for the prediction of customers’ WOM.

From Pair sample t-test, its table determined that the first pair (i.e., Word of mouth and Friendly Staff) and found it to be statistically significant based on the obtained p-value. Similarly, the second pair (i.e., Word of mouth and product range) showed to be statistically insignificant. The third pair (i.e., Word of mouth and return policy), showed to be statistically significant. Even the fourth and fifth pairs (i.e., Word of mouth and product quality; Word of mouth and product price), showed statistical significance, respectively. Hence, the result concludes that, friendly staff, return policy, product quality and product price factors are the best predictors for word of mouth.

The strength of word of mouth is realized as it is mentioned by Sweeney, Soutar and Mazzarol (2008). Then as per the argument of Cuesta-Valiño, Gutiérrez-Rodríguez and García-Henche (2022), the loyalty of the retail store and the WOM communication are also the key driving factors in online shopping, it even works without including the digitization (i.e., via customers’ WOM). Further, with respect to Itsarintr (2011), it is contradicting for the positive words of mouth by the retailers, as it is found that the retailers avoid to share positive words of mouth, so as to keep their source of wholesale purchase a secret for their purchase.

In this data analysis, there were no limitations, as the dataset was perfect, then the questions asked were clear for determining the independent and dependent variable. Respectively, the suitable techniques (i.e., multiple linear regression and the Paired sample T-test) were conducted successfully using the SPSS tool.

Conclusion

Finally, the results conclude that, friendly staff, return policy, product quality and product price factors are the best predictors for WOM. And, they are improving the overall WOM generated from the customers.

For predict customer’s word of mouth, “Product quality” and “Price” are determined to have more weightage in the word of mouth. Thus, WOM is powerful as a customer acquisition tool. Then, the retailers must ensure to add authentic value to its brand, which will attract the customers and help them to stay loyal to their brand.

References

Aspelmeier, J. and Pierce, T., 2015. SPSS. New York: Worth Publishers, Incorporated.

ATTRAH, S., 2021. MULTIPLE LINEAR REGRESSION AND IT,S STATISTICAL INDICATORS BY USING STATISTICAL PROGRAM SPSS. International Journal of Humanities and Educational Research, 03(03), pp.119-133.

Bhatti, N., Siyal, A., Qureshi, A. and Bhatti, I., 2019. Socio-Economic Impact Assessment of Small Dams Based on T-Paired Sample Test Using SPSS Software. Civil Engineering Journal, 5(1), p.153.

Cuesta-Valiño, P., Gutiérrez-Rodríguez, P. and García-Henche, B., 2022. Word of mouth and digitalization in small retailers: Tradition, authenticity, and change. Technological Forecasting and Social Change, 175, p.121382.

Golhar, A., Choudhari, N. and Patil, A., 2021. Prediction of aluminium content in a metal using SPSS based linear regression analysis. Journal of Physics: Conference Series, 1913(1), p.012002.

Itsarintr, K., 2011. FACTORS AFFECTING POSITIVE WORD OF MOUTH AND REPURCHASE INTENTION: A CASE STUDY OF CLOTHING RETAILERS IN BANGKOK. Assumption Journals, [online] pp.92- 98. Available at: <https://core.ac.uk/download/pdf/233620477.pdf> [Accessed 13 April 2022].

Jones, K., 2018. Spss. US: Tritech Digital Media.

Mclean, P., 2018. Spss. US: Tritech Digital Media.

Sweeney, J., Soutar, G. and Mazzarol, T., 2008. Factors influencing word of mouth effectiveness: receiver perspectives. European Journal of Marketing, 42(3/4), pp.344-364.

Yao, W. and Li, L., 2013. A New Regression Model: Modal Linear Regression. Scandinavian Journal of Statistics, 41(3), pp.656-671.

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