Sustainability issues in international apparel companies: problems and success factors



3.2 Analysis and results

The first stage of the statistical analysis is to identify the indices of correlation between each independent variable and the dependent one. The SPSS technical tools will be used for this deal. However, different correlation coefficients will be applied due to the diverse nature of variables included into the model. Thus, measured variable has a binary nature, and that fact limits us in usage of Pearson correlation coefficient - the most popular instrument of correlation calculation. According to basic statistical recommendations, dichotomous variables may be considered as ordinal, and the main instrument to count correlation with ordinal variables is the coefficient of Spearman.

However, the variable “Company's geographical features” containing the list of countries where companies' headquarters are placed belongs to the group of categorical variables. This means that correlation indices cannot be correctly applied in the analysis of the variable. Therefore, this element of the model will be described simply by statistical method of counting frequencies. In detail, the frequencies of each country name appeared on the list will be calculated, separately for two considering groups of companies, and then the result will be visualized with the instrument of Word Cloud.

Correlation results will be considered in the order of decreasing correlation strength. The strongest correlation has been identified between the experimental variable and the rate of ACSI (American Customer Satisfaction Index). The results was equal to 0,653 with the significance level of 0,01.



Table 8. Spearman correlation coefficient for the rate of ACSI.

Indeed, after looking at the following graph represented the result above it can be noticed that the values of the Index in the group of `sustainable' companies are in average higher, so that the row of points is shifted to the right relative to the values of the second group.

Figure 6. Visual interpretation of correlation for ACSI.

According to the coefficient, there is a weak correlation between the output variable and the rate of informational transparency of the company towards sustainable practices (on the strong level of significance equal to 0,01). The table of correlation describing this result is represented below.



Table 9. Spearman correlation coefficient for the rate of company's informational transparency in aspect of sustainability.

Moreover, the table that shows the distribution of observations in different combinations of characteristics was conducted for better understanding of received numbers.

Table 10. Distribution of observations for companies' levels of informational transparency about sustainable practices.

In this case, correlation is counted between one binary variable and the variable with three available options. According to the hypothesis, higher rate of informational transparency of the company should lead to its belonging to `sustainable' group of companies, and the low rate means the company is not sustainable enough. Thus, more values get into matrix cells with extreme codes (0;0) and (2;2), stronger will be the correlation. The middle column may also play a role if there is a mismatch between two groups of companies in this value.

To make thoughts clear, the table of transparent information case was also supported by the graphical interpretation.



Figure 7. Graphical interpretation of correlation for company's rate of transparency in aspect of sustainability

In this graph, bigger circle stands for larger concentration of cases in this combination of values. It can be identified that the mean value of transparency for group 0 will be located approximately in the middle of the scale, while for group 1 there will be a shift of mean value closer to the right end of the scale.

The next relatively strong correlation is observed in the link with transparency of information about contractors. The coefficient here is 0,36 with the high significant level equal to 0,01.

Table 11. Spearman correlation coefficient for the transparency of information about contractors.



Table 12. Distribution of observations for transparency of information about contractors.

Figure 8. Graphical interpretation of correlation for transparency of information about contractors

Then, the factor of Social Media Marketing comes. As in all previous cases, the coefficient of Spearman was calculated, and the result was 0,32 with the level of significance in 0,05.



Table 13. Spearman correlation coefficient for quality of companies' SMM.

Figure 9. Visual interpretation of correlation for quality of companies' SMM.

In the graph that interprets the result of correlation we can see the certain positive trend, as there is a number of low values in group 0. On the other hand, the largest part of values in group 0 are approximately equal to the ones in group 1, so the correlation is definitely weak.

The last significant correlation has been found between explained variable and the share of stakeholders' equity in companies. The value of the coefficient is 0,295 and it is significant on the 0,05 level.

Table 14. Spearman correlation coefficient for company's equity ratio.

Figure 10. Visual interpretation of correlation for company's equity ratio.



Indeed, the graph shows approximately same rows of points with slight shift to the left of the group 0.

Two correlations were defined as insignificant. The first one is correlation with the firm size expressed through the number of employees.

Table 15.Spearman correlation coefficient for number of employees in the company.

Figure 11. Visual interpretation of correlation for number of employees in the company.

Another insignificant correlation is in the confirmations of using sharing or renting economies models.

Table 16. Spearman correlation coefficient for facts of using sharing or renting economies models by the company.

What is more, the coefficient is insufficient. Indeed, if we look at the distribution table, we will see that each cell possesses almost same number of cases.

Table 17. Distribution of observations for facts of using sharing or renting economies models by companies.

The following word clouds represent the geographical distribution of both groups of companies. If the country has large frequency, the name becomes bigger in the word cloud (Figure 12). The left word cloud is conducted for `sustainable' group of companies, and the right one is for the opposite group.

Figure 12. Word clouds: countries of companies' head offices.

As it can be noticed, there are a lot of countries that can be found in both word clouds with approximately same sizes. We can characterize USA, Canada, Finland and Italy as neutral countries, where any geographical impact cannot be identified. At the same time, Germany, Sweden, Great Britain, France and Switzerland are countries that are somehow associated exactly with the group of `sustainable' companies, while Russia, Japan, Australia and China are more on the opposite side of the sample.

The next stage of the analysis is the conduction of the regression model. The binary logistic regression model has been conducted with SPSS program, as the explanatory variable has dichotomous nature. The results of the correlation analysis force us to exclude two insignificant variables out of the model: number of employees in the company and the fact of sharing or renting economy models usage. Tables with main results of regression analysis are represented below.

Table 18. Omnibus Tests of Model Coefficients.

As we can see from Omnibus Tests table, the model as significant. However, this indicator is not enough to claim the model is well-fitted. The further diagnostics of the model has been done, and its description is put into the Appendix E.

Table 19. Regression Classification Table.

According to classification table, 82,9% of all results were correctly predicted by the conducted regression model, which can be considered as sufficient percent.

Table 20. Variables in the Equation.



The most interesting table of regression from the point of result is the table “Variables in the Equation”. According to the column with significance of each variable, we can make a conclusion that only one variable among all included has a statistically significant influence on the explained variable. This fact is about ACSI, that represents the satisfaction level of customers.



4.Interpretation and implications of the results

4.1. Conclusions about hypothesis and research model correction

Factors influencing on the level of companies' sustainability will be discovered and the reasons will be received why some corporations are successful at implementing sustainability procedures, while others are not. The survey will indicate customers' perceptions of sustainability and attitudes towards certain companies' business models in this sphere. As a result, some recommendations will be developed for increasing the level of success in this field.

Hypothesis H1 was not approved. After correlation analysis it becomes obvious there is no relation between the size of the company and its sustainability rate.

The same thing can be said about the second hypothesis. The significance of the correlation between this variable and the explained one was too low to continue the consideration of this variable in the regression.

Hypothesis 3 can be partly accepted. According to the frequency analysis, approximately 2/3 of countries had a certain tendency: companies from these countries were more likely to be in the first of the second group of companies compared in the research. However, the further research is required due to the limitations of the current research in sample size and possibilities to statistically measure categorical variables.

Hypothesis H4 should also be rejected. There weak rate of relation between equity ratio and output variable has been identified in the correlation analysis, but the regression model has shown there was no significant interconnection.

Thus, we have to eliminate factors with no significant influence from the model. The final theoretical model that can be counted as a result of this paper is represented below.

As significant relation for all success factors of sustainability featured in the statistical model wasn't found, the poor amount of recommendations could be given for apparel companies on this stage. The hypothesis about geography of headquarters was partly approved, so the main recommendation for companies is to try to take into account cultural and geographical features of countries where these companies operate. Moreover, it is recommended to study the experience and specific features of apparel companies from the following countries: Germany, Sweden, Great Britain, France and Switzerland. The environment of these countries may somehow positively influence companies' sustainability levels. On the opposite, the experience of companies from Russia, Japan, Australia and China may be studied to determine the patterns that could lead to worse sustainability rates.

4.2 Limitations of the study

Unfortunately, this study did not avoid certain limitations, the list of which is the following:

1. The main limitation of the study is the lack of data. It leads to the scarcity of observations number and the narrowness of the sample, that can subsequently lead to the distortion of the final result.

It is important to say that most companies are closed in questions of data provision, including financial statistics. Moreover, the shortage of required information is caused by the specific research topic: nowadays we still have incredibly little amount of opened internet sources with statistics about sustainability issues. This leads us to the second crucial limitation;

2. There is a deficit of methodology on sustainability researches in scientific society. Sustainability issues and indicators are quite subjective and abstract, and it is extremely difficult to univocally measure the sustainability rate of any company. As a result, certain assumptions should be made. As for this research, the main assumption is that the rate of sustainability directly influence the societal perception of company's sustainability. Due to absence of definite indicators the author needed to use the indicator of sustainability perception as the main output instead of the `true' sustainability rate. However, it made possible to make the quantified research at all.

Moreover, due to the limitation in indicators it was impossible to cover all success factors influencing companies' sustainability that were developed through theoretical background. From 15 factors that were put in the model and additionally added, only 4 were considered during the analysis, and only 2 remained being in the model after that.

4.3 Fields for future research

The works leaves an incredibly wide field for future research. In particular,

1. More factors from mentioned theoretical model should be included into statistical analysis to check their significance and level of impact. To do this, the appropriate indicators for such factors should be developed. In particular, the important task is to consider the influence of technological factors that were not affected in this work.

2. The results of the current research may be checked on the larger sample and through other possible research methods (e.g. qualified research with expert interviews).

3. The try could be made to develop a better output indicator that will not include side influencers (like image of the company was in this case).

4. The research with the same methodology may be repeated in the scope of other industries to check on the existence of similarities, differences and patterns.

5. The separate research may be conducted for deeper study of geographical and cultural influences of company's sustainability.



Conclusion

Sustainability matters are becoming more and more vital each and every day. More people and organizations realize the need to maintain and evolve sustainability issues. Thus, a lot of companies provide information of their activities in this field in their annual reports, in Social Media and web-sites. It often helps to increase customers' loyalty towards brand.

However, there is a plenty of factors that influence the rate of companies' sustainability, and it is important to focus on them, as the image improvement will not make the environment or society better. The task of scientific and business societies is to develop a comprehensive model that would take into consideration all possible influencers on sustainability rate of companies in each industry. Predictable model will give companies the clear picture of this (still new for them) subject, and it will be possible for them to optimize their activities in order to decrease their harmful impact and at the same time increase profits.

In this paper the effort is made to start the development of such model for apparel industry: the most environmentally harmful industry in the world according to many experts.

This work covers vital aspects of our life and can make a contribution towards our common future on the planet Earth. The author hopes this research will be continued and sustainability concepts will become more clear and applicable for business.



References

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4.Baraka, D. (2017). Making Sustainability Matter: How to Make Materiality Drive Profit, Strategy and Communications. Routledge, London. doi: https://proxylibrary.hse.ru:2120/10.4324/9781351274920

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9.Dickson, M. A., & Chang, R. K. (2015). Apparel Manufacturers and the Business Case for Social Sustainability: World Class CSR and Business Model Innovation. Journal of Corporate Citizenship, 2015(57), 55-72. https://doi.org/10.9774/gleaf.4700.2015.ma.00006

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Appendix A

The list of companies involved into the research

Group 1 - companies recognized as highly sustainable

Ninety percent

Veja

Allbirds

Alternative apparel

People Tree

Pact

Amour Vert

Patagonia

Reformation

H&M Conscious Collection

Everlane

Eileen Fisher

Cuyana

Levi's

Adidas

Burberry

HUGO BOSS

Kering SA

Moncler SpA

New Balance Athletic Shoe

Fenix Outdoor AB

Recreational Equipment

Marimekko

Gildan Activewear

NIKE

PUMA

Group 0 - companies that are not recognized as highly sustainable

AEFFE

PVH

Amer Sports

Kappa

LBrands

Melon Fashion Group

Skechers

XTEP

Tom Tailor

Lululemon

GAP

WACOAL

Noni B Ltd

Onward Holdings Company Lim

Toyobo Company Limited

Alexium International Group

Iconix Brand Group

Canada Goose Holdings

Oroton Group

Gunze limited

The Cato Corporation

HLA Corp.

Daphne International Holdin

Jihua Group

Albany International Corp.

Giordano Int. Holdings Limited?



Appendix B

The procedure of companies' selection

First, the lists of sustainable companies from mentioned journals have been considered, and all the companies from these lists were written down. Many companies were placed in many lists at the same time, so the frequency of names' appearance has also been notified. The long-list of companies, names of which appeared at least once is represented below (the frequency of appearance of each name is represented in numbers near the name).

Maggie Marilyn

Boyish Jeans

Mother of Pearl

Phoebe English

House of Sunny

Bethany Williams

BITE

Misha Nonoo

Stine Goya

Away That Day

Reformation

Stella McCartney

Tome

Gabriela Hearst

Ninety Percent x2

Simon Miller

Baum Und Pferdgaten

Bottletop

Nak

Veja x2

Plastic Freedom

Tretorn

Allbirds x2

Alice Early

Kitx

Lark & Berry

Alternative apparel x3

Ecovibe Apparel

People Tree x4

Pact x4

United by blue

Amour Vert x4

Patagonia x4

Reformation x4

Study NY

H&M Conscious Collection x4

Threads for thought

Krotchet kids international

Everlane x3

The kit

Tribe Alive

DL 1961

Boyish Jeans

Eileen Fisher x3

Stella Mccartney

Prana

Outdoor Voices

Girlfriend collective

Cuyana x2

Toms

Rothy's

Tentree

Levi's x3

thredUP

Polo Ralph Lauren

Rent the Runway

ABLE

Verloop

Vera Bradley

Columbia

Athleta

Manduka

Then, the companies that were short-listed to DJSI were added to this list:

Adidas

Burberry

HUGO BOSS

Kering SA

Moncler SpA

The main limitation of the sample is the list of `unsustainable' companies. The search of such companies started from the bottom of CSR-Hub rating. Each company has been checked on data availability about chosen variables. The search was stopped when the mark in 20% of rating has been reached. The stop was made to be sure the difference between two groups of companies is sufficient. Overall, 26 companies were selected by this method.

To make two groups of companies statistically comparable, it was decided to put the same number of 26 companies to the list of `sustainable' companies. This list contains 19 companies from the mentioned long-list (all companies that have been mentioned more than once and 5 companies from DJSI). Other companies have been taken from the top of CSR-Hub rating. Some companies from the mentioned long-list have already been placed to the top of this numeral rating, so it can be said that the impact of all used sources is approximately equal.



Appendix C

Meanings of variables' names and the dataset

Type - variable that shows the belonging of each company to `sustainable' (1) or `not sustainable' group (0);

Employees - Number of employees;

ACSI - American Customer Satisfaction Index;

Social_nets - Quality indicator: quality of social networking and site maintenance;

Eco_facts2 - Quality indicator: specification of sustainability information on company's website;

Contractors - Quality indicator: specification of information about contractors;

Sharing - Quality indicator: the existence of information about sharing or renting economy initiatives on the official website;

Headquarters - The country of the headquarters;

Equity_ratio - Equity ratio;



Appendix D

The results of companies' SMM evaluation

The table that is represented above shows the calculations of points received by the companies as a demonstration of their SMM quality. As it has been already said in paper, the website of the company and their official pages in Instagram and Facebook were evaluated by three experts in SMM, two of whom had certain experience of work in apparel industry. These “experienced” experts are marked with codes E1 and E2, and their weight in overall mark for each social net is equal to 0,4, while marks of the third expert (E3) are multiplied on 0,2.

Columns colored in green represent the weighted evaluation of company's website, Facebook page and Instagram page. The final point is the simple sum of three marks for website and two social nets. The choice of social nets for evaluation has been explained in the paper. The weights of website and both social nets are decided to be equal, as there is no logical reason to somehow shift the weights: there is no confirmation of higher importance of any internet source for apparel industry.

Table 20

Table 21



Appendix E

Regression model diagnostics

The first thing that has been checked after regression analysis is the diagnostics on influential cases by counting Cook's distances. It was found there is an influential case in face of WACOAL company, as Cook's distance for it was more than 1. Therefore, the influential case has been removed and the regression analysis has been done from the beginning.

Then, after fixing this problem, the regression model itself has been check on basic statistic biases. To be sure the model fits the data Hosmer and Lemeshow Test was conducted, and the result of the test is represented below.

As it can be noticed, the value of Chi-square coefficient is not significant, so we can make a conclusion that the model is well-fitted.

The last, but not the least stage of diagnostics is the check on multicollinearity bias. For that, all explanatory variables were checked on the correlation between each other. Due to diverse nature of variables, some of them should be considered through coefficient of Pearson, while some relations should be notified with the usage of Spearman indicator. However, according to coefficient tables listed below, all relations between variables are not strong (all the values are below 0,8).

Thus, it can be stated there is no problem of multicollinearity in this regression model. Overall, the model is well-fitted, and its results can be clearly interpreted.

international clothing industry



Table 22

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