A comparative study of the effect of online stores on sales in the fast fashion industry

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Bachelor thesis

A comparative study of the effect of online stores on sales in the fast fashion

industry.

Author:

Alexandra Konunnikova – 11105747

Programme

: Economics and Business - Economics

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Abstract

Lately, the fast fashion industry was a topic to study for various researchers. Mainly because of industrial conditions that differentiate it from other markets. Among those are uncertain demand and quick response to the rapidly changing trends. Technological innovations gave corporations a chance to expand their businesses into the online environment. The main purpose of this study is to find out whether launching the electronic commerce (online store) actually increases total sales and how it is different in various geographical regions. To achieve this goal a regression analysis was performed using a differences in difference approach. The sample studied was Inditex, which is currently the biggest player in the market. The results show that developing an online store indeed increases total sales in the fast fashion industry. Moreover, opening the online shop in Europe (excluding Spain) eventually leads to higher sales in comparison to Spain, ceteris paribus. Nevertheless further research should be conducted in order to gain an in-depth understanding of industry environment.

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Statement of originality

This document is written by Alexandra Konunnikova who declares to take full responsibility for the contents of this document. I declare that the text and the work presented in this document is original and that no sources other than those mentioned in the text and its references have been used to create it. The Faculty of Economics and Business is only responsible for the supervision of completion of the work, not for the contents.

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1. Introduction

Every year fashion integrates more and more into society’s everyday life. The emergence of the fashion industry dates back to the 19th century when the first Haute Couture house was opened in Paris. Nowadays, this industry consists of luxury, high-fashion, and supermarket sectors (Bruce and Daly, 2006).Since the very beginning, this industry was undergoing different changes, especially over the past couple of decades (Bhardwaj and Fairhurst, 2010). The reason for these fluctuations in the industrial conditions is highly uncertain demand, which is a common aspect of the fashion industry in general. Because of these transitions in the market environment, it is almost impossible to predict profitable strategy for the future. Thus, the vast majority of corporations operating within the industry started to experiment with different approaches in order to stay in the apparel industry. These included lowering production costs, chasing the flexibility of the design and increasing turnover of the collections (Bhardwaj and Fairhurst, 2010). Moreover, changes in the customer demand from Pret-a-porter collections to ready-to-wear clothing has also had an effect on the strategic choice of the corporation (Crane, 1997).

These various changes in strategies aligned with shifts in consumer preferences gave an opportunity for fast fashion to gain a big share in the global market (Choi et al., 2010). This subpart of the fashion industry emerged at the end of 20th - beginning of the 21st century in Europe. It can be defined as an industry in which styles of the clothes quickly move from the catwalk of famous boutiques to retailer stores in order to seize rapidly changing fashion trends (Zhenxiang and Lijie, 2011). Firms in this area of the fashion industry pursuit strategies, the fundamental goal of which is a quick and cost-efficient response to newly appearing fashion trends. These strategic approaches make it possible to match supply with highly uncertain demand (Barnes and Lea-Greenwood, 2006; Cahon and Swinney, 2011). But designers still use seasonal fashion weeks, popular fashion magazines and celebrity’s clothing styles as a source of inspiration (Bhardwaj and Fairhurst, 2010; Simona Segre, 2005). All of the points mentioned above were preselected by ¨gatekeepers¨, meaning that probability of their success is high enough (Throsby, 2008).

The main difference between high fashion and fast fashion is the ability of fast fashion companies to produce lower quantities but in the meantime having a greater range of goods at affordable prices (Taplin, 2014). In order to achieve cost efficiency retailers began outsourcing their production to developing countries which have high concentration of cheap and low skilled labour, that is one of the crucial aspects of the industry (Bhardwaj and Fairhurst, 2010;

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5 Simona Segre, 2005). Consequently, developed and developing countries play distinct roles in this market. The former represents the demand side, whereas the latter in most cases shows the supply side (Taplin, 2014). Apart from highly uncertain demand and rapidly changing market conditions, globalization and technological developments have influenced the environment within the industry (Taplin, 2014). To be more precise, development of the Internet gave fashion retailers a possibility to include and use many features in the business (Doherty and Ellis-Chadwick, 2010). These features include: better communication with customers, ability to make a general market analysis, greater product variety, brand extensions, cost efficiency and expanding geographical boundaries through the launch of the online store (Doherty and Ellis-Chadwick, 2010). Furthermore, clothing collections represented in stores, even from the same chain, vary from country to country (Doherty and Ellis-Chadwick, 2010). So that launching of the electronic commerce (e-commerce) could probably not only increase profitability but also drive up customer satisfaction. Previous studies have already uncovered that the public excitingly responded to the implementation of IT innovations.

The emergence of online shopping, however, has raised a number of concerns regarding the future of the businesses and industry as a whole. Therefore, it might be considered crucial for companies to find out whether developing of e-commerce will have a significant effect on the total revenues. The main aim of this paper is answering the following research question: ¨To what extent has the introduction of e-commerce resulted in a significant improvement of total sales amongst different geographical areas in the fast fashion industry? ¨.This analysis will provide corporations in the fast fashion industry with an insight into the possible effects of launching e-store and will help find out whether it is actually beneficial.

The remainder of the paper is structured as follows. First of all, the existing literature on this topic is examined, giving a theoretical background to this paper. Secondly, the research methodology that is used is discussed. The results are provided as well as analyzed. After that, limitations of the research itself are discussed. Finally, the main question is answered aligned with that it is concluded if the development of e-commerce has a significant effect on the sales increase for various retailers operating within the fast fashion industry.

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2. Literature review

Over the last couple of decades a great number of scientific articles, that shed a light on the fast fashion industry, were published. Many authors claim that fast fashion is a completely different part of the fashion industry itself (Bhardwaj and Fairhurst, 2010; Cachon and Swinney, 2011). There are various characteristics of fast fashion which explain this distinction. Barnes and Lea-Greenwood (2006) provide readers with a historical background regarding industry environment. They claim that 20th century apparel industry was dominated by a small number of large conglomerates, resulting in high market concentration aligned with significant barriers to entry.

Producers also used to predict customer demand long before the consumption occurs (Bhardwaj and Fairhurst, 2010), meaning that retailers themselves were trend-setters while creating ready-to-wear collections only a couple of times per year (Bhardwaj and Fairhurst, 2009; Cahon and Swinney 2009). By that time, the introduction of new fashion trends and their acceptance by “gatekeepers” was the first stage of the product life-cycle. Hirsch (1972) defines “gatekeepers” as experts and opinion leaders in the industry. Designers main concern was making styles that appeal to mass media representatives, celebrities, critics, etc (Throsby, 2008).

By the end of the century, however, when the fast fashion started to develop all these conditions had changed. In the late 1980s, clothing corporations started to focus their attention on faster responsiveness through outsourcing (Bhardwaj and Fairhurst, 2009; Taplin 2014). A great number of manufacturing processes have moved to Asia, Middle East, Portugal and Spain (Vinhas da Silva et al., 2002; Miller, 2013). Production companies assist retailer conglomerates through enhancing timing and cost, as well as market response efficiency (Taplin, 2014). Moreover, delivering commodities became faster and cheaper due to the development of the channel integration. These logistical improvements enabled corporations to better match supply with the uncertain demand (Taplin, 2014). Correspondingly, fast fashion retailers started to make use of this matching in order to have a bit more control over customer behavior.

Despite all the advantages of producing in other countries, there were some disadvantages that made this strategy less profitable than it seemed before (Birtwistle, Siddiqui, & Fiorito, 2003). On the other hand, Meichry (2007) shows that some companies still try to enhance their quick response through trendy design, because of either cultural or logistical problems that arise on the supply side. As a result, the optimum strategy was a combination of cost efficiency and reaction to consumer demand instead of forecasting trends (Jackson, 2001;

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7 Cahon and Swinney 2011). Also state that the complementary and substitution effect of quick response and enhanced design should be distinguished. Accordingly, they have found that it is much more profitable to implement both strategies at the same time, proving their complementary effect on each other, than using them separately. On top of that, Cahon and Swinney (2011) have found enough statistical evidence to show market strategies that pay a lot of attention to design will create more appealing goods to customers and consequently higher willingness to pay. These schemes will allow companies to charge more for fashionable clothes in contrast to basic collections.

Nevertheless, enhancing design facilities might possibly lead to higher fixed and variable costs. Apparently, these expenditures generate an opportunity cost between larger revenues and expenses. One of the main challenges faced by businesses within the industry is that consumers postpone their purchases until the price reduction periods (Rozhon, 2004). If consumers actually wait for drop in prices, it has a negative impact on financial performance of the company. But some action plans in the fast fashion industry give an opportunity to make potential customers to buy the product as soon as it is delivered to the store. This happens because people may be risk averse when it comes to merchandise that can be out of fashion soon.

Moreover, the emergence of the fast fashion industry has made circumstances within the market much simpler. To be more specific it has lowered barriers to entry as well as made speed to market faster (Bhardwaj and Fairhurst, 2010; Barnes and Lea-Greenwood, 2006). Furthermore, firms began to enhance flexibility and responsiveness in order to maintain connections between demand and supply sides (Wheelright and Clark, 1992). Cahon and Swinney (2011) also mentions that businesses face constantly changing consumer demand and an increasing quantity of rivals. Barnes and Lea-Greenwood (2006) came to the conclusion that none of the currently existing economic theories or models, including supply chain management can be applied to this industrial area.

Nowadays information technologies (IT) play an important part in our everyday life. The main aim of Blazques (2014) quantitative research was to get a proper insight of the position that IT has on the industry as a whole, as well as experience that customers get in it. In her article, she discusses the obstacles that fast-fashion retailers face while implementing new technological developments into their businesses. She also mentions that this industry, in particular, was one of the last to adopt electronic commerce since it is hard to transfer experience that people get in physical stores into the online environment. It is also important to mention that various firms try to make a good impression on customers and increase their brand

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8 loyalty through making physical stores more attractive. The author explains that clothes are considered to be high-involvement goods, because all of them need to be experienced individually. In this context, experience means seen, touched, felt and at least tried on, because it is challenging to assess the quality for someone else. In order to gain a competitive advantage in this highly competitive market, firms started to operate on the multichannel level. Blazques (2014) refers to a study conducted by research company Mintel, which has found that brick-and-mortar stores have lost their market share, because of online retailing. She also points out Chu and Paglucia (2002) who have found that the main concern in this business is customer satisfaction (Bhardwaj and Fairhurst, 2010, Christopher et al, 2004).

Technological developments have fundamentally changed consumer perception of shopping (Drapers, 2012). For instance, potential customers started to search for inspiration from fashion magazines, bloggers, etc or information regarding product qualifications prior to buying it (Blazques, 2014). Therefore it became important to analyze their experiences (Mathwick, Malhotra, & Rigdon, 2002; Puccinelli et al., 2009). It was found that if an individual had a positive experience with online purchasing, he or she is likely to buy from the internet again (Martín et al, 2009; Scarpi 2012; Yoh et al. 2003). As a result of the expansion of the apparel industry in the online environment has decreased time people spend in physical stores (Clifford, 2012). However, it is still important for fashion retailers to take into account experience from the brick-and-mortar stores, due to its importance for this channel (Blazques 2014). Results of some research show that consumers do not actually differentiate between multiple ways of shopping (Dholakia et al., 2005). It might be crucial for retailers to try to use distinct channel capabilities as complementary strategies.

Besides all the factors that have an impact on the online shopping, intermediates through which customers complete their orders (eg. websites) should be inspected (Eroglu, Machleit and Davis, 1999). When launching an electronic store, companies should consider aspects of the website, having an influence on customers’ intention to buy (Park and Kim, 2003). They are as follows: availability of a good searching engine, product description, customer service in terms of answering frequently asked questions, delivery, return and payment methods data, convenient navigation, attractive interface, and secure payment procedures (Park & Kim, 2003). The very last one can be said to be the core concern of online shoppers. A lot of existing websites do not explain how exactly the customers' information is secured and how payment goes through safely (Elliot and Fowell, 2000). As a consequence, the safety of personal data became one of the biggest issues of the online retailing. Careful and accurate development of

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9 the website will enhance a relationship with consumers by keeping them satisfied and loyal to the brand.

Since the development of the online shopping there was not a big number of scientific articles published that emphasize how customers actually make their decision while purchasing on the World Wide Web (WWW). Haubl and Trifts (2000) say that an establishment of modern technological innovations has had a positive effect on buying intentions. These mechanisms vary from search engines to systems that match diverse products available with individual preferences. Haubl and Trifts (2000) claim that shopping online makes it possible to carefully evaluate and gather more information about all possible alternatives. Furthermore, they have found that after using the ecommerce customers make better decisions aligned with the facts that they are making less effort while spending a smaller amount of time.

Apart from the points that make online environment beneficial for consumers, it is also advantageous for clothing companies. Specifically, it saves them a considerable amount of money that would have been otherwise spent on rent and warehouse payments. Authors state that these savings are appearing because of the unlimited storage space in the online services. Van der Heijen and Verhagen (2003) indicate that there is a certain amount of factors that should be considered when a company is expanding into an online setting. They claim that these elements are the ones that influence customer intentions to purchase online. Among those the following can be named: usefulness of the website, ease of use and more importantly online store image. Authors say that the last point has not received a lot of attention from modern researchers (Park and Kim, 2003). Van der Heijen and Verhagen (2003) list enjoyment, store style, familiarity, trustworthiness, and settlement performance as components of the online store image.

Some studies have already found the effect of the brick and mortar image on the purchasing motivation, therefore authors believe that these results can be generalized to the online world. However, there is still a need for adjustment of the already existing instruments. They came to the conclusion that store image is able to explain approximately 30% of the variation in motivation for using online shopping. Moreover, it was found that multichannel retailers should focus their attention on making online stores more useful, satisfying and safe, instead of improving its design.

One of the main aspects of every industry is consumer behavior. The first research regarding this topic dates back to the 20th century. Stokey (1981) and Bulow (1982) have formulated that if an individual is patient enough they will wait until prices drop to the marginal cost level. Some of the papers look at the way how customer purchasing behavior is influenced

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10 by the supply of goods. For instance, Liu and van Ryzin (2008) investigate that dynamic pricing is highly likely to lead to an increase in overall economic performance of the company. Authors define dynamic pricing as a way in which apparel companies charge high prices for fashionable items, but in the meantime low-demanded goods face a rapid decline in their prices overtime. There are a lot of factors that have an effect on consumer decision making. Among those can be included: weather conditions, current trends as well as economic circumstances. As a result, it becomes harder for corporations to set a quantity of inventories before the sale takes place (Liu and van Ryzin, 2008). As a consequence businesses might want to consider creating shortages of supplies (Liu and van Ryzin, 2008; Cachon and Swinney, 2009). Small quantity of goods available can increase customer willingness to buy clothes without large reduction in prices, because people want to possess fashionable clothes exactly at the time when they are popular (Liu and van Ryzin, 2008; Cachon and Swinney, 2009).

Miller (2013) notes that many loyal customers of the fast fashion industry have, to some extent, a hedonic behaviour. The reason for that might be that customers purchase replicas or somewhat similar clothes of the worldwide known fashion designers in order to fill themselves with pleasure (Juggessur and Cohen, 2009). In contemporary society possession of some goods are associated with the foundation of social identity, which leads to moderately hedonistic behaviour. In this context, the term hedonism can be defined as a point of view in which getting pleasure is the main purpose of human lives. Prior analysis has shown that hedonic customers, in comparison to non-hedonic ones, buy more goods in fashion shops (Scarpi 2006). Miller (2013) has shown that fast fashion shoppers are highly affected by hedonistic behaviour. They enjoy purchasing itself and the process of creating new outfits with their items. Miller elaborates that the latter one is the main action that gives the population happiness and emotional value for a long period of time after the purchase. She concluded that people keep purchasing in this industry, because strategies implemented by firms have a positive impact on hedonistic attitude, which still occurs after a person obtains the clothes.

After taking into consideration the literature explained above, it can be concluded that many topics regarding either the fast fashion industry or electronic shopping have been studied. However, this thesis is concentrated on an area that has not been fully studied before, it specifies the relationship between commence of an online store with the total sales of the fast fashion retailers. On the top of that, the analysis will account for regional differences between developed and developing countries and evaluate in which region it might be considered more lucrative to open an electronic platform. Additionally, the results found in this paper are based

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11 on an econometric approach, whilst the vast majority of the articles are based on either interviews or literature review.

3. Context, treatment and design

3.1 Context

Inditex was chosen to be a representative of the industry. This apparel company was preferred to a great number of firms, because it is believed to be a pioneer of “fast fashion”. The founder of Inditex, Amancio Ortega, started operating in the fashion industry in 1963 by producing textiles in his home country (Spain). In 1975 he was able to open the first shop, which concept breeds success. In 1985, the holding company Inditex was formed. Almost from the creation this retailer has gained a large market share and it has started to extend its brands, geographical boundaries and open even more shops every year.

At the moment Inditex serves 94 markets worldwide with 7502 opened stores. Out of these 94, 48 also have an online shop. The firm consists of 8 brands, which more or less serve all the possible target categories. They are: Zara, Bershka, Massimo Dutti, Uterque, Pull and Bear, Zara Home, Stradivarius, Lefties. Among these subsidiaries are the ones that produce women’s fashion, men’s clothes, products for kids and adolescents, even home wear. Each of the brands has various brand extensions associated with the unique style. Moreover, various product lines at affordable prices give an opportunity to satisfy a great number of customers. The company’s main goal is to “globally focus on the key elements of fashion production –

design, manufacture, distribution and retail.”

In addition to that, Inditex’s strategic approach aligned with economic performance make it a good sample for the population of the research. This holding company is viewed as the most dominant and successful actors in the market. Its strategic design is consistent with the theoretical overview provided in the previous section. All of the brands seek to provide fashionable items at reasonable prices, within a short period of time. Which, in other words, is developing a quick response to the fashion-conscious public at a fast speed to market. Moreover, the company has outsourced its production to partially or non-industrialized countries make it more cost efficient (Tokatli, 2008). Along with this, Inditex operates on every continent in the world, allowing the examination of regional differences in fast fashion. From year to year, the company continues to expand its geographical boundaries through using the

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12 full potential of information technologies. Organizational environment is highly opened to innovation, so that new devices are implemented in physical stores, while electronic stores are improved in general. Besides, Inditex was one of the first fast fashion companies to launch an online platform to exert technologies that can enhance performance in many different ways. These points combined make Inditex an appropriate unit of study.

3.2 Treatment & Design

The main objective of this thesis is to investigate the significance of the initiation of the online outlet on overall revenues of the enterprise in fast fashion. To be able to achieve this goal a regression analysis is performed, the results gotten show the impact on the total revenues.

The main strategy of the study is comparing regional differences, because some of the regions are less developed in e-commerce than others. A difference in difference - is used to capture the strength of the effect. This way of analysis views 2 groups in the study. The first one of them is considered to be the one undergoing treatment. In our case, this means that start of the online store has actually occurred. Originally, another group is treated as the one that never faced any changes, however, in this sample, it represents geographical regions in which the electronic store has appeared sometime later. After the regression is arranged, it is crucial to look at the significance of the main independent variable. For these purposes, the ordinary least squares method is chosen. P-value is used to assess whether the estimators acquired are different from zero so that it is possible to empirically check the validity of the hypothesis.

After doing the research, the expectation is to find enough statistical evidence to infer that launching of the e-store will have a significant effect on the total revenues, considering different geographical segments. This might be explained by the fact that various technologies developed during the age of digitalization have led to a faster and more convenient shopping process, which may possibly lead to the improvement of total sales. The main hypothesis of this paper is as follows:

H0: Launching of the online store in the fast fashion industry does not have significant effect

on total sales among different geographical regions.

H1: Launching of the online store in the fast fashion industry has significant effect on total sales

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4. Data and empirical specification

The dataset for the sample used is unique and was specifically created for this study. Numbers needed for the empirical part of this research were collected manually from annual reports of Inditex. The time framework chosen for the study is from 2008 to 2016. This period is chosen because from 2010 onwards e-stores started to open in each region. Nevertheless, the first online store was Zara Home in 2007, it cannot be taken as a representative of the fast fashion industry because it specializes on home wear. The first online platforms were opened in 2010 in 16 European countries, including Spain. The main independent variable is the possibility to buy online from this particular region. This is going to be represented by a dummy variable, which is 1 if e-store is present and 0 otherwise. Since some regions contain more countries than others it was decided that the obligatory condition to claim that the online store exists is that at least 5 out of 8 possible brands have an e-commerce. In the meantime, the dependent variable is the total sales in each region at a specific point in time.

All the countries in which company functions were divided into subgroups (regions): Spain, rest of Europe (excluding Spain), North & South Americas, Asia and the rest of the world. Spain is taken as a separate region, since it is the largest market in which company operates. Only in this country there are 1731 shops, which continue to open every year. Furthermore, Inditex items’ range alters between geographical areas, because the retailer adapts for cultural, environmental and social conditions. The year in which online shopping was developed varies from one region to another as well. Because of that year dummies are included. Using them eliminates the variation between region differences in sales. As it was elaborated in the literature review, people are capable of finding alternative ways or products. At the moment a lot of substitutes can be found online, so that the variable that shows a presence of the rivals’ online store is added. In the presented case the main competitor is Hennez & Mauritz (H&M). This firm is a runner-up in the fast fashion. Similar to Inditex it has multiple brands which are selling their products in around 6000 shops worldwide. H&M developed an online program in more or less the same time. H&M electronic commerce started operating in 2011. This makes this Swedish company a valid representative of the competitor. To compute this, a binary variable is used in the regression. The matrix with all necessary figures consisted of: total sales, dummies regarding the existence of the online shop, year dummies, regional dummies as well as dummies representing the presence of the rivals electronic commerce.

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14 The model itself looks like this:

𝑌_{𝑠𝑎𝑙𝑒𝑠=}𝛽₀+ 𝛽₁𝑒𝑠𝑡𝑜𝑟𝑒 + 𝛾𝑋 + 𝛼_𝑖 + ʎ_𝑡+ µ_𝑖+ 𝜀_𝑖

Where each variable represents one of the defined above. 𝐸𝑠𝑡𝑜𝑟𝑒 is a binary variable for the launch of electronic store. All four geographical regions used in the study are represented by 𝛼_𝑖. The timeframe chosen (2008-2016) is showed by ʎ_𝑡. Moreover, µ_𝑖stands for opening of the main opponents shop in the online environment. Two interaction variables are, also, included in the regression. These are interactions between region and year as well as between region and e-store. The former helps to observe the trend with which e-store impacts total sales at the particular region and at the exact year, whereas the latter captures the effect of having an online store in a specific geographical area.

The variable of interest of this study is sales of Inditex in every region. This is depicted by Ysales . As it is shown on the Graph 1 for all of the regions and overall sales there is an

increasing trend in this variable throughout the whole chosen time period. Only sales in Spain do not vary a lot.

0.00 2,000,000.00 4,000,000.00 6,000,000.00 8,000,000.00 10,000,000.00 12,000,000.00 2008 2009 2010 2011 2012 2013 2014 2015 2016

Sales by region

Spain Rest or Europe (excluding Spain)

Americas Asia and the rest of the world

(Inditex, 2016)

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15 The regression analysis is performed in order to investigate potential and sudden change in the dependent variable among different geographical areas due to the commencement of the online store for this particular company, taking into account the launch of the online store in a rival company. In addition to that it is important to add that 𝜀_𝑖 is an error term, which is completely independent from the main variable of study.

5. Main results

In the subsequent section the results of the study are outlined. Firstly, the tables with descriptive statistics are presented. Afterwards, the results obtained are shown.

5.1 Descriptive Statistics

In the table below the summary of the descriptive statistics for the panel dataset from statistical software and data analyst (STATA) is displayed.

Table 1

Descriptive statistics

Variable Mean Std. Dev. Minimum Maximum

Sales 3.966601 2.465856 1.056347 10.74986

E-store 0.6666667 0.4780914 0 1

Rival 0.5555556 0.5039526 0 1

Region 2.5 1.133893 1 4

As it was mentioned before, the data was collected for the period of 9 years for 4 regions, therefore each variable ended up having 36 observations. Sales, which are shown in millions items, have a great difference between the highest and lowest values. It can be noticed that the maximum value is 10 times larger than the minimum one. Therefore, these figures show that there was a rapid increase in sales throughout the 9 years. Standard deviation is high, meaning that sales can vary a lot from the mean. Variables e-store and rival are binary, so that both of them can only take values 1 or 0, interpreting numbers for mean and standard deviation is impossible. Region has a minimum value of 1 and maximum of 4, since the initial names of the regions chosen were encoded to have a numerical value. Without this coding the regression analysis would simply omit the variable.

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5.2 Regression analysis

In the subsequent part, the results of the analysis discussed above, are examined. The attention should be focused only the interpretation of the coefficient for the e-store. Brief interpretation of some other coefficients is also provided in this section. Overall, there is four regressions conducted, variables contained in them make them different from one another. In the base of all of them are the following independent variables: e-store, regional and yearly dummies.

The values of adjusted R-squared for each regression are provided at the bottom of table 2. The numbers are 88.84%, 88.44%, 99.73% and 93.75% respectively. These figures show that for all of the analyses more than 80% of variation in dependent variable can be explained by variation in independent ones, which are included into the model of this paper.

Table 2

Regression analysis (1)-(4) of the launch of e-store on total sales in fast fashion

Dependent variable: total sales in millions

Regressors (1) (2) (3) (4) E-store 0.109 (0.609) 0.005 (0.659) 0.087 (0.104) -1.817** (0.769) Rival

-

_(0.578)0.266 _(0.089)0.003 _(0.445)0.446 Asia 0.548 (0.411) 0.554 (0.419) -364.212*** (47.602) 0.055 (0.481) Europe (ex Spain) 5.359***

(0.394) 5.312*** 0.414 -911.572*** (47.763) 3.879*** (0.586) Spain 1.659*** (0.394) 1.582*** 0.435 473.124*** (48.230) 2.556*** (0.586) Year 2009 0.172 (0.583) 0.172 (0.593) -0.163* (0.092) 0.172 (0.436) 2010 0.478 (0.657) 0.464 (0.669) -0.181* (0.106) 0.549 (0.532) 2011 0.767 (0.740) 0.712 (0.762) -0.222* (0.122) 0.897 (0.596) 2012 1.305* (0.740) 1.251 (0.762) -0.019 (0.127) 1.435** (0.596) 2013 1.472* (0.842) 1.378 (0.882) -0.182 (0.147) 1.440** (0.659) 2014 1.821** (0.842) 1.659* (0.926) -0.169 (0.158) 1.677** (0.689) 2015 2.516*** (0.842) 2.355** (0.926) 0.192 (0.165) 2.372*** (0.689) 2016 3.119*** (0.842) 2.958*** (0.926) 0.459** (0.173) 2.975*** (0.689) i.region#c.year

_-

-Americas

-

0.234*** _(0.024)

-Asia

-

0.416*** _(0.024)

-Europe (ex Spain)

-

0.69*** _(0.024)

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-17 i.region#e-store

_-

-Americas#1

-

1.247* (0.709) Asia#1

-

2.129*** (0.733)

Europe (ex Spain)

-

3.119***

(0.702) Spain

-

Omitted constant 0.708** (0.476) 0.005 (0.659) -469.415*** (48.178) 0.977** (0.448) N ₃₆ ₃₆ ₃₆ ₃₆ Adjusted R2 0.8884 0.8844 0.9973 0.9375

After having a look at the table 2 it is possible to decipher the results of the regression. In the first regression it can be noticed that coefficient of the variable e-store, actually, is not different from 0.1 (0.109), thus it shows that launch of the online store increases total sales by thousands of items, while sales in this paper are measured in millions, holding everything else constant. In the second analysis variable rival is added. In this case variable representing online store has a coefficient that is even closer to zero (0.005), ceteris paribus. The next regression, apart from variable rival, also contains an interaction between region and year. Coefficient (0.086) in front of e-store is bigger than in the regression (2). Last regression instead of the interaction between geographical area and region includes multiplication of the variables region and e-store. This analysis gives a negative number for the coefficient of the interest (-1.817), all things being equal.

One of the goals of this study is to compare the regional differences. As is shown in the table 2, regression (4), selling in different geographical areas actually has an effect on sales. In comparison to Spain, having an online shop in Asia, Europe excluding Spain and North & South Americas increases total sales by larger amounts. Moreover, rest of Europe (without Spain) has the biggest increase in total sales, which is shown by the coefficient of the interaction variable (3.118). Furthermore, sales face an increasing pattern over the entire time period regressions (1) – (3). In addition to that it should be mentioned that some of the variables are not statistically significant. Including an interaction between region and the year lead to huge regional differences. After looking at the p-values for each estimator of the e-store it can be claimed that in regressions (1) – (3) there is not enough statistical evidence that the launch of the online shopping tools have a significant effect on the total sales in the fast fashion industry. Variable rival, also, does not have a significant effect on the primary variable of interest. On

Table 2 - Continued

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18 the other hand in analysis (4) developing electronic commerce, indeed, has a statistically significant negative effect on the overall number of items sold. In the analysis (4) the interaction between region and e-store does have significant effect on the total sales.

6. Discussion and conclusion

The fast fashion industry has been in a constant state of evolution over the past decades. Industrial conditions were heavily impacted by uncertainties surrounding the demand, as well as the development of various technological innovations. As a result, fast fashion retailers took the required measures to modify and adapt their strategies in order to maintain successful and profitable operations in the increasingly complex industry. The primary objective of this paper is, therefore, to contribute to the existing research by analyzing the relationship between the increasing levels of electronic commerce and the overall sales in fast fashion, taking geographical differences into consideration.

In order to investigate the strength of the impact of the possibility of online shopping, difference in difference approach was taken. This approach uses the differences between the regions in which electronic commerce was launched at different times. Net sales from 2008 to 2016 of one of the largest fast fashion retailers Inditex was taken as a dependent variable. A number of independent variables were manipulated in four stages of regression analysis. Among those are: emergence of the e-store, year dummies and indicator whether rival has an online store in the same region.

The results of the analyses brought forward in the previous section have pointed at an insignificant statistical evidence of a relationship in 3 cases, while the ultimate regression analysis indicated an existing significant effect of developing electronic commerce on the total sales of the apparel retailers.

Overall sales of the examined fast fashion firm consist of instore and online quantities sold. During the whole time span sales of Inditex were increasing in all of the regions. In this case, however, the existence of the online shop has a minimal effect on all sales of the firm. This result might have occurred because sales on the internet simply substitute the number of goods purchased in the physical stores. The possible reasons for such a result are the convenience of buying in the online market since it makes shopping process itself more effortless and advantageous. On top of that, worldwide expansion of the information technologies made it possible to easily shop in distinct parts of the world.

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19 On the other hand, magnitude of the electronic commerce, in case of Inditex, completely changes when the effect of the introduction of the online shop in a specific area is taken into account. Under this circumstances, launch of the online store has a negative impact on the overall sales of the fast fashion retailer. This outcome can be supported by the following arguments. First of all, shopping online has a negative effect on sales because clothes is an experience good and it is nearly impossible to assess the quality of the product online. Secondly, return and delivery policy may have affected consumers’ decisions to use electronic commerce. The reasoning behind that is time and effort spent on both obtaining and returning the parcel.

Besides the effect of the e-commerce, enough statistical evidence was found to claim that number of sales is different in distinct parts of the world. It was found that Inditex business in Europe (excluding Spain) is larger than in other regions. This area is Inditexs’ main scope of operation, therefore it may be claimed that organizational structure aligned with strategic paths were developed in order to match economic and cultural conditions of the region.

The results of all four regressions, however, show that having a competition in the online world in the same geographical area does not have a significant effect on the total sales of the company, focusing on the fast fashion market. This might have occurred because of the brand loyalty which exits almost in every business. Findings of this study are completely aligned with the expectation that was made after studying academic articles regarding this topic. Nonetheless, it was not predicted that quantities sold online replace physical sales. Also, it should be taken into account that launching electronic commerce in companies that are part of the fast fashion industry will decrease sales by a significant amount, only if the e-store was opened in a particular geographical region.

Nevertheless, this research has a number of limitations. First of all, even though the company chosen is a good representative of the industry, it would be better to include a number of companies. Second of all, the sample is small, it has only 36 observations, quarterly data could have been used instead. Small number of observations make it difficult to make reliable claims. Thirdly, geographical areas could have been spread unequally in a sense of the accessibility of Inditexs’ both online and of physical store. Finally, one of the regressions that contains the interaction between region and e-store resulted in a significant decrease in the total sales. This outcome might show that there was a problem with regression itself.

Further research would be interesting to conduct since the industry can be categorized as one of the most rapidly changing. Thus, finding more statistical evidence regarding other areas of the fashion industry such as famous fashion houses and the relationship of the firms with the online shopping tools. All in all, this paper has found a considerable effect of the

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20 launch of the e-commerce in a specific region on the total sales. Despite all the limitations it still provides fast fashion retailors with an insight about the relationship between the multiple channels of operating business.

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21

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A comparative study of the effect of online stores on sales in the fast fashion industry

Bachelor thesis