Identification of the optimal business model on the example of st. Petersburg book market

Several statistical packages and programs were used to process the data: RStudio, Microsoft Excel, Microsoft Power Query and 2GIS computer application.

In total information about 261 booksellers was obtained. The final sample include 118 observations. This difference in quantity of units is explained by the availability of financial data. The unit observation is a bookseller who sells printed books and also may sell some complementary products. All information is up to date.

The dataset contains the following characteristics of booksellers. The table containing full description of variables is presented in appendix (Table 8).

Names of book shops and their names of legal entity;

Links to pages in Rusprofile and SPARK databases;

Type of organization, SME register, city of registration;

District, nearest metro station, distance to metro;

Number of branches;

Publishing bookstore;

Selling of new or used books;

Belonging to the Krupskaya Fair;

Form of the shop (online, conventional of both);

Existence of website, and pages in several networks, mobile application;

Presence of cafй in the bookshop;

Revenues.

As there are a lot of online booksellers in Russia and a citizen of Saint Petersburg can practically make an order in any shop that has a delivery by post in her town some selection was applied to limit the scope. The assumption was made that a citizen of Saint Petersburg will purchase book either from local online shops of from the biggest and most popular ones in Russia because the fee of delivery by post from other cities is almost always higher than delivery from the key players of market (for example “labirint.ru”, “ozon.ru” and others)

The sample includes Saint Petersburg book sellers and several shops that are located in Leningrad region, near metro station “Devyatkino”, as it is easy to get to these shops. The coverage radius is approximately 17 km with the center located near the Rostral Columns (Appendix: Picture 1).

The author of the current paper mostly applied content analysis and synthesis of the domestic and foreign press and social networks to collect the data. The descriptive analysis was applied to make a clear picture of the Saint Petersburg book market. Author uses regression analysis to find factors influencing revenue of bookseller.

Quantitative variables were described by their mean, median, standard deviation, minimum and maximum values. See Table 4 in Regression Analysis section. Shapiro-Wilk tests were used to test these variables for normality. The logarithm of revenue was taken as the dependent variable since it had an abnormal distribution and large values relative to other variables. Several Grubbs tests were conducted to detect outliers. The outliers were not deleted from the sample. Instead there were created several dummy variables denoting booksellers with high values in these quantitative variables. Author tested some hypotheses with and without the outliers and decided they were significant for the study. The sample contains many dummy and factor variables so booksellers were divided into several groups. Mann-Whitney test was used for comparison of two independent groups. For more than two groups, Kruskal-Wallis tests were carried out. Categorical variables were expressed with percentages. Association between variables was measured by Spearman`s rho correlation coefficient.

To estimate influence of different factors on revenue multiple linear regression models were built. To assess the quality of models and to decide which variables should be included following tests were performed: Chow test for the presents of structural breaks, Ramsey RESET test for detecting missing variables, MacKinnon-White-Davidson PE test for comparing linear vs. log-linear specifications in linear regressions, Davidson-MacKinnon J-test for comparing non-nested models, F-test (the hypothesis that a data set in a regression analysis follows the simpler of two proposed linear models that are nested within each other). Author also tested models for multicollinearity with VIF coefficients and for heteroskedasticity with Breusch-Pagan test.

All statistical analyses were performed using RStudio and a value of p < 0.1 was considered statistically significant.

The current analysis has several limitations. There are a lot of participants in the Saint Petersburg book market and this study concentrates only on booksellers who distributes their products to private customers. The author considers but does not make analysis of points of book-crossing, companies engaged in the acquisition of funds and libraries, libraries, book applications for smartphones, online book clubs, electronic libraries, and illegal internet resources. Another limitation is that the database could have missing data (some booksellers may not be counted there). This could happen because the data was gathered from 2GIS maps, this resource may not include some booksellers due to some reasons. What is more, this source includes only registered firms, information about unregistered entities is unobtainable.

Results and Discussion

Market overview

Saint Petersburg book market has various participants. Nowadays it could be hard to find pure book shops, i.e. stores that sell only books without complementary goods like stationery. Moreover, a book shop may sell different kinds of literature: fiction, non-fiction, specialized, comics and other. The author conditionally distinguished the following types of booksellers existing in Saint Petersburg book market:

Book and stationery shops;

Specialized literature shops (comics stores, religious literature shops, foreign literature shops, etc.);

Antiquarian book shops;

Commission shops that sell books;

Publishing bookstores;

Art galleries/centers;

Children`s bookstores;

Other.

Here are some characteristics of Saint Petersburg book market. The author conditionally distinguished the following types of characteristics: basic (quantity, locations, forms), legal/official (conclusions based on the data from SPARK and Rusprofile concerning legal entities), cultural (presence in the Internet, presence of cafй, mobile application).

Basic. - In Saint Petersburg book shops are almost evenly distributed throughout the city (and consequently throughout metro stations). It is shown in the map in appendix (Picture 1). For independent bookstores, districts, nearest metro stations, and distance to the metro were identified. Most book shops are situated in central districts: Central 17%, Nevskiy 17%, Admiralteyskiy 6%, Petrogradskiy 4% and Vasileostrovskiy 4%. The shares of other districts are shown in the graph in the appendix (Figure 14). The high percentage of stores in Nevskiy district is due a lot of independent book shops are at the Krupskaya Fair which is in this district near metro station Elizarovskaya.

H1: Booksellers which are in central districts have higher revenues than those in outskirts.

85% of booksellers are located within 1 km from nearest metro, 10% within 1 km-2 km. The rest of stores are from 2 km to 7 km far from nearest metro (Figure 13).

H2: Booksellers which are located near metro stations have higher revenues than those at a great distance.

When comparing booksellers, they could also be divided into single shops and chains (2 shops or more). In total there are almost 10% of booksellers that have 2 or more shops in their chain. The obvious leader in the number of branches is a Bookvoed with 84 stores. This bookseller differs a lot from all other market participants, therefore the author will examine this case in more detail further. The graph shows that the majority (65,5%) of booksellers are independent book shops. Another big part (24,5%) consists of online sellers which do not have physical shops. Several leaders including Bookvoed are also presented in the graph (Figure 1). Grafit and Nautilus have common owner, these two chains are like 2 different brands. Grafit shop with 32 branches is better known as stationery store, although, it also sells books. Nautilus mainly sells books and stationery as a complementary good. Both firms are successful and bring about the same income. The Territoriya is an interesting case for this study.

This chain of stores is an “EKSMO publishing” franchise and has a modern development strategy, which will be discussed later. However, the fact that each branch has its own owner makes difficult to get the financial data about this chain. AzBooka shops are also quite popular and successful but this bookseller has several legal entities, therefore it cannot be considered as a single firm. Another leader Respublika is a modern bookstore. It sells not only books but also stationery, travel goods and other things that attract youth.

Figure 1. Share of booksellers grouped by number of branches, %

There is a permanent book fair called after N.K. Krupskaya. It is a special phenomenon that need to be taken into account when analyzing the book market. Nowadays this book fair is an example of traditional platform where many sellers and buyers meet. The majority of these small sellers do not have their own websites but there is a website of this book fair and it works like an aggregator: a consumer can search through books offered there. 14% of sample are shops in this book fair and 2% of booksellers have branch in Krupskaya (Figure 15). This cluster of stores is very convenient for buyers. If the buyer did not find the book, she needed in one store, she can find it in a nearby one, not going far. This creates tougher competition. However, there is a problem for researcher to get much information about these booksellers. Most of them are individual entrepreneurs which means that there is no financial information about them in databases and it is hard to know the name of owner of such small shop.

11% (29 stores) of all companies sell used books (Figure 16). These are antiquarian or commission shops. It cannot be definitely said who is more successful, stores selling new or old books: consumers have different interests. Moreover, nowadays people tend to think about ecological situation so buying used books is good thing within this concept. One more argument in favor of buying used books is reprinting of books: some people prefer the concrete publishing houses. The author supposes that the profitability of a commissioned bookstore is affected not so much by the sale of second-hand books as the popularity of the store and its availability.

6 of 29 stores have website;

11 of 29 stores have at least one page in social networks, in most cases a bookshop has page in “Vkontakte” (9 out of 11).

These statistics tell us that such bookstores tend to be on the Internet (i.e. be known). However, these figures could also tell that this segment is not developed yet. Currently manly people use the Internet to find place where to buy less expensive or rare book. The shops that are not presented in the Internet are harder to find. Therefore, they have opportunities to grow if they make their own pages in the Internet.

There 15% of booksellers are publishers (Figure 22). Like on foreign book markets the majority of publishers in Saint Petersburg sell their books online directly to consumers (i.e. have their own conventional shops or sell online). Almost all publishers (36 of 39) have their own website. Among all publishers 7 of 39 are conventional shops, 8 have both internet and brick-and-mortar shops, 24 are online shops.

One of the main factors which is considered in this paper is a form of a bookseller. A half of Saint Petersburg book market consists of conventional shops. 25% and 25% respectively are online and conventional+online shops (Figure 23). Online shop means that the bookseller sells online only. Conventional and online means that bookseller has both conventional (physical) store and online shop. Conventional booksellers sell in the usual way and even if they have websites, they do not sell books through it. The author supposes that bookseller which has both forms of shops is more successful as it covers customers who prefers to make purchases online and those who makes purchases in the usual way and likes the atmosphere of bookstores. Independent conventional store may be known only by its district residents but ignored by residents of other districts because they do not know about it. Online shop may lose, for example, elderly people as a target group because they rarely use the Internet than younger generations. The author expects significant differences between these groups.

H3: Booksellers which are both online and conventional have higher revenues than other groups.

Official. - These factors apply to book sellers as legal entities. Rusprofile and SPARK Interfax pages were found for 71% (186 obs.) of all booksellers in the dataset (Figure 17). 45% of these booksellers are limited liability companies. 21% are individual entrepreneurs. There is no financial data about this category in Rusprofile or SPARK. The graph in appendix (Figure 10) shows the whole range of types of organizations. Book shops are mainly not very big companies so many of them are in the SME register (microenterprises 33%, 7% small enterprises and 1% medium enterprises) (Figure 11).

One of the controversial issues in the current study is a region of firm`s registration. A bookseller could be in Saint Petersburg, but its legal entity is registered in another region. The author decided to keep firms registered outside St. P. in the sample because such booksellers are present at the market and compete with other booksellers therefore cannot be ignored. Clearly most firms (62%) are registered in St. P. 7% are registered in Moscow. The whole picture is presented in the graph in appendix (Figure 12).

Cultural. - These factors are the key for the current study. The author supposes that nowadays book shops are experiencing a transformation to cultural centers. It means such creative spaces where visitors can have a rest, communicate with other visitors, get together, organize a cultural event. The Internet also plays a major role here. Customers of such spaces should be aware of cultural events taking place, get news about bookseller and goods, sales, discounts, etc.

With the number of Internet and social media users growing worldwide, it is essential for managers of bookstores to understand online consumer behavior. Consumers tend to no longer use traditional media such as television, radio, and magazines, but are increasingly using social networking sites to search for information. Social media platforms provide customers with the ability to interact with other consumers; thus, companies are no longer the only source of brand communication. Nowadays companies use social media for online marketing and branding and these methods are popular among consumers (Schivinski and Dabrowski, 2016). According to Leitao et al. (2018) social media influence consumers` decisions and perceptions. The Internet is a place where customers can be involved in a life of their preferred bookseller. In the interview in the journal “University Book” Sergey Kuznetsov (general director of SKCG agency) noted that in Russia readers migrate to the Internet these days. Russian website “Vkontakte” becomes a major social network for young people and many companies seek to make their own page there to keep users aware if their products and events.

66% of all booksellers in the dataset are somehow presented in the Internet (Figure 21). The figure (Figure 2) below shows shares of booksellers which have either website or a page in a social network.



Figure 2. Share of book shops which have website or a page in a social network, 5

It can be seen from a figure that bigger halves of booksellers have website or Vkontakte page. A bookseller should pay for having its own website, but it does not have to pay for Vkontakte page, it may explain why this network is preferred. Instagram and Facebook are not as popular as two previous forms, however, many shops prefer to be presented in these social networks also. YouTube and Twitter are more specific networks (one is for video, other is for short quotes) that can explain why they are less popular. Odnoklassniki is a Russian social network and major contingent is not youth. It is less popular alternative to Vkontakte. The author also grouped booksellers by the number of social networks (Figure 3). There are a few booksellers which have pages in all considered social networks. They are: OZON, Bookvoed, Kniga Plus, Labirint.ru, Planeta Muzyki, St. Petersburg Book House, and Chitai-Gorod. Almost equal parts have from 1 to 4 pages. The author also takes into consideration the variant in which not the certain networks influence success of a bookseller but the number of pages.

H4: Booksellers with more pages in social networks have higher revenues.



Figure 3. Share of booksellers grouped by number of social networks, %

Also, there was included one factor concerning those booksellers which have websites. Sometimes a website has links to networks so that visitors if a site could easily go over to needed page. The author supposes that presence of such links makes booksellers` social network pager more popular and successful. From those book shops which have a website 67% have links to social networks (Figure 19).

H5: Presence of link to social networks on the bookseller`s site influence positively on revenue.

Nowadays most people have smartphones and it is a good way of promotion of a shop via such application. Several booksellers in Saint Petersburg market have their own mobile application (3% or 7 booksellers) (Figure 20). It could be concluded that it is a rare practice to have a mobile application among booksellers. Devar, My-shop.ru, OZON, Bookvoed, Labirint.ru, Respublika and Chitai-Gorod have mobile application. These are not only online shops, but also conventional+online (Figure 35). What is more, not every shop can afford to have its own application because they need some extra money to develop and maintain it.

H6: Presence of mobile application influence positively on revenue.

Finally, the presence of a cafй inside a book shop make it a cultural space and it contributes to the emergence of another source of income for the store. Only 3% (9) of all booksellers have a cafй (Figure 18).

H7: Presence of cafй contribute higher revenues.

The tables below (Table 2, Table 3) reflects key numbers that were mentioned above.

Table 2. Key basic and cultural characteristics of Saint Petersburg book market

Variable		N	%
District	Central	45	17.2%
	Nevskiy	44	16.9%
	Branches	26	10.0%
	Admiralteyskiy	16	6.1%
	Vasileostrovskiy	11	4.2%
	Petrogradskiy	11	4.2%
	Kirovskiy	9	3.4%
	Krasnogvardeyskiy	7	2.7%
	Frunzenskiy	7	2.7%
	Vyborgskiy	6	2.3%
	Kalininskiy	6	2.3%
	Leningrad region	3	1.1%
	Primorskiy	3	1.1%
	Moskovskiy	2	0.8%
	Online shops	65	24.9%
Distance to nearest metro	< 1 km	223	85.4%
	1 km - 2 km	25	9.6%
	2 km - 3 km	6	2.3%
	3 km - 4 km	5	1.9%
	4 km - 5 km	1	0.4%
	6 km - 7 km	1	0.4%
Number of branches	Online shops	64	24.5%
	1	171	65.5%
	2	14	5.4%
	3	5	1.9%
	4 - SPbU store	1	0.4%
	5 - Respublika, Nautilus	2	0.8%
	19 - AzBooka	1	0.4%
	20 - Territoriya	1	0.4%
	32 - Grafit	1	0.4%
	84 - Bukvoed	1	0.4%
Krupskaya Fair	Not in Krupskaya	220	84.3%
	In Krupskaya	36	13.8%
	Has branch in Krupskaya	5	1.9%
Selling new/used books	New	232	88.9%
	Used	29	11.1%
Form of bookseller	Conventional	130	49.8%
	Conventional+online	66	25.3%
	Online	65	24.9%
Bookseller is a publisher	Publisher	39	14.9%
	Other	222	85.1%
Cafй	Have a cafй	9	3.4%
	Do not have	252	96.6%
Website	Have a site	141	54.0%
	Do not have	120	46.0%
Site contains links to social networks	Have links	95	67.4%
	Do not have	46	32.6%
Vkontakte	Have page	135	51.7%
	Do not have	126	48.3%
Instagram	Have page	103	39.5%
	Do not have	158	60.5%
Facebook	Have page	92	35.2%
	Do not have	169	64.8%
Youtube	Have page	45	17.2%
	Do not have	216	82.8%
Twitter	Have page	37	14.2%
	Do not have	224	85.8%
Odnoklassniki	Have page	20	7.7%
	Do not have	241	92.3%
Number of social networks	0	110	42.1%
	1	34	13.0%
	2	29	11.1%
	3	38	14.6%
	4	31	11.9%
	5	12	4.6%
	6	7	2.7%
Bookseller is not present in the Internet	Present	173	66.3%
	No links	88	33.7%
Mobile application	Have	7	2.7%
	Do not have	254	97.3%



Table 3. Key official characteristics of Saint Petersburg book market

Variable		N	%
Rusprofile/SPARK	Have a page	186	71.3%
	Do not have	75	28.7%
Type of organization	LLC (ООО)	118	45.2%
	Individual entrepreneur (ИП)	54	20.7%
	JSC (АО)	5	1.9%
	Non-profit organization (АНО)	3	1.1%
	CJSC (ЗАО)	2	0.8%
	OJSC (ОАО)	1	0.4%
	Other	5	1.9%
	Not determined	73	28.0%
SME Register	Microenterprise	87	33.3%
	Small enterprise	17	6.5%
	Medium enterprise	2	0.8%
	Not included in SME	155	59.4%
Region of registration	Saint Petersburg	162	62.1%
	Moscow	17	6.5%
	Leningrad region	4	1.5%
	Chelyabinsk	2	0.8%
	Lipetsk region	1	0.4%
	Not determined	75	28.7%

To sum up, the structure of Saint Petersburg book market is similar to that of foreign book markets described by foreign authors: the main key players are the same (authors, publishers and so on). And relationships between these key players are also similar: there are trends such as self-publishing or publishers sell books directly to consumers.

Russian book market seems not to be influenced by big international companies like Amazon. This is happening probably because Russia has a rich literary heritage. Many great works were written in Russia, so people mostly read in Russian and more advanced readers who knows other languages buy foreign literature. The majority of foreign literature that comes to Russia is translated into Russian and is available in Russian bookstores. Therefore, Russians in most cases do not need Amazon, which sells books written in foreign languages.



2. Trend towards monopolization

Several scientists, for example, Szenberg and Ramrattan (2015) highlight that larger bookstores seem to have monopoly position on the book markets. The closer a market is to a monopoly, the higher the market's concentration (and the lower its competition). The author of current paper checks if this statement is true for the Russian market. For this, the Herfindahl index is calculated using the following formula.

,

Where:

,

,

Booksellers registered only in Saint Petersburg were included;

N = 102;

HHI = 1875;

For the calculations companies` revenues were taken.

This HHI value is considered a moderately concentrated industry, as expected since there are only 102 firms. But the number of firms in an industry does not necessarily indicate anything about market concentration, which is why calculating the HHI is important. Having a closer look at market shares, it is possible to identify bookseller whose position can be considered as a monopoly. The Bookvoed has 41% of market share and no other company scored more than 9%.

It could be said that situation on a book market in Russia is quite like in Europe and USA. There is a trend of closing small brick-and-mortar book shops and tendency to monopolization of the market: a particular big book distributor that has both online and offline sales. Mergers and consolidation of publishing companies and book sellers entails a decrease in the mobility of the industry's response to new consumer demands. An example is the joint creation of a “Territoriya” store by an EKSMO publishing house and an AzBooka chain of stores.

2.1 Regression analysis

In the current paper a multivariate linear regression is estimated with revenue of booksellers as a dependent variable and the characteristics related to booksellers as independent variables. Author`s first objective is to test how different factors that make book shop be more like a cultural center influence its financial success (measured by revenue). These factors mean, for example, that bookseller is present in the Internet, has a cafй or is located at the city center and is within walking distance of the metro. The choice of the dependent variable is determined by the fact that revenue by definition means quantity of goods sold multiplied by their prices, it is not heavily influenced by costs, taxes, tax regime and so on. A firm has different financial indicators including various income indicators, which are reflected in the income statement. However, the main goal of the study is to show how successfully books are sold or how big are the sales for each bookseller according to non-financial factors.

The author is mainly interested in how the revenue gained by a bookseller depends on actions taken by the seller to make the book store be a cultural center where customers can not only buy some books but also have a rest, have some fun, communicate with other visitors, get useful information and be enlightened.

In the previous section all the booksellers in the dataset were taken into account to get the whole picture of Saint Petersburg book market. However, the author could not find financial data for a number of stores. The regression analysis is performed on a sample of 118 observations. It means that there is a possibility not to get expected results. For example, there are only 9 stores which have cafй in the dataset, although, in the current sample there are only 4 of them. This may lead to insignificance of the coefficient of cafй variable. The following tables present the summary statistics of variables.

Some key statistics of numeric variables are summarized in the table below (Table 4).

Table 4. Summary statistics of numeric variables

Variable	N	Mean	St. Dev.	Min	Max
Distance to nearest metro (m)	118	396.61	617.53	0	4,300
Number of branches	118	1.79	8.21	0	84
Number of social networks	118	2.25	1.85	0	6
Revenue 2018 (th. RUB)	118	633,761.10	3,686,077	20	37,434,775

First of all, numeric variables were checked for normality of distribution. Especially, the author was interested in the dependent variable - revenue. Histograms and “qqplots” were used to visualize variables. Shapiro-Wilk tests were also applied. Revenue had abnormal distribution and several outliers. Moreover, it is measured in high values compared to other numeric variables, so the author decided to take a logarithm of revenue. The graphs showing distribution of revenue and log(revenue) are in the appendix (Figure 24, Figure 25).

Taking into consideration outliers in revenue variable, the dummy denoting these outliers was created. OZON, EKSMO publishing and Labirint.ru have the highest revenue values. The graph below shows distribution of revenue without these outliers grouped by form of a shop. It can be seen from the graph that conventional shops tend to have lower revenues than online and conventional+online stores.

For further selection of variables, it is also necessary to consider the relationship between them. Spearman`s rho coefficients were found for pairs:

Revenue and distance to nearest metro (-0.027);

Revenue and number of social networks (0.3***);

Number of social networks and distance to metro (-0.26***).

The signs of the obtained coefficients coincide with the assumptions of the author; however, the first coefficient is insignificant. Actually, shops located far from the metro have fewer pages in social networks. This fact could lead to lower revenues among such bookstores.

Figure 4. Distribution of revenue 2018 without outliers

From the graph below can be seen that group of booksellers with 6 pages in social networks has higher revenues. Also, a reader can notice that a group with 4 pages has wider range of revenues. What is more, there is a tendency to growth of averages with growth of number of social networks.

Figure 5. Revenue dependence on the number of social networks



Some key statistics of factor and dummy variables are summarized in table below (Table 5).

Table 5. Key statistics of factor and dummy variables

Variable		N	%
Type of organization	LLC (ООО)	109	92.4%
	JSC (АО)	3	2.5%
	CJSC (ЗАО)	2	1.7%
	Non-profit organization (АНО)	1	0.8%
	Other	3	2.5%
SME Register	Microenterprise	80	67.8%
	Small enterprise	17	14.4%
	Medium enterprise	2	1.7%
	Not included in SME	19	16.1%
Region of registration	Saint Petersburg	101	85.6%
	Moscow	16	13.6%
	Chelyabinsk	1	0.8%
District	Central	18	15.3%
	Nevskiy	11	9.3%
	Admiralteyskiy	7	5.9%
	Petrogradskiy	5	4.2%
	Kirovskiy	4	3.4%
	Kalininskiy	3	2.5%
	Vasileostrovskiy	2	1.7%
	Vyborgskiy	2	1.7%
	Krasnogvardeyskiy	2	1.7%
	Frunzenskiy	2	1.7%
	Moskovskiy	1	0.8%
	Branches	16	13.6%
	Online shops	45	38.1%
Distance to nearest metro	< 1 km	109	92.4%
	1 km - 2 km	5	4.2%
	2 km - 3 km	3	2.5%
	4 km - 5 km	1	0.8%
Number of branches	Online shops	45	38.1%
	1	57	48.3%
	2	8	6.8%
	3	4	3.4%
	5 - Respublika, Nautilus	2	1.7%
	32 - Grafit	1	0.8%
	84 - Bukvoed	1	0.8%
Krupskaya Fair	Not in Krupskaya	104	88.1%
	In Krupskaya	10	8.5%
	Has branch in Krupskaya	4	3.4%
Selling new/used books	New	113	95.8%
	Used	5	4.2%
Form of bookseller	Conventional	35	29.7%
	Conventional+online	38	32.2%
	Online	45	38.1%
Bookseller is a publisher	Publisher	34	28.8%
	Other	84	71.2%
Bookseller has high revenue in 2018	Not leader	105	89.0%
	Leader	13	11.0%
Cafй	Have a cafй	4	3.4%
	Do not have	114	96.6%
Website	Have a site	93	78.8%
	Do not have	25	21.2%
Site contains links to social networks	Have links	61	51.7%
	Do not have	57	48.3%
Vkontakte	Have page	81	68.6%
	Do not have	37	31.4%
Instagram	Have page	58	49.2%
	Do not have	60	50.8%
Facebook	Have page	59	50.0%
	Do not have	59	50.0%
Youtube	Have page	31	26.3%
	Do not have	87	73.7%
Twitter	Have page	23	19.5%
	Do not have	95	80.5%
Odnoklassniki	Have page	13	11.0%
	Do not have	105	89.0%
Number of social networks	0	30	25.4%
	1	20	16.9%
	2	13	11.0%
	3	21	17.8%
	4	20	16.9%
	5	8	6.8%
	6	6	5.1%
Bookseller is not present in the Internet	Present	101	85.6%
	No links	17	14.4%
Mobile application	Have	6	5.1%
	Do not have	112	94.9%

In the data set there are many factor and binary variables. To analyze these types of variables, the author builds “box and whiskers” diagrams. To verify the presence/absence of differences in the group averages, analysis of variance can be performed using nonparametric criteria: Mann-Whitney for 2 groups and Kruskal-Wallis for 3 or more (taking into consideration abnormal distribution of revenue variable).

The differences in group revenue averages were found among following factors: book market business selling

Presence of site;

Presence of link to social networks;

Presence of Instagram page;

Presence of YouTube page;

Presence of Odnoklassniki page;

Presence of mobile application;

Presence of cafй;

SME groups;

Regions of registration;

Forms;

Number of social networks.

The differences in group revenue averages were not found among following factors:

Sales of new/used books;

Presence of Vkontakte page;

Presence of Facebook page;

Presence of Twitter page;

The bookseller is a publisher;

The bookseller is in the Krupskaya Fair;

Organizational type;

District.

These results will be taken into account in the process of selecting variables for the final regression model.

Before multivariate regression building the author tested several assumptions. The dependence of revenue on the distance to the metro with a grouping according to the type of store was considered. Indeed, the graph below shows the distinguishable difference in revenue between conventional and conventional+online shops, however, there is no clear dependence between revenue and distance to the nearest metro. That is why the correlation coefficient mentioned below was insignificant. The similar analysis was performed while dependence of revenue and number of social networks was considered. Six scatterplots in appendix (Figure 26, Figure 27, Figure 28, Figure 29, Figure 30, Figure 31) show this dependence grouped by presence of social networks. As was mentioned above, Vkontakte is the most popular network among others, because it is easy to use, to create and is free of charge. Then booksellers tend to register in Instagram and Facebook.

The author identified another group of leaders based on the logarithm of revenue and created dummy variable. This group included 13 booksellers: OZON, EKSMO publishing, Labirint.ru, Bookvoed, My-shop.ru, Respublika, Russkoe Slovo publishing, ECO-vektor, Saint Petersburg book house, Clever, Azbookvarik, Akademkniga publishing and METEK. Then similar scatterplot displaying dependence of revenue and number of social networks grouped by “leaders” dummy variable was built. From this graph could be concluded that market leaders actively use social networks (have 3, 5 or 6 groups in that case). Although, there are a couple of exceptions.

It would be useful to consider the dependence of revenue on a variable “form” separately. The difference between conventional and conventional+online can be seen from the figure below (Figure 6). Conventional+online book shops tend to have higher revenues; it is also noticeable in the difference between the average and median meanings. Although, it is hard to unambiguously assess the position of online stores. The author can only say that the revenue spread of this category is greater: from low to very high values. From this we can draw the following conclusion: for conventional store it is better to have online shop, but online shop can be either very successful or bring poor revenue. Adding division by city to this graph sheds light on some details. Booksellers that are registered in Moscow have highest revenues in all three groups, especially they are the outliers in the group of online stores, which increases the dispersion of the group. Taking into consideration only St. P. booksellers, it is clear that conventional+online form of the shop is the most successful. Moreover, the shops in which there is a cafe belong to this particular group. Shops from conventional+online and online groups have mobile application and these shops have highest revenues. Primarily, the bookshops from Moscow have mobile application. See the correspond graph in appendix (Figure 33).

Figure 6. Revenue dependence on distance to the nearest metro station (m) grouped by form of a shop



Figure 7. Revenue dependence on number of social networks grouped by “leaders” variable

Figure 8. Revenue dependence on the form of the shop with mean and median meanings

Another significant factor that the author considered in detail is the presence of a cafй. It is important to notice that booksellers that have cafйs are active in the Internet, which is shown in the figure below (Figure 9).

Figure 9. Revenue dependence on cafй grouped by number of social networks

After a detailed study of all variables separately, the author proceeded to construct a multivariate regressions. The author mostly used forward method or regression building and used tests mentioned in methodology section to decide which variables to include and assess quality of the models. The group of variables describing official characteristics stand out as control variables.

The author intentionally highlighted several models, because some variables could not be used in the same model, it could lead to multicollinearity. Eventually the author made her choice on two models:



,

and

,

Table 6. Regression models results

<...

	Dependent variable:
	log(revenue)
	(1)	(2)
number of social networks	0.326***
	(0.114)
number of branches	0.041*
	(0.024)
form: conventional+online	1.448***
	(0.518)
form: online	0.398
	(0.505)
site		0.872**
		(0.430)
vkontakte		-0.288
		(0.429)
instagram		0.759*
		(0.422)
facebook		-0.415
		(0.419)
youtube		-0.336
		(0.532)
twitter		0.439
		(0.459)
odnoklassniki		0.892
		(0.633)
mobile application		2.468***
		(0.891)
city: SPB		-1.939***
		(0.572)
city: Chelyabinsk		-0.216
		(1.822)
SME: small

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