Because a representative number of respondents is found only in Moscow and Saint-Petersburg, other regions are grouped together.

Among those people there are 55% who participate in sharing economy at least once in month and 82,6% of those who take part in once in several months. Only 5,2% have never taken part in sharing economy activities. However, according to answers on questions about interaction with particular services (question 2) there are 8,3% of those who have never tried collaborative consumption.



Results

Market structure

Concerning the awareness about service group existence a primary leaders are online taxi, short term flat rental and redistribution market (99,7%, 98,8% and 98,4 respectively). The worst results re shown by things sharing and ridesharing services. They accomplished to achieve no more than 83,1% of awareness. The total picture of awareness distribution is shown in Diagram 1.

segmentation market consumption brand

Diagram 1 Awareness distribution in branches of sharing economy

The dispersion of usage is anticipated bigger. 74,9% of respondents used online taxi services. The least percentage of users again is possessed by things sharing and ridesharing branches (16,9% and 18,1% respectively tried those services). The entire picture is represented in the Diagram 2.



Diagram 2 The usage distribution of sharng economy branches in Russia

Finally, the most perspective branches (determined by dividing “Want to try” variant of answer on the sum of “Know” and “Want to try variants”, thus people who know, did not try, but want to start using are represented) are carsharing and ridesharing. The summarized results are presented in Diagram 3.

Diagram 3 "Want to try" answers distribution



A pivot table about Russian brands of sharing economy is represented below. Data is sorted by the percentage of people aware of the brand. A pouring reflects the most noticeable numbers in the column.

	Know	Used	Want to try	Don't' know
YandexTaxi	97,0%	55,0%	8,9%	2,9%
Avito	95,2%	66,2%	4,0%	4,7%
Uber	89,8%	43,9%	8,6%	10,1%
BlaBlaCar	80,7%	20,3%	11,9%	19,2%
GetTaxi	77,4%	35,1%	5,2%	22,5%
Yula	75,1%	14,2%	5,1%	24,9%
AirBnB	54,7%	22,6%	22,1%	45,3%
Delimobil'	23,8%	1,9%	12,7%	76,2%
YouDrive	17,5%	0,9%	14,0%	82,5%
Sobaka Gulyaka	10,6%	0,4%	18,9%	89,3%
Anytime	10,5%	0,9%	14,7%	89,5%
BelkaCar	9,8%	0,7%	16,9%	90,1%
Rentmania	6,2%	0,5%	20,3%	93,8%

Table 4 Russian brands of sharing economy efficiency

The biggest awareness round all the country gained YandexTaxi, Avito and Uber. More than 50% users have YandexTaxi and Avito brands. The most perspective companies are AirBnB and Rentmania. However, the distribution above is strongly connected with the presence territory of brands. Several of them for now are represented just in Moscow (Sobaka Gulyaka, Anytime), others only in Moscow and Saint Petersburg (BelkaCar, Delimobil', YouDrive).

It should be mentioned that some sharing economy services were not included in the investigation (Arendorium, Tvil.ru, BeepCar, Boombilla, Rutaxi, Freelansim, Upwork, FL.ru, Qlean, YouDo). Those services were written by respondents in the field other of the question about sharing economy companies.

Factors revealed

As it was mentioned above different sharing economy services employ different motives of participation. For the reason that Distributional market and Skills sharing (freelance) are not new for the Russian market, they were thrown out the intensives study. The exploratory analysis has shown that there is a group of users who do not like “sharing economy” (as they understood it), but have a strong inclination to proceed using it. This situation could be connected with the fact that those respondents got used to the services and do not care much about its quality. This research, however, aims at new and innovative for the market model short-term access to a good, including P2P interactions or sharing the common good (a car in this case).

To reduce the bias of different motives only those respondents who have tried carsharing, things sharing or ridesharing (these branches seems to employ similar benefits and risks) and, thus, knows the proper meaning of sharing economy were left in the study.

Short-term flat sharing was also excluded from the research, because of ambiguity of answers. 832 respondents answered, that they used this kind of sharing. Meanwhile, only 326 mentioned that used AirBnB - the main player on the market. Just a few respondents put a Tvil.ru service in the other field. Thus, a bigger part of those who reported the usage of short-term flat rental likely meant apartment rent from the company.

After selecting only those respondents who took part in carsharing, things sharing or ridesharing there are 766 cases left. Among them only 609 cases have answers on the all needed for further cluster analysis questions (cases were excluded listwise everywhere in the research).

A series of subsequent factor analysis was conducted to reveal the latent variables standing behind the dispersion of answers. For all the analysis conducted a reliable method of Varimax rotation with Kaiser normalization was used. Extraction based on eigenvalues greater than one by the principal component method.

Firstly, a preliminary exploratory analysis was held and it showed 11 factors. An item “The collaborative consumption is difficult” (No. 32 in Appendix 1) showed a strong negative loading with items associated with drivers and, thus, was recoded reversely.

After that a confirmatory analysis was carried out on the particular groups of indicators: drivers, stoppers and values characteristics.

Drivers

Due to the exploratory factor analysis items No. 1-11, 16-17, 28-32 (32 reversed), 34-38 were included in this step. 23 indicators were included and revealed 5 factors. KMO was on the level of god reliability and was equal 0,905 and exceeds the threshold of 0,8 (Cerny and Kaiser 1977). The model achieved to explain 63,55% of variance.

Mostly all indicators loadings exceed 0,5 threshold, Cronbach's alpha of revealed factors is above 0,7.

	Factor
	Inc	PU	Ease	Eco	Ref
Overall, sharing goods and services within a collaborative consumption community makes sense.	0,741
Collaborative consumption is a better mode of consumption than selling and buying individually.	0,693
I can see myself engaging in collaborative consumption more frequently in the future.	0,682
It is likely that I will frequently participate in collaborative consumption communities in the future.	0,656
All things considered, I find participating in collaborative consumption to be a wise move.	0,655
All things considered, I expect to continue collaborative consumption often in the future.	0,644
If I need something, I will rent it.	0,639
Collaborative consumption experience meets my expectations.	0,597
Collaborative consumption is innovative.		0,729
To me, sharing represents an up-to-date life style.		0,729
Sharing is in tune with the times.		0,704
I think collaborative consumption is interesting.		0,657
Collaborative consumption is comfortable.		0,56
Collaborative consumption saves my time		0,541
Collaborative consumption is useful		0,485
Difficult_reverse			0,796
Collaborative consumption is affordable.			0,766
Collaborative consumption is accessible.			0,69
Collaborative consumption helps save natural resources.				0,897
Collaborative consumption is environmentally friendly.				0,87
Sharing allows me to lower my expenses.				0,534
My nearest and dearest consider that I should take part in Collaborative consumption.					0,874
My nearest and dearest support my decision take part in Collaborative consumption.					0,861
Cronbach's Alpha	0,882	0,851	0,749	0,785	0,825

Table 5 Driving factors of sharing economy participance

To sum up, five factors were extracted due to the drivers' factor analysis. First of them represent Inclination and is called Inc. The second reveals the Perceived Usefulness of sharing economy and is named PU and the third stands for Ease of use and is called Ease. The fourth is called eco, for it combines items connected with environment friendly side of sharing. The last factor is associated with the influence of referent groups and, thus, is called Ref.

Impediments

The analysis of stoppers revealed three latent variables, all containing indicators with loading more than 0,4 and Cronbachs's alpha above 0,75 (except Mistrust factor with Cronbach's alpha equal to 0,591). The KMO of the factor analysis is 0,790. Factors with indicators are presented in Table 6.

	Factor
	Neg	PEOU	MisTr	Own
People can rent to others badly working things	0,829
There are scammers in collaborative consumption communities.	0,777
Using rented goods is not totally hygienically.	0,719
If I participate in collaborative consumption, thing owned by me could be stolen.	0,654
There's a risk that I will not be able to get the res. That I want at the time I want to use it.	0,617
I do not like using thing that have been already used by other people.	0,543
It takes a long time to get acquainted to sharing.		0,84
I would have to familiarize with sharing a lot first.		0,818
Sharing appears to be too circumstantial to me.		0,778
It is cumbersome to participate in sharing activities.		0,66
It is inconvenient for me to facilitate access to by belongings to other people.		0,413
To what extent do you trust people in collaborative consumption communities?			-0,788
How do you trust collaborative consumption services?			-0,741
People with many possessions have a high profile.				0,929
Having many possessions is a status symbol.				0,927
Cronbachs's Alpha	0,808	0,791	0,591	0,863

Table 6 Impediments to participate in sharing economy

The level of 0,591 of Cronbach's Alpha is more than minimal required 0.5 threshold for the analysis (Cronbach 1951), however it is located in the boundary zone. Because it is close to the normal value of 0.6, there was made a decision to leave a factor in the research.

As it could be seen from the Table 6 all indicators proposed by authors combined together in the single latent variable that could be interpreted as Negative Expectations from sharing economy and as follows this variable would be further used as Neg.

The second factor evidently and anticipated reflects Percieved Ease of Use variable (PEOU).

The third one have strong negative loading and is associated with mistrust to sharing economy and, consequently, is named MisTr.

The last factor revealed stands for the opinion that ownership is a better way of consumption and is named Own.

Innovativeness

Factor analysis revealed 4 latent variables that are determined as Hard adapter, Leader, Easy adapter and Inventor. Hard and easy adapters differ in their attitude to innovations, Leader is an influential person in his referential field and Inventor tries to implement new innovations and technologies.

The KMO criteria is equal to 0,901, all factors exceed a Cronbach's alpha 0,8 value, all factor loadings are higher then 0,4.

	Factor
	1	2	3	4
I rarely trust new ideas until I can see whether the vast majority of people around me accept them.	0,777
I am reluctant about adopting new ways of doing things until I see them working for people around me.	0,747
I often find myself skeptical of new ideas.	0,724
I must see other people using new innovations before I will consider them.	0,717
I am suspicious of new inventions and new ways of thinking.	0,706
I am aware that I am usually one of the last people in my group to accept something new.	0,633
I am generally cautious about accepting new ideas.	0,619
I find it stimulating to be original in my thinking and behavior.		0,776
I consider myself to be creative and original in my thinking and behavior.		0,732
I am an inventive kind of person.		0,671
I tend to feel that the old way of living and doing things is the best way.		0,616
I feel that I am an influential member of my peer group.		0,615
I enjoy taking part in the leadership responsibilities of the group I belong to.		0,527
My peers often ask me for advice or information.		0,411
I enjoy trying new ideas.			0,805
I seek out new ways to do things.			0,734
I am receptive to new ideas.			0,559
I frequently improvise methods for solving a problem when an answer is not apparent.			0,453
I am challenged by unanswered questions.				0,785
I am challenged by ambiguities and unsolved problems.				0,721

Table 7 Innovativeness

Clusters

To conduct a clusterization a list of subsequent steps was held. A cluster definition process was done on the all drivers and impediments variables gained in factor analysis.

Because the cluster analysis is held on the cases, selected from the main sample it is important to provide a socio-demographic description of the new sample. A smartphone usage is an additional variable; it is considered to be influential in such mobile-oriented field.

	N	%			N	%
Gender		Marital status
Male	227	29,7		Married	376	49,1
Female	537	70,3		Divorced	56	7,3
		Single	334	43,6
Age
Under 18	2	0,3		Children
18-25	262	34,2		Yes	252	33,2
26-30	226	29,5		No	508	66,8
31-35	138	18
36-40	57	7,5		Subject RF
41-50	70	9,2		Moscow	268	35
51-60	10	1,3		Saint-Petersburg	91	11,9
		Regions	407	53,1
Education
Incomplete secondary education	3	0,4		City size
Secondary general education	13	1,7		More than 1 mln.	522	69,9
Secondary special education	24	3,1		500 ths. - 1 mln.	98	13,1
Incomplete higher education	121	15,8		100-500 ths	92	12,3
Higher education	500	65,3		50-100 ths	17	2,3
Two or more higher	77	10,1		Less than 50 ths	18	2,4
PhD	28	3,7		Smartphone usage
		Little	22	2,9
Income		Moderatly	88	11,5
Not enough money even for food	7	0,9		Active	317	41,4
Evough money only for food	25	3,3		Fan	339	44,3
Enough money for food and clothes, but we can not allow bigger aqusitions	369	48,2
We can not but a car	229	29,9
We can allow a new car, but a flat is not affordable.	115	15
There is enough money not to reject anything.	20	2,6

Table 8 Socio demographic description of people who used carsharing, things sharing or ridesharing

Initially, a hierarchical cluster analysis was carried out by means of Ward method with the use of Squared Euclidean distance as a measure. Table of agglomeration aided in defining the maximum number of segments (8). Then, an analysis was produced by conducting a clustering procedure until there will be a compatible number of cases (>30) in each segment. The appropriate solution was found in a 7-clusters model. The number of cases in each cluster in the hierarchical cluster analysis could be seen in Table 9.



No. of cluster	N
1	74
2	51
3	143
4	110
5	124
6	76
7	31
Total	609

Table 9 The number of cases in each cluster in the hierarchical cluster analysis

After that number of cluster and their centers were used to conduct a K-means clustering procedure. The number of cases in each segment in conjunction with mean values of basing variables is presented in Table 10. Red filling demonstrates numbers less than -0,6, green filling shows numbers higher than 0,6.

Clusters	1	2	3	4	5	6	7	Total
N	88	54	110	115	104	102	36
Int	0,32	0,24	-0,22	-0,54	0,68	0,30	-1,63	-0,01
PU	-0,06	-0,03	-0,21	0,01	0,43	0,24	-1,12	0,00
Ease	0,17	0,33	-1,25	0,58	0,13	0,17	0,11	0,00
Eco	-1,47	0,22	0,07	0,47	0,40	0,28	-0,38	0,00
Ref	0,24	-1,96	0,20	0,37	0,38	0,06	-0,79	-0,01
Neg	-0,27	0,16	0,07	0,58	-0,68	0,14	-0,15	-0,01
PEOU	-0,53	0,00	1,07	-0,66	-0,20	-0,24	-0,35	-0,10
MisTr	0,08	0,01	0,46	0,06	-0,92	-0,56	1,72	-0,04
Own	0,16	0,41	0,11	0,51	0,50	-1,24	-0,74	0,01

Table 10 No. of cases in clusters and clusters' centers

The socio-demographic description of clusters is shown in the Appendix 2. Mainly, all clusters follow the general tendency of the research, however there are particular for every segment significant differences, which could be described. For the sake of concision all those differences are aggregated in Appendix 3 and described by means of standardized residuals. In the table provided values higher than 1,0 or less than -1,0 are reflected. Moreover, all cells, where a number of answers was less than 10 are excluded from the research.

The innovativeness scale does not achieve any representative results in any cluster except the seventh one. Table 11 concludes gained cluster centers in every latent variable from the innovativeness scale.

Hard_adapter_exp	-0,04	-0,11	0,52	-0,04	-0,33	-0,25	-0,46
Leader_exp	-0,04	-0,29	0,04	0,01	0,32	0,05	-0,53
Easy_adapter_exp	-0,04	0,20	-0,28	0,11	0,27	0,10	-0,81
Inventor_exp	0,02	0,01	-0,11	0,30	0,02	-0,09	-0,12

Table 11 Cluster centers of innovativeness factors

Below the full description of clusters is provided. Conclusions about a tendency concerning drivers, impediments or innovativeness are done due to mean deviations reflected in Table 10, findings in socio-demographic sphere are mane with the help of Appendix 3 and standardized residuals mentioned there.

Cluster 1

The main distinguishing feature of this cluster concerning the deviation from clusters centers is that people in this group do not consider sharing economy to be environmentally friendly. Analyzing the socio demographical cross-tab we can see that men are more likely to be in this cluster than in any other. Education of people in this cluster is mainly higher or better. They are inclined to be more wealthy and they are less likely to be single.

They could be named as “Educated Male”.

The only practical recommendation for this cluster could be not to use ecology connected messages in communication and not to accent on the easiness of usage.

Cluster 2

People in cluster 2 are tend to be male and active users of smartphone, they also incline to be single and without children. Their key feature that determined a cluster was a particularly low level of referential influence. It probably could be connected with their single lifestyle.

Unfortunately, little information is gathered about this segment to describe it properly, consequently, no consistent recommendations on the work with this segment could be done.

Cluster 3

This segment is one better described by different variables. It also remarkable for the strongly marked attitude towards sharing. Those one included in this segment suppose sharing to be a complex process, difficult to get acquainted with. This group tends to be elder and to have children. They also are inclined to have a middle income and live in regions in towns with population from 100 thousand to 500 thousand. It is likely that one in this cluster would be woman (a gender ratio in the segment is 78%/22%).

It should be mentioned that age is frequently associated with harder innovation acceptance (Venkatesh, et al. 2003).

This segment could be named “Elder Hard Adapters Women” and it is not considered to be a target group for sharing economy companies. However, if this segment is chosen as a prior one the appropriate communication message should command attention on the Ease of Use and the lack of obstacles to take part in.

Cluster 4

This group of people are smartphone fans (use almost all smartphone functions) and they are inclined to suppose sharing economy to be not a difficult business model. However, they have a slight negative attitude towards collaborative consumption and do not have inclination to take part in it. They also have a positive movement from the mean value in the Ownership variable that could be associated with the opinion buying things implies a status confirmation process.

People in this group are also likely to be young (not elder than 25) and to live in small towns with population less than 500 ths.

This cluster could be named “Small Cities Youngsters”. They are considered to be the less effective target group for their negative attitude towards sharing, low inclination and ownership attraction qualities.

Cluster 5

This clusters seems to be the most perspective one for sharing economy companies. People included in this group tend to have positive inclination to collaborative consumption, they are likely to trust it and do not have negative associations with it. They are the unique cluster that have a city inclination: Saint Petersburg (they also are likely to live in cities with more than 1 mln. population). They tend to be from 26 to 30 years, have higher education, to earn enough money to instantly buy home appliances, but could not buy a car and no more. People in this group more inclined to have a family and to be single.

“Young City Husbands” is supposed to be a name for this cluster.

The appropriate communication message for this group is a perspective way for further research, however it could be assumed that with low not a high income and a family they could be interested in economic value of sharing economy services.

Cluster 6

This cluster tends to consist of elder women with higher income and with two or more higher educations. They strongly do not support an “ownership as a status symbol” viewpoint and has a slight inclination to trust sharing economy. However, they do not have a strong inclination or attitude to collaborative consumption and could not be named a significant target group.

This segment could be called “Educated Women”. It seems not to be definitely negative or positive and it it a vast field for further research to define if this segment is inclined to participate in sharing or not.

Cluster 7

This group is the smallest in the research and has only 36 members, however, it has a strong and distinguished position. This group could be named “Nihilists”, for they have combined not acceptation of property as a status, negative attitude towards sharing and low inclination to take part in it. They also tend to say that referential group do not consider that they should participate in sharing. Also a well marked Mistrust variable could be seen in this group.

The innovativeness scale shows that an Easy adapters behavior model has negative meaning in this group.

This last cluster represents the worst target group for collaborative consumption services. Fortunately for sharing economy firms, members of this group account for only 6% of the sample.

Concluding, empirical research on the sample of Russian people (N=1488) actively using mobile devices was conducted revealing the market structure and segmenting users in particular clusters by dint of drivers or/and stoppers inspiring them to participate or not to participate in collaborative consumption.

Market research showed that three branches gained nearly 100% awareness in Russia. They are online taxi, short term flat rental and redistribution markets. 74,9% of users tried online taxi at least ones and it is the best result across all sharing economy services. The worst score is achieved by things sharing and ridesharing branches. The most perspective from respondents' point of view spheres are carsharing and ridesharing. Concerning brands, the most successful ones from the number of users viewpoint are Avito and YandexTaxi.

Factor analysis was conducted in three steps and revealed totally 13 factors, 9 of which were used in followed cluster analysis.

Cluster analysis found 7 clusters in the restricted sample of 609 respondents. Among those clusters one could be named as target, three as not-target, one as perspective for further research and two as neutral.



Conclusions

An analysis of secondary data provided a deep understanding of the benefit segmentation as a best segmentation method to describe consumer groups by means of complex and not direct indicators as inner motives, attitude and inclination.

Besides, a versatile notion of sharing economy was widely discussed and, finally, defined particularly for this research as a P2P or B2C business model that entirely depends on the mobile Internet, implies temporal access to a good rather than ownership and uses an underutilized asset of the product. “Collaborative consumption” term was used as substitutional for sharing economy.

Then a UTAUT model of Vencatesh et. al was discussed in conjunction with two its basing concepts - Theory of Planned Behavior and Technology Acceptance Model. An indicators suggested in this work were adapted to assess the attitude, inclination and approach towards sharing.

A practical part of the work consisted of the secondary data analysis connected with previous research of the motives in sharing economy. Two works were chosen as a practical baseline to adapt indicators and expected factors: The Sharing Economy: “Why People Participate in Collaborative Consumption” (Hamari, Sjoklint and Ukkonen 2016) and “Understanding the Sharing Economy-- Drivers and Impediments for Participation in Peer-to-Peer Rental” (Hawlitschek, Teubner and Gimpel 2016).

Market analysis revealed the awareness, usage and potentiality structure of sharing economy in Russia. The most acknowledgeable branches are Taxi, Redistibitional markets and short-term flat rental. The biggest usage level is achieved by Taxi services, second and third position with nearly the same result is shareв by Redistributional markets and short-term flat rental. Concerning brands structure of the awareness leading position are affirmed after online taxi services and, of coarse, Avito. The percentage of users distributed in the same way. The best scores in “Want to try” question are achieved by the least known brands (except AirBnB) - SobakaGulyaka and Rentmania.

The investigation proceeded with factor analysis, that was divided in three parts: drivers, impediments and innovativeness. The analysis resulted in 13 latent variables: 5 drivers, 4 barriers and 4 innovativeness measures.

Finally, a cluster analysis was carried out revealing seven clusters of customers divided by their attitude towards sharing inclination to participate in it and factors inspiring them to take part in collaborative consumption or distracting them from it. From those clusters one (“Young City Husbands”) was defined as a target one, one as a perspective for further investigation (“Educated Women”), three (No. 3, 4, 7) as a non-target groups for their negative inclination or attitude and two could not been definitely referred to positive or negative.



Recommendations

1. Cluster analysis revealed a significantly big (17%) target group (Young City Husbands). Likely they live in big cities with population more than 1 million, have middle income, married, have higher education and use the majority of smartphone functions. The proposed communication strategy as it was mentioned above is vast field for further marketing research, for this study accomplished only to denote strong inclination towards sharing. Thus, value positioning for this cluster is an open question.

2. Cluster number 6 (Educated Women) shows the significant trust to sharing economy services and, meanwhile, a considerable disagreement with the demonstration a status through ownership. This group seems to be potentially perspective. It is possible that they just have not found something appropriate for them in sharing economy yet. May be premium brands handbag or clothes sharing or P2P money loaning services (that still are not presented in the Russian market) could be popular among this audience. New investigations should be conducted to prove or refuse the potential of the segment.

3. Cluster 1 (Educated Male) is also considered to be further examined because the attitude of its members to sharing economy is still not certain.

4. Three segments (Elder Hard Adapters Women, Small Cities Youngsters and Nihilists) are positioned as not-target for their negative inclination or attitude to sharing. They combined form 43% of the sample.

5. An interesting note is that all groups associated with with population less than 500 thousand have negative attitude towards sharing, thus bigger cities are recommended to be a starting place for a new sharing company or an extanding the business platform for existing one.

6. Market research has shown that the most attractive to try segment of sharing economy is carsharing. Though, Moscow market has been fast growing in recent years, regions are just starting to develop this business direction and seems to be a perspective vector for investment, basing on the Moscow experience.

7. On the contrary both secondary data analysis and empirical research prove the lowest growth of things sharing market. In the last three years this branch is considered to gain less development. However, Rentmania - the main player on the P2P rental market gained the second result (20,3%) in “Want to try” scale, and it is possible that this market sector still has not achieved its target segment. It also could be interesting to the one who is willing to work in this field that the biggest share of usage in thing sharing (15,7%) refers to the Cluster 5 (main target cluster of sharing economy according to the results of this study).



References

1. Ajzen, I. From intentions to actions: A theory of planned behavior. Berlin, Heidelber, New York: Springer-Verlag, 1985.

2. Botsman R., и R. Rogers . What's Mine Is Yours: The Rise of Collaborative Consumption. New-York: HarperBusiness, 2011.

3. Frank, Ronald, William F. Massey, и Yoram Wind. Market segmentation. Madison: Prentice-Hall, 1972.

4. Kamakura, W.A. Marketing for MBA. Duke: Fuqua School of Business, 2010.

5. Kotler, Philip, и Kevin Lane Keller. Marketing Management (14th Edition). Cambridge: Pearson, 2011.

6. Stephany, Alex . The Business of Sharing. Palgrave: Macmillan UK, 2015.

7. Wedel, Michel, и Wagner Kamakura. Market Segmentation: Conceptual and Methodological Foundations. -: Springer US, 1999.

8. Wrenn, Bruce, и Philip Kotler. Marketing Management: Analysis, Planning, Implementation, and Control, 6th Ed. : Instructor's Manual. -: Prentice-Hall, 1988. Russian periodicals

9. Barkan, David I, и Alina S. Rucheva. «Strategic Marketing: Value navigation of the firm.» Вестник С.-Петерб. ун-та. Сер. Менеджмент, 2015: 1-38.

Foreign periodicals

10. Bardhi, Fleura, и Giana M. Eckhardt. «Access-Based Consumption: The Case of Car Sharing.» Journal of Consumer Research, December 2012: 881-898.

11. Belk, Russell. «Why Not Share Rather Than Own?» The ANNALS of the American Academy of Political and Social Science, 2007: 126-140.

12. Belk, Russell. «You are what you can access: Sharing and collaborative consumption online.» Journal of Business Research, 2014: 1595-1600.

13. Calantone, Roger J., и Alan G. Sawyer. «The Stability of Benefit Segments.» Journal of Marketing Research Vol. 15, No. 3 (1978): 395-404.

14. Cerny, C.A., и H.F. Kaiser. «A study of a measure of sampling adequacy for factor-analytic correlation matrices.» Multivariate Behavioral Research, 1977: 43-47.

15. Cronbach, LJ. «Coefficient alpha and the internal structure of tests.» Psychometrika, 1951: 297-334.

16. Davis, F. D. «Perceived usefulness, perceived ease of use, and user acceptance of information technology.» MIS Quarterly, 1989: 319-340.

17. Felson, M., и J. Speath. «Community structure and collaborative consumption.» American Behavioral Scientist, 1978: 614-624.

18. Halley, Russell I. «Benefit Segmentation: A Decision-Oriented Research Tool.» Journal of Marketing Vol. 32 No. 3 (July 1968): 30-35.

19. Hamari, Juho, Mimmi Sjoklint, и Antti Ukkonen. «The Sharing Economy: Why People Participate in Collaborative Consumption.» Journal of the Assossiation for Infromation Science and Technology, 2016: 2047-2059.

20. Hawlitschek, Florian, Timm Teubner, и Henner Gimpel. «nderstanding the Sharing Economy-- Drivers and Impediments for Participation in Peer-to-Peer Rental.» 2016 49th Hawaii International Conference on System Science. Hawaii: IEEE, 2016. 4782-4791.

21. Hinkin, Timothy R. «A brief tutorial on the development of measures for use in survey questionnaires.» Organizational Research Methods, 1998: 104-121.

22. Hurt, H. T., K. Joseph, и C. D. Cook. «Scales for the measurement of innovativeness.» Human Communication Research, 1977: 58-65.

23. Saussier, Julie . «Sharing as a disruptive force.» Global Investor, November 2015: 18-22.

24. Smith, W.R. «Product differentiation and market segmentation as alternative marketing strategies.» Journal of Marketing Vol. 21, No. 1 (July 1956): 3-8.

25. Venkatesh, Viswanath, Michael G. Morris, Gordon B. Davis, и Fred D. Davis. «User Acceptance of Information Technology: Toward a Unified View.» MIS Quarterly Vol. 27 No. 3 (September 2003): 425-478.

26. Wind, Yoram. «Issues and Advances in Segmentation Research.» Journal of Marketing Research Vol. 15, No. 3 (August 1978): 317-337.

Web-resources

27. Коммерсантъ. Mail.ru Group раскручивает "Юлу". 10 August 2016 г. http://kommersant.ru/doc/3059539 (дата обращения: 29 April 2017 г.).

28. Кречетова, Ангелина. Россия стала ключевым рынком для BlaBlaCar. 10 April 2017 г. http://www.forbes.ru/tehnologii/342359-rossiya-stala-klyuchevym-rynkom-dlya-blablacar (дата обращения: 29 April 2017 г.).

29. Botsman, R. Defining The Sharing Economy: What Is Collaborative Consumption-And What Isn't? . 27 05 2015 г. https://www.fastcompany.com/3046119/defining-the-sharing-economy-what-is-collaborative-consumption-and-what-isnt (дата обращения: 28 April 2017 г.).

30. Cartagena, J. What is the right name for the “sharing-economy”? 13 December 2014 г. https://medium.com/ouishare-connecting-the-collaborative-economy/is-there-a-better-name-for-the-sharing-economy-2d7489e1f56d (дата обращения: 7 April 2017 г.).

31. Chernikova, Nastua. Какие квартиры сдают на Airbnb в России. 28 Jule 2014 г. http://www.the-village.ru/village/city/tourism/161035-neobychnye-kvartiry-i-hozyaeva-airbnb (дата обращения: 29 April 2017 г.).

32. Eckhardt, G. M., и F. Bardhi . The Sharing Economy Isn't About Sharing at All. 28 January 2015 г. https://hbr.org/2015/01/the-sharing-economy-isnt-about-sharing-at-all (дата обращения: 7 April 2017 г.).

33. Forbes. 20 самых дорогих компаний рунета - 2017. 27 February 2017 г. http://www.forbes.ru/kompanii-photogallery/339445-20-samyh-dorogih-kompaniy-runeta-2017 (дата обращения: 29 April 2017 г.).

34. --. Mail.ru Group запустил сервис BeepCar для поиска попутчиков. 06 February 2017 г. http://www.forbes.ru/kompanii/338581-mailru-group-zapustil-servis-beepcar-dlya-poiska-poputchikov (дата обращения: 29 April 2017 г.).

35. GFK. «ПРОНИКНОВЕНИЕ ИНТЕРНЕТА В РОССИИ: ИТОГИ 2016 ГОДА.» GFK. 15 January 2017 г. http://www.gfk.com/fileadmin/user_upload/dyna_content/RU/Documents/Press_Releases/2017/Internet_Usage_Russia_2016.pdf (дата обращения: 30 April 2017 г.).

36. GFK. Амбициозных и Традиционалистов стало больше, Гедонистов - меньше: как меняются ценностные ориентиры, а вслед за ними потребление товаров и медиа. Москва, Москва, 4 Май 2017 г.

37. Killick, Rob. 'Sharing economy' companies like Uber and Airbnb aren't really 'sharing' anything . 6 October 2015 г. http://www.businessinsider.com/sharing-economy-companies-like-uber-and-airbnb-arent-really-sharing-anything-2015-10 (дата обращения: 28 April 2017 г.).

38. Meelen, Toon, и Koen Frenken. Stop Saying Uber Is Part Of The Sharing Economy. 14 01 2015 г. https://www.fastcompany.com/3040863/stop-saying-uber-is-part-of-the-sharing-economy (дата обращения: 28 April 2017 г.).

39. Merced, Michael J. De La. Uber Attains Eye-Popping New Levels of Funding. 6 June 2014 г. https://dealbook.nytimes.com/2014/06/06/uber-raises-new-funds-at-17-billion-valuation/?_r=1 (дата обращения: 20 November 2016 г.).

40. Nielsen. «Is Sharing The New Buying?» Nielsen.com. 1 May 2014 г. http://www.nielsen.com/content/dam/nielsenglobal/eu/nielseninsights/pdfs/Nielsen-Global-Share-Community-Report-May-2014.pdf (дата обращения: 30 September 2016 г.).

41. UBER. 4 Septembers of uberX in NYC. 6 October 2015 г. https://newsroom.uber.com/us-new-york/4-septembers-of-uberx-in-nyc/ (дата обращения: 29 April 2017 г.).

42. Пузырев, Денис, Наталья Телегина, и Владислав Серегин. Частники против интернет-гигантов: как устроен рынок такси в Москве. 3 February 2016 г. http://www.rbc.ru/business/03/02/2016/56b20ef49a7947344cbca265 (дата обращения: 29 April 2017 г.).

43. Артамонова, Анна . Купить нельзя - арендовать: сработают ли сервисы совместного потребления только для дорогостоящего имущества? 04 April 2017 г. http://www.forbes.ru/tehnologii/341871-kupit-nelzya-arendovat-srabotayut-li-servisy-sovmestnogo-potrebleniya-tolko-dlya (дата обращения: 28 April 2017 г.).

44. Соколова, Анна. Кто рулит на российском рынке такси. 1 February 2016 г. https://rb.ru/story/taxi-market/ (дата обращения: 29 April 2017 г.).

45. Сухаревская, Алена. В России запустили конкурента BlaBlaCar. 01 March 2016 г. http://www.rbc.ru/technology_and_media/01/03/2016/56d55a239a7947997569b2ca (дата обращения: 29 April 2017 г.).



Appendix 1. Indicators used in empirical research

	Индикатор на английском	Индикатор на русском	Вторичный источник	Первчичный источник
1	All things considered, I find participating in collaborative consumption to be a wise move.	В целом, участвовать в совместном потреблении - мудрое решение.	Hamari J., Sjoklint M., Ukkonen A. (2015). The Sharing Economy: Why People Participate in Collaborative Consumption	[1] Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211.
2	Overall, sharing goods and services within a collaborative consumption community makes sense.	В целом, имеет смысл делиться вещами в рамках совместного потребления.
3	Collaborative consumption is a better mode of consumption than selling and buying individually.	Совместное потребление - лучше чем покупка и продажа вещей индивидуально.
4	All things considered, I expect to continue collaborative consumption often in the future.	Я буду продолжать участвовать в совместном потреблении.		[2] Bhattacherjee, Anol. 2001. "Understanding Information Systems Continuance: An Expectation-Confirmation Model," MIS Quarterly, (25: 3).
5	I can see myself engaging in collaborative consumption more frequently in the future.	Я планирую увеличить участие в совместном потреблении в будущем.
6	It is likely that I will frequently participate in collaborative consumption communities in the future.	Я буду участвовать в совместном потреблении в будущем.
7	I think collaborative consumption is interesting.	Совместное потребление - это интересно.		[3] Van der Heijden, H. (2004). User acceptance of hedonic information systems. MIS Quarterly, 28(4), 695-704.
8	Sharing allows me to lower my expenses.	Совместное потребление позволяет снижать затраты .	Hawlitschek F., TeubneT., Gimpel H. (2016). “Understanding the Sharing Economy--Drivers and Impediments for Participation in Peer-to-Peer Rental”. 2016 49th Hawaii International Conference on System Sciences, 4782-4791.
9	My participation in collaborative consumption saves me time.	Совместное потребление экономит время.	Hamari J., Sjoklint M., Ukkonen A. (2015). The Sharing Economy: Why People Participate in Collaborative Consumption	[4] Bock, G.-W., Zmud, R.W., Kim, Y.-G., & Lee, J.-N. (2005). Behavioral intention formation in knowledge sharing: Examining the roles of extrin- sic motivators, social-psychological forces, and organizational climate. MIS Quarterly, 29(1), 87-111.
10	Collaborative consumption helps save natural resources.	Совместное потребление сохраняет природные ресурсы.	Hamari J., Sjoklint M., Ukkonen A. (2015). The Sharing Economy: Why People Participate in Collaborative Consumption
11	Collaborative consumption is environmentally friendly.	Совместное потребление сохраняет окружающую среду.

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