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Article

Evaluation of the Consumer Perception of Sharing Economy: Cases of Latvia, Russia, Ukraine and Belarus

by
Tatjana Tambovceva
1,*,
Jelena Titko
2,
Anna Svirina
2,
Dzintra Atstaja
3,4 and
Maria Tereshina
5
1
Institute of the Civil Engineering and Real Estate Economics, Riga Technical University, LV1048 Riga, Latvia
2
EKA University of Applied Sciences, LV1019 Riga, Latvia
3
Legal Department, Faculty of Law, Rīga Stradiņš University, LV1007 Riga, Latvia
4
Department of Management, BA School of Business and Finance, LV1013 Riga, Latvia
5
Department of Public Policy and Public Administration, Kuban State University, 350040 Krasnodar, Russia
*
Author to whom correspondence should be addressed.
Sustainability 2021, 13(24), 13911; https://doi.org/10.3390/su132413911
Submission received: 25 November 2021 / Revised: 8 December 2021 / Accepted: 9 December 2021 / Published: 16 December 2021
(This article belongs to the Special Issue Environmental Management Approaches and Tools to Boost Circular Economy)
Versions Notes

Abstract

:
The overwhelming goal of large-scale cross-country research is to evaluate consumers’ perception of a sharing economy. The research was limited by the number of respondents, as well as by the countries represented in the survey. Latvia, Russia, Ukraine, and Belarus were mostly represented, and only these responses (757) were analyzed. The study used multilevel modelling of sharing economy elements (dependent variable) in relation to personal characteristics (age, gender, income, industry) nested by the self-assessed level of eco-friendliness (a key predictor for the attitude towards sharing economy). Findings: The key personal characteristics, which influence a person’s intention to be involved in the sharing economy practices, are level of income, education, and also self-perceived ecological friendliness. The sharing economy is not only a topic for investigation among academicians, but also an issue on the agenda of the European Commission, because it is considered as a driver for growth and job creation in the European Union. Despite an increasing interest and many studies, there is a limited number of studies focused on difference in perception of sharing economy depending on personal characteristics of respondents. This indicates the necessity of conducting such surveys, involving participants from different European countries. The given paper could be used as a methodological framework for other European researchers who are interested in the exploration of the topic regarding perception of the sharing economy.

1. Introduction

Sharing economy, or collaborative economy, is on the agenda of the European Commission due to its rapid dissemination across Europe. It is considered that sharing economy can make an “important contribution to jobs and growth in the European Union” [1]. The idea of sharing economy is based “on the philosophy of access-based consumption where, instead of buying and owning things, consumers want access to goods and prefer to pay for the experience of temporarily accessing them” [2]. Eurostat survey showed that 21% of EU citizens used a website or an app to arrange accommodation from another person, and 8% have done the same for transport services [3].
The current research continues the previous study conducted by the authors [4], which was based on a Latvian sample only. In the Latvian survey, 244 respondents participated, and one of the main goals was to test the reliability of the research instrument.
The goal of this paper was to evaluate consumers’ perception of a sharing economy. The results of the research were based on data received from 877 respondents representing 34 countries, but mainly Latvia, Russia, Ukraine, and Belarus. The only data received from respondents (757) of the most represented countries was analyzed.
The research question was “What personal characteristics of respondents affect their attitude to sharing economy?”
The data analysis was performed in the SPSS, applying multilevel regression analysis. Testing of measurement scales of separate questions was done by means of reliability analysis, assessing an internal consistency with Cronbach’s alpha coefficient.
The current research contributes to investigation of the relationship between attitude to sharing economy and personal characteristics of the potential participants.

2. Definition of the Sharing Economy, Related Concepts, and Methodological Approaches

2.1. Concept of Sharing Economy

Sharing economy is based on the idea of sharing underused assets in order to improve efficiency and sustainability. This idea overlaps significantly with concepts such as solidarity economy, social economy, access economy, platform economy, and community economy [5,6,7,8,9]. At the same time, the term itself and the scope of its application are not the subject of scientific consensus and cover a wide variety of disciplines, such as business modelling, psychology, economics, innovation, law, management, marketing, sociology, and sustainable development [5,10].
The authors have found that the sharing economy is compared to the peer-to-peer economy (P2P) in scientific publications [5,11,12,13,14]. This can be explained by the service provider’s analysis, which is more often characterized by a horizontal network of participants’ economic self-organizations, ensuring the production, exchange, distribution, and consumption of tangible and intangible goods without a hierarchical management regime [10,15]. Inspired by a study by Daniel Schlagwein et al. in 2019, the authors agree with their definition of a sharing economy [16].
The concept of a peer-to-peer economy, which emerged from the framework of a purely technical association (peer-to-peer network), is currently considered as a social model with such characteristics as voluntary cooperation between equal economic actors, the distributed nature of decision-making and management, self-organization, the creation of public goods, and a decrease in the importance of monetary compensation as the main incentive to participate in peer-to-peer economic production [17]. In addition to peer-to-peer communication, Chase [18] identified two other key drivers of the sharing economy: information platforms and underutilized resources. Despite some common points and the exponential growth of research on sharing economics [19,20], definitions and conceptual approaches are diverse, complex, and somewhat controversial [5,21,22,23,24,25].
It seems that this difficulty in conceptualizing the concept is associated with a wide variety of practices in the sharing economy [26], as well as the lack of generally accepted criteria for its definition. In a broad sense, the sharing economy includes shared consumption (sharing apartments, cars), shared lifestyles (coworking, cohabitation, shared housing), co-financing (crowdfunding, peer-to-peer exchange and cash lending, alternative currencies), and related production (digital manufacturing, 3D printers, etc.) [27]. The most clear and comprehensive definitions, on our opinion, are those proposed by Munoz and Cohen [28] and Wang and Ho [29], defining the sharing economy as (1) “a socio-economic system that provides an intermediary set of exchanges of goods and services between individuals and organizations, which are aimed at increasing efficiency and optimizing the resources used in society” and as (2) “…an emerging social and technological phenomenon based on developments in information and communications technology (ICT) that implies the collaborative consumption of physical, virtual, and intellectual goods”.
Among the methodological concepts, within the framework of which the sharing economy develops, the following can be distinguished. Cost minimization and the formation of new transaction methods are in line with the theory of transaction costs [30]. Analyzing the sharing economy, Gibbs et al. [31] used the theory of hedonic prices, since the prices of goods and services in the sharing economy depend not only on the product itself, but also on its characteristics. The logic of the service economy, according to Heo [10], is the most relevant for explaining the phenomenon of the sharing economy. In the work of Cheng [32], sharing economics is considered from the standpoint of the theory of social presence and the methodology of multilevel analysis. A social presence that builds trust between people is a fundamental element in ensuring the proper functioning of the sharing economy. For disclosing the relationship between service providers (agents) and authorities (principals), agent management theory is important [33]. The networking concept underpins research on the sharing economy through social networks [34]. Pricing in the sharing economy can also be viewed from the perspective of dynamic pricing [35]. Institutional theory is extremely relevant for the study of sharing economics, due to the presence of different types of stakeholders, low levels of trust, the presence of ethical dilemmas, and undeveloped institutional structures [36,37].
Kim and co-authors [38] used social capital theory to study the impact on other stakeholders, particularly in the tourism industry. Since the conclusions about the role of the sharing economy in sustainable development are ambiguous, the theory of the transition to sustainable development is of great importance for the study of the sharing economy [39].
The practices of joint consumption are far from new; however, in combination with digitalization, its tools, and the consequences of the formation of a networked information society, they give rise to completely new forms of ownership, models of labor organization, and exchange, which, according to some experts, will “cost” USD 335 billion by 2025 (it was USD 15 billion in 2015) [40].

2.2. Perception of the Sharing Economy GAP and Research Framework

A significant number of scientific works are devoted to the study of the factors influencing the perception of the sharing economy in the minds of consumers, while there is no unambiguous understanding of which factors are key in this process in various socio-economic conditions. Most researchers rightly believe that user perception in general is related to their motivation. Perception determines the change in motives, as well as their weakening or strengthening among users and producers in the sharing economy. Hamari et al. [41] and Hellwig et al. [42] highlight ideological and economic factors. Davidson et al. [43] emphasize the diversity of motives (convenience, flexibility, interaction, local authenticity, economic benefit) and perceptions of the participants. Pisano et al. [44] show that participation in the sharing economy practices, on the one hand, is based on trust, but, on the other hand, can change the perception and thinking of users towards increased transparency, openness, collaboration, and sharing. Acquier et al. [5] and Möhlmann [45] consider extrinsic and intrinsic motivation. At the same time, Chung and Lee [46] emphasize the importance and prevalence of extrinsic motivation, since utilitarian and hedonistic motivation, as well as perceived trust, have a positive effect on consumer propensity [47]. Barnes and Mattsson [48] highlight economic, environmental, political, social, and technological factors that influence consumer perception. Rebiazina with co-authors [49] explain the attitudes and expectations of participants in the sharing economy by socio-technological, economic-political, and personal groups of factors, emphasizing that perception largely depends on the socio-demographic and psychological characteristics of the participants, as well as the sphere of the sharing economy.
The perception of participants can be quite rational from an economic point of view, on the basis of the possibility of obtaining benefits from lower prices and reducing transaction costs, as well as the realization that it is meaningless to invest in expensive goods for limited use. At the same time, attitudes are not always based on financial incentives. Changing cultural and social norms towards sustainable consumption fosters a positive perception. At the same time, a significant proportion of consumers attribute environmental benefits to the sharing economy and believe that such practices contribute to strengthening social ties [50]. It is probably the case that millennials are readier for a positive perception of new ways of consumption; moreover, this is due not only to the evolution of consumption patterns from generation to generation, but is also an additional consequence of the more intensive dissemination of digital practices among them, creating reputation and rating online mechanisms and reducing risks in consumer perception.
Investigating the factors that determine the context of sensitivity of consumers to sharing services based on empirical evidence is extremely important from several points of view. Firstly, such an analysis makes it possible to understand the prospects for the development of the sharing economy in various socio-economic conditions, and secondly, the identification of key factors allows us to customize the marketing strategy of the sharing services. Finally, “working” with each factor that determines the consumer attitude to the sharing economy, individuals are able to strengthen the existing drivers and level the existing barriers.
There is a quite limited number of studies focused on difference in perception of the sharing economy depending on personal characteristics of respondents. Interesting research was conducted by the group of researchers within EU H2020 project, i.e., “Millennials and the Sharing Economy: European Perspectives” [51]. The authors of the report state that Millennials show “divergent consumption patterns when compared to older generations”. They also refer to other researchers as Xu et al. [52] or Klein and Smart [53], who have concluded that Millennials are less likely to be homeowners and are more likely to choose public or shared transportation over owning their own car. There are also other studies confirming that Millennials are most likely engaged in the sharing economy activities than older generations. For instance, a survey in North America based on the answers of 1000 adults revealed that “Millennials were almost three times as likely to use a space to stay, like Airbnb, or use professional services, like tax preparation, than people ages 35 and older” [54]. Thus, we can assume that the age is one of the dominant factors determining the intention for engagement in the sharing economy.
The results of the research conducted by Buda [55] revealed that “openness” to the sharing economy services is influences by socio-economic characteristics such as “economic status” (openness is overrepresented among active workers and students), “generation” (representatives of Y and Z generations are much more open to using sharing economy services; the Baby Boomers refuse sharing economy services), and education. In this study, gender was not a determining factor. However, other researchers state that women are more motivated to participate in the sharing economy [42,56,57].
The sharing economy is an interdisciplinary field. Research methods such as monitoring framework, system thinking, surveys, mapping and assessing indicator-based framework, or mapping the social dimension are commonly used in research. We tried to improve what was found in other studies to unify. Our sample can be used for further analysis of other industries.
In this case, research gaps will provide new essential areas to the sharing economy and help align the performance with the sustainable development goals. As for the research carried out in our analyzed countries, they are also very limited. In previous research, the authors investigated Latvian consumers’ perception of the sharing economy [4,58]. Ivanova et al. tried to examine the overall state of the sharing economy in Russia [59]. Revinova et al. [60] analyzed “the current level of use and prospects for the development of models of the sharing economy in Russia”. The authors surveyed students from two Russian universities but did not pay attention on perception. In turn, Barkhatov et al. [61] studied features and tendencies of development of the sharing economy in Russia, with much attention paid to the problem of trust. Gonchar et al. [62] analyzed the main sharing economy international practices and generalized Ukrainian experience in the implementation of the sharing economy projects.
From the description of the situation and the theoretical substantiation described above follows the research question that is formulated as “What personal characteristics of respondents affect their attitude to the sharing economy?”. From the researched theory, a research gap was found. To date, there are no practical studies that could be used as a methodological framework to substantiate the relevance of the sharing economy, its support, and promotion mechanisms. In order for hasty conclusions to be avoided, measurable and comparable values were examined, which can be justified cross-border with our study. The authors chose research methods that were studied in order how to eliminate the uncertainty completely, and our hypotheses has been put forward for it [63].
In search of answers, researchers have researched and analyzed scientific articles in various industries and continents. Eco-friendliness concerning sustainable solutions has been raised as a priority, paying attention to specific actions in consumer groups [64,65,66,67,68,69].
Referring to the research question and on the basis of the literature review, we state the first hypothesis:
Hypothesis 1.
Personal characteristics, such as age, income, and education, are predictors of a person’s involvement in sharing economy practices.
Assuming that combinations of personal characteristics could be explanatory factors of engagement in the sharing economy (not standing-alone variables), we state the second, technical hypothesis:
Hypothesis 2.
A specific configuration of personal characteristics are predictors of person’s involvement in sharing economy practices.

3. Materials and Methods

For our research, we used questions that were taken from the original questionnaire created by the Veridu in 2016 and conducted in the United Kingdom and USA [70]. According to the Veridu study, as well as other studies [71,72,73], younger consumers are far more likely to participate in the sharing economy. This is why the main part of the respondents were students who participated in various study courses related to sustainable development issues.
In the current research, the questionnaire with only two main sections was used: A—respondent profile, B—experience in sharing economy services, and C—attitude to the sharing economy. In turn, each of the sections involves several multiple-choice questions. The structure of the questionnaire is presented in Table 1.
The current study analyzed the only data from section A and section C.
Within section C1, respondents were offered to answer the question “How comfortable are you with each of the following sharing economy scenarios?” on a 5-point scale (“1”—extremely uncomfortable, “5”—extremely comfortable), which was suggested for the rating of 8 pre-determined scenarios. Labels of each scenario are presented in Table 2.
Within section C2, respondents were offered the opportunity to evaluate on a 5-point scale (“1”—absolutely not important, “5”—very important) the importance of various aspects/factors when using sharing economy services. All the factors were labelled, as is shown in Table 3.
Within section C3, respondents were offered the opportunity to evaluate their readiness/willingness to make a checking procedure before engaging in a transaction with another member of the sharing economy platform. A 5-point scale (“1”—not likely at all, “5”—completely likely) was suggested. Checking procedures and their labels are summarized in Table 4.
The total number of respondents in the sample was 877, representing 34 countries. Most respondents represented Russia, Ukraine, Belarus, and Latvia—23.26%, 22.12%, 8.55%, and 32.38%, respectively. Age representativeness of these countries can be explained by the fact that Russia, Ukraine, Belarus, and Latvia participated in the Baltic University program (BUP). The lecturers of these universities participated together in various events organized by BUP and agreed to conduct joint research.
The representativeness of all other countries did not exceed 1–3% of the sample and can be explained by the presence of foreign students in groups. Therefore, we decided to conduct an analysis only for 4 countries, i.e., we left only answers of 757 respondents. An adequate sample size, based on statistics of a population collected by the World Bank and using 99% confidence level and 5% confidence interval, was found to be 666 respondents.
The distribution between male and female respondents was 30% and 70%, respectively. Among the respondents, 49% had completed secondary education, and 18%, 8%, and 18% held a bachelor’s degree, a master’s degree, and a doctoral degree, respectively. The remaining respondents selected the answer “other”. Most of the respondents—25% and 38%, respectively—represented the age groups “less than 20 years” and “20–25 years”. In all other age groups, the number of respondents was distributed almost equally. Although the sample was somewhat biased in terms of gender, it was robust according to testing, yet we recognize consequential study limitations.
The reliability analysis to check the internal consistency of the measurements scales was conducted within the previous study. Analyzing the item-total correlation coefficients and alpha value “if item deleted”, we concluded that when the statement “joining a platform” from C1 scale and “communication” from C3 scale are deleted, total alpha of the scale can be increased. The items were not removed, but it was suggested to iterate the test in the following cross-country research. The results, based on the cross-country sample, are presented in Table 5.
Cronbach’s alphas for the whole measurement scales (C1, C2, C3 questions) were 0.756, 0.798, and 0.809, respectively. As shown in Table 5, deleting of two items—“sharing a house” and “communication”—increased the overall consistency of the measurement scales C1 and C3, respectively. However, the improvement was not significant enough to remove the items from the analysis.
Data analysis was performed by the implementation of a multilevel regression model to find the relationship between the perception of the sharing economy (perceived eco-friendliness) and personal characteristics of the respondents. Multilevel regression was chosen as the key methodology due to the fact that suggested hypothesis aimed to test the portrait of a person who is highly likely to become engaged in sharing economy practices.
Applying the multilevel regression model, we found that the parameters (regression coefficients) were used to assess probability with which the dependent variable would reach certain value. The nesting parameter, or the second level of modeling data, is an independent variable that is moderating the relationship between the lower-level independent variable and the dependent variable. Multilevel models are commonly addressed as hierarchical, due to the implemented data structure and the hyper-parameters of the upper-level model that are used as a controlling variable that affects the influence of lower-level variables [74].
This type of statistical analysis is in use when one needs to consider the social contexts as well as the individual respondents or subjects, which applies to the studied case of the sharing economy perception in 4 countries. According to Snijders and Bosker [75], there are “Two kinds of argument to choose for a multilevel analysis instead of an OLS regression of disaggregated data:
  • Dependence as a nuisance. Standard errors and tests based on OLS regression are suspect because the assumption of independent residuals is invalid.
  • Dependence as an interesting phenomenon. It is interesting in itself to disentangle variability at the various levels; moreover, this can give insight in the directions where further explanation may fruitfully be sought.”
As the relationship studied within this research tended to be influenced by personal characteristics of the respondent, this study used multilevel modelling of the sharing economy elements (dependent variable) in relation to personal characteristics (age, gender, income, industry) nested by the self-assessed level of eco-friendliness (a key predictor for the attitude towards circular economy). The assessment was performed with SPSS Statistics 22.0.

4. Results

The first question to the respondents was about the general self-perception as an “eco-friendly” person. Less than 1% answered “not at all”. The distribution of other answers, using 5-point scale from “not at all” to “very much”, was the following: “2”—15%, “3”—46%, “4”—35%, and “5”—4%. Thus, approximately 38% of respondents perceived themselves as “eco-friendly” people, and 61% evaluated their level of eco-friendliness as average.
At the first stage of research, we assessed direct relationships between the variables retrieved from the questionnaire by means of regular regression analysis; yet, it appeared that no significant relationship existed when we looked for the interdependence of a person’s involvement in the sharing economy and their personal characteristics (and an example of ANOVA is found in Table 6, where the dependent variable is Joining_a_platform, but predictors are constant and education). In order to follow the path of multilevel regression analysis, we needed to estimate whether the level of education can be seen as a predictor for joining the platform.
The results of single-level regression modelling with the same dependent variable, run for age, gender, and country as independent variables, indicated the same—the results were statistically insignificant, and therefore each personal characteristic cannot be considered as a predictor for a person’s involvement in the sharing economy. Yet, respondent’s age appeared to be statistically significant, although it explained only 2.5% of the variance in terms of involvement in sharing economy practices. Thus, Hypothesis 1 was partly supported (for age).
As a sequence to non-revealed result, we continued statistical modeling by means of multilevel regression analysis (as suggested by Snijders and Bosker [75]). The results of sharing economy elements modeling in relation to personal characteristics (age, gender, income, industry) were nested by the self-assessed level of eco-friendliness. The set of initial models tested (the set of models was defined on the basis of correlation analysis, where age had been proven to be insignificant) can be seen in Table 7.
Each of the initial models was tested as the primary level of multiple regression, which was connected to the dependent variable (involvement in the sharing economy on either supply or demand side) via intercept. The statistically significant results can be seen in Table 8.
Out of the models tested, only eight appeared to be statistically significant enough to predict the level of personal involvement in the sharing economy, both as a supplier and provider of services. These included a multiplicative model that included income and education as basic level personal characteristics; a multiplicative model with country and eco-friendliness self-perception as personal characteristics; a multiplicative model using income, education, and self-perceived eco-friendliness as personal characteristics (this model also works if education level is replaced by country of origin or respondent’s gender); and a multiplicative model using education, country, and self-perceived eco-friendliness. Moreover, the two four-factor models proved to be statistically significant, i.e., multiplicative model using income, education, country, and self-perceived eco-friendliness as personal characteristics and the model with income, education, sex, and self-perceived eco-friendliness as independent variables of the bottom-level variables. All the other models appeared to be insignificant. The same results were achieved when a set of variables, presented in the questionnaire, were used as nesting ones. Thus, the authors can conclude that key personal characteristics, which influence a person’s probable involvement in the sharing economy practices, are his or her levels of income, education, and self-perceived ecological friendliness. Other factors appeared influential only in some cases, and thus they can be dropped for predicting the level of involvement in the sharing economy.
Summarizing the analysis, we identified the following results. First, in case of nesting the model with eco-friendliness, none of the personal characteristics appeared to be statistically significant as predictors of the sharing economy perception. At the same time, such models as Income × Age, Education × Age, and Income × Country × Gender appeared to be statistically significant in predicting the perception of certain sharing economy elements. Second, in the case of nesting by personal characteristics (for instance, age), only the models that considered eco-friendliness as one of the elements appeared to be statistically significant predictors of sharing economy elements perception. Thus, Hypothesis 2 was supported, confirming that a set of personal characteristics can be a predictor of personal involvement in sharing economy practices. Our results partly match with the results of Ranzini et al. [51].

5. Discussion

Hypothesis 1, which aimed to assess individual behavior in relevance to a set of personal characteristics, was partly confirmed. It appeared, that although age is the only statistically significant direct predictor of person’s involvement in sharing economy practices, it appeared to be statistically significant; this direct relation did not appear earlier in the literature. Moreover, the range of influence shown by this predictor was very low, so in practice, it should not be considered a significant factor.
The set of personal characteristics, including age, gender, country of origin, education, and personal income, appeared to be a better predictor for the sharing economy involvement. In this research, we confirmed seven models that appeared to be statistically significant in terms of predicting personal involvement in the sharing economy practices (one was aged-based and included gender also), four were income-based and included a set of characteristics, and two were education-based and also included more than two predictors. These findings are in line with the results achieved by Hamari et al. [41] and Hellwig et al. [42], for whom ideology (education) and economics (income) were the key factors. Moreover, the findings partly confirm the suggestion of Pisano et al. [44], who outline that the sharing economy can change the perception and thinking of users towards increased transparency, openness, collaboration, and sharing—each of these is indirectly related to a set of personal characteristics, especially defined by country (see Hofstede dimensions). Barnes and Mattsson [48] highlighted economic, environmental, political, social, and technological factors that influence consumer perception, and this was partly confirmed by the significance of multifactor models our research had identified. Rebiazina with co-authors [49] outlined socio-technological, economic-political, and personal groups of factors, and these findings were also confirmed by the models developed as a part of this research.

6. Conclusions

The current paper reflects the results of the authors’ conducted cross-country survey aimed to evaluate consumers’ perception of the sharing economy. The results of the research are based on data received from 757 respondents representing four countries: Latvia, Russia, Ukraine, and Belarus. The research applied a multilevel regression analysis to identify the link between personal characteristics of respondents and their attitude to sharing economy services.
Approximately 41% of respondents perceived themselves as an “eco-friendly” person. A total of 44% evaluated their level of eco-friendliness as average. Personal characteristics that have an impact on respondents’ perception of the sharing economy were income level and education level. Moreover, the analysis revealed that the level of perceived “eco-friendliness” is a predictor of respondents’ evaluation of suggested statements regarding sharing economy activities. Gender, income, and education were not dominant predictors of attitude of respondents towards the sharing economy that, actually, is a contradictory result with the previously conducted studies. On the contrary, age appeared to be a significant predictor of adapting sharing economy practices, although variance explained by this factor was low.
Hypothesis 1 was confirmed only for age, which appeared to be a statistically significant predictor of personal involvement in the sharing economy. Hypothesis 2 was partly confirmed—one age-based, four income-based, and two education-based multifactor models were confirmed by multilevel analysis.
On the basis of the achieved results, this research confirms some of the findings in the existing literature. First, the role of age was proven to be significant, although it had a very low impact on involvement in the sharing economy. Second, multilevel regression models indicated that mainly the mix of age, gender, and education are the predictors for adapting sharing economy behavior.
The potential directions for further research include: (1) analysis of the most popular sharing economy activities to be engaged from the viewpoint of respondents, (2) analysis of the main reasons for avoiding sharing economy activities, (3) analysis of the difference in attitude of respondents representing different countries, and (4) statistically significant predictors serving as a methodological framework to justify the sharing economy.
Moreover, it is important to mention that the results of this research were somewhat biased by the dominance of females in the original sample; this has to be considered as one of the research limitations that should be taken into account.

Author Contributions

Conceptualization, T.T. and J.T.; methodology, T.T., J.T. and D.A.; software, A.S.; validation, A.S.; formal analysis, T.T. and J.T.; investigation, T.T., D.A. and M.T.; resources, T.T.; data curation, T.T., J.T. and M.T.; writing—original draft preparation, T.T., J.T., A.S., D.A. and M.T.; writing—review and editing, T.T. and J.T.; visualization, A.S.; supervision, T.T. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

The authors would like to thank all the respondents for participating in the survey.

Conflicts of Interest

The authors declare no conflict of interest.

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Table
Table 1. Structure of the questionnaire.
Table 1. Structure of the questionnaire.
Section ARespondent ProfileDetails
Q A1AgeClosed: 8 alternative responses
Q A2GenderClosed: 2 alternative responses
Q A3Education levelClosed: 4 alternative responses
Q A4Self-perception as an eco-friendly person5-point scale
Q A5Household income per personClosed: 6 alternative responses
Section BExperience in sharing economy servicesDetails
Q B1Experience as a sharing economy services receiverMultiple-choice: 9 alternatives
Q B2Experience as a sharing economy services providerMultiple-choice: 9 alternatives
Q B3Reasons for non-participation in sharing economy activitiesMultiple-choice: 8 alternatives
Section CAttitude to sharing economyDetails
Q C1Self-perception in various sharing economy scenariosC1.1–C1.8 statements evaluation with 5-point scale
Q C2Perceived importance of sharing economy servicesC2.1–C2.9 statements evaluation with 5-point scale
Q C3 C3.1–C3.6 statements
Safety checkEvaluation with 5-point scale
Source: designed by the authors.
Table
Table 2. Statements in the questionnaire section C1 and their labels.
Table 2. Statements in the questionnaire section C1 and their labels.
Statements/ScenariosContentLabel
C1.1Joining a platform to meet/share with people in your street/communityJoining a platform
C1.2Swapping skills with someone you have never metSwapping skills
C1.3Sharing a ride with someone you have never metSharing a ride
C1.4Lending money to someone you have never metLending money
C1.5Letting someone you have never met rent your house/apartmentRent a house
C1.6Sharing or swapping a house/apartment with someone you have never metSharing a house
C1.7Borrowing money from someone you have never metBorrowing money
C1.8Letting someone you have never met borrow your carBorrowing a car
Source: designed by the authors.
Table
Table 3. Statements in the questionnaire section C2 and their labels.
Table 3. Statements in the questionnaire section C2 and their labels.
FactorsContentLabel
C2.1Personal safetySafety
C2.2Property/home will be well-treatedProper attitude
C2.3Saving moneySaving money
C2.4Making moneyEarnings
C2.5Knowing something about the other personHuman interactions
C2.6Reputation of the sharing platformReputation
C2.7Helping the environmentEnvironment
C2.8Having a unique or new experienceExperience
C2.9New relationships/friends or being part of a communityNew friends
Source: designed by the authors.
Table
Table 4. Statements in the questionnaire section C3 and their labels.
Table 4. Statements in the questionnaire section C3 and their labels.
FactorsContentLabel
C3.1Read reviews from other membersReviews
C3.2Check their profile on sharing platformPlatform profile
C3.3Research reputation of sharing platformReputation
C3.4Get in touch with them directlyCommunication
C3.5Run a search of their name on the internetWeb search
C3.6Check their social media profileSocial media profile
Source: designed by the authors.
Table
Table 5. Cronbach’s alpha of the scales if individual items are deleted.
Table 5. Cronbach’s alpha of the scales if individual items are deleted.
C1 StatementsAlphaC2 StatementsAlphaC3 StatementsAlpha
Joining a platform0.746Safety0.790Reviews0.789
Swapping skills0.736Proper attitude0.786Platform profile0.766
Sharing a ride0.723Saving money0.779Reputation0.770
Lending money0.699Earnings0.789Communication0.817
Rent a house0.717Human interactions0.769Web search0.767
Sharing a house0.797Reputation0.789Social media profile0.763
Borrowing money0.712Environment0.777
Borrowing a car0.694Experience0.758
New friends0.764
Source: authors’ compilation based on data processing in SPSS.
Table
Table 6. ANOVA model of the sharing economy perception.
Table 6. ANOVA model of the sharing economy perception.
ModelSquare SumsStandardSquare MeanFSignificance
Regression0.72610.72612020.273
Residual445,9617550.604
Total445,687756
Source: authors’ compilation based on data processing in SPSS.
Table
Table 7. A set of models of multilevel regression testing.
Table 7. A set of models of multilevel regression testing.
Group of ModelsModel FactorsModel Type
AgeIncomeEducationCountrySexEco Friendliness
Simple additive model+ Additive
Income-based models++ Multiplicative
+ + Multiplicative
+ + Multiplicative
+ +Multiplicative
++ + +Multiplicative
++ + Multiplicative
+++ +Multiplicative
+ ++ Multiplicative
+ + +Multiplicative
+++++ Multiplicative
++++ +Multiplicative
+ Multiplicative
Education-based models+ ++ Multiplicative
+ + + Multiplicative
+ ++ +Multiplicative
+ + ++Multiplicative
+ ++++Multiplicative
Source: authors’ compilation based on data processing in SPSS. Plus indicates the factor is significant.
Table
Table 8. Multilevel regression for sharing economy sector perception (statistically significant models).
Table 8. Multilevel regression for sharing economy sector perception (statistically significant models).
EffectValueFHypothesis dfError dfSig.
InterceptPillai’s Trace0.8281145.3362501.0000.000
Wilks’ Lambda0.1791145.3362000501.0000.000
Hotelling’s Trace4.5721145.3362000501.0000.000
Roy’s Largest Root4.5721145.3362000501.0000.000
Age × GenderPillai’s Trace0.042152414,0001,004,0000.096
Wilks’ Lambda0.959153114,0001,002,0000.093
Hotelling’s Trace0.043153814,0001,000,0000.091
Roy’s Largest Root0.03927667000502,0000.008
Income × Age × GenderPillai’s Trace0.037191310,0001,004,0000.040
Wilks’ Lambda0.963191710,0001,002,0000.039
Hotelling’s Trace0.038192210,0001,000,0000.039
Roy’s Largest Root0.03232415000502,0000.007
Income × Age × Eco_friendlinessPillai’s Trace0.438156411,0001,004,0000.003
Wilks’ Lambda0.556174711,0001,002,0000.003
Hotelling’s Trace0.432174611,0001,000,0000.003
Roy’s Largest Root0.126233828,000502,0000.005
Income × Gender × Eco_friendlinessPillai’s Trace0.3401395135,0002,580,0000.002
Wilks’ Lambda0.7001404135,0002,530,3300.002
Hotelling’s Trace0.3741413135,0002,552,0000.002
Roy’s Largest Root0.150286027,000516,0000.000
Education × Country × Eco_friendlinessPillai’s Trace0.9781125486,0009,522,0000.032
Wilks’ Lambda0.3581130486,0007,759,6430.029
Hotelling’s Trace1.0791133486,0009,182,0000.026
Roy’s Largest Root0.184361227,000529,0000.000
Income × Education × Country × Eco_friendlinessPillai’s Trace0.215146981,0001,542,0000.005
Wilks’ Lambda0.798148381,0001,532,2130.004
Hotelling’s Trace0.237149681,0001,532,0000.003
Roy’s Largest Root0.144273827,000514,0000.000
Education × Country × Gender × Eco_friendlinessPillai’s Trace0.4601172216,0004,152,0000.047
Wilks’ Lambda0.6191179216,0003,955,6180.041
Hotelling’s Trace0.5021186216,0004,082,0000.035
Roy’s Largest Root0.140269827,000519,0000.000
Source: authors’ compilation based on data processing in SPSS.
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Tambovceva, T.; Titko, J.; Svirina, A.; Atstaja, D.; Tereshina, M. Evaluation of the Consumer Perception of Sharing Economy: Cases of Latvia, Russia, Ukraine and Belarus. Sustainability 2021, 13, 13911. https://doi.org/10.3390/su132413911

AMA Style

Tambovceva T, Titko J, Svirina A, Atstaja D, Tereshina M. Evaluation of the Consumer Perception of Sharing Economy: Cases of Latvia, Russia, Ukraine and Belarus. Sustainability. 2021; 13(24):13911. https://doi.org/10.3390/su132413911

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Tambovceva, Tatjana, Jelena Titko, Anna Svirina, Dzintra Atstaja, and Maria Tereshina. 2021. "Evaluation of the Consumer Perception of Sharing Economy: Cases of Latvia, Russia, Ukraine and Belarus" Sustainability 13, no. 24: 13911. https://doi.org/10.3390/su132413911

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