Trust in Sharing Economy Business Models from the Perspective of Customers in Szczecin, Poland

Abstract

The concept of a sharing economy, as part of a wider collaborative economy concept, is among the most important economic and technological trends that will influence socioeconomic development in the future. Interest in using the opportunities offered by sharing platforms is increasing; hence, the subject is a current and important issue. Confidence in technology, service providers and application providers is a key issue when making decisions about using such solutions. The aim of the paper is to examine the level of trust in sharing economy business models considering two groups of factors, trust in people and in technology, among several demographic groups. The paper has an empirical character and the results are provided on the basis of a survey conducted in Szczecin, Poland, with 403 respondents who are current and potential users of sharing platforms. The obtained results show that platform management requires more attention focused on building mutual trust networks among participants rather than strengthening the confidence in using the technology.

1. Introduction

The popularity of sharing platforms is constantly growing. This is confirmed both by studies assessing society’s awareness of business models of this type [1] and by the number of users involved. Interest in the sharing economy is most visible in the hospitality and ride sharing markets. The most recognizable platforms can serve as examples. In 2018 the number of Airbnb users in the United States was 38.4 million [2] and the number of Uber users worldwide was 95 million [3], and the forecasts indicate further growth. Customer interest arising from the opportunities offered by collaboration platforms is increasing also in Poland, where 1.5 million people use Uber [4]. In addition to the giants focusing on the hospitality and ride sharing markets, smaller and more recently established platforms are functioning with a more niche character. Their activity often better matches the idea of sustainability than those well-established international services. One example is leftover food sharing platforms [5].

Trust is the foundation of every business. It is also one of the most important factors influencing consumers’ decisions about participating in the sharing economy [6]. Members of sharing economy transactions must trust each other to act in good faith [7]. In business models based on the sharing economy, trust is a complicated issue, because not only does it exist between the service provider and the user, but the third entity, the sharing platform, is also included in the mutual trust network.

The sharing economy, as a research area, requires seeking patterns and creating a new theory, a body of knowledge explaining the basis of its functioning, as well as business and social determinants and relationships involved in such models. The theories that lie behind new business models must include the issue of trust and the willingness to join the sharing platform on the demand as well as the supply side. This paper presents the results of empirical research, which, together with the results obtained by other researchers, can enrich the current state of knowledge.

The aim of the paper is to examine the level of trust in sharing economy business models considering two groups of factors, trust in people and in technology, among several demographic groups. Although there are papers concerning the impact of social referrals and technological enablers on trust in sharing platforms [8], our research differs in factors that characterize each of these groups. The paper has an empirical character and the results are provided based on a survey conducted by the authors. The analytical part of the study is based on answers given by 403 respondents from Szczecin, Poland. The statistical analysis of the results was conducted with the use of basic statistics measures, as well as Cronbach’s alpha measure, nonparametric Kolmogorov–Smirnoff test, Kruskal–Wallis test, centre of gravity (CoG), histograms, box plots, and radar charts.

The paper is divided as follows: First we introduce the idea of the sharing economy as a business model that can act for sustainability, then we provide a literature review on trust in the sharing economy. After that, we explain our research procedure and methods, discuss the main findings and provide managerial implications for sharing platforms.

2. Theoretical Background

2.1. The Sharing Economy as a New Sustainable Business Model

The sharing economy is a relatively new concept, which is why a single definition and classification of this phenomenon have not been yet developed. The concept is in its formative stages and various authors have contributed to disentangling and classifying it [9]. The differences between related concepts such as collaborative economy, collaborative consumption, access-based economy, peer-to-peer, and the “mesh” (coined by Gansky [10]) have not been precisely specified. The definitions and subdivisions proposed in the literature are not always consistent [11]. The problem of ambiguous classification of concepts in this thematic area is highlighted by, among others, Rachel Botsman, one of the pioneers of the sharing economy. According to Botsman, the term “collaborative economy” is the most widely used and covers, among other aspects, sharing economy, collaborative consumption and access economy, as well as areas such as collaborative production, finance and education [12]. In this paper, the terms “sharing economy” and “collaborative consumption,” as well as “sharing platforms” and “collaborative platforms,” are used interchangeably.

In the most general approach, a collaborative economy refers to “business models where activities are facilitated by collaborative platforms that create an open marketplace for the temporary usage of goods or services often provided by private individuals” [13]. For society, it means giving up the purchase of goods for the joint use of assets, resources, time, skills, or capital. The joint use of goods by several people is not a new idea; on the contrary, it has been practiced for years. However, the development of information and communication technology (ICT) has enabled expansion of the range of people involved, and “stranger sharing” was created [14].

Including the information technology (IT) application as an additional party next to the provider and the user made the sharing develop a different dimension. The two most important features that distinguish this type of business model are the use of temporary access to goods and services over ownership, and reliance on the internet [11].

The collaborative economy involves three categories of actors [13]:

• Service providers, who share assets, resources, time and/or skills; these can be private individuals offering services on an occasional basis (peers) or service providers acting in their professional capacity (professional service providers);
• users of services;
• online platform operators, which connect providers with users.

The idea of a business model based on a collaborative economy is schematically presented in Figure 1. It indicates the trust between people and technology. Our paper focuses on analysing the level of trust for relationships marked A and B in the figure.

There are several features of a route to a sustainable economy. Ideas that make up that course include encouraging the minimization of consumption; designing it to maximize societal and environmental benefit, rather than prioritizing economic growth; basing it on a closed-loop approach where nothing is allowed to be wasted or discarded in the environment; and emphasizing delivery of functionality and experience, rather than product ownership [16]. Some of them are coherent with sharing economy business models.

Many scientists consider the sharing economy, at the level of the idea itself, as acting in the spirit of sustainability. Many reasons are given. For example, such systems provide significant environmental benefits by increasing usage efficiency, reducing waste, encouraging the development of better products and reducing the surplus created by overproduction and overconsumption [17,18,19]. Depending on the branch in which the platform is acting, different features are connected with an orientation toward sustainability. For example, a sustainable approach to collaborative consumption penetrates the lodging market not only as a low-cost alternative to hotels, but as a new, more socially responsible and rewarding way of travelling [20]. Curtis [21] proposes the following characteristics, the elements of a sharing economy that contribute to its meaning for sustainability: ICT-mediated, non-pecuniary motivation for ownership, temporary access, and rivalrous and tangible goods.

However, there is also criticism of the idea of a sharing economy as a sustainable business model concerning whether the right assumptions in practice may have different effects. “Collaborative economy” is a broad term, which includes small start-ups as well as international corporations primarily focused on making a profit and using the sharing platform as a tool for linking demand and supply on the market. Often there is no place or desire for sharing, and customers are only looking for more convenient and cheaper service [22]. It is worth asking what to do to strengthen relationships between the sharing economy and sustainability. The solution could include aligning with mutual interests, collaborating for shared success and committing to social responsibility [23].

Despite concerns raised about the positive impact of the sharing economy on the environment and society, there is scientific evidence of positive perceptions and the appreciation of possibilities offered by this model, such as reduced waste and better resource use, increased access to unaffordable goods and increased community cohesion [24]. Sustainability-oriented platforms are still emerging in the sharing economy. One example is leftover food sharing platforms [5]. Other popular subsectors that show a strong orientation towards environmental sustainability are fashion, on-demand services, and logistics [25].

The well-known social thinker J. Rifkin is optimistic about socioeconomic changes such as transformation from ownership to access, the growing popularity of the idea of cooperative community and the increased importance of sustainable development. Rifkin believes in a generational change in the approach to ownership and the potential to change the capitalist approach to a new paradigm of a hybrid economy, partly capitalist and partly based on the collaborative community idea [26]. It is considered that the collaborative movement can be the basis for creating a new socioeconomic system. The transition that we are witness to is also called “crowd-based capitalism,” a new way of organizing economic activity that may replace the traditional corporate-centred model [27].

2.2. Trust in Sharing Economy

Trust is a complex problem, difficult to operationalize, define, and measure. The idea of trust is analysed in many fields of science, including philosophy, social psychology, sociology, economics and management. For this reason, it is not always defined in the literature nor always presented in a coherent manner [28]. Hence the definitions are quite diverse and pay attention to different aspects of the term. In this paper, the definition grounded in management sciences was chosen, because it perfectly suits the idea of a sharing economy. In this perspective, trust can be defined as “an actor’s expectation of the other party’s competence and goodwill” [29].

In the literature, lack of trust has been repeatedly indicated as the main barrier to engaging in transactions carried out via the Internet. Concerns are primarily related to the provision of financial and personal data [30]. There are many research works that analyse trust in the online environment; however, researchers note that there is scarce literature on trust in the collaborative housing market [31] and collaboration consumption in general [8]. This paper attempts to fill this gap.

The issue of trust in the sharing economy is described in various ways. Research focuses on various elements of the platforms as research objects and uses various research methods. All elements of the system appear among the examined objects, including service providers [32,33], users’ attitudes [34], recommendations and opinions [35], regulatory frameworks [36], and others. Among the methods used for testing customers’ trust, there are, for example, bibliographic research [37], experiments [18], game theory scenarios [38], and questionnaire surveys [8,34,39]. A questionnaire was used as a research method in several papers devoted to trust in the sharing economy. A questionnaire survey was also used in this paper, so it is worth looking at some of them in more detail.

Hsiao [34] examined four groups of variables that can affect an individual’s participation in the sharing economy: demographic variables, trust, computer self-efficacy, and ease of use of technology. Trust is identified on the basis of three factors: trust in strangers, trust in known others (e.g., family, friends, co-workers), and trust in institutions. The research, carried out on a sample of 508 respondents, showed that trust in institutions (e.g., authorities, courts, police, and churches), computer self-efficacy, and perceived ease of use positively correlate with willingness to use sharing economy services. In Hsiao’s study, as in our paper, the research was not limited to one specific sharing platform or industry, but examined the trust in the model as a certain concept of operating on the market and in society.

The second paper using the questionnaire survey method limited the study to only one car-sharing platform operating in China [39]. The research sample included 309 respondents. The paper sought to find determinants affecting customers’ trust in the selected platform. The results indicated that the most important determinant for consumers’ trust in the car-sharing platform was the platform’s reputation. Additionally, an increased degree of familiarity with the car-sharing service platform had a positive impact on consumers’ trust.

In our paper, the factors affecting the level of trust in sharing platforms are split into two groups, representing trust in people and trust in technology. To some extent, a similar approach was used in the work of Kong [8], where the importance of two types of aspects, social referrals and technological enablers, was also investigated. However, the understanding of these two aspects is completely different in the two papers. For example, social aspect in Kong’s research is represented by three questions about respondents’ (N = 211) consideration of other users’ opinions on the use of a particular platform, Airbnb. Respondents were asked if they had heard from others that the Airbnb platform is useful, easy to use, or reliable. Meanwhile, the questions used in our research in the area of trust in people were constructed in a completely different way. They affect the relationship between the respondents and the service/goods providers rather than an analysis of recommendations, and relate to identifying the respondent’s own concerns, such as fear of insincere intentions of the service provider. Also, the research model and aims of both papers are different. Kong’s paper was looking for an answer to the question about the impact of the two main aspects on the customer’s decision to post positive opinions and continuing to use the e-marketplace. The original contribution of our paper consists in breaking down each of these two perspectives, technical and social, into smaller elements and ultimately creating a synthetic measure allowing the studied factors to be combined.

Research in the area of trust in the sharing economy with a questionnaire-based method addressed to the younger generation was also conducted by Mittendorf [31], who focused on the generation of millennials. Mittendorf’s paper shows the empirical evidence that both trust in the intermediary and trust in providers are decisive for users’ intention to use the Airbnb platform.

There are also papers that use questionnaire surveys to address issues completely different from those analysed in our research; they examine the influence of selected factors on trust in the sharing economy model, such as the issue of legal regulations [40] and the service provider’s attributes [32].

2.3. The Role of Trust in Sustainable Business

Trust plays a crucial role in relations between an enterprise and its stakeholders. The importance of trust mechanisms within organisations is emphasised with regard to, among other factors, employee relationships [41], supply chains [42], and (this dimension is the most interesting for our research) organisations and their customers [43]. Developing trust in a company is a significant part of building company–customer relationships [44].

The relationship between consumer trust and company performance strengthens as the company’s reputation and brand become increasingly important [45]. Intangible assets determine the brand value, i.e., any loss of trust in a company could result in measurable financial losses. Even in traditional industries, the issues of sustainability and social performance are becoming the most important drivers of a company’s processes and decisions [46]. Moreover, consumers themselves can co-create brand trust, as well as change brand competitiveness (e.g., by electronic word-of-mouth) [47].

Relations between trust and sustainability are related to the social dimension of the triple-bottom-line concept. Trust is the key concern that supports social sustainability [48]. Goodland argued that “Social sustainability (…) create(s) the basic framework for society. It lowers the cost of working together and facilitates cooperation: trust lowers transaction costs” [49]. Scientific research shows that trust in the relations between parties is considered critical for sustainable collaboration in order to deal with complex problems [50].

It is not only scientists who pay attention to the importance of trust in running a sustainable business. Practitioners, consultants, and politicians also appreciate its role. Confirmation for this can be found in the Sustainable Development Impact Summit organised by the World Economic Forum in 2018, during which participants argued that “sustainability will require more trust in markets” [51]. Nowadays, establishing an environment of trust is particularly important when transactions are carried out in online markets. It requires multidisciplinary approaches by authorities charged with ensuring fair competition, consumer protection, data protection, and other regulations [52]. The key pillar of trust in business is the belief that companies conduct their operations in compliance with the law [52]. It is also important that companies are “’open and honest’—it is the strongest driver of trust in companies with the global public” [53].

3. Materials and Methods

3.1. Designing a Research Tool

The research questions posed in this paper concern the level of trust in sharing platforms. In order to obtain answers to the research questions, the questionnaire survey method was used. The questions formulated in the survey apply collectively to all types of collaborative platforms. The tool created for the purpose of this study, a questionnaire, is not limited to one type of goods or services offered. This was a deliberate action. The authors’ intention was to evaluate trust in the formula of the business model based on the idea of sharing rather than analysing selected platforms. The questionnaire gives examples of the most recognizable platforms in the transport and hospitality industry, though this was done only to help the respondents better understand the research object.

This approach is very similar to the idea presented by Botsman, who believes that the first level is to trust the idea, then the company and, finally, the other person [54]. It can be compared to a ladder. You will not reach the top level of the ladder, i.e., you will not use the service, if you do not first trust the sharing economy idea and a specific platform provider. When we analyse the number of platform users, then we know how many people are at the top level of this ladder. However, it is impossible to get there without going through the first two stages.

In our study, particular attention was paid to determining the level of confidence in sharing platforms represented by the Z generation, which refers to people born after 1995. In the literature, generation Z is sometimes referred to as the Facebook generation or iGeneration, and the main characteristic features that connect this group are close contact with the world of new technologies, online social connections, a practical approach to problem solving, flexibility, impatience, and searching for solutions on the internet [55,56]. They are characterised as open-minded and caring, and appreciating family and relationships [57]. They are described as savvy consumers who do not trust brands [58]; however, they pay attention to online reviews and recommendations [59], which makes them an interesting group for research on the sharing economy, a business model largely based on such recommendations. The results obtained for representatives of the Z generation are compared with those of older respondents.

While conceptualising and designing this research, the results of one of the most important surveys in this thematic area carried out on a European scale were taken into account [1]. The results of the pan-European survey showed that over half of the respondents (N = 14,050) from 28 Member States had heard of collaborative platforms and 17% had used them [1]. The study focused on the popularity of sharing platforms and identifying their advantages and disadvantages. No factors determining the level of trust in the sharing economy were identified. In this paper, this task has been undertaken.

The survey prepared by the authors consisted of several parts. The first part included two questions about the gender and age of the respondents. These were single-choice questions. The second part consisted of four questions that were aimed at determining the level of knowledge, experience and plans of the respondents related to the sharing economy. The first two questions posed in this part were single-choice questions and the other two were multiple choice. The third part of the survey was about the concerns and fears of respondents related to the use of services provided in the sharing economy model, and the questions it contained covered two areas: concerns related to interpersonal relationships, and concerns regarding new technologies.

The Likert scale was used for these questions. The level of trust in a society depends on common cultural rules widespread in a given community, which is called “thick trust” [60]. This type of trust results from the specific historical or cultural conditions of a given country. “Thick trust” in Poland is rated extremely low. This clearly shows that Poland is dominated by a culture of mistrust rather than trust. Research on Polish public opinion shows that over three-quarters of Poles (69%) believe in the principle of maintaining extreme caution and suspicion in relations with others [61]. Hence, Poles may see more threats associated with the use of sharing platforms than residents of other countries, and consequently, the results of surveys on trust in the sharing economy conducted in Poland may differ from those obtained in other countries. In addition, Szczecin is a city with no indigenous population. The people who immigrated to these areas after World War II created a population with various cultures, traditions, customs, and national and ethnic identification, which certainly contributed to even greater caution and suspicion in relations with other people compared to other regions of Poland. Due to this specificity, the survey questions in our research were based on an assessment of the level of fear and concern, not trust. It is easier for respondents in this region to express their own fears than to quantify trust. Hence, the level of fear and concern was treated here as the opposite of the level of trust. Treating trust and concerns as coexisting and complementary concepts is asserted in the literature [62]. Some studies that have analysed risk and its negative effect on consumer behaviour argue that risk represents distrust [63].

The division of the survey into three main parts is shown schematically in Figure 2.

In each area, the questions reflected factors that affect the level of trust in business models based on the idea of sharing. Then, a list of potential factors that affect the level of trust in each of the two areas was created. The model concept diagram used to test the level of trust in the sharing economy business model is shown in Figure 3.

Factors that affect the level of trust in new technologies are classified into two subgroups: security and reliability. Factors related to interpersonal trust are classified into two subgroups as well: credibility and legal aspects. The combination of these two subgroups in each area determines the specificity of models of online sharing platforms.

To achieve the research goals, four auxiliary research questions were formulated:

RQ1. 

What is the level of trust in sharing economy business models of the Z generation representatives in relation to the level of trust of older age groups?

RQ2. 

Is the level of trust in sharing economy business models the same among men and women?

RQ3. 

Which of the two main areas of trust in sharing economy business models, technology or people, is characterized by higher intensity among all age groups?

Based on the results obtained, it is possible to draw conclusions regarding respondents’ perception of the idea of a sharing economy and the possibility of its future development in Szczecin.

3.2. Conducting the Survey

It is worth enriching the existing sharing economy literature with results of surveys conducted in countries with specific characteristics, cultures and levels of socioeconomic development [20]. This paper presents an approach to the sharing economy expressed by the inhabitants of Szczecin, Poland, a country that has undergone a difficult economic transformation and was promoted to the status of very high human-developed country according to Human Development Index [64]. The adopted sample selection corresponds with studies known from the literature that focus on testing new concepts in regional conditions. The research was carried out in Szczecin, a city where collaborative consumption is gaining in popularity, which is proven by one of the highest average annual growth rates in the hospitality market in Poland [65], and where recognizable platforms in the car-mobility market have begun to operate.

For research purposes, the diagnostic survey method was used with a computer-assisted web interviewing technique. The survey questionnaires were sent via e-mail and shared via social media. The request to provide questionnaires to potential respondents was directed to Szczecin universities, offices and libraries and directly to citizens. We assumed that each respondent might potentially be interested in using sharing platforms. The survey was limited to residents of Szczecin. In the conducted survey, 403 completed questionnaires were returned. The sample group included potential and current users. The research was conducted in October and November 2018. The characteristics of the tested sample are shown in

Table 1. Characteristics of respondents (N = 403).

Demographic Traits		Frequency	Percentage
Gender	Male	166	41.2%
	Female	237	58.8%
Age (years)	18–25	185	45.9%
	26–35	64	15.9%
	36–45	89	22.1%
	46–55	65	16.1%

.

The first part of the survey shows that the majority of respondents were women (58.8%), and the largest group of respondents (45.9%) was in the 18-to-25-year-old age group, representing generation Z. Representatives of older generations accounted for 54.1%.

Figure 4 shows averages (dashed lines), quartiles and maximum and minimum values regarding the respondents’ age divided by gender. The average age of the respondents was 31.8 years; the average age of women was 33.27 years and men 29.7 years. Based on the median, 50% of respondents were under 28.58 years of age; the median was 32.14 years for women and 25.55 years for men. In addition, the lower and upper quartiles indicate that 25% of respondents were under 22.36 years of age and 25% over 41.98 years; the same proportions of women were under 22.89 years and over 44.03 years. The quartiles referring to men’s ages are correspondingly lower; 25% of men surveyed were under 21.77 years of age and 75% were under 38.34 years.

The questions formulated in the second part of the survey allowed the authors to determine the respondents’ knowledge of sharing platforms (Figure 5 and Figure 6), their experiences (Figure 7 and Figure 8) and future plans (Figure 9 and Figure 10) regarding the use of this type of offering.

When analysing the results regarding the knowledge of sharing platforms (Figure 5 and Figure 6), we see that 93.8% of people reported knowing about these platforms. The share of people reporting knowledge of sharing platforms was as follows: 18–25 years, 97%; 26–35 years, 94%; 36–45 years, 92%; and 46–55 years, 80%. It was found that 90.7% of women and 95.8% of men knew about the idea of sharing platforms.

Figure 6 presents statistics on the age of respondents divided by gender based on their knowledge of sharing platforms. The average age of people who knew about sharing platforms is 31.14 years. The average age of people who reported no knowledge of sharing platforms was 40.33 years.

The obtained answers show that initiatives based on the idea of sharing platforms are very well known (6.2% of respondents had not heard of this type of solution). Considering respondents who had heard about these types of platforms, 50.3% had never used them (Figure 7). In contrast, 8.3% of respondents used a sharing platform once, 6.7% twice and 34.8% more than twice. Those who used such platforms more often decided to do so several times, which may indicate a satisfactory assessment of the activities of these platforms.

Figure 8 presents box plots showing statistics of the age of respondents depending on the frequency of using sharing platforms among those who had heard of them. The average age of people who have never used sharing platforms was 31.88 years; 50% were between 22.22 and 42.3 years of age. The average age of people who used this type of sharing platform once was 34.37 years; twice, 28.62 years; and more times, 29.79 years. We also see that 50% of people who used sharing platforms twice or more were under 26 years of age. The median of people who used a sharing platform once is 32.88 years of age.

Figure 9 shows the percentage of respondents by age who answered the question about whether they intend to use sharing platforms among respondents who had heard about them. Possible answers were yes, no, or perhaps. Only 3.8% of all respondents did not plan to use sharing platforms in the future. In each age group more people answered “perhaps” than “yes,” but the difference was not large.

When analysing people’s age depending on the answer given (Figure 10), the average age was similar for “yes” and “perhaps” answers, about 31–32 years old; for “no,” the average age was 27.5 years.

4. Results Analysis

4.1. The Level of Trust in Sharing Economy Business Model

In order to check respondents’ level of trust in the sharing economy and the people offering services through the platforms, we posed 11 questions in the third part of the survey.

Questions about trust in technology included:

• T1: mobile payment
• T2: sharing of personal data
• T3: handling applications
• T4: service availability

Questions checking trust in people offering services through sharing platforms included:

• P1: level of independence (adapting to conditions set by service providers)
• P2: credibility of issued opinions
• P3: credibility of the offer
• P4: honesty of the service provider
• P5: honouring a deal
• P6: complaint service
• P7: legal liability in the event of an accident

Respondents answered questions on a scale of 1 to 5, where level 1 meant very low trust and level 5 meant very high trust. In further analysis, only those respondents who had heard about sharing platforms were taken into account; a further examined sample consisted of 374 respondents. The reliability of the survey was evaluated with Cronbach’s alpha. Cronbach’s alpha for the obtained variables equals 0.8316. This means that the analysed survey results are a good measure of trust in sharing platforms.

As many statistical tests, analyses, and graphical presentations prepared for the purpose of this research referred to all research questions, it was decided to carry out a joint analysis of all three research questions together. This was a deliberate procedure. Summary answers to research questions are included in the conclusion.

Table 2. Average value (1–5) and standard deviation of responses about the level of trust in technology and people, taking into account respondents’ age.

	Question	T1	T2	T3	T4	P1	P2	P3	P4	P5	P6	P7	All
Z generation (18–25 yo)	Average value	3.32	3.02	3.88	3.31	2.95	2.62	2.56	2.67	2.65	2.45	2.57	3.07
	Standard deviation	1.44	1.32	1.27	1.25	1.14	1.15	1.08	1.14	1.12	1.15	1.09	1.33
Older respondents (26–55 yo)	Average value	3.36	2.58	3.75	3.33	2.96	2.78	2.68	2.84	2.81	2.53	2.36	3.11
	Standard deviation	1.51	1.42	1.30	1.25	1.22	1.12	1.08	1.22	1.24	1.28	1.22	1.27

Note: yo stands for years old.

presents the average values and standard deviations of the responses of Z generation (18–25 years old) and older (26–55 years old) respondents.

Similarly,

Table 3. Average value (1–5) and standard deviation of responses about the level of trust in technology and people, taking into account respondents’ gender.

	Question	T1	T2	T3	T4	P1	P2	P3	P4	P5	P6	P7	All
Women	Average value	3.25	2.68	3.67	3.27	2.93	2.65	2.64	2.65	2.64	2.47	2.34	2.84
	Standard deviation	1.43	1.43	1.29	1.21	1.18	1.19	1.10	1.24	1.24	1.27	1.15	1.31
Men	Average value	3.46	2.94	4.01	3.38	2.99	2.78	2.60	2.91	2.86	2.53	2.62	3.01
	Standard deviation	1.54	1.33	1.26	1.31	1.19	1.07	1.06	1.08	1.09	1.16	1.17	1.28

presents the average value and standard deviation considering the answers based on the respondents’ gender.

In both tables, in each of the 28 cases, the average values referring to trust in people have a trust level below 3. The average values referring to trust in technology, presented in Table 2 and Table 3, have in 13 cases out of 16 a level above 3. This indicates that the respondents trust more in technology than in people (RQ3). Considering the answers of women and men, in 10 of 11 cases the average values are higher for men than for women. This indicates that men have a higher level of trust in the sharing economy business model in general (RQ2).

Before verifying the statistical hypothesis, a nonparametric Kolmogorov–Smirnov test was used to verify the normality of all items included in the analysis. This test showed that none of the samples had a distribution close to the normal distribution, with a significance level of p = 0.001. Therefore, the Kruskal–Wallis test was used to verify the statistical hypotheses.

The statistical hypotheses were verified regarding the existence of differences between the responses of the Z generation and older respondents (RQ1).

The following hypotheses for questions T1–T4 and P1–P7 were investigated:

Null Hypothesis (H0a).

There is no difference between the answers of the Z generation and older respondents.

Alternative Hypothesis (H1a).

There is a difference between the answers of the Z generation and older respondents.

The Kruskal–Wallis test showed that we could not reject the null hypothesis (H0a) at a set level of significance presented in the table. The test results are presented in

Table 4. Kruskal–Wallis test results for analysed values.

		Dataset Size (N)	Degrees of Freedom (Df)	Test Statistic (H)	Significance Level
T1: mobile payment	Z generation	180	1	0.1158	≤0.05
	Older	194
T2: sharing of personal data	Z generation	180	1	9.7658	≤0.001
	Older	194
T3: handling applications	Z generation	180	1	1.0238	≤0.05
	Older	194
T4: service availability	Z generation	180	1	0.0262	≤0.05
	Older	194
P1: level of independence	Z generation	180	1	0.0022	≤0.05
	Older	194
P2: credibility of issued opinions	Z generation	180	1	1.6491	≤0.05
	Older	194
P3: credibility of the offer	Z generation	180	1	1.0374	≤0.05
	Older	194
P4: honesty of the service provider	Z generation	180	1	1.7782	≤0.05
	Older	194
P5: honouring a deal	Z generation	180	1	1.1681	≤0.05
	Older	194
P6: complaint service	Z generation	180	1	0.1139	≤0.05
	Older	194
P7: legal liability in the event of an accident	Z generation	180	1	4.1497	≤0.01
	Older	194

.

The general conclusion is that there are no significant differences between the answers given by the Z generation and older respondents. However, when considering the significance level, we can conclude that for the significance level of 0.05 for questions T2 and P7, we can reject the null hypothesis (H0a). Therefore, for this level of significance, we conclude that there are noticeable differences in the responses of the Z generation and older respondents regarding questions T2, sharing of personal data, and P7, legal liability in the event of an accident. It is worth emphasizing that in these questions the average values differ significantly. For question T2, the average value for the Z generation is 3.02 and for older respondents it is 2.58, whereas for question P7 these values are 2.57 and 2.36, respectively.

Similarly, the Kruskal–Wallis test was performed for the analysed data taking into account the gender of respondents (RQ2). The hypotheses in this case are as follows:

Null Hypothesis (H0b).

There is no difference between the answers of women and men.

Alternative Hypothesis (H1b).

There is a difference between the answers of women and men.

Table 5. Kruskal–Wallis test results for analysed values.

		Dataset Size (N)	Degrees of Freedom (Df)	Test Statistic (H)	Significance Level
T1: mobile payment	Women	215	1	2.5054	≤0.05
	Men	159
T2: sharing of personal data	Women	215	1	3.2385	≤0.05
	Men	159
T3: handling applications	Women	215	1	7.1359	≤0.001
	Men	159
T4: service availability	Women	215	1	0.7473	≤0.05
	Men	159
P1: level of independence	Women	215	1	0.2028	≤0.05
	Men	159
P2: credibility of issued opinions	Women	215	1	1.4979	≤0.05
	Men	159
P3: reliability of the offer	Women	215	1	0.1671	≤0.05
	Men	159
P4: honesty of the service provider	Women	215	1	4.7179	≤0.01
	Men	159
P5: honouring a deal	Women	215	1	3.9166	≤0.01
	Men	159
P6: complaint service	Women	215	1	0.5871	≤0.05
	Men	159
P7: legal liability in the event of an accident	Women	215	1	5.2823	≤0.01
	Men	159

presents the results of the Kruskal–Wallis test together with the significance level values, for which there is no reason to reject the null hypothesis (H0b). Therefore, for the levels of significance presented in Table 5, we conclude that there is no rejection of the null hypothesis, that there are no significant differences in the responses of women and men. However, performing the Kruskal–Wallis test assuming a significance level of 0.05 for questions T3, P4, P5 and P7, we get the conclusion that the null hypothesis (H0b) should be rejected. So for questions T3, handling applications; P4, honesty of the service provider; P5, honouring a deal; and P7, legal liability in the event of an accident at a significance level of 0.05 we conclude that there are differences between the answers of women and men. Analysing the average values of each of these questions shows higher average values for men than women, i.e., men show greater trust in the issues considered in the questions.

Next, we analysed the results comparing trust in technology and trust in people as two separate groups.

Table 6. The percentage distribution of responses regarding trust in technology and trust in people depending on age and gender among respondents.

		Level 1	Level 2	Level 3	Level 4	Level 5
		Very Low	Low	Medium	High	Very High
Trust in technology	18–25 yo	12.1%	14.6%	25.6%	18.5%	29.3%
	26–35 yo	15.8%	16.3%	22.9%	15.4%	29.6%
	36–45 yo	14.3%	12.8%	20.7%	21.3%	30.8%
	46–55 yo	25.0%	10.1%	26.9%	17.3%	20.7%
	18–55 yo	15.0%	13.8%	24.3%	18.4%	28.5%
	Women	15.7%	14.7%	27.1%	17.1%	25.5%
	Men	14.0%	12.7%	20.4%	20.3%	32.5%
Trust in people	18–25 yo	18.9%	26.4%	32.8%	16.0%	6.0%
	26–35 yo	15.7%	30.2%	26.7%	15.5%	11.9%
	36–45 yo	18.5%	22.6%	30.0%	21.8%	7.1%
	46–55 yo	23.9%	27.2%	28.0%	12.6%	8.2%
	18–55 yo	19.0%	26.3%	30.5%	16.7%	7.5%
	Women	21.5%	26.9%	28.0%	15.7%	7.9%
	Men	15.3%	25.8%	34.0%	18.1%	6.9%

Note: yo stands for years old.

presents the percentages of results obtained regarding trust in the technology used by sharing platforms and trust in people, depending on the age and gender of the respondents (RQ1, RQ2, RQ3).

Based on the results obtained, a very high level of trust (level 5) in technology is noticeable. When considering all respondents aged 18–25, 28.5% indicated very high trust (level 5). An average level of trust (level 3) was indicated by 24.3% of people and a high level (level 4) by 18.4%.

Similar relationships occur in the division of respondents into age intervals. The largest percentage was a very high level of trust (level 5) by people aged 18–25, 26–35, and 36–45. The exception is respondents aged 46–55, where the largest percentage was level 3 (26.9% of respondents).

To sum up, it can be stated that trust in the technology used in sharing platforms is very high (level 5), except for people over the age of 46, for whom trust can be assessed as medium (level 3).

Figure 11 presents responses regarding the area of trust in technology in the form of average values. Graphic visualization shows that the lowest average value was obtained for people aged 46–55 and it was 2.99.

Figure 12 shows average values of respondents’ trust in technology depending on gender (RQ2). The higher average value was obtained for men and its value is 3.45.

When analysing the percentage distribution of answers (Table 6) it can be seen that for men, a very high level of trust (level 5) is observed; 32.5% of respondents indicated this level. For others the level is lower.

For women, the largest percentage is medium trust (level 3), accounting for 27.1% of respondents. Above average levels (high (level 4) and very high (level 5)) were chosen by 42.6% of women. A below average level of trust in technology (level 3) was indicated by 30.4% of women.

To sum up, men assess trust in the technology used in the provision of services at a very high level (level 5), while women assess it at a high level (levels 4 and 5). The average value for men is higher than for women.

The results of the survey regarding trust in people indicate medium or low trust (RQ1, RQ3). Detailed percentage results are given in Table 6. A total of 75.8% of respondents indicated medium (level 3), low (level 2), or very low (level 1) trust in people. When analysing respondents’ answers divided by age group that way, similar results were found in all age groups. Figure 13 presents average values of trust in the people offering services through sharing platforms.

Similar results of trust in service providers were obtained by analysing the results of the survey based on gender. Figure 14 shows the average values of trust in service providers using sharing platforms depending on the gender of the recipient.

The average value of trust in people is higher for men than for women and its value is 2.76. The analysis of the more detailed results from Table 6 shows that in both cases, the largest percentage is medium trust (level 3); however, there is a right asymmetry in the distribution of trust in people. For women, 76.4% indicated medium (level 3), low (level 2), or very low (level 1) trust, while these levels of trust were indicated by 75.1% of men.

The results of the survey shown in Table 6 indicate low trust (level 2) in people offering services through sharing platforms. Analysing various cases, the results show that over 75% of respondents indicate trust at a medium (level 3), low (level 2), or very low (level 1) level.

Next, the centre of gravity values for respondents’ trust in people and technology are considered.

Table 7. Values of centre of gravity (CoG) regarding trust in technology and people presented in Figure 11 and Figure 13; scale is 1–5.

	18–25 yo	26–35 yo	36–45 yo	46–55yo	18–55 yo
CoG of trust in technology	3.38	3.27	3.41	2.99	3.32
CoG of trust in people	2.64	2.78	2.76	2.54	2.67

Note: yo stands for years old.

presents the centre of gravity (CoG) values, which were determined on the basis of results (Table 6) regarding trust in people and technology (RQ1, RQ3). As a reminder, the minimum trust value is 1, the maximum is 5.

The centre of gravity values indicate that respondents have more trust in technology than people.

Regarding trust in technology, CoG values in four of the five groups are above 3. In one group, the CoG value of trust in technology is 2.99. If you trust people, all CoG values are below 3. The above statement is also confirmed by the average values presented in Table 2 and Table 3.

This relationship, i.e., greater confidence in technology than people, is also seen in Figure 12 and Figure 14.

Table 8. CoG values regarding trust in technology and people presented in Figure 12 and Figure 14; scale is 1–5.

	Women	Men
CoG of trust in technology	3.22	3.45
CoG of trust in people	2.62	2.76

shows the centre of gravity values calculated with the division for men and for women. Also, here we can see that CoG values are higher for trust in technology than for trust in people. Once again, the results obtained show higher trust in technology than in people (RQ1, RQ3). The CoG values are higher for men than women for both trust in technology and in people. The conclusion is that men have a higher level of trust in sharing economy business models (RQ2).

Based on the analyses of the results obtained, we can refer to previously submitted auxiliary research questions. For question RQ1, regarding the level of trust in sharing economy business models, among representatives of the Z generation, the results indicate that this group is characterized by a definitely high trust in technology (73.3% of respondents indicated very high (level 5), high (level 4), or medium (level 3) level of trust in this area) and quite low trust in people (78.1% indicated medium (level 3), low (level 2), or very low (level 1) level of trust in this area) (see Figure 15).

Referring to question RQ1, we can conclude that the level of trust in technology in Z generation is consistent with that demonstrated by other age groups, except for respondents aged 46–55 yo, whose trust in this area can be assessed as medium (level 3). The level of trust in people for all age groups is convergent and was assessed as low (level 2) (see Figure 16).

For question RQ2, regarding the level of trust in sharing economy business models, the results indicate that men show a slightly higher level of trust than women in both technology and people (see Figure 17).

When analysing responses to question RQ3, Which of the two main areas of trust in sharing economy business models, technology or people, are characterized by a higher intensity in all age groups? Trust in technology is assessed at a much higher level (a very high level (level 5) was indicated by the majority of respondents in all age groups, except for those aged 46–55) than trust in people (75.8% of all respondents indicated trust at a medium (level 3), low (level 2), or very low (level 1) level (see Figure 18).

Further in the paper, individual factors from two main areas affecting the level of trust in technology and people will be analysed.

4.2. Factors Affecting the Level of Trust in Sharing Economy

In accordance with the concept of the model used to examine the level of trust in the sharing economy (see Figure 3), factors affecting the level of trust in two main areas were identified: technology and interpersonal relations. Then, factors affecting trust in technology were grouped into two sub-areas, security and reliability, and factors affecting trust in people were grouped into another two sub-areas, credibility and legal aspects, as shown in

Table 9. Division of factors affecting the level of trust in sharing economy in specific areas.

Symbol	Factor	Sub-Area	Area
T1	Mobile payment	Security	Technology
T2	Sharing of personal data	Security	Technology
T3	Handling applications	Reliability	Technology
T4	Service availability	Reliability	Technology
P1	Level of independence	Credibility	People
P2	Credibility of issued opinions	Credibility	People
P3	Credibility of the offer	Credibility	People
P4	Honesty of the service provider	Credibility	People
P5	Honouring a deal	Legal aspects	People
P6	Complaint service	Legal aspects	People
P7	Legal liability in the event of an accident	Legal aspects	People

.

All the results obtained, broken down by individual factors, are presented in detail in

Table 10. The percentage distribution of responses regarding the trust in technological and human factors in sharing business models among respondents.

		Level 1	Level 2	Level 3	Level 4	Level 5
		Very Low	Low	Medium	High	Very High
Technological factors	T1–mobile payment	19.3%	9.1%	21.1%	19.5%	31.0%
	T2–sharing of personal data	24.9%	18.2%	26.5%	13.9%	16.6%
	T3–handling applications	7.2%	10.4%	18.7%	20.9%	42.8%
	T4–service availability	8.6%	17.6%	30.7%	19.5%	23.5%
Human factors	P1–level of independence	14.2%	17.9%	38.0%	18.2%	11.8%
	P2–credibility of issued opinions	16.3%	27.5%	32.9%	16.0%	7.2%
	P3–credibility of the offer	16.0%	30.5%	34.2%	13.6%	5.6%
	P4–honesty of the service provider	16.8%	27.0%	27.3%	21.4%	7.5%
	P5–honoring a deal	17.4%	27.5%	26.7%	21.1%	7.2%
	P6–complaint service	27.0%	25.9%	25.9%	13.9%	7.2%
	P7–legal liability in the event of an accident	25.1%	27.8%	28.6%	12.6%	5.9%

.

Considering the factors affecting the level of trust in technology in sharing economy business models, it was verified which of them had the greatest impact. Figure 19 shows average values for individual factors from the area of trust to technology.

Respondents indicated the highest trust in handling applications—the highest average value (3.82) was obtained for T3. It is interesting to analyse the results in more detail. Regarding Table 10, 63.6% of all respondents indicated trust in this factor at a very high (level 5; 42.8%) or high (level 4; 20.9%) level. Another factor that respondents showed great trust in is mobile payment (T1); 50.5% of all respondents indicated trust in this factor at a very high (level 5; 31%) or high (level 4; 19.5%) level. The situation is similar for service availability (T4); 43% of all respondents indicated trust in this factor at a very high (level 5; 23.5%) or high (level 4; 19.5%) level. Sharing of personal data (T2) raised the biggest concerns by respondents; 43% of all respondents indicated trust in this factor at a very low (level 1; 24.9%) or low (level 2; 18.2%) level.

Figure 20 presents the level of trust of all respondents in individual factors affecting trust in technology in two sub-areas: security and reliability.

Regarding factors affecting the level of trust in people, respondents indicated much less trust in these than in factors affecting the level of trust in technology (Table 10). Figure 21 shows average values calculated for individual factors from the area of trust in people. It shows that respondents considered P7, legal liability in the event of accident, and P6, complaint service, as the most worrying factors.

The results can be also analysed regarding Table 10. A large proportion of respondents—52.9%—trusted P6 at a very low (level 1; 27.0%) or low (level 2; 25.9%) level. Respondents expressed similar concerns in relation to P7; 52.9% of all respondents indicate trust in this factor at a very low (level 1; 25.1%) or low (level 2; 27.8%) level. Another factor of concern was P3, credibility of the offer; 46.5% of all respondents indicated trust in this factor at a very low (level 1; 16.0%) or low (level 2; 30.5%) level. Fewer concerns among respondents were raised by factors P5, honouring a deal; P2, credibility of issued opinions; and P4, honesty of the service provider. The proportion of respondents indicating trust at very low (level 1) and low (level 2) levels were as follows: P5, 44.9%; P2, 43.9%; and P4, 43.9%. The least concerning factor among the respondents was P1, level of independence; 32.1% of all respondents indicated trust in this factor at a very low (level 1) or low (level 2) level. In addition, for P1, 38.0% of respondents indicated an average level of trust (level 3) in this factor. Such a lack of resolution and the choice of a neutral response at level 3 may result from the assessment of the sharing economy as imposed in the survey as one idea, without distinguishing between types of activities and industries.

Figure 22 presents the levels of trust in individual factors affecting trust in people in two sub-areas, credibility and legal aspects.

5. Conclusions

The development of business models based on the sharing economy has become possible thanks to the development of ICT tools and easy access to the Internet, among other aspects. However, further strengthening the presence of the sharing economy in socioeconomic life does not depend only on the efficient use of technology and the lack of fear of new applications. Whether potential customers will trust this model and use its services depends to a large extent on trust in the human factor present in the model.

RQ1: The level of trust by the Z generation in the sharing economy, measured with average value, is 3.07, which is just above the middle of the 1–5 scale, indicating an average level of trust in the sharing economy business model. The Z generation is characterised by high trust in technology and quite low trust in people. Regardless of age, respondents have a similar level of confidence in the sharing economy. This is confirmed by results of the Kruskal–Wallis test and considering the centre of gravity values of the distribution of trust in the sharing economy business model.

RQ2: Women have lower trust in the sharing economy business model than men (average values, 2.84 and 3.01, respectively). Men show greater trust in factors in the field of technology than women, and in both cases it is significant. The results are confirmed with the use of the same statistical methods as in RQ2.

RQ3: Trust in the technology used by sharing platforms is very high for almost all age groups. The only exception is people over 46, whose trust can be assessed as average. The highest trust, measured by the average value of answers, respondents have in the T3 factor (3.82): handling applications. Trust in the human factor of the sharing economy model looks completely different; it is at a low level in every cross-section of the sample, for each age group and regardless of gender. In the area of trust in people, the highest average value (2.95) was achieved by P1: level of independence, although it is still below 3. The difference (0.87) between the highest average values obtained for factors attributed to trust in technology and trust in people indicates that the level of trust of respondents in these areas is not the same. At the same time, it is worth noting that women are more stringent in assessing their trust in people; more often they rated it at the lowest possible level (21.5% at level 1); 15.3% of men indicated the lowest level of trust in people (level 1).

The results have significant managerial implications. They indicate the important direction that should be strengthened in platform marketing strategies. Platforms should place more emphasis on convincing potential sharing economy users to trust other participants in this business model. The responsibility for improving trust in factors of the sub-areas of interpersonal relations lies largely with platform managers. The biggest concerns among the respondents are factors from the legal aspects sub-area, i.e., P6, complaint service; P7, legal liability in the event of an accident; and P5, honouring a deal. An example would be strengthening security in terms of the possibility of recovering money in a situation of unsatisfactory service. This means that managers should pay more attention to guarantees for their clients by creating very detailed contracts and regulations that clearly define liability issues. This would allow potential users to feel safer, and thus more willing to use such services. Many legal aspects related to liability and guarantees are not yet resolved by legal regulations, hence it is worthwhile for platform managers to dispel doubts and indicate possible solutions to disputable issues in the regulations themselves. This will strengthen the good interpersonal relations that are part of a sustainability approach to business models. Research results indicate that the lowest level of trust concerns the issue of legal liability and integrity of model participants, therefore it is important to ensure the following:

• Clear regulations of platform service usage,
• Quick and easy contact with the platform service enabling ongoing resolution of conflict situations (chat, phone),
• A reliable system of mutual recommendations for both parties participating in the model,
• Transparency of the sharing platform (shared information about the owner, capital connections, etc.),
• Limiting the provision of personal data by clients to the necessary minimum,
• Highlighting the pro-social and pro-environmental goals pursued by the sharing platform in the marketing strategy, due to the increased sustainability awareness in Polish society,
• Training or other additional support in using online tools for people over 46, and
• Getting involved in the lives of local communities by having a presence at cultural events relevant to the community for platforms operating in local markets.

Research shows that respondents are open to new business models. Only 5% of respondents definitely do not want to use sharing economy business models. Others are definitely interested or at least do not rule out the possibility. This means a very large market of potential users. The survey was conducted in Szczecin, where not all of the known cooperation platforms offer their services yet. An example from the area of urban mobility is Uber; its service does not include Szczecin. For platforms that are considering expanding their activity to Szczecin, as well as for new initiatives that are just emerging, information about high interest in this market is certainly valuable. However, drawing conclusions about specific business solutions and prospects for new initiatives would require in-depth research into particular types of platforms.

Future research should consider repeating the questionnaire survey in the same area, i.e., in Szczecin, as well as conducting a survey based on the same questionnaire form in other parts of Poland and the world. Repeating the survey in Szczecin, which is specific because of its historical and cultural conditions, would help to check whether, with the spread of shared platforms offer, trust in this business model changes.