“The impact of taxi drivers’ characteristics on the propensity to do business: Case study from a sharing economy”

This paper aims to quantify the impact of selected demographic, financial, and economic factors on the propensity to do business in the taxi sector of the sharing economy. The sample comprised 375 taxi drivers from the Czech Republic and Slovak Republic. Data were collected using the query method via a questionnaire in April 2022. The structure of the respondents is divided into shared taxi service providers (N = 294) and traditional taxi service providers (N = 69). The study selected 14 factors: demographic (4), financial (7), and economic (3). The SEM approach was applied to evaluate the hypotheses. Shared taxi providers have a stronger propensity to do business than traditional taxi drivers. Demographic characteristics of a traditional taxi driver are the most significant factors with a strong influence on the propensity to do business (βS = 0.525 > βT = 0.425). On the other hand, the financial and economic characteristics of shared taxi drivers strongly influence the propensity to do business (βT = 0.565 > βS = 0.212). The characteristics of the enterprise are on the verge of significance in relation to the tendency to do business with shared taxi drivers, as opposed to traditional taxi drivers. For traditional taxi drivers, there is a strong influence of the characteristics of the enterprise on the propensity to do business (βT = 0.476 > βS = 0.026). This study contributes to understanding how participating in sharing economy may stimulate the propensity to do business.


INTRODUCTION
The term "sharing economy" (also known as collaborative economy, collaborative consumption, or gig economy) and many similar terms refer to similar concepts and patterns of behavior that revolve around the provision, sharing, giving, and receiving products and services among individuals, often through various online platforms, rather than traditional purchasing from business and institutions (Perkumiené et al., 2021).
Sharing is a form of social exchange that is either free or limited in cost (Ključnikov et al., 2018;Eckhardt & Bardhi, 2015;Pu et al., 2021).Information technologies have brought many innovations to the realm of digital sharing.The sharing economy, which arises from peer-topeer lending, exchanging, and gifting among individuals, represents a new economic-technological phenomenon that enables access to ownership (Hamari et al., 2016), with a key aspect being the sharing of individuals' private assets (Bencsik et al., 2019).Peer-to-peer activities allow the online purchase and sale of goods and services through in-formation technologies and online platforms, facilitating shared utilization or consumption of resources (Cheng et al., 2018;Edward et al., 2023).
The growth of business platforms in the sharing economy is driven by the internet and mobile technologies, rapid advancements in analytics, artificial intelligence, big data, and shifting consumer preferences and patterns.Business models based on shared economy platforms often facilitate direct interactions and transactions between individuals in unprecedented ways (Caldieraro et al., 2018).
Ridesharing, as a form of the sharing economy, brings significant benefits and impacts to the economy and society (Ngo, 2015).This new form of transportation and service provision fundamentally changes the traditional model of taxi services.
Firstly, ridesharing enables more efficient utilization of available resources such as vehicles and drivers (Schwieterman & Smith, 2018; Van et al., 2022).Through ridesharing platforms, individuals can simultaneously use their vehicles and share their journeys with other passengers, reducing transportation costs and helping alleviate traffic congestion in urban areas (Etminani-Ghasrodashti & Hamidi, 2019).It also provides new opportunities for entrepreneurship and income generation (Chen et al., 2017).Individuals who own a vehicle can leverage ridesharing platforms to provide services to other passengers and earn additional income.This model offers flexibility and the ability to work at one's own pace, which appeals to many people (Cramer & Krueger, 2016).
Although the ridesharing economy is expected to grow significantly in the coming years, research on the theme is in its infancy and heavily reliant on various contradictory theories and concepts.At the same time, there is a lack of more profound research into the entrepreneurial prerequisites of ridesharing providers.

LITERATURE REVIEW AND HYPOTHESES
Entrepreneurship is gaining attention on a global level because it contributes to streamlining resource utilization and addresses the issue of unemployment.There exists a consensus in available scientific studies regarding why people choose entrepreneurship.Entrepreneurs are presumed to be agents facing uncertainty and possessing a certain risk propensity.Regarding the relationship between risk propensity and entrepreneurship, research indicates that a higher willingness to take risks significantly and positively correlates with the likelihood of engaging in entrepreneurship (Selina, 2022).
The propensity to do business can be understood as the intention to start a new venture and choose an alternative career path instead of traditional employment (Ward et al., 2019;Yi, 2020).The propensity to do business is the best predictor for measuring entrepreneurial behavior (Ajzen & Sheikh, 2013).Previous research has found that individuals with a high level of entrepreneurial inclination positively and significantly affect future entrepreneurial behavior (Neneh, 2019).Also, awareness of circularity principles and circular .Further, the gender differences in perception of business perspectives caused by complex demographic factors lead to gender-related differences in business behavior (Apostol, 2022).
Motivating the general public to share their assets differs from inspiring entrepreneurs to start businesses in a traditional sense (Kim et al., 2020).Therefore, understanding the formation of entrepreneurial intention and inclination is fundamental in expanding knowledge about entrepreneurship in an innovative, sharing economy.
The sharing economy provides less demanding alternatives to traditional entrepreneurial ventures by allowing individuals to become self-employed.Additionally, the experience of working in the sharing economy could serve as a transitional step toward establishing a new independent business, acting as a catalyst for entrepreneurial aspirations (Barrios et al., 2022;Frenken & Schor, 2017).
The development of the sharing economy can stimulate microentrepreneurship (Zhang et al., 2019) by providing individuals with experiences in online income-generating activities on platforms, helping them overcome concerns related to risks.Conversely, online platforms can be perceived as entrepreneurial incubators shaping workers' identities and fostering their propensity to do business (Bellesia et al., 2019).
Based on all of the above, the aim of the paper is to quantify the impact of selected demographic, financial, and economic factors on the propensity to do business in the taxi sector of the shared economy.To achieve the objective of the study, the following statistical hypotheses were formulated: H1_A: Respondent demographic characteristics affect shared taxi drivers' propensity to do business in the taxi sector of the shared economy.
H1_B: Respondent demographic characteristics affect traditional taxi drivers' propensity to do business in the taxi sector of the shared economy.
H2_A: Business characteristics affect shared taxi drivers' propensity to do business in the taxi sector of the shared economy.
H2_B: Business characteristics affect traditional taxi drivers' propensity to do business in the taxi sector of the shared economy.
H3_A: Financial and economic characteristics affect shared taxi drivers' propensity to do business in the taxi sector of the shared economy.
H3_B: Financial and economic characteristics affect traditional taxi drivers' propensity to do business in the taxi sector of the shared economy.

METHODOLOGY
The data collection occurred in the Czech Republic (CR) and the Slovak Republic (SR) business environment in April 2022.Data were collected us-ing the query method via a questionnaire.A respondent engaged in gainful employment in the taxi sector of the shared economy completed the questionnaire.The selection of respondents was provided by the MNFORCE survey agency, which is an established service agency in the Visegrad Group countries (MNFORCE, n.d.).Random selection, as a statistical method, was applied by MNFORCE in the selection of respondents.The research methodology was identical in both countries studied.The survey agency, using its interviewers, surveyed attitudes from respondents in the form of face-to-face structured interviews while driving in a taxi.
The results of the analysis of the size of the sample files found it necessary to obtain at least 124/187 completed questionnaires in the business environment of the SR/CR.The total number of respondents was at least 311.The ranges of respondents' sample files were verified with the following parameters: error rate -5%, confidence level -99% (Fan et al., 1999).
The questionnaire consisted of 21 questions, both closed and open.The questionnaire consisted of: 1) demographic characteristics of the respondents (DCHR: gender, age, attained education, nationality); 2) enterprise characteristics (CHE: form of business, length of operation, place of business); 3) financial and economic characteristics of the respondents (FECHR; mileage per week, number of driving days per month, type of income, average gross monthly income, average net monthly income, average driving time), and 4) a question asking about shared economy platforms used.
The second part of the questionnaire contained questions about the tendency to do business and take business risks.The questions in the second part of the questionnaire were generated randomly.It also included a control question to verify the consistency of the respondent's attitudes to the questions in the questionnaire.The total number of questionnaires collected was 375 (100%).This met the requirements for analyzing the size of respondents' sample files.Of the total number of questionnaires collected, 12 (3.3%)questionnaires were excluded from the empirical evaluation, and 363 (96.7%) were correctly completed questionnaires (N = 363).The most common reasons for excluding the questionnaire from the evaluation are incomplete questionnaires, inconsistent answers of the respondent to formulated questions, and meaninglessly filled-in questions (e.g., age -105 years, etc.).
Statements about the inclination to do business (EI) include: "For me, being an entrepreneur means more advantages than disadvantages." Respondents had to answer the claim with one of the following answers (according to a Likert scale): I completely disagree with the statement (1), ...., I fully agree with the statement (5).Also, respondents' answers to selected characteristics of respondents were transformed into numerical values (see Table 1).
To evaluate the formulated hypotheses, the SEM statistical method was applied.Applying the SEM method to empirical data is the best option because it verifies and quantifies the magnitude of the influence among the selected factors.The maximum assurance method was used to estimate parameters in SEM models (FM_S shared taxi providers; FM_T -traditional taxi service providers).
The significance of SEM models was verified using the fit test summary (FTS) adapted from Fan et al.The significance level (α) is 5%.Descriptive and factor analysis was performed in IBM SPSS Statistics 28.The visualization of the relationships between variables was done by IBM SPSS Amos 28 Graphics software.
The structure of the respondents is divided based on the main criterion of the study into shared taxi service providers (S; N = 294) and traditional taxi service providers (T; N = 69; see Table 1).

RESULTS
The results of descriptive characteristics and internal consistency of variables examined (S/T) are presented in Characteristics of the research sample of respondents (see Table 2): 1) traditional taxi providers have a lower attained education and, at the same time, operate longer in the business environment than shared taxi providers; 2) traditional taxi drivers' income is largely their primary income and their single income; 3) traditional taxi drivers tend to have a lower number of rides per day and drive up to 15 days per month, unlike shared taxi providers.
Table 3 contains the results of the reliability and validity of the variables examined.The results of factor loadings (see Table 3) confirmed the fact that the correlation between indicators and factors is at a good level (FLs are better than the minimum value of FL = 0.5) (Kaiser, 1974).The Cronbach's alpha and composite reliability values for each factor reach values better than the minimum value of 0.7 (Byrne, 2009).AVE values are also better than the minimum value of 0.  Figure 1 and Figure 2 present the final models (FMs) of relationships between manifest variables (e.g., DCHR1, …DCHR4) to latent variables (DCHR), as well as quantifies the relationships between latent variables (dependent variable: EI; independent variables: DCHR, CHE, FECHR) depending on the type of respondent (shared respondents: FM_S; traditional respondents: FM_T).
Table 4 contains a statistical verification of the causal relationships between the selected characteristics (respondent, enterprise, financial and economic) and their propensity to do business.
Table 5 contains an evaluation of FIT model characteristics.The results confirmed that both FIT models (S/T; see Figure 1) are acceptable and show an optimal solution between defaults and saturated models.Table 6 presents the results of the positive attitudes of respondents toward the propensity to do business based on the selected characteristics of the respondent.The results confirmed statistically significant causal relationships between independent variables (DCHR, CHE, FECHR) to the dependent variable (EI), regardless of whether they are a shared or a traditional taxi provider.All formulated hypotheses (H1_A, ..., H3_B) were accepted at the 5% level of significance.
The results (see

DISCUSSION
The results showed several significant findings.

CONCLUSION
The study aimed to quantify the impact of selected demographic, financial, and economic factors on the propensity to do business in the taxi sector of the shared economy.The study was conducted based on data on traditional and shared taxi drivers in the Czech Republic and Slovak Republic in April 2022.The MNFORCE survey agency provided the selection of respondents.The research methodology was identical in both countries studied.
It is concluded that shared and traditional taxi providers do not perceive the propensity to do business identically.Shared taxi providers (69.0%) are more inclined to do business than traditional ones (50.7%).A taxi provider's financial and economic characteristics (average gross/net income, number of rides per day, type of income) are the most significant characteristics that play a role in the tendency to do business with shared taxi drivers.For traditional taxi service providers, the most significant factors are the characteristics of the enterprise (country of business, length of operation, and legal form of business).
Conducting quantitative research also entails certain specifics or limitations.The research was conducted in only two Central European countries with interdependent business environments.In addition, the sample of respondents shows a greater number of shared taxi drivers than traditional taxi providers.An equally significant factor is that the research was conducted only with the subjective attitudes of taxi drivers (even incompetence -not understanding the basic economic concepts -e.g., gross and net income) when collecting data directly while driving.On the other hand, the research is unique in its scope, as well as in the depth of processing.The data collection was carried out during the Russia-Ukraine war (e.g., rising fuel prices), bringing greater pessimism and livelihood concerns among taxi drivers in the short term.

Table 1 .
Selected characteristics of shared and traditional taxi service providers Source: Own data collection.

Financial and economic characteristics of the respondent (FECHR) Number of driving days per month (FECHR1) Number of driven km per week (FECHR2)
(Kaiser, 1974vidual indicators (items) could be explained by background factors (KMO values are better than 0.7)(Kaiser, 1974).Bartlett's sphericity tests confirmed that data are suitable for PCA Note: () -Percentage.

Table 1 (
cont.).Selected characteristics of shared and traditional taxi service providers

Table 6 .
Representation of respondents with a positive attitude toward the propensity to do business Source: Own data collection.

Financial and economic characteristics of the respondent (FECHR) Number of driving days per month (FECHR1) Number of driven km per week (FECHR2) Respondent (S/T) S T Respondent (S/T) S T
Bogatyreva et al. (2021)compared workers' propensity to do business in the sharing economy to the general population using a sample of 1257 respondents from Russia.The results indicate that workers in the sharing economy have significantly higher entrepreneurial inclination than the general population.Regarding the predecessor of participation in the sharing economy and propensity to do business, similar effects were found related to age, entrepreneurial social capital, previous business exit, and intrapreneurial experiences, while perceived self-efficacy was only associated with engagement in digital platforms.Another important finding of this study is that experiences in the sharing economy demonstrated a significant positive impact on the propensity to do business and entrepreneurial intentions. (2)p://dx.doi.org/10.21511/im.19(2).2023.14