Introduction

Every sudden disruption interferes with the everyday life of society, especially when the implementation of restrictions on mobility, work, teaching, and relationships with other people accompanies it. As transport supports achieving the goals of travelers, restrictions not only limit mobility but may have long-term consequences (e.g., changing travel priorities and influencing public policies) (Bounie et al., 2020). Moreover, the future of sustainable mobility and the development of multimodality is also in question, e.g., when limiting the number of passengers in means of transport. Furthermore, besides the restrictions, because of the high number of infections, the smooth operation of public transport may be limited (e.g., caused by sick leaves) (De Matteis et al., 2022). Additionally, restrictions on long-distance travel (especially forbidding journeys to another country) influence commuting—especially in big international transportation hubs (Shortall et al., 2022). If so, then the transport restrictions influence the accessibility of different services.

On the other hand, changes in mobility can result from not only transport restrictions but also limiting the accessibility of services—closing restaurants, clubs, cinemas, and other leisure entities, schools, and workplaces (not necessarily because of quarantine, but also because of the swinging mode of work) (Ren, 2020). If so, then the restrictions regarding satisfying the needs of residents influence the lower demand for mobility. Additionally, the analysis should include personal beliefs since the fear of infection and its adverse effects on the respondent’s health (Albzeirat et al., 2020). Those issues were considered while analyzing the data collected and building the final analysis model.

The aim of the study was twofold. Firstly, we wanted to verify the differences between the car enthusiasm of respondents during the second and fourth waves of the pandemic. Secondly, we wanted to discover, what determines the car enthusiasm generally and if it differed in 2020 and 2021. This paper joins a growing number of research about mobility choices. However, it fills the research gap about the changes in this field for Poland and, more generally, Central European countries. It shows the reactions to mobility restrictions and their effects—attitudes towards using cars in Poland is one of the most public transport-focused societies. Moreover, it is one of the few studies comparing the car enthusiasm of the same society in two waves of the COVID-19 pandemic. There is usually a fragmentary approach used in the current research, studying only the results of changes, not the exact causes of those changes. Underestimation of the social characteristics and processes in this regard is unreasonable. Moreover, many research papers are based on non-randomized datasets of global IT providers, and only a few on random samples (Borkowski et al., 2020). This distorts the picture of reality and can lead to wrong conclusions. Only the randomization of the research sample authorizes the extrapolation of the results to the population, which was attempted in our study.

In fact, car enthusiasm is a widely known area of research. Many papers focused on the so-called “car culture” dominating many countries and replaced step-by-step by multimodality, shared mobility, and sustainable mobility (Hagman, 2010; Paterson, 2000; Szmelter-Jarosz and Suchanek, 2021). Furthermore, a few years ago, the “peak car” phenomenon was discussed, so if the use of the car as the primary transport mode reached its peak in world history or not, and if yes, then in what countries (Goodwin, 2012). Finally, we want to discuss if the pandemic disturbed or changed car replacement with more sustainable modes.

Firstly, the paper presents the most important insights about changes in mobility during the pandemic in different countries. In this section, the research questions are raised based on the literature review. The next part presents the chosen methodological approach, namely the information about surveys and the main econometric model to analyze the obtained data. The following section shows the results of the analysis and answers the research questions. The last part discusses the results, verifies them with existing literature, and concludes the paper.

Literature review

Car culture, car enthusiasm, and mobility

Mobility is not only moving people from point A to point B. It is a complex construct consisting of interacting features: priorities, opinions, habits, beliefs, personality, patterns obtained in the family and peer environment, notions of community, social identities, culture, and many others (Jensen, 2008). Therefore, mobility research has to cover psychology, sociology, information technologies, transportation, logistics, and human geography. Mobility choices can be based on using single or many modes (multimodality). In cities, the number of possible choices is higher due to the rich offer of public transport services and services of private providers (e.g., car-sharing, bike-sharing). However, for over a century, the car is a central point of interest in mobility research.

The car culture grew out of the fact that only a car satisfied the eternal need to move always when the traveler needed it, with the least effort and door-to-door. The car also began to fulfill other roles—to prove the owner’s wealth, lifestyle, and independence. For this reason, the car culture lasts and probably will last for many years (Bladh, 2019).

However, the car culture is more than only the massive use of cars in particular societies. W. Andrews proposes two approaches on how to define car culture (Andrews, 2018). The first follows an approach based on human geography to understand identity, community-based features, behaviors, and choices. The second one (more sociology-based) focuses more on the behaviors only—if car enthusiasts care about emissions, quality of life, and safety of others. This approach is focused more on positioning policy responses.

However, as mobility is not the same as traveling, car enthusiasm cannot be defined as driving. It is a term examined in emotional geography or geographies of enthusiasm, mentioned as an “intimate relationship between cars and people” (Miller, 2001) in highly localized cultures of car mobility (micro-scale) and in the broader context—larger car cultures covering whole car-centric societies (macro scale). Sometimes, car enthusiasm is defined as more developed, even “the way in which the humanized car feeds back to the automobilized human in a symbiotic relationship of sorts” (Andrews, 2018). However, there is a lack of a unified approach to categorizing the elements helping to measure this enthusiasm or its causes. Therefore, in this paper, we try to build the framework for this assessment and try to catch the features that might impact mobility choices in the situation of global disruption lasting longer than expected. Thus, it is an attempt to find if the car’s enthusiasm is resistant to this disruption or if this disruption can help to grow stronger.

The literature shows different levels of car enthusiasm in different parts of the society. For example, one of the research topics is examining the mobility choices of generations. Usually, the Y generation, representing people born between 1981 and 1996 is presented as less car-oriented, more ecology-focused, and willing to use sharing economy solutions (Hopkins, 2016). The opposite attitudes are assigned to the X generation (people born between 1963 and 1980). Therefore, in our study, we defined the first and second research questions:

RQ1: Was the car enthusiasm correlated with the age of respondents (the generation)?

RQ2: Was the car enthusiasm correlated with the ecological orientation of respondents?

Changes in mobility during the COVID-19 pandemic

The course of the pandemic was specific for world regions. Firstly, the time of the coronavirus expansion differed, as well as the intensity of adverse effects, number of infections, number of deaths, and the dynamics of the spread. Secondly, countries implemented different methods and tools to fight the pandemic. Some countries introduced quarantines for incoming tourists; others announced a national lockdown, closing almost every business activity for some time. One of the restrictions was limiting the number of passengers on public transport, also in Poland (Wielechowski et al., 2020).

The COVID-19 pandemic affected the lives of almost all countries worldwide, even if they did not identify any confirmed cases of infected residents or visitors. Three countries were perceived as most affected by the COVID-19 epidemic: China (the first country with many deceased infected patients), Italy (most affected in Europe), and the USA (with the highest dynamic of a number of infections) (Chen et al., 2020; Ren, 2020). As the situation in Europe in 2021 was still unstable, Italy seemed to be the most influenced by the pandemic (Gatto et al., 2020).

It is worth noticing that mobility was guilty of spreading the virus (Pan et al., 2020). Both short- and long-distance travel caused the spread has increase dynamically. Therefore, among others, all the planned massive gatherings, rallies, and concerts, were banned. The lockdown caused a sharp reduction in the number of trips and distance traveled, especially in urban areas. Since the COVID-19 epidemic is still ongoing, some initial research results on mobility changes are now available, although they are based mainly on Google data (Aktay et al., 2020; Luther, 2020; Yilmazkuday, 2020) not the primary data gathered by particular researchers.

The basic of the restrictions implemented by governments worldwide is a group of solutions described as social distancing. It resulted in many changes not only in the mobility area but also in short-term lifestyles. Since the epidemic’s beginning, many forecasts have appeared trying to explain the possible scenarios in the mobility area (Engle et al., 2020; Kaplan, 2020). Because of the restrictions, long-distance travel, especially international ones, was sharply limited or banned. So, the spread of the new coronavirus caused changes on an international scale. Firstly—travelers were controlled and the possibilities of traveling were limited. Passengers were also monitored after the journey. Several pieces of advice occurred to minimize the risk of getting infected (Biscayart et al., 2020) generally for urban areas and specifically for using public means of transport. After some time, the epidemic also affected short-distance travel, which was noticeable, especially in urban areas (Rubin et al., 2020). It is worth noting that the spread of disease is due to individual decisions, also on mobility issues but to some extent—on public policy.

There is a set of changes regarding mobility during the COVID-19 epidemic. As mobility usually depends on the primary purpose of the journey, the change in the purposes (leisure, health, work, education, shopping, etc.) decided about the changes in mobility, also in Poland (Borkowski et al., 2020). Firstly, the character of the jobs has changed—many workers moved their workstations to their houses, many of them lost their jobs, others had to take care of children, sometimes combining it with their usual job, so many obstacles and changes appeared in the life of both rural and urban residents (Yilmazkuday, 2020). A huge effect on mobility was the lockdown of schools and companies (Liu et al., 2020). More time was spent on leisure and recreation, especially using green spaces (for Oslo by 291%) (Venter et al., 2020) and active transport (cycling, walking). This was observable, especially in urban areas, which started the discussion about the true needs of urban residents and how to shape future cities to meet their requirements (Morita et al., 2020), including green spaces and recreation areas (Marcelo et al., 2022).

The research about mobility changes because of COVID-19 is in its initial stage while the epidemic still lasts. However, several studies only mention this topic, among others for Canada (Chan, 2020), China (Kraemer et al., 2020), Japan (Morita et al., 2020), Italy (Pepe et al., 2021), USA (de Paz et al., 2020), India (Gunthe and Patra, 2020), France (Bounie et al., 2020), Sweden (Dahlberg et al., 2020). The most comprehensive research about mobility changes in Poland was made by Borkowski et al. (Borkowski et al., 2020) and dealt with the effects observed during the first wave of the pandemic; in fact, the “fresh” effect of the lockdown but still a low number of confirmed infections within the country. Daily announcements about the high number of infections became “a new normal” after some time and did not reveal any shock among society but only when it increased sharply.

To sum up, the above-mentioned issues it is important to note that the interrelations of restrictions of access to mobility and access to daily activities influenced the self-assessed quality of life, which was confirmed, e.g., for Germany (Ravens-Sieberer et al., 2022), USA (Pan et al., 2020) and Israel (Lipskaya-Velikovsky, 2021). Therefore, we also wanted to check if this is true for Poland. We formulated the third research question as follows:

RQ3: Did the self-assessment of life quality influence on car enthusiasm?

COVID-pandemic-related restrictions in Poland

The beginnings of introducing pandemic restrictions in Poland were very intense. The initial approach in Poland involved conducting tests on suspected cases, tracing their contacts, and mandating quarantine for all individuals returning from overseas. Mass gatherings were canceled on March 10th, 2020, and cultural events were suspended on March 12th, 2020. Schools and universities were also shut down on the same day. New regulations were introduced on March 24th, limiting group sizes to a maximum of two individuals and prohibiting non-essential travel except for work or necessary everyday activities such as shopping, medical visits, or engaging in individual sports deemed necessary for health. Occupancy rates on public transport were reduced, while private vehicle use remained unrestricted. Non-essential services, including hairdressers and shopping malls (except for food retailers), parks, forests, and beaches were also closed. People were required to maintain a distance of at least two meters in public spaces. On April 20th, restrictions on mobility changed, allowing for, e.g., recreational travel. The number of infections decreased in June 2020. Then, in September–December 2020 the second wave of the pandemic, with limited mobility restrictions, took many victims, and the fourth was observed one year later (September–December 2021). In between, the third wave was less intense and lasted from February to May 2021 with the peak of infections in the end of April (Kłos-Adamkiewicz and Gutowski, 2022; Przybylowski et al., 2021). The course of the second and fourth waves of the pandemic in Poland were very similar, also in terms of the dynamics of demand for mobility services, including public transport, traffic intensity, and restrictions on the mobility of people.

Considering the issues mentioned above, we formulated the last research question:

RQ4: Did car enthusiasm rise between October 2020 and October 2021 in Poland between the second and fourth waves of the pandemic?

Research methods

Survey method

Data collection in this research was based on the survey among Polish people, preceded by a pilot survey (437 respondents, non-random sampling, May 2020). The questionnaire was amended according to the comments of respondents. Then, an external company was recruited to perform the survey (September 2020). The data was collected using the CAWI (computer-assisted web interview) approach in two periods. A total of 3700 complete and reliable observations were collected, 1700 for October 2020 and 2000 for October 2021. For every round of the survey, the respondents were randomly selected by an external company, which was obliged to stratify the population of Polish citizens according to gender, age, and place of living (ca. 60% of urban residents, ca. 40% of country residents including ca. 20% of suburban zones up to 20 kilometers from the city center). If so, the stratified random sampling method was applied. In the case of age, only four generations were taken into consideration (Baby Boomers—people born between 1945–62, generation X—people born between 1963 and 1980, generation Y—people born between 1981 and 1999; generation Z—people born after 2000; only persons aged 16 or more were respondents). The sampling process provided an error parameter of 3% with at least a 95% confidence interval.

The first step of the research involved calculating the intenseness of the millennial values and beliefs in a given person by summarizing the number of positive responses on a scale suggested by Sapsford (Sapsford, 2011). The main focus was to grasp the inter-generational or inter-gender differences in car enthusiasm and catch if there were differences between the answers given during the second and fourth waves of the pandemic in Poland. The specifics of the millennial generation (generation Y) were mentioned many times in the literature and therefore, was one of the main research areas in this study because today, this generation sets trends and has the most significant purchasing power (Hopkins, 2017; Simons et al., 2014; Szmelter-Jarosz and Suchanek, 2021).

The main body of the survey consisted of three parts: the characteristics of respondents’ opinions, beliefs, and attitudes, the mobility choices, and individual characteristics about life choices (but anonymized). The modes of transport proposed in the questions were specified based on two chosen approaches (Buehler, 2011; Curtis et al., 2019) and referred only to everyday mobility. The questionnaire had 23 closed questions, including 5 scale questions about the preferences and choices of transport modes.

Building the structural equation modeling (SEM) model

The scale verification was achieved by performing the reliability analysis and calculating the Cronbach’s alpha parameter, which had a value of 0.71 thus making it reliable according to Dimitrov (Dimitrov, 2003). All of the presented statements were described in the literature (Hwang and Griffiths, 2017; Lavieri et al., 2017; McDonald, 2015; Newbold and Scott, 2017; Villwock-Witte and Clouser, 2016) as characteristic of generations Y and Z, significantly different from those presented for older generations: X and Baby Boomers (Berliner et al., 2018; Parment, 2013; Simons et al., 2014; Siren and Haustein, 2013; Weber et al., 2018; Young and Lachapelle, 2017). Because the alpha value was acceptable (see Table 1), as well as global Cronbach’s alpha (0.71), all of the proposed statements were included in further data processing, namely the sum of positive answers to the questions 1.1–1.35 was thus aggregated into one variable—StatMil.

Table 1 Results for reliability analysis—defining Millennials’ attitudes.
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Afterward, reliability analysis was also performed on variables KES1-KES6, representing the responses (on a five-point Likert scale) to the statements constituting Kessler’s 6-item NSPD—non-specific psychological distress scale (Kessler et al., 2002). The reliability analysis showed that this approach is highly acceptable. The Cronbach’s alpha for such a set of variables was 0.90 (see Table 2), warranting the proper use of the scale in further research. The statements were treated together as constitutive of latent variables representing the distress of the respondent at the moment of research.

Table 2 Reliability analysis—NSPD scale.
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Other independent variables in the model include:

  • QoL (self-declared perceived quality of life of the respondent on a scale of 1–10),

  • DOM_Car (a binary variable representing the dominant mode of transport used by the respondent: 1—car, 2—else),

  • Generation (a nominal variable representing the generation of the respondent: 1—generation Z (born in 2000 or later), 2—generation Y (born between 1981 and 1999), 3—generation X (born between 1963 and 1980), 4—Baby Boomers (born between 1945 and 1962),

  • Gender (a nominal variable representing the declared gender of the respondent: 1—female, 2—male, 3—other),

  • Urban_01 (a nominal variable representing the area of residence: 1—urban, 2—suburban, 3—rural),

  • Year (a binary variable representing the point of time of the response: 0–2020, 1–2021).

The independent variable was constructed based on the responses to questions 14.01–14.23, representing (on a five-point Likert scale) the respondent’s attitude to mobility-related statements. An exploratory factor analysis (EFA) was then conducted to identify the latent variables that had been identified for this study to reduce the number of dimensions and diminish the effect of collinearity. In contrast to an observable variable, a latent variable is a variable that is not directly observed but inferred from other variables. According to Yong and Pearce (Yong and Pearce, 2013) the “[…] purpose of factor analysis is to summarize data so that relationships and patterns can be easily interpreted and understood.”. Thus, it is often used to regroup variables and retrieve a limited set of factors based on shared variance.

Originally, EFA was developed to reduce dimensions to prepare methods like regression and minimize the risk of collinearity. However, using EFA to analyze a dataset’s internal variability is common before applying other methods, such as regression or SEM.

Typically, the Kaiser–Meyer–Olkin (KMO) criterion is verified as a first step of the EFA. Furthermore, Bartlett’s test of sphericity is conducted to verify the possibility of applying EFA to the given dataset. The extracted factors can be analyzed if the test is significant and the eigenvalues that are the basis for the criterion are higher than 1. Classically, the principal component analysis (PCA) method was applied to extract the factors (Johnson and Wichern, 2007). In the next step, the extracted factors were analyzed regarding their eigenvalues. Also, the total variance explained as well as the internal structure based on the factor loadings for each item within the factors were looked at. Within PCA, rotation methods are often applied to ease interpretability; thus, each factor is associated with a reduced block of observed variables (Acal et al., 2020). The type of rotation applied is based on whether the factors are believed to be correlated (oblique) or uncorrelated (orthogonal). According to the literature, there are four main orthogonal methods: equamax, orthomax, quartimax, and varimax, where varimax is the most common one. Therefore, we initially assume that the factors are uncorrelated (the factor analysis results imply no correlation) and apply the most common rotation method, the varimax rotation.

There is an ongoing discussion within the literature about the minimal acceptable value for the factor loadings for the item that is considered significant (McNeish et al., 2018). Regarding this thesis, a factor loading for an item higher than 0.5 is a relatively good representation of a given item within the factor (see Table 3).

Table 3 The first approach to EFA in regards to the mobility-related statements.
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This first approach suggests a very dispersed set of responses regarding the mobility statements. Furthermore, the results suggested that the models of higher-order values were not well-represented by the data with the original item structure. As a consequence, two methods were used to improve the model fit until the CFI for the subsequent SEM reached a value of at least 0.90:

1) items with non-significant loadings were excluded from the factor analysis (variables 14.04, 14.14, 14.16, 14.19 were excluded at this point),

2) the item with the lowest factor load was deleted from the first four domains (variables 14.05, 14.23, 14.18, 14.21 were removed). Given that the last factor was fully represented by one-factor loading, its structure was left intact.

This led to a second recalculation of the factor analysis (see Table 4), which resulted in the extraction of 3 significant latent variables connected with mobility:

  • factor 1 representing the intensity of car fondness (the main dependent variable in the model)

  • factor 2 representing the attitude toward shared mobility

  • factor 3 representing the intensity of sustainable and “green” beliefs

Table 4 The second approach to EFA regarding the mobility-related statements.
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In order to establish a full model of relations within the problem, a SEM has been created. Structural equation modeling is a multivariate data analysis technique for examining intricate interactions between constructs and indicators. Researchers typically use two techniques: partial least squares SEM (PLS-SEM) and covariance-based SEM (CB-SEM) to estimate structural equation models. PLS represents a causal-predictive approach to SEM that stresses prediction in estimating models, whose structures are intended to provide causal explanations, in contrast to CB-SEM, which is largely employed to confirm hypotheses. PLS-SEM is helpful for validating measurement models as well. Due to the specifics of the relations in question, PLS-SEM has been used as a model of choice. The final structural equation model was estimated in SmartPLS 3 software. The list of variables included in the model is presented in Appendix A. The results of the application of this methodology are presented in the next section.

Results

As stated before, SEM was the most appropriate econometric model for this kind of analysis, considering many latent and independent variables. The KES1-KES6 variables were declared to be loading the latent variable of non-specific psychological distress (NSPD). Car enthusiasm, ecology, and shared mobility attitudes were declared latent variables loaded by the appropriate observable variables resulting from the EFA. The StatMil was partially mediated by the generation to which the person belongs. Such a model was bootstrapped with 500 subsamples with bias-corrected and accelerated (BCa) bootstrap as the confidence interval method (Awang et al., 2015). The resulting model is presented in Fig. 1.

Fig. 1: Structural equation model for car enthusiasm.
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The figure presents the bootstrapped SEM model. All of the connections are statistically significant (p-value < 0.05), thus proving the significance of individual parameters. Furthermore, the total significance of the model for the model indicates generally acceptable goodness of fit.

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The parameters of the estimated model are presented in the table below. All of the connections are statistically significant (p-value < 0.05), thus proving the significance of individual parameters (see Table 5). Furthermore, the total significance of the model (see Table 6) for the model indicate generally acceptable goodness of fit. The SRMR for the model fits the widely acceptable criterion of being lower than 0.1 (Shi et al., 2020). The Normed Fit Index (NFI), while on the lower side, can be assumed to be correct because bootstrapping usually underestimates the value of this parameter (Yadama and Pandey, 1995).

Table 5 Estimates for the SEM.
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Table 6 The goodness of fit for the estimated model.
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The model results indicate that the Year variable is significant concerning car enthusiasm (increasing the enthusiasm along with the progress of the pandemic). It might result from restrictions and the growing number of infected people in Poland. Social distancing and restrictions regarding the number of people in public transport might influence the propensity to use modes other than car mobility. However, the other variables were significant for car enthusiasm. Being a millennial (belonging to generation Y) decreases car enthusiasm which can be related to inter-generational differences in Polish society. What is more, regarding the perception of own life quality, the increase of the distress scale leads to a decrease in the quality of life as could be assumed, but interestingly enough, increases the car enthusiasm. If so, an increase in distress is correlated with a higher proneness to use cars. Again, this might be related to risk aversion according to the effects of the pandemic.

At the same time, what is considered surprising, also the year variable was significant to the sustainability of the respondent. There was a significant decrease in the importance of this attitude in the eyes of the respondents along with the progress of the pandemic but also, interestingly enough, with the quality of life. However, this relation seems to be counterintuitively increasing its positivity—the self-assessed quality of life increases in the year 2021 on average compared to 2020. This might be characteristic of Poland since, during the pandemic, the death ratio was one of the highest in Europe, and surviving the first two waves of the pandemic helped to appreciate own life and relationships with others, not only financial situation.

Interestingly, the proposed model shows that car enthusiasm and ecology orientation are positively correlated. This stands in opposition to the decrease in the sustainability of the respondent. However, due to some inconsistencies in this matter, it is worth to be discussed.

Discussion and conclusions

The results showed interesting relations between the variables. Firstly, the development of the pandemic changed the perception of the car and life quality. Car enthusiasm grew during the pandemic. It might result from mobility restrictions and the growing number of infected people in Poland—it was safer to use a car than any public means of transport (Abdullah et al., 2020). The fear of society being infected resulted in minimizing chances to meet other people. One of the possible solutions, if mobility could not be reduced because of commuting to work or school (obligatory travel) was using other means of transport than public ones. The car was an obvious and safe alternative, especially in winter when using scooters and bicycles is difficult. Additionally, social distancing and restrictions regarding the number of people in public transport might influence the propensity to use modes other than car mobility.

Being a millennial (belonging to generation Y) also decreases car enthusiasm. It confirms the other research results for many countries worldwide (Hopkins, 2017; Lavieri et al., 2017). This may be explained by the well-known assumption of multimodality of generation Y (Hopkins, 2017; Simons et al., 2014), using shared mobility services (Bieliński and Ważna, 2020; Menon et al., 2019; Suchanek and Szmelter-Jarosz, 2019) and being “green” consumers (Bhavana and Thiruchanuru, 2018; Moroz and Polkowski, 2016). However, it denies the idea of changing the mobility choices over the life course (Döring et al., 2014; Newbold and Scott, 2017), stating that people having young children (and now they mostly represent generation Y) are more willing to use the car because of its convenience and are taking the mobility habits from the X generation, so their parents (Döring et al., 2014). Furthermore, still in Poland, having a car is perceived in society as a matter of financial status and showing the ability to afford it (Piotrowska, 2017). This could contribute to the perception of Poles as a nation that fits into the concept of car culture, but our results seem to contradict this. On the other hand, Baby Boomers and generation X were always car-oriented generations, so it is obvious that they can be more willing to use a car than the younger people (Newbold and Scott, 2017; Parment, 2013b; Siren and Haustein, 2013). However, what is surprising, the other findings present the Polish residents as a whole group as more car-oriented than it seems.

The increase of the distress scale leads to a decrease in the quality of life as could be assumed but increases the car enthusiasm. Therefore, it concludes that increased distress correlates with a higher proneness to use cars. This partially confirms the findings of Ding et al. (Ding et al., 2014) that driving might be associated with higher distress levels and also that the choice of the mode of transport is significantly associated with mental well-being.

Interestingly enough, contrary to positive belief, car enthusiasm and ecology orientation are positively correlated, indicating a hint of “environmental hypocrisy” of people declaring ecology-oriented declarations without any behavior change, as described by Martinsson and Lundqvist (Martinsson and Lundqvist, 2010). However, being a car enthusiast might correlate with ecological attitudes if the driver wants to use a zero-emission or low-emission vehicle, optionally a microcar (Mirhedayatian and Yan, 2018; Mu and Yamamoto, 2019). It is essential to accept that travel comfort will be an important value for the traveler who will not give up on it in many societies. Therefore, public policies should find a compromise between public and private interests and take seriously this correlation between using a car and being positive about ecological solutions. Specifically, policy should be focused on reducing the distress connected with the use of public transport, thus improving the relative comfort and consequently the subjective perception of the quality of life of the commuters. At the same time, the reduction of car use comfort via parking policies, traffic buffering, and congestion charges is highly advised.

Despite the careful planning of this research, some limitations can be highlighted. Firstly, the survey was not designed as a panel survey, but two separate surveys were carried out on the other research samples, even with the same stratification criteria. Therefore, the conclusions cannot be made about changing the habits or mobility choices of the same respondents, but the parts of the Polish society. Nevertheless, research samples were randomly chosen with the stratification, so the reliability of the results is high and meets the assumed parameters.

The main contribution of this study is the analysis of social causes that indicate the changes in mobility. Filling the research gap for one country can be a reference point for future research about mobility choices and the results of global disruptions with local effects on mobility of people living in urban zones and countryside, people of different ages, on different life stages. We hope we have started the scientific discussion on that matter and many interesting research insights will appear in academic literature in the near future.