External Environmental Analysis for Sustainable Bike-Sharing System Development

Abstract

The paper introduces a discussion regarding the development of a public bike-sharing system, considering random factors, based on selected external environmental analysis methods. The global energy crisis is forcing scientists to continuously improve energy-efficient sustainable methods and scientific solutions. It is particularly important in transportation since transport activities and the constant increase in the number of vehicles have a large share in global energy consumption. The following study investigates the social, technological, economic, environmental, and political aspects of bike-sharing systems in cities. The research purpose of the article is to select the most important macro-environmental factors and their mutual interaction influencing the sustainable development of bike-sharing systems based on the Polish cities case study. The evaluation was carried out through expert methods with STEEP environmental analysis, evaluation of factors with the weighted score, and structural analysis method with MICMAC computer application. The classification of key factors influencing the development of a bike-sharing system has divided them into five groups. It can support public transport service providers and organizers. This can optimize the planning process with decision-making based on future environmental trends.

1. Introduction

The bike-sharing system (BSS) is becoming an increasingly popular item of the transport system in urban spaces in many cities around the world and in Poland. It is based on the idea of sustainable mobility, identified with emission-free mobility or zero-emission mobility. The concept of BSSs is much broader, which is to ensure the balancing of mobility in cities not only considering global environmental challenges consisting in creating energy-saving solutions, but also technological, social, and economic megatrends. It is assumed that the functioning of such systems will be based on the values of the sharing economy and circular economy. The BSS can generally be divided into two groups.: the first is a system where bikes can be rented from one docking station located in one place and returned to another docking station located in the same or another place. Such station-based bike-sharing (SBBS) systems are functioning in many cities in America, Europe, and Asia, e.g., in Paris, Warsaw, Wuhan, Hong Kong, Canton, Montreal, Washington, Boston, and Minneapolis; the second group consists of systems that enable the rental of bikes located at different points in a given area. There are no docking stations in these systems. Users can locate and unlock bikes from any place using a smartphone app. The second type of free-floating bike-sharing (FFBS) system is often found in Asian, American, and Australian cities, e.g., in Guangzhou, Shenzhen, Hangzhou, Xi’an, Singapore, Seattle, Denver, and Sydney. In some cities, the systems enable both the rental of bikes from SBBS systems as well as from FFBS systems, e.g., in Berlin, Beijing, and Shanghai.

The main reasons for the implementation of the BSSs in cities are related to the need to increase the efficiency of urban transport systems on the last mile commuting, reduce the problems associated with the negative impact of traditional means of transport on the environment, and improving the health of city residents. The public bike also successfully becomes a part of multimodal transport, connecting with other means of public transport. BSSs have been operating in Poland since 2008, expanding its offer to new cities each year. Currently, BSSs are already operating in almost 100 of the largest cities and agglomerations in Poland. The oldest BSS was the Krakow Wavelo system (2008–2020), while the largest is the Veturilo system operating in Warsaw, consisting of over 300 stations and over 5000 bikes. BSSs operators in Poland are mainly: Nextbike, BikeU, Roovee, Acro Bike, Comdrev, GeoVelo, Romet Rental Systems, HomePort, Blinkee, and Mevo.

BSS, as well as a private bike (PB), has many advantages, which mainly include [1]:

• traffic characteristics, e.g., speed of movement in areas with high traffic volume of motor vehicles. The bicycle is the fastest form of traveling with travel distances from 5 to 6 km. The bicycle also offers the advantages of an individual means of transport, such as privacy, and the possibility of door-to-door travel;
• economics, social costs of traveling (including costs of road construction and maintenance, vehicle construction, and running costs) by bicycle are many times lower than by private car and public transport;
• energetic efficiency and environmental protection;
• health benefits;
• urban space management, e.g., a bicycle occupies an area twelve times smaller than a motor vehicle in the city space.

The bicycle can play an important role in reducing the risk caused by road transport to a minimum. Unfortunately, this vehicle also has disadvantages, the most important of which are [1]: sensitivity to weather conditions, exposure to road accidents, and dependence on the condition of technical infrastructure. The full use of the bicycle’s capabilities depends on the density of the road network and bicycle lanes, the condition of the road surface, the possibility of parking a bicycle, exposure to attacks, especially in the absence of lighting, road routes through undeveloped areas, etc.

Due to the advantages and limitations associated with BSSs in cities, as well as complex and volatile economies, it becomes important to understand not only the structure of the system but also its environment, i.e., factors positively or negatively influencing on this system. The external environment covers the surrounding elements, processes, and events outside of the system, which has a significant impact and influence on it. The environment of BSSs can be divided into macro-environment and micro-environment, general and targeted, further and closer environments. The research presented in this paper deals with the analysis of the external macro-environment of BSSs, as shown in Figure 1.

The research purpose of this article is to select the most important macro-environmental factors and their mutual interaction influencing on the sustainable development of BSSs based on the example of Polish cities.

The macro-environmental analysis is usually used for strategic management in decision making and can be used in planning infrastructural development related to shared mobility. There are many methods used to analyze macroeconomic groups of factors referring to the most important aspects of sustainable development and being the acronym of these groups [2]:

• PEST (STEP)—political, economic, social, and technological;
• STEEP—social, technological, economic, environmental, and political;
• STEER—social, technological, economic, environmental, and regulatory;
• PESTEL (PESTLE)—political, economic, social, technological, environmental, and legal;
• STEEPLE—social, technological, economic, environmental, political, legal, and ethical;
• STEEPLED—social, technological, economic, environmental, political, legal, ethical, and demographic.

The main research goal is to select the most important macro-environmental factors influencing the sustainable development of BSSs in social, technological, economic, environmental, and political groups (STEEP). Basing on the selected factors, the mutual interaction is performed with the cross-impact analysis.

The scientific literature review of the manuscript outlines the achievements of scientists in five major research areas. In addition, it presents, briefly, a summary of the scientific literature dedicated to the development of BSSs in STEEP macro-environmental groups. The materials and methods present three main stages covering theoretical and practical analysis. The environmental research of BSSs in Poland is divided into two parts: selection of key factors influencing on the bike-sharing systems’ development and their structural analysis, which allows for classifying the variables into five groups. Their interpretation is presented in the discussion part. The paper ends with the conclusions resulting from the theoretical and research parts.

2. Scientific Literature Review

Considering the research work in the field of BSSs development, several main directions of research can be distinguished. There are:

• BSSs planning and design issues, especially station location, infrastructure, and equipment issues;
• forecasting the demand for bicycles and relocation of bicycles;
• motivation to use the BSS;
• business model and sharing economy of BSSs;
• BSSs in a post-COVID pandemic world.

According to the assumptions of the STEEP analysis, the starting point for the analysis of the macro-environment of the BSS system is a summary of the main external factors of the analyzed system in terms of social, technological, economic, environmental, and political factors. Hence, a literature review was performed classifying research papers into these four subject areas (

Table 1. The summary of the most important directions of research work dedicated to the development of BSSs.

Research Group	Year	Key Research Works	Research Location	Data	Research Description	Key Findings
Social	2018	L. Ma et al. [6]	China	Survey data	The social influence and social interactions on the adoption of new technologies, such as BSSs.	Hedonic value has the greatest impact on users’ well-being, followed by social and utilitarian values. Moreover, the ease of use and usefulness of the BSS have positive effects on users’ trust attitudes.
	2019	F. Manca et al. [7]	Greece	Survey data	Social interactions connected with road users’ attitudes towards BSSs.	The lack of cycling infrastructure and road safety concerns were identified as possible usage barriers.
	2014	E. Fishman et al. [8]	Australia	Survey data	Motivators and barriers to BSS usage.	The most important barriers to using BSSs result from the fact that traveling by motorized means of transport is more convenient and the docking stations are far from home, work, and other places.
	2021	J.F. Teixeira et al. [9]	Portugal	Survey data	Insights on motivations for using BSSs during the COVID-19 pandemic.	The motivations related to using a BSS to avoid public transport and maintain social distancing while traveling are as important as the motivations related to the well-being and personal interests of the travelers.
	2019	X. Li et al. [10]	China	Survey data	Research on factors influencing use of PB, SBBS, or FFBS.	FFBS and PB are more attractive for long-distance travel compared to SBBS. PB is rarely used for suburban transfers, while FFBSS are most in demand in a combination of other means of travel. High maintenance costs and the problem of theft are the main obstacles for BF. In addition, the non-student, high-income, and older groups tend to prefer BSSs, while the student, low-income, and young groups tend to prefer FFBS.
	2021	W.L. Shang et al. [11]	Bejing, China	Trip data of three main FFBS operators	The impact of the COVID-19 pandemic on the degree of use of BSSs.	A method for calculating travel distances and trajectories has been proposed to estimate the environmental benefits of BSSs. The results show that the pandemic significantly affected user behavior, e.g., the average travel time of BSSs was extended.
Technological	2021	K. Mouratidis et al. [12]	-	Research papers of other authors	Analysis of teleactivity, sharing economy, and emerging transportation technologies impact on the built environment and travel behavior.	Teleactivities may substitute some trips but generate others.
	2020	L. Caggiani, et al. [13]	Italy	-	Bike-sharing docks or stations location	Proposed area service model to complement the coverage of the public transport network. The model covers the issues of accessibility to stations, range, location, considering the aspects of equal access to stations for different user groups.
	2021	Ch. Fu et al. [14]	China	Open data source	New integrated station location and rebalancing vehicle service design model	The model aims to maximize daily revenue for station location and bike acquisition.
	2021	F. Kon et al. [15]	USA	Open data source	A novel analytical method to analyze BSS mobility, abstracting relevant mobility flows across urban areas.	This method presents an extensive set of analytical tools to support public authorities in making planning and policy decisions.
	2021	H.I. Ashqar et al. [16]	San Francisco, USA	Two bike trip dataset	Modeling the number of available bikes at the station level.	Demographic information and other environmental variables were significant factors to model bikes in BSSs.
	2021	E.A.A. Alaoui, and S.C.K. Tekouabou, [17]	London, United Kingdom	Data from BSSs	BSS management using machine learning and IoT.	Tool proposal for predicting the number of bikes shared per month, day, or hour by taking several dynamic parameters.
Economic	2018	L. Li et al. [18]	Korea	Data from BSSs, reports, newspaper, and social media	The analysis of the overseas expansion of Mobike, Korea, that has partnered with a local government.	Results on the actual obstacles and market strategies for the development of Mobike, Korea.
	2021	L. Lou et al. [19]	China	User behavior data	Analysis of the influences of user-user, user-provider, and user-service interaction-related factors on user participation in the context of BSS services.	Information about implications to both policymakers, and managers of BSS services.
	2021	X. Tian et al. [20]	China	Data from firms	Analysis of the lack of profitability of shared-bike enterprises.	Suggestions for BSS risk management and profitability.
	2021	S. Si et al. [21]	China	Bike-sharing Industry Report	Exploring how innovation-based business project creates, delivers, and captures value in a sharing economy.	The proposition of an innovation-based business of BSS.
	2020	J. Chu et al. [22]	China	Dataset of resale apartments	Research on the prices of apartments located at different distances with FFBS systems.	The presence of FFBS reduces the housing price premium by 29%/km away from a subway station.
Environmental	2022	Y. Wang and S. Sun [23]	China	Emissions data	Estimation of the impact of large-scale FFBS on greenhouse gas emissions based on real-world transportation big data.	Research results suggest that effective and rational market surveillance is essential to obtain the desired environmental benefits of FFBS.
	2020	V.E. Sathishkumar and Y. Cho [24]	South Korea	Open data source	A rule-based regression predictive model for BSS demand prediction.	In the prediction of hourly rental bike demand, the hour of the day, and temperature are the most influential variables.
	2021	A. Li et al. [25]	Shanghai, China	FFBS transaction data	Assessing environmental influence of FFBS.	It was found that the use of FFBS contributes to a significant reduction of the annual greenhouse gas emissions
	2021	S. Sun and M. Ertz [26]		Resource utilization efficiency data	Investigation how the transition to the fourth generation of BSS, known as FFBS, presents an environmental and technological leap.	FFBS, in comparison with SBBS, is characterized by greater protection of natural resources, i.e., reduce steel and aluminum consumption, rubber and plastic consumption for each bicycle trip in the city.
	2021	G. Mao et al. [27]	Tianjin, China	Resources and emissions data	A Life Cycle Assessment of BSSs was presented to estimate the negative environmental impacts of the stages of the whole life cycle.	Among all stages consisting of the production stage, the use stage, daily management, and transportation stage, and waste treatment and recycling stage, the production stage contributes to the greatest negative environmental impacts.
Political	2020	H. Chen et al. [28]	China	Survey data and observations	The analysis of the relationship between different stakeholders and their influence factors from the perspective of consumers.	The consumers are willing to participate in BSS co-management, while the researched influence factors showed different impacts on BSSs.
	2019	A. Nikitas [29]	Sweden, Greece	Two survey-based studies	Searching for factors influencing the success of BSSs.	A set of guidelines for the introduction and launch of BSSs in the city.
	2019	L. Peters and D. Mac Kenzie [30]	Seattle, USA	Survey data, reports, BSS data	Analysis of the factors determining the success of the FFBS in comparison to the failure of SBBS.	Factors contributing to the failure of BSSs include mainly insufficient station density, inadequate system scale, the pricing structure, geographic coverage area, and ease of use.
	2020	B. Laa and G. Emberger [31]	Vienna, Austria	Expert interviews and literature review	Analysis of the situation of FFBS with a focus on regulation. The situation is compared to selected cities around the world.	A legal framework is needed to cope with new forms of mobility, such as FFBS.
	2020	L. Bocker et al. [32]	Oslo, Norway	Trip records of the BSS	The policy of a less car-dependent and more sustainable, healthy, and socially inclusive urban transport future.	PB is more often used on routes to and from the last railway or metro stations. Furthermore, important are such accompanying factors as time of the day, urban form, route distance, bike dock, as well as capacity. Moreover, a BSS is less accessible to, suited to, and used by older age and women groups.

). Therefore, many research works have comprehensively described the factors influencing bike-sharing demand, analyzing many factors at the same time, e.g., the built environment and land use, weather conditions, public transportation, temporal factors, sociodemographic attributes, and safety issues [3] along with proposals for balancing the number of bicycles between stations [4,5].

In the group of research works dedicated to social factors related to BSSs, one can distinguish research on the quantitative assessment of the risk to health and the benefits of replacing travel by car with BSSs. In the research work [33], an analysis of the twelve major BSSs in Europe has concluded that for people, the advantages of cycling are more important than the risks they take. Promoting a change of mode of transport when traveling on a public bike can significantly increase the health benefits. Therefore, BSSs can be used as a health promotion and prevention tool. Some research works emphasize that BSSs provide a healthy lifestyle and a sustainable environment to the world as well as encourage pro-environmental behavior of people [34]. Sometimes city residents use BSSs and e-bikes as a means of transport for trips outside the urban area [35,36,37]. One of the serious barriers to the use of BSSs is people habits, which are the fundamental basis of many daily activities. Habits can be a big obstacle in changing travel behavior in favor of cycling, or in the use of BSSs in general [38]. Further research in this area concerned the influence of various important socio-demographic factors determining the use of public bicycles. The results indicated that important factors when choosing this form of travel were such features as population density, median household income, age, gender, and station availability [39]. In the research work [40] simplicity of use of the system was observed and cycling pleasure was identified as a key element influencing the intention to use BSSs in the future. These factors have been identified as playing an important role in increasing the noticed value of the BSS as well as confidence in the service provider.

In the group of research works dedicated to the use of new technologies in BBSs, the work focuses mainly on the bike-sharing services without docks, which in recent years have revolutionized the bike-sharing markets both in Asia and in many European cities [41]. There are no docking stations in these systems; the subscription and payment for the service are made using the smartphone application. Bicycles are located based on GPS and the subscriber’s identity. Bicycles can be parked in all legal places allowing the parking of bicycles. Currently, the expansion of FFBS systems in many cities around the world exceeds previous expectations [42]. Understanding the impact on the transportation system of these new FFBS systems is critical to BSS operation, transport, and urban research. Research in this area shows that the high elasticity and productivity of FFBS often makes the integration of the system with public transport even tighter than conventional BSSs, providing an effective last and first mile ride option [43]. One of the most serious problems of FFBS is the improper behavior of bicycle users when parking, which includes leaving bikes in places not intended for this [44] and a significant degree of vandalism [45].

Equipping the stations with e-bikes is a bit of a challenge compared to traditional BBSs. E-bikes are popular among BBS users due to the lack of physical effort while driving, especially in mountainous areas, but they are, nevertheless, sensitive to travel speed, journey length, and duration of the activity, as these parameters determine the endurance of the batteries [46]. In turn, the need to charge the batteries requires access to a reliable source of energy, which to some extent complicates the choice of the location of BSS stations [47]. Sometimes photovoltaic energy generated on the roof of the parking lot of smart e-bike charging stations is used as an auxiliary for this purpose [48]. Some scientific works prove that e-bike sharing becomes a competitive solution for car-sharing, bike-sharing, mopeds, and taxis as well as selected public transport services [49].

For several years, the bikes have contained GPS equipment, which makes this system very user-friendly [50]. Finding the nearest bike and renting is possible through the smartphone application, as well as settlement in case of prior registration. Moreover, the analysis of the spatial location of the bikes is much easier because the dataset contains GPS coordinates of the start and end position, which can be easily read by any GIS system. Hence, in recent years, the analysis of the space-time patterns of bicycle use in the studied period is often carried out, thanks to which it is possible to understand the spatio-temporal patterns of mobility [51]. These data also allow for the qualification of the spatial integration of BBS stations and much other detailed analysis [52].

BSSs are fast evolving as a key form of the sharing economy. In the group of research works dedicated to economic factors affecting the BBS usage, one can mainly mention works aimed at identifying factors regulating the demand for BSSs [53]. Moreover, several research works indicate that the use of bike-sharing is precisely related to income and age. For instance, the elderly and the poor people are less likely to benefit from BBSs [54]. In turn, in work [55] it was indicated that the main users of BSSs are well-educated, young, and rich people. In the research work [56] the life cycle of BSSs was analyzed. The analysis covered infrastructural, social, and economic factors. The results point out that the system capacity, coverage area, and type of payment will have an impact on the operation of BSSs in the future. In addition, purchasing power, parity, and financial support are distinguishing items that seem to affect the probability of BSS prosperity. Research has also shown that the way of payment influences the survival rate of the BSS, but only after the system has stabilized [57].

The popularity of FFBS, manifested by a large number of system users and numerous daily rentals, makes pricing strategies in the field of bike-sharing essential in terms of income management for BSSs companies as the price is one of the most efficient items that can be used to manage demand [58]. The analysis [59] covered the optimal monthly strategy pricing of the FFBS platform. The results allowed for the conclusion that the increased rate of the platform’s revenue with the monthly price policy is connected with the cost of purchasing bicycles. The lower the purchase cost, the higher the platform’s revenue growth rate. A significant part of the work was also devoted to the characteristics of public bike trips at the level of zonal origin-destination (O-D) concerning the pricing scheme [60]. The conclusions of this work indicate that the change in the pricing pattern has an impact on a short-length trip as well as O-D travel if other transit services are given.

About 20% of greenhouse gas emissions in the US and 40% in Europe are due to the use of road vehicles powered by an internal combustion engine [3]. As the use of fossil fuels is the principal reason for greenhouse gas emissions, the use of motor vehicles for road transport continues to be one of the most polluting human activities in the world [61]. Attempts are made to reduce the consumption of natural sources and air pollution by striving for sustainable use of individual means of transport, an example of which, in urban transport systems, are also BSSs. A lot of scientific works have already been published in the group of works devoted to environmental factors of the BSSs functioning. This work mainly focuses on quantifying the environmental benefits of bike rental, energy consumption, and emissions [62]. For example, in the research [63], a macroscopic model of bicycle patterns was developed and its use in planning new bicycle routes was proposed. The model considers the influence of longitudinal slope connections and the surface type of bicycle routes on demand, and distribution of bicycle traffic volume. The results of this work permit for assessing the influence of the planned bicycle routes on reducing the traffic volume of motor vehicles, which is of key importance for reducing energy utilization and the unfavorable impact on the environment. All studies in this area clearly emphasize the enormous potential of BSSs to whittle energy use and emissions in the situation of a large share of trips made by bicycle. In turn, unfavorable weather conditions are indicated as a serious barrier to cycling [64].

In the group of research works dedicated to political factors shaping the future of BSSs, the works mainly concern the implementation of the postulates of sustainable transport development, in which the BSS can contribute to reducing the use of cars, especially in the case of short-distance journeys [65]. The effectiveness of the implementation of the policy of sustainable transport development is usually analyzed by examining changes in the modal split [66,67]. Some research work in this area has also been devoted to incentives for the safe use of BBSs [68]. Moreover, in recent years, social media has become an important platform enabling interested public and private entities to participate and interact in different transport policies in the field of BSSs [69]. Further research focused on the role of economic incentives in modeling user behavior [70]. An interesting solution seems to be the introduction of an innovative dynamic pricing system for renting a bike with negative prices. Normally, users pay operators a positive price for the use of the bicycle. However, when the unsustainable distribution of bicycles appears in the BSS, users who move from an oversupplied area to an area with undersupply will receive a cash reward from the operator, i.e., negative prices apply [71].

The scientific literature provides elaborations of macro-environmental factors influencing transport systems with PESTEL method by Belwal [72], PEST method [73], STEEPLE method [74], and with SWOT analysis [75]. The research of SWOT analysis of bicycle systems can be found in the works of Ma et al. [76], Mátrai and Tóth [77], Guyandi et al. [78], and Yang et al. [79], who investigated the connected factors that are essential to the sustainability of FFBS programs. The gap of knowledge identified is that there is a lack of scientific research that analyses macro-environmental groups of factors and their mutual influence in the BSS. Therefore, in the following sections of the article, the methodology was presented and research was carried out on the identification and influence of macroeconomic factors on the functioning of BSSs.

3. Materials and Methods

The studies were carried out in three main stages covering theoretical and practical analysis. The research focuses on the statement that it is possible to define external factors influencing BSSs with heuristic methods. The diagram showing the implementation of the theoretical and analytical research stages is illustrated in Figure 2.

Stage I was the starting stage dedicated to the scientific literature review and preliminary activities focused on the preparation of expert research. The outcome of this stage was the report of environmental analysis of BSSs. Simultaneously, the database of experts in the planning, construction, implementation, and use of the BSS was prepared. The invitation to participate in the research was sent to experts from the Southern Poland area to allow easier access to further meetings. A group of 22 experts was established to work in the expert panel. The last part at this stage was an overview of computer applications facilitating data processing in structural analysis through which the MICMAC software was chosen.

Stage II was focused on the research analysis. First, the summary report of the analysis of BSSs in Polish cities has been developed and distributed to the experts together with their working plan. Finally, the expert research was conducted during three panel meetings. During the first panel, experts were asked to prepare a list of key factors influencing the BSS using the brainstorming method. During the second panel meeting, the results were collected using the weighted score evaluation of factors in individual groups: social, technical, economic, ecological, and political. The quantification of the factors made it possible to rank them and select the most significant ones. The list of 25 key factors was the basis for the structural analysis carried out at the third meeting of the expert panel. The results of this meeting were processed in the MICMAC program and, on this basis, the matrix of direct and indirect influence was created.

Stage III covered the construction of matrixes of direct and indirect key factors influencing the BSS. It also contains a summary and conclusions resulting from the external environmental analysis.

In the scientific literature review section, the results and achievements of global scientists were quoted. The subject of the research is BSSs operating since 2008 in most medium-sized and large cities in the southern agglomeration part of Poland. The research part, based on expert knowledge, was conducted in the manner of traditional meetings.

4. Environmental Research of Bike-Sharing Systems in Poland

Environmental research of BSSs in Poland was conducted in stage II of the research. There were two main elements: (1) selection of key factors influencing BSSs development and (2) structural analysis of those factors, which is presented in this section.

4.1. Selection of Key Factors Influencing Bike-Sharing System Development

After the analysis of BSSs in Poland, the studies based on the heuristic method of expert research part proceeded. First, the database with experts on BSSs was prepared. It included over 60 people responsible for designing paths, planning traffic systems in cities, public bike operators, users, researchers, ecologists, and members of public bikes’ fan clubs. Due to the planning of the research in traditional contact meetings, the database included people familiarized with the specificity of public bike systems in Southern Polish cities. Twenty-two people agreed to participate in three meetings of the expert group. The scheme of the procedure and the scope of the analysis assigned to each meeting is presented in Figure 3.

To find the key factors influencing BSSs development, each factor was evaluated with a weighted assessment. This was done at the second meeting where experts were first asked to evaluate the weight of each criterion so that their sum was equal to 1 in each group. Then each factor was assessed individually on a scale of 0—5 according to their importance (0—not at all important, 1—slightly important, 2—important, 3—fairly important, 4—very important, 5—most strongly important). The results of the weighted assessments thus obtained were then summed up. The ranking of factors’ assessment allowed for the selection of the five factors most important for each group. The final list of key factors influencing BSSs with a sum of weighted scores is presented in

Table 2. Key STEEP factors influencing the BSSs.

STEEP Group	Abbreviation	Factors	Total Weighted Score
Social	S1	A healthy lifestyle for city residents	18.4
	S2	The digitization of society increasing the availability of mobile applications	9.11
	S3	Increase in the size of the population, especially the age group most frequently using bicycles	8.84
	S4	Increasing inhabitants’ awareness of the pro-environmental aspects of cycling	8.52
	S5	Trends and fashion related to the cycling mobility of urban residents	8.16
Technological	T1	Modernization of public bikes	9.54
	T2	Extension and modernization of bicycle city routes	7.88
	T3	Increased use of mobile applications supporting public bike parking stations	6.63
	T4	Frequent servicing of docking stations and bikes to improve their safety	5.57
	T5	Improvement of the marking of bike lanes and paths and regional attractions along the paths	4.26
Economic	F1	Free public bike ride to work, schools, and university	10.85
	F2	The ratio of the price of renting a public bike and traveling by bus, tram, or other means of public transport	10.55
	F3	Maintenance costs of the public bike system in relation to other types of public transport	9.25
	F4	The increasing level of public bike rental prices	9
	F5	EU projects co-financing investments in the modernization and expansion of the public bicycle system	6.8
Environmental	E1	Increased interest in zero-emission mobility	15.75
	E2	Eco-friendliness of the use of a public bike	12.79
	E3	Using renewable energy sources for charging public bike stations	11.1
	E4	Mounting smartphone chargers in public bikes for the use of renewable energy	9.99
	E5	Low emission of harmful substances of BSSs	8.1
Political	P1	Promotional programs and events for the public bike	11.54
	P2	Programs to educate/motivate residents on bicycle mobility	8.09
	P3	Implementation of the bicycle priority rule on selected road sections	7.32
	P4	Regulating legal rules regarding the rental of public bikes	6.93
	P5	Coordination of international and EU legal regulations in the field of urban mobility	5.57

.

The final list of key factors influencing BSSs consists of 25 factors divided into five groups. The highest result presented in the table was recorded for the S1 factor, amounting to 18.40, indicating its importance to the BSS. Most of them have a positive influence, but some, such as F4, have a negative influence on the development of BSSs in the cities. The indicated ranking on the list does not fully reflect the importance of each variable. For this, it is necessary to present the mutual relations for the selection of those most essential to the evolution of the system.

4.2. Structural Analysis of Key Factors Impacts Influencing Bike-Sharing System Development

In the method of structural analysis (also known as cross-impact analysis), the mutual relations of key variables are examined. For this purpose, the MICMAC computer program developed by the LIPSOR organization was used. The name of the MICMAC software reflects the possibilities of its application, namely: analysis of the cross-impact matrix for their classification. Comparing factor rankings for different classifications (direct and indirect impact) allows for confirmation of their importance for the entire system, but also allows for identification of those that play a dominant role in it [80].

The structural analysis methodology includes three phases: creating a list of variables, describing the relationships between the variables, and then identifying key variables. Figure 4 presents how the 25 factors (variables) of BSSs were implemented into the application. When entering variable data into a computer program, it is also possible to assign any label to them. To facilitate identification, the graphs of the dependencies previously used abbreviations in accordance with Table 1, reflecting the assignment to a particular STEEP group.

To avoid errors in determining whether there is a relationship between the two variables, the following three questions were answered:

• Whether the variable i accidentally affects the variable j or vice versa (Figure 5a)?
• Whether the variable i affects the variable j, or is there any correlation, e.g., when the third variable k affects variable i and the variable j (Figure 5b)?
• Whether the connection between variable i and variable j is direct or indirect, i.e., does it occur through another listed variable l (Figure 5c)?

Complementing the direct impact matrix was done both by identifying the impact itself or its absence and by determining the strength of the impact of individual pairs of variables on each other. The matrix of BSSs factors’ direct influences (

Table 3. Matrix of direct influence of BSSs’ factors.

	S1	S2	S3	S4	S5	T1	T2	T3	T4	T5	F1	F2	F3	F4	F5	E1	E2	E3	E4	E5	P1	P2	P3	P4	P5	Σ
S1	-	0	3	3	3	0	0	1	0	1	2	1	3	0	0	3	3	2	1	0	3	3	0	0	2	34
S2	1	-	0	3	3	2	2	3	1	3	3	2	3	1	1	3	3	1	3	0	3	3	0	0	1	45
S3	3	3	-	2	2	2	3	2	2	1	1	1	1	1	2	1	1	1	1	0	3	3	0	0	1	37
S4	3	3	3	-	1	1	3	1	1	3	1	1	1	0	3	3	3	2	0	0	3	3	0	0	1	40
S5	3	3	1	3	-	1	3	0	0	0	3	2	0	0	0	3	3	1	1	0	3	3	0	1	1	35
T1	0	2	1	1	1	-	0	3	3	0	0	2	3	2	3	1	1	2	3	1	1	1	1	0	0	32
T2	0	3	0	0	3	0	-	3	1	3	3	2	3	1	3	3	3	3	2	2	1	3	2	0	0	44
T3	1	3	0	3	3	0	0	-	0	1	3	0	1	1	1	0	0	0	3	0	3	3	0	0	0	26
T4	3	0	0	2	3	3	3	1	-	2	0	1	1	1	1	2	2	2	2	3	1	3	0	1	1	38
T5	3	3	0	3	3	1	3	3	1	-	3	2	2	1	2	3	1	2	1	0	3	3	2	0	1	46
F1	3	3	0	3	3	0	0	3	0	0	-	3	2	2	2	3	3	3	1	1	3	3	1	3	3	48
F2	3	3	2	3	3	2	2	3	1	3	3	-	3	3	3	2	2	1	1	1	3	3	2	2	0	54
F3	3	3	1	3	3	3	3	3	2	3	3	2	-	1	2	3	1	1	3	3	3	3	1	1	2	56
F4	1	2	0	0	3	3	3	1	3	3	3	3	0	-	3	2	0	0	3	2	2	1	0	2	2	42
F5	0	2	0	1	0	3	3	2	3	3	2	3	0	3	-	1	1	3	3	2	1	1	0	0	0	37
E1	3	3	0	3	3	0	0	2	0	0	3	3	3	1	0	-	2	2	2	0	3	3	2	1	2	41
E2	3	2	0	3	3	2	2	2	3	1	2	2	2	1	1	1	-	1	3	3	2	2	2	2	2	47
E3	3	0	2	3	3	3	3	1	3	1	3	2	2	0	0	3	3	-	0	2	3	1	1	1	1	44
E4	3	3	0	3	3	3	0	3	1	0	0	2	2	2	3	2	3	2	-	3	3	3	0	1	1	46
E5	3	3	0	3	3	3	0	1	3	0	1	2	3	2	3	3	3	3	0	-	2	3	0	1	1	46
P1	3	3	0	3	3	2	2	3	0	3	3	3	3	2	2	3	3	2	3	2	-	3	1	1	1	54
P2	3	3	0	3	3	0	1	2	0	0	2	0	1	0	0	0	2	1	0	0	3	-	1	0	2	27
P3	1	1	0	1	2	0	1	1	0	3	2	1	2	1	1	3	3	3	0	0	3	3	-	3	3	38
P4	3	3	1	3	3	3	3	3	2	3	3	2	3	2	2	2	2	2	3	3	2	2	3	-	3	61
P5	3	3	0	3	3	3	3	3	3	3	3	2	3	1	1	2	2	2	2	2	3	3	3	3	-	59
Σ	55	57	14	58	63	40	43	50	33	40	52	44	47	29	39	52	50	42	41	30	60	62	22	23	31	1077

Where: 0 = no influence, 1 = weak influence, 2 = moderate influence, 3 = strong influence.

) was created, showing the total direct impact index, which measures the intensity with which a given variable influences the system, and the total direct dependency index, which is a measure of the intensity with which the system affects a given variable.

The algorithms used in the MICMAC program to transform the matrix of direct influence into the matrix of indirect influences were based on the classical theory of Boolean algebra [81].

The impact of individual factors on each other can be visualized with the graph of direct impacts (Figure 6) obtained with the MICMAC program. As can be seen in the graph, most of the factors (numbered in squares) show either no or the strongest influence on each other.

The advantage of the MICMAC computer application is the quick classification of the indirect dependence of variables. The program makes it possible to observe the spread of the influence through the paths and loops of feedback, and, consequently, to organize the variables according to the influence index (bearing in mind the number of paths and loops of length 1, 2 ... N resulting from the variables) and the dependency index (considering the number of paths and loop lengths 1,2,… N for each variable). The impact index and the dependency index in the indirect classification were determined by transforming the direct influence matrix, shown in Table 3, into the indirect influence matrix through multiple iterations carried out in the MICMAC program. The indirect influence of individual factors on each other can be visualized with the indirect influence graph (Figure 7). The greatest indirect influence, marked with a red line in the graph, is noticeable between the factors S2, S4, S5, E5, P1, P2, P4, P5.

The impact of individual factors can also be presented graphically on the impact map [81], in which the x-axis determines the dependency index (the intensity of the system’s impact on a given variable), and the y-axis defines the impact index (the intensity with which a given variable affects the system). Graphical characterization of the relationship between the variables enables the identification of key factors and the examination of the role played by individual variables in a given system according to Figure 8.

Graphical presentation of the map of the indirect influence of factors determining the development of the BSS together with the classification is shown in Figure 8.

Based on the presented map of influences and dependencies, it was possible to distinguish individual groups of factors conditioning the functioning of BSSs. Two political factors were qualified to the input factors, six to the intermediate factors, another six to the resultant factors, five to the excluded factors, and six to the clustered factors.

Summarizing the results obtained in the research part, it can be stated that the key factors influencing BSSs can be divided into five groups:

• Input factors:

  ◦

  P4—Regulating legal rules regarding the rental of public bikes,

  ◦

  P5—Coordination of international and EU legal regulations in the field of urban mobility.

• Intermediate factors:

  ◦

  S2—The digitization of society increasing the availability of mobile applications,

  ◦

  F1—Free public bike ride to work, schools, and universities,

  ◦

  F2—The ratio of the price of renting a public bike and traveling by bus, tram, or other means of public transport,

  ◦

  F3—Maintenance costs of the public bike system in relation to other types of public transport,

  ◦

  E2—Eco-friendliness of the use of a public bike,

  ◦

  P1—Promotional programs and events for the public bike.

• Resultant factors:

  ◦

  S1—A healthy lifestyle for city residents,

  ◦

  S4—Increasing the inhabitants’ awareness of the pro-environmental aspects of cycling,

  ◦

  S5—Trend and fashion related to the cycling mobility of urban residents,

  ◦

  T3—Increased use of mobile applications supporting public bike parking stations,

  ◦

  E1—Increased interest in zero-emission mobility,

  ◦

  P2—Programs to educate/motivate residents on bicycle mobility.

• Excluded factors:

  ◦

  S3—Increase in the size of the population, especially the age group most frequently using bicycles,

  ◦

  T1—Modernization of public bikes

  ◦

  T4—Frequent servicing of docking stations and bikes to improve their safety,

  ◦

  F5—EU projects co-financing investments in the modernization and expansion of the public bicycle system,

  ◦

  P3—Implementation of the bicycle priority rule on selected road sections.

• Clustered factors:

  ◦

  T2—Extension and modernization of bicycle city routes,

  ◦

  T5—Improvement of the marking of bike lanes and paths and regional attractions along the paths,

  ◦

  F4—The increasing level of public bike rental prices,

  ◦

  E3—Using renewable energy sources for charging public bike stations,

  ◦

  E4—Mounting smartphone chargers in public bikes for the use of renewable energy,

  ◦

  E5—Low emission of harmful substances of BSSs.

5. Discussion

In the research on environmental analysis of BSSs in cities, the expert panel is composed of a team of specialists in the field of planning such kind of systems. In addition, the group included representatives of interdisciplinary fields, experts in the field of economics, sociology, political science, as well as practitioners: outstanding managers, representatives of business environment institutions, and the users of public bikes themselves.

In the research methodology, expert knowledge was used three times. During the first stage, the brainstorming method was used to generate many ideas in the field of external factors conditioning BSSs. Its greatest advantage was the intense involvement of all participants, allowing everyone to freely express themselves and generate even unrealistic solutions to the problem. The more difficult stage was gathering all ideas and assigning them to groups according to the STEEP analysis. For example, factor P1 (Promotional programs and events for the public bike) contained several brilliant ideas in the field of promotion, such as free bikes on the “car-free day”, in the days of pre-Christmas road congestion, free first test rides, loyalty program, etc.). Some of the factors were interdisciplinary and the decision to assign them to one group was the panel’s consensus. The assignment to these groups was not essential as their mutual influence was later emphasized in the cross-impact analysis anyway. Examples of such interdisciplinary factors are E1—Increased interest in zero-emission mobility, which is strictly connected with the growth in the prices of energy fuels, such as gas, gasoline, or crude oil (economic-environmental factor), or E4—Mounting smartphone chargers in public bikes for the use of renewable energy (technology- environmental factor).

Assessment according to the importance of a given factor using a weighted score presented a variety of assessments by each of the experts. This was already evident in assigning ratings to each of the factors. More careful people distributed the weight evenly, and some of them put a lot of weight on 1-2 factors at once. For this reason, the summary evaluation of the weighted evaluation made it possible to collect the opinions of all experts. It is worth showing that most of the indicated factors include those that positively affect the development of the BSSs, but also those that may slow this development down or prevent it (such as F3—Maintenance costs of the public bike system in relation to other types of public transport and F4—The increasing level of public bike rental prices).

Creation and evaluation of the structural analysis matrix allowed for the description of the relationships amongst the factors (variables). The relationships between the factors were so large that it was not possible to select the most important combinations of influences only based on the direct influence graph. The strongest dependence shown in the indirect influence graph was identified between social and political factors. The factors were classified according to their function in BSSs.

Factors P4 (Regulating legal rules regarding the rental of public bikes) and P5 (Coordination of international and EU legal regulations in the field of urban mobility) are highly influential, independent, and qualified as input variables. These variables tend to provide a comprehensive description of the examined BSS and determine its dynamics. They relate to political issues of legal regulations regarding urban mobility and the rental business of public bikes. These factors should also be prioritized when considering strategic action plans for the expansion of the BSS. In the study, the factors showed the highest factor of influence on BSSs, which is also confirmed by the research of Wu and Lei [82], Reddic et al. [83], and Laa and Emberger [31].

Indirect variables show both the greatest influence and dependence. In this group, the one with the highest indexes is another political issue connected with promotional programs of biking mobility in cities (P1). The importance of marketing tools supporting the demand for public bike services was also emphasized in the work by Yang et al. [84], Martens [85], and Ge et al. [86]. The economic issues are crucial according to freeware bike ride (F1), prices (F2), and modernization investments (F3) factors. The financial issues were raised in many studies [29,59,87]. Many research works also focus on free-floating bike-sharing [26,57,88,89]. In Poland, as of today, there are only traditional SBBS systems in the cities. The research proves that the main ecological aspect is the environmental friendliness of biking (E2). It was also underlined in the work of Chen [90] and Bieliński et al. [35]. Moreover, the digitization of society (S2) is among these unstable variables that may dynamically alter the system and its development. Diez et al. [91] claim that among other factors, digitization makes it possible to consider the new stage of development of BSSs as cyber-physical systems.

The group of resultant variables is not influential, but mostly dependent. Its behavior in BSSs, therefore, explains the impacts resulting from other variables, mainly the intermediate and input variables. The most dependent factors are educational and motivational programs (P2) and fashion trends of the society (S5). In addition, the healthy lifestyle of city residents (S1) and their environmental awareness (S4) as well as the interest in zero-emission mobility (E1) factors are included in this group. Increased use of mobile applications supporting public bike parking stations (T3) is the least influential factor. According to the scientific literature, education was also one of the main factors affecting bike-sharing demand in Eren and Uz work [3]. The social factors connected with the popularity of bike-sharing were investigated by Ouyang et al. [92], and Böcker and Anderson [93]. The work of Ma et al. [94] also proved that the practicality of bike-sharing applications is the most significant element contributing to users’ intention to recommend this mobility possibility.

Excluded variables are neither dependent nor influential. Consequently, they have little influence on the BSSs. Often these variables simply describe inertial or prevailing trends, which change little over time. Sometimes, these variables are autonomous and, thus, have little influence on the system. Surprisingly, among these factors, there is an increase in the size of the population (S3), bicycle priority rules on selected road sections (P3), the EU project co-financing investments (F5), and technological issues related to the modernization of bikes (T1) and frequent servicing of biking stations (T4). Excluding these variables, therefore, will have few consequences for the analysis of BSSs. Indeed, the attention of scientists notices the dependence of the use of public bikes on the population density, but rather focuses on social groups, such as tourists in Yang et al. work [84], or students in Manca et al. [7], Zainuddin et al. [95], and Torrisi et al. [96] publications. The issue of bicycle lane priority is usually understood in terms of traffic safety [97], but also as a promotional tool by Bagloee et al. [98].

Finally, the group of clustered variables tends to assemble. These variables are not sufficiently influential or dependent to be included among the previous classifications. There are no definitive conclusions about these variables and their impact on the system. This reflects an increasing level of public bike rental prices (F4), as analyzed by Wu et al. [60] who noted pricing scheme change can alleviate demand-supply imbalance. There are also three environmental factors clustered in this group: using renewable energy sources for charging public bike stations (E3), smartphone chargers in public bikes (E4), and low emission of harmful substances (E5). A similar analysis of renewable energy usage in BSSs was presented by Usama et al. [99], Jia et al. [100], Matias et al. [101], carbon dioxide emissions by Chen et al. [102], and Kou et al. [103]. There are also two technological factors in this group: the extension and modernization of bicycle city routes (T2) and improvement of their markings (T5), which are naturally connected, and change as the network of bike-sharing connections between the cities of the conurbation in the southern part of Poland grow. It concerns the planning of BSSs, which should be part of an integral project that includes not only docking stations, but a dedicated cycle path network connecting them, as analyzed by Jin et al. [104], Bao et al. [105], and Zhuang et al. [106].

6. Conclusions

The goal of the paper was to select the most important macro-environmental factors and their mutual interaction influencing the sustainable development of BSSs based on the example of Polish cities. It was done according to 25 key factors with the use of expert knowledge.

Expert panels are a popular method in research on the external environment of transport systems, allowing for analysis in a wide and very diverse context. The selection of appropriate stakeholders enables an objective estimation of the state of knowledge in a given detailed field, initial prioritization of problems, and research areas and areas of technology. The research goals formulation and discussion can be a tool for the synthesis of various research results and expert views. This makes it possible to extend the spectrum of research.

The key factors influencing the sustainable development of BSSs were chosen among social, technological, economic, environmental, and political issues. They were also qualified into five groups of factors with the different roles they play in the system. The key factors influencing BSSs also include those supporting energy-saving solutions, e.g., mounting smartphone chargers in public bikes for the use of renewable energy (E4) and using renewable energy sources for charging public bike stations (E5). Qualification of these variables can optimize the planning process with decision-making based on future environmental trends, which may support public transport service providers and organizers.

Future research directions can be focused on checking the impact of the classified factors on the actual development of BSSs in Polish cities and rechecking the macroeconomic factors of the environment if they have changed. According to the research results, the future research should mainly focus on the following factors: political issues connected with promotional programs of biking and mobility in cities (P1); free public bike ride to work, schools, and universities (F1); the ratio of the price of renting a public bike, and traveling by bus, tram, or other means of public transport (F2); maintenance costs of the public bike system in relation to other types of public transport (F3); eco-friendliness of the use of a public bike (E2); and the digitization of society increasing the availability of mobile applications (S2).