Citizen science is the participation of people in scientific processes that are not institutionally linked to that field of science. It is emerging as “the favored twenty-first-century model for conducting large-scale scientific research” (Toerpe, 2013, p. 44). CS is often associated with monitoring (e.g., taking pictures using a plant ID app) or data collection (e.g., beach litter audits). This is usually referred to as ‘crowd-sourced science’. However, there is a spectrum of involvement, and participation can range from short-term data collection to more intensive use of leisure time to analyze or research the topic with other scientists/volunteers (volunteer researchers may have an academic background, but this is not a prerequisite for participation). Thus, the central idea of CS is the involvement of the general public, which can include (to varying degrees) proposing, designing, collecting, managing, analyzing, and sharing scientific investigations.

According to Haklay et al. (2018), in the context of geographic information, beyond using citizens as sensors (Level 1 crowdsourcing), there are three additional levels of citizen engagement: Level 2 Distributed Intelligence (e.g., training citizens, interpretation of data), Level 3 Participatory Science (e.g., defining the problem and collecting data), Level 4 Extreme CS (e.g., defining the problem, collecting the data, and analyzing it) (Haklay et al., 2018). In the context of weather monitoring CS, the hierarchy of Haklay et al. (2018) can be applied to the following project examples: the Community Collaborative Rain, Hail and Snow Network (CoCoRaHS) consists of crowd-sourced data of precipitation observations measured from citizen backyards in the United States (Level 1, Reges et al., 2016), SmartPhones4Water citizen scientists from Nepal were trained to collect precipitation measurements (Level 2, Prajapati et al., 2021), disaster risk mapping was conducted in schools in the Philippines using scientist designed tools refined by participatory discussions with teachers and students (Level 3, Gaillard and Pangilinan, 2010), the project Extreme Citizen Science: Analysis and Visualisation (ECSAnVis): in the Congo was the result of scientists and citizens working together throughout the entire process (Level 4, Time4CS, 2024).

Overall, CS is an inclusive scientific methodology where full-time experts and volunteers can learn from and with each other on an equal footing (Bonn et al., 2022; EU Cordis, 2023). The benefits are innumerable: it increases social awareness, leads to more relevant policies for the communities they serve, and expands observational networks and databases in scope and availability to a greater extent or variety of spatial/temporal coverage.

While institutional science is traditionally associated with academic and professional research institutions, CS is defined as collaborative scientific research by non-professional researchers in cooperation with institutional (professional) scientists and academic institutions, often realized in heterogeneous consortia (Lukyanenko et al., 2020; Göbel et al., 2022).

Public participation in scientific research describes different approaches, including crowd-sourcing, community-based research, or volunteered geographic information (Bonney et al., 2016). Participatory data collection from the public has a long history in environmental observations (Bonney et al., 2016). Wynne (2007) distinguished two types of public engagement in science and technology research: invited (scientific expert-driven) and uninvited (public/bottom-up mobilized) participation. Further, in this sense, CS activities can be divided into two categories concerning the collaboration between non-professional volunteers and institutional researchers: the science-driven collaborative/contributory projects and the community-driven co-created/collegial projects (Shirk et al., 2012).

In CS projects, two distinct approaches to emergence must be distinguished. The first approach is driven by the interests of citizens who are non-professional researchers and are motivated by a scientific question. The second approach is initiated by institutional formations of professional researchers who aim to involve citizens in a scientific process. CS draws on the interactions and partnerships between professional academic scientists and non-professional volunteer researchers in scientific research (Göbel et al., 2022). Therefore, scholars repeatedly point to the importance of CS projects to identify the different people and communities involved, i.e., the stakeholders and stakeholder communities (citizens, scientists, policymakers, and media), and to identify stakeholders’ capacities to participate, to assess their project expectations and their influence on a project, to analyze their interactions (Parmar et al., 2010; Skarlatidou et al., 2019).

Citizen and institutional science are not mutually exclusive but synergistic and mutually beneficial. Citizen scientists enhance institutional science by contributing valuable long-term data, especially in areas with limited access or scarce resources. The diversity of participants in CS projects often results in data collection on a spatial and temporal scale that would be unattainable within the confines of traditional institutional research (Wiggins et al., 2011; Forrester et al., 2015). However, by involving citizens who contribute by collecting large amounts of data, science remains its own institutional sphere with professional expertise. (Sauermann et al., 2020). Hence, institutional scientists provide critical guidance, expertise, and resources to ensure the scientific validity of CS efforts.

The motivations for engaging in CS are similar to those for volunteering, and the research conducted to understand why people volunteer often applies to CS participation (Land-Zandstra et al., 2021) but only a few studies have examined this link (Phillips et al., 2019). In particular, people are more likely to participate in CS activities when the projects address issues relevant to their lives and interests (Rotman et al., 2014; West and Pateman, 2016; Phillips et al., 2019). Participants can be recruited using a variety of online and offline approaches, such as flyers at public events, survey registration links (e.g., Facebook campaigns), or email invitations via mailing lists (Brouwer and Hessels, 2019). However, recruiting sufficient and diverse participants and keeping them engaged throughout the project is often a crucial challenge for the scientists involved (West and Pateman, 2016; Arienzo et al., 2021).

Targeted invitations to participate in CS projects, in contrast to a generic invitation strategy, have been proven to be an effective way of recruiting a wide range of participants and also reaching marginalized groups, such as elderly people and citizens with lower education (Brouwer and Hessels, 2019). Targeted approaches via recruitment through third-party organizations, such as the collaboration with the Bund für Umwelt und Naturschutz Deutschland (BUND), a German non-governmental organization dedicated to nature conservation, in the water monitoring CS project FLOW (www.Flow-project.de, Engel et al., 2023), have been proven very successful in reaching a much broader spectrum of participants and enabling the CS project to achieve nationwide participation. This is consistent with the recommendations of the recently launched Citizen Science Strategy 2030 for Germany (Bonn et al., 2022), which highlight the importance of building collaborations with established volunteer structures, such as regional associations in the field of volunteer management, for succeeding in volunteer recruitment (Bonn et al., 2022). In line with Sorensen et al. (2019), Pateman et al. (2021). point further to the importance of reaching out to key individuals who are already locally active and trusted by local citizen groups for achieving inclusivity in recruiting participants.

Involving people with diverse interests, motivations, and backgrounds (Jennet et al., 2016; Tiago et al., 2017) and creating relationships between professional and non-professional researchers remain challenging in CS projects. Regarding the engagement of broader civil society in science projects, Wynne (2007) distinguishes between “invited participation” which typically involves elite “stakeholders” who have the scientific expertise and are connected to policy-making, and the engagement of “spontaneous and independent, uninvited forms of civil participatory action”. Within the different phases of a CS project, the participation of diverse stakeholders can occur in different ways.

According to Shirk et al. (2012), five levels of involvement in the research process can be identified based on the degree of participation: “Contract” (citizens hire scientists to conduct a study on a topic of particular concern to them), “Contribute” (citizen scientists participate in data collection), “Collaborate” (citizen scientists participate in research design, data collection, and analysis), “Co-Create” (joint work between professional scientists and citizen scientists on a research problem), and “Colleagues” (citizen scientists and professional scientists generate independently new knowledge in a research area). Yet, within these different types of public engagement in CS projects, discrepancies exist regarding the willingness to participate. For example, concerning participant diversity in environmental CS projects, Pateman et al. (2021) found that already marginalized social groups in terms of age, gender, ethnic background, education, and income are underrepresented in CS and are the least likely to participate in CS projects. The authors caution that a lack of diversity among citizen scientists has implications that CS project leaders should consider. More recently, Göbel et al. (2022) in their study on stakeholder participation in Citizen Social Science (CSS) in Germany, showed that CS activities do much more than capacitate lay people to do science. CSS activities are vital for facilitating cooperation with co-researchers and other partners in associations “originating inside and outside of academic research (or ‘academic’ and ‘non-academic’ initiatives)". Hence, the authors introduce the concept of ‘cooperation capacity’ which “emphasizes the ability of individual and collective actors to establish connectivity and relations inside the heterogeneous consortia as well as between them and other actors outside to generate scientific knowledge through participatory research” (Göbel et al., 2017).

Based on an international stakeholder analysis conducted by Göbel et al. (2017), four main stakeholder groups are involved in CS initiatives: 1) institutional research, 2) civil society, 3) public administration, and 4) the private sector. The degree of engagement and contribution varies between each group.

Building on Göbel et al. (2017) and looking at the urban CS context in which we will analyze a case study in this paper, Figure 1 shows the interdependencies between stakeholder communities: institutional Science represents the group of professional researchers, employed by a research institution. Further stakeholder groups are private businesses, civil society, and public administration. Eventually, stakeholders are directly interconnected; e.g., schools are part of the public administration and directly linked to their students and teachers, who might act on distinct projects as amateur scientists in their spare time.

Figure 1 also emphasizes that some groups, such as existing CS initiatives and special local associations might be related to and consist of members of all stakeholder groups.

Stakeholder analysis is an important tool in stakeholder management. It involves the systematic identification of stakeholders, the assessment of the interests, needs, and concerns of the stakeholders involved, and their relevance and influence on a project (Eden et al., 2013). It is crucial for subsequent stakeholder engagement in the implementation of actions to influence their habits. Figure 2 shows the main steps in the stakeholder analysis process and the overarching questions that need to be answered.

Thus, it is essential to devise targeted strategies for how citizens from diverse backgrounds (such as individuals in private settings, employees of companies, or members of existing organizations) can be effectively engaged and committed to participating in research tasks and comprehensive environmental monitoring. As stated in the Citizen Science Strategy 2030 for Germany, one of the main challenges is establishing a new collaboration model between scientists and volunteers in terms of more tailored roles for volunteers (Bonn et al., 2022). Providing tailored training to specific target groups of citizens typically results in collecting higher-quality data. Research also shows that people are more likely to participate in CS activities when the projects address issues of relevance to their lives and interests (Rotman et al., 2014; West and Pateman, 2016; Phillips et al., 2019) and that keeping people engaged throughout the project duration is often a challenge for the scientists involved (West and Pateman, 2016; Arienzo et al., 2021). This evidence suggests that a targeted approach is essential to understanding a citizen group’s interests and the factors that would keep them motivated.

According to Tiago (2016) and Hacklay et al. (2018), applying stakeholder analysis to CS can be critical for the success of CS projects. For CS projects, a successful engagement mainly covers the establishment of a customized communication and feedback culture and the development of tailored participatory techniques for collaboration with the different stakeholder groups (e.g., co-creating project design, collecting data, data analysis, formulating results; Göbel et al., 2022; San Lorente Capdevila et al., 2020). Skarlatidou et al. (2019) highlighted the relevance of stakeholder mapping for enhanced co-creation in CS projects and “for more effective stakeholder communication, more successful implementation, and a greater impact for CS initiatives’'.

The value proposition canvas (VPC) is a strategic tool that helps businesses understand their customers’ needs and design products or services that effectively meet those needs. The VPC reflects the added value or benefit that a product or service provides to the market or customer (Osterwalder et al., 2014). This value should be as high as possible to eliminate a customer’s problem or create an advantage for them.

Generally, the VPC consists of two components: the customer profile and the value map, which together identify the key features and benefits of a successful product or service. The left-hand side of the VPC is dedicated to the product or service itself. This section identifies potential pain relievers, which help reduce customer barriers, and gain creators, which are factors that improve the overall customer experience. Gain creators can be expected or even positively surprise customers. The right-hand side, the customer profile, presents the individual tasks (customer jobs) the customer/user has to accomplish in his daily life and outlines the gains (benefits) and gains (obstacles).

The VPC-based marketing approach can be innovatively adapted to CS projects by considering the individual stakeholders and their individual needs and concerns regarding different issues. Therefore, it is crucial to assess each stakeholder specifically, taking into account their gains and pains, to determine their suitability for a project. The use of the VPC allows for a more in-depth stakeholder analysis, especially the identification of approaches and tools to motivate more citizens to get involved. To achieve this, there are three essential aspects, the customer profile and value map as parts of a marketing VPC, and the communication channel as a new important aspect (see Figure 3). All these aspects must be incorporated into a CS-specific consideration, including the recognition that CS is focused on providing valuable services or tools rather than selling a product, which differentiates it from traditional business theory.

Hecker et al. (2018) highlighted the need for an appropriate communication strategy as a key motivating factor to retain participants and exchange information through multi-way channels. Therefore, the “Channel” object in the center of the adapted VPC is implemented to emphasize the important role of communication. Communication plays a crucial role in connecting both components. Clear and persuasive communication is the key to successful CS project implementation. Appropriate means of communication are freely available information materials (manuals, newsletters, private policies), regularly updated social channels to inform about results and upcoming events, websites, regular communication through emails, and the organization of workshops and meetings. The publication of success stories in local media also increases the recognition of the valuable contributions of citizens.

Institutional scientists, representing professional science, often take the lead in initiating academic CS projects. However, in our specific context, they are active participants in these projects, and at the same time, integral members of the stakeholder community. Therefore, they also need to define clear project goals and consider their tasks and goals in terms of benefits and obstacles as they seek to obtain long-term, high-quality data for their research. To achieve this, they must examine their own gains and pains and communicate them effectively to other stakeholders through appropriate communication channels.

In addition, there are two perspectives to gain a more complete understanding of the mutual services and tasks to be performed. This requires a thorough adaptation of the value map (gain creators, pain relievers) and the stakeholder profile (gains and pains). To facilitate this process, a number of questions can help to identify an appropriate VPC for the CS project under consideration (see Table 1).

As seen in Table 1, it is crucial to assess the requirements and the added value for, e.g., the participating citizens, in order to tailor the services appropriately. This involves clear communication of expectations from the citizens for the project. Selecting suitable, easy-to-use sensors and providing the necessary support through workshops and comprehensive manuals can serve as a gain creators and increase the citizen’s motivation to invest time and effort in this activity. In addition, ongoing support in cases of problems and questions should also be provided. Nonetheless, it is essential that all services and communication are accessible in the native language of the involved stakeholders.

The economic theory provides another valuable tool, known as Key Performance Indicators (KPIs), which organizations can use to track their progress over time, identify areas for improvement, make data-driven decisions, and measure success. KPIs are particularly useful for monitoring project objectives, with each organization setting KPIs unique to its specific project. To be effective, KPIs should be measurable, specific, realistically achievable, and have a clear deadline (Hassler, 2016).

According to the European Commission, Directorate-General for Research and Innovation, the KPIs are used as core indicators focused on assessing the impact of Horizon 2020 and will be considered in its evaluation and monitoring system (European Union, 2015). The fact, that for the first time, these KPIs are being identified before the start of the Framework Programme is a significant development, as it provides a solid and coherent basis for the monitoring and evaluation system for Horizon 2020, coupled with a focus on measuring the results and the impacts of this Programme.

Assessing the impact of CS is often challenging to quantify since activities could cover various dimensions of impact (e.g., social, scientific, and policy). Within these dimensions, aspects of success are difficult to measure (e.g., how do you measure innovation?).

The selection of measurable KPIs is essential for assessing the impact of project activities and whether the overall project objectives have been achieved, and it also serves as a tool for recognition.

Additionally, project funding should include evaluating the project’s impact (Citizen Science Strategy 2030 for Germany) (Bonn et al., 2022). Demonstrating impact and tangibly showing that aims have been achieved is beneficial for the long-term sustainability of a CS project (e.g., evidence of success, further recognition, applying for future funding, upscaling). An evaluation and assessment framework of impact for CS projects is often set by funding agencies or an aspect of the project management. For example, during the funding period, a project may be required to submit interim reports detailing future milestones and goals, with subsequent reports addressing how these goals were met. However, as mentioned above, an impact assessment should not only be used to ensure effective project self-management; adapting an open framework for impact evaluation would also promote recognition for the project, for example, through a “score” that the project meets/exceeds a certain standard.

When evaluating CS programs, inputs, activities, and outputs tend to be easily quantifiable through KPI’s (Sauermann and Franzoni, 2015; Burgess et al., 2017; Sauermann et al., 2020; Schaefer et al., 2021). As an example, Liñán et al., 2022 proposed the following KPIs to measure the achievement of the scientific project goal and its accumulated value at the end of the project: 1) number of observations; 2) number of quality checked observations; 3) number of participants; 4) number of participants uploading observations to the data portal; and 5) number of volunteers repeating the activity. Furthermore, KPIs can be introduced to evaluate data quality and reliability by establishing metrics that assess the accuracy, completeness, consistency, and timeliness of the provided data. Thus, the defined measures could focus on data completeness and timeliness, such as measuring the percentage of data that is collected within a specific timeframe or the percentage of data that is complete and not missing any required information. By regularly tracking and analyzing these KPIs, project coordinators can gain insights into the quality and reliability of the data and identify areas for improvement. This information can be provided to stakeholders and reflects that the data is reliable and can be used confidently.

The public participation in the scientific research framework developed by Shirk et al. (2012) spans a continuum from institutionalized science-driven “contributory” research projects to public-driven “collegial” projects. To illustrate the possible added value of marketing approaches in the context of CS, we started with the consideration of two projects representing two individual cases on this spectrum. One is a locally initiated collegial CS project (project “Meteorologie hautnah” ["Meteorology Up Close"]) at the Leipzig Institute for Meteorology, University Leipzig (Germany), which was funded as part of the “Deutsche Hochschulwettbewerb 2022”. The 3-month project period was divided into three phases of 1 month each. 20–30 people took part in each phase, and became aware of the project through various media reports and social media platforms. The participants from the cycling and meteorology interest groups and members of the university were then invited to an opening event and allocated to the project phases. The aim of “Meteorology Up Close” was to communicate the scientific process to a broad section of the population and to jointly investigate the urban heat island.

The other case is a science-initiated and science-driven CS project called CityCLIM-project, funded by the European Union’s Horizon 2020 research and innovation program and comprising six consortium partners and four participating pilot cities (Karlsruhe/Germany, Valencia/Spain, Thessaloniki/Greece, and Luxembourg.

An essential aspect of both projects was the involvement of citizens to better understand climate impacts in cities by collecting in situ sensor data in urban environments using miniaturized mobile weather stations called MeteoTracker (meteotracker.com, Italy). This MeteoTracker is a miniature weather station specially designed and patented to measure air temperature, relative humidity, and air pressure on the move.

The method of stakeholder selection varied between the two projects. The “Meteorologie hautnah” project advertised the project to the general public in various media. With such a general call, the project reached a wide and non-targeted group of people interested in meteorology and urban climate. No stakeholder analysis or other tools were used in the project “Meteorologie hautnah” to analyze the requirements of the participants. The main objective was simply to find citizens who, for example, cycle regularly and would be interested in a mobile measurement. For example, travel times or routes to achieve an even coverage of the area were not considered. We could see this as a non-targeted approach because the main aim was to find a broader group of people interested in cycling. Such a non-targeted approach is applied to many CS projects.

The CityCLIM project, on the other hand, specifically identified a target group consisting of citizens who use the bicycle primarily for commuting or as a hobby and who have specific requirements in terms of route, duration, and frequency. The main difference to “Meteorology hautnah” is the main focus on the collection of well-distributed meteorological data on a temporal and spatial scale. The German-wide initiative “Stadtradeln” program (‘City Cycling’) encourages various groups in the participating cities to cycle as much as possible within 21 days to promote climate action (STADTRADELN, 2023 - www.stadtradeln.de). Therefore, CityCLIM contacted this regional cycling initiative to involve different stakeholders. It should be emphasized that targeted actions such as the “Stadtradeln” program are intended for specific temporary campaigns rather than long-term data collection. In the following sub-sections, the aim is to explain our targeted participation strategy.

The ‘Stadtradeln’ initiative represents a competition that involves completing as many daily journeys as possible in an environmentally-friendly manner using the bike over a 21-day period. The aim of the initiative is to encourage more people to cycle and reduce carbon emissions, and to show where the city is already cycle-friendly and where improvements are needed. With this idea, this program involves a representative cross-section of society from all kinds of stakeholder-groups.

As shown in Figure 2 the CityCLIM stakeholder analysis of the ‘Stadtradeln’ initiative included several steps that focused on the different stakeholder groups associated with the “Stadtradeln” program.

Stakeholder identification: In our case, we will focus on three key stakeholder groups that have to be analyzed in the stakeholder analysis: the institutional scientist, the “Stadtradeln” association, and the participants in the “Stadtradeln” initiative. The institutional scientists are interested in collecting high quality spatial and temporal environmental data for a wide area in urban areas. The ‘Stadtradeln’ initiative is a climate alliance of participating cities in Germany with the goal of promoting climate protection by strengthening local cycling. In addition, the “Stadtradeln” association provides comprehensive support in finding sponsorships and promoting sustainable urban mobility in general. Typically, this competition involves a representative cross-section of society from all kinds of stakeholders, shown in Figure 1. The large community of participants consists of individual, hobby, company or club organized cyclists who are interested in this competition to do something for the environment and their health. All participants can combine various daily activities (e.g., cycling to work, going to the gym, shopping) with the aspect of collecting data on the number of kilometers traveled or their own health indicators, such as calories burned. However, the data shows that the ‘Stadtradeln’ program is mainly targeted at companies/organizations within cities, that formed teams for the competition aspect and the aim of winning the game for their company/organization. Of course, other stakeholders, such as local politicians or decision-makers, also play a role. However, they are not considered here in detail.

Analysis of relationships: Institutional scientists need data to answer recent research questions, such as the impact of climate change on urban environments. CS approaches can be applied here, and people who are willing to take part in CS projects collecting environmental data are required. The “Stadtradeln” association can provide a connection to interested people who like to combine cycling with collecting data and being part of such a competition. Based on this collaboration and the added value of environmental data collection, the program can also be used to formulate a specific environmental goal in that city. This may also attract additional participants who are particularly interested in the urban climate or local climate actions. The “Stadtradeln” initiative also enables local politicians and decision-makers to learn how to improve cycling infrastructure in their city by providing them with information from participants. Normally, all participants share an interest in cycling and in participating in this competition. Therefore, there is a strong sense of a larger community with the same interests. In addition, this initiative shows a broad cross-section of society building a unique network of interactions between local associations, the community’s activities, local companies and organizations, and individual interests. Numerous stakeholders can be reached through such a platform initiative.

Analysis of influences: The main influences of the “Stadtradeln” association lie in coordinating the initiative, setting the framework for the competition, and providing all the necessary means of communication. Institutional scientists can use these main influences to enhance the impact of the initiative in terms of achieving their scientific goals. Hereby, they can create sufficient motivation for the participants to invest more effort in collecting additional environmental data during their bike ride. The competitive nature of the ‘Stadtradeln’ program is the main incentive for all stakeholders involved. The team spirit, including the prospect of winning for the team as well as the reward of kilometer points for the cycling teams, influences the activity of the participants to use the bicycle instead of the car. Another influencing factor is the potential combination of several daily activities (e.g., cycling to work, to the gym, to the store) with the aspect of data collection, e.g., the number of kilometers cycled or personal health indicators such as e.g., calories burned. All these influencing factors can be used to motivate participants to collect urban climate data for the CityCLIM project.

Analysis of actions: The “Stadtradeln” association is responsible for the organizational framework, including various services for the participating cities and cyclists. This includes, e.g., the provision of IT infrastructure and apps, participant events, award ceremonies, and keeping the communication channels up to date. The institutional scientist can use the established communication channels to inform the interested cyclists about the opportunity to support a specific task involving the measurement of environmental variables in the city and to emphasize the high value of this scientific work. It is up to the scientist to remove obstacles that arise and to assess and respond to the needs of the participants throughout the duration of the initiative in order to keep the citizen scientists informed and the motivation high. The participants are able to provide a valuable amount of measured data when cycling, if they use devices to collect environmental variables. However, depending on the daily behavior of the participants the collected data may not be evenly distributed over time and space. While commuters may collect most of their data during peak commuting times (7–9 a.m.; 3–6 p.m.) on their way to and from work, recreational cyclists may be data providers mainly in their free time and at weekends. The consideration of all groups involved led to better spatial and temporal coverage of the city.

Communication + Involvement: The “Stadtradeln” association has a user-friendly website to inform about the initiative, upload the kilometers ridden, and show the current ranking. Typically, the collected data is ingested directly into a web dashboard, showing the amount of carbon dioxide emissions avoided and the current ranking of the participant’s company/organization. In addition, the use of local communication channels such as billboards, press releases and TV reports are particularly effective in reaching a broad local audience and motivating people to participate in that “game”. Institutional scientists provide comprehensive information about the additional scientific programs that these communication channels offer to help participants. Social media plays an important role in providing information about the interim results of the competition or the first results of the measurements. It is important to make it as easy as possible to give feedback and to raise questions and concerns.

It is important to consider that activating different groups will result in different distributions of data, depending on the everyday “jobs” to be done (as referred to in the VPC) The CityCLIM VPC for understanding the recruitment and motivation of the targeted approach using the “Stadtradeln” group is presented in Figure 4. The described VPC is created to investigate the needs and perspectives of the “Stadtradeln” target group.

(1) Target group analysis: The crucial aspect of the “Stadtradeln” target group - the participating cyclists-is that the participating citizens already possess a certain level of exposure or motivation to engage in climate action. As expected, pre-motivated individuals in the targeted group might be more involved and engaged in the campaign compared to a non-targeted selection group. However, if individuals are already actively engaged in climate action, the Value Proposition Canvas analysis needs to assess the additional value that the CityCLIM project can provide in conjunction with their current efforts.

The definition of KPIs depends on the exact research questions. The measurement of urban climate data (air temperature, pressure, and humidity) in a suitable temporal and spatial coverage was the primary task to be solved by CS in the CityCLIM project. Hence, KPIs were defined to cover two aspects:

• The impact of the targeted approach within the CS initiative and

The determination of the impact is related to the number of participants per target group and the received feedback. The overall data quality can be evaluated by the number of measured data points, the number of quality-controlled data points, and the spatial and temporal distribution of quality-controlled data points. Table 2 provides a list of defined KPIs.

Despite the limited duration of our 15-day measurement campaign, our data contained substantial and comprehensive information concerning the significant research question and the further elaboration of the presented approach. Figure 5 compares the daily accumulated data points resulting from the targeted individuals (CityCLIM group). The CityCLIM group demonstrated high engagement in the measurement campaign, contributing a larger number of data points than originally expected (see KPI list in Tab. 2). Additionally, the non-uniform distribution of weekly data becomes evident in Figure 5.

The achieved results revealed the performance of the CS biking initiative (see Tab. 2): The impact KPI evaluation slightly underscores the targeted results, but it also serves as a guide for strategic adjustments in terms of a clearer and more detailed definition of the objectives. The KPIs to evaluate data quality uncover gaps in temporal and spatial data coverage. This information can be used to identify critical deviations and allow for timely adjustments, for instance, in order to effect recruitment of new participants.

A noticeable decline in the quantity of data points at the weekends can be observed. However, the prevailing weather conditions significantly influence the availability of the data. Specifically, inclement weather, such as rain, leads to the absence of measurement data as it adversely affects the use of bicycles. A first analysis of the data showed an anomaly in the daily data distribution due to the specific use of cycling in the morning and evening hours for the targeted CityCLIM group. Such an observed distribution might be unsuitable for assessing the temporal variation of the urban climatic conditions during the day and must be considered to evaluate the target group. These results have to be included in the recruitment of new target groups (e.g., to fill the arising data gaps) in terms of a data-driven adaptation of the targeted approach.