Braving the elements: Understanding and promoting winter cycling behavior

Braving the elements: Understanding and promoting winter cycling behavior Laura Ebbinghaus a,b,* , Dominik Georgi a , Marcel Zbinden a , Larissa Dahinden a a Lucerne School of Business, Institute of Communication and Marketing IKM, Lucerne University of Applied Sciences and Arts, Zentralstrasse 9, 6002 Lucerne, Switzerland b Chair of Marketing Management and Sustainability, HHL Leipzig Graduate School of Management, Jahnallee 59, 04109 Leipzig, Germany ARTICLE INFO Keywords: Winter cycling Behavioral interventions Sustainable mobility Active transport Stimulus–organism–response theory Modal shift ABSTRACT Cycling is among the healthiest and most sustainable forms of transportation, with the potential to reduce congestion and emissions. However, many cyclists switch to motorized vehicles when temperatures drop, raising questions about promoting winter cycling, an area with limited research. Our study addresses this gap by examining factors influencing winter cycling and intervention strategies. Using stimulus–organism–response theory and a literature review on winter cycling, we surveyed 11,034 Swiss cyclists online. Exploratory and confirmatory factor analyses revealed four latent factors impacting winter cycling intentions: cycling identity, health consciousness, adverse weather safety concerns, and winter road safety concerns. We also assessed five behavioral interventions aimed at promoting winter cycling: a monetary incentive, social comparison, an additional paid day of holidays per year, a competition, and goal setting. All interventions increased participants’ winter cycling intentions, with the paid day off being the most effective and social comparison the least effective. Further moderation analyses revealed that adverse weather safety concerns moderate the link between the interventions and winter cycling intentions. This research contributes to the transportation literature by providing new insights into the psychological factors facilitating active transport behavior under adverse conditions. This study offers guidance for policy-makers and practitioners interested in promoting sustainable mobility, specifically, winter cycling. 1. Introduction Promoting year-round cycling is essential for achieving climate goals and advancing sustainable mobility (Hudde and Wessel, 2024). While cycling is widely recognized as a low-emission (Pucher and Buehler, 2008), cost-effective, and health-promoting (Galway et al., 2021) mode of transport, seasonal weather variations significantly impact cycling behavior, leading to reduced cycling rates during the winter months (Chapman and Larsson, 2021; Amiri and Sadeghpour, 2015; Nahal and Mitra, 2018). Studies indicate that cycling rates can decrease by 47–73 % in the winter months (Nahal and Mitra, 2018; Bergstr¨ om and Magnusson, 2003). These fluctuations are more pronounced than those caused by other adverse weather conditions, such as extreme heat (Chapman and Larsson, 2021); making winter a particularly challenging season for promoting active transportation. Hence, understanding how to encourage * Corresponding author at: Lucerne School of Business, Institute of Communication and Marketing IKM, Lucerne University of Applied Sciences and Arts, Zentralstrasse 9, 6002 Lucerne, Switzerland. E-mail address: [email protected] (L. Ebbinghaus). Contents lists available at ScienceDirect Transportation Research Part F: Psychology and Behaviour journal homepage: www.elsevier.com/locate/trf https://doi.org/10.1016/j.trf.2025.05.001 Received 24 October 2024; Received in revised form 2 May 2025; Accepted 3 May 2025 Transportation Research Part F: Psychology and Behaviour 113 (2025) 517–535 Available online 22 May 2025 1369-8478/© 2025 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ ). winter cycling is crucial for potential sustainable alternatives to motorized transport. While existing research has extensively examined the role of infrastructure and environmental conditions in facilitating winter cycling—such as the impact of road maintenance and snow clearance (Galway et al., 2021; Chapman and Larsson, 2021; Amiri and Sadeghpour, 2015; Nahal and Mitra, 2018)—far less attention has been given to behavioral interventions that could encourage cycling during the winter. A meta-analysis by Do˘ gru, Webb, and Norman (2021) suggested that behavioral interventions, such as selfmonitoring, can be more effective than infrastructure modifications in increasing cycling uptake. However, little is known about whether such interventions are equally effective under winter conditions, where additional barriers such as cold temperatures, darkness, and icy roads (Bergstr¨ om and Magnusson, 2003; Flynn et al., 2012) may limit their impact. To date, no study has systematically tested behavioral interventions for winter cycling or empirically examined the psychological mechanisms that drive cycling behavior in colder months. 1 To explain the psychological mechanisms underlying winter cycling behavior, this study applies the Stimulus–Organism–Response (SOR) framework. Developed by Mehrabian and Russell (1974), the SOR model suggests that external stimuli (S)—in this case, behavioral interventions—influence internal psychological processes (O), such as cycling identity, safety perceptions, and health considerations, which in turn shape the behavioral response (R)—increased winter cycling. Moreover, this study addresses these research gaps by investigating behavioral interventions aimed at increasing winter cycling volume in Central Europe—a region with well-developed cycling infrastructure, significant seasonal variations in cycling behavior, and strong policy commitments to sustainable mobility (Federation and European Cyclists, 2024). While winter conditions in Central Europe are less extreme than those in Nordic countries are, cycling rates still decline substantially during colder months (Velo-Hotspots, 2025), making this region an ideal setting for testing behavioral interventions aimed at sustaining cycling throughout the winter. Understanding behavioral interventions in this context can offer insights applicable to regions with similar infrastructure and climate conditions while contributing to the broader literature on winter cycling behavior. Given the gaps in research and the potential for behavioral interventions to promote winter cycling, this study aims to identify the underlying factors influencing winter cycling volume and to test the effects of five behavioral interventions designed for its promotion. These interventions—a monetary incentive, social comparison, an additional paid holiday per year, a competition, and goal setting—were chosen on the basis of the behavioral change literature and their practical applicability, allowing organizations and policymakers to implement them with relatively low structural requirements compared with large-scale infrastructure changes. The second section details the development of these interventions. To achieve the research objective, this study addresses the following research questions (RQs): RQ 1 How effective are the following interventions in encouraging more winter cycling: a monetary incentive, social comparison, an additional paid day off per year, a competition, and goal setting? RQ 2.1 Which psychological factors best predict the intention to increase winter cycling volume? RQ 2.2 To what extent do these psychological factors moderate the influence of the tested interventions on the intention to increase winter cycling volume? The empirical results make an important contribution to transportation research, providing valuable insights into active transport, sustainable mobility choices, and behavioral interventions to promote winter cycling. Moreover, they have significant implications for policy-makers, urban planners, companies, and nonprofit organizations interested in promoting cycling, particularly in the winter. The article is organized as follows: Section 2 introduces the theoretical concept of the stimulus–organism–response model, justifies its relevance for examining psychological factors influencing winter cycling intentions, provides a literature review on interventions to promote winter cycling, and links these interventions to internal factors influencing winter cycling behavior. Section 3 details the online experimental procedure. Section 4 empirically identifies winter cycling determinants and analyzes intervention effects on the intention to increase winter cycling volume using ANOVA, exploratory and confirmatory factor analyses, structural equation modeling, and moderation analyses. Section 5 discusses the underlying mechanisms and delves into practical and theoretical implications. Finally, Section 6 examines limitations and offers suggestions for future research. 2. Theoretical Background 2.1. Stimulus-organism-response model The stimulus–organism–response (SOR) model (Mehrabian, 1974) explains human behavior by positing that stimuli (S) trigger internal processes within the organism (O), which in turn influence response behavior (R). While stimuli (S) and responses (R) are directly observable, organism (O) processes—such as involvement, attitudes, activation, emotions and motives—are inferred through empirical indicators (Meffert et al., 2019). Although the SOR model has been widely applied in sustainability and consumer behavior research (e.g., (Raj et al., 2023; Sultan et al., 2021; Wu et al., 2023), its application in transportation mode choices remains limited (Djakfar et al., 2021). However, recent studies have begun integrating the SOR model into cycling research (e.g., Chi et al. (2020), demonstrating its ability to capture both tangible (e.g., infrastructure, incentives) and psychological (e.g., identity, safety perception) determinants (Chang et al., 2011) of 1 Abbreviations throughout this article are used for adverse weather safety concerns (AWSC), health consciousness (HC), intention to increase winter cycling volume (IWCV), stimulus–organism–response (SOR), and winter road safety concerns (WRSC). L. Ebbinghaus et al. Transportation Research Part F: Psychology and Behaviour 113 (2025) 517–535 518 active transport decisions. Since winter cycling behavior is influenced by both external and internal factors, the SOR model provides a suitable framework for examining these complex interactions. In this study, five behavioral interventions—a monetary incentive, social comparison, an additional paid day of holiday, competition and goal setting—serve as stimuli (S) aimed at encouraging winter cycling. Moreover, the SOR model enables the consideration of latent psychological mechanisms (Kroeber-Riel and Gr¨ oppel-Klein, 2013); which influence how individuals process stimuli. Here, cycling identity, winter cycling safety concerns, and health consciousness serve as organism (O) factors, as identified in prior research on cycling behavior, including limited studies on winter cycling. These organism factors are expected to influence behavioral intentions, specifically whether and how much an individual intends to increase winter cycling after exposure to an intervention. The theory of planned behavior (TPB) (Ajzen, 1991) provides a complementary perspective on behavioral intentions by suggesting that intentions are shaped by attitudes toward the behavior, subjective norms, and perceived behavioral control. The strengths of both the TPB and SOR lie in their broad applicability across behavioral contexts (Li, et al., 2025). However, these models are sometimes criticized for not being clear about the meaning of their factors and for not specifying the relative importance of their constructs across different contexts (Fishbein and Stasson, 1990). Research by Bamberg (2003) suggests that situation-specific constructs are stronger determinants of specific behaviors than are general ones (e.g., environmental concerns), supporting the inclusion of contextualized psychological constructs in this research. The selected organism factors—cycling identity, winter cycling safety concerns, and health consciousness—provide such specificity within the SOR framework. The response component (R) refers to actual winter cycling behavior. Prior research has suggested that behavioral intentions are strong predictors of actual behavior, with correlations typically ranging from 0.75 to 0.82 for behaviors involving a choice among alternatives (Ajzen, 1991). Fig. 1 illustrates the theoretical framework undergirding this research. The remainder of this section provides a detailed literature review of each framework component—interventions (i.e., winter cycling stimuli) as well as winter cycling organism and response factors—and their alignment with the SOR framework. 2.2. Winter cycling stimuli Behavioral interventions have shown promise in promoting active transportation (Ciccone et al., 2021), yet findings on their effectiveness remain mixed, varying across contexts and intervention types, and controlled studies integrating psychological determinants of mode choice with incentive-based approaches for promoting active transport remain scarce (Bird et al., 2013). Moreover, while most research has focused on general cycling behavior, little is known about how behavioral interventions can mitigate seasonal barriers to cycling. This study focuses on behavioral interventions as stimuli (S), given their potential to be more effective than (infra)structural Fig. 1. Theoretical framework. Note: The figure illustrates the Stimulus–Organism–Response (SOR) model (Mehrabian, 1974) applied to winter cycling intentions. It shows how behavioral interventions (stimuli) influence winter cycling intentions (response), with psychological and perceptual factors (organism) postulated to moderate this relationship and exert direct effects. AWSC refers to adverse weather safety concerns, and WRSC denotes winter road safety concerns. AWSC captures general concerns about challenging weather conditions, whereas WRSC specifically reflects concerns about road-related hazards such as snow-covered or icy streets. IWCV represents the intention to increase winter cycling volume. L. Ebbinghaus et al. Transportation Research Part F: Psychology and Behaviour 113 (2025) 517–535 519 modifications (Do˘ gru, Webb, & Norman, 2021), particularly in contexts where infrastructure improvements may be costly or impractical in the short term. A systematic review by Bird et al. (2013) identified various behavioral interventions to promote walking and cycling, with mixed results. While self-monitoring and goal-setting techniques were frequently associated with positive effects, interventions using social comparison strategies had inconsistent outcomes, depending on their framing. Similarly, Ciccone et al. (2021) demonstrated that monetary incentives can effectively increase cycling behavior in a field experiment, but the effects differed depending on the type of monetary incentive (fixed rewards vs. conditional lotteries). These findings highlight the need for context-specific investigations into behavioral interventions, particularly in winter cycling, where external barriers such as cold temperatures, precipitation, and road conditions add complexity to intervention effectiveness. To address this gap, this study tests and compares the effects of five interventions aimed at increasing winter cycling intentions: a monetary incentive, social comparison, an additional paid holiday per year, a competition, and goal setting. These interventions were selected on the basis of a comprehensive literature review of behavior change strategies in sustainable mobility and active transportation. Given the limited research on winter cycling, these interventions aimed to clarify whether behavioral interventions can encourage cycling in cold-weather conditions and which type of intervention are most effective. Moreover, these interventions were chosen for their practical applicability, allowing organizations and policy-makers to implement them with minimal structural requirements compared with large-scale infrastructure changes. Grounded in the SOR framework, each intervention represents a stimulus (S) intended to influence winter cycling behavior (R), with its effectiveness shaped by psychological processes (O) such as cycling identity, health consciousness, and safety perceptions (see Section 2.3 for details). The interventions were selected to reflect different pathways to behavioral change by leveraging extrinsic and intrinsic motivation. According to self-determination theory (Deci and Ryan, 1985), extrinsic motivation occurs when an activity is pursued to achieve a “separable outcome” (e.g., monetary incentives, winning a competition), whereas intrinsic motivation stems from engaging in an activity for its “inherent satisfaction” (e.g., achieving a personal goal) (Ryan and Deci, 2000). While both motivation types can drive behavior change (Vaezipour et al., 2019), their relative effectiveness in winter cycling remains underexplored. The following sections provide a detailed discussion of each intervention, their theoretical foundations, and their relevance to winter cycling behavior. 2.2.1. Monetary incentive Monetary incentives primarily leverage extrinsic motivation (Deci and Ryan, 1985). They can boost behavior change (Martin et al., 2012; White et al., 2019) and motivate people to implement sustainable behaviors (Diamond and Loewy, 1991; Wilhite and Ling, 1995; Slavin et al., 1981). Unlike intrinsic motivators, such as goal setting, financial incentives reinforce behavior through tangible benefits (Ryan and Deci, 2000). Monetary incentives can take the form of gifts, cash, and subsidies. Interestingly, definite and probabilistic rewards may produce different effects. For example, in a laboratory experiment by Diamond and Loewy (1991); lotteries evoked significantly more changes in recycling attitudes and more frequent recycling behavior than cash payments did. Some countries have used monetary incentives to promote cycling (Ciccone et al., 2021). In a field study testing different payment schemes for active transportation (i.e., walking and cycling), Ciccone et al. (2021) reported that monetary incentives encouraged more frequent cycling. Participants receiving a definite reward in the form of a flat rate (2 Norwegian kroner, about EUR 17 cents per km cycled) increased their cycling volume by 36 % compared with the control group. This finding raises the question of whether similar effects could be achieved for winter cycling. 2.2.2. Social comparison Humans naturally seek consistency in their behavior but often adapt their actions on the basis of others’ behavior (Festinger, 1954). Social comparison can enhance intrinsic motivation by providing individuals with a benchmark that fosters self-improvement (Zell et al., 2017). According to social comparison theory (Festinger, 1954), individuals evaluate their abilities and behaviors by comparing themselves to others, particularly when objective standards are lacking. Research suggests that people are most influenced by comparisons with others who have similar skill levels or characteristics (Kruglanski & Mayseless, 1990; Suls & Wheeler, 2000). Extensive research has shown that social norms can drive behavioral changes across various domains, including littering (Cialdini et al., 1990; Kallgren et al., 2000), physical activity, environmentally friendly behaviors (Goldstein et al., 2008; Jansson et al., 2017; Schultz, 1999) and travel mode choices (Hunecke et al., 2001). Drawing attention to social norms is often called “nudging,” a concept defined by Thaler and Sunstein (2008) as modifications in the decision-making environment that predictably change individual behavior without limiting options or substantially altering economic incentives. Lehner et al. (2016) advocate for further experimental research on nudges in mobility and travel behavior, given the significant differences in effect sizes found across settings and as their effectiveness in the transportation sector remains underexplored (Lehner et al., 2016). Hence, examining the potential of a social comparison intervention in the context of winter cycling provides an important opportunity to advance the understanding of whether and how social comparison strategies can promote sustainable mobility practices. 2.2.3. An additional paid holiday An additional paid day off serves as an extrinsic motivator, offering a tangible benefit that reinforces behavior through an external reward mechanism. Research on employee incentives shows that paid holidays or time off can effectively motivate individuals to adopt environmentally sustainable behaviors (Govindarajulu and Daily, 2004). Similarly, research on prosocial behavior conducted by Lacetera and Macis (2013) indicates that time off work encourages blood donations. Their investigation of a legal provision granting a one-day paid holiday for blood donors revealed a significant annual increase of approximately 40 % in donations among participants L. Ebbinghaus et al. Transportation Research Part F: Psychology and Behaviour 113 (2025) 517–535 520 who received the incentive. Importantly, this increase persisted even after the incentive eligibility ended, indicating a lasting effect on behavior. Our literature review revealed a lack of detailed investigations into the effectiveness of time-based incentives, specifically paid time off, in promoting winter cycling. However, exploring paid time off as a motivational factor for winter cycling shows promise, as it has been identified as a potential way to encourage cycling, especially among individuals not typically engaged in cycling activities (F´ elix et al., 2019). 2.2.4. Competition Competition may foster both extrinsic and intrinsic motivation, depending on how it is structured. While external rewards (e.g., prizes) provide a clear extrinsic incentive (Ryan and Deci, 2000), competition may also enhance intrinsic motivation when individuals participate for the enjoyment of the challenge itself or the personal satisfaction of improvement. In Chapman and Larsson’s (2021) qualitative study, participants advocated for the implementation of competitions, either between companies or on social media, to increase motivation for winter cycling. Unlike lotteries, where winners are chosen randomly, competitions take place between individuals. In the context of winter cycling, prizes could be awarded to participants covering the greatest number of kilometers. Such competitions are already established in many countries to encourage cycling. For example, in Switzerland, the Bike to Work campaign, organized by the cycling association Pro Velo Switzerland; takes place annually in the summer (May and June). The aim for participants is to cycle to work on as many workdays as possible. In 2023, more than 97,000 people participated, covering more than twentyeight million kilometers, with prizes totaling more than CHF 130,000 (approximately 138,350 euros). Similar initiatives, such as the Sydney Rides Business Challenge in Australia, reward organizations with the highest staff participation percentages. However, these initiatives typically occur in milder weather conditions. Hence, it is worth testing whether competition could be as effective in promoting winter cycling under more adverse conditions. 2.2.5. Goal setting Individuals are more likely to adopt a behavior if they perceive it as instrumental in achieving their goals. Research in industrial and organizational psychology has highlighted the effectiveness of specific, quantifiable goals in improving task performance (Locke et al., 1981). Unlike extrinsic incentives, goal setting primarily enhances intrinsic motivation by reinforcing self-regulation and commitment, encouraging individuals to engage in behavior for personal achievement rather than external reward (Locke and Latham, 2019). Self-monitoring is closely related to goal setting due to reciprocal effects, making it difficult to separate the two. The formal requirement to set goals could lead to informal self-monitoring and vice versa (Morgan, 1987). Both goal setting and self-monitoring are effective tools for behavior change (Bandura, 1986) and involve systematically tracking one’s behavior (Morgan, 1987). Self-monitoring can be a promising tool for prompting behavioral changes (Michie, Abraham, Whittington, McAteer, & Gupta, 2009). Researchers of active travel have found that self-monitoring is particularly promising for increasing walking by enhancing selfefficacy (Du, 2011) and reducing perceived barriers (Wilbur et al., 2003); findings that are also supported by Bird et al. (2013). However, intervention studies remain limited, and it is unclear whether the positive associations of self-monitoring observed in previous research apply to cycling (Bird et al., 2013), especially in the winter. A recurring observation is that goals are most consistently effective in regulating performance when articulated in specific, quantitative terms rather than vague requests for effort (Morgan, 1987). Thus, this study focuses on setting measurable goals to clarify their effectiveness in promoting winter cycling. On the basis of the discussion of stimuli, we propose the following: H1. A monetary incentive, social comparison, an additional paid holiday per year, and goal setting will positively influence cyclists’ intended winter cycling volume. 2.3. Winter cycling organism and response factors The SOR model’s organism component includes an individual’s cognitive and emotive inner life, encompassing values, emotions, scripts, moods, experiences, internalizations, and personality (Jacoby, 2002; Abbott, 2023). These factors can directly impact responses or mediate/moderate external stimuli (Abbott, 2023). Numerous studies have investigated various aspects of cycling behavior, including barriers, motivators, experiences, and patterns (F´ elix et al., 2019; Swiers et al., 2017; Heesch et al., 2012; Nkurunziza et al., 2012; Zander et al., 2013; de Souza et al., 2017). However, academic research on winter cycling remains limited despite growing advocacy and extensive gray literature (Nahal and Mitra, 2018). The scarce scientific attention given to winter cycling behavior—characterized by cold temperatures and challenging weather conditions—raises crucial questions about its barriers and facilitators. Debates on promoting winter cycling often focus on the view that few people ride during colder months (Nahal and Mitra, 2018), yet research findings are mixed (Chapman and Larsson, 2021). Some studies have suggested that cold weather deters cyclists (Bergstr¨ om and Magnusson, 2003; Flynn et al., 2012; Semenescu and Coca, 2022); whereas others have shown that temperature has a minimal impact (Amiri and Sadeghpour, 2015), with year-round cycling possible even in cold-climate communities (Chapman and Larsson, 2021). Similarly, in warmer climates, a threshold must be exceeded before heatwaves reduce cycling (Rabassa et al., 2021). Geographical location (Chapman and Larsson, 2021), personal experience, resilience to weather, and cycling culture may also shape individual responses (Goldmann and Wessel, 2021). Although published work on winter cycling is scarce, a few studies have identified motivational and deterrent factors influencing L. Ebbinghaus et al. Transportation Research Part F: Psychology and Behaviour 113 (2025) 517–535 521 individuals’ decisions to cycle in winter. We discuss these findings next, as they form the basis for further hypotheses in this study. 2.3.1. Cycling identity In their qualitative study conducted in a northern climate city, Galway et al. (2021) reported that cyclists are often motivated by a sense of community and identity associated with cycling. The participants described how interactions with fellow cyclists, such as waving, smiling, or exchanging greetings, created a sense of belonging. This community connection not only encouraged them to start cycling but also motivated them to ride more frequently. Cyclists noted that being part of this broader cycling community reinforced their commitment to making cycling an integral part of their lifestyle. One participant, for instance, stated that his identity revolves around cycling, which he believes motivates him to continue cycling regularly. Similarly, Lois et al. (2015) reported a strong connection between viewing oneself as a cyclist and one’s perceived self-efficacy in cycling, as well as a positive effect on the intention to commute by bicycle. Moreover, Forward (1998) emphasized that transportation choices, such as cycling, are strongly influenced by personal attitudes and lifestyle alignment. In their study, compared with drivers, regular cyclists perceived cycling as more time-efficient and comfortable than driving and were rarely deterred from it. Thus, a sense of identity and commitment to cycling may help cyclists cope with the challenges posed by adverse weather conditions. This aligns with self-identity theory (Stryker and Burke, 2000), which assumes that stronger identification with a particular role or behavior leads to greater behavioral stability, even when external obstacles or challenges arise. Hence, individuals who strongly identify as cyclists may be more inclined to continue cycling during the winter months. 2.3.2. Adverse weather safety concerns Although safety concerns exist year-round, adverse weather conditions and road safety challenges become more pronounced in winter, especially in northern climates, creating significant obstacles to cycling (Amiri and Sadeghpour, 2015; Nahal and Mitra, 2018; Bergstr¨ om and Magnusson, 2003; Pucher and Buehler, 2006; Winters et al., 2007). Winter conditions, including snowfall, extreme temperatures (Nahal and Mitra, 2018), poor lighting, and icy roads (Amiri and Sadeghpour, 2015), present significant barriers. Our exploratory factor analysis (detailed in Section 4.3) identified two distinct dimensions of safety concerns: Adverse Weather Safety Concerns (AWSC) and Winter Road Safety Concerns (WRSC). AWSC captures concerns about general adverse weather conditions that may affect visibility, comfort, and perceived control over cycling. These concerns include cycling in wet conditions, cold temperatures, and poor lighting in winter. Importantly, these concerns are not necessarily based on objective weather conditions but rather on individuals’ subjective perceptions of how challenging these conditions are. For example, Hudde (2023) reported that winter cycling rates are not solely determined by climatic conditions but also by how strongly individuals react to them, which varies across cultures. AWSC aligns with the theory of planned behavior (Ajzen, 1991), which influences perceived behavioral control, a key determinant of behavioral intention. When individuals perceive cold, wet, or poorly lit conditions as hazardous, they may feel less in control of their cycling behavior, reducing their likelihood of cycling in the winter. 2.3.3. Winter road safety concerns Safety is crucial for promoting cycling (Pucher and Buehler, 2008), and perceived traffic danger is a major barrier (Noland, 1995). Differences in accident rates help explain varying cycling rates across countries. For example, lower accident rates in the Netherlands, Germany, and Denmark may account for their higher cycling levels, despite similar car ownership levels (Pucher and Buehler, 2008). While AWSC reflects broader concerns about adverse weather, winter road safety concerns (WRSCs) specifically capture fears related to road conditions in winter, particularly icy and snow-covered roads. However, WRSC is not necessarily based on objective road conditions but rather on how safe individuals perceive winter roads to be. According to risk perception theory (Slovic, 2000; Slovic, 2016), individuals assess risk not solely on the basis of statistical probabilities but also through cognitive biases and emotional responses. Higher perceived risks—such as slipping on ice or encountering unsafe road conditions—can lead to avoidance behaviors, making individuals less likely to cycle in winter. Empirical research supports this: For example, Hudde (2023) demonstrated that cycling behavior is not merely a reaction to objective environmental conditions but rather to how strongly individuals subjectively perceive and respond to them. Accordingly, WRSC reflects an individual’s subjective assessment of winter road conditions rather than objective infrastructure conditions. 2.3.4. Health consciousness Regular cycling is associated with numerous health benefits. As noted by Herman and Larouche (2021), regular cycling is linked to lower risks of various cardiometabolic conditions and cancer as well as reduced overall mortality. Moreover, they found that active commuting to work or school enhances subjective well-being and promotes a more favorable work-life balance. Similarly, Mueller et al. (2015) concluded that active transportation provides substantial net health benefits regardless of geographic context, as the health benefits of increased physical activity far outweigh the potential risks associated with traffic accidents and air pollution. Health benefits appear to be particularly relevant as a motivating factor for winter cycling. Galway et al. (2021) reported that many participants associated winter cycling with physical health benefits such as weight management, improved balance, cardiovascular health, and muscle strength. These benefits were perceived as especially important by cyclists over 50 years of age, highlighting the perceived value of cycling for staying healthy and active, particularly later in life. Similarly, Bergstr¨ om and Magnusson (2003) reported that exercise was the most important reason why frequent winter cyclists continued cycling despite harsh conditions. In addition to physical benefits, mental health benefits have been cited as a motivation for cycling. For example, participants in Galway et al.’s (2021) study noted that cycling provided greater mental clarity, stress relief, and happiness, especially compared to L. Ebbinghaus et al. Transportation Research Part F: Psychology and Behaviour 113 (2025) 517–535 522 nonactive modes of transport such as driving. Some participants reported that cycling reduced work-related anxiety and stress during commutes. From a theoretical perspective, Health Consciousness (HC) aligns with the health belief model (Rosenstock, 1974), which posits that individuals are more likely to engage in and maintain health-related behavior when they perceive its benefits to outweigh potential barriers. Individuals who strongly prioritize physical fitness may see cycling as an essential means of maintaining their health, making them less likely to be deterred by winter conditions. Consequently, individuals who prioritize cycling for its health benefits may be more motivated to continue cycling through the winter. 2.4. Moderation hypotheses We aimed to explore the internal factors discussed—cycling identity, AWSC, WRSC, and health consciousness—as potential moderators to reveal the cognitive and emotional processes through which interventions shape individuals’ intentions to increase winter cycling volume. Building on the discussion of individual winter cycling factors and stimuli, we propose the following: H2.1 – H2.3. The effects of (a) the monetary incentive, (b) social comparison, (c) an additional paid holiday per year, (d) competition, and (e) goal setting on cyclists’ intended winter cycling volume are moderated by (1) cycling identity, (2) health consciousness, (3) adverse weather safety concerns, and (4) winter road safety concerns. 2.5. Control variables We examined whether the associations among key variables remained valid when accounting for control variables. Research on sustainable behavior often uses sociodemographic factors such as gender and age as controls (Jansson et al., 2017). The cycling literature extends this view by considering the influence of both constructed and natural surroundings on individual cycling behavior (Bean et al., 2021; B¨ ocker et al., 2013). Researchers have identified altitude (Semenescu and Coca, 2022), city size (Ljungberg, 1987), and perceived proximity to destinations (Cabral et al., 2018) as factors influencing cycling volume. Broberg and Sarjala (2015) reported that urban density is negatively associated with active transportation. Furthermore, research on gender differences in winter cycling indicates that women cycle less than men do in the winter (Nahal and Mitra, 2018). However, this observation may be overestimated because of men’s higher year-round cycling rates and similar reactions to adverse weather conditions among both genders (Sears et al., 2012). With respect to age, Bergstr¨ om and Magnusson (2003) and Winters et al. (2007) reported that increasing age is associated with lower rates of winter cycling. Additionally, we controlled for language region (French vs. German-speaking Switzerland), as regional differences in infrastructure and cycling culture may influence winter cycling behavior (Hudde, 2023). We also included individual winter cycling adaptations, such as increased winter bicycle maintenance (e.g., chain care after snow and/or salt exposure) and the installation of additional bicycle components (e.g., larger mudguards), to account for variation in personal strategies to cope with winter conditions. Finally, we controlled for the perceived effectiveness of a winter cycling challenge, 2 as participants’ preexisting attitudes toward such initiatives could influence their responses. Fig. 1 illustrates the associations discussed in this chapter. 3. Method We conducted an online survey incorporating a between-subjects experiment. After providing consent, the participants shared details on their general transportation use. The questionnaire subsequently examined their cycling behavior both year-round and in the winter. The experiment next tested and compared the effectiveness of five interventions: a monetary incentive, social comparison, an additional paid holiday per year, a competition, and goal setting. Sociodemographic information was collected at the end of the survey. 3.1. Procedure Pro Velo, a Swiss cycling association, distributed the survey via their online newsletter to approximately 50,000 Swiss people who previously participated in their “Bike to Work” initiative. The survey invitation emphasized prior involvement in the program and asked individuals if they continued cycling to work in the winter. A call to action encouraged survey participation, with a prize detailed below the start button. The participants could win a “Dirtlej Commute Suit” worth 229 Swiss francs (approximately EUR 243) or one of ten Bike to Work mugs, each valued at 20 Swiss francs (approximately EUR 21), as incentives can increase response rates (Galea and Tracy, 2007). Upon following the survey link, the respondents received general information about the study, including its duration (approximately 12–15 min), assurances of data anonymity, and an emphasis on personal opinions—there were no ‘right’ or ‘wrong’ answers. This addition aimed to counteract social desirability bias, which can affect self-reported sustainable behavior (Durmaz, Dursun, & Kabadayı, 2023). The survey, conducted in German and French, targeted participants from German-speaking Switzerland (84.8 %) and French-speaking Switzerland (15.2 %). It screened out respondents who had cycled, on average, less than once a month in the last 12 months or who lacked access to a bicycle. 2 Participants were asked to rate the extent to which the described challenge would motivate people to cycle in the winter. While the item referred to “people” in general (rather than the participants themselves), we included it as a control to capture general openness toward the intervention. L. Ebbinghaus et al. Transportation Research Part F: Psychology and Behaviour 113 (2025) 517–535 523 3.1.1. Measures The questionnaire was drawn from the literature review and theoretical framework outlined in Section 2. We calculated participants’ intention to increase winter cycling volume by measuring the additional number of minutes they intended to cycle after experimental treatment, recorded as a whole number. Cycling identity was assessed using three items adapted from Galway et al. (Galway et al., 2021) and Schahn (Schahn, 1999). Winter cycling safety concerns were evaluated with five items adapted from Galway et al. (Galway et al., 2021) and Amiri and Sadeghpour (Amiri and Sadeghpour, 2015). Health consciousness was measured using three items adapted from Schwartz (Schwartz, 1992) and Herman and Larouche (Herman and Larouche, 2021). Table A.1 (in Appendix A) provides detailed specifications for each construct. 3.1.2. Experimental manipulation During the experiment, the participants were presented with the following hypothetical scenario 3 : “Please imagine the below scenario for the following questions. Pro Velo aims to promote cycling, including in winter. A Winter Bike Challenge is therefore planned. The primary goal of the challenge is to encourage participants to cycle on as many days as possible in February—not only for commuting but also for other daily trips—regardless of weather conditions such as rain, storms, or snow. Distances can be logged manually in an online calendar or automatically tracked via the Bike to Work app. After reading this scenario, the participants were randomly assigned to one of five experimental groups via an automatic randomization procedure in the survey tool. No stratification was applied. Each group received an additional intervention message, which represented different behavioral stimuli designed to encourage winter cycling, as shown in Fig. 2. Irrespective of group assignment, all participants were first presented with their baseline winter cycling volume, which was calculated by multiplying their reported weekly winter cycling frequency (number of trips per week) by the average trip duration (in minutes), on the basis of previously collected data: “You stated that you cycle an average of [baseline value] minutes per week in winter. The goal of the Winter Bike Challenge is to increase this time.” Each experimental group then received an additional intervention message: Monetary incentive (n ¼2,210): “Health insurance companies have partnered with the winter campaign. They will offer a 10 % discount on supplementary insurance to cyclists who increase their cycling time to at least [baseline value +17] minutes per week in winter.”. The 17-minute threshold was chosen for practical reasons, being high enough to suggest a difference but not so high as to be unrealistic or unmotivating. Prior research has indicated that effective comparisons require reference values that feel achievable and relevant to the individual (Suls et al., 2002). Thus, 17 min was selected as a threshold that provided a moderate challenge while still being attainable, ensuring that participants perceived the intervention as motivating rather than discouraging. The choice of a 10 % discount on supplementary insurance was also intentional, as it aligns with conventional norms, and a higher discount could be perceived as unrealistic. Social comparison (n ¼2,199): “You can compare yourself with individuals who are similar to you via the online calendar or app. On the basis of previous data, cyclists with similar characteristics to yours spend an average of [baseline value +17] minutes per week cycling in winter.”. Paid day off (n ¼2,213): “Your employer would be willing to grant an additional paid day off during the Easter period to cyclists who spend at least [baseline value +17] minutes per week cycling in winter.”. Competition (n ¼2,207): “Various sponsors have agreed to provide prizes (e.g., vouchers for weatherproof clothing, bicycle helmets, high-quality lamps) worth up to 150 Swiss francs (approximately EUR 159) to cyclists who rank at the top of the winter cycling leaderboard. On the basis of previous data, at least [baseline value +17] minutes per week of cycling in winter are required to qualify for a prize.”. Goal setting (n ¼2,205): Unlike the other experimental conditions, the goal-setting condition explicitly encouraged participants to define their own cycling targets. The intervention message for this group stated, “You can set personal goals and track your progress using the online calendar or app. Your goal could be to exceed your [baseline value] minutes of cycling per week.”. Following the treatment, participants in all five groups were asked, “How many extra minutes do you think you would spend cycling each week in the winter if you took part in the challenge?”. At the end of the survey, the participants were thoroughly debriefed. 3.2. Analyses The following section presents the results and begins by outlining the sample’s demographic characteristics. Then, aligned with the SOR model, we apply one-way ANOVA to analyze direct intervention effects—i.e., ‘stimuli’—on the intention to Increase Winter Cycling Volume (IWCV). We subsequently identify winter cycling factors and their influence on IWCV using exploratory and confirmatory factor analyses and structural equation modeling. We then use the SPSS PROCESS macro for moderation tests to examine whether these factors moderate the relationship between interventions and IWCV. This mixed-method approach provides profound 3 Translated from German. L. Ebbinghaus et al. Transportation Research Part F: Psychology and Behaviour 113 (2025) 517–535 524 insights into the determinants of winter cycling and intervention strategies to promote it. 4 4. Results 4.1. Demographic characteristics of the sample Among the 11,034 Swiss cyclists surveyed, 55 % were male, with most respondents aged 31–45 years (40 %) and 46–60 years (44 %). Notably, 68 % lived within 12 km of their workplace or educational institution. Table A.2 (in Appendix A) contains further demographic details. 4.2. Direct intervention effects on IWCV We tested Hypothesis H1 using a one-way ANOVA, with the intervention conditions as the independent variable and the additional number of minutes cyclists intended to ride per week in the winter, posttreatment, as the dependent variable. Levene’s test indicated unequal variances F (4,11024) =2.76, p =0.026; therefore, we applied Welch’s ANOVA. This test revealed significant differences in the mean values for additional intended winter-cycling minutes across experimental groups, F Welch (4,5504.67) =3.84, p =0.004. Games–Howell post hoc analysis (Table A.3 (in Appendix A)) revealed a significant difference (p =0.001) between the paid day off group and the social comparison group (10.06, 95 % CI [2.94, 17.19]). The social comparison group had a significantly lower mean IWCV score (M =40.06, SD =78.08) than did the paid day off group (M =50.12, SD =94.56). The mean IWCV scores did not significantly differ among the other groups: the monetary incentive group (M =43.86, SD =78.01), the goal setting group (M =45.17, SD =90.31), and the challenge group (M =45.20, SD =83.5). All interventions increased IWCV, supporting H1. Fig. 2. Experimental groups, stimuli, and randomization process. 4 Implausible values for the outcome variable (IWCV) were identified through distributional checks and removed during preprocessing to ensure data quality. L. Ebbinghaus et al. Transportation Research Part F: Psychology and Behaviour 113 (2025) 517–535 525 Given the sustainability benefits of cycling, the potential for expanding winter cycling, and the urgent need for a drastic transformation toward more environmentally friendly transportation, we hope that our results inspire further research. Future studies could conduct field experiments with tracking mechanisms to assess real-world impact, use broader samples, and employ controlled trials. Additionally, follow-up assessments could be used to evaluate the long-term effects of interventions. Finally, more cross-national studies are needed to further the winter cycling debate. Funding sources This work was supported by the Mercator Foundation Switzerland. CRediT authorship contribution statement Laura Ebbinghaus: Writing – review & editing, Writing – original draft, Visualization, Validation, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Dominik Georgi: Writing – review & editing, Supervision, Resources, Project administration, Funding acquisition. Marcel Zbinden: Writing – review & editing, Resources, Project administration, Funding acquisition. Larissa Dahinden: Writing – review & editing, Resources, Project administration, Investigation. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgement The authors gratefully acknowledge the assistance of the Swiss cycling association Pro Velo in distributing the survey. Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.trf.2025.05.001. Data availability Data will be made available on request. References Abbott, R., et al. (2023). The role of dark pattern stimuli and personality in online impulse shopping: An application of S-O-R theory. Journal of Consumer Behaviour, 22 (6), 1311–1329. https://doi.org/10.1002/cb.2208 Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91) 90020-T Amiri, M., & Sadeghpour, F. (2015). 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