An experiential perspective on uncertainty in peer-to-peer platform services

Consumers worldwide are increasingly using digital peer-to-peer (P2P) platforms ^[1] (Mody et al., 2020; Wirtz et al., 2019), which represent services that offer the infrastructure and rules to connect producers (peers who are creators of the offerings) and consumers (buyers/users of the offerings) (Malthouse et al., 2019; Mody et al., 2020). For instance, in Finland, the Netherlands, Norway, and Denmark, over 30% of consumers used such platforms to sell goods or services to other consumers in 2021 (Eurostat, 2023), while Airbnb surpassed 5 million active hosts in the last quarter of 2023 (Forbes, 2024). With their emergence, P2P platforms have been recognized as contributors to economic empowerment and the advancement of the circular economy (Karpen et al., 2023) as well as a service research priority (Field et al., 2021).

A key characteristic of P2P platforms is consumer uncertainty, which stems from nonstandard offerings, the peer nature of this domain, and the online environment (Dimoka et al., 2012; Fernando et al., 2018; Field et al., 2021). Given the importance of consumer uncertainty, studies have investigated its antecedents (Dimoka et al., 2012; Yan and Gong, 2023), consequences (Fernando et al., 2018; Yeh et al., 2012), and trust-building mechanisms (Chen et al., 2021) in platforms.

Despite the widespread discussion, the platform services literature ^[2] has narrowly conceptualized uncertainty, overlooking the full range of uncertain objects and individual’s reactions to uncertainty. Uncertainty is defined as an absence of information due to information asymmetry (Yan and Gong, 2023), as part of a perceived risk concept (Marth et al., 2022; Wei et al., 2019), or as something individuals avoid or experience negatively (Gupta et al., 2019; Zhang and Srite, 2021). Some studies have not even defined uncertainty, simply assuming that its meaning is clear (Lu and Yi, 2023; Ter Huurne et al., 2017). The current research on uncertainty in P2P platforms centers on decision-making related to product transactions, focusing on product, platform, and service provider uncertainties (Chen et al., 2021; Tang et al., 2023; Yan and Gong, 2023). However, uncertainty may occur beyond decision-making and be present at other stages of the journey with P2P platforms. Hence, a more thorough conceptualization is needed, given that uncertainty influences consumers’ perceptions of, participation in, and decisions about P2P platforms (Fernando et al., 2018; Yan and Gong, 2023; Yeh et al., 2012).

We argue that a customer experience lens (e.g. Becker and Jaakkola, 2020; De Keyser et al., 2020) enables the theorizing of uncertainty in P2P platforms. Such a lens can help overcome the shortcomings of how uncertainty has previously been defined in the platform services literature, capturing the richness of the uncertainty experience through a multidimensional perspective that exceeds information asymmetry. Moreover, it can yield insights into how the uncertainty experience emerges as a response to stimuli under different actors’ control throughout the customer journey. Finally, considering the recursive role of the customer experience in the customer journey (Becker et al., 2020), this lens clarifies how the uncertainty experience influences consumers’ behaviors in their journeys in response to their experiences (e.g. consumers can adopt strategies to reduce uncertainty). Thus, the goal of the current paper is to develop an experiential perspective on uncertainty in digital P2P platform services. Specifically, we aim to answer the following three research questions:

To answer these questions, we conducted a qualitative study through focus groups and in-depth interviews with consumers ^[3] who play dual roles of service providers and customers in P2P platforms. Our findings reveal that uncertainty is a rich, complex, and multidimensional experience directed at multiple objects of uncertainty across the customer journey. The uncertainty experience emerges as a response to different types of stimuli under various actors’ control. We identified the types of consumer strategies to manage this experience in P2P platforms. Finally, we determined the boundary conditions affecting the emergence of uncertainty experience and/or consumer strategies to manage uncertainty experience in such platforms.

Our findings contribute to the literature on platform services and customer experience by offering an alternative experiential perspective to conceptualize uncertainty in P2P platforms. First, by conceptualizing uncertainty as an experience, we move beyond informational uncertainty to the rich assemblage of emotions, thoughts, and sensations and uncover the potential positive nature of uncertainty in P2P platforms. Second, we provide a nuanced understanding of how the uncertainty experience emerges, differentiating the objects of uncertainty from the triggers of the uncertainty experience. Third, we identify consumers’ strategies for managing uncertainty during the customer journey. Finally, we contribute to prior research on P2P platform services by studying uncertainty from the perspective of consumers acting as producers (e.g. sellers or hosts) and customers (e.g. buyers and guests), which is particularly relevant in this area. These insights can help service providers in P2P digital platforms mitigate uncertainty throughout the customer journey and better manage customer experience.

P2P platforms are inherently seen as risky and uncertain environments. The combination of the online environment, inherent information asymmetry, nonstandardized offerings, and P2P exchanges (Chen et al., 2021; Tang et al., 2023) creates greater uncertainty in P2P platforms than in other contexts, such as second-hand offline stores or B2C markets. However, simply acknowledging high uncertainty as a property of an environment is insufficient to fully understand this concept. Therefore, in this section, we examine how uncertainty has been defined in the platform services literature, its emergence, and consumers’ strategies to manage uncertainty.

The literature contains various definitions of and distinct views on uncertainty. We identified four key perspectives that inform uncertainty in P2P platforms (Table 1).

The first perspective represents uncertainty as an inherent characteristic of the context. Uncertainty, while lacking a workable definition, is mentioned interchangeably with risk and lack of information and is strongly connected to trust (e.g. Liu et al., 2023; Lu and Yi, 2023; Ter Huurne et al., 2017). The second perspective focuses on individuals’ ability to handle uncertainty psychologically—that is, the likelihood that individuals would react negatively to uncertain or ambiguous situations. In psychology research, this perspective offers measures such as the Need for Cognitive Closure (Webster and Kruglanski, 1994), the Intolerance of Uncertainty Scale (Carleton et al., 2007), or the Ambiguity Intolerance Scale (SAIS-7) (Pedović et al., 2023). However, studies on platform services rely on the measure of Uncertainty Avoidance (Hofstede, 1984) to examine how this characteristic of an individual member of a culture influences attitudinal and behavioral outcomes in P2P platforms (Gupta et al., 2019; Zhang and Srite, 2021).

The third perspective examines the fundamental uncertainty inherent in decisions, often explored in research through perceived risk (e.g. Wei et al., 2019; Xue et al., 2020). While perceived risk comprises two elements in the consumer behavior literature, namely, uncertainty about and seriousness of undesirable consequences (Conchar, 2004; Dowling and Staelin, 1994), not all platform studies explicitly mention uncertainty in their perceived risk definitions. Instead, they refer to the probability or the likelihood of future negative events (Marth et al., 2022; Mittendorf, 2017).

Finally, uncertainty in the platform services literature is predominantly addressed from the perspective of information asymmetry, focusing on situations where “the information available deviates from the consumer’s ideal information state” (Shiu et al., 2011, p. 585). Many platform studies have adopted Dimoka et al.’s (2012, p. 399) definition of uncertainty as “the buyer’s difficulty in predicting the outcome of an online transaction due to seller-related and product-related information asymmetry.” Information asymmetry can be defined as buyers perceiving “sellers to have a greater quantity or quality of information than they have” (Pavlou et al., 2007, p. 111). This perceived absence or imbalance of essential information hinders decision-making, creates uncertainty, and impedes the desired market behaviors (e.g. Kanani and Glavee-Geo, 2021; Tang et al., 2023).

The abovementioned perspectives have strengths that can contribute to a better understanding of uncertainty in P2P platforms. However, each perspective may be limited due to the narrow focus. For instance, the perceived risk perspective treats the range of uncertain objects and situations when uncertainty emerges narrowly, focusing on the uncertainty of negative outcomes arising from a purchase. While considering a broader range of what can be uncertain, information asymmetry disregards the range of potential reactions to uncertainty, offering an overly cognitive and information-focused perspective. The uncertainty avoidance perspective focuses on an individual’s characteristic and negative reactions to uncertainty, ignoring the potential positive valence of reactions and the range of situations or objects involved.

Regarding the process by which uncertainty emerges, extant research broadly agrees that the main reason is either a lack of information or conflict of information (e.g. Fernando et al., 2018; Li and Fang, 2022). The literature frequently addresses a lack of information about service providers (e.g. trustworthiness, morality, and ability to provide good service) (e.g. Bai et al., 2015; Kanani and Glavee-Geo, 2021; Tang and Lin, 2019), the offering (e.g. hygiene of products, product fit or performance, and quality of accommodation) (e.g. Fernando et al., 2018; Xu et al., 2014; Yan and Gong, 2023), consumers (Mittendorf, 2017; Ter Huurne et al., 2017), the online context (e.g. information security and privacy) (Lu and Yi, 2023; Marth et al., 2022), and platform transparency and functioning (Tang et al., 2023; Wei et al., 2019).

Studies have indicated that, as factors influencing uncertainty, strong relationships with and trust in the platform and the seller community mitigate some of these uncertainties (Lu and Yi, 2023; Wei et al., 2019). Additionally, trust-building mechanisms (e.g. reputation systems and guarantees, support and delivery services) (Chen et al., 2021; Fernando et al., 2018; Perren and Kozinets, 2018), visual and textual information quality (e.g. product descriptions and third-party infomediaries), and social support (through comments and experience sharing) may reduce uncertainty (e.g. Dimoka et al., 2012). Nonetheless, the literature does not clearly differentiate between uncertainty triggers (e.g. what triggers the perceived lack of information about a product) and objects of uncertainty (e.g. the product/service features customers do not know about). Thus, such studies do not clearly identify how uncertainty emerges throughout a customer’s journey.

The literature also offers insights into the strategies consumers use to manage uncertainty in P2P platforms. The information asymmetry perspective reveals that consumers experiencing uncertainty search for information about offerings, platforms, partners, and other participants (Yan and Gong, 2023). Using uncertainty reduction theory, Yan and Gong (2023) deconstructed strategies into passive, active, and interactive, all aimed at gathering information (e.g. consumers may passively browse other consumers’ reviews, request information about a counterpart from a third party, or interact with their counterpart directly).

Meanwhile, the perceived risk perspective (Mitchell, 1999) explains how consumers react to uncertainty, either by taking risks or avoiding them, such as avoiding or delaying purchases until more information is available (Kanani and Glavee-Geo, 2021; Tang and Lin, 2019). Despite these insights, current research on platform services has focused more on attitudinal outcomes, failing to fully explain how consumers deal with uncertainty in this context (Marth et al., 2022; Wei et al., 2019).

In this paper, we theorize uncertainty in P2P platforms using customer experience as an enabling lens (Becker and Jaakkola, 2020; De Keyser et al., 2020). Customer experience refers to consumers’ spontaneous responses and reactions to offering-related stimuli throughout their journey (Becker and Jaakkola, 2020; De Keyser et al., 2020). These reactions can be affective, sensorial, cognitive, behavioral, and social (De Keyser et al., 2020) and are directed toward something specific at a touchpoint in the customer journey (Gahler et al., 2023). A customer journey represents the entire process before, during, and after a purchase (Lemon and Verhoef, 2016). Touchpoints represent moments in which consumers interact directly or indirectly with a firm, organization, or seller (Becker and Jaakkola, 2020), varying in the level of a focal firm’s control (De Keyser et al., 2020). At each touchpoint, consumers respond to offering-related stimuli, which are the smallest units influencing customer experience, such as product descriptions and images (Bolton et al., 2014).

Adopting a customer experience lens can enrich the understanding of uncertainty in P2P platforms for several reasons. First, we add an experiential perspective to the existing narrow views in this context, allowing us to describe how uncertainty is experienced during the customer journey in ways that go beyond conceptualizations of it as information asymmetry or an element of perceived risk (e.g. Fernando et al., 2018). We can also question the predominant focus on negatively valenced feelings associated with uncertainty (e.g. discomfort, tension, nervousness, and anxiety) in marketing and psychology research by exploring all potential experiential responses. Second, we can identify stimuli triggering uncertainty that are controlled by diverse actors. While the literature has focused on the lack or conflict of information as a trigger for uncertainty in the early stages of the customer journey (e.g. Lu and Yi, 2023), a customer experience lens can offer a more granular description of these triggers, leading to better account of how uncertainty experience emerges throughout the journey. Third, we can expand the understanding of uncertainty’s recursive role in the customer journey, thus identifying behaviors in the journey as responses to uncertainty experience (Becker et al., 2020). Overall, the use of a customer experience lens contributes to a more holistic and expanded understanding of the nature of uncertainty in P2P platforms.

To achieve our research purpose, we followed the principles of enabled theorizing (Dolbec et al., 2021) and systematic combining (Dubois and Gadde, 2002). These are similar approaches in that the former refers to the use of previous research to inform data collection and analysis (Dolbec et al., 2021), while the latter relies on the iterative interplay between theory and empirical observations to develop new concepts and models (Dubois and Gadde, 2002). Both refer to abductive approaches, with the goal of developing new theoretical perspectives (Dolbec et al., 2021; Dubois and Gadde, 2002). We chose enabled theorizing with a customer experience lens, which brings a particular vocabulary (e.g. customer experience, customer journey, touchpoints; Becker and Jaakkola, 2020) that sensitizes researchers and helps them promote new ways of understanding a phenomenon (Dolbec et al., 2021). Systematic combining involves combining, in an iterative process, empirical data and customer experience as an enabling lens in the generation of a new experiential perspective on uncertainty (vs. the confirmation of a theory) (Dubois and Gadde, 2002).

We collected data through focus groups and semi-structured interviews between March and October of 2023. Focus groups are suitable for collecting data on customer experience because they generate rich qualitative data (Hague et al., 2013) and can explore informants’ experiences (Trevinal and Stenger, 2014), while in-depth interviews—one of the most commonly used qualitative data collection methods to capture customer experiences (Becker, 2018)—are appropriate for generating rich data for theorizing purposes (Miles and Huberman, 1984). While both data collection techniques provide rich data about experiences, they also have complementary features in that focus groups allow participants’ joint discussion to enrich the findings (Hydén and Bülow, 2003), while interviews create a comfortable climate that allows interviewees to speak more freely without social pressure (Överlien et al., 2005).

We used purposive sampling (Belk et al., 1988) to select participants who used P2P platform services in both roles (e.g. as a guest/host on Airbnb or as a buyer/seller on Facebook Marketplace) at least once. We recruited the informants through a combination of social media posts, snowballing technique, and personal connections. For the focus groups, our sampling criteria sought to achieve homogeneity within groups (Hague et al., 2013) and variation between groups, with each group focusing on a single, distinct platform. We investigated diverse types of P2P platforms (e.g. services and products as objects of exchange and supporter and connector platforms ^[4]; Yrjölä et al., 2021) to capture the potential facets of uncertainty experience that would not emerge in a single context (Table 2). As recommended (Hague et al., 2013), we conducted seven focus groups with 3–7 informants and interviewed 10 people, for a total of 41 informants. Table 3 presents information about the informants, and Web Appendix A presents more detailed information about their platform behaviors.

The focus groups lasted between 2.5 and 3 h, while each interview lasted between 30 min and 1.5 h. Initially, we asked questions about customers’ journey and experience in P2P platforms, following the touchpoints and experiences throughout the entire journey for both roles (e.g. buyer/seller, passenger/driver, and guest/host). As emerging findings about uncertainty became clearer, we focused our questions in the later stages of data collection to capture the uncertainty experience in more depth (Dubois and Gadde, 2002). In the focus groups, the informants were encouraged to share their experiences using an experience chart. The experience chart and an example of a script for product platforms are shown in Web Appendix B.

After transcribing all the focus groups and interviews, we first coded the data using customer experience concepts (e.g. touchpoints, stimuli, experiences, and contextual factors) as the enabling lens (Dolbec et al., 2021). As themes about uncertainty experience emerged, we let them guide the next steps in the data collection and analysis (Dubois and Gadde, 2002), enabling us to code and recode data into more specific themes that also emerged (e.g. uncertainty experience, strategies, and objects of experience). Second, we wrote narrative memos and memos around key concepts to share data among the authors, including themes from the raw data. Third, two authors read, compared, and discussed the themes in the memos to group them into higher-order themes and concepts, thus identifying uncertainty experience as the core theme of our model, along with related concepts, their properties, and dimensions. An example of this coding process is presented in Figure 1. Finally, we conducted nine member checks, providing evidence that our data interpretation was adequate and that no important uncertainty experience theme was missing. While described linearly, the data collection and analysis were iterative processes of going back and forth between the data, emerging themes, and the literature (Dubois and Gadde, 2002).

In this section, we present the emergent key themes from our study: uncertainty experience, uncertainty triggers, strategies to manage uncertainty in digital P2P platforms, and boundary conditions. Web Appendix C presents additional quotes.

The key component of uncertainty experience is the feeling of not knowing something. Our data reveal that this feeling emerges at several points in the customer journey, regardless of the role adopted (e.g. buyer/seller or host/guest). This was illustrated by our informants, who discussed their experiences as Airbnb hosts:

However, adopting a customer experience lens reveals that uncertainty goes beyond the feeling of not knowing, as shown in the previous quote. Consumers experience uncertainty in P2P platforms affectively (e.g. “scared”), cognitively (e.g. “concerned”), behaviorally (e.g. “feeling like running away”), and/or sensorially/physically (e.g. “like choking”), as shown in the consumers’ expressions when talking about their uncertainty experience (e.g. feeling afraid, stressed, worried, suspicious, unsure, restless, and anxious):

While commonly described as negative, uncertainty experience can also be positive in P2P platforms. Our data show that it can be described as excitement, hope, or even as a thrilling/exciting feeling in response, for example, to the possibility of finding a bargain or a unique/exclusive product:

Furthermore, uncertainty experiences vary in intensity, from low (e.g. “a little uncomfortable,” “a bit tense,” and “feel a kind of fear”) to high (e.g. “truly excited,” “very scared,” and “very stressing”):

Given how our informants described their experiences of uncertainty, we argue that uncertainty experience consists of a combination of its key component—the feeling of not knowing—with a customer experience (e.g. an affective experience). Uncertainty experience varies in valence (neutral, negative, or positive) and intensity (e.g. from low to high intensity) (De Keyser et al., 2020). Therefore, we argue that in P2P platforms:

However, uncertainty experience cannot be adequately described without understanding what the consumer does not know (about). Such an experience is directed at objects that can be identified by answering the following question: “What is it that the consumer does not know (about)?” Our data show that such an experience can be directed at objects before service/purchase (e.g. not knowing how to price), during service/purchase (e.g. not knowing whether the counterpart is going to show up), and after the service/purchase (e.g. not knowing what to expect from the review). These objects of uncertainty can refer to oneself (e.g. not knowing if one will find a meeting point), the offering (e.g. not knowing the conditions of the accommodation), the counterpart (e.g. not knowing their intentions), the platform (e.g. not knowing if one can be removed by not following the rules), and even partners in the customer journey (e.g. not knowing whether a product is going to be safely delivered).

Uncertainty experience can be directed at past, present, or future objects in the customer journey. For example, uncertainty experience commonly emerges at the pre-purchase or pre-core service encounter stage, referring to objects of uncertainty that are part of the purchase or core service encounter (e.g. uncertainty about how neighbors in an accommodation may behave is experienced while searching for the accommodation). Uncertainty about past events can also emerge. For example, a host can experience uncertainty in the post-core service encounter stage about something that may or may not have happened in the service stage:

These objects of uncertainty also vary in their levels of specificity. Sometimes, consumers merely suspect something is “wrong,” “weird,” or “funky,” giving a vague description of the object. At other times, the objects of uncertainty are described in a specific manner and are clearly identifiable (e.g. whether a consumer will smoke, party, or be noisy; whether a driver made a mistake; or whether someone might leave them a negative review):

Significantly, these objects of uncertainty can be intertwined. For example, consumers may feel uncertain about the seller’s honesty, which makes them uncertain about the product’s conditions. Therefore, we propose the following:

Uncertainty experience emerges as a reaction to offering-related stimuli (cf. Becker and Jaakkola, 2020). Our data show varied uncertainty triggers controlled by different actors (i.e. platform, counterpart, other consumers, and partners), which can influence uncertainty experience positively or negatively throughout the customer journey.

Platform stimuli influence the uncertainty experience. For example, trust-building mechanisms, such as guarantees, payment systems, and automatic booking (Shen et al., 2011), reduce the uncertainty experience. Moreover, stimuli related to the platform interface, such as automatic translation and ease of use, can positively or negatively influence such experiences:

Varied counterpart stimuli also act as uncertainty triggers, including the person’s characteristics (e.g. appearance and personality), profile (e.g. presence of pictures and friends in common), communication and interaction (e.g. responsiveness, honesty, and use of language), and behavior (e.g. delay and reckless driving), as well as stimuli related to the offering and listing (e.g. pictures in the listing and accommodation). For example, certain informants explained how counterpart stimuli made them suspicious of scams or triggered uncertainty about the seller and product conditions:

Other consumers’ stimuli also influence the uncertainty experience. In particular, the presence and content of ratings, reviews, and comments can make consumers more/less uncertain in their customer journeys. Moreover, where consumption is shared, other consumers’ appearances, interactions, and behaviors can influence the experience:

Our data reveal that partners and even external stimuli can also influence the uncertainty experience. For example, delays in delivery and product treatment relate to the delivery partner and can influence the experience:

Our data also show cases in which distinct stimuli simultaneously and interactively influence the uncertainty experience (cf. Becker and Jaakkola, 2020). For example, platform stimuli mitigate the uncertainty experience triggered by a counterpart’s stimuli:

Based on the abovementioned information, we argue that in P2P platforms:

Our data reveal that consumers use four types of strategies to manage their uncertainty experience throughout the customer journey, often seeking to reduce it when it is perceived as negative: (1) information search, (2) anticipation and precaution, (3) avoidance, and (4) acceptance.

Information search refers to behaviors inside and outside the platform to reduce information deficit. Consumers search for information in reviews about, for example, prices, counterparts, and products, often adding touchpoints to their customer journeys. This information search can be more passive (e.g. by checking a counterpart’s profile) or active (e.g. by asking questions). When conducting an information search, consumers encounter new triggers that influence their uncertainty experience. For example, consumers commonly check the prices of similar products and services in the platform’s listings or on other platforms, websites, and stores, encountering product stimuli that influence their experience:

Anticipation and precaution refer to consumers imagining different possibilities of uncertain outcomes and then proactively doing something to address them, often adding touchpoints to the journey or changing how they engage with common touchpoints. For example, consumers on second-hand platforms (especially women) often take precautions concerning potential outcomes during face-to-face meetings with counterparts. They may meet in a public or crowded place, have another person accompany them, or disclose the location only when their counterpart is on their way (i.e. changing how they engage in common touchpoints). Consumers can also anticipate that products might be contaminated and take precautions to avoid infestation. Sellers may anticipate potential complaints if products are not delivered as promised, so they take pictures of the packaged products as proof. Airbnb hosts can imagine what their guests are like and what they value before deciding how to list and clean their accommodations, thus adding touchpoints to their journeys.

Avoidance behaviors reduce uncertainty through the avoidance of or disengagement from touchpoints. When facing uncertainty regarding the likelihood of potential negative outcomes, consumers can simply terminate the customer journey with their counterparts. Alternatively, they may completely avoid specific product categories they consider uncertain:

Finally, acceptance is used as a strategy to cope with uncertainty. An attitude of “loss assumption” was prevalent in our data. As uncertainty is a characteristic experience throughout the customer journey, consumers become accustomed to, expect, and tolerate uncertainty and negative outcomes. Thus, they proceed on their journeys and engage in touchpoints with an accepting attitude despite the uncertainty:

Given the above information, we argue that in P2P platforms:

We identified two key boundary conditions influencing uncertainty experience, uncertainty triggers, and/or strategies to manage uncertainty experience: (1) type of platform and (2) individual characteristics.

The type of platform (connector or supporter) influences the uncertainty experience through uncertainty triggers. Connector platforms, such as Facebook Marketplace, often lack trust-building mechanisms, relying mostly on user reviews and private messaging for trust-building (Perren and Kozinets, 2018; Yrjölä et al., 2021). Conversely, supporter platforms, such as Vinted and Airbnb, present a range of trust-building mechanisms, including ID verification, guarantees, and insurance policies, which actively address consumer uncertainty (Perren and Kozinets, 2018; Yrjölä et al., 2021). These trust-building mechanisms reduce uncertainty experience and act as buffers, minimizing the impact of other triggers interactively. Naturally, they can also shape the strategies consumers use to manage uncertainty. For instance, in cases where the platform secures payments or offers guarantees, consumers feel less of a need to take precautions against monetary losses:

Our data also show four individual characteristics that influence consumers’ uncertainty experiences and strategies: (1) demographic characteristics, (2) personality traits, (3) previous experience, and (4) level of involvement with an offering.

Demographic characteristics, such as gender and age, were recognized as factors influencing the intensity of uncertainty experience. Women, especially, tend to experience more intense uncertainty when not knowing men’s intentions while interacting with them. Furthermore, age can influence how uncertain one is:

Personality traits also influence the degree of uncertainty experienced by consumers and their strategies. The ability to tolerate uncertainty—a trait often measured in psychology research (e.g. Carleton et al., 2007; Pedović et al., 2023)—seems to influence many consumers. However, the participants also mentioned other personality traits, such as proactivity, risk-taking and aversion, and propensity to trust:

Previous experience also influences consumers’ uncertainty levels and strategies: the more previous experience consumers have, the less uncertainty they experience (e.g. how to rate or prepare an accommodation). Specifically, not knowing something is not coupled with negative experiences (e.g. fear) when consumers are more experienced. Moreover, previous experience influences consumers’ strategies. Based on their previous experiences, they learned how to use these strategies to gain product/service and platform knowledge (e.g. what kind of information/visual cues to look for, and what trust-building mechanisms are available at the platform):

The level of involvement with the offering can also influence uncertainty experience, especially regarding consumers’ strategies. Involvement with the offering may be due to the value or importance given by consumers to a product based on their interests and needs (Serravalle et al., 2023). In general, one’s involvement with the offering is higher when the associated price and risks are higher, leading to a greater information search (Peng et al., 2019). The higher the level of consumers’ involvement, the more likely they are to intensify their strategies to manage uncertainty:

Given the previous information, we propose the following:

In the previous section, we described the uncertainty experience in P2P platforms, its emergence, consumer strategies to manage it, and boundary conditions. Together, these key themes form the conceptual framework presented in Figure 2.

Premise 1 answers RQ1: How can uncertainty be defined as an experience in P2P platforms? Central to the conceptual framework, uncertainty experience consists of the feeling of not knowing and a customer experience that varies in terms of dimension, valence, and intensity (Premise 1). This definition of uncertainty experience goes beyond common perspectives in the platform services literature (Table 1) and responds to broader calls for uncertainty research to address both informational (a deficit in knowledge) and subjective (a feeling of not knowing) aspects of uncertainty (Bar-Anan et al., 2009).

Our conceptualization also challenges how consumers’ uncertainty-related feelings and thoughts have previously been addressed. Marketing and psychology research have considered experiences of fear, anxiety, wariness, and hesitation—mostly negative experiences—as outcomes of uncertainty (Tang and Lin, 2019; Knowles and Olatunji, 2020; Wei et al., 2019). Only a recent conceptual work in psychology has questioned the valence of anxiety, suggesting that uncertainty comprises a mix of opposite emotions, such as “fearful hope,” representing a more complex experience (Vazard, 2024), while uncertainty management theory recognizes uncertainty as neutral (Brashers, 2001). Finding excitement, hope, thrill, and titillation demonstrates that uncertainty experiences are more complex than previously explored.

Our conceptual framework also shows the objects—what the consumers do not know (about)—to which uncertainty experience is directed: oneself, the offering, the counterpart, the platform, and the partners (Premise 2). Uncertainty about oneself is similar to personal uncertainty, defined in psychology as “an aversive feeling that you experience when you feel uncertain about yourself” and separated from informational uncertainty (Van Den Bos, 2009, p. 197). This aspect has not previously been addressed in the literature due to its overwhelming focus on the informational perspective.

Objects of uncertainty vary in their level of specificity (Premise 2). Moreover, these objects can take place in the past, simultaneously, or in the future within a customer journey, represented by the crossing arrows in the conceptual framework. Both factors are relatively understudied in the platform services literature. Uncertainty is typically related to objects in the future (e.g. Fernando et al., 2018), while e-commerce research seldom mentions post-purchase uncertainty, reflecting consumers’ lack of confidence about the appropriateness of their past choices (Littler and Melanthiou, 2006). As for understanding specificity levels, risk and uncertainty research suggests that a lack of information or ignorance about possible outcomes increases decision ambiguity (Camerer and Weber, 1992). In other words, not knowing what awaits might be experienced even more acutely than not having information about something in particular.

Uncertainty experience also emerges as a response to various stimuli (and their interactions) under different actors’ control: platform, counterpart, other consumers, partner, and external (Premise 3; upper part of Figure 2), thus answering RQ2: How does uncertainty emerge in the journey with P2P platforms when adopting an experiential perspective? While the platform services literature has considered some of these stimuli (Dimoka et al., 2012), especially trust-building mechanisms and counterpart stimuli (e.g. Perren and Kozinets, 2018), the literature has failed to differentiate between the stimuli triggering uncertainty and its objects. This distinction is valuable, because triggers emphasize why uncertainty emerges, while the objects emphasize what the potential uncertain outcomes are, both contributing to how consumers experience uncertainty.

Premise 4 answers RQ3: What strategies do consumers use to manage uncertainty during the customer journey with P2P platforms? Consumers in both roles (e.g. sellers and buyers) employ information search, anticipation and precaution, avoidance, and acceptance strategies to manage uncertainty throughout the customer journey (Premise 4; lower part of Figure 2). The literature has focused on information search strategies in accordance with uncertainty reduction theory (e.g. Berger, 1979; Berger and Cabresse, 1975) and uncertainty management theory (e.g. Yan and Gong, 2023).

Other strategies have been less commonly studied. For example, while the platform services literature recognizes consumers’ anticipation of second-hand products being tainted by the scent of previous owners (Xu et al., 2014), it does not consider the precautions taken by consumers to address these uncertainties. Regarding avoidance strategies, only a few studies have considered consumers’ avoidance behaviors (e.g. not using a P2P platform or purchase deferral) (Gupta et al., 2019; Zhang and Srite, 2021). Some avoidance strategies are consistent with instrumental conditioning (Skinner, 1938) in that consumers learn to avoid the same outcomes based on previous behaviors, which result in negative experiences. Acceptance strategies have been identified in the psychology literature, such as when individuals adapt to chronic uncertainty derived from a difficult economic situation (Li and Song, 2024) or a severe disease (Rains and Tukachinsky, 2015). However, such chronic uncertainty responds to stressful life events far away from the temporary stressor, causing uncertainty in P2P platforms. Emotion-focused coping, which is directed at regulating emotional responses due to a stressful event and maintaining affective equilibrium (Lazarus and Folkman, 1984), resembles the acceptance strategies identified in this context, as there is no attempt to reduce the key component of uncertainty (i.e. not knowing), only the attenuation of the accompanying negatively valenced experiences.

The conceptual framework presents a recursive link between strategies and new stimuli encounters in the customer journey. By employing strategies to manage uncertainty experience, especially information search and anticipation and precaution, consumers turn to extra touchpoints (e.g. conduct an information search outside the platform) or engage with a common touchpoint (e.g. pay through the platform). With these strategies, they encounter new stimuli, which then act as triggers for the uncertainty experience and reduce it. For example, an uncertain seller (uncertainty experience at t₁) who investigates their counterpart’s Facebook profile (strategy at t₂) will encounter a range of new stimuli (e.g. picture, mutual friends, and date of profile creation) that can recursively influence the uncertainty experience by reducing it (uncertainty experience at t₂). This process demonstrates the recursive influence of such strategies on the uncertainty experience. This has been previously proposed by Becker et al. (2020), who found that customers use their current experiences as drivers for subsequent behaviors in the customer journey in iterative, recurrent processes.

Finally, our conceptual model accounts for boundary conditions, adding important nuances to how uncertainty emerges (RQ2) and consumers’ strategies to manage it (RQ3). Specifically, the type of platform (supporter vs. connector) and individual characteristics (demographic characteristics, personality traits, previous experience, and level of involvement with an offering) influence the emergence of consumers’ uncertainty experience and/or strategies to manage it (Premise 5; left side of Figure 2). Such findings are consistent with customer experience literature, which agrees that contextual factors ranging from individual/internal (e.g. personality traits) to broader/external (e.g. type of platform) influence the customer experience (Becker and Jaakkola, 2020; De Keyser et al., 2020).

The type of platform (supporter vs. connector) illustrates how stimuli interactions can influence uncertainty experience. For supporter platforms, trust-building mechanisms act as buffers that compensate for, for example, the lack of information about the counterpart. While the influence of interactive stimuli has been recognized in the customer experience literature (Becker and Jaakkola, 2020), especially in a multiplicative way (Bolton et al., 2014), our findings suggest that causal complexity, which considers necessary and sufficient conditions and the right combinations of stimuli for an outcome to occur (Meyer et al., 1993), might be appropriate for understanding the emergence of uncertainty experience.

Personality traits have also been addressed in the psychology and consumer research literature (e.g. Pedović et al., 2023). The ability to tolerate uncertainty and ambiguity as a personality trait has been studied in consumer research with regard to its effects on attitudes. Factors such as ambiguity tolerance, consumer familiarity, and perceived behavioral control play roles in shaping consumer attitudes toward ambiguous products (Wang et al., 2018). Ambiguity tolerance also predicts prosocial behavior, with more tolerant individuals engaging in costly cooperative and trusting actions (Vives and FeldmanHall, 2018).

The e-commerce literature has shown that previous experiences help consumers learn whether and how strategies can be used to manage uncertainty (e.g. Mudambi and Schuff, 2010). Thanks to the learning process based on previous experience (Skinner, 1938), consumers can better identify strategies to manage uncertainty. While the platform services literature has mainly focused on how consumers base their information search strategies on previous experiences, we have shown that they can also learn from such experiences to take precaution (e.g. meeting their counterpart in a crowded place) and avoidance strategies (e.g. not paying outside the platform).

Regarding involvement with the offering, our findings align with psychology research, which argues that people engage with coping strategies, such as emotion-focused strategies, denial, or cognition avoidance, when involvement is high (Li and Song, 2024). In marketing, research has found that involvement in uncertain contexts leads to higher information search (Peng et al., 2019). The present research has identified that, in P2P platforms, high involvement can increase uncertainty, driving consumers to apply information search strategies and take precautions during payment.

Our conceptual framework (Figure 2) contributes to the platform services and customer experience domains in service research in several ways. First, we contribute to the platform services literature by introducing an experiential conceptualization of uncertainty, thus moving beyond the commonly accepted perspective based on information asymmetry (Dimoka et al., 2012), advancing the literature that has considered uncertainty as a negative response with negative outcomes (e.g. Tang and Lin, 2019), and adding to conceptual formulations stating that an experience can be considered positive or negative depending on contextual factors (Becker and Jaakkola, 2020).

Second, we describe how the uncertainty experience emerges throughout the entire customer journey. The findings reveal that uncertainty experience emerges as a reaction to (interactions of) offering-related stimuli throughout the journey, thereby advancing the literature that has traditionally focused on the pre-purchase stage of the customer journey (e.g. Tang et al., 2023). While the literature has previously referred to sources of uncertainty without identifying them as objects—that is, what consumers do not know about (e.g. product quality, Fernando et al., 2018; sellers’ opportunistic behavior, Shen et al., 2011; or platform transparency, Yeh et al., 2012), we also differentiate between uncertainty triggers (stimuli that influence the uncertainty experience) and objects of uncertainty (what the uncertainty is about), providing a more nuanced description of the emergence of uncertainty experience. Such a differentiation contributes to the customer experience literature by exploring objects of customer experiences that, so far, have been undefined or assumed to be a brand or firm (e.g. Lemon and Verhoef, 2016).

Third, we identify strategies used by consumers to manage uncertainty experience in their customer journeys, thus advancing the literature that has focused on information search (e.g. Yan and Gong, 2023). In the customer experience literature, these strategies showcase the active role played by consumers in shaping their own customer experiences through the recursive role of such an experience in the customer journey (Becker et al., 2020).

Fourth, we contribute to the platform services literature by considering dual roles (e.g. sellers/buyers and hosts/guests) in our conceptual framework. Our findings show that consumers in both roles experience uncertainty similarly as a response to a variety of stimuli and that both employ the same categories of strategies to manage uncertainty. This is a significant point of departure from the perspective of information asymmetry in the literature, which tends to assume that customers have less information than providers (e.g. Dimoka et al., 2012; Pavlou et al., 2007; Shiu et al., 2011). However, in P2P platforms, both sides lack relevant information about their counterparts.

Our findings provide interesting managerial implications for both P2P platforms and the consumers who participate in them. First, our findings highlight the fundamental and multidimensional role of uncertainty experience in platform services. Consequently, P2P platform managers should recognize the holistic nature of uncertainty that goes beyond information gaps and develop an in-depth understanding of the dimensions, valence, and intensity of consumers’ uncertainty experiences. Importantly, uncertainty is not inherently negative; when things go well, this experience can create a competitive advantage in relation to the consumption of new products or services. Overall, acknowledging uncertainty as an experience is a critical starting point, for example, in platforms’ customer journey development.

Second, identifying the uncertainty triggers offers a concrete avenue for improvement for all actors involved in P2P platforms. While some stimuli fall within the platform’s control, others are outside its direct control, such as consumer and partner stimuli. Thus, actors engaged in P2P platform services are encouraged to meticulously evaluate their verbal and nonverbal communication and their impact on eliciting stimuli. For instance, platforms should educate consumers about conspicuous stimuli that generate negative uncertainties. In pursuit of this objective, consumers in diverse roles could derive value from conventional customer journey mapping methodologies meant to dissect uncertainty. Alternatively, platform managers might consider offering such mapping services to facilitate this understanding for their users.

Third, consumers employ different strategies to manage uncertainty, but managers may lack guidelines for supporting these, especially for anticipating and taking precautions, as well as avoidance and acceptance behaviors. Consumers proactively imagine and evaluate different scenarios that prompt them to take steps to mitigate negative outcomes (e.g. bringing a friend along to meet a seller). These measures represent the costs of uncertainty for consumers, so companies should develop trust-building mechanisms, such as seller verification and secure payments, throughout the customer journey to help reduce these costs. Avoidance behaviors, in turn, represent a critical issue for platforms, as they limit growth opportunities when consumers avoid, for example, certain product categories or abandon their journeys altogether. To identify this behavior, platforms can interview consumers who actively browse certain categories while refraining from others or investigating clickstreams (i.e. listings that are clicked on but not bought). The ability to identify and address the causes of avoidance will open up new opportunities to innovate mechanisms that can result in new competitive advantages.

While this research developed an experiential perspective on uncertainty in P2P platforms, it is not without limitations. First, our conceptual framework was derived from enabled theorizing and systematic combining, with the goal of developing new concepts and models (Dubois and Gadde, 2002). Hence, the findings could be complemented with a theory-testing approach, wherein future research could derive hypotheses from our conceptual framework and test them. For example, we showed that uncertainty experience emerges as a response to varied offering-related stimuli. Future studies could test the relative strength of these stimuli’s influence on uncertainty experience.

Second, we identified that uncertainty can be experienced positively in certain circumstances. However, we did not systematically explore when and how it is experienced negatively versus positively. Furthermore, our data did not reveal consumers’ strategies for managing positive experiences of uncertainty (e.g. thrill or curiosity), although we assumed they may exist (e.g. purposefully avoiding spoilers to maintain uncertainty). Thus, future studies could investigate in greater depth the positive side of uncertainty in P2P platforms.

Third, we identified instances in which offering-related stimuli interact to trigger an uncertainty experience in compensatory or additive ways. However, we did not systematically identify these interaction types. Identifying the types of interactions between stimuli that jointly influence the customer experience can be a fruitful research endeavor.

Fourth, we identified a few boundary conditions of our conceptualization: type of platform and individual characteristics. We assume that other boundary conditions, such as the type of offering (product vs. service), situational contingencies, and cultural differences, might be relevant, although we could not identify significant differences in the data. We suggest that future studies identify other boundary conditions more systematically.

Finally, we studied how consumers experience uncertainty on P2P platforms, characterized by high-risk perceptions, the nonstandard nature of the offerings, and peer nature. Our findings are applicable across P2P platforms, as we sought to collect data from them. This empirical context reveals specific stimuli influencing the uncertainty experience, specific objects of experience, and consumers’ strategies to manage it in P2P platforms. However, our richer definition of uncertainty experience can be applied to contexts beyond P2P platforms, such as high-involvement offerings, traditional flea markets, and healthcare. For example, in waiting for a biopsy result or swiping endless possibilities on a dating app, the uncertainty experience goes beyond not knowing; it can encompass visceral, rich, multidimensional experiences that are likely the same as those described by our participants. Overall, our experience-based definition of uncertainty could be adopted by service research beyond platform services to enable a richer perspective on this concept.

Dr Panina gratefully acknowledges the Foundation for Economic Education for funding this research (ref: DESINEX research project). Dr Raúl Pérez-López wishes to express his gratitude for the financial support received from the Grant PID2020-114874GB-I00 funded by MCIN/AEI/ 10.13039/501100011033; the funding received from the Government of Aragón and the European Social Fund (GENERES Group S-54_20R) and the University of Zaragoza (JIUZ-2020-SOC-09). Dr Pérez-López and Dr Becker thank the program “IEDIS atrae talento” from University of Zaragoza.

The supplementary material for this article can be found online.