Abstract
Purpose: This study examines whether trust in the sharing economy (SE) is driven more by decentralised, peer-based mechanisms (normative or cultural-cognitive institutions) or centralised regulatory authority.
Design/methodology/approach: Structural equation modelling was performed to test a multi-level trust model using data from 635 respondents exposed to a between-subjects experimental vignette online survey.
Findings/results: The mechanisms of peer pressure, micro-level platform reputation, and meso-level platform brand assurance are the primary drivers of consumer trust and participation intention. The authority of macro-level independent regulation plays a significantly weaker role. The collective judgement of peers holds more sway for consumers than the oversight of formal authorities in establishing SE legitimacy.
Practical implications: Service providers must prioritise curating excellent platform reputations, as high peer ratings are a de facto market requirement. Platforms should strengthen their brand’s perceived reliability. Policymakers should adopt a nuanced regulatory approach, recognising that traditional top-down assurances are less influential than decentralised, social proof mechanisms for legitimising most SE services.
Originality/value: This is one of the first studies to integrate and contrast trust-building institutions across micro-, meso- and macro-levels within a single SE framework. It provides empirical evidence that normative and cultural-cognitive institutions are more effective than regulatory ones in legitimising the SE, highlighting a pivotal shift in how trust is established in digital, peer-to-peer markets.
Keywords: institutional theory; peer influence; platform reputation; regulatory trust; sharing economy; trust.
Introduction
In the sharing economy (SE), consumers use services that may not meet the quality and safety standards of the traditional economy. Digital platforms transfer value from underused assets, such as apartments, between consumers and service providers, contributing to sustainable consumption through more efficient resource usage (Dabbous & Tarhini, 2019) and disintermediating incumbents (Gerwe & Silva, 2020; Hawlitschek et al., 2018; Wirtz et al., 2019). The SE’s virtual nature enables platforms to circumvent regulations (Davlembayeva et al., 2020) and established industry standards (Lee et al., 2020), creating information asymmetry (Akerlof, 1978; Sundararajan, 2016) and concerns about information manipulation, undermining consumer trust (Zervas et al., 2021). Consumer trust is thus pivotal for SE’s legitimacy and institutionalisation, shaping its ability to evolve within, or be limited by, societal values, norms and rules. The current article seeks to identify the sources and role of trust in the consumption of an SE service, Airbnb.
Airbnb was selected as the research context for several reasons. As a globally recognised consumer-to-consumer (C2C) platform, it provides a paradigmatic case for examining trust between strangers (Belk, 2014; Gerwe & Silva, 2020). Its two-sided reputation system operationalises micro-level normative institutions, while its strong brand enables the assessment of meso-level cultural-cognitive institutions. Operating within the regulated hospitality sector creates natural tension with macro-level regulatory institutions, making Airbnb an ideal setting to contrast peer-driven and authority-based trust mechanisms.
Trust enables SE participation, ameliorating the lack of standards and information asymmetries while legitimising C2C interactions. Platforms build trust to facilitate interactions and value exchange among parties (Rangaswamy et al., 2020). Moreover, normative, cultural-cognitive and regulatory institutions (Scott, 2014) shape resource coordination (Edvardsson et al., 2014) and consumption markets (Chaney & Slimane, 2014). Accordingly, this article draws on institutional theory and trust literature to examine how institutions shape consumer trust and, ultimately, their intention to participate in the SE.
This article addresses calls to analyse trust in ‘a platform business on multiple levels’ (Breidbach & Brodie, 2017, p. 768; Lumineau & Schilke, 2018). At the micro-level, platforms enable trust through service provider reputations from past consumer interactions (Ert & Fleischer, 2019), consistent with normative institutions. At the meso-level, trust stems from brand reliability (Akhmedova et al., 2021), reflecting cultural-cognitive institutions. Combined, these levels illustrate ‘the evolving interplay between decentralised digital cues and centralised corporate brands in generating consumer trust at scale’ (Sundararajan, 2019, p. 32). At the macro-level, regulators establish standards to reduce moral hazards, creating trust consistent with regulatory institutions in traditional markets (Voytenko Palgan et al., 2021). As platform regulation aligns with traditional frameworks (Chalmers & Matthews, 2019), we ask whether trust built through reputation systems can ultimately replace trust in formal regulators (Eckhardt et al., 2019).
This article examines how trust and participation intention in the SE are shaped by three distinct institutional mechanisms: the decentralised platform reputation of service providers (micro, normative), the centralised platform brand (meso, cultural-cognitive) and regulatory-independent reputation (macro, regulatory). All constructs can be reviewed in the Glossary in Appendix 1.
Our analysis yields four key findings that constitute the primary contributions of this research. Firstly, we demonstrate that trust and participation intention are most strongly driven by the normative, peer-based platform reputation at the micro-level. Secondly, while the cultural-cognitive dimension of the platform brand fosters trust at the meso-level, trust in individual service providers exerts a stronger influence. Thirdly, we find that regulatory, independent reputation at the macro-level plays a markedly limited role in building trust in this context. Finally, the results reveal a ‘rating floor’ effect, where exceptionally high normative platform ratings are a predominant prerequisite for trust.
The next section integrates insights from trust literature and institutional theory across these three levels to develop the novel conceptual model that guides this investigation. The subsequent section outlines the quantitative approach to data collection, validation and analysis, resulting in a revised model, which is discussed alongside extant literature. The article concludes with implications, limitations and future research pathways.
Background
Trust in the sharing economy
Trust is an essential lubricant in situations of dependence amid uncertainty (Rousseau et al., 1998). In the SE, trust is particularly crucial because of its unique characteristics: C2C interactions between strangers (Belk, 2014), enabled by digital platforms, involving underutilised assets (Gerwe & Silva, 2020). Unlike traditional dyadic trust (Mayer et al., 1995), SE transactions involve three parties, consumer, provider and platform, with consumers trusting peer-providers rather than brand-affiliated ones (Ramphal, 2024). This digitally-enabled sharing among strangers (Schor, 2016; Sutherland & Jarrahi, 2018) entails risks like asset damage or misrepresentation, making consumer willingness to trust strangers foundational to the SE.
The classic trust preconditions, uncertainty, risk and interdependence (Rousseau et al., 1998), are inherent in SE transactions (Mittendorf et al., 2019). Without traditional regulatory enforcement (Ferrari, 2016; Sundararajan, 2019), SE platforms rely on alternative trust mechanisms: interaction-based trust (from consumer ratings and reviews) and institution-based trust. This raises the question of where trust is most effectively built: at micro (individual), meso (brand) or macro (regulatory) levels.
Interaction-based trust develops through technology-enabled consumer ratings (Abrahao et al., 2017) and reviews (Cheng et al., 2019), with platforms institutionalising decentralised feedback into provider reputations. This emergent, peer-driven mechanism challenges whether traditional regulatory trust can be superseded.
Institution-based trust stems from specific institutional arrangements (Bachmann, 2011), where favourable conditions enable situational success (McKnight & Chervany, 2001). Regulators create this through independent standards-based reputation (Bartlett et al., 2013), while platforms rely on brand assurances like secure transactions and guarantees (Akhmedova et al., 2021; Kong et al., 2020). Consumers expect competent, integrity-based performance within established norms for legitimacy (Mayer et al., 1995; Scott, 2014), making institution-based trust essential alongside interaction-based trust.
An institutional theoretical perspective
Marketing involves institutions and processes that create, deliver and exchange value (American Marketing Association, 2017). Gundlach and Wilkie (2009) highlight marketing’s role in ‘institutions that … facilitate and govern these activities (e.g. governmental agencies, legislators, courts, professional associations, social norms, ethics and individual values)’ (p. 262) and assert ‘that marketing systems … are a part of marketing as are social processes (e.g. regulations and norms)’ (p. 261). Institutions, comprising cultural-cognitive, normative and regulatory dimensions together constrain and enable behaviour (North et al., 2009; Scott, 2014). As an emergent market form, the SE relies on these institutions for legitimacy, shaping trust and participation through norms and values (normative), socially constructed meanings (cultural-cognitive) and rules governing consumption (regulatory) (Scott, 2014).
Therefore, the SE’s institutional landscape and marketing dynamics are co-shaped by consumers, firms, platforms and regulators (Eckhardt et al., 2019). A core feature of this landscape is the consumer’s dual role as a prosumer, simultaneously providing and consuming services. The resulting institutions facilitate value creation (Duncan, 1920, as cited in Rosenbloom, 2013) and coordinate resources across the ecosystem (Edvardsson et al., 2014). By applying institutional theory, this study specifically examines which institutional pillar, normative, cultural-cognitive or regulatory, most effectively influences consumer trust and participation intention in the SE.
Levels of analysis in the sharing economy
Research on the SE typically analyses trust at the micro (individual), meso (platform) and macro (regulatory) levels (Maurer et al., 2020; Trenz et al., 2018). While studies have examined micro-level peer reputations and meso-level platform assurances, macro-level regulatory influences remain underexplored (Mao et al., 2020; Yang et al., 2019). This fragmented approach highlights a need for integrated multi-level theorisation (Cheng, 2016). Trust in this ecosystem is inherently layered – originating in peer interactions (micro), shaped by platforms (meso) and contextualised by regulation (macro) – requiring a unified model to explain its development across interdependent levels (Figure 1).
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FIGURE 1: Levels and sources of analysis in the sharing economy. |
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Institution-based trust in the SE may arise through different reputation mechanisms operating at distinct levels. Platform reputation emerges endogenously through repeated peer-to-peer interactions and is embedded within the digital platform. It is typically reflected in aggregated user ratings and reviews, such as buyer-seller feedback on online marketplaces or passenger ratings of drivers in ride-hailing platforms, which function as socially constructed signals based on prior exchanges (Mauri et al., 2018; Pavlou, 2002). Platform reputation, therefore, reflects normative pressures generated and reinforced through peer communities.
In contrast, independent reputation originates exogenously from third-party institutions outside the platform’s governance structure. Such mechanisms are commonly associated with formal regulation, certification or standard-setting bodies, including quality assurance certifications or licensing requirements, and are grounded in rule-based assessments (Scott, 2014; Zucker, 1986). Independent reputation thus reflects regulatory pressures, whereby trust is conferred through compliance with externally defined checks and balances.
This distinction is central to the present study, which examines how different institutional pillars substitute for trust under uncertainty in digitally mediated markets (Eckhardt et al., 2019). Specifically, it compares the influence of platform-based and independent reputation mechanisms on consumer trust and participation intentions in the SE.
Normative role of platform reputation at the micro-level
At the micro-level, consumer trust is rooted in the expectation that a service provider will act in the consumer’s best interest, a set of expectations conceptualised as trusting beliefs and intentions, which form the basis of interpersonal trust (McKnight & Chervany, 2001). In the virtual context of the SE, where transactions are initiated before physical service delivery, this translates into interaction-based trust. This form of trust is predicated on the perceived ability, integrity and benevolence of the provider, a framework established in e-commerce (Pavlou & Fygenson, 2006) and extended to SE contexts (Tussyadiah & Park, 2018; Yang et al., 2019). The inherent information asymmetry of interacting ‘at a distance’ presents a central challenge. Platforms mitigate this by institutionalising the aggregated experiences of past users into a platform reputation, enabling consumers to base their trust on this normative, peer-derived signal.
Rather than explicitly assessing the provider’s ability, integrity and benevolence, platforms fabricate a proxy for interaction-based trust by aggregating consumers’ ratings (Abrahao et al., 2017) to infer the provider’s reputation (Vavilis et al., 2014). Consumers rely on ratings, even from those they have not engaged with. As a normative institution, platform reputation reflects shared norms (Scott, 2014) and serves as a consumer decision-making heuristic (Cialdini et al., 1991), with reviews and ratings shaping provider reputations (Mao et al., 2020; Mauri et al., 2018; Yang et al., 2019). However, ratings themselves vary in degree and interact with the platform’s overall reputation. This leads to our first hypothesis:
H1: The level of a service provider’s platform reputation (PR) significantly affects consumer trust in the service provider (TSP).
H1a: TSP is much lower at a low (1-star) PR rating than at a medium (3-star) rating.
H1b: TSP is much higher at a high (5-star) PR rating than at a medium (3-star) rating.
Meso-level trust from the cultural-cognitive platform brand
Brand trust has evolved from traditional mechanisms like insurance and escrows (Zucker, 1986) to corporate brands (Möhlmann & Geissinger, 2018), reflecting consumer confidence in reliability and intent (Delgado-Ballester et al., 2003). In digital contexts, brands signal quality and engender trust through structural assurances (McKnight & Chervany, 2001; Shankar et al., 2002). Similarly, SE platforms build institution-based trust through website reliability, transaction security, fair policies, verification and reduced user friction. These features enhance the platform’s brand appeal, aid user acquisition and retention (Akhmedova et al., 2021) and reinforce trust in its ability and integrity (Mayer et al., 1995). The platform brand thus functions as a cultural-cognitive institution through which consumers develop taken-for-granted expectations (Scott, 2014), as seen when brand names become verbs (e.g. ‘Ubering’). Hence, we hypothesise:
H2: The strength of a platform’s brand (B) has a positive effect on the level of consumer trust in that platform (TP)
Macro-level trust from the regulatory, independent reputation of the service provider
Institution-based trust also operates at the macro-level through standards, regulations and certifications (Lawrence, 1999; Shao et al., 2020). Independent accreditation institutionalises credibility by substituting for reputation and alleviating information asymmetry (Bartlett et al., 2013; Pavlou, 2002; Martin-Fuentes et al., 2018). Official star ratings enhance reputation and trust (Kang et al., 2016; Sutherland et al., 2021), reflecting how normative institutions can evolve into regulatory ones over time (Scott, 2014). Since regulations are externally enforced and carry coercive power, they may exert a stronger influence on trust. This leads to our third hypothesis:
H3: A service provider’s independent, regulatory reputation (IR) influences consumer trust in the service provider (TSP).
H3a: A low (1-star) IR rating reduces TSP compared to a medium (3-star) rating.
H3b: A high (5-star) IR rating increases TSP compared to a medium (3-star) rating.
Given the potential for regulatory ratings to function as mere ‘hygiene factors’ rather than key discriminants, we ask whether trust in formal regulators is as influential as trust in platform reputation systems (Eckhardt et al., 2019). Institutional arrangements often combine multiple elements (Scott, 2014), and contrasting independent regulatory ratings with peer-derived platform ratings offers a novel way to examine how macro- and micro-level trust signals interact. Thus, we hypothesise:
H4: There is a significant interaction between platform reputation (PR) and independent reputation (IR) in predicting trust in the service provider (TSP). The relationship between PR and TSP differs significantly across low, medium and high levels of IR.
Intention to participate in the sharing economy
Key drivers behind consumers’ changing perceptions, intentions and actions are evolving cultural, institutional and technological norms (Zhang & Chang, 2020), which have permeated consumers’ lives through the SE. Consumers’ intention to participate in the SE can be defined as their likelihood to request or use a sharing service (Mittendorf et al., 2019). Sellers with better reputations tend to attract more customers (Tadelis, 2016). Consumers’ trust in providers shapes their intentions in e-commerce (Fang et al., 2014), accommodation sharing (Mao et al., 2020; Nisar et al., 2020) and ride-sharing (Mittendorf et al., 2019). Our fifth hypothesis is therefore:
H5: Trust in the service provider (TSP) has a positive influence on consumers’ intention to participate (IP) in the sharing economy.
When a provider’s rating was experimentally manipulated, higher values signalled a greater willingness to use the service (Rosenthal et al., 2020). Additionally, higher ratings indicated that providers would remain in the market, thus serving as a proxy of consumers’ support of participation in the SE service (Leoni, 2020). Our sixth and seventh hypotheses, therefore, accommodate both participation intention and the role of trust as a mediator:
H6: The positive effect of platform reputation (PR) on intention to participate (IP) is mediated by trust in the service provider (TSP).
Correspondingly, consumers value standard-setting as it leads to more bookings and greater market share (Ballina et al., 2020):
H7: The effect of independent reputation (IR) on intention to participate (IP) is mediated by trust in the service provider (TSP).
Consumer intention to participate in the SE is predicated on trust in the platform itself. This platform trust is multifaceted, encompassing dimensions such as safety measures, guarantees and perceived service quality, which collectively constitute the platform’s brand (Ter Huurne et al., 2017). Empirical research substantiates that trust in the platform is a key driver of participation intentions (Lee et al., 2018; Mittendorf, 2018; Mittendorf et al., 2019). Consequently, a platform’s ability to foster adoption hinges on reinforcing this brand-based trust (Akhmedova et al., 2020). This establishes the foundational logic for our final hypothesis:
H8: Trust in the platform (TP) has a positive influence on consumers’ intention to participate (IP) in the sharing economy.
H9: The positive effect of the platform brand (B) on intention to participate (IP) is mediated by trust in the platform (TP).
Figure 2 represents the conceptual model of the hypotheses.
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FIGURE 2: Conceptual model of the hypotheses. |
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Methodology
Research design
This research examined the accommodation sharing-economy platform, Airbnb, in South Africa using a between-subjects experimental vignette online survey (Aguinis & Bradley, 2014). Airbnb was selected as its service is relatively homogeneous (short-term lodging) compared to other SE sectors, allowing for clearer experimental manipulation of reputation variables. The platform also has a high level of awareness and usage within our target population, ensuring respondents are familiar with the context, which is essential for valid responses in a vignette-based survey. The selection of the South African context, along with the Tourism Grading Council of South Africa (TGCSA) as the independent regulatory body, provided a clear, nationally recognised macro-level institution against which Airbnb’s platform reputation could be contrasted. This allowed for a clean experimental design where both peer (Airbnb) and authority (TGCSA) ratings could be manipulated on the same 5-star scale that they share.
Since short-term accommodation incumbents operate in an institutionalised sector (Weber et al., 2019), TGCSA standardisation constitutes a recognised quality indicator (Du Plessis & Saayman, 2011). Both PR and IR were manipulated at three levels (1-, 3- and 5-star). Participants were randomly assigned to one of the resulting nine (3×3) treatment conditions (Figure 3). They answered 14 Likert-scale statements (1 = strongly disagree to 5 = strongly agree) (Table 2).
The target population comprised individuals familiar with both Airbnb and the South African context, who had either used or considered short-term accommodation services. Participants were recruited via the authors’ professional email and LinkedIn networks. To extend reach, a snowball sampling technique was employed, where initial participants were encouraged to share the survey within their own networks. From an initial pool of 760 responses, 125 were removed because of incompleteness or failure to meet other validity criteria, resulting in a final analytic sample of 635.
Data validation
Exploratory factor analysis (Bartlett’s test: χ2 = 4734.473, df = 66, p < 0.001; Kaiser−Meyer−Olkin = 0.865) returned four factors explaining 69% of variance (Pallant, 2001). Multicollinearity was not an issue as tolerance values for TSP, TP and B exceeded 0.1 (0.865, 0.419 and 0.450, respectively), and variance inflation factors remained below 3 (1.157, 2.388 and 2.221, respectively) (Hair et al., 2010). Non-response bias was minimal (Table 1), as Levene’s test showed no significant differences between early and late completers (Fulton, 2018).
Factor loadings were significant and exceeded 0.5 (Table 2), confirming convergent validity (Hair et al., 2010). The measurement model demonstrated acceptable fit (Table 3). Convergent validity and reliability were obtained because the average variance extracted (AVE) and composite reliability per construct exceeded 0.5 and 0.7, respectively (Hair et al., 2010; Hancock & Mueller, 2001). Discriminant validity was confirmed, with AVE square roots exceeding inter-factor correlations (Table 3) (Fornell & Larcker, 1981) and heterotrait-monotrait ratio of correlations being below 0.85 (Table 4) (Henseler et al., 2015).
| TABLE 2: Confirmatory factor analysis measurement model with standardised estimates. |
| TABLE 3: Internal consistency of measures. |
| TABLE 4: Heterotrait-monotrait ratio results. |
Data analysis
Covariance-based structural equation modelling (CB-SEM) using analysis of moment structures (IBM AMOS®) was selected as the primary analytical method. This choice was appropriate given the data’s general normality, the large sample size (n = 635), and the use of reflective constructs. CB-SEM was particularly suited for testing the hypothesised model as it allows for the simultaneous estimation of direct and indirect (mediation) effects while incorporating control variables. Following established literature, prior experience as an Airbnb user and general online shopping habits were included as controls, as these factors are known to influence participatory intentions in digital contexts (Abramova et al., 2015; Mittendorf et al., 2019; Pavlou & Fygenson, 2006). To test the mediation hypotheses (H6, H7 and H9), indirect effects were calculated as the product of the constituent path coefficients, with bias-corrected bootstrapping (5000 samples) used to establish confidence intervals (Collier, 2020).
For the experimental manipulation, indicator-coded dummy variables were created to represent the three levels (1-star, 3-star and 5-star) of both PR and IR. In the direct effects model, the 3-star level for each variable was designated as the zero-coded reference category, allowing the 1-star and 5-star conditions to be compared against this baseline (Collier, 2020). To test the interaction hypothesis (H4), which posits that the effect of PR on trust depends on the level of IR, a standard interaction term within CB-SEM (as per a multiple-indicator-multiple-cause model) is unsuitable with dummy-coded categorical predictors. Therefore, the interaction was assessed by estimating separate structural models for each of the nine treatment conditions. This analysis was complemented by visual interpretation of the mean differences using analysis of variance (ANOVA) graphs.
Ethical considerations
The ethical clearance to conduct this study was obtained from the Gordon Institute of Business Science, University of Pretoria and Gordon Institute of Business Science (GIBS) Master’s Research Ethical Clearance Committee.
Results
Demographics
The sample was characterised by a majority (68%) of respondents who were existing SE service users. Demographically, over half of the participants (56%) fell within the age range of 24–39 years, a finding consistent with prior research that identifies millennials as the core users of SE platforms (Amaro et al., 2019; Mao et al., 2020; Yang et al., 2019). Geographically, a majority of respondents (83.94%) were residents of South Africa. The remaining participants were distributed across other regions: Europe (5.51%), other African countries (5.04%), Asia (2.52%), North America (1.42%), Oceania (1.42%) and South America (0.16%).
Structural equation modelling results
Direct effects
The structural model tested five direct hypotheses, of which four were supported: H1, H2, H5, and H8 (see Table 5 for complete results). H3 was not supported, indicating that IR does not function as hypothesised.
| TABLE 5: Direct effects for the structural equation model. |
Analyses of PR confirmed its strong normative influence. Compared to the 3-star reference, a 1-star PR rating significantly reduced TSP, supporting H1a. Conversely, a 5-star PR rating produced a stronger positive effect on TSP (H1b). This asymmetric pattern underscores that consumers’ interaction-based trust is highly sensitive to peer ratings, with perfect scores providing a substantial boost and low scores imposing a severe penalty.
Supporting H2, B exerted a significant positive influence on TP. This finding confirms that the brand acts as a critical cultural-cognitive institution, providing the structural assurance necessary for institution-based trust at the meso-level.
The results for IR were mixed and revealing. While a 1-star IR rating significantly reduced TSP (H3a), a 5-star rating had no statistically significant effect (H3b). This pattern indicates that a poor regulatory signal can damage trust, but a positive one does not comparably enhance it. This asymmetry suggests that relative to powerful peer-based signals (PR), macro-level regulatory assurances (IR) have a limited and non-linear role in building consumer trust, with consumers appearing to rely more heavily on collective peer experiences than on formal ratings.
Finally, both TSP and TP were confirmed as significant drivers of participation intention (IP), supporting H5 and H8, respectively. The standardised path coefficient for TSP (β = 0.564) was substantially larger than that for TP (β = 0.201), indicating that trust in the individual provider is a more decisive factor in the participation decision than trust in the platform itself.
Mediation effects
The mediation analysis, detailed in Table 6, examined the indirect pathways to participation intention (IP). The results indicate significant mediation for most hypothesised paths (H6 and H9), with one key exception: the effect of a high (5-star) IR rating in H7.
| TABLE 6: Mediation test using bootstrap analysis with a 95% confidence interval. |
Platform reputation (PR) as a mediated pathway: The analysis for H6 confirmed a significant indirect effect of PR on IP through TSP. For the 1-star PR rating (versus the 3-star reference), both the direct and indirect paths to IP were negative, indicating complementary mediation. This suggests that a poor peer rating not only directly deters participation but also does so by eroding trust in the provider. Conversely, the 5-star PR rating showed significant positive direct and indirect effects, also demonstrating complementary mediation, whereby an excellent peer rating boosts both trust and intention directly.
The limited, mediated role of independent reputation (IR): The results for H7 were more nuanced. The direct effects of both low and high IR ratings on IP were non-significant (p = 0.922 and p = 0.727, respectively). However, for the 1-star IR rating, a significant full mediation was observed (β = −0.368, p = 0.001). This indicates that a negative regulatory signal only influences participation intention by first undermining TSP, with no direct effect. The non-significant mediation for the 5-star IR rating reinforces that positive regulatory signals have no reliable effect – neither direct nor indirect – on participation.
Brand trust as a mediating mechanism: Supporting H9, B exhibited a significant positive indirect effect on IP through TP, demonstrating full mediation. This confirms that the platform’s brand does not influence participation intention directly; its effect is entirely channelled through the trust it fosters in the platform itself.
Factorial design
The analysis of the interaction between PR and IR (H4) reveals a nuanced pattern in how these signals jointly shape TSP, as detailed in Table 7 and visualised in Figure 4.
| TABLE 7: Results across nine treatment conditions. |
The effect of PR was contingent on its level. As hypothesised in H1a and H1b, 1-star PR ratings (TCs 1, 4 and 7) consistently reduced TSP, while 5-star PR ratings (TCs 3, 6 and 9) consistently enhanced it, regardless of the accompanying IR level. Critically, medium (3-star) PR ratings (TCs 2, 5 and 8) yielded non-significant coefficients, suggesting that average peer ratings are insufficient to build trust and may be functionally equivalent to a lack of information.
The role of IR was more complex and context-dependent. A 1-star IR rating (TCs 1, 2 and 3) exerted a uniformly negative influence on TSP, supporting H3a. However, contrary to H3b, higher IR ratings (3- and 5-stars) showed positive coefficients across conditions, though their impact was not uniformly strong enough to achieve significance in the main effects model. This indicates that while a poor regulatory signal is damaging, a positive one is not a consistently powerful trust signal on its own.
The interaction is clearly illustrated in Figure 4. The slope representing the relationship between PR and TSP is steepest when paired with a 5-star IR rating, indicating that regulatory endorsement amplifies the positive effect of high peer ratings. Conversely, a provider with a 1-star PR rating suffers a severe trust deficit that even a perfect 5-star IR rating (TC 7) cannot fully offset. In contrast, the penalty for a low IR rating is less absolute; it lowers trust but does not negate the powerful positive effect of a 5-star PR score. This pattern confirms the primacy of peer-based (normative) signals, while revealing that macro-level (regulatory) signals can act as a moderating amplifier, particularly for high-quality providers.
Figure 5 summarises results, greying out unsupported hypotheses (H3 and H4) and highlighting partial mediation (H6).
Discussion and conclusion
This research responds to calls for a multi-level examination of trust within the SE (Breidbach & Brodie, 2017; Ter Huurne et al., 2017). By integrating institutional theory, we conceptualise trust-building mechanisms at three distinct levels: the micro-level (service provider’s platform reputation; Mittendorf et al., 2019), the meso-level (platform brand, Sundararajan, 2019) and the macro-level (service provider’s independent, regulatory reputation; Eckhardt et al., 2019). While prior studies have often focused on one or two of these levels in isolation (e.g. Mao et al., 2020; Mittendorf, 2018; Mittendorf et al., 2019; Yang et al., 2019), this study is among the first to integrate all three, contrasting their effects on participation intention and examining the combined impact of peer-driven and regulatory rating systems. Our findings provide a clearer, more nuanced understanding of the institutional mechanisms that underpin consumer trust and participation in the South African SE context.
Contributions to theory
Our findings offer four key theoretical contributions that advance the understanding of trust in digital marketplaces. Firstly, we emphasise the importance of peer influence. At the micro-level, we confirm that a service provider’s platform reputation, a normative institution built on peer reviews, is a dominant force. The strong support for H1 and H5 demonstrates that interaction-based trust, derived from the collective judgement of other consumers, is a more powerful predictor of trust and participation intention than formal regulatory signals. This extends existing literature (Mao et al., 2020; Yang et al., 2019) by showing that in a C2C ecosystem, decentralised peer cues can not only influence but also trump centralised authority. While some studies, such as Ert et al. (2016), found that visual cues like photos could overshadow ratings, our results reaffirm the profound weight of aggregated peer opinions in the South African SE landscape.
Secondly, we found the enabling role of the platform brand. At the meso-level, our study reinforces the importance of the platform brand as a cultural-cognitive institution (Akhmedova et al., 2021; Wang & Jeong, 2018; Wu & Shen, 2018; Yang et al., 2019). The support for H2 indicates that a reliable and well-known platform brand provides essential structural assurance, fostering institution-based trust. Furthermore, this trust in the platform positively influences participation intentions (H8 and H9), consistent with global findings (Lee et al., 2018; Mittendorf, 2018). A critical insight, however, is that trust in the service provider (micro-level) exerts a stronger influence on participation than trust in the platform (meso-level). This underscores the fundamental peer-to-peer nature of the SE, where consumers relate more directly to individual providers than to the facilitating platform itself (Costello & Reczek, 2020).
Thirdly, we found, contrary to expectations and prior research, that regulatory assurance plays a limited role at the macro-level. A pivotal finding, with implications for institutional theory, is the limited role of macro-level regulatory trust. The lack of support for H3 indicates that independent reputation ratings, such as those from the Tourism Grading Council, have a negligible direct impact on trust in a service provider. This contrasts with studies that highlight the importance of standard-setting (Kang et al., 2016; Sutherland et al., 2021). This divergence may be uniquely context-dependent. In South Africa, regulatory accreditation in the hospitality sector may be less visible or trusted by locals compared to the immediate, social proof offered by platform reviews. This suggests a shift where ‘the changing peer-to-peer review internet platforms are challenging the need for quality assurance through such institutions’ (Visser & Eastes, 2020, p.74). Trust, it appears, is shedding its authoritative character for a more interactive, social one (Botsman, 2015), at least within the local consumer base.
Fourthly, we discuss the ‘rating floor’ effect. When micro and macro mechanisms are combined (H4), the results reveal a powerful ‘rating floor’ effect. The severe trust penalty for 1-star platform ratings and the non-significant effect of 3-star ratings indicate that a de facto quality threshold exists near the 5-star level; ratings below this threshold fail to assure consumers. This institutionalised norm, where 95% of providers maintain near-perfect ratings (Zervas et al. 2021), transforms high platform ratings into a hygiene factor. This skew can be attributed to platform design (reflecting platforms’ efforts to minimise visible negative reviews, Zamani et al., [2019]), consumer reluctance to leave negative feedback (Berg et al., 2020), and fear of retaliation (Newlands et al., 2019), creating a market where exceptional quality is the baseline expectation.
Practical implications
Our findings offer actionable insights for key stakeholders in the SE ecosystem, service providers, platform managers and policymakers, aimed at fostering trust and sustainable growth. For service providers operating at the micro-level, the imperative is unequivocal: a near-perfect platform reputation is essential for survival. Our findings reveal a rating floor effect, where five-star ratings have become the normative baseline. This dynamic creates an intensely competitive environment where the margin for differentiation is exceptionally narrow, forcing providers to compete on subtle cues beyond the star rating itself and amplifying their dependence on the qualitative content of informal reviews. The platform, therefore, enforces a rigorous, consumer-driven standard of excellence. In this system, providers with less-than-stellar reputations are systematically marginalised, effectively ‘voted off’ the platform, while those who maintain top ratings become pervasive. Consequently, the stakes for micro-level reputation management are exceptionally high; even minor service failures can trigger swift marginalisation, as consumer trust and choice are overwhelmingly anchored in these peer-generated evaluations.
Platform managers, who operate at the meso-level, should understand that their brand is a critical trust enabler. Investing in website reliability, transaction security and fair dispute-resolution policies strengthens the platform’s cultural-cognitive legitimacy. Furthermore, since high-performing providers create a positive halo effect for the brand itself, platforms have a vested interest in supporting providers through training and engagement initiatives to help them maintain the high ratings that drive the entire ecosystem.
For policymakers, who operate at the macro-level, a nuanced approach is appropriate. While oversight is necessary, our results indicate that imposing traditional regulatory reputation systems may be an inefficient way to build trust in the SE. Standard-setting bodies ensure consistent service encounters, yet SE services, provided and consumed by individuals, are inherently heterogeneous. Policymakers should consider contextual applications for independent ratings, which differ according to platform offering, provider and consumer maturity. Standardised SE services (e.g. ride-sharing) may benefit more from regulation, while unique, experience-based offerings (e.g. Airbnb’s ‘authentic’ stays) are better trusted through peer and brand mechanisms. New market entrants, lacking reputational history, would benefit from independent accreditation (e.g. a TGCSA rating) as a valuable trust signal in the absence of an established platform reputation. Cautious new users, visiting the platform or the country for the first-time, may be hesitant to trust strangers, but could be reassured by the presence of official regulatory endorsements.
Limitations and further research
This study has several limitations that also delineate valuable pathways for future research. Firstly, concerning methodology, relying on self-reported intentions may not fully capture actual booking behaviour. Future research would benefit from experimental designs or field studies that track real SE transactions to validate these findings and examine how specific institutional mechanisms (e.g. the presence of pictorial ratings) influence concrete choices.
Secondly, the geographic and cultural context may limit generalisability, given the predominance of South African residents in the sample. The attenuated effect of macro-level IR could be partially explained by lower institutional trust in regulatory bodies within this specific setting. Replicating this model in countries with stronger institutional trust would help determine if the primacy of peer-based (normative) trust is a universal characteristic of the SE or a context-contingent finding. Such cross-cultural work could also investigate if tourists, seeking brand consistency abroad, assign greater weight to official regulatory ratings than to platform-specific peer reviews.
Thirdly, the sample characteristics present a potential bias. The respondents were predominantly experienced Airbnb users, who likely have well-established trust in platform-native reputation systems. To better understand initial trust formation, future studies should target new or prospective users. Employing broader, more heterogeneous consumer or tourism panels would also enhance the representativeness of the findings.
Fourthly, the generalisability across SE sectors requires further validation. Although the multi-level institutional framework is theoretically portable, its empirical application depends on how micro-, meso- and macro-level signals are operationalised in different contexts; for example, applying the model to ride-sharing would involve defining a driver’s ‘independent reputation’ (macro) and contrasting it with platform-based ratings (micro). Future research should identify which specific trust signals are most impactful across different SE domains. Promising avenues include examining the relative influence of visual cues (e.g. service provider photos) versus textual reviews, or how the trust-building role of the platform brand (meso) varies between asset-heavy (e.g. accommodation) and service-heavy (e.g. task-based) platforms.
Finally, this study evaluates institutional trust solely within the SE. Future research could provide a richer understanding by comparing these dynamics with those in the traditional economy, where established ratings (e.g. 3-star vs 5-star hotels) have long guided consumer expectations. Such a comparison would directly assess whether and how platform-based reputation systems alter consumers’ fundamental reliance on macro-level institutional trust.
Acknowledgements
This article includes content that overlaps with research originally conducted as part of Avikaar Ramphal’s doctoral thesis entitled, ‘An institutional theory perspective on sustainable consumption’, submitted to the Gordon Institute of Business Science, University of Pretoria, in 2024. The thesis was supervised by Morris Mthombeni and Kerry Chipp. Portions of the data, analysis, and/or discussion have been revised, updated, and adapted for journal publication. The original thesis is publicly available at: https://repository.up.ac.za/items/27ee9b16-ace5-4bc0-a684-751bb5e65969. The authors affirm that this submission complies with ethical standards for secondary publication, and appropriate acknowledgement has been made of the original work.
Competing interests
The authors declare that they have no financial or personal relationships that may have inappropriately influenced them in writing this article. The author, Morris Mthombeni serves as an editorial board member of this journal. The peer review process for this submission was handled independently, and the author had no involvement in the editorial decision-making process for this article. The authors have no other competing interests to declare.
CRediT authorship contribution
Avikaar Ramphal: Conceptualisation, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Software, Validation, Visualisation, Writing – original draft, Writing – review & editing. Morris Mthombeni: Conceptualisation, Methodology, Project administration, Resources, Supervision, Validation, Visualisation, Writing – review & editing. Kerry Chipp: Conceptualisation, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualisation, Writing – review & editing. All authors reviewed the article, contributed to the discussion of results, approved the final version for submission and publication, and take responsibility for the integrity of its findings.
Funding information
This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors.
Data availability
The data that support the findings of this study are available from the corresponding author, Kerry Chipp, upon reasonable request.
Disclaimer
The views and opinions expressed in this article are those of the authors and are the product of professional research. They do not necessarily reflect the official policy or position of any affiliated institution, funder, agency or the publisher. The authors are responsible for this article’s results, findings, and content.
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Appendix 1
Glossary of key terms
The following key terms are used throughout this study and are defined here for clarity:
- Institutional Theory: A theoretical framework that explains how established rules, norms, and beliefs (institutions) shape behaviour and provide stability in social systems. In this study, we focus on three types of institutions: normative (peer-based reputations), cultural-cognitive (shared understandings of platform brands), and regulatory (formal rules and oversight). (Scott, 2014)
- Peer Influence: The social pressure exerted by other consumers, whose collective opinions and behaviours shape an individual’s trust and decision-making. In the sharing economy, this influence is primarily channelled through digital reputation systems (e.g. star ratings and reviews). (Mauri et al., 2018)
- Platform Reputation: The aggregated digital record of a service provider’s past performance, as rated by previous consumers on a sharing economy platform (e.g. Airbnb, Uber). This serves as a primary, decentralised cue for trust. (Mauri et al., 2018)
- Regulatory Trust: The confidence consumers derive from formal, top-down rules, standards, and oversight provided by governmental or independent authorities, which are designed to reduce risk and ensure fairness in markets. (Cheng et al., 2019)
- Sharing Economy: A socio-economic ecosystem built on the sharing of access to underutilised goods or services, often facilitated by digital platforms that connect private providers with consumers. (Davlembayeva et al., 2020)
- Trust: The psychological willingness of a consumer to rely on a service provider and become vulnerable in a transaction, based on positive expectations of the provider’s behaviour, despite inherent uncertainty and information asymmetry. (Cheng et al., 2019)
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