Signaling in times of uncertainty: The effectiveness of risk-coping measures disclosure in the hospitality sector

Abstract

In health-related crises, hotels tend to disclose risk-coping measures (RCMs) as a vital element of crisis management. While there has been active research on these measures, much of the work has focused on consumer perceptions and travel intentions. Drawing on signaling theory, we address this gap by rigorously quantifying the underexplored linkage between RCMs disclosure and performance outcomes. Leveraging a quasi-experimental design that combines difference-in-differences method with propensity score matching, we analyze matched hotel samples with/without RCMs disclosure. Our findings reveal significant performance gains for disclosing hotels, driven primarily by firm-operated measures, whereas consumer-involved initiatives yield neutral or adverse effects. Pandemic severity, government responses, and hotel status further moderate efficacy. Additionally, mediation analysis reveals that performance improvements are partially attributable to the increase in positive consumer feedback. Our findings contribute to the crisis management and tourism literature and offer managerial insights for enhancing operational performance in the hospitality industry.

Keywords

risk-coping measures hotel performance signaling theory crisis management hospitality industry

Introduction

Health-related crises profoundly disrupt industries, particularly tourism and hospitality (Boto-García and Leoni, 2022; Brandano et al., 2024). For instance, the World Tourism Organization (UNWTO, 2020) estimated that the COVID-19 pandemic could reduce global tourism export revenues by $910 billion to $1.2 trillion in 2020. During such periods, heightened uncertainty and elevated risk perceptions substantially depress travel demand (Park et al., 2022). In response, service providers (e.g., hotels) increasingly develop and disclose risk-coping measures (RCMs), such as enhanced sanitation protocols and comprehensive safety strategies (Hsieh et al., 2021; Smart et al., 2021), to mitigate consumers’ perceived risks. However, how these RCMs affect organizational performance remain unclear.

Existing research predominantly investigates the impact of RCMs on tourists’ travel intentions (Banos-Pino et al., 2023; Hüsser and Ohnmacht, 2023), risk perceptions (Shin and Kang, 2020), and satisfaction (Yu et al., 2022) from a consumer perspective. These studies primarily rely on experimental or qualitative data and tend to evaluate RCMs in isolation. While such research provides valuable insights into consumer-side implications, the effects of RCMs on organizational performance remain insufficiently explored—particularly when considering strategic portfolios that integrate firm-operated measures (implemented without consumer involvement) and consumer-involved measures (requiring consumer participation). Research outside the hospitality sector similarly focuses on one type of RCMs at a time. For instance, prior studies show that product guarantees and risk disclosures (i.e., firm-operated measures) generally reduce perceived risk and enhance consumer trust and purchase likelihood (Ozpolat et al., 2013; Patel et al., 2021), indicating their positive influence on firm outcomes. In contrast, insurance or return policies (i.e., consumer-involved measures) require consumer participation and impose additional cognitive or financial burdens, which may reduce purchase intentions or satisfaction (Bower and Maxham III, 2012; Li et al., 2022). In sum, the joint effects of these measures—particularly in the context of a health crisis—remain largely unexplored.

To bridge this gap, we empirically examine how hotel performance responds to RCMs portfolio that disclosed during health-related crises. Leveraging TripAdvisor’s COVID-19 Travel Safe Tools module, introduced in April 2020, which allows hotels to disclose their RCMs (see Figure 1), we exploit a unique opportunity to assess the real-world effects of this disclosure on organizational outcomes.

From signaling theory perspective (Connelly et al., 2011; Spence, 1973), RCMs can be interpreted as signals that hotels (senders) convey to potential consumers (receivers) to reduce uncertainty during crises. By disclosing RCMs, hotels signal their commitment to consumer well-being, which is particularly valued in high-risk environments, thereby potentially enhancing revenue. However, RCMs are typically implemented as a portfolio rather than isolated actions, their effects can vary. Specifically, firm-operated measures may improve performance by mitigating risk without imposing direct costs on consumers (Patel et al., 2021), while consumer-involved measures can impose burdens that diminish consumption intentions (Kang et al., 2021). As such, the overall impact of RCMs disclosure, as a composite signal, remains uncertain. This ambiguity motivates our first question: (1) Does disclosing RCMs enhance hotel performance during a crisis, and how do the effects differ between various RCMs?

We further explore boundary conditions shaping the effectiveness of RCMs disclosure. First, the external environment can moderate signal effectiveness (Friske et al., 2023; Lim et al., 2023). For instance, infection rates and government interventions (Blengini et al., 2026; Litvin, 2024) can alter how consumers perceive risk, potentially affecting the potency of safety signals. Consequently, RCMs may be more impactful in high-threat contexts but offer limited incremental benefits in benign environments. Second, the characteristics of the signaler matter. Existing research suggests that operational improvements tend to benefit low-status firms (Gu and Ye, 2014; Zhu and Zhang, 2010). However, whether this pattern holds in a health crisis remains unclear. Signaling theory posits that high-status organizations with greater resources and recognized reputations are better positioned to send credible signals at a lower cost (Connelly et al., 2011). Hence, consumers may trust RCM disclosures more when issued by high-status hotels. These insights motivate our second question: (2) When are hotel-disclosed RCMs effective, and who benefits from such disclosure?

In the hotel industry, consumer satisfaction is essential for fostering repurchase behavior and organizational performance (Han and Hyun, 2017; Ladhari and Michaud, 2015). Notably, a hotel’s crisis management approach significantly shapes guest attitudes and behaviors (Chen and Jai, 2019; Liu et al., 2015). Although prior research has examined crisis management’s influence on consumer perceptions, less is known about how it translates into improved performance through guest feedback. According to signaling theory, effective signals are reinforced by positive receiver feedback (Connelly et al., 2011). Thus, we propose that RCMs disclosure not only signals commitment to safety but also fosters positive guest feedback and then enhances performance. This leads to our third question: (3) Does RCMs disclosure enhance hotel performance by fostering more positive guest feedback?

To address these questions, we develop and test five hypotheses rooted in signaling theory. We combine difference-in-differences (DID) approach with propensity score matching (PSM) to estimate the causal effects of RCMs disclosure. The results reveal that RCMs disclosure leads to a 7.25%–16.53% increase in hotel revenue. Additionally, firm-operated measures, including Cleaning Sanitation and Medical services, positively affect performance, whereas consumer-involved measures, like Social Distancing and Mask Policy, exhibit neutral or even negative effects. Furthermore, the severity of the pandemic and government interventions negatively/positively affect performance, with these impacts amplified/mitigated when hotels disclose RCMs. We also find that RCMs disclosure yields stronger positive effects for high-status hotels, highlighting the role of firm reputation. Finally, we demonstrate that RCMs disclosure fosters more positive guest feedback, which mediates the relationship between disclosure and performance.

This study makes several contributions. First, it advances the tourism risk management literature by examining the effects of bundled firm-operated and consumer-involved measures, offering a holistic view absent in prior research that focused on isolated RCMs (Lai and Wong, 2020) or consumer intentions (Banos-Pino et al., 2023; Hüsser and Ohnmacht, 2023). Second, it elucidates the mechanisms behind RCMs disclosure effectiveness, addressing not only the “what” but also the “why” and “when,” by incorporating signaling environment and signaler characteristics into the analysis. We also reveal a mediating role of guest feedback. Third, this study advances signaling theory in crisis contexts by systematically integrating its five elements into hypothesis development and testing. While signaling theory is widely cited (Fang et al., 2025; Friske et al., 2023), it is often used superficially. Our study enriches theoretical understanding and offers a framework for future research in crisis management.

Literature review and hypothesis development

RCMs during health-related crisis

Perceived risk has been recognized as a crucial factor influencing customers’ purchase intention and firm performance. To alleviate customers’ concerns and facilitate their purchase decisions, timely risk-coping practices are necessary for organizations (Li et al., 2023), especially for tourism and hospitality industry (Williams and Baláž, 2015). There is a wealth of literature emphasizing the importance of RCMs for the recovery of hotels during health-related crises. For example, hotels typically implement stricter cleaning standards and procedures for dining areas, public restrooms, and guest rooms during crises (Hsieh et al., 2021; Smart et al., 2021). They may also deploy technological innovations (e.g., robots) to sanitize and disinfect guest rooms without contact (Shin and Kang, 2020). Furthermore, due to the nature of health-related crises, minimizing interactions between customers and employees is crucial. Consequently, non-pharmaceutical interventions, including the mask-wearing, testing, and social distancing, have become prevalent (Hüsser and Ohnmacht, 2023; Kim and Pomirleanu, 2021; Park and Lehto, 2021). All these measures have the potential to influence the perceived health risk during the customer decision-making process.

The numerous RCMs mentioned above can be categorized into two main types based on the subject implementing them: firm-operated and customer-involved measures. Firm-operated measures refer to various safeguard programs provided by firms to protect customers against uncertainty. Implementing these measures can alter customers’ perceived risk levels (Shin and Kang, 2020) without any extra costs paid by the customers, such as cleaning and sanitizing. In contrast, customer-involved measures are programs that need customers’ cooperation and execution to alleviate uncertainty. When implementing such measures, customers always need to pay additional costs (e.g., monetary or psychological costs) to safeguard their interests (Hüsser and Ohnmacht, 2023; Kang et al., 2021). One ubiquitous measure is mask-wearing policy. Hotels adopt it to reduce the infection risk but require customers to bear the additional costs and effort associated with its implementation.

In sum, while this stream of research acknowledges the importance of RCMs, two gaps remain. First, previous studies have primarily focused on qualitative analyses of recommended RCMs in the tourism industry and explored customer travel and accommodation intentions in response to such measures during crises, little is known about the collective effects of different types of RCMs on hotel performance from the hotel perspective. Firms often launch a portfolio that bundles firm-operated and customer-involved measures during a health crisis. Thus, the role of both firms and customers must be considered when examining the effectiveness of such a portfolio. Second, while existing research has studied the effect of various measures on business outcomes, these studies often rely on prospective data (e.g., survey). However, due to the unpredictability of crises, significant disparities may exist between prospective data and real-world data. These disparities can potentially lead to conclusions that diverge from real-world scenarios. As such, our study can develop more valuable insights for potential future occurrences of a similar crisis by employing real-world data and analyzing in terms of causality.

Signaling theory

This study is grounded in signaling theory, which explains how parties address information asymmetries (Connelly et al., 2011). Widely applied in marketing and management research, signaling theory comprises five key elements: the signaler, the signal, the receiver, the signaling environment, and receiver feedback (Connelly et al., 2011). Signalers possess privileged information and communicate it through signals—either intentional or unintentional—to reduce information asymmetry. Effective signals are observable, costly to imitate, and credible (Ross, 1977). Receivers interpret signals and leverage the conveyed information to make informed decisions. The signaling environment also plays a crucial role in shaping both the transmission and reception of signals, affecting how signals are perceived and acted upon (Connelly et al., 2011). Furthermore, receivers’ interpretation may, in turn, generate feedback to signalers.

We apply this theoretical framework to our research context. In this study, hotels serve as signalers, conveying information to receivers (i.e., customers) by disclosing their RCMs on TripAdvisor. These signals are intended to demonstrate their capability to ensure safety and maintain service stability during crisis, thereby influencing potential customers’ perceptions and booking decisions. However, the extent to which the effectiveness of these signals is influenced by the characteristics of the signal itself, the signaling environment, and the attributes of the signalers remains unclear. Moreover, the receiver feedback in response to these signals—such as post-stay attitudes toward the hotel—requires further investigation. Accordingly, we develop and test five hypotheses to address these gaps. Figure 2 shows an overview of the hypotheses.

Hypothesis development

Signals: Perceived cost

In this research, we first examine the portfolio of RCMs that hotels disclose in response to the COVID-19 crisis, specifically distinguishing between firm-operated measures and customer-involved measures. It is not immediately clear how potential customers will respond to various RCMs disclosed by hotels during the crisis. Although hotels may be motivated by a genuine commitment to guest safety, they must also consider that customers expect not only reassurance but also a hassle-free experience. This tension gives rise to two competing effects in customer responses.

On the one hand, we hypothesize that firm-operated measures will generate positive business outcomes. This expectation is grounded in signaling theory (Connelly et al., 2011; Spence, 1973), which posits that actions perceived as more costly serve as stronger signals of a firm’s commitment (Yoon and Chung, 2018). When a hotel discloses a high-cost response—such as enhanced cleaning or medical services—it signals a willingness to incur significant expenses to ensure safety, thereby bolstering its credibility and attracting more bookings. On the other hand, customer-involved measures, such as mandatory mask policies and enforced social distancing, impose low direct costs for the hotel but may be perceived less favorable as they shift the burden of compliance onto guests. Moreover, such measures could introduce inconvenience, potentially discouraging customers and adversely affecting their experience. Therefore, we hypothesize customer-involved measures may elicit less favorable or even negative responses from customers, leading to adverse effects on hotel performance.

Overall, we expect firm-operated measures to positively impact hotel performance, while consumer-involved measures may have the opposite effect. However, we hypothesize a net positive effect of RCMs disclosure for three reasons. First, even consumer-involved measures are not inherently detrimental; they can enhance perceptions of a hotel’s diligence and safety commitment, particularly among risk-averse consumers who value proactive precautions. Second, disclosing two types of measures signals a comprehensive crisis response, enhancing consumer trust in the hotel’s competence. Third, by signaling transparency and accountability, such disclosure can enhance the credibility of the hotel’s overall crisis response, thereby translating into improved performance outcomes. Taken together, these arguments motivate the following hypotheses:

In general, RCMs disclosure has a net positive impact on hotel performance.

H2a

Firm-operated measures have a positive impact on hotel performance.

H2b

Consumer-involved measures have a negative effect on hotel performance.

Environment: Perceived safety

We further examine how the signaling environment moderates the relationship between hotels’ RCMs disclosure and performance. As factors associated with the signaling environment influence how receivers interpret signals (Friske et al., 2023), the effectiveness of RCMs disclosure is not static but context-dependent. Specifically, pandemic severity (Ding et al., 2021) and the intensity of government responses (Hsieh et al., 2021; Litvin, 2024) may serve as critical external moderators that shape how potential customers interpret RCMs.

In regions with high pandemic severity and strong governmental interventions, consumers may perceive heightened risk and vulnerability. According to Han et al. (2023), individuals cognitively assess threats before engaging in protective behaviors. As such, RCMs disclosure in such contexts become salient indicators of a hotel’s commitment to guest safety. These signals may enhance consumer trust and perceived safety, positively influencing visit intentions (Hsieh et al., 2021; Quan et al., 2022). Conversely, when pandemic severity is low and government responses are limited, consumers may not perceive RCMs as necessary. Under such conditions, the same signals may be perceived as less critical or even redundant, as consumers do not feel the same level of urgency or threat, weakening their influence on consumer behavior and hotel performance.

Thus, the signaling environment acts as a contextual filter: it amplifies signal effectiveness during periods of heightened threat while attenuating it under benign conditions. This dual role underscores the importance of perceived risk in shaping protective behavior (Han et al., 2023) and aligns with Lim et al.’s (2023) emphasis on contextual influences. Based on this reasoning, we propose our third hypothesis:

H3a

Pandemic severity moderates the impact of RCMs disclosure, amplifying positive effects on hotel performance during periods of high pandemic severity.

H3b

Government response intensity moderates the impact of RCMs disclosure, amplifying positive effects on hotel performance during periods of strong government interventions.

Signaler: Perceived credibility

Signaling theory suggests that signaler is a critical component of effective communication, especially during crises (Fang et al., 2025). The underlying logic behind this is that the effectiveness of signals depends on their observability, imitation cost, and credibility (Bafera and Kleinert, 2023), all of which can vary significantly depending on who delivers the message. Prior research shows that high-status signalers are more effective in conveying credible information (Fang et al., 2025; Friske et al., 2023), particularly when their signals are backed by external endorsements. For instance, Fang et al. (2025) find that CEO-led announcements elicit stronger reactions than generic corporate statements, as they convey authenticity and value alignment. Similarly, Friske et al. (2023) demonstrate that third-party assurances enhance the credibility and value impact of CSR disclosure.

In our context, hotels voluntarily disclose their RCMs on TripAdvisor. We argue that hotel status significantly shapes how these signals are received. High-status hotels (e.g., those with higher rankings, where lower numerical values indicate better quality) possess greater reputational capital and operational capabilities, which lend credibility to their disclosures. Consumers are more likely to perceive such hotels as capable of implementing effective safety measures, thereby increasing trust and performance outcomes. Conversely, signals from low-status hotels may suffer from credibility deficits. Due to resource constraints and higher imitation costs, their disclosures may be interpreted as less genuine or even as strategic “cheap talk,” diminishing their impact or potentially backfiring. Therefore, we propose the following hypothesis:

The positive impact of RCMs disclosure on hotel performance is more pronounced for high-status hotels compared to low-status hotels.

Receiver: Consumer feedback

In the hotel industry, consumer satisfaction is a central objective for hotels and a critical outcome of effective operational strategies, directly influencing repurchase intentions (Han and Hyun, 2017). For example, Ladhari and Michaud (2015) show that higher levels of consumer satisfaction are associated with an increased likelihood of repeat patronage, highlighting satisfaction’s pivotal role in driving organizational performance. During crises, hotels implement RCMs not only to mitigate negative stakeholder impacts but also to signal their proactive management to consumers, aiming to prevent adverse behaviors such as reduced satisfaction. Extending signaling theory, hotels act as signalers by disclosing their RCMs through platforms such as TripAdvisor, while consumers serve as receivers who interpret these signals, forming perceptions that influence their behavioral intentions (Chen and Jai, 2019; Liu et al., 2015). Crucially, the feedback generated (e.g., positive reviews) by consumers represents an essential component of the signaling process. Positive consumer feedback reinforces the effectiveness of the hotel’s RCM signals, enhancing both reputation and performance. The following hypothesis is therefore proposed:

RCMs disclosure leads to more positive guest feedback, which in turn mediates the relationship between RCM disclosure and improved hotel performance.

Research context, data and methodology

Research context

To understand whether and how RCMs disclosure affects firm performance, we leverage a module change in TripAdvisor, one of the largest online platforms for accommodations, restaurants, and attractions in the world (Ding et al., 2022). At the end of 2019, the outbreak of COVID-19 triggered a global health crisis, exposing consumers to substantial risks during travel. To protect consumers’ health, TripAdvisor launched a module named COVID-19 Travel Safe Tools in April 2020. This feature aids consumers in finding, filtering, and verifying health and safety information. Specifically, it allows hotels to display RCMs details on their webpage (see Figure 1). Our study includes 14 different RCMs. We first explore their total effect. Alongside this aggregate effect, we divide 14 measures into two categories and five subcategories: firm-operated measures (Clean Sanitation, Medical, and Organization) and consumer-involved measures (Mask Policy and Social Distancing). Then, we examine these different measures to understand their actual effectiveness.

Research data

Following Hollenbeck (2018), we select Texas as our research setting. Our data are drawn from three main sources: the Texas Comptroller of Public Accounts (TCPA), the Centers for Disease Control and Prevention (CDC), and TripAdvisor, which respectively provide information on hotel revenues, COVID-19 conditions and policies, and hotels’ RCMs and online reviews, respectively. The Texas lodging sector plays an important role in the state’s economy given that Texas is the second largest U.S. state. The TCPA provides a complete and authentic record of monthly tax data for the lodging sector in the area. It also includes basic information, such as hotel name, address, and capacity.

We first obtain monthly hotel revenue data from TCPA, an official state agency that publishes comprehensive tax records. From March 2020 to March 2021, we collect revenue, name, address, and capacity data for over 10,000 Texas hotels. Second, from TripAdvisor, we extract RCMs, including the date and content of measures, as well as review information such as ratings, review content, and posting dates. We also gather hotel attributes including name, address, and star class. Third, recognizing that pandemic severity and policy responses may influence hotel performance, we collect county-level data from the CDC, including monthly COVID-19 fatalities and government interventions such as mask mandates, stay-at-home orders, gathering restrictions, and restaurant limitations. Finally, because our data originates from three distinct sources, accurate data matching is essential. As detailed in the Section A1 of Appendix, we develop a structured data integration framework that successfully matches 5880 of 7524 hotels (78.15%), yielding a monthly panel dataset of 70,946 hotel-month observations from March 2020 to March 2021.¹

Empirical model

The dependent variable in our study is RevPAR (Revenue per Available Room), a standard performance measure for hotel i in month t (Hollenbeck, 2018). To ensure stationarity, we apply a natural logarithm transformation to RevPAR. Our independent variable captures whether hotel i discloses RCMs in month t, determined by browsing historical snapshots of the hotel’s webpage via the Internet Archive (https://archive.org/web). We specify the following panel fixed-effects model for our DID analysis:

\log ({R e v P A R}_{i t}) = β {R C M_D i s c l o s u r e}_{i t} + γ {C o n t r o l s}_{i t} + {H o t e l}_{i} + {T i m e}_{t} + ε_{i t}

(1)

A key concern is potential self-selection bias, wherein hotels that disclose RCMs may systematically differ from those that do not. To address this, we construct a matched control group using PSM method, ensuring comparability in observable characteristics (see Section A2 of the Appendix for details). Additionally, we employ coarsened exact matching (CEM) and look-ahead PSM (LA-PSM) methods to obtain matched data as alternative approaches to PSM in robustness checks. To further mitigate endogeneity, we include hotel and time fixed effects, controlling for unobserved time-invariant hotel characteristics and temporal shocks.

We also incorporate several sets of control variables based on prior research (Ding et al., 2022; Hollenbeck, 2018). First, given the critical role of online reviews in shaping hotel performance, we control for review-related variables: cumulative review volume (AccVolume), average rating (AccRating), average review length (AccLength), standard deviations of ratings (AccRatingSD) and review lengths (AccLengthSD) as of month t-1. Second, to account for competition, we control for the average RevPAR of other hotels in the same zip code (Mean_OtherRevPAR). Third, recognizing the impact of COVID-19, we control for monthly county-level fatalities (Fatalities). We also include monthly county-level government measures to account for the variation in local regulatory interventions, which may influence both hotel revenue and the effectiveness of RCMs. These government measures include mask order (Mask_Order), stay-at-home order (StayAtHome_Order), gathering order (Gathering_Order), and restaurant restrictions (Restaurant_Order). Lastly, we control for ownership changes (OwnerChange) during the study period. Definitions and summary statistics of all variables are provided in Table 1.

Table 1.

Main variables definition and summary statistics.

Variable	Definition	Mean	SD	Min	Median	Max
RevPAR	Revenue per available room of the hotel	36.44	72.65	0	27.74	9235
RCM_Disclosure	Whether a date is after the hotel’s RCMs disclosure	0.177	0.382	0	0	1
AccVolume	Monthly cumulative number of reviews	210.0	428.4	1	71	8341
AccAvgRating	Monthly cumulative average rating of reviews	3.742	0.922	1	3.985	5
AccAvgLength	Monthly cumulative average length of reviews	454.8	96.7	95	452.4	789
AccRatingSD	Monthly cumulative standard deviation of review rating	1.129	0.431	0	1.167	2.828
AccLengthSD	Monthly cumulative standard deviation of review length	200.7	42.46	0	207.2	495.7
MeanOtherRevPAR	Monthly average RevPAR of other hotels in the same zip-code	32.80	38.18	0	27.93	2025
OwnerChange	Whether a hotel changes its ownership recently	0.004	0.064	0	0	1
Fatalities	Monthly number of fatalities	678.4	1202	0	117	6098
Mask_Order	Whether governmental mask order executed	0.696	0.460	0	1	1
StayAtHome_Order	Whether governmental stay-at-home order executed	0.924	0.265	0	1	1
Gathering_Order	Whether governmental gathering order executed	0.638	0.461	0	1	1
Restaurant_Order	Intensity of governmental restaurant order executed	1.052	0.354	0.29	1	2

Results

Effects of RCMs disclosure

We first examine effects of RCMs disclosure on hotel performance (H1). Table 2 displays the estimation results of the DID model. In column (1), we provide the estimates using the full sample. Columns (2) to (4) show the estimates using matched samples, in which the observations are made up of hotels that are matched within the same county, city and zip-code region. We are interested in the coefficients of RCM_Disclosure, which we observe to be positive and statistically significant at the 0.001 level for all samples, thus indicating that the disclosure of RCMs is positively related to hotel revenue. Specifically, hotels that disclose RCMs experience a revenue increase of 7.25% (exp (0.07)−1 = 0.0725) to 16.53% compared to those that do not. These findings provide empirical support for H1, implying the important role of disclosing RCMs during a health crisis.

Table 2.

The effects of RCMs disclosure.

	log (RevPAR)
	(1)	(2)	(3)	(4)
RCM_Disclosure	0.153^*** (0.014)	0.070^*** (0.020)	0.070^*** (0.021)	0.084^*** (0.022)
Controls	YES	YES	YES	YES
Time, hotel FEs	YES	YES	YES	YES
Matched level	All	County	City	Zip
Observations	54,422	15,607	15,057	13,127
R-square	0.307	0.369	0.358	0.395

Notes. Standard errors are robust and clustered at the hotel level.

*p < 0.05, **p < 0.01, ***p < 0.001.

The effects of different RCMs

We demonstrate that RCMs are generally effective in improving business performance. However, since these measures are presented in the form of a portfolio, we want to explore the effect of each form to test H2a and H2b. Thus, we extend our model as follows:

\log ({R e v P A R}_{i t}) = β_{1} {C l e a n_S a n i t a t i o n}_{i t} + β_{2} {M e d i c a l}_{i t} + β_{1} {O r g a n i z a t i o n}_{i t} + β_{1} {M a s k_P o l i c y}_{i t} + β_{1} {S o c i a l_D i s t a n c i n g}_{i t} + γ {C o n t r o l s}_{i t} + {H o t e l}_{i} + {T i m e}_{t} + ε_{i t}

(2)

In equation (2), Clean_Sanitation_it, Medical_it, Organization_it, Mask_Policy_it, and Social_Distancing_it are binary variables indicating whether hotel i discloses the corresponding measures in month t. The first three are firm-operated measures, and the last two are consumer-involved measures. Table 3 displays the regression results: Clean Sanitation and Medical measures have significant and positive impacts on RevPAR, whereas Mask Policy measures have a significantly negative impact (under the strictest matched level). Specifically, the results indicate that following the disclosure of Clean Sanitation or Medical measures, hotels experience improved performance, with RevPAR increasing by 17.6% and 16.1%, respectively. In contrast, hotels that disclose mask-related measures see a decline in performance, with RevPAR decreasing by 18.8% overall. According to psychological reactance theory, when individuals are compelled to modify their behaviors in a specific manner, they perceive these demands as threats to personal freedom (Kang et al., 2021). As such, people may view mask-related measures as violations of freedom, which consequently has a negative effect. Moreover, Social Distancing and Organization measures do not have significant impacts on RevPAR. Accordingly, the findings provide nuanced support for H2a and H2b: some measures boost hotel performance, whereas others can be neutral or even counterproductive. To advance empirical understanding, we also scrutinized the moderating role of social and demographic contextual factors on the adverse impact of mask-related measures (see Section A3 in Appendix).

Table 3.

The effects of different RCMs.

	log (RevPAR)
	(1)	(2)	(3)	(4)
Firm-operated measures
Clean Sanitation	0.220^*** (0.033)	0.191^** (0.063)	0.194^** (0.059)	0.176^** (0.054)
Medical	0.114^*** (0.032)	0.075 (0.044)	0.127^** (0.049)	0.161^** (0.055)
Organization	0.012 (0.036)	−0.042 (0.049)	−0.042 (0.049)	−0.006 (0.060)
Consumer-involved measures
Mask Policy	−0.208^*** (0.035)	−0.212^*** (0.064)	−0.216^*** (0.062)	−0.208^*** (0.060)
Social Distancing	0.108^*** (0.031)	0.091^* (0.042)	0.064 (0.047)	0.066 (0.052)
Controls	YES	YES	YES	YES
Time, hotel FEs	YES	YES	YES	YES
Matched level	All	County	City	Zip
Observations	54,422	15,607	15,057	13,127
R-square	0.309	0.372	0.361	0.399

Notes. Standard errors are robust and clustered at the hotel level.

*p < 0.05, **p < 0.01, ***p < 0.001.

We further examine the causality of different measures through the PSM analysis, where we regard each form as the treatment variable while controlling for the other forms in the matching procedure. For example, when examining the effect of Mask_Policy, we carry out matching to ensure that other forms of measures are consistent, and the only difference is whether to disclose Mask_Policy measures. Table C in the Appendix provides details of these robustness checks, which indicate that such matching is consistent with the results of our regression analysis. To enhance clarity and provide a clearer understanding of the differential effects of various types of RCMs, we present a visual summary in Figure D in the Appendix.

Robustness checks

Alternative validation

To mitigate endogeneity concerns, we further implement several additional methods. First, we utilize CEM to obtain matched data, which achieves balance automatically without extensive post-matching assessment. Second, we employ LA-PSM approach, as suggested by Kumar et al. (2018), to account for unobserved hotel characteristics that may influence both the propensity to disclose RCMs and hotel performance. In this design, control units are hotels that have not disclose RCMs temporarily but will do so in the future, which further ensures that the treatment and control groups are similar in unobserved characteristics. Third, we incorporate a Heckman-type model to consider hotel’s self-selection of disclosing action following observable characteristics (Kumar et al., 2018). The corresponding results for the three methods are presented in columns (1) to (3) of Table 4. All these results consistently support the validity of our identification strategy.

Table 4.

Results of robustness checks.

	log (RevPAR)			First stage RCM_Disclosure	Second stage log(RevPAR)
	(1)	(2)	(3)	(4)	(5)
RCM_Disclosure	0.088^*** (0.025)	0.107^* (0.044)	0.204^*** (0.016)		0.299^* (0.137)
IMR			0.282^** (0.105)
Lag_RatioOtherZip (SameCounty-DiffCity)				0.226^*** (0.027)
Controls	YES	YES	YES	YES	YES
Time, hotel FEs	YES	YES	YES	YES	YES
Method	CEM	LA-PSM	Heckman	IV
Matched level	Zip	Zip	All	All	All
Observations	9356	3,062	36,830	54,360	54,360
R-square	0.361	0.489	0.378	-	0.303
KP (F-stat)				68.40
CD (F-stat)				441.05

Notes. Standard errors are robust and clustered at the hotel level.

*p < 0.05, **p < 0.01, ***p < 0.001.

Furthermore, we utilize the instrumental variable (IV) method to address the potential influence of unobserved hotel characteristics that may confound the RCMs disclosure and affect hotel outcomes. Specifically, we use the ratio of RCMs disclosed by hotels in other cities but in the same county during the t-1 period as an ideal instrument. Hotels in the same county area face the same pandemic situation and may therefore adopt similar responses, so whether the focal hotel discloses RCMs will be related to the proportion of RCMs disclosed by hotels in other cities but in the same county area. However, this proportion is unlikely to correlate with focal hotel revenue, as consumers are unlikely to change their original destination because of this proportion. Columns (4) and (5) of Table 4 present the estimation results of the two-stage least squares method with IV. The result is in line with prior results.

Other robustness checks

We also provide other robustness checks in the study. First, we repeat our estimation by setting the disclosure time to the following month if the date of disclosing RCMs is in the second half of the month. Second, we examine whether the positive effects persist if we measure the independent variable in terms of the number of measures (see Table D in the Appendix). Third, we conduct parallel trend tests (Figure B and Table E in the Appendix) and falsification tests (Figure C in the Appendix). All robustness checks consistently validate our findings.

Moderating effects of the environment

Our main findings suggest that RCMs disclosure generally enhances hotel performance; however, we posit that this effect is contingent on contextual factors, specifically pandemic severity and government intervention intensity (H3). To test these boundary conditions, we introduce interaction terms between RCM_Disclosure and two key variables: pandemic severity, measured by the logarithm of COVID-19 fatalities (Ding et al., 2021), and government responses, captured by a composite index (GovResponseIndex) that aggregates various intervention dimensions, such as mask mandates and economic support policies. Specifically, this index consolidates multiple policy measures into a single composite score for each county and month. It is directly sourced from the CDC, which monitors and reports various public health orders at the local level. The GovResponseIndex enables us to capture the overall regulatory environment in which each hotel operates, reflecting both the breadth and intensity of local government interventions.

While such measures are intended to provide institutional assurance, we argue that stronger government actions may inadvertently heighten consumer vigilance and risk perceptions through three primary mechanisms. First, rooted in negativity bias (Rozin and Royzman, 2001), consumers are evolutionarily predisposed to weight threat-related signals more heavily than safety-related ones. Consequently, the “safety” provided by an intervention is often perceived as abstract, while the underlying “risk” it signals remains immediate and existential. Second, drawing on signaling theory (Connelly et al., 2011), the intensity of government mandates serves as a proxy for the severity of the environmental hazard. Stringent policies may be interpreted as a heuristic signal that the crisis is severe or difficult to control, thereby increasing consumers’ sense of vulnerability. Finally, intrusive interventions can trigger psychological reactance (Kang et al., 2021), where the perceived loss of autonomy and personal freedom amplifies a sense of being in a “high-risk” state. Ultimately, these mechanisms suggest that the risk-aggravating informational value of government intervention might dominate its functional safety benefits. This heightened risk perception can, in turn, suppress consumer demand and negatively affect hotel performance.

Table 5 presents the results. In columns (1) to (3), the negative coefficients for log(Fatalities) confirm that greater pandemic severity adversely affects hotel performance. However, the positive and significant interactions (RCM_Disclosure × log(Fatalities)) indicate that the benefits of RCMs disclosure are amplified under higher pandemic severity. This finding aligns with the notion that consumers place greater value on proactive safety measures amid heightened risk, thereby strengthening the signaling effect. Notably, the negative coefficients for RCM_Disclosure suggest that in regions with lower pandemic severity, such disclosures may appear unnecessary or burdensome, weakening their immediate positive impact.

Table 5.

The moderating effects of pandemic severity and government response.

	log (RevPAR)
	(1)	(2)	(3)	(4)	(5)	(6)
RCM_Disclosure	−0.119^** (0.045)	−0.110 (0.057)	−0.065 (0.058)	0.398^** (0.150)	0.634^*** (0.179)	0.446^** (0.166)
log (Fatalities)	−0.036^** (0.011)	−0.044^*** (0.012)	−0.031^* (0.013)
GovResponseIndex				−0.061^*** (0.007)	−0.057^*** (0.006)	−0.057^*** (0.006)
RCM_Disclosure × log (Fatalities)	0.032^*** (0.008)	0.030^** (0.010)	0.025^* (0.010)
RCM_Disclosure × GovResponseIndex				−0.006^* (0.003)	−0.011^** (0.003)	−0.007^* (0.003)
Controls	YES	YES	YES	YES	YES	YES
Time, hotel FEs	YES	YES	YES	YES	YES	YES
Matched level	County	City	Zip	County	City	Zip
Observations	15,607	15,057	13,127	15,607	15,057	13,127
R-square	0.370	0.359	0.396	0.369	0.358	0.395

Notes. Standard errors are robust and clustered at the hotel level.

*p < 0.05, **p < 0.01, ***p < 0.001.

Columns (4) to (6) reveal a different pattern with respect to government responses. The negative and significant interactions (RCM_Disclosure × GovResponseIndex) imply that strong government interventions diminish the incremental benefits of RCMs disclosure. In areas with robust governmental measures, consumers may already feel sufficiently protected, reducing the additional assurance provided by individual hotel disclosures. Thus, government actions may weaken the signaling value of RCMs. Overall, the results provide support for H3a, but fail to support H3b. While higher pandemic severity amplifies the positive effect of RCMs disclosure on hotel performance, stronger governmental responses attenuate this benefit. Furthermore, we also provide visual representations (see Figures E-F in the Appendix) to illustrate how the effects of RCMs disclosure change with pandemic severity and the stringency of government interventions.

Moderating effects of the hotel status

We further examine the heterogeneity in the effects of RCMs to offer insights and actionable guidance for hotels during health crises. Table 6 presents the results examining how hotel status moderates the relationship between RCMs disclosure and hotel performance. Across multiple specifications, the interaction terms between RCM_Disclosure and various indicators of hotel status (e.g., quality, ranking, and star-class) are generally positive and statistically significant. These findings indicate that higher-status hotels benefit more from disclosing RCMs. For instance, the positive interaction in columns (1) and (2) suggest that hotels with higher ratings or market positioning experience a stronger effect of RCMs disclosure on RevPAR. Similarly, column (3) reveals that mid- and luxury-segment hotels exhibit significant, positive interactions, further underscoring the amplifying role of hotel status. These results collectively support H4, which posits that the positive impact of RCMs disclosure on performance is more pronounced for high-status hotels.

Table 6.

The moderating effects of hotel status.

	log (RevPAR)
	(1)	(2)	(3)
RCM_Disclosure	−0.031 (0.031)	0.076^*** (0.022)	0.005 (0.025)
RCM_Disclosure × Quality	0.181^*** (0.039)
RCM_Disclosure × Rank		0.318^* (0.143)
RCM_Disclosure × Mid			0.145^*** (0.040)
RCM_Disclosure × Luxury			0.409^* (0.165)
Controls	YES	YES	YES
Time, hotel FEs	YES	YES	YES
Matched level	Zip	Zip	Zip
Observations	13,127	13,127	13,127
R-square	0.398	0.396	0.399

Notes. Standard errors are robust and clustered at the hotel level. The variables “Quality” and “Rank” are determined based on the median of hotels’ average ratings prior to RCMs disclosure and the median of their relative rankings (ranking divided by the number of hotels) within the same city, respectively; hotels with values above the median are coded as 1. Economic (1–2 stars), Mid (2.5–3.5 stars), and Luxury (4–5 stars) are classified by star-class.

*p < 0.05, **p < 0.01, ***p < 0.001.

Theoretically, these findings align with signaling theory, whereby high-status hotels possess greater legitimacy and resource capabilities, allowing them to implement and communicate their risk-coping strategies more credibly. In contrast, lower-status hotels may struggle to establish the same level of trust among potential guests, diminishing the performance benefits associated with such disclosure. Consequently, hotel status serves as a key boundary condition that shapes the efficacy of RCMs in crisis contexts.

Mediation analysis: Consumer feedback

Previous research suggests that positive reviews convey quality signals to consumers and enhance firm performance (Zhu and Zhang, 2010). Applying this logic, hotels disclosing RCMs may generate additional positive reviews and boost revenue. Following Baron and Kenny (1986), we conduct mediation analyses using the number of positive reviews (ratings of 4 and 5) as a mediator. Table 7 presents the bootstrap-based mediation test results for H5. Columns (2) and (3) show that RCM_Disclosure positively and significantly affects the number of positive reviews, indicating that disclosing RCMs enhances guests’ favorable perceptions, while its effect on negative reviews is negative but statistically insignificant. In column (4), where both RCM_Disclosure and NumPos predict RevPAR, the coefficient for RCM_Disclosure remains positive but diminishes in magnitude, indicating partial mediation. The significant coefficient for NumPos confirms that increased positive feedback improves hotel performance. With a mediation proportion of 10.72%, a meaningful share of the effect of RCM_Disclosure on RevPAR operates through enhanced positive reviews. These findings provide empirical support for H5, demonstrating that RCM disclosure improves hotel performance partly by influencing consumer perceptions and feedback.

Table 7.

Mediation regression: The role of consumer feedback.

	(1)	(2)	(3)	(4)
	log (RevPAR)	NumPos	NumNeg	log (RevPAR)
RCM_Disclosure	0.084^*** (0.022)	0.164^** (0.053)	−0.008 (0.016)	0.075^*** (0.021)
NumPos				0.054^*** (0.006)
Controls	YES	YES	YES	YES
Time, hotel FEs	YES	YES	YES	YES
Matched level	Zip	Zip	Zip	Zip
Observations	13,127	13,127	13,127	13,127
R-square	0.395	0.047	0.150	0.405
Proportion of mediation				10.72%

Notes. Standard errors are robust and clustered at the hotel level.

*p < 0.05, **p < 0.01, ***p < 0.001.

Conclusion and discussion

Summary of findings

We examine the role of RCMs disclosed by firms to understand how such disclosure enhances firm performance in the hotel industry. Our empirical analysis shows a positive association between RCMs and hotel performance in general. We further find that the effects of different types of RCMs are heterogeneous. Specifically, firm-operated measures, such as Cleaning Sanitation and Medical, have positive effects on hotel performance. However, consumer-involved measures, such as Mask Policy, exert a negative impact. Consistent with signaling theory, our results demonstrate that both the signaling environment and the characteristics of the signalers moderate the positive impact of RCMs disclosure on firm performance. Finally, we reveal that the disclosure of RCMs can lead to more positive guest feedback, which in turn improves hotel performance.

Theoretical contributions

Our study makes three key theoretical contributions to the literature on crisis management and signaling in hospitality. First, we advance tourism risk management research by empirically evaluating a comprehensive portfolio of RCMs that integrates both firm-operated and consumer-involved strategies. Prior studies in the tourism and hospitality literature have largely relied on qualitative assessments of isolated RCMs (Lai and Wong, 2020) or focused primarily on consumer-side outcomes such as travel intentions and stay behavior during crises (Hsieh et al., 2021; Hüsser and Ohnmacht, 2023; Kang et al., 2021). In contrast, our study adopts a performance-based perspective, offering rigorous quantitative evidence from the hotel standpoint. Drawing on signaling theory (Connelly et al., 2011; Spence, 1973), we conceptualize RCMs as strategic signals that convey credible information about a hotel’s risk management capability. Our findings reveal that firm-operated measures serve as more effective signals than consumer-involved ones, enhancing performance without imposing additional burdens on guests. By demonstrating that RCMs disclosure produces tangible firm-level benefits rather than merely shaping consumer perceptions, our research bridges a critical gap between perception-oriented and performance-oriented crisis management studies.

Second, we extend the theoretical understanding of how, why, and when RCM disclosures affect hotel performance by unpacking their underlying mechanisms. While previous crisis management studies have primarily focused on the existence or visibility of RCMs (e.g., Hsieh et al., 2021), we integrate signaling theory to capture how the signaling environment (Friske et al., 2023) and signaler characteristics (Fang et al., 2025) condition the credibility and effectiveness of RCMs. Specifically, we show that contextual factors—such as pandemic severity and government interventions—shape the signaling environment, while hotel-specific features, such as status and reputation, affect the perceived credibility of the signaler. Moreover, by identifying guest feedback as a mediating mechanism, we reveal how positive consumer responses reinforce satisfaction and subsequently enhance firm performance. This multi-layered mechanism clarifies not only whether but also under what conditions RCMs disclosures generate performance gains, thereby enriching the contingency-based understanding of signaling in crisis contexts.

Third, our research advances signaling theory in the context of crisis management by rigorously operationalizing its five core elements—signaler, signal, receiver, signaling environment, and feedback—to construct and test our hypotheses. While signaling theory is widely employed in the literature (Fang et al., 2025; Friske et al., 2023), its application has often been limited to hypothesis support without a thorough exploration of underlying mechanisms. By employing a robust quantitative approach, our study not only confirms that RCMs disclosure serves as effective signals but also elucidates how external conditions and hotel characteristics moderate these effects. Additionally, feedback in the form of guest responses and subsequent satisfaction acts as a reinforcing mechanism that can influence hotel performance. This comprehensive analytical framework enriches our theoretical understanding and offers a robust analytical framework for future research on crisis communication and performance enhancement within the hospitality industry.

Practical implications

Our study provides several important implications for both hotel managers and online platforms. First, RCMs have proven effective in boosting hotel revenue during health crises. As such, hotel managers should proactively disclose these measures to reassure consumers, reducing their concerns and increasing their likelihood to book. While existing measures are valuable, managers should consider implementing additional firm-operated measures, such as offering risk-related tips and service refunds to further ease consumer worries (Pappas, 2018). Hotels that already offer consumer-involved measures should focus on minimizing the effort and cost consumers incur when coping with risks. For instance, hotels can distribute free masks and rearrange furniture to ensure adequate social distancing in rooms and dining areas. Furthermore, implementing non-contact services powered by artificial intelligence or remote technology can enhance consumers’ safety and comfort by automating the enforcement of social distancing.

Second, hotel managers must tailor RCMs development to their unique circumstances and the broader market environment, as the effectiveness of these measures varies across different types of hotels. Specifically, high-end, high-ranking, and luxury hotels should prioritize the disclosure of RCMs, as these measures can provide significant benefits in terms of consumer trust and loyalty. On the other hand, lower-quality or lower-ranked hotels should focus on other signals that emphasize safety and low risk, such as promoting their location in less crowded areas. Additionally, hotels should remain flexible and adapt their communication strategies based on evolving external conditions, as the impact of RCMs disclosure can fluctuate over time.

Finally, platform managers can play a critical role in facilitating the disclosure of RCMs by offering tools and functions that make it easier for hotels to communicate these measures to consumers. For example, platforms could introduce features that allow hotels to display RCMs more effectively, as well as interactive elements like consumer forums or real-time seller responses, fostering direct communication between hotels and guests. Additionally, platforms could create mechanisms that encourage hotels to share credible, private safety information with potential customers, helping to build trust and differentiate themselves during crises.

Limitations and future research

Our study has a few limitations. First, the generalizability of our results is restricted due to the limited scope of our research sample, which focuses solely on hotels in Texas. Researchers can extend our analysis to different regulatory and cultural contexts in the future. Second, while we have endeavored to control for observable characteristics to the greatest extent possible, the potential confounding influence of unobserved hotel strategies (e.g., pricing and advertising) remains a concern. These factors may coincide with the disclosure of RCMs, thereby complicating the interpretation of our findings. Future research should explore more sophisticated methodological approaches to mitigate this issue (e.g., natural experiments or richer operational data). Third, future research could address the limitations of this study by incorporating additional performance indicators, such as occupancy rates or average daily rate (ADR), to explore how these metrics interact with various crisis management measures. This would offer a more comprehensive understanding of hotel performance during health crises. Finally, while the mediation effect through positive reviews exists in this study, it represents only one of several potential pathways through which RCMs could impact hotel performance. Other mediators (e.g., employee behavior, word-of-mouth, and media coverage) may also play important roles in this relationship. Future research could explore these additional mechanisms to provide a more comprehensive understanding of how RCMs influence hotel performance.

Supplemental material

Supplemental material - Signaling in times of uncertainty: The effectiveness of risk-coping measures disclosure in the hospitality sector

Supplemental material for Signaling in times of uncertainty: The effectiveness of risk-coping measures disclosure in the hospitality sector by Yanan Cheng, Baojun Gao, Shan Liu in Tourism Economics

Footnotes

ORCID iDs

Yanan Cheng

Baojun Gao

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the [National Natural Science Foundation of China] under Grant [72371192 and 72032006], and the [Humanities and Social Science Fund of Ministry of Education of the People’s Republic of China] under Grant [22YJA630021].

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Supplemental material

Supplemental material for this article is available online.

Note

Author biographies

Yanan Cheng is a PhD student of Economics and Management School at Wuhan University. His research interests focus on business analytics, tourism management and information technologies.

Baojun Gao is a Professor of Economics and Management School at Wuhan University. His research interests are in the areas of online reviews, hospitality analytics, and social media. His research has appeared in Management Science, Tourism Economics, Tourism Management, International Journal of Hospitality Management, Annals of Tourism Research, Decision Support Systems, Information & Management, and other journals.

Shan Liu is a Professor and Vice Dean at School of Management in Xi’an Jiaotong University. His research interests focus on business analytics and intelligence, IT-enabled supply chain management, IT project management, and healthcare informatics. His research papers have been published in Journal of Operations Management, Tourism Management, International Journal of Hospitality Management, Information Systems Journal, Information & Management, European Journal of Operational Research, and other journals.

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