Destination recovery during COVID-19 in an emerging economy: Insights from Perú

Abstract

The onslaught of the COVID-19 pandemic has had a critical impact on the travel and tourism sector. Tourist destinations in developing countries are even more susceptible to negative trends of this sort due to the importance of tourism in emerging economies and the peculiarities of their infrastructures and healthcare systems. In such a context, the research develops a Partial Least Square (PLS) path modeling to analyze the impact of destination image and perceived health safety on perceived destination quality. We also assess the extent to which perceived quality impacts degree of destination loyalty and customer engagement. Results are based on a sample of 250 travelers visiting Lima during December 2020-January 2021.

1

Introduction

The COVID-19 pandemic has turned out to be one of the most shocking phenomena of the 21st century, seriously impacting countries, economic sectors and consumer behavior across the board—and around the world (e.g., Vázquez-Martínez et al., 2021). The travel and tourism sector has unquestionably been one of the hardest hit (Gössling et al., 2021; Jaaron et al., 2021); border closures and mobility restrictions aimed at slowing the spread of the virus have reduced tourism and hospitality activity to marginal figures (Zhong et al., 2021).

In such a context, Zenker and Kock (2020) identify potential impact on destination image as an interesting line of research—based on the premise that the pandemic, and the way it is being managed in different places, affects factors like perceived safety and destination quality. From a progressive tourism recovery standpoint, previous health crises like SARS, Ebola, influenza A and bird flu have shown that such events negatively impact destination image. In terms of tourist destination management, both the magnitude of the health crisis and the way public and private institutions manage it—effectiveness of communication campaigns and measures aimed at guaranteeing safety at destination, for instance—will have a decisive impact on destination image and the decision to return to or avoid specific destinations (Farmaki, 2021; Li et al., 2021a).

This is all much more relevant for emerging economy destinations, where less economic development and inferior healthcare infrastructures are a factor; and, in many cases, where tourism is an essential source of income for the local population—explaining why such destinations tend to be more susceptible to health crises of this sort. Hence, we believe understanding how destination image and perceived health safety impact perceived destination quality is key to proposing actions which effectively foster destination recovery. Moreover, perceived quality can also have an impact on destination loyalty and degree of engagement—understood as a set of transactional/non-transactional behaviors that both add value (Van Doorn et al., 2010) and have consequences of great current and future value for the tourist destination.

In the particular case of Peru, inbound tourism dropped dramatically by more than 75% in 2020 (MINCETUR, 2020) due to border and airspace closures. The tourism and hospitality industry plays a vital role in Peru's economy and labor market (Huerta-Álvarez et al., 2020). In 2019 it accounted for just over 5% of GDP and employed almost 10% of the formal workforce (INEI, Insituto de Estadística e Informática de Perú, 2021)). Destinations such as Machu-Pichu, Nazca and Titicaca are some of the most popular in the country; while capital city Lima, with its international airport, is the gateway to Peru and little by little is earning a reputation as destination of reference in its own right (Huerta-Álvarez et al., 2020).

Hence, we chose Lima (Peru) as our reference for a destination analysis in an emerging economy context. This research assesses the impact of destination image and perceived health safety on perceived quality. It also proposes that perceived quality impacts both potential destination loyalty and degree of customer engagement. With a view to achieve our research objectives, the paper is structured as follows: theoretical background and hypothesis development are presented in the following section. The third section details key study characteristics and methodology. Results are based on a sample of 250 travelers visiting Lima during December 2020-January 2021. Data are analyzed by using the Partial Least Square (PLS) technique. Section 4 presents our principal findings. Finally, we discuss results and provide a series of recommendations aimed at streamlining and enhancing tourist destination management in the post-COVID-19 world. Results are of great interest for managers as the pandemic seems to continue in the near future while most economies can not endure a new total lockdown. Authorities and managers may reflect on the results of this research to reinforce the perceived health safety, the travelers’ confidence, destination loyalty and engagement.

2

Theoretical background and hypothesis development

On the whole, perceived quality refers to consumer evaluation of a product or service, the outcome being superior to the rest of compared options (Aaker, 1991; Zeithaml, 1988). This evaluation can be based both on previous experiences/consumption and on customer references regarding the brand (name, advertisement, e-WOM) (e.g., Sürücü et al., 2019). Adapted to the context of tourism/travel destination, perceived quality is a global judgment regarding destination excellence—linked to tourists’ associations in relation to a destination's infrastructure, facilities and other tangible and intangible aspects (Bianchi et al., 2014; Prados-Peña & Del Barrio-García, 2021).

Comparison of perceptions and expectations serves as the basis for perceived quality evaluation (Mortazavi, 2021; Parasuraman et al., 1985; Prentice et al., 2019). With a view to achieve positive evaluation results, then, tourist destination managers must be well aware of what tourists’ expectations are (Mortazavi, 2021; Ye et al., 2019). Moreover, a decisive factor in the travel decision-making process is the search for information most tourists carry out prior to travel (Villamediana-Pedrosa et al., 2020). This is even more evident in the current pandemic context where perceived health risks rooted in fear—not only of contracting the coronavirus but of the uncertainty regarding quality and availability of adequate resources to care for patients as well—drive tourists to seek more information about destinations prior to travel.

The information travelers and tourists have access to, however, is not always as comprehensive as one would wish. In fact, information asymmetry is a characteristic problem in experiential services like tourism (Rocha & Fink, 2017)—making it essential for signalers (destination or territory agent) to convey and share information with receivers (tourist). In this sense, the Signaling Theory Perspective (STP) helps reduce information asymmetry (Li et al., 2021; Spence, 1973), and allows us to propose links between our constructs of reference.

STP proposes that destinations (signal sender)—through effective signals communication to travelers (signal receiver) regarding destination capabilities and quality- transmit information to potential and current tourists. According to this theory, senders transmit signals capable of influencing receivers who, in turn, will adopt behaviors in line with their interpretation of said signals; for instance, transmitting credible information to others in the market (Spence, 1973). Given the impact such signals can have on customer perceptions, knowing how to communicate successfully with travelers is as important in tourism destination management as quality infrastructures, services, accommodations, etc. (Li et al., 2021a)—even more so in emerging destinations where, under unprecedented pandemic circumstances like COVID-19, tourists may form unfavorable preconceived ideas about health crisis management due to a lack of access to comprehensive information.

High service quality perceptions, on the other hand, drive a range of favorable relational behaviors like positive word of mouth (WOM), referrals and repeat visits, as numerous studies have confirmed (e.g., Žabkar et al., 2010). Relationship management—as a process for attracting, maintaining and promoting customer relationships aimed at achieving profitability through customer loyalty (Grönroos, 2000; Morgan & Hunt, 1994)—offers support for the last chain of effects in our causal model.

Given the highly competitive nature of the tourism sector—due to the fact that most destinations offer similar attractions, services and experiences (Mirzaalian & Halpenny, 2021)—aspects like customer loyalty and engagement have become key differentiating factors in management (Cossío-Silva et al., 2019; Kumar & Pansari, 2016; Li, 2021; Thi et al., 2020). It is widely accepted that customer loyalty drives long-term outcomes (Cossío-Silva et al., 2019; Stylidis et al., 2021). Similarly, the literature indicates that customer engagement contributes to building longer-lasting company-customer relationships and generates positive interactive behaviors beyond mere repurchase among users (So et al., 2016). In the tourism context, customer behaviors of this sort provide fellow tourists with very useful additional information—not only contributing to a positive destination image but to minimizing uncertainty linked to pandemic-related health risks. In turn, recommendations by loyal customers and the range of interactions springing from customer engagement will generate new signals, thus completing a virtuous circle.

Hence, taking STP and the Relationship Marketing (RM) paradigm as references, our theoretical model proposes analyzing the potential impact of destination image and perceived health safety on perceived destination quality in the context of COVID-19—and how such perceptions can impact tourist responses in terms of engagement with and loyalty to the destination. Fig. 1

Fig. 1.

Proposed theoretical model.

2.1

Impact of destination image and perceived health safety on perceived destination quality

Destination image considers individuals’ knowledge, perceptions, feelings and beliefs about a tourist destination (Al Saed et al., 2020; Tan & Wu, 2016). Specifically, Kock et al. (2016) define destination image as tourists’ cognitive, affective associations in relation to a tourist destination. Destination image is a key factor impacting both destination choice and subsequent evaluation of the experience (Loureiro & Jesus, 2019)—while providing valuable insight into tourists’ behavioral intentions (Afshardoost & Eshaghi, 2020; Palos-García et al., 2021; Sánchez-Cañizares et al., 2021; Tan & Wu, 2016). Yet, despite being one of the most explored concepts in the tourism literature (e.g., Al Saed et al., 2020;; King et al., 2015), there is still little evidence confirming the impact of destination image during the COVID-19 pandemic.

It is logical to assume that a positive destination image will drive favorable tourist assessments regarding destination quality—i.e., destination products, services and experiences (Huerta-Álvarez et al., 2020). In the midst of a global health crisis, however, destination image, per se, may be not be enough. In such a context, tourists not only consider the usual elements comprising destination image in their evaluation of destination quality; factors like risk of contracting disease and health care capabilities and quality in the case of falling ill (i.e., perceived health safety) become equally important.

Undertaking a trip always involves a certain degree of risk—like safety and health-related risks, for instance (Sánchez-Cañizares et al., 2021). The literature has shown that perceived risk negatively impacts visitors' perceptions regarding the destination (e.g., Khan et al., 2017; Loureiro & Jesus, 2019); perceived safety, on the contrary, may be one of the most critical factors driving destination choice (Alrawadieh et al., 2019; Slevitch & Sharma, 2008). Hence, while prior to a health crisis, visitors’ overall image of a destination may impact their perception of destination quality, in times of health crisis, perceived health safety becomes another key factor in the perceived destination quality equation—clearly impacting behavioral responses.

In this sense, the Protection Motivation Theory (PMT; Rogers, 1975) complements the Signaling Theory Perspective (STP). PMT is helpful in predicting health-related behavior as it suggests that individuals—when detecting possible threats to health or well-being—take protective measures to avoid the risk of contracting a disease, based on perceived severity and vulnerability to the risk (Floyd et al., 2000; Rogers, 1975). Hence, the likelihood of preventive, protective behavior is positively connected with the availability of information (Vázquez-Martínez et al., 2021; Wong & Yeh, 2009); seeking medical information before traveling, for instance (Wang, Liu-Lastres et al., 2019). In the tourism context, PMT has been widely used to assess the impact of destination risk and safety perceptions on tourist behavior (Dryhurst et al., 2020; Rather, 2021; Wang et al., 2019; Wong & Yeh, 2009; Zheng et al., 2021)). In health crisis scenarios, factors like safety measures and regulations aimed at preventing the spread of a disease and/or authorize return to normal activity—or the existence/lack of adequate healthcare infrastructures can impact safety perceptions (Novelli et al., 2018). Hence, in today's ongoing pandemic world, it is essential to consider both destination image itself and perceived health safety at destination as an antecedent to perceived destination quality.

Based on the above arguments, we hypothesize that:

H1: Destination image has a positive impact on perceived destination quality.

H2: Perceived health safety at destination has a positive impact on perceived destination quality.

2.2

Impact of perceived destination quality on destination loyalty and engagement

Destination loyalty is very often considered an extension of customer loyalty (Zhang et al., 2014). However, there are certain key differences, since the principle that a loyal customer will generally be more likely to repeat purchase of a product or brand is not equally applicable in the case of travel and tourism. With leisure travel, people seek restorative experiences—to disconnect, break out of routine and explore new places (Ragb et al., 2020; Yang and Wong, 2020). Hence, for tourism contexts, we suggest defining loyalty in terms of intent to return and/or recommend a destination to other travelers (Huerta-Álvarez et al., 2020).

Perceived quality is the result of comparing consumer expectations with perceived service quality (Gao et al., 2020); hence, if reality exceed expectations, customer satisfaction has been achieved (Huerta-Álvarez et al., 2020). It is logical, then, to assume that if visitors have positive perceptions of destination quality they will be likely to return, as well as recommend the destination to others (Cossío-Silva et al., 2019; Palos-García et al., 2021; Prados-Peña & Del Barrio-García, 2021; Prayag & Ryan, 2012). The positive impact of perceived quality has been established in the literature—brand loyalty (Liu et al., 2017) and tourist destination loyalty, for instance (Chen et al., 2020; Huerta-Álvarez et al., 2020).

Customer engagement, on the other hand, has been defined as “a customer's behavioral manifestations that have a brand or firm focus, beyond purchase, resulting from motivational drivers” (Van Doorn et al., 2010). Engagement springs from interactivity, hence requires a connection between two parties (Prentice et al., 2019; Vivek et al., 2014). In an increasingly interconnected, technological world, customer engagement research is no longer limited to the study of company-customer interactions; rather, engagement is approached as a set of complex network relationships involving different actors, where the behaviors of some directly or indirectly impact the behaviors of others which, in turn, impact others, etc. (Shawky et al., 2020). According to Kumar et al. (2010), customer engagement—beyond economic transactions—can generate value indirectly (a) through customer referrals, (b) via customer-to-customer influence in terms of information sharing and eWOM and (c) by way of customer feedback or suggestions that help the company to improve existing products and services or even create new ones.

From the Social Exchange Theory perspective—which complements the RM approach—customer engagement involves mutual interaction between customers and brands, where consumers (i.e. tourists) are expected to display positive behaviors (e.g., advocacy) towards the brand (destination) if they perceive the relationship as beneficial (Hollebeek, 2011). Hence, under the principle of reciprocity we can assume that, when tourists have positive perceptions regarding destination quality (benefit received), feelings of gratitude are awakened triggering a desire to reciprocate—either helping directly by providing suggestions aimed at improving destination products/services or indirectly, by sharing positive personal experiences with other individuals/tourists—thus balancing the relationship (Hollebeek, 2011; Islam et al., 2019).

Hence, we propose that:

H3: Positive perceived destination quality has a positive impact on destination loyalty.

H4: Positive perceived destination quality has a positive impact on degree of customer engagement with destination.

3

Methodology

3.1

Research design

According to the most recent Morgan Stanley Capital International (MSCI) annual ranking based on major world stocks indexes (2021), Peru classifies as an emerging economy. Emerging markets are currently the major drivers of global growth and The World Bank (2021) expects emerging economies to grow faster than developed economies over the next few years—Latin America being a key area of reference. By country, Peru is ranked second in the global emerging markets (GEM) ranking conducted every six months by Bank of America (2020); above Russia, Israel or China, for instance.

In terms of tourism—prior to the onslaught of COVID-19—the 2018 World Travel & Tourism Council (WTTC) projected the LATAM region would sustain growth at 3.4% through 2028. Yet, the pandemic has unleashed a crisis of seismic proportions in the travel and tourism sector triggering losses of $4.5 trillion in GDP globally (−49.1%) (WTTC, 2021a). In the case of Peru, tourism contributed 8.3% of GDP before COVID-19. Due to mobility restrictions and lockdown measures throughout 2020, however, that contribution has slumped to 4.6% (WTTC, 2021b)—slightly higher than the average drop in tourism revenues worldwide (WTTC, 2021a). That said, vaccines are proving to work despite the appearance of new variants, successfully mitigating the most adverse effects of the disease (Wouters et al., 2021) and driving optimistic forecasts for a gradual recovery (WTTC, 2021c). In this vein, the Peruvian Minister of Foreign Trade and Tourism, Claudia Cornejo, affirmed at the 2021 FITUR International Tourism Fair that the sector can expect a 15% increase in international arrivals this year compared to 2020; moreover, there is confidence in recovering pre-pandemic figures (four million foreign tourists) in the next three years (Hosteltur, 2021b). In such a context, our study is highly relevant.

3.2

Measurement scales

To test the proposed model, a quantitative research approach was adopted using a structured questionnaire. The constructs studied were measured using scales adapted from the literature (see Appendix I). Specifically, destination image (3 items), destination perceived quality (4 items), destination loyalty (4 items) and customer engagement with destination (6 items) were measured with Huerta-Álvarez et al. (2020) proposal. Perceived health safety (3 items) was adapted from the scale proposed by Simpson et al. (2016). All items were measured using a 7-point Likert scale, from 1: “strongly disagree” to 7: “strongly agree”.

3.3

Data collection and sample profile

This fieldwork was carried out through travelers who visited Lima during the COVID-19 pandemic, during the months of December 2020 and January 2021. The method for collecting information was determined by simple random selection of guests staying in three-, four- and five-stars hotels in Metropolitan Lima. Prior to commencing fieldwork, permission was requested from area hotels. Questionnaires were self-administered by trained interviewers in hotel lobbies during mornings and evenings. Our final sample size was 250 travelers -all legal adults- achieving a sampling error of 0.063 (p = q = 0.5 and infinite population). Just over half of our sample is comprised of men (53.6% - 134 respondents), where 46% of the respondents were under 35 years old. Due to sanitary restrictions, the main purpose of the tryp was business/work for the entire sample −31.6% employed; 68.4% self-employed-.

3.4

Common-method bias test

In order to avoid common bias method, measurement scales were adapted from previously validated studies, a pre-test was carried out to correct potential ambiguities, and explaining to the respondent that there are no right or wrong responses. Following Kock and Lynn's (2012: 353) procedure, the full collinearity test was performed and none of the variance inflation factors were higher than the maximum threshold of 3.3 (VIF_{Dest_Image}=2.56; VIF_{Perc_Health_Safety}=1.65; VIF_{Dest_Quality}=1.05; VIF_{Dest_Loyalty}=2.61; VIF_{Cust_Engagement}=1.35). These results indicated the absence of collinearity issues. Furthermore, Table 2 shows that none correlations between latent variables exceed 0.9 (Bagozzi et al., 1991).

4

Analysis and results

Partial least squares (PLS) path modeling was employed by using software SmartPLS 3.3.3 (Ringle et al., 2015) with the bootstrap method of resampling of 5000 to evaluate the significance of the estimated parameters (Henseler et al., 2009). Firstly, the psychometric properties of the measurement scales were analyzed, and subsequently the hypotheses were tested estimating the effects between latent variables.

4.1

Measurement scales: dimensionality, reliability, and validity

A first-order measurement model was estimated considering all reflective items towards their latent construct to assess the internal consistency of the measurement scales. One item of the destination quality scale was eliminated to increase Cronbach's Alpha (see Table 1). With regard to the convergent validity, Table 1 shows that standardized loadings from the measurement model estimations were all over 0.7 and significant at 99%, and composite reliability (CR>0.7) and average variance extracted (AVE>0.5) were also above the minimum thresholds. These results allowed us to confirm the convergent validity (Hair et al., 2017).

Table 1.

Measurement model factor loadings (internal consistency and convergent validity assessment).

Construct & Items	Loading	t-value	Cronbach's α	Composite Reliability	AVE
Destination Image	0.934	0.953	0.834
DIM1	0.902**	42.59
DIM2	0.900**	57.52
DIM3	0.938**	70.50
DIM4	0.912**	38.65
Perceived Health Safety	0.839	0.904	0.758
PHS1	0.830**	20.18
PHS2	0.925**	48.27
PHS3	0.854**	21.41
Perceived Destination Quality	0.944	0.964	0.899
DQ1	0.947**	88.45
DQ2	0.955**	75.43
DQ3	0.941**	60.33
Destination Loyalty	0.884	0.914	0.729
DLO1	0.888**	30.99
DLO2	0.925**	97.78
DLO3	0.781**	10.84
DL4	0.907**	57.37
Customer Engagement with Destination	0.899	0.919	0.655
CE1	0.819**	12.81
CE2	0.824**	12.01
CE3	0.787**	12.98
CE4	0.777**	23.47
CE5	0.844**	32.03
CE6	0.803**	11.74

Note: **: Significant at 0.01

The discriminant validity was confirmed via the correlations between latent variables based on the criterion of Fornell and Larcker (1981). Table 2 shows that the square root of AVE for each latent variable was higher that the correlation, verifying that all scales showed discriminate validity. Validity was analyzed in detail using the heterotrait-monotrait (HTMT) method (Henseler et al., 2015)—the highest correlation ratio being 0.848 between destination image and destination loyalty, below the maximum threshold of 0.9 (see Table 2).

Table 2.

Discriminant validity assessment.

	1.	2.	3.	4.	5.
1. Destination Image	0.913	0.813	0.719	0.848	0.587
2. Perceived Health Safety	0.714	0.871	0.652	0.644	0.686
3. Perceived Destination Quality	0.681	0.581	0.948	0.631	0.413
4. Destination Loyalty	0.804	0.583	0.66	0.854	0.726
5. Customer Engagement	0.549	0.603	0.417	0.677	0.809

Note: Values along the main diagonal (bold) are the square root of the AVEs. Off-diagonal values are the correlations between constructs, and HTMT ratios are above the diagonal.

4.2

Structural model estimation

To test the proposed hypotheses, a structural equation model was estimated where direct and indirect effects were analyzed. Table 3 displays the estimated path coefficients and indirect effects (with its t-value associated). To check the direction of the research hypotheses, the one-tailed test at a 5% significance level is performed (Kock, 2014).

Table 3.

Effects on endogenous variable.

Direct effects	Standardized coefficient	t-value	R^2	Q^2	Decision
Destination Image → Perc. Destination Quality	0.543**	7.96	0.482	0.428	H1: supported
Perc. Health Safety → Perc. Destination Quality	0.194**	2.00			H2: supported
Perc. Destination Quality → Destination Loyalty	0.660**	14.74	0.435	0.267	H3: supported
Perc. Destination Quality → Customer Engagement	0.417**	4.69	0.174	0.093	H4: supported
Specific indirect effects	Standardized coefficient	t-value
Destination Image → Destination Loyalty	0.358**	6.44
Perc. Health Safety → Destination Loyalty	0.128*	1.914
Destination Image → Customer Engagement	0.227**	3.99
Perc. Health Safety → Customer Engagement	0.081^+	1.65

Notes: **: significant at 0.01; *: significant at 0.05.

+

: significant at 0.1.

Destination image and perceived health safety are significantly, positively correlated to perceived quality of destination (γ=0.543*, γ=0.194*, respectively), confirming hypotheses H₁ and H₂. Destination quality also has a significant impact on travelers’ destination loyalty (β=0.660**) and degree of engagement (β=0.417**), thus H₃ and H₄ were supported. Moreover, the indirect effects of destination image and perceived health safety on loyalty towards destination and customer engagement with destination through quality of destination are significant at 90%, at least (see Table 3).

Explained variability of destination quality via the image and health safety constructs is R²=0.482. With regard to consequences, the explained variability of destination loyalty is R²=0.435; and tourist engagement, R²=0.174.Finally, the predictive capacity of the model -testing with Q²Stone-Geisser- yielded positive values for all endogenous variables (see Table 3). It should be noted as well that global model fit (SRMR=0.074) is adequate, falling below the established 0.08 maximum threshold (Hu & Bentler, 1999).

5

Discussion

The travel and tourism sector is among the hardest hit by natural disasters, terrorism, financial crises and pandemics—as the fear such events trigger has a direct, immediate impact on travel planning (Gössling et al., 2021; Jaaron et al., 2021). In the context of COVID-19, this research aimed to provide insights for destination image management as well as test the potential impact of perceived quality on a set of relevant positive outcomes, from the tourist's standpoint. Our findings are of great interest—for Destination Management Organizations (DMOs), in general, and for emerging economy DMOs in particular.

Informed by the Signaling Theory Perspective (STP) and the RM paradigm, we have analyzed how certain signals (i.e., destination image and perceived health safety) impact perceived destination quality; and how this, in turn, influences tourist actions like loyalty and customer engagement—which may generate new signals in their own right. This virtuous circle is of great interest for optimal tourist destination management, particularly in the (post)COVID-19 era. This is due to the fact that prior to traveling, tourists tend to look for signals which enable them to make rational decisions (Ballina et al., 2020). Hence, when tourists have never visited a given destination before, they will likely construct a destination image from the stimuli they receive from different sources—DMO marketing efforts and word of mouth, for instance (Prayag, 2009).

On the other hand, while the literature has traditionally considered destination image to be stable and resistant to change, major disasters and crises can be extremely disruptive (Lehto et al., 2008); more specifically, health and safety-related risks have a negative impact on visitor perceptions regarding the destination (Khan et al., 2017; Loureiro & Jesus, 2019). Moreover, the pandemic unleashed by COVID-19 is proving to be very dynamic: since its declaration on March 11, 2020 by WHO, several distinct waves and variants have spanned the globe—affecting countries asymmetrically, impacting different age/demographic groups, etc.. As a result, perceptions regarding health and safety have fluctuated considerably as well.

Our findings suggest that—in a COVID-19 context—destination image has a significant impact on perceived quality. Yet, it is also evident that perceived health safety has an even stronger impact. Clearly, a destination's general characteristics together with its tourism and hospitality infrastructures determine travelers’ experiences; but in health crises scenarios, as Kock et al. (2016) suggest, it is logical to assume that perceived health safety not only complements destination image but, more importantly, becomes an essential ingredient in perceived destination quality.

In such a context, an obvious recommendation is for both governments and DMOs to provide—and guarantee compliance with—effective measures/protocols designed to minimize risk of contagion; adequate healthcare infrastructures and contingency plans should also exist. However, we believe effectively communicating such measures and infrastructures is at least as important when it comes to winning back visitors. Of course, traditional, formal channels exist for transmitting information and recommendations to travelers—often institutional in nature. Yet, visitor-generated other types of signals not controlled by the destination—can have a far greater impact on travelers’ perceptions and decisions (Huerta-Álvarez et al., 2020). Information of this sort is readily available via the Internet and social media; it surfaces spontaneously and freely and is based on the first-hand experiences of travelers themselves. For many travelers visitor-generated content is more credible than the information DMOs and local governments offer.

This year, the New York Times has included Lima on its “52 Places to Go” list for 2021—hardly a surprise given the impact destination image has on destination choice. Moreover, Reputation Institute's (currently named RepTrak) ranked Peru as the country with the best reputation (i.e., image) in Latin America in 2019, ahead of Chile and Argentina. The travel and tourism sector is known for being intensely competitive—further aggravated by the way divergent pandemic management styles around the world are impacting tourists’ choice of destination. At the meeting of Tourism Ministers of the Americas held in May of this year, the Secretary General of the World Tourism Organization (UNWTO), Zurab Pololikashvili, asserted that “reestablishing trust in travel is a key first step towards tourism recovery” (UNWTO, 2021). Our results suggest that the attributes related with destination image and perceived health safety should be a priority in the marketing strategies for tourist destinations, especially those from emerging economies where some health infrastructures are weaker than those in developing economies. In this sense, obtaining certain quality and health-safety certifications can be a considerable boon in reactivating the sector—due to the substantial impact this has on perceived safety among tourists, as confirmed by the empirical evidence in our study.

Hence, to maintain Peru's competitiveness as a tourist destination, our best advice would be to adopt a hybrid communication strategy—a two-pronged approach: on one hand providing comprehensive, updated information via destination-controlled channels and engaging proactively with travelers to answer questions or attend to issues/events that may arise during their trip/stay; on the other, carefully monitoring and analyzing visitor-generated content online. Tools like Social Web-Web 2.0, Social CRM and Natural Language Processing (NLP) allow for collecting information on target audience attitudes/behaviors and establishing two-way contact flows—with a view to impact traveler perceptions and ensure satisfactory consumer experiences at destination.

Our findings also confirm that perceived quality has a decisive impact on tourists’ relational behavior with respect to the destination in terms of loyalty and engagement. While some discrepancy exists in the literature as to whether there is a causal relationship linking perceived quality and destination loyalty, our results prove that such a relationship exists, as authors like Huerta-Álvarez et al. (2020) and Herrero et al. (2017) have corroborated. On the other hand, our findings confirm that positive tourist perceptions regarding destination quality drive adoption of a series of non-transactional behaviors that go beyond mere repurchase (So et al., 2016)—also in line with the literature; these behaviors in turn trigger new signals to potential tourists, completing the virtuous circle.

A tourist destination is a highly experiential service where, if customers’ expectations are met, an emotional bond is created (Cambra-Fierro et al., 2021; Ostrom et al., 2015). Thus, by the principle of reciprocity, if tourists really perceive quality in a destination, they will want to do something positive by helping to promote it—e.g., sharing positive experiences with friends, family, other potential tourists, etc. (positive WOM)—or even engaging with the destination to help it improve. In a health crisis scenario like COVID-19, sharing information can be even more motivating as it contributes to others being better informed, hence, better equipped to make smart decisions and protect themselves and others.

We must therefore not forget that first-traveler perceptions will be very relevant for people thinking of traveling to the destination down the road—so early actions and messages must be solidly rooted in the reality of the destination and the evidence collected. Once optimal levels of perceived quality are achieved, our data indicate that destinations can expect positive behavior from tourists in the form of loyalty and engagement.

We should keep in mind that loyalty is not only readiness to return to a destination; it also enhances likelihood of recommending the destination to other potential visitors. Customer engagement, in turn, fosters DMO-visitor interaction—facilitating intelligence-gathering on consumer experiences and direct feedback from travelers regarding destination strengths/weaknesses and proposals for improvement. All of this is highly valuable in terms of early destination reopening processes and progressive tourism recovery.

6

Conclusions

This study contributes to the literature in three fundamental ways. First, by adopting the Signaling Theory Perspective (STP) and RM paradigm, we establish a theoretical framework for analyzing the virtuous circle and other relevant positive outcomes born of the impact destination image and perceived health safety have on perceived destination quality. Secondly, we apply a causal model containing variables which have been analyzed, primarily, in the context of established tourist destinations in developed economies—scarcely in emerging destination contexts (Huerta-Álvarez et al., 2020; Marques et al., 2021). Finally, we explore the impact of COVID-19 on tourist perceptions and behaviors.

Despite the relevance of our research, however, we must recognize several limitations. Firstly, this is an exploratory study limited to the Metropolitan Lima (Peru) arena; hence, it would be interesting to conduct the same study in other emerging countries/cities with a view to corroborate our findings. Secondly, our findings are based on personal opinions. That said—to avoid potential bias—we followed recommendations by Baumgartner and Steenkamp (2006), Podsakoff et al. (2003) and others, such as guaranteeing participant anonymity, clarifying that there are no right or wrong answers, using previously validated scales, adapting to the reality under analysis and, finally, using pre-tests to eliminate potential ambiguities in item wording. Lastly, though our sample may seem somewhat small (250 responses) the fact that (i) it is a random sample and (ii) fieldwork was conducted during a brief, early, transitory phase of tourism reopening in Peru, allows us to effectively achieve our research objectives and offer relevant insights for DMO destination recovery management.

Given that the variables we explore focus towards the destination context in general, an interesting future line of research would be to determine whether the relationships we establish can be confirmed in specific subsectors like hospitality and leisure. Assessing the extent to which tourist demographics (e.g., age, gender, income, education) inform destination image-building and perceived health safety would also be of interest. Findings from such a study would provide DMOs with the tools they need to delineate consumer profiles and effectively streamline market segmentation; hence, to i) provide service in line with expectations, and ii) better tailor signals to segment profiles.

Author statement

All authors have contributed equally.