Journal of Destination Marketing & Management 20 (2021) 100564

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Journal of Destination Marketing & Management

journal homepage: www.elsevier.com/locate/jdmm

Research paper

Demystifying the effects of perceived risk and fear on customer

engagement, co-creation and revisit intention during COVID-19: A
protection motivation theory approach
Raouf Ahmad Rather
Department of Management Studies (South Campus), University of Kashmir, Jammu & Kashmir, Srinagar, India

A R T I C L E I N F O A B S T R A C T

Keywords: Applying protection motivation theory, the purpose of this study is to investigate the effects of social media on
Fear of COVID-19 customer brand engagement (CBE) and their consequent impact on co-creation and revisit intention in pandemic
Perceived risk environment. This study also examines the moderation impact of fear of COVID-19 and perceived risk on as­
Social media
sociations between social media, CBE, and co-creation/revisit intention, thus further contributing to existing
Customer brand engagement
literature. Partial least squares structural equation modeling (PLS-SEM) was adopted to examine the data
Co-creation
Revisit intention collected from key tourism-destinations in Jammu/Kashmir. The findings propose that social media positively
Tourism and significantly effects the CBE, which subsequently affects co-creation and revisit intention in COVID-19 times.
Destination Second, findings found that CBE’s positive impact on co-creation and revisit intention. Third, findings indicated
the social media’s indirect effect on co-creation and revisit intention, as mediated via CBE. Thus, social media’s
effect on co-creation and revisit intention are more prominent under elevated CBE in pandemics. Finally, fear of
COVID-19 and perceived risk negatively moderates the linkage between social media, CBE, and co-creation/
revisit intention. This study concludes with key implications arising from the analyses and further research
opportunities.

1. Introduction b). Recently, many studies have focused on tourism crises, while very
few of them examined health- or pandemic-linked risks (Bhati,
The tourism sector has been acknowledged as one of the major prone Mohammadi, Agarwal, Kamble, & Donough-Tan, 2020; Hall, 2010). In
sectors to crises, disasters or pandemics (Gössling, Scott, & Hall, 2020; the past few decades, various health-linked crises, particularly pandemic
Yu et al., 2020). The COVID-19 (coronavirus) outbreak has shocked risks leads to callous damage to tourism business in international or
humanity, causing unparalleled socio-economic repercussions and hu­ domestic level like SARS (e.g. Zeng, Carter, & De Lacy, 2005) and Ebola
manitarian, or health-safety problems worldwide (Bae & Chang, 2020; (e.g. Novelli et al., 2018). Bhati et al. (2020), Wen, Aston, Liu, and Ying
UNWTO, 2020a). Pandemic/health-linked issues like perceived risk (2020) and Wong and Yeh (2009) highlighted different psychological
(PRK) and fear of COVID-19 (FCV) has closed destinations/cities in and perceived-health risks/issues like tourists’ perceived risk or fear as
many countries (Bae & Chang, 2020). Health-linked crises may have key factors affecting travel behaviour/future travel intentions, and
negative and direct effects on tourism, as travel can play a crucial role in Protection Motivation Theory (PMT) as an essential approach to explore
expansion of pandemics between destinations (Hollingsworth, Fergu­ it. As the health-related or pandemic risk perceptions may differ among
son, & Anderson, 2006; Sánchez-Cañizares et al., 2020). The negative different tourists/visitors, it is regarded an important aspect in
effects of proactive measures imposed to restrain the coronavirus decision-making process while selecting destinations (Bhati et al., 2020;
pandemic, have negatively influenced most industries including tourism Rather, 2021; Reisinger & Mavondo, 2005), to recover the demand for
globally (e.g. Crossley, 2020; UNWTO, 2020a). Rooted in World Health tourism.
Organization (WHO) advices, many countries controlled peoples’ Social media is a crucial factor in disaster- and health-related crises/
movement, advanced the closure of tourism-attractions and suspension pandemic including COVID-19 (Yu et al., 2020). As a growing world­
of business activities/public events (e.g. WHO & UNWTO, 2020). wide phenomenon (Li, Larimo, & Leonidou, 2020; Narangajavana et al.,
Accordingly, national governments and destination marketing or­ 2017), social media sees key effects in tourism, specifically regarding the
ganizations (DMOs) have promoted travel and tourism as a suitable way tourists use (or access) information (Ebrahimi, Hajmohammadi, &
strategy during crises that inhibit international travel (UNWTO, 2020a, Khajeheian, 2020; Huerta-Álvarez, Cambra-Fierro, & Fuentes-Blasco,

https://doi.org/10.1016/j.jdmm.2021.100564
Received 6 November 2020; Received in revised form 4 February 2021; Accepted 5 February 2021
Available online 22 February 2021
2212-571X/© 2021 Elsevier Ltd. All rights reserved.
R.A. Rather Journal of Destination Marketing & Management 20 (2021) 100564

2020; Li, Teng & Chen, 2019). Widespread COVID-19 publicity into PMT-informed theory in many contexts propose threat- and coping-ap­
social media can effect tourist’s perception of risk, thereby changing praisal elements of PMT are essential in envisaging health-linked
their behaviour, perception, and attitude (e.g. fear) (Bhati et al., 2020; behaviour or intentions (Bhati et al., 2020; Rogers & Prentice-Dunn
Hassan & Soliman, 2020; Rather, 2021). Thus, tourism-based social 1997; Wang et al., 2019). Threat-appraisal process evaluates risk
media literature has gathered substantial academic interest generally based on perceived severity and perceived vulnerability (Floyd,
(Harrigan, Evers, Miles, & Daly, 2018; Li et al., 2019; Rather et al., 2021) Prentice-Dunn, & Rogers, 2000; Rogers, 1975). Perceived severity denotes
and particularly in pandemics (Yu et al., 2020). Further, in marketing, the perceived degree of harm to a person which is linked with the event,
customer brand engagement (CBE) promotes value co-creation and while as perceived vulnerability implies the perceived probability that a
thereby reveals an important strategic marketing approach, including in threatened event will happen (Rogers 1975). Next, persons undergo
tourism (Harrigan et al., 2018; Huerta-Álvarez et al., 2020; Rather et al., coping-appraisal process wherein they assess behaviors to cope with risk
2021; Rather, Hollebeek, & Islam, 2019). Engaged customers are likely on the basis of response efficacy and self-efficacy (Floyd, Prentice-Dunn,
to increase positive sales, word-of-mouth, and profitability (Brodie, & Rogers, 2000; Rogers 1975). Response efficacy refers the perception of
Hollebeek, Jurić, & Ilić, 2011; Chen, Zhou, Zhan, & Zhou, 2020; Pansari effectiveness of recommended risk-preventative behaviour, while as
& Kumar, 2017), warranting the concept’s strategic importance. None­ self-efficacy refers the perceived individuals ability to exhibit behaviour
theless, empirical tourism-led CBE research in the social media context (Floyd et al., 2000).
remains relatively scant under less-regular conditions, involving pan­ In the tourism context, PMT-informed perspective has been further
demics/crises (Li et al., 2019; Rather, 2021; Rather & Hollebeek, 2021; adopted in examining tourist’s behaviour as they are affected by per­
So et al., 2020), thus revealing an another essential research gap. ceptions of risk and safety of a tourism destination (Rather, 2021; Wang
This research makes the following contributions. First, no research et al., 2019; Wong & Yeh, 2009; Zheng, Luo, & Ritchie, 2020). A recent
has explored the relationship between pandemic/health-linked issues (e. study by Bhati et al. (2020) revealed that destination health-risk image,
g. fear of COVID-19 and perceived risk), social media, CBE, brand co- media engagement, and tourist’s health-protective behaviour signifi­
creation (BCO) and revisit intention (RVI) in a COVID-19 environment cantly effects tourist’s post-COVID-19 travel behaviour. However, many
to-date, as explored in this research. The investigation is based on one of works on tourism response (or management) of health-linked risks exist,
the most comprehensive cognitive and affective theoretical-frameworks very little contend with customer-linked health/pandemic issues during
in the analysis of pandemics or health-linked behaviours, attitudes or travel or post-pandemic (e.g. Bhati et al., 2020; Novelli et al., 2018;
travel intention (Wang, Liu-Lastres, Ritchie, & Mills, 2019; Wong & Yeh, Zheng et al., 2020). Thus, focusing on health/pandemic-related risks
2009) namely Protection Motivation Theory (Rogers, 1975). including the moderating roles of perceived risk during COVID-19 and
Second, as outlined, despite CBE-research has developed key insight; fear of COVID-19 between (a) social media and CBE; (b) CBE and brand
most research works to-date investigate CBE under free-market, regular co-creation/revisit intention further develops the PMT framework.
situations (Harrigan et al., 2018; Hollebeek et al., 2014, 2019b; Huer­ In the current digital era, social media platforms/online communi­
ta-Álvarez et al., 2020; Rather et al, 2018, Rather et al., 2021; Rather & cations are well-known and strongly impact tourist’s behaviour or travel
Hollebeek, 2021; Shawky et al., 2019) generating a significant knowl­ intent (Bhati et al., 2020; Narangajavana et al., 2017). From the last
edge gap about its manifestation under less-regular situations, including decade, various destination marketing activities and campaigns have
pandemic/crises. Thus, it is crucial to investigate social media’s role in been performed via digital platforms, particularly social media (Ebra­
CBE, which in turn affects customers travel intentions and co-creation in himi et al., 2020; Harrigan et al., 2018). Such different media messages
COVID-19 environment to revive tourism demand (Huynh, 2020; Wen from service providers and user generated perspectives lead to image
et al., 2020). formation that ultimately effect tourist’s perception and revisit inten­
Third, this research examines the moderating roles of perceived risk tion. Many studies adopt PMT framework to investigate the marketing
and fear of COVID-19 on the association between social media and CBE, communication techniques and social media’s effectiveness (Anderson
and between CBE and co-creation/revisit intention, revealing another & Agarwal, 2010; Bhati et al., 2020; Rather, 2021). The present litera­
key managerial insight. Thus, unlike most extant research that views ture proposes that tourist destinations utilize social media platforms to
perceived risk and fear of COVID-19 as drivers of revisit intention ease and alleviate visitors’ perceived risks and fears, and impact their
(Sánchez-Cañizares et al., 2020), this study postulates that perceived destination perception (e.g. Huynh, 2020). The present research extends
risk and fear of COVID-19 to exert vital moderating effects on the pro­ PMT in context of COVID-19 outbreak to identify the social media’s role
posed association. Fourth, this research also investigates the mediating on CBE, which subsequently effecting co-creation and revisit intention
role of CBE in the proposed associations, thereby underlining CBE’s in administration tourism-brands during pandemic. In addition, this
pivotal role in managing tourism brands during COVID-19 (Abou-Shouk study also investigates the role of mediator (CBE) and the role of mod­
& Soliman, 2021; Rather et al., 2019). Finally, the empirical findings can erators (perceived risk/fear of COVID-19) in effecting co-creation and
contribute to the devise of most efficient strategies/actions in devel­ travel re-visit intentions thus further extends the PMT framework, as
oping the safety measures within tourism services and to re-store proposed in the theoretical model (Fig. 1). Further, as examined by
tourism in crises or pandemic times.
Regarding paper’s structure, section-2 elucidates the theoretical
background. The section-3 reveals the conceptual framework and hy­
potheses development followed by an overview of our research methods
in the section-4. The 5-section provides the analysis and results followed
by discussion, implications and future research directions in section-6.

2. Theoretical background

2.1. Protection motivation theory

Introduced by Rogers (1975), PMT is an affective and cognitive

model of behaviour with the objective of predicting (or mediating)
health-linked behaviour. PMT was developed as a framework in un­
derstanding the effect of fear appeals and well-being of individuals
(Rogers, 1975; Rogers & Prentice-Dunn 1997). Findings based on Fig. 1. Conceptual framework.

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R.A. Rather Journal of Destination Marketing & Management 20 (2021) 100564

Wang et al. (2019), past tourism studies involving PMT focused on a streams (Li et al., 2020; Shawky et al., 2019). Social media, as a kind of
single element (e.g. only on perceived severity and/or perceived social connectedness and social interaction can provide significant value
vulnerability) out of all components. In line with Wang et al. (2019), this to service providers/firms in attaining customer brand engagement (Li
research assumes that if tourists perceive a high threat potential (e.g. et al., 2020). In tourism, more deeper and meaningful relationships may
from pandemic like COVID-19) during their trip, visitors’ intentions to be accomplished by developing active interactions between tourists and
protect themselves from threats may be increased. Thus, the present brands (Harrigan et al., 2018). Such interaction can improve tourists’
study considers the threat- and coping-appraisal (e.g. perceived risk/­ emotional or affective involvement, thereby strengthening their
fear) of PMT to predict tourists’ brand engagement, co-creation and engagement with the destination; Cabiddu, De Carlo, & Piccoli, 2014; So
revisit intention during pandemic. et al., 2020). Based on these arguments, first hypothesis is proposed as:
H1: Tourism destination-derived social media positively affects
2.2. Social media-based CBE and co-creation customer’s brand engagement.

Owing to the development of social media, there is an increasing 3.2. CBE’s influence on brand co-creation
focus from researchers as well as practitioners on social media-based
user/CBE (Huerta-Álvarez et al., 2020; Li et al., 2020). Social media Co-creation refers to “a customer’s perceived value arising from
platforms offer users an interactive means to create value and engage interactive, joint, collaborative or personalized brand-related activities
with the brand/firm (e.g. by likes, shares, or comments; Harrigan et al., for or with stakeholders in service systems” (Hollebeek et al., 2019a,
2018; Narangajavana et al., 2017), which can also invite other users’ 2019b, p. 170). That is, on interacting with a firm/brand, consumers will
contributions. As a result, social media-based CBE may assist customers’ build a specific level or (valence) of perceived-value from the interaction
influence over others or sharing of personal experiences or recommen­ (Chathoth et al., 2014; Im & Qu, 2017), as evolved in service-dominant
dations (Brodie et al., 2011). Likewise, these researchers refer to social logic (S-D logic), in turn effecting their future brand-linked behaviors,
media-based CBE as a highly interactive process on the basis of emotions, and cognitions (Cabiddu et al., 2014; Ranjan & Read, 2016).
“learning, sharing, advocating, socializing and co-developing” (p. 112). With reference to S-D logic (Ahn et al., 2020; Vargo & Lusch 2004,
Many definitions of consumer/customer engagement have emerged. 2016), value is not merely co-created by firms/brands, but may be
Following scholars including Harrigan et al. (2018) and Hollebeek et al. co-created by tourists (Im & Qu, 2017). Since tourists become engaged
(2014), this research employs the CBE concept, given its brand focus increasingly, they are more expected to share their personal information
(Islam et al., 2019; Rather, 2018b; Rather & Camilleri, 2019a, b, pp. (or experience) with other visitors (Prebensen et al., 2014; Zhang et al.,
429–443). Regardless the discussion around CBE’s conceptualization, 2017). For instance, when tourists identify a high level of interaction, a
various authors agree regarding the concepts two-way, interactive na­ positive influence on their resulting co-creation is anticipated. Overall,
ture (Hollebeek et al., 2014; Mollen & Wilson, 2010; Rather, 2020). For as consumers engage with a firm/brand, co-creation arises, rendering it
example, Hollebeek et al. (2019b, p. 166) defined CBE as “a customer’s a CBE consequence (Cabiddu et al., 2014; Hollebeek et al., 2019a,
motivationally driven, volitional investment of focal operant resources 2019b; Rather et al., 2019). Thus, the second hypothesis is proposed as:
(including cognitive, emotional, behavioral, and social knowledge/­ H2: Tourists’ destination brand engagement has a positive effect on
kills), and operand resources (e.g. equipment) in brand interactions,” brand co-creation.
thereby fitting with the PMT-informed perspectives proactive customer
view. For example, users are not only capable to obtain brand-linked 3.3. CBE’s effect on revisit intention
content, but to actively contribute to its creation also (i.e. through
marketing communication techniques’ or user-generated content), Oliver (1997) defines behavioral intention as an individual’s “stated
therefore reflecting CBE’s two-way interactive aspect (Harrigan et al., likelihood to engage in a behavior (p. 28).” Revisit intention, which is
2018; Li et al., 2020). Further, during regular and free-market situations, one of the behavioral intention components, refers to a customer’s
CBE’s volitionality level can be restrained by factors including in­ intention to re-experience the same tourist destination or product (Goh­
stitutions- (like control/rules) and/or lacking- (like financial) resources ary, Pourazizi, Madani, & Chan, 2018; Zhang, Wu, & Buhalis, 2018).
(Hollebeek et al., 2014; Vargo & Lusch, 2016). Although, in lockdown CBE acts as a key instrument in strengthening brand intent (Harrigan
period, CBEs voluntariness is further controlled by service systems such et al., 2018; Islam et al., 2019), while empirical examination into this
as government-imposed restrictions, involving prohibited association remains meager, especially in the tourism destination brand
social-interactions, travel bans/restrictions, and temporary context (Huerta-Álvarez et al., 2020; So et al., 2020).
service-closure (Hollebeek et al., 2020), thereby further effecting CBE. In the social media-led destination brand context, CBE aids the
Furthermore, like CBE, brand co-creation presumes customers to be improvement of affective bonds with consumers, increasing their brand
active (vs. passive) users of a brand/firm and its communications (e.g. revisit intent (Harrigan et al., 2018; Li, Wen, & Ying, 2018). Thus this
Ahn, Back, Barišić, & Lee, 2020; Prahalad & Ramaswamy, 2004; Vargo research proposes that tourist’s revisit intention can be strengthened by
& Lusch, 2004, pp. 207–231). Vallaster and von Wallpach (2013, p. engaging them in brand-linked social media interactions (Li et al.,
1513) contend: “Organizations no longer unilaterally define and control 2019). In addition, entertaining CBE activities provided by a
the brand, rather that the brand is co-created by customers.” That is, by tourism-destination brand serves as a prominent driver of consequent
engaging consumers/users, social media-led tourism communications tourists’ revisit intent (Rather, 2020; So et al., 2020). The proposed
are helpful to cocreating value (e.g. Grissemann & Stokburger-Sauer, association between CBE and revisit intent is further reinforced by social
2012). exchange theory, which advocates that the resources (intangible/tan­
gible) accumulated by consumers by engaging with a focal object/brand
3. Conceptual framework and hypotheses development (tourism-destination) will lead them to re-visit further that object/des­
tination (e.g. Harrigan et al., 2018; Rather, 2018, pp. 207–231). Hence,
3.1. Social media’s effect on CBE this study posits:
H3: Tourists’ destination brand engagement has a positive effect on
Researchers contend that brands/firms aiming to use social media revisit intention.
strategically, “should take deliberate initiatives to motivate and
empower customers to maximize their engagement” (Li et al., 2020, p. 3.4. Mediating role of CBE
4). Due to the current academic developments in CBE domain (Pansari &
Kumar, 2017; So et al., 2020), thus scholars need to link the two research As per PMT, tourism-destinations use social media platforms and/or

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R.A. Rather Journal of Destination Marketing & Management 20 (2021) 100564

marketing communication techniques that affect tourist’s travel inten­ Relatedly, Yin et al. (2020, pp. 207–231) examined that perceived risk
tion and behaviour (Anderson & Agarwal, 2010; Huynh, 2020). This moderates the link between physical/human crowding and destination
study extends PMT approach in the context of COVID-19 to identify the attractiveness, emotion and evaluation of tourists. Therefore, our study
role of mediator (CBE) in effecting travel intentions (Bhati et al., 2020). considers that perceived risk resulting from COVID-19 moderates the
Based on PMT-informed perspective, social media can improve CBE, relationship between social media and CBE, and between CBE and
which in-turn affects customer behaviors, recommendations, and co-creation/revisit intention with the tourist destination. Based on these
behavioral intentions. arguments, following hypotheses are proposed as:
Past studies have argued that in consumer-brand relationships, CBE H6: Perceived risk from COVID-19 moderates the relation between
plays an important mediating role between customer perceptions and social media and CBE.
behavioral intentions (Abou-Shouk & Soliman, 2021; Harrigan et al., H7: Perceived risk from COVID-19 moderates the relation between
2018; Rather et al., 2019). As CBE is a psychological state occurring CBE and co-creation.
within the service experience process (Brodie et al., 2011; So et al., H8: Perceived risk from COVID-19 moderates the relation between
2020), many authors have used CBE as a mediator (e.g. Abou-Shouk & CBE and revisit intention.
Soliman, 2021; Hollebeek et al., 2014). For example, Hollebeek et al.
(2014) argued that CBE mediates the link between self-brand connec­ 3.6. Moderating role of fear of COVID-19
tion, brand involvement and brand-usage intent within social media
contexts. Abou-Shouk and Soliman (2021) investigated that customer As soon as WHO confirmed coronavirus disease as a pandemic (e.g.
engagement mediates the association between gamification UNWHO, 2020), many individuals understand the fear/threat and risk
adoption-intention and tourist’s brand awareness and loyalty in tourism. of COVID-19 epidemic on their lives and health (Reznik et al., 2020).
Furthermore, extant literature proposes social media’s direct effect on Thus, fear of COVID-19 has been significantly increased and spread
CBE (e.g. Narangajavana et al., 2017). Likewise, social media’s effect on contagiously among individuals due to sharing their feelings of fear and
tourists’ revisit intent and/or co-creation and CBE’s effect on revisit observing the behaviors of others (Ahorsu et al., 2020).
intent/co-creation have been documented (e.g. Harrigan et al., 2018). Studies have verified that fear can be an intervening factor which
Despite these developments, little remains known about CBE’s potential affects individual’s behaviors and attitudes toward the pandemic/threat
indirect effect on co-creation and tourists’ revisit intention. Considering (Hassan & Soliman, 2020; Zheng et al., 2020). The present research
the key role of CBE, this research proposes that social media will develop ought to investigate if fear of COVID-19 can change the behavior and
CBE, which in-turn affects co-creation and tourists’ revisit intention attitude of visitors to re-visit tourist destination in future. In view of,
towards the destination, leads to next hypotheses as: Hassan and Soliman (2020) revealed that fear of COVID-19 moderates
H4: CBE mediates the correlation between social media and brand the link between destination reputation and tourist’s revisit intention,
co-creation. and between visitors’ perceived trust and revisit intention. As outlined
H5: CBE mediates the correlation between social media and revisit earlier, till-date no study has investigated the moderating effect of fear
intention. of COVID-19 on the proposed associations. Thus, this study explores that
fear resulting from COVID-19 moderates the link between social media
3.5. Moderating role of risk perception and CBE, and between CBE and co-creation/revisit intention with the
destination. Anchored in these arguments, subsequent hypotheses are
In recent decades, the construct of risk has been used extensively developed as:
(Bae & Chang, 2020; Karl, 2016; Quintal et al., 2010; Tavitiyaman & Qu, H9: Fear of COVID-19 moderates the relationship between social
2013), and is conceptualized as a potential-loss resulting from media and CBE.
un-certainty to which some possibility of incidence can be allocated. H10: Fear of COVID-19 moderates the relationship between CBE and
Though, the definition of risk differs based on the context or service-with brand co-creation.
which it is connected. H11: Fear of COVID-19 moderates the relationship between CBE and
In tourism context, perceived risk typifies situations wherein it revisit intention.
forecasts the decision(s) to evade visiting (traveling) to some destina­
tions; such as, because of health risks-, political insecurity - or terrorism 4. Materials and methods
(Bae & Chang, 2020; Neuburger & Egger, 2020; Sonmez & Graefe, 1998,
pp. 207–231). In tourism, Chew and Jahari (2014, p. 383–384) define The present empirical study is anchored in primary data obtained via
perceived risk as “consumer perception of the probability that an action self-administrated survey to test the research hypotheses generated in
may expose them to danger that can influence travel decisions if the proposed theoretical framework.
perceived danger is deemed to be beyond an acceptable level”. Thus, in
the present research, perceived risk is integrated into the PMT, as in the 4.1. Survey instrument
studies of Bhati et al. (2020) and other related works. Here perceived
risk is defined as the degree of possible loss-perceived by a person, Fear of COVID-19 was gauged by employing Ahorsu et al.’s (2020, pp.
resulting from unfavorable outcomes of traveling due to global 207–231) 7-item scale. Perceived risk was measured by using Karl’s
COVID-19 outbreak situation. (2016, pp. 207–231) and Tavitiyaman and Qu’s (2013, pp. 207–231)
While perceived risk is often-cited antecedent of tourist media 3-items. Social media was gauged by adopting Ebrahimi et al.’s (2020,
engagement, attitude or behavioral intention (e.g. Bae & Chang, 2020; pp. 207–231) 4-item scale. To measure CBE, Vivek et al.’s (2014, pp.
Bhati et al., 2020), it’s potentially moderating role in particular relations 207–231) 10-item, three-dimensional scale was adopted that in­
remains much less well-understood, despite having received little corporates conscious attention- 3 items), enthused participation- 4
attention. For example, Tavitiyaman and Qu (2013) showed that items), and social connection-3 items; see also Hollebeek et al., 2014;
perceived risk moderates the relationship between destination image/­ Rather, 2020). Brand co-creation was gauged by using 4-item scale sug­
overall satisfaction, and overall satisfaction/behavioral intention of gested by Grissemann and Stokburger-Sauer’s (2012, pp. 207–231),
tourists who traveled Thailand after SARS incident. Thus it looks clear which reflects co-production and value-in-use aspects (Ranjan & Read,
that the risk perception acts as a crucial factor in tourists’ or traveler’s 2016). At last, revisit intention was measured by adopting Zhang et al.’s
decision-making process (Kozak et al., 2007; Quintal et al., 2010), and (2017, pp. 207–231) 3-item instrument. All the items were collected on
might even change rational decisions about travel or choice of desti­ seven-point scale ranging from strongly disagree (1) to strongly agree (7;
nation (Bae & Chang, 2020; Karl, 2016; Neuburger & Egger, 2020). please refer Appendix A).

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4.2. Research site Table 1

Respondents’ demographic/travel characteristics.
Placed in the Great Himalayas, Jammu and Kashmir is situated in Variables Categories Respondents’ Respondents
India’s Northern-most union territory. The well-known tourism desti­ proportion
nations/sites include Kokernag, Daksum Gulmargh, and Phalgam, which Gender Male 53% 172
were thus chosen for empirical exploration. These destinations offer Female 47% 153
diverse activities, involving recreation-, adventure-, cultural-, and reli­ Marital Married 50% 163
gious experiences, thus fitting properly with our research aims. Second, status Unmarried 44% 143
Divorced/separated 6% 19
tourism’s inherently high levels of tourist-provider interaction warrant Age (years) 18–28 34% 111
examination of our proposed model in this context (Ebrahimi et al., 29–39 31% 101
2020; Grissemann & Stokburger-Sauer, 2012; Rather & Sharma, 2019). 40–50 24% 78
Third, given the Indian tourism sector’s substantial recent growth (IBEF, Above 51 11% 35
Education Secondary level or 10% 52
2019), this sector is also of practical significance. Fourth, the emerging
lower
market context of India was selected, which has been predicted to Graduation 40% 208
develop to the world’s 4th-largest GDP-based tourism economy, after Post-graduation 40% 208
China, the U.S., and Germany, by 2027 (WTTC, 2018). Others 10% 53
The COVID-19 pandemic has affected the health/human life, econ­ Occupation Self-employee 25% 83
Student 27% 87
omy, employment, and travel/tourism/hospitality industry in India. For Public employee 29% 94
example, at the end of September, there are nearly 6-million confirmed Retired 10% 32
cases, of which nearly 940,000 are active, over 5-million have recov­ Unemployed 9% 29
ered, and 97,529 have died in India (Observer Research Foundation, Past visit One time 10% 32
2 times 23% 75
2020). Second, International Monetary Fund(IMF) revised its projection
3 times 34% 111
of contraction in India’s GDP for 2020–21, from 4.5% to 10.3% due to 4 and more 33% 107
rise in COVID-19 cases (Business Standard, 2020). Third, in September
2020, India witnesses an unemployment rate of more than 6%. Unem­
ployment went up to 24% in May 2020 (Statista, 2020). Lastly, as per the 4.5. Common method variance (CMV)
Indian Civil Aviation Ministry, in 2020, India may lose about 25–30%
inbound international tourists and 40% domestic visitors in view of Next, CMV was evaluated based on the two methods. First of all,
COVID-19 threats (ICC, 2020). Harman’s single factor method was operated. The results indicated that,
total variance explained by one-factor was 34.6% (below 50%), which
implies that CMV is not an issue for this research (e.g. Podsakoff et al.,
4.3. Data collection and research design 2003). Second, CMV was also assessed based on variance inflation fac­
tors (VIFs). Table 2 indicated that, the values of VIFs were below 5,
In this study, a questionnaire was used to measure the constructs hence also verifying the lack of CMV and/or multi-collinearity in this
outlined in our model. First, respondents were screened by only allowing research (Hair, Hult, Ringle, & Sarstedt, 2017).
those who had utilized social media to search for travel information, and
second, at least once traveled to major destination sites in India (e.g. 5. Analysis and results
Gulmargh, Phalgam). Before the main study, questionnaire was tested
by pilot-study with a sample of 40 respondents over a five-day period. The research model was tested by partial least squares structural
Results showed no issues regarding the questionnaire’s item clarity or equation modeling (PLS-SEM) using SmartPLS v.3.2.6 software to
readability. analyze the data. Because PLS-SEM betters deals with complex models-,
A convenience-sample was acquired (Hollebeek & Rather, 2019; non-normally data distribution-, smaller sample size, predictive- or
Rather et al., 2019; Sánchez-Cañizares et al., 2020), with data gathered
exploratory-research (Hair et al., 2017). Further, previous (tourism)
for the empirical examination through self administered questionnaires scholars have broadly adopted PLS-based analysis considering its ben­
in June–July 2020. Tourists were approached in key hotspots, including
efits (vs. covariance-based methods), including higher statistical power
hotels, tourism sites etc (Rather & Hollebeek, 2020; 2021). Out of 400 (Do Valle & Assaker, 2016, pp. 207–231; Rather, 2021) and its ability to
distributed surveys, 325 valid responses were received, representing
test hypothesized relations, while taking a prediction focus to estimate
81.25% response rate. Descriptive analysis showed that 53% of the re­ the proposed research model (Raza et al., 2020; Sarstedt et al., 2019),
spondents were male (47% female). Moreover, 34% were 18–28 years of
reflecting its dual explicative and predictive roles (Hair et al., 2017;
age, 31% were 29–39, 24% were aged 40–50, and 11% were 51 years of Rather, 2021).
age or over. Regarding tourists past visits towards tourism destinations,
10% of respondents were visited one-time, 23% visited 2-times, 34%
5.1. Measurement model assessment
visited 3-times and 33 visited 4-times. Table 1 provides the
socio-demographic data of the sample.
Tables 2 and 3 revealed the reliability and validity of all the con­
structs used in theoretical model. All the constructs factor loadings were
4.4. Non-response bias statistically significant and above 0.70 threshold value (Hair et al., 2017;
Parrey, Hakim, & Rather, 2019). Construct’s average variance extracted
Following Armstrong and Overton (1977) method, this study eval­ (AVEs) values were greater than the cutoff value of 0.50, signifying that
uates non-response bias by comparing early respondents-top 10% with constructs had acceptable convergent validity (Fornell & Larcker, 1981,
late respondents-bottom 10% on demographic variables (age, gender) pp. 207–231; Hair et al., 2017; Rather et al., 2021). Composite reliability
and measurement items. Chi-square tests show no significant differences for the measures ranged from 0.86 to 0.93, whereas the Cronbach’s
(α = .05) between early and late participants with respect to their alpha values extended from 0.90 to 0.94, indicating a strong reliability
characteristics. Further, the results of t tests confirm that all the mea­ of the scales (Table 1).
surement items were not significantly different (α = .05) between early The discriminant validity was first evaluated by Fornell and Larck­
and late respondents. Thus non-response bias was not evident in this er’s (1981, pp. 207–231) criterion followed by heterotrait-monotrait
research. ratio of correlations (HTMT) (Hair et al., 2017). As presented in

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R.A. Rather Journal of Destination Marketing & Management 20 (2021) 100564

Table 2
Reliability and validity of measurable items.
Constructs Items AVE CR α VIF SD M Factor loadings

Perceived risk (PRK) 0.75 0.86 0.93 3.14 1.45 4.65

PRK1 0.85
PRK2 0.86
PRK3 0.81
Fear of COVID-19 (FCV) 0.72 0.88 0.93 3.03 1.27 5.97
FCV1 0.85
FCV2 0.86
FCV3 0.85
FCV4 0.84
FCV5 0.78
FCV6 0.79
FCV7 0.83
Social Media (SM) 0.75 0.88 0.90 3.02 1.42 4.73
SM1 0.86
SM2 0.78
SM3 0.79
SM4 0.83
Enthused Participation (EP) 0.71 0.92 0.93 2.51 1.48 3.94
EP1 0.88
EP2 0.89
EP3 0.85
EP4 0.82
Conscious Attention (CA) 0.74 0.91 0.93 2.45 1.31 3.79
CA1 0.82
CA2 0.81
CA3 0.80
Social Connection (SC) 0.71 0.93 0.94 2.03 1.42 3.82
SC1 0.90
SC2 0.91
SC3 0.86
Brand Co-creation (BCO) 0.69 0.88 0.91 3.02 1.54 3.71
BCO1 0.90
BCO2 0.91
BCO3 0.88
BCO4 0.89
Revisit intention (RVI) 0.73 0.89 0.93 2.85 1.61 4.58
RVI1 0.92
RVI2 0.90
RVI3 0.89

Note: α = Cronbach’s alpha, AVE = average variance extracted, CR = composite reliability, SD = standard deviation, M = mean.

Table 3
Fornell-Larcker criterion and HTMT ratios.
Construct FCV SM EP CA SC BCO PRK RVI

Fear of COVID-19 (FCV) 0 .87 0.50 0.52 0.55 0.54 0.58 0.59 0.56
Social media (SM) 0.53 0.85 0.54 0.56 0.59 0.55 0.59 0.53
Enthused Participation (EP) 0.54 0.56 0.83 0.57 0.55 0.54 0.56 0.57
Conscious Attention (CA) 0.55 0.49 0.48 0.89 0.56 0.51 0.57 0.55
Social Connection (SC) 0.51 0.52 0.53 0.50 0.88 0.53 0.58 0.54
Brand Co-creation (BCO) 0.52 0.55 0.57 0.53 0.61 0.86 0.53 0.50
Perceived risk (PRK) 0.55 0.58 0.50 0.58 0.52 0.55 0.82 0.57
Revisit Intention (RVI) 0.58 0.52 0.59 0.52 0.50 0.57 0.52 0.83

Note: Bold font = square-root of the AVE. The above bold diagonal factors are the HTMT ratios. Under the bold diagonal are estimated correlations.

Table 3, the square root of AVE of each construct was greater than the variables. All R2 values were higher than 0.10 level suggested by Falk
respective inter-correlations, thus supporting the first condition. Second, and Miller (1992) (CBE: 0.42; RVI: 0.69; BCO: 0.67). Also, all the
all the HTMT values were below the threshold level of 0.9, which Stone-Geisser’s Q2 values for endogenous constructs- (CBE: 0.14; RVI:
established discriminant validity. 0.17; BCO: 0.16) were positive, which corroborates the adequate pre­
dictive relevance of the proposed model (Hair et al., 2017).
Table 4 indicates the estimated path coefficients for the model’s
5.2. Structural model evaluation proposed relationships. As proposed in H1, social media encourages
CBE, exerting a strong impact (β = 0.61, p < 0.001). As postulated in H2
Structural/path model was evaluated by using various criterions. and H3, CBE leads to increased brand co-creation (β = 0.62, p < 0.001),
Henseler et al.’s (2016, pp. 207–231) advocated that the standardized and revisit intention (β = 0.63, p < 0.001), thus emitting strong effects
root mean square residual or (SRMR) was evaluated as the approximate respectively. Next is to test the effect size (f2), which is assessed based on
model fit criterion. These authors suggested that, a SRMR value <0.10 is Cohen’s cut-off values: 0.02 (small); 0.15 (medium); and 0.35 (strong)
regarded a good model-fit. The result of this research (SRMR = 0.081) effect of exogenous variables on endogenous factors (Cohen, 1988; Hair
thus confirms an acceptable model-fit. In addition, the prediction power et al., 2017). The effect size (f2) values for paths ranged from 0.28 to
of the model was examined using R2 and Q2 values of predicted

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Table 4 Table 6
Structural model results. Moderating effects.
Hypotheses Paths β t-value f2 Remarks Hypotheses β SD t- p- Decision Moderation
statistics value
H1 SM→ CBE 0.61 8.49 0.28 Supported
H2 CBE → BCO 0.62 9.10 0.30 Supported H6: SM × − 0.27 0.20 2.42*** .005 Supported Yes
H3 CBE → RVI 0.63 9.94 0.34 Supported PRK→ CBE
H7: CBE × − 0.29 0.21 3.48*** .004 Supported Yes
Note: * represents non-significance. Other effects are significant at 0.001 level. PRK →
CBE: R2 = 0.42; Q2 = 0.14; RVI: R2 = 0.69, Q2 = 0.17, BCO: R2 = 0.67; Q2 = BCO
0.16. H8: CBE × − 0.32 0.24 4.56*** .002 Supported Yes
PRK → RVI
H9: SM × − 0.33 0.24 5.32*** .002 Supported Yes
0.34, indicating strong effects (Please refer Table 4).
FCV→ CBE
H10: CBE × − 0.36 0.26 6.43*** .002 Supported Yes
5.3. Mediation analysis FCV→ BCO
H11: CBE × − 0.37 0.28 8.53*** .002 Supported Yes
FCV→ RVI
Mediation was assessed by using product-of-coefficients approach
via PROCESS (e.g. Hayes, 2013) by 5000 bootstrap samples and at 95% Note: ***p < 0.01.
confidence interval (CI). The CIs for indirect effect did not include zero,
signifying that mediation was supported for H4 and H5. Social media Third, findings also revealed the social media’s indirect effect on co-
was found to exert an indirect impact on co-creation via CBE (coefficient creation and revisit intention, as mediated through CBE, aligns with the
= 0.33, 95% CI [0.13, 0.26]). Also, the direct effect of social media and studies of Abou-Shouk and Soliman (2021) and Rather et al. (2019).
co-creation was statistically significant (coefficient = 0.13), signifying Therefore, social media’s effect on co-creation and revisit intention are
partial mediation and supporting H4. Relatedly, social media was found more prominent under elevated CBE in pandemic environment.
to exert an indirect impact on revisit intention via CBE (coefficient = Finally, fear of COVID-19 and perceived risk negatively moderates
0.36, 95% CI [0.29, 0.39]). Moreover, the direct effect of social media the linkage between social media, CBE, and co-creation/revisit intention
and revisit intention was significant (coefficient = 0.13), implying during crises, supports with the recent works of Hassan and Soliman
partial mediation and supporting H5. On the whole, the findings suggest (2020) and Sánchez-Cañizares et al. (2020). From the results achieved
that social media indirectly increases co-creation and revisit intention by here, various academic and practical implications can be drawn.
enhancing CBE during pandemic (please see Table 5).
6.1. Theoretical implications
5.4. Moderation analysis
By integrating fear of COVID-19, perceived risk, social media, CBE,
Finally, in structural model, the moderating effect of perceived risk co-creation and revisit intention in a theoretical model, this research
and fear of COVID-19 in proposed causal relationships between social contributes to the tourism risk perception-, FCV-, social media-, and
media and CBE; and between CBE and co-creation/revisit intention was CBE-literature. This research provides insight into the role of social
evaluated using Fassott, Henseler, and Coelho’s (2016) PLS approach. media-based engagement and its effect on co-creation and revisit
The results indicate the existence of significant and negative moderating intention during pandemic. Second, this research also offers extended
interaction effects of perceived risk, thereby confirming H6, H7 and H8. understating into the moderating roles of perceived risk and fear of
Relatedly, findings also revealed the significant/negative moderating COVID-19 and mediating effect of CBE on proposed relationships during
interaction effects of fear of COVID-19, thereby confirming H9, H10 and pandemic. While extant research establishes a direct/positive effect of
H11. The overall results are presented in Table 6. social media on engagement/revisit intention (Harrigan et al., 2018; Li
et al., 2020; Rather, 2021; Shawky et al., 2019), empirical insight into
6. Discussion and implications the association of proposed model concepts remains sparse (e.g. MSI,
2018, pp. 207–231), particularly in social media-based tourism context
Using PMT approach, this research is one of the first empirical in crises/pandemic times.
studies to investigate how fear of COVID-19, perceived risk and social Third, this research builds on the ingrained PMT-informed frame­
media transform engaged customers with the brand/firm and how these work and provides important implications for tourism researchers to
engaged consumers become brand-loyal during COVID-19 pandemic. adopt these additional moderator(s) and mediator(s) to the theory-based
Finding of this research indicated that social media have positive effects on COVID-19 epidemic. However, extant linear research works centered
on CBE, supports the studies of Cabiddu et al. (2014), Narangajavana on reasoned action and planned behaviour theories in tourism behavioral
et al. (2017), and Li et al. (2020), which conceptualized the role of social studies (e.g. Sánchez-Cañizares et al., 2020), the present research em­
media to develop engaged and loyal consumers. ploys PMT-informed lens, based on its cognitive and affective compo­
Second, findings verify the CBE’s positive effect on co-creation and nents which include the pandemic condition. Thus, it develops extant
revisit intention. These finding align with the theoretical works of theory by integrating the social media, tourist’s destination brand
Harrigan et al. (2018), Islam et al. (2019), which conceptualized that engagement, co-creation and revisit intention in COVID-19 crises.
brand co-creation and revisit intent is driven by customer engagement. Exploring the relationships between these variables can contribute to­
CBE is documented as a crucial source of value for brands/firms, spe­ wards building economic-resilience during pandemic in the tourism
cifically in the context of the services (Rather et al., 2021; So et al., industry.
2020). Finally, while perceived risk and fear was examined in literature

Table 5
Mediation model analysis.
IV Mediator DV(s) β S.E LLCI ULCI Conclusion

H4 SM CBE BCO 0.33 0.03 0.13 0.26 Supported

H5 SM CBE RVI 0.36 0.04 0.29 0.39 Supported

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R.A. Rather Journal of Destination Marketing & Management 20 (2021) 100564

amongst several contexts (e.g. business management, human resource guidelines including border management; attractions and thematic
management, marketing, health concerns), very little studies have parks; tour operators and travel agencies; hospitality; air travel; meet­
explored the key roles of these factors (i.e. FCV and perceived risk) ings and events; private sector; and destination planning and manage­
relating to tourism-destination issues during pandemics. Empirical ment, in supporting tourism/travel sectors to reopen their operations in
findings reveal the interaction role of perceived risk and FCV as a psy­ a responsible, safe, and seamless means (UNWTO, 2020b). To recover
chological consequence appears from coronavirus epidemic. In contrast tourism from COVID-19 pandemic, continual vigilance besides the
to past research, the present study explored the moderation roles of FCV tourism value-chain is critically important in reducing tourist’s negative
and perceived risk providing empirical foundation on how FCV/ feelings (e.g. fear/risk) about the destination. Relatedly, actions to be
perceived risk linked to coronavirus negatively impacted the direct under-taken by policymakers or managers can incorporate
positive association between social media, CBE, co-creation and revisit promotional-led initiatives, aiming tour operators from many other
intention of tourists. nations via familiarization trips, formal market research, or offering
reports generated by independent experts verifying the safety of tourism
6.2. Practical implications destinations/sites (Sánchez-Cañizares et al., 2020). Managers may also
provide closed travel-packages with pre-arranged visits and planned
This research also offers insight for tourism (marketing) practitioners itinerary, which generate barriers to risky/pandemic ecosystems (Hol­
in crises/pandemic times. First, this study identifies the key role of lebeek et al., 2020).
tourism-based social media in developing CBE. The results confirm that Finally, the results also revealed that CBE plays an important medi­
firm-based investments in social media can grant important benefits. ating role on the proposed associations during pandemic times. Thus,
Thus, tourism marketers are advised to develop differing marketing destination marketers should consider the significance of CBE
tactics/strategies that underline the role of tourism social media-based (Abou-Shouk & Soliman, 2021; Rather & Hollebeek, 2021). For
CBE in pandemic. Second, the study uncovers CBE’s crucial role in instance, managers can use social-networks, like communication stra­
driving co-creation and revisit intention, thus confirming CBE’s strategic tegies or brand communities to engage tourists in facilitating them to
significance in developing customer/brand relationships in pandemic transcend the restrictions of time and space, which not only develops
times (Hollebeek et al., 2020; Rather, 2021). For example, to cater tourism-brand co-creation, but also increases tourists revisit inten­
prospective/potential tourists in pandemic, managers are suggested to tion/recommendations in pandemic situations.
sustain and develop various service interaction platforms (Huerta-Ál­
varez et al., 2020; Narangajavana et al., 2017), encompassing mobile 6.3. Limitations and future research
apps, e-commerce websites, virtual reality/VR based touch-points or
brand communities (Hollebeek et al., 2020; Rather et al., 2021; Yu et al., This research also has various limitations, which need further
2020), that are helpful in engaging such customers. This study suggest attention. First, this study is cross-sectional, suggesting that data was
managers to initially recognize those tourists which have started collected at one particular point in time. Customers were surveyed in
adopting new brand-linked platforms in pandemic and who desire to times of proactive measures of coronavirus like travel restrictions,
resume adopting these post-pandemic and continue targeting them with isolation, and quarantine. Thus, longitudinal research will offer more
related brand-linked content through these platforms. Such results generalizable results after pandemic.
provide firms with insights into the adoption of CBE strategies and the Second, this research examined the moderating effects of perceived
advancement of tourist’s connection or revisit intent with the firm/­ risk and FCV; thus, future researchers are suggested to explore extra
brand during crises (Rather et al., 2021; Rather & Hollebeek, 2021). interaction factors like psychological-perceived safety and behavioral
Third, findings confirm the moderation roles of perceived risk and control or socio-demographic moderating variables, including gender,
FCV instigated by COVID-19 and showed that perceived risk and FCV age (Rather et al., 2021; Rather & Hollebeek, 2021).
negatively impacted the relationships between social media, CBE, and Third, further study would also explore the mediating roles of some
co-creation/revisit intention. Therefore, marketers and tourism man­ other factors like brand commitment, brand happiness. Fourth, future
agers should consider their marketing practices/strategies and adver­ research may investigate other antecedents of CBE including satisfac­
tising to strengthen the CBE and to increase brand co-creation and revisit tion, perceived value, service recovery, brand image, and visitors’
intention, specifically during pandemic/crises. Tourism industry should emotions to offer additional insights (Abou-Shouk & Soliman, 2021;
be (pro)-active and obtain the benefits of technology (like to implement Rather, 2017, Rather & Hollebeek, 2019; Shams et al., 2020a, b, pp.
touch-less operations in all possible touch-points; UNWTO, 2020b) in 429–443). Fifth, this study explores two key CBE consequences,
protecting tourists against pandemic/health-related risk in key tourism including co-creation and revisit intention. Thus, additional variables
destinations and underline such commitments in communication stra­ may be used, such as brand love, brand trust, brand attachment, brand
tegies. Destinations should focus on diminishing tourists’ identification or wellbeing (Islam et al., 2020; Rather, 2018a, pp.
health/pandemic-related risk or psychological-risk perceptions (e.g. 207–231; So, King, & Sparks, 2014) may generate extra insight in
fear) by stressing spatial-distance and/or hypotheticality. For example, a post-COVID19.
well-known Chinese tourist attraction/destination (Mutianyu Great Sixth, this research was carried out in a single country, India,
Wall), various measures have assisted to control traveler’s risk percep­ thereby; the generalizations of findings have to be prepared with care.
tions including (a) tourist’s book trips via online platforms or purchase Thus, further study is suggested to perform the analysis in other nations
tickets by QR codes, (b) before enter the destination, the employees with divergent cultural contexts (Rather & Shakir, 2018).
verifies tourist’s health codes to confirm visitors/tourists are outside Finally, it should be re-called that, there are some precedents for
14-days quarantine, and (c) while queuing, 1-m lines demarcate the COVID-19 (Gössling et al., 2020; Rather, 2021). Although, a re-born
waiting region (Zhou et al., 2020). By employing parallel and related tourism industry can be likely to have a different shape/form in a
measures, DMOs and marketers can intervene psychological and spatial post-COVID-19, thus it would be important to review this matter in the
distance to diminish tourists’ perceived risks. future.
Furthermore, during COVID-19 pandemic situation, tourism and
travel industry has to be vigilant and prioritize health/safety-related Appendix A. Measurement Items - Study Constructs
issues (Li et al., 2021; Neuburger & Egger, 2020). Preparedness and
responsiveness of tourist’s fear, anxiety or risk reduction should be Perceived risk (PRK)
prioritized so as to minimize tourist’s negative feelings and cope with
tourist flows. WHO provides an extensive compilation of international PRK1: In the current situation, I prefer to avoid traveling to large

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R.A. Rather Journal of Destination Marketing & Management 20 (2021) 100564

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RVI1: I tend to visit this destination again.
Interactive Marketing, 28(2), 149–165.
RVI2: I think I will come back to this destination in near future.
RVI3: I would love to come to this destination again.

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R.A. Rather Journal of Destination Marketing & Management 20 (2021) 100564

Hollebeek, L. D., & Rather, R. A. (2019). Service innovativeness and tourism customer Rather, R. A. (2017). Investigating the impact of customer brand identification on
outcomes. International Journal of Contemporary Hospitality Management, 31(11), hospitality brand loyalty: A social identity perspective. Journal of Hospitality
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Marketing. Forthcoming.. https://doi.org/10.1108/JSM-05-2020-0199 empirical insights (pp. 207–231). United Kingdom: Emerald Publishing.
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Synopsis and guiding propositions. European Journal of Marketing, 53(9), 2018–2023. Rather, R. A. (2020). Customer experience and engagement in tourism destinations: The
Hollebeek, L. D., Srivastava, R. K., & Chen, T. (2019b). SD logic–informed customer experiential marketing perspective. Journal of Travel & Tourism Marketing, 37(1),
engagement: Integrative framework, revised fundamental propositions, and 15–32.
application to CRM. Journal of the Academy of Marketing Science, 47(1), 161–185. Rather, R. A. (2021). Monitoring the impacts of tourism-based social media, risk perception
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control the international spread of pandemic influenza? Nature Medicine, 12(5), Current Issues in Tourism.
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Huerta-Álvarez, R., Cambra-Fierro, J. J., & Fuentes-Blasco, M. (2020). The interplay brand value congruity on hospitality brand loyalty. Anatolia, 30(4), 547–559.
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Raouf Ahmad Rather, Ph.D, is Marketing Researcher, Research Analyst, and Research
Theory and Practice, 22(4), 401–420.
Assistant at the Department of Management Studies (South Campus), University of
Wang, J., Liu-Lastres, B., Ritchie, B. W., & Mills, D. J. (2019). Travellers’ self-protections
Kashmir, Jammu and Kashmir, Srinagar, India. His research interests center on customer
against health risks: An application of the full Protection Motivation Theory. Annals
experience, co-creation, customer engagement, service innovation, customer loyalty and
of Tourism Research, 78, 102743.
tourism crises. His work to-date has published in journals, including the Journal of Travel
Wen, J., Aston, J., Liu, X., & Ying, T. (2020). Effects of misleading media coverage on
Research, European Journal of Marketing, Journal of Retailing & Consumer Services, In­
public health crisis: A case of the 2019 novel coronavirus outbreak in China.
ternational Journal of Contemporary Hospitality Management, Journal of Hospitality
Anatolia, 31(2), 331–336.
Marketing & Management, Service Industries Journal, among others. He is the recipient of
WHO & UNWTO. (2020). February 27). A joint statement on tourism and COVID-19 -
SIJ Best Paper Award (2020) of Routledge Taylor & Francis Group, and is co-editor of Book
UNWTO and WHO call for responsibility and coordination. World Health Organization.
Brand Co-creation Tourism Research: Contemporary Issues and Challenges (Apple Aca­
https://www.who.int/news-room/detail/27-02-2020-a-joint-statementon-touris
demic Press, USA and CRC Press, Taylor & Francis Group). Rather serves as an Editorial
m-and-covid-19-unwto-and-who-call-for-responsibility-and-coordination.
Team Member and Reviewer of several reputed SSCI Journals. He love to visit the tourism
Wong, J.-Y., & Yeh, C. (2009). Tourist hesitation in destination decision making. Annals
destinations and attractions.
of Tourism Research, 36(1), 6–23.
WTTC. (2018). Travel and tourism economic impact. http://www.wttc.org/site_
media/uploads/downloads/world2018_1.pdf.

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