The destination marketing and media profile – travel motives nexus amid tourism crisis: the mediating effect of the nation brand

Abstract

Purpose

The ongoing COVID-19 pandemic highlights the importance of destination marketing and media profiling to re-engage international tourists. However, potential crisis-induced nation brand (NB) deficits must be addressed to re-ignite tourism demand. The study examines the possible intervening effect of the contemporary NB in the international destination marketing and media-travel motives nexus.

Design/methodology/approach

A deductive quantitative study was undertaken with an online Amazon Mechanical Turk sample of n = 454 respondents. Hypotheses were tested using PROCESS Macro, Model 4.

Findings

The results show that the NB [people and negative events] had a practically significant partial mediating effect in the destination marketing – nature-cultural oriented travel motivation nexus.

Practical implications

New insights are provided via a practical model which facilitates the measurement of potential nuances in the influence of destination marketing and media profiling on leisure tourists' travel motives amid crises. The intervening effect implies that a better understanding of the NB as an indirect antecedent to travel motivation may result in more effective crisis communications and tourism recovery-oriented marketing.

Originality/value

The study is amongst the first to extend marketing and behavioural theory to explore the interplay between the marketing and media profile, a nation's brand and tourists' travel behaviour amid a crisis. The study addresses a discernible dearth of knowledge related to the influence of the NB on tourist behaviour from an emerging market perspective.

Citation

Matiza, T. and Slabbert, E. (2024), "The destination marketing and media profile – travel motives nexus amid tourism crisis: the mediating effect of the nation brand", Journal of Hospitality and Tourism Insights, Vol. 7 No. 1, pp. 1-27. https://doi.org/10.1108/JHTI-05-2022-0174

Introduction

The COVID-19 pandemic is an epoch-defining crisis for global tourism. Not since Second World War has the tourism sector experienced such a far-reaching crisis across the spectrum of its value chain. Contextually, the effects of the ongoing COVID-19 pandemic, infection and death rates surpass that of the Severe Acute Respiratory Syndrome (SARS) outbreak of 2002–2004, as well as the 2009 (H1N1) and Ebola (2014–2016) outbreaks combined (Riles, 2020). However, with the advent of various effective pharmaceutical and non-pharmaceutical interventions (vaccines) and interventions such as social distancing, mask mandates as well as general health-oriented behaviour change, the global tourism industry is transitioning from the tourism response to a cautious tourism recovery phase (Castañeda-García et al., 2022). One key pillar in post-crisis tourism recovery is a concerted crisis and marketing communications strategy (Matiza, 2021; Yeh, 2021). Given the contemporary global tourism environment, tourism marketing has become critical to re-igniting tourism demand. However, potential changes in the nation brand (NB) of destinations may constrain or accentuate the effect or influence that destination marketing efforts exert on tourists' travel behaviour.

Tourist sensitivities to risk and risk perceptions in travel and tourism imply that information asymmetry due to inadequate communication during and after the crisis may result in the development of long-lasting organic stereotypes that may negatively impact the images of countries (how they are perceived) and the subsequent travel behaviour of potential international tourists (Avraham and Ketter, 2017; Xie et al., 2021). The residual effects of the pandemic include reputational damage accruing to some of the top destinations of the world and source market nations (Khan, 2021), including China (Rasoolimanesh et al., 2021a), Italy (Codagnone et al., 2020) and the USA (Wike et al., 2020), owing to various pandemic related events. To this end, the empirical evidence emerging from contemporary studies on COVID-19 and tourist behaviour indicates a shift in tourist behaviour towards predictable, low-risk travel and tourism activity in familiar and trusted destinations (Rasoolimanesh et al., 2021a; World Travel and Tourism Council, 2021). This may indicate an increased crisis-induced reliance of tourists on heuristic cues that aid their decision-making. These heuristic cues are manifested by inherent stereotypes and subjective generalisations of destination countries [either based on accurate perceptions or misnomers] and summed up as NBs (Avraham, 2020; Hafeez et al., 2016).

Tourism destinations must address NB oriented nuances due to current market conditions dominated by considerations associated with COVID-19 and its potential after-effects, as they may include likely trust deficits towards specific countries because of the severity of the pandemic, health-crisis-induced xenophobia or poor vaccine and pandemic diplomacy (Lee, 2021a; Tessler et al., 2020; Yeh, 2021). Furthermore, heterogeneity in the impact of COVID-19 on various countries and the subsequent idiosyncratic interventions (moratoriums on travel and tourism, vaccination, health protocols and multiple states of disaster) have invariably influenced tourist perceptions of destination countries as brands (Castañeda-García et al., 2022; Xie et al., 2021). Three pertinent knowledge gaps that allude to the influence of marketing and media on the travel behaviour of tourists amid a crisis have thus far been identified in contemporary tourism research (Avraham, 2020; Avraham and Ketter, 2017; Lee, 2021b; Xie et al., 2021). First, there is limited empirical evidence chronicling tourists' travel behaviour during crises. Second, there is inadequate in situ data on the effect of risk-related framing of information through media and marketing on tourists' travel motivations considering global concerns such as the ongoing COVID-19 pandemic. Third, there is a discernible gap in knowledge about the influence of the NB on destination marketing and media profiles, as well as travel behaviour amid a crisis. The present study addresses these gaps and considers how the relationship between the international media and marketing profile of a destination and tourists' travel behaviour may be nuanced by the NB during a crisis.

Within the African tourism context, the identified gaps in academic inquiry, exacerbated by scholarly inquiry into predominantly positive image formation within the tourist decision-making process, are disproportionately focussed on developed [Western] countries. This has resulted in African destination countries such as South Africa remaining relatively unknown or susceptible to the global media's inherent stereotypes of war, disease and poverty being promulgated (Nandonde, 2015). Before the COVID-19 pandemic, Africa's experience with health-related crises being detrimental to tourism on the continent was limited to the impact and effect of the HIV/AIDS outbreak (Cossens and Gin, 1995) and the Ebola epidemic (Novelli et al., 2018). What may be particularly concerning about these health-related crises is the continued, often outdated and exclusive association they have with the African continent, despite the endemic nature of the health crisis globally or the ad hoc and localised nature of the outbreaks. As a result, the African experience of COVID-19 must be explored by tourism practitioners to be more proactive in managing tourism recovery.

The study aims to explore the probable mediating effect of the NB in the relationship between the marketing and media profile of a destination and the travel motives of international tourists. To the best of the authors' knowledge, no study has yet to explore the intervening effect of crisis-induced NB perceptions in the nexus, more so from an African tourism destination perspective. Thus, the study contributes a unique emerging market perspective regarding the impact of COVID-19 on tourism concerning destination media and marketing profiling during a crisis, NB perceptions towards affected countries and the travel behaviour (cognition and affect) of tourists towards a prominent African tourism destination.

Literature review

Destination-oriented media and marketing in tourism

Destination marketing refers to the undertaking of various activities aimed at favourably positioning a tourism destination whereby destinations promote their offering and seek to attract tourism based on the destination's uniqueness via their brand image and identity (Avraham and Ketter, 2017). The effectiveness of contemporary destination marketing is, therefore, predicated on the ability of destinations to influence the behaviour of tourists via their induced perceptions and communicated image (Winter, 2009). However, in a digital age increasingly characterised by the proliferation of information about countries and places across multiple media platforms, tourists have become significantly more circumspect in their consumptive decision-making (Gaffar et al., 2022). Crisis events further heighten this circumspection. Despite this phenomenon, studies have shown that “[…] tourists are certainly ‘not opposed to’ and ‘do not find it inappropriate’ for destinations to continue their marketing and promotional activities ‘during’ and ‘post disaster’” (Khan, 2021, p. 71). Hence, effectively facilitating information symmetry via destination marketing is key to managing and influencing travel behaviour when destinations experience exogenous shocks (Avraham, 2020).

Closely related to destination marketing amid crises is media profiling. Mass and socially-oriented media are both critical to tourist information symmetry and influential on public opinion (Wang et al., 2023; Zarezadeh et al., 2019), implying that how a destination is framed or profiled within the context of a crisis can immediately affect consumer tourist perceptions of a destination (Avraham, 2020). Consequently, specific authors (Avraham, 2020; Avraham and Ketter, 2017; Batista-Sánchez et al., 2022) concede that a “substantial” proportion of the stereotypes and misnomers that negatively influence tourist perceptions and behaviour towards African tourism destinations is manifest by and projected via various media.

Destination marketing and media profiling amid a crisis are unique and entail three imperatives: (1) the engagement in concerted crisis communications to mitigate risk perceptions (Matiza, 2022); (2) the implementation of recovery marketing to reposition destinations (Avraham, 2020); and (3) adopting a hybrid strategy of both crisis communications and recovery marketing to repair reputational damage resulting from the crisis (Khan, 2021). This multi-pronged approach is consistent with the rationale of Palmgreen and Rayburn's (1979) Use and Gratification Theory (UGT), which acknowledges that information symmetry-based heuristic cues are important in the consumptive decision-making of consumers. Thereby, consumers access multiple marketing, and media touchpoints to satisfy their information symmetry needs to appraise or confirm their perceptions and attitudes towards an offering (Chavez et al., 2020; Moon and An, 2022).

Moreover, promoting tourism and influencing tourists' perceptions and travel behaviour amid crises prompts the need for an integrative approach to destination marketing. The paid, earned, shared and owned (PESO) model formulated by Dietrich (2020) sustains the multi-modal approach to marketing, positing that multi-dimensional and bespoke marketing via an integrated framework of multiple public relations and marketing-oriented strategies is a practical approach to targeted marketing. Integrating PESO media facilitates implementing a multi-modal system for promoting tourism by simultaneously communicating and engaging consumers with marketing and media content via multiple channels and integrated messaging (Dietrich, 2020; Khan, 2021). To this end, the extant of literature has explored and recognised the influence of tourism-oriented advertising (Lee, 2021b); social media (Batista-Sánchez et al., 2022); tourism websites (Joseph and Anandkumar, 2021); government initiatives to promote tourism (Matiza, 2022); movies and other television content (Avraham, 2020); as well as media coverage (Lee, 2021a) on the conative behaviour (including travel motives) of tourists.

Tourist's travel behaviour and the nation brand

Travel motivation is a principal antecedent in tourist decision-making. It represents the intrinsic or extrinsic drivers for why tourists engage in tourism and where they ultimately choose to consume tourism offerings (Aebli et al., 2022). The push–pull framework (PPF - Crompton, 1979; Dann, 1977) is a seminal explanatory framework in travel motivation theory. The PPF delineates the susceptibility of tourists to intrinsic psychological forces that motivate or “push” tourists to engage in travel and tourism activity and the specific attractions, features and attributes (extrinsic factors) that subsequently attract or “pull” tourists to visit specific destinations (Pattanayak et al., 2022). It follows that, while destination marketing may trigger the intrinsic desire of tourists to travel, the competitive positioning of tourism destinations principally influences the extrinsic “pull” travel motives of tourists. One such competitive positioning heuristic cue is the NB, which may be a multi-dimensional construct that provides tourists with information symmetry via heuristic cues about a tourism destination based on the cognitive value proposition of the tourism destination, as well as the inherent perceptions of the destination (image/reputation) from a broader country perspective (Matiza, 2021).

A NB is a complex summative construct that simplifies the perceptions of a country based on the subjective beliefs, impressions and associations that people hold of the country as a distinct place (Hao et al., 2021). The NB theory recognises that, from a demand perspective, the country as a “brand” represents an existing perception or reputation and is susceptible to the subjective insights of consumers (Belloso, 2010). Hence, NB theory contemplates the susceptibility of consumer decision-making to the influence of subjective biases and external forces (Kaneva, 2011; Matiza and Slabbert, 2020). The technical economic approach (TE-A) to NB is pertinent to the present study. This functionalist perspective contextualises the influence of NB within the marketing paradigm as a utilitarian construct (Kaneva, 2011).

Consumers such as tourists make consumptive decisions influenced by one or a combination of any of six distinct perceptual heuristic cues, modelled as the NB Hexagon (Anholt, 2004; Belloso, 2010; Hassan and Mahrous, 2019; Lee et al., 2022), namely the governance of a country; exports associated with a country; investment and immigration policy; inherent perceptions of the people; the culture and heritage of the country; and its tourism offering. However, the variables are interchangeable due to the subjective and reflexive nature of the NBH. For the study, the NBH was adapted to exclude exports and tourism and include negative events and infrastructure as NBH dimensional measures. Based on these dimensions, the NB is critical to a country's strategic positioning in the minds of international tourists for its ability to influence their extrinsic travel motives via brand marketing-oriented information symmetry.

Previous studies have determined that the NB influences tourists' travel behaviour, including their re-visit intentions (Papadimitriou et al., 2018) and tourist perceptions and attitudes towards destinations (Hassan and Mahrous, 2019). However, Matiza and Slabbert (2020) observed an inverse relationship between immigration policy (visa regime), tourists' travel behaviour and tourism demand. Malaysian law enforcement as a governance indicator was viewed as a critical antecedent to tourism demand (Seow et al., 2017). Aspects regarding the residents of the destination are perceived (people dimension) to be tolerance of cultural diversity and the friendliness of citizens, which have also been found to influence tourist decision-making and behaviour (Hemmonsbey and Tichaawa, 2021; Tessler et al., 2020). In addition, a correlation has been observed between wine exports and uptake in wine tourists interested in visiting wine-producing countries (Guedes et al., 2022).

Hypothesis development

Within the growing body of tourism literature on travel behaviour amid crises, prior studies have yet to examine the indirect effects of the NB in destination marketing and media–travel motives nexus amid a global crisis such as the COVID-19 pandemic. There is, however, empirical evidence of the direct effects between the exogenous (destination marketing and media profile) and endogenous (NB and travel motives) variables under examination. The direct influence of the information symmetry provided by destination marketing and media profiles on tourist perceptions [NB] and travel motives is consistent with the UGT. Thereby, tourists actively seek information symmetry via heuristic cues provided by destination marketing and the media profiles of destinations to inform both their perceptions of countries and their attribute-based extrinsic travel motivations, respectively (Avraham, 2020; Moon and An, 2022).

In line with the PESO model, a vast amount of marketing and media information has influenced tourist perceptions and travel motives. Tourism studies have also shown that perception formation is a multi-dimensional process that derives various heuristic cues. These cues include different marketing and media channels, such as television and movie content and media coverage, as well as first-hand and second-hand experiences (Hafeez et al., 2016; Wang et al., 2023). For instance, the extensive American news media coverage and social media framing of the COVID-19 pandemic led to the unintended consequence of negative brand association for China and its citizens (Tessler et al., 2020). Hence, this illustrates a direct predictive influence between marketing media and tourists' contemporary perceptions of countries like China as potential tourism destinations (Rasoolimanesh et al., 2021a).

A study by Joseph and Anandkumar (2021) observed that non-commercial generic media content directly influences tourist behaviour. Aebli et al. (2022) ascertain that destination marketing and promotion communicate destination attributes to tourists. Thus, this directly affects their extrinsic-oriented travel motives by providing information symmetry which subsequently eases tourist decision-making. A notion also hypothesised by Pawaskar and Goel (2016) posits that information symmetry touchpoints influence the extrinsic travel motives of tourists by attracting them to specific destinations based on their attributes. The extant of literature (Avraham, 2020; Lee, 2021b; Lee et al., 2022; Matiza and Slabbert, 2020) also corroborates the NB's direct effect on tourist's travel motives, submitting that public perceptions of a country [via the NB] are a significant antecedent to tourists' conative behaviour, including their travel motives. This evidence aligns with the demand-oriented TE-A perspective of NB theory which supports the notion of inherent organic perceptions towards countries potentially exerting a direct predictive effect on consumptive decision-making. In the study context, this includes the extrinsic travel motives of international tourists. There is theoretical and empirical support for the direct relationships between destination marketing/media profile and the NB and the direct relationship between NB and travel motives. Therefore, in line with the literature (Rasoolimanesh et al., 2021b), an examination of the potential mediating effect of the NB in the relationship between the marketing and media of a destination and the travel motives of international tourists amid the COVID-19 pandemic was tested in terms of the following hypothesis:

H1.

The NB mediates the relationship between destination marketing and media profile and the travel motives of international tourists.

Methodology

The present study was deductive in nature and conducted as a cross-sectional quantitative study under the ethical clearance of a leading South African university. An online approach was adopted due to the restrictive effects of the COVID-19 pandemic on international travel, which limited access to international tourists. A self-administered online survey questionnaire was developed in QuestionPro and distributed to a purposive-convenient online population on the crowdsourcing platform Amazon Mechanical Turk (MTurk) between the 30th of January and the 4th of February 2022 (Cobanoglu et al., 2021; Jeong et al., 2021). A link to the published QuestionPro survey questionnaire was shared on the MTurk platform.

Sample and procedure

A total of 800 respondents viewed the survey on MTurk. 676 responses were received, 221 were incomplete and one respondent stemmed from outside the sampled countries and was disqualified. A final practically significant sample (Krejcie and Morgan, 1970, p. 607) of n = 454 respondents was obtained. The sample comprised respondents from the USA, the UK and Germany [South Africa's principal pre-pandemic source markets], as well as India and Brazil [South Africa's emerging source markets] (Kruger and Snyman, 2017; Organisation for Economic Co-operation and Development, 2020). Each respondent submitting a complete self-administered questionnaire received a reward of US$1. In line with Cobanoglu et al. (2021), data cleaning, applying timed responses and utilising Captcha verification enhanced the quality (the validity and reliability) of the survey data.

Measuring instrument

Unique composite scales were developed based on the literature (Table S1, Supplementary) to accommodate the subjective nature of the measured variables. The survey instrument was peer-reviewed by a scientific committee of tourism experts and subjected to a rigorous ethical clearance process. The structured questionnaire comprised of several questions on multiple scales. However, the data within the scope of this study were examined on three primary five-point Likert scale-based scales.

1. Independent variable: We adapted 12 statements from the literature (Table S1, Supplementary) to measure international destination marketing and media profiles across the spectrum of paid (publishing; sponsored content; media advertising), earned (media, influencer or investor relations; building links; word-of-mouth), shared (organic social; reviews; social forums) and owned (brand journalism; content marketing; visual content) (PESO) destination marketing and media (Chavez et al., 2020; Dietrich, 2020; Khan, 2021). An influence-oriented five-point Likert scale captured tourist responses ranging between not at all influential (1) and extremely influential (5).

2. Mediating variable: 30 statements were adapted from contemporary literature (Table S1, Supplementary). The scale to measure NB fell within the NB Hexagon framework (Anholt, 2004; Belloso, 2010). A five-point Likert scale to measure influence captured participant responses, ranging between not at all influential (1) and extremely influential (5).

3. Dependent variable: This study explores the supply perspective to the PPF of travel motivation (Dann, 1977) by examining the travel motives of international tourists based on their extrinsic motives for visiting South Africa based on its perceived attributes. Ten item statements were drawn from the extant of literature (Table S1, Supplementary) to measure leisure-oriented attributes as pull travel motives. Responses were recorded on a five-point Likert scale of likelihood ranging between extremely unlikely (1) and extremely likely (5).

Data analysis

The data were analysed using the Statistical Package for the Social Sciences (SPSS). The Kaiser-Meyer-Olkin (KMO) test of sampling adequacy (KMO >0.7) and Bartlett's test of sphericity (p < 0.05) established the sample adequacy and factorability of the data (Hair et al., 2014). Employing the Oblimin rotation with Kaiser normalisation, exploratory factor analysis (EFA: minimum factor loading coefficient of ≥0.5), and principal components analysis (PCA: Eigenvalue [EV] > 1) reduced the data into discernible and reliable (Cronbach's alpha: α > 0.6) scales. Regressions estimated the direct effect and predictive power of the respective potential path relationships and established the viability of mediation analysis (Kane and Ashbaugh, 2017). Prior behavioural studies (Hayes et al., 2017; Rasoolimanesh et al., 2021b), parallel mediation was used to test the hypotheses using PROCESS Macro (Model 4) in SPSS (Hayes, 2013). The PROCESS Macro programme was deemed to be suitable as it was an appropriate (automatic calculation of all the relevant statistics and methods of inference) multi-model statistical programme that explored underlying mechanisms related to causal effects without the need to construct the paths required in SEM, while generating similar results (Hayes, 2013; Hayes et al., 2017). Additionally, PROCESS Macro decomposes outputs into total, direct and indirect effects for a more straightforward interpretation of the outputs for reporting.

Results

Respondent profile

Most of the 454 respondents stemmed from the USA (27%), Brazil (24%), and the UK (22%) and indicated that they would consider visiting South Africa as a tourist someday in the future (65%). In contrast, the remainder had visited South Africa before the survey (22%), had considered visiting South Africa as a tourist before but decided not to (9%), and would never travel to South Africa for tourism (4%). Most respondents were male (66%), were in possession of a bachelor's degree (55%), were married (60%) and were employed in the private sector (70%). The largest age cohort was the 25–34-year-olds, with most travelling with their partners (33%) or their family (25%), earning the average income in their country (40%), and having travelled at least once (75%) in the two years before to the survey. The Internet was the most influential media channel for respondents (53%), with 85% and 82% of the respondents indicating their intention of domestic and international travel, respectively, within the next year. A cumulative 55% of respondents were willing to spend between US$ 3 000 and US$ 5 000 on a trip to South Africa.

Dimension reduction

The KMO and Bartlett's statistics for all the constructs confirmed the factorability of the data and adequacy of the sample. Table 1 summarises the results of the PCA and EFA for all variables (see Table S2, Supplementary). International Media and Marketing Profile (IMMP) extracted two dimensions [EV > 1; loading coefficient of ≥0.5], namely Destination Media Profile [DMED - Seven items; x¯ = 3.50; α = 0.838] and Destination Marketing Profile [DMKT - Four items; x¯ = 3.80; α = 0.665]. Both dimensions were “quite” influential on how tourists perceive South Africa as a tourism destination, accounting for a collective 49.27% of the data variance. The PCA and EFA for South Africa's NB extracted six dimensions [EV > 1; loading coefficient of ≥0.5], namely Infrastructure [INF – four items; x¯ = 3.60; α = 0.823]; Governance [GOV – four items; x¯ = 3.74; α = 0.713]; People [PEO – three items; x¯ = 3.75; α = 0.664]; Culture and Heritage [CUH – three items; x¯ = 3.40; α = 0.774]; Immigration Policy [IMMPOL – four items; x¯ = 3.69; α = 0.726]; and Negative Events [NEG – five items; x¯ = 3.39; α = 0.812]. The CUH and NEG NB dimensions were “somewhat” influential on how tourists perceived South Africa as a tourism destination. At the same time, INF, GOV, PEO and IMMPOL were “quite” influential on tourist perceptions. The extracted NB dimensions cumulatively accounted for 59.03% of the data variance.

Table 1 also shows that the PCA and EFA of travel motives extracted two dimensions [EV > 1; loading coefficient of ≥0.5], namely Natural-cultural [NATCUL – seven items; x¯ = 4.13; α = 0.849] and Entertainment–Leisure [ENTLEI – three items; x¯ = 3.70; α = 0.658]. The statistics suggest that international tourists were likely to engage in the two tourism typologies, accounting for a cumulative 55.94% variance in the data.

Direct effect testing

The Pearson-Product Correlation statistics (Table S3, Supplementary) indicated statistically significant [p < 0.01] small (ENTLEI/NEG: r = 0.165) to large (DMED/CUH: r = 0.625) linear associations between the dimensions, with one statistically insignificant correlation reported between NATCUL and NEG (r = −0.047, p = 0.321). All the NB dimensions were cognate, exhibiting small (CUH/GOV: r = 0.251) to large (DEMED/CUH: r = 0.625) linear associations; however, with no discernible causal relationship between the NB dimensions, parallel mediation analysis is supported (Hayes, 2013). All the variables explored in the study were subjected to Harman's single-factor test to assess common method variance (CMV) - resulting in a variance accounted for that is significantly below the 50% threshold (Rodríguez-Ardura and Meseguer-Artola, 2020) at 24.83%. This suggests that bias associated with CMV was not a concern regarding the data.

Direct effect testing via regressions sought to establish the predictive relationships on the various paths tested by the mediation analysis. Regression analyses determined the following predictions: X of Y (path c); X of M (path a); M of Y (path b), where X₁ is DMED and X₂ is DMKT; M₁ is INF, M₂ is GOV; M₃ is PEO; M₄ is CUH; M₅ is IMMPOL; M₆ is NEG; Y₁ is NATCUL and Y₂ is ENTLEI. The two outcome variables (Y₁ and Y₂) necessitated the initiation of separate direct effects tests. Based on the statistical models and residuals (Tables S4 and S5, Supplementary), no violations were identified in the linear regression relationships (Hayes, 2013; Kane and Ashbaugh, 2017). Particularly, normality was assessed as part of the direct effect testing, where the normal probability plots were assessed to verify the underlying assumption of normality in the data. Additionally, the VIF and Tolerance statistics confirmed the absence of multicollinearity for the predictive relationships analysed. In the first model (Table S4, Supplementary) all the paths reported significant predictive effects except patch c for X₁ − Y₁ (p = 0.202); path b for M₁ − Y₁ (p = 0.195); and path b for M₄ − Y₁ (p = 0.728). Therefore, since DMED, INF and CUH were not predictive dimensions, they were deemed unviable for mediation analyses and therefore omitted from further analysis. Subsequently, the following extended hypothesised model was formulated based on the EFA and direct effect testing, as illustrated in Figure 1.

There is empirical evidence of the direct relationships hypothesised by the extended hypotheses that emerged. Critical destination marketing aspects such as tourist perceptions (Rasoolimanesh et al., 2021a) and mass media news coverage (Tessler et al., 2020) significantly influence the NB of tourism destination countries. At the same time, the availability of information and the natural attractiveness of a destination (Le and Bui, 2022) are some key destination marketing aspects that directly influence the travel motives of nature (Giddy and Webb, 2016), as well as cultural experience-seeking tourists (Dai et al., 2019). NB aspects such as perceptions held of the people of a destination (Tessler et al., 2020), as well as governance (Richards, 2018), have also been found to influence experience-oriented (nature or cultural) tourists' travel behaviour. The following expanded hypothesis was thus tested via parallel mediation analysis:

H1.

The NB (governance [H1a], people [H1b], immigration policy [H1c] and negative events [H1d]), mediates the relationship between the international destination marketing profile and the nature-cultural oriented travel motives of international tourists.

The second model (Table S5, Supplementary) summarises the direct effect tests for the mediation effect of South Africa's NB in the destination media profile – leisure–entrainment-oriented travel motives nexus. All respective paths reported statistically significant direct effects, except for X₂ – Y₁ (p = 0.119); path b for M₁ – Y₂ (p = 0.148); M₂ – Y₂ (p = 0.715); and M₅ – Y₂ (p = 0.603), respectively. Therefore, DMKT, INF, GOV and IMMPOL were not predictive. Thus, they were not viable for mediation analyses and were omitted from further analysis. Subsequently, PEO, CUH and NEG were included in the parallel mediation in the relationship between South Africa's international destination media profile and the leisure–entertainment travel motives of international tourists. Subsequently, the following extended hypotheses were formulated and illustrated (Figure 2) based on the EFA and direct effect testing.

The empirical evidence supports the direct destination media profile and leisure–entertainment-oriented travel motivation nexus. The influence of social media (Avraham, 2020; Zarezadeh et al., 2019) and digitalisation in the form of information websites (Zemanek, 2018) have been observed to influence tourist perceptions of NBs within the tourism context. At the same time, a direct correlation has been established between destination media profile aspects such as movie and television content (Wen et al., 2018), as well as government-initiated promotion of tourism (Matiza, 2022) and the travel motivation of leisure-oriented tourists. The influence of NB aspects such as culture and heritage (Lee et al., 2022; Xu et al., 2022) and negative events such as the impact of COVID-19 and poor vaccination programmes in destination countries (Castañeda-García et al., 2022; Vogler, 2022) have also been found to influence travel motives of leisure and entrainment-seeking tourists. The following expanded hypothesis was thus tested via parallel mediation analysis:

H2.

The NB (people [H2a], culture and heritage [H2b] and negative events [H2c]) mediates the relationship between the international destination media profile and the entertainment–leisure-oriented travel motives of international tourists.

Mediation analysis results

Model 4 of the PROCESS Marco (v4.0) in SPSS (v27) was utilised to undertake the parallel mediation analyses. Table 2 summarises the parallel mediation statistics for the mediating effect of (1) GOV, PEO, IMM, NEG in South Africa's DMKT – NATCUL nexus, and (2) PEO, CUH, NEG in South Africa's DMED – LEIENT nexus.

Table 2 summarises the parallel mediation effects (Hayes, 2013), based on unstandardised coefficients 95% confidence intervals (CI) based on 5,000 bootstrap samples (bias-corrected). Figures 3 and 4 illustrate the respective parallel mediation models.

DMKT (Figure 3) reported a statistically significant (p < 0.001) positive direct effect on all four NB dimensions (Path a_1-4), namely GOV (β = 0.4729, p = 0.0000); PEO (β = 0.4247, p = 0.0000); IMMPOL (β = 0.5857, p = 0.0000); and NEG (β = 0.3830, p = 0.0000). However, only the PEO (β = 0.2316, p = 0.0000) and NEG (β = -0.2100, p = 0.0000) NB dimensions reported statistically significant (p < 0.001) positive and negative effects on NATCUL (Path b), respectively. Path b₁ for GOV and Path b₃ for IMMPOL were not significant. Hence, the mediation of the DMKT– NATCUL nexus via South Africa's NB is based on tourist perceptions of PEO: positive indirect effect a₂b₂ = 0.0983 [95% bootstrap CI (LL = 0.0646, UL = 0.1391)] and NEG: negative indirect effect a₄b₄ = −0.0804 [95% bootstrap CI (LL = −0.1211, UL = −0.0464)]. Subsequently, hypotheses H1b and H1d were accepted, while H1a and H1c were rejected. The effect of DMKT on NATCUL [c’ = 0.2377, p < 0.001] adjusted for South Africa's NB indicated partial mediation with a practically significant variance accounted for (VAF) of 29.13%.

DMED (Figure 4) reported statistically significant (p < 0.001) positive direct effects on all three NB dimensions (Path a_1-3), namely PEO (β = 0.4284, p = 0.0000); CUH (β = 0.7890, p = 0.0000); and NEG (β = 0.4783, p = 0.0000). The NB dimensions (Path b_1-3) PEO (β = 0.1761, p = 0.0003) and CUH (β = 0.0927, p = 0.0260) reported statistically significant positive effects on ENTLEI, whereas NEG reported statistically significant (β = -0.1408, p = 0.0005) negative effect on ENTLEI. Hence, the mediation of the DMED - ENTLEI nexus via South Africa's NB is based on tourist perceptions of PEO: positive indirect effect a₁b₁ = 0.0754 [95% bootstrap CI (LL = 0.0184, UL = 0.1300)] and NEG: negative indirect effect a₃b₃ = −0.0674 [95% bootstrap CI (LL = −0.1151, UL = −0.0275)]. Subsequently, hypotheses H2a and H2c were accepted. The indirect effect of CUH on the DMED - ENTLEI nexus was not significant (a₂b₂ = 0.0732 [95% bootstrap CI (LL = −0.0046, UL = 0.1501)] since the CI lower limit (LL) and upper limit (UL) passed through zero – confirming the null hypothesis. Thus, H2c was rejected (Preacher and Hayes, 2004). The effect of DMED on ENTLEI [c’ = 0.5232, p < 0.001] adjusted for South Africa's NB indicated partial mediation, albeit with a small, practically insignificant VAF of 13.43%.

Discussion and conclusions

Conclusions

The study is one of the first to examine the potential interaction of South Africa's international destination marketing and media profile, NB and the travel motives of tourists in the era of COVID-19. One model (Figure 3) was found to be of practical significance. In line with the contemporary literature, the findings affirm that South Africa's destination marketing efforts directly and positively influence the travel motives of tourists (Avraham and Ketter, 2017; Khan, 2021; Lee, 2021a; Tessler et al., 2020), more-so nature and cultural experience-oriented international tourists (Gaffar et al., 2022). Furthermore, the findings corroborate the literature on the influence of destination marketing on how the citizens of tourism destinations are perceived (Hemmonsbey and Tichaawa, 2021; Tessler et al., 2020; Winter, 2009), as well as the extent to which governance (Wiysonge et al., 2022) immigration policy (Matiza and Slabbert, 2020) and negative events (Khan, 2021; Rasoolimanesh et al., 2021a; Wike et al., 2020) impact on the decision-making process of tourists via information symmetry (Batista-Sánchez et al., 2022). Thus, the findings generally align with the notion of a symbiotic relationship between increased cognition and affective association (Gaffar et al., 2022; Nandonde, 2015).

Significantly, the findings chronicle the complexity of tourists' travel behaviour during crises. The empirical evidence indicates that South Africa's NB [based on the organic perceptions of its people and the impact of adverse events] influences the travel motives of tourists, confirming some of the findings from prior academic inquiry (Avraham, 2020; Hassan and Mahrous, 2019). However, in the absence of previous studies examining the intervening effect of the NB in the relationship between destination marketing and the nature/culture destination-oriented “pull” travel motives of international tourists, the literature affirming the direct relationships supports the present study's statistically and practically significant mediation model. Therefore, we conclude that despite the impact of crisis-induced negative events, the overall positive intervening influence of aspects related to perceptions of the South African people perhaps indicates the enduring and underlying goodwill and brand strength of South Africa. To the best of the author's knowledge, the mediation model is novel in the African tourism context and is supported by the extant literature that validates the direct predictive relationships that emerged.

Theoretical implications

The study contributes to and extends the burgeoning knowledge of tourist behaviour during a crisis. The study shows evidence of the utility and effectiveness of implementing the PESO model (Dietrich, 2020) as a bespoke multi-dimensional marketing approach to manage the tourists' organic perception and influence travel behaviour amid a global crisis. Moreover, the empirical evidence extends the UGT (Jibril and Adzovie, 2022; Palmgreen and Rayburn, 1979) to a crisis context, whereby information symmetry is critical to the cognitive (heuristic cue interpretation) and conative behaviour of the tourists (Chavez et al., 2020; Moon and An, 2022). Additionally, the study's re-examination of the TPB extends the comprehension of the predictive power of the TPB in travel behaviour to crises situations and acknowledges the potential influence of both induced and organic marketing stimuli on tourist attitudes and stereotypes (subjective norms of tourists); hence, extending the growing literature (Ojo et al., 2022).

Establishing the direct effect of destination marketing as an exogenous variable influencing the perceptions (NB) and behaviour (motivation) of tourists improves the conceptualisation of marketing within tourism. The findings also re-affirm the nuanced role of the NB in tourists' consumptive decision-making due to its intervening effect in the marketing efforts of tourism destinations. More so when considering that during crises, the already established pre-crisis NBs are susceptible to the impact of the crisis in line with the evolving subjective perceptions of tourists, suggesting that strong, positive NBs are critical to tourism (Avraham, 2020). The study also contributes to an under-researched perspective on tourism by examining and affirming the mediation model from an African tourism destination perspective, thereby enriching and advancing tourism theory. Moreover, determining the NB's mediating effect goes the current thinking around the role of extrinsic forces such as NBs during a crisis, thus substantiating some of the previous studies (Aebli et al., 2022; Anholt, 2004; Avraham and Ketter, 2017; Matiza and Slabbert, 2020; Matiza, 2022).

The study developed a mediation model incorporating composite scales to measure the interaction between critical exogenous and respective endogenous variables in tourism. To the best of the author's knowledge, the composite scales are proficient in measuring and generating data to model the relationship between destination marketing and media profiles, a country's NB aspects, and tourists' travel motives amid a crisis. The statistical and practical significance of the mediation model (Figure 3) enhances the understanding of the relationship between the triad of dimensions from an international tourist perspective, albeit within a uniquely African tourism context. The scale is reflexive and can be replicated for other markets or adapted to explore the relationship between the triad of dimensions in the context of future man-made or natural crises. Hence, this study is critical to tourism practitioners' better understanding of the crisis and post-crisis tourism marketing and tourist behaviour, as well as opening new avenues for academic inquiry.

Managerial implications

The findings of our study may be of interest to destination marketing practitioners from emerging tourism markets, as well as tourism academics. Destination marketing amid a crisis serves a dual purpose: (1) managing crisis messaging and (2) managing meaning (perceptions). Notwithstanding the significance of destination marketing [as a part of a concerted positive post-crisis communication strategy] to the travel motives of international nature and cultural-oriented tourists, the optimistic post-crisis recovery scenario for South African tourism is predicated on an NB-oriented multistakeholder government policy. Crisis-management-oriented tourism marketing incorporating the NB requires a reflexive process that integrates government and industry-led policies and strategies to provide critical differentiation, value proposition and competitive advantage in tourism recovery.

The business case for the importance of tourism to Africa is well established due to tourism's economic multiplier effect and value chain integration in destination economies (see Lee, 2021b). In the post-COVID-19 scenario, the role of African tourism businesses is to gain tourist confidence by “reassuring” tourists of their health and safety within a redefined tourism value proposition. Thus, agenda-setting marketing and crisis media communications that manage the “narrative’ and crisis risk messaging will be critical to tourism recovery in South Africa and Africa. Meanwhile, targeted, proactive international marketing communications and positioning will be essential to spur global tourism demand. From a supplier perspective, NB is a government-driven multi-stakeholder approach, “[…] whereby governments adapt and utilise competitive marketing strategies as a tactical approach to managing their identities and brand images to improve their global images and reputations” (Matiza, 2021, p. 110), will be paramount.

The pervasiveness of the impact of the COVID-19 pandemic across the tourism value chain suggests that, from a policy perspective, African governments must be reflective and acknowledge how the pandemic has transformed the designs of tourism service-oriented industries and how they are delivered. Moreover, the study provides new insights by distinguishing between destination marketing and the media profiling of destinations and considering how this dichotomy influences the crisis-impacted travel behaviour of the two tourist typologies that emerged. Beyond the conventional international destination marketing and media profiling, the results of the study also suggest that it would be useful for African tourism destinations such as South Africa to adopt an NB-oriented multi-stakeholder approach post-COVID-19 tourism recovery as a broad-based approach that is inclusive of state and non-state tourism organisations, as well as tourism and non-tourism actors in tourism a multi-pronged, cross-spectrum policy and strategy formulation. Such an inclusive and concerted approach will be particularly critical to image repair in cases where the pandemic has negatively impacted the NB, ensuring more effective crisis communications and recovery marketing.

Limitations and future research

Akin to other tourism marketing-oriented studies, the study has some limitations. This study is cross-sectional and deductive, implying that the data provide a snapshot of tourist behaviour during a specific time during the ongoing pandemic. Contingent on resource availability, a longitudinal study approach would mitigate this limitation and broaden academic inquiry into the subject matter. The sample is limited to specific source markets of interest to South African tourism recovery. Regarding the study by Uner et al. (2022), it would be interesting to explore the perceptual differences related to South Africa's NB based on the respondents' nationality and their implications for mediating the destination marketing-travel motives nexus. The scope of the study is also limited to the emerging market perspective of South Africa. Therefore, the replication of the study in multiple markets is recommended, particularly from the perspective of other emerging and more developed tourism markets, to validate the scale and potential utility of the model. It would also be interesting to conduct a comparative study using PLS-SEM for mediation analyses to contribute to the emerging methodological debate around the utility of Process Macro and other regression-based methods in mediation analysis.

Figures

Figure 1

Conceptual parallel mediation analysis of the nation brand (I)

Figure 2

Conceptual parallel mediation analysis of the nation brand (II)

Figure 3

Empirical parallel mediation analysis of the nation brand (I)

Figure 4

Empirical parallel mediation analysis of the nation brand (II)

Table 1

EFA and PCA results

Factor	*Items	Eigenvalue (EV)	Variance (percent)	Factor loading (>0.50)		Cronbach alpha (α)	Mean (x¯)	Communalities
				Min	Max			Min	Max
1International Media and Marketing Profile
Destination Media Profile (DMED)	DMP1; DMP2; DMP3; DMP5; DMP6; DMKT1; DMKT 5	4.837	40.31	0.511	0.827	0.838	3.50	0.506	0.619
Destination Marketing Profile (DMKT)	DMP4; DMKT2; DMKT4; DMKT6	1.076	8.96	0.509	0.802	0.665	3.80	0.440	0.550
2Nation Brand
Infrastructure (INF)	INF1; INF2; INF3; INF4	9.889	32.96	0.618	0.738	0.823	3.60	0.623	0.726
Governance (GOV)	GOV1; GOV2; IMM3; IMM5	2.157	7.19	0.526	0.695	0.713	3.74	0.544	0.662
People (PEO)	PEO1; CUH2; CUH4	1.930	6.43	0.552	0.762	0.664	3.75	0.500	0.633
Culture (CUH)	PEO5; CUH1; CUH3	1.561	5.20	0.682	0.758	0.774	3.40	0.638	0.715
Immigration Policy (IMMPOL)	GOV4; GOV5; IMM1; IMM2	1.117	3.72	0.569	0.667	0.726	3.69	0.581	0.646
Negative Events (NEG)	NEG1; NEG2; NEG3; NEG4; NEG5	1.006	3.53	0.551	0.758	0.812	3.39	0.556	0.742
3Travel Motives
Natural-cultural (NATCUL)	DAI2; DAI4; DAI6; DAI7; DAI8; DAI9; DAI10	4.307	43.07	0.570	0.826	0.849	4.13	0.416	0.661
Entertainment - Leisure (ENTLEI)	DAI1; DAI3; DAI5	1.287	12.87	0.501	0.840	0.658	3.70	0.464	0.687

Note(s): DMP = Destination Media Profile; DMKT = Destination Marketing; INF = Infrastructure; GOV = Governance; IMM = Immigration; PEO = People; CUH = Culture and Heritage; NEG = Negative Events; DAI = Destination Attributes

Oblimin with Kaiser Normalisation, (Coefficient ≥0.50): ¹KMO of 0.891 and Bartlett's test of Sphericity of (χ² (66) = 1694.669, p < 0.000); ²KMO of 0.918 and Bartlett's test of Sphericity of (χ² (435) = 6084.953, p < 0.000); ³KMO of 0.869 and Bartlett's test of Sphericity of (χ² (45) = 1522.239, p < 0.000)

*Details of items and factor loading co-efficient provided in the supplementary data (Table S2, Supplementary)

Table 2

Parallel mediation analyses

Testing path	β	BootSE	95% BootCI		t-value	Sig
			Lower limit CI	Upper limit CI
DMKT - NB [GOV, PEO, IMMPOL, NEG] - NATCUL
Path c: R2 = 0.1359, F(1,452)71.071, p = 0.0000
DMKT - NATCUL	0.3354	0.0398	0.2572	0.4136	8.4308	0.0000***
Path a: DMKT - NB [GOV, PEO, IMMPOL, NEG]
Path a1: R2 = 0.1985, F(1,452)111.9404, p = 0.0000	0.4729	0.0447	0.3851	0.5607	10.5802	0.0000***
Path a2: R2 = 0.1534, F(1,452)81.8841, p = 0.0000	0.4247	0.0469	0.3325	0.5169	9.0490	0.0000***
Path a3: R2 = 0.2818, F(1,452)177.3550, p = 0.0000	0.5857	0.0440	0.4993	0.6721	13.3175	0.0000***
Path a4: R2 = 0.0876, F(1,452)43.3845, p = 0.0000	0.3830	0.0582	0.2688	0.4983	6.5867	0.0000***
Path b and c: R2 = 0.2518, F(5,448)30.1534, p = 0.0000
Path b1: GOV	0.0697	0.0442	−0.0171	0.1566	1.5578	0.1153
Path b2: PEO	0.2316	0.0404	0.1522	0.3109	5.7339	0.0000***
Path b3: IMMPOL	0.0799	0.0454	−0.0092	0.1691	1.7620	0.0788
Path b4: NEG	−0.2100	0.0338	−0.2764	−0.1436	−6.2154	0.0000***
Path c’: DMKT - NATCUL	0.2377	0.0457	0.1479	0.3275	5.2009	0.0000***
Effect: a1b1+ a2b2+ a3b3+ a4b4	0.0977	0.0320	0.0378	0.1623
a1b1	0.0330	0.0292	−0.0197	0.0952
a2b2	0.0983	0.0189	0.0646	0.1391
a3b3	0.0468	0.0307	−0.0123	0.1089
a4b4	−0.0804	0.0192	−0.1211	−0.0464

DMED - NB [PEO, CUH, NEG] - ENTLEI
Path c: R2 = 0.3347, F(1,452)227.3884, p = 0.0000
DMKT - ENTLEI	0.6044	0.0401	0.5257	0.6832	15.0794	0.0000***
Path a: DMED - NB [PEO, CUH, NEG]
Path a1: R2 = 0.2048, F(1,452)116.3802, p = 0.0000	0.4284	0.0397	0.3504	0.5065	10.7880	0.0000***
Path a2: R2 = 0.3906, F(1,452)289.7488, p = 0.0000	0.7890	0.0464	0.6980	0.8801	17.0220	0.0000***
Path a3: R2 = 0.1792, F(1,452)98.6531, p = 0.0000	0.4783	0.0482	0.3837	0.5730	9.9324	0.0000***
Path b and c: R2 = 0.3730, F(4,449)66.7902, p = 0.0000
Path b1: PEO	0.1761	0.0477	0.0823	0.2699	3.6888	0.0003***
Path b2: CUH	0.0927	0.0415	0.0111	0.1743	2.2330	0.0260*
Path b3: NEG	−0.1408	0.0399	−0.2192	−0.0625	−3.5320	0.0005***
Path c’: DMED - ENTLEI	0.5232	0.0526	0.4198	0.6266	9.9477	0.0000***
Effect: a1b1+ a2b2+ a3b3+ a4b4	0.0812	0.0385	0.0075	0.1517
a1b1	0.0754	0.0308	0.0184	0.1300
a2b2	0.0732	0.0403	−0.0046	0.1501
a3b3	−0.0674	0.0219	−0.1151	−0.0275

Note(s): Statistically significant at *p < 0.05, **p < 0.01, ***p < 0.001

Table S1

Summary of EFA items

Code	Statement	Source
International media and marketing profile (1 = Not at all influential – 5 = Extremely influential)
DMP1	South Africa's tourism offering on travel and tourism websites	Adeola and Evans (2019), Gong and Tung (2017), Huong and Lee (2017), Hyun (2006), Kapu and Richards (2016), McCabe (2014), No and Kim (2015), Reitsamer and Brunner-Sperdin (2017), Soliman (2011)
DMP2	Social media posts about South Africa (Facebook, Instagram, Snapchat, Twitter and YouTube)
DMP3	The information available on South Africa's official tourism website
DMP4	Coverage of South Africa in the media (News, documentaries)
DMP5	The image of South Africa shown in entertainment content (Movies, series and reality shows)
DMP6	South Africa's product placement in adverts
DMKT1	The South African government's initiatives to promoting tourism
DMKT2	Generally sufficient information about South Africa as a tourism destination
DMKT3	The value for money that I would receive from South African tourism products
DMKT4	The attractive uniqueness of South Africa compared to other destinations
DMKT5	Positive marketing promotions related to tourism to South Africa
DMKT6	Perception of South Africa as an international tourism destination of choice
Nation brand dimensions (1 = Not at all influential – 5 = Extremely influential)
GOV1	The political stability in South Africa	Adams et al. (2015), Cowling et al. (2020), Filistanova (2017), Lee (2012), Lee et al., 2012, Liu et al. (2021), Lunt et al. (2012), Musuva (2015), Saiprasert (2011), Singh (2013), Verissimo (2012)
GOV2	Visible policing and safety from crime in South Africa
GOV3	The relations between South Africa and my own country
GOV4	Official COVID-19 related information availability on South African government website
GOV5	Control measures by the South African government to manage the COVID-19 pandemic
IMM1	Ease of immigration visa procedures when travelling to South Africa
IMM2	Visa policy of South Africa towards my home country
IMM3	Quality of life in South Africa
IMM4	South Africa's public resources (health and education)
IMM5	Availability of efficient basic service utilities in South Africa (water, electricity)
PEO1	Friendliness and helpfulness of South Africans
PEO2	Common language with South Africa (English, Dutch)
PEO3	High competence level of South Africans
PEO4	Acceptance of foreigners by South Africans
PEO5	Famous citizens from South Africa (Nelson Mandela, Charlize Theron, Wayde van Nierkerk)
CUH1	Commonality of my cultural values with South Africans
CUH2	Tolerance/openness to cultural diversity/change in South Africa
CUH3	The colonial heritage of South Africa
CUH4	Preservation of South Africa's cultural practices and heritage
CUH5	Societal equality in South Africa
NEG1	Persistent drought in water scarce South Africa (the drought in Cape Town and the Eastern Cape region)
NEG2	Food safety and insecurity (Listeria outbreak in South Africa)
NEG3	Lower vaccination levels compared to developed countries
NEG4	South Africa's economic sluggishness due to the pandemic
NEG5	Association of South Africa with the Beta and Delta COVID-19 strains
INF1	Access to affordable medical treatment
INF2	World-class health infrastructure (private health sector)
INF3	Less restrictive bio-ethical/health laws
INF4	Technologically advanced health systems
INF5	Access to high quality of medical services
Destination attributes (1 = Extremely unlikely – 5 = Extremely likely)
DAI1	Attend festivals, arts events and music concerts	Filistanova (2017), Gautam (2018), Mapingure et al. (2019), Pesonen et al. (2011), Saiprasert (2011), Seyidov and Adomaitienė (2016)
DAI2	Visit museums, monuments, and historical locations and artefacts
DAI3	Engage in entertainment activities (sports, theme parks, water parks, casinos and resorts)
DAI4	Experience unique food/cuisine experiences (wine, traditional, Western and Asian)
DAI5	Engage in outdoor activities (Quad-biking, hiking, bungee jumping and rafting)
DAI6	Visit locations with beaches (Durban, Cape Town and Port Elizabeth)
DAI7	Travel to places that offer a variety of unique of flora and fauna
DAI8	Visit national parks, conservancies and nature reserves
DAI9	Enjoy various natural attractions (mountains, lakes and rivers)
DAI10	Experience great weather in the country

Table S2

Exploratory factor analysis results

Code	Item	Factor loading coefficient (>0.05)	Comm
Destination Media Profile (1 = Not at all influential – 5 = Extremely influential)
DMP1	South Africa's tourism offering on travel and tourism websites	0.752	0.506
DMP2	Social media posts about South Africa (Facebook, Instagram, Snapchat, Twitter and YouTube)	0.511	0.406
DMP3	The information available on South Africa's official tourism website	0.692	0.477
DMP5	The image of South Africa shown in entertainment content (Movies, series and reality shows)	0.636	0.472
DMP6	South Africa's product placement in adverts	0.752	0.537
DMKT1	The South African government's initiatives to promoting tourism	0.827	0.617
DMKT 5	Positive marketing promotions related to tourism to South Africa	0.696	0.537
Destination Marketing Profile (1 = Not at all influential – 5 = Extremely influential)
DMP4	Coverage of South Africa in the media (News, documentaries)	0.509	0.440
DMKT2	Generally sufficient information about South Africa as a tourism destination	0.607	0.505
DMKT4	The attractive uniqueness of South Africa compared to other destinations	0.802	0.541
DMKT6	Perception of South Africa as an international tourism destination of choice	0.592	0.550
Infrastructure (INF) (1 = Not at all influential – 5 = Extremely influential)
INF1	Access to affordable medical treatment	0.618	0.660
INF2	World-class health infrastructure (private health sector)	0.649	0.656
INF4	Technologically advanced health systems	0.738	0.623
INF5	Access to high quality of medical services	0.731	0.726
Governance (GOV) (1 = Not at all influential – 5 = Extremely influential)
GOV1	The political stability in South Africa	0.526	0.662
GOV2	Visible policing and safety from crime in South Africa	0.695	0.589
IMM3	Quality of life in South Africa	0.591	0.544
IMM5	Availability of efficient basic service utilities in South Africa (water, electricity)	0.686	0.575
People (PEO) (1 = Not at all influential – 5 = Extremely influential)
PEO1	Friendliness and helpfulness of South Africans	0.762	0.633
CUH2	Tolerance/openness to cultural diversity/change in South Africa	0.552	0.500
CUH4	Preservation of South Africa's cultural practices and heritage	0.727	0.606
Culture (CUH) (1 = Not at all influential – 5 = Extremely influential)
PEO5	Famous citizens from South Africa (Nelson Mandela, Charlize Theron and Wayde van Nierkerk)	−0.682	0.715
CUH1	Commonality of my cultural values with South Africans	−0.743	0.684
CUH3	The colonial heritage of South Africa	−0.758	0.638
Immigration Policy (IMM) (1 = Not at all influential – 5 = Extremely influential)
GOV4	Official COVID-19 related information availability on South African government website	−0.667	0.611
GOV5	Control measures by the South African government to manage the COVID-19 pandemic	−0.569	0.643
IMM1	South Africa's public resources (health and education)	−0.647	0.646
IMM2	Availability of efficient basic service utilities in South Africa (water, electricity)	−0.622	0.581
Negative Events (NEG) (1 = Not at all influential – 5 = Extremely influential)
NEG1	Persistent drought in water-scarce South Africa (the drought in Cape Town and the Eastern Cape region)	0.551	0.556
NEG2	Food safety and insecurity (Listeria outbreak in South Africa)	0.613	0.651
NEG3	Lower vaccination levels compared to developed countries	0.758	0.661
NEG4	South Africa's economic sluggishness due to the pandemic	0.735	0.742
NEG5	Association of South Africa with the Beta and Delta COVID-19 strains	0.723	0.705
Natural-cultural (NATCUL) (1 = Extremely unlikely – 5 = Extremely likely)
DAI2	Visit museums, monuments and historical locations and artefacts	0.603	0.661
DAI4	Experience unique food/cuisine experiences (wine, traditional, Western and Asian)	0.749	0.567
DAI6	Visit locations with beaches (Durban, Cape Town and Port Elizabeth)	0.686	0.418
DAI7	Travel to places that offer a variety of unique of flora and fauna	0.570	0.483
DAI8	Visit national parks, conservancies and nature reserves	0.822	0.604
DAI9	Enjoy various natural attractions (mountains, lakes and rivers)	0.700	0.535
DAI10	Experience great weather in the country	0.826	0.604
Leisure -Entertainment (LEIENT) (1 = Extremely unlikely – 5 = Extremely likely)
DAI1	Attend festivals, arts events and music concerts	0.840	0.661
DAI3	Engage in entertainment activities (sports, theme parks, water parks, casinos and resorts)	0.819	0.687
DAI5	Engage in outdoor activities (Quad-biking, hiking, bungee jumping, rafting)	0.501	0.464

Table S3

Correlation matrix

	DMED	DMKT	INF	GOV	PEO	CUH	IMMPOL	NEG	NATCUL	ENTLEI
DMED	1	0.590**	0.446**	0.368**	0.453**	0.625**	0.522**	0.423**	0.263**	0.579**
DMKT		1	0.385**	0.446**	0.392**	0.377**	0.531**	0.296**	0.369**	0.293**
INF			1	0.522**	0.415**	0.433**	0.544**	0.616**	0.160**	0.267**
GOV				1	0.375	0.251**	0.513**	0.460**	0.214**	0.186**
PEO					1	0.424**	0.476	0.345**	0.352**	0.381**
CUH						1	0.387	0.445**	0.126**	0.425**
IMMPOL							1	0.434**	0.279**	0.264**
NEG								1	−0.047	0.165**
NATCUL									1	0.483**
ENTLEI										1

Note(s): DMED = Destination Media; DMKT = Destination Marketing; INF = Infrastructure; GOV = Governance; PEO = People; CUH = Cultural Heritage; IMMPOL = Immigration Policy; NEG = Negative Events; NATCUL = Natural-Cultural; ENTLEI = Entertainment-Leisure

**Correlation is significant at the 0.01 level (2-tailed)

Table S4

Direct effect of dimensions on NATCUL travel motives

	Unstandardised coefficients		Standardised coefficients
	B	Std. Error	β	t-value	Sig
X1 (DMED) and X2 (DMKT) – Y1 (NATCUL): path c
R2 = 0.135, F(2,453)36.405, p = 0.000
X1 (DMED) − Y1 (NATCUL)	0.055	0.043	0.069	1.277	0.202
X2 (DMKT) − Y1 (NATCUL)	0.298	0.049	0.328	6.059	0.000***
Infrastructure (INF)
X2(DMKT) − M1(INF): path a	0.473	0.053	0.385	8.873	0.000***
M1(INF) − Y1 (NATCUL): path b	0.058	0.045	0.078	1.298	0.195
Governance (GOV)
X2(DMKT) − M2 (GOV): path a	0.473	0.045	0.446	10.580	0.000***
M2 (GOV) − Y1 (NATCUL): path b	0.106	0.046	0.123	2.135	0.021*
People (PEO)
X2(DMKT) − M3(PEO): path a	0.425	0.047	0.392	9.049	0.000***
M3(PEO) − Y1 (NATCUL): path b	0.253	0.043	0.031	5.920	0.000***
Culture (CUH)
X2(DMKT) − M4(CUH): path a	0.546	0.063	0.377	8.660	0.000***
M4(CUH) − Y1 (NATCUL): path b	0.011	0.032	0.018	0.348	0.728
Immigration Policy (IMM)
X2(DMKT) − M5(IMM): path a	0.586	0.044	0.531	13.317	0.000***
M5(IMMPOL) − Y1 (NATCUL): path b	0.139	0.046	0.169	3.038	0.003**
Negative Events (NEG)
X2(DMKT) − M6(NEG): path a	0.383	0.058	0.296	6.587	0.000***
M6(NEG) − Y1 (NATCUL): path b	−0.237	0.040	−0.337	−5.979	0.000***

Note(s): Statistically significant at *p < 0.05, **p < 0.01, ***p < 0.001

Table S5

Direct effect of dimensions on LEIENT travel motives

	Unstandardised coefficients		Standardised coefficients
	B	Std. Error	β	t-value	Sig
X1 (DMED) and X2 (DMKT) – Y2 (LEIENT): path c
R2 = 0.335, F(2,453)115.274, p = 0.000
X1 (DMED) − Y2 (LEIENT)	0.650	0.050	0.622	13.117	0.000***
X2 (DMKT) − Y2 (LEIENT)	−0.089	0.057	−0.074	−1.561	0.119
Infrastructure (INF)
X2(DMED) − M1(INF): path a	0.477	0.045	0.446	10.587	0.000***
M1(INF) − Y2(LEIENT): path b	0.084	0.058	0.086	1.450	0.148
Governance (GOV)
X2(DMED) − M2 (GOV): path a	0.341	0.041	0.368	8.415	0.000***
M2 (GOV) − Y2 (LEIENT): path b	0.022	0.059	0.019	0.360	0.715
People (PEO)
X2(DMED) − M3(PEO): path a	0.428	0.040	0.456	10.788	0.000***
M3(PEO) − Y2 (LEIENT): path b	0.254	0.055	0.230	4.612	0.000***
Culture (CUH)
X2(DMED) − M4(CUH): path a	0.789	0.046	0.625	17.022	0.000***
M4(CUH) − Y2 (LEIENT): path b	0.278	0.041	0.336	6.773	0.000***
Immigration Policy (IMM)
X2(DMED) − M5(IMMPOL): path a	0.503	0.039	0.522	13.009	0.000***
M5(IMM) − Y2 (LEIENT): path b	0.031	0.059	0.028	0.521	0.603
Negative Events (NEG)
X2(DMED) − M6(NEG): path a	0.478	0.048	0.423	9.932	0.000***
M6(NEG) − Y2 (LEIENT): path b	−0.128	0.051	−0.138	−2.805	0.013*

Note(s): Statistically significant at *p < 0.05, **p < 0.01, ***p < 0.001

Disclosure statement: The authors report that there are no competing interests to declare.

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