VR tourism experiences and tourist behavior intention in COVID-19: an experience economy and mood management perspective

PurposeVirtual reality (VR) technology is a potential tool for tourism marketers to maintain the attractiveness of their destinations and recover from the COVID-19 pandemic. However, the effectiveness of VR technology in motivating potential tourists' visit intention under lockdown conditions remains unknown. An integrated model based on the experience economy framework and mood management theory was, therefore, used to explain how tourists' VR experiences affect their mood management processes and subsequent behaviors. This research also examined how perceived travel risk influenced the relationship between mood management processes and future decisions.Design/methodology/approachThis study used a cross-sectional design based on a sample collected from a Chinese survey company, Sojump. The author surveyed 285 respondents who had experienced VR tourism activities during the COVID-19 pandemic. The research model was tested using partial least squares–structural equation modeling.FindingsThe results demonstrated that the four dimensions of VR experiences differently affected mood management processes, while perceived travel risk differently moderated the influence of mood management processes on visit intention and VR stickiness. This provides insights for tourism marketers to adapt to the current tourism environment and develop recovery strategies.Originality/valueIn response to gaps in the literature, this research examined the effectiveness of VR technology in driving tourists' visit intention during the COVID-19 pandemic, providing insights for tourism marketers to successfully implement VR tourism and plan timely recovery strategies.

The role of virtual reality interactivity in building tourists' memorable experiences and post-adoption intentions in the COVID-19 era. (Special Issue: Challenges and prospects of AIoT application in hospitality and tourism marketing.)

Purpose: This study aims to examine the impact of virtual reality (VR) interactivity elements, including synchronicity, two-way communication and active control, on tourists' VR memorable experiences, and the subsequent effect on tourists' word-of-mouth (WOM) and VR continuance intention. Design/methodology/approach: The research model was tested using self-administrated survey data from 285 Chinese tourists who were experienced in travel and VR tourism activities. Partial least squares-structural equation modelling was adopted to perform latent variable analysis. Findings: VR interactivity elements, including synchronicity, two-way communication and active control, play a considerable role in driving tourists' VR memorable experiences, which in turn drive tourists' WOM and VR continuance intention. Research limitations/implications: Cross-sectional, single country data was used and thus the generalisability of the findings may be limited. Future research is recommended adopting a longitudinal approach to compare the relationship between constructs in countries with diverse cultures. Practical implications: The findings provide guidance for tourism marketers to use the interactive features of VR, including synchronicity, two-way communication and active control, to drive tourists' memorable experiences and thereby strengthen tourists' referral and continuance intention. Originality/value: Scholarly attention on the importance of VR interactivity in driving tourists' memorable experiences and behavioural intention is limited. This study contributes to the tourism marketing literature by empirically testing the research model to reveal the importance of VR interactivity elements, including synchronicity, two-way communication and active control, to drive tourists' VR memorable experiences and behavioural intentions.

Economic evaluations of inactivated COVID-19 vaccines in six Western Pacific and South East Asian countries and regions: A modeling study.

OBJECTIVE: The present study aimed to document the economic profiles of inactivated COVID-19 vaccines in Hong Kong SAR, Indonesia, mainland China, Philippines, Singapore, and Thailand, the evidence on which is currently absent. METHODS: Decision tree models were developed to assess the cost-effectiveness of two doses of inactivated COVID-19 vaccines at a population vaccination rate of 50% in the base case, which was an estimate of feasible vaccination coverage according to previous studies. Epidemiological, mortality, cost, and health state utility information were sourced from the literature. Vaccine efficacy against COVID-19 cases by severity were estimated using meta-analyses of publicly accessible phase 3 trial results of inactivated vaccines. The health outcomes were quantified as quality-adjusted life years (QALYs) and compared across the vaccination and no vaccination strategies. In scenario analyses, incidence and vaccination rates were changed semi-continuously over spectrums, the results of which were presented as contour lines informing the efficiency frontiers of vaccination strategies. One-way and probabilistic sensitivity analyses were also conducted. RESULTS: The vaccination strategy was dominant in all jurisdictions in the base case by producing 105.18, 98.15, 99.70, 60.48, 112.00, and 103.47 QALYs while saving US$40.26 million, US$5.26 million, US$7.60 million, US$5.91 million, US$21.33 million, and US$7.18 million in Hong Kong SAR, Indonesia, mainland China, Philippines, Singapore, and Thailand per every 100,000 vaccinated individuals, respectively. Results were robust in alternative model specifications. CONCLUSIONS: Inactivated COVID-19 vaccines may be cost-saving options in Hong Kong SAR, Indonesia, mainland China, Philippines, Singapore, and Thailand. Mass vaccination programs using inactivated COVID-19 vaccines should be considered in these jurisdictions.

Association between IL-6 and severe disease and mortality in COVID-19 disease: a systematic review and meta-analysis.

BACKGROUND: So far, SARS-CoV-2 is the seventh coronavirus found to infect humans and cause disease with quite a strong infectivity. Patients diagnosed as severe or critical cases are prone to multiple organ dysfunction syndrome, acute respiratory distress syndrome and even death. Proinflammatory cytokine IL-6 has been reported to be associated with the severity of disease and mortality in patients with COVID-19. OBJECTIVE: This systematic review and meta-analysis were carried out to evaluate the association between IL-6 and severe disease and mortality in COVID-19 disease. METHODS: A systematic literature search using China National Knowledge Infrastructure, Wanfang databases, China Science and Technology Journal Database, Chinese Biomedical Literature, Embase, PubMed and Cochrane Central Register of Controlled Trials was performed from inception until 16 January 2021. RESULTS: 12 studies reported the value of IL-6 for predicting the severe disease in patients with COVID-19. The pooled area under the curve (AUC) was 0.85 (95% CI 0.821 to 0.931). 5 studies elaborated the predictive value of IL-6 on mortality. The pooled sensitivity, specificity and AUC were 0.15 (95% CI 0.13 to 0.17, I2=98.9%), 0.73 (95% CI 0.65 to 0.79, I2=91.8%) and 0.531 (95% CI 0.451 to 0.612), respectively. Meta-regression analysis showed that country, technique used, cut-off, sample, study design and detection time did not contribute to the heterogeneity of mortality. CONCLUSION: IL-6 is an adequate predictor of severe disease in patients infected with the COVID-19. The finding of current study may guide clinicians and healthcare providers in identifying potentially severe or critical patients with COVID-19 at the initial stage of the disease. Moreover, we found that only monitoring IL-6 levels does not seem to predict mortality and was not associated with COVID-19's mortality. PROSPERO REGISTRATION NUMBER: CRD42021233649.

Prediction of the Severity of the Coronavirus Disease and Its Adverse Clinical Outcomes.

This study aims to investigate blood and biochemical laboratory findings in patients with severe coronavirus disease 2019 (COVID-19) and to develop a joint predictor for predicting the likelihood of severe COVID-19 and its adverse clinical outcomes and to provide more information for treatment. We collected the data of 88 patients with laboratory-confirmed COVID-19. Further, the patients were divided into a non-severe group and a critical group (including critically ill cases). Univariate analysis showed that the absolute lymphocyte count, albumin level, albumin/globulin ratio, lactate dehydrogenase level, interleukin-6 (IL-6) level, erythrocyte count, globulin level, blood glucose level, and age were significantly correlated with the severity of COVID-19. The multivariate binary logistic regression model revealed that age, absolute lymphocyte count, and IL-6 level were independent risk factors in patients with COVID-19. The receiver operating characteristic curve revealed that the combination of IL-6 level, absolute lymphocyte count, and age is superior to a single factor as predictors for severe COVID-19, regardless of whether it is in terms of the area under the curve or the prediction sensitivity and specificity. Early application is beneficial to early identification of critically ill patients and timing individual treatments to reduce mortality.

Prevalence and factors associated with postpartum depression during the COVID-19 pandemic among women in Guangzhou, China: a cross-sectional study.

BACKGROUND: The 2019 coronavirus disease (COVID-19) is a public health emergency of international concern. To date, there are limited studies that have investigated the impact of COVID-19 pandemic on mental health among female population. Therefore, the study aims to investigate the prevalence of postpartum depression (PPD) and it's related factors among women in Guangzhou, China, during the COVID-19 pandemic. METHODS: A cross-sectional study was performed from 30th March 2020 to 13th April 2020 using anonymous online questionnaire among 864 women at 6-12 weeks postpartum. The Chinese version of Edinburgh Postnatal Depression Scale and a questionnaire regarding associated factors were administered to all participants. Multivariate logistic regression was used to determine factors that were significantly associated with PPD. RESULTS: The prevalence of PPD among women at 6-12 weeks postpartum was 30.0%. A multivariate logistic regression model identified significant factors as: immigrant women, persistent fever, poor social support, concerns about contracting COVID-19 and certain precautionary measures. CONCLUSIONS: The findings suggest the need for policies and interventions to not only mitigate the psychological impacts but also target disadvantaged sub-groups of women following childbirth during the COVID-19 pandemic.

Prediction of adverse clinical outcomes in patients with coronavirus disease 2019.

OBJECTIVE: This study aims to investigate blood and biochemical laboratory findings in patients with coronavirus disease (COVID-19) and analyze the potential predictors of poor outcome in patients with COVID-19. METHODS: The clinical, laboratory, and outcome data of 87 patients with COVID-19 were collected and retrospectively analyzed. Only data collected at the time of admission were used in the analysis for predictors of poor outcome. These patients were divided into two groups: the adverse prognosis group (36 patients) and the non-adverse prognosis group (51 patients). The adverse prognosis of COVID-19 patients was defined as admission to the intensive care unit or death. RESULTS: On the univariate analysis, age, white blood cell (WBC) count, neutrophil counts, lymphocytes count, neutrophils-to-lymphocytes ratio (NLR), interleukin-6, albumin-to-globulin ratio (AGR), albumin, lactate dehydrogenase, glutamyl transpeptidase, and blood glucose were found to be the significant predictors. On the multivariate analysis, the predictors of poor outcome of patients with COVID-19 were NLR (OR = 2.741, [95% CI = 1.02 ~ 7.35], P = .045) and IL-6 (OR = 1.405, [95% CI = 1.04 ~ 1.89, P = .025]). The receiver operating characteristic (ROC) curve revealed that the AUC of NLR, interleukin-6, pneumonia severity index (PSI) score, and Confusion-Urea-Respiratory Rate-Blood pressure-65 (CURB-65) score were 0.883, 0.852, 0.824, and 0.782, respectively. CONCLUSION: High interleukin-6 (6 pg/mL, cuff value) and NLR (4.48, cuff value) can be used to predict poor outcomes in patients with COVID-19 on admission, thus can serve as a beneficial tool for timely identifying COVID-19 patients prone to poor outcome and reduce patient mortality through early intervention.