How Tourists’ Perceived Risk Affects Behavioral Intention through Crisis Communication in the Post-COVID-19 Era
Abstract
:1. Introduction
2. Literature Review
2.1. Perceived Risk
2.2. Crisis Communication
Relationship between Perceived Risk and Crisis Communication
2.3. Non-Pharmaceutical Interventions
Relationship between Perceived Risk and Non-Pharmaceutical Interventions
2.4. Emotional Attachment
2.4.1. Relationship between Crisis Communication Strategy and Emotional Attachment
2.4.2. Relationship between Non-Pharmaceutical Interventions and Emotional Attachment
2.5. Behavioral Intention
2.5.1. Relationship between Emotional Attachment and Behavioral Intention
2.5.2. Relationship between Non-Pharmaceutical Interventions and Behavioral Intention
3. Methodology
3.1. Setting and Sample
3.2. Measures
3.3. Methods
4. Results
4.1. Characteristics of the Respondents
4.2. Measurement Model
4.3. Structural Equation Model
5. Conclusions
5.1. Discussion
5.2. Theoretical Implication
5.3. Managerial Implication
5.4. Limitations and Future Research Recommendations
Author Contributions
Funding
Conflicts of Interest
References
- Kim, S.H.; Holland, S.; Han, H.S. A Structural Model for Examining How Destination Image, Perceived Value, and Service Quality Affect Destination Loyalty: A Case Study of Orlando. Int. J. Tour. Res. 2013, 15, 313–328. [Google Scholar]
- Tian-Cole, S.; Crompton, J.L.; Willson, V.L. An Empirical Investigation of the Relationships Between Service Quality, Satisfaction and Behavioral Intentions among Visitors to a Wildlife Refuge. J. Leis. Res. 2002, 34, 1–24. [Google Scholar] [CrossRef]
- Parasuraman, A.; Zeithaml, V.; Berry, L. A Conceptual Model of Service Quality and its Implications for Future Research. J. Mark. 1985, 49, 41–50. [Google Scholar] [CrossRef]
- Engel, J.F.; Blackwell, R.D.; Miniard, P.W. Consumer Behavior, 6th ed.; Dryden Press: Chicago, IL, USA; New York, NY, USA, 1995. [Google Scholar]
- Teng, C.Y.; Jahari, S.A.B. Destination image as a mediator between perceived risks and revisit intention: A case of post-disaster Japan. Tour. Manag. 2014, 40, 382–393. [Google Scholar]
- Reisinger, Y.; Mavondo, F. Travel anxiety and intentions to travel internationally: Implications of travel risk perception. J. Travel Res. 2005, 43, 212–225. [Google Scholar] [CrossRef]
- Adam, I. Backpackers’ risk perceptions and risk reduction strategies in Ghana. Tour. Manag. 2015, 49, 99–108. [Google Scholar] [CrossRef]
- Lepp, A.; Gibson, H. Tourist Roles, Perceived Risk and International Tourism. Ann. Tour. Res. 2003, 30, 606–624. [Google Scholar] [CrossRef]
- Sönmez, S.F.; Apostolopoulos, Y.; Tarlow, P. Tourism in Crisis: Managing the Effects of Terrorism. J. Travel Res. 1999, 38, 13–18. [Google Scholar]
- Huang, J.; Min, J.C. Earthquake Devastation and Recovery in Tourism: The Taiwan Case. Tour. Manag. 2002, 23, 145–154. [Google Scholar] [CrossRef]
- Park, K.; Reisinger, Y. Differences in the Perceived Influence of Natural Disasters and Travel Risk on International Travel. Tour. Geogr. 2010, 12, 1–24. [Google Scholar] [CrossRef]
- Saling, B.M.; Baharuddin Semmaila, A.G. Effect of Service Quality and Marketing Stimuli on Customer Satisfaction: The Mediating Role of Purchasing Decisions. J. Bus. Manag. Sci. 2016, 4, 76–81. [Google Scholar]
- Law, R. The perceived impact of risks on travel decisions. Int. J. Tour. Res. 2006, 8, 289–300. [Google Scholar] [CrossRef]
- Obembe, D.; Kolade, O.; Obembe, F.; Owoseni, A.; Mafimisebi, O. COVID-19 and the tourism industry: An early-stage sentiment analysis of the impact of social media and stakeholder communication. Int. J. Inf. Manag. Data Insights 2021, 1, 100040. [Google Scholar] [CrossRef]
- Wang, J.; Liu-Lastres, B.; Ritchie, B.W.; Mills, D.J. Travelers’ self-protections against health risks: An application of the full Protection Motivation Theory. Ann. Tour. Res. 2019, 78, 102743. [Google Scholar] [CrossRef]
- Abdelrahman, M. Personality Traits, Risk Perception, and Protective Behaviors of Arab Residents of Qatar During the COVID-19 Pandemic. Int. J. Ment Health Addict. 2022, 20, 237–248. [Google Scholar] [CrossRef] [PubMed]
- Çınar, K.; Kavacık, S.Z.; Bişkin, F.; Çınar, M. Understanding the behavioral intentions about holidays in the shadow of the COVID-19 pandemic: Application of protection motivation theory. Healthcare 2022, 10, 1623. [Google Scholar] [CrossRef]
- Li, C.H.; Chao, P.J. Impact of emotional contagion through social network sites on travel willingness in the pandemic. J. Qual. Assur. Hosp. Tour. 2022, 1–18. [Google Scholar] [CrossRef]
- Kapuściński, G.; Richards, B. News Framing Effects on Destination Risk Perception. Tour. Manag. 2016, 57, 234–244. [Google Scholar] [CrossRef]
- Zhang, J.; Xie, C.; Chen, Y.; Dai, Y.-D.; Yi-Jun, W. The Matching Effect of Destinations’ Crisis Communication. J. Travel Res. 2022, 1–26. [Google Scholar] [CrossRef]
- Wallis, P.; Nerlich, B. Disease metaphors in new epidemics: The UK media framing of the 2003 SARS epidemic. Soc. Sci. Med. 2005, 60, 2629–2639. [Google Scholar] [CrossRef]
- Maser, B.; Weiermair, K. Travel Decision-Making: From the Vantage Point of Perceived Risk and Information Preferences. J. Travel Tour. Mark. 1998, 7, 107–121. [Google Scholar]
- Maulana, N.; Astuti, R.D.; Sukamdani, H.B.; Tjiptoherijanto, P. Risk Perception in the Post COVID-19 Pandemic Era: An Analysis of Tourist Accommodation and Travel Behavior in the New Normal Era. Sustainability 2022, 14, 14758. [Google Scholar] [CrossRef]
- Larissa Neuburger & Roman Egger. Travel risk perception and travel behaviour during the COVID-19 pandemic 2020: A case study of the DACH region. Curr. Issues Tour. 2021, 24, 1003–1016. [Google Scholar] [CrossRef]
- Sujood; Hamid, S.; Bano, N. Behavioral intention of traveling in the period of COVID-19: An application of the theory of planned behavior (TPB) and perceived risk. Int. J. Tour. Cities 2022, 8, 357–378. [Google Scholar]
- Mandina, S.P.; Du Preez, E.A. Travelers’ Risk Perceptions and Intentions to Visit African Destinations amidst COVID-19: The Case of Brands South Africa and Zimbabwe. Afr. J. Hosp. Tour. Leis. 2022, 11, 975–995. [Google Scholar]
- Chan, C.S. Developing a Conceptual Model for the Post-COVID-19 Pandemic Changing Tourism Risk Perception. Int. J. Environ. Res. Public Health 2021, 18, 9824. [Google Scholar] [PubMed]
- Zhang, X.; Tang, J. A Study of Emotional Solidarity in the Homestay Industry between Hosts and Tourists in the Post-Pandemic Era. Sustainability 2021, 13, 7458. [Google Scholar]
- Bauer, R.A. Consumer Behavior as Risk Taking. In Dynamic Marketing for a Changing World, Proceedings of the 43rd Conference of the American Marketing Association; Hancock, R.S., Ed.; American Marketing Association: Chicago, IL, USA, 1960; pp. 389–398. [Google Scholar]
- Jacoby, J.; Kaplan, L.B. The Components of Perceived Risk. In Proceedings of the Third Annual Conference of the Association for Consumer Research, Chicago, IL, USA, 3–5 November 1972; Venkatesan, M., Ed.; Association for Consumer Research: Chicago, IL, USA, 1972; pp. 382–393. [Google Scholar]
- Mitchell, V.M. Consumer Perceived Risk: Conceptualizations and Models. Eur. J. Mark. 1999, 33, 163–195. [Google Scholar] [CrossRef]
- Mansfeld, Y. Cycles of war, terror, and peace: Determinants and management of crisis and recovery of the Israeli tourism industry. J. Travel Res. 1999, 38, 30–36. [Google Scholar] [CrossRef]
- Sönmez, F.S.; Graefe, R.A. Influence of Terrorism Risk on Foreign Tourism Decisions. Ann. Tour. Res. 1998, 25, 112–144. [Google Scholar] [CrossRef]
- Richard, George. International tourists’ perceptions of crime-risk and their future travel intentions during the 2010 FIFA World Cup™ in South Africa. Crime Prev. Community Saf. 2012, 14, 79–103. [Google Scholar] [CrossRef]
- Huan, T.T.; Beaman, J.J.; Shelby, L.B. No-escape natural disaster: Mitigating Impacts on Tourism. Ann. Tour. Res. 2004, 31, 255–273. [Google Scholar]
- Quintal, V.A.; Lee, J.A.; Soutar, G.N. Risk, Uncertainty and the Theory of Planned Behavior: A Tourism Example. Tour. Manag. 2010, 31, 797–805. [Google Scholar]
- Cahyanto, I.; Wiblishauser, M.; Pennington-Gray, L.; Schroeder, A. The dynamics of travel avoidance: The case of Ebola in the U.S. Tour. Manag. Perspect. 2016, 20, 195–203. [Google Scholar] [CrossRef] [PubMed]
- Cowling, B.J.; Ng, D.M.; Ip, D.K.; Liao, Q.; Lam, W.W.; Wu, J.T.; Lau, J.T.; Griffiths, S.M.; Fielding, R. Community psychological and behavioral responses through the first wave of the 2009 influenza A(H1N1) pandemic in Hong Kong. J. Infect. Dis. 2010, 202, 867–876. [Google Scholar]
- SCrossReCañizares, S.M.; Cabeza-Ramírez, L.J.; Muñoz-Fernández, G.A.; Fuentes-García, F.J. Impact of the perceived risk from Covid-19 on intention to travel. Curr. Issues Tour. 2020, 24, 970–984. [Google Scholar]
- Matiza, T. Post-COVID-19 crisis travel behaviour: Towards mitigating the effects of perceived risk. J. Tour. Futures 2022, 8, 99–108. [Google Scholar]
- Abraham, V.; Bremser, K.; Carreno, M.; Crowley-Cyr, L.; Moreno, M. Exploring the consequences of COVID-19 on tourist behaviors: Perceived travel risk, animosity and intentions to travel. Tour. Rev. 2020, 76, 701–717. [Google Scholar] [CrossRef]
- Hakim, M.P.; Zanetta, L.D.; da Cunha, D.T. Should I stay, or should I go? Consumers’ perceived risk and intention to visit restaurants during the COVID-19 pandemic in Brazil. Food Res. Int. 2021, 141, 110152. [Google Scholar]
- Morgan, R.M.; Hunt, S.D. The commitment-trust theory of relationship marketing. J. Mark. 1994, 58, 20–38. [Google Scholar] [CrossRef]
- Zhong, L.; Liu, J.; Morrison, A.M.; Dong, Y.; Zhu, M.; Li, L. Perceived differences in peer-to-peer accommodation before and after COVID-19: Evidence from China. Int. J. Contemp. Hosp. Manag. 2023. [Google Scholar] [CrossRef]
- Zhao, Y.; Wang, H.; Guo, Z.; Huang, M.; Pan, Y.; Guo, Y. Online reservation intention of tourist attractions in the COVID-19 context: An extended technology acceptance model. Sustainability 2022, 14, 10395. [Google Scholar] [CrossRef]
- Arpan, L.M.; Pompper, D. Stormy weather: Testing “stealing thunder” as a crisis communication strategy to improve communication flow between organizations and journalists. Public Relat. Rev. 2003, 29, 291–308. [Google Scholar] [CrossRef]
- Coombs, W.T.; Holladay, S.J. Helping Crisis Managers Protect Reputational Assets: Initial Tests of the Situational Crisis Communication Theory. Manag. Commun. Q. 2002, 16, 165–186. [Google Scholar]
- Coombs, W.T.; Holladay, S.J. Communication and Attributions in a Crisis: An Experiment Study in Crisis Communication. J. Public Relat. Res. 1996, 8, 279–295. [Google Scholar] [CrossRef]
- Coombs, W.T. Information and Compassion in Crisis Responses: A Test of Their Effects. J. Public Relat. Res. 1999, 11, 125–142. [Google Scholar] [CrossRef]
- Rather, R.A. Monitoring the impacts of tourism-based social media, risk perception and fear on tourist’s attitude and revisiting behaviour in the wake of COVID-19 pandemic. Curr. Issues Tour. 2021, 24, 3275–3283. [Google Scholar]
- Coombs, W.T. An Analytic Framework for Crisis Situations: Better Responses From a Better Understanding of the Situation. J. Public Relat. Res. 1998, 10, 177–191. [Google Scholar]
- Taecharungroj, V.; Avraham, E. From tsunami through terror attacks to Covid-19: Crisis communication strategies and recovery campaigns to combat Thailand’s tourism crises. Asian J. Commun. 2021, 32, 41–64. [Google Scholar]
- Liu, B.; Pennington-Gray, L.; Krieger, J.L. Tourism crisis management: Can the Extended Parallel Process Model be used to understand crisis responses in the cruise industry? Tour. Manag. 2016, 55, 310–321. [Google Scholar]
- Acar, A.; Muraki, Y. Twitter for crisis communication: Lessons learned from japan’s tsunami disaster. Int. J. Web Based Communities 2011, 7, 392–402. [Google Scholar] [CrossRef]
- Shah, Z.; Chu, J.; Feng, B.; Qaisar, S.; Ghani, U.; Hassan, Z. If you care, I care: Perceived social support and public engagement via SNSs during crises. Technol. Soc. 2019, 59, 101195. [Google Scholar]
- Ritchie, B.; Dorrell, H.; Miller, D.; Miller, G. Crisis communication and recovery for the tourism industry: Lessons from the 2001 foot and mouth disease outbreak in the UK. J. Travel Tour. Mark. 2003, 15, 199–216. [Google Scholar]
- Bill Faulkner. Towards a framework for tourism disaster management. Tour. Manag. 2001, 22, 135–147. [Google Scholar]
- World Health Organization. Coronavirus Disease (COVID-19) Pandemic. WHO. 2020. Available online: https://www.who.int/emergencies/diseases/novel-coronavirus-2019 (accessed on 1 October 2020).
- Rimal, R.N.; Real, K. Perceived risk and efficacy beliefs as motivators of change: Use of the risk perception attitude (RPA) framework to understand health behaviors. Hum. Commun. Res. 2003, 29, 370–399. [Google Scholar]
- Zhang, J.; Xie, C.; Wang, J.; Morrison, A.M.; Coca-Stefaniak, J.A. Responding to a major global crisis: The effects of hotel safety leadership on employee safety behavior during COVID-19. Int. J. Contemp. Hosp. Manag. 2020, 32, 3365–3389. [Google Scholar]
- Sturges, D.L. Communicating through Crisis: A Strategy for Organizational Survival. Manag. Commun. Q. 1994, 7, 297–316. [Google Scholar]
- Xiao, J.; Shiu, E.Y.C.; Gao, H.; Wong, J.Y.; Fong, M.W.; Ryu, S.; Cowling, B.J. Nonpharmaceutical Measures for Pandemic Influenza in Nonhealthcare Settings-Personal Protective and Environmental Measures. Emerg. Infect. Dis. 2020, 26, 967–975. [Google Scholar]
- Pannu, J. Nonpharmaceutical Measures for Pandemic Influenza in Nonhealthcare Settings—International Travel-Related Measures. Emerg. Infect. Dis. 2020, 26, 2298–2299. [Google Scholar]
- Nigmatulina, K.R.; Larson, R.C. Living with influenza: Impacts of government imposed and voluntarily selected interventions. Eur. J. Oper. Res. 2009, 195, 613–627. [Google Scholar] [CrossRef]
- Cowling, B.J.; Fung, R.O.; Cheng, C.K.; Fang, V.J.; Chan, K.H.; Seto, W.H.; Yung, R.; Chiu, B.; Lee, P.; Uyeki, T.M.; et al. Preliminary findings of a randomized trial of non-pharmaceutical interventions to prevent influenza transmission in households. PLoS ONE 2008, 3, e2101. [Google Scholar]
- Mniszewski, S.; Del Valle, S.; Priedhorsky, R.; Hyman, J.; Hickman, K. Understanding the Impact of Face Mask Usage Through Epidemic Simulation of Large Social Networks. Theor. Simul. Complex Soc. Syst. 2013, 52, 97–115. [Google Scholar]
- Stutt, R.O.J.H.; Retkute, R.; Bradley, M.; Gilligan, C.A.; Colvin, J. A modelling framework to assess the likely effectiveness of facemasks in combination with ‘lock-down’ in managing the covid-19 pandemic. Proc. R. Soc. A Math. Phys. Eng. Sci. 2020, 476, 20200376. [Google Scholar]
- Ngonghala, C.N.; Iboi, E.; Eikenberry, S.; Scotch, M.; MacIntyre, C.R.; Bonds, M.H.; Gumel, A.B. Mathematical assessment of the impact of non-pharmaceutical interventions on curtailing the 2019 novel coronavirus. Math. Biosci. 2020, 325, 108364. [Google Scholar]
- Lee, C.K.; Song, H.J.; Bendle, L.J.; Kim, M.J.; Han, H. The impact of non-pharmaceutical interventions for 2009 H1N1 influenza on travel intentions: A model of goal-directed behavior. Tour. Manag. 2012, 33, 89–99. [Google Scholar] [CrossRef]
- Kim, M.J.; Lee, C.K.; Petrick, J.F.; Kim, Y.S. The influence of perceived risk and intervention on international tourists’ behavior during the Hong Kong protest: Application of an extended model of goal-directed behavior. J. Hosp. Tour. Manag. 2020, 45, 622–632. [Google Scholar]
- Zucker, H. The Emotional Attachment of Children to their parents as Related to Standards of Behavior and Delinquency. J. Psychol. 1943, 15, 31–40. [Google Scholar] [CrossRef]
- Hsiang, S.; Allen, D.; Annan-Phan, S.; Bell, K.; Bolliger, I.; Chong, T.; Druckenmiller, H.; Huang, L.Y.; Hultgren, A.; Krasovich, E.; et al. The effect of large-scale anti-contagion policies on the COVID-19 pandemic. Nature 2020, 584, 262–267. [Google Scholar]
- Eikenberry, S.E.; Mancuso, M.; Iboi, E.; Phan, T.; Eikenberry, K.; Kuang, Y.; Kostelich, E.; Gumel, A.B. To mask or not to mask: Modeling the potential for face mask use by the general public to curtail the COVID-19 pandemic. Infect. Dis. Model. 2020, 5, 293–308. [Google Scholar] [CrossRef]
- Ainsworth, M. The Bowlby-Ainsworth attachment theory. Behav. Brain Sci. 1978, 1, 436–438. [Google Scholar] [CrossRef]
- Escalas, J.E.; Bettman, J.R. Self-Construal Reference Groups and Brand Meaning. J. Consum. Res. 2005, 32, 378–389. [Google Scholar] [CrossRef]
- Hazan, C.; Shaver, P. Attachment as an Organizational Framework for Research on Close Relationships. Psychol. Inq. 1994, 5, 1–22. [Google Scholar] [CrossRef]
- Thomson, M.; MacInnis, D.J.; Park, C.W. The ties that bind: Measuring the strength of consumers’ emotional attachments to brands. J. Consum. Psychol. 2005, 15, 77–91. [Google Scholar]
- Fournier, S. Consumers and Their Brands: Developing Relationship Theory in Consumer Research. J. Consum. Res. 1998, 24, 343–353. [Google Scholar] [CrossRef]
- Lee, J.; Kyle, G.; Scott, D. The mediating effect of place attachment on the relationship between festival satisfaction and loyalty to the festival hosting destination. J. Travel Res. 2012, 51, 754–767. [Google Scholar] [CrossRef]
- Kil, N.; Holland, S.M.; Stein, T.V.; Ko, Y.J. Place attachment as a mediator of the relationship between nature-based recreation benefits and future visit intentions. J. Sustain. Tour. 2012, 20, 603–626. [Google Scholar]
- Trauer, B.; Ryan, C. Destination Image, Romance and Place Experience—An Application of Intimacy Theory in Tourism. Tour. Manag. 2005, 26, 481–491. [Google Scholar]
- Rajkumar, R.P. COVID-19 and Mental Health: A Review of the Existing Literature. Asian J. Psychiatry 2020, 52, 102066. [Google Scholar] [CrossRef]
- Ajzen, I. Attitude structure and behavior. In Attitude Structure and Function; Pratkanis, A.R., Breckler, S.J., Greenwald, A.G., Eds.; Lawrence Erlbaum Associates, Inc.: Mahwah, NJ, USA, 1989; pp. 241–274. [Google Scholar]
- Cong, G.; Zhang, H.; Chen, T. A Study on the Perception of Authenticity of Tourist Destinations and the Place Attachment of Potential Tourists—The Case of Ding Zhen’s Endorsement of Ganzi, Sichuan. Sustainability 2022, 14, 7151. [Google Scholar] [CrossRef]
- Mousavi, S.H.; Delshad, M.H.; Acuti Martellucci, C.; Bhandari, D.; Ozaki, A.; Pourhaji, F.; Pourhaji, F.; Reza Hosseini, S.M.; Roien, R.; Ramozi, A.A.; et al. Community Behavioral and Perceived Responses in the COVID-19 Outbreak in Afghanistan: A Cross-Sectional Study. Disaster Med. Public Health Prep. 2021; 1–7, advance online publication. [Google Scholar]
- Dunn, L.H.; Hoegg, J. The Impact of Fear on Emotional Brand Attachment. J. Consum. Res. 2014, 41, 152–168. [Google Scholar] [CrossRef]
- Kuroki, M.; Yamamoto, K.; Goldfinch, S. Factors influencing the adoption of voluntary nonpharmaceutical interventions to control COVID-19 in japan: Cross-sectional study. JMIR Form. Res. 2022, 6, e34268. [Google Scholar]
- Hang, H.; Aroean, L.; Chen, Z. Building emotional attaching during COVID-19. Ann. Tour. Res. 2020, 83, 103006. [Google Scholar] [CrossRef]
- Ullah, F.; Saqib, S.E.; Ahmad, M.M.; Fadlallah, M.A. Flood Risk Perception and its Determinants Among Rural Households in Two Communities in Khyber Pakhtunkhwa, Pakistan. Nat. Hazards 2020, 104, 225–247. [Google Scholar]
- Ajzen, I.; Fishbein, M. The prediction of behavioral intentions in a choice situation. J. Exp. Soc. Psychol. 1969, 5, 400–416. [Google Scholar] [CrossRef]
- Venkatesh, V.; Thong, J.Y.; Xu, X. Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology. MIS Q. 2012, 36, 157–178. [Google Scholar]
- Bock, G.; Zmud, R.W.; Kim, Y.; Lee, J. Behavioral Intention Formation in Knowledge Sharing: Examining the Roles of Extrinsic Motivators, Social-Psychological Factors, and Organizational Climate. MIS Q. 2005, 29, 87–111. [Google Scholar]
- Cao, S.; Gao, X.; Niu, S.; Wei, Q. A Comparison Study of Doctor-Patient Internet Interactions in Traditional and Modern Medicine: Empirical Evidence from Online Healthcare Communities. Evid.-Based Complement. Altern. Med. 2022, 2022, 4619914. [Google Scholar] [CrossRef]
- Lam, T.; Hsu, C.H. Predicting behavioral intention of choosing a travel destination. Tour. Manag. 2006, 27, 589–599. [Google Scholar]
- Yang, Y.; Sun, S. Tourism demand forecasting and tourists’ search behavior: Evidence from segmented Baidu search volume. Data Sci. Manag. 2021, 4, 1–9. [Google Scholar] [CrossRef]
- Gotham, K.F.; Campanella, R.; Lauve-Moon, K.; Powers, B. Hazard Experience, Geophysical Vulnerability, and Flood Risk Perceptions in a Postdisaster City, the Case of New Orleans. Risk Anal. 2018, 38, 345–356. [Google Scholar] [CrossRef]
- Haverila, M.J.; McLaughlin, C.; Haverila, K. The impact of social influence on perceived usefulness and behavioral intentions in the usage of non-pharmaceutical interventions (NPIs). Int. J. Healthc. Manag. 2022, 1–12. [Google Scholar] [CrossRef]
- Xu, W.; Youn, H.-J.; Lee, C.-K. Role of Non-Pharmaceutical Interventions for COVID-19 in Cruise Tourists’ Decision-Making Process: An Extended Model of Goal-Directed Behavior. Sustainability 2021, 13, 5552. [Google Scholar]
- Yıldırım, M.; Güler, A. Factor analysis of the COVID-19 Perceived Risk Scale: A preliminary study. Death Stud. 2022, 46, 1065–1072. [Google Scholar]
- Gao, Y.; Chen, L. Impact of COVID-19 risk perception on residents’ behavioural intention towards forest therapy tourism. Sustainability 2022, 14, 11590. [Google Scholar]
- Song, H.; You, G.J.; Reisinger, Y.; Lee, C.K.; Lee, S.K. Behavioral intention of visitors to an Oriental medicine festival: An extended model of goal directed behavior. Tour. Manag. 2014, 42, 101–113. [Google Scholar]
- Schoofs, L.; Claeys, A. Communicating sadness: The impact of emotional crisis communication on the organizational post-crisis reputation. J. Bus. Res. 2021, 130, 271–282. [Google Scholar] [CrossRef]
- Steenkamp, J.-B.E.M.; Baumgartner, H. On the use of structural equation models for marketing modeling. Int. J. Res. Mark. 2000, 17, 195–202. [Google Scholar] [CrossRef]
- Anderson, J.C.; Gerbing, D.W. Structural Equation Modeling in Practice: A Review and Recommended Two-Step Approach. Psychol. Bull. 1988, 103, 411–423. [Google Scholar] [CrossRef]
- Kelloway, K.E. Using LISREL for Structural Equation Modeling: A Researcher’s Guide; Sage Publications: Thousand Oaks, CA, USA, 1998. [Google Scholar]
- Matiza, T.; Kruger, M. Ceding to their fears: A taxonomic analysis of the heterogeneity in COVID-19 associated perceived risk and intended travel behaviour. Tour. Recreat. Res. 2021, 46, 158–174. [Google Scholar] [CrossRef]
- Nunnally, J.C.; Bernstein, I.H. The Assessment of Reliability. Psychom. Theory 1994, 3, 248–292. [Google Scholar]
- Bagozzi, R.P.; Yi, Y. On the Evaluation of Structure Equation Models. J. Acad. Mark. Sci. 1998, 16, 76–94. [Google Scholar]
- Fornell, C.; Larcker, D.F. Structural Equation Models with Unobservable Variables and Measurement Error: Algebra and Statistics. J. Mark. Res. 1981, 18, 382–388. [Google Scholar] [CrossRef]
- Yang, J.; Luo, J.M.; Yao, R. How fear of COVID-19 affects the behavioral intention of festival Participants—A case of the HANFU festival. Int. J. Environ. Res. Public Health 2022, 19, 2133. [Google Scholar] [CrossRef] [PubMed]
- Schneiders, M.L.; Naemiratch, B.; Cheah, P.K.; Cuman, G.; Poomchaichote, T.; Ruangkajorn, S.; Stoppa, S.; Osterrieder, A.; Cheah, P.K.; Ongkili, D.; et al. The impact of COVID-19 non-pharmaceutical interventions on the lived experiences of people living in Thailand, Malaysia, Italy and the United Kingdom: A cross-country qualitative study. PLoS ONE 2022, 17, e0262421. [Google Scholar]
- Oltra González, I. SOS to my followers!: The role of marketing communications in reinforcing online travel community value during times of crisis. Tour. Manag. Perspect. TMP 2021, 39, 100843. [Google Scholar]
Feature | Frequency | % | |
---|---|---|---|
Gender | Man | 411 | 39.26% |
Woman | 636 | 60.74% | |
Age | ≦18 | 38 | 3.63% |
19–30 | 177 | 16.91% | |
31–40 | 241 | 23.02% | |
41–50 | 268 | 25.60% | |
51–60 | 193 | 18.43% | |
≧61 | 130 | 12.42% | |
Educational background | Junior high school or below | 40 | 3.82% |
Senior high (vocational) school | 207 | 19.77% | |
University (college) | 569 | 54.35% | |
Postgraduate or above | 231 | 22.06% | |
Visited Dadaocheng in the past for Chinese medicine | Yes | 528 | 50.43% |
No | 519 | 49.57% | |
Purpose of using herbal medicine | Health maintenance | 821 | 78.41% |
Illness treatment | 132 | 12.61% | |
Never used before | 94 | 8.98% |
Measure | Value | Suggested Value |
---|---|---|
Chi-square/df | 3.315 | <5 |
Goodness of Fit index (GFI) | 0.948 | >0.9 |
Root mean square error of approximation (RMSEA) | 0.048 | <0.08 |
Standard Root mean square residual (SRMR) | 0.031 | <0.06 |
Adjusted Goodness of Fit Index (AGFI) | 0.931 | >0.9 |
Normed Fit Index (NFI) | 0.970 | >0.9 |
Relative Fit Index (RFI) | 0.964 | >0.9 |
Incremental Fit Index (IFI) | 0.979 | >0.9 |
Comparative Fit Index (CFI) | 0.979 | >0.9 |
Constructs | Items | Factor Loading | t-Value | Cronbach’s α | AVE | CR |
---|---|---|---|---|---|---|
Perceived risk | PR4 | 0.674 *** | 23.949 | 0.901 | 0.723 | 0.911 |
PR6 | 0.923 *** | 38.127 | ||||
PR7 | 0.959 *** | 40.767 | ||||
PR8 | 0.817 *** | 31.366 | ||||
Non-pharmaceutical interventions | NPI5 | 0.764 *** | 28.574 | 0.932 | 0.751 | 0.937 |
NPI7 | 0.781 *** | 29.522 | ||||
NPI8 | 0.862 *** | 34.312 | ||||
NPI9 | 0.967 *** | 41.837 | ||||
NPI10 | 0.94 *** | 39.719 | ||||
Crisis communication strategy | CCS2 | 0.686 *** | 24.635 | 0.929 | 0.778 | 0.932 |
CCS4 | 0.921 *** | 38.345 | ||||
CCS5 | 0.961 *** | 41.328 | ||||
CCS6 | 0.932 *** | 39.111 | ||||
Emotional Attachment | EA2 | 0.811 *** | 30.698 | 0.912 | 0.725 | 0.913 |
EA3 | 0.84 *** | 32.433 | ||||
EA4 | 0.919 *** | 37.495 | ||||
EA5 | 0.833 *** | 32.007 | ||||
Behavioral intention | BI1 | 0.925 *** | 38.387 | 0.939 | 0.844 | 0.942 |
BI2 | 0.956 *** | 40.633 | ||||
BI3 | 0.873 *** | 34.846 |
PR | NPI | CCS | EA | BI | |
---|---|---|---|---|---|
PR | 0.723 | ||||
NPI | 0.065 | 0.751 | |||
CCS | 0.205 | 0.027 | 0.778 | ||
EA | 0.002 | 0.028 | 0.018 | 0.725 | |
BI | 0.003 | 0.047 | 0.006 | 0.430 | 0.844 |
Path Coefficient | t-Value | p-Value | ||
---|---|---|---|---|
H1 | PR→CCS | 0.454 | 12.350 | *** |
H2 | PR→NPI | 0.256 | 7.616 | *** |
H3 | CCS→EA | 0.106 | 3.206 | ** |
H4 | NPI→EA | 0.148 | 4.452 | *** |
H5 | EA→BI | 0.637 | 20.532 | *** |
H6 | NPI→BI | 0.110 | 4.200 | *** |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Chen, S.-L.; Hsu, H.-T.; Chinomona, R. How Tourists’ Perceived Risk Affects Behavioral Intention through Crisis Communication in the Post-COVID-19 Era. Mathematics 2023, 11, 860. https://doi.org/10.3390/math11040860
Chen S-L, Hsu H-T, Chinomona R. How Tourists’ Perceived Risk Affects Behavioral Intention through Crisis Communication in the Post-COVID-19 Era. Mathematics. 2023; 11(4):860. https://doi.org/10.3390/math11040860
Chicago/Turabian StyleChen, Shui-Lien, Hsiang-Ting Hsu, and Richard Chinomona. 2023. "How Tourists’ Perceived Risk Affects Behavioral Intention through Crisis Communication in the Post-COVID-19 Era" Mathematics 11, no. 4: 860. https://doi.org/10.3390/math11040860
APA StyleChen, S. -L., Hsu, H. -T., & Chinomona, R. (2023). How Tourists’ Perceived Risk Affects Behavioral Intention through Crisis Communication in the Post-COVID-19 Era. Mathematics, 11(4), 860. https://doi.org/10.3390/math11040860