Risk perception and purchase intention during the COVID-19 pandemic
DOI:
https://doi.org/10.5585/remark.v23i4.23697Keywords:
COVID-19, Risk perception, Purchase intention, Theory of planned behavior, PLS-SEMAbstract
Objective: To verify the influence of COVID-19 risk perception and in-store purchase intention estimated by the dimensions of an extended TCP model.
Method: The research conducted was descriptive with a quantitative approach involving 596 consumers. The technique used was Structural Equation Modeling, using SmartPLS version 3.
Originality/Relevance: This research incorporated anticipated fear into the TCP model to construct an extended model. The extended TCP model assists in enabling a comprehensive analysis and a better understanding of consumer in-person purchase intention during the COVID-19 pandemic.
Results: The results showed that COVID-19 risk perception negatively influenced the elements of the TCP model and positively influenced anticipated fear. In addition, attitude, subjective norm, and perceived behavioral control had a positive impact on purchase intention. Therefore, the antecedents of the TCP model mediate the relationship between COVID-19 risk perception and purchase intention, but anticipated fear has no influence.
Theoretical/Methodological Contributions: This study adds to the knowledge on how COVID-19 risk perception influences purchase intention by using a TCP model.
Contributions to Management: The findings can contribute to understanding consumer behavior during a pandemic and help government and marketing departments take measures to reduce losses.
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References
Adiyoso, W., & Wilopo. (2021). Social distancing intentions to reduce the spread of COVID-19: The extended theory of planned behavior. BMC Public Health, 21(1), 1-12. https://doi.org/10.1186/s12889-021-11884-5
Addo, P. C., Jiaming, F., Kulbo, N. B., & Liangqiang, L. (2020). COVID-19: Fear appeal favoring purchase behavior towards personal protective equipment. The Service Industries Journal, 40(7-8), 471-490. https://doi.org/10.1080/02642069.2020.1751823
Ahmed, R. R., Streimikiene, D., Rolle, J-A, & Duc, P. A. (2020). The COVID-19 pandemic and the antecedents for the impulse buying behavior of US citizens. Journal of Competitiveness, 12(3), 5-27. https://doi.org/10.7441/joc.2020.03.01
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Process, 50, 179-211. https://doi.org/10.1016/0749-5978(91)90020-T
Ajzen, I. (1985). From intentions to actions: A theory of planned behavior. In J. Kuhl & J. Beckman (Eds.), Action-control: From cognition to behavior (pp. 11–39). Heidelberg: Springer.
Ajzen, I. (2011). The theory of planned behaviour: Reactions and reflections. Psychology & health, 26(9), 1113-1127. https://doi.org/10.1080/08870446.2011.613995
Akter, S., D'Ambra, J., & Ray, P. (2011). An Evaluation Of Pls Based Complex Models: The Roles Of Power Analysis, Predictive Relevance And Gof Index. Paper presented at the AMCIS 2011 Proceedings. Recuperado de https://aisel.aisnet.org/amcis2011_submissions/151
Armitage, C. J., & Conner, M. (2001). Efficacy of the theory of planned behaviour: A meta analytic review. British journal of social psychology, 40(4), 471-499. https://doi.org/10.1348/014466601164939
Asmundson, G. J., & Taylor, S. (2020). How health anxiety influences responses to viral outbreaks like COVID-19: What all decision-makers, health authorities, and health care professionals need to know. Journal of anxiety disorders, 71, 102211. 10.1016/j.janxdis.2020.102211
Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the academy of marketing science, 16(1), 74-94. https://doi.org/10.1007/BF02723327
Chen, M. F. (2017). Modeling an extended theory of planned behavior model to predict intention to take precautions to avoid consuming food with additives. Food Quality and Preference, 58, 24-33. https://doi.org/10.1016/j.foodqual.2017.01.002
Chen, H., Qian, W., & Wen, Q. (2021). The impact of the COVID-19 pandemic on consumption: Learning from high-frequency transaction data. In AEA Papers and Proceedings(Vol. 111, pp. 307-311). 2014 Broadway, Suite 305, Nashville, TN 37203: American Economic Association.
Chin, W., & Marcoulides, G. (1998). The partial least squares approach to structural equation modeling. Modern methods for business research, 295(2), 295-336.
Cohen, J., 1988. Statistical Power Analysis for the Behavioral Sciences. second ed. Lawrence Erlbaum Associates, Hillsdale, NJ.
Costa, J. C. N., Camargo, S. M., Toaldo, A. M. M., & Didonet, S. R. (2019). Managers’ influence on company capabilities. RAM. Revista de Administração Mackenzie, 20(6), eRAMD190061. https://doi.org/10.1590/1678-6971/eramd190061
Daellenbach, K., Parkinson, J., & Krisjanous, J. (2018). Just how prepared are you? An application of marketing segmentation and theory of planned behavior for disaster preparation. Journal of nonprofit & public sector marketing, 30(4), 413-443. https://doi.org/10.1080/10495142.2018.1452830
de Matos, C. A., & Leis, R. P. (2013). The antecedents of complaint behaviour for B razilian and F rench consumers of services. International Journal of Consumer Studies, 37(3), 327-336. https://doi.org/10.1111/ijcs.12002
de Souza Bido, D., & Da Silva, D. (2019). SmartPLS 3: especificação, estimação, avaliação e relato. Administração: Ensino e Pesquisa, 20(2), 488-536. https://doi.org/10.13058/raep.2019.v20n2.1545
Dowling, G. R., & Staelin, R. (1994). A model of perceived risk and intended risk-handling activity. Journal of consumer research, 21(1), 119-134. https://doi.org/10.1086/209386
Eger, L., Komárková, L., Egerová, D., & Mičík, M. (2021). The effect of COVID-19 on consumer shopping behaviour: Generational cohort perspective. Journal of Retailing and Consumer Services, 61, 102542. https://doi.org/10.1016/j.jretconser.2021.102542
El Khatib, A. S. (2021). Acúmulo de Alimentos durante a Pandemia da COVID-19: Uma Análise à luz da Teoria do Comportamento Planejado (TCP)/Food Accumulation during the COVID-19 Pandemic: An Analysis in the Light of Theory of Planned Behavior (TCP). ID on line. Revista de psicologia, 15(54), 743-759. https://doi.org/10.14295/idonline.v15i54.2949
Farooq, A., Laato, S., & Islam, A. N. (2020). Impact of online information on self-isolation intention during theCOVID-19 pandemic.Journal of Medical Internet Research,22(5), 1–15. https://doi.org/10.2196/19128
Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error: Algebra and statistics. https://doi.org/10.1177/002224378101800313
Graham-Rowe, E., Jessop, D. C., & Sparks, P. (2015). Predicting household food waste reduction using an extended theory of planned behaviour. Resources, Conservation and Recycling, 101, 194-202. https://doi.org/10.1016/j.resconrec.2015.05.020.
Hair, J.F., Hult, G.T.M., Ringle, C.M. & Sarstedt, M. (2021). A primer on partial least squares structural equation modeling (PLS-SEM) (2nd ed.). Thousand Oaks, CA: Sage.
Hamdah, D. F. L., Rahmadya, R. R., & Nurlaela, L. (2020). The Effect of Attitude, Subjective Norm, and Perceived Behavior Control of Taxpayer Compliance of Private Person in Tax Office Garut, Indonesia. Review of Integrative Business and Economics Research, 9, 298-306. https://sibresearch.org/uploads/3/4/0/9/34097180/riber_9-s1_23_k19-086_298-306.pdf
Han, H., & Kim, Y. (2010). An investigation of green hotel customers’ decision formation: Developing an extended model of the theory of planned behavior. International journal of hospitality management, 29(4), 659-668. https://doi.org/10.1016/j.ijhm.2010.01.001
Han, T., Zhang, L., Zhao, X., & Deng, K. (2023). Total-effect test may erroneously reject so-called “full” or “complete” mediation. arXiv. http://arxiv.org/abs/2309.08910
Harman, H. H. (1976). Modern factor analysis. The University of Chicago Press.
Henseler, J., Hubona, G., & Ray, P. A. (2016). Using PLS path modeling in new technology research: updated guidelines. Industrial management & data systems. 116(1), 2-20. https://doi.org/10.1108/IMDS-09-2015-0382
Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009). The use of partial least squares path modeling in international marketing. In New challenges to international marketing. Emerald Group Publishing Limited. https://doi.org/10.1108/S1474-7979(2009)0000020014
Höck, M., & Ringle, C. M. (2006). Strategic networks in the software industry: An empirical analysis of the value continuum. In IFSAM VIIIth World Congress.
Høie, M., Moan, I. S., & Rise, J. (2010). An extended version of the theory of planned behavour: Prediction of intentions to quit smoking using past behaviour as moderator. Addiction Research & Theory, 18(5), 572-585. https://doi.org/10.3109/1 6066350903474386.
Hsu, C. H., & Huang, S. (2012). An extension of the theory of planned behavior model for tourists. Journal of Hospitality & Tourism Research, 36(3), 390-417. https://doi.org/1 0.1177/1096348010390817.
Hu, P., Bhuiyan, M. A., Rahman, M. K., Hossain, M. M., & Akter, S. (2022). Impact of COVID-19 pandemic on consumer behavioural intention to purchase green products. Plos one, 17(10), e0275541. https://doi.org/10.1371/journal.pone.0275541
Irvin, C. B., Cindrich, L., Patterson, W., & Southall, A. (2008). Survey of hospital healthcare personnel response during a potential avian influenza pandemic: will they come to work?. Prehospital and disaster medicine, 23(4), 328-335. doi:10.1017/ S1049023x00005963
Iwaya, G. H., Cardoso, J. G., Sousa Júnior, J. H. D., & Steil, A. V. (2020). Preditores da intenção de permanecer em distanciamento social. Revista de Administração Pública, 54, 714-734. https://doi.org/10.1590/0034-761220200177
Jamovi. (2024). jamovi (Versão 2.5) [Software de computador]. Recuperado de https://www.jamovi.org
Khachfe, H. H., Chahrour, M., Sammouri, J., Salhab, H., Makki, B. E., & Fares, M. (2020). An epidemiological study on COVID-19: a rapidly spreading disease. Cureus. https://doi.org/10.7759/cureus.7313
Kozak, M. J. (1986). Emotional processing of fear: Exposure to corrective information. Psychological Bulletin, 99(1), 20-35. 10.1037/0033-2909.99.1.20
Kim, J., Yang, K., Min, J., & White, B. (2022). Hope, fear, and consumer behavioral change amid COVID‐19: Application of protection motivation theory. International Journal of Consumer Studies, 46(2), 558-574. https://doi.org/10.1111/ijcs.12700
Kumar, A., & Smith, S. (2018). Understanding local food consumers: Theory of planned behavior and segmentation approach. Journal of Food Products Marketing, 24(2), 196-215. https://doi.org/10.1080/10454446.2017.1266553
Kuruppu, G. N., & De Zoysa, A. (2020) COVID-19 and Panic Buying: An Examination of the Impact of Behavioural Biases. http://dx.doi.org/10.2139/ssrn.3596101
Lazarus R. S. (1991). Progress on ac ognitive-motivational-relational theory of emotion. Am Psychol 46(8), 819. https://doi.org/10.1037/0003-066X.46.8.819
Leung, X. Y., & Cai, R. (2021). How pandemic severity moderates digital food ordering risks during COVID-19: An application of prospect theory and risk perception framework. Journal of Hospitality and Tourism Management, 47, 497-505. https://doi.org/10.1016/j.jhtm.2021.05.002
Li, J., Hallsworth, A. G., & Coca-Stefaniak, J. A. (2020). Changing Grocery Shopping Behaviours Among Chinese Consumers At The Outset Of The COVID-19 Outbreak. Tijdschrift voor economische en sociale geografie. 111(3), 1–10. https://doi.org/10.1111/tesg.12420
Liu, C., Sun, C. K., Chang, Y. C., Yang, S. Y., Liu, T., & Yang, C. C. (2021). The impact of the fear of COVID-19 on purchase behavior of dietary supplements: Integration of the theory of planned behavior and the protection motivation theory. Sustainability, 13(22), 12900. https://doi.org/10.3390/su132212900
Long, N. N., & Khoi, B. H. (2020). An Empirical Study about the Intention to Hoard Food during COVID-19 Pandemic. EURASIA Journal of Mathematics, Science and Technology Education, 16(7), em1857. https://doi.org/10.29333/ejmste/8207
Ministério da Saúde (2020). Painel Coronavírus. Recuperado de https://covid.saude.gov.br/
Murphy, R., Calugi, S., Cooper, Z., & Dalle Grave, R. (2020). Challenges and opportunities for enhanced cognitive behaviour therapy (CBT-E) in light of COVID-19. The Cognitive Behaviour Therapist. https://doi.org/10.1017/S1754470X20000161
Nações Unidas (2020) Tire suas dúvidas sobre o novo coronavírus. Recuperado de https://nacoesunidas.org/tire-suas-duvidas-sobre-o-novo-coronavirus/
Pan American Health Organization [PAHO]. (2020). OMS declara fim da Emergência de Saúde Pública de Importância Internacional referente à COVID-19. Washington, DC: PAHO.
Paul, J., Modi, A., & Patel, J. (2016). Predicting green product consumption using theory of planned behavior and reasoned action. Journal of retailing and consumer services, 29, 123-134. https://doi.org/10.1016/j.jretconser.2015.11.006
Podsakoff, P. M., MacKenzie, S. B., & Podsakoff, N. P. (2012). Sources of method bias in social science research and recommendations on how to control it. Annual Review of Psychology, 63, 539–569. https://doi.org/10.1146/annurev-psych-120710-100452
Rather, R. A. (2021). Demystifying the effects of perceived risk and fear on customer engagement, co-creation and revisit intention during COVID-19: A protection motivation theory approach. Journal of Destination Marketing & Management, 20, 100564. https://doi.org/10.1016/j.jdmm.2021.100564
Richards, T. J., & Rickard, B. (2020). COVID-19 impact on fruit and vegetable markets. Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie. https://doi.org/10.1111/cjag.12231
Ringle, C. M., Da Silva, D., & de Souza Bido, D. (2014). Modelagem de equações estruturais com utilização do SmartPLS. REMark-Revista Brasileira de Marketing, 13(2), 56-73. https://doi.org/10.5585/remark.v13i2.2717
Rivis, A., Sheeran, P., & Armitage, C. J. (2009). Expanding the affective and normative components of the theory of planned behavior: A meta‐analysis of anticipated affect and moral norms. Journal of applied social psychology, 39(12), 2985-3019. https://doi.org/10.1111/j.1559-1816.2009.00558.x
Roberts, J. A., & David, M. E. (2021). Improving predictions of COVID-19 preventive behavior: Development of a sequential mediation model. Journal of Medical Internet Research, 23(3), e23218. https://doi.org/10.2196/23218
Sampieri, R. H., Collado, C. F., & Lucio, M. del P. B. (2006). Metodologia de pesquisa (3a ed). São Paulo: McGraw-Hill.
Seabra, C., Abrantes, J. L., & Kastenholz, E. (2014). The influence of terrorism risk perception on purchase involvement and safety concern of international travellers. Journal of Marketing Management, 30(9-10), 874-903. https://doi.org/10.1080/0267257X.2014.934904
Sjöberg, L. (2000). Specifying factors in radiation risk perception1. Scandinavian Journal of Psychology, 41(2), 169-174. https://doi.org/10.1111/1467-9450.0018
Sobel, M. E. (1987). Direct and indirect effects in linear structural equation models. Sociological Methods & Research, 16(1), 155-176. https://doi.org/10.1177/0049124187016001006
Soper, D. S. (2024). Sobel test calculator for the significance of mediation [Software]. Available from https://www.danielsoper.com/statcalc
Song, W., Jin, X., Gao, J., & Zhao, T. (2020). Will Buying Follow Others Ease Their Threat of Death? An Analysis of Consumer Data during the Period of COVID-19 in China. International Journal of Environmental Research and Public Health, 17(9), 3215. https://doi.org/10.3390/ijerph17093215
Stefani, G., Cavicchi, A., Romano, D., & Lobb, A. E. (2008). Determinants of intention to purchase chicken in Italy: the role of consumer risk perception and trust in different information sources. Agribusiness: An International Journal, 24(4), 523-537. https://doi.org/10.1002/agr.20177
Stone, R. N., & Grønhaug, K. (1993). Perceived risk: Further considerations for the marketing discipline. European Journal of marketing, 27(3), 39-50. https://doi.org/10.1108/03090569310026637
Thøgersen, J. (2010). Country differences in sustainable consumption: The case of organic food. Journal of Macromarketing, 30(2), 171-185. https://doi.org/10.1177/0276146710361926
Thomas, M. S., & Feng, Y. (2021). Consumer risk perception and trusted sources of food safety information during the COVID-19 pandemic. Food Control, 130, 108279. https://doi.org/10.1016/j.foodcont.2021.108279
Van Bavel, J. J., Baicker, K., Boggio, P. S., Capraro, V., Cichocka, A., Cikara, M., ... & Drury, J. (2020). Using social and behavioural science to support COVID-19 pandemic response. Nature Human Behaviour. https://doi.org/10.1038/s41562-020-0884-z
Vuković, D., Jurič, B., & Krnjak, I. (2022). Influence of the emotion of fear on patterns of consumer behavior toward dietary supplements during the COVID-19 pandemic. Journal of risk and financial management, 15(6), 257. https://doi.org/10.3390/jrfm15060257
Wijaya, T. (2020). Factor Analysis of Panic Buying During the Covid-19 Period in Indonesia. SSHO-D-20-00135. http://dx.doi.org/10.2139/ssrn.3603750
Zhang, Y., Yang, H., P., & Luqman, A. (2019). Predicting consumers’ intention to consume poultry during an H7N9 emergency: an extension of the theory of planned behavior model. Human and Ecological Risk Assessment: An International Journal. https://doi.org/10.1080/10807039.2018.1503931
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