The Impact of Regional COVID-19 Outbreak on Consumers’ Risk Perception of Purchasing Food Online
Abstract
:1. Introduction
2. Literature Review and Research Hypotheses
2.1. Online Food Safety Risk Perception
2.2. Regional COVID-19 Epidemic and Risk Perception of Online-Purchased Food Containing Coronavirus
2.3. Online Food Purchase as Infection Source
2.4. Social Media Effect
3. Materials
3.1. Survey Design and Data Collection
3.2. Sample Description and Variable Selection
4. Methods
5. Empirical Results
5.1. Baseline Regression
5.2. Mechanism Analysis
5.3. Robustness Test
5.4. Heterogeneity Analysis
5.4.1. Regional/Province and City Influences
5.4.2. Perceived Risk Differences in Various Food Categories Purchased Online
6. Conclusions and Policy Implications
6.1. Conclusions
6.2. Policy Implications
6.3. Limitation and Future Work
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
Gender | Male = 1; Female = 2 |
Age | Younger than 22 = 1; 23 to 30 = 2; 31 to 40 = 3; 41 to 50 = 4; older than 51 = 5 |
Education | less than high school = 1; college = 2; undergraduate = 3; Master = 4; Doctoral degree = 5 |
Net monthly income | Less than 2000 yuan = 1; 2001–4000 yuan = 2; 4001–6000 yuan = 3; 6001–8000 yuan = 4; 8001–12,000 yuan = 5; 12,001–20,000 yuan = 6; more than 20,001 yuan = 7 |
Household size | 1 person = 1; 2 persons = 2; 3 persons = 3; 4 persons = 4; 5 or more = 5 |
Pregnant female in the family | Yes = 1; No = 2 |
Older than 60 years | Yes = 1; No = 2 |
COVIDRi | Respondent location. other regions of non-P province = 1; P province, but not C city = 2; C city = 3 |
COVIDC | the city with the major epidemic (C city, P province) = 1; other = 0 |
Packaging | Degree of agreement that online food ingredients or packaging surfaces may contain SARS-COV-2/or its variants: completely disagree = 1; basically disagree = 2; neutral = 3; agree = 4; total agree = 5 |
Social media use | Do you use online social media to learn about COVID-19? Yes = 1; No = 0 |
Food purchased online source of COVID-19 | Degree of risk of COVID-19 from food purchased online: no risk at all = 1; little risk = 2; neutral = 3; higher = 4; very high risk = 5 |
Online purchase higher COVID-19 risk | Degree of agreement that online-purchased food is more susceptible to COVID-19 contamination than off-line food: completely disagree = 1; basically disagree = 2; neutral = 3; agree = 4; totally agree = 5 |
References
- National Bureau of Statistics. The Consumer Market Improved Quality, Expanded Circulation and Innovated Development. The Seventh in a Series of Reports on Achievements in Economic and Social Development since the 18th CPC National Congress. Available online: http://www.stats.gov.cn/tjsj/sjjd/202209/t20220922_1888578.html (accessed on 10 July 2022). (In Chinese)
- Gordon-Wilson, S. Consumption practices during the COVID-19 crisis. Int. J. Consum. Stud. 2021, 46, 575–588. [Google Scholar] [CrossRef] [PubMed]
- Toiba, H.; Efani, A.; Rahman, M.S.; Nugroho, T.W.; Retnoningsih, D. Does the COVID-19 pandemic change food consumption and shopping patterns? Evidence from Indonesian urban households. Int. J. Soc. Econ. 2022, 49, 1803–1818. [Google Scholar] [CrossRef]
- National Bureau of Statistics. Total Retail Sales of Consumer Goods Increased by 3.1 Percent in June 2022. Available online: http://www.stats.gov.cn/tjsj/zxfb/202207/t20220715_1886422.html (accessed on 10 July 2022). (In Chinese)
- FAO; WHO. Assuring Food Safety and Quality: Guidelines for Strengthening National Food Control Systems; Food and Agriculture Organization of the United Nations/World Health Organization: Rome, Italy, 2003. [Google Scholar]
- Wu, L. China’s approach to food safety risk management. Red Flag Manuscr. 2019, 19, 35–36. (In Chinese) [Google Scholar]
- Han, S.; Roy, P.K.; Hossain, M.I.; Byun, K.-H.; Choi, C.; Ha, S.-D. COVID-19 pandemic crisis and food safety: Implications and inactivation strategies. Trends Food Sci. Technol. 2021, 109, 25–36. [Google Scholar] [CrossRef] [PubMed]
- Liu, P.; Yang, M.; Zhao, X.; Guo, Y.; Wang, L.; Zhang, J.; Lei, W.; Han, W.; Jiang, F.; Liu, W.J.; et al. Cold-chain transportation in the frozen food industry may have caused a recurrence of COVID-19 cases in destination: Successful isolation of SARS-CoV-2 virus from the imported frozen cod package surface. Biosaf. Health 2020, 2, 199–201. [Google Scholar] [CrossRef] [PubMed]
- Daniels, N.A.; Bergmire-Sweat, D.A.; Schwab, K.J.; Hendricks, K.A.; Reddy, S.; Rowe, S.H.; Fankhauser, R.L.; Monroe, S.S.; Atmar, R.L.; Glass, R.I.; et al. A foodborne outbreak of gastroenteritis associated with Norwalk-like viruses: First molecular traceback to deli sandwiches contaminated during preparation. J. Infect. Dis. 2000, 181, 1467–1470. [Google Scholar] [CrossRef]
- Gao, X.; Shi, X.; Guo, H.; Liu, Y. To buy or not buy food online: The impact of the COVID-19 epidemic on the adoption of e-commerce in China. PLoS ONE 2020, 15, e237900. [Google Scholar] [CrossRef]
- Madan, A.; Bindal, S.; Gupta, A.K. Social distancing as risk reduction strategy during COVID-19 pandemic: A study of Delhi-NCT, India. Int. J. Disaster Risk Reduct. 2021, 63, 102468. [Google Scholar] [CrossRef]
- Mansilla Dominguez, J.M.; Font Jimenez, I.; Belzunegui Eraso, A.; Peña Otero, D.; Díaz Pérez, D.; Recio Vivas, A.M. Risk perception of COVID-19 community transmission among the spanish population. Int. J. Environ. Res. Public Health 2020, 17, 8967. [Google Scholar] [CrossRef]
- Wang Weiquan, T.D. Regional Emergency Coordination Mechanism and Efficiency Optimization in COVID-19 Prevention and Control. J. Shenzhen Univ. 2020, 2, 117–123. (In Chinese) [Google Scholar]
- Cipolletta, S.; Andreghetti, G.R.; Mioni, G. Risk Perception towards COVID-19: A Systematic Review and Qualitative Synthesis. Int. J. Environ. Res. Public Health 2022, 19, 4649. [Google Scholar] [CrossRef] [PubMed]
- Bosa, I.; Castelli, A.; Castelli, M.; Ciani, O.; Compagni, A.; Galizzi, M.M.; Garofano, M.; Ghislandi, S.; Giannoni, M.; Marini, G.; et al. Corona-regionalism? Differences in regional responses to COVID-19 in Italy. Health Policy 2021, 125, 1179–1187. [Google Scholar] [CrossRef] [PubMed]
- Yang, H.L.; Wu, Y.Y.; Yao, Y.D.; Zhang, S.; Zhang, S.; Xie, L.; Li, Z.; Tang, L. How to Reach a Regional Cooperation Mechanism to Deal with the Epidemic: An Analysis from the Game Theory Perspective. Front. Public Health 2021, 9, 738184. [Google Scholar] [CrossRef] [PubMed]
- Ma, N.L.; Peng, W.; Soon, C.P.; Hassim, M.F.N.; Misbah, S.; Rahmat, Z.; Yong, W.T.L.; Sonne, C. COVID-19 pandemic in the lens of food safety and security. Environ. Res. 2021, 195, 110405. [Google Scholar] [CrossRef]
- Rogers, R.W. Cognitive and physiological processes in fear appeals and attitude change: A revised theory of protection motivation. Soc. Psychophysiol. 1983, 19, 153–176. [Google Scholar]
- Zanetta, L.D.; Hakim, M.P.; Gastaldi, G.B.; Seabra, L.M.J.; Rolim, P.M.; Nascimento, L.G.P.; Medeiros, C.O.; da Cunha, D.T. The use of food delivery apps during the COVID-19 pandemic in Brazil: The role of solidarity, perceived risk, and regional aspects. Food Res. Int. 2021, 149, 110671. [Google Scholar] [CrossRef]
- Prentice, C.; Quach, S.; Thaichon, P. Antecedents and consequences of panic buying: The case of COVID-19. Int. J. Consum. Stud. 2020, 46, 3–364. [Google Scholar] [CrossRef]
- Liberman, N.; Trope, Y. The role of feasibility and desirability considerations in near and distant future decisions: A test of temporal construal theory. J. Personal. Soc. Psychol. 1998, 75, 5–18. [Google Scholar] [CrossRef]
- Xu, J.; Gao, M.; Zhang, Y. The variations in individual consumption change and the substitution effect under the shock of COVID-19: Evidence from payment system data in China. Growth Chang. 2021, 52, 990–1010. [Google Scholar] [CrossRef]
- Cheong, F.; Law, R. Will Macau’s Restaurants Survive or Thrive after Entering the O2O Food Delivery Platform in the COVID-19 Pandemic? Int. J. Environ. Res. Public Health 2022, 19, 5100. [Google Scholar] [CrossRef]
- Willems, K.; Verhulst, N.; Brengman, M. How COVID-19 could accelerate the adoption of new retail technologies and enhance the (E-) servicescape. In The Future of Service Post-COVID-19 Pandemic, Volume 2: Transformation of Services Marketing; Springer: Singapore, 2021; pp. 103–134. [Google Scholar]
- Watanabe, T.; Omori, Y. Online consumption during and after the COVID 19 pandemic: Evidence from Japan. In The Impact of COVID-19 on E-Commerce; Proudpen: London, UK, 2020; Volume 10, pp. 971–978. [Google Scholar]
- Habib, S.; Hamadneh, N.N. Impact of Perceived Risk on Consumers Technology Acceptance in Online Grocery Adoption amid COVID-19 Pandemic. Sustainability 2021, 13, 10221. [Google Scholar] [CrossRef]
- Poon, W.C.; Tung, S.E.H. The rise of online food delivery culture during the COVID-19 pandemic: An analysis of intention and its associated risk. Eur. J. Manag. Bus. Econ. 2022. [Google Scholar] [CrossRef]
- Mehrolia, S.; Alagarsamy, S.; Solaikutty, V.M. Customers response to online food delivery services during COVID-19 outbreak using binary logistic regression. Int. J. Consum. Stud. 2021, 45, 396–408. [Google Scholar] [CrossRef]
- Anelich, L.E.C.M.; Lues, R.; Farber, J.M.; Parreira, V.R. SARS-CoV-2 and Risk to Food Safety. Front. Nutr. 2020, 7, 7580551. [Google Scholar] [CrossRef]
- Min, S.; Xiang, C.; Zhang, X. Impacts of the COVID-19 pandemic on consumers’ food safety knowledge and behavior in China. J. Integr. Agric. 2020, 19, 2926–2936. [Google Scholar] [CrossRef] [PubMed]
- Duda-Chodak, A.; Lukasiewicz, M.; Zięć, G.; Florkiewicz, A.; Filipiak-Florkiewicz, A. COVID-19 pandemic and food: Present knowledge, risks, consumers fears and safety. Trends Food Sci. Technol. 2020, 105, 145–160. [Google Scholar] [CrossRef]
- Zhu, X.; Yuan, X.; Zhang, Y.; Liu, H.; Wang, J.; Sun, B. The global concern of food security during the COVID-19 pandemic: Impacts and perspectives on food security. Food Chem. 2021, 370, 130830. [Google Scholar] [CrossRef]
- Byrd, K.; Liu, Y.; Fan, A.; Her, E.; Almanza, B.; Leitch, S. Consumers’ self-protection practices related to consuming take-out/delivery restaurant foods during the COVID-19 pandemic. J. Acad. Nutr. Diet. 2021, 121, A51. [Google Scholar] [CrossRef]
- Ali, S.H.; Keil, R. Global cities and the spread of infectious disease: The case of severe acute respiratory syndrome (SARS) in Toronto, Canada. Urban Stud. 2006, 43, 491–509. [Google Scholar] [CrossRef]
- Teerawattananon, Y.; Dabak, S.V.; Isaranuwatchai, W.; Lertwilairatanapong, T.; Shafie, A.A.; Suwantika, A.A.; Oh, C.; Srisasalux, J.; Cheanklin, N. What Can We Learn from Others to Develop a Regional Centre for Infectious Diseases in ASEAN? Comment on “Operationalising Regional Cooperation for Infectious Disease Control: A Scoping Review of Regional Disease Control Bodies and Networks”. Int. J. Health Policy Manag. 2022, 11, 3141–3144. [Google Scholar] [CrossRef]
- Liu, L.; Shi, M.; Xiao, B. Impact of COVID-19 epidemic on purchase channel selection of consumers in China. J. China Agric. Univ. 2021, 7, 272–284. (In Chinese) [Google Scholar]
- Meng, X. Research on Public Risk Perception and Response Behavior in Low-risk Areas and Medium/High-risk Areas Under the COVID-19 Epidemic. Med. Soc. 2022, 4, 85–89. [Google Scholar] [CrossRef]
- Caserotti, M.; Girardi, P.; Rubaltelli, E.; Tasso, A.; Lotto, L.; Gavaruzzi, T. Associations of COVID-19 risk perception with vaccine hesitancy over time for Italian residents. Soc. Sci. Med. 2021, 272, 113688. [Google Scholar] [CrossRef] [PubMed]
- Van Asselt, E.D.; Meuwissen, M.P.M.; van Asseldonk, M.A.P.M.; Teeuw, J.; van der Fels-Klerx, H.J. Selection of critical factors for identifying emerging food safety risks in dynamic food production chains. Food Control 2010, 21, 919–926. [Google Scholar] [CrossRef] [PubMed]
- Scarpin, M.; Scarpin, J.E.; Musial, N.; Nakamura, W.T. The implications of COVID-19: Bullwhip and ripple effects in global supply chains. Int. J. Prod. Econ. 2022, 251, 108523. [Google Scholar] [CrossRef]
- Ha, T.M.; Shakur, S.; Pham Do, K.H. Linkages among food safety risk perception, trust and information: Evidence from Hanoi consumers. Food Control 2020, 110, 106965. [Google Scholar] [CrossRef]
- Wan, J.M.; Ichinose, G.; Small, M.; Sayama, H.; Moreno, Y.; Cheng, C. Multilayer networks with higher-order interaction reveal the impact of collective behavior on epidemic dynamics. Chaos Soliton Fract. 2022, 164, 112735. [Google Scholar] [CrossRef]
- Kohler, U.; Karlson, K.B.; Holm, A. Comparing Coefficients of Nested Nonlinear Probability Models. Stata J. 2011, 11, 420–438. [Google Scholar] [CrossRef]
- Karlson, K.B.; Holm, A.; Breen, R. Comparing Regression Coefficients Between Models using Logit and Probit: A New Method. Sociol. Methodol. 2012, 42, 286–313. [Google Scholar] [CrossRef]
- Baron, R.M.; Kenny, D.A. The Moderator-mediator Variable Distinction in Social Psychological Research: Conceptual, Strategic, and Statistical Considerations. J. Personal. Soc. Psychol. 1986, 51, 1173–1182. [Google Scholar] [CrossRef]
- Carney, C. P04 Mediators of the association between socio-economic position and type two diabetes using the British Household Panel Survey. J. Epidemiol. Community Health 2016, 70, A52–A55. [Google Scholar] [CrossRef]
- Bai, L.; Wang, Y.; Wang, Y.; Wu, Y.; Li, N.; Liu, Z. Controlling COVID-19 transmission due to contaminated imported frozen food and food packaging. China CDC Weekly 2021, 3, 30. [Google Scholar] [CrossRef] [PubMed]
- Velebit, B.; Milojevic, L.; Jankovic, V.; Lakicevic, B.; Baltic, T.; Nikolic, A.; Grkovic, N. In Surface adsorption and survival of SARS-CoV-2 on frozen meat. IOP Conf. Ser. Earth Environ. Sci. 2021, 854, 12101. [Google Scholar] [CrossRef]
- Zhu, W.W.; Wang, X.; Li, H.Z. Multi-Modal Deep Analysis for Multimedia. IEEE Trans. Circuits Syst. Video Technol. 2020, 30, 3740–3764. [Google Scholar] [CrossRef]
- Oster, E. Unobservable Selection and Coefficient Stability: Theory and Evidence. J. Bus. Econ. Stat. 2019, 37, 187–204. [Google Scholar] [CrossRef]
- Shi, J.Z.; Wen, Z.Y.; Zhong, G.X.; Yang, H.; Wang, C.; Huang, B.; Liu, R.; He, X.; Shuai, L.; Sun, Z.; et al. Susceptibility of ferrets, cats, dogs, and other domesticated animals to SARS-coronavirus 2. Science 2020, 368, 1016. [Google Scholar] [CrossRef] [PubMed]
Sample Area | P Province, C City | P Province, not C City | Outside P Province | Total | ||||
---|---|---|---|---|---|---|---|---|
Number | % | Number | % | Number | % | Number | % | |
First stage | 8 | 1.08 | 4 | 0.54 | 204 | 27.49 | 216 | 29.11 |
Second stage | 113 | 15.23 | 103 | 13.88 | 310 | 41.78 | 526 | 70.89 |
Total | 121 | 16.31 | 107 | 14.42 | 514 | 69.27 | 742 | 100 |
Variable | Variable Definition | Mean | Standard Deviation |
---|---|---|---|
Dependent variable | |||
Food purchased online source of COVID-19 | “Degree of risk of COVID-19 from food purchased online ”: 1 = no risk at all; 2 = little risk; 3 = neutral; 4 = higher; 5 = very high risk | 3.24 | 0.88 |
Online purchase higher COVID-19 risk | “Degree of agreement that online-purchased food is more susceptible to COVID-19 contamination than off-line food”: 1 = completely disagree; 2 = basically disagree; 3 = neutral; 4 = agree; 5 = totally agree | 3.27 | 1.06 |
Key explanatory variable | |||
COVIDRi | Respondent location. 1 = other regions of non-P province; 2 = P province, but not C city; 3 = C city | 1.47 | 0.76 |
COVIDC | 1 = the city with the major epidemic (C city, P province); 0 = other | 0.163 | 0.37 |
Mechanism variables | |||
Packaging | “Degree of agreement that online food ingredients or packaging surfaces may contain COVID-19/or its variants”: completely disagree = 1; basically disagree = 2; neutral = 3; agree = 4; total agree = 5 | 3.76 | 0.86 |
Social media use | “Do you use online social media to learn about COVID-19?”, yes = 1; no = 0 | 0.57 | 0.50 |
Control variables | |||
Gender | Male = 1; Female = 2 | 1.54 | 0.50 |
Age | Younger than 22 = 1; 23 to 30 = 2; 31–40 = 3; 41 to 50 = 4; older than 51 = 5 | 2.78 | 1.02 |
Education | 1= less than high school; 2 = college; 3 = undergraduate; 4 = Master; 5 = Doctoral degree | 3.11 | 0.89 |
Net monthly income | Less than 2000 yuan = 1; 2001–4000 yuan = 2; 4001–6000 yuan = 3; 6001–8000 yuan = 4; 8001–12,000 yuan = 5; 12,001–20,000 yuan = 6; more than 20,001 yuan = 7 | 4.14 | 1.63 |
Household size | 1 person =1; 2 persons = 2; 3 persons = 3; 4 persons = 4; 5 or more = 5 | 3.31 | 1.05 |
Pregnant female in the family | 1 = yes; 0 = no | 0.01 | 0.16 |
Older than 60 years | 1 = yes = 1; 0 = no | 0.25 | 0.43 |
Variable Name | Description | Frequency | Percentage |
---|---|---|---|
Gender | Male | 339 | 45.69% |
Female | 403 | 54.31% | |
Age | Younger than 22 | 64 | 8.62% |
23 to 30 years old | 257 | 34.64% | |
31 to 40 years old | 241 | 32.48% | |
41 to 50 years old | 140 | 18.87% | |
older than 51 | 40 | 23.18% | |
Education | Less than high school | 41 | 5.79% |
College | 75 | 10.11% | |
Undergraduate | 435 | 58.63% | |
Master | 134 | 18.06% | |
Doctoral degree | 55 | 7.41% | |
Net monthly income | <2000 yuan | 74 | 9.97% |
2001–4000 yuan | 59 | 7.95% | |
4001–6000 yuan | 98 | 13.21% | |
6001–8000 yuan | 153 | 20.62% | |
8001–12,000 yuan | 203 | 27.36% | |
12,001–20,000 yuan | 124 | 16.71% | |
>20,001 yuan | 31 | 4.18% | |
Household size | 1 person | 38 | 5.12% |
2 persons | 96 | 12.94% | |
3 persons | 328 | 44.2% | |
4 persons | 161 | 21.7% | |
5 or more persons | 119 | 16.04% | |
Pregnant female in the family | Yes | 10 | 1.35% |
No | 732 | 98.65% | |
Older than 60 years | Yes | 184 | 24.8% |
No | 558 | 75.2% |
Variable Name | Estimated Coefficients | |||
---|---|---|---|---|
(1) | (2) | (3) | (4) | |
COVID-19 from online food purchase | COVID-19 from online food purchase | Online purchase riskier than offline purchase | Online purchase riskier than offline purchase | |
Regional effect | 0.359 *** | - | 0.198 ** | - |
(3.75) | (2.20) | |||
City effect | - | 0.533 *** | - | 0.473 *** |
(2.77) | (2.59) | |||
Packaging | 0.866 *** | 0.869 *** | 0.631 *** | 0.630 *** |
(9.72) | (9.75) | (7.81) | (7.80) | |
Social media use | 0.314 ** | 0.338 ** | 0.320 ** | 0.323 ** |
(2.15) | (2.32) | (2.27) | (2.30) | |
Gender | 0.241 * | 0.240* | −0.159 | −0.144 |
(1.71) | (1.70) | (−1.18) | (−1.06) | |
Age | 0.023 | 0.010 | 0.062 | 0.059 |
(0.31) | (0.13) | (0.88) | (0.84) | |
Education | −0.027 | −0.050 | −0.035 | −0.044 |
(−0.31) | (−0.58) | (−0.43) | (−0.54) | |
Net monthly income | −0.029 | −0.035 | −0.040 | −0.042 |
(−0.61) | (−0.76) | (−0.88) | (−0.93) | |
Household size | 0.108 | 0.118 | 0.024 | 0.030 |
(1.46) | (1.60) | (0.34) | (0.41) | |
Pregnant female in the family | −1.176 * | −1.154 * | 0.550 | 0.528 |
(−1.72) | (−1.68) | (0.77) | (0.75) | |
Elderly | −0.254 | −0.265 | 0.099 | 0.104 |
(−1.42) | (−1.48) | (0.57) | (0.61) | |
N | 742 | 742 | 742 | 742 |
LR chi2(11) | 142.78 *** | 136.25 *** | 82.50 *** | 84.42 *** |
Pseudo R2 | 0.078 | 0.075 | 0.038 | 0.039 |
Regional Effect | City Effect | |||
---|---|---|---|---|
(1) | (2) | (3) | (4) | |
Dependent variable | COVID-19 from online food purchase | Online purchase riskier than offline purchase | COVID-19 from online food purchase | Online purchase riskier than offline purchase |
Total effect | 0.448 *** | 0.270 *** | 0.697 *** | 0.603 *** |
(4.69) | (3.01) | (3.63) | (3.32) | |
Direct effect | 0.377 *** | 0.218 ** | 0.561 *** | 0.504 *** |
(3.96) | (2.43) | (2.92) | (2.77) | |
Indirect effect | 0.071 * | 0.052 * | 0.136 * | 0.099 * |
(1.85) | (1.83) | (1.75) | (1.74) | |
Control variables | YES | YES | YES | YES |
N | 742 | 742 | 742 | 742 |
Pseudo R2 | 0.08 | 0.04 | 0.07 | 0.04 |
Regional Effect | City Effect | |||
---|---|---|---|---|
(1) | (2) | (3) | (4) | |
Dependent variable | COVID-19 from online food purchase | Online purchase riskier than offline purchase | COVID-19 from online food purchase | Online purchase riskier than offline purchase |
Total effect | 0.398 *** | 0.255 *** | 0.601 *** | 0.570 *** |
(4.26) | (2.85) | (3.19) | (3.14) | |
Direct effect | 0.379 *** | 0.237 *** | 0.573 *** | 0.545 *** |
(4.04) | (2.63) | (3.04) | (3.00) | |
Indirect effect | 0.019 * | 0.018 * | 0.028 | 0.025 |
(1.69) | (1.67) | (1.35) | (1.32) | |
Control variables | YES | YES | YES | YES |
N | 742 | 742 | 742 | 742 |
Pseudo R2 | 0.02 | 0.01 | 0.02 | 0.01 |
(1) Ordered Probit | (2) OLS | (3) Ordered Probit | (4) OLS | |
---|---|---|---|---|
Variable Name | COVID-19 from Online Food Purchase | Online Purchase Riskier than Offline Purchase | ||
Regional effect | 0.208 *** | 0.153 *** | 0.119 ** | 0.115 ** |
(3.76) | (3.79) | (2.24) | (2.27) | |
Packaging | 0.485 *** | 0.346 *** | 0.359 *** | 0.341 *** |
(9.89) | (10.20) | (7.86) | (8.01) | |
Social media use | 0.193 ** | 0.151 ** | 0.171 ** | 0.169 ** |
(2.28) | (2.43) | (2.09) | (2.17) | |
Control variables | YES | YES | YES | YES |
N | 742 | 742 | 742 | 742 |
LRchi2/F | 141.95 *** | 15.13 *** | 80.12 *** | 8.39 *** |
PseudoR2 | 0.077 | 0.171 | 0.037 | 0.103 |
Item | (1) | (2) |
---|---|---|
COVID-19 from Online Food Purchase | Online Purchase Riskier than Offline Purchase | |
Regional measure | ||
Not P province | 0 | 0 |
(.) | (.) | |
P province, but not C city | 0.616 *** | −0.0567 |
(2.87) | (−0.28) | |
C city | 0.652 *** | 0.462 ** |
(3.31) | (2.48) | |
Packaging | 0.866 *** | 0.630 *** |
(9.73) | (7.81) | |
Social media use | 0.307 ** | 0.326 ** |
(2.10) | (2.31) | |
Control variables | YES | YES |
N | 742 | 742 |
Loglikelihood ratio | 144.59 *** | 84.50 *** |
Pseudo R2 | 0.079 | 0.039 |
Food Category | Completely Disagree | Basically Disagree | Neutral | Agree | Completely Agree | Agree and Completely Agree |
---|---|---|---|---|---|---|
Online-ordered meal | 2.43% | 13.88% | 38.54% | 34.64% | 10.51% | 45.15% |
Fresh agri-products | 1.62% | 8.89% | 26.28% | 42.32% | 20.89% | 63.21% |
Vegetables and fruits | 2.7% | 14.82% | 31.4% | 35.98% | 15.09% | 51.07% |
Frozen food | 1.89% | 10.51% | 22.91% | 39.08% | 25.61% | 64.69% |
Shelf-stable food | 5.53% | 19.68% | 36.12% | 29.51% | 9.16% | 38.67% |
Variable | Online Ordered Meal | Fresh Agri-Products | Vegetables and Fruits | Frozen Food | Shelf-Stable Food |
---|---|---|---|---|---|
Marginal Effects (dy/dx) | |||||
Regional effect | 0.0186 ** | 0.0521 *** | 0.0117 | 0.0315* | 0.0106 |
(2.20) | (3.78) | (1.08) | (1.96) | (1.44) | |
Packaging | 0.100 *** | 0.157 *** | 0.130 *** | 0.178 *** | 0.0833 *** |
(8.71) | (11.73) | (10.34) | (12.58) | (7.84) | |
Social media | −0.000834 | 0.0408 * | 0.0362 ** | 0.0195 | 0.0159 |
(−0.07) | (1.92) | (2.16) | (0.80) | (1.40) | |
Control variables | YES | YES | YES | YES | YES |
N | 742 | 742 | 742 | 742 | 742 |
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
Liu, W.; Cao, M.; Florkowski, W.J. The Impact of Regional COVID-19 Outbreak on Consumers’ Risk Perception of Purchasing Food Online. Healthcare 2023, 11, 1571. https://doi.org/10.3390/healthcare11111571
Liu W, Cao M, Florkowski WJ. The Impact of Regional COVID-19 Outbreak on Consumers’ Risk Perception of Purchasing Food Online. Healthcare. 2023; 11(11):1571. https://doi.org/10.3390/healthcare11111571
Chicago/Turabian StyleLiu, Weijun, Mengzhen Cao, and Wojciech J. Florkowski. 2023. "The Impact of Regional COVID-19 Outbreak on Consumers’ Risk Perception of Purchasing Food Online" Healthcare 11, no. 11: 1571. https://doi.org/10.3390/healthcare11111571