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Article

The Effect of COVID-19 on Food Consumers’ Channel Purchasing Behaviors: An Empirical Study from Poland

1
Department of Computer Engineering in Management, The Faculty of Management, Rzeszow University of Technology, Al. Powstańców Warszawy 12, 35-959 Rzeszow, Poland
2
Department of Marketing, The Faculty of Management, Rzeszow University of Technology, Al. Powstańców Warszawy 12, 35-959 Rzeszow, Poland
3
Department of Quantitative Methods, The Faculty of Management, Rzeszow University of Technology, Al. Powstańców Warszawy 12, 35-959 Rzeszow, Poland
4
Institute of Economics and Finance, University of Rzeszow, 2 Ćwiklińskiej, 35-601 Rzeszow, Poland
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(5), 4661; https://doi.org/10.3390/su15054661
Submission received: 2 February 2023 / Revised: 26 February 2023 / Accepted: 1 March 2023 / Published: 6 March 2023
(This article belongs to the Special Issue Economic and Social Consequences of the COVID-19 Pandemic)

Abstract

:
The aim of the research was to analyze consumer behavior regarding changes in the place where the food was purchased during the COVID-19 pandemic. An analysis of the relationship between sociodemographic characteristics and changes in the frequency of food purchases in specified retail outlets was presented. Different consumer reactions to the threats and restrictions that resulted from the COVID-19 pandemic were found. Gender had a statistically significant impact on the changes in the place of purchase in the case of supermarkets and discount stores. Women were reported to be more cautious about the risks associated with shopping in supermarkets and discount stores. Age had a statistically significant impact on the frequency of grocery shopping in small local/rural stores, in medium-sized self-service stores, in supermarkets and in discount stores. Among the group of respondents aged 46 and over, greater trust in larger commercial units was observed. Education had a statistically significant impact on the frequency of shopping for groceries only in supermarkets. In turn, the place of residence had a statistically significant impact on the change in the frequency of making purchases in medium-sized self-service stores and discount stores. The results of our own research are not unambiguous, but they indicate certain tendencies in the perception of health safety when shopping among various social groups.

1. Introduction

The COVID-19 pandemic has had many repercussions for the global economy, changing the way businesses operate as well as consumer behavior [1,2]. Due to the transmission of the disease by droplets, through direct contact with infected people, the governments of many countries have introduced restrictions on direct contact between people. At the same time, the food sector is almost the only sector that is not subject to such restrictions and the availability of food has not been disrupted [3,4,5,6]. However, it does not mean that concerns about food safety and food purchases are of minor importance to customers. The pandemic caused not only changes in the sanitary and hygienic rules of society, but also secondary psychological effects and behavioral changes related to, among other things, eating or purchasing food [7,8]. In fact, oral transmission of the virus is very unlikely. Coronaviruses cannot proliferate in foods as they need a living animal or human host to replicate. Even the small risk of foodborne infection can be reduced by heating food, as viruses are sensitive to higher temperatures. At the same time, the SARS-CoV virus is stable at low temperatures, which may allow its transmission in fresh products exposed to contact with virus-containing droplets before freezing or chilling, or through fresh fruit and vegetables. This may suggest to consumers the possibility of transmission of the virus from food products in the cold chain [5,9]. It may also determine consumer behavior, causing fears for one’s own health safety. Although infection with SARS-CoV viruses in this way is unlikely, such a probability cannot be completely ruled out [10,11,12].
The COVID-19 pandemic has affected consumer behavior around the world. There are many studies on the changes in eating and grocery shopping behavior observed during the pandemic. However, to the best of our knowledge, there is a lack of research that focuses on the changes in the choice of places to shop for food and the possibility of their preservation. Consumer concerns, as well as sanitary restrictions, could influence shopping behavior, changing it or strengthening it for longer. Therefore, the aim of the research was to analyze consumer behavior regarding changes in the place where food was purchased during the COVID-19 pandemic.

2. Review of the Literature

Numerous research studies have assessed the impact of the COVID-19 pandemic on aspects related to food consumption, diet quality, food safety, food availability, food supply, food demand, food prices and the functioning of international and national food supply chains [4,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28]. Very few studies address the impact of the pandemic on consumer purchasing behavior on an individual level [29,30,31,32] and, to the best of our knowledge, there is a clear lack of research on changes in purchasing behavior, especially literature on the choice of consumer purchasing channels during the pandemic and the determinants of these behaviors. The switching to other purchase habits that took place during the COVID-19 pandemic left a permanent mark on the behavior of customers, including the choice of the purchase channel, forcing enterprises to change their business models. Food products represent a unique product category, satisfying not only primary needs, such as satisfying hunger and nourishing the body, but also improving the well-being of consumers. Currently, food also responds to psychological and socio-cultural needs, which is why food choices are increasingly complex [33].
Purchasing decisions are an inseparable element of consumer behavior [34,35]. The making of purchasing decisions by consumers is a complex process in which consumer behavior is determined by many factors [36,37,38,39,40,41,42,43,44,45,46,47,48,49,50]. Among the factors considered by consumers in the purchasing process is the place where they make a purchase [51]. The process of choosing a place to make a purchase is as interesting and complex as the process of making a decision about choosing a specific product [52].
The changes caused by the pandemic were reflected in consumer behavior [53,54]. Previous studies have shown that consumer expectations toward brands and their purchase channels have changed and buyers have started to evaluate companies from a new perspective [55,56]. The recent literature shows that consumers, fearing a shortage of products, demonstrated food hoarding behavior that significantly changed their daily shopping habits [57,58]. Jeżewska-Zychowicz et al. [59] pay attention to the fear of limited access to food, which increased the likelihood of buying larger amounts of it. In addition, health concerns have resulted in a change in purchasing channel habits and an increased demand for alternative purchase channels, thereby enabling consumers to meet their needs effectively and safely [60,61,62]. Digital methods of purchasing have become a popular tool for purchasing products [63,64]. Amberg & Fogarassy [65] and Gao et al. [66] discovered that the amount of confirmed COVID-19 cases is positively correlated to the possibility of buying foods online. Online shopping has been extended to many product categories that were not previously available to consumers in the digital space [67]. COVID-19 forced older consumers and those living in rural areas to engage in online shopping [57,68].
The shopping environment plays a large role in consumers’ decisions on what type of retail facility to choose [69]. Numerous studies are concerned with the role of the store atmosphere and consumers’ responses [70,71,72,73,74]. Currently, an interesting factor shaping the shopping environment is the COVID-19 pandemic. A study by Grashuis et al. [75] found that as COVID-19 spread at an increasing rate, consumers were less likely to buy food from brick-and-mortar grocery stores. Fearing congestion, consumers have moved away from close contact with other consumers, shifting from large-format retail to small local retailers and shopping [76]. There has also been an increased interest in buying local food [77,78], and the importance of online food shopping has increased [79], mainly in high-income countries [80]. In a study, Ellison et al. [81] analyzed the shopping habits of American consumers, revealing that food during the COVID-19 pandemic was bought mainly in grocery stores, then in corner stores and online. The same study showed that the level of importance varied depending on the value of the food, such as taste, price, nutritional value, ease of preparation and storage, with taste being the most important factor driving food purchases [81]. On the other hand, Italian consumers bought food in supermarkets, then grocery stores, local markets and then online [82]. In contrast, in Romania, during the pandemic, buyers chose to buy groceries in stores close to home, directly from producers, rather than in larger stores such as supermarkets or hypermarkets [83]. These research studies [83] also revealed that purchases of food from stores close to home were not influenced by household income, level of education or the presence of children, while purchases of food products directly from producers were not influenced by gender, income or level of education. Additionally, in the study by Brumă et al. [84], it was found that Romanian consumers felt safer in open spaces, like food markets, compared to closed spaces, be they supermarkets or grocery stores. In the research of Szymkowiak et al. [85], it was found that the perceived risk of contagion in the store had an effect on the increase in arousal and, at the same time, on the decrease in the perceived pleasure while shopping. The perception of the risk associated with the COVID-19 pandemic also reduced the frequency of food shopping [80,86,87], which may suggest more frequent use of supermarkets, allowing access to a wider range of products and greater shopping convenience. Serbian studies by Petković et al. [88] showed that the vast majority of customers combined purchasing channels and shopping became part of a complex experience. In the research of Bareja-Wawryszuk et al. [89], attention was drawn to the geographical differences in purchasing practices and consumer concerns related to the COVID-19 pandemic. As a result of the comparative analysis, a clear difference has been showed in the nature of the changes in consumer behavior between the respondents from Turkey and Poland. It has been found that the restrictions imposed during the COVID-19 pandemic had a greater impact on consumer behavior, especially for students in Turkey.
Changes in the frequency of using food services were also observed, shopping in restaurants decreased and the demand for food in retail trade increased [90], interest in healthy eating increased [91], while in the case of Giacalone et al. [92], these changes were rather unfavorable.
The results of the research presented above show that the COVID-19 pandemic had a significant impact on food shopping behavior, changes in eating habits and changes in food shopping preferences.

3. Materials and Methods

The following research was based on a quantitative approach. The Internet survey method was used. An online survey is an effective tool to study customer behavior [93,94]. The choice of this method was also necessary due to access to respondents because of the restrictions during the pandemic. This procedure made it easier for respondents to complete the survey. The instrument was developed based on the past literature [57,58,59,60,61,62,76,81,83,93]. The research was conducted from November 2021 to February 2022. A non-probability sampling method was used to select the research sample. In the choice of the research sample, the criterion of convenience was used due to the easier and cheaper way of reaching the respondents. Non-probability sampling techniques are often used in exploratory research. This type of research allows for an initial understanding of the processes and phenomena occurring in insufficiently studied populations. The data was collected using Google Forms. A link to the questionnaire was shared and sent to various organizations. Many personalized messages inviting and reminding respondents to complete the survey were sent. Before the main study, a pilot study was conducted among 50 people, which allowed us to assess the comprehensiveness, clarity and accuracy of the questions asked to the respondents. After this stage, modifications were made to some of the questions. The questionnaire included questions about: A. Changes in the frequency of food shopping during the COVID-19 pandemic with the option of selecting one of the following answers: (1) I buy without changing the frequency; (2) I do not buy; (3) I buy less often; (4) I buy more often; and a question regarding B. Changes in grocery shopping habits during the COVID-19 pandemic in brick-and-mortar stores with the option of choosing one of the following answers: (1) I go to brick-and-mortar shops without changing; (2) I am limiting going to brick-and-mortar shops; (3) I go to brick-and-mortar stores more often; (4) I do not go to stores, I shop through other channels. In total, 467 completed questionnaires were received, but after removing incomplete questionnaires, a sample of 401 respondents was accepted for the analysis. The Kruskal–Wallis ANOVA test was used in this research.
The data was collected from Polish consumers over 18 years old. Table 1 presents the demographic and social characteristics of the respondents. Table 2 presents the characteristics of the population in Poland for comparison. Compared to these data, it can be seen that, in the surveyed population, there is a smaller share of elderly people, a larger share of women and the surveyed respondents are better educated. Our research sample is similar to the structure of Internet users.

4. Results and Discussion

As a rule, people have difficulty with changing their eating behaviors, which are often habitual and resistant to change [96,97]. However, a disruption may be an impulse for favorable and unfavorable changes in eating habits [98] and their consolidation, implying changes in food distribution channels and from the consumer’s point of view, also changes shopping behavior. There is much speculation about the impact of the pandemic on food retail. Some believe that the pandemic will cause permanent structural changes in retail food purchases, e.g., it will increase the popularity of online sales. Others believe that the shock was temporary and customers will return to their old habits. At the same time, there is a consensus that the restrictions during the pandemic affected food distribution channels in different ways, causing more or less visible changes [90].
Our own research shows that consumers changed their behavior regarding places to buy food during the pandemic. Table 3 presents the results of the research on the relationship between sociodemographic characteristics and changes in the frequency of food purchases in specified retail outlets. Gender statistically significantly influenced changes in the place of purchase in the case of supermarkets (p = 0.0155) and in the case of discount stores (p = 0.0001). In the case of supermarkets, 41% of women did not change the frequency of shopping during the COVID-19 pandemic, while for men, a much higher percentage, namely as much as 64%, continued to shop at these retail outlets without changing the frequency (Figure 1). Similarly, in the case of discount stores, a greater percentage of men than women did not change their shopping habits (Figure 2). This indicates that lasting changes in purchasing behavior affected women more than men. Men can go back to their old habits faster than women. It is also worth mentioning that the statistical analysis confirmed the differences only for supermarkets and discount stores, i.e., the most popular places to buy food. This also indicates the fact that men were less concerned about security issues. Similar results were obtained in research about online shopping, where women were more cautious [99]. The differences in shopping behavior based on gender are a topic often discussed in the literature [100,101,102]. Research results suggest that women and men (both biologically and culturally) attribute different meanings and values to different types of food, which translates into gender preferences for specific types of food, food attributes or shopping behavior [103,104,105,106,107,108,109]. Gender, through interactions in shopping situations, moderates the relationship between experience and shopping behavior. In real situations, women, compared to men, pay more attention to their experience with the shopping environment [110]. Compared to males, females tend to eat healthier, have higher nutrition knowledge, have higher engagement in food-related activities and show higher preference toward food items that are commonly included in dietary guidelines [104,111,112]. At the same time, it is emphasized that women play a more important role in managing household expenses [113,114]. Therefore, changing the purchasing behavior of women from the point of view of managers of shopping facilities may be more important.
Another demographic variable considered in the research was the age of the respondents. The research emphasizes the significant impact of this variable on shopping behavior [115,116,117]. The influence of age on purchasing behavior results from the cohort theory [118]. The model of consumer socialization and cohort theory supports differences in attitudes, norms and behaviors across different social groups, such as generational groups. This is due to different life experiences. Age will have a statistically significant impact on the frequency of grocery shopping in small local/rural stores (p = 0.0407), medium-sized self-service stores (p = 0.0001), supermarkets (p = 0.0021) and discount stores (p = 0.0456) (Table 3). Among the group of respondents aged 46 and over, the smallest percentage of people who buy groceries in small local or rural shops without changing the frequency was observed (Figure 3). This may be due to concerns about their own health related to the belief that hygienic standards in this type of commercial outlet might be lower. The research of Li. et al. [119] and Marinković and Lazarević [120] shows that consumers’ perception of risk when shopping during the COVID-19 pandemic was an important factor influencing their behavior. Additionally, in the research by Brumă et al. [84], it was stated that people generally developed different personal protection strategies during the pandemic period, reacting in a responsible way to the risks imposed by the sanitary crisis. A small percentage of respondents increased the frequency of purchases in this type of trading post. Lockdown related to COVID-19 might have contributed to the increase in the attractiveness of small grocery stores, often located close to the place of residence. However, such a tendency was not observed in our own research. In the case of medium-sized self-service stores, the biggest changes in the choice of this type of retail outlet were observed in the 36–45 age group (Figure 4). This age group most often limited shopping in these places. In the case of supermarkets, the oldest people were the most loyal customers: 96% of respondents aged 56 and over used this form of retail sales with no change in shopping frequency (Figure 5). A similar situation was also noted in the case of discount stores (Figure 6). This indicates that this group of customers is very loyal to these types of retail outlets. The age component modulates the relationship between satisfaction and loyalty, so that this relationship is stronger for older consumers [121,122].
Another socio-economic variable that was analyzed was education. Education had a statistically significant effect on the frequency of shopping for groceries only in supermarkets (p = 0.0009) (Table 3). It was observed that a significant percentage of people with the lowest level of education increased the frequency of shopping in this type of commercial vessel (Figure 7). As the education level increases, the percentage of people who shop more often in this type of facility decreases. This may have resulted from the desire to avoid excessive crowds in such places. Such motives were also observed in the research by Brumă et al. [84]. The obtained results indicate the need to conduct further research on consumer perception of care for sanitary and hygienic standards, service quality, availability of a wide range of products in individual commercial facilities and the possibility of accepting lower standards in exchange for expected benefits, e.g., lower prices, the possibility of taking advantage of short distribution chains, etc.
Pandemic restrictions have reduced the frequency of shopping due to movement restrictions [90]. For this reason, the authors’ own research assessed changes in the frequency of shopping depending on the place of residence. The statistical analysis shows that only statistically significant places of residence affected the change in the frequency of shopping in medium-sized self-service stores (p = 0.0257) and discount stores (p = 0.0289) (Table 3). In the case of medium-sized self-service stores in cities with more than 300,000 residents, the smallest percentage (54%) of people made purchases without changing the frequency (Figure 8). On the other hand, in smaller towns, a much higher percentage of residents did not change the frequency of shopping in this type of facility. This may be due to the lower availability of diverse food retail outlets in these towns. In turn, in the case of discount stores, only 36% of rural residents made purchases without changing the frequency and only 9% of them increased the frequency of shopping (Figure 9). These were the smallest changes among the analyzed places of residence of the respondents. Discount stores are located in larger towns and therefore pandemic movement restrictions may have influenced decisions of rural residents regarding the places of purchases.
In the further research, attention was paid to how much sanitary restrictions related to the COVID-19 pandemic influenced the consolidation of behavioral changes and their maintenance in the conditions at the end of pandemic restrictions. The literature on the subject emphasizes the need for research on the issues of maintaining shopping habits determined by the pandemic [29,96]. Shopping habits developed during the pandemic may return to previous ones after the pandemic restrictions are eased or completely lifted, or new ones may develop. The analysis shows that the age (p = 0.001) and education of the respondents (p = 0.0001) had a statistically significant effect on changes in purchasing behavior (Table 4). The respondents’ answers indicated that the smallest changes in the frequency of shopping in brick-and-mortar stores concerned the 20–25 and 26–35 age groups. Older people have reduced the frequency of shopping in brick-and-mortar stores to a greater extent and the increase in the level of education will also affect the change in the frequency of shopping in brick-and-mortar stores (Table 5). The results of these studies suggest the need for further research on the frequency of use and importance of other household food supply channels. In particular, it seems necessary to conduct research on food purchases via the Internet and in short distribution channels. As highlighted by recent research [93,123], online shopping can represent a new model of consumption and path of agri-food development.

5. Conclusions

Consumers’ responses to the COVID-19 pandemic provide important lessons for unexpected situations like that in the future. Our research results shed light on the characteristics of changes in food purchasing behavior depending on the sociodemographic characteristics of consumers. In our own research, varied consumer reactions to the threats and restrictions resulting from the COVID-19 pandemic were found. Gender had a statistically significant impact on the changes in the place of purchase in the case of supermarkets and discount stores. A greater percentage of men than women did not change their shopping habits in these retail outlets. This indicates that women were more cautious about the risks associated with shopping in supermarkets and discount stores. This suggests that retail facility managers should consider the moderating role of gender concerning customers’ psychological feedback. Especially in the case of women, attention should be paid to investments in improving the experience in the purchasing environment and the customer service procedure in order to increase satisfaction and safety during shopping. In other words, retailers need to consider gender roles when creating differentiated experiences and use this to create a sustainable competitive advantage.
Age had a statistically significant impact on the frequency of grocery shopping in small local/rural stores in medium-sized self-service stores, in supermarkets and discount stores. Among the group of respondents aged 46 and over, the smallest percentage of people who buy groceries in small local or rural shops without changing the frequency was observed. This indicates a greater trust of this age group in larger commercial units. Education had a statistically significant impact on the frequency of shopping for groceries only in supermarkets. It was observed that a significant percentage of people with the lowest level of education increased the frequency of shopping in this type of commercial unit. In turn, the place of residence had a statistically significant impact on changes in the frequency of making purchases in medium-sized self-service stores and discount stores. The results of our own research are not unambiguous, but they indicate certain tendencies in the perception of health safety when shopping among various social groups. It should also be noted that the product purchase channel is an important place for receiving visual, auditory and olfactory stimuli and creating sensations, impressions and creating experiences with brands and the store itself. Sometimes consumers express their lifestyle and seek entertainment through shopping. The pandemic disrupted these processes, triggering various fears among consumers and changing the pattern of purchase habits. Current consumer behavior is a dynamic composition of old and new habits and patterns toward the places of purchase.
This study is somewhat limited in scope. First of all, it is a cross-sectional study on a non-representative sample of adults, with responses predominantly from the younger and better educated. In addition, the survey was targeted only to respondents with Internet access, which introduces a systematic bias. Despite its limitations, this study provides new and key findings on changing shopping patterns during the COVID-19 crisis and acts as an impetus for more systematic research. Future research should address changes in food distribution channels and consumer perceptions of sanitation and hygiene in different food distribution channels.
Changes in consumer behavior during the COVID-19 pandemic from the perspective of a country in Central and Eastern Europe provide useful information for researchers and practitioners, in particular for enterprises to implement appropriate business models. Getting to know the consumer purchasing channels and the conditions affecting this process is important both from the point of view of production and commercial enterprises operating in the food industry. In the case of production companies, it allows for effective management of distribution channels. On the other hand, it allows trading companies to better adjust their commercial offerings to the requirements of buyers. In addition, knowledge on this subject can help them prepare a marketing campaign and support the involvement of enterprises in building customer relationships at the point of sale by removing fears, ensuring safety and appropriately designing the store atmosphere and creative customer experience management in these specific (post-)covid times. This study can also support companies’ efforts to strengthen customer loyalty and satisfaction. This study reveals changes in consumer behavior toward purchasing channels, giving an insight into future trends.
The contribution can be useful not only for enterprises and stores in designing effective marketing strategies and hygiene and sanitary security systems, but also can be helpful for public administration in shaping food safety policy. Therefore, understanding consumer behavior is an important aspect in creating sustainable strategies in the food sector.
Future research should continue to follow whether switching to other purchase channels is a sustainable change. Reactions to the pandemic may differ from the cultural factors that determine shopping habits and should be explored in future research.

Author Contributions

Conceptualization, B.K., L.W. and D.K.; methodology, B.K., L.W., K.C.-L. and D.K.; software, K.C.-L.; validation, B.K. and K.C.-L.; formal analysis, B.K., L.W., D.K., K.C.-L. and P.O.; investigation, B.K., L.W., D.K., K.C.-L. and P.O.; resources, B.K. and D.K.; data curation, B.K.; writing—original draft preparation, B.K., L.W., D.K., P.O. and A.W.; writing—review and editing, B.K., L.W., D.K., K.C.-L., P.O. and A.W.; visualization, B.K. and D.K.; supervision, B.K.; project administration, B.K.; funding acquisition, B.K., L.W., D.K., K.C.-L., P.O. and A.W.; All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable. Approval for the study was not required in accordance with the local/national legislation.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Kosieradzka, A.; Smagowicz, J.; Szwed, C. Ensuring the business continuity of production companies in conditions of COVID-19 pandemic in Poland—Applied measures analysis. Int. J. Disaster Risk Reduct. 2022, 72, 102863. [Google Scholar] [CrossRef] [PubMed]
  2. Cruz-Cárdenas, J.; Zabelina, E.; Guadalupe-Lanas, J.; Palacio-Fierro, A.; Ramos-Galarza, C. COVID-19, consumer behavior, technology, and society: A literature review and bibliometric analysis. Technol. Forecast. Soc. Chang. 2021, 173, 121179. [Google Scholar] [CrossRef]
  3. Béné, C.; Bakker, D.; Chavarro, M.J.; Even, B.; Melo, J.; Sonneveld, A. Global assessment of the impacts of COVID-19 on food security. Glob. Food Secur. 2021, 31, 100575. [Google Scholar] [CrossRef]
  4. Gutiérrez-Villar, B.; Melero-Bolaños, R.; Carbonero-Ruz, M. COVID-19’s First Wave: Examination of Impact on Food Purchasing Behaviour in the Eurozone. Foods 2021, 10, 1179. [Google Scholar] [CrossRef] [PubMed]
  5. Arnaboldi, S.; Mangeri, L.; Galuppini, E.; Righi, F.; Tilola, M.; Scarazzato, A.; Bertasi, B.; Finazzi, G.; Varisco, G.; Filipello, V.; et al. Is SARS-CoV-2 a Concern for Food Safety? A Very Low Prevalence from a Food Survey during the COVID-19 Pandemic in Northern Italy. Foods 2022, 11, 2096. [Google Scholar] [CrossRef]
  6. Vatta, K.; Bhogal, S.; Green, A.S.; Sharma, H.; Petrie, C.A.; Dixit, S. COVID-19 Pandemic-Induced Disruptions and Implications for National Food Security and Farm Incomes: Farm-Level Evidence from Indian Punjab. Sustainability 2022, 14, 4452. [Google Scholar] [CrossRef]
  7. Oliveira, J.G.A.; Sampaio, A.C.S.; Lapenta, O.M. Impacts of COVID-19 Sanitary Cues on Hedonic Appreciation of Foods. Foods 2022, 11, 1753. [Google Scholar] [CrossRef] [PubMed]
  8. Laborde, D.; Martin, W.; Swinnen, J.; Vos, R. COVID-19 risks to global food security. Science 2020, 369, 500–502. [Google Scholar] [CrossRef]
  9. Feng, X.-L.; Li, B.; Lin, H.-F.; Zheng, H.-Y.; Tian, R.-R.; Luo, R.-H.; Liu, M.-Q.; Jiang, R.-D.; Zheng, Y.-T.; Shi, Z.-L.; et al. Stability of SARS-CoV-2 on the Surfaces of Three Meats in the Setting That Simulates the Cold Chain Transportation. Virol. Sin. 2021, 36, 1069–1072. [Google Scholar] [CrossRef]
  10. Zang, R.; Gomez Castro, M.F.; McCune, B.T.; Zeng, Q.; Rothlauf, P.W.; Sonnek, N.M.; Liu, Z.; Brulois, K.F.; Wang, X.; Greenberg, H.B.; et al. TMPRSS2 and TMPRSS4 promote SARS-CoV-2 infection of human small intestinal enterocytes. Sci. Immunol. 2020, 5, 1–10. [Google Scholar] [CrossRef]
  11. Chin, A.W.H.; Chu, J.T.S.; Perera, M.R.A.; Hui, K.P.Y.; Yen, H.-L.; Chan, M.C.W.; Peiris, M.; Poon, L.L.M. Stability of SARS-CoV-2 in different environmental conditions. Lancet Microbe 2020, 1, e10. [Google Scholar] [CrossRef] [PubMed]
  12. Dibner, J.J. Direct COVID-19 infection of enterocytes: The role of hypochlorhydria. Am. J. Infect. Control 2021, 49, 385–386. [Google Scholar] [CrossRef]
  13. Aday, S.; Aday, M.S. Impact of COVID-19 on the food supply chain. Food Qual. Saf. 2020, 4, 167–180. [Google Scholar] [CrossRef]
  14. Arouna, A.; Soullier, G.; Mendez del Villar, P.; Demont, M. Policy options for mitigating impacts of COVID-19 on domestic rice value chains and food security in West Africa. Glob. Food Secur. 2020, 26, 100405. [Google Scholar] [CrossRef]
  15. Galanakis, C.M. The Food Systems in the Era of the Coronavirus (COVID-19) Pandemic Crisis. Foods 2020, 9, 523. [Google Scholar] [CrossRef] [Green Version]
  16. Deaton, B.J.; Deaton, B.J. Food security and Canada’s agricultural system challenged by COVID-19. Can. J. Agric. Econ. 2020, 68, 143–149. [Google Scholar] [CrossRef]
  17. Hobbs, J.E. Food supply chains during the COVID-19 pandemic. Can. J. Agric. Econ. 2020, 68, 171–176. [Google Scholar] [CrossRef] [Green Version]
  18. Emiliata, T.; Asem, P.; Levi, J.; Iosua, T.; Ioane, A.; Seupoai, V.; Eteuati, M.; Solipo, P.; Nuu, T.; Boodoosingh, R. Capturing the Experiences of Samoa: The Changing Food Environment and Food Security in Samoa during the COVID-19 Pandemic. Oceania 2020, 90, 116–125. [Google Scholar] [CrossRef]
  19. Lopes de Sousa Jabbour, A.B.; Chiappetta Jabbour, C.J.; Hingley, M.; Vilalta-Perdomo, E.L.; Ramsden, G.; Twigg, D. Sustainability of supply chains in the wake of the coronavirus (COVID-19/SARS-CoV-2) pandemic: Lessons and trends. Mod. Supply Chain Res. Appl. 2020, 2, 117–122. [Google Scholar] [CrossRef]
  20. Fan, S.; Teng, P.; Chew, P.; Smith, G.; Copeland, L. Food system resilience and COVID-19—Lessons from the Asian experience. Glob. Food Secur. 2021, 28, 100501. [Google Scholar] [CrossRef]
  21. Marcuta, L.; Popescu, A.; Tindeche, C.; Smedescu, D.; Marcuta, A. 2021, Food Security of The European Union and The Influence of COVID-19. Sci. Papers Ser. Manag. Econ. Eng. Agric. Rural Dev. 2021, 21, 383–392. Available online: http://managementjournal.usamv.ro/pdf/vol.21_2/Art46.pdf (accessed on 12 December 2022).
  22. Picchioni, F.; Goulao, L.F.; Roberfroid, D. The impact of COVID-19 on diet quality, food security and nutrition in low and middle income countries: A systematic review of the evidence. Clin. Nutr. 2021, 41, 2955–2964. [Google Scholar] [CrossRef]
  23. Chenarides, L.; Manfredo, M.; Richards, T.J. COVID-19 and Food Supply Chains. Appl. Econ. Perspect. Policy 2021, 43, 270–279. [Google Scholar] [CrossRef]
  24. Alsuwailem, A.A.; Salem, E.; Saudagar, A.K.J.; AlTameem, A.; AlKhathami, M.; Khan, M.B.; Hasanat, M.H.A. Impacts of COVID-19 on the Food Supply Chain: A Case Study on Saudi Arabia. Sustainability 2022, 14, 254. [Google Scholar] [CrossRef]
  25. Codjia, C.O.; Saghaian, S.H. Determinants of Food Expenditure Patterns: Evidence from U.S. Consumers in the Context of the COVID-19 Price Shocks. Sustainability 2022, 14, 8156. [Google Scholar] [CrossRef]
  26. Rokicki, T.; Bórawski, P.; Bełdycka-Bórawska, A.; Szeberényi, A.; Perkowska, A. Changes in Logistics Activities in Poland as a Result of the COVID-19 Pandemic. Sustainability 2022, 14, 10303. [Google Scholar] [CrossRef]
  27. Seuring, S.; Brandenburg, M.; Sauer, P.C.; Schünemann, D.-S.; Warasthe, R.; Aman, S.; Qian, C.; Petljak, K.; Neutzling, D.M.; Land, A.; et al. Comparing regions globally: Impacts of COVID-19 on supply chains—A Delphi study. Int. J. Oper. Prod. Manag. 2022, 42, 1077–1108. [Google Scholar] [CrossRef]
  28. Tabe-Ojong, M.P.; Gebrekidan, B.H.; Nshakira-Rukundo, E.; Börner, J.; Heckelei, T. COVID-19 in rural Africa: Food access disruptions, food insecurity and coping strategies in Kenya, Namibia, and Tanzania. Agric. Econ. 2022, 53, 719–738. [Google Scholar] [CrossRef]
  29. Goddard, E. The impact of COVID-19 on food retail and food service in Canada: Preliminary assessment. Can. J. Agric. Econ. 2020, 68, 157–161. [Google Scholar] [CrossRef]
  30. Silva, F.B.; Osborn, D.E.; Owens, M.R.; Kirkland, T.; Moore, C.E.; Patterson, M.A.; Tucker, W.J.; Miketinas, D.C.; Davis, K.E. Influence of COVID-19 Pandemic Restrictions on College Students’ Dietary Quality and Experience of the Food Environment. Nutrients 2021, 13, 2790. [Google Scholar] [CrossRef]
  31. O’Meara, L.; Turner, C.; Coitinho, D.C.; Oenema, S. Consumer experiences of food environments during the Covid-19 pandemic: Global insights from a rapid online survey of individuals from 119 countries. Glob. Food Secur. 2022, 32, 100594. [Google Scholar] [CrossRef] [PubMed]
  32. Muresan, I.C.; Harun, R.; Brata, A.M.; Brata, V.D.; Chiciudean, D.I.; Tirpe, O.P.; Porutiu, A.; Dumitras, D.E. Factors Affecting Food Consumers’ Behavior during COVID-19 in Romania. Foods 2022, 11, 2275. [Google Scholar] [CrossRef] [PubMed]
  33. Gracia, A. Consumers’ preferences for a local food product: A real choice experiment. Empir. Econ. 2014, 47, 111–128. [Google Scholar] [CrossRef]
  34. Solomon, M.R. Buying, Having, and Being; Prentice Hall: London, UK, 1994. [Google Scholar]
  35. Kumar, A.A. Factors Influencing Customers Buying Behavior. Glob. J. Manag. Bus. Res. E Mark. 2016, 16, 30–35. Available online: https://globaljournals.org/GJMBR_Volume16/5-Factors-Influencing-Customers.pdf (accessed on 27 September 2022).
  36. Kotler, P.; Keller, K.L. Principles of Marketing Kotler, 14th ed.; Pearson Education Limited: Essex, UK, 2012. [Google Scholar]
  37. Lukman, A.; Vukasović, T. The Factors Influencing the Buying Decision of Customers Behaviour. Management 2020, 15, 221–233. [Google Scholar] [CrossRef]
  38. Kusz, D.; Kusz, B.; Hydzik, P. Changes in the Price of Food and Agricultural Raw Materials in Poland in the Context of the European Union Accession. Sustainability 2022, 14, 4582. [Google Scholar] [CrossRef]
  39. Dean, D.; Rombach, M.; Koning, W.d.; Vriesekoop, F.; Satyajaya, W.; Yuliandari, P.; Anderson, M.; Mongondry, P.; Urbano, B.; Luciano, C.A.G.; et al. Understanding Key Factors Influencing Consumers’ Willingness to Try, Buy, and Pay a Price Premium for Mycoproteins. Nutrients 2022, 14, 3292. [Google Scholar] [CrossRef]
  40. Rombach, M.; Dean, D.; Vriesekoop, F.; de Koning, W.; Aguiar, L.K.; Anderson, M.; Mongondry, P.; Oppong-Gyamfi, M.; Urbano, B.; Gómez-Luciano, C.A.; et al. Is cultured meat a promising consumer alternative? Exploring key factors determining consumer’s willingness to try, buy and pay a premium for cultured meat. Appetite 2022, 179, 106307. [Google Scholar] [CrossRef]
  41. Vandenbroele, J.; Vermeir, I.; Geuens, M.; Slabbinck, H.; Van Kerckhove, A. Nudging to get our food choices on a sustainable track. Proc. Nutr. Soc. 2020, 79, 133–146. [Google Scholar] [CrossRef]
  42. Kusz, B.; Kilar, J.; Kusz, D. Sensory Attractiveness of Local Smoked Bacons. Mod. Manag. Rev. 2018, 25, 159–169. [Google Scholar] [CrossRef]
  43. Stasi, A.; Songa, G.; Mauri, M.; Ciceri, A.; Diotallevi, F.; Nardone, G.; Russo, V. Neuromarketing empirical approaches and food choice: A systematic review. Food Res. Int. 2018, 108, 650–664. [Google Scholar] [CrossRef]
  44. Pilone, V.; Stasi, A.; Baselice, A. Quality preferences and pricing of fresh-cut salads in Italy: New evidence from market data. Br. Food J. 2017, 119, 1473–1486. [Google Scholar] [CrossRef]
  45. Kaya, I. Motivation Factors of Consumers’ Food Choice. Food Nutr. Sci. 2016, 7, 149–154. [Google Scholar] [CrossRef] [Green Version]
  46. McDermott, M.S.; Oliver, M.; Svenson, A.; Simnadis, T.; Beck, E.J.; Coltman, T.; Iverson, D.; Caputi, P.; Sharma, R. The theory of planned behaviour and discrete food choices: A systematic review and meta-analysis. Int. J. Behav. Nutr. Phys. Activity 2015, 12, 162. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  47. Dimitri, C.; Rogus, S. Food Choices, Food Security, and Food Policy. J. Int. Aff. 2014, 67, 19–31. Available online: http://www.jstor.org/stable/24461733 (accessed on 12 December 2022).
  48. Johansen, S.B.; Næs, T.; Hersleth, M. Motivation for choice and healthiness perception of calorie-reduced dairy products. A cross-cultural study. Appetite 2011, 56, 15–24. [Google Scholar] [CrossRef]
  49. Botonaki, A.; Polymeros, K.; Tsakiridou, E.; Mattas, K. The role of food quality certification on consumers’ food choices. Br. Food J. 2006, 108, 77–90. [Google Scholar] [CrossRef]
  50. Blaylock, J.; Smallwood, D.; Kassel, K.; Variyam, J.; Aldrich, L. Economics, food choices, and nutrition. Food Policy 1999, 24, 269–286. [Google Scholar] [CrossRef] [Green Version]
  51. Wierzbiński, B.; Surmacz, T.; Kuźniar, W.; Witek, L. The Role of the Ecological Awareness and the Influence on Food Preferences in Shaping Pro-Ecological Behavior of Young Consumers. Agriculture 2021, 11, 345. [Google Scholar] [CrossRef]
  52. Angowski, M.; Domańska, K. The Factors Affecting the Choice of Yellow Cheese Purchasing Place on the Example Long-Maturing Cheese. Zesz. Nauk. Uniw. Szczec. Probl. Zarz. Finans. Mark. 2015, 41, 385–398. [Google Scholar] [CrossRef] [Green Version]
  53. Das, D.; Sarkar, A.; Debroy, A. Impact of COVID-19 on changing consumer behaviour: Lessons from an emerging economy. Int. J. Consum. Stud. 2022, 46, 692–715. [Google Scholar] [CrossRef]
  54. Sheth, J. Impact of Covid-19 on consumer behavior: Will the old habits return or die? J. Bus. Res. 2020, 117, 280–283. [Google Scholar] [CrossRef] [PubMed]
  55. Arora, N.; Charm, T.; Grimmelt, A.; Ortega, M.; Robinson, K.; Sexauer, C.; Staack, Y.; Whitehead, S.; Yamakawa, N. A Global View of How Consumer Behavior Is Changing Amid COVID-19; McKinsey & Company: New York, NY, USA, 2020. [Google Scholar]
  56. Witek, L.; Kuźniar, W. Green Purchase Behavior: The Effectiveness of Sociodemographic Variables for Explaining Green Purchases in Emerging Market. Sustainability 2021, 13, 209. [Google Scholar] [CrossRef]
  57. Pantano, E.; Pizzi, G.; Scarpi, D.; Dennis, C. Competing during a pandemic? Retailers’ ups and downs during the COVID-19 outbreak. J. Bus. Res. 2020, 116, 209–213. [Google Scholar] [CrossRef]
  58. Sayyida, S.; Hartini, S.; Gunawan, S.; Husin, S.N. The Impact of the Covid-19 Pandemic on Retail Consumer Behavior. APTISI Trans. Manag. 2021, 5, 79–88. [Google Scholar] [CrossRef]
  59. Jeżewska-Zychowicz, M.; Plichta, M.; Królak, M. Consumers’ Fears Regarding Food Availability and Purchasing Behaviors during the COVID-19 Pandemic: The Importance of Trust and Perceived Stress. Nutrients 2020, 12, 2852. [Google Scholar] [CrossRef] [PubMed]
  60. Nanda, A.; Xu, Y.; Zhang, F. How would the COVID-19 pandemic reshape retail real estate and high streets through acceleration of E-commerce and digitalization? J. Urban Manag. 2021, 10, 110–124. [Google Scholar] [CrossRef]
  61. Oliveira, J.; Santos, T.; Sousa, M.; Lopes, J.M.; Gomes, S.; Oliveira, M. Physical Health of Food Consumers during the COVID-19 Pandemic. Soc. Sci. 2021, 10, 218. [Google Scholar] [CrossRef]
  62. Shamim, K.; Ahmad, S.; Alam, M.A. COVID-19 health safety practices: Influence on grocery shopping behavior. J. Public Aff. 2021, 21, e2624. [Google Scholar] [CrossRef]
  63. Petrescu-Mag, R.M.; Vermeir, I.; Petrescu, D.C.; Crista, F.L.; Banatean-Dunea, I. Traditional Foods at the Click of a Button: The Preference for the Online Purchase of Romanian Traditional Foods during the COVID-19 Pandemic. Sustainability 2020, 12, 9956. [Google Scholar] [CrossRef]
  64. Al-Hattami, H.M. Determinants of intention to continue usage of online shopping under a pandemic: COVID-19. Cogent Bus. Manag. 2021, 8, 1936368. [Google Scholar] [CrossRef]
  65. Amberg, N.; Fogarassy, C. Green Consumer Behavior in the Cosmetics Market. Resources 2019, 8, 137. [Google Scholar] [CrossRef] [Green Version]
  66. 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, e0237900. [Google Scholar] [CrossRef]
  67. Lu, M.; Wang, R.; Li, P. Comparative analysis of online fresh food shopping behavior during normal and COVID-19 crisis periods. Br. Food J. 2022, 124, 968–986. [Google Scholar] [CrossRef]
  68. Dannenberg, P.; Fuchs, M.; Riedler, T.; Wiedemann, C. Digital Transition by COVID-19 Pandemic? The German Food Online Retail. Tijds. Econ. Soc. Geog. 2020, 111, 543–560. [Google Scholar] [CrossRef] [PubMed]
  69. Turley, L.W.; Milliman, R.E. Atmospheric Effects on Shopping Behavior: A Review of the Experimental Evidence. J. Bus. Res. 2000, 49, 193–211. [Google Scholar] [CrossRef]
  70. Kotler, P. Atmospherics as a Marketing Tool. J. Retail. 1973, 49, 48–64. [Google Scholar]
  71. Bitner, M.J. Evaluating Service Encounters: The Effects of Physical Surroundings and Employee Responses. J. Mark. 1990, 54, 69–82. [Google Scholar] [CrossRef]
  72. Tian, Y.; Kamran, Q. A Review of Antecedents and Effects of Loyalty on Food Retailers toward Sustainability. Sustainability 2021, 13, 13419. [Google Scholar] [CrossRef]
  73. Vadhwani, S.; Tandon, S. Analysing Relationship Between Store Atmospherics and Consumer Purchase Intention in Select Hypermarkets. Int. J. Res. Soc. Sci. Hum. 2018, 4, 1–7. [Google Scholar]
  74. Lyu (Daisy), J.; Krasonikolakis, I.; Vrontis, D. A systematic literature review of store atmosphere in alternative retail commerce channels. J. Bus. Res. 2022, 153, 412–427. [Google Scholar] [CrossRef]
  75. Grashuis, J.; Skevas, T.; Segovia, M.S. Grocery Shopping Preferences during the COVID-19 Pandemic. Sustainability 2020, 12, 5369. [Google Scholar] [CrossRef]
  76. Li, J.; Hallsworth, A.G.; Coca-Stefaniak, J.A. Changing Grocery Shopping Behaviours Among Chinese Consumers at The Outset Of The COVID-19 Outbreak. Tijds. Econ. Soc. Geog. 2020, 111, 574–583. [Google Scholar] [CrossRef]
  77. Godrich, S.L.; Lo, J.; Kent, K.; Macau, F.; Devine, A. A mixed-methods study to determine the impact of COVID-19 on food security, food access and supply in regional Australia for consumers and food supply stakeholders. Nutr. J. 2022, 21, 1–10. [Google Scholar] [CrossRef] [PubMed]
  78. Zhang, T.; Chen, J.; Grunert, K.G. Impact of consumer global–local identity on attitude towards and intention to buy local foods. Food Qual. Prefer. 2022, 96, 104428. [Google Scholar] [CrossRef]
  79. Ben Hassen, T.; El Bilali, H.; Allahyari, M.S.; Charbel, L. Food shopping, preparation and consumption practices in times of COVID-19: Case of Lebanon. J. Agribus. Dev. Emerg. Econ. 2022, 12, 281–303. [Google Scholar] [CrossRef]
  80. Ben Hassen, T.; El Bilali, H.; Allahyari, M.S. Food shopping during the COVID-19 pandemic: An exploratory study in four Near Eastern countries. J. Islam. Mark. 2022. ahead-of-print. [Google Scholar] [CrossRef]
  81. Ellison, B.; McFadden, B.; Rickard, B.J.; Wilson, N.L.W. Examining Food Purchase Behavior and Food Values During the COVID-19 Pandemic. Appl. Econ. Persp. Policy 2021, 43, 58–72. [Google Scholar] [CrossRef]
  82. Principato, L.; Secondi, L.; Cicatiello, C.; Mattia, G. Caring more about food: The unexpected positive effect of the Covid-19 lockdown on household food management and waste. Socio-Econ. Plan. Sci. 2022, 82, 100953. [Google Scholar] [CrossRef]
  83. Dumitras, D.E.; Harun, R.; Arion, F.H.; Chiciudean, D.I.; Kovacs, E.; Oroian, C.F.; Porutiu, A.; Muresan, I.C. Food Consumption Patterns in Romania during the COVID-19 Pandemic. Foods 2021, 10, 2712. [Google Scholar] [CrossRef]
  84. Brumă, I.-S.; Ulman, S.-R.; Tanasă, L.; Cautisanu, C. Implications of COVID-19 pandemic on sustainable consumption patterns. Evidence from Iasi County, Romania. Front. Sustain. Food Syst. 2022, 6, 1050977. [Google Scholar] [CrossRef]
  85. Szymkowiak, A.; Gaczek, P.; Jeganathan, K.; Kulawik, P. The impact of emotions on shopping behavior during epidemic. What a business can do to protect customers. J. Consum. Behav. 2021, 20, 48–60. [Google Scholar] [CrossRef]
  86. Ben Hassen, T.; El Bilali, H.; Allahyari, M.S.; Berjan, S.; Radosavac, A.; Cvijanovic, D.; Bogevska, Z.; Despotovic, A.; Vaško, Ž. No social distancing from food: How the COVID-19 pandemic shaped student food-related activities in the Western Balkans. Nutr. Health 2023. [Google Scholar] [CrossRef] [PubMed]
  87. Kombanda, K.T.; Margerison, C.; Booth, A.; Worsley, A. The Impact of the COVID-19 Pandemic on Young Australian Adults’ Food Practices. Curr. Dev. Nutr. 2022, 6, 6003005. [Google Scholar] [CrossRef]
  88. Petković, G.; Dokić, A.; Stojković, D.; Bogetić, Z. The Effects of Covid-19 Pandemics on Changes in Shopping Behavior across Different Market Segments. J. Serv. Innov. Sustain. Dev. 2020, 1, 69–86. [Google Scholar]
  89. Bareja-Wawryszuk, O.; Pajewski, T.; Çakaröz, K.M.; Kavas, B. Changes in Consumer Behavior during the COVID-19 Pandemic: A Comparative Analysis between Polish and Turkish Consumers. Sustainability 2022, 14, 10276. [Google Scholar] [CrossRef]
  90. Panzone, L.A.; Larcom, S.; She, P.-W. Estimating the impact of the first COVID-19 lockdown on UK food retailers and the restaurant sector. Glob. Food Secur. 2021, 28, 100495. [Google Scholar] [CrossRef]
  91. Molina-Montes, E.; Uzhova, I.; Verardo, V.; Artacho, R.; García-Villanova, B.; Guerra-Hernández, E.J.; Kapsokefalou, M.; Malisova, O.; Vlassopoulos, A.; Katidi, A.; et al. Impact of COVID-19 confinement on eating behaviours across 16 European countries: The COVIDiet cross-national study. Food Qual. Prefer. 2021, 93, 104231. [Google Scholar] [CrossRef]
  92. Giacalone, D.; Frøst, M.B.; Rodríguez-Pérez, C. Reported changes in dietary habits during the COVID-19 lockdown in the Danish population: The Danish COVIDiet study. Front. Nutr. 2020, 7, 592112. [Google Scholar] [CrossRef]
  93. Bryła, P. Organic food online shopping in Poland. Br. Food J. 2018, 120, 1015–1027. [Google Scholar] [CrossRef]
  94. Markovitch, D.G. Comparing online and mail survey methods in a sample of chief marketing officers. Innov. Mark. 2009, 5, 55–62. Available online: https://www.businessperspectives.org/images/pdf/applications/publishing/templates/article/assets/2945/im_en_2009_4_Markovitch.pdf (accessed on 27 July 2022).
  95. Demographic Yearbook of Poland; Statistics Poland: Warsaw, Poland, 2022. Available online: https://stat.gov.pl/obszary-tematyczne/roczniki-statystyczne/roczniki-statystyczne/rocznik-demograficzny-2022,3,16.html (accessed on 22 February 2023).
  96. Rodrigues, J.F.; Cunha dos Santos Filho, M.T.; Aparecida de Oliveira, L.E.; Brandemburg Siman, I.; Barcelos, A.F.; Ramos, G.L.P.A.; Almeida Esmerino, E.; Gomes da Cruz, A.; Arriel, R.A. Effect of the COVID-19 pandemic on food habits and perceptions: A study with Brazilians. Trends Food Sci. Technol. 2021, 116, 992–1001. [Google Scholar] [CrossRef] [PubMed]
  97. van’t Riet, J.; Sijtsema, S.J.; Dagevos, H.; De Bruijn, G.J. The importance of habits in eating behaviour. An overview and recommendations for future research. Appetite 2011, 57, 585–596. [Google Scholar] [CrossRef] [PubMed]
  98. Schäfer, M.; Jaeger-Erben, M.; Bamberg, S. Life Events as Windows of Opportunity for Changing Towards Sustainable Consumption Patterns? J. Consum. Policy 2012, 35, 65–84. [Google Scholar] [CrossRef]
  99. Hui, T.-K.; Wan, D. Factors affecting Internet shopping behaviour in Singapore: Gender and educational issues. Int. J. Consum. Stud. 2007, 31, 310–316. [Google Scholar] [CrossRef]
  100. Cooke, L.J.; Wardle, J. Age and gender differences in children’s food preferences. Br. J. Nutr. 2005, 93, 741–746. [Google Scholar] [CrossRef] [Green Version]
  101. Bellows, A.C.; Alcaraz, V.G.; Hallman, W.K. Gender and food, a study of attitudes in the USA towards organic, local, U.S. grown, and GM-free foods. Appetite 2010, 55, 540–550. [Google Scholar] [CrossRef] [PubMed]
  102. Pienwisetkaew, T.; Wongthahan, P.; Naruetharadhol, P.; Wongsaichia, S.; Vonganunsuntree, C.; Padthar, S.; Nee, S.; He, P.; Ketkaew, C. Consumers’ Intention to Purchase Functional Non-Dairy Milk and Gender-Based Market Segmentation. Sustainability 2022, 14, 11957. [Google Scholar] [CrossRef]
  103. Fagerli, R.A.; Wandel, M. Gender differences in opinions and practices with regard to a “Healthy Diet”. Appetite 1999, 32, 171–190. [Google Scholar] [CrossRef]
  104. O’Doherty Jensen, K.; Holm, L. Preferences, quantities and concerns: Socio-cultural perspectives on the gendered consumption of foods. Eur. J. Clin. Nutr. 1999, 53, 351–359. [Google Scholar] [CrossRef] [Green Version]
  105. Beardsworth, A.; Bryman, A.; Keil, T.; Goode, J.; Haslam, C.; Lancashire, E. Women, men and food: The significance of gender for nutritional attitudes and choices. Br. Food J. 2002, 104, 470–491. [Google Scholar] [CrossRef]
  106. Badora, A.; Kud, K.; Woźniak, M. Consumer Attitudes as Part of Lifestyle in the COVID-19 Emergency. Sustainability 2022, 14, 9521. [Google Scholar] [CrossRef]
  107. Bukachi, S.A.; Ngutu, M.; Muthiru, A.W.; Lépine, A.; Kadiyala, S.; Domínguez-Salaset, P. Gender and sociocultural factors in animal source foods (ASFs) access and consumption in lower-income households in urban informal settings of Nairobi, Kenya. J. Health Popul. Nutr. 2022, 41, 30. [Google Scholar] [CrossRef]
  108. Kuster-Boluda, I.; Vila-Lopez, N. Gender differences among teenagers: Healthy and unhealthy lifestyle habits and eating behaviours, food involvement and packaging cues. Br. Food J. 2022. ahead-of-print. [Google Scholar] [CrossRef]
  109. Gundala, R.R.; Nawaz, N.; Harindranath, R.M.; Boobalan, K.; Gajenderan, V.K. Does gender moderate the purchase intention of organic foods? Theory of reasoned action. Heliyon 2022, 8, e10478. [Google Scholar] [CrossRef] [PubMed]
  110. Pei, X.-L.; Guo, J.-N.; Wu, T.-J.; Zhou, W.-X.; Yeh, S.-P. Does the Effect of Customer Experience on Customer Satisfaction Create a Sustainable Competitive Advantage? A Comparative Study of Different Shopping Situations. Sustainability 2020, 12, 7436. [Google Scholar] [CrossRef]
  111. Wardle, J.; Haase, A.M.; Steptoe, A.; Nillapun, M.; Jonwutiwes, K.; Bellisle, F. Gender Differences in Food Choice: The Contribution of Health Beliefs and Dieting. Ann. Behav. Med. 2004, 27, 107–116. [Google Scholar] [CrossRef]
  112. Motevalli, M.; Wagner, K.-H.; Leitzmann, C.; Tanous, D.; Wirnitzer, G.; Knechtle, B.; Wirnitzer, K. Female Endurance Runners Have a Healthier Diet than Males—Results from the NURMI Study (Step 2). Nutrients 2022, 14, 2590. [Google Scholar] [CrossRef]
  113. Naseri, M.; Elliott, G. Role of demographics, social connectedness and prior internet experience in adoption of online shopping: Applications for direct marketing. J. Target Meas. Anal. Mark. 2011, 19, 69–84. [Google Scholar] [CrossRef] [Green Version]
  114. Ramprabha, K. Consumer shopping behaviour and the role of women in shopping—A literature review. Res. J. Soc. Sci. Manag. 2017, 7, 50–63. [Google Scholar]
  115. Fekete-Farkas, M.; Gholampour, A.; Bouzari, P.; Jarghooiyan, H.; Ebrahimi, P. How gender and age can affect consumer purchase behavior? Evidence from A microeconomic perspective from Hungary. AD-Minister 2021, 39, 25–46. [Google Scholar] [CrossRef]
  116. Ciucan-Rusu, L.; Vasile, V.; Stefan, D.; Comes, C.-A.; Stefan, A.-B.; Timus, M.; Oltean, A.; Bunduchi, E.; Popa, M.-A. Consumers Behavior Determinants on Online Local Market Platforms in COVID-19 Pandemic—A Probit Qualitative Analysis. Mathematics 2022, 10, 4281. [Google Scholar] [CrossRef]
  117. Chiang, M.-C.; Yen, C.; Chen, H.-L. Does Age Matter? Using Neuroscience Approaches to Understand Consumers’ Behavior towards Purchasing the Sustainable Product Online. Sustainability 2022, 14, 11352. [Google Scholar] [CrossRef]
  118. Ryder, N.B. The cohort as a concept in the study of social change. Am. Sociol. Rev. 1965, 30, 843–861. [Google Scholar] [CrossRef] [PubMed]
  119. Li, S.; Kallas, Z.; Rahmani, D.; Gil, J.M. Trends in Food Preferences and Sustainable Behavior during the COVID-19 Lockdown: Evidence from Spanish Consumers. Foods 2021, 10, 1898. [Google Scholar] [CrossRef] [PubMed]
  120. Marinković, V.; Lazarević, J. Eating habits and consumer food shopping behaviour during COVID-19 virus pandemic: Insights from Serbia. Br. Food J. 2021, 123, 3970–3987. [Google Scholar] [CrossRef]
  121. Tiruwa, A.; Yadav, R.; Suri, P.K. Moderating effects of age, income and internet usage on Online Brand Community (OBC)-induced purchase intention. J. Adv. Manag. Res. 2018, 15, 367–392. [Google Scholar] [CrossRef]
  122. Jackson, V.; Stoel, L.; Brantley, A. Mall attributes and shopping value: Differences by gender and generational cohort. J. Retail. Consum. Serv. 2011, 18, 1–9. [Google Scholar] [CrossRef]
  123. Alaimo, L.S.; Fiore, M.; Galati, A. How the Covid-19 Pandemic Is Changing Online Food Shopping Human Behaviour in Italy. Sustainability 2020, 12, 9594. [Google Scholar] [CrossRef]
Figure 1. Gender and change in the frequency of shopping in supermarkets (percentage of responses). Note: own study, based on research survey.
Figure 1. Gender and change in the frequency of shopping in supermarkets (percentage of responses). Note: own study, based on research survey.
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Figure 2. Gender and change in the frequency of purchases in discount (percentage of responses). Note: own study, based on research survey.
Figure 2. Gender and change in the frequency of purchases in discount (percentage of responses). Note: own study, based on research survey.
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Figure 3. Age and change in shopping frequency in small local and rural shops (percentage of responses). Note: own study, based on research survey.
Figure 3. Age and change in shopping frequency in small local and rural shops (percentage of responses). Note: own study, based on research survey.
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Figure 4. Age and change in shopping frequency in medium-sized self-service (percentage of responses). Note: own study, based on research survey.
Figure 4. Age and change in shopping frequency in medium-sized self-service (percentage of responses). Note: own study, based on research survey.
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Figure 5. Age and change in the frequency of shopping in supermarkets (percentage of responses). Note: own study, based on research survey.
Figure 5. Age and change in the frequency of shopping in supermarkets (percentage of responses). Note: own study, based on research survey.
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Figure 6. Age and change in the frequency of purchases in discount stores (percentage of responses). Note: own study, based on research survey.
Figure 6. Age and change in the frequency of purchases in discount stores (percentage of responses). Note: own study, based on research survey.
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Figure 7. Education and the change in the frequency of shopping in supermarkets (percentage of responses). Note: own study, based on research survey.
Figure 7. Education and the change in the frequency of shopping in supermarkets (percentage of responses). Note: own study, based on research survey.
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Figure 8. Place of residence and frequency of shopping in medium-sized self-service stores (percentage of responses). Note: own study, based on research survey.
Figure 8. Place of residence and frequency of shopping in medium-sized self-service stores (percentage of responses). Note: own study, based on research survey.
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Figure 9. Place of residence and the frequency of shopping at discount (percentage of responses). Note: own study, based on research survey.
Figure 9. Place of residence and the frequency of shopping at discount (percentage of responses). Note: own study, based on research survey.
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Table 1. Characteristics of the researched group of respondents.
Table 1. Characteristics of the researched group of respondents.
VariableCategoryN%
GenderFemale28872
Male11328
Age20–2511328
26–356616
36–4513233
46–556717
56 and over236
EducationPrimary and vocational82
Secondary10827
Higher28571
Place of residenceVillage17644
City up to 50 thousand5614
City from 50–100 thousand297
City from 100–300 thousand11228
City over 300 thousand287
Note: own study, based on research survey.
Table 2. Characteristics of the general population in Poland in 2021.
Table 2. Characteristics of the general population in Poland in 2021.
VariableCategoryGeneral Population (%)Population 20 Years and More (%)
GenderFemale51.752.4
Male48.347.6
under 2020.3x
Age20–256.17.6
26–3513.717.2
36–4516.620.8
46–5513.016.3
56 and over30.438.2
EducationPrimary and vocational39.7
Secondary35.2no data available
Higher25.1
Place of residenceVillage40.339.1
City up to 100 thousand31.832.2
City 100 thousand and more27.828.7
Note: own calculation, based on statistical data [95].
Table 3. Results of the Pearson chi-square test of independence. Sociodemographic characteristics and the change in the frequency of purchase in particular places.
Table 3. Results of the Pearson chi-square test of independence. Sociodemographic characteristics and the change in the frequency of purchase in particular places.
VariableGenderAgeEducationPlace of Residence
In small, local/country shops0.68190.0407 **0.76710.4445
In medium-sized self-service stores0.35650.0001 ***0.47090.0257 **
In supermarkets0.0155 **0.0021 ***0.0009 ***0.4358
In discount stores0.0001 ***0.0456 **0.06140.0289 **
At bazaars/marketplaces 0.1507 0.05620.7483 0.8814
From the farmer’s (direct) 0.2401 0.2673 0.63150.2299
Over the Internet 0.10920.4567 0.1862 0.7805
Doorstep sales 0.1118 0.5276 0.8945 0.1882
** α ≤ 0.05, *** α ≤ 0.01. Note: own study, based on research survey.
Table 4. Results from the Pearson chi-square test of independence. Sociodemographic characteristics and the change in groceries shopping habits in brick-and-mortar shops.
Table 4. Results from the Pearson chi-square test of independence. Sociodemographic characteristics and the change in groceries shopping habits in brick-and-mortar shops.
Variablep
Gender0.3609
Age0.001 ***
Education0.0001 ***
Place of residence0.8520
*** α ≤ 0.01. Note: own study, based on research survey.
Table 5. Has the COVID-19 pandemic changed the shopping habits for groceries in brick-and-mortar shops? (% answers).
Table 5. Has the COVID-19 pandemic changed the shopping habits for groceries in brick-and-mortar shops? (% answers).
VariableI Go to Brick-and-Mortar Shops without ChangingI Am Limiting Going to Brick-and-Mortar ShopsI Go to Brick-and-Mortar Stores More OftenI Do Not Go to Stores, I Shop through Other Channels
Gender
– Female55.2143.401.040.35
– Male62.8334.511.770.88
Age
– 20–2573.4524.781.77-
– 26–3560.6136.36-3.03
– 36–4547.7350.761.51-
– 46–5547.7650.751.49-
– 56 and over52.1747.83--
Education
– Primary and vocational75.012.512.5-
– Secondary71.3027.780.92-
– Higher51.5846.671.050.70
Place of residence
– Village54.5543.181.700.57
– City up to 50 thousand55.3644.64--
– City from 50–100 thousand72.4127.59--
– City from 100–300 thousand56.2541.071.790.89
– City over 300 thousand67.8632.14--
Note: own study, based on research survey.
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MDPI and ACS Style

Kusz, B.; Witek, L.; Kusz, D.; Chudy-Laskowska, K.; Ostyńska, P.; Walenia, A. The Effect of COVID-19 on Food Consumers’ Channel Purchasing Behaviors: An Empirical Study from Poland. Sustainability 2023, 15, 4661. https://doi.org/10.3390/su15054661

AMA Style

Kusz B, Witek L, Kusz D, Chudy-Laskowska K, Ostyńska P, Walenia A. The Effect of COVID-19 on Food Consumers’ Channel Purchasing Behaviors: An Empirical Study from Poland. Sustainability. 2023; 15(5):4661. https://doi.org/10.3390/su15054661

Chicago/Turabian Style

Kusz, Bożena, Lucyna Witek, Dariusz Kusz, Katarzyna Chudy-Laskowska, Paulina Ostyńska, and Alina Walenia. 2023. "The Effect of COVID-19 on Food Consumers’ Channel Purchasing Behaviors: An Empirical Study from Poland" Sustainability 15, no. 5: 4661. https://doi.org/10.3390/su15054661

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