1. Introduction
The coronavirus/SARS-CoV-2 (COVID-19), officially recognized by the World Health Organization on 11 February 2020, rapidly spread around the world with significant social and economic repercussions. The pandemic has caused a catastrophic loss of life on a global scale and presents an unprecedented threat to food systems, public health, and the workplace. Governments throughout the world have used using non-pharmaceutical measures like social distancing regulations and civic lockdowns to restrict the spread of the virus because there was no vaccine or viable treatment to stop the disease’s spread. Economic activities have been significantly harmed by the impact of preventing people from being able to work, gather, and socialize, particularly in the service and food sectors.
Therefore, the COVID-19 outbreak has significantly impacted supply chains and has caused several supply chain disruptions in almost all industries worldwide. Moreover, increased transportation costs, labor shortages, and insufficient storage facilities all led to food loss during the pandemic, and this disruption has affected the logistics in the food value chains, including transportation, warehousing, procurement, packaging, and inventory management. Retail businesses are thus facing supply issues, which affect consumer behavior by causing uncertainties and stresses about food. This has a negative impact on the amount of food that is available as well as its quality, freshness, safety, access to markets, and affordability. The pandemic’s enormous demands created significant logistical difficulties for both governments and individuals.
The COVID-19 pandemic, according to the Food and Agricultural Organization (FAO), has had a substantial impact on both supply and demand in the short food supply chains (SFSCs). Additionally, it has caused an impact on the dairy products industries, a significant sector of the world economy, in large part because of the protective measures implemented at the local or regional level.
The UAE is particularly vulnerable to the effects of the epidemic on other nations due to its reliance on imported food and its open economy, which makes it exposed to unstable international markets. Additionally, there has been a significant shift in the global food demand’s composition. Not only was there a collapse in demand from restaurants, hotels, and catering services as well as the closure of some open markets [1], but there was also a surge in consumer demand faced by supermarkets, neighbourhood grocers, and grocery-related e-commerce channels [2,3]. Therefore, this study intends to investigate how changes in the food supply chain and the food security issue during COVID-19 affected consumer behavior through a quantitative survey. This research will determine the influence of COVID-19 on the global food supply chain, the operations of the food supply chain in the UAE, and how to evaluate it, and it will identify methods for minimizing the pandemic’s impact on UAE businesses.
This article has several interlinked goals. We will highlight the existing gap in the literature regarding the explicit connection between consumer behavior and the supply chain, specifically focusing on the UAE food sector during the COVID-19 pandemic, and emphasizing that the current literature lacks in-depth exploration and understanding of how consumer behavior influences supply chain dynamics and operations. The objective of this study is to examine the impact of consumer behavior on the supply chain in the UAE food sector during the COVID-19 pandemic, and this research aims to contribute to filling the existing research gap and provide valuable insights into this important relationship. This research will contribute to the existing body of knowledge by identifying key consumer behavior factors, such as changing preferences, purchasing patterns, and online shopping behaviors that have had a direct impact on supply chain operations during COVID-19.
This article presents an examination of the impact of consumer behavior on the supply chain in the UAE food sector during the COVID-19 pandemic. The organization of this paper is broken up into sections after this one. In Section 2, we provide a theoretical background, followed by reviewing relevant literature on consumer behavior and supply chain management and contextualizing our study in Section 3. Section 4 outlines our methodology, detailing our research design, data collection methods, and analysis techniques. Section 5 presents our findings and analysis, offering insights into the specific ways consumer behavior has influenced the supply chain in the UAE food sector. Section 6 relates our findings to existing literature and presents the managerial implications of our findings. Finally, in Section 7, we summarize our key findings, contributions, and recommendations for supply chain managers. Overall, the paper aims to present a coherent and insightful exploration of the relationship between consumer behavior and the supply chain during the COVID-19 pandemic in the UAE food sector.
2. Background
The COVID-19 pandemic, since its emergence globally, has affected several divisions in industries. The food industry is one of the most vulnerable sectors in the economy of any country. In the United Arab Emirates, the food industry supply chain has seen tremendous change over the past few decades, as the government continues to invest in agriculture to minimize the nation’s dependency on imported supplies. The transit of food across borders had a substantial impact via the outbreaks of COVID-19 on a global level. However, due to the lack of demand, the closure of food production facilities, and financial constraints, business operations and the supply of various food products have been temporarily suspended.
The food supply chain is a dynamic sector that relies on an efficient and effective supply chain to function properly. It is a process that involves various phases that food products go through during the movement from supplier to consumer and finally to customer. Every phase in the supply chain requires natural resources or humans. The supply chain processes are linked together, and when one stage in the process is affected, the whole supply chain will be affected. The processes involve production, handling and storage, processing and packaging, distribution, retailing, and consumption.
In the food supply chain, the food moves from the supplier to producer (farmer/food manufacturer) to the final consumer during the processes shown in Figure 1 below, and the money paid by the consumer then moves from the consumer to the producer in a reversed process [4]:
The movement of both food and money is facilitated by push/pull dynamics. In the food supply chain, the food is pushed or supplied by producers and processors, and is also pulled or demanded by consumers. Processors and producers also pull the money, while the consumer pushes money to facilitate its flow from the consumer to the producer.
3. Literature Review
The COVID-19 pandemic has had a profound and multifaceted impact on the global food supply chain, leading to disruptions, logistical challenges, and shifts in consumer behavior. While there is existing literature on the overall impact of the pandemic on food systems and supply chains, there is a scientific vacuum regarding the specific strategies and solutions to minimize the pandemic’s impact on UAE businesses operating in the food supply chain sector. A comprehensive literature review is required to identify this scientific gap and contribute to the understanding of effective measures that can be implemented to mitigate the challenges faced by UAE companies. By addressing this research gap, the study aims to provide valuable insights and recommendations for improving the resilience and sustainability of the UAE’s food supply chain in the face of future disruptions or similar crises.
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Impacts of COVID-19 on the Food Supply Chain
The food supply chain is comprised of all the stages that food products go through during their movement from producer to customers and consumers. However, there are few occurrences and instances that could disrupt the food chain and cause complications down the line. Because of the recent challenges in the food supply chain, there is now considerable concern about food production, processing, distribution, and demand. COVID-19 resulted in the movement restrictions of workers, changes in demand of consumers, closure of food production facilities, restricted food trade policies, and financial pressures in food supply chain. However, the main impacts that we will be focusing on are (1) lack of communication, (2) the panic of consumers regarding the shortage of labour, and (3) the shortage of raw materials.
3.1. Lack of Communication
The supply chain during the COVID-19 crisis has faced critical challenges in the communication lines with business-related stakeholders [5]. The stakeholders share their assets and capabilities to minimize uncertainty, share cost, and risk, and they satisfy customers by serving them. The relevant information is required by the supply chain members to make informed and appropriate decisions [6].
During the lockdowns introduced almost universally by governments around the world, various markets have recorded a significant increase of the demand for food. The food supply chain system experienced a panic response globally, it seems, because of the unprecedented uncertainty of the COVID-19 pandemic and the measures introduced to combat it. As a result, and related to the high demand of customers, suppliers should manage and produce extra products to satisfy customers, but because the pandemic happened suddenly, and because of the panic buying when the lockdowns started, the suppliers dis not have a plan for communicating to consumers or for how to produce and access food. The absence of communication between the company suppliers and the customer can result in unsatisfied customers and insufficient quantities for the orders that are demanded. The process of the supply chain can thereby be affected via the strategy implementation and the decision-making process. Based on [7], poor communication between the company and the transportation drivers is an additional internal uncertainty and risk, and the resulting inefficiency can contribute to delayed deliveries and increase costs.
Furthermore, based on the studies in the Valguarnera Industry regarding communication outside the consortium, the exchange of information between the suppliers and enterprises was weak because of the geographical distances, which was severely exacerbated by the lockdown of the borders between the countries and the difficulty of getting even good across them, at least at first. As a result, the research analysis found that a lack of awareness of new technologies is an issue that can lead to inefficient production processes outside the consortium and delays in receiving data [8].
3.1.1. Coordination between Stakeholders
The food chain must obtain support by communicating with all stakeholders involved in the supply chain, irrespective of the physical location [9], although inconsistent communication due to the low capacity of wireless connections can make it challenging to obtain data for planning and also for managing instability with external and internal stakeholders. To reduce risks and build a more resilient food system, there has to be more cooperation between the parties involved in the food supply chain—a concept of interactions between supply chain partners to achieve objectives by working together to complete duties. This emphasizes the importance of horizontal collaboration in the food supply chain and the use of IT technologies, which could enhance supply chain coordination [10].
3.1.2. Coordination between Stakeholders
Sharing information is essential for preserving coordination among the FSC stakeholders. Additionally, information strategies that differ in the types of connections between supply chain stakeholders can influence how much information is shared. However, during the COVID-19 pandemic and the resultant lockdown, the lack of awareness of how to use technologies and digital communication was the main issue that caused a bottleneck in the supply chain process, since, in this process, each chain is linked to the other, and all have to share information on the levels of stock, trend sales, and trend demands, which leads to an increase in communication and collaboration by implementing effective strategies due to a well-informed decision-making process [8].
In the modern era, businesses collect data about the activities occurring along the food supply chain utilizing technology-based traceable systems [10].
3.2. Consumer Panic in the COVID-19 Pandemic
Globalization has brought about not only advantages but also risks to the supply chain, and one of these risks is the effect of consumer behaviour in crises, such as pandemics. Panic buying is a human behaviour indicated by a rapid increase in purchase volume before or during natural disasters and man-made crises, or in view of a large price increase or shortage. However, consumers are commonly observed to stock up on consumer goods to an extent that greatly exceeds levels observed in normal times. Ref. [11] provides a comprehensive exploration of consumer behavior, delving into the factors that shape consumer decision-making processes and offer valuable insights into understanding consumer motivations and behaviors.
If there is a lack of awareness and education about food availability and security, this can lead to anxiety, stress, and panic. This, in turn, can cause a change in consumer behaviour that triggers a demand shock in the food supply and in food demand, specifically in supermarkets and retailers, and the number of stockouts would thereby increase significantly as shown in Figure 2. This is what happened during the pandemic. Indeed, serious stock-out situations have arisen in many countries for consumer staples in 2020. Stock-outs are costly for consumers in general. Consumers’ food consumption habits have changed as a result of less frequent grocery shopping, a bad income shock, and skyrocketing food prices. In addition, lockdowns, transportation disruptions, and panic buying led to shortages of products in almost every sect, which made it difficult for producers to reach markets and limited consumer access to the inputs where the cost of transporting food has gone up. This only goes to show the way in which human behaviour is impacted by a sense of fear or anxiety, especially during disasters or extreme situations [12].
3.2.1. Food Availability
Food availability refers to the regular accessibility of food at the local level, ensuring that individuals and households can obtain their essential food items without facing difficulties [13]. Consumer preferences are significantly influenced by the perceived scarcity or abundance of goods, including food [14]. During pandemics, the unavailability of food in retail stores has a direct impact on consumer purchasing behavior and contributes to food-related stress. Without intervention from countries or governments in the food industry, there is a risk of severe disruptions in the food supply chain, leading to a doubling of the number of people experiencing hunger. The absence of available food in local markets during the COVID-19 pandemic has been particularly distressing for parents of infants [2]. To avoid future food shortages, the Food and Agriculture Organization (FAO) has recommended that countries maintain resilient food supply systems [15]. However, when foods are not accessible in the market, consumers are unable to make purchases, which directly impacts their buying behavior [16].
The product availability perceptions and shortages can have a big influence on customer preferences. Consumer purchasing patterns and stress levels during the epidemic are affected by the availability of food in retail outlets, resulting in an increase in the number of customers in ordering goods, and causing food shortages and the unavailability of many essential products. Lockdowns brought may, indeed, quadruple the number of hungry individuals [17].
The only alternative left for people to buy food products during the CVOID-19 pandemic was supermarkets and online services with home delivery. However, even here, there were major problems in obtaining products. The problem of understocking was also widespread at this time, and finding necessary products in the market was another challenge. In times of shortage, retailers raised prices as they saw fit, and access became expensive. The biggest problem for supermarkets was imported products, and shelves were left empty due to border closures [18].
3.2.2. Food Stress
Due to the lockdowns, which generate panic in food purchases, the majority of nations experienced a shortage of goods and services. Regional stores do not carry food, and the cost of basic items increased by 300%. Customers will therefore be concerned about buying and eating food. Therefore, stress played a crucial part in the increased food consumption that occurred during the COVID-19 epidemic. Consumer food stress is increased by the disruption of the food supply chain, indeed, and by lack of access to food, rising food prices, and unemployment [17].
3.2.3. Food Insecurity
Direct food stress brought on by food insecurity has a detrimental effect on consumer spending behaviour and access to affordable and nutritious food. During the COVID-19 pandemic, more than 1 billion people suffered from nutritional deficiencies, which is an increasing global issue due to food insecurity, and, therefore, these problems can have detrimental effects on public health, including diabetes, heart disease, depression, etc. As a result, there are now more people who are food insecure than there were a few years ago. A total of 8.9% of the world’s population (690 million) suffered from malnutrition in 2019, and this is one of the reasons that food prices have risen since February 2020, increasing by more than 10% in Belarus, Bolivia, Ghana, and Myanmar, and more than 20% in Guyana, Sudan, and Zambia [19].
3.2.4. Food Quality and Safety
Consumer purchase intentions and consumption behavior are significantly impacted by the quality and safety of food [20,21,22,23,24]. Consumers have a preference for purchasing high-quality products, and food quality and safety encompass various criteria, such as taste, naturalness, freshness, safety, production methods, healthiness, control, sensory appeal, shelf life, and overall condition [25]. Describing food quality and safety can be challenging as they are considered as credibility attributes, which are characteristics that consumers cannot directly verify. Consumer judgments of quality and safety rely on comparisons with other products, considering intrinsic factors (e.g., product aesthetics) and extrinsic factors (e.g., quality labels) [26]. Food choices continuously involve decisions related to food quality and safety, which, in turn, influence consumer purchasing behavior [24]. It is important to note here that [27] has clarified that coronaviruses are not transmitted through food, as demonstrated by previous coronavirus outbreaks like MERS and SARS. However, it is still recommended to follow proper hygiene practices, such as thorough handwashing or sanitizing after handling food packages in order to minimize the risk of contact with coronavirus-contaminated food [28].
The most important aspects of consumer purchase decisions that significantly affect consuming behaviour are food quality and safety. Customers like to buy high-quality goods. Food quality and safety refers to a number of food standards, including good flavor, freshness, safety, appropriate production processes, and healthiness. In Bangladesh, an outbreak that resulted in a decline in food quality and safety was caused by a number of businessmen mixing infected food. People are consequently concerned about food, and their shopping patterns are quickly changing [17].
3.2.5. Demand Shock
At the beginning of the lockdown, panic buying has led to a food shortage, shocking the supply chain’s rhythm. For instance, the initial lockdown period in South Africa was imposed for a period of 21 days to stop the spread of COVID-19 infections [9]. People stocked their pantries with necessities as a result of the uncertainty and panic that the lockdown period caused. It has been observed that the biggest obstacle was not a shortage of food but rather a consumer’s ability to access that food, a factor that is primarily determined by economic and social variables.
With spikes and demand shocks at the start of the lockdown, numerous markets observed a considerable rise in the demand for food, household goods, and home electronics. In the United States, a study on food demand was conducted. The food demand in the United States from March to May 2020, with an initial shock in March and stabilization from April 2020 onwards [29]. Uncertainty brought on by the shutdown of distribution routes was the cause of the initial shock period. The shock in the food supply chain may have been caused by a panic response rather than by actual threats of food scarcity.
Consumer demand for food products initially increased as a result of stockpiling behaviour, which also caused demand for basic goods to increase. For instance, in the protein industry, manufacturers increased production in response to this spike. The loss of nearly all food services as well as a shift in consumer behaviour towards preventative saving had a negative impact on demand as economic activity slowed, which was a result of changes in consumer behaviour brought on by the pandemic, and which was also a result of active government initiatives (i.e, full or partial lockdowns) [30]. However, on the supply side, the severe negative shock was reflected through plant slowdowns and shutdowns as COVID-19 kept workers at home due to illness, quarantine, or risk avoidance. Additionally, COVID-19 encouraged restrictions on processing due to social distancing measures and additional health, safety, and sanitation measures, which further constricted supply.
Short-term stockouts have been caused by consumer stockpiling and panic buying. Large scale supermarkets often operate on a just-in-time delivery system [12]. A constant supply of items is maintained on the shelves of grocery stores due to sophisticated inventory management and planning procedures that take into consideration typical supply-and-demand trends. The system is functional and efficient in normal conditions, it should be said, but the demand shock’s unexpected nature put it under a significant amount of strain.
3.3. Labor Shortages in COVID-19
Labor shortages are one of the potential factors that could cause supply-side disruptions in the food supply chains. It is important to consider the possibility of labor shortages in the networks of downstream food processing and distribution as a result of the illness, isolation, or limitations on movement suffered by workers [31].
The COVID-19 epidemic has caused massive shortages in the labor supply, and it has affected several countries and, also, a diverse range of industries, especially ones that require a large amount of human interaction, such as hospitality and food as well as manufacturing. The increased labor shortages post COVID-19 may be partially a result of structural changes, particularly preferences changes as some workers may no longer accept low pay and unfavorable working circumstances.
Recent surveys, conducted in [32,33,34,35], have provided evidence linking long-COVID to individuals leaving the labor force. These surveys revealed that around 20% of their respective respondents were not employed due to health issues associated with COVID-19. By combining these survey findings with COVID-19 case rates and the prevalence of long-COVID symptoms, researchers have estimated a loss of approximately 1.5 million workers from the labor force [36,37,38]. However, it is important to note that the existing survey evidence has certain limitations, such as the absence of control groups, reliance on self-reported reasons for non-employment attributed to long-COVID, and, in some cases, samples that may not be representative. Furthermore, while [38] discovered that COVID-19 negatively impacts worker performance in the context of professional soccer, these findings are in just one profession/field and may, of course, not be universally applicable.
Logistics became another major problem during the pandemic as harvested products were not reaching to the market. Researchers have explained that in the UAE, cargo drivers and their assistants took regular COVID-19 tests to make sure that they were virus free. This obviously took time in terms of administration of the tests and obtaining the results, and this caused delays.
Whenever a driver tested positive, they had to keep themselves in quarantine, of course, hence companies had to find replacements in a short timespan as the perishable products could not be kept for long periods of time, and so the situation was quite challenging. Since most of the labor in the UAE came from other countries, processing of documents of new entrants also became an issue as the borders were closed. Some officials were also afraid of contracting disease and employed unusual strategies, which caused further strains in the movement of goods. As a result, the shortage of labor in the UAE caused delayed logistics, which impacted the whole supply chain.
COVID-19 Illnesses and Work Absences
It has been demonstrated that absences due to illness result in consistent reductions in the labor force. According to studies, workers who miss a full week of work due to suspected COVID-19 diseases are 7% less likely to be employed 1 year after their absence than workers who did not leave work due to illness. COVID-19 diseases force older employees into retirement, which is one reason why there was a decrease in the labor supply.
Along with producers, distributors, and consumers, the supply chain also has an impact on labor-intensive food processing facilities. Many workers were determined not to be COVID-19 positive and who were thus unwilling to go to work because they believed that they would get sick there at the time of the epidemic, and, therefore, many firms temporarily stopped, postponed, or decreased production. Due to these factors, it was estimated that by the end of April, the output capacity of facilities producing pork, to take just one example, had fallen by around 25% [39].
In the United States, there were 93 farms and production facilities affected by COVID-19 infections, as well as at least 462 meat packaging and 257 food-processing industries. At least 232 workers died and at least 54,036 workers were found to have been COVID-19 positive. In Brazil, 2400 workers of meat processing plants in 24 slaughterhouses across 18 towns were found to be COVID-19 positive. In Ghana, 534 workers of a firm that processed fish tested positive for the virus. More than 100 cases were reported at slaughterhouses in France, and 1553 cases of COVID-19 were discovered at meat-processing facilities in Germany [39].
Health-related absence rates have increased faster during the epidemic among workers in occupations with potentially higher rates of COVID-19 exposure. The monthly counts of excess absences due to illness and COVID-19 cases during the CPS reference week are shown in Panel A in millions [40]. Excess health-related absences are calculated by deducting actual health-related absences from the seasonal (monthly) trend number of absences, which is predicted from January 2010 through to February 2020. Before the pandemic, health-related absences were growing with age, but the pandemic increase was substantially attributed to younger workers [40].
The link between health-related absenteeism and occupation-level indicators of COVID-19 exposure risk before and during the epidemic [40].
3.4. Shortages of Raw Materials
The imbalance in the supply of raw materials, which impacts the worldwide market in all economic sectors that depend on these resources for their production processes, is one of the main problems caused by the COVID-19 pandemic.
The worldwide lack of raw materials has caused supply bottlenecks in all industries and the movement of the materials has come to a standstill. The reasons for this are, on the one hand, rising demand due to the pandemic-related economic slowdown and, on the other hand, that raw material suppliers have further reduced their production capacities. Moreover, other elements of business are suffering as a result of the disruption to the flow of materials and goods, including a sudden end to incoming financial flows and a shift in the workforce’s skill distribution. Every supply chain was interrupted by the obstruction of material and people’s movement [41].
3.4.1. The Labor Dilemma
One of the main reasons for the raw material shortages is labor-related problems brought on by the pandemic, such as absenteeism, rising unionization, and the difficulties filling unfilled positions, all of which continue to restrict the industrial sector’s potential for expansion [12]. For example, a labor shortage at JBS, the world’s largest meat supplier, is affecting operations in every developed country, limiting production increases and rising costs.
3.4.2. Raw Material Scarcity
Due to the gradual lifting of the continuing nationwide lockdown, the country’s industries are experiencing raw material scarcity. Indian industry, for example, struggles to import materials for which locally manufactured substitutes are very difficult to obtain due to limited capacity at India’s major ports for both sea and air freight [41].
An insufficient supply of raw materials signifies that manufacturers have to start working with different suppliers. For example, this means different types of raw materials with different consistencies, quality, etc. This will increase the variance of raw materials used in production and affect production and throughput [42,43].
3.4.3. Movement Restriction
Distribution of basic goods suffers from restrictions between cities, provinces, regions, and nations [39]. High-value products require a lot more labor to produce than everyday necessities.
The difficulties brought on by consumer demand shifts and transportation restrictions (such as national or international border closures) are significant. Due to the limitations, consumers prepare their own meals at home instead of dining out. Additionally, customers are reluctant to visit marketplaces and supermarkets, since they risk contracting COVID-19 in such establishments [39].
4. Methodology
This case study aims to identify the impacts of COVID-19 on the food supply chain in the UAE in order to understand how the food supply chain has been affected. To address this objective, a comparison strategy will be used to compare the impacts before and during the pandemic. Moreover, we will be collecting historical data about the food demand and supply before and during the outbreak. To evaluate consumers’ behavior, two approaches will be applied. The first approach involves constructing a questionnaire to test if food availability and security was affected, and the second involves applying hypothesis testing for measuring the behaviour of consumers as shown in Figure 3.
4.1. Questionnaire Development
This questionnaire will examine the food consumption behaviour of consumers in the UAE using quantitative models. The questionnaire structure is divided into six sections, which are consumer behavior (three questions), food stress (two questions), food price (two questions), food availability (three questions), food quality and safety (three questions), and food insecurity (two questions). After collecting the results from the consumers and customers, we will analyze the responses.
4.1.1. Data Collection and the Sample
A self-administered questionnaire was developed to collect the survey data used to measure the six variables on consumer buying behavior, which are consumer behavior, food anxiety, food price, food availability, food quality and safety, and food insecurity. To design the measurement-related question, a 5-point Likert scale was applied: 1 = strongly disagree, 2 = disagree, 3 = neutral, 4 = agree, and 5 = strongly agree. The survey was sent to 200 people, and out of 200 respondents, 172 completed the questionnaire. The response consisted of 86.6% females and 13.4% males. In terms of the age group, the majority were 18–25 (42.4%); those aged 26–45 accounted for 36.6%; those aged 46 and above were 18%; and those under 18 constituted 2.9% of the respondents. The majority of the people that responded had a bachelor’s degree (57.6%). Based on the survey results, the food sector that people think was most affected was the meat, poultry, and seafood one, with 40.1%. The survey was divided into six variables with 13 structured questions. The main aim of the survey was to analyze the effects of the COVID-19 pandemic on the food supply chain and to analyze the relationship between anxiety and purchasing behaviors.
4.1.2. Data Analysis
Structural Equation Modelling (SEM) is a multivariate statistical analysis technique for studying structural relationships. Analysis of the structural relationship between measured variables and latent constructs is performed using this method, which combines multiple regression analysis and factor analysis. In this research, this tool will be used to analyse the collected data from the consumer behaviour questionnaire by collecting all the information and determining the percentage of each question. As a result, we will be able to identify the variables that influence the food supply chain during COVID-19 in the UAE.
Structural Equation Modeling (SEM) can be justified as a suitable research approach for studying the impact of COVID-19 on the supply chain in the UAE food sector due to many reasons. SEM allows for the examination of complex relationships among multiple variables simultaneously. In the context of the UAE food sector and the impact of COVID-19, there are likely to be various interrelated factors, such as supply disruptions, changes in consumer demand, operational challenges, and financial implications. SEM can capture these complex relationships and provide a holistic understanding of how these factors interact and influence the supply chain.
SEM provides a framework for testing and validating theoretical models. By specifying a theoretical framework that represents the relationships between different constructs or variables, researchers can empirically test the proposed relationships and assess their significance. This approach enables the validation or refinement of existing theories and the development of new ones based on empirical evidence. SEM also allows researchers to validate and refine measurement instruments used to assess constructs of interest. In the context of studying the impact of COVID-19 on the supply chain in the UAE food sector, this can involve assessing the reliability and validity of measurement scales used to capture variables, such as supply disruptions, changes in consumer behavior, or operational performance. Validating these measurement instruments ensures the accuracy and reliability of the data collected.
4.2. Hypothesis Testing
This study proposes a new framework and hypotheses, as shown in Table 1 and Figure 4, that are based on the existing literature. Food prices and food stress are the mediating variables, and consumer consumption behavior is the dependent variable. This conceptual model can be used to evaluate how current consumer habits are affecting food security in the wake of the pandemic [17].
5. Results and Discussion
5.1. Model Fit
Table 2 demonstrates that Model 1, with 172 observations, has an AIC (Akaike Information Criteria) of 6897.376, which indicates the measure of goodness-of-fit of a model that balances the fit of the model with the number of parameters. Moreover, the BIC (Bayesian Information Criteria) of Model 1 is 7035.866, which is similar to AIC but places a stronger penalty on the number of parameters in the model. However, these two measurement criteria indicate a better fit model when they have a lower value. The chi-square (χ2) has a value of 121.730 with 60 degrees of freedom (df) and a p-value of 0.001 with a sample size of 172, suggesting that the effect being measured is statistically significant at the 0.05 significance level. Therefore, the p-value of 0.001 provides evidence against the null hypothesis and supports the alternative hypothesis, suggesting that there is a relationship between the predictor and the outcome variables.
5.2. Additional Fit Measures
Table 3 displays several fit indices for Model 1. Each index’s value represents how accurately the model fits the data, the goodness-of-fit indices, and indicates the degree to which the model fits the data better than a null model. To start with, the first index, which is the Comparative Fit Index (CFI), examines the discrepancy between the data and the proposed model in order to analyze the model fit, and the range of CFI values is 0 to 1, with higher values reflecting the better fit. The value of (CFI) for Model 1 is 0.835, which indicates a fair fit. Furthermore, the Tucker–Lewis Index (TLI) and the Bentler–Bonett Non-Normed Fit Index (NNFI) are both 0.785, which also indicates a fair fit and the Bentler–Bonett Normed Fit Index (NFI) is 0.730, which also indicates a fair fit. Additionally, the parsimony fit indices (PNFI and RFI) measure how well the model fits the data while using the fewest possible parameters. The greater the value, the better the fit, while keeping the number of parameters to a minimum. The Parsimony Normed Fit Index (PNFI) is 0.562, which indicates a poor fit, and Bollen’s Relative Fit Index (RFI), which is 0.650, indicates a fair fit. Moreover, IFI and RNI, often known as incremental fit indices, measure how much better a model fits the data compared to a null model. A greater value denotes a better model fit improvement. Bollen’s Incremental Fit Index (IFI), which is 0.842, indicates a fair fit. Lastly, the Relative Noncentrality Index (RNI) is 0.835, which indicates a fair fit.
Table 4 demonstrates other fit measures. The first metric is Root mean square error of approximation (RMSEA), and it measures the overall fit of the model. Values close to 0 indicate a good fit, and values close to 1 indicate a poor fit. Therefore, based on the result value, which is 0.077, the model fits the data well. The second metric is RMSEA 90% CI lower bound with a value of 0.057, and a value between 0.05 and 0.08 is acceptable. RMSEA 90% CI upper bound has a value of 0.097, meanwhile, which is poor. The RMSEA p-value is known as the probability that RMSEA is less than or equal to 0.05. If a p-value is greater than 0.05, the RMSEA value does not indicate a model rejection. Therefore, the p-value above is 0.014, which is less than 0.05, suggesting that the model does not fit the data well. The Standardized Root Mean Square Residual (SRMR) has a value of 0.076, and values close to 0 indicate a good fit whereas values close to 1 indicate a poor fit, which means, in this case, that a good fit is indicated. Hoelter’s critical N (α = 0.05) has a value of 112.740 and Hoelter’s critical N (α = 0.01) has a value of 125.877, and they are used to determine the minimum sample size needed to achieve a specified level of fit. The Goodness of Fit Index (GFI) and the McDonald Fit Index (MFI) are both measures of the overall fit of the model. High values of GFI and MFI indicate a good fit. A value of GFI greater than 0.9 means a satisfactory fit. Thus, the value of the GFI of the model above is 0.992, which indicates a satisfactory fit. The Expected Cross Validation Index (ECVI) has a value of 1.219, which is a low value, and lower values of ECVI indicate a better generalizability. Based on the above criteria, the model fits are acceptable.
The R-squared values for each predictor variable are given in Table 5. They show how much of the variance in the dependent variable is accounted for by each predictor. While an R-squared number close to 0 indicates that the predictor variable explains relatively little variance, an R-squared value close to 1 indicates that the predictor variable accounts for a significant amount of the variance in the dependent variable. In this case, the R-squared value for “Food Quality & Safety” is 0.720, which means that 72.0% of the variation in “Food Quality & Safety” can be explained by the independent variables. Moreover, the highest R-squared value for a single independent variable is 1.000 for “x4”, which indicates that this variable is a perfect predictor of the dependent variable.
5.3. Parameter Estimates
Table 6 illustrates the factor loading for each latent variable (Consumer Behaviour, Food Availability, Food Anxiety, Food Insecurity, Food Price, and Food Quality and Safety) and their corresponding indicators (x1–x13). Therefore, it shows the correlation between the latent variables and the indicator variables. The p-value indicates the statistical significance of the factor loading, and a low p-value demonstrates a strong relationship between the latent and indicator variables. The p-value for each latent and its indicator variable has a significant relationship because the p-value is less than 0.05, which shows that there is a strong correlation between all the latent variables and the indicator variables, and that they are linked to each other.
The factor variances for each latent variable are shown in Table 7 (Consumer Behaviour, Food Anxiety, Food Price, Food Availability, Food Quality and Safety, Food Insecurity, and Food Stress). Given that all other independent variables remain constant, the estimated change in the dependent variable is shown in the estimate column for each unit increase in the related independent variable, where all the values are below 0 and the Food Anxiety 2 is higher than 0, being 1.727. The standard error of the estimate for each coefficient is shown in the Std. Error column, and can be used to determine the confidence interval for each estimate. A range of values that probably includes the true population value of the coefficient is provided by the confidence interval.
The z-value column displays each estimate’s standard score, or z-score, which calculates how many standard deviations the estimate deviates from the estimated population’s mean. Each coefficient’s significance is evaluated using the z-value by comparing it to a normal distribution of known values. This indicates that the estimate is considerably different from 0, and that the associated independent variable likely to have an impact on the dependent variable is a large positive or negative z-value (for example, larger than 2). Food Anxiety has a larger z-value of 8.893, and has a greater impact on the dependent variable. Food Availability 4 and Food Insecurity are the next other latent variables, with z-values of 3.528 and 3.126, respectively.
The likelihood of witnessing a z-value as large as the computed one is shown in the p-value column, assuming that the relevant independent variable has no impact on the dependent variable. This indicates that the estimate is significant and that the associated independent variable likely to have an impact on the dependent variable is a low p-value (e.g., less than 0.05). Consumer Behaviour is 0.026, Food Anxiety is 0.001, Food Availability 4 is 0.001, and Food Insecurity is 0.002, among the latent variables with p-values less than 0.05. The lower and upper limits of the 95% confidence range for each estimate are shown in the lower and upper columns, respectively. At a 95% confidence level, these intervals offer a range of values that are most likely to include the true population value of the coefficient. Based on the statistic, the p-value of Food Quality and Safety and Food Stress is not accepted because it is greater than 0.05.
Table 8 shows the estimate, standard error, z-value, p-value, lower bounds, and upper bounds of the 95% confidence interval for residual variances (x1–x13). The estimate represents the estimated residual variance for each variable. The standard error is the standard deviation of the estimate. The z-value is a standardized estimate of the difference between the estimate and the population parameter (mean) divided by the standard error. The p-value is the probability that the difference between the estimate and the population parameter is due to chance. The lower and upper bounds of the 95% confidence interval give the range within which we expect the true residual variance to lie, with a 95% confidence. The p-values for all of the variables are less than 0.05, which indicates that the differences between the estimate and the population parameter are statistically significant.
5.4. Estimation of the Structural Model
The structural equation model is evaluated based on the importance and applicability of the path coefficients of the VIF between constructs and the predictive relevance. For each relationship in the dataset, path coefficients and p-values have been determined. Therefore, the study assessed the significant relationship between the hypothesis testing where the p value is less than 0.05, as shown in Table 9 and Figure 5.
6. Discussion and Recommendations
Here, we promote the development of a comprehensive theoretical framework that incorporates relevant theories and concepts related to supply chain management, crisis management, and resilience. This framework should serve as a foundation for understanding the impact of COVID-19 on the supply chain in the UAE food sector and guide the analysis of the empirical data.
Based on the theoretical framework and the existing literature, hypotheses should be formulated that represent specific relationships between the variables. These hypotheses can be derived from theories or prior empirical findings, and they should aim to explain the underlying mechanisms and dynamics of the impact of COVID-19 on the supply chain.
The collected data should be utilized to test and validate the formulated hypotheses. This can be done through statistical analysis using techniques such as SEM or regression analysis. By examining the empirical evidence, the study can contribute to the validation or refinement of existing theories or propose new theoretical explanations for the observed phenomena.
Potential mediating or moderating factors that influence the relationship between COVID-19 and the supply chain in the UAE food sector should be explored. This can involve investigating the role of factors such as government policies, technological advancements, organizational capabilities, or market conditions. By identifying these factors, the study can provide valuable insights into the underlying mechanisms that shape the impact of the pandemic on the supply chain.
The theoretical findings should be translated into practical implications for supply chain practitioners and policymakers in the UAE food sector. Actionable recommendations based on the identified relationships and dynamics should be provided. These recommendations should aim to enhance the resilience, efficiency, and sustainability of the supply chain in the face of future disruptions.
This study has examined customer food consumption patterns in relation to the principles of food security. The suggested conceptual framework links key components of food security like food availability, food access, food quality and safety, and food prices to the food consumption behaviour and food stress of consumers. Therefore, managerial and policy implications should be used in the following ways to address the food supply crisis and the food stress of consumers during a pandemic.
To maintain a lead time between changes in consumer demand and the suppliers’ response, a new agile strategy must be established. To prevent deleterious consumption behaviors, the government should plan educational events about food security and intensify education campaigns using a variety of media, including social media, newspapers, and news channels. In order to avoid disruptions in the food supply chain, the government must also support different systems of food production and consumption, from agriculture to transportation management, food storage, and marketing policy to regulations that are supportive of farmers and producers. Governments could also assist local farmers by providing zero-interest loans to assist them in producing food during the pandemic.
During the pandemic, supply chain management should switch from traditional planning strategies to innovative organizational transformation techniques. Diverse food flows and value networks must be ensured on a local and international level. It is important to ensure the proper supply chain management process, which includes identifying food processing, marketing, and distribution as essential services, guaranteeing worker safety, and keeping open trade channels between nations. Finally, to minimize the effects of restructuring food logistics systems, all sectors and stakeholders must collaborate to guarantee sustainable food production and consumption development during the outbreak of a crisis such as the COVID-19 pandemic.
7. Conclusions
The COVID-19 epidemic has suddenly and drastically disrupted the whole global food supply chain, including the whole process from production to logistics to the distribution to retail and the individual consumers. Structural Equation Modelling (SEM) was used to analyze the collected data from the consumer behavior questionnaire by collecting all the information and determining the percentage of each question. This enabled us to identify the variables that have had the most influence on the food supply chain during COVID-19 pandemic in the UAE.
Through the introduction of a new approach to understanding consumer purchasing behavior, this study has provided important findings in this research field. In the context of the pandemic in the UAE, this study explains the effects of the psychological food stress of the consumer and consumption behavior. The primary need of all humans is food. Consumers have been more anxious during COVID-19 due to the noticeable shift in food production and delivery. The impact of food availability and food insecurity on consumer behavior was significant. Food availability and food stress, as well as food insecurity and the price of food, were found to be strongly correlated. This suggests that if food is not easily accessible in local stores to satisfy daily demands during COVID-19, consumers will become more anxious.
The model fit proves that the measures fit in the model based on two measures, the AIC of 6897.376 and the BIC of 7035.866, which indicate a good fit to the model when they have a low value. The chi-square (χ2) is 121.730, with a degree of freedom (df) is 60, and a p-value < 0.001 with a sample size of 172, which demonstrates that the measures are statistically significant because the p-value is <0.5. There is a strong correlation between the latent variables and the indicator variables.
Consumer behavior is significantly related to the decision of purchasing (H1), storing food (H2), and reducing the wastage of food (H3), whereas food availability is correlated with unavailable food in grocery shops and local shops (H4) and the availability of enough food for customers to be satisfied (H5). Food anxiety is related to food running out for the customer (H6). Food insecurity has a strong correlation with feeling secure regarding food (H7) and the ability to meet daily nutritional needs (H8). In terms of food price, we found that this has a relationship with increasing the price of food (H9), and the ability to afford to buy food (H10). Food quality and safety are strongly related to the quality of food (H11), the food supplier (H12), and the low quality of food with contamination (H13). Last of all, food stress has a significant relationship to consumer behavior, food availability, food insecurity, food anxiety, the price of food, and food quality and safety.
Methodology, H.A.A., S.A. (Shouq Alzarooni), S.A. (Shaikha Almulla) and F.A.; Software, B.A.A., H.A.A., S.A. (Shouq Alzarooni), S.A. (Shaikha Almulla) and F.A.; Data curation, H.A.A., S.A. (Shouq Alzarooni), S.A. (Shaikha Almulla) and F.A.; Writing—original draft, B.A.A., H.A.A., S.A. (Shouq Alzarooni), S.A. (Shaikha Almulla) and F.A.; Supervision, Y.A.N. All authors have read and agreed to the published version of the manuscript.
Not applicable.
Not applicable.
Data can be made available by contacting the correspondence author.
The authors declare no conflict of interest.
Footnotes
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.
Hypothesis.
| Hypothesis | Relationship |
|---|---|
| H1 | Consumer Behaviour → Purchase Decision |
| H2 | Consumer Behaviour → Storing Food |
| H3 | Consumer Behaviour → Reduce Food Wastage |
| H4 | Food Availability → not available in grocery shops and local shops |
| H5 | Food Availability → Availability of Food for Customer Satisfaction |
| H6 | Food Anxiety → Food Runs Out |
| H7 | Food Insecurity → Feel Secured Regarding Food |
| H8 | Food Insecurity → Meeting Daily Nutritional Needs |
| H9 | Food Price → Increasing in Food Price |
| H10 | Food Price → Affording Food Buying |
| H11 | Food Quality & Safety → Food Quality |
| H12 | Food Quality & Safety → Food Supplier |
| H13 | Food Quality & Safety → Low Food Quality with Contamination |
| H14 | Food Stress → Food Anxiety |
| H15 | Food Stress → Food Price |
| H16 | Food Stress → Food Availability |
| H17 | Food Stress → Food Quality & Safety |
| H18 | Food Stress → Food Insecurity |
| H19 | Food Stress → Consumer Behaviour |
Model Fit.
| Baseline Test | Difference Test | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| AIC | BIC | n | χ² | df | p | Δχ² | Δdf | p | |
| Model 1 | 6897.376 | 7035.866 | 172 | 121.730 | 60 | <0.001 | 121.730 | 60 | <0.001 |
Fit Indices.
| Index | Value |
|---|---|
| Comparative Fit Index (CFI) | 0.835 |
| Tucker-Lewis Index (TLI) | 0.785 |
| Bentler-Bonett Non-normed Fit Index (NNFI) | 0.785 |
| Bentler-Bonett Normed Fit Index (NFI) | 0.730 |
| Parsimony Normed Fit Index (PNFI) | 0.562 |
| Bollen’s Relative Fit Index (RFI) | 0.650 |
| Bollen’s Incremental Fit Index (IFI) | 0.842 |
| Relative Noncentrality Index (RNI) | 0.835 |
Other Fit Measures.
| Metric | Value |
|---|---|
| Root mean square error of approximation (RMSEA) | 0.077 |
| RMSEA 90% CI lower bound | 0.057 |
| RMSEA 90% CI upper bound | 0.097 |
| RMSEA p-value | 0.014 |
| Standardized root mean square residual (SRMR) | 0.076 |
| Hoelter’s critical N (α = 0.05) | 112.740 |
| Hoelter’s critical N (α = 0.01) | 125.877 |
| Goodness of fit index (GFI) | 0.992 |
| McDonald fit index (MFI) | 0.836 |
| Expected cross validation index (ECVI) | 1.219 |
R-Squared.
| R² | |
|---|---|
| x1: Purchase Decision | 0.305 |
| x2: Storing Food | 0.338 |
| x3: Reduce Food Wastage | 0.098 |
| x4: Food Runs Out | 1.000 |
| x5: Increasing in Food Price | 0.325 |
| x6: Affording Food Buying | 0.319 |
| x7: Food is not available in grocery shops and local shops | 0.548 |
| x8: Availability of Food for Customer Satisfaction | 0.612 |
| x9: Food Quality | 0.670 |
| x10: Food Supplier | 0.133 |
| x11: Low Food Quality with Contamination | 0.343 |
| x12: Feel Secured Regarding Food | 0.466 |
| x13: Meeting Daily Nutritional Needs | 0.551 |
| Consumer Behaviour | 0.365 |
| Food Anxiety | 0.084 |
| Food Price | 0.206 |
| Food Availability | 0.378 |
| Food Quality & Safety | 0.720 |
| Food Insecurity | 0.377 |
Factor Loadings.
| Latent | Indicator | Estimate | Std. Error | z-Value | p | Lower | Upper |
|---|---|---|---|---|---|---|---|
| Consumer Behaviour | x1 | 1 | 0 | 1 | 1 | ||
| x2 | 1.218 | 0.348 | 3.502 | <0.001 | 0.536 | 1.9 | |
| x3 | 0.565 | 0.21 | 2.697 | 0.007 | 0.155 | 0.976 | |
| Food Availability | x7 | 1 | 0 | 1 | 1 | ||
| x8 | 1.071 | 0.198 | 5.4 | <0.001 | 0.682 | 1.459 | |
| Food Anxiety | x4 | 1 | 0 | 1 | 1 | ||
| Food Insecurity | x12 | 1 | 0 | 1 | 1 | ||
| x13 | 0.993 | 0.213 | 4.666 | <0.001 | 0.576 | 1.41 | |
| Food Price | x5 | 1 | 0 | 1 | 1 | ||
| x6 | 1.141 | 0.475 | 2.4 | 0.016 | 0.209 | 2.073 | |
| Food Quality and Safety | x9 | 1 | 0 | 1 | 1 | ||
| x10 | −0.415 | 0.104 | −3.976 | <0.001 | −0.619 | −0.21 | |
| x11 | 0.69 | 0.121 | 5.721 | <0.001 | 0.453 | 0.926 | |
| Food Stress | Food Anxiety | 1 | 0 | 1 | 1 | ||
| Food Price | 0.72 | 0.332 | 2.165 | 0.03 | 0.068 | 1.371 | |
| Food Availability | 1.397 | 0.503 | 2.774 | 0.006 | 0.41 | 2.384 | |
| Food Quality & Safety | 2.105 | 0.707 | 2.978 | 0.003 | 0.72 | 3.49 | |
| Food Insecurity | −1.201 | 0.445 | −2.699 | 0.007 | −2.073 | −0.329 | |
| Consumer Behaviour | 0.982 | 0.389 | 2.52 | 0.012 | 0.218 | 1.745 |
Factor Variances.
| 95% Confidence Interval | ||||||
|---|---|---|---|---|---|---|
| Variable | Estimate | Std. Error | z-Value | p | Lower | Upper |
| Consumer Behaviour | 0.265 | 0.119 | 2.226 | 0.026 | 0.032 | 0.499 |
| Food Anxiety | 1.727 | 0.194 | 8.893 | <0.001 | 1.346 | 2.108 |
| Food Price | 0.315 | 0.162 | 1.945 | 0.052 | −0.002 | 0.632 |
| Food Availability | 0.507 | 0.144 | 3.528 | <0.001 | 0.225 | 0.789 |
| Food Quality and Safety | 0.272 | 0.169 | 1.615 | 0.106 | −0.058 | 0.603 |
| Food Insecurity | 0.377 | 0.121 | 3.126 | 0.002 | 0.141 | 0.613 |
| Food Stress | 0.158 | 0.098 | 1.608 | 0.108 | −0.035 | 0.351 |
Residual Variances.
| 95% Confidence Interval | ||||||
|---|---|---|---|---|---|---|
| Variable | Estimate | Std. Error | z-Value | p | Lower | Upper |
| x1 | 0.951 | 0.157 | 6.052 | <0.001 | 0.643 | 1.258 |
| x2 | 1.217 | 0.219 | 5.566 | <0.001 | 0.788 | 1.645 |
| x3 | 1.235 | 0.145 | 8.533 | <0.001 | 0.951 | 1.518 |
| x4 | 0 | 0 | 0 | 0 | ||
| x5 | 0.822 | 0.188 | 4.371 | <0.001 | 0.453 | 1.191 |
| x6 | 1.105 | 0.247 | 4.48 | <0.001 | 0.622 | 1.589 |
| x7 | 0.672 | 0.156 | 4.297 | <0.001 | 0.365 | 0.978 |
| x8 | 0.593 | 0.171 | 3.467 | <0.001 | 0.258 | 0.929 |
| x9 | 0.479 | 0.148 | 3.225 | 0.001 | 0.188 | 0.769 |
| x10 | 1.093 | 0.124 | 8.821 | <0.001 | 0.85 | 1.336 |
| x11 | 0.885 | 0.118 | 7.471 | <0.001 | 0.653 | 1.117 |
| x12 | 0.695 | 0.142 | 4.883 | <0.001 | 0.416 | 0.973 |
| x13 | 0.486 | 0.13 | 3.728 | <0.001 | 0.23 | 0.741 |
Hypothesis Testing.
| Hypothesis | Relationship | Decision Support | |
|---|---|---|---|
| H1 | Consumer Behaviour à Purchase Decision | 0 | Yes |
| H2 | Consumer Behaviour à Storing Food | <0.001 | Yes |
| H3 | Consumer Behaviour à Reduce Food Wastage | 0.007 | Yes |
| H4 | Food Availability à not available in grocery shops and local shops | 0 | Yes |
| H5 | Food Availability à Availability of Food for Customer Satisfaction | <0.001 | Yes |
| H6 | Food Anxiety à Food Runs Out | 0 | Yes |
| H7 | Food Insecurity à Feel Secured Regarding Food | 0 | Yes |
| H8 | Food Insecurity à Meeting Daily Nutritional Needs | <0.001 | Yes |
| H9 | Food Price à Increasing in Food Price | 0 | Yes |
| H10 | Food Price à Affording Food Buying | 0.0016 | Yes |
| H11 | Food Quality and Safety à Food Quality | 0 | Yes |
| H12 | Food Quality and Safety à Food Supplier | <0.001 | Yes |
| H13 | Food Quality and Safety à Low Food Quality with Contamination | <0.001 | Yes |
| H14 | Food Stress à Food Anxiety | 0 | Yes |
| H15 | Food Stress à Food Price | 0.03 | Yes |
| H16 | Food Stress à Food Availability | 0.006 | Yes |
| H17 | Food Stress à Food Quality & Safety | 0.003 | Yes |
| H18 | Food Stress à Food Insecurity | 0.007 | Yes |
| H19 | Food Stress à Consumer Behaviour | 0.012 | Yes |
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; Budur Al Ali; Hind Al Ali; Alzarooni, Shouq; Almulla, Shaikha; Alteneiji, Fatima