impact of covid 19 on consumer buying behaviour research paper

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• Published: 19 January 2024

Analyzing the impact of COVID-19 on consumption behaviors through recession and recovery patterns

• Rui Chen 1 ,
• Tong Li 1 &
• Yong Li 1  

Scientific Reports volume  14 , Article number:  1678 ( 2024 ) Cite this article

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• Computational science
• Health care
• Scientific data

The COVID-19 outbreak has dramatically impacted the economy, particularly consumption behaviors. Studies on how consumption responses to COVID-19 can be a powerful aid for urban consumption recovery. In this paper, based on a high-frequency consumption dataset from January 6, 2020, to April 28, 2020 covering 18 sectors and dataset from the corresponding lunar period in 2021, we look at how COVID-19 changed how people spent their money by looking at patterns of recession and recovery during the pandemic. Specifically, we first explore the recession-recovery pattern of national consumption and the effects of various policies and quantify it using regression methods. Then, recession-recovery patterns across cities are widely studied. We also reveal how consumption structures change during a pandemic and the relationship between patterns of change in citizens’ consumption and the socioeconomic characteristics of cities. And the specific empirical analysis is provided through panel regression models. In general, national consumption represented a Vshaped pattern during the pandemic, experiencing a dramatic decline and a rapid rebound. Consumption is significantly inhibited by lockdown, while it is stimulated positively but gradually by easing policies. Consumption patterns at the city level are associated with socioeconomic characteristics. Cities with high-income groups experience a more significant decline, and cities with a high share of the secondary sector have a higher recovery rate in consumption. The consumption structure redistributes but does not fundamentally change. During the recession and early recovery phase, consumption related to basic living saw a significant rise, whereas leisure-related consumption dropped dramatically and recovered slowly. Our study can assist policymakers in implementing diversified market provisions and targeted lockdown policy adjustments for consumption recovery in cities with different socioeconomic backgrounds.

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Introduction.

The beginning of COVID-19 in Wuhan in 2020 was described as the biggest ‘black swan’ event to hit China in more than a decade. Incalculable loss of life and property has been caused by the COVID-19 outbreak. To combat this incredibly contagious coronavirus, the Chinese government has successfully implemented preventive measures 1 , 2 , 3 . Policies like city lockdown, home quarantine, travel restrictions, the closure of entertainment venues, and bans on public gatherings were swiftly implemented throughout China within a few days 4 , 5 , 6 . It has been demonstrated that these anti-epidemic measures stop the coronavirus from spreading 6 , 7 , 8 , 9 , 10 , 11 , however, the pandemic and lockdown measures together had a significant negative impact on China’s economy, particularly on citizens’ consumption 12 , 13 .

Urban consumption is an important driver of economic growth. Meanwhile, urban consumption can be significantly influenced by major economic shocks, such as the Great Recession of 2007–2008, which had a substantial impact on household consumption behavior due to factors like rising home equity and permanent income shocks 14 . Significant external economic shocks may jointly determine income, household consumption, and borrowing dynamics. However, the use of micro-level consumption data helps mitigate this concern. Many researchers have paid close attention to how the COVID-19 outbreak affects consumption. Studies on the consumption responses to COVID-19 pandemic are rapidly emerging 15 , 16 , 17 , 18 , 19 . Previous studies have examined the pandemic impacted consumption behaviors from multiple perspectives, including impacts of the COVID-19 and lockdown policies 20 , 21 , 22 , 23 , changes in consumption structure 21 , 22 , 23 , 24 , social-economic factors affecting consumption during COVID-19 pandemic 25 , 26 , 27 . Some have studied the direct impact of COVID-19 on consumption and identified that it had a huge negative impact on consumption, resulting in a dramatic drop in consumption volume in various countries and regions around the world 15 , 20 , 23 , 28 , 29 . Specifically, these studies focused on studying the trend of consumption during the recession phase. For example, the downward trends of state-level consumption and consumption across sectors were widely investigated in various countries or regions like Europe 30 , North America 31 , 32 , 33 and China 23 , 29 , 34 . Some studies have focused on the effects of COVID-19 response policies, as it is highly useful to test intervention effectiveness and offer policy-relevant lessons. They have measured how representative COVID-19 lockdown policies, such as city lockdown, capacity restrictions and social distancing laws, affect consumption behaviors during the pandemic 20 , 35 , 36 , 37 .

Existing studies only explain how the COVID-19 pandemic causes a drop in consumption behavior 15 , 20 , 28 , 29 , however, it is potentially assumed that there exists a consumption basket whose ingredients do not change over this course. Therefore, the previous research is almost silent on how the consumption structure, the proportion of various consumption expenditures in the total expenditure, changes before and during the pandemic. Additionally, they do not delve into the whole recession-recovery patterns of both consumption expenditure and consumption structure over the course of the pandemic. Many research studied changes in consumption at the national level, whereas our understanding of consumption recession-recovery patterns at the city level is insufficient. The relationship between socioeconomic characteristics at the city level and consumption patterns during recessions and recoveries is also much less well understood. Consumption is an essential part of economic development. For the purpose of identifying the consumer market pattern in the post-COVID period and the development of consumer upgrading among citizens, it is crucial to comprehend changes in consumption during COVID-19. Understanding how the COVID-19 outbreak and response policies impact the consumption behaviors of citizens over the pandemic period is critical to helping design strategies to prevent the potential economic and societal impacts of changing consumption demands. To achieve this goal, extensive and large-scale research that goes beyond simple descriptive analysis is required.

In this study, we investigate the impact of COVID-19 on consumption behaviors by revealing the recession-recovery pattern of consumption at the national and city levels over the pandemic period. We use high-frequency consumption data from Meituan as a proxy for official national consumption statistics, to reveal consumption responses to the COVID-19 outbreak, which has high precision in the temporal dimension. Meituan is one of the largest big data platforms for life services in China, records and stores a large number of personal daily life consumption records generated in the course of national economic life, and provides us with high-frequency consumption data to track the dynamic changes of citizens’ consumption during the pandemic. The consumption data covers the period from January 6, 2020 (pre-lockdown) to April 28, 2020 (post-unblock) in 53 major cities across mainland China. Notably, our intention is not to capture all possible consumption behaviors but to use a proxy measure to detect an important state- and city-level temporal change pattern in citizens’ consumption during the COVID-19 pandemic. Specifically, we investigate the whole recession and recovery patterns of consumption in the whole country and more than 50 Chinese cities during the pandemic. Then we quantify and compare the effects of various COVID-19 response policies, with a focus on the easing policies’ diminishing beneficial effects on consumption recovery. We also reveal the patterns of temporal changes in consumption structure. Furthermore, the relationship between a city’s socioeconomic characteristics and its recession-recovery pattern of consumption is also explored.

The main contributions of our study are as follows. First, furthering our understanding of consumption responses to COVID-19, we find that aggregate national consumption displays an apparent V-shaped pattern of recession and recovery, which is characterized by several phases with distinct features. Second, we assess the differential effects of COVID-19 response policies, especially easing policies during the recovery phase. And we fully quantify the impact of policies on aggregate national consumption and consumption at the city level, which is important for a large country with a large population such as China. The city’s lockdown policy has a significant negative impact on consumption. On the contrary, easing policies have diminishing positive effects on consumption. We show that the resumption of work and production greatly contributes to consumption recovery. Reopening of gathering places has only a weak boost to consumption, while lifting lockdown has the mildest or even non-significant effect on consumption compared with other easing policies. Third, we provide preliminary evidence on the nature of temporal change patterns in consumption across sectors during the pandemic, which gives us insights into how consumption structure changes over the pandemic period. We observe that COVID-19 alters consumption structure in the short term, but without fundamental changes. In particular, we discuss in what sense these changes in consumption reshape sustainable consumption. Last, we further investigate in depth the association between city-level recession-recovery patterns of consumption and socioeconomic attributes, which has not been considered in any previous studies. Specifically, we focus more on the dynamic patterns of different consumption sectors and used the panel regression model to demonstrate the heterogeneous effects of pandemic changes on urban consumption across different socioeconomic backgrounds.

Notably, income significantly influences the recession of city-level consumption, while the economic structure plays a key role in the recovery phase. It suggests that COVID-19 has exacerbated the shortcomings of economic inequalities across social and economic groups.

Temporal variations of consumption behaviors

Temporal variations of national consumption behaviors. ( a ) Year-over-Year growth rate of aggregate national consumption and cases of COVID-19 infection. ( b ) Time series of aggregate national consumption expenditure in 2020 and 2021.

Consumption expenditures of citizens exhibit a distinctive V-shaped temporal pattern in terms of aggregate national statistics (Fig.   1 a). Notably, the impact of the COVID-19 pandemic on consumption can be analyzed by measuring the change in consumption from two perspectives: the consumption expenditures and the volume of consumption orders. Here, we conduct analysis based on consumption expenditures, and the analysis of the volume of consumption orders is detailed in Supplementary Note 4 . With the onset of COVID-19, citizens’ consumption has drastically reduced because of both rising COVID-19 infections and national lockdown policies. Once the pandemic was contained, consumption quickly and significantly recovered. To be specific, national consumption expenditures bottomed out after a sharp decline of about 60% on February 10, 2020. After that, production and work are permitted to resume as the number of COVID-19 cases gradually decreases. As a result, national consumption recovers rapidly. When cities reopen on April 28, 2020, aggregate national consumption recovers by approximately 90%, falling only slightly short of normal levels.

The V-shaped consumption pattern can be separated into three distinct phases, including recession, recovery I and recovery II (Fig.   1 b).

Recession phase (January 23, 2020–February 9, 2020).

On January 23, 2020, the city lockdown policy was implemented following the COVID-19 outbreak. The drastic reduction in citizens’ consumption behaviors is brought on by store closures and infection-related fear. Notably, the Chinese New Year (January 25) causes consumption spending to decline in both 2020 and 2021. Consumption spending bounces back quickly in 2021 after the holiday, but due to the COVID-19 outbreak, it remains severely depressed (with the lowest level of consumption recorded as − 62%) until February 10 in 2020. Accordingly, year-over-year consumption growth plummets sharply during the recession phase (Fig.   1 a), demonstrating the COVID-19 outbreak and lockdown policy have a significant negative impact on residents’ consumption behaviors.

Recovery phase I (February 10, 2020–March 2, 2020).

The government declared the resumption of work and production on February 10, 2020, which marks the beginning of recovery phase I. After people gradually resume their regular work and production, their consumption behaviors rebound sharply, creating a V-shaped reversal in national consumption expenditures (Fig.   1 a). Notably, consumption expenditure volume is still substantially below normal levels at this phase. Also, cyclical fluctuations, which are visible in consumption temporal variations during normal times, are not present in this phase (Fig.   1 b). These show that consumption is still far from returning to normalcy in terms of the expenditure volume and the cyclical pattern.

Consumption activities for basic needs, including dining, education, and life services, are the main sectors to recover during this phase (Supplementary Note 2 ). It is interesting to note that hotel consumption has significantly increased, which may be related to the need for workers to undergo a quarantine period before returning to the office. Leisure-related consumption sectors, on the other hand, such as entertainment, karaoke, and travel, remain subdued and do not appear to be on the mend (Supplementary Note 2 ), where the entertainment sector specifically includes gyms, camping, arcade game centers, massage, etc. The prohibition of group gatherings at this time prevents citizens from engaging in leisure-related consumption behaviors, contributing to the continued depression in related sectors of the economy.

Recovery phase II (March 3, 2020–April 28, 2020).

Recovery phase II begins on March 3, 2020, when the government proclaimed that gathering places were once again open. During this phase, national consumption expenditures keep increasing, but at a relatively slower rate than before. By the end of April 2020, national consumption had returned to its pre-epidemic level (Fig.  1 a). In this phase, an apparent cyclical fluctuation pattern also appears (Fig.  1 b). Both of them imply that citizens’ consumption activities have returned to normalcy at the end of this phase.

The beginning of a leisure-related consumption rebound, including karaoke, entertainment, and travel, can be seen during this phase. In contrast to recovery phase I, this phase is marked by significant cyclical fluctuations in the majority of consumption sectors (Supplementary Note 2 ). The reopening of gathering places is supposed to encourage residents to engage in more offline consumption, particularly in leisure-related sectors, resulting in a weekend peak in consumption (Fig.  1 b).

Effects of lockdown and its easing

The trend in aggregate national consumption changes dramatically after the lockdown policy is implemented Fig. 1 a. Specifically, we can observe that there is a very significant drop in aggregate national consumption after the implementation of lockdown policies (Fig. 1 a, January 23, 2020). Correspondingly, after the lifting of restrictions, the aggregate national consumption rebounds immediately (Fig. 1 a, February 9, 2020). Clearly, the changes in consumption are associated with the pandemic and intervention policies.

The impact of pandemic response policies on consumption has been observed in relevant literature. Lockdown policies resulted in a sharp decline in consumption, particularly among low-income households 38 . However, the policy effects vary significantly 39 . Therefore, we next investigate how response policies affect economic activity using the first lockdown in China that was imposed in January 2020 and its subsequent gradual easing. To be specific, we perform regression analysis on the year-over-year growth index (YGI) and the recovery gap index (RGI) of consumption activities to quantify the effects. The year-over-year growth index (YGI) is the ratio of the difference in national citizens’ consumption expenditures between 2020 and 2021. The recovery gap index (RGI) is the difference between the national citizens’ consumption expenditures in 2020 and 2021. According to Table  1 , the city lockdown policy has a considerable negative effect on consumption activity. Also, COVID-19 infections show strong detrimental effects on the economy, which may be a result of people’s voluntary social distancing in response to rising infections. The lockdown policy shows more harmful impacts on consumption with a larger regression coefficient than COVID infections. Because stores are closed and people are unable to go out, the lockdown restricts both the supply and demand sides of consumption activity. Easing policies, such as the resumption of work and production, the reopening of gathering places, and the lifting of the lockdown, have a positive impact on consumption activity, though their effects are gradually fading. Specifically, consumption greatly benefits from the resumption of work and production, as it has the largest positive coefficients for both indexes. The reopening of gathering places provides a modest boost to consumption. Surprisingly, the coefficients of the lifting lockdown in the two regressions are small or non-significant, indicating that its incentive effect on citizens’ consumption is very limited.

Recession and recovery comparison between cities

By revealing city-specific recession-recovery rates, we explore how the COVID-19 outbreak influences urban consumption across different cities. The recession rate represents the ratio of the difference in consumption expenditure between the pre-lockdown level and the minimal level to the number of days it took to reach the minimal level. The recovery rate is defined as the ratio of the difference in consumption expenditure between the normalcy level and the minimal level to the number of days needed to return to the normalcy level. The details of the definition of recession and recovery rates are given in Supplementary Note 1 . Figure 2 a shows the scatter plot of the recession and recovery rates of 53 cities with various degrees of COVID-19 pandemic severity. Cities with more COVID-19-infected cases experienced a significant decline in consumption expenditure within a short time, showing a large recession rate. In general, recession and recovery rates are positively and linearly correlated with one another, corresponding to the V-shaped pattern (Fig.   1 a), reflecting a high level of economic resilience in cities.

Recession and recovery pattern for 53 cities.

Based on the relative positions to the regression line, cities can be divided into two groups (Fig. 2 a). Cities in group 1, with scatter points above the regression line, have a faster recovery in consumption compared with cities in group 2 (Fig. 2 a, Group 1). Alternatively, cities in group 2 with scatter points below the regression line have a lower relative recovery rate (Fig. 2 a, Group 2). It is interesting to note that this difference for the two groups of cities is related to one socioeconomic indicator, namely the share of the secondary and tertiary sectors in the city-level economy (Fig. 2 b). Specifically, the secondary sector is dominated by the manufacturing and infrastructure industries. The tertiary sector is dominated by service-related industries, such as recreation, socializing, and education (details in Supplementary Note 7 ). In our case, we focus on the consumption behaviors of citizens and adopt the share of employees in these two sectors to characterize this socioeconomic indicator. Cities in group 1 have a high share ( \(60\%\) on average) of employees in the secondary sector (Fig. 2 b), making them with a developed secondary sector. Cities in group 2 possess a high share of employees in the tertiary sector ( \(60\%\) on average) (Fig. 2 b), which can be classified as cities with a developed tertiary sector. Therefore, the recovery rate observed is associated with differences in city-level economic structure.

We next conduct regression analysis to quantitively reveal how socioeconomic factors affect city-level consumption. Specifically, using a panel regression model (see “ Methods ” for details), we further quantified the combined effect of city-level socioeconomic variables (labor force, GDP, income, secondary, and tertiary sectors), response policies (the city lockdown and its easing), and daily COVID-19 incidence (nationwide and city-level cases) on the year-over-year growth index (YGI) and the recovery gap index (RGI) of city-level consumption during the period from January 6 to April 28, 2020 and the same period in 2019 (lunar calendar) (Tables 2 , 3 ). The first column gives the basic city-level time series pattern in the data, as a function of all variables mentioned above. It is apparent that city-level consumption is significantly correlated with each variable in the benchmark regression (Tables 2 and 3 , column (1)). Cities with a larger labor force, a larger GDP, higher income, or a larger fraction of secondary and tertiary sector workers consume more on average (Tables 2 and 3 , column (1)). In columns (2), we included a interaction term between the nationwide COVID-19 incidence and socioeconomic variables to explore how these socioeconomic factors impact city-level consumption, especially during the pandemic (Tables 2 and 3 , column (2)). For example, GDP has an interaction term of − 0.078 (Tables 2 , column (2)), which implies that the positive impact of GDP on daily city-level YGI continues to weaken as the number of COVID-19 cases increases. In short, the growth of COVID-19 cases weakens the positive impact of GDP on city-level consumption growth. Alternatively, per capita GDP (Tables 2 and 3 , column (2)) means that as the number of pandemic cases increases, the positive impact of per capita GDP on total urban consumption continuously diminishes. In such a scenario, it is possible that once the pandemic reaches a certain severity threshold, the positive impact of per capita GDP on total urban consumption weakens to zero or even becomes negative. Therefore, it can be inferred that when the pandemic is below a certain critical value, per capita GDP positively affects aggregate urban consumption. However, when the pandemic exceeds this critical value, per capita GDP may have a negative impact on aggregate urban consumption.

City-level recession-recovery patterns of consumption are associated with socioeconomic attributes during the pandemic. When compared to cities with lower-income groups, cities with higher-income groups see a bigger drop in consumption. Also, cities with a high share of tertiary sector workers recover more slowly in terms of consumption, whereas they recover more rapidly in cities with a high share of secondary sector workers (Tables 2 and 3 , column (2)). Such a conclusion persists after additionally controlling for city fixed effects (Tables 2 and 3 , column (3)). Moreover, socioeconomic variables have differential impacts on city-level consumption at different phases (Supplementary Note 3 ). Income and GDP are the most impactful variables in the recession phase, while the economic structure contributes more to the recovery phase. Specifically, the secondary sector counts more in Recovery Phase I, and the tertiary sector is crucial to Recovery Phase II (Supplementary Note 3 ), which supports our earlier finding about the gradual recovery of leisure-related consumption (Supplementary Note 2 ).

Evolution of the share of aggregate national consumption across sectors during COVID-19 pandemic.

Consumption structure of citizens during COVID-19 pandemic

The national consumption structure exhibits a remarkable dynamic pattern in alignment with lockdown and easing policies in the recession and recovery phase I (Fig.   3 ), but eventually recovers to its pre-lockdown pattern. Specifically, consumption structure refers to the share of each type of consumption expenditure in the total consumption expenditure. The details of the definition of consumption structure are given in Supplementary Note 6 . Market shares of national consumption across sectors were quite stable prior to the implementation of city lockdown, with each sector distributing roughly even except for a small fraction of life essentials, medicine, and car consumption. Immediately following the city lockdown, a clear pattern of redistribution emerges: spending on life essentials and medicines grows greatly, with these two sectors alone accounting for over 60% of national consumption by early February, while other sectors like education, dining, and leisure shrink entirely, reflecting the sharp growth in demand for daily necessities and health, as well as the collapse in demand for non-essential consumption during the recession phase (Fig.   3 ). National consumption recovers after the easing of policy restrictions, and the market share of consumption for basic living and working, like dining, education, and car consumption, recovers strongly after the resumption of work and production on February 10, 2020 (Fig.   3 ). Except for a slow recovery in leisure consumption, the market share of consumption steadily returns to the pre-lockdown distribution in recovery phase II after the reopening of gathering places on March 3, 2020 (Fig.  3 ).

Dynamics of city-level consumption structure patterns during COVID-19 pandemic. Shares are computed as seven-day moving averages (MA).

To investigate the relationship between consumption structure and income levels during the pandemic, we reveal the dynamic consumption patterns of the bottom 10 and top 10 cities ranked by per capita income (Fig.  4 a and b). It shows results consistent with previous analysis of national consumption structure (Fig.  3 ). They exhibit similar dynamic patterns of national consumption structure, but with some remarkable distinctions in terms of market shares. In recession and recovery phase I, the share of medicine and life essentials increase rapidly and significantly in all cities, with other sectors shrinking to varying degrees (Fig.  4 a and b). In particular, consumption of education, dining and other (pet and beauty) sectors experience a much faster recovery in high-income cities, when compared with the slow recovery in most consumption, except for medicine and life essentials, in low-income cities (Fig.  4 a). Therefore, throughout the entire economic trauma of the pandemic, with medical and daily life-related consumption comprising nearly 80% of the consumption pattern (Fig.  4 a), residents in low-income cities emphasized the consumption of daily necessities. Conversely, residents in high-income cities exhibited a faster recovery in consumption across sectors other than life-related and medical consumption (Fig.  4 b), with a particular emphasis on education investment (during the recovery period I, education consumption accounts for the second highest share at around 24%) (Fig.  4 b) . Notably, during the recovery phase I, their share of education consumption is the highest among all sectors, except for essential daily life-related and medical consumption (Fig.  4 b).

By ranking the proportion of secondary and tertiary sectors, we reveal the relationship between dynamics of city-level consumption structure and economic structure (Fig.  4 c and d). In recession and recovery phase I, living (hotel and homestay) and education consumption recover faster and account for a large proportion of total consumption in cities with a developed tertiary sector (Fig.  4 c), whereas dining and car consumption recover faster and make up a significant proportion of total consumption in industrially developed cities (Fig.  4 d). It suggests that citizens place more emphasis on practicality in daily consumption owing to the needs of commuters in cities with a developed secondary sector. Nevertheless, citizens are more development and enjoyment-oriented when they consume in cities with a developed tertiary sector.

Our research concurs, in certain aspects, with the outcomes of preceding studies conducted in China amidst the COVID-19 pandemic. Analogous investigations have noted a swift decline in consumer behaviors subsequent to the enforcement of quarantine measures. Additionally, there was a more pronounced decrease in discretionary spending as compared to essential goods consumption. However, our study diverges from previous works in several pivotal respects. Primarily, we have quantified the role of response policies, discerning a gradual attenuation in the positive effects of policy relaxation on consumption. Secondly, we have meticulously examined, through panel regression models, how socioeconomic factors influence the shifts in consumption behaviors during the pandemic, an area that has not been extensively explored in earlier research. Moreover, by contrasting with former studies, our analysis further reveals that the structural changes in consumption during the pandemic did not fundamentally reverse. We also observed that, notwithstanding the abundance of research on consumer behaviors during the pandemic, studies focusing on how the consumption structure evolves over the course of the pandemic remain relatively scarce. Utilizing high-frequency consumption data provided by Meituan, our study offers a novel perspective to understand this phenomenon.

There are limitations to our study, including the focus on the first wave of the epidemic in China and the need for long-term data analysis. Due to data limitations, we only studied the first wave of the epidemic in China, and more than one year of data analysis may be needed to understand long-term changes in consumption behavior. Additionally, we only studied typical socioeconomic characteristics and did not consider more heterogeneous characteristics at the city level, such as racial differences. Still, our study focuses on tracking dynamic changes in consumption behavior, and future research could further develop predictive methods for consumption behavior prediction.

Our study fills a void in the existing studies on the consumption response to the epidemic by ingeniously demonstrating that the high-resolution data provided by Meituan, a big data platform for life services, can be used as a proxy for national consumption items to analyze the dynamics of consumption during the epidemic. We did a large-scale analysis using different types of real-world data from more than 50 major cities to measure how consumption patterns change over the course of an epidemic’s life cycle and how they relate to socioeconomic factors at the city level. Our results show that government departments can use the data to understand how consumption changed during COVID-19, keep track of changes in the structure of consumption, and change response measures accordingly. Specifically, we find that higher-income groups experience greater consumption declines than lower-income groups during the pandemic. This is likely due to the fact that COVID-19 and lock-down policies, by their very nature, have a greater impact on the enjoyment-focused consumption pattern prevalent among wealthier citizens. With our findings, the government has the opportunity to take radical decisions regarding consumption recovery and addressing inherent inequalities. The provision of more opportunities to support leisure-related consumption, such as travel, movies, and sports, may be crucial to enhancing the openness of our society and economy. Also, it is suggested that the government take steps to create more jobs and ease the unemployment crisis caused by COVID-19. This would raise people’s incomes and encourage them to consume. According to the analysis of the shift in consumption structure during the lockdown, we propose that market supply must be diversified to effectively increase consumer demand. As the change in consumption structure during the pandemic varies across social and economic groups, the market will exhibit characteristics of consumption stratification. In order to achieve a better balance between supply and demand, the market supply must meet the diverse consumption needs of various urban groups. Businesses and service providers in high-income areas are encouraged to increase the number of goods and services that help people grow and have fun. In low-income areas, however, there is an urgent need for goods and services that are affordable and help people live better lives.

Our research shows how COVID-19 and response policies affect the way people go about their daily lives after a pandemic. First, we discover a V-shaped pattern, as the general consumption recovery is led by a natural increase in consumption without policy-driven effects. After experiencing a sharp decline, consumption rebounded rapidly with the implementation of the resumption of work and production, exhibiting a V-shaped recession-recovery pattern. Second, for the policy response, both the recession and the recovery of consumption are related to the physical constraints imposed by the quarantine. When the quarantine is relaxed, such as with the resumption of work and production, the suppressed consumption for basic needs recovers quickly. We also notice that the positive effect of easing policies on consumption is diminishing. The essence of consumption recovery, which has almost returned to pre-epidemic levels, lies in the strong recovery in consumption for basic needs in the early phase, in contrast to leisure-related consumption, which starts to recover only at a later stage. Third, by comparing recession and recovery patterns of consumption across cities, we reveal that they are associated with differences in city-level socioeconomic attributes like economic structure and citizens’ income. The COVID-19 outbreak has a larger negative impact on consumption recovery for those from cities with developed tertiary sectors. Underlying this result is a slow recovery in leisure-related consumption. On the contrary, cities with developed secondary sectors, often considered to provide more employment and material security, exhibit a faster recovery in consumption. Moreover, we document that higher-income groups suffer larger declines in consumption compared with lower-income groups during the pandemic. Finally, despite the redistribution throughout the lockdown period, there is no fundamental change in consumption structure, which to some extent indicates that consumption is relatively resilient and the trend of consumption upgrading is not reversed by COVID-19.

The vast bulk of our research is based on Meituan’s consumption data in mainland China from 18 different sectors, which consists of daily consumption records. Specifically, the 18 sectors include Entertainment, sports, travel, karaoke, movie, life services, households, shopping, weddings, offspring, hotel, homestay, dining, education, medicine, car, pet and beauty. Meituan platform records consumption data through users’ activities. This includes various user actions such as browsing, searching, ordering, paying, and evaluating on the app. These activities generate a large amount of data, including user behavior, timestamps, geographic location, consumption categories, and other information. Each activity and order is timestamped to record when the user performed an action. To examine the pandemic impact on consumption, we collected consumption records from January 6, 2020, to April 28, 2020, and the data from the same lunar calendar period in 2021 for comparison, thus eliminating the impact of the Chinese Lunar New Year. The dates of January 6 and April 27 are 2 weeks before and 2 weeks after city lockdown, for a total of 112 days, allowing us to analyze the entire COVID-19 period.

Except for movie and karaoke (two sectors that have been missing for a long time due to restriction policies), we filter out cities with inactive consumption by selecting cities with at least one consumption record in each sector within a month. As a result, \(72\%\) of all cities in the mainland including 53 cities, are represented in Meituan data.

Due to the sparse format of the data, consumption records for some cities on certain dates may be missing. To solve this problem, we perform data interpolation to recover temporal no-consumption entries. Temporally no record is normal in urban space, but long-term vacancy is likely owing to lockdown policies. If a certain sector of consumption has no record for less than three continuous days, we fill the discontinuous entries with mean values within the gaps. For long-term vacancies that exceed 3 days, we preserve the gaps.

We also collect data on consumption from urban households from the National Bureau of Statistics and the China Urban Statistical Yearbook for comparison and validation. To validate the representativeness of our data, we examine its Pearson correlation with household consumption expenditure data from the China Urban Statistical Yearbook from the temporal and spatial dimensions, which shows a significant correlation, proving that our consumption data can be used as a proxy indicator of official consumption data series (details in Supplementary Note 5 ).

Evaluation indicators

We summarize the symbolic representation of variables used in our paper in Table  4 . We construct two indicators of year-over-year growth index and recovery gap based on city granularity to further evaluate the recession-recovery pattern of consumption, where i represents a city, t represents time, and tc represents total city-level consumption. The Year-over-Year Growth Index (YGI) is a measure of relative recession and recovery in city-level consumption:

The Recovery Gap Index (RGI) measures the absolute gap in city-level consumption recovery. The Year-over-Year Growth Index cannot reveal the absolute distance of the city returning to normal because it only reflects the relative degree of recovery. Although consumption in some cities is showing signs of improvement, the absolute gap remains large, the distance to full recovery is long, and the risk of a consumption downturn is high. As a result, we utilize the recovery gap as another indicator of city-level consumption recovery:

Empirical strategy

Linear regression modeling is used to study the impact of COVID-19 and its response policises on aggregate national consumption. Linear regression model is a statistical model used to predict the relationship between a dependent variable and independent variables. Its core idea is to minimize the difference between actual and observed values in the dataset so that the difference is greater than or equal to zero. The aggregate national consumption is primarily influenced by lockdown and easing policies and the severity of the pandemic. Therefore, in our study, the independent variables consisted of four different policy variables (all binary) and the number of daily COVID-19 infections (continuous variable). The dependent variable YGI or RGI is a proxy for aggregate national consumption. The purpose of the regression model is to derive the effects of various independent variables on the two consumption indicators according to the results of the regression analysis. The daily YGI and RGI of aggregate national consumption are linearly regressed on lockdown and easing policies. To handle categorical variables, we convert them into dummy variables (also known as indicator variables), where each categorical variable is encoded into multiple binary variables to represent its different categories. This allows us to incorporate policy variables into the model. If China enters a particular phase of the lockdown or lockdown easing on a given calendar day, each lockdown or easing dummy-a binary variable for each period-takes on a value of one and zero otherwise. The reported coefficients can thus be read as the excess percentage point growth of YGI or RGI as a function of COVID-19 cases and the policy adopted at each phase of the pandemic. Taking − 0.418 as an example, for every 1 unit increase in the number of cases, YGI decreases by 0.418 percentage points on average. This means that for each one-unit increase in the independent variable X, the average change in the dependent variable YGI is an increase of 0.418 percentage points. It is important to note that the dependent variable YGI itself is a percentage, so the increase in the average value of YGI as independent variable X increases is measured in percentage points. Let \(Y^{(r)}\) be YGI or RGI of aggregate national consumption, and X be the number of infected COVID-19 cases each day around China. \(P_{j}\) is an indicator variable for whether a day falls in the post-lockdown period, where j can be four separate policies. \(\mu _{i}\) is the error term and \(\beta _{0}\) is an intercept. We perform linear regressions on the following specifications 40 :

To handle categorical variables, we convert them into dummy variables (also known as indicator variables), where each categorical variable is encoded into multiple binary variables to represent its different categories. This allows us to incorporate categorical variables into the model and model their effects. To validate whether the linear regression model is suitable for the dataset, we use the coefficient of determination ( \(R^2\) ) to measure the model’s goodness of fit and prediction accuracy

The panel regression model is capable of capturing individual effects that do not vary over time, such as city-level income and the proportion of secondary and tertiary sectors (in Tables 5 and 6 ) Also, it can observe time-invariant effects that do not vary across cities, such as nationwide infected cases of COVID-19. Socio-economic indicators are considered as control variables at the city level and are typically assumed to remain constant within a specific time frame (from 2019 to 2020). These macroeconomic indicators generally do not experience significant fluctuations over the short term, thus providing a reasonable basis for the assumption of relative stability within this short time period. Here, we use appropriate methods to deal with non-stationarity, such as introducing dummy variables. The dummy variables in the panel data are stationary and do not change over time, so they are not affected by non-stationarity. When using dummy variables in panel regression, the focus is on estimates of fixed effects or fixed-effect models, rather than estimates of time series features. Note that the panel regression model can automatically handle multicollinearity between variables so that the impact of each variable on YGI or RGI can be analyzed independently. In addition, we need to include interaction terms between socioeconomic attributes and COVID-19 cases to test for heterogeneity in the impact of city-level socioeconomic attributes on consumption during COVID-19. Thus, we develop a panel regression model to analyze the interaction between different independent variables and COVID-19 cases in 53 cities in relation to city-level consumption changes. The final formulation of the panel regression model is as follows 50 , 51 :

where \(y_{i,t}\) denotes YGI or RGI in city i on day t , \(x_{i,t}\) is the number of infected COVID cases in city i on day t , \(c_{t}\) is the number of infected COVID-19 cases on day t across China, \(p_{j}\) is an indicator variable that states whether a certain policy is implemented on that day, X represents other independent variables, such as various socioeconomic attributes, and \(u_{i}\) is a city fixed effect that controls for any time-invariant city characteristics that might affect disease outcomes or other variables, \(\varepsilon _{i,t}\) is the error term, and \(\alpha\) is an intercept. All variables are normalized to eliminate the effect of dimensions.

The first column of Table 2 gives the basic city-level time-series pattern for YGI and RGI, as a function of COVID-19, lockdown and easing policies, and socioeconomic attributes. Considering that the impact of COVID-19 on consumption may vary across cities with regard to socioeconomic attributes, to capture this heterogeneity, we include interaction terms for each socioeconomic attribute with COVID-19 in the second column of Table 2 , which also improves the fit of both models. In columns (3), we additionally control for city fixed effects to capture unobserved city-specific factors, which provides the best fit.

Data availability

Social-economic data is available from the China Urban Statistical Yearbook on the website of the National Bureau of Statistics ( https://data.stats.gov.cn ). The consumption data that support the findings of this study are available from the Meituan platform. The daily COVID-19 confirmed case for each infected city and the whole country are updated daily by the National Health Commission of China or city-level Health Commissions since January 21, 2020. he datasets used and/or analysed during the current study available from the corresponding author on reasonable request.

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Acknowledgements

This research has been supported in part by the National Natural Science Foundation of China under Grant U21B2036 and Grant U20B2060; in part by the International Postdoctoral Exchange Fellowship Program (Talent-Introduction Program) under YJ20210274; in part by the China Postdoctoral Science Foundation under Project 2022M721891.

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Chen, R., Li, T. & Li, Y. Analyzing the impact of COVID-19 on consumption behaviors through recession and recovery patterns. Sci Rep 14 , 1678 (2024). https://doi.org/10.1038/s41598-024-51215-3

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Research Article

Psychological factors and consumer behavior during the COVID-19 pandemic

Contributed equally to this work with: Adolfo Di Crosta, Irene Ceccato

Roles Data curation, Formal analysis, Investigation, Methodology, Writing – original draft, Writing – review & editing

Affiliation Department of Neuroscience, Imaging and Clinical Sciences, G. d’Annunzio University of Chieti-Pescara, Chieti, Italy

Roles Conceptualization, Formal analysis, Methodology, Writing – original draft, Writing – review & editing

Roles Conceptualization, Formal analysis, Methodology

Affiliation Department of Psychological, Health and Territorial Sciences, G. d’Annunzio University of Chieti-Pescara, Chieti, Italy

Roles Investigation, Writing – review & editing

Roles Writing – original draft, Writing – review & editing

Affiliations Department of Neuroscience, Imaging and Clinical Sciences, G. d’Annunzio University of Chieti-Pescara, Chieti, Italy, Center for Advanced Studies and Technology (CAST), G. d’Annunzio University of Chieti-Pescara, Chieti, Italy

Affiliation Department of Business Studies, Grenon School of Business, Assumption University, Worcester, MA, United States of America

Roles Conceptualization, Writing – review & editing

Roles Conceptualization, Methodology, Writing – review & editing

* E-mail: [email protected]

Roles Conceptualization, Writing – original draft, Writing – review & editing

• Adolfo Di Crosta, 
• Irene Ceccato, 
• Daniela Marchetti, 
• Pasquale La Malva, 
• Roberta Maiella, 
• Loreta Cannito, 
• Mario Cipi, 
• Nicola Mammarella, 
• Riccardo Palumbo, 

• Published: August 16, 2021
• https://doi.org/10.1371/journal.pone.0256095
• Reader Comments

The COVID-19 pandemic is far more than a health crisis: it has unpredictably changed our whole way of life. As suggested by the analysis of economic data on sales, this dramatic scenario has also heavily impacted individuals’ spending levels. To better understand these changes, the present study focused on consumer behavior and its psychological antecedents. Previous studies found that crises differently affect people’s willingness to buy necessities products (i.e., utilitarian shopping) and non-necessities products (i.e., hedonic shopping). Therefore, in examining whether changes in spending levels were associated with changes in consumer behavior, we adopted a fine-grained approach disentangling between necessities and non-necessities. We administered an online survey to 3833 participants (age range 18–64) during the first peak period of the contagion in Italy. Consumer behavior toward necessities was predicted by anxiety and COVID-related fear, whereas consumer behavior toward non-necessities was predicted by depression. Furthermore, consumer behavior toward necessities and non-necessities was predicted by personality traits, perceived economic stability, and self-justifications for purchasing. The present study extended our understanding of consumer behavior changes during the COVID-19 pandemic. Results could be helpful to develop marketing strategies that consider psychological factors to meet actual consumers’ needs and feelings.

Citation: Di Crosta A, Ceccato I, Marchetti D, La Malva P, Maiella R, Cannito L, et al. (2021) Psychological factors and consumer behavior during the COVID-19 pandemic. PLoS ONE 16(8): e0256095. https://doi.org/10.1371/journal.pone.0256095

Editor: Marcel Pikhart, University of Hradec Kralove: Univerzita Hradec Kralove, CZECH REPUBLIC

Received: March 8, 2021; Accepted: July 31, 2021; Published: August 16, 2021

Copyright: © 2021 Di Crosta et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All data are available from the figshare database (accession number(s) DOI: 10.6084/m9.figshare.14865663.v2 , URL: https://figshare.com/articles/dataset/RawData_PO_sav/14865663 ).

Funding: The authors received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Coronavirus disease 2019 (COVID-19) refers to an infection (SARS-CoV-2) of the lower respiratory tract [ 1 , 2 ], which was first detected in Wuhan (China) in late December 2019. Since then, the number of contagions by COVID-19 has been increasing globally each day [ 3 ]. In March 2020, the World Health Organization (WHO) declared the COVID-19 outbreak a global pandemic [ 4 ]. Subsequently, several national governments implemented long-term full or partial lockdown measures to reduce the spread of the virus. Although these strict measures have proven to be quite effective in containing the further spread of the virus, they have severely impacted the global economic system and caused an unprecedented shock on economies and labor markets [ 5 ]. As a matter of fact, the COVID-19 pandemic can be defined as far more than just a health crisis since it has heavily affected societies and economies. COVID-19 outbreak has unpredictably changed how we work, communicate, and shop, more than any other disruption in this decade [ 6 ]. As reflected by the analysis of economic data on sales, this dramatic situation has greatly influenced consumer attitudes and behaviors. According to a study conducted by the Nielsen Company, the spread of the COVID-19 pandemic led to a globally manifested change in spending levels related to consumer behavior [ 7 ]. Specifically, a growing tendency in the sales of necessities has been observed: consumer priorities have become centered on the most basic needs, including food, hygiene, and cleaning products. In Italy, consumer shopping preferences have changed throughout the pandemic. Initially, when Italy was the first country in Europe to experience the spreading of COVID-19 (between March and April 2020). Consumer behavior tended to compulsively focus on purchasing essential goods, especially connected with preventing the virus, such as protective devices and sanitizing gel [ 8 ]. The pandemic changed the consumption patterns, for instance reducing sales for some product categories (e.g., clothes), and improving sales for other categories (e.g., entertainment products) [ 9 ]. Also, research indicated that job insecurity and life uncertainty experienced during the pandemic negatively impacted on consumer behavior of Italian workers [ 10 ].

It comes as no surprise that in such a situation of emergency, the need for buying necessities takes precedence [ 11 ]. However, the investigation of antecedent psychological factors, including attitudes, feelings, and behaviors underlying changes in consumer behavior during the COVID-19 pandemic, have received less attention. Nevertheless, understanding the psychological factors which drive consumer behavior and products choices can represent a crucial element for two main reasons. First, such investigation can extend our understanding of the underpinnings of the changes in consumer behavior in the unprecedented context of COVID-19. Second, obtained results could be helpful in the development of new marketing strategies that consider psychological factors to meet actual consumers’ needs and feelings [ 12 ]. On the one side, companies could benefit from this knowledge to increase sales during the COVID-19 pandemic [ 13 ]. Moreover, understanding these needs and feelings could be fundamental to improve the market’s preparedness to face future pandemics and emergencies [ 14 , 15 ]. On the other hand, consumers could take advantage of this new market’s preparedness to respond to their actual needs and feelings. As a result, in case of future emergency, factors such as anxiety and a perceived shortage of essential goods could be reduced [ 16 ], whereas well-being and the positive sense of self of the consumers could be supported [ 17 ]. Furthermore, the novelty of the present study lies in two main aspects. First, based on previous studies highlighting that crises differently affect people’s willingness to buy necessities and non-necessities products [ 11 , 18 ], we adopted a fine-grained approach and disentangled between necessities and non-necessities. Second, considering the unprecedented context of the COVID-19 pandemic, we adopted an integrative approach to investigate the role of different psychological factors such as fear, anxiety, stress, depression, self-justifications, personality traits, and perceived economic stability in influencing consumer behavior. Noteworthy, all these factors have been implicated in consumer behavior in previous research, but, to our knowledge, no study has considered all of them at once. Therefore, considering both the lack of studies that have focused on these factors at once and the unique opportunity to study them in the context of such an unprecedented global pandemic, we adopted an integrative approach to get one of the first overviews of the role of the several psychological factors influencing consumer behavior.

Previous studies in consumer psychology and behavioral economics have highlighted that several psychological factors impact consumer behavior differently [ 18 – 20 ]. Consumer behavior refers to the study of individuals or groups who are in the process of searching to purchase, use, evaluate, and dispose of products and services to satisfy their needs [ 12 ]. Importantly, it also includes studying the consumer’s emotional, mental, and behavioral responses that precede or follow these processes [ 21 ]. Changes in consumer behavior can occur for different reasons, including personal, economic, psychological, contextual, and social factors. However, in dramatic contexts such as a disease outbreak or a natural disaster, some factors, more than others, have a more significant impact on consumer behavior. Indeed, situations that potentially disrupt social lives, or threaten individuals’ health, have been proven to lead to strong behavioral changes [ 22 ]. An example is panic buying, a phenomenon occurring when fear and panic influence behavior, leading people to buy more things than usual [ 23 ]. Specifically, panic buying has been defined as a herd behavior that occurs when consumers buy a considerable amount of products in anticipation of, during, or after a disaster [ 24 ]. A recent review on the psychological causes of panic buying highlighted that similar changes in consumer behavior occur when purchase decisions are impaired by negative emotions such as fear and anxiety [ 25 ]. Noteworthy, in the context of the COVID-19 pandemic, Lins and Aquino [ 23 ] showed that panic buying was positively correlated with impulse buying, which has been defined as a complex buying behavior in which the rapidity of the decision process precludes thoughtful and deliberate consideration of alternative information and choice [ 25 ]. The analysis of the different psychological factors involved in consumer behavior and changes in purchase decisions still represents an area that is scarcely explored. Arguably, during an uncertain threatening situation, such as a health crisis or a pandemic, the primitive part of our brain usually becomes more prominent, pushing individuals to engage in behaviors that are (perceived as) necessary for survival [ 26 – 29 ]. Importantly, these primitive instinctual behaviors can override the rational decision-making process, having an immense impact on usual consumer behavior. Therefore, the basic primitive response of humans represents the core factor responsible for changes in consumer behavior during a health crisis [ 16 ]. Specifically, fear and anxiety originated from perceived feelings of insecurity and instability, are the factors driving these behavioral changes [ 30 ]. In line with the terror management theory [ 31 ], previous studies have shown that external events, which threaten the safety of individuals, motivate compensatory response processes to alleviate fear and anxiety [ 32 , 33 ]. These response processes can prompt individuals to make purchases to gain a sense of security, comfort, and momentarily escape, which can also serve as a compensatory mechanism to alleviate stress. However, as such buying motivation represents an attempt to regulate the individuals’ negative emotions, the actual need for the purchased products is often irrelevant [ 34 ].

Pandemics and natural disasters are highly stressful situations, which can easily induce negative emotions and adverse mental health states [ 35 – 37 ] such as perceived lack of control and instability, which are core aspects of emergency situations, contribute directly to stress. In turn, research has highlighted that stress is a crucial factor in influencing consumer behavior. For example, past studies have shown that individuals may withdraw and become passive in response to stress, and this inaction response can lead to a decrease in purchasing [ 38 , 39 ]. However, some studies point out that stress can lead to an active response, increasing impulsive spending behaviors [ 40 , 41 ]. Moreover, event-induced stress can lead to depressive mood. In some cases, the depressive mood may translate into the development of dysfunctional consumer behavior, such as impulsive (the sudden desire to buy something accompanied by excessive emotional response) and/or compulsive buying (repetitive purchasing due to the impossibility to control the urge) [ 41 , 42 ]. In this context, Sneath and colleagues [ 37 ] highlighted that changes in consumer behavior often represent self-protective strategies aimed at managing depressive states and negative emotions by restoring a positive sense of self. Importantly, a recent study conducted during the COVID-19 pandemic showed that depression predicted the phenomenon of the over-purchasing, which was framed as the degree to which people had increased their purchases of some necessities goods (e.g. food, water, sanitary products, pharmacy products, etc.) because of the pandemic [ 43 ].

A recent study recommended a differentiation between necessity and non-necessity products to better understand consumer behavior in response to stressful situations [ 18 ]. According to the authors, contrasting findings on the link between stress and consumer behavior may be due to the fact that stress affects certain purchasing behaviors negatively, but others positively, depending on the type of product under investigation. On one side, it has been argued that consumers may be more willing to spend money on necessities (vs. non-necessities) by making daily survival products readily available. Accordingly, recent research documented an increase in buying necessities products (i.e., utilitarian shopping) during and after a traumatic event [ 11 ]. However, other findings showed that impulsive non-necessities purchasing (i.e., hedonic shopping) could also increase as an attempt to escape or minimize the pain for the situation. That is, non-necessities buying is used as an emotional coping strategy to manage stress and negative emotional states [ 44 ]. To reconcile these findings, Durante and Laran [ 18 ] proposed that people adopt strategic consumer behavior to restore their sense of control in stressful situations. Hence, high stress levels generally lead consumers to save money and spend strategically on products perceived as necessities. Importantly, regarding the impact of perceived stress due to the COVID-19 pandemic on consumer behavior, a recent study showed that the likelihood of purchasing quantities of food larger than usual increased with higher levels of perceived stress [ 45 ].

Another psychological factor implicated in consumer behavior that deserves special attention is self-justification strategies [ 46 ]. Self-justification refers to the cognitive reappraisal process by which people try to reduce the cognitive dissonance stemming from a contradiction between beliefs, values, and behaviors. People often try to justify their decisions to avoid the feeling of being wrong to maintain a positive sense of self [ 17 ]. In consumer behavior research, it is widely acknowledged that consumers enhance positive arguments that support their choices and downplay counterarguments that put their behavior in question [ 47 ]. Based on previous research, it is plausible that, within the context of the COVID-19 pandemic, self-justifications for buying non-necessities products may also include pursuing freedom and defying boredom [ 11 , 48 ]. Further, the hedonistic attitude of “I could die tomorrow” or “You only live once” could certainly see a resurgence during the COVID-19 emergency [ 48 ], and become a crucial mechanism accounting for individual differences in consumer behavior. Based on these considerations, in the context of the COVID-19 pandemic, self-justifications strategies could be relevant for non-necessities, since products for fun or entertainment could be more suited to the pursuit of freedom and to defy boredom. Conversely, self-justifications strategies related to necessities could be implemented to a lesser degree, due to the very nature of the products. The unprecedented context of the pandemic could already justify the purchase of those essential goods by itself, and additional justifications may not be necessary.

Furthermore, several studies have shown that household income has a significant impact in determining people’s expenses [ 49 – 51 ]. Not surprisingly, the research highlighted a positive relationship between income and spending levels [ 52 ]. Income is defined as money received regularly from work or investments. Interestingly, a different line of research pointed out that self-perceived economic stability is a more appropriate determinant of consumer behavior than actual income [ 53 , 54 ]. Usually, people tend to report subjective feelings of income inadequacy, even when their objective financial situation might not support such attitude [ 55 ]. An interesting explanation for this bias draws on the social comparison process. Indeed, the study of Karlsson et colleagues [ 53 ] showed that, compared to families who considered themselves to have a good financial situation, households which considered themselves to be worse off economically than others reported fewer purchases of goods, perceived the impact of their latest purchase on their finance to be greater, and planned purchases more carefully. Furthermore, a recent study in the context of the COVID-19 emergency showed that people who believed to have limited financial resources were the most worried about the future [ 56 , 57 ]. Therefore, in the present study, we measured both the income and the perceived economic situation of the respondents to respectively consider the objective economic information and the subjective perception of respondents. However, considering the state of uncertainty experienced by many households during the COVID-19 pandemic [ 58 ], we changed the comparison from other families to participants’ economic situation in different time frames. We asked respondents to report perceived economic stability before, during, and after the emergency.

Finally, besides situational factors related to the specific emergency, the individuals’ personality traits are likely to have a role in determining consumer behavior as well. Past research has highlighted that the Big Five personality traits [ 59 ] can differently predict consumer behavior [ 60 ]. Specifically, conscientiousness, openness, and emotional stability (alias neuroticism) were related to compulsive buying, impulsive buying, and utilitarian shopping. Nevertheless, how different personality traits are related to consumer behavior is still an open question [ 61 ].

We conducted a nationwide survey in the Italian population to examine consumer behavior during the lockdown phase due to the COVID-19 pandemic. Since the COVID-19 emergency has emphasized the usefulness of essential goods (e.g. food, medications, etc.) compared to non-essential products (e.g. luxury items such as clothes and accessories) [ 62 ], in our study, we categorized products in necessities and non-necessities. Furthermore, changes in spending levels (necessities vs. non-necessities) were examined to confirm the effect that COVID-19 had on people’s expenses. Moreover, we tried to clarify the relationship between changes in spending levels and changes in consumer behavior. Finally, we focused on the psychological factors underlying changes in consumer behavior toward the target products. Based on the literature, we expected to find an increase in purchases with a more noticeable rise in necessity products. Specifically, we explored potential underpinnings of consumer behavior by examining mood states and affective response to the emergency, perceived economic stability, self-justification for purchasing, and personality traits. All these factors have been implicated in consumer behavior in previous research, but, to our knowledge, no study has considered all of them at once. Therefore, in this study, we adopted an integrative approach to study the contribution of different psychological factors by considering their mutual influence (see Fig 1 ). Specifically, based on the empirical findings and theoretical accounts presented above, we hypothesized that during the COVID-19 pandemic:

• Higher levels of anxiety and COVID-related fear would explain changes in consumer behavior, increasing the need for buying necessities.
• Higher levels of stress would lead consumers to save money or, in alternative, would increase the need to spend money on necessities (i.e., utilitarian shopping).
• Higher levels of depressive state would be associated with an increase in the need for buying, both necessities and non-necessities.
• Higher implementation of self-justification strategies would be associated with a higher need for buying, especially for non-necessities.
• Higher perceived economic stability would be associated with an increase in the need for both necessities and non-necessities.

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https://doi.org/10.1371/journal.pone.0256095.g001

The construct involved in the study is placed in the center of the figure. Arrows depart from these constructs to show the hypothesized relationship between the constructs and the outcomes of the present study (Necessities and Non-necessities). The symbol “±” was used to take into consideration two possible opposite directions.

Materials and methods

Data were collected through a series of questionnaires, using a web-based survey implemented on the Qualtrics software. The survey was active in the period starting from April 1st, 2020, to April 20th, 2020, during the first peak of the contagion in Italy. We used a convenience sample due to the exceptional situation of the COVID-19 pandemic and the time constraints to conduct our investigation. Therefore, participants were recruited through word-of-mouth and social media. Inclusion criteria were the age over 18 and be resident in Italy. First, socio-demographic information was collected, including gender, age, annual income, and education. Then, questions on spending levels and consumer behavior, both before the COVID-19 pandemic and during the first week of lockdown in Italy, were presented, separating necessities and non-necessities. Finally, a series of specifically created questionnaires and standardized measures were administered to investigate psychological and economic variables.

Participants

A total of 4121 participants were initially recruited. For the present study, we adopted a rigorous approach, excluding 104 participants over the age of 64, since they relied on retirement benefits and -from an economic point of view- were considered a specific population, not comparable to the rest of the sample [ 63 ]. Furthermore, we excluded 184 participants who did not report spending any money before the COVID-19 pandemic on buying necessities and/or non-necessities. Therefore, 3833 Italian participants (69.3% women, age M = 34.2, SD = 12.5) were included in this study. All participants provided their written informed consent before completing the survey. The study was conducted following the ethical standards of the Declaration of Helsinki and was approved by the Institutional Review Board of Psychology (IRBP) of the Department of Psychological, Health and Territorial Sciences at G. d’Annunzio University of Chieti-Pescara (protocol number: 20004). Participants did not receive monetary or any other forms of compensation for their participation.

Demographic variables

A demographic questionnaire was administered to collect background information. The questions considered age, gender, annual income, and education. The annual income was then categorized into five levels, based on the income brackets established by the Italian National Statistical Institute [ 64 ]. Education was categorized into five levels, from elementary to school to postgraduate degree.

Consumer behavior during COVID-19

We created this questionnaire from scratch to get a comprehensive overview of people’s economic attitudes and behaviors during the COVID-19 emergency. The idea of this new questionnaire was developed based on a series of previous studies on consumer behavior [ 43 , 65 – 67 ]. However, specific items were developed from scratch adapting them to the specific unprecedented context of the COVID-19 pandemic. Specifically, these items were created following a series of group discussions between all co-authors of the present study. To directly measure changes in consumer behavior due to the COVID-19 pandemic, participants were requested to compare their actual behavior to their normal behavior before the COVID-19 outbreak. Therefore, the initial statement in the questionnaire underlined that answers had to be given by referring to the COVID-19 emergency period compared to everyday life before the outbreak.

The factor structure and reliability were evaluated in the larger sample ( n = 4121), using principal component analysis (PCA) and Cronbach’s alpha. The results revealed a six-factor structure and satisfactory reliability values (see S1 Table for more details). Note that the PCA and reliability analyses were also conducted on the current subsample, and the pattern of results did not change.

For the present study’s aims, we focused on three scales: “Necessities”, “Non-necessities”, and “Self-justifications”. Items are shown in Table 1 . The first two scales investigated consumer behavior toward the different framed products. Specifically, items addressed the individual’s attitudes, feelings, and behaviors toward necessities and non-necessities. Thus, higher scores reflected greater value (e.g., need, utility) placed on the target products.

https://doi.org/10.1371/journal.pone.0256095.t001

The self-justifications scale referred to consumers’ thoughts to justify their purchases, with no distinction between necessity and non-necessity products. Higher scores reflected a frequent use of self-justifications in purchasing items.

For all these scales, responses were given on a Likert scale ranging from 0 ( not at all ), to 100, ( extremely ). Total scores on each scale were obtained by averaging all items.

Change in spending levels due to COVID-19

A fourth scale, i.e. “Spending Habits,” was extracted from the questionnaire mentioned above. As we aimed at measuring changes in the spending levels due to the COVID-19 emergency, we decided to use single items instead of the total scale score (items are presented in Table 1 ). Specifically, we created three percentage scores: “Changes in General Spending”, “Changes in Necessities spending”, and “Changes in Non-necessities spending” considering the difference between the money spent during the first week of lockdown, and the money spent on average in a week before the emergency (see Table 1 notes). Scores reflect the change in the amount (in Euro) that people devolved in purchasing the target products (hypothetical range from -1999 to +1999).

Big Five Inventory 10-item (BFI-10)

Big Five Inventory 10-item (BFI-10) is a short scale designed to briefly assess the five personality traits with two items for each trait. Specifically, these traits are: Agreeableness (example item: “I see myself as someone who is generally trusting”), Conscientiousness (example item: “I see myself as someone who does a thorough job”), Emotional stability (example item: “I see myself as someone who is relaxed, handles stress well”), Extraversion (example item: “I see myself as someone who is outgoing, sociable”), and Openness (example item: “I see myself as someone who has an active imagination”) [ 68 ]. In addition, respondents are asked to indicate whether they agree or disagree with each statement on a 5-point Likert-type scale, ranging from 1 ( not agree at all ) to 5 ( totally agree ). A previously validated Italian version was used in the present study [ 69 ].

Generalized anxiety disorder (GAD-7)

The GAD-7 [ 70 ] is a 7-item self-reported measure designed to screen for generalized anxiety disorder and to measure the severity of symptoms, based on the DSM-IV criteria. This measure is often used in both clinical practice and research. Specifically, respondents are asked the frequency they have experienced anxiety symptoms in the past two weeks (e.g., “Not being able to stop or control worrying”) on a 4-point Likert scale, ranging from 0 ( not at all ) to 3 ( nearly every day ). The total score ranges from 0 to 21, with higher scores indicating worse anxiety symptomatology.

Patient health questionnaire (PHQ-9)

The patient health questionnaire (PHQ-9) is a 9-item self-reported brief diagnostic measure for depression [ 71 ]. Specifically, respondents are asked of the frequency they felt bothered by several depressive symptoms during the past two weeks (e.g., “Little interest or pleasure in doing things”) on a 4-point Likert scale, ranging from 0 ( not at all ) to 3 ( nearly every day ). Total score ranges from 0 to 27, with higher scores indicating higher depressive symptoms.

Perceived Stress Scale (PSS)

The Perceived Stress Scale (PSS) is a 14-item self-report measure designed to assess the degree to which situations are appraised as stressful [ 72 ]. Each item (e.g., “In the last month, how often have you been upset because of something that happened unexpectedly?”) is rated on a 5-point Likert scale ranging from 0 ( never ) to 4 ( very often ). Thus, the total score ranges from 0 to 56, with a higher score indicating a higher level of perceived stress during the COVID-19 emergency.

Fear for COVID-19

We administered the Fear for COVID-19 questionnaire to measure fear and concerning beliefs related to the COVID-19 pandemic [ 35 , 36 , 73 ]. This questionnaire was created from the assumption that, during a health crisis, the individual’s fear is determined by both the hypothesized susceptibility (i.e., probability of contracting a disease) and the expected severity of the event (i.e., perceived consequences of being infected) [ 25 ]. Therefore, the 8 items dealt with the perceived probability of being infected by COVID-19 (Belief of contagion) and the possible consequences of the contagion (Consequences of contagion). See Table 1 for the complete list of the items. Previous studies have reported the PCA and reliability of the questionnaire [ 36 ]. Responses were given on a Likert scale ranging from 0 ( not at all ), to 100, ( extremely ). A total score was obtained by averaging the items (range 0–100).

Perceived economic stability

This questionnaire was developed to assess the subjective perception of an individual’s economic situation. The PCA in the larger sample revealed a unidimensional structure (see S2 Table for more details). The scale assessed perceived economic stability in three different timepoints: before, during, and after (in terms of expectation) the COVID-19 pandemic. Responses were given on a Likert scale ranging from 0 ( not at all ), to 100, ( extremely ). The total score was calculated by averaging these three items (range 0–100).

Statistical analysis

We preliminary investigated changes in spending levels due to the COVID-19 pandemic, comparing expenses before the emergency to expenses during the COVID-19 pandemic. First, we analyzed changes in the average general spending level. Then, we performed dependent (paired) sample t -tests between “Changes in necessities spending” and “Changes in non-necessities spending” to examine differences between products framed as necessities and non-necessities.

Afterward, we checked whether changes in spending levels were associated with changes in consumer behavior by conducting Pearson’s correlation analyses, respectively between “Changes in necessities spending” and “Necessities”, and “Changes in non-necessities spending” and “Non-necessities” scores.

Finally, to investigate the psychological underpinnings of consumer behavior, we performed two hierarchical multiple regressions, respectively, with “Necessities” (Model 1) and “Non-necessities” (Model 2) as outcomes. The same predictors were entered in Model 1 and Model 2. Specifically, the order of the steps was designed to include at first the socio-demographic information as control variables. Hence, we entered the age, gender, annual income brackets, and education in the first step. In Step 2, we included the personality measures (i.e., Big-Five personality traits) since these traits are stable and are not affected by the specific situation. In Step 3, Anxiety, Depression, and Stress were entered, to analyze the impact of emotional antecedents of consumer. Further, we decided to include Fear for the COVID-19 in a separate fourth step to evaluate the effect of this specific aspect. We included perceived economic stability at Step 5 after the psychological variables. This choice allowed to analyze the impact of the perceived economic stability after controlling for the role of emotional antecedents on consumer behavior. Finally, following the same logic, we included self-justifications strategies.

Considering “Changes in General spending”, our results showed that our sample reported, on average, an increase of 60.48% in the general spending level during the first week of lockdown. Furthermore, significant differences between “Changes in Necessities spending” and “Changes in Non-necessities spending”, t (3832) = 11.99, p < .001, were detected. Indeed, the spending level for necessities products showed an increase of 90.69%, while for non-necessities products, the average increase was only 36.11%. Means and standard deviations are presented in Table 2 .

https://doi.org/10.1371/journal.pone.0256095.t002

The results of the correlation analyses indicated that there was a significant positive association between “Changes in necessities spending” and “Necessities”, r (3831) = .22, p < .001. Furthermore, a significant positive association was highlighted between “Changes in non-necessities spending” and “Non-necessities”, r (3831) = .23, p < .001. Therefore, people’s changes in spending levels were related to their attitudes and feelings toward specific products. This finding supported our choice to investigate the psychological underpinnings of people’s consumer behavior.

Hierarchical multiple regression analyses were performed on the two consumer behavior scores. In addition, control variables, psychological factors, and economic variables were entered as predictors as detailed above.

Regarding Model 1 (Necessities), results showed that all the steps explained a significant amount of additional variance (see Table 3 for detailed results). When personality traits were entered in the model (Step 2), only agreeableness, openness, and emotional stability negatively predicted the outcome. However, when anxiety, depression, and stress were entered in the model (Step 3), only openness remained statistically significant. The variables entered in Step 3 contributed to explaining 7% of the variance, with anxiety and stress positively predicting the outcome. Adding fear for COVID-19 in the following step increased the explained variance by 6%, reduced the impact of anxiety, and completely overrode the effect of stress, which became non-significant. In the following steps, perceived economic stability offered a small but significant contribution (1%), and Self-justifications explained even further variance (4%). Overall, in the final step, the final model explained 23% of the variance in Necessities. Inspecting coefficients, we found that, after accounting for control variables, openness ( p < .001), anxiety ( p < .001), fear for COVID-19 ( p < .001), perceived economic stability ( p < .001), and self-justifications ( p < .001) emerged as significant predictors.

https://doi.org/10.1371/journal.pone.0256095.t003

In Model 2 (Non-necessities), results indicated that each step significantly contributed to explaining the outcome (see Table 4 ). In Step 2, personality traits explained 2% of the outcome variance, with consciousness and openness emerging as significant predictors and remaining significant until the final step. Notably, consciousness was negatively associated with non-necessities behavior, while high scores in openness were associated with higher scores on the Non-necessities scale. In Step 3, only depression was significantly and positively related to the outcome and remained so in subsequent models. Both fear for COVID-19 and perceived economic stability further significantly explained the outcome, albeit weakly (about 1% of variance each one). Higher levels of fear and perceived economic stability were associated with higher scores on the Non-necessities scale. Noteworthy, adding Self-justifications in the final step explained a substantial share of variance, equal to 12%. Specifically, higher scores on self-justifications were associated with higher scores on the Non-necessities scale. Furthermore, self-justifications also had a greater impact on non-necessities compared to those had on necessities, t (7664) = -10.60, p < .05. Total variance explained in the final step was 22%, with conscientiousness ( p < .001), openness ( p = .001), depression ( p = .002), perceived economic stability ( p = .009), and self-justifications ( p < .001) being significant predictors.

https://doi.org/10.1371/journal.pone.0256095.t004

The present study aimed to examine changes in consumer behavior and their psychological antecedents during the lockdown period due to the COVID-19 pandemic. We were specifically interested in separating necessity and non-necessity products since previous studies suggested that such a distinction is helpful to better understand consumer behavior[ 18 , 74 ]. First, our results indicated a 61% increase in spending levels during the first week of the lockdown, compared to the average expenses before the health crisis. Furthermore, spending levels were differently increased for buying products framed as necessities (91%) and non-necessities (36%). Second, we examined consumer behavior through Necessities and Non-necessities scales, which included measures related to the psychological need of buying, the specific aspects of the purchase experience (e.g., impulsiveness, perceived utility, satisfaction), and the number of products purchased. Our results highlighted that changes in consumer behavior were positively associated with changes in spending levels during the COVID-19 emergency.

Finally, we focused on psychological factors that can explain these changes in consumer behavior. In this context, our hypothesis about the role of the identified psychological factors in predicting consumer behavior during COVID-19 was supported. Also, our findings confirmed the importance of separating necessities from non-necessities products, as we found that they had different psychological antecedents. Regarding the investigation on spending levels, our findings are in line with sales data reporting that, during the COVID-19 pandemic, consumer priorities have become more centered on necessities, including food, hygiene, and cleaning products[ 7 , 62 ]. Therefore, the present study confirmed the greater tendency to buy necessities products during the COVID-19 pandemic. It is noteworthy to mention that our sample also reported an increase in spending levels related to non-necessities products. These data can be explained by referring to previous research that considered increases in non-necessities spending levels to respond to the hedonistic pursuit of freedom, defying boredom, restoring the sense of self, and compensatory mechanism, to alleviate negative psychological states[ 16 , 32 , 34 , 37 , 44 , 75 ]. However, as highlighted in the study by Forbes and colleagues[ 76 ] these hedonic needs and compensatory mechanisms can have a different impact during or in the aftermath of a crisis. In addition, the authors highlighted that the consumption of non-necessities products increased, as a way of coping to alleviate negative psychological states, particularly in the short term after a natural disaster. According to these results, a recent study conducted during the COVID-19 pandemic suggested that some factors, such as the degree of perceived threat, may vary during the COVID-19 pandemic, thus, having a different impact on consumer behavior[ 77 ]. Therefore, future research could delve into the analysis of changes in consumer behavior over time in relation to the different phases of the COVID-19 pandemic.

Regarding our investigation of consumer behavior’s antecedent psychological factors, we found partly different antecedents for necessities and non-necessities. Regarding demographic effects, in the present study, we found that men were more oriented in terms of needs and feelings toward non-necessities than women. A possible explanation could consider the context of the COVID-19, whereas the lockdown has imposed the closure of physical stores. In this context, it could be appropriate to refer to those studies that found several gender differences between consumer e-commerce adoption and purchase decision making. Specifically, research has shown that men and women have different psychological pre-disposition of web-based purchases, with men having more positive attitudes toward online shopping[ 78 , 79 ]. Furthermore, a study conducted during COVID-19 showed that women spent more time on necessities such as childcare and chores compared to men[ 80 ]. Regarding age differences, we found that younger people were more oriented toward non-necessities products. A study conducted in Italy during the COVID-19 pandemic highlighted that older adults showed lower negative emotions than younger adults[ 73 , 81 , 82 ]. In this view, it is possible that lower emotional antecedents, such as depressive states, lowered the need to buy non-necessities for more aged people. Another study conducted during the COVID-19 pandemic showed that older adults, aged 56 to 75, had significantly reduced the purchase of non-necessities goods compared to younger people[ 83 ]. Furthermore, considering the closure of physical stores, it is possible that younger people were more able and got used to buy a broader range of non-necessities products by e-commerce. However, it is important to note that we excluded in the present study people aged over 65. We also found a positive effect of income on necessities. A possible explanation is that people more stable from an economic point of view were more oriented to feel the need to buy products. However, surprisingly we did not find this effect for non-necessities. Finally, we found a positive effect of education on non-necessities. This data is congruent with another study conducted during the COVID-19 pandemic, showing that people with higher education (e.g., bachelor’s degrees and graduate or professional degrees) tended to buy an unusual amount of goods than people with lower education[ 84 ].Furthermore, another study highlighted that during COVID-19 pandemic entertainment and outdoor expenses significantly varied across different education groups[ 85 ]. Considering the present results, further studies should better investigate the impact of socio-demographic factors on the need to purchase necessities and non-necessities during health emergency and natural disaster.

Furthermore, after accounting for control variables (gender, age, income brackets, and education), consumer behavior toward necessities was explained by personality traits (openness), negative emotions (anxiety and COVID- related fear), perception of economic stability, and self-justifications. On the other side, consumer behavior toward non-necessities was explained by conscientiousness, openness, depression, perceived economic stability, and self-justifications.

Present findings showed that negative feelings have a considerable role in predicting changes in consumer behavior related to necessities products. This result is consistent with previous literature showing that, during a health crisis, fear and anxiety are developed from perceived feelings of insecurity and instability[ 30 ]. To reduce these negative feelings, people tend to focus on aspects and behaviors that can help them regain control and certainty, such as buying[ 86 ]. Therefore, changes in consumer behavior could be explained as a remedial response to reduce fear and anxiety related to the COVID-19 emergency. According to our hypothesis, present findings indicated that fear and anxiety play an important role in predicting changes in consumer behavior related to necessities. In contrast, no significant effects were found on non-necessities. A possible explanation for this remarkable difference can be provided by research in survival psychology, which highlighted that individuals might undergo behavioral changes during events such as natural disasters or health crises, including herd behavior, panic buying, changes in purchasing habits, and decision making[ 8 , 76 ]. Following these changes, individuals can be more engaged in behaviors that are necessary for survival[ 26 , 87 ]. In this view, COVID-related fear and anxiety could lead individuals to feel the need to buy necessities products useful for daily survival.

Stress is another factor suggested to differently affect changes in consumer behavior toward necessities and non-necessities[ 18 ]. It is noticeable that consumers experiencing stressful situations may show increased spending behavior, explicitly directed toward products that the consumer perceives to be necessities and that allow for control in an otherwise uncontrollable environment[ 18 ]. Our results partly support this position, showing that stress has a specific role in predicting changes in consumer behavior related to necessities but not to non-necessities. However, the role of stress was no longer significant when fear was entered in the regression model. Noteworthy, we focused on fear for COVID-19, therefore, it is possible that in such an exceptionally unprecedented situation, fear had a prominent role compared to stress. Moreover, previous literature shows that the relationship between fear and consumer behavior increases as the type of fear measured becomes more specific[ 88 ]. In this sense, further studies could delve into the relationship between fear and stress in relation to consumer behavior.

Notably, past studies had found a relationship between depressive states and consumer behavior, suggesting that changes in consumer behavior can represent self-protective behaviors to manage negative affective states[ 37 ]. The role of depression was highlighted by our results in respect to consumer behavior only related to non-necessities. Therefore, conversely to the study conducted in the UK and Ireland during the COVID-19 pandemic by Bentall et colleagues (2021), we did not find a relationship between depression and buying necessities. It is important to note that we described non-necessities products as “products for fun or entertainment”. In our opinion, people with higher levels of depressive symptoms may feel a greater need for this kind of product. Thus, people were drawn more toward this category of purchases because it was better suited to satisfy compensatory strategies to improve their negative emotional states. However, future studies are required to investigate this possibility and deepen the relationship between depressive states and the need to buy necessities and non-necessities. Furthermore, considering that depressive mood can be related to severe dysfunctional aspects of consumer behavior, such as impulsivity and compulsivity, future clinical studies should further investigate this relationship.

Furthermore, based on the limited and contrasting literature on this topic, we considered the role of personality traits. As suggested by previous studies, conscientiousness and openness were found to be associated with consumer behavior[ 89 – 91 ]. Interestingly, we found that personality traits were more relevant in consumer behavior toward non-necessities than necessities products. Only openness had a role in (negatively) predicting consumer behavior toward necessities, whereas conscientiousness (negatively) and openness (positively) predicted consumer behavior toward non-necessities. Unexpectedly, we found that people with a high level of openness showed high scores in consumer behavior toward non-necessities but low scores in necessities products. We speculated that individuals with higher levels of openness, which are more inclined to develop interests and hobbies[ 92 ], might have experienced a higher need to purchase non-necessities products during the lockdown. On the other hand, individuals with lower scores of openness, which tend to prefer familiar routines to new experiences and have a narrower range of interests, might have been more focused on purchasing necessity products. However, further studies should investigate the different roles of openness on necessities vs non-necessities consumer behavior. Globally, we acknowledge that the specific role and directions of these different personality traits on consumer behavior toward necessities and non-necessities is still an unexplored question, fully deserving of further investigations.

Finally, in both regression models, perceived economic stability and self-justifications predicted changes in consumer behavior. It comes as no surprise that individuals who perceived themselves and their family as more economically stable were prone to spend more in both products categories, necessities and non-necessities [ 52 , 53 ]. More intriguing, we found that the self-justifications that consumers adopted to motivate their purchases were a strong predictor of consumer behavior, especially in relation to non-necessities, where it explained the largest amount of variance (12%). Therefore, our hypothesis on the greater impact of self-justifications strategies on non-necessities compared to necessities was confirmed. Non-necessities, framed as products for fun or entertainment, seem more suited to satisfy that pursuit of freedom and the need to defy boredom that people increasingly experienced during the COVID-19 pandemic[ 48 ]. Therefore, we confirmed that the hedonistic attitude is an important predictor of consumer behavior during the COVID-19 pandemic. This result supported and extended previous literature showing that, during a crisis, changes in consumer behavior are related to self-justifications and rationalizations that people formulate to feel right in making their purchases, including the pursuit of freedom and the reduction of boredom[ 11 , 48 ]. Companies and markets can acknowledge this process and use it to develop new marketing strategies to meet consumers’ actual needs, feelings, and motivation to purchase during the COVID-19 emergency[ 12 ]. On the one hand, satisfying these needs could support and favor well-being and the positive sense of self, which are essentially sought by the consumer developing such self-justification strategies[ 17 ]. On the other hand, focusing on strategies that consider these psychological self-justifications could be a winning marketing strategy for increasing sales, contributing to the economic recovery after the COVID-19 outbreak[ 13 ].

The results of the present study highlighted that the COVID-19 pandemic had a considerable impact on consumer behavior. In our sample, this impact resulted in increased spending levels accompanied by an increase in the psychological need to purchase both necessities and non-necessities products. Furthermore, our findings demonstrated that several psychological factors predicted these changes in consumer behavior. Notably, consumer behavior respectively toward necessities and non-necessities differed on some psychological predictors.

Some limits of the current study need to be acknowledged. First, we studied consumer behavior from a broad perspective on a non-clinical sample, therefore we did not include dysfunctional aspects related to consumer behavior, such as impulsivity and compulsivity buying and hoarding behavior, which the emergency may elicit. Hence, in relation to the COVID-19 pandemic, it would be interesting to integrate our results with investigations of dysfunctional aspects of consumer behavior. Furthermore, since the unique opportunity to study psychological factors and consumer behavior during this unprecedented period, we adopted an integrative approach to consider the impact of several psychological factors at once, obtaining one of the first overviews of consumer behavior during the COVID-19 pandemic. However, combining all these psychological factors could have led to an aggregation bias[ 93 ], which could have masked the specific roles of each of the individual factors influencing consumer behavior. Therefore, future studies could adopt a more fine-grained approach to disentangle the role of each factor. Another limit is that we collected data during the initial stage of the COVID-19 outbreak in Italy. Notably, we reasoned that focusing on the very first period of the lockdown would likely allow us to capture the greater shift in consumer behavior, thus offering compelling evidence on the first impact of the pandemic on consumers. Nevertheless, it is likely that consumer behavior will undergo further changes in the longer term. Hence, future studies should investigate the evolution of consumer behaviors in relation to the development of the pandemic. Indeed, it is likely that when the “sense of urgency” and the negative affective reaction to the emergency will decrease, also the need for buying and purchases preferences would change. Furthermore, since we asked participants to estimate their weekly expenditures before and during the COVID-19 pandemic, it is important to keep in mind that our study focused on the people’s perception of changes in expenses. We did not know how much reliable these estimations were, and it is possible that objective assessment of change in the amount of money spent before and during the pandemic diverge from subjective views. In the present study, we focused on individual internal factors that could influence consumer behavior. However, other external factors, including the lockdown restrictions as the closure of physical stores, had certainly had a further impact on consumer behavior. Notwithstanding these limitations, this study represents one of the first attempts to examine changes in consumer behavior during the COVID-19 pandemic from a behavioral economic perspective, providing a thorough analysis of the psychological factors driving changes in consumer behavior, with a direct link to previous psychological research in consumer behavior. Furthermore, our results provided new evidence on the role of psychological factors influencing necessities and non-necessities spending and extended our knowledge of the antecedents of consumer behavior changes during the unprecedented health crisis we are experiencing.

In conclusion, the present study, by shedding new light on changes in people’s behavior due to the pandemic, fits into the growing body of research which helps increase economic and psychological preparedness in the face of future health emergencies.

Supporting information

S1 table. pattern matrix of the pca for the questionnaire on consumer behavior during the covid-19 pandemic..

https://doi.org/10.1371/journal.pone.0256095.s001

S2 Table. PCA for the “Perceived economic stability” questionnaire.

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Impact of Covid-19 on consumer behavior: Will the old habits return or die?

Affiliation.

• 1 Goizueta Business School, Emory University.
• PMID: 32536735
• PMCID: PMC7269931
• DOI: 10.1016/j.jbusres.2020.05.059

The COVID-19 pandemic and the lockdown and social distancing mandates have disrupted the consumer habits of buying as well as shopping. Consumers are learning to improvise and learn new habits. For example, consumers cannot go to the store, so the store comes to home. While consumers go back to old habits, it is likely that they will be modified by new regulations and procedures in the way consumers shop and buy products and services. New habits will also emerge by technology advances, changing demographics and innovative ways consumers have learned to cope with blurring the work, leisure, and education boundaries.

Keywords: COVID Pandemic; Consumer habits; Customer experience; New regulations for shopping.

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Immediate Impact of Covid-19 on…

Immediate Impact of Covid-19 on Consumption Behavior.

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E-commerce and Impact of COVID-19 on Consumer Behaviors Globally: A Review

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The background of e-commerce, shopping, online banking, and E-enterprise are only a few of the services offered by e-commerce. Consumers may buy, order, and watch goods and services online using their gadgets, no matter where they are in the world. This industry has emphasized the importance of doing business in rural and remote areas. This paper suggests that e-commerce platforms must ensure consumer privacy and security and protect data from violence and fraud. Consumers are afraid of online transactions and purchases because e-commerce platforms may not ensure the proper protection, trust, and trust. Companies can guarantee fast delivery nationwide to attract customers to e-commerce and shop online. Hence, consumers’ desire for online purchases will increase, and e-commerce will become more used. The study also examined the leading theories of adopted behaviour and the theory of planned behaviour (TPB). A survey of selected countries will be conducted to examine literature challenging the Covid 19 issues and their effect on consumers' behaviour. The COVID-19 situation has had an immediate and widespread impact on consumer behaviour. Public health laws are contributing to increased Internet usage.

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Darwish, S., Gomes, A. (2023). E-commerce and Impact of COVID-19 on Consumer Behaviors Globally: A Review. In: Musleh Al-Sartawi, A.M.A., Razzaque, A., Kamal, M.M. (eds) From the Internet of Things to the Internet of Ideas: The Role of Artificial Intelligence. EAMMIS 2022. Lecture Notes in Networks and Systems, vol 557. Springer, Cham. https://doi.org/10.1007/978-3-031-17746-0_36

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Impact of Covid-19 on consumer behavior: Will the old habits return or die?

The COVID-19 pandemic and the lockdown and social distancing mandates have disrupted the consumer habits of buying as well as shopping. Consumers are learning to improvise and learn new habits. For example, consumers cannot go to the store, so the store comes to home. While consumers go back to old habits, it is likely that they will be modified by new regulations and procedures in the way consumers shop and buy products and services. New habits will also emerge by technology advances, changing demographics and innovative ways consumers have learned to cope with blurring the work, leisure, and education boundaries.

1. Introduction:

The purpose of this research paper is to examine the impact of Covid-19 pandemic on consumer behavior. Will the consumers permanently change their consumption habits due to lockdown and social distancing or will they go back to their old habits once the global crisis is over? Will there be new habits consumers will acquire due to new regulations related to air travel, shopping at the shopping centers and attending concerts and sports events? Will consumers find that going to a store or attending an event in person is much of a hassle, and therefore, it is better to let the store or the event come to home? To some extent, this has been happening for quite some time in sports tournaments and entertainment by broadcasting them on television and radio.

All consumption is location and time bound. Consumers develop habits over time about what to consume, when and where ( Sheth, 2020a , Sheth, 2020b ). Of course, this is not limited to consumption. It is also true of shopping, searching for information and post consumption waste disposal. And consumer behavior is highly predictable, and we have many good predictive models and consumer insights based on past repetitive buying behavior at the individual level.

While consumption is habitual it is also contextual. Context matters and there are four major contexts which govern or disrupt consumer habits. The first is change in the social context by such life events as marriage, having children and moving from one city to another. The social context includes workplace, community, neighbors, and friends. The second context is technology. And as breakthrough technologies emerge, they break the old habits. The most dramatic technology breakthroughs in recent years are smart phones, internet and ecommerce. Online search and online ordering have dramatically impacted the way we shop and consumer products and services.

A third context that impacts consumption habits is rules and regulations especially related to public and shared spaces as well as deconsumption of unhealthy products. For example, consumption of smoking, alcohol, and firearms are regulated consumption by location. Of course, public policy can also encourage consumption of societally good products and services such as solar energy, electric cars, and mandatory auto and home insurance services and vaccines for children.

The fourth and less predictable context are the ad hoc natural disasters such as earthquakes, hurricanes, and global pandemics including the Covid-19 pandemic we are experiencing today. Similarly, there are regional conflicts, civil wars as well as truly global wars such as the World War II, cold war, and Great Depression of the late twenties and the Great Recession of 2008–2009. All of them significantly disrupted both consumption as well as production and supply chain. The focus of this paper is to examine both the immediate as well as the long-term impact of Covid-19 on consumption and consumer behavior.

2. Immediate impact on consumer behavior

As mentioned before, all consumption and consumer behavior are anchored to time and location. Since World War II, more and more women have been working resulting in reduction of discretionary time. It is estimated that today more than 75 percent for all women with children at home are working fulltime. This has resulted in time shortage and time shift in family as well as personal consumption. Monday through Fridays, no one is at home between 8am to 5 pm for service technicians to do installations and maintenance of appliances as well as repairs of broken heating and cooling systems. The supplier has to make appointments with the household to ensure there will be someone at home to open the door.

There is also time shortage as the discretionary time of the homemaker is now nondiscretionary due to her employment. This time shortage has resulted in consumers ordering online and have products delivered at home. Similarly, vacations are no longer two or three weeks at a time but are more minivacations organized around major holidays such as Easter, Christmas, Thanksgiving, Memorial Day, and Labor Day extended weekends.

With lockdown and social distancing, consumers’ choice of the place to shop is restricted. This has resulted in location constraint and location shortage. We have mobility shift and mobility shortage. Working, schooling and shopping all have shifted and localized at home. At the same time, there is more time flexibility as consumers do not have to follow schedules planned for going to work or to school or to shop or to consume.

Shortage of space at home is creating new dilemmas and conflicts about who does what in which location space at home. As homo sapiens, we are generally more territorial and each one needs her or his space, we are all struggling with our privacy and convenience in consumption.

Fig. 1 summarizes eight immediate effects of Covid-19 pandemic on consumption and consumer behavior.

Immediate Impact of Covid-19 on Consumption Behavior.

1. Hoarding. Consumers are stockpiling essential products for daily consumption resulting in temporary stockouts and shortages. This includes toilet paper, bread, water, meat, disinfecting and cleaning products.

Hoarding is a common reaction to managing the uncertainty of the future supply of products for basic needs. Hoarding is a common practice when a country goes through hyperinflation as it is happening in Venezuela. In addition to hoarding, there is also emergence of the gray market where unauthorized middlemen hoard the product and increase the prices. This has happened with respect to PPE (personal protection equipment) products for healthcare workers including the N95 masks. Finally, the temporary extra demand created by hoarding, also encourages marketing of counterfeit products. We have not done enough empirical research on the economic and the psychology of hoarding in consumer behavior.

2. Improvisation. Consumers learn to improvise when there are constraints. In the process, existing habits are discarded and new ways to consume are invented. The coronavirus unleashed the creativity and resilience of consumers for such tradition bound activities as weddings and funeral services. Sidewalk weddings and Zoom funeral services substitute for the traditional location centric events. This was also true for church services especially on Easter Sunday.

Improvisation to manage shortage of products or services is another area of future research. It leads to innovative practices and often leads to alternative option to location centric consumption such as telehealth and online education. Once again, there is no systemic empirical or scientific research on improvisation. The closest research is on improvisation is Jugaad in India. It means developing solutions that work by overcoming constraints imposed by social norms or government policy. Jugaad also means doing more with less, seeking opportunity in adversity and thinking and acting flexibly and following the heart ( Radjou, Prabhu and Ahujo, 2012 ).

3. Pent-up Demand. During times of crisis and uncertainty the general tendency is to postpone purchase and consumption of discretionary products or services. Often, this is associated with large ticket durable goods such as automobiles, homes, and appliances. It also includes such discretionary services as concerts, sports, bars, and restaurants. This results in shift of demand from now into the future. Pent up demand is a familiar consequence when access to market is denied for a short period of time for services such as parks and recreation, movies, and entertainment. While economists have studied impact of pent up demand on the GDP growth, there is very little research in consumer behavior about the nature and scope of pent up demand.

4. Embracing Digital Technology. Out of sheer necessity, consumers have adopted several new technologies and their applications. The obvious example is Zoom video services. Just to keep up with family and friends, most households with the internet have learned to participate in Zoom meetings. Of course, it has been extended to remote classes at home for schools and colleges and to telehealth for virtual visits with the physician and other health care providers.

Most consumers like social media including Facebook, WhatsApp, YouTube, WeChat, LinkedIn, and others. The internet is both a rich medium and has global reach. The largest nations in population are no longer China and India. They are Facebook, YouTube, and WhatsApp. Each one has more than a billion subscribers and users. This has dramatically changed the nature and scope of word of mouth advices and recommendations as well as sharing information. One of the fastest growing areas is influencer marketers. Many of them have millions of followers. Impact of digital technology in general and social media in particular on consumer behavior is massive in scale and pervasive in consumer’s daily life. It will be interesting to see if technology adoption will break the old habits. While we have studied diffusion of innovation for telephones, television, and the internet, we have not experienced a global adoption of social media in highly compressed cycle.

5. Store Comes Home. Due to complete lockdown in countries like India, China, Italy, and other nations, consumers are unable to go to the grocery store or the shopping centers. Instead, the store comes home. So does work and education. This reverses the flow for work, education, health and purchasing and consumption. In home delivery of everything including streaming services such as Disney, Netflix, and Amazon Prime is breaking the odd habits of physically going to brick and mortar places. It is also enhancing convenience and personalization in consumer behavior. What we need is to empirically study how “IN-home everything” impacts consumer’s impulse buying and planned vs unplanned consumption.

6. Blurring of Work-Life Boundaries . Consumers are prisoners at home with limited space and too many discrete activities such as working, learning, shopping, and socialization. This is analogous to too many needs and wants with limited resources. Consequently, there is blurring of boundaries between work and home and between tasks and chats. Some sort of schedule and compartmentalization are necessary to make home more efficient and effective.

7. Reunions with Friends and Family. One major impact of the coronavirus is to get in touch with distant friends and family, partly to assure that they are okay but partly to share stories and experience. This resembles high school or college reunions or family weddings. What is ad hoc event to keep in touch is now regular and scheduled get togethers to share information and experiences. Symbolically, we are all sitting on our porch and talking to our neighbors globally. The global reach of the social get togethers through social media such as Zoom and WhatsApp is mind boggling. We need to study sociological and cultural assimilations of consumption practices. Similar to the classic studies such as Reisman et al., 1950 , Linder, 1970 , Putnam, 2000 , we should expect dramatic changes in consumer behavior as a consequence of speedier and universal adoption of new technologies accelerated by the Covid pandemic.

8. Discovery of Talent. With more flexible time at home, consumers have experimented with recipes, practiced their talent and performed creative and new ways to play music, share learning, and shop online more creatively. With some of them going viral, consumers are becoming producers with commercial possibilities. YouTube and its counterparts are full of videos which have the potential for innovation and commercial successes.

3. Will old habits die or return?

It is expected that most habits will return back to normal. However, it is inevitable that some habits will die because the consumer under the lockdown condition has discovered an alternative that is more convenient, affordable, and accessible. Examples include streaming services such as Netflix and Disney. They are likely to switch consumers from going to movie theatres. This is similar to ride sharing services such as Uber which is more user friendly than calling a taxi service. Due to coronavirus, consumers may find it easier to work at home, learn at home and shop at home. In short, what was a peripheral alternative to the existing habit now becomes the core and the existing habit becomes the peripheral.

There is a universal law of consumer behavior. When an existing habit or a necessity is given up, it always comes back as a recreation or a hobby. Examples include hunting, fishing, gardening, baking bread, and cooking. It will be interesting to see what existing habits which are given up by adopting the new ways will come back as hobbies. In other words, will shopping become more an outdoor activity or hobby or recreation?

Modified Habits. In most cases, existing habits of grocery shopping and delivery will be modified by the new guidelines and regulations such as wearing masks and keeping the social distance. This is evident in Asia where consumers wear masks before they go for shopping or use the public transit systems. Modified habits are more likely in the services industries especially in personal services such as beauty parlors, physical therapies, and fitness places. It will also become a reality for attending museums, parks and recreation centers, and concerts and social events, just to name a few.

New Habits . There are three factors which are likely to generate new habits. The first is public policy. Just as we are used to security checks at the airports after 9/11, there will be more screening and boarding procedures including taking the temperature, testing for the presence of the virus and boarding the flight. All major airlines are now putting new procedures for embarking and disembarking passengers as well as meal services. As mentioned before, government policy to discourage or encourage consumption is very important to shape future consumptions.

As mentioned earlier a second major driver of consumer behavior is technology. It has transformed consumer behavior significantly since the Industrial Revolution with the invention of automobiles, appliances, and airplanes. This was followed by the telephone, television, internet and now the social media and the user generated content. The digital technology is making wants into needs. For example, we did not miss the cell phone but today you cannot live without it. Today internet is as important as electricity and more important than television. How technology transforms wants into needs has significant impact on developing new habits such as online shopping, online dating, or online anything. More importantly it has equally significant impact on the family budget between the old necessities (food, shelter, and clothing) in the new necessities (phone, internet, and apps).

The third context which generates new habits is the changing demographics ( Sheth and Sisodia, 1999 ). A few examples will illustrate this. As advanced economies age, new needs for health preservation (wellness) and wealth preservation (retirement) arise. Also, aging population worries about personal safety and the safety of their possessions. Finally, their interest in recreation (both active and passive) changes as compared to the younger population. Similarly, as more women enter the workforce, the family is behaving more like a roommate family. Eating meals together at home every evening is no longer possible. And the dinner together is more of a chore to be completed as fast as possible. Right after the dinner each family member goes to their own private room or space and engage in text messages, YouTube, or watching television. Shared consumption is giving way to individual consumption at the convenience for each family member.

There is also a growing trend of living alone by choice. More than one third of the U.S. households today are single adult households. This is due to delay in first time marriage from age eighteen to age twenty-nine. And with aging of the population, many senior citizens (especially women) are living alone by choice. As a single person household, new habits are formed about what to buy, and how much to buy and from where to buy. In conclusion, changing demographics, public policy and technology are major contextual forces in developing new habits as well as giving up old habits.

4. Managerial implications

There are three managerial implications from the impact of Covid-19 on consumer behavior. First, just as consumers have learned to improvise, business also has to learn to improvise and become more resident during the pandemic crisis. Unfortunately, companies are governed by formal processes and they are often unable to change them quickly. This has been evident in the government’s inability to process the PPP (payroll protection program) loans in the U.S. as well as applying for unemployment benefits.

Fortunately, as more large enterprises have transitioned to cloud computing, it has been easier to improvise. This has been the case with supermarkets and large retailers such as Walmart and Target. The latter, in any case, were converging their brick and mortar stores with their online shopping and even capable of omnichannel delivery. In short, companies can learn how to make their infrastructure, systems and processes to be more resilient; and in the process, manage global crises such as the Covid-19.

A second managerial implication is matching demand and supply. At each retailer ranging from the supermarkets to hyper stores to drug stores, there were chronic shortages due to hoarding and “run on the bank” mentality of consumers in a crisis. Supply chain, logistics, and warehousing operations are critical functions which need to be integrated with the volatile fluctuations in demand. In other words, unlike the current practice of stocking the products on the shelf with a backup inventory in the back of the store, it will be increasingly necessary to encourage online procurement and reverse the process from the merchandise waiting on the shelf for the customer to customer ordering first and the supermarket warehouse assembling the order and delivering it to the customer. As mentioned above, customers coming to the store is not the same as store going to the customer.

A third implication for management is that consumers will go back to their old habits unless the technology they learn to use such as Zoom video services and online ordering brings significant changes in their lives. Customers experience in the virtual world as well as post purchase services (customer support) will be strategic investments.

5. Research implications

As the lockdown and social distancing disrupted the whole range of consumer behavior (ranging from problem recognition to search from information to shopping to delivery to consumption and waste disposal), it has generated several new research opportunities anchored to anchored to the real world. These areas of empirical research with some theoretical propositions on hoarding, blurring the work-life boundaries, use of social media in a crisis are good opportunities to enrich the discipline of consumer behavior.

A social major area for the academic research has to do with consumer resilience and improvisation. It is a new field of research and the Covid-19 crisis has surfaced it as a great research opportunity. For example, are there cultural differences in improvisation across the globe? What are the different techniques used by consumers globally to isolate themselves from the infection?

Finally, Covid-19 has increased the use of social media on Facebook, Instagram, WhatsApp, Twitter, and Zoom. They are generating enormous amount of data on word of mouth. Current analytic techniques are not as useful with video conversations. Just as we developed Natual Language Processing (NLP) to analyze the text data, we will have to develop other techniques to analyze the video content probably anchored to machine learning and artificial intelligence ( Sheth, 2020a , Sheth, 2020b ). The virtual world is becoming more interesting to consumers compared to the physical world as we have seen in video games and virtual sports. Will artificial become real? For example, is a relationship with a chatbot girlfriend more comfortable and enjoyable as compared to a real girlfriend or boyfriend? In a recent article in Wall Street Journal, Parmy Olson describes several anecdotes of how individuals are interacting with chatbots. According to the author, Microsoft XiaIce social chatbot has more than 660 million users in China alone. In short, the artificial has become real.

6. Conclusion

The lockdown and social distancing to combat the covid-19 virus has generated significant disruptions on consumer behavior. All consumption is time bound and location bound. With time flexibility but location rigidity, consumers have learned to improvise in creative and innovative ways. The work-life boundaries are now blurred as people work at home, study at home, and relax at home. Since the consumer is unable to go to the store, the store has to come to the consumer.

As consumers adapt to the house arrest for a prolonged period of time, they are likely to adopt newer technologies which facilitate work, study and consumption in a more convenient manner. Embracing digital technology is likely to modify existing habits. Finally, public policy will also impose new consumption habits especially in public places such as airports, concerts, and public parks.

Jagdish N. Sheth is Charles H. Kellstadt Professor of Business in the Goizueta Business School at Emory University. He is globally known for his expertise in consumer behavior, relationship marketing, competitive strategy, and geopolitical analysis. Professor Sheth has over 50 years of combined experience in teaching and research at the University of Southern California, the University of Illinois at Urbana-Champaign, Columbia University, MIT, and Emory University. Professor Sheth is the recipient of all four top awards given by the American Marketing Association: the Richard D. Irwin Distinguished Marketing Educator Award , the Charles Coolidge Parlin Award for market research, the P.D. Converse Award for outstanding contributions to theory in marketing, and the William Wilkie Award for marketing for a better society. Professor Sheth is the recipient of an Honorary Doctorate in Science, awarded by the University of Illinois at Urbana-Champaign (2016), and Honorary Doctorate of Philosophy, awarded by Shiv Nadar University (2017). Professor Sheth has authored or coauthored more than three hundred papers and several books. His latest book is Genes, Climate and Consumption Culture: Connecting the Dots (2017). He is the co-founder, with his wife Madhuri Sheth, of the Sheth Family Foundation, which contributes to many charities both in India and in Atlanta.

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This research significantly contributes to understanding the trends and effects of the COVID-19 pandemic on consumer buying patterns. The results, which are of utmost importance, highlight changes in consumer behavior that emerged at the beginning of the second wave of the pandemic. A questionnaire survey was conducted using an online panel to identify how consumers changed their shopping habits and the needs they prioritized pre and post-pandemic crises concerning their fears. The paper delves into the concept of mindful consumption pre- and post-COVID-19.

COVID-19 , Pandemic , Consumer Behavior , Mindful Behaviour , Mindful Consumption , Acquisitive Consumption , Repetitive Consumption

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1. Introduction

The world witnessed a pandemic that changed how humans live, consume, think, and behave. COVID-19 has resulted in a disruption of lifestyle and consumer behavior. On March 11 th , 2020, the coronavirus epidemic was declared a pandemic by the Director General of WHO (Cucinotta & Vanelli, 2020) . In the absence of a vaccine to curb the spread of the highly contagious virus COVID-19, countries around the world were forced to take preventive measures in the form of imposing social distancing and declaring country-wide lockdowns (Kaplan et al., 2020) . Consumer sentiment varies significantly across countries impacted by COVID-19. Consumers in China, India, and Indonesia consistently report higher optimism than the rest of the world, while Europe and Japan remain less optimistic about their countries’ economic conditions after COVID-19. Divergent sentiment is also reflected in spending intent across categories. Divergent sentiment is also reflected in spending intent across categories. In most countries, consumers intend to continue shifting their spending to essentials while cutting back on most discretionary categories (Charm et al., 2020) .

Consumer behavior tended to compulsively focus on purchasing essential goods, especially those connected with preventing the virus, such as protective devices and sanitizing gel. The pandemic changed consumption patterns, for instance, reducing sales for some product categories (e.g., clothes) and improving sales for other categories (e.g., entertainment products) (Cannit et al., 2021) . Also, research indicated that job insecurity and life uncertainty experienced during the pandemic negatively impacted consumer behavior (Degli Esposti et al., 2021) .

The behavioral responses during epidemic outbreaks such as EBOLA, SARS, MERS, swine flu, and dengue have been studied in the past (Chirumbolo et al., 2021) . The areas affected by pandemics generally witnessed unemployment, uncertainties, and economic recession. Food, face masks, and sanitizer purchases were increased during the swine flu outbreak (Balinska & Rizzo, 2009) . During the first phase of the coronavirus lockdown in India, citizens experienced unprecedented situations, leading to an unparalleled preference shift among consumers. Unsurprisingly, in such an emergency, the need to buy necessities takes precedence (Goodwin et al., 2009) . Since there is a colossal uncertainty in the world that brought about changes in how individuals consume, it is essential to understand how the consumption patterns of individuals have changed during COVID-19 and post-COVID-19. Thus, this study included both COVID-19 and post-COVID-19 periods.

Owing to the economic uncertainty due to the long period of lockdown in India that resulted in unemployment and financial instability, it is essential to find an answer to the impact of the pandemic on the consumption pattern of individuals. Thus, the paper intends to study the pre and post-COVID-19 consumer behavior and the major factors that drove consumers’ purchase and investment decisions, & the reasons for the changes in behavior, if any. Secondly, considering the economic impact, would consumers continue impulse buying or shift towards mindful consumption?

2. Literature Review

2.1. Consumer Behaviour

Consumer Buyer behavior is influenced by four key sets of buyer characteristics: cultural, social, personal, and psychological. These factors can help identify interested buyers and shape products and appeals to serve consumer needs better (Kotler & Lee, 2008) . Culture is the most critical determinant of a person’s wants and behavior. Social Factors influence an individual’s buying behavior. A person’s reference group, social networks, family and friends, and professional associations strongly affect product and brand choices. Consumer Lifestyles, the whole pattern of acting and interacting in the world also influences purchase decisions.

Consumer behavior studies individuals or groups searching to purchase, use, evaluate, and dispose of products and services to satisfy their needs (Larson & Shin, 2018) . It also includes studying the consumer’s emotional, mental, and behavioral responses that precede or follow these processes (Kardes el al., 2011) . Changes in consumer behavior can occur for different reasons, including personal, economic, psychological, contextual, and social factors. However, in dramatic contexts such as a disease outbreak or a natural disaster, some elements, more than others, have a more significant impact on consumer behavior. Indeed, situations that potentially disrupt social lives or threaten individuals’ health have been proven to lead to solid behavioral changes (Leach, 1994) . An example is panic buying, a phenomenon occurring when fear and panic influence behavior, leading people to buy more things than usual (Lins & Aquino, 2020) . Specifically, panic buying has been defined as a herd behavior that occurs when consumers buy a considerable amount of products in anticipation of, during, or after a disaster (Steven et al., 2014) .

Pandemics and natural disasters are highly stressful situations, which can quickly induce negative emotions and adverse mental health states (Cannito et al., 2020; Sneath et al., 2009) such as perceived lack of control and instability, which are core aspects of emergencies, contribute directly to stress. Research has highlighted that stress is a crucial factor in influencing consumer behavior. For example, past studies have shown that individuals may withdraw and become passive in response to stress, and this inaction response can lead to a decrease in purchasing (Henry, 1993; Landau et al., 2011) . However, some studies point out that stress can lead to an active response, increasing impulsive spending behaviours (Burroughs & Rindfleisch, 2002; Duhachek, 2005) . This study aims to understand whether the pandemic triggered impulsive spending or whether there were instances of mindful consumption, within the turmoil caused by COVID-19.

2.2. Mindful Consumption

The Concept of Mindfulness:

Mindful practice implies breaking from habitual scripts, breaking free from the conditioning of the mind, where individuals are more aware of internal and external stimuli to regulate their minds better to stay in the present moment. This enables individuals to make better and more informed decisions and choices, away from the habitual ways of making decisions (Milne et al., 2020) .

Mindfulness is “a state of conscious awareness characterized by active distinction drawing that leaves the individual open to novelty and sensitive to context and perspective.” Mindfulness has four main features: greater sensitivity to one’s context or environment, more openness to new information, more excellent aptitude for cognitive categorization, and enhanced awareness of multiple perspectives in problem-solving (Sheth et al., 2011) .

Mindful Consumption :

Mindful consumption is viewed as a process that requires consumers to pay attention to their bodily sensations, emotions, and thoughts, with the attitude of acceptance as an ongoing process of inquiry to make consumption choices based on one’s direct experience of needs, values, and insight (Bahl et al., 2016) .

A core attribute that characterizes mindful consumption (MC) is mindset and behavior. For perspective, it is a sense of caring about the implications and consequences of one’s consumption, and for behavior in MC, the core attribute is temperance in consumption. Sheth defines a mindful mindset as caring for nature, the self, and the community. Caring for oneself is not selfish or self-centered but is about paying heed to one’s well-being. There are two main aspects of personal well-being: eudemonic—meaning happiness or flourishing, and economic. An increased level of consumption not only works counter to welfare in the eudemonic sense but also reduces economic well-being for vast segments of consumers. Caring for the natural environment is also a part of mindful consumption, wherein this view provides a motive to conserve the background so that it continues to remain helpful to humans.

A sense of caring for self, community, and nature would each motivate temperance in consumption. Their combined effect would give such motivation a more significant boost.

Mindful Behaviour :

Mindful consumption connotes temperance in acquisitive, repetitive, and aspirational consumption at the behavior level, ensuing from and reinforced by a mindset that reflects a sense of caring toward self, community, and nature. For behavior change, temperance is the pivotal concept in mindful consumption. Temperance does not imply a rejection of consumption per se, but temperance is about making consumption an ideal situation that provides ultimate satisfaction. Temperance must be exercised in three behaviors most often associated with overconsumption: accumulative, repetitive, and aspirational.

Acquisitive consumption : The most basic form of excessive consumption involves acquiring things at a scale that exceeds one’s needs or even one’s capacity to consume.

Repetitive consumption : The cycle of buying, discarding, and buying again is another path to excessive consumption. Many things are discarded and purchased repeatedly because they are meant to be consumed repeatedly.

Aspirational Consumption : Competitive consumption, as the name suggests, is related to a subtle variation of aspiration-driven consumption, and it is no longer limited to those at the top of the income pyramid (Sheth et al., 2011) . Conspicuous consumption is not about people trying to keep up with their neighbors or even people of the same income status or those of similar socio-eco- nomic standing. Instead, the trend is an upward shift in consumer aspirations, coming with the vertical stretching out of reference groups—which means people are more likely to compare with others whose incomes are three, four, or five times one’s own.

3. Objective of the Study & Methodology

This study aims to understand the impact of COVID-19 on consumer behavior, whether the sudden pandemic has brought about changes (if any) in individuals’ consumption behavior, and the type of changes that have resulted in buying behavior owing to the crises. It will also examine the factors influencing purchase decisions and whether there were instances of impulse buying and mindful consumption when economic activities resumed post-lockdown.

Data was collected online through a structured questionnaire. Google Forms was used to create the questionnaire. The respondents had already experienced the lockdown and were also aware of the possible impact of this pandemic on the world’s economy and its consequences on the Indian economy. The questionnaire consisted of three sections: the first section comprised questions related to planned and unplanned purchases before and after the COVID-19 situation, The second section consisted of questions regarding impulse buying and mindful consumption, the last team comprised queries related to the demographic profile of the respondents; A 5-point Likert scale was used to get responses for some of the questions while some questions were open-ended to delve into the factors that influenced purchase decisions post lockdown.

Simple random sampling and convenience sampling were used to collect data, and the respondents were contacted by sending survey links through email and social media platforms like WhatsApp and Facebook. A total of 43 responses were collected. 100 questionnaires were sent online, while only 43 responded accurately, filling the entire questionnaire. The number of respondents might be considered as a limitation of the study.

4. Data Analysis

4.1. Expenditure Comparison Pre COVID & Post COVID ( Figure 1 and Figure 2 )

Figure 1 . Expenditure of consumers pre-COVID-19.

Figure 2 . Expenditure of consumers during COVID-19.

➢ There is a decrease in expenditure in every segment except for Food Delivery & Grocery.

➢ Eating at restaurants is the most impacted sector due to COVID-19, and consumers prefer home delivery.

4.2. Buying Behavior of Consumers Post COVID ( Figure 3 )

Figure 3 . Post COVID-19 buying behaviour.

➢ There is significant cost-cutting in skincare & makeup.

➢ Expenditure on healthcare & household supplies has been increased.

4.3. Consumer Preference towards Reusable Products ( Figure 4 )

Figure 4 . Preference of reusable products during COVID-19.

➢ Almost 65% of people prefer reusable products to disposable ones.

4.4. Impact of Peer Influence on Consumer Buying Behavior ( Figure 5 )

Figure 5 . Influence of peer buying behaviour on consumers.

➢ 28% of the survey respondents chose to buy the same brands as their peers/relatives.

➢ A notable 40% consumers demonstrated a strong preference for autonomy in their purchasing decisions, opting to buy brands of their own choice.

4.5. Change in Consumer Buying Behavior towards Electronics Pre COVID & Post COVID ( Figure 6 and Figure 7 )

Figure 6 . Repurchase of branded appliances.

Figure 7 . Repurchase of updated versions of technology.

➢ Expenditure on electronics has increased because of online education & work from home.

4.6. Shift in Consumer Buying Behavior during Pandemic & Post-Pandemic (Figures 8-11)

During COVID-19

Figure 8 . Purchase of items during COVID-19, that seemed useless post COVID-19.

Post-COVID 19

Figure 9 . Instances of buying luxurious brands post COVID-19.

Figure 10 . Instances of unused products in the cupboard of consumers.

Figure 11 . Shift in buying behaviour during and after COVID-19.

➢ A significant majority of the consumers perceived that the apparel and fashion accessories they purchased during the pandemic had limited utility as it lost value once the pandemic subsided. This may be due to overconsumption, impulsive buying or changes in their fashion preference and lifestyle with the new normal.

➢ During the pandemic most individuals stockpiled healthcare products, that seemed redundant or unnecessary with the cessation of Covid-19. Over preparation during the pandemic and evolving public health guidelines post pandemic may have contributed to these phenomena.

➢ In the Post Pandemic era consumer spending habits have shifted, with a significant inclination towards fashion accessories & apparel. This trend is characterized by resurgence in demand for fashion & a renewed lifestyle with the new normal, return to pre-pandemic routines, a need of self expression and creativity after a prolonged period of restriction.

➢ A significant majority of consumers have admitted of possessing items that remained unused throughout the pandemic. This phenomenon highlights hoarding behaviour, stockpiling items through frequent purchases due to fear of uncertainty, leading to unused purchases. Overconsumption-excessive unnecessary buying leading to surplus purchases resulting from fear.

➢ A significant majority of respondents reported a significant shift in their purchasing habits during the pandemic, thereby adopting a more mindful approach to consumption.

5. Discussion

Of the 43 respondents, 67.4% were male, and 32.6% were female. The majority of the respondents, that is, 41.8%, were in the age group of 25 to 44 years; 23.3% were between 31 to 40 y years of age; only 9.3% were below 18 years, and the remaining 9.3 % were between 41 - 54 years.11.6% were within the age group of 18 - 30 years. Most of the respondents (37.2%) had a monthly family income of Rs 70,000 or less; 25.6% had a monthly income of Rs 70,137 - Rs 273,098; 9.3% had an income between Rs 273,167 - Rs 845,955 while, 27.9% earned Rs 846,023 or more every month.

Occupations of the respondents ranged from Chartered Accountant, Accountant, HR Professional, homemaker, Engineer, Software Engineer, Software Developer, Product Designer, IT professional, Regional Sales Manager, Media Professional, Product Marketing Manager, Interior Designer, Sales Manager, Product Marketing Manager, Banker, Software Engineer, Teacher, Lawyer, Entrepreneur, and Lawyer. Regarding educational qualifications, 53.5% of the respondents were graduates with a professional degree, while 34.9% were postgraduates without professional qualifications; 7% were diploma holders.

The notable feature of expenditure before the lockdown (24th March 2022) is that the money spent on healthcare products and Kits was significantly low, which increased dramatically during and after the lockdown.

Groceries classified as a commodity of high necessity show no significant change in expenditure before, during, and after the lockdown. This implies that people went on to buy the same quantity of groceries for the family, and no expenses were curtailed regarding grocery items. Consumption of Fashion Accessories (Shoes, watches, eyewear, sunglasses, bags, purses, belts) increased during the lockdown period compared to the pre-lockdown era. This finding might point towards the psychological condition of the respondents during the lockdown when all social ties were cut off owing to the crises. Besides, financial in-security like unemployment and salary cuts, might have contributed to an unstable mental state. Consumption of commodities like fashion accessories and apparel provided temporary satisfaction and enjoyment to many. Thus, many individuals considered shopping a recreational activity during the lockdown period. Several factors might have contributed towards this trend: 1. Pent-up Demand: Post lock-down there was a sudden surge in retail spending due to pent-up emotions and demands. 2. New Normal: With lock-down being lifted, consumers adopted to the new normal and thus, buying more of fashion accessories and apparel, as work resumed and social gatherings were permitted. Besides, some respondents noted that they did not change their consumption patterns post-lockdown.

23% of the respondents demonstrated a remarkable commitment towards apparel consumption, electing not to cut costs even during the lockdown period, a time of uncertainty and restrictions. This statistics represents a resilient and dedicated approach to their lifestyle choices, a prioritization of fashion, & lifestyle over uncertainties. A notable 9% of respondents exhibited financial prudence by intentionally cutting costs on apparel during the lockdown period, demonstrating a proactive approach, driven by a desire to build resilience, mitigate financial uncertainty or simply adopt to the new economic and social reality.

Their mindful approach towards consumption could indicate a growing awareness of the importance of sustainable spending habits. Electronic Appliances depicted a balanced trend: 37% of the population did not cut costs, while 34% focused on intensive cost-cutting on electronic items during the lockdown.

Healthcare products show a predictable trend, as 41% of the respondents never did any cost-cutting regarding healthcare commodities. However, 34% did focus on some cost-cutting when it came to healthcare products, and 79% of the population did conscious cost-cutting when it came to eating at restaurants post-lockdown. It might be that the fear of getting infected through restaurants. Covid-19’s impact on consumer behaviour is starkly evident in the fashion industry, with a significant 67% of the population exhibiting no or very minimal restraint in their spending on fashion accessories. This suggests that the prolonged stress and uncertainty of the pandemic led to impulsive buying behaviour, as individuals found solace in retail therapy. The reality that respondents turned to online shopping as a means of entertainment to cope with the crises and stressful situations, reveals a deeper psychological dynamics at play. Buying apparel and fashion accessories provided a temporary solace and escape from anxiety and stress that the pandemic had induced. This phenomenon predicts a trend of acquisitive consumption, during and post-lock down period, highlighting the significance of brands and e-commerce as a primary channel for consumption during times of crises and uncertainty caused by a pandemic like Covid-19. This finding has important implications for the Fashion industry. A significant majority (81%) of respondents prioritized household supplies as a necessity and abstained from cutting costs in this area. Similarly, a substantial proportion (61%) of respondents considered skincare and makeup products essential. These might be the fashion conscious people, who are immensely concerned about the way they present themselves to the world. Although a notable minority of 37% adopted cost cutting measures in the skin care and makeup category, indicating a more cautious approach to spending. The data suggests that household supplies are considered important and essential, with a high percentage of respondents unwilling to compromise on these necessities. Make up and skincare is also considered important, though at a lesser extent. Factors like demographic insights, age, income, occupation, gender, social status must have impacted the buying behaviour of respondents significantly. Economic Implications: Skincare and make up industry must alter their marketing strategies during a crisis. Industries catering to household supplies may have a stable business running during a crisis, yet they too must have marketing strategies to combat a crisis. Marketing implications: The survey results indicate that consumers prioritize essential items over discretionary spending during a pandemic that brings with it economic uncertainty. This finding can indeed influence marketing and sales strategies across various industries. No panic buying/herd buying was noted in this study, even when respondents’ lives were uncertain.

Repetitive consumption is a means of overconsumption (as the theory states). Temperance in repetitive consumption has been noticed in this study, as respondents preferred buying reusable products post-pandemic. Preference for disposable products was not solely due to the pandemic but also due to concern for the environment, as noted by many respondents who bought reusable items because they felt they would be environmentally friendly. They bought reusable products long before COVID-19. However, due to the pandemic, few used reusable products to save money and focus on cost-cutting.

Acquisitive Consumption was high during the lockdown and post-lockdown. Respondents bought apparel and fashion accessories that they later felt were unnecessary. This finding also corroborates the theory that stress/fear/depression caused by the lockdown and the sudden pandemic, where all social ties were suddenly cut off, resulted in a buying spree in many individuals. Consumption became an entertainment and provided a feel-good factor. These respondents also admitted that before the crises, they consumed items, especially apparel and fashion accessories, that they later felt were unnecessary and wanted to give away to charity. Consumers plan to keep spending on clothing and Fashion accessories in the new normal. Thus, even after the economic crises, acquisitive consumption is on the rise without any hint of mindful consumption.

Aspirational consumption was significantly high as consumers developed a savings habit during the lockdown. Aspirational consumption was high in the case of mobile phones, especially where respondents planned to acquire premium mobiles and, in many cases, tabs and laptops. Considerable money was spent by respondents on mobile phones, tablets, and laptops even during lockdown.

6. Findings

1) The pandemic has taught many financial lessons; people have developed savings habits and focus only on buying necessities. This is mainly due to salary cuts and fear of unemployment.

2) Acquisitive Consumption is on the rise even after the pandemic. Acquisitive Consumption behavior is exceptionally high for apparel, fashion accessories, mobile phones, laptops, and tablets. Mindful behavior regarding acquisitive consumption is absent among respondents.

3) For 53% of respondents, nothing has changed due to the pandemic, which means their consumption pattern has not altered during and after the lockdown. This implies there has not been any change in buying behavior owing to COVID-19. So, it cannot be generalized that COVID-19 or any other epidemic/ pandemic will change consumption behavior. Some of the population remains undeterred by such crises. They continue to consume in a way that suits their lifestyle and needs without focusing on cost-cutting or even being mindful of their consumption.

4) Part of the sample who agreed that there was a significant change in buying behavior after the pandemic focussed on purchasing necessary items (primarily groceries, household supplies, and health care products).

5) Big brands and peer pressure still play critical roles in influencing individuals’ aspirational consumption behavior as life returns to the new normal.

6) Most consumers have a strong sense of self identity and confidence in their choice of brands and decision making abilities. These consumers are less likely to be influenced by social norms or fear of missing out. They prioritize their personal choice and preferences over external opinions. Thus, brands need to target and appeal this segment with personalized marketing efforts highlighting unique selling points (USP) of the brand.

7) The findings highlight the complex and adaptive nature of consumer behavior during times of crises. While some individuals like (53%) may have turned to hoarding and stock piling, depicting a trend of compulsive consumption as against mindful consumption. Contrarily, 47% respondents had become more intentional and thoughtful with their purchases, carefully considering each purchase. A shift towards mindful consumption possible driven by various factors like financial constraints, reduced and induced consumption habits owing to the pandemic uncertainty.

7. Conclusion

According to the survey, respondents preferred using reusable products over disposable ones to care for the natural environment. This attitude was not a result of the pandemic, but rather a mindset individual had developed long before the COVID-19 crisis. In other words, there has not been a significant shift in consumption behavior due to the pandemic. The respondents’ consumption habits remained unchanged before, during, and after the lockdowns. Furthermore, acquisitive consumption was prevalent, even during the pandemic, indicating that shopping became a coping mechanism for stress, anxiety and uncertainty caused by the pandemic. Retail therapy as it is often called, provided a temporary escape and solace from the fear that dominated social life during the pandemic. The act of browsing, purchasing and acquiring new items offered a sense of control, comfort and momentary happiness, helping to alleviate the emotional toll to the pandemic situation. Contrary to expectations, the pandemic’s economic instability had a surprisingly limited impact on individual’s purchasing behavior. Despite the widespread financial uncertainty and an ambiance of fear & anxiety, consumer spending habits did remain more or less consistent. A profound shift in consumer behavior marked by growing awareness of the need and importance of mindful consumption has been noted. During the pandemic, consumers have developed an awareness while consuming products and have prioritized needs over wants, embracing mindful consumption.

Conflicts of Interest

The author declares no conflicts of interest regarding the publication of this paper.

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Could all-to-all trading improve liquidity in the Government of Canada bond market?

Introduction.

Government bond markets are typically considered to be among the most-liquid fixed-income markets. Even so, liquidity conditions in government bond markets can deteriorate sharply when a sudden imbalance occurs between the demand for and supply of liquidity. In fact, in recent extreme episodes of market turmoil like the onset of the COVID‑19 crisis in March 2020 and the UK gilt crisis in September 2022, market liquidity deteriorated so much that central banks intervened by buying government bonds to stabilize and restore orderly functioning of these markets.

Some policy-makers, academics and financial market participants believe that one reason market liquidity of government bonds is vulnerable to periods of market turmoil is because the structure of these markets is centred around dealers. In this type of market structure, dealers use their own balance sheets or act as a broker to match offsetting transactions between their different clients. In periods of turmoil, however, client demands for liquidity can surge, and trading can become more one-sided. In other words, many clients trade bonds in the same direction, by, for example, buying government bonds in a flight to safety or selling government bonds in a dash for cash.

Dealers may charge a higher fee to intermediate such trades as compensation for the additional risks that they bear on their own balance sheets when market conditions are volatile. 1 In extreme cases, dealers may stop intermediating completely, either due to constraints on their balance sheets or their inability to hedge risks. This could prevent transactions from occurring among clients who would otherwise be willing to transact with one another.

Adopting an all-to-all market structure is one of several proposals for improving the resilience of government bond markets during periods of turmoil (Duffie 2023). In this structure, market participants would be able to transact directly with each other on an electronic trading platform and therefore avoid any limits in dealers’ ability to intermediate. An all-to-all structure could also attract new market participants through increased transparency of executable and executed prices on all-to-all trading platforms. This increased transparency could improve the bargaining power of market participants and reduce barriers to entry that may exist in a dealer-intermediated structure. Supporters of all-to-all trading argue that a more diverse set of market participants could improve market liquidity. This is because it could increase the likelihood and amount of bond transactions occurring in opposite directions, even during periods of turmoil. These offsetting transactions could reduce pressures on market liquidity.

To examine these considerations of all-to-all trading, we use granular transaction-level data to assess how much client-to-client trading could be possible in the Government of Canada (GoC) bond market. We find, on average, almost half of the GoC bond transactions of dealers’ clients could potentially be offset with those of other clients over the trading day. This share is stable over time, including during the COVID-19 crisis in March 2020. This demonstrates that clients were indeed transacting in the opposite direction of other clients’ transactions in a period of market turmoil. For GoC bond futures—instruments that are like GoC bonds but trade on an all-to-all platform—we find that almost all clients’ transactions can be offset by those of other clients and that this high offsetting share is also stable.

So would all-to-all trading support liquidity in the GoC bond market? The answer remains unclear. On the one hand, our results shed some light on the potential for clients’ transactions to offset each other. On the other hand, our methodology overlooks important considerations for the sake of simplicity. For instance, we do not account for differences in the prices when offsetting client transactions or for the influence that client-dealer relationships may have on trading behaviours. These considerations make it challenging to understand whether our estimated extent of client offsetting would take place if GoC bonds were traded entirely on an all-to-all platform. In addition, several other aspects of all-to-all trading merit further investigation.

All-to-all trading presents a range of risks and considerations

While all-to-all trading may bring benefits, it presents a range of risks and considerations. Critics argue that the potential for matching transactions across different participants could be minimal in periods of market turmoil. They argue that even new entrants could have demands for liquidity that lead them to transact government bonds in the same direction as other clients, amplifying one-sided trading rather than promoting matching. Some critics also argue that new entrants and existing clients would have fewer incentives to maintain intermediation services in periods of turmoil. They cite the lower importance on relationships with clients on an all-to-all platform compared with a dealer-intermediated structure as the reason for the potential reduction in intermediation services.

Other considerations that require further analysis include:

• the impact of greater price transparency on an all-to-all platform can affect the behaviour of market participants
• the impacts to settlement and clearing risks as clients’ transactions are executed on an all-to-all platform
• the potential negative impacts to other services provided by dealers, such as access to primary bond markets
• the implications for markets, such as repurchase (repo) or derivatives markets, that trade in conjunction with GoC bonds
• the potential reduction of capital that dealers allocate to their intermediation businesses

Almost half of transactions could potentially be offset on any given trading day

Using data from October 2019 to November 2023 on GoC bond transactions from the Market Trade Reporting System, we calculate the share of transactions of clients of dealers that could have potentially been offset by transactions with other clients within a trading day, following Chaboud et al. (2022). 2 In other words, for each purchase of a specific bond that a client makes, what share of the amount bought was sold by other clients on the same trading day and vice versa. The methodology allows buys and sells to offset each other despite potential differences in the prices of trades or the exact times within the day that trade trades occurred. 3 It also does not match exact trade amounts; instead, it looks at total buys and sells for a specific bond. Despite these limitations, our methodology helps establish estimates of how many client transactions in a trading day could potentially be matched.

Chart 1 reports the average daily amount of GoC bond transactions that can be offset by clients as a share of the amount of transactions offset plus the residual amount that is not offset. Across all types of GoC bonds, 49% of client transactions could be offset on an average trading day. Across bond tenors, the offsetting shares range from 57% to 65% for benchmark bonds and from 23% to 46% for non-benchmark bonds.

The higher offsetting shares for benchmark bonds is likely due to their greater liquidity, which leads to higher turnover of trading volume compared with that of non-benchmark bonds (Gungor and Yang 2017). The relatively lower offsetting share for non-benchmark bonds suggests that dealers play an important role in using their balance sheets to intermediate these less-liquid bonds. Overall, high offsetting transactions among clients promote two-sided markets. This can help dealers more easily find the bonds or cash they need to fulfill their clients’ transactions, which could promote market liquidity (Sandhu and Vala 2023a).

Chart 1: Benchmark bonds have higher average daily shares of offsetting client transactions than those of non-benchmark bonds

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The share of offsetting transactions was stable during the COVID‑19 crisis

In this section, we examine how the share of clients’ offsetting GoC bond transactions changes over time, including in periods of market turmoil. Client-to-client trading could be most beneficial in periods of market turmoil when a larger share of clients, like asset managers, may experience an increased need for liquidity. This type of client-to-client trading would also reduce the volume of transactions that dealers would need to intermediate using their own balance sheets, which they may be more reluctant to do in periods of turmoil.

Chart 2 shows the 21-day moving average of the total daily share of clients’ offsetting GoC bond transactions with the offsetting and residual amounts. The share of clients’ offsetting transactions is fairly stable, at around 50%, from October 2019 to November 2023. The offsetting share was also stable during the COVID‑19 crisis in March 2020, indicating that many clients were transacting GoC bonds in the opposite direction despite significant market turmoil.

Chart 2: The share of offsetting client transactions was stable during the COVID-19 crisis

While our analysis sheds light on the share of client transactions that could potentially be offset, our estimates of clients’ offsetting transactions could have been different if clients had actually traded on an all-to-all platform. This difference arises because our estimates are based on trading behaviour that is influenced by the relationships between dealers and their clients. These relationships are typically formed in dealer-intermediated market structures, like the repo market for GoC treasury bills (Sandhu, Walton and Lee 2019). For instance, dealers can encourage their clients to conduct a trade that they otherwise may not have entered. They could do this by offering price concessions or packaging a GoC bond with other services or products offered to their clients.

As a result, our estimates may be higher than what we would observe on an all-to-all platform given the influence of relationships between dealers and their clients.

Almost all bond futures transactions could potentially offset each other

To understand what client-to-client trading could look like without the influence of dealer-to-client relationships on trading behaviour, we apply our methodology to the GoC bond futures market. In other words, we calculate the share of client transactions that could be offset by other clients for each bond futures contract each day, ignoring individual transaction amounts and any difference in prices or time of the trade over the course of the trading day. 4 Like our analysis of GoC bonds, this exercise is useful for understanding whether trading among clients is two-sided at the daily level for GoC bond futures.

GoC bond futures can be a substitute for GoC bonds because they offer similar exposure to interest rates. GoC bond futures, however, are transacted entirely on a futures exchange, which is akin to an all-to-all platform, where clients can trade directly with other clients or dealers anonymously. In this market structure, relationships between dealers and clients are less likely to form because dealers do not directly benefit from their clients’ transactions.

Our comparison is imperfect due to several differences between GoC bonds and bond futures. These differences include the set of clients and dealers, standards for settling transactions and the amount of cash required to enter into positions. As well, futures trades are much smaller than those in the GoC bond market. Moreover, trading is more concentrated in bond futures because there are fewer active bond futures than there are different GoC bonds, possibly increasing their liquidity.

Chart 3 presents the 21-day moving average of the total daily share of offsetting client transactions in the GoC bond futures market as well as the offsetting and residual amounts. The offsetting share is quite stable over our entire sample, from January 2020 to June 2023, and transactions among clients that could potentially offset each other is high, at around 90% on average. In other words, approved participants—who are comparable to dealers in the GoC bond market—are on the other side of only 10% of client transactions over the trading day. These results demonstrate that a large amount of client-to-client trading indeed takes place on an all-to-all platform for an asset class that is like GoC bonds and is less influenced by the relationship between dealers and clients.

Chart 3: The share of offsetting client transactions in Government of Canada bond futures was stable during the COVID-19 crisis

We find that almost half of the GoC bond transactions from clients could be potentially offset by the transactions of other clients over the trading day. This share remains stable in periods of market turmoil. These results help address concerns with all-to-all trading around the potential for overwhelmingly one-sided transactions from clients in periods of turmoil. In the GoC bond futures market, we find that almost all of client transactions are ultimately offset by transactions of other clients. This demonstrates that clients are willing to trade a security similar to a GoC bond but traded on an all-to-all platform. Our results are an important first step in understanding the relevance of broader all-to-all trading in the GoC bond market. However, it remains unclear whether broader all-to-all trading would improve liquidity given the simplicity of our methodology as well as several other risks and considerations that merit further investigation.

Appendix: Robustness of clients’ offsetting transactions

To ensure the approach of measuring clients’ offsetting transactions used in our analysis is robust, we consider alternative methods and find that our overall conclusions hold. One caveat, however, is that we do not account for differences in the prices of buy and sell transactions in our offsetting methodology. Despite the robustness exercises discussed below, ignoring price differences overstates our offsetting potential results.

We calculate the same measure, but instead of offsetting buy and sell transactions for individual GoC bonds, we offset transactions according to broader categories based on the term to maturity of bonds. Specifically, we allow buys and sells of different bonds to offset each other if their term to maturity is between 1 and 3 years; between 3 and 8 years; between 8 and 12 years; and greater than 12 years.

Matching transactions within these broader buckets is plausible because bonds of similar maturity are close substitutes. Clients trading on an all-to-all platform may adjust their behaviour to trade substitutable bonds because the platform may foster greater price transparency and lower costs to locate available bonds for trade. Under this approach, the average daily clients’ offsetting transaction for non-benchmark bonds, across all term-to-maturity buckets, increases by between 14 and 16 percentage points, depending on the term-to-maturity bucket.

For our analysis, we allow buy and sell transactions to offset at the daily level. This is a useful theoretical exercise to help understand whether client trading is one-sided during the day. In reality, clients may not be willing to be exposed to the risks of holding or being short a GoC bond or GoC bond future over the course of the trading day and may want to offset their trades immediately.

To understand the extent of client two-sided trading at shorter intervals, we apply our same offsetting approach, but at 1-hour and 15-minute intervals. For GoC bonds, this approach is only a rough approximation because the time stamps in the Market Trade Reporting System may be inaccurate. As expected, the share of clients’ offsetting transactions declines at shorter intervals. 5 Despite these lower levels of client offsetting, the results remain indicative of two-sided trading among clients at shorter intervals, albeit to a lesser extent.

Chaboud, A., E. Correia Golay, C. Cox, M. Fleming, Y. Huh, F. Keane, K. Lee, K. Schwarz, C. Vega and C. Windover. 2022. “ All-to-All Trading in the U.S. Treasury Market. ” Federal Reserve Bank of New York Staff Report No. 1036.

Duffie, D. 2023. “ Resilience Redux in the US Treasury Market. ” Stanford University Graduate School of Business Research Paper No. 4552735. Presented at the Jackson Hole Symposium, Federal Reserve Bank of Kansas City.

Gungor, S. and J. Yang. 2017. “ Has Liquidity in Canadian Government Bond Markets Deteriorated? ” Bank of Canada Staff Analytical Note No. 2017-10.

Sandhu, J. and R. Vala. 2023a. “ Do Hedge Funds Support Liquidity in the Government of Canada Bond Market? ” Bank of Canada Staff Analytical Note No. 2023-11.

Sandhu, J. and R. Vala. 2023b. “ The Government of Canada Bond Market: Discussion on Market Structure. ” Presentation to the Canadian Fixed-Income Forum, Ottawa, Ontario, November 21.

Sandhu, J., A. Walton and J. Lee. 2019. “ Borrowing Costs for Government of Canada Treasury Bills. ” Bank of Canada Staff Analytical Note No. 2019-28.

Acknowledgements

We thank David Cimon, Jean-Philippe Dion, Toni Gravelle, Stéphane Lavoie and Adrian Walton for their comments. We are grateful to Harri Vikstedt and Corey Garriott for their advice and suggestions and to members of the Canadian Fixed-Income Forum for sharing their perspectives at their meeting in November 2023. We also thank Alain Chaboud, Ellen Correia Golay, Michael Fleming and Frank Keane for helpful discussions comparing our analysis to their work on all-to-all trading in the US Treasury Market. Finally, we are grateful to Robert Tasca and members of his team at the Montréal Exchange for providing data for our analysis.

• 1. Some market participants contend that measures of liquidity should be evaluated on a volatility-adjusted basis. When adjusted for volatility, some recent periods of worsened liquidity appear less severe.[ ← ]
• 2. See Sandhu and Vala (2023a) for a description of the Market Trade Reporting System.[ ← ]
• 3. Differences in prices of buy and sell transactions could be an important deterrent for clients to trade with each other. If price differences were considered, the potential for offsetting could be even lower when prices are volatile.[ ← ]
• 4. We exclude 30-year GoC bond futures contracts from our analysis given the limited trading activity in this tenor.[ ← ]
• 5. See Sandhu and Vala (2023b) for details of how clients’ offsetting changes at shorter intervals.[ ← ]

Bank of Canada staff analytical notes are short articles that focus on topical issues relevant to the current economic and financial context, produced independently from the Bank’s Governing Council. This work may support or challenge prevailing policy orthodoxy. Therefore, the views expressed in this note are solely those of the authors and may differ from official Bank of Canada views. No responsibility for them should be attributed to the Bank.

DOI: https://doi.org/10.34989/san-2024-17

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