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• Published: 09 October 2023

Evidence of the time-varying impacts of the COVID-19 pandemic on online search activities relating to shopping products in South Korea

• Jiam Song   ORCID: orcid.org/0000-0002-7975-0909 1 ,
• Kwangmin Jung   ORCID: orcid.org/0000-0002-5615-8865 2 &
• Jonghun Kam   ORCID: orcid.org/0000-0002-7967-7705 1  

Humanities and Social Sciences Communications volume  10 , Article number:  669 ( 2023 ) Cite this article

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• Environmental studies

A Correction to this article was published on 30 October 2023

This article has been updated

The COVID-19 pandemic has changed the level of the received risk of the public and their social behavior patterns since 2020. This study aims to investigate temporal changes of online search activities of the public about shopping products, harnessing the NAVER DataLab Shopping Insight (NDLSI) data (weekly online search activity volumes about +1,800 shopping products) over 2017–2021. This study conducts the singular value decomposition (SVD) analysis of the NDLSI data to extract the major principal components of online search activity volumes about shopping products. Before the pandemic, the NDLSI data shows that the first principal mode (15% of variance explained) is strongly associated with an increasing trend of search activity volumes relating to shopping products. The second principal mode (10%) is strongly associated with the seasonality of monthly temperature, but in advance of four weeks. After removing the increasing trend and seasonality in the NDLSI data, the first major mode (27%) is related to the multiple waves of the new confirm cases of corona virus variants. Generally, life/health, digital/home appliance, food, childbirth/childcare shopping products are associated with the waves of the COVID-19 pandemic. While search activities for 241 shopping products are associated with the new confirmed cases of corona virus variants after the first wave, 124 and 190 shopping products are associated after the second and third waves. These changes of the public interest in online shopping products are strongly associated with changes in the COVID-19 prevention policies and risk of being exposed to the corona virus variants. This study highlights the need to better understand changes in social behavior patterns, including but not limited to e-commerce activities, for the next pandemic preparation.

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Introduction.

The COVID-19 pandemic has spread out since early 2020. The corona virus is contagious and has transformed to variants. The global community has been suffered with multiple waves of new confirmed cases of the corona virus variants. The herd community of the corona virus consists of the natural infected groups and vaccinated groups. However, the herd immunity for COVID-19 is required to prevent multiple variants from Alpha through Omicron (Moghnieh et al. 2022 ). Particularly, the absence of vaccines for corona virus and its variants has exacerbated the pandemic over the world. The variants have decreased the probability to form the herd immunity.

The spread of corona virus variants has significantly increased public risk perception, thereby leading people to avoid in-person activities (Dryhurst et al. 2020 ). Markets have responded to such changes in socioeconomic landscape by rapidly adapting digital transformations, which consequently boosted online platforms relating to shopping. The public have become preferred to online shopping, rather than in-person shopping, particularly when the number of infected people increases (Grashuis et al. 2020 ; Li et al. 2020 ; Mouratidis and Papagiannakis, 2021 ; Pham et al. 2020 ). This shift of the public’s lifestyle provides an opportunity to understand the impact of the COVID-19 pandemic on socioeconomic change via big social monitoring data relating to online information seeking activities.

The impact of the COVID-19 pandemic can be examined by comparing socioeconomic activities before and after COVID-19 pandemic. However, the long-lasting pandemic crisis makes it difficult to investigate the time-varying impact of the COVID-19 pandemic. Few literature has considered temporal changes of the impact of COVID-19 through its multiple waves due to the cost of collecting relevant data and the time-consuming data preprocessing. Online social monitoring data enables us to investigate the impact of the multiple waves of the corona virus variants and relevant prevention policies on online socioeconomic activities, which are costly-efficient and real-time monitoring. Recent studies have investigated changes in online activity patterns during the COVID-19 pandemic (Gu et al. 2021 ; Lampos et al. 2021 ; Nasser et al. 2021 ). However, the socioeconomic impact of the multiple waves of the corona virus variants remains unknown.

During the COVID-19 pandemic, online shopping patterns has been investigated in various ways. A previous study discussed a chance to die or modify old purchasing habits from in-person activities and to create new habits (Sheth, 2020 ). The new habits are likely to be influenced by socioeconomic constraints, such as public policy, technology and changing demographics. Another study proposed this behavior pattern change during the COVID-19 pandemic introducing the “react”, “cope”, and “adapt” phases of the Reacting Coping Adapt (RCA) framework (Kirk and Rifkin, 2020 ). At the “react” phase, the public change their purchasing behavior based on pandemic risk perception as a social response to dynamic social distancing policies. At the “coping” phase, they start adopting new purchasing pattern based on the public policy level. At “adapt” phase, they establish/stabilize new purchasing pattern and less reactive to the pandemic situation (Guthrie et al. 2021 ; Kirk and Rifkin, 2020 ). The RCA framework has been validated by the online shopping patterns in France before, during, and after the COVID-19 pandemic (Guthrie et al. 2021 ). The application of this RCA framework to other countries and social behaviors is still lacking.

Nowadays, the Internet service providers monitor and record online search activities through data logging and analyze these online search activity data to detect changes in the user’s interest and optimize the search algorithm for most relevant information to their interest in a timely manner. For example, increased online search activities about a specific shopping product hint an emerging demand of the shopping product, which is a practical information for inventory and supply chain management.

Online social network data, such as Twitter, have been already used to predict stock market price change (Almehmadi, 2021 ). Online information search activity data, such as Google Trends, have been used to forecast the near-term values of economic indicators (Carrière‐Swallow and Labbé, 2013 ; Choi and Varian, 2012 ), private consumption (Vosen and Schmidt, 2011 ), and epidemics (Carneiro and Mylonakis, 2009 ; Teng et al. 2017 ). Recently, the utility of these data has been examined in investigating spatiotemporal changes of social response to natural disasters, such as earthquakes and droughts (Gizzi et al. 2020 ; Kam et al. 2021 ; Kam et al. 2019 ; Kim et al. 2019 ). However, These social monitoring big data have been underutilized to investigate the changes of socioeconomic activities during the multiple waves of the corona virus variants.

The NAVER Shopping website is the most popular online shopping platform among the citizens of the Republic of Korea with online sales valued at about 2.7 billion KRW in the third quarter of 2021 (2.3 million USD) ( https://www.wiseapp.co.kr/insight/detail/89 ). Online shopping activities via the NAVER shopping website can capture major modes of online shopping activities of the Koreans. For example, increased online search activities relating to a specific shopping product hint an emerging demand of the NAVER’s users relating to the shopping product (Woo and Owen, 2019 ). Rumors about an emerging topic can affect the public’s social behavior patterns via social media (Alkhodair et al. 2020 ). However, the quality of social monitoring data determines an appropriate analysis spatial scale, and a careful design of data preprocesses is necessary for quality control (Wilcoxson et al. 2020 ). Recently, it has been found that the public interest in nationwide natural disasters and global pandemics can reduce the impact of rumors on social media and online seeking activities because the rumors can be verified by the direct and indirect experience of the public from the disaster or pandemic (Park et al. 2022 ; Kam et al. 2021 ).

Recent studies found a relationship between decision making and consumer behavior patterns at the individual level during the COVID-19 pandemic (Birtus and Lăzăroiu, 2021 ; Smith and Machova, 2021 ; Vătămănescu et al. 2021 ). Statewise sentimental alterations have been also found from the public’s complaints about water pollution during the COVID-19 pandemic (Liu et al. 2023 ). However, the impact of the COVID-19 pandemic and associated prevention policies on national-level social behavior pattern remains unknown. Online social monitoring data provides a unique opportunity to examine the relationship between decision making and consumer behaviors as response to changes of the COVID-19 pandemic prevention policies.

This study aims to investigate the impact of multi-year COVID-19 pandemic, using the NAVER DataLab Shopping Insight (NDLSI) data that provided by the NAVER Corporation. The data provides online search activity volumes relating to +1,800 shopping products at the nation level, which can detect an emerging change of online purchasing activities of the Koreans. The NAVER Corporation has operated the online search engine since 1999 and is the most popular internet search engine platform in South Korea. It had 1.2 billion visits from August through October 2022, and 94% of these visits solely from the Republic of Korea ( https://www.similarweb.com/website/naver.com/#traffic ). The NAVER Coporation provides weekly online search activity volume data of 1,800 shopping times since 2017 via the NDLSI platform. Such big social monitoring data provide a unique research opportunity to examine the COVID-19 impact on online shopping activities of the Koreans within the RCA framework by answering the following questions:

What are the major components of the dynamic patterns of online search activities before and after COVID-19?

How have the social behavior patterns related to online shopping search activities changed along multiple waves of corona virus variants?

Which prevention policies are key factors of the temporal changes of online shopping search activities during the COVID-19 pandemic?

To answer these questions, this study extracts the major modes of information seeking behavior patterns relating to shopping products from the NDLSI data (2017–2021) via the singular value decomposition algorithm-based Principal Component Analysis (PCA). Furthermore, the RCA framework is validated by the major modes of the NDLSI data during the multiple waves of the COVID-19 pandemic. The PCA analysis of the NDLSI data will advances the current understanding about changes in e-commerce before and after the two-year long COVID-19 pandemic.

Data and methods

Naver datalab shopping insight (ndlsi) data.

The NDLSI data includes the number of clicks on 1,837 shopping products from the NAVER Shopping platform. This study uses the NDLSI data that provide 214-week online search activities relating to 1,837 shopping products (July 31, 2017 through August 30, 2021). Weekly relative search activity volumes of the NDLSI data range from 0 to 100 (normalized by the maximum number of clicks during the search period and multiplied by 100). The NDLSI data is classified at the three levels: 11 categories for the first level, 204 categories for the second level, and 1,837 items for the third level (see Table S1 . in Supplementary Material). These categories of shopping products are provided from NAVER shopping platform, which are based on the merchant category codes (MCCs) that a credit card issuer to uses to categorize the transactions consumers complete using a particular card. The MCCs is used to classify merchants and businesses by the type of goods or services provided in order to keep a track of transactions. Recently, changes in credit/debit card spending in the MCCs have been analyzed during the COVID-19 pandemic (Darougheh, 2021 ; Dunphy et al. 2022 ). The first level categories include Fashion clothing, Fashion Miscellaneous Goods, Cosmetics/Beauty, Digital/Home Appliance, Furniture/Interior, Childbirth/Childcare, Food, Sports/Leisure, Life/Health, Leisure/Life convenience, and Duty-free shops. The category and product names are provided in Korean. In this study, the category and product names are translated in English via the Google Translator.

Six COVID-19 metrics

This study uses the six COVID-19 metrics from the Center for Systems Science and Engineering at Johns Hopkins University (JHU CSSE) COVID-19 dataset (Dong et al. 2020 ). The six COVID-19 metrics include new confirmed cases, stringency index, residential index, vaccination index, new death cases, and fatality. New confirmed/death cases are the number of the corresponding case of the Koreans over the study period. The stringency index is estimated based on the nine metrics: school closures, workplace closures, cancellation of public events, restrictions on public gatherings, closures of public transport, stay-at-home requirements, public information campaigns, restrictions on internal movements, and international travel controls. The stringency index shows the strictness of the government prevention policies in quantitative method (Dong et al. 2020 ). The value ranges from 0 (lowest stringency) to 100 (highest stringency). Higher stringency values represent more strict prevention policies. The residential index shows the number of people who spend more time at home after the COVID-19 pandemic than before. The vaccination index is a partial vaccinated index that represents the percent of who have vaccinated at least once. The fatality index is the ratio of the number of the number of new death cases to the number of new confirmed cases. While these daily six metrics are available, this study computes and uses the weekly sums of new confirmed cases and the weekly averages of the other five COVID-19 metrics, which is a consistent temporal scale with the NDLSI data analysis. The Korea Meteorological Administration (KMA) provides the historical meteorological data of the Republic of Korea through the Open MET Data Portal platform ( https://data.kma.go.kr/cmmn/main.do ). In this study, weekly temperature averages of the 95 stations in the Republic of Korea are computed to extract the seasonality of the regional climate system.

Singular value decomposition (SVD)-based principal component analysis

In the machine learning field, the principal component analysis (PCA) is a popular unsupervised learning method. The PCA technique is known as a data compressing technique to extract key features of the high dimension data. Singular value decomposition (SVD) algorithm can be used to extract the PCA major modes (Vosen and Schmidt, 2011 ; Wilks, 2011 ). The SVD algorithm-based PCA decomposes a covariance matrix into three matrixes if the A matrix has m x n dimension (n < m; Eq. 1 ). These matrixes include the U matrix (an m by m matrix), the Σ matrix (a m diagonal matrix) and the V transpose matrix (an n by n matrix). The Σ matrix is a diagonal matrix which have one to one correspondence with the U matrix. The U matrix shows the orthogonal eigenvectors, which are known as the principal components (PCs).

In this study, the SVD algorithm is employed to the covariance matrix of the NDLSI data over the five different periods. The five periods include the period before the COVID-19 pandemic (July 31, 2017–December 31, 2019), Wave 1 (July 31, 2017–May 25, 2020), Wave 2 (July 31, 2017–October 19, 2020), Wave 3 (July 31, 2017–March 1, 2021), and Wave 4 (July 31, 2017–August 31, 2021) of the corona virus variants. Here, the waves are defined based on the surges of the new confirm cases. To explore shopping products with an increasing/decreasing interest of the public during each wave of the COVID-19 pandemic, the SVD analysis period for the wave of interest covers before the emergence of the next wave, which includes the overlapped analysis period of the previous wave. It enables us to investigate the impact of the wave of interest on the public interest relating to shopping products compared to that of the previous wave.

Two major modes are found before the COVID-19 pandemic: the increasing line trend and the seasonality pattern of the online search activities. Before employing the SVD-based PCA analysis to the NDLSI data, the linear trend and the seasonality are removed from the NDLSI matrixes over the period of Wave 1, 2, 3 and 4. The detrended NDLSI data over the different periods enable us to investigate changes of online search activities relating to shopping products over the multiple waves of COVID-19. Not available values in the NDLSI data were replaced with zeros. The U and V matrixes are the same eigenvectors of the covariance matrix and the Σ matrix includes the eigenvalues. The Σ matrix’s diagonal values show the quantitative contribution of the corresponding vector to the total variance of the covariance matrix.

Spearman’s rank correlation

Spearman’s rank correlation is a non-parametric metric to find a relationship between two variables based on their ranks (Spearman, 1904 ). This study uses Spearman’s rank correlation because online search activities of most items in the NDLSI data do not have normal distribution. Spearman’s rank correlation efficiency ranges from −1 (negative perfect relation between two variables) to +1 (positive perfect). In this study, Spearman’s rank correlation is used to trace the user’s interest in shopping products that are associated with the wave of corona virus variants. Furthermore, Spearman’s rank correlation is computed between the COVID-related metrics and NDLSI data to examine which socio-economic factors associated on e-commerce search activities. First, Spearman’s rank correlation coefficients are computed between the first principal component (PC1; one time series) and the search activities of +1,800 shopping products (>1,800 time series) during the periods of Wave 1 through 4. Furthermore, the distribution of Spearman’s rank correlation coefficient is constructed by the kernel density estimate (KDE) method from the Joyplot python package ( https://github.com/leotac/joypy ). Shopping products with a high coefficient have increased from Wave 1 through 4 (Fig. S 1 ). In this study, 0.45 of Spearman’s coefficient is a threshold value to detect up to 20% of associated item with the PC1 mode with the COVID-19 pandemic.

Quantile-Quantile plot (QQ plot)

The number of the PC-associated shopping products affect the construction of the reliable correlation distributions with the COVID-19 metrics. A Quantile-Quantile (QQ) plot is a common visualization method to determine whether two data sets are came from same distributions or not. Despite different numbers of the COVID-19 associated shopping products during each wave period, the QQ plot can detect the stability of the correlation distribution shape. The QQ plot is based on the ranks of each data, which gives an advantage that the two dataset still can be compared in the QQ plot even though the sample sizes of the two datasets are different. The one-to-one line is a reference line of the QQ plot. When the quantile line of the two data is close to the reference line, the two sample data are from the same distribution (Nist, 2006 ). In this study, the QQ plots are constructed for the sensitive analysis of the stability of the correlation distribution shape to shopping products numbers. This analysis can determine how many shopping products are needed to generate the reliable distributions of its correlation with the waves of corona virus variants (Figs. S 2 and S 3 ).

Principal components of NDLSI data

Before the COVID-19 pandemic (hereafter, Wave 0), the first and second Principal Component (PC) modes (PC1 and PC2, respectively) explained around 15% and 10% of the total variance, respectively. PC1 was a monotonic increasing trend of online search activities for shopping products. PC2 was strongly associated with the seasonality of weekly mean temperature, however the seasonal cycle of online search activities relating to shopping products was four weeks ahead of the seasonality of the temperature (Fig. 1A, B ). Based on Spearman’s rank correlation coefficients with the PC1 and PC2, the top 10 shopping products showed that these two major modes captured well an increasing trend of shopping product-specific e-commerce and the seasonality of online search relating to shopping items during Wave 0 (Fig. 1C–F ).

Weekly time series of Principal Component 1 (PC1) of NDLSI data before COVID-19 ( A ) and PC2 ( B ) with heatmap of correlation coefficients of top 10 correlated items. Associated online search activities of top 10 shopping products with the PC1 time series ( A ): Positive ( C ) and negative correlation ( D ). Associated online search activities of top 10 shopping products with seasonality ( B ): Summer- and winter-related shopping products in ( C ) and ( D ), respectively.

Results showed that the top 10 PC1-related shopping products included toothpaste, table tennis shoes, and cleaning tissue, packed lunch, and hair spray. Shopping products with a negative correlation coefficient with the PC1 mode included (car) hands free, sea fishing, Random Access Memory (RAM), and Network Attached Storage (NAS). Shopping products with a positive (negative) correlation coefficient with the PC2 mode (the seasonality of temperature in advance of four weeks) were summer (winter) season shopping products. Based on the correlation coefficients with PC2, summer season shopping products included fan, parasol, yeolmu kimchi (a type of kimchi for summer), and tarp. Winter season shopping products included brooch, beanie, and neck cape. These PC2-based items were the well-known popular shopping products for summer and winter, respectively, confirming that the PCA technique is useful to extract and interpret key features in the NDLSI data when the principal major mode is associated with a certain temporal pattern (herein, the seasonality of temperature).

Flow of PC1 related items during the COVID-19 pandemic

Results from the PCA analysis of the detrended NDLSI data showed that PC1 resembled the new confirm cases of COVID-19 over the four waves of the corona virus variants (Fig. 2 ). The percent of explained variance by the PC1 mode increased from the first wave (20%) through the fourth wave (27%), which means that associated shopping products with the corona virus variants increased during the COVID-19 pandemic. The first-level category shopping products associated with the PC1 mode showed temporal changes from Wave 1 through 4 (Fig. 3 ). For visualization, the Sankey diagram was constructed, which has been often used as an efficient visualization for changes of the flow/volume of the data (Lupton and Allwood, 2017 ).

Weekly time series of the PC1 mode of the detrended NDLSI data up to Wave 1 through Wave 4 (gray dash lines) along South Korea’s COVID-19 new confirmed cases (a sky line).

Sankey diagram of COVID-19 associated shopping products during the four waves.

Based on the result of the explained variance by the PC1 mode (around 20% of the total variance), changes in online search activities relating to shopping products with the correlation coefficient, 0.45, or higher (close to 20% of total items) were analyzed. Overall, life/health, digital/home appliance items showed a large percentage during the study periods). Outdoor activity-related category items, including cosmetics/beauty, fashion clothing and fashion miscellaneous goods, account for small portions than other category items. Associated items with the corona virus variants have increased from Wave 1 through Wave 4 by more than twice (from 327 to 714). After the first wave, new 241 shopping products showed the correlation coefficient, 0.45 or above. This inflow of online search activities were associated with shopping items in the categories of life/health (25%), digital/home appliances (15%), and food (15%) (Fig. 4 ).

Percentages of the first-level shopping product categories of inflow after Wave 2, 3, and 4.

After Wave 2, the inflow of the 125 items included life/health (29%), digital/home appliance (19%), and childbirth/childcare (12%) items with decreased item numbers (125 items). After Wave 3, the inflow of 190 items included life/health (22%), digital/home appliance (17%), and childbirth/childcare (19%) items. Interestingly, duty-free shopping products and leisure/life convenience items first appeared after the Wave 2 and 4, respectively. The leisure/life convenience category items included work out class (fitness/personal training and Pilates) abroad travel items (abroad travel package, airline ticket, Wi-Fi/ Universal Subscriber Identity Module (USIM)). Increasing online search activities relating to work out class may be come from a concern about health due to a restrict quarantine policy. Increased interest in abroad travel cases after Wave 4 suggests that the public in South Korea might have a low perceived risk of the COVID-19 pandemic and begin to consider that the pandemic is over.

To investigate the temporal change of the third-level (product-specific) category shopping products associated with the waves of the corona virus variants, changes in the correlation coefficients of the top 10 items were selected for each waves (Fig. 5 ). The results showed that 31 shopping products were associated with the PC1 component throughout the four waves. More than 32% shopping products were in the category of life/health shopping products. These 31 items can be classified into two groups: the items with a higher and lower correlation coefficient over time. The first group items included minidisc player monitor arms, webcam, interphone box, fabric, handicraft supplies/subsidiary materials, character card/ticket, processed snacks, cooking oil/oil, bread, tuning supplies, craft, feed, seeds/seedlings, water aperture, gravel/sands/soil, landscape tree/sapling. These first group shopping products showed a persistent increase in the correlation coefficient through the multiple waves. The second group items included gas range, microwave, toothbrush, hula hoop. These second group shopping products showed a decrease in the correlation coefficient (Fig. 5 ).

The numbers of the Wave 1 through 4 heatmaps are Spearman’s rank correlation coefficients of the shopping products with the PC1 mode. The Wave 2 to 4 heatmap depict the percent changes of Spearman’s rank correlation coefficients compared with the correlation coefficients after Wave 1 (( Corr X – Corr 1 )/ Corr 1 ) * 100, where X depicts the wave occurrence order (X = 2, 3, and 4).

Weekly time series of the COVID-19 new confirmed cases ( A ), the stringency ( B ), residential index ( C ), vaccinated rate ( D ), new deaths by the corona virus ( E ), and fatality ( F ).

These two shopping product groups might originate from the different social response to the strictness of prevention policies. During the first wave, the government forced the public to stay at home to minimize the risk of being exposed to the corona virus. However, the prevention policies became less strict at Wave 4 to account for the fatigue of the public from the multi-year pandemic and revive local business and industry sectors. While the first group items have become more associated with the waves of the corona virus variants, the second group items no longer showed a high correlation coefficient with the corona virus variants.

Association with the six COVID-19 metrics

A surge of new confirmed cases of corona virus variants can influence social behavior patterns relating to e-commerce in a different way due to a different level of the COVID-19 prevention policy and the easy access of online shopping activities. In this study, Spearman’s rank correlation coefficients between the six COVID-19 metrics and the NDLSI data are computed to investigate potential causes of changes in online search activity volumes of shopping products (Fig. 6 ).

The six COVID-19 metrics showed different correlation distributions with the six COVID-19 metrics (Fig. 7 ). As the sensitivity test of the correlation distribution shape to the number of shopping products, the Quantile-Quantile (QQ) plots have been made along the different shopping times (see Figs. S 2 and S 3 ). According to the QQ plots, the top 50 items were chosen to construct the correlation distributions of the top 50 shopping products with the vaccination index. The correlation coefficients were widely distributed, indicating a relatively weak association with online search activities relating to the shopping products (Fig. 7A ). The correlation distributions with the stringency and fatality indices showed a low variance with high correlation coefficients above 0.8. The correlation distribution with the residential index showed a relatively low correlation coefficients than those with the stringency and fatality indices. New confirmed and death cases showed a relatively high variance than the correlation distributions with the fatality and stringency data. The categories of the top 50 shopping products included life/health (20%), digital/home appliance (16%) and food (16%), shopping products (Fig. 7B ).

Distributions of Spearman’s rank correlation coefficient of top 50 items related to the COVID-19 pandemic with six COVID-19 metrics ( A ), and the pie chart of first category percentage of items of top 50 items ( B ).

To investigate associations of online search activities relating shopping products with the six COVID-19 metrics, the Spearman’s rank correlation coefficients with the 31 PC1-associated items associated with the COVID-19 pandemic were computed (Fig. 8 ). New confirmed cases, stringency, residential index, new death cases and fatality showed a high correlation coefficient with the most of top 10 shopping products. The vaccination index showed no significant correlation coefficient with the top 10 shopping products. Gas range, baby walker and toothbrush items showed a relatively low correlation with the COVID-19 metrics than other shopping products. Online search activities relating to these shopping items showed a decreasing correlation during COVID-19 pandemic (see Fig. 5 ), that is, these items no longer show a significant effect of the COVID-19 pandemic after the Wave 4.

Heatmap of Spearman’s rank correlation coefficient between COVID-19 metrics and the 31 shopping products.

Overall, the stringency and fatality metrics generally have high association with the changes in online search activity patterns for shopping product. Stringency can be regarded as how government control public strictly. Fatality shows seriousness of pandemic. The results indicate that consumer behavior response sensitively to extent of restriction policies and seriousness of pandemic.

This study used the NDLSI data about the online search activity volumes for shopping products, not real purchasing data. Using the data of online search activities can provide an evidence on emerging purchasing patterns of the public in the next regime, implying that the public might tend to purchase items that have been most searched in the previous timeframe (Chen et al. 2017 ). Lately, credit card data Footnote 1 and bar cord data Footnote 2 include the records of actual purchase activities. Integrating the actual purchase data and online search activity data can provide more practical guidelines and plans for socio-economic changes not only during the COVID-19 pandemic, but also the post pandemic period.

This study revealed that the public interest in online shopping products had been changed not only after the first wave of the COVID-19 pandemic but also during the following three waves. These dynamic patterns of the public interest in online shopping products were possibly explained by the RCA framework (Kirk and Rifkin, 2020 ). The RCA framework consists of reacting, coping, and adapting phases, and significant changes in social behavior patterns are expected during a transition period from one to another phase. The first wave was a typical ‘react’ phase because people responded to the pandemic situation. A large inflow volume after Wave 2 (241 items) indicated a coping phase. The new confirmed cases were relatively low during Wave 2 (see a line colored in sky in Fig. 2 ) compared with those during other waves. Inflow of online search activities relating shopping products was the minimum after Wave 2. This finding suggests that a transition from a ‘react’ to ‘coping’ phase might occur between Wave 2 and Wave 3. After Wave 3, the public coped with the long-term pandemic. During Wave 4, the categories related with outdoor activities show a low percentage, indicating a low level of the public interest in outdoor activities due to the COVID-19 quarantine policy. The result that the inflow of online search activities relating to leisure/life convenience items (workout class, abroad travel) at Wave 4 indicates that the public became less reactive to the wave of the corona virus variants, which hints an emerging signal of a low perceived risk of the COVID-19 pandemic after Wave 4. Therefore, the ‘adapt’ phase transition is expected after Wave 4.

Understanding the public’s purchasing patterns amid a global crisis via big social monitoring data is critical from the risk management perspective. Risk control (e.g., self-protection) and financing (insurance) strategies can be improved for the next global crisis by understanding and predicting changes in social behaviors. This study found that the shopping products with an increased interest of the public have been changed during the two year-long COVID-19 pandemic, which can be explained by different stages of the RCA framework. The social behavior patterns found by this study had been also reported from the observed reacting and coping consumer behaviors in mass media and online and reacting public behavior to social distance during the COVID-19 pandemic (Guthrie et al. 2021 ; Kirk and Rifkin, 2020 ; Tintori et al. 2020 ). Specifically, better understanding and predicting of which products can help markets manage inventory of shopping products that are in an emerging high/low demand throughout different regimes of the crisis. This study found that associations of these products were more clear when they were used for self-protection measures (e.g., facial masks in the COVID-19 pandemic).

Governments and authorities can accordingly implement changes in the public’s actions to prevent potential market failures that, for example, self-protection measures may not be sufficiently supplied, or big market players use their power to dominate necessity markets (Stiglitz, 2021 ). These responses from the public and private sectors can be optimized with prevention plans in a timely manner of different waves of the crisis by analyzing big social monitoring data. This study found changes in the interest and demand of the shopping products related to self-protection measures during the COVID-19 pandemic, which hints how to facilitate big social monitoring data to mitigate the adverse effects of daily infections. Furthermore, this information can help insurance industries manage systematic risks that cannot be fully controlled by individuals or other industry sectors, which can offer risk transfer measures (Alonso et al. 2020 ; Harris et al. 2021 ; Peiffer-Smadja et al. 2020 ; Rita et al. 2019 ). This study also found a strong association between changes in online search activities of the public relating to shopping items and perceived risk, which was previously found in the travel insurance purchasing patterns (Al Mamun et al. 2022 ; Tan and Caponecchia, 2021 ). This information can give an insight for how to increase the public’s willingness to prepare for the next pandemic.

Search engine optimization (SEO) algorithms for searching items have been developed, particularly in the e-commerce sector to increase the customer’s satisfaction and loyalty (Husain et al. 2020 ; Liu et al. 2008 ; Pratminingsih et al. 2013 ). Some online search engine platforms collect the data of the user’s online activities and optimize the customized recommendation algorithm that could give more relevant result of searching. Especially, e-commerce sites, such as Amazon, have developed this customized SEO algorithm to increase a chance to purchase the products (Heng et al. 2018 ; Linden et al. 2003 ). In this study, the observational evidence of the COVID-19 impact on online search activities about shopping products was reported, which was also found in online shopping pattern for apparel (Watanabe et al. 2021 ). The SEO algorithms developed by the data before the COVID-19 pandemic increased the user’s complaint by three times (Dahiya et al. 2021 ), implying that the COVID-19 pandemic was an unprecedented event since the advent of Internet that supposedly cause a drastic context difference. Therefore, the SEO algorithms are needed to update until the data after the pandemic is sufficient. Furthermore, the expected continued growth of online commerce industries requires the coping strategies to adapt an increasing trend of not only pandemics but also other disasters such as climatic extremes, pandemic, war, and terror.

This study provides an insight about how social big monitoring data can help authorities to better understand the social response to COVID-19 via near real-time social monitoring data. In this study, the NDLSI data about the online search activity volumes relating to shopping products, not real purchasing data, were used. The NDLSI data analysis provided a possible evidence on an emerging change in the public’s purchasing patterns at the shopping product level. Previously, it was found that the public tended to purchase shopping products that have been most searched in the previous timeframe (Chen et al. 2017 ). Associations between the public interest in shopping products and purchase records can be explored using credit card data Footnote 3 and barcode data Footnote 4 . These data have been used to investigate changes in spending associated with stringent nonpharmaceutical interventions during the COVID-19 pandemic (Horvath et al. 2023 ). Integrating the actual purchase data and online search activity data can give more practical guidelines and plans for socio-economic changes during not only the COVID-19 pandemic, but also the post pandemic period. Furthermore, the e-commerce sector can harness social big monitoring data to develop their strategic plans for supply chain management for the next pandemic.

This study also explored associations of changes of online search activity patterns with the COVID-19 metrics. The results showed that the COVID-19 metrics, except for vaccination, were strongly associated with changes in online search activity patterns relating to shopping products. The stringency index was a reliable indicator of the strictness of the government’s response to the COVID-19 pandemic and had a significant impact on social behavior patterns, which is in line with the findings of Makki et al. ( 2020 ) that the timing and duration of the stringency implementation are key factors to prevent the spread of the corona virus variants. Furthermore, a recent study found that policy perceptions affect the practice of volunteered prevention behaviors, such as mask waring and social distancing (Lee et al. 2021 ). They found that the perceived policy stringency was associated with actual risk and political ideology, causing noncompliance in communities during the COVID-19 pandemic.

The proposed methods in this study have some limitations. For example, the results based on the correlation analysis provide potential, not actual, triggers of changes in the social behavior patterns during the COVID-19 pandemic, which have previously known as the caveat of the correlation analysis (Haley and Drazen, 1998 ; Stigler, 2005 ). The findings of this study about potential triggers however can help design more effective and efficient interview and survey questionnaires to investigate true triggers of changes in the public interest in shopping products. Combined information from big social monitoring and survey/interview data will create new knowledge about the dynamics of social behavior patterns and help develop a reliable social behavior prediction modeling.

Conclusions

This study succeeded to extract the major modes of the public’s interest in shopping products and investigate changes in online search activities relating to associated shopping products with the COVID-19 pandemic. The SVD algorithm-based PCA analysis of the NDLSI data showed the dynamic patterns of online search activities relating to shopping products during the two year-long COVID-19 pandemic. Before the COVID-19 pandemic, an increasing trend and seasonality of online search activity volumes about shopping products are the major mode of the NDLSI data. After the COVID-19 pandemic, the impact of COVID-19 on online search activities relating to shopping products were various during the four waves of the corona virus variants, particularly when the objective risk was dramatically increased. Changes of the online search activity patterns were associated with the change of the COVID-19 prevention policy and objective risk of being exposed to the corona virus variants. This study attempted to explain the changes of these online search activity patterns within the RCA framework by identifying the react, coping, and adapt phases.

This study highlights the utility of online social monitoring data in developing strategic plans for preparation, mitigation, and recovery policies for the next pandemic. Furthermore, the findings of this study can guide how to design interview and survey questionnaires to investigate actual drivers of social behavior changes during the COVID-19 pandemic. Integrated studies using online social monitoring data and survey and interview data will advance the current knowledge and prediction skill of social behavior changes, which can provide actionable information to mitigate its adverse effects for the sustainable development of our communities Kim et al. ( 2019 ), Spearman ( 1904 ).

Data availability

The data used in this study are available at Harvard Dataverse: https://doi.org/10.7910/DVN/JT8RCK .

Change history

30 october 2023.

A Correction to this paper has been published: https://doi.org/10.1057/s41599-023-02297-3

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Acknowledgements

We thank the NAVER DataLab for making available the NAVER DataLab Shopping Insight (NDLSI) data. This study was supported by a grant from the National Research Foundation of Korea (NRF-2021R1A2C1093866).

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Song, J., Jung, K. & Kam, J. Evidence of the time-varying impacts of the COVID-19 pandemic on online search activities relating to shopping products in South Korea. Humanit Soc Sci Commun 10 , 669 (2023). https://doi.org/10.1057/s41599-023-02183-y

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The pandemic has changed consumer behaviour forever - and online shopping looks set to stay

More and more consumers are ordering goods online. Image:  REUTERS/Danish Siddiqui

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• Consumer shift to digital channels will remain after the pandemic -PwC report.
• Customer loyalty has plummeted, with buyers switching brands at unprecedented rates.
• The use of smartphones for online shopping has more than doubled since 2018.

Billions of people affected by the COVID-19 pandemic are driving a “historic and dramatic shift in consumer behaviour” – according to the latest research from PwC.

The consulting and accounting firm's June 2021 Global Consumer Insights Pulse Survey reports a strong shift to online shopping as people were first confined by lockdowns, and then many continued to work from home. Other trends in this shift towards digital consumption include online shoppers being keen to find the best price, choosing more healthy options and being more eco-friendly by shopping locally where possible.

Another significant finding from the report is that consumers do not think they’ll go back to their old ways of shopping once the pandemic is over.

A consumer pivot to digital and devices

More than 8,600 people across 22 territories took part in PwC’s survey. They were asked how often, in the past 12 months, they had bought clothes, books and electronics using a range of shopping channels.

Have you read?

Covid-19 pandemic accelerated shift to e-commerce by 5 years, new report says, these charts show how covid-19 has changed consumer spending around the world.

The chart below illustrates their answers, and shows a shift to digital and a growing trend for shopping using connected devices such as smartphones, tablets and smart voice assistants such as Amazon Echo, Google Home and Samsung SmartThings.

More than 50% of the global consumers responding to the June 2021 survey said they had used digital devices more frequently than they had six months earlier, when they had taken part in a prior PwC survey. The report also finds the use of smartphones for shopping has more than doubled since 2018.

COVID-19 has exposed digital inequities globally and exacerbated the digital divide. Most of the world lives in areas covered by a mobile broadband network, yet more than one-third (2.9 billion people) are still offline. Cost, not coverage, is the barrier to connectivity.

At The Davos Agenda 2021 , the World Economic Forum launched the EDISON Alliance , the first cross-sector alliance to accelerate digital inclusion and connect critical sectors of the economy.

Through the 1 Billion Lives Challenge , the EDISON Alliance aims to improve 1 billion lives globally through affordable and accessible digital solutions across healthcare, financial services and education by 2025.

Read more about the EDISON Alliance’s work in our Impact Story.

Medicines and groceries on demand

A survey of US consumers by McKinsey & Company gives a more detailed breakdown of the shift to digital shopping channels and the kinds of purchases consumers are making.

The survey found a 15-30% overall growth in consumers who made purchases online across a broad range of product categories. Many of the categories see a double-digit percentage growth in online shopping intent, led by over-the-counter medicines, groceries, household supplies and personal care products.

And McKinsey noted that “consumer intent to shop online [post-pandemic] continues to increase, especially in essentials and home-entertainment categories”.

A decline in brand loyalty

With consumers shopping from their sofas and home offices, another trend flagged up by McKinsey is a marked decline in brand loyalty.

In total, 75% of US consumers have tried a new shopping behaviour and over a third of them (36%) have tried a new product brand. In part, this trend has been driven by popular items being out of stock as supply chains became strained at the height of the pandemic. However, 73% of consumers who had tried a different brand said they would continue to seek out new brands in the future.

What is the World Economic Forum doing to manage emerging risks from COVID-19?

The first global pandemic in more than 100 years, COVID-19 has spread throughout the world at an unprecedented speed. At the time of writing, 4.5 million cases have been confirmed and more than 300,000 people have died due to the virus.

As countries seek to recover, some of the more long-term economic, business, environmental, societal and technological challenges and opportunities are just beginning to become visible.

To help all stakeholders – communities, governments, businesses and individuals understand the emerging risks and follow-on effects generated by the impact of the coronavirus pandemic, the World Economic Forum, in collaboration with Marsh and McLennan and Zurich Insurance Group, has launched its COVID-19 Risks Outlook: A Preliminary Mapping and its Implications - a companion for decision-makers, building on the Forum’s annual Global Risks Report.

Companies are invited to join the Forum’s work to help manage the identified emerging risks of COVID-19 across industries to shape a better future. Read the full COVID-19 Risks Outlook: A Preliminary Mapping and its Implications report here , and our impact story with further information.

Healthy, hygienic and sustainable

The trend towards online shopping has also seen consumers focus on staying healthy during long periods in lockdown. McKinsey notes a desire to reduce touchpoints to ensure greater hygiene with the shopping experience.

One enterprise in the US has tapped into these trends to provide a service for shopping online at a range of farm shops local to the buyer. To qualify for the FarmMatch scheme, farmers must grow their food using sustainable methods.

As the world navigates its way out of the pandemic, the way we all act as consumers has been changed fundamentally by COVID-19. The research points to this change becoming permanent, leaving retailers and manufacturers with the challenge of attracting and retaining consumers in an 'omnichannel' world, where customer loyalty is hard-won.

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A study on factors limiting online shopping behaviour of consumers

Rajagiri Management Journal

ISSN : 0972-9968

Article publication date: 4 March 2021

Issue publication date: 12 April 2021

This study aims to investigate consumer behaviour towards online shopping, which further examines various factors limiting consumers for online shopping behaviour. The purpose of the research was to find out the problems that consumers face during their shopping through online stores.

Design/methodology/approach

A quantitative research method was adopted for this research in which a survey was conducted among the users of online shopping sites.

As per the results total six factors came out from the study that restrains consumers to buy from online sites – fear of bank transaction and faith, traditional shopping more convenient than online shopping, reputation and services provided, experience, insecurity and insufficient product information and lack of trust.

Research limitations/implications

This study is beneficial for e-tailers involved in e-commerce activities that may be customer-to-customer or customer-to-the business. Managerial implications are suggested for improving marketing strategies for generating consumer trust in online shopping.

Originality/value

In contrast to previous research, this study aims to focus on identifying those factors that restrict consumers from online shopping.

• Online shopping

Daroch, B. , Nagrath, G. and Gupta, A. (2021), "A study on factors limiting online shopping behaviour of consumers", Rajagiri Management Journal , Vol. 15 No. 1, pp. 39-52. https://doi.org/10.1108/RAMJ-07-2020-0038

Emerald Publishing Limited

Copyright © 2020, Bindia Daroch, Gitika Nagrath and Ashutosh Gupta.

Published in Rajagiri Management Journal . Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

Introduction

Today, people are living in the digital environment. Earlier, internet was used as the source for information sharing, but now life is somewhat impossible without it. Everything is linked with the World Wide Web, whether it is business, social interaction or shopping. Moreover, the changed lifestyle of individuals has changed their way of doing things from traditional to the digital way in which shopping is also being shifted to online shopping.

Online shopping is the process of purchasing goods directly from a seller without any intermediary, or it can be referred to as the activity of buying and selling goods over the internet. Online shopping deals provide the customer with a variety of products and services, wherein customers can compare them with deals of other intermediaries also and choose one of the best deals for them ( Sivanesan, 2017 ).

As per Statista-The Statistics Portal, the digital population worldwide as of April 2020 is almost 4.57 billion people who are active internet users, and 3.81 billion are social media users. In terms of internet usage, China, India and the USA are ahead of all other countries ( Clement, 2020 ).

The number of consumers buying online and the amount of time people spend online has risen ( Monsuwe et al. , 2004 ). It has become more popular among customers to buy online, as it is handier and time-saving ( Huseynov and Yildirim, 2016 ; Mittal, 2013 ). Convenience, fun and quickness are the prominent factors that have increased the consumer’s interest in online shopping ( Lennon et al. , 2008 ). Moreover, busy lifestyles and long working hours also make online shopping a convenient and time-saving solution over traditional shopping. Consumers have the comfort of shopping from home, reduced traveling time and cost and easy payment ( Akroush and Al-Debei, 2015 ). Furthermore, price comparisons can be easily done while shopping through online mode ( Aziz and Wahid, 2018 ; Martin et al. , 2015 ). According to another study, the main influencing factors for online shopping are availability, low prices, promotions, comparisons, customer service, user friendly, time and variety to choose from ( Jadhav and Khanna, 2016 ). Moreover, website design and features also encourage shoppers to shop on a particular website that excite them to make the purchase.

Online retailers have started giving plenty of offers that have increased the online traffic to much extent. Regularly online giants like Amazon, Flipkart, AliExpress, etc. are advertising huge discounts and offers that are luring a large number of customers to shop from their websites. Companies like Nykaa, MakeMyTrip, Snapdeal, Jabong, etc. are offering attractive promotional deals that are enticing the customers.

Despite so many advantages, some customers may feel online shopping risky and not trustworthy. The research proposed that there is a strong relationship between trust and loyalty, and most often, customers trust brands far more than a retailer selling that brand ( Bilgihan, 2016 ; Chaturvedi et al. , 2016 ). In the case of online shopping, there is no face-to-face interaction between seller and buyer, which makes it non-socialize, and the buyer is sometimes unable to develop the trust ( George et al. , 2015 ). Trust in the e-commerce retailer is crucial to convert potential customer to actual customer. However, the internet provides unlimited products and services, but along with those unlimited services, there is perceived risk in digital shopping such as mobile application shopping, catalogue or mail order ( Tsiakis, 2012 ; Forsythe et al. , 2006 ; Aziz and Wahid, 2018 ).

Literature review

A marketer has to look for different approaches to sell their products and in the current scenario, e-commerce has become the popular way of selling the goods. Whether it is durable or non-durable, everything is available from A to Z on websites. Some websites are specifically designed for specific product categories only, and some are selling everything.

The prominent factors like detailed information, comfort and relaxed shopping, less time consumption and easy price comparison influence consumers towards online shopping ( Agift et al. , 2014 ). Furthermore, factors like variety, quick service and discounted prices, feedback from previous customers make customers prefer online shopping over traditional shopping ( Jayasubramanian et al. , 2015 ). It is more preferred by youth, as during festival and holiday season online retailers give ample offers and discounts, which increases the online traffic to a great extent ( Karthikeyan, 2016 ). Moreover, services like free shipping, cash on delivery, exchange and returns are also luring customers towards online purchases.

More and more people are preferring online shopping over traditional shopping because of their ease and comfort. A customer may have both positive and negative experiences while using an online medium for their purchase. Some of the past studies have shown that although there are so many benefits still some customers do not prefer online as their basic medium of shopping.

While making online purchase, customers cannot see, touch, feel, smell or try the products that they want to purchase ( Katawetawaraks and Wang, 2011 ; Al-Debei et al. , 2015 ), due to which product is difficult to examine, and it becomes hard for customers to make purchase decision. In addition, some products are required to be tried like apparels and shoes, but in case of online shopping, it is not possible to examine and feel the goods and assess its quality before making a purchase due to which customers are hesitant to buy ( Katawetawaraks and Wang, 2011 ; Comegys et al. , 2009 ). Alam and Elaasi (2016) in their study found product quality is the main factor, which worries consumer to make online purchase. Moreover, some customers have reported fake products and imitated items in their delivered orders ( Jun and Jaafar, 2011 ). A low quality of merchandise never generates consumer trust on online vendor. A consumer’s lack of trust on the online vendor is the most common reason to avoid e-commerce transactions ( Lee and Turban, 2001 ). Fear of online theft and non-reliability is another reason to escape from online shopping ( Karthikeyan, 2016 ). Likewise, there is a risk of incorrect information on the website, which may lead to a wrong purchase, or in some cases, the information is incomplete for the customer to make a purchase decision ( Liu and Guo, 2008 ). Moreover, in some cases, the return and exchange policies are also not clear on the website. According to Wei et al. (2010) , the reliability and credibility of e-retailer have direct impact on consumer decision with regards to online shopping.

Limbu et al. (2011) revealed that when it comes to online retailers, some websites provide very little information about their companies and sellers, due to which consumers feel insecure to purchase from these sites. According to other research, consumers are hesitant, due to scams and feel anxious to share their personal information with online vendors ( Miyazaki and Fernandez, 2001 ; Limbu et al. , 2011 ). Online buyers expect websites to provide secure payment and maintain privacy. Consumers avoid online purchases because of the various risks involved with it and do not find internet shopping secured ( Cheung and Lee, 2003 ; George et al. , 2015 ; Banerjee et al. , 2010 ). Consumers perceive the internet as an unsecured channel to share their personal information like emails, phone and mailing address, debit card or credit card numbers, etc. because of the possibility of misuse of that information by other vendors or any other person ( Lim and Yazdanifard, 2014 ; Kumar, 2016 ; Alam and Yasin, 2010 ; Nazir et al. , 2012 ). Some sites make it vital and important to share personal details of shoppers before shopping, due to which people abandon their shopping carts (Yazdanifard and Godwin, 2011). About 75% of online shoppers leave their shopping carts before they make their final decision to purchase or sometimes just before making the payments ( Cho et al. , 2006 ; Gong et al. , 2013 ).

Moreover, some of the customers who have used online shopping confronted with issues like damaged products and fake deliveries, delivery problems or products not received ( Karthikeyan, 2016 ; Kuriachan, 2014 ). Sometimes consumers face problems while making the return or exchange the product that they have purchased from online vendors ( Liang and Lai, 2002 ), as some sites gave an option of picking from where it was delivered, but some online retailers do not give such services to consumer and consumer him/herself has to courier the product for return or exchange, which becomes inopportune. Furthermore, shoppers had also faced issues with unnecessary delays ( Muthumani et al. , 2017 ). Sometimes, slow websites, improper navigations or fear of viruses may drop the customer’s willingness to purchase from online stores ( Katawetawaraks and Wang, 2011 ). As per an empirical study done by Liang and Lai (2002) , design of the e-store or website navigation has an impact on the purchase decision of the consumer. An online shopping experience that a consumer may have and consumer skills that consumers may use while purchasing such as website knowledge, product knowledge or functioning of online shopping influences consumer behaviour ( Laudon and Traver, 2009 ).

From the various findings and viewpoints of the previous researchers, the present study identifies the complications online shoppers face during online transactions, as shown in Figure 1 . Consumers do not have faith, and there is lack of confidence on online retailers due to incomplete information on website related to product and service, which they wish to purchase. Buyers are hesitant due to fear of online theft of their personal and financial information, which makes them feel there will be insecure transaction and uncertain errors may occur while making online payment. Some shoppers are reluctant due to the little internet knowledge. Furthermore, as per the study done by Nikhashem et al. (2011), consumers unwilling to use internet for their shopping prefer traditional mode of shopping, as it gives roaming experience and involves outgoing activity.

Several studies have been conducted earlier that identify the factors influencing consumer towards online shopping but few have concluded the factors that restricts the consumers from online shopping. The current study is concerned with the factors that may lead to hesitation by the customer to purchase from e-retailers. This knowledge will be useful for online retailers to develop customer driven strategies and to add more value product and services and further will change their ways of promoting and advertising the goods and enhance services for customers.

Research methodology

This study aimed to find out the problems that are generally faced by a customer during online purchase and the relevant factors due to which customers do not prefer online shopping. Descriptive research design has been used for the study. Descriptive research studies are those that are concerned with describing the characteristics of a particular individual or group. This study targets the population drawn from customers who have purchased from online stores. Most of the respondents participated were post graduate students and and educators. The total population size was indefinite and the sample size used for the study was 158. A total of 170 questionnaires were distributed among various online users, out of which 12 questionnaires were received with incomplete responses and were excluded from the analysis. The respondents were selected based on the convenient sampling technique. The primary data were collected from Surveys with the help of self-administered questionnaires. The close-ended questionnaire was used for data collection so as to reduce the non-response rate and errors. The questionnaire consists of two different sections, in which the first section consists of the introductory questions that gives the details of socio-economic profile of the consumers as well as their behaviour towards usage of internet, time spent on the Web, shopping sites preferred while making the purchase, and the second section consist of the questions related to the research question. To investigate the factors restraining consumer purchase, five-point Likert scale with response ranges from “Strongly agree” to “Strongly disagree”, with following equivalencies, “strongly disagree” = 1, “disagree” = 2, “neutral” = 3, “agree” = 4 and “strongly agree” = 5 was used in the questionnaire with total of 28 items. After collecting the data, it was manually recorded on the Excel sheet. For analysis socio-economic profile descriptive statistics was used and factors analysis was performed on SPSS for factor reduction.

Data analysis and interpretation

The primary data collected from the questionnaires was completely quantified and analysed by using Statistical Package for Social Science (SPSS) version 20. This statistical program enables accuracy and makes it relatively easy to interpret data. A descriptive and inferential analysis was performed. Table 1 represents the results of socio-economic status of the respondents along with some introductory questions related to usage of internet, shopping sites used by the respondents, amount of money spent by the respondents and products mostly purchased through online shopping sites.

According to the results, most (68.4%) of the respondents were belonging to the age between 21 and 30 years followed by respondents who were below the age of 20 years (16.4%) and the elderly people above 50 were very few (2.6%) only. Most of the respondents who participated in the study were females (65.8)% who shop online as compared to males (34.2%). The respondents who participated in the study were students (71.5%), and some of them were private as well as government employees. As per the results, most (50.5%) of the people having income below INR15,000 per month who spend on e-commerce websites. The results also showed that most of the respondents (30.9%) spent less than 5 h per week on internet, but up to (30.3%) spend 6–10 h per week on internet either on online shopping or social media. Majority (97.5%) of them have shopped through online websites and had both positive and negative experiences, whereas 38% of the people shopped 2–5 times and 36.7% shopped more than ten times. Very few people (12%), shopped only once. Most of the respondents spent between INR1,000–INR5,000 for online shopping, and few have spent more than INR5,000 also.

As per the results, the most visited online shopping sites was amazon.com (71.5%), followed by flipkart.com (53.2%). Few respondents have also visited other e-commerce sites like eBay, makemytrip.com and myntra.com. Most (46.2%) of the time people purchase apparels followed by electronics and daily need items from the ecommerce platform. Some of the respondents have purchased books as well as cosmetics, and some were preferring online sites for travel tickets, movie tickets, hotel bookings and payments also.

Factor analysis

To explore the factors that restrict consumers from using e-commerce websites factor analysis was done, as shown in Table 3 . A total of 28 items were used to find out the factors that may restrain consumers to buy from online shopping sites, and the results were six factors. The Kaiser–Meyer–Olkin (KMO) measure, as shown in Table 2 , in this study was 0.862 (>0.60), which states that values are adequate, and factor analysis can be proceeded. The Bartlett’s test of sphericity is related to the significance of the study and the significant value is 0.000 (<0.05) as shown in Table 2 .

The analysis produced six factors with eigenvalue more than 1, and factor loadings that exceeded 0.30. Moreover, reliability test of the scale was performed through Cronbach’s α test. The range of Cronbach’s α test came out to be between 0.747 and 0.825, as shown in Table 3 , which means ( α > 0.7) the high level of internal consistency of the items used in survey ( Table 4 ).

Factor 1 – The results revealed that the “fear of bank transaction and faith” was the most significant factor, with 29.431% of the total variance and higher eigenvalue, i.e. 8.241. The six statements loaded on Factor 1 highly correlate with each other. The analysis shows that some people do not prefer online shopping because they are scared to pay online through credit or debit cards, and they do not have faith over online vendors.

Factor 2 – “Traditional shopping is convenient than online shopping” has emerged as a second factor which explicates 9.958% of total variance. It has five statements and clearly specifies that most of the people prefer traditional shopping than online shopping because online shopping is complex and time-consuming.

Factor 3 – Third crucial factor emerged in the factor analysis was “reputation and service provided”. It was found that 7.013% of variations described for the factor. Five statements have been found on this factor, all of which were interlinked. It clearly depicts that people only buy from reputed online stores after comparing prices and who provide guarantee or warrantee on goods.

Factor 4 – “Experience” was another vital factor, with 4.640% of the total variance. It has three statements that clearly specifies that people do not go for online shopping due to lack of knowledge and their past experience was not good and some online stores do not provide EMI facilities.

Factor 5 – Fifth important factor arisen in the factor analysis was “Insecurity and Insufficient Product Information” with 4.251% of the total variance, and it has laden five statements, which were closely intertwined. This factor explored that online shopping is not secure as traditional shopping. The information of products provided on online stores is not sufficient to make the buying decision.

Factor 6 – “Lack of trust” occurred as the last factor of the study, which clarifies 3.920% of the total variance. It has four statements that clearly state that some people hesitate to give their personal information, as they believe online shopping is risky than traditional shopping. Without touching the product, people hesitate to shop from online stores.

The study aimed to determine the problems faced by consumers during online purchase. The result showed that most of the respondents have both positive and negative experience while shopping online. There were many problems or issues that consumer’s face while using e-commerce platform. Total six factors came out from the study that limits consumers to buy from online sites like fear of bank transaction and no faith, traditional shopping more convenient than online shopping, reputation and services provided, experience, insecurity and insufficient product information and lack of trust.

The research might be useful for the e-tailers to plan out future strategies so as to serve customer as per their needs and generate customer loyalty. As per the investigation done by Casalo et al. (2008) , there is strong relationship between reputation and satisfaction, which further is linked to customer loyalty. If the online retailer has built his brand name, or image of the company, the customer is more likely to prefer that retailer as compared to new entrant. The online retailer that seeks less information from customers are more preferred as compared to those require complete personal information ( Lawler, 2003 ).

Online retailers can adopt various strategies to persuade those who hesitate to shop online such that retailer need to find those negative aspects to solve the problems of customers so that non-online shopper or irregular online consumer may become regular customer. An online vendor has to pay attention to product quality, variety, design and brands they are offering. Firstly, the retailer must enhance product quality so as to generate consumer trust. For this, they can provide complete seller information and history of the seller, which will preferably enhance consumer trust towards that seller.

Furthermore, they can adopt marketing strategies such as user-friendly and secure website, which can enhance customers’ shopping experience and easy product search and proper navigation system on website. Moreover, complete product and service information such as feature and usage information, description and dimensions of items can help consumer decide which product to purchase. The experience can be enhanced by adding more pictures, product videos and three-dimensional (3D), images which will further help consumer in the decision-making process. Moreover, user-friendly payment systems like cash on deliveries, return and exchange facilities as per customer needs, fast and speedy deliveries, etc. ( Chaturvedi et al. , 2016 ; Muthumani et al. , 2017 ) will also enhance the probability of purchase from e-commerce platform. Customers are concerned about not sharing their financial details on any website ( Roman, 2007 ; Limbu et al. , 2011 ). Online retailers can ensure payment security by offering numerous payment options such as cash on delivery, delivery after inspection, Google Pay or Paytm or other payment gateways, etc. so as to increase consumer trust towards website, and customer will not hesitate for financial transaction during shopping. Customers can trust any website depending upon its privacy policy, so retailers can provide customers with transparent security policy, privacy policy and secure transaction server so that customers will not feel anxious while making online payments ( Pan and Zinkhan, 2006 ). Moreover, customers not only purchase basic goods from the online stores but also heed augmented level of goods. Therefore, if vendors can provide quick and necessary support, answer all their queries within 24-hour service availability, customers may find it convenient to buy from those websites ( Martin et al. , 2015 ). Sellers must ensure to provide products and services that are suitable for internet. Retailers can consider risk lessening strategies such as easy return and exchange policies to influence consumers ( Bianchi and Andrews, 2012 ). Furthermore, sellers can offer after-sales services as given by traditional shoppers to attract more customers and generate unique shopping experience.

Although nowadays, most of the vendors do give plenty of offers in form of discounts, gifts and cashbacks, but most of them are as per the needs of e-retailers and not customers. Beside this, trust needs to be generated in the customer’s mind, which can be done by modifying privacy and security policies. By adopting such practices, the marketer can generate customers’ interest towards online shopping.

Conceptual framework of the study

Socioeconomic status of respondents

KMO and Bartlett’s test

Cronbach’s α

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Wei , L.H. , Osman , M.A. , Zakaria , N. and Bo , T. ( 2010 ), “ Adoption of e-commerce online shopping in Malaysia ”, In 2010 IEEE 7th International Conference on E-Business Engineering , IEEE , pp. 140 - 143 .

Yazdanifard , R. and Godwin , N.W. ( 2011 ), “ Challenges faced by customers: Highlighting E-shopping problems ”, Paper presented at international Conference on Economics, Business and Marketing Management (CEBMM 2011) , Shanghai, China , available at: http://www.researchgate.net/profile/Assc_Prof_Dr_Rashad_Yazdanifard/publication/268507745_Challenges_faced_by_customers_Highlighting_E-shopping_problems/links/546d4ade0cf26e95bc3cb0a1/Challenges-faced-by-customers-Highlighting-E-shopping-problems.pdf ( accessed 20 March 2020 ).

Further reading

Grabner-Kräuter , S. and Kaluscha , E.A. ( 2003 ), “ Empirical research in on-line trust: a review and critical assessment ”, International Journal of Human-Computer Studies , Vol. 58 No. 6 , pp. 783 - 812 .

Nurfajrinah , M.A. , Nurhadi , Z.F. and Ramdhani , M.A. ( 2017 ), “ Meaning of online shopping for indie model ”, The Social Sciences , Vol. 12 No. 4 , pp. 737 - 742 , available at: https://medwelljournals.com/abstract/?doi=sscience.2017.737.742

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The great consumer shift: Ten charts that show how US shopping behavior is changing

Anyone who has hosted a game night over video chat or ordered groceries to be delivered at home for the first time understands how profoundly the COVID-19 crisis has changed our behavior as consumers. But which of these changes will stick? We see several that are key:

Flight to online

Shock to loyalty, need for hygiene transparency, back to basics and value, rise of the homebody economy.

We’ve boiled down extensive McKinsey consumer research into ten exhibits to illustrate the trends and the consumer segments associated with each.

1. Digital shopping is here to stay

Physical distancing and stay-at-home orders have forced whole consumer segments to shop differently. A few months into COVID-19, consumer shopping online has increased significantly across many categories. Consumer intent to shop online continues to increase, especially in essentials and home-entertainment categories. More interestingly, these habits seem like they’re going to stick as US consumers report an intent to shop online even after the COVID-19 crisis. Categories where expected growth in online shoppers exceeds 35 percent include essentials such as over-the-counter (OTC) medicine, groceries, household supplies, and personal-care products. Even discretionary categories such as skin care and makeup, apparel, and jewelry and accessories show expected customer growth of more than 15 percent.

2. Millennials and high-income earners are in the lead when it comes to shopping online

While the shift to online shopping has been near universal across categories, high-income earners and millennials are leading the way in shifting spend online across both essential and nonessential items. Gen X has experienced a similar online shift, although not at the same scale as millennials. Gen Z has concentrated its shift online in particular categories: apparel and footwear, at-home entertainment, and food takeout/delivery.

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3. consumers are switching brands at unprecedented rates.

The crisis has prompted a surge of new activities, with an astonishing 75 percent of US consumers trying a new shopping behavior in response to economic pressures, store closings, and changing priorities. This general change in behavior has also been reflected in a shattering of brand loyalties, with 36 percent of consumers trying a new product brand and 25 percent incorporating a new private-label brand. Of consumers who have tried different brands, 73 percent intend to continue to incorporate the new brands into their routine. Gen Z and high earners are most prone to switching brands.

The beneficiaries of this shift include big, trusted brands, which are seeing 50 percent growth during the crisis, and private labels, which have outpaced the retail market. Some 80 percent of customers who started using a private brand during the pandemic indicate they intend to continue using it once the COVID-19 crisis subsides.

4. Brands need to ensure strong availability and also convey value

Shoppers have cited a number of reasons for switching brands, with availability (in-store and online), convenience, and value leading the pack.

For marketers, this highlights the need to quickly become aware of when shoppers are migrating brands or retailers and then to manage the logistics to ensure product and service availability. Looking at China, which is further along in its recovery cycle than most countries, the increase in promotional activity to cater to consumers’ focus on value in apparel is expected to continue.

5. US consumers are changing how they shop in response to health and safety concerns

As Americans contemplate going back out to shop, hygiene and hygiene transparency have emerged as important sources of concern. It is becoming increasingly important for stores and restaurants to not only follow hygiene protocols (thorough cleaning and masks for consumers and employees are top priorities) but also communicate effectively that they are following those procedures.

US consumers have already started to change their behavior in response to hygiene concerns. Technologies that enhance hygiene, particularly contactless activities such as food and grocery delivery and curbside pickup, are taking off. There is strong intent to continue contactless activities across the United States. As an example, 79 percent of consumers intend to continue or increase their usage of self-checkout in retail after COVID-19. Millennials and Gen Z are the widest adopters of contactless activities.

6. Consumer shopping intent is focused on essentials

Around 40 percent of US consumers have reduced spending in general, and they expect to continue to cut back on nonessentials specifically. This reality reflects profound discomfort about the state of the economy.

With overall consumer spending declining, intent to spend in essential categories is increasing. Even among those with higher incomes, we see that while essentials show spending momentum, intent to buy discretionary products still lags significantly. As the worst of the crisis abates, we do see online spending in nonessential categories such as apparel and footwear starting to come back. This effect is strongest among high-income earners, consumers in the Northeast, and Gen Z.

7. Consumers want value for their money—especially in essential categories

Tied to the concern about the state of the economy is an increasing consumer focus on value—especially for essential categories. For example, in shampoo on Amazon, value and mass products have experienced the greatest increase in share, at two- and five-percentage-points gains, respectively. Premium shampoo products have seen significantly less growth in comparison, losing more than five points of volume.

How marketing leaders can both manage the coronavirus crisis and plan for the future

8. americans are changing how they spend their time at home.

Americans are spending more of their at-home time on domestic activities, media, and news. Intent to eat more at home post-COVID-19 has strengthened significantly over the past three months. Usage of popular online entertainment platforms has skyrocketed. (The popular video game Fortnite recently hosted a concert that was “attended” by 12.3 million users. 1 Andrew Webster, “More than 12 million people attended Travis Scott’s Fortnite concert,” The Verge , April 23, 2020, theverge.com. ) Investment in at-home fitness through equipment purchases and online activity is growing. Consumers still expect to spend more time on at-home activities, even in less-restricted regions.

9. Americans are concerned about going back to regular activities outside the home

As economies reopen, 73 percent of consumers are still hesitant to resume regular activities outside the home. They are concerned about going to a hair salon, gym, or restaurant, but are especially worried about shared environments, such as public transportation, ride sharing, air travel, and being in crowded spaces, such as attending large indoor or outdoor events.

Behaviors vary by consumer segment

10. ‘great consumer shift’ trends vary by consumer segment.

US consumer-segment behavior varies significantly across the next-normal trends. We have identified five customer segments driven by optimism, health, and financial concerns, each of relatively similar size. These five segments exhibit the consumer trends to a different degree and have the following characteristics:

Affluent and unaffected: These consumers express general optimism about the future (~20 percent higher than the overall US consumer population), skew male (60 percent), and make more than $100,000 a year. They tend to be able to stay at home during the pandemic crisis, allowing them to shop more online. This group is slightly less price sensitive than other cohorts due to greater job stability.

Uprooted and underemployed: These consumers are feeling major impact on both their finances and health due to job insecurity. They are cautious about how they spend money, with low optimism about future economic conditions. Not surprisingly, this group is trading down to essentials and value, swapping out brands, and shopping online when possible.

Financially secure but anxious: This population is largely 65 years old and older and is generally pessimistic about economic conditions after COVID-19, which has had a major impact on their habits. This group has expressed the greatest need for hygiene transparency, with above-average concerns on safety and well-being and concerns about the ability to get necessary supplies.

Out trying to make ends meet: These consumers are being cautious about how they spend money and feel that their jobs and job security have been heavily impacted by COVID-19. This group has significant representation from minority groups and rural populations. They are less likely to be able to stay at home (hence their lower likelihood to be part of the homebody economy), but they are strongly moving toward shopping for essentials and value.

Disconnected and retired: This category denotes those who are retired, over 65, and have a lower income level than the financially-secure-but-anxious segment. They are broadly optimistic about economic conditions after COVID-19 and are less likely to display any of the next-normal characteristics. Predominantly from Southern and suburban areas of the country, this group has not exhibited significant changes in shopping behavior.

As retailers contemplate the changes in consumer behavior, they will need to adjust their strategies and execution to adapt to the new norms, including:

• Adjusting mix and spend to where the consumer is now (go digital, ensure full coverage of bottom-funnel marketing and demand capture, think region-by-region)
• Revamping messaging and creative to be in sync with the times, particularly in terms of hygiene and value
• Ensuring the end-to-end journey meets the new hygiene and at-home needs
• Managing corporate social-responsibility efforts to build brand strength authentically
• Refocusing on online and pickup solutions and rebuilding real-time measurement plans, as traditional media-mix models won’t suffice

Further, it will be important for brands to reevaluate and reprioritize their target audience and consumer segments, as the emphasis on each of the next-normal trends will vary based on the target consumer.

Tamara Charm is a senior expert in McKinsey’s Boston office, where Jamie Wilkie is a partner; Becca Coggins is a senior partner in the Chicago office; and Kelsey Robinson is a partner in the San Francisco office.

The authors wish to thank Nidhi Aurora, Sarah Coury, Resil Das, and Salvador Tormo for their contributions to this article.

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New technologies are impacting a wide range of Americans’ commercial behaviors, from the way they evaluate products and services to the way they pay for the things they buy

Table of contents.

• 1. Online shopping and purchasing preferences
• 2. Online reviews
• 3. New modes of payment and the ‘cashless economy’
• Acknowledgments
• Methodology

Americans are incorporating a wide range of digital tools and platforms into their purchasing decisions and buying habits, according to a Pew Research Center survey of U.S. adults. The survey finds that roughly eight-in-ten Americans are now online shoppers: 79% have made an online purchase of any type, while 51% have bought something using a cellphone and 15% have made purchases by following a link from social media sites. When the Center first asked about online shopping in a June 2000 survey, just 22% of Americans had made a purchase online. In other words, today nearly as many Americans have made purchases directly through social media platforms as had engaged in any type of online purchasing behavior 16 years ago.

But even as a sizeable majority of Americans have joined the world of e-commerce, many still appreciate the benefits of brick-and-mortar stores. Overall, 64% of Americans indicate that, all things being equal, they prefer buying from physical stores to buying online. Of course, all things are often not equal – and a substantial share of the public says that price is often a far more important consideration than whether their purchases happen online or in physical stores. Fully 65% of Americans indicate that when they need to make purchases they typically compare the price they can get in stores with the price they can get online and choose whichever option is cheapest. Roughly one-in-five (21%) say they would buy from stores without checking prices online, while 14% would typically buy online without checking prices at physical locations first.

Although cost is often key, today’s consumers come to their purchasing decisions with a broad range of expectations on a number of different fronts. When buying something for the first time, more than eight-in-ten Americans say it is important to be able to compare prices from different sellers (86%), to be able to ask questions about what they are buying (84%), or to buy from sellers they are familiar with (84%). In addition, more than seven-in-ten think it is important to be able to try the product out in person (78%), to get advice from people they know (77%), or to be able to read reviews posted online by others who have purchased the item (74%). And nearly half of Americans (45%) have used cellphones while inside a physical store to look up online reviews of products they were interested in, or to try and find better prices online.

The survey also illustrates the extent to which Americans are turning toward the collective wisdom of online reviews and ratings when making purchasing decisions. Roughly eight-in-ten Americans (82%) say they consult online ratings and reviews when buying something for the first time. In fact, 40% of Americans (and roughly half of those under the age of 50) indicate that they nearly always turn to online reviews when buying something new. Moreover, nearly half of Americans feel that customer reviews help “a lot” to make consumers feel confident about their purchases (46%) and to make companies be accountable to their customers (45%).

But even as the public relies heavily on online reviews when making purchases, many Americans express concerns over whether or not these reviews can be trusted. Roughly half of those who read online reviews (51%) say that they generally paint an accurate picture of the products or businesses in question, but a similar share (48%) say it’s often hard to tell if online reviews are truthful and unbiased.

Finally, this survey documents a pronounced shift in how Americans engage with one of the oldest elements of the modern economy: physical currency. Today nearly one-quarter (24%) of Americans indicate that none of the purchases they make in a typical week involve cash. And an even larger share – 39% – indicates that they don’t really worry about having cash on hand, since there are so many other ways of paying for things these days. Nonwhites, low-income Americans and those 50 and older are especially likely to rely on cash as a payment method.

Among the other findings of this national survey of 4,787 U.S. adults conducted from Nov. 24 to Dec. 21, 2015:

• 12% of Americans have paid for in-store purchases by swiping or scanning their cellphones at the register.
• Awareness of the alternative currency bitcoin is quite high, as 48% of Americans have heard of bitcoins. However, just 1% of the public has actually used, collected or traded bitcoins.
• 39% of Americans have shared their experiences or feelings about a commercial transaction on social media platforms.

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Determinants of Consumers’ Online/Offline Shopping Behaviours during the COVID-19 Pandemic

1 Institutional Research Team, Office of Graduate School, Korea University, 145, Anam-Ro, Seongbuk-Gu, Seoul 02841, Korea; moc.revan@88msiruot

Yunseon Choe

2 School of Community Resources and Development, Arizona State University, 411 N. Central Ave, Phoenix, AZ 85004, USA; [email protected]

3 The Hainan University—Arizona State University Joint International Tourism College, Hainan University, 58 Renmin Road, Haikou 570004, China

HakJun Song

4 Department of Hotel and Convention Management, Pai Chai University, 155-40 Baejae-Ro, Doma-Dong, Seo-Gu, Daejeon 35345, Korea

Associated Data

The dataset used in this research is available upon request from the corresponding author. The data are not publicly available due to restrictions i.e., privacy and ethical.

The COVID-19 pandemic has wreaked havoc in Korean society since the end of 2019. Unlike prior to the pandemic, when online and offline activities were conducted side-by-side, many aspects of consumers’ daily lives are only conducted online, especially shopping and meetings. This study analysed the characteristics of consumers who have used offline shopping channels during the pandemic. In addition, participants were asked how often they will use online and offline shopping channels after society stabilizes from COVID-19 in order to analyse what determinants will be used to select either online or offline shopping channels after the pandemic. This study will contribute to provide a deeper understanding of the consumption patterns of consumers (online vs. offline) during times of deep external impact, such as a pandemic.

1. Introduction

In the age of the fourth industrial revolution, the buying patterns of consumers have switched from traditional digital purchases to online or mobile channels due to consumers’ easy access to digital technology as well as the availability of world markets with this technology [ 1 ]. Smart digital devices and technology have enabled the service industry to provide services with precision and allow consumers to interact with service providers without ever having to meet face-to-face with an employee. These types of non-contact services have recently become a focus for the consumption patterns of consumers due to the unprecedented COVID-19 pandemic. Even before the beginning of the COVID-19 pandemic, untact marketing was a trend in the distribution industry. Untact is the combination of contact and the negative prefix un, thereby creating the word (un + contact), which means not having any physical interaction. Untact can be defined as a form of service exchange that prevents direct contact with providers and consumers, such as restaurant kiosks, VR (Virtual Reality) shopping, chatbots, and other apps with high technology aspects. The most representative forms of untact services are the kiosks at McDonalds, KFC, and multiplexes. Furthermore, mobile food delivery apps, such as Baedal Minjok and Yogiyo, have become increasingly popular in South Korea (hereafter Korea) which is the subject of the current study. The popularity of untact marketing can be attributed to the following factors: an increase in the number of single person households; changes in the population and demographics of consumers; changes in the social climate; and other external factors, such as the COVID-19 pandemic. Recently, the COVID-19 pandemic has increased consumers’ desire to not meet with others and, as such, increases in untact consumerism have been observed, not only in younger demographics, but also in older demographics, where the use of smart phones and displays has become increasingly prevalent. Furthermore, through the transition into the fourth industrial revolution, development in untact technology has gained ground. It was only natural that the distribution and service industries would reflect these changing trends [ 2 ]. A heightened perception exists that the 21st century is the age of globalization and pandemics. Through the progress of globalization, much development has occurred in industrialization and the economy; however, it has also had negative effects, such as increases in the quickness and coverage of viruses throughout the world. Different from the past, as people exchange goods and services, diseases could also inevitably be included in this exchange. Even before the COVID-19 pandemic, Korea was experiencing a difficult situation due to Middle East Respiratory Syndrome (MERS) [ 3 ]. Specifically, on 20 May 2015, a quarantine system in Korea came into jeopardy when a MERS outbreak occurred. MERS was previously only thought to have been prevalent in the Middle East, but its influx into Korea caused psychological and physical torment to Koreans.

Additionally, the 2015 incident hit the distribution and services industries strongly, causing growth in these sectors to freeze. This incident created a national understanding of the severity of pandemics, and manuals and policies were put into place to prevent such incidents from happening again internationally or domestically; however, despite all of these efforts, it seems that future epidemics could not be completely prevented. To this end, during the COVID-19 pandemic, the Korean government has increasingly taken a larger role to operate a quarantine management system. When panic occurred that resulted in a mass buying of masks, which caused mask supplies to become dangerously low, the government stepped in and prevented online sales and the export of crucial medical supplies. Additionally, the government implemented a five-day system at local pharmacies through which consumers could only buy masks on certain days of the week based on their national registration number. Although some people began panic buying and became hoarders, an increasingly large proportion of the population turned to internet shopping and online food delivery services. These changes caused a heavy burden on the postal services and restaurants where the number of deliveries increased tremendously. The COVID-19 pandemic has caused consumer habits to change from contact consumerism to untact consumerism. Although pandemics are not common occurrences, a single occurrence of a pandemic can cause an unspoken amount of damage. Most analyses of and research trends related to diseases and epidemics are based in the medical or legal fields. As such, little research exists from the social science perspective. Therefore, even though the world is faced with severe threats from this pandemic, it is difficult to find research focused on changes in online and offline shopping patterns. As such, this study tries to analyse the distribution and retail sectors, which have become increasingly competitive, and the changes in the industries brought about by the COVID-19 pandemic.

More specifically, this study aims to analyse the determinant factors based on online and offline shopping patterns by grouping consumers into online and offline shoppers. In this study, various factors affecting the selection of online and offline shopping channels will be compared and analysed. Although this study follows a similar flow as used in previous research, such as [ 4 , 5 , 6 , 7 ], it has a unique aspect to it in that it mainly focuses on the use of retail and distribution by consumers related to the COVID-19 pandemic. The study is expected to provide meaningful data because it investigates the development of and changes in competition between the offline and online shopping industry in the post-COVID-19 era. Additionally, this study aims to analyse the retail patterns of consumers during the COVID-19 pandemic through the use of an ordered logit model, which is a sub-section of the binary choice model. Most related studies have analysed the characteristics of consumers using a selected shopping channel through a model estimation regarding the binary choices of selecting or not selecting a certain shopping channel. These forms of analyses can clearly compare characteristics between the consumers of specific retail channels [ 7 ]. Based on the results of this study, the retail industry could be supplemented with evidential data to determine which marketing strategies should be employed during such a crisis as the COVID-19 pandemic. Furthermore, this study can also contribute to providing a deeper understanding of the consumption patterns of consumers (offline vs. online) during times of deep external impact, such as a pandemic.

1.1. Literature Review

1.1.1. usage patterns of retail channels.

Consumer consumption patterns have changed numerous times in history. For example, approximately 100 years ago, consumption occurred in the form of bartering or purchases made from traveling merchants. Since then, purchases have been made via catalogues; retail or boutique stores; and convenience stores, supermarkets, and department stores. The consumption paradigm now seems to be shifting toward online retail through the Internet. In the case of Korea, major retail corporations such as the Shinsegae Group (i.e., E-mart, Shinsegae Department Store) and Lotte Group (i.e., Lotte Mart, Lotte Department Store) have dominated the retail industry in Korea. Additionally, the competition between these two corporations shifted to the competition between the offline and online in Korean retail sectors. For example, Lotte Mart (offline store) and Lotte Mart Mall (online retail) are in competition although they are retail companies in the same brand. In the 2000s, the increased availability of smart phones dramatically increased consumption via mobile apps [ 8 ]. For example, in Korea, a mobile app called “Baedal Minjok” came into the delivery industry as the representative of the untact form of online consumption, where its users can order food delivery through the app without ever making contact with any employee or restaurant. This untact form of online consumption shows that a small number of companies are now dominating the entire retail industry [ 9 ].

This paradigm shifted again when the COVID-19 pandemic began, causing a sharp increase in demand for online and untact consumption. K-Prevention is a form of social distancing that is recommended by the national government. It requires one to refrain from external activities, such as meeting with another individual or simply going out of one’s home. Consumers are also decreasing the frequency of their visits to large supermarkets or other offline stores due to the fear that there may be COVID-19-positive individuals there; thus, a large proportion of consumers have shifted to online consumption, causing new sales records for online retail [ 9 ]. Offline retail sales, on the other hand, have decreased sharply due to fears of infection causing people to stay at home, refraining from external activities, and social distancing. It was reported that the decline in retail sales from COVID-19 is the second largest since the revision of the retail industry sales trend statistics that occurred in June 2016 [ 10 ]. However, it is plausible that this trend has not just been isolated to Korea, but is occurring globally. For example, on June 16, 2020, the sales for Walmart’s online store overtook eBay’s sales for the first time [ 11 ]. In the e-commerce sector of the United States, Amazon ranked first with a market share of 38%, followed by Walmart at 5.8%, eBay at 4.5%, Apple at 3.5%, and Home Depot at 1.9% [ 11 ]. Amazon’s online sales sharply surged due to the increase in their number of users during the COVID-19 pandemic [ 11 ]. Increased online sales reveal that the consumption paradigm shift from offline to online is accelerating due to online retail’s advantage of providing retail experiences from the comfort and safety of one’s home. In terms of research on online and offline shopping patterns, several studies in the fields of marketing and retail have focused on the characteristics of consumers who use specific retail channels. Home shopping, which uses catalogues, has been around for several decades and, as such, has been well-studied [ 4 , 12 , 13 , 14 ]. These studies utilized gender, age, education, occupation, and income to study the characteristics of consumers who used catalogues for their retail purchases. The characteristics of television home shopping channel consumers, which first became popular in the 1980s, were analysed by James and Cunningham [ 15 ] and Freedberg [ 16 ]. The factors that they utilized in their analyses were TV consumption and shopping affinity along with other demographical factors, such as gender, age, and education. In the 1990s, much research was conducted on the expansion of the Internet and the popularity of Internet shopping. For example, several studies analysed demographical variables with other traits such as shopping affinity and perception and understanding of shopping because they usually affect online retail consumption [ 5 , 17 ]. Since the 2000s, retail channels have diversified with the changes in traditional markets and supermarkets in the retail industry. Kim et al. [ 18 ] analysed consumers’ selections among traditional and other competing markets—Internet shopping malls and superstores—with the factors of customer satisfaction; physical sizes of the markets; parking capacities; and government policies, including laws and regulations, and found that Internet shopping malls were used more often when satisfaction and education levels were higher. Additionally, the results showed that government policies were found not to be influential over consumers’ selections. Kim et al. [ 6 ] analysed factors influencing one’s selection of online and offline shopping channels using a logit model and the factors of individual characteristics, product characteristics, and purchasing circumstances. The results showed that major factors influencing the selection of shopping channels were knowledge of the selected channel, purchasing experience, and the value of consumers. Particularly, consumers who valued their time most tended to shop online, while consumers who had available time usually shopped offline.

In the second half of the 2010s, the competition between online and offline retail channels increased. Lee [ 7 ] compared the traits of consumers who used Internet retail channels and the traits of consumers who used teleshopping with a nested logit model. The results showed that married females with high incomes and older people usually used teleshopping, while single females along with younger people generally used online shopping. Furthermore, it was found that consumers who preferred Internet shopping accrued information before their purchases and tended to trust others. Lee’s [ 7 ] study was specifically based on the notion that the affinity or characteristics of consumers were different between online and offline shoppers. For example, the foundational notion states that online shoppers cannot see the item itself, thus they must rely on information provided by other consumers through their reviews [ 19 ]. This process indicates that these types of consumers are more trusting of others and are not confined to traditional consumption patterns. More specifically, online shoppers are exposed to indirect forms of information from the Internet or TV due to the inability to view the item itself. This variable is widely prevalent in all forms of online retail, which is very different from its offline counterpart. Based on the review of the literature, this study hypothesizes that online and offline consumption patterns will be different based on the consumer’s demographic and sociopsychological traits during the COVID-19 pandemic.

1.1.2. Protection Motivation Theory

The protection motivation theory (PMT), a major theory of the current study, was initially developed to explain one’s personal motivation to respond to threats or dangerous actions. According to the PMT, one’s reaction to certain situations can lead to positive changes through one’s protection motivation to overcome that specific situation [ 20 ]. Protection motivation is a powerful desire to protect oneself. Thus, when an individual is exposed to a message that threatens the safety or health of him/herself, a change in the individual’s actions occurs to remove this threat. Thus, the individual is able to protect him/herself with the individual’s changed actions [ 21 ]. Threat appraisal, a component of the PMT, is related to perceived severity, which dictates that the extent of a threat is based on the situation. Threat appraisal also includes perceived vulnerability, which is one’s perception of the extent to which one is exposed to a threat [ 20 ]. Severity is one’s perceived degree of harm that may be caused from the threat. According to the PMT, an individual will form a protection motivation, which allows the individual to have a positive attitude toward a recommended action and, thus, carry out the action. In this study, severity is the degree of the psychological threat related to the COVID-19 pandemic. Vulnerability is the negative expectation of becoming exposed to the COVID-19 virus. Therefore, when a consumer’s recognition of severity and vulnerability is high, a high likelihood exists that the consumer will experience a significant level of personal threat. His/her protection motivations will lower his/her frequency of outside activities, such as using offline retail, and could impact the consumer’s online retail purchases. Another component of the PMT, coping appraisal, includes perceived response efficacy and self-efficacy, allowing an individual to assess the potency of one’s response. Coping appraisal represents the belief of the individual regarding his/her ability to properly respond to threatening situations [ 22 , 23 ]. Coping appraisal consists of response efficacy and self-efficacy. Response efficacy is an individual’s expectancy that the selection of a recommended action can eliminate a threat. Self-efficacy is trust in the ability of oneself to successfully carry out the recommended action. Response efficacy and self-efficacy increase an individual’s potential to effectively carry out protective measures. Therefore, these attributes will increase an individual’s frequency of offline retail purchases as the individual’s protective action effectively decreases the threat (high response efficacy) and increase the expectation of the successful adaptation of the action (high self-efficacy). In summary, based on the PMT, the consumer will not tend to perform actions, such as pursuit of the offline retail channel, to avoid the threat that may expose him/her to the adverse situation if his/her cognitive level of threat appraisal is high. Additionally, if the consumer has a high cognition of response efficacy and self-efficacy, he/she will be more likely to prefer the offline retail channel by carrying out protective measures, such as using hand sanitizers, wearing masks, and avoiding outside activities.

1.1.3. Theory of Planned Behaviour

The theory of planned behaviour (TPB) was introduced by Ajzen [ 24 , 25 ] and has been widely applied to understand various human behaviours. According to the TPB, the leading factors of human behaviours include attitudes to behaviour, subjective norms, and perceived behavioural control. Specifically, the TPB systematically explains factors that are directly related to behavioural intentions during courses leading up to behaviours. This explanation is the reason why this theory is widely acknowledged as providing a useful theoretical framework for explaining human behaviours [ 26 , 27 ]. The attitude toward behaviour in the TPB refers to learned tendencies related to responding either favourably or unfavourably to certain objects. Subjective norm is similar to the social pressures felt by an individual regarding a decision on whether to conduct a certain action. In other words, subjective norm represents the opinion of the reference group, and stands for an individual’s perception of how most people think about a particular action [ 28 ]. Perceived behavioural control refers to a person’s subjective evaluation of the level of difficulty of conducting a certain behaviour. By adding the perceived behavioural control to the TPB, it can explain behaviours that are not within the boundaries of a human’s will [ 29 , 30 , 31 ], and has also shown a high level of accuracy in forecasting behavioural intentions [ 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 ].

Therefore, when we apply the TPB variables of attitude, subjective norm, and perceived behavioural control, we are able to explain social concerns and an individual’s tendency and capacity related to outside activities due to COVID-19. If a person has a negative attitude toward COVID-19 and a strong perception of the subjective norm, then he/she will prefer using online shopping channels rather than offline channels. In addition, perceived behavioural control will have a positive influence on using offline shopping channels because behavioural intentions are likely to increase when an individual perceives that his/her capability, opportunity, and resources are sufficient and the barriers to his/her behaviours are relatively insignificant. This indicates that if the perceived behavioural control is relatively high, then the person will prefer online shopping channels; otherwise, the person will prefer offline shopping channels.

2. Materials and Methods

2.1. research method.

In order to achieve the goal of this research, we carried out parallel documentary and empirical studies. International and domestic articles and statistics were referenced in order to systematize the background of this study. An empirical investigation was conducted by analysing the collected data using an SPSS statistics package and a STATA econometric statistics package. In terms of specific research methodology, frequency and basic statistical analyses were conducted in order to identify the characteristics of the survey respondents. The feasibility and reliability of the metric variables in the collected data were investigated using Cronbach’s alpha and exploratory factor analysis. Furthermore, an ordered logit model was employed using the econometric statistics package STATA.

2.2. Measurement

The research variables in this study were derived using the following process. The variables were largely categorized into sections. The first section of the survey questionnaire includes severity (4 categories), vulnerability (4 categories), response efficacy (4 categories), and self-efficacy (3 categories). The degree of effect that respondents had on the intention of the protection response of the consumers during the study period (April–May 2020) was calculated. This study utilized the framework of an expanded PMT. Specifically, based on prior research, this study additionally considered the four major variables of the PMT (i.e., severity, vulnerability, response efficacy, and self-efficacy) and supplementary variables (e.g., cognition of government policy, negative attitude toward COVID-19, degree of knowledge regarding COVID-19, degree of participation in social distancing). Demographic characteristic questions that are included in a traditional demand model, such as marital status, gender, and income, were asked. Through the variables of attitude (3 items), subjective norm (3 items), and perceived behavioural control (3 items), consumers’ intentions of using certain shopping channels during COVID-19 were measured. A Likert scale (1: totally disagree to 5: strongly agree) was used for the above items except for the characteristics on the population statistics.

Based on the literature review, items for the PMT and TPB were deduced. The severity of the PMT can be considered a key variable in this study. The survey items for vulnerability (i.e., degree of damage caused by COVID-19) were taken from Ruan, Kang, and Song [ 33 ]. The items of response efficacy were based on Cheng, Wei, Marinova, and Guo’s [ 34 ] research. The items about self-efficacy were based on Lee, Song, Bendel, Kim, and Han’s [ 35 ] study and they were used to investigate the awareness of respondents’ abilities to control results for individuals’ measures for COVID-19. The survey items related to the TPB were borrowed from Ajzen [ 25 ] and Lam and Hsu [ 36 ]. The awareness of government policy variable was adapted from Ruan, Kang, and Song [ 33 ]. The demographical characteristics were selected based on Lee’s [ 7 ] study. The protection behaviours were measured by the frequency of the participant’s visit to online markets and offline markets. We categorized the frequency related to visiting offline and online markets into infrequent (0), neutral (1), and frequent (2) for measurement. Since the consumers were placed into three groups based on their frequency of visiting offline markets, an ordered logit model was employed in our study.

2.3. Data Collection

The research data for this study were collected via an online survey conducted during April and May 2020. The respondents were required to be at least 20 years old. In terms of conducting a survey, this study adopted a quota sampling regarding age, gender, and regions based on census data so as to prevent distortions in the survey results due to population characteristics. The overall response rate for this survey was 92.9% (i.e., 251 completed surveys from the 271 customers contacted). Of the responses, 34 questionnaires were eliminated due to incomplete responses or irregularities. As such, 251 questionnaires were coded and used for the analysis.

2.4. Research Model

As the dependent variable of this study is ordered, an ordered logit model was used. An ordered logit model usually analyses the response of dependent variables which is ordered, ranked, and hierarchical through a regression equation. As such, the ordered logit model is a more developed form of a traditional regression analysis [ 37 ]. Compared with the ordered logit model, the affecting factors in the logit model can only be identified within the two categories of the dependent variable: 0 or 1 when the logit model is used. However, the dependent variables can be identified within three or more different ranked categories in the ordered logit model. Generally, the dependent variable of an ordered logit model does not take hierarchy; it is only defined as an ordered form of data. Hierarchy is defined as something needing a response that is dependent on another response. In terms of a research item, order is needed when the response is not dependent on another response and has an equal position among all the responses; thus, order is defined as the “order” in which the response proceeds from one response to another response for a research item in a questionnaire. The ordered logit model can handle casual effects models for qualitative as well as quantitative independent variables with a discrete dependent variable. In that, traditional regression methods cannot consider discrete responses [ 38 ]; when a traditional regression model is applied to an ordered response, it is simply used to calculate the average or conduct a regression on the response number. However, when a designated number is used (i.e., when an ordered Likert-scale is used), the predilection of the respondents cannot be determined if the average is calculated to be 2.5. An ordered logit model can solve this issue by using probability [ 38 ]. Therefore, a need exists to utilize an ordered logit model in this study so as to analyse various ordered, dependent variables. Thus, the model that will be used for this study is as follows.

The y ∗ in Equation (1) is a latent dependent variable (frequency of online and offline retail channel use in this study) and, proposing the standard, the final observed response. x ′ in Function (1) is the determinant (explanatory variables) and ε is the margin of error. If there are numbers present for the value, then the latent and observed responses are as follows.

In Function (2), above the μ is defined as the range value of the y ∗ . Thus, l is the most applicable value within the range values among L number of observable responses and the former description on the Function (2) is defined as the statistical notion held by the dependent variable in the ordered logit model. Therefore, the probability that y will select the specific value l is as follows.

Based on Function (3), the final ordered logit model equation used to analyse the relationship among the variables used in this study is as follows.

In the above equation, Y denotes the following dependent variables respectively: the frequency of using offline shopping channels in the past and the frequencies of intended use of either online or offline shopping channels in the future. Xn , respectively, denotes severity, vulnerability, response efficacy, self-efficacy, attitude, subjective norm, perceived behavioural control, level of complying with social distancing, knowledge of COVID-19, and recognition of government policy. Finally, ε denotes the error term.

3.1. Sample Characteristics

Table 1 shows the characteristics of the respondents in this study. Gender was similar with 49.8% male and 50.2% female. The age of the respondents was most prevalent as in their 40s (31.5%), in their 20s and 50s (23.5%), and 21.5% were in their 30s. For marital status, 60.6% of respondents were married. For education, 45.8% were college educated. For occupation, the majority of respondents were office workers, small business owners, or self-employed. Income was evenly distributed. As 60% of the respondents were married, a three-person household was the most prevalent with 39.8%, followed by a two-person household with 33.9%, and a single person household at 26.3%.

Demographic characteristics of the respondents.

Note: * United States Dollar is equivalent to 1122 Korean Won (KRW).

In addition, 41.8% of perceptions of COVID-19 were reported to be positive—the COVID-19 situation would improve in the future; 58.2% said that the COVID-19 situation will change more negatively in the future (see Table 2 ). For the category of the most severe disease or epidemic issue, the COVID-19 issue was the highest with 70.1%. It was observed that the majority of the respondents were participating well in social distancing. A total of 67.7% of respondents were found to not be interested in visiting offline retail channels in the future.

Awareness of COVID-19.

3.2. Results of the Ordered Logit Model Analysis

By employing major variables from the PMT and TPB, this study analysed how consumers’ behaviours related to online and offline shopping channels were different from each other during the COVID-19 crisis. Due to social distancing to curb COVID-19, sales revenue for offline markets, such as large grocery markets and department stores, fell significantly, while the sales revenue for online shopping soared. The pandemic has restrained—through social distancing and refrained outside activities—consumers from using offline distribution channels, where contact among consumers and salespersons is constantly occurring. Regardless of these circumstances, it is the purpose of this study to analyse the sociopsychological characteristics of the consumers who usually conduct shopping in offline markets and compare them to customers who usually conduct shopping in online markets. To achieve the purposes of this study, we estimated three dependent variables: frequency of using offline shopping channels during the COVID-19 crisis, intention to use online shopping channels after the stabilization of COVID-19, and intention to use offline shopping channels after the stabilization of COVID-19. Since the frequency of using offline shopping channels during the COVID-19 crisis was measured as “0 (will not nearly use it or have not nearly used it), 1 (will use it when necessary or have used it several times), and 2 (will use it a lot or have used it a lot)”, we used the ordered logit model. The goodness of fit of the ordered logit model can be verified by the likelihood ratio chi-square test, pseudo-R 2 , etc. First, when we analysed the models with the ordered logit model, we found them to be statistically significant at the significance level of 1%. The likelihood ratio chi-square test examined changes in likelihood (or goodness of fit) for each model that included only constants or independent variables. All of the models for this study had goodness of fit since they had statistical significance at the significance level of 1% [ 38 ]. Since the t-value of the boundary value, which distinguishes frequency of using a distribution channel, had significance at the significance level of 1%, models 1, 2, and 3 were statistically fit as ordered logit models. A summary of the analysis of the ordered logit model can be found in Table 3 .

Summary of analysis of ordered logit model.

Note: *: p < 0.1, **: p < 0.05, ***: p < 0.01.

First, the analysis results of Model 1 for using offline shopping channels during the COVID-19 crisis are as follows (see Table 4 ). For using offline shopping channels during the COVID-19 crisis, males ( p < 0.10) in their 20s and 30s ( p < 0.05) were found to have statistical significance. The major variables of the TPB were not found have statistical significance. Meanwhile, vulnerability ( p < 0.05) of the PMT had a negative influence (−), and response efficacy and self-efficacy ( p < 0.001) of the PMT had a positive influence (+). Level of compliance with social distancing was found to have negative influence (−) on using offline shopping channels during the COVID-19 crisis ( p < 0.001).

Result of analysis by ordered logit model of determinants leading to selection of offline shopping channels during COVID-19 crisis.

Note: (*) dummy variable from 0 to 1; Number of obs = 251, Log likelihood = −181.855, LR chi 2 (13) = 74.78; Prob > chi 2 = 0.000, Pseudo R 2 = 0.171; *: p < 0.1, **: p < 0.05, ***: p < 0.01.

Second, the analysis results of Model 2 for using offline shopping channels after the stabilization of COVID-19 are as follows (see Table 5 ).

Determinants leading to selection of offline shopping channels after stabilization of COVID-19.

Note: (*) dummy variable from 0 to 1; Number of obs = 251, Log likelihood = −181.855, LR chi 2 (13) = 74.78.; Prob > chi 2 = 0.000, Pseudo R 2 = 0.171.; *: p < 0.1, **: p < 0.05.

For using offline shopping channels after the stabilization of COVID-19, individuals in their 20s and 30s ( p < 0.05) were found to have statistical significance. Similar to Model 1, the major variables of the TPB were not found to have statistical significance. Both severity and vulnerability ( p < 0.05) of the PMT were found to have a negative influence (−). Knowledge of COVID-19 ( p < 0.01) had a positive influence (+) and the recognition of government policy ( p < 0.05) had a negative influence on using offline shopping channels (−). Finally, the analysis results of Model 3 for using online shopping channels after the stabilization of COVID-19 are as follows (see Table 6 ). Both attitude and subjective norm ( p < 0.001) of the TPB had a negative influence (+) on the intention to use online shopping channels. However, perceived behavioural control ( p < 0.05) was found to have a positive influence (+) on the intention. Both severity and vulnerability ( p < 0.05) of the PMT were found to have negative influences (−) on the intention to use online shopping channels. Both response efficacy and self-efficacy ( p < 0.05) were found to have a positive influence (+) on the intention to use online shopping channels. In addition, knowledge of COVID-19 ( p < 0.01) and recognition of government policy ( p < 0.05) were found to have a positive influence (+) on the intention to use online shopping channels.

Determinants leading to selection of online shopping channels after stabilization of COVID-19.

Note: (*) dummy variable from 0 to 1; Number of obs = 251, Log likelihood = −129.444, LR chi 2 (13) = 232.25; Prob > chi 2 = 0.000, Pseudo R 2 = 0.4729; *: p < 0.1, **: p < 0.05, ***: p < 0.01.

4. Discussion and Limitations

4.1. discussion.

Due to several factors such as the fourth industrial revolution and other technological developments, untact online sales are increasing. Adding to the trend, the unexpected COVID-19 pandemic has made untact shopping indispensable. As a result, the sales of offline distribution channels are gradually decreasing and the sales of online distribution channels are rapidly increasing, thereby the structure of competition in the retail sector is significantly changing. Based on these circumstances, this study intended to study how online and offline shopping behaviours have changed during the unprecedented COVID-19 pandemic. The government of Korea has implemented various policies to fight COVID-19, such as social distancing, compulsory mask wearing when using mass transportation, and scanning QR codes when visiting public facilities. Additionally, the prosperous trend to significantly reduce direct contacts among people was burgeoning while working at home and video conferencing were becoming the daily norm. In this study, an ordered logit model was used for these analyses since the respondents’ answers ranged from 0 (not likely to use) to 2 (likely to use a lot).

Specifically, in the first model, in terms of using offline distribution channels during the COVID-19 pandemic, it was found that men in their 20s and 30s tended to use offline distribution channels. Moreover, response efficacy and self-efficacy were found to have positive influences on the use of offline distribution channels. However, the vulnerability of the PMT and the level of practicing social distancing negatively influenced using offline shopping channels. These results showed that people who did not comply with social distancing usually used offline shopping channels and vulnerability in the PMT diminishes one’s use of offline shopping channels. This also indicates that the more that people recognized that they are vulnerable under current circumstances [ 36 ], the less likely they are to use offline shopping channels. In addition, it seems that the more they thought they could defend against threats like COVID-19, the more likely they were to use offline shopping channels. Based on the results of Model 2, in terms of using offline distribution channels after stabilization of COVID-19, it was analysed that people in their 20s and 30s are expected to use offline shopping channels. Both severity and vulnerability of the PMT and recognition of government policy are found to negatively affect using offline shopping channels. However, knowledge of COVID-19 positively affected using the channels. These results implied that when the circumstances of COVID-19 are severe and people think they are vulnerable, offline shopping decreases. In addition, it was revealed as people’s disregard of government policy increased, they seemed to use more offline shopping channels. Moreover, when people have enough knowledge of COVID-19, they actively perform self-protection measures, which leads to more online shopping. It was interesting that attitude, subjective norm, and perceived behavioural control did not have statistical significance in both Models 1 or 2. This shows that the TPB was not enough to explain consumers’ behaviours during the COVID-19 crisis.

In the third model, with regard to using online shopping channels after the stabilization of COVID-19, it was found that none of the characteristics in the population statistics had any statistical influence. Both the severity and vulnerability of the PMT were found to have a negative influence on using online shopping channels. This result shows that when circumstances are severe and vulnerable as with COVID-19, it is more likely that consumption through offline distribution channels will decrease. The model also showed that response efficacy and self-efficacy were found to have a positive influence on intention to use online shopping channels. This result implies the confidence and feeling that they can cope with crises like COVID-19 increase use of online shopping channels. Knowledge of COVID-19 and recognition of government policy were found to have a positive influence on intention to use online shopping channels. Therefore, we can expect that consumers are more likely to use online shopping channels if they have enough knowledge of COVID-19 and positive recognition of government policy. However, both attitude and subjective norm have a negative influence on the intention to use online shopping channels. These results indicated that when consumers are more negative against circumstances like COVID-19, they are more likely to use online shopping channels. Consumers are also expected to use online shopping channels more when the people around them are negative against using offline shopping channels. During the COVID-19 crisis, while consumers conducted behaviours to protect themselves, they were also concerned about the negative reactions of those people around them regarding their own behaviours, such as not wearing masks. As such, perceived behavioural control was found to have a positive influence on intention to use online shopping channels (i.e., when consumers believe that they can control their behaviours, they are more likely to use online shopping channels). Summarizing the results of this study, we can expect that consumers who do not meticulously practice social distancing, are individuals in their 20s and 30s, or confident that they can effectively cope with COVID-19 are very likely to use offline shopping channels. In addition, we can expect consumers who have knowledge of COVID-19, have positive recognition of government policy, and are confident that they can effectively cope with COVID-19 to use online shopping channels. The major variables that influenced the consumers’ selection of offline distribution channels or online shopping channels were social distancing, recognition of government policy, attitude toward COVID-19, and subjective norm. Consumers who had negative attitudes toward COVID-19 and believed that the people around them might respond to their behaviours negatively indicated that they would use more online instead of offline shopping channels.

From the social psychological perspective, this study examines the characteristics of consumers who use offline or online shopping channels through the PMT and TPB and shows that consumers against the COVID-19 pandemic are more likely to use online shopping channels than offline channels. In this study, the findings revealed that the PMT is primarily more appropriate than the TPB in interpreting consumer behaviour under COVID-19 circumstances. This suggests that the PMT is a theoretical approach that can better explain consumer behaviour than the TPB under serious risk and the unprecedented pandemic situation. It also indicates that, consistent with the previous studies, consumers are trying to take sufficient measures to protect themselves through “protective actions” on the issues of fine dust, environmental pollution, privacy, and health. In terms of comprehensive implications for marketers, based on the results of the current study, it is necessary to comply with social distancing guidelines and encourage consumption activities through online rather than offline shopping channels as COVID-19 is still prevalent in our society. In addition, as consumers who become familiar with government regulations are likely to use online shopping channels than offline shopping channels, government officials should do their best to earn the trust of consumers and raise awareness of consumer protection issues regarding health and safety. In other words, if government policies are well implemented and make consumers trust them more, they will prefer online shopping channels. Therefore, government officials need to continue to seek opportunities to increase consumers’ trust in government policies to effectively cope with COVID-19.

Online and offline shopping channels generally have a complementary relationship rather than competition. In the new era of retail, many consumers have moved from offline retail channels to safe and convenient online channels. Consumers have selectively used offline or online shopping channels depending on the consumers’ specific needs before COVID-19; however, in a situation where COVID-19 is likely to last for a long time, consumers are expected to actively use online shopping channels in a short period of time. As the government has implemented further strengthened social distancing rules, the role and revenues of offline retail channels are further decreasing due to government policy. Since the first case was identified in Wuhan in December 2019, COVID-19 has continued until 2021. Although online channels seek continuous increases in revenues amid the prolonged situation of the COVID-19 pandemic, offline channels need to seek different ways such as closing down stores, reducing spaces, shifting spaces with different usages, and so on. Offline retail channels should also make their survival strategies continuously such as developing entertainment of the space, new space design by combining the fourth industrial revolution technologies and customers’ needs, role shift of purchase to the space of experiences, and so on. In this regard, this study has meaning to reveal that the PMT explained the pandemic situation better than the TPB, which was a well-known theory on consumers’ behaviours under the unprecedented pandemic situation. Like Korean cases, the retail apocalypse also happened in the United States due to the COVID-19 pandemic, shutting down retailers such as J.C. Penny, Sears, Neiman Marcus, and so on. Because of this situation, online shopping has grown in importance compared to offline shopping, but it should not be overlooked that offline retail channels still have value that online channels cannot match. Since offline distribution channels have the aspects of the offline retail experience as a part of the national economy, it is necessary to find a direction to carry out economic activities combining online and offline shopping. In offline distribution channels, consumers can see and touch in real time, have a real-world offline experience, and purchase niche products not available for online purchase (i.e., liquor, gourmet foods, art supplies). The space of the offline retail channel can be moved to the real world such as “showrooms” and “meeting places”. In addition, offline shopping channels need to diversify the functions of store spaces to survive declining demand for offline retail spaces. The government’s support for this can help shift the balance of the retail market between online and offline channels.

4.2. Limitations and Future Research

This study has a limitation in that it was conducted during April and May 2020, when COVID-19 was still rampant in society. Since confirmed cases of COVID-19 are still occurring, this time period could be a limitation for this study. Therefore, it is necessary to conduct another study after COVID-19 is gone from society in order to determine whether changes have occurred in consumers’ decision processes. Although this study was conducted by focusing on online and offline shopping channels, it could also be meaningful to attempt to conduct similar studies focusing on more specific subsections of the channels, such as department stores, large grocery stores, and convenience stores. In terms of the development process of the incident, research comparing the characteristics and behaviour of actual consumers before, during, and after COVID-19 needs to be conducted in the future. It will be meaningful for future research to be performed in other countries affected by COVID-19 in order to increase the likelihood of generalization. It will be a very interesting scholarly endeavour to examine the impact of protection motivations and consumer sociopsychological characteristics on shopping behaviour during COVID-19 through examples from different countries. Finally, future investigations need to use social network modelling to more diversely examine consumers’ intentions of using certain shopping channels during COVID-19 and future virus disease outbreaks.

In spite of these limitations of this study and additional future research topics, the current study can be said to be of high academic value in that it analysed the characteristics of consumers who used offline shopping channels during the pandemic. In other words, the findings of this research add to better understanding of the consumers’ perception on the retail channels at the initial COVID-19 pandemic situation by conducting research between April and May 2020 during the expansion phase of COVID-19. The study also contributed to investigating how many of these consumers plan to use offline and online shopping channels after the stabilization of COVID-19 and analysed the determinants for their shopping decisions. The results of this study have significance in regard to the characteristics, as expressed by the PMT and the TPB variables, of consumers who have used either offline or online shopping channels. The results showed that, during crises like COVID-19, the PMT was more adequate than the TPB for interpretation of consumer behaviours.

Author Contributions

J.M.: writing—original draft preparation, methodology, conceptualization; Y.C.: writing—review and editing, investigation; H.S.: data collecting, supervision, other assistance. All authors have read and agreed to the published version of the manuscript.

This research received no external funding.

Institutional Review Board Statement

It is not applicable.

Informed Consent Statement

Informed consent has been obtained from all subjects involved in this study to publish this paper.

Data Availability Statement

Conflicts of interest.

There are no conflict of interest to declare.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Upcoming events, participants needed for online grocery shopping study.

If you are a healthcare professional in Nova Scotia who shops online for groceries, you may be interested in taking part in the Groceries Online – Eating, Acquisitions, & Technology (GO-EAT) Study at Dalhousie University.

The GO-EAT Study is looking at the relationship between online grocery shopping and food-related habits. We are looking for healthcare professionals in Nova Scotia, who shop online for groceries, to help fill out questionnaires about their food purchases, technology use, and food intake.

This study is part of a PhD thesis and has received ethics approval from the Research Ethics Board at Dalhousie University [REB #2023-6616].

For more information or to sign up to participate, please visit https://redcap.link/GO-EAT_Study or email Helen Wong at [email protected]

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