The Shift of Consumer Online Purchase Behavior: Covid-19 Pandemic as A Situational Variable
Daniel Kristian Purwanto1*, Albert Kriestian Novi Adhi Nugraha1
1 Faculty of Economics and Business, Universitas Kristen Satya Wacana, Salatiga, Indonesia
*Corresponding Author: [email protected]
Accepted: 15 September 2022 | Published: 1 October 2022
DOI:https://doi.org/10.55057/ijbtm.2022.4.3.34
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Abstract: The spread of the Covid-19 pandemic affected many aspects of our daily lives. In Indonesia, the government implemented PSBB or big-scale social limitations to reduce the spread of the Covid-19 virus. It was not easy getting groceries and goods from the store, and E-commerce was a promising alternative to obtain goods without risking getting the Covid-19 virus. E-commerce is more convenient, cheaper, and safer as it does not require social interaction. This research aimed to compare data on consumer online purchasing behavior before, during, and after the Covid-19 pandemic. This research applied a quantitative approach by collecting primary data. The subjects for this research were 201 respondents that lived in Salatiga, Central Java, Indonesia, with a convenience sampling technique. Repeated measure ANOVA and Chi-square analysis were used to analyze the data collected from the questionnaire. The results indicated that the Covid-19 pandemic had influenced online shopping behavior regarding shopping frequency, product category, and budget spent.
Keywords: Covid-19 pandemic, e-commerce, online purchasing behavior
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1. Introduction
The Coronavirus disease or Covid-19 spreads quickly, more than people can anticipate (Zu et al., 2020). The deadly virus dispersed from Wuhan, one of the largest cities in China, back in December 2019 (Kim, 2020). On April 2, 2020, the total confirmed cases reached 1 million people across 204 countries, with the fatality rate at 5·2% (Phua et al., 2020). Most people implemented self-quarantine to decrease the spread of this virus. Nearly every aspect of daily life was affected by this pandemic, and people could not go outside freely and limited their activities only to critical or urgent things. Many retailers temporarily shut down their businesses, implemented the work-from-home system, and studied online meetings for students. Traveling across cities is also prohibited, thus making a massive drop in hotel stays and travel business (Baldwin, 2020). These lead to decreasing economic growth (Hasanat et al., 2020). Some industries, such as transportation, traveling, tourism, restaurants, and cinemas, suffered from the decreasing demand because most people are implementing physical and social distancing. On the other hand, businesses such as the internet, video games, and television programs experience a significant increase in demand (Barua, 2020).
Online shopping has been around for quite a long time. Shopping online is more convenient and economical than shopping in physical stores, and online shopping is also more flexible in terms of location, time, and variety of products. In this case, the pandemic may eventually drive
people who never use online shopping to shop online (Kim, 2020). A survey that had been participated by 2.200 adults in the United States stated that 37% of survey respondents considered switching to online shopping (Kim, 2020). Some people who have started shopping online due to this pandemic have already experienced online shopping convenience. Thus, it is safer anyway because we do not have to go directly to the physical store and maintain physical and social distancing. During the pandemic, a new 10% consumer demographic adopted online shopping because the pandemic has paid the price and experienced the effectiveness of online shopping. It is aware that a few days of waiting for delivery might be worth it than risking our lives to shop in physical stores (Kim, 2020).
There are plenty of e-commerce websites in Indonesia. Some popular local e-commerce platforms that dominates in Indonesia are Bukalapak, Tokopedia, Traveloka, and Go-Jek (Lestari, 2019). Foreign-owned e-commerce platforms such as Shopee, Alibaba, JD.ID, Zalora, and Aliexpress also exist in the Indonesian e-commerce market. Sea Group is a Singaporean company that owns Shopee, Alibaba Group holds Lazada, and JD.id is a branch of a Chinese e-commerce platform named JD.com. Zalora is also an e-commerce platform owned by a Singaporean company, and lastly, AliExpress is an e-commerce platform owned by the Alibaba Group.
Some scholars have conducted studies on purchase behavior during the pandemic. Hasanah et al. (2020) examined how the pandemic affected online business in the context of product supply, especially Chinese products sold in Malaysia. Other studies discussed how the pandemic might accelerate digital transformation and consumer behavior changes in the marketplace (Kim, 2020). Another study examined the possible effect of the pandemic on macroeconomics in general (McKibbin & Fernando, 2020). Dinesh & MuniRaju (Dinesh &
MuniRaju, 2021) show differences in frequency, budget, and consumer online purchase behavior categories. However, the study did not compare the frequency, budget, and category data before and after the pandemic. The current study mainly investigates the shift of online purchase behavior by comparing before, during, and post-pandemic behavior. Does the Covid- 19 pandemic influence the change in online purchase behavior, particularly in frequency, the choice of product category, and budget? Furthermore, the current study answers whether the pandemic acts as a situational variable that forms an enduring shift in online purchase behavior.
2. Literature Review
Online shopping has been quite a trend; shopping for goods online is considered more efficient and economical than shopping in physical stores. Also, convenience is one of the main factors that motivate people to adopt online shopping (Jiang et al., 2013). Online shopping is flexible regarding location and time, and consumers can purchase goods at any time and place. Online shopping gives customers high transparency and information about the products. Consumers know the condition and product quality through the review by the previous consumers, and this review can increase the trust of the next buyer. These benefits of shopping online can affect consumer purchase intention and thus develop online purchasing behavior (Al-Debei et al., 2015).
The Covid-19 virus has been spreading rapidly since late 2019. The government has implemented various preventive procedures such as social distancing and the stay-at-home rule. The stay-at-home rule forced people to limit their outdoor activities and leave the house only if they had an essential need. This situation makes internet usage increase rapidly caused of the limitation of contact with other people, face-to-face contact and working from home. All
of these lead to the shifting towards the usage of online media. The stay-at-home procedures and the reduction of face-to-face activities increase online retailing. One of the top reasons online retailing is rising is the infected concern. Online shopping is safer than offline retailing because it requires no face-to-face contact, thus decreasing infection. A survey in Germany on March 27 indicates that 44% of customers ordered groceries through an online platform for the first time (Dannenberg et al., 2020).
In this pandemic era, shopping online is safer than in physical stores because people can still maintain social and physical distancing as people do not have to meet face-to-face while shopping. Thus, most e-commerce provides online payment methods that can reduce the risk of getting infected by this virus as we do not have to pay using cash and avoid physical contact with other people. This pandemic may have accelerated the rise of online purchases; it can eventually drive people who never shop online to shop online. 10% of the new consumer demographic had started adopting online shopping (Kim, 2020). They already paid the learning cost and realized that waiting several days is better than risking our lives when shopping in physical stores. They plan to continue to shop online even after the pandemic ends. A survey also showed that of 2.200 adults in the US, 37% considered switching to online shopping.
Because of this pandemic, approximately 11% of generation Z, 10% of millennials, 12% of generation X, and 5% of boomers have started purchasing something online.
There are differences in online purchase behavior in frequency, budget, and product category due to the Covid-19 pandemic (Dinesh & MuniRaju, 2021). There is an increase in the frequency of online shopping. Furthermore, there was a rise in online purchase spending during the Covid-19 pandemic, with the majority (30% out of 195 respondents) spending 2001-3000 Indian Rupee per month on shopping online. And lastly, the top 3 most purchased product categories are home and kitchen essential (39.2%), personal and health care (34.4%), and groceries (32.5%) sequentially (Dinesh & MuniRaju, 2021).
3. Research Method
Sampling Technique
The chosen sample was a group of respondents who live in Salatiga city, Indonesia. Most respondents were students of Universitas Kristen Satya Wacana, located in Salatiga, and their relatives and friends. The current study collected data using convenience sampling as the respondents were easily accessible (Sedgwick, 2013). According to (Roscoe et al., 1975), the sample size should be 30 and below 500 respondents. The respondents for the current study were 201 people, which also followed the minimum sample size for marketing research studies (Malhotra, 2019).
Data Measurement
The questionnaire consists of three parts: the demographic profile, internet usage, and online shopping behavior across three periods: before, during, and after the pandemic. The demographic profile consists of gender, age, education, job, and domicile question items, and internet usage includes questions on the device, internet hours, and browser preference.
Subsequently, the questionnaires detailed questions on online shopping behavior into three dimensions: frequency, product category, and budget. Respondents answered questions on online shopping behavior based on their experience before and during the pandemic and their expectations after the pandemic. For instance, the respondents answered how frequently they shopped online before, during, and after the Covid-19 pandemic, ranging from "never shop
online" to "more than five times a month."
Data Collection and Data Analysis
The current study applies a quantitative approach by collecting primary data. Data were collected through an online survey using the Google form platform. An online survey is a cheaper and more flexible data collection tool regarding time and location (Buchanan &
Hvizdak, 2009). The data from the online questionnaire were then analyzed using the Repeated Measure ANOVA method and Chi-square analysis that were available in IBM SPSS Statistic.
4. Results and Discussions
Demographic Profile
The data collection took roughly two months, from August 24, 2021, to October 26, 2021. In the end, the researchers get 200 respondents who live in Salatiga city. Table 1 indicates that most respondents are male (52.7%), and the rest are women (47.3%). Most consumer respondents were aged 17-25 years, with a percentage of 45.8%, with the minority sample held by respondents over 46 years (2.0%). Furthermore, based on the educational background of the respondents, 67.7% are bachelor's degree graduates, and the least number is master's degree graduates, with a percentage of 1%. In addition, the respondent's main job is dominated by private employees, as much as 32.3%, while government employee jobs are the jobs with the least number of jobs in this study, with 6.5%. Lastly, the respondents live in Salatiga city with different sub-districts. Most respondents live in the Sidorejo district (35.8%), while a minor percentage of 19.4% live in the Argomulyo district.
Table 1: Respondent Demographic Profile
No Category Sub-Category Frequencies Percentage Total
1 Gender Male 106 52.7%
Female 95 47.3% 201
2 Age
17 - 25 92 45.8%
26 - 35 52 25.9% 201
36 - 45 53 26.4%
>= 46 4 2.0%
3 Educational Level
Junior High School 3 1.5%
High School 60 29.9% 201
Bachelor Degree 136 67.7%
Master Degree 2 1.0%
4 Job
Entrepreneur 36 17.9%
201
Students 64 31.8%
Private Employee 65 32.3%
Household 23 11.4%
Government
Employee 13
6.5%
5
Domicile (Salatiga
City)
Sidorejo 72 35.8%
Tingkir 47 23.4% 201
Argomulyo 39 19.4%
Sidomukti 43 21.4%
Internet Usage
Respondents vary in the way they use the internet during the pandemic. Based on Table 2, most respondents use their smartphones to access internet services (68.2%), and a minor percentage of respondents use tablet computers (5.0%). They mostly spend around 5 to 9 hours/day surfing the internet (60.7%), and the minority of respondents spend more than 16 hours/day (5.5%). Google Chrome is still a mainstay for respondents in surfing the internet.
Furthermore, more than half of the sample used Google Chrome (71.6%), and a minor proportion of respondents used Safari (2%).
Table 2: Respondent's Profile as Internet Users During COVID-19
No Category Sub-Category Frequencies Percentage Total
1 Device
Smartphone 137 68.2%
Laptop 43 21.4% 201
Tablet Computer 10 5.0%
Personal Computer 11 5.5%
2 Internet Hour
< 5 hour/day 30 14.9%
5 - 9 hour/day 122 60.7% 201
10 - 15 hour/day 38 18.9%
> 16 hour/day 11 5.5%
3 Browser Internet
Google Chrome 144 71.6%
201
Internet Explorer 18 9.0%
Mozilla Firefox 29 14.4%
Opera 6 3.0%
Safari 4 2.0%
Reasons for Online Shopping
Table 3 shows the reason for online shopping. Most respondents prefer online shopping because of the practicality of place and time (85.07%). They can shop without limitation as long as they can access the internet through their smartphone and laptop. Online shopping is also more preferred due to its payment flexibility, affordable transaction cost, secure payment, and time efficiency.
Table 3: Reasons for Online Shopping
Characteristic Frequency Percentage
More practical (Accessible) 171 85.07%
Affordable Transaction Fee 5 2.49%
Secure Payment 5 2.49%
Various Product Alternative 3 1.49%
Flexible Payment Mode 7 3.48%
Relative Affordable Price 4 1.99%
Time Efficiency. 5 2.49%
Promos and Discounts 1 0.50%
The Frequency of Online Shopping Before, During, and Post-Pandemic
This study investigates whether online shopping frequency differs in three periods (before, during, and after the pandemic). The current study applied repeated measures ANOVA, and the first stage was to investigate the assumption of sphericity, as shown in Table 4. The result indicates that Mauchly's test of sphericity is significant, F(2, 400) = 111.002, Sig=0.001 < 0.05.
Therefore the assumption of sphericity is violated. The next stage was to interpret the test of within-subjects effects using an alternative univariate test (i.e., the Greenhouse-Geisser test).
The result from the Greenhouse-Geisser test was significant, F(1.835, 366.940) = 111.002, Sig=0.001 < 0.05. Thus, we can differentiate online shopping frequency based on three periods (before, during, and after the pandemic).
Table 4: Test of Within-Subject Effects for Online Shopping Frequency
Category Type III Sum
of Squares df Mean
Square F Sig
Online Shopping Frequency
Sphericity Assumed Greenhouse-Geisser Huynh-Feift Lower-bound
97.088 97.088 97.088 97.088
2 1.835 1.851 1.000
43.544 47.467 47.053 87.088
111.002 111.002 111.002 111.002
0.001 0.001 0.001 0.001 Table 5 presents the unique comparison between the means of online shopping frequency among three time periods using tests of within-subject contrasts. The results indicate the first contrast between online shopping frequency before and during the COVID-19 pandemic. The first contrast was statistically significant at F(1.835, 366.940) = 261.445, Sig=0.001 < 0.05.
The first contrast indicates an increase in online shopping frequency during the pandemic compared to before the pandemic. Meanwhile, the second contrast was also statistically significant between online shopping frequency during the pandemic and post-pandemic, F(1.835, 366.940) = 7.055, Sig=0.009 < 0.05.
Table 5: Tests of Within-Subjects Contrasts for Online Shopping Frequency
Online Shopping Frequency Comparison F Sig.
Before and During the pandemic 261.445 0.001
During and After the pandemic 7.055 0.009
*significant at p level .05
Table 6 shows the differences between each period. The mean score shows that online shopping frequency increased significantly during the pandemic (mean=2.577) compared to before the pandemic (mean=1.701). The respondent tends to shop online during the pandemic as they cannot go out to purchase goods from an offline store. Although online shopping frequency after the pandemic was significantly lower than during the pandemic (mean=2.413), the frequency is still considerably higher than before the pandemic. The results indicate that respondents started to become more comfortable with online shopping. However, the respondents might expect to be able to shop online and eat at the restaurant after the pandemic ends. Hence, online shopping frequency decreased compared to during the pandemic.
Nevertheless, they will continue to shop for goods from online store even after the pandemic ends.
Table 6: Pairwise Comparison for Online Shopping Frequency
(I) Period Mean (J) Period
Mean Difference
(I-J)
Std.
Error Sig.b
95% Confidence Interval for Differenceb Lower Bound
Upper Bound Before 1.701 During -.876* .054 <0.001 -1.006 -.745
After -.711* .070 <0.001 -.881 -.541
During 2.577 Before .876* .054 <0.001 .745 1.006
Online Shopping Based on Product Category Before, During, and Post-Pandemic
Table 7 shows that respondents differ in their choice of product category when they shop online for a three-time period based on the Chi-square test result (Chi-square=113.495, p = <.0,05).
Respondents tended to shop online more for fashion products, hobbies, toys, food and beverage before the pandemic. The choice of product category changed when respondents dramatically increased their purchases of food & beverage products and health & medicine products during the pandemic. Subsequently, respondents asked what they expected to buy after the pandemic ended. Although there are slight differences, it is considered insignificant compared with during the pandemic. The respondents tend to maintain similar purchased product category with during the pandemic.
Table 7: The Choice of Product Category Before, During, and Post-Pandemic
Budget Allocation for Online Shopping Before, During, and Post-Pandemic
Overall, there was a statistically significant period effect on budget allocation for online shopping, F(1.872, 374.314) = 97.377, Sig=0.001. Table 8 indicates the contrast between online shopping budgets before, during, and post-pandemic. Tests of within-subjects differences show the first statistically significant contrast between online shopping budgets before and during the pandemic F(1,200) = 154.547, Sig=0.001 < 0.05. The online shopping budget increased significantly during the pandemic compared to before. Interestingly, the second contrast for online shopping budget during and post-pandemic was insignificant, F(1,200) = 1.387, Sig=0.240 > 0.05. The results show that the respondents increase their budget for online shopping during the pandemic and expect to spend relatively the same budget after the pandemic ends.
Table 8: Tests of Within-Subjects Contrasts for Online Shopping Budget
Online Shopping Frequency Comparison F Sig.
Before and During the pandemic 154.547 0.001
During and After the pandemic 1.387 0.240
*significant at p level .05
Table 9 further shows the differences in budget spent in each period. There was a significant increase in budget spent during the pandemic (mean=2.697) compared to before the pandemic (mean=1.960). However, the budget spent after the pandemic (mean=2.637) had no significant differences compared to during the pandemic, with slightly less (-0.60) than during the pandemic. The respondents spent significantly more budget during the pandemic than before the pandemic. Furthermore, the respondents tend to maintain relatively the same budget after
After .164* .062 .026 .015 .313
After 2.413 Before .711* .070 <0.001 .541 .881
During -.164* .062 .026 -.313 -.015
Period Chi-Square
(p-value) Before During After
Product Category
Electronics 34 21 36 Overall Chi-square = 113.495, p = 0.000
Before vs During Chi-square = 95.284, p = 0.000 During vs After Chi-square =
9.942, p = 0.77
Fashion 116 125 143
Food & Beverages 56 155 135
Furniture 17 43 39
Health & Medicine 20 122 100 Hobbies and Toys 75 88 102
the pandemic as during the pandemic. Overall the budget spent during the pandemic was the highest among the three periods.
Table 9: Pairwise Comparison for Online Shopping Budget
(I)
Period Mean (J) Period
Mean Difference
(I-J)
Std.
Error Sig.b
95% Confidence Interval for Differenceb
Lower Bound Upper Bound
Before 1.960 2 -.736* .059 <0.001 -.879 -.593
3 -.677* .065 <0.001 -.834 -.520
During 2.697 1 .736* .059 <0.001 .593 .879
3 .060 .051 .721 -.063 .182
After 2.637 1 .677* .065 <0.001 .520 .834
2 -.060 .051 .721 -.182 .063
5. Conclusion, Implication, Limitations, and Further Study
Conclusion
The current study indicates that the COVID-19 pandemic has influenced online shopping behavior regarding shopping frequency, product category, and budget. Before the lockdown and stay-at-home regulation, respondents could still easily access brick-and-mortar stores. The shift occurs that respondents have to minimize purchasing offline significantly. The pandemic forces respondents to change how they buy products depending on online purchases. During the pandemic, online shopping was considered the best option to anticipate safety and health issues. In addition, given the practicality, affordable transaction fee, secure payment, and time efficiency, respondents feel convenient and expect to increase their online shopping habits after the pandemic.
The respondents increased their frequency significantly during the pandemic compared to before. However, the respondent decreased their frequency significantly after the pandemic compared to during the pandemic. Even so, the frequency of online shopping after the pandemic is still considerably higher than before the pandemic. Thus, the respondent will maintain a relatively high online shopping frequency even after the pandemic ends.
Before the pandemic, respondents purchased more fashion and, hobbies & toys. During the pandemic, they shifted their online purchase by dramatically increasing their online purchase for food & beverages and health & medicine. The respondents are more likely to maintain their online habits after the pandemic.
Respondents spent more budgets on online shopping than before the pandemic. It is hardly surprising because they can not get access to conduct offline purchases as they could before the pandemic. However, they maintain relatively the same budget as their current budget spending during the pandemic to anticipate future purchases after the pandemic.
Implication
The COVID-19 pandemic plays an essential role as a situational variable that dramatically changes online shopping behavior. As a result, this situation becomes a promising opportunity for business owners to utilize the behavior shift by increasing the commitment to building an online business. Business owners can use existing e-commerce platforms or construct websites to generate revenue from online businesses. Although respondents prefer electronic also health
and medical products that they potentially purchase after the pandemic, there become product categories that respondents
Limitations and Further Study
The current study identifies several methodological issues. Firstly, the number of participants during the survey is deemed relatively small, although it is still acceptable for marketing research studies. Increasing the sample size might generate more diversified perceptions of and in-depth insights into online shopping behavior. Second, respondents have a potential issue distinguishing the period between during and after the pandemic. Respondents might think that the period during a pandemic is a new normal similar to after the pandemic. Perhaps respondents perceive that the same lifestyle continues, such as wearing masks, washing hands, using sanitizers, and limiting social interactions.
Future studies can extend the sample size and the scope of respondents to different regions.
Future studies may also break down the analysis into consumer segments such as generation cohorts. Another statistical method to profile the behavior based on segmentation is recommended, such as cluster analysis and a causal model.
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