Next Article in Journal
Potential Use of Bio-Oleogel as Phase Change Material
Previous Article in Journal
Teaching and Learning Approaches: Curriculum Framework for Sustainability Literacy for Technical and Vocational Teacher Training Programmes in Malaysia
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Sustainability
Volume 15
Issue 3
10.3390/su15032545
sustainability-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

The Impact of the COVID-19 Pandemic on Online Consumer Behavior: Applying the Theory of Planned Behavior

by
Anastasia Theodorou
1,
Leonidas Hatzithomas
1,
Thomas Fotiadis
2,
Anastasios Diamantidis
2,* and
Antonios Gasteratos
2
1
Department of Business Administration, University of Macedonia, 54636 Thessaloniki, Greece
2
Production & Management Engineering Department, Democritus University of Thrace, 12 Vas. Sofias St., 67100 Xanthi, Greece
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(3), 2545; https://doi.org/10.3390/su15032545
Submission received: 31 October 2022 / Revised: 9 January 2023 / Accepted: 13 January 2023 / Published: 31 January 2023
(This article belongs to the Section Sustainable Management)
Browse Figure
Versions Notes

Abstract

:
The present study applied the theory of planned behavior (TPB) to examine consumers’ intention to shop online during the COVID-19 pandemic and its influence on purchasing behavior. The fear of shopping in a physical store due to COVID-19 was added to the model’s key factors, which included attitudes towards e-shopping, subjective norms, and perceived behavioral control. The study involved 406 participants aged over 18, who were all residents of the Regional Unit of Thessaloniki. The findings indicated that during the pandemic, the factors that influenced online purchase intention differed from those that would affect future purchase intention, with the fear of shopping in a physical store being the most influential. In particular, the results revealed that attitudes towards e-shopping, subjective norms, perceived behavioral control and fear of shopping at a physical store positively affected the intention to make online purchases during the pandemic. In contrast, only attitudes toward e-shopping and perceived behavioral control affect future purchase intention. Furthermore, as expected, the intention to shop online influences actual online purchase behavior.

1. Introduction

On 31 December 2019, an infectious disease, subsequently named COVID-19 [1], was detected in Wuhan, China, and quickly spread throughout the world. On 11 March 2020, it was declared a pandemic, as it had spread simultaneously in many countries worldwide [2]. By 25 February 2021, confirmed COVID-19 cases numbered 112,209,815 worldwide, while deaths officially attributed to the disease stood at 2,490,776, and the number of countries it had spread to amounted to more than 223 [3]. Apart from health problems, the outbreak of the pandemic had serious financial ramifications [4,5,6], and caused significant changes in consumers’ lifestyles and behaviors.
Due to the COVID-19 pandemic, consumer behavior has shifted, with an exponential increase in e-shopping. With the advent of the internet, businesses have become more familiar with selling products online, while consumers are attempting to make sound decisions about how and where to purchase a diverse array of products from a wide range of online stores. Consumers can buy products from online stores at any time and on any day, they want, without leaving their homes, and they can choose from a wide range of products [7]. Consumers also have free access to information about products or services and the ability to compare product/service features and prices [8], saving time and money [9]. Making online purchases is easier, faster [10], and more convenient [11,12]. On the other hand, e-shopping also hides several risks such as security of payments [13], lack of contact with the product, and inability to conduct product testing before making a purchase [8]. Complaints about e-shopping in Greece totaled 1396 in 2019, while 2770 were lodged in the first ten months of 2020, representing a 25.80% increase.
Several theories have been used to predict consumers’ online behavior, such as the theory of reasoned action (TRA), the theory of planned behavior (TPB), and the technology acceptance model (TAM). The current study employs the theory of planned behavior [14] to describe the impact of the COVID-19 pandemic on Greek consumers’ online purchasing behavior. According to Ajzen (1991) [14], TPB is applicable in many contexts, and hence we use it to predict consumer intention and online purchase behavior during and after the pandemic. The model’s three determinants [14]—attitudes toward online shopping, subjective norm, and perceived behavioral control—have been used in a few studies to predict intention to shop online [15,16]. Several researchers, however, have examined them in conjunction with other factors [8,17,18,19,20,21,22,23,24] to explain which one influences intentions and behaviors in online shopping. Factors studied include perceived risks, perceived consequences, innovation, trust, confidentiality issues, and reliability, among others.
A study by [25] focusing on the adoption of e-banking has shown that the extended TPB model predicts intention better than the basic model. Taking this stream of research, we seek to sufficiently understand and predict consumers’ intent to make online purchases. It’s also intriguing to see how the theory relates to e-shopping during the global pandemic crisis. Thus, the current study employs the main components of the planned behavior theory [14]—attitudes toward online shopping, subjective norm, and perceived behavioral control—and the fear of shopping in a physical store. According to [26] (p. 2), the later factor is “a potential conditional factor that impact on purchase behavior, especially during the COVID-19 pandemic”. In the present study, the dependent variables are the intention to shop online during and after the pandemic and the actual online behavior. The present study extends the literature on the theory of planned behavior to the understanding of online shopping by showing that amid a pandemic the factor “fear of shopping at a physical store” is very important for providers and consumers. Additionally, the outcomes of this research can be helpful for sale managers and executives as it provides a framework of customer behavior during pandemic (present or future).

2. Literature Review

2.1. Theory of Planned Behavior

Ref. [27] described the theory of planned behavior (TPB) as a framework for the conception, measurement, and empirical determination/identification of factors that determine behavior and intentional behavior (p. 544). The theory can help us understand what leads consumers to take a particular action [14,28]
The theory of reasoned action (TRA) is the basis for the creation of TPB, which was developed about 35 years ago by [29]. Ref. [14] states that the TPB’s core is the intention.. This is determined by attitudes, subjective norms, and perceived behavioral control. Attitude toward a behavior refers to an individual’s favorable or unfavorable evaluation or appreciation of a specific behavior. Subjective norm is an individual’s perception of specific behavior that is affected by the opinions of significant others (e.g., friends, family, or media). Behavioral control refers to how easy or difficult a particular behavior appears to an individual. The latter is also a distinction between the two theories because it is not included in TRA and aims to investigate behaviors over which people do not have complete control [14]. The more positive the intention factors are, the more likely the individual is to be engaged in the behavior [30].
TPB has been used to understand consumers’ intentions to visit green hotels [31] and to purchase consumer grocery products [32], as well as consume sustainable food [27] and make different food choices [32,33]. TPB has been also employed as the basis for studies related to physical activity/exercise [34], understanding behavioral intentions in a work environment [35], and searching for jobs [36]. Finally, studies on the prediction of health-related behaviors [37], information systems [38,39], m-commerce [40], intention to use e-banking [25,41,42], have been interpreted through the TPB.

2.2. Theories on E-Shopping

According to several studies [41,43,44], the theory of reasoned action (TRA), the theory of planned behavior (TPB) and the technology acceptance model (TAM) are the most appropriate to predict e-shopping. All three models emphasize intention, which is influenced by attitudes and beliefs.
According to [29], TRA is a socio-psychological model that concerns behaviors, which the individual wishes to exhibit, i.e., they are under his/her control. The focus is on the intention that has two antecedents: attitudes toward behavior and subjective norms. These are determined by behavioral and normative beliefs, respectively. TRA has not been used alone to predict intention to shop online, but it has been combined with other theories [45].
The TRA is the basis of TAM, which was developed about 35 years ago and has been used mainly in the field of informatics [46,47]. The two dimensions of the model–perceived usefulness and perceived ease of use–influence attitudes towards adopting a novel technology, which in turn affects intention [46,48]. Hong, Thong, and Tam [49] (p. 1822) explicitly state that the intention to adopt technology is a good predictor of its actual use. In online shopping, perceived usefulness describes the degree to which an individual believes that using the internet will improve their performance in e-shopping [50]. TAM has been used as a basis in studies to understand the factors that influence consumers’ intention to shop online, in conjunction with other factors, such as the legal framework, trust, the cultural environment, and the perceived risks [51], and has been combined with consumer confidence and perceived control in Taiwan [52]. Another study comparing online consumers in Canada and Pakistan used TAM in conjunction with trust and perceived risks [53]. Other researchers such as [50,54], combined TAM and TPB to examine their variables in terms of intention and online purchasing behavior.
All three of the theories mentioned above have been studied in the context of online shopping to better understand the intention to shop. With the addition of perceived behavioral control, TPB can better predict and understand behavior [14]. Ref. [32] compared TRA and TPB, concluding that the latter is the best for predicting consumer behavior for online grocery shopping. There are also a few other studies that agree that TPB better predicts consumer behavior in e-shopping [23,55]. Thus, it appears that TAM and TPB predict online consumer behavior more successfully.
According to [56], the TPB also better and more accurately explains consumers’ online behavior, whereas TAM is better for predicting the adoption of novel technology on the internet. Finally, [41] agree that TPB better explains the intention to shop online, as it includes the subjective norm and the perceived behavioral control, which are not parts of TAM. Therefore, based on a review of the other theories, TPB was considered more appropriate for predicting the intention to make online purchases.

3. Research Hypotheses

3.1. Attitudes

The first antecedent of intention is the attitude toward the behavior [14]. Behavioral beliefs influence attitude toward the behavior. It is important to note that it refers to the attitude toward the behavior and not to objects, people, or other things [29]. It is also related to emotions [9] and many studies have indicated that it affects the intention [57,58].
In this study, attitude is defined as an overall assessment of the feasibility of a possible transaction with a specific online retailer [59] (p. 241). The more positive the attitude, the stronger the intention to behave in this way, and the stronger the exhibition of the specific behavior [60]. According to various studies, attitude positively affects the intention to make online purchases [15,18,19,61,62,63,64,65] Hence, the following hypotheses have been formulated:
H1: 
Attitude toward online shopping has a positive effect on the intention to shop online during the COVID-19 pandemic.
H2: 
Attitude toward online shopping has a positive effect on the intention to shop online in the future (after the pandemic).

3.2. Subjective Norm

Subjective norm is the second most important antecedent of intention that is determined by normative beliefs, i.e., beliefs about how much other important people (e.g., friends or family) believe they should or should not perform certain behaviors [14,28,29]. Ref. [14] (p. 188) defines the subjective norm as the perceived social pressure to perform/execute the behavior (or not). Family, friends, colleagues, and mass media are some of the groups that influence consumers’ behavior [8,10,66,67,68]. Little is known about how each of these groups will influence online purchasing intentions [69]. Our social circle’s attitudes toward online shopping, for example, can influence our online purchases by pressuring us to conform to their expectations; [59]. Ref. [70] demonstrated that people are initially influenced by others, especially when they have never used this specific method to purchase a product. When reference groups like friends and family approve of online shopping, the intention to shop online increases [71]. Ref. [64] also found a positive relationship between the subjective norm and the intention to make online purchases. Hence, the following hypotheses are proposed:
H3: 
Subjective norm has a positive effect on the intention to shop online during the COVID-19 pandemic.
H4: 
Subjective norm has a positive effect on the intention to shop online in the future (after the pandemic).

3.3. Perceived Behavioral Control

TPB was developed by incorporating perceived behavioral control into TRA [72], and applies to situations where individuals do not have complete control over their behavior [14,73]. It reflects an individual’s perception of how much control he or she has over certain behaviors; it is typically measured by evaluations of the ease/difficulty of performing the behavior” [74] (p. 394). It refers to the perceived and the actual control over the behavior [29]. Perceived behavioral control has both a direct and indirect effect, through intention, on behavior [18].
In e-shopping, perceived behavioral control is defined as the consumer’s perception concerning the control of a potential transaction [59] (p. 241). This variable reflects an individual’s level of control over online transactions [8,75]. Perceived behavioral control is a significant factor in determining consumers’ intentions to make online purchases [76,77,78,79], especially during a volatile time for e-commerce.
Several studies have shown that perceived behavioral control positively influences the intention to shop online [80,81] and behavior [18,23]. Hence, the following hypotheses can be extracted:
H5: 
Perceived behavioral control has a positive effect on the intention to shop online during the COVID-19 pandemic.
H6: 
Perceived behavioral control has a positive effect on the intention to shop online in the future (after the pandemic).

3.4. Fear of Shopping in a Physical Store

Feelings of anxiety arise when people experience some threat [82]. Regarding the initial outbreak of the COVID-19 pandemic, the absence of a vaccine created insecurity and fear among consumers [26,83,84] resulting in a change in their behavior [85]. Fear is a negative emotion accompanied by a high level of arousal [86] (p. 591). Ref. [4] claim that the traffic bans imposed during lockdowns led consumers to empty the store shelves of certain essential products such as toilet paper. Markets change in scope, time, and volume [5]. Ref. [87] points out that the fear of transmission of COVID-19 has changed e-commerce.
Consumers appear to be reluctant to buy groceries while COVID-19 infection rates are rising [88]. Ref. [89] concluded that consumers were not happy with their in-store purchases because of the possibility of virus transmission. According to [90], Londoners’ shopping habits have changed primarily because most of them now work from home and do not go shopping in city center supermarkets daily, as they did before COVID-19. They turn to local stores for their purchases, thus reducing the turnover of the city stores. Therefore, the fear of shopping in a physical store during a pandemic is commensurate with feelings about consumer protection.
As can be deduced, consumer behavior has changed significantly due to isolation and insecurity. The traditional way of making purchases (in a physical store) has given way to online shopping. Ref. [91] showed that during the COVID-19 pandemic, 27% of consumers shopped online for the first time. Generally, familiarity with the internet and the COVID-19 pandemic increased electronic purchases. In 2019, online shopping covered 60% of total shopping in the [92]. The Ref. [93] reported that Amazon, the largest online retail store in the world, increased its workforce from January to October 2020 by 427,300 employees, indicating that online shopping has grown during the pandemic [93].
In a similar vein, high consumer demand and staff shortages to deliver products purchased through online shopping were observed in Canada’s food supply chain [94]. Another country that had to deal with the shift of businesses and consumers to e-commerce was India, which experienced a significant increase in cases [95]. Walmart USA, the world’s largest multinational retailer, saw a 79% increase in e-commerce sales in the third quarter of 2020 [96]. TESCO, the UK’s largest supermarket chain, saw profits rise in the first half of the pandemic as more customers turned to the internet to make purchases. Londoners increased their online shopping by 62 percent, and 44 percent plan to continue shopping online even after stores reopen in the post-COVID period. Hence, the following hypotheses are formulated:
H7: 
Fear of shopping in a physical store due to the COVID-19 pandemic positively affects the intention to shop online during the pandemic.
H8: 
Fear of shopping in a physical store due to the COVID-19 pandemic does not affect the intention to shop online in the future (after the pandemic).

3.5. Purchase Intention and Its Relation to the Actual Purchase

The intention is central to TPB, thanks to which behavior can be better predicted and fulfilled [50,97]. Behavioral intent is defined as the cognitive representation of an individual’s willingness to exhibit given behaviors and is considered a direct precedent for actual behavior [98] (p. 41). The individual must decide, on his/her initiative, whether or not to perform the behavior; therefore, the exhibition of a particular behavior is determined by how intense the intention to exhibit it is [14]. It has to do with how much people desire ([57] and how much effort they make [14,29] to perform the behavior. Intention to make online purchases refers to individuals’ intention to shop online [99,100,101]. Hence, the following hypothesis is formulated:
H9: 
The intention to shop online during the COVID-19 pandemic has a positive effect on the actual online purchasing behavior.
The extensive research model based on the theory of planned behavior is presented in Figure 1.

4. Methodology

4.1. Data Collection

The survey methodology was used in this study to collect primary data; respondents were asked to answer questions in a structured questionnaire. The research was conducted by telephone as well as online and was designed to gather information in the Regional Unit of Thessaloniki, Greece about consumers’ intentions to make online purchases. As regards this study’s ethical considerations, it was explicitly and fully explained to the potential participants that their participation was voluntary and anonymous.

4.2. Population and Research Sample

Population: households in the Regional Unit of Thessaloniki.
Population unit: a household of the Regional Unit of Thessaloniki.
Sampling unit: a male or female household member in the Regional Unit of Thessaloniki, over 18 years of age.
Area: the geographical boundaries of the Regional Unit of Thessaloniki.
Time: December 2020–January 2021.
Sampling Framework: The most recent unified telephone directory in 2020 of the Regional Unit of Thessaloniki.
Sample size: 515 questionnaires were collected, of which 73 were conducted via telephone and 442 via the internet. During the telephone study, simple random sampling was followed. The snowball method was used for the digitally collected questionnaires.
Of the 515 participants, 35.1% (181 respondents) were men, and 64.9% (334) were women. The survey was open to everyone, except those who stated that they did not make an online purchase during the COVID-19 pandemic, and who could only fill out their demographic information. Bearing this in mind, Table 1 lists the demographic characteristics collected, divided into those who made online purchases during COVID-19 the pandemic (Yes) and those who did not (No). It is important to note that 73 responses came from the telephone survey while 442 came via the internet. The statistical software package SPSS 25.0 has been used for this study’s statistical analysis (demographics analysis, correlation analysis and regression analysis).

4.3. Research Questionnaire

Given that the survey was conducted both by telephone and online, the questionnaire did not contain many questions (which may have been an inhibiting factor). The questions were drawn from previous studies; a pilot study had preceded the final configuration of the questionnaire. Questions 1–5 generally addressed consumers’ online shopping behavior during the COVID-19 pandemic. Initially, respondents indicated whether or not they had made an online purchase during the pandemic (Question 1). If they answered negatively, only their demographic data was recorded in Section 3. If they answered positively, they could continue to the other parts of the questionnaire: they were required to state how many times they made online purchases during the pandemic (Question 2), the types of products they bought (Question 4), and how they paid for them (Question 5). The aforementioned questions were drawn from the research of [8]. Furthermore, Question 3 was also added to indicate how much money they spent on their online purchases, to have an approximate idea of how much money they were spending during the pandemic.
Respondents were then asked questions related to the research model, which was designed according to TPB, in order to understand how intention was affected in making online purchases: one question was related to their actual online behavior, one question to their intention to make online purchases during the pandemic [102], one on the subjective norm [23], one on the perceived behavioral control [103], three questions on their attitude towards online shopping [104], one on the intention to buy in the future [104] and one concerning the fear of buying in physical stores. These questions were measured on a 5-point Likert scale (1 = Strongly disagree/5 = Strongly agree). Questions 7–10 regarding demographic characteristics included gender, age, education level, and area of residence. On the questionnaire’s cover page it was explicitly mentioned that the gathered questionnaire data was anonymous and coded. Further it was stressed that the participant could resign from the research whenever he/she wanted. The communication time spent on completing the questionnaire did not exceed 3.5 min.

5. Results Analysis

5.1. Correlations

The research continued with the correlation analysis to describe the relationships between the variables, as shown in Table 2. Spearman’s test was used to check the correlations, as the variables were ordinal.

5.2. Regression Analysis

5.2.1. Intention to Make Online Purchases during the COVID-19 Pandemic

We conducted a regression analysis (Table 3) with attitude towards online shopping, perceived behavioral control, subjective norm, and fear of buying in a physical store as independent variables, gender, age, and education level as covariates, and purchase intention during the pandemic as the dependent variable. The R2 score is 0.383; hence, 38.3% of the variation in the intention to make online purchases during the pandemic is due to the independent variables. In the ANOVA analysis, the p-value < 0.001 of the F-statistic is less than 0.05, thus, the model is statistically significant.
The estimated B coefficients of the variables: age, attitude towards online shopping, perceived behavioral control, subjective norm, and fear of buying in a physical store, are significant at the p < 0.05 level. Thus, age (t = −3.71, p < 0.001), attitude towards online shopping (t = 5.03, p < 0.001), perceived behavioral control (t = 2.44, p < 0.05), subjective norm (t = −5.11, p < 0.001), and fear of buying in a physical store (t = 6.62, p < 0.001) all have a significant effect on the intention to make online purchases during COVID-19. In other words, as age decreases and those of the other independent variables increase, the intention to buy online during the pandemic increases. Hence, H1, H3, H5, and H7 are supported.
In contrast, gender (t = −0.1, p = 0.922), and education level (t = 0.07, p = 0.941) do not affect the intention to make online purchases during the COVID-19 pandemic.

5.2.2. Intention to Make Online Purchases after the Pandemic

We conducted a regression analysis (Table 4) with attitude towards online shopping, perceived behavioral control, subjective norm, and fear of buying in a physical store as independent variables, gender, age, and education level as covariates, and purchase intention after the pandemic as the dependent variable. The R2 score is 0.330; hence, 33.0% of the variation in the intention to make online purchases in the future is explained by the independent variables. In the ANOVA analysis, the p-value = 0.001 for the F-statistic is less than 0.05; thus, the model is statistically significant.
The estimated B coefficients of the variables: age, education level, attitude towards online shopping, and perceived behavioral control are significant at the p < 0.05 level. Thus, age (t = −2.62, p < 0.01), education level (t = 2.22, p < 0.05), attitude towards online shopping (t = 7.77, p < 0.001), and perceived behavioral control (t = 3.31 p = 0.001) all have a significant effect on the intention to make online purchases after COVID-19. In other words, as age decreases and those of the other independent variables increase, the intention to buy online after the pandemic increases. Hence, H2, and H6 are supported. In contrast, gender (t = −0.75, p = 0.455), subjective norm (t = 0.860, p = 0.390), and fear of buying in a physical store (t = 1.38, p = 0.169) do not affect the intention to make online purchases after the COVID-19 pandemic.

5.2.3. Actual Online Behavior during the COVID-19 Pandemic

We conducted a regression analysis (Table 5)with purchase intention during the pandemic as the independent variable, gender, age, and education level as covariates, and actual online behavior as the dependent variable. The R2 score is 0.141; hence, 14.1% of the variation in the actual online behavior during the COVID-19 pandemic is due to the independent variables. In the ANOVA analysis, the p-value = 0.001 of the F-statistic is less than 0.05; thus, the model is statistically significant.
The estimated B coefficients of the variables for gender and intention to make online purchases during the COVID-19 pandemic are significant, as p-value < 0.05. Thus, there is a significant effect in the actual online behavior with gender (t = 2.94, p < 0.01) and the intention to make online purchases during the COVID-19 pandemic (t = 6.78, p < 0.001). Thus, H9 is supported.
Table 6 presents this research’s overall hypotheses testing results.

6. Discussion

Initially, regarding consumer online behavior, the results of the research showed that almost four out of five residents in the Regional Unit of Thessaloniki made online purchases during the COVID-19 pandemic. Most e-consumers shopped online up to nine times and spent €101–500 on their purchases during the pandemic. The products they bought the most were clothing/footwear/sports and electronics, while the payment methods were mainly limited to the use of debit cards and cash on delivery.
Moreover, the results showed that attitudes towards online shopping, subjective norm, perceived behavioral control, and fear of shopping in a physical store due to COVID-19 positively influenced purchase intentions during the COVID-19 pandemic. The result showing that the attitudes of the residents of the Regional Unit of Thessaloniki towards online shopping positively affected the intention to make online purchases during the COVID-19 pandemic confirms the findings of several other researchers [15,18,19,23,64,65].
Age also has a significant negative effect on the intention to shop online, i.e., an increase in age leads to a decrease in the intention to shop online. This affected younger ages, as shown in research by [105], in contrast to research by [106] where intent did not seem to be affected.
Research on gender [107,108,109,110] and education level [63,108]; have proven that these factors influence the intention to shop online. We found a positive effect of the intention to shop online on the actual shopping behavior during the COVID-19 pandemic, which is consistent with prior studies such as [57,102].
The significant effect of fear of buying in a physical store on purchase intention is in line with the findings of several studies (e.g., [26,84,89,111]). This finding suggests that consumers prioritize their health over other factors in determining their behavior.
The fear of shopping in a physical store due to the pandemic, as well as the subjective norm, have no effect on the intention to shop online after the pandemic. The intention to shop online after the pandemic is influenced by the attitude toward online shopping as well as perceived behavioral control, indicating that different factors influence the intention to shop online during and after the pandemic.
The influence of family, friends and colleagues (subjective norm) is also very important during the COVID-19 pandemic; therefore, the positive widespread use of the online store plays an important role and leads to the intention to shop online. At the same time, businesses have to target younger age groups because as age increases, the intention to shop online decreases. The internet saves consumers time by not having to go to/browse in a physical store to look for a product and buy it, factors that will lead them in the future to shop online. Thus, from a business standpoint, an online store and business website should be simple to use and have a simple payment process so that customers can easily place orders.

7. Conclusions

The outbreak of the COVID-19 pandemic and fear of the spread of the virus brought about changes in people’s lifestyles, and consequently in their consumption habits. In conjunction with the lockdowns where physical stores remained closed, it was of interest to investigate whether consumers turned to e-shopping since the beginning of the pandemic, i.e., since March 2020. The study aimed to investigate the factors that influenced the intention to shop online during and after the COVID-19 pandemic with the use of an extended model of the theory of planned behavior.
The main contribution of this study to the literature is that it emphasizes the role of the fear of shopping in a physical store due to COVID-19 on consumers’ intention to shop online. This finding points out that a better understanding of how customers’ fears for health provide better insights into their behavior. In particular, based on this research’s findings, it can be stated that a greater consumers’ fear leads to a greater change in their shopping behavior.
Our findings confirm perceived behavioral control as a key predictor of intention and behavior in the theory of planned behavior model, demonstrating that it positively affects intention during the pandemic. In other words, during and after the COVID-19 pandemic, the control factor in the purchase process plays a critical role in the intention to shop online.
The results of the research can provide suggestions to companies to help them strengthen their customers’ intention to shop online, both during the COVID-19 pandemic and in the future. In particular, businesses should reach out to those who have a positive attitude towards online shopping, both during and after the COVID-19 pandemic, by creating an entertaining experience on their site, and offering products people want to buy through safe and easy transactions. Residents of the Regional Unit of Thessaloniki have a favorable attitude toward online shopping, implying that the internet is useful for their purchases. Increasing positive attitudes toward online shopping leads to an increase in online shopping intentions, and thus to an increase in the sales and revenues of online stores. Fear of the spread of the virus also affects online shopping. As a result, businesses could provide more secure product deliveries and safe methods for making card transactions, thereby preventing the virus from spreading.
In summary, residents of the Regional Unit of Thessaloniki made several purchases, spent a lot of money, and used debit cards during the COVID-19 pandemic. Their intention to shop online was also driven by their attitude towards online shopping, perceived behavioral control, their fear of the spread of the virus, and influence from family, friends and colleagues to avoid physical stores, as well as the age factor (the older they were, the lower their intention to shop online). Consumers in the Regional Unit of Thessaloniki are willing to use the internet for future shopping and this is due to their perceived behavioral control and attitudes towards online shopping.

7.1. Research Limitations

This research sought to link the COVID-19 pandemic to consumer behavior in online shopping. However, as with any research, there are always some limitations that need to be considered. Initially, the research only covered the COVID-19 pandemic period. The number of questions could have been increased, but this was difficult to achieve because the survey was originally intended to be conducted over the phone, so the number of questions could not exceed twenty.
According to the way the research was conducted, and more specifically with the telephone research, several restrictions arose. It is particularly important to mention that the survey was addressed only to households in the Regional Unit of Thessaloniki, by making calls only to landlines. However, the telephone directory also included business numbers that were not listed in a professional capacity; thus, some calls were rejected as business calls. Many cell phones were not included in the search and several ‘dead’ numbers. But the biggest limitation that arose was the many missed calls and negative attitude of the respondents toward answering calls about a survey. This is probably due to the negative attitude many people had concerning such calls because they are already inundated by pushy salespeople, the negative psychological state of mind of many respondents due to the COVID-19 pandemic and because the calls were made using a hidden number. Finally, another limitation is that most respondents who picked up the calls were older, so the sample could not be regarded as representative. For the above reasons, the research was also conducted online.

7.2. Future Research

Future research could compare the shopping intentions of traditional consumers (i.e., those who shop at physical stores) with those who did their shopping online during the COVID-19 pandemic. It would also be interesting to investigate the medium used by consumers (e.g., mobile phones, laptops, and computers) for making online purchases and how it affected their intention to make purchases. Finally, after the end of the pandemic and for at least two years, a comparison could be made between the two time periods with an analysis of the older age groups, to see if they continued to use the internet as a means of making purchases or if they did not use it at all, even in emergencies such as the COVID-19 pandemic.

Author Contributions

Conceptualization, A.T. and L.H.; methodology, L.H. and T.F.; formal analysis. A.T.; curation, A.T.; writing—original draft preparation, A.T.; writing—review and editing, L.H., T.F., A.D. and A.G.; visualization, A.G., T.F. and A.D.; supervision, L.H. and T.F.; funding acquisition, L.H. All authors have read and agreed to the published version of the manuscript.

Funding

The study was funded by the Chamber of commerce in Thessaloniki.

Institutional Review Board Statement

The study was conducted in accordance with the set of ethical principles the Declaration of Helsinki.

Informed Consent Statement

What is the purpose, scientific, and social benefits of the research? This re-search is conducted in order to examine consumers’ intention to shop online during the COVID-19 pandemic and its influence on purchasing behavior, in Greece.

Data Availability Statement

Data available on request due to restrictions e.g., privacy or ethical. The data presented in this study are available on request from the corresponding author. The data are not publicly available due to ethical reasons.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

References

  1. World Health Organization. Coronaviruses (COVID-19). WHO. 2020. Available online: https://www.who.int/emergencies/diseases/novel-coronavirus-2019/question-and-answers-hub/q-a-detail/coronavirus-disease-covid-19 (accessed on 1 July 2020).
  2. World Health Organization. What Is a Pandemic? 2020. Available online: https://www.who.int/csr/disease/swineflu/frequently_asked_questions/pandemic/en/ (accessed on 1 July 2020).
  3. World Health Organization. Coronavirus Disease (COVID-19) Pandemic. 2020. Available online: https://www.who.int/emergencies/diseases/novel-coronavirus-2019?gclid=EAIaIQobChMIvZma5cKe7QIVhO3tCh2mbAlzEAAYASAAEgKp5PD_BwE (accessed on 1 July 2020).
  4. Laato, S.; Islam, A.N.; Farooq, A.; Dhir, A. Unusual purchasing behavior during the early stages of the COVID-19 pandemic: The stimulus-organism-response approach. J. Retail. Consum. Serv. 2020, 57, 102224. [Google Scholar] [CrossRef]
  5. Loxton, M.; Truskett, R.; Scarf, B.; Sindone, L.; Baldry, G.; Zhao, Y. Consumer Behaviour during Crises: Preliminary Research on How Coronavirus Has Manifested Consumer Panic Buying, Herd Mentality, Changing Discretionary Spending and the Role of the Media in Influencing Behaviour. J. Risk Financial Manag. 2020, 13, 166. [Google Scholar] [CrossRef]
  6. Neger, M.; Uddin, B. Factors Affecting Consumers’ Internet Shopping Behavior During the COVID-19 Pandemic: Evidence from Bangladesh. Chin. Bus. Rev. 2020, 19, 91–104. [Google Scholar] [CrossRef]
  7. Jusoh, Z.M.; Ling, G.H. Factors Influencing Consumers’ Attitude Towards E-Commerce Purchases Through Online Shopping. Int. J. Humanit. Soc. Sci. 2012, 2, 223–230. [Google Scholar]
  8. Javadi, M.H.; Dolatabadi, H.R.; Nourbakhsh, M.; Poursaeedi, A.; Asadollahi, A.R. An Analysis of Factors Affecting on Online Shopping Behavior of Consumers. Int. J. Mark. Stud. 2012, 4, 81–98. [Google Scholar] [CrossRef] [Green Version]
  9. Al-Debei, M.M.; Akroush, M.N.; Ashouri, M.I. Consumer attitudes towards online shopping: The effects of trust, perceived benefits, and perceived web quality. Internet Res. 2015, 25, 707–733. [Google Scholar] [CrossRef]
  10. Nwaizugbo, I.C.; Ifeanyichukwu, C.D. Understanding Consumers’ Behaviour Towards Online Shopping: A Study of Online Shoppers in Anambra State. Int. J. Sales Retail. Mark. 2016, 5, 28–37. [Google Scholar]
  11. Bashir, R.; Mehboob, I.; Bhatti, W.K. Effects of Online Shopping Trends on Consumer-Buying Behaviour: An Empirical Study of Pakistan. J. Manag. Res. 2015, 2, 1–24. [Google Scholar]
  12. Beauchamp, M.B.; Ponder, N. Perceptions of retail convenience for in-store and online shoppers. Mark. Manag. J. 2010, 20, 49–65. [Google Scholar]
  13. Mittal, A. E-commerce: It’s Impact on consumer Behavior. Glob. J. Manag. Bus. Stud. 2013, 3, 131–138. [Google Scholar]
  14. Ajzen, I. The Theory of Planned Behavior. Organ. Behav. Hum. Decis. Process. 1991, 50, 179–211. [Google Scholar] [CrossRef]
  15. Noor, N.M.; Noranee, S.; Zakaria, M.F.; Unin, N.; Suaee, M.A.H.M. Online Shopping: The Influence of Attitude, Subjective Norm and Perceived Behavioral Control on Purchase Intention. In Proceedings of the 2020 The 6th International Conference on E-Business and Applications, Kuala Lumpur, Malaysia, 25–27 February 2020; pp. 33–36. [Google Scholar]
  16. Wang, M.S.; Chen, C.C.; Chang, S.C.; Yang, Y.H. Effects of online shopping attitudes, subjective norms and control beliefs on online shopping intentions: A test of the theory of planned behaviour. Int. J. Manag. 2007, 24, 296–302. [Google Scholar]
  17. Bhatti, A.; Ur Rahman, S. Perceived benefits and perceived risks effect on online shopping behavior with the mediating role of consumer purchase intention in Pakistan. Int. J. Manag. Stud. 2019, 26, 33–54. [Google Scholar] [CrossRef]
  18. George, J.F. The theory of planned behavior and Internet purchasing. Internet Res. 2004, 14, 198–212. [Google Scholar] [CrossRef] [Green Version]
  19. Ketabi, S.N.; Ranjbarian, B.; Ansari, A. Analysis of the Effective Factors on Online Purchase Intention through Theory of Planned Behavior. Int. J. Acad. Res. Bus. Soc. Sci. 2014, 4, 3. [Google Scholar] [CrossRef] [Green Version]
  20. Khalifa, M.; Limayem, M. Drivers of Internet shopping. Commun. ACM 2003, 46, 233–239. [Google Scholar] [CrossRef]
  21. Lee, S.H.; Ngoc, H.T.B. Investigating the on-line shopping intentions of Vietnamese students: An extension of the theory of planned behaviour. World Trans. Eng. Technol. Educ. 2010, 8, 471–476. [Google Scholar]
  22. Mat, N.K.N.; Sentosa, I. The integration of theory of planned behavior (TPB) and technology acceptance model in internet purchasing: A structural equation modeling (SEM) approach. In Proceedings of the Applied International Business Conference, Cairo, Egypt, 1–4 December 2008. [Google Scholar]
  23. Limayem, M.; Khalifa, M.; Frini, A. What makes consumers buy from Internet? A longitudinal study of online shopping. IEEE Trans. Syst. Man, Cybern.—Part A Syst. Humans 2000, 30, 421–432. [Google Scholar] [CrossRef]
  24. Sinha, J.; Kim, J. Factors affecting Indian consumers’ online buying behavior. Innov. Mark. 2012, 8, 46–57. [Google Scholar]
  25. Shih, Y.Y.; Fang, K. The use of a decomposed theory of planned behavior to study Internet banking in Taiwan. Internet Res. 2004, 14, 213–223. [Google Scholar] [CrossRef] [Green Version]
  26. Wang, S.; Cheah, J.H.; Lim, X.J.; Leong, Y.C.; Choo, W.C. Thanks COVID-19, I’ll reconsider my purchase: Can fear appeal reduce online shopping cart abandonment? J. Retail. Consum. Serv. 2022, 64, 102843. [Google Scholar] [CrossRef]
  27. Vermeir, I.; Verbeke, W. Sustainable food consumption among young adults in Belgium: Theory of planned behaviour and the role of confidence and values. Ecol. Econ. 2008, 64, 542–553. [Google Scholar] [CrossRef]
  28. Ajzen, I. Consumer attitudes and behavior: The theory of planned behavior applied to food consumption decisions. J. Agric. Econ. 2015, 70, 121–138. [Google Scholar] [CrossRef]
  29. Ajzen, I. From Intentions to Action: A Theory of Planned Behavior. In Action Control: From Cognitions to Behaviors; Kuhl, J., Beckman, J., Eds.; Springer: New York, NY, USA, 1985. [Google Scholar]
  30. Gentry, L.; Calantone, R. A comparison of three models to explain shop-bot use on the web. Psychol. Mark. 2002, 19, 945–956. [Google Scholar] [CrossRef]
  31. Chen, M.-F.; Tung, P.-J. Developing an extended Theory of Planned Behavior model to predict consumers’ intention to visit green hotels. Int. J. Hosp. Manag. 2014, 36, 221–230. [Google Scholar] [CrossRef]
  32. Hansen, T.; Jensen, J.M.; Solgaard, H.S. Predicting online grocery buying intention: A comparison of the theory of reasoned action and the theory of planned behavior. Int. J. Inf. Manag. 2004, 24, 539–550. [Google Scholar] [CrossRef]
  33. Alam, S.; Sayuti, N. Applying the Theory of Planned Behavior (TPB) in halal food purchasing. Int. J. Commer. Manag. 2011, 21, 8–20. [Google Scholar] [CrossRef]
  34. Feng, H.; Hwang, J.; Hou, L. Understanding Physical Activity and Exercise Behavior in China University Students: An Application of Theories of the Flow and Planned Behavior. J. Environ. Public Health 2022, 2022, 7469508. [Google Scholar] [CrossRef]
  35. Greaves, M.; Zibarras, L.D.; Stride, C. Using the theory of planned behavior to explore environmental behavioral intentions in the workplace. J. Environ. Psychol. 2013, 34, 109–120. [Google Scholar] [CrossRef]
  36. Song, Z.; Wanberg, C.; Niu, X.; Xie, Y. Action–state orientation and the theory of planned behavior: A study of job search in China. J. Vocat. Behav. 2006, 68, 490–503. [Google Scholar] [CrossRef]
  37. Bosnjak, M.; Ajzen, I.; Schmidt, P. The theory of planned behavior: Selected recent advances and applications. Eur. J. Psychol. 2020, 16, 352–356. [Google Scholar] [CrossRef]
  38. Kim, B. An empirical investigation of mobile data service continuance: Incorporating the theory of planned behavior into the expectation–confirmation model. Expert Syst. Appl. 2010, 37, 7033–7039. [Google Scholar] [CrossRef]
  39. Martono, S.; Mukhibad, H.; Anisykurlillah, I.; Nurkhin, A. Evaluation of acceptance of information systems in state university with theory of planned behavior and theory of acceptance model approaches. Manag. Sci. Lett. 2020, 10, 3225–3234. [Google Scholar] [CrossRef]
  40. Gangwal, N.; Bansal, V. Application of decomposed theory of planned behavior for m-commerce adoption in India. In Proceedings of the 18th International Conference on Enterprise Information Systems (ICEIS 2016), Rome, Italy, 25–28 April 2016; Volume 2, pp. 357–367. [Google Scholar]
  41. Yousafzai, S.Y.; Foxall, G.R.; Pallister, J.G. Explaining internet banking behavior: Theory of reasoned action, theory of planned behavior, or technology acceptance model? J. Appl. Soc. Psychol. 2010, 40, 1172–1202. [Google Scholar] [CrossRef]
  42. Al-Ajam, A.S.; Nor, K.M. Customers’ adoption of Internet banking service: An empirical examination of the theory of planned behavior in Yemen. Int. J. Bus. Commer. 2013, 2, 44–58. [Google Scholar]
  43. Wu, J.; Song, S. Older Adults’ Online Shopping Continuance Intentions: Applying the Technology Acceptance Model and the Theory of Planned Behavior. Int. J. Human–Computer Interact. 2021, 37, 938–948. [Google Scholar] [CrossRef]
  44. Delafrooz, N.; Paim, L.H.; Khatibi, A. Understanding consumers internet purchase intention in Malaysia. Afr. J. Bus. Manag. 2011, 5, 2837–2846. [Google Scholar]
  45. Amaro, S.; Duarte, P. An integrative model of consumers’ intentions to purchase travel online. Tour. Manag. 2015, 46, 64–79. [Google Scholar] [CrossRef]
  46. Lee, Y.; Kozar, K.A.; Larsen, K.R. The technology acceptance model: Past, present, and future. Commun. Assoc. Inf. Syst. 2003, 12, 752–780. [Google Scholar] [CrossRef]
  47. Lim, W.M.; Ting, D.H. E-shopping: An Analysis of the Technology Acceptance Model. Mod. Appl. Sci. 2012, 6, 49–62. [Google Scholar] [CrossRef]
  48. Ha, S.; Stoel, L. Consumer e-shopping acceptance: Antecedents in a technology acceptance model. J. Bus. Res. 2009, 62, 565–571. [Google Scholar] [CrossRef]
  49. Hong, S.; Thong, J.Y.; Tam, K.Y. Understanding continued information technology usage behavior: A comparison of three models in the context of mobile internet. Decis. Support Syst. 2006, 42, 1819–1834. [Google Scholar] [CrossRef]
  50. Sentosa, I.; Mat, N.K.N. Examining a theory of planned behavior (TPB) and technology acceptance model (TAM) in internetpurchasing using structural equation modeling. Res. World 2012, 3, 62–77. [Google Scholar]
  51. Akhlaq, A.; Ahmed, E. Intention Towards Online Shopping: A Pakistan Based Study. In Proceedings of the UK Academy for Information Systems Conference, London, UK, 20–24 June 2011. [Google Scholar]
  52. Zhu, D.-S.; O’Neal, G.S.; Lee, Z.-C.; Chen, Y.-H. Effect of Trust and Perceived Risk on Consumers’ Online Purchase Intention. In Proceedings of the 2009 International Conference on Computational Science and Engineering, Vancouver, BC, Canada, 29–31 August 2009; pp. 771–776. [Google Scholar] [CrossRef]
  53. Ashraf, A.R.; Thongpapanl, N.; Auh, S. The Application of the Technology Acceptance Model under Different Cultural Contexts: The Case of Online Shopping Adoption. J. Int. Mark. 2014, 22, 68–93. [Google Scholar] [CrossRef]
  54. Ha, N.T.; Nguyen, T.L.H.; Nguye, T.D. The effect of trust on consumers’ online purchase intention: An integration of TAM and TPB. Manag. Sci. Lett. 2019, 9, 1451–1460. [Google Scholar] [CrossRef]
  55. Rehman, S.U.; Bhatti, A.; Mohamed, R.; Ayoup, H. The moderating role of trust and commitment between consumer purchase intention and online shopping behavior in the context of Pakistan. J. Glob. Entrep. Res. 2019, 9, 43. [Google Scholar] [CrossRef] [Green Version]
  56. Turan, A.H. Internet Shopping Behavior of Turkish Customers: Comparison of Two Competing Models. J. Theor. Appl. Electron. Commer. Res. 2012, 7, 13–14. [Google Scholar] [CrossRef] [Green Version]
  57. Pavlou, P.A. Fygenson Understanding and Predicting Electronic Commerce Adoption: An Extension of the Theory of Planned Behavior. MIS Q. 2006, 30, 115–143. [Google Scholar] [CrossRef]
  58. Jafarkarimi, H.; Saadatdoost, R.; Sim, A.T.H.; Hee, J.M. Behavioral intention in social networking sites ethical dilemmas: An extended model based on Theory of Planned Behavior. Comput. Hum. Behav. 2016, 62, 545–561. [Google Scholar] [CrossRef]
  59. Pavlou, P.A.; Chai, L. What drives electronic commerce across cultures? A cross-cultural empirical investigation of the theory of planned behavior. J. Electron. Commer. Res. 2002, 3, 240–253. [Google Scholar]
  60. Aboelmaged, M.; Gebba, T.R. Mobile Banking Adoption: An Examination of Technology Acceptance Model and Theory of Planned Behavior. Int. J. Bus. Res. Dev. 2013, 2, 35–50. [Google Scholar] [CrossRef]
  61. Alhaimer, R. Fluctuating Attitudes and Behaviors of Customers toward Online Shopping in Times of Emergency: The Case of Kuwait during the COVID-19 Pandemic. J. Internet Commer. 2022, 21, 26–50. [Google Scholar] [CrossRef]
  62. Yelamanchili, R.; Wukadada, B.; Jain, A.; Pathak, P. A study on consumers attitude towards online grocery shopping in Covid19 pandemic. Acad. Mark. Stud. J. 2021, 25, 1–9. [Google Scholar]
  63. Karaaslan, K. Determinants of online shopping attitudes of households in Turkey. J. Model. Manag. 2021, 17, 119–133. [Google Scholar] [CrossRef]
  64. Hasbullah, N.A.; Osman, A.; Abdullah, S.; Salahuddin, S.N.; Ramlee, N.F.; Soha, H.M. The Relationship of Attitude, Subjective Norm and Website Usability on Consumer Intention to Purchase Online: An Evidence of Malaysian Youth. Procedia Econ. Finance 2016, 35, 493–502. [Google Scholar] [CrossRef] [Green Version]
  65. Yang, B.; Lester, D.; James, S. Attitudes Toward Buying Online as Predictors of Shopping Online for British and American Respondents. CyberPsychology Behav. 2006, 10, 198–203. [Google Scholar] [CrossRef]
  66. Redda, E.H. Attitudes towards Online Shopping: Application of the Theory of Planned Behaviour. Acta Univ. Danub. 2019, 15, 148–159. [Google Scholar]
  67. Ha, N.T.; Nguyen, T.L.H.; Pham, T.V.; Nguyen, T.H.T. Factors influencing online shopping intention: An empirical study in Vietnam. J. Asian Financ. Econ. Bus. 2021, 8, 1257–1266. [Google Scholar]
  68. Hidayat, S.; Wibowo, W.; Gunawan, Y.E.; Dewi, G.C.; Wijayaningtyas, M. Factors influencing purchase intention of healthcare products during the COVID-19 pandemic: An empirical study in Indonesia. J. Asian Financ. Econ. Bus. 2021, 8, 337–345. [Google Scholar]
  69. Tan, M.; Teo, T.S. Factors influencing the adoption of Internet banking. J. Assoc. Inf. Syst. 2000, 1, 1–42. [Google Scholar] [CrossRef] [Green Version]
  70. Bhatti, A.; Akram, H. The Moderating Role of Subjective Norms Between Online Shopping Behaviour And Its Determinants. Int. J. Soc. Sci. Econ. Rev. 2020, 2, 1–9. [Google Scholar] [CrossRef]
  71. Foucault, B.E.; Scheufele, D.A. Web vs campus store? Why students buy textbooks online. J. Consum. Mark. 2002, 19, 409–423. [Google Scholar] [CrossRef]
  72. Ajzen, I.; Madden, T.J. Prediction of goal-directed behavior: Attitudes, intentions, and perceived behavioral control. J. Exp. Soc. Psychol. 1986, 22, 453–474. [Google Scholar] [CrossRef]
  73. Ajzen, I. Perceived Behavioral Control, Self-Efficacy, Locus of Control, and the Theory of Planned Behavior. J. Appl. Soc. Psychol. 2002, 32, 665–683. [Google Scholar] [CrossRef]
  74. Sheeran, P.; Trafimow, D.; Armitage, C.J. Predicting behavior from perceived behavioural control: Tests of the accuracy assumption of the theory of planned behaviour. Br. J. Soc. Psychol. 2003, 42, 393–410. [Google Scholar] [CrossRef]
  75. Pavlou, P.A. What drives electronic commerce? A theory of planned behavior perspective. Acad. Manag. Proc. 2002, 2002, A1–A6. [Google Scholar] [CrossRef]
  76. Peña-García, N.; Gil-Saura, I.; Rodríguez-Orejuela, A.; Siqueira-Junior, J.R. Purchase intention and purchase behavior online: A cross-cultural approach. Heliyon 2020, 6, e04284. [Google Scholar] [CrossRef]
  77. Puiras, E.; Oliver, C.; Cummings, S.; Mazmanian, D. Shopping Intentions during COVID-19: What Can Canadian Businesses Learn to Encourage Future Consumer Support? J. Int. Consum. Mark. 2022, 1–15. [Google Scholar] [CrossRef]
  78. Odilia, G.; Sulistiobudi, R.A.; Fitriana, E. Factors Contributing to Online Shopping Behavior During COVID-19 Pandemic: The Power of Electronic Word of Mouth in Digital Generation. Acad. Entrep. J. 2022, 28, 1–10. [Google Scholar]
  79. Siripipatthanakul, S.; Siripipattanakul, S.; Limna, P.; Pholphong, L. Predicting Intention to Choose the Online Degree During the COVID-19 Pandemic: The Mediating Role of Perceived Effectiveness. Asia-Pac. Rev. Res. Educ. 2022, 1, 1–19. [Google Scholar] [CrossRef]
  80. Jabari, M.A.A.; Othman, S.N.; Mat, N.K.N. Actual Online Shopping Behavior among Jordanian Customers. Am. J. Econ. 2012, 2, 125–129. [Google Scholar] [CrossRef]
  81. Venkatesh, V.; Morris, M.G.; Davis, G.B.; Davis, F.D. User acceptance of information technology: Toward a unified view. MIS Q. 2003, 27, 425–478. [Google Scholar] [CrossRef] [Green Version]
  82. Cauberghe, V.; Van Wesenbeeck, I.; De Jans, S.; Hudders, L.; Ponnet, K. How Adolescents Use Social Media to Cope with Feelings of Loneliness and Anxiety During COVID-19 Lockdown. Cyberpsychol. Behav. Soc. Netw. 2021, 24, 250–257. [Google Scholar] [CrossRef]
  83. Li, S.; Wang, Y.; Xue, J.; Zhao, N.; Zhu, T. The Impact of COVID-19 Epidemic Declaration on Psychological Consequences: A Study on Active Weibo Users. Int. J. Environ. Res. Public Health 2020, 17, 2032. [Google Scholar] [CrossRef] [Green Version]
  84. Rossolov, A.; Aloshynskyi, Y.; Lobashov, O. How COVID-19 Has Influenced the Purchase Patterns of Young Adults in Developed and Developing Economies: Factor Analysis of Shopping Behavior Roots. Sustainability 2022, 14, 941. [Google Scholar] [CrossRef]
  85. Harper, C.A.; Satchell, L.P.; Fido, D.; Latzman, R.D. Functional Fear Predicts Public Health Compliance in the COVID-19 Pandemic. Int. J. Ment. Health Addict. 2020, 19, 1875–1888. [Google Scholar] [CrossRef]
  86. Witte, K.; Allen, M. A Meta-Analysis of Fear Appeals: Implications for Effective Public Health Campaigns. Health Educ. Behav. 2000, 27, 591–615. [Google Scholar] [CrossRef]
  87. Tran, L.T.T. Managing the effectiveness of e-commerce platforms in a pandemic. J. Retail. Consum. Serv. 2020, 58, 102287. [Google Scholar] [CrossRef]
  88. Grashuis, J.; Skevas, T.; Segovia, M.S. Grocery Shopping Preferences During the COVID-19 Pandemic. Sustainability 2020, 12, 5369. [Google Scholar] [CrossRef]
  89. Szymkowiak, A.; Gaczek, P.; Jeganathan, K.; Kulawik, P. The impact of emotions on shopping behavior during epidemic. What a business can do to protect customers. J. Consum. Behav. 2021, 20, 48–60. [Google Scholar] [CrossRef]
  90. Nielsen. Central London Grocery See Huge Sales Decline as City Workers Become Home Workers. 2020. Available online: https://www.nielsen.com/eu/en/insights/article/2020/central-london-grocery-see-huge-sales-decline-as-city-workers-become-home-workers/ (accessed on 1 December 2020).
  91. Nielsen. COVID-19 Has Flipped the Value Proposition of Omnichannel Shopping for Constrained Consumers. 2020. Available online: https://www.nielsen.com/gr/en/insights/article/2020/covid-19-has-flipped-the-value-proposition-of-omnichannel-shopping-for-constrained-consumers/ (accessed on 1 December 2020).
  92. Eurostat. Online Shopping Continues to Grow. 2020. Available online: https://ec.europa.eualmartt/web/products-eurostat-news/-/DDN-20200420-2 (accessed on 1 August 2020).
  93. The New York Times. Pushed by Pandemic, Amazon Goes on a Hiring Spree without Equal. 2020. Available online: https://www.nytimes.com/2020/11/27/technology/pushed-by-pandemic-amazon-goes-on-a-hiring-spree-without-equal.html (accessed on 1 December 2020).
  94. Hobbs, J.E. Food supply chains during the COVID-19 pandemic. Can. J. Agric. Econ. /Rev. Almart Nee Agroecon. 2020, 68, 171–176. [Google Scholar] [CrossRef] [Green Version]
  95. Goel, S. Online Shopping: Evolving Consumer Behavior Amid the Pandemic. 2020. Available online: https://www.entrepreneur.com/article/356122 (accessed on 1 December 2020).
  96. Repko, M.; Coombs, B. Walmart Earnings Top Expectations as Customers’ New Shopping Habits Send E-Commerce Sales Soaring 79%. 2020. Available online: https://www.cnbc.com/2020/11/17almartt-wmt-q3-2021-earnings-.html?&qsearchterm=Walmart (accessed on 1 December 2020).
  97. Trafimow, D. The theory of reasoned action: A case study of falsification in psychology. Theory Psychol. 2009, 19, 501–518. [Google Scholar] [CrossRef]
  98. Putit, L. Examining consumer’ e-transaction behaviour adoption: An exploratory study. J. Hum. Cap. Dev. 2008, 1, 41–46. [Google Scholar]
  99. Warganegara, D.L.; Hendijani, R.B. Factors That Drive Actual Purchasing of Groceries through E-Commerce Platforms during COVID-19 in Indonesia. Sustainability 2022, 14, 3235. [Google Scholar] [CrossRef]
  100. Pavlou, P.A. Consumer Acceptance of Electronic Commerce: Integrating Trust and Risk with the Technology Acceptance Model. Int. J. Electron. Commer. 2003, 7, 69–103. [Google Scholar] [CrossRef]
  101. Salisbury, W.D.; Pearson, R.A.; Pearson, A.W.; Miller, D.W. Perceived security and worldwide web purchase intention. Ind. Manag. Data Syst. 2001, 101, 165–177. [Google Scholar] [CrossRef]
  102. Lim, W.M. Antecedents and consequences of e-shopping: An integrated model. Internet Res. 2015, 25, 184–217. [Google Scholar] [CrossRef]
  103. Hsua, M.H.; Yenb, C.H.; Chiuc, C.M.; Changa, C.M. A longitudinal investigation of continued online shopping behavior: An extension of the theory of planned behavior. Int. J. Hum. -Comput. Stud. 2006, 64, 889–904. [Google Scholar] [CrossRef]
  104. Abdul-Muhmin, A.G. Repeat Purchase Intentions in Online Shopping: The Role of Satisfaction, Attitude, and Online Retailers’ Performance. J. Int. Consum. Mark. 2010, 23, 5–20. [Google Scholar] [CrossRef]
  105. Joines, J.L.; Scherer, C.W.; Scheufele, D.A. Exploring motivations for consumer Web use and their implications for e-commerce. J. Consum. Mark. 2003, 20, 90–108. [Google Scholar] [CrossRef]
  106. Kwon, W.; Noh, M. The influence of prior experience and age on mature consumers’ perceptions and intentions of internet apparel shopping. J. Fash. Mark. Manag. Int. J. 2010, 14, 335–349. [Google Scholar] [CrossRef]
  107. Black, G.S. Consumer demographics and geographics: Determinants of retail success for online auctions. J. Targeting, Meas. Anal. Mark. 2007, 15, 93–102. [Google Scholar] [CrossRef] [Green Version]
  108. El-Ansary, O.; Roushdy, S.A. Factors Affecting Egyptian Consumer Intentions for Accepting Online Shopping. J. Am. Acad. Bus. Camb. 2013, 19, 191–201. [Google Scholar]
  109. Lim, P.L.; Yazdanifard, R. Does gender play a role in online consumer behavior? Glob. J. Manag. Bus. Res. 2014, 14, 36. [Google Scholar]
  110. Thamizhvanan, A.; Xavier, M. Determinants of customers’ online purchase intention: An empirical study in India. J. Indian Bus. Res. 2013, 5, 17–32. [Google Scholar] [CrossRef]
  111. Xu, X.; Siqin, T.; Chung, S.H.; Choi, T.M. Seeking survivals under COVID-19: The WhatsApp platform’s shopping service operations. Decis. Sci. 2021, 1–19. [Google Scholar] [CrossRef]
Sustainability 15 02545 g001 550
Figure 1. Research Model.
Figure 1. Research Model.
Sustainability 15 02545 g001
Table
Table 1. Sample characteristics.
Table 1. Sample characteristics.
Online ShoppingNo Online Shopping
VariablesCategoryFrequencyPercentage%FrequencyPercentage%
GenderMale13332.84844
Female27367.26156
Age18–22297.198.3
23–281263176.4
29–345413.31412.8
35–40611587.3
41–465212.81412.8
47–524511.11816.5
53–58266.41211
59–6471.71211
65+61.51513.8
EducationPrimary school/Middle school/
High school		7017.26256.9
Vocational institution5313.11715.6
College4110.9
Degree18746.12422
Master8621.243.7
Doctorate61.510.9
Municipalities of Western Thessaloniki: Ampelokipoi–Menemeni, Oraiokastro, Kordelio-Evosmos, Chalkidonos, Volvi, Neapoli-Sykies, Lagkada, and Delta. Municipalities of Eastern Thessaloniki: Thermi, Pylaia-Chortiatis, Thermaikos, and Kalamaria. Center of Thessaloniki: Thessaloniki.
Table
Table 2. Correlation analysis.
Table 2. Correlation analysis.
Attitude towards Online ShoppingSubjective NormPerceived Behavioral ControlFear of Buying in a Physical StoreOnline Purchase Intention during the PandemicOnline Purchase Intention after the Pandemic
Attitude towards Online Shopping1.000−−−−−
Subjective Norm0.398 **1.000−−−−
Perceived Behavioral Control0.383 **0.204 **1.000−−−
Fear of Buying in a Physical Store0.239 **0.222 **0.196 **1.000−−
Online Purchase Intention During the Pandemic0.478 **0.407 **0.296 **0.396 **1.000−
Online Purchase Intention After the Pandemic0.508 **0.252 **0.335 **0.194 **0.392 **1.000
Note: ** p value < 0.05.
Table
Table 3. Regression Analysis–Coefficients (a).
Table 3. Regression Analysis–Coefficients (a).
ModelUnstandardized CoefficientsStandardized Coefficientstp-Value
BStd. ErrorBeta
1 (Constant)3.5870.381 9.4070.000
Gender−0.2450.150−0.081−1.6280.104
Age−0.0150.006−0.123−2.4530.015
Education Level0.0610.0500.0611.2150.225
2 (Constant)0.2400.408 0.5890.556
Gender−0.0120.123−0.004−0.0990.922
Age−0.0190.005−0.159−3.7040.000
Education Level0.0030.0410.0030.0730.941
Perceived Behavioral Control0.1210.0500.1082.4370.015
Attitude towards Online Shopping0.3880.0770.2505.0330.000
Subjective Norm0.3000.0590.2285.1050.000
Fear of Buying in a Physical Store0.2980.0450.2796.6190.000
Dependent Variable: Intention to make online purchases during the COVID-19 pandemic.
Table
Table 4. Regression Analysis–Coefficients (a).
Table 4. Regression Analysis–Coefficients (a).
ModelUnstandardized CoefficientsStandardized Coefficientstp-Value
BStd. ErrorBeta
1 (Constant)4.4790.311 14.3900.000
Gender−0.3000.123−0.119−2.4500.015
Age−0.0170.005−0.170−3.4350.001
Education Level0.0980.0410.1182.3820.018
2 (Constant)1.5880.353 4.4960.000
Gender−0.0790.106−0.031−0.7480.455
Age−0.0120.005−0.117−2.6220.009
Education Level0.0780.0350.0952.2240.027
Perceived Behavioral Control0.1420.0430.1533.3100.001
Attitude towards Online Shopping0.5190.0670.4037.7720.000
Subjective Norm0.0440.0510.0400.8600.390
Fear of Buying in a Physical Store0.0540.0390.0611.3790.169
Dependent Variable: Intention to make online purchases after the COVID-19 pandemic.
Table
Table 5. Regression Analysis–Coefficients (a).
Table 5. Regression Analysis–Coefficients (a).
Coefficients a
ModelUnstandardized CoefficientsStandardizedCoefficientstp-Value
BStd. ErrorBeta
1(Constant)3.0220.313 9.6430.000
Age−0.0900.035−0.130−2.5880.010
Gender0.3200.1400.1122.2840.023
Education0.0850.0470.0911.8150.070
2(Constant)2.0010.333 6.0070.000
Age−0.0610.033−0.088−1.8310.068
Gender0.3920.1330.1372.9350.004
Education0.0680.0450.0731.5230.129
Online Purchase Intention During the Pandemic0.2990.0440.3196.7780.001
a. Dependent Variable: Actual online behavior.
Table
Table 6. Hypotheses testing results.
Table 6. Hypotheses testing results.
HypothesisPathRemarks
H1Attitude toward online shopping → Intention to shop online during the pandemicSupported
H2Attitude toward online shopping → Intention to shop online after the pandemicSupported
H3Subjective norm → Intention to shop online during the pandemicSupported
H4Subjective norm → Intention to shop online in the futureDropped
H5Perceived behavioral control → Intention to shop online during the pandemicSupported
H6Perceived behavioral control → Intention to shop online after the pandemicSupported
H7Fear of shopping in a physical store due to the COVID-19 pandemic → Intention to shop online during the pandemicSupported
H8Fear of shopping in a physical store due to the COVID-19 pandemic → Intention to shop online after the pandemicDropped
H9Intention to shop online during the pandemic→ Intention to shop online in the futureSupported
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Theodorou, A.; Hatzithomas, L.; Fotiadis, T.; Diamantidis, A.; Gasteratos, A. The Impact of the COVID-19 Pandemic on Online Consumer Behavior: Applying the Theory of Planned Behavior. Sustainability 2023, 15, 2545. https://doi.org/10.3390/su15032545

AMA Style

Theodorou A, Hatzithomas L, Fotiadis T, Diamantidis A, Gasteratos A. The Impact of the COVID-19 Pandemic on Online Consumer Behavior: Applying the Theory of Planned Behavior. Sustainability. 2023; 15(3):2545. https://doi.org/10.3390/su15032545

Chicago/Turabian Style

Theodorou, Anastasia, Leonidas Hatzithomas, Thomas Fotiadis, Anastasios Diamantidis, and Antonios Gasteratos. 2023. "The Impact of the COVID-19 Pandemic on Online Consumer Behavior: Applying the Theory of Planned Behavior" Sustainability 15, no. 3: 2545. https://doi.org/10.3390/su15032545

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop