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Abstract

The primary objective of this study was to explore how situational factors and perceived severity impact online purchase decisions, emphasizing the potential moderating role of perceived risk in influencing the relationship between situational factors and these decisions. Additionally, perceived risk was a moderator to assess the association between perceived severity and online purchase decisions. Furthermore, the study investigated whether online purchase decisions could lead to a sustained intention for online adoption post-pandemic. To accomplish these goals, an online survey questionnaire was employed, and Confirmatory Factor Analysis (CFA) was conducted to validate the data. The Structural Equation Model (SEM) using SmartPLS was then utilized to test the hypotheses. The study found that both situational factors and perceived severity during the COVID-19 pandemic significantly and directly impacted the decision to make online purchases. The moderation analysis results indicated that the influence of situational factors on online purchase decisions is lessened when perceived risk is higher and strengthened when perceived risk is lower. These findings contribute to the expanding literature, particularly in developing countries, by providing evidence for formulating evidence-based policies during the post-pandemic recovery stage. They emphasize the critical need for the country in question to strengthen its information and communication technology (ICT) infrastructure and implement robust security measures to ensure safe and secure online transactions.

Plain language summary

This study aimed to understand how different factors, like the situation and the perceived seriousness of the situation, influence people’s decisions to buy things online. The researchers were particularly interested in how the perceived risk of online shopping plays a role in this relationship. They also wanted to see if the decisions made during the COVID-19 pandemic would continue after the pandemic. They used an online survey to collect data and then analyzed it using statistical methods. The results showed that both the situation and how serious people perceived the COVID-19 pandemic directly affected their choice to shop online. The study also found that the impact of the situation on online shopping was weaker when people felt there was a higher risk, but stronger when they felt the risk was lower. In other words, the perceived risk influenced how much the situation affected online shopping decisions. The findings suggest that these insights can be useful for creating policies in developing countries during the post-pandemic recovery phase. Additionally, the study emphasized the importance of improving information technology infrastructure and implementing strong security measures for secure online transactions in the concerned country.

Keywords

situational factor perceived risk perceived severity online purchase decision COVID-19

Introduction

The rise of advanced online technology has significantly transformed grocery shopping in China, with online supermarkets improving their services to meet growing consumer demands (Xiang et al., 2014). Many consumers have shifted to online shopping due to the convenience of purchasing groceries from home at their preferred time (Jiang et al., 2013). However, the COVID-19 pandemic drastically altered consumer behavior, forcing many to adopt online food purchasing due to movement restrictions and temporary closures of dine-in services. Consequently, the food and beverage industry, one of the most impacted economic sectors, transitioned to online meal delivery services to sustain operations. Research by Alaimo et al. (2020) revealed that numerous food businesses, including supermarkets and restaurants, introduced technological solutions to facilitate online orders, home delivery, and in-store collection in response to the crisis.

Building upon this shift, a critical gap in the literature is the limited exploration of how situational factors, perceived severity, and perceived risk interact to shape online purchase behavior during a pandemic crisis. Situational factors, defined as external influences unrelated to individual decision-making traits (Hand et al., 2009; Ross & Robertson, 2003), play a crucial role in shaping and sustaining online purchasing behavior. Perceived severity refers to an individual’s subjective assessment of an illness’s seriousness, influenced by present realities and future expectations (Green et al., 2020). Additionally, perceived risk serves as a moderator that may weaken, strengthen, or alter the relationship between situational factors, perceived severity, and online purchase decisions. Despite the growing popularity of online food purchasing, some consumers remain hesitant due to perceived risk (Hansen, 2008; Yen, 2010). Prior studies have established that consumer purchase behavior is influenced by perceived risk (Pires et al., 2004; Rouibah et al., 2016; Yeung & Morris, 2006). However, these studies primarily focused on normal conditions without considering external crisis-driven factors. In contrast, consumers often overlook perceived risks when they recognize benefits such as convenience, time-saving, enjoyment, hedonic motivation, and post-use satisfaction (Forsythe et al., 2006; Rouibah et al., 2016). Pires et al. (2004) found no direct association between frequent online buyers and perceived risk. Nevertheless, the majority of prior research (Aghekyan-Simonian et al., 2012; Forsythe et al., 2006; Garbarino & Strahilevitz, 2004; Yen, 2010) has examined perceived risk in stable environments, where consumer interactions were unaffected by external crises (Dabholkar & Bagozzi, 2002).

Connecting this to historical contexts, epidemic outbreaks such as SARS in 2003 (Forster & Tang, 2005), the coronavirus outbreak (Alsulaiman, 2018), and the MERS outbreak (Jung et al., 2016) have historically triggered social anxiety and altered consumer behavior. Social anxiety, defined as discomfort arising from the awareness of others’ perceptions (Fenigstein et al., 1975), has led consumers to shift from traditional in-store shopping to online purchases voluntarily (L. Lu & Reardon, 2018). This shift suggests that consumer adoption of online shopping is driven by situational factors rather than long-term behavioral changes (Hand et al., 2009). However, limited research explores how consumers’ temporary adoption of online shopping during crises can translate into sustained behavioral change post-pandemic.

Lastly, this study investigates the impact of situational factors and perceived severity on online food purchasing behavior, with perceived risk serving as a moderator to determine whether it weakens, strengthens, or alters these relationships. Additionally, the study examines whether the online purchase decisions made during the pandemic lead to long-term adoption intentions.

By addressing these gaps, this research contributes to the literature by offering new insights into how situational factors, perceived severity, and perceived risk influence online purchasing behavior, especially in times of crisis. Furthermore, it expands the understanding of perceived risk as a moderating variable in these relationships. Unlike prior studies that focused on consumer behavior in normal, controllable environments, this study emphasizes the unique context of a pandemic-induced shift in consumer behavior. Additionally, it extends the work of Hand et al. (2009) by examining not only the initial adoption but also the retention of online purchasing behavior post-pandemic. Recognizing the importance of situational factors in driving online purchase adoption can help marketers develop effective strategies to retain customers who transitioned to online shopping during the crisis.

Theoretical Background and Hypotheses Development

E-Commerce in China

Jack Ma established e-commerce in 1999, including the business-to-business online platform Alibaba.com. In 2003, eBay entered China as the first foreign e-commerce company (Lucy, 2019). Chinese e-commerce experienced significant growth after the SARS pandemic in 2003, leading to the development of Alipay, a secure online payment system (Forster & Tang, 2005). More recent studies have further examined the evolution of digital payment systems and their role in shaping consumer trust in online transactions (Joshi, 2025; Krishna et al., 2025). China remains a dominant force in global e-commerce, particularly in the Asia-Pacific region. Reports indicate that China accounted for nearly half (47.0%) of the global retail e-commerce market, valued at $899.09 billion in 2016, surpassing North America’s $423.34 billion (Emarketer, 2016). The number of internet users in China increased significantly to approximately 804.5 million in 2018, with around 610.1 million individuals engaging in online purchasing that year (Agne, 2019). More recent reports highlight that by 2023, China’s e-commerce market had exceeded $2.5 trillion, reinforcing its position as the world’s largest online marketplace (Statista, 2023). This substantial and growing online consumer base has driven the remarkable expansion of Chinese e-commerce, making it a critical area of study for understanding global digital retail trends (Fang & Fang, 2022). The pervasiveness of e-commerce affects how, where, and when consumers shop, and it indirectly influences daily lifestyles (J. Cao & Mokhtarian, 2005). More recent studies emphasize the role of mobile commerce, artificial intelligence, and personalized marketing in further shaping consumer shopping experience (Bansal & Gupta, 2024). To better evaluate and anticipate the profound impacts of e-commerce, it is essential to refine our understanding of consumers’ online shopping behavior, particularly in the context of increased digital adoption and technological advancements (Raji et al., 2024).

On the other hand, online food delivery (OFD) refers to the process through which food purchased online is prepared and delivered to consumers (Huang & Siao, 2023). Over the past few years, customer demand for OFD services has increased significantly and is expected to continue rising. By 2024, the global OFD market is projected to surpass $182.3 billion in total revenue, up from $107.4 billion in 2019 (Report, 2020). More recent projections suggest that the market may exceed $200 billion by 2025, driven by rapid urbanization and evolving consumer preferences (Deloitte, 2023). The COVID-19 pandemic further accelerated the adoption of OFD due to its contactless ordering and delivery mechanisms, which have continued to attract new customers (Maida, 2020). Moreover, studies indicate that post-pandemic consumer behavior may sustain higher reliance on OFD services due to convenience, health considerations, and improvements in delivery logistics (Basile et al., 2022).

Situational Factor

Situational factors, which are defined as “any aspects that are not related to the decision maker as an individual (e.g., personality and physical traits) or to the decision alternatives,” are crucial in forming and sustaining online purchasing motives (Bazi et al., 2022). All those characteristics specific to a time and place of observation which do not follow from a knowledge of personal (intra-individual) or stimulus (choice alternative) traits are referred to as situational factors (Belk, 1975; S. Cao et al., 2023). Situational factors play a significant role in influencing consumer behavior (Belk, 1975). Life events such as health problems have been identified as major drivers of online purchasing behavior (Hand et al., 2009). Although situational factors have not been widely explored in the literature, their importance became evident during the COVID-19 pandemic. The COVID-19 outbreak had a substantial situational impact on consumer behavior concerning online food purchases (Tien et al., 2021). Therefore, in this study, situational factors particularly the impact of COVID-19 are considered as an independent variable influencing the purchasing behavior gap in the context of online food purchases. COVID-19, a highly infectious respiratory illness caused by the coronavirus, has had a profound impact on global markets, especially the food sector (Richards & Rickard, 2020). The restaurant industry, in particular, has been significantly affected by the pandemic (Fryer, 2021). Government-imposed lockdowns and consumer health concerns led to the closure of food establishments, resulting in a sharp decline in demand for restaurant meals and services (Richards & Rickard, 2020). As a result, consumers rapidly transitioned from in-person to online purchases, reshaping their lifestyles and shopping behaviors (Watanabe & Omori, 2020). The surge in online meal ordering through restaurant websites and food delivery platforms further illustrates this behavioral shift (Hall et al., 2021). This drastic shift in consumer behavior underscores the role of situational factor caused by COVID-19 pandemic in influencing online purchase decisions. As such, this study examines this perspective by testing the following hypothesis:

H1: Situational factor influences online purchase decision

The Relationship Between Perceived Severity and Online Purchase Decision

Perceived severity refers to an individual’s subjective assessment of the seriousness of a threat, particularly in relation to its potential consequences on personal well-being. It reflects the extent to which people believe an event such as a health crisis poses a significant risk to their safety and quality of life (Green et al., 2020). In the context of health-related risks, perceived severity is a critical determinant of behavior change, as individuals are more likely to take precautionary measures when they anticipate serious negative outcomes (Reiss, 1991).

The COVID-19 pandemic significantly heightened perceived severity by creating widespread fear of infection and serious health complications (Wiebers & Feigin, 2020). As a result, consumers actively sought ways to minimize physical contact, leading to an accelerated shift toward online food purchasing as a safer alternative. Studies indicate that food enterprises, including supermarkets, restaurants, and fast-food chains, adapted quickly to these concerns by integrating advanced technological solutions for online ordering, home delivery, and contactless pick-up services (Alaimo et al., 2020). This widespread industry response was driven by consumers’ high perceived severity of COVID-19 and their urgent need to mitigate the associated risks. Therefore, the next hypothesis may be postulated as follows:

H2: Perceived severity influences online purchase decision

Online Purchase Decision and Future Online Adoption

During COVID-19 pandemic, consumers decided to use online for fresh produce, such as meat, fish, fruit, and vegetables(Lee et al., 2020). Consumer shifted to order food online during COVID-19, the situation that left them with no option. Users of digital technology have been forced to adopt and employ particular technological apps for online purchasing as a result of this catastrophe (Abbas et al., 2021). Consumer intention determines whether consumers are prepared to adopt and make an attempt to do so. Consumers with strong intentions are more likely to execute an action (Ajzen, 1991; Lai & Cheng, 2016). The unanswered question is whether or not consumers will continue to use the online form of payment after COVID-19. The study by Spurgeon and Niehm (2020) revealed that the likelihood of the desire to use the online service repeatedly may be influenced by the buying experience or satisfaction brought on by the purchasing intention. However, the situational and temporal contexts in which events take place influence how a person thinks about the reasons behind and consequences of one’s behavior (Bandura & Walters, 1977). The adoption of online grocery shopping by consumers can be caused by circumstances (in this case, COVID-19) rather than by a cognitive elaboration and decision as majority of prior literature suggest (Hand et al., 2009). It appears that the adoption of online purchasing is conditional and subject to termination by consumers when the initial circumstances alter (Hand et al., 2009). However, the findings of the Gomes and Lopes (2022) study show that favorable online purchasing experiences during the pandemic can have a positive impact on future online shopping intentions. Following is a hypothesis based on the gathered information:

H3: Online purchase decision influences online adoption

Moderator Variable: Perceived Risk in Online Purchase

In the current investigation, perceived risk is hypothesized to act as a moderator, influencing the strength of the relationship of interest. Consumers’ assessments, decisions, and behaviors are thought to be heavily influenced by how they perceive risk (Arslan et al., 2013; Siegrist & Árvai, 2020). Consumer researchers define perceived risk in terms of uncertainty and consequences; perceived risk rises when there is more uncertainty or when there is a larger likelihood of negative consequences (Rouibah et al., 2016). Consumer research has identified and quantified a number of risk factors, including those related to money, performance, relationships, society, psychology, and physical health. Rundmo (1999) indicated that people take perceived risk into account while making purchases online. This assertion is consistent with the findings of the study done by Masoud (2013), Bashir and Madhavaiah (2015), which found that perceived risk affects consumers’ decisions to shop online. The COVID-19 influenced consumers to buy their food online (Laguna et al., 2020), despite the fact that perceived risk has been negatively influencing online purchasing decisions(Masoud, 2013). This could affect the strength of the relationship between perceived severity and online purchase decision as well as the between situational factor and online purchase decisions. The following hypotheses are produced as a result of the previously mentioned information (Figure 1):

H4: Perceived risk moderates the relationship between situational factor and online purchase decision

H5: Perceived risk moderates the relationship between perceived severity and online purchase decision

Figure 1.

Conceptual framework.

Data and Methodology

This study employed a cross-sectional survey design, chosen for its effectiveness in quantitatively describing trends or opinions within a population, drawing insights from a representative sample of the local community. In essence, a research design delineates the specific inquiry approach in a study, providing guidance and methods for conducting a scientific investigation (Creswell & Creswell, 2017).

A survey designed for Google’s tool (Google Forms) that was distributed to respondents via social media over a 2-month period between January and March 2022 served as the basis for our empirical investigation. It was the time China was restricting people’s movements to stop the COVID-19 virus from spreading. We focused on students in the 10 universities of Beijing city. Consumers were invited to take part in the survey utilizing snowball sampling and invitations posted on popular websites and WhatsApp accounts. Using the snowball sampling technique, participants in a study enlist more people to take part in the study. It is used when it is challenging to locate suitable individuals, as it was in our case during the COVID-19 pandemic. In this study, the sampling strategy involved utilizing primary data sources to identify potential additions to the study. A questionnaire, administered through Google Forms, was distributed to potential respondents via a provided link, and they were requested to complete and electronically submit it. The questionnaire was initially developed in English, then translated into Chinese, and back-translated into English. The process of translating a survey instrument from one language to another and then back-translating it into the original language is a widely recognized method in cross-cultural research to ensure semantic and conceptual equivalence (Kowal, 2024). Back-translation helps identify inconsistencies, misinterpretations, or culturally inappropriate terms that may arise due to linguistic differences (Tuğsal, 2020). According to Kowal (2024), this method enhances the validity and reliability of translated instruments by reducing bias and ensuring that the intended meaning of the original questionnaire is preserved.

To ensure the validity of measurements, we developed items for the components in our hypothesized model based on existing research. While incorporating established measuring questions from the literature, we made necessary modifications to suit our specific needs while maintaining the original intent. The questionnaire comprised various sets of questions, encompassing demographic inquiries and variables (independent, moderator, and dependent), measured on a Likert scale of 1 to 5 (where 1 denotes complete disagreement and 5 signifies complete agreement).

For sample determination, the study employed the N: q rule, a well-known method for selecting a sample with sufficient statistical power. The “N” in the rule represents the number of cases (respondents), while “q” denotes the number of model parameters requiring statistical estimates. With 28 parameters in the hypothesized model requiring estimation, the N: q rule determined the ideal maximum sample size to be 15 times 28, resulting in 420 cases. The collected and filtered sample amounted to 798, meeting the maximum sample size criteria according to this approach (Jackson, 2003).

Results

The research utilized a descriptive analysis method to scrutinize various sample characteristics, encompassing gender, age, education level, occupation, monthly income, and the frequency of online food purchases. An overview of the respondents’ profiles is presented in Table 1. Upon scrutinizing the data, it was noted that, among the 450 responses, 56% identified as male, while the remaining respondents were female. The majority of participants (53%) fell within the age range of 18 to 23 years. Notably, 417 individuals (52%) reported engaging in daily online food purchases, while 295 respondents (37%) indicated making such purchases a few times a week. Surprisingly, only a marginal percentage (1%) of respondents, according to the survey findings, reported refraining from online food purchases. The summary is indicated in Table 1.

Table 1.

Demographic Characteristics of the Respondents.

Profile	Frequency	Percentage
Gender
Male	450	56
Female	348	44
Total	798	100
Age
Below 18	92	12
From 18 to less than 23	421	53
From 23 to less than 35	186	23
From 35 to less than 45	66	8
From 45 to less than 60	25	3
From 60 and above	8	1
Total	798	100
Education
Current doing bachelor or got bachelor’s degree	521	65
Current doing postgraduate or got postgraduate	277	35
Total	798	100
Occupation
Student	699	88
Lecturer at the university	90	11
Other	9	1
Total	798	100
Monthly income (RMB)
Less than 1,000	428	54
From 1,000 to less than 5,000	281	35
From 5,000 to less than 10,000	12	2
From 10,000 to less than 15,000	55	7
From 15,000 and above	22	3
Total	798	100
Online food purchase frequency
Almost every day	417	52
Few times a week	295	37
Once a week	59	7
Not even once a month	9	1
Once or twice a month	18	2
Total	798	100

Evaluation of the Measurement Model

To ensure the reliability of the measurements applied to the four constructs, we conducted a confirmatory factor analysis using SmartPLS (Hair et al., 2019). As highlighted in Table 2, the composite reliability scores not only exceeded the 0.7 thresholds but also surpassed the recommended level of 0.5 for average variance extracted (AVE) values, as advised by Fornell and Larcker (1981). Additionally, Cronbach’s alpha, serving as an extra reliability coefficient, surpassed the suggested threshold of .7 (Hair et al., 2019). This validation of the measures’ reliability across the five constructs underscores their trustworthiness and the likelihood of consistent results in other research endeavors. In summary, these findings point to a satisfactory correspondence between the measurement model and the data (Bagozzi & Yi, 1988).

Table 2.

CFA to Check the Convergent Validity.

Construct	Cronbach’s alpha	Composite reliability	Average variance extracted
Perceived severity (PS)	.88	0.74	0.75
Perceived risk (PR)	.87	0.75	0.65
Adoption intention (AI)	.90	0.71	0.70
Online purchase decision (OPD)	.85	0.79	0.75
Situational influences (SIF)	.90	0.74	0.60

Model Fit Indices Analysis

The study assessed the model fit using several widely recognized indices, including Chi-square (χ²), Root Mean Square Error of Approximation (RMSEA), Comparative Fit Index (CFI), Normed Fit Index (NFI), and Adjusted Goodness of Fit Index (AGFI). These indices provide a comprehensive evaluation of how well the proposed model aligns with the observed data, offering insights into its overall validity and reliability.

The analysis indicates that the model demonstrates an acceptable fit based on these indices. The RMSEA value of 0.04 suggests a strong fit, indicating that the model closely approximates the data. The CFI, NFI, and AGFI values of 0.90, 0.90, and 0.85, respectively, fall within acceptable ranges, though they are slightly below the ideal thresholds for a more robust fit. These results suggest that while the model is adequately specified, there may be room for improvement to achieve a higher level of fit and predictive accuracy.

Structural Model Analysis

The study incorporated hypotheses encompassing both direct and moderation relationships. Table 3 provides a summary of the study’s effects utilizing the SmartPLS4 Structural Equation Model (SmartPLS SEM). It elucidates the relationship between path coefficients, standard deviation (STDEV), probability values (P-value), and the outcomes of each research construct. The study disclosed that situational factors exhibit a positive and statistically significant direct effect on online purchase decisions (Beta value = .359, p = .00), thereby supporting Hypothesis 1. Hypothesis 2, which posited a positive and significant direct relationship between Perceived Severity during COVID-19 and online purchase decisions, was also supported (Beta value = .401, p = .009). Additionally, the study explored the impact of online purchase decisions on online adoption. However, Hypothesis 3 reported an insignificant direct relationship between online purchase decisions and online adoption, indicating that this hypothesis was not supported.

Table 3.

Summary of the Direct Relationship Hypotheses Results.

Hypotheses	Hypotheses number	Beta values	Standard deviation (STDEV)	T statistics (\|O/STDEV\|)	p-Values	Remarks
Situational factor (SI) → Online purchase decision (OPD)	H1	.359	0.068	5.291	.000	Supported
Perceived severity (PS) → Online purchase decision (OPD)	H2	.401	0.062	2.101	.009	Supported
Online purchase decision (OPD) → Online Adoption	H3	.009	0.001	0.002	.115	Not supported

Moderation Results

The result of shows that situational factor has high effect (β = .301, p < .01) on online purchase decision when perceived risk is low. This result indicated that hypothesis H4 is supported. However, perceived risk could not moderate the relationship between perceived severity and online purchase decision (β = 0.021, p > 0.01). Thus, the consumer could purchase online food no matter level of risk available (Table 4 and Figure 2).

Table 4.

Summary of the Moderation Hypotheses Results.

Hypothesis	Hypotheses number	Estimate	Significant	Results
Perceived risk moderates the relationship between situational factor and online purchase decision	H4	0.301	.01	Supported
Perceived risk moderates the relationship between perceived severity and online purchase decision	H5	0.021	.10	Not Supported

Figure 2.

Moderation results.

Discussion

This study aimed to enhance the comprehension of current consumer behavior in online food purchases, encompassing both present actions and future intentions. The research concentrated on assessing the influence of situational factors and perceived severity on online food purchasing behavior during the COVID-19 period, exploring how customers react to situations perceived as threatening in their immediate surroundings. The findings indicated a significant direct impact of both situational factors and perceived severity during COVID-19 on online purchase decisions. This outcome is partly consistent with Satish et al.’s (2021) findings, which identified a shift in consumer purchasing patterns during lockdowns or crises. The pandemic has evidently triggered a substantial alteration in consumer behavior, reflecting a situational effect. The surge in the popularity of online food purchases during the COVID-19 situation was further supported by M. Lu et al.’s (2022) research. Additionally, Gumasing et al. (2022) demonstrated that perceived severity significantly influenced online food purchasing intentions and the usage of online grocery apps during the pandemic. The mentioned studies align with our results by suggesting that external crises, such as pandemics, trigger shifts in consumption habits. However, it is noteworthy that our results contrast with those of Hong et al. (2021), who found no significant impact of perceived severity on online food purchases. It is essential to recognize that Hong et al.’s (2021) study was conducted in the United States of America, which may explain the observed disparity in results. The discrepancy with Hong et al. (2021), who found no significant relationship between perceived severity and online food purchases, suggests that contextual factors (e.g., geographic location, economic stability, cultural differences) play a role in shaping consumer responses to crises.

Additionally, the present study revealed an insignificant direct relationship, suggesting that situational adoption does not necessarily translate into habitual or sustained behavior. This challenges the Habit Formation Theory (Keller et al., 2021), which assumes that repeated behavior under specific conditions leads to long-term adoption. Unlike the study by Fatima et al. (2022) which found the significance on the relationship between online purchase decision and adoption of online method, our findings suggest that post-pandemic consumer behavior is more complex and may be influenced by additional factors which are that were not studied in the present study.

And finally, the moderator used in the current study was perceived risk to find out if it may weaken, strengthening or alter the relationship between situational factor, perceived severity and online purchase decision. On our case the perceived risk is the level of risk consumers perceive when using online methods. The result shows that situational factor has low effect on online purchase decision when perceived risk is higher effect. It is also shows that situational factor has high effect on online purchase decision when perceived risk is low. This results partially aligned with those by Nguyen et al. (2021), Habib and Hamadneh (2021), and Zhang et al. (2014) that found out that fraud risk of the seller negatively affect the intention to buy food via online shopping channels. Thus, previous studies supported that perceived risk weaken the relationship between situation factor, perceived severity of COVID 19 on online food purchase decision. Despite the fact that consumers were involuntary locked in their houses but they were also aware about the possible scam distraction during online food purchase. For example in the perceived risk item by Mortimer et al. (2016)“Compared with other ways of making purchases, I think that using this supermarket’s website is more risky.” Therefore, the results obtained show that although consumer were required to utilize online method, but they were aware of the risk involved in online transactions.

Implication

The findings confirm that external crises, such as pandemics, significantly influence consumer purchasing behavior, driving a shift toward online shopping. Businesses should enhance their digital infrastructure, logistics, and delivery systems to accommodate sudden demand surges during crises. Policymakers should also develop contingency plans and support mechanisms for online food retailers to ensure the availability of essential goods in emergency situations. The study highlights a contrast with Hong et al. (2021), who found no significant impact of perceived severity on online food purchases in the U.S. This suggests that geographic, economic, and cultural factors influence consumer responses to crises, implying that businesses should adopt localized marketing and service strategies rather than a one-size-fits-all approach. Policymakers should recognize that regional differences in digital literacy, trust in e-commerce, and economic stability play a critical role in shaping online purchasing behavior and should tailor intervention strategies accordingly.

The study found that situational adoption of online food purchasing does not necessarily lead to long-term behavioral change, challenging the Habit Formation Theory. This suggests that businesses must implement strong customer retention strategies, such as loyalty programs, personalized experiences, and post-pandemic incentives, to encourage continued online purchasing. Additionally, policymakers should invest in consumer education programs to increase digital literacy and trust in e-commerce beyond crisis periods.

The study confirmed that perceived risk weakens the effect of situational factors on online purchasing decisions, meaning that even during crises, consumers remain cautious about fraud and security issues. Businesses must strengthen cybersecurity measures, enhance transparency in transactions, and improve consumer confidence in online platforms through secure payment systems, clear return policies, and fraud prevention mechanisms. Policymakers should implement stronger consumer protection laws and regulations to address online fraud risks and boost trust in digital commerce.

Limitation

A primary constraint in this study is survey bias, stemming from the random selection of survey respondents. The questionnaire was administered to volunteers who participated without any form of compensation, relying solely on their personal interest in the subject matter. Consequently, the sample exhibited a notable concentration of educated individuals, deviating from a representation of the broader population in the studied country. This renders the generalization of findings inaccurate in this particular case. Lastly, two of hypotheses could not result into statistical significance that give the room to future researcher to use different population that contain different characteristics for more useful observations.

Footnotes

ORCID iDs

Shouchao He

Vijay Shankar Chand

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: publish from Zhejiang Provincial Department of Education [Y202457332], project titled ‘Research on the Mechanism and Strategy of Industrial Chain Community Driving the High-quality Development of Zhejiang Cross-border E-commerce Industry'.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

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Factors Influencing Online Food-Related Grocery Purchase: Future Online Purchase Adoption