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How the pandemic has changed the online sales landscape.

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Online sales is a relatively young industry and, as such, it’s going through constant changes. The sophisticated ways to generate user data and use it for marketing efforts today were almost unimaginable a decade ago.

Artificial intelligence, intelligent automation, inbound sales — all of these and many other methods have been propelling the sales industry forward and help close deals faster and more efficiently than ever before.

On the other hand, the global pandemic has stifled the progress of many industries and hindered economic growth, even for the most significant financial players in the world. However, the online space has often been mentioned as a way to bypass issues like social distancing, the lack of office work and isolation.

So, how has the crisis impacted the online sales industry?

Increased Online Ad Spending And More Online Purchases

WWE Raw Results, Winners And Grades After Great Wyatt Sicks Follow-Up

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The quarantines and curfews all over the world contributed to increased online commercial activity. This was to be expected: With reduced in-store traffic, retailers and salespeople focused on getting people to make online purchases to keep generating revenue. People seem to have responded, as online sales worldwide have been rising steadily since the Covid-19 outbreak.

Just take a look at this dataset from the U.K.’s Office for National Statistics. When it comes to the percentage of online sales to total retail sales, the uptick from February to May 2020 is not only noticeable but staggering.

In less than a year, from February 2020 to January 2021, the percentage of online sales to total retail sales nearly doubled, going from 19.1% to 36.3%. The trend is starting to slow down as things are opening up, but the overall popularity and awareness of online shopping will likely remain a factor.

This seems to be supported by customer sentiment: In this coronavirus wave 4 study, GWI found that 46% of people surveyed in 2020 believe they will be doing more online shopping even after the pandemic ends. The initial shift might have been motivated by circumstances but its effects are here to stay.

The Need For Automation Tools In B2B Sales

The B2B field has always, in some way, been viewed as a personalized space that’s all about connections and face-to-face meetings. We always think of B2B salespeople who schedule appointments and hold presentations in rooms filled with people.

The global pandemic is slowly but surely transforming this industry as well. Not only is remote B2B selling already a big thing in the industry but both buyers and sellers seem to prefer it. According to one McKinsey study , focusing on Brazil, up to three-quarters of B2B decision-makers surveyed prefer either a contactless (digital self-serve) or a remote selling experience.

These interactions make it quicker and more convenient for users to place their orders, get information and contact support. A digital approach to selling also allows salespeople to close more deals faster. The only potential issue is keeping that personal touch throughout the entire process.

Modern sales automation tools continue to address these issues and evolve to support the efforts of online sales. With sales automation tools, you can now:

• Get customer data, qualify and warm up leads.

• Send connection requests and automated emails to lots of leads at the same time.

• Leverage personalization in your messages to make it seem like you’re sending your messages manually.

• Send personalized videos where you address your prospects directly.

• Set up triggers to automatically send emails when a prospect completes a particular action (creates an account, makes their first purchase, etc.).

Shifting Team Collaboration

Another significant effect of the Covid-19 pandemic is the shift to remote team collaboration.

Sales and marketing teams have been forced to coordinate their efforts remotely, with tools like Zoom experiencing massive growth during the pandemic. This new trend is something that companies still need to deal with. Staying productive and meeting KPIs when working from home is challenging, especially in sales, where meeting targets is critical.

It may seem like remote work is almost old news at this point but, compared to 2010, the number of people who work remotely has grown. One survey of more than 900 people found the number of people working remotely has grown by a staggering 400%.

How we navigate these unique challenges remains to be seen, but isolation, time off and the work/home distinction erosion seem to be critical issues.

The Final Word

The global pandemic has had a profound impact on the global economy — in many ways, we’ve still to see its full effect in the years to come.

The online sales industry hasn’t been immune to these effects. Increased spending with increased customer preference of online channels has contributed to more revenue and a significant shift in consumer habits.

Even the B2B industry, which has typically relied on human contact and face-to-face meetings, has relied on sales automation software and machine learning.

As things slowly open up and people get back to their old habits, it’s reasonable to be skeptical about the continued growth of online sales. However, it’s also sensible to believe that many users have permanently changed their habits and rediscovered trust in doing business online.

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Research Article

A theoretical model of factors influencing online consumer purchasing behavior through electronic word of mouth data mining and analysis

Roles Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

Affiliation School of Economics and Management, Zhengzhou University of Light Industry, High-tech District, Zhengzhou City, Henan Province, China

Roles Conceptualization, Funding acquisition, Project administration, Supervision

* E-mail: [email protected]

Affiliation School of Politics and Public Administration, Soochow University, Gusu District, Suzhou City, Jiangsu Province, China

Roles Data curation, Funding acquisition, Project administration

Roles Formal analysis, Funding acquisition, Project administration

• Qiwei Wang, 
• Xiaoya Zhu, 
• Manman Wang, 
• Fuli Zhou, 
• Shuang Cheng

• Published: May 18, 2023
• https://doi.org/10.1371/journal.pone.0286034
• Peer Review
• Reader Comments

The coronavirus disease 2019 pandemic has impacted and changed consumer behavior because of a prolonged quarantine and lockdown. This study proposed a theoretical framework to explore and define the influencing factors of online consumer purchasing behavior (OCPB) based on electronic word-of-mouth (e-WOM) data mining and analysis. Data pertaining to e-WOM were crawled from smartphone product reviews from the two most popular online shopping platforms in China, Jingdong.com and Taobao.com . Data processing aimed to filter noise and translate unstructured data from complex text reviews into structured data. The machine learning based K-means clustering method was utilized to cluster the influencing factors of OCPB. Comparing the clustering results and Kotler’s five products level, the influencing factors of OCPB were clustered around four categories: perceived emergency context, product, innovation, and function attributes. This study contributes to OCPB research by data mining and analysis that can adequately identify the influencing factors based on e-WOM. The definition and explanation of these categories may have important implications for both OCPB and e-commerce.

Citation: Wang Q, Zhu X, Wang M, Zhou F, Cheng S (2023) A theoretical model of factors influencing online consumer purchasing behavior through electronic word of mouth data mining and analysis. PLoS ONE 18(5): e0286034. https://doi.org/10.1371/journal.pone.0286034

Editor: Ahmad Samed Al-Adwan, Al-Ahliyya Amman University, JORDAN

Received: April 19, 2023; Accepted: May 5, 2023; Published: May 18, 2023

Copyright: © 2023 Wang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript and its Supporting Information files.

Funding: The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by the Henan Province Philosophy and Social Science Planning Project (grant number. 2020CZH012), the Henan Key Research and Development and Promotion Special (Soft Science Research) (grant number. 222400410126), the Jiangsu Province Social Science Foundation Youth Project (grant number. 21GLC012) and the Doctor Fund of Zhengzhou University of Light Industry (grant number. 2020BSJJ022, 2019BSJJ017). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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

1. Introduction

A prolonged quarantine and lockdown imposed by the coronavirus disease 2019 (COVID-19) pandemic has changed the human lifestyle worldwide. The COVID-19 pandemic has negatively impacted various sectors such as manufacturing, import and export trade, tourism, catering, transportation, entertainment, especially retail and hence the global economy. Consumer behavior has gradually shifted toward contactless services and e-commerce activities owing to the COVID-19 [ 1 ].

Consumers are relying on e-commerce more than ever to protect their health. Recent advances in information technology, digital transformation, and the Internet helped consumers to encounter the COVID-19 to meet the needs of the daily lives, which led to an increase in the importance of e-commerce and changes in consumers’ online purchasing patterns [ 2 ]. When consumers shop online, their behavior is considered non-traditional, and is illustrated by a new trend and current environment. To analyze the influencing factors of online consumer purchasing behavior (OCPB), it is necessary to consider several factors, such as the price and quality of a product, consumers’ preferences, website design, function, security, search, and electronic word-of-mouth (e-WOM) [ 3 ]. As the current website design and payment security have become a user-friendly and guaranteed system compared with a decade ago, some factors are no longer considered as essential. By contrast, greater diversity and complexity have become the main characteristics of the influencing factors. Furthermore, under the traditional sales model, consumers’ purchase decisions were simple, while online consumers have more options in terms of shopping channels and decision choices. Meanwhile, in recent years, consumers’ preferences have gradually shifted from standardized products to customized and personalized. In line with these changes, information technology and data science, such as big data analytics, data mining from e-WOM, and machine learning (ML), adaptively analyze data regarding online consumers’ needs to obtain more accurate data.

Since the concept of big data was proposed in 2008, it has been applied and developed lasting 14 years, emerging as a valuable tool for global e-commerce recently. However, most enterprises have failed to seize the benefits generated from big data. In the context of big data, a huge number of comments were posted regarding e-malls (Amazon, Taobao, etc.) and online social media (blogs, Bulletin Board System, etc.). For instance, Amazon was the first e-commerce company to establish an e-WOM system in 1995, which provided the company with valuable suggestions from online consumers. E-WOM has greater credibility and persuasiveness, compared with traditional word of mouth (WOM), which is limited by various subjective factors. Moreover, e-WOM has the advantage of containing not only structured data (e.g., ratings) but also unstructured data (e.g., the specific content of consumer reviews). However, e-WOM provides product-related information that cannot be directly transformed to a research objective. Thus, an innovative method of big data analytics needs to be utilized to explore the influencing factors of OCPB, which shows the advantage of interdisciplinary applications.

The research problems are to explore the factors influencing OCPB through e-WOM data mining and analysis and explain the most important influencing factors for online consumers that are likely to exist in the future within the context of the COVID-19. The study fulfills the literature gaps on exploring influencing factors of OCPB from the perspective of e-WOM. The study makes a significant contribution to the consumer study because its findings can adequately identify the influencing factors of OCPB. It also provides the theoretical and managerial implications of its findings including how e-commerce platforms can use such data to adapt their platforms and marketing strategies to diverse situations.

The remainder of this is organized as follows. Section 1 presents the introduction. Section 2 discusses the literature review and hypotheses. Section 3 provides the methodology, including data mining and analysis. Section 4 describes the results, including K-means results, performance metrics, hypotheses results, and a theoretical model. Sections 5 and 6 provide discussion and conclusion, respectively.

2. Literature review and hypotheses

2.1 influencing factors of ocpb.

Online shopping has an increasing sales volume each year, which has become huge challenges for offline retailers. Venkatesh et al. [ 4 ] found that culture, demographics, economics, technology, and personal psychology were the main antecedents of online shopping, and the main drivers of online shopping were congruence, impulse buying behavior, value consciousness, risk, local shopping, shopping enjoyment, and browsing enjoyment by a comprehensive model of consumers online purchasing behavior. Within the context of COVID-19, OCPB is positively impacted by attitude toward online shopping [ 5 ]. Melović et al. [ 6 ] focused on millennials’ online shopping behavior and noted that the demographic characteristics, the affirmative characteristics, risks and barriers of online shopping were the key influencing factors. Based on the stimulus-organism-response (SOR) theory model, consumers’ actual impulsive shopping behavior is impacted by arousal and pleasure [ 7 ]. Furthermore, the influencing factors of consumers’ purchase behavior toward green brands are green perceived quality, green perceived value, green perceived risk, information costs saved, and purchase intentions by perceived risk theory [ 8 ]. The positive and negative effects of corporate social responsibility practices on consumers’ pro-social behavior are moderated by consumer-brand social distance, although it also impacts consumer behavior beyond the consumer-brand dyadic relationship [ 9 ]. Green perceived value, functional value, conditional value, social value, and emotional value may impact green energy consumers’ purchase behavior [ 10 ]. Recipients’ behavior and WOM predict distant consumers’ behavior [ 11 ]. Moreover, consumer behavior is significantly impacted by financial rewards, perceived intrusiveness, attitudes toward e-mail advertising, and intentions toward the senders [ 12 ]. Store brand consumer purchase behavior is positively impacted by store image perceptions, store brand price-image, value consciousness, and store brand attitude [ 13 ]. A meta-analysis summarizes the influencing factors of consumer behavior, household size, store brands, store loyalty, innovativeness, familiarity with store brands, brand loyalty to national brands, price consciousness, value consciousness, perceived quality of store brands, perceived value for money of store brands, and search versus experience positively impact consumer behavior, whereas price–quality consciousness, quality consciousness, price of store brands, and the consequences of making a mistake in a purchase negatively impact consumer behavior [ 14 ].

Based on protection motivation theory and theory of planned behavior (TPB), consumers are more likely to use online shopping channels than offline channels during the COVID-19 pandemic [ 15 ]. The TPB is also adapted to explain the influencing factors of consumers’ behavior in different areas. For instance, the attitude, perceived behavioral control, policy information campaigns, and past-purchase experiences significantly impact consumers’ purchase intention, whereas subjective and moral norms show no significant relationship based on the extended TPB [ 16 ]. Although green purchase behavior has different antecedents, only personal norms and value for money have fully significant relationships with green purchase behavior, environmental concern, materialism, creativity, and green practices. Functional value positively influences purchase satisfaction, physical unavailability, materialism, creativity, and green practices, and negatively influences the frequency of green product purchase by extending the TPB [ 17 ]. Meanwhile, Nimri et al. [ 18 ] utilized the TPB in green hotels and showed that knowledge and attitudes, as well as subjective injunctive norms, positively impacted consumers’ purchase intention. Yi [ 19 ] observed that attitude, social norm, and perceived behavioral control positively impacted consumers’ purchase intention based on the TPB. The factors of supportive behaviors for environmental organizations, subjective norms, consumer attitude toward sustainable purchasing, perceived marketplace influence, consumers’ knowledge regarding sustainability-related issues, and environmental concern are the influencing factors of consumers sustainable purchase behavior [ 20 ]. Consumers’ green purchase behavior is impacted by the intention through support of the TPB [ 21 ].

2.2 Influencing factors of emergency context attribute

Consumers exhibited panic purchase behavior during the COVID-19, which might have been caused by psychological factors such as uncertainty, perceptions of severity, perceptions of scarcity, and anxiety [ 22 ]. In the reacting phase, consumers responded to the perceived unexpected threat of the COVID-19 and intended to regain control of lost freedoms; in the coping phase, they addressed this issue by adopting new behaviors and exerting control in other areas, and in the adapting phase, they became less reactive and accommodated their consumption habits to the new normal [ 23 ]. The positive and negative e-WOMs may have significant influence on online consumers’ psychology. Specifically, e-WOM that conveys positive emotions (pride, surprise) tends to have a greater impact on male readers’ perception of the reviewer’s cognitive effort than female readers, whereas e-WOM that conveys negative emotions (anger, fear) has a greater impact on cognitive effort of female readers than male readers [ 24 ]. When online consumers believe their behavioral effect is feasible and positive, while their behavioral decision is related to the behavioral outcome [ 25 ]. Traditionally, there are five stages of consumer behavior that include demand identification, information search, evaluation of selection, purchase, and post-purchase evaluation. In addition, online purchase behavior involved in the various stages can be categorized into: attitude formation, intention, adoption, and continuation. Most of the important factors that influence online purchasing behavior are attitude, motivation, trust, risk, demographics, website, etc. “Internet Adoption” is widely used as a basic framework for studying “online buying adoption”. Psychological and economic structures associated with the IT adoption model can be used as the online consumer’s behavior models for innovative marketers. The adoption of online purchasing behavior is explained by different classic models of attitude behavior [ 26 ]. Consumer behaviors represented by customer trust and customer satisfaction, influence repurchase and positive WOM intentions [ 27 ]. Return policy leniency, cash on delivery, and social commerce constructs were significant facilitators of customer trust [ 28 ]. Meanwhile, seller uncertainty was negatively influenced by return policy leniency, information quality, number of positive comments, seller reputation, and seller popularity [ 29 ]. Social commerce components were a necessity in complementing the quality dimensions of e-service in the environment of e-commerce [ 30 ]. Perceived security, perceived privacy and perceived information quality were all significant facilitators of online customer trust and satisfaction [ 31 ].

E-service quality, consumer social responsibility, green trust and green perceived value have a significant positive impact on green purchase intention, whereas greenwashing has a significant negative impact on green purchase intention. In addition, consumer social responsibility, green WOM, green trust and green perceived value positively moderated the relationship between e-service quality and green purchase intention, while greenwashing and green participation negatively moderated the relationships [ 32 ]. Large-scale online promotions provide mobile users with a new shopping environment in which contextual variables simultaneously influence consumer behavior. There is ample evidence suggesting that mobile phone users are more impulsive during large-scale online promotion campaigns, which are the important contextual drivers that lead to the occurrence of mobile users’ impulse buying behavior in the “Double 11” promotion. The results show that promotion, impulse buying tendency, social environment, aesthetics, and interactivity of mobile platforms, and available time are the key influencing factors of impulse buying by mobile users [ 33 ]. Environmental responsibility, spirituality, and perceived consumer effectiveness are the key psychological influencing factors of consumers’ sustainable purchase decisions, whereas commercial campaigns encourage young consumers to make sustainable purchases [ 34 ]. The main psychological factors affecting consumers’ green housing purchase intention include the attitude, perceived moral obligation, perceived environmental concern, perceived value, perceived self-identity, and financial risk. Subjective norms, perceived behavioral control, performance risk, and psychological risk are not included. Meanwhile, the purchase intention is an important predictor of consumers’ willingness to buy [ 35 ]. The perceived control of flow and focus will positively affect the utilitarian value of consumers, while focus and cognitive enjoyment will positively impact the hedonic value. Moreover, utilitarian value has a greater impact on satisfaction than hedonic value. Finally, hedonic value positively impacts unplanned purchasing behavior [ 36 ]. Utilitarian and hedonic features achieve high purchase and WOM intentions through social media platforms and also depend on gender and consumption history [ 37 ].

Therefore, we present the following hypothesis:

• Hypothesis 1 (H1): Perceived emergency context attribute is the influencing factor of OCPB.

2.3 Influencing factors of perceived product attribute

Product quality and preferential prices are the major factors considered by online consumers, especially within the context of the COVID-19. Specifically, online shopping offers lower price, more choices for better quality products, and comparison between them [ 1 ]. Under the circumstance of online reviews, an original equipment manufacturer (OEM) selling a new product carefully decides whether to adopt the first phase remanufacturing entry strategy or to adopt the phase 2 remanufacturing entry strategy under certain conditions. Meanwhile, the OEM adopts penetration pricing for new and remanufactured products, when the actual quality of the product is high. Otherwise, it adopts a skimming pricing strategy, which is different from uniform pricing when there are no online reviews. Online reviews significantly impact OEM’s product profits and consumer surplus. Especially when the actual quality of the product is high enough, the OEM and the consumer will be also reciprocal [ 38 ]. Online reviews reduce consumers’ product uncertainty and improve the effect of consumer purchase decisions [ 39 , 40 ]. Uzir et al. [ 41 ] utilized the expectancy disconfirmation theory to prove that product quality positively impacts customer satisfaction, while product quality and customer satisfaction are mediated by customer’s perceived value. Product quality and customer’s perceived value will have greater influence with higher frequency of social media use. Nguyen et al. [ 42 ] studies consumer behavior from a cognitive perspective, and theoretically develops and tests two key moderators that influence the relationship between green consumption intention and behavior, namely the availability of green products and perceived consumer effectiveness.

Both sustainability-related and product-related texts positively influence consumer behavior on social media [ 43 ]. Online environment, price, and quality of the products are significantly impacted by OCPB. Godey et al. [ 44 ] explained the connections between social media marketing efforts and brand preference, price premium, and loyalty. Brand love positively impacts brand loyalty, and both positively impact WOM and purchase intention [ 45 ]. Brand names have a systematic influence on consumer’s product choice, which is moderated by consumer’s cognitive needs, availability of product attribute information, and classification of brand names. In the same choice set, the share of product choices with a higher brand name will increase and be preferred even if it is objectively inferior to other choices. Consumers with low cognitive needs use the heuristic of “higher is better” to select options labeled with brand names and choose brands with higher numerical proportions [ 46 ].

• Hypothesis 2 (H2): Perceived product attribute is the influencing factor of OCPB.

2.4 Influencing factors of perceived innovation attribute

Product innovation increases company’s competitive advantage by attracting consumers, whereas the enhancement of innovative design according to consumer behavior accelerates the development of sustainable product [ 47 , 48 ]. The innovation, WOM intentions and product evaluation can be improved positively by emotional brand attachment and decreased by perceived risk [ 49 ]. Based on the perspective of evolutionary, certain consumer characteristics, such as buyer sophistication, creativity, global identity, and local identity, influence firms’ product innovation performance, which can increase the success rate of product innovation, and enhance firms’ research and development performance [ 50 ]. However, technological innovation faces greater risk as it depends on market acceptance [ 51 ]. Moreover, electronic products rely more on technological innovation compared with other products, which maintain the profit and market [ 52 ]. The technological innovation needs to apply logical plans and profitable marketing strategies to reduce consumer resistance to innovation. Thus, Sun [ 53 ] explains the relationship between consumer resistance to innovation and customer churn based on configurational perspective, whereas the results show that response and functioning effect are significant but cognitive evaluation is not.

Based on the perspective of incremental product innovation, aesthetic and functional dimensions positively impact perceived quality, purchase intention, and WOM, whereas symbolic dimension only positively impacts purchase intention and WOM. By contrast, aesthetic and functional dimensions only positively impact perceived quality, whereas symbolic dimension positively impacts purchase intention and WOM. Furthermore, perceived quality partially mediates the relationship between aesthetic and functional dimensions and purchase intention and WOM by incremental product innovation, whereas perceived quality fully mediates the relationship between aesthetic and functional dimensions and purchase intention and WOM by radical product innovation [ 54 ]. Contextual factors, such as size of organizations and engagement in research and development activity, moderate the relationship between design and product innovation outcomes [ 55 ]. For radical innovations, low level of product innovation leads to more positive reviews and less inference of learning costs. As the functional attribute of radical innovations is not consistent with existing products, it is difficult for consumers to access relevant product category patterns and thus transfer knowledge to new products. The product innovation of aesthetics, functionality, and symbolism positively impact willingness to pay, purchase intention, and WOM through brand attitude [ 56 ]. This poor knowledge transfer results in consumers feeling incapable of effectively utilizing radical innovations, resulting in greater learning costs. In this case, product designs with low design novelty can provide a frame of reference for consumers to understand radical innovations. However, incremental product innovation shows no significant difference between a low and high level of design newness [ 57 ].

• Hypothesis 3 (H3): Perceived innovation attribute is the influencing factor of OCPB.

2.5 Influencing factors of perceived motivation attribute

The research has proven that almost all consumers’ purchases are motivated by emotion. Under this circumstance, an increase in online consumers’ positive emotions increases, their purchase frequency, whereas an increase in online consumers’ negative emotions reduces their purchase frequency. Additionally, user interface quality, product information quality, service information quality, site awareness, safety perception, information satisfaction, relationship benefits and related benefit factors have negative impacts on consumers’ online shopping emotionally. Nevertheless, only product information quality, user interface quality, and safety perception factors have positive effects on online consumer sentiment [ 58 ]. E-WOM carries emotional expressions, which can help consumers express the emotions timely. Pappas et al. [ 59 ] divides consumers’ motivation into four factors, namely entertainment, information, social-psychological, and convenience, while emotions into two factors, namely positive and negative. Specifically, according to complexity and configuration theories, a conceptual model by a fuzzy-set qualitative comparative analysis examines the relationship between a combination of motivations, emotions, and satisfaction, while results indicate that both positive and negative emotions can lead to high satisfaction when combing motivations.

From the perspective of SOR theory, consumers’ motivation is greatly influenced by self-consciousness, while conscious cognition plays the role of intermediary. First, after being stimulated by the external environment, online consumers will form “cognitive structure” depending on their subjectivity. Instead of taking direct action, they deliberately and actively obtain valid information from the stimulus process, considering whether to choose the product, and then react. Second, the stimulation stage in the retail environment can often attract the attention of consumers and cause the change of their psychological feelings. This stimulation is usually through external environmental factors, including marketing strategies and other objective influences. Third, organism stage is the internal process of an individual. It is a consumers’ cognitive process about themselves, their money, and risks after receiving the information they have seen or heard. Reaction includes psychological response and behavioral response, which is the decision made by the consumer after processing the information [ 60 ]. Based on literature review, 10 utilitarian motivation factors, such as desire for control, autonomy, convenience, assortment, economy, availability of information, adaptability/customization, payment services, absence of social interaction, and anonymity and 11 hedonic motivation factors, such as visual appeal, sensation seeking/entertainment, exploration/curiosity, escape, intrinsic enjoyment, relaxation, pass time, socialize, self-expression, role shopping, and enduring involvement with a product or service, are refined [ 61 ]. Consumers’ incidental moods can improve online shopping decisions impulsivity, while decision making process can be divided into orientation and evaluation [ 62 ]. Sarabia‐Sanchez et al. [ 63 ] combine K-means cluster and ANOVA analyses to explore the 11 motivational types of consumer values, which are achievement, tradition, inner space, universalism, hedonism, ecology, self-direction (reinforcement, creativity, harmony, and independence), and conformity.

• Hypothesis 4 (H4): Perceived motivation attribute is the influencing factor of OCPB.

3. Materials and methods

3.1 research design.

Given the present study’s objective to identify the influencing factors of OCPB, we analyzed e-WOM using big data analysis. To obtain accurate data of the influencing factors on OCPB, smartphones were the main object of data crawling. The rationale behind this choice is as follows. First, the time people spend using their smartphones is gradually increasing. Nowadays, smart phones are not only used for telephone calls or text messages, but also for taking photographs, recording video, surfing the web, online chatting, online shopping, and other such uses [ 64 ]. Second, smartphones have become a symbol of personal identification, as users’ using fingerprint or facial scans are frequently used to unlock devices, conduct online transactions, and make reservations, etc. Finally, smartphones’ software and hardware are updated frequently, so they may be considered high-tech products. Therefore, smartphones were chosen as the research object to determine which influencing factors affect OCPB.

Fig 1 shows the e-WOM data mining process and methods used. A dataset obtained from Taobao.com and Jingdong.com was collected by utilizing a Python crawling code, additional details of which are provided in Section 2.3. Section 2.4 addresses issues regarding language complexity. Moreover, Section 2.5 refers to the clustering of the influencing factors of OCPB through the K-means method of ML.

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https://doi.org/10.1371/journal.pone.0286034.g001

3.2 Data collection

The data were crawled from the e-commerce platforms Jingdong.com and Taobao.com by utilizing Python software. Jingdong and Taobao are the most powerful and popular platforms in China having professional e-WOM and user-friendly review systems. Specifically, the smartphone brands selected for analysis were Apple, Samsung, and Huawei because these three smartphone companies occupy the largest percentage of the smartphone market.

The authors determined that the analysis of the influencing factors of OCPB would be more persuasive and realistic by choosing smartphone models with high usage rate and liquidity. Thus, products reviews were crawled for the purchase of newly launched smartphones from Apple, Samsung, and Huawei in 2022. Specifically, to guarantee high-quality data, reviews from Taobao flagship stores and Jingdong directly operated stores were selected. However, we only collected reviews’ text content instead of images, videos, ratings, or rankings, the rationale was to ensure the reliability of data and meet research objectives. For instance, some e-commerce sellers attempt to increase their sales volume through deceitful methods, such as by faking ratings, rankings, and positive comments. Furthermore, online sellers and e-commerce companies (rather than consumers) often decide which smartphones are highest-rated and highest-selling. Finally, nowadays, the content of online reviews is not limited to text, as they also involve pictures, videos, and ratings, which have limited contribution in analyzing influencing factors of OCPB. Thus, the analyzed data regarding e-WOM in reviews was limited to text content.

In addition, to accurately reflect the real characteristics of OCPB during the COVID-19 pandemic, the study period ranged between February and May, 2022 (4 months). During that 4-month period, consumers exhibited a preference for buying products from e-commerce platforms. Specifically, the number of text reviews for the aforementioned types of smartphones was 51,2613 and 44,3678 in Taobao and Jingdong, respectively, for a total of 956,291 reviews.

3.3 Textual review processing method

As the crawled data exhibited noise, several data cleaning methods were adopted to filter noise and transform unstructured data of complex contextual review into structured data. Fig 1 shows the main procedures of the reviews’ pre-processing and the details are as follows.

First, to identify the range of sentences and for further data processing, sentences were apportioned using Python’s tokenizer package.

Second, this study employed Python’s Jieba package to perform word segmentation. The Jieba package is the Python’s best Chinese word segmentation module, comprising three modes. The exact mode was used to segment the sentences as accurately as possible, so they may be suitable for textual context analysis. The full mode was used to scan and process all words in each sentence, although it had a relatively high speed, it had a low capacity to resolve ambiguity. Additionally, the search engine mode segmented long words a second time, which allowed for the improvement of the recall rate, and was suitable for engine segmentation based on Jieba’s exact mode.

Third, stop words were deleted by referring to a stop words list. These included conjunctions, interjections, determiners, and meaningless words, among others. Finally, Python’s Word-to-vector (Word2vec) package was imported in the next step. Word2vec is an efficient training word vector model proposed by Mikolov [ 65 , 66 ]. The basic starting point was to match pairs of similar words. For instance, when “like” and “satisfy” appeared in a same context, they showed a similar vector, as both words had a similar meaning. Kim et al. [ 67 ] stated that a word could be considered a single vector and real numbers in the Word2vec model. In fact, most supervised ML models could be summarized as f ( x )−> y . Moreover, x could be considered a word in a sentence, while y could be considered this word in the context. Word2vec aimed to decide whether the sample of ( x , y ) could match the laws of natural language. Namely, after the process of Word2vec, the combination of word x and word y could be reasonable and logical or not. Table 1 shows the results of text processing.

https://doi.org/10.1371/journal.pone.0286034.t001

3.4 Influencing factors analysis by K-means

ML styles are divided into supervised and unsupervised algorithms. This study mainly utilized unsupervised algorithms to analyze the clusters of influencing factors of OCPB. Unsupervised algorithms consist in the clustering of unknown or unmarked objects without a trained sample [ 68 ]. This study utilized K-means to cluster the influencing factors.

For a given sample set, the K-means algorithm divides the sample set into k clusters according to the distance between samples. The main algorithm’s logic is to make the points in the cluster as close as possible, and to make the distance between the clusters as large as possible. Assuming that clusters can be divided into ( C 1 , C 2 ,…, C k ), the Euclidean distance of E is shown in Eq 1 .

The main procedures of K-means were the following.

Step 1 consisted of inputting the samples D = { x 1 , x 2 ,… x m }, K is the number of clusters, and appears as C = { C 1 , C 2 ,… C k }.

In Step 2, K samples were randomly selected from data set D as the initial K centroid vectors: { μ 1 , μ 2 ,… μ k }.

For Step5, it was necessary to repeat Steps 3 and 4, until all the centers μ remained steady. The final clustering result can be shown as C = { C 1 , C 2 ,… C k }.

The main procedures of K-means, according to Jain [ 69 ], are shown in Table 2 .

https://doi.org/10.1371/journal.pone.0286034.t002

4.1 K-means results

Based on the main procedures of K-means ( Table 2 ), the results are presented in Figs 2 – 4 .

https://doi.org/10.1371/journal.pone.0286034.g002

https://doi.org/10.1371/journal.pone.0286034.g003

https://doi.org/10.1371/journal.pone.0286034.g004

Four clusters of influencing factors of OCPB can be clearly identified in the analyses of the Jingdong dataset, Taobao dataset, and combined Jingdong and Taobao dataset. After checking the context of four clusters, even though small differences were found, their influence was found to be negligible for our analyses. Thus, Fig 4 was chosen as the benchmark of influencing factors of OCPB. In Section 4.3, the explanation and analysis of influencing factors of OCPB will be presented.

4.2 Performance metrics

First, performance metrics of sum of the square errors (SSE) and silhouette coefficient were adapted to verify the clustering results of K-means.

When the number of clusters does not reach the optimal numbers K, SSE decreases rapidly with the increase of the number of clusters, while SSE decreases slowly after reaching the optimal numbers, and the maximum slope is the optimal numbers K.

Where C i is the i th cluster, p is the sample point in C i (the mean value of all samples in C i ), and SSE is the clustering error of all samples, which represents the quality of clustering effect.

Fig 5 indicates that the SSE decreases rapidly when K equals the number of four.

https://doi.org/10.1371/journal.pone.0286034.g005

The range of sc i is between -1 and 1, the clustering effect is bad when sc i is below zero, whereas the clustering effect is good when sc i is near 1 conversely.

Based on Fig 6 , it is obviously to show that the silhouette coefficient reaches highest when K equals the number of four. Therefore, the results of the SSE and the silhouette coefficient jointly prove the number of K is four.

https://doi.org/10.1371/journal.pone.0286034.g006

4.3 Hypotheses results

Based on the K-means analysis, this section presents the influencing factors identified in the data from Jingdong and Taobao, which indicate the influencing factors influencing OCPB.

The first cluster comprises the perceived emergency context attribute, such as logistics, expressage, delivery, customer service, promotion, and reputation.

The second cluster comprises the perceived product attribute, such as appearance, brand, hand feeling, color, cost-performance ratio, price, design, and usability.

The third cluster comprises the perceived innovation attribute, such as photograph, quality and effects, screen quality, audio and video quality, pixel density, image resolution, earphone capabilities, and camera specifications.

The fourth cluster comprises the influencing factors, such as processing speed, operation, standby time, battery, system, internal storage, chip, performance, and fingerprint and face recognition, which cannot represent the perceived motivation attribute.

The results match the findings of Zhang et al. [ 70 ] to some extent, who identified 11 smartphone attributes based on online reviews: performance, appearance, battery, system, screen, user experience, photograph, price, quality, audio and video, and after-sale service. In addition, other scholars have explained the relationship between feature preferences and customer satisfaction [ 71 , 72 ], usage behavior and purchase [ 73 , 74 ], importance and costs of smartphones’ features and services [ 75 ], brand effects [ 76 ], and purchase behavior of people of different ages and gender groups [ 77 – 79 ]. Thus, H1, H2 and H3 are supported, while H4 is not supported according to the results of the K-means analysis.

4.4 Theoretical framework and validity of OCPB influencing factors

Kotler’s five product level model states that consumers have five levels of need comprising the core level, generic level, expected level, augmented level, and potential level. First, the core benefit is the fundamental need or want that consumers satisfy by consuming a product or service. Second, the generic level is a basic version of a product made up of only those features necessary for it to function. Third, the expected level includes additional features that the consumer might expect. Fourth, the augmented level refers to any product variations or extra features that might help differentiate a product from its competitors and make the brand a preferred choice amongst its competitors. Finally, a potential product includes all augmentations and improvements that a product might experience in the future [ 80 ].

In contrast with these levels, this study proposed the four influencing factors of OCPB. Based on Table 3 , first, the perceived emergency context H1 is not included in Kotler’s five products level, while the influencing factor expresses the significant characteristics of OCPB compared with Kotler’s model. Second, the perceived product attribute H2 could be considered the core and generic level. Third, the perceived innovation attribute H3 could be considered the potential level. Fourth, the results of H4 mainly reflects additional or special function of product, which meets the definition of the expected and augmented level. To refine the theoretical framework, H4 changes to the perceived functionality attribute by combing the explanation of the expected and augmented level, instead of the perceived motivation attribute. The details are shown in Fig 7 .

https://doi.org/10.1371/journal.pone.0286034.g007

https://doi.org/10.1371/journal.pone.0286034.t003

Fig 7 shows the four influencing factors of the theoretical framework of OCPB. Specifically, according to Kotler’s five products level, the perceived product attribute is the necessary influencing factor of OCPB, which meets the core drive and basic requirement. For instance, the core drive of purchasing of a smartphone is the core function of communication, and then the appearance, brand, color, etc. The perceived functionality attribute is the additional influencing factor of OCPB, which meets the expected and augmented requirement. For instance, when smartphones are in the same price range, consumers prefer to choose a smartphone belonging to better quality, smarter design, or better functionality. Moreover, the perceived innovation attribute is the attractive influencing factor of OCPB, which reflects the potential level. For instance, most consumers are the Apple fans mostly because the Apple products offer innovative usage experience and different technology elements yearly. Finally, the perceived emergency context attribute is the adaptive influencing factor of OCPB, which shows the main distinction with Kotler’s five products level. Further, because of the COVID-19, consumers only have online channel to purchase product under a prolonged quarantine and lockdown. Thus, in the emergency context, consumers primarily consider whether the product can be purchased in the e-commerce platform, whether the product can be delivered normally, or whether the packaged has been disinfected fully.

5. Discussion

Traditional consumer behavior is mainly affected by psychological, social, cultural, economic, and personal factors [ 81 , 82 ]. Park and Kim [ 83 ] conducted an empirical study to identify the key influencing factors that impact OCPB, which include service information quality, user interface quality, security perception, information satisfaction, and relational benefit. Further, Sata [ 84 ] conducted an empirical study and found that price, social group, product features, brand name, durability and after-sales services were important to consumers’ buying behavior when choosing a smartphone for purchase. Simultaneously, some studies have utilized big data technology to explore OCPB, exploring online consumers’ attitude toward products in different countries, and identified product features. However, these studies do not identify the influencing factors of OCPB and ignore e-WOM. To better explain OCPB influencing factors, e-WOM should be integrated into the theoretical framework and used in practical applications. Thus, this study contributes to OCPB research by data mining and analysis that can adequately identify the influencing factors based on e-WOM.

5.1 Theoretical implications

First, perceived emergency context attribute is the influencing factor of OCPB. Because of the COVID-19, e-commerce is the priority choice for consumers under circumstances of prolonged quarantine and lockdown, and then considering logistics and delivery. Furthermore, customer service, packaging, promotion, and reputation are critical to online consumers.

Second, perceived product attribute is the influencing factors of OCPB. The basic features of product, such as appearance, brand, hand feeling, price, and design, positively attract online consumers. Elegant appearance, famous brand, better hand feeling, lower price, and better design would be more impactful to OCPB.

Third, perceived innovation attribute is the influencing factor of OCPB. For smartphone, online consumers would show more interest in the innovation of speed, operation, standby time, chip, etc. Scientific and technological innovation for most products could improve the level of OCPB. Thus, the guarantee and improvement of functionality of a product could create more opportunities for online consumers to make purchasing decisions.

Fourth, according to Kotler’s five products level, perceived product attribute satisfies the characteristics of core drive and basic, while the perceived innovation attribute satisfies the characteristics of the potential level. Because hypothesis of perceived motivation attribute is not supported. Based on the analyzing results, the perceived functionality attribute is refined instead of the perceived motivation functionality attribute, which satisfies the expected and augmented. Meanwhile, the perceived emergency context attribute is not included, which shows the main difference with Kotler’s five products level.

5.2 Managerial implications

The influencing factors of OCPB were clustered into four categories: perceived emergency context, product, innovation, and function attributes. The definition and explanation of these categories may have important managerial implications for both OCPB and e-commerce. First, the findings of this study suggest that e-commerce enterprises should pay more attention to improving the quality, user experience, and additional design features of their products to arouse the interest of OCPB. However, this may be difficult for e-commerce enterprises because achieving these goals requires updating the software and hardware constantly, which involves significant investment. For most scientific and technical corporations, making heavy investments is not particularly difficult, however, service-type enterprises and small and medium enterprises may have insufficient funds to afford such heavy investments. This is the main reason that most online consumers buy products from famous brands instead of small and medium enterprises. Therefore, to improve their situation, both types of companies could jointly develop products or services, for instance, small and medium enterprises may purchase patents from large enterprises, jointly researching and developing products, or large enterprises could share their achievements at a price.

Second, the pandemic has accelerated the spread of e-commerce considerably, changing consumers’ shopping style in the process. Accordingly, e-commerce enterprises should adapt their marketing strategies, especially as the COVID-19 pandemic is still ongoing, due to the rapid development of the economy and its dynamic environment. For instance, e-commerce platforms should realize that changes in OCPB will continue to contribute to the growth of the e-commerce market. Moreover, e-commerce enterprises should combine their online presence with brick-and-mortar stores. Even more importantly, e-commerce enterprises should successfully operate their supply chain to adapt to the implementation of lockdown measures and the closing of manufacturing factories. Consumers should exercise caution when facing e-commerce enterprises’ adaptive financial policy, such as interest-free rates, which may cause financial burden.

Third, e-commerce enterprises should offer a simple and smooth shopping experience, clearly display practical information, increase the value of goods (by improving the quality, design, and performance of products or services) and improve their brand image for online consumers. However, e-commerce enterprises sometimes rely on certain fraudulent methods to increase their sales volume, such as falsifying positive e-WOM and deleting negative feedback, as was identified during the data processing stage. Therefore, online consumers should select online stores cautiously to avoid buying products of poor quality or performance.

Fourth, nowadays, technology is constantly evolving at an accelerated rate, particularly in the smartphone industry, as companies launch new products with innovative functions each year. Thus, e-commerce enterprises should strive to innovate to secure their position in the market. In addition, consumers should reconsider the need to experience the state-of-the-art products because these may have high prices.

6. Conclusion and limitations

In conclusion, during the COVID-19 pandemic, consumers highly preferred to buy products online, because most brick-and-mortar stores were closed due to lockdowns and social distancing measures. Additionally, with the rapid development of e-commerce, online shopping has become the most popular shopping style because it allows consumers to not only save time and money, but also review e-WOM before purchasing a product. Moreover, e-WOM is much more reliable compared with traditional WOM. Thus, this study proposed a theoretical framework to explore and define the influencing factors of OCPB based on e-WOM data mining and analyzing. The data were crawled from Jingdong and Taobao, while the data process was also fully demonstrated. Comparing the results, the influencing factors of OCPB were clustered around four categories: perceived emergency context, product, innovation, and function attributes. Moreover, perceived emergency context attribute is the main difference compared with Kotler’s five products level, while perceived product attribute meets the core and generic level, perceived functionality attribute meets the expected and augmented level, and perceived innovation attribute meets the potential level.

However, this study still has certain limitations. First, the data were crawled from Chinese e-commerce websites, hence, they may not be generalized in contexts where the influencing factors and dimensions may vary compared with other countries or regions. Second, this study only explored and defined the antecedents of OCPB. Data should be added from Western e-commerce websites. Moreover, the present study’s results should be compared with Western studies to generate a more comprehensive view of the antecedents of OCPB. Future studies should explore the underlying mechanisms influencing OCPB.

Supporting information

S1 dataset..

https://doi.org/10.1371/journal.pone.0286034.s001

• View Article
• Google Scholar
• PubMed/NCBI
• 25. Krishna A, Strack F. Reflection and impulse as determinants of human behavior. Knowledge and Action,Springer, Cham; 2017. p. 145–67.
• 37. Mikalef P, Pappas IO, Giannakos M. Consumer intentions on social media: a fsQCA analysis of motivations. Conference on e-Business, e-Services and e-Society: Springer, Cham; 2016. p. 371–86.
• 58. Cinar D. The effect of consumer emotions on online purchasing behavior. Tools and Techniques for Implementing International E-Trading Tactics for Competitive Advantage. USA: IGI Global; 2020. p. 221–41.
• 61. Martínez-López F. J. P-G, C., Gázquez-Abad JC, Rodríguez-Ardura I. Online consumption motivations: an integrated theoretical delimitation and refinement based on qualitative analyses. Strategic e-Business Management. Berlin, Heidelberg: Springer; 2014. p. 347–70.
• 80. Kotler P. Principles of marketing. Boston: Pearson; 2016.

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• > Journals
• > Journal of Agricultural and Applied Economics
• > Volume 53 Issue 3
• > US Consumers’ Online Shopping Behaviors and Intentions...

Article contents

• Introduction
• Methodology
• Implications and Conclusions

Author Contributions

Financial support, conflict of interests, data availability, us consumers’ online shopping behaviors and intentions during and after the covid-19 pandemic.

Published online by Cambridge University Press:  31 August 2021

A study of 1,558 US households in June 2020 evaluated utilization of online grocery shopping during the COVID-19 pandemic, influences on utilization, and plans for future online grocery shopping. Nearly 55 percent of respondents shopped online in June 2020; 20 percent were first-timers. Cragg model estimates showed influences on online shopping likelihood and frequency included demographics, employment, and prior online shopping. Illness concerns increased likelihood, while food shortage concerns increased frequency of online shopping. A multinomial probit suggested 58 percent respondents planned to continue online grocery shopping regardless of pandemic conditions.

1. Introduction

1.1. the covid-19 pandemic and policy response.

The COVID-19 pandemic has led many American consumers to rapidly and sometimes dramatically change their food shopping behaviors in response to changes in policy, and personal or public health concerns. In March 2020 as the virus started spreading widely in the United States (US), state and local governments began issuing orders to close restaurants to in-person dining to mitigate the spread of the virus. In response to these conditions, many consumers responded by shifting their food expenditures away from food service (e.g. restaurants and eating establishments) to food retailers (Kowitt and Lambert, Reference Kowitt and Lambert 2020 ). In some cases, consumers stockpiled groceries due to concerns about supply chain disruptions and shortages (Acosta, Reference Acosta 2020 ). Part of this stockpiling may also have been due to averting behaviors, as some consumers preferred to shop in-store less frequently, thus reducing the number of their potential exposures. Some of these increased food expenditures were conducted through online purchases, which showed a significant increase in utilization from the early months of the pandemic through the next stage of the pandemic policy response in April when states started issuing stay-at-home or shelter-in-place orders (Redman, Reference Redman 2020 b).

From March 1 to May 31, 2020, 42 states and territories issued stay-at-home orders that covered about 73 percent of US counties (Moreland et al., Reference Moreland, Herlihy, Tynan, Sunshine, McCord, Hilton, Poovey, Werner, Jones, Fulmer, Gundlapalli, Strosnider, Potvien, García, Honeycutt and Baldwin 2020 ). These orders continued the closure of restaurants to in-person dining, while keeping many food retailers open, and asked households to limit their activity outside of their home. The duration of the stay-at-home orders varied from state to state, but they represented a significant disruption to the way households typically acquire food.

As consumers entered into a new phase of pandemic policies marked by the end of stay-at-home orders during the late spring and early summer, questions have arisen as to how consumers will navigate this new environment and which behaviors adopted during the earliest months of the pandemic will endure (Foster and Mundell, Reference Foster and Mundell 2020 ). Even as state policies changed, consumers have continued to encounter some elements of the pandemic including shortages of food at retailers and the concern of contracting the virus while making in-person grocery shopping. Thus, the pandemic likely continued to influence shopper behavior into the summer of 2020. Consumers likely adopted some behaviors, such as online grocery shopping, that they may continue even beyond the end of the pandemic. Therefore, this study not only investigates determinants of online grocery shopping, including delivery and curbside pickup services, in June 2020, but also intentions among online grocery shoppers for future online grocery shopping. The influences on plans for future online shopping are measured, given that the scenarios the pandemic could continue or subside. Hence, the study provides insights into how shoppers may behave with regard to online shopping in the post-pandemic era. The study uses results from an original national US survey administered in July 2020.

This study complements the current literature in two ways. First, we incorporate both previously explored and pandemic-specific variables into our model of current online grocery store use. Prior research has shown that age, income, and the presence of children in the household influence the decision to the utilization of online grocery shopping (Etumnu et al. Reference Etumnu, Widmar, Foster and Ortega 2019 , Hansen, Reference Hansen 2005 ; Hansen, Reference Hansen 2005 ; Jaller and Pawha, Reference Jaller and Pahwa 2020 ; Melis et al., Reference Melis, Campo, Lamey and Breugelmans 2016 ; Van Droogenbroeck and Hove, Reference Van Droogenbroeck and Van Hove 2017 ). However, it is unclear how the pandemic has influenced the consumer decision-making process. While Ellison et al. ( Reference Ellison, McFadden, Rickard and Wilson 2020 ) documented an increase in online grocery shopping, their primary focus was on changes in purchasing behavior and not the decision to use online grocery shopping. Therefore, we have included pandemic-specific measures to capture how risk perceptions about COVID-19 or food supply chain disruptions influence the choice of online grocery shopping and frequency of online shopping.

Second, we investigate consumers’ anticipated use of online shopping in the future. We consider the possibility that online grocery shopping will persist only during the pandemic, that it will continue regardless of the pandemic, or that it will not be continued in the future. We examine the prevalence of each behavior and then investigate possible determinants of future online grocery shopping using a multinomial logistic regression. Understanding the potential future grocery shopping behavior and its determinants could assist grocers and retailers to reidentify their marketing strategies and enhance online shopping service to better serve online grocery shoppers.

The first section of this paper provides a brief literature overview of studies of online grocery shopping both pre-pandemic and in the pandemic-shaped grocery markets. This literature review helps define hypotheses about how shopper demographics and attitudes may influence online grocery shopping, frequency of online grocery purchases during the pandemic, and plans to continue online grocery shopping. Following the literature review and hypotheses development, the next section presents information about the survey and data collection and model estimations. Results and policy implications are discussed next, along with conclusions.

1.2. Prior Studies of Online Grocery Shopping and Behaviors During the Pandemic

1.2.1. online grocery shopping patterns pre-pandemic.

Several studies have examined the effects of shopper demographics and attitudes on online grocery shopping. Younger shoppers are more likely to use online grocery shopping, perhaps because they are more familiar and comfortable with online shopping in general and related technology (Etumnu et al., Reference Etumnu, Widmar, Foster and Ortega 2019 ; Farag et al., Reference Farag, Schwanen, Dijst and Faber 2007 b; Hiser, Nayga, and Capps, Reference Hiser, Nayga and Capps 1999 ; Van Droogenbroeck and Hove, Reference Van Droogenbroeck and Van Hove 2017 ). While some studies have found positive influence of female gender on online grocery shopping (Jaller and Pawha, Reference Jaller and Pahwa 2020 ), others have found the opposite (Etumnu et al., Reference Etumnu, Widmar, Foster and Ortega 2019 ; Farag et al. Reference Farag, Schwanen, Dijst and Faber 2007 b). Prior studies have suggested that presence of younger children has a positive effect on online grocery shopping adoption (Etumnu et al. Reference Etumnu, Widmar, Foster and Ortega 2019 , Hansen, Reference Hansen 2005 ; Jaller and Pawha, Reference Jaller and Pahwa 2020 , Melis et al., Reference Melis, Campo, Lamey and Breugelmans 2016 ), indicating food shoppers with accompanying children may find in-store trips more time-consuming and challenging than those without children.

Studies have found positive effects of household income (Hansen, Reference Hansen 2005 ) or full-time employment in the household (Van Droogenbroeck and Hove, Reference Van Droogenbroeck and Van Hove 2017 ) on online grocery shopping. Greater likelihood of online grocery shopping has also been associated with higher levels of education (Etumnu et al., Reference Etumnu, Widmar, Foster and Ortega 2019 ; Hiser, Nayga, and Capps, Reference Hiser, Nayga and Capps 1999 ; Jaller and Pawha, Reference Jaller and Pahwa 2020 ; Van Droogenbroeck and Hove, Reference Van Droogenbroeck and Van Hove 2017 ). Melis et al. ( Reference Melis, Campo, Lamey and Breugelmans 2016 ) found that, if shoppers lived farther from a brick-and-mortar store, they were more likely to spend a larger share of their grocery spending at the online chain. They posited that shoppers would experience relatively higher transportation costs and thus were more inclined to shift more of their purchases to the online store. Findings by Melis et al. ( Reference Melis, Campo, Lamey and Breugelmans 2016 ) might suggest that urban consumers would be less likely to choose online shopping over brick-and-mortar shopping. However, this finding might not hold in more rural areas where there are limited online grocery shopping opportunities. But, with large chains such as WalMart offering online shopping with curbside pickup and Amazon delivery of grocery items even in rural areas, these limitations may be less than in the past (Germain, Reference Germain 2020 ).

Researchers have also investigated the relationship between in-store shopping and online shopping. Farag, Krizek, and Dijst ( Reference Farag, Krizek and Dijst 2007 a) found Dutch online buyers make more shopping trips than non-online buyers and have a shorter duration of shopping trips. Their results were suggestive of a complementary relationship between online buying and in-store shopping. Furthermore, Pozzi ( Reference Pozzi 2013 ) found only limited cannibalization of traditional brick-and-mortar store grocery sales by online sales.

1.2.2. Influences of Attitudes and Pre-Pandemic Lifestyles

Several studies have examined the influence of convenience and perceived risks on online grocery shopping (Campo and Breugelmans Reference Campo and Breugelmans 2015 ; Melis et al. Reference Melis, Campo, Lamey and Breugelmans 2016 ; Ramus and Nielsen, Reference Ramus and Nielsen 2005 ; Rohm and Swaminathan, Reference Rohm and Swaminathan 2004 ; Verhoef and Langerak, Reference Verhoef and Langerak 2001 ). Verhoef and Langerak ( Reference Verhoef and Langerak 2001 ) found that consumers who believed the reduction in the physical efforts of grocery shopping were an important advantage associated with online grocery shopping. Rohm and Swaminathan ( Reference Rohm and Swaminathan 2004 ) found that store-oriented shoppers who derived satisfaction from immediate product possession and contact shopping were much less likely to shop online than were convenience shoppers. Campo and Breugelmans ( Reference Campo and Breugelmans 2015 ) noted that the vast majority of online grocery shoppers were actually multichannel shoppers who visited both online and offline, brick-and-mortar, and grocery stores. Melis et al. ( Reference Melis, Campo, Lamey and Breugelmans 2016 ) found that consumers who had moderate time constraints, indicated by frequency of shopping trips, were more likely to adopt the online channel for grocery retailers. Ramus and Nielsen ( Reference Ramus and Nielsen 2005 ) found that shoppers perceived internet grocery shopping to be convenient, but more likely to result in purchasing poorer quality products that they would either have to accept or return.

A few studies have examined frequency of online grocery shopping. Hansen ( Reference Hansen 2007 ) found that increased utilization of online grocery shopping was associated with the perception of increased physical effort of in-store shopping and decreased perception of the complexity of online shopping, internet grocery risk, and enjoyment of shopping in-store. Hand et al. ( Reference Hand, Dall’Olmo Riley, Rettie, Harris and Singh 2009 ) found that situational factors, for example, birth of a child, health problems, or family circumstances, often were precipitating factors that influenced shoppers to buy groceries online. However, once these precipitating factors were gone, the shoppers tended to return to brick-and-mortar grocery shopping. These results elicit the question of whether those who have initiated or increased their online grocery shopping during the pandemic will plan to continue online shopping or revert to prior brick-and-mortar grocery shopping patterns after the pandemic conditions have eased. Furthermore, some shoppers may plan to continue online grocery shopping only as long as the pandemic conditions prevail. However, this represents an empirical question yet to be answered.

1.2.3. Online and In-Store Shopping During the COVID-19 Pandemic

During the first few months of the pandemic, several changes in food shopping behaviors were found. In a study of Spanish consumers (Laguna et al., Reference Laguna, Fizman, Puerta, Chaya and Tarrega 2020 ), no changes in percentages of where consumers said they mainly purchased their foods (supermarkets, small shops, or online) were found; however, consumers reduced their frequency of shopping trips. While there was not a shift toward online shopping found in their study, the decrease in frequency of shopping suggests averting behaviors. Another study found consumer in the United States and China had changed their food purchase behaviors toward more use of takeout and delivery orders (Dou et al., Reference Dou, Stefanovsi, Galligan, Lindem, Rozin, Chen and Chao 2020 ). In addition, some studies showed that grocery shopping online increased with social distancing measures and concerns about shopping in crowded grocery stores (Ellison et al., Reference Ellison, McFadden, Rickard and Wilson 2020 ; Melo, Reference Melo 2020 ). Melo ( Reference Melo 2020 ) noted during the first few months of the pandemic certain foods were stockpiled by consumers.

Grashius and Skevas ( Reference Grashius and Skevas 2020 ) used a choice experiment to determine how online shopping attributes and COVID-19 conditions might influence preferences for online grocery shopping. They also examined how the spread of COVID-19 may impact consumer preferences. Respondents who were presented with the hypothetical case where COVID-19 was spreading at an increasing rate had the most disutility of shopping in-store. However, where COVID-19 was hypothetically spreading at a decreasing rate, consumer preferences for the home delivery over other methods were not as pronounced. Hence, they postulated that consumer online shopping behavior is motivated at least in part by concerns of shopping inside grocery stores. Their results suggest that when pandemic conditions subside, many online shoppers will choose to return to brick-and-mortar shopping.

The possibility that concerns regarding COVID-19 influence consumer behavior was also investigated by Goolsbee and Syverson ( Reference Goolsbee and Syverson 2020 ) who used cell phone records to track customer visits to 2.25 million businesses across 110 industries during the early months of the pandemic. They found that overall consumer traffic fell by 60 percentage points, but legal restrictions explained only about 7 percentage points of this decline, while individual choices were more explanatory of the decline. They noted, however, that shutdown orders did reallocate consumers from restaurants and bars toward groceries and other food sellers. Hence, during the early months of the COVID-19 pandemic, a portion of sales gains online may be attributable to both concerns regarding COVID-19 and declines in food-away-from-home purchases.

2. Methodology

2.1. survey and data collection.

The data for this study were collected via an online survey through the Qualtrics survey platform in July 2020. The survey panel consisted of US primary household food shoppers (person primarily responsible for most of the food shopping in their household) aged 18 years and over, who had lived in the same state since February 1, 2020. Prior to the survey being fielded, a pretest of 50 respondents was conducted and the survey was deemed suitable for broader distribution. The sample panel was drawn by Qualtrics to reflect the distribution of US households according to the American Community Survey (ACS) (U.S. Census Bureau, 2019 ) based on their 2019 income, age, and geographical region (i.e. Northeast, Midwest, West, and South). Qualtrics solicited responses until a total of 2,000 responses were received from respondents who met the qualifications described above while ensuring the age, income, and regional quotas were met. Table  1 displays sample averages for several demographic and household variables compared with ACS estimates for the US population. As can be seen in Table  1 , the sample respondent is more likely to be female than the US average. This may be attributed to the primary shopper inclusion criteria because a higher percentage of primary food shoppers are female (Schaefer, Reference Schaefer 2019 ). Also, our sample has a higher percentage of college graduates than the US average. The sample average household size was larger than the US average and a higher percentage have children under the age of 18 years compared with the US average.

Table 1. Survey sample demographics compared with US American Community Survey (ACS) estimates

The survey instrument consisted of several sections including methods of acquiring food in June 2020 (online or in-person grocery store, in-person or takeaway from restaurants, and other sources), food expenditures, COVID-19 experiences and attitudes, and other demographic and household questions. Appropriate human subjects’ protocols were followed and institutional review board approvals obtained (UTK-IRB-20-05882-XM).

2.2. Modeling of Grocery Purchases Online During the Pandemic and Post-Pandemic Plans

Two consumer decisions regarding the use of online shopping were examined in this study. The first is whether the consumer used online grocery shop during the month of June 2020 and if so, how frequently. The online grocery shopping question respondents asked how many times in June 2020 they purchased groceries online including curbside pickup, and any delivery service (e.g. supermarket delivery, Amazon, and Instacart). The second decision among these online grocery shoppers is whether they planned to continue purchasing groceries online in the future regardless of the pandemic, only under pandemic conditions, or not at all.

Based on the prior studies, we hypothesized that households that are younger, higher income, have children, and more employed individuals in the household will be more likely to utilize online grocery shopping and plan to utilize it in the future regardless of continuation of the pandemic (Hansen, Reference Hansen 2005 ; Etumnu et al., Reference Etumnu, Widmar, Foster and Ortega 2019 ; Farag et al., Reference Farag, Schwanen, Dijst and Faber 2007 b; Hiser, Nayga, and Capps, Reference Hiser, Nayga and Capps 1999 ; Van Droogenbroeck and Hove, Reference Van Droogenbroeck and Van Hove 2017 ). Due to mixed findings regarding female gender, the direction of influence of female gender was not hypothesized a priori (Etumnu et al., Reference Etumnu, Widmar, Foster and Ortega 2019 ; Farag et al., Reference Farag, Schwanen, Dijst and Faber 2007 b; Jaller and Pawha, Reference Jaller and Pahwa 2020 ). While several studies have found complementarity between traditional in-store and online grocery shopping, given that the time frame studied here is the span of a month, the influence of greater number of in-store trips is difficult to hypothesize a priori (Farag, Krizek, and Dijst, Reference Farag, Krizek and Dijst 2007 a; Pozzi, Reference Pozzi 2013 ). However, it is more likely that a greater number of in-store trips is more likely to influence the frequency of online shopping, rather than the choice the shop online at all. In addition, it is likely that prior online shopping will strongly influence both online grocery shopping behaviors in June 2020 and plans for online grocery shopping in the future.

Two concerns precipitated by the pandemic that may influence shopping behavior are concern with contracting COVID-19 and concern with food shortages at retailers due to food supply chain disruptions (Ferguson, Reference Ferguson 2020 ). Either has the potential to increase the use of online shopping as consumers seek to avoid stores or plan food expenditures to avoid shortages or stockpile items of which they may experience a shortage at retailers or grocers. To assess consumers’ perceptions of these two pandemic-related risks, we asked respondents to rank their concern with either becoming ill with COVID-19 or that COVID-19 will cause food shortages, both on a scale from 1 to 9, where 1 indicated no concern and 9 indicated extremely concerned. Footnote 1 If the respondent was moderately concerned or greater, the variable was assigned a value of “1” and “0” otherwise.

2.2.1. Cragg Model of Number of Times Purchased Groceries Online

The expected value of the number of times shopper i purchases online groceries conditional on at least one purchase is:

The marginal effect of the jth explanatory variable on the probability of the ith shopper choosing online groceries at least once is

The marginal effect of the jth explanatory variable for the ith individual on the conditional level of Times Online is

The average marginal effects, which are the average of the individual level effects, are estimated using the Delta method (Greene, Reference Greene 2018 ). The craggit module in STATA 16.0 was used to estimate the Cragg model and the marginal effects of evaluated determinants (Burke, Reference Burke 2009 ).

2.2.2. Multinomial Probit Model of Future Online Grocery Shopping Decisions

The survey question regarding plans for future online shopping for groceries includes three possible outcomes ( M  = 3), these are a) 3 = Yes, b) 2 = Yes, but only if COVID-19 is a concern, and c) 1 = No. With three outcomes, a multinomial probit model is used to estimate the probability of each future planned online grocery shopping outcome. The probability that the lth option is chosen among M alternatives by the ith consumer is then (Cameron and Trivedi, Reference Cameron and Trivedi 2005 ):

3.1 Summary Demographics of Respondents Used in the Models

Table  2 contains statistics summary for the variables used in the Cragg and multinomial models. These summary measures are for the 1,558 respondents out of the 2,000 who answered all questions needed for the analysis. Notably, about 54.8 percent of the 1,558 respondents shopped for groceries online in June 2020 and overall 48.3 percent indicated they had not shopped for groceries online before the pandemic. Also, among the June 2020 online grocery shoppers surveyed in this study, about 58.4 percent indicated they planned to continue to shop for groceries online regardless of the pandemic ( Future Online  = 3). About 29.5 percent said they would continue to shop online for groceries, but only if COVID-19 remained as a problem ( Future Online  = 2). Only 12.1 percent indicated they would not shop online for groceries in the future ( Future Online  = 1).

Table 2. Names, definitions, and means for variables used in the Cragg model for online grocery shopping and multinomial model of future online grocery shopping plans

a The baseline region is Midwest. States in the West include AK, AZ, CA, CO, HI, ID, MT, NM, NV, OR, UT, WA, and WY, in the South include AL, AR, DC, FL, GA, KY, LA, MS, NC, OK, SC, TN, TX, VA, and WV, in the Northeast include CT, DE, MA, MD, ME, NH, NJ, NY, PA, RI, and VT, and in the Midwest include IL, IN, IA, KS, MI, MN, MO, ND, NE, OH, SD, and WI.

As shown in Table  2 , similar to the full sample, the responses used in estimating the models were more likely to be female, have children in the household, and be college graduates as compared to the ACS estimates shown in Table  1 . The percentage of respondents residing in each region from Table  2 were similar to ACS regional percentages shown in Table  1 .

3.2. Cragg Model of Number of Times Shopped Online for Groceries in June 2020

Table  3 contains the results from the Cragg model for online grocery shopping in June 2020. The log-likelihood ratio (LLR) test against an intercept-only model (-2*(LL Intercept Only-LL Model)) indicates that the model with the covariates is significant overall. Additionally, the LLR test comparing the Cragg model to the Tobit model, with a test statistic of 188.766 with 17 degrees of freedom from a chi-square distributed LLR test (-2*(LL Tobit-LL Cragg)), indicates that the model fit is improved by using the Cragg specification. The pseudo R 2 for the model is 0.274, while the percent correctly classified for Shopped Online is over 81 percent. The mean variance inflation factor of 1.34 suggests no statistically problematic multicollinearity found within the covariates.

Table 3. Estimated Cragg model of number of times shopped for groceries online and associated marginal effects (ME)

a ***Significance at α = 0.01, **significance at α = 0.05, and *significance at α = 0.1

b The baseline region is Midwest.

c The marginal effect of Grocery Expenditures is calculated as the ME Ln Groc. Expend./Grocery Expenditures.

The second column of Table  3 includes the estimated coefficients for the choice to use online grocery shopping in June 2020 (the probit portion of the Cragg model for Shopped Online = 1) and the third column contains the coefficients for the frequency of online shopping (the truncated portion of the Cragg model for Times Online ). The associated average marginal effects for the explanatory variables on probability of shopping online and the number of times shopped online for groceries were calculated using the estimated coefficients and equations ( 4 ) and ( 5 ), and shown in the third and fourth columns of Table  3 .

As seen in Table  3 , age ( Age ) negatively influences the likelihood of shopping online for groceries by 0.2 percent for each year, which is consistent with prior research findings (Etumnu et al. Reference Etumnu, Widmar, Foster and Ortega 2019 ; Farag et al., Reference Farag, Schwanen, Dijst and Faber 2007 b; Hiser, Nayga, and Capps, Reference Hiser, Nayga and Capps 1999 ; Van Droogenbroeck and Hove, Reference Van Droogenbroeck and Van Hove 2017 ). However, age does not significantly influence the number of times shopped online. Consistent with Etumnu et al. ( Reference Etumnu, Widmar, Foster and Ortega 2019 ) and (Farag et al., Reference Farag, Schwanen, Dijst and Faber 2007 b), identifying as female ( Female ) negatively influences the probability of shopping for groceries online by 6.5 percent ( Shopped Online ) and the number of times ( Times Online ) the respondent grocery shopped online by 0.439 times.

Respondents with children in the household ( Children ) are 8.4 percent more likely to have shopped for groceries online in June 2020 and made about 0.731 more trips than households without children. These findings are similar to some previous studies (e.g. Etumnu et al. Reference Etumnu, Widmar, Foster and Ortega 2019 , Hansen, Reference Hansen 2005 ; Jaller and Pawha, Reference Jaller and Pahwa 2020 , Melis et al., Reference Melis, Campo, Lamey and Breugelmans 2016 ) that suggested that household food shoppers may find it more challenging to shop in-store with accompanying children.

In line with other studies (Etumnu et al., Reference Etumnu, Widmar, Foster and Ortega 2019 ; Hiser, Nayga, and Capps, Reference Hiser, Nayga and Capps 1999 ; Jaller and Pawha, Reference Jaller and Pahwa 2020 ; Van Droogenbroeck and Hove, Reference Van Droogenbroeck and Van Hove 2017 ), this study found that having a college degree ( College Graduate ) increases the frequency of online grocery shopping 0.546 times among those who used online shopping at least once. However, being a college graduate does not significantly influence the initial decision to shop online.

While full-time employment status ( Employed Full Time ) does not influence the number of times the respondent online grocery shopped, it positively influences the probability that the respondent shopped for groceries online by about 4.4 percent. This finding is consistent with those of Van Droogenbroeck and Hove ( Reference Van Droogenbroeck and Van Hove 2017 ). Identifying as an essential worker ( Essential ) does not significantly influence the probability of online grocery shopping but does decrease the frequency of online shopping by 0.456 trips among those who chose to shop online. For essential workers, the potential convenience of online shopping may not have been outweighed by the convenience and ability to visit their regular store during their commute to or from work. This finding may have implications for retailers as more households return to working in-person in the future and have to re-evaluate the trade-offs of ordering online with the ability to shop at stores along commuting routes.

Poverty status ( Low Income ) does not have the anticipated positive influence on online shopping as suggested by pre-pandemic research (Hansen, Reference Hansen 2005 ). Rather, our study found that Low Income has no significant influence on the choice to shop online, or the frequency of online shopping. Two theories could explain this finding. First, in this study’s definition, both grocery purchases that were delivered or curbside pickup are included. If previous studies did not include curbside pickup, and if lower-income families are more likely to use curbside pickup because it does not incur a delivery fee, then previous studies may have been less likely to detect lower-income households’ use of online grocery shopping. Second, prior to the pandemic lower-income households that participated in the Supplemental Nutrition Assistance Program (SNAP) could not use their benefits to purchase groceries online. The USDA Food and Nutrition Services (USDA-FNS), which manages the SNAP program, began a limited pilot program pre-pandemic to allow SNAP participants to use their benefits to purchase groceries online, and in March 2020 they began expanding the program into additional states (USDA/FNS, 2020 ). While SNAP benefits could be used for online groceries, they should not be used to pay for delivery fees and could be redeemed at a very limited number of retailers. However, Walmart and Amazon were an option in most states. Reports suggest this policy change may have dramatically increased online purchases by SNAP households since the beginning of the pandemic (Day, Reference Day 2020 ). Additionally, several supermarket chains and Walmart began accepting SNAP as a form of payment for curbside pickup during the pandemic and independent of the FNS pilot program (Berthiaume, Reference Berthiaume 2020 ; Redman, Reference Redman 2020 a; WalMart, 2020 ). Jointly, these may have increased the utilization of online grocery shopping among lower-income households.

Lack of experience with online grocery shopping ( Not Shopped Online Before ) has a large effect both on the use and frequency of online grocery shopping in June 2020. Those who had not shopped online for groceries before are about 40 percent less likely to have shopped online for them in June 2020 and among those who did, they used online shopping 0.567 fewer times than those with previous online shopping experience.

In-store shopping trips ( Store Trips ) have mixed effects on online grocery shopping. Each additional grocery trip decreases the probability of shopping online by 0.8 percent but increases the frequency of online grocery shopping by 0.120 times. This latter finding suggests a complementary, rather than substitution, relationship between online and brick-and-mortar shopping trips which is similar to findings by Farag et al. ( Reference Farag, Krizek and Dijst 2007 a) and Pozzi ( Reference Pozzi 2013 ). However, additional research would be needed to substantiate this hypothesis.

Restaurant trips do not influence probability of shopping online; however, among those who shopped online, each additional restaurant trip increases the frequency they shopped online by 0.092 trips. As the share of restaurant trips for pickup or delivery increases, the probability of shopping online decreases by 6.1 percent hinting at possible substitutability between pickup/delivery restaurant trips and online grocery shopping. Yet, among those who shopped online, increasing the share of restaurant trips that were pickup or delivery by a point increases the number of times the respondent shopped online by 0.615. Since increasing the share of restaurant trips that are pickup/delivery increases the frequency of online grocery shopping, after controlling for the influence of total restaurant trips, this result could reflect averting behaviors during the pandemic among online grocery shoppers. Findings regarding the effects of pandemic risk variables discussed below further support this hypothesis.

As might be expected, overall grocery expenditures, as measured by the natural log of June 2020 grocery expenditures ( Ln Grocery Expenditures ) increases the number of times shopped online ( Times Online ). Footnote 3 Using the untransformed values, for each 100 dollars of grocery expenditures in June 2020, the number of times shopped online increases by 0.160.

Compared with respondents from the Midwest , Northeast respondents who shopped for groceries online did so 0.533 times more often. While large metro area was expected to have a positive influence on online shopping, it does not significantly affect probability of buying groceries online and it negatively affects the frequency for those who used online shopping by 0.533 trips. One possible explanation for the negative effect is that perhaps metro shoppers were less likely to say they were spending more on groceries than normal than the suburban or more rural counterparts. As part of debriefing questions, it was asked whether the respondent thought they spent more or less on groceries than usual in June 2020. However, while 40.42 percent of the metro respondents said they spent more on groceries in June 2020 than normal, only 32.40 percent of the non-metro respondents spent more than usual. Another potential explanation is that metro shoppers may be more experienced with and trusting of online grocery services and hence be willing to purchase more per online shopping trip. Given that they were spending more than usual on groceries in June 2020 than their non-metro counterparts, this seems more plausible. However, additional research would be needed to evaluate metro versus non-metro shopper knowledgeability and trust in online shopping for groceries.

Concerns about becoming ill with COVID-19 or possible pandemic-associated food shortages influenced online grocery shopping but in different ways. Being at least moderately concerned about becoming ill with COVID-19 ( Concern Becoming Ill ) increases the probability of online grocery shopping by 7.1 percent but does not significantly influence the number of times shopped online. However, being at least moderately concerned about food shortages ( Concerned Food Shortages ) positively influence frequency of online grocery shopping by 0.489 times. While the exact reasons for this relationship require further study, they do suggest that COVID-19 concerns related to the food system continued to influence consumer behavior even into the summer of 2020. Some possible explanations may include the increased utilization of online grocery shopping to maintain a continuous supply of items that were previously in short supply, or stockpiling goods. However, these are hypotheses that would require future study.

3.3. Reasons for Not Shopping Online for Groceries in June 2020

The survey also asked respondents the reasons for not online grocery shopping in June 2020 and the results are reported in Figure  1 . Personal preference for shopping in-store is the dominant reason (˜73%), distantly followed by delivery fees being too expensive at 9.64 percent. Only 5 percent indicated that they did not like the previous experiences in online grocery shopping, while less than 5 percent indicated they did not have the services available where they live, that their SNAP benefits were not accepted, or that they had purchased online before, but just did not do so in June 2020.

Figure 1. Reasons for not shopping for groceries online in June 2020.

3.4. Multinomial Probit Model of Plans for Future Online Grocery Shopping

The estimated multinomial probit model of future plans for online grocery shopping ( Future Online ) among households that currently use online grocery is reported in Table  4 . The reference category is Future Online  = 1, or no plans to grocery shop online in the future. The associated average marginal effects for the explanatory variables on probability of shopping online and the number of times shopped online for groceries are calculated using the estimated coefficients and equation (7) and are shown in the third to fifth columns of Table  4 . The standard errors associated with the marginal effects were calculated using the Delta method. The model was significant overall as indicated by the LLR test against an intercept-only model. The model correctly classified just under 62.3 percent of the observations regarding future online shopping plans.

Table 4. Estimated multinomial probit model of future online grocery shopping plans ( Future Online ) and associated marginal effects (ME) a

a The baseline category is Future Online  = 1 or will not shop online.

b ***Significance at α = 0.01, **significance at α = 0.05, and *significance at α = 0.10.

c The baseline region is Midwest.

d The marginal effect of Grocery Expenditures is calculated as the ME Ln Groc. Expend./Grocery Expenditures .

Several consumer characteristics influenced plans to use online grocery shopping in the futur e, regardless of the pandemic, as indicated by their relationship with the Future Online  = 3 outcome in column 3 of Table  4 . The presence of a child ( Children ) increased the probability of respondents stating they would shop online in the future by 6.4 percent, which suggests that respondents with children value the convenience afforded by shopping online that may reduce the number of brick-and-mortar store trips with children even after the pandemic ends (Etumnu et al. Reference Etumnu, Widmar, Foster and Ortega 2019 ; Hansen, Reference Hansen 2005 ; Jaller and Pawha, Reference Jaller and Pahwa 2020 ; Melis et al., Reference Melis, Campo, Lamey and Breugelmans 2016 ).

College Graduate increased the probability of planning to shop online in the future by 3.1 percent and decreased the probability of not planning to do so by 5.3 percent. This result is similar to findings from prior research about the effects of education on online grocery shopping (Etumnu et al., Reference Etumnu, Widmar, Foster and Ortega 2019 ; Hiser, Nayga, and Capps, Reference Hiser, Nayga and Capps 1999 ; Jaller and Pawha, Reference Jaller and Pahwa 2020 ; Van Droogenbroeck and Hove, Reference Van Droogenbroeck and Van Hove 2017 ) and suggests that higher educated online grocery shoppers in June 2020 plan to continue to so into the future. In addition, full-time employment increased the probability of planning to use online grocery shopping in the future by 7.6 percent and decreased the probability of saying they would not by 8.4 percent. Full-time workers likely value the convenience afforded by online grocery shopping and plan to continue it into the future which is consistent with prior research findings (Van Droogenbroeck and Hove, Reference Van Droogenbroeck and Van Hove 2017 ).

With each additional year of age ( Age ), the probability of planning to shop online in the future regardless of the pandemic increased by 0.4 percent, while the probability of planning to online shop only if the pandemic persists decreased by 0.4 percent. This finding about the effects of age on future plans is unlike findings from prior research of negative effects of age on online grocery shopping (Etumnu et al. Reference Etumnu, Widmar, Foster and Ortega 2019 ; Farag et al., Reference Farag, Schwanen, Dijst and Faber 2007 b; Hiser, Nayga, and Capps, Reference Hiser, Nayga and Capps 1999 ; Van Droogenbroeck and Hove, Reference Van Droogenbroeck and Van Hove 2017 ). This finding could reflect that older shoppers who have experienced online grocery shopping may now value the convenience it affords so as to plan to continue it into the future.

Compared with higher-income households, lower-income households ( Low Income ) that shopped online in June 2020 are 7.4 percent less likely to continue shopping online in the future and 7.5 percent more likely to choose to shop online only under pandemic conditions. This finding suggests that compared with higher-income households, lower-income households will be more likely to continue online shopping if pandemic conditions persist. This finding could reflect a perceived trade-off in the minds of lower-income online grocery shoppers during the pandemic, weighing any additional grocery costs incurred by online shopping against concerns about becoming ill shopping in-store if the pandemic persists.

Full-time employment status ( Employed Full Time ) positively influences probability of shopping online for groceries in the future (7.6 percent) and negatively influenced probability of not shopping online in the future (−8.4 percent). Furthermore, essential workers ( Essential ) are 6.6 percent more likely to say they would shop online for groceries in the future regardless of the pandemic. These results echo prior research suggesting a link between busy schedules and the convenience of online shopping (Verhoef and Langerak, Reference Verhoef and Langerak 2001 ).

Results also show that regional location and urbanization of residence influence plans for future online grocery shopping. Compared with those in the Midwest , those in the South are 5.3 percent less likely to say they do not plan to shop online for groceries in the future. Also, compared with those outside metro areas, those in the largest metro areas are 3.6 percent less likely to say they do not plan to shop online for groceries in the future.

Lack of previous online grocery purchasing experience ( Not Shopped Online Before ) decreases the probability of plans to shop in the future by 27.6 percent and increases the likelihood of not shopping online in the future by 13.1 percent. However, lack of experience also positively influences shopping online only if the pandemic continues to be a problem (14.4 percent). As this variable gauges experience with shopping online prior to June 2020, it may suggest that households that adopt online shopping during the later period of the pandemic are primarily concerned with minimizing their exposure to COVID-19 and will be unlikely to continue online grocery shopping behaviors after the pandemic ends. This would concur with the finding by Hand et al. ( Reference Hand, Dall’Olmo Riley, Rettie, Harris and Singh 2009 ) that once situational factors that precipitate use of online shopping are removed, shoppers tend to return back to brick-and-mortar shopping.

More frequent in-store grocery store trips positively influence probability of the respondent indicating they do not plan to shop online for groceries in the future (0.5 percent) and negatively influence probability that they plan to shop online for groceries in the future (−0.8 percent). Restaurant trips do not significantly influence future online shopping plans, but as the share of restaurant trips that were pickup or delivery increases, the probability of not shopping online for groceries in the future decreases by 7.4 percent. These findings could reflect planned averting behaviors, with those currently shopping in-store less and using drive through or pickup dining more, being less likely to say they would not shop online in the future.

The natural log of overall grocery expenditures for June 2020 ( Ln Grocery Expenditures ) positively influences respondents’ intentions for future online grocery shopping. For every $100 spent on groceries, the effect on probability of shopping online in the future is 11.0 percent, shopping online only if the pandemic continues is −4.1 percent, and not planning to shop online in the future is −6.9 percent.

The pandemic concern variables primarily influence planned future online shopping behavior related to the pandemic. Moderate concern with becoming ill with COVID-19 ( Concerned Becoming Ill ) decreases the probability of planning to shop online in the future regardless of pandemic conditions (−9.4 percent) but increases the probability of continuing to shop online only while the pandemic continues (10.9 percent). This result suggests that greater concerns about becoming ill will likely only drive online shopping while the pandemic persists. Although concerns about food shortages ( Concerned Food Shortages ) influenced the frequency of online grocery shopping in June 2020, it has no effect on future plans for online grocery shopping. This suggests that grocery shoppers were responding to supply chain disruptions early in the pandemic but do not see this as likely problematic in the future and plan to adjust their shopping plans accordingly. It is possible that those who are most concerned about food shortages may not see online shopping as a preferred means to stockpile as compared to brick-and-mortar shopping. However, additional research would be needed to investigate this possibility further.

4. Implications and Conclusions

While online grocery shopping had been increasing in popularity prior to the start of the COVID-19 pandemic in early 2020, the onset of the pandemic accelerated its adoption. With this rapid increase in use of online grocery shopping, developing a better understanding of drivers of its use is of interest not only to the grocery retailing industry but also to policymakers. This study investigated the influence of pandemic-specific drivers, such as concerns about becoming ill and potential food shortages, as well shopper demographics, food shopping behaviors, and grocery expenditure patterns. Furthermore, to understand the potential staying power of online grocery shopping, this study also examined factors influencing online grocery shoppers’ intentions to continue online shopping in the future, under pandemic and non-pandemic conditions.

Many of the household determinants found in pre-pandemic research to increase online grocery shopping were also found in this research to increase online grocery shopping during the pandemic (younger age, full-time employment, college education, and the presence of children). Unexpectedly, low income had no influence on either the use or frequency of using online grocery shopping, whereas in past research it was generally associated with lower utilization of online grocery shopping. While this finding needs to be investigated further in future research, it may suggest that the pandemic has been particularly influential on the choice to grocery shop online among low-income households, who were previously less likely to utilize online grocery shopping. This may be related to the expansion of curbside pickup and the expansion of USDA pilot program allowing SNAP participants to use their benefits to make online grocery purchases (USDA/FNS, 2020 ; Hansen, Reference Hansen 2005 ).

While low income did not influence probability or frequency of online shopping in June 2020, low-income shoppers are more likely to say they would shop online if the pandemic continues, suggesting that lower-income shoppers do not believe the benefits of online shopping will persist beyond the pandemic. These households may be most sensitive to the additional delivery fees or cost associated with online grocery shopping that cannot be paid for with their SNAP benefits (USDA/FNS, 2020 ). This finding is in contrast to the influence of full-time employment and essential worker status which both increase the likelihood of future online shopping regardless of the pandemic. Full-time and essential workers may have less time to shop in-person and value the convenience of online grocery shopping beyond the duration of the pandemic. Given the potential of online grocery shopping to improve access to supermarkets for low-income households, future research should focus on the barriers and benefits of online grocery shopping among lower-income households.

Older populations are another vulnerable population of concern during the pandemic, because they may be more susceptible to serious illness if they contract COVID-19, and thus could benefit from policies and programs to reduce their exposure, such as those encouraging online grocery shopping. However, our results showed that age negatively influenced the probability of shopping for groceries online, perhaps reflecting that older populations are less comfortable with the concept of and technology needed to shop for groceries online. As evidenced by reasons for not shopping online, that majority of those who did not shop online preferred to shop in-store, despite the pandemic. Interestingly, among those who did shop online for groceries, older age had the opposite effects on plans for future grocery shopping. Older age increases the likelihood of continuing to shop online in the future, regardless of the pandemic. These findings suggest that once older shoppers try online shopping, compared with younger shoppers, they are more likely plan to continue it, perhaps due to the convenience, and in some cases, to avoid the physical demands associated with grocery shopping. Thus, developing policies to address barriers to use and increase online grocery shopping among older populations may not only benefit them during the pandemic, but also beyond. For example, some programs might focus on how to access and use online grocery shopping for the more nascent online shopper, while other programs might focus on how to use meal planning with online shopping and online list-making to more efficiently use food budgets and potentially reduce food waste.

Concerns with becoming ill with COVID-19 increased the likelihood of utilizing online grocery shopping in June 2020, while the frequency of online grocery shopping, among those who use online grocery shopping, was driven in part by fears of food shortages. Combined with the finding that increasing total grocery expenditures and in-store trips also increased the frequency of online grocery store shopping, this may suggest stockpiling behaviors among grocery shoppers. However, this behavior was not directly addressed in this study and requires future study for more definitive conclusions. Additional research should likely examine how consumers may be shopping in-store and supplementing with items they cannot find in-store with online purchases and vice versa.

The long-term effects of the pandemic on online grocery shopping will require further analysis, but our research does provide several preliminary insights. Those who had not previously purchased groceries online were 40.3 percent less likely to shop online for groceries in June 2020; however, among online grocery shoppers, new online shoppers were only 27.6 percent less likely to say they would shop online in the future regardless of the pandemic. This latter result suggests that some first-timers will be likely to stay with online shopping regardless of the pandemic. However, among respondents who utilized online grocery shopping in June 2020, about 12 percent indicated they do not plan to continue and 29.5 percent indicated they will continue to shop online only if COVID-19 continues. This foretells that at least some of the increased utilization of online grocery shopping will not persist beyond the pandemic.

Only concerns about becoming ill influence future online shopping intentions, while concerns about food shortages do not. While shoppers may have seen food supply chain disruptions that occurred in the first few months of the pandemic, they may have confidence in the supply chain to resolve disruptions and shortages in the longer term. However, being moderately concerned about becoming ill increased the probability that a respondent would shop online in the future but only if the pandemic persists. This latter finding could suggest that online shoppers who are driven by concerns about becoming ill from COVID-19 may revert to their usual in-store shopping behaviors when the pandemic subsides. Taken together with the finding that those who had not shopped online before were less likely to plan to do so in the future, inexperienced online shoppers who were more driven by pandemic concerns may be less likely to sustain online grocery shopping in the future beyond the pandemic.

This study has several limitations. First, it represents a snapshot of time in June 2020. Hence, some of the variables included in the model of June 2020 online grocery shopping, such as June grocery expenditures, restaurant trips, in-store grocery trips, and share of restaurant trips that were pickup, could represent endogenous decision-making during that month. Additional research including consumer behaviors from multiple time frames could help alleviate this issue. Furthermore, future research should focus on the long-term impacts of the increased utilization of online grocery that began during the pandemic, including how retailers are adapting their online shopping services to meet changing shopper preferences and perhaps improving their services during the pandemic. While out of the scope of this article, future research should examine the availability of online grocery by retailer type to determine if current trends will disproportionately benefit large, chain grocers who may be better able to support online services, while harming smaller, independent grocers. This could have implications for communities that rely on smaller grocers, or for individuals who cannot easily access online services.

Second, we did not ask detailed food shopping questions to investigate how the types of food items purchased may have changed as a result of the pandemic. This could potentially be of importance as some items may be more readily deliverable through online shopping than others. Etumnu and Widmar ( Reference Etumnu and Widmar 2020 ) found certain types of foods were more likely to be ordered online than others among US food shoppers. Additional research should likely examine whether more perishable items, such as fresh fruits and vegetables, are purchased in a brick-and-mortar setting rather than online, particularly in rural areas where delivery services for these types of items may be lacking.

Third, our survey was an online survey, and not an in-person or intercept survey. This could potentially create some sample bias toward those who are more familiar with the internet, and possibly, online shopping. Additional research findings from an in-person or intercept survey in-store could complement the findings from this research.

Conceptualization: K.L.J., J.Y., X.C., and T.Y.; Methodology: K.L.J., J.Y., X.C., and T.Y.; Formal Analysis: K.L.J. and J.Y.; Data Curation: K.L.J. and J.Y.; Writing—Original Draft: K.L.J., J.Y., X.C., and T.Y.; Writing—Review and Editing: K.L.J., J.Y., X.C., and T.Y.

Funding for this study was provided by in part by Ag Research at The University of Tennessee Institute for Agriculture. The findings and views represented in this paper are solely those of the authors and do not necessary represent those of the institution.

Drs. Jensen, Yenerall, Chen, and Yu declare no competing interests.

Data for this study were collected under UTK IRB Approval UTK IRB-20-05882-XM and were done so with respondents being assured of confidentially. Therefore, individual data may not be released.

1 Taherdoost ( Reference Taherdoost 2019 ) provided an overview of use of differing Likert scales, proposing use of a 7-point Likert scale. However, as noted in Taherdoost’s paper, Preston and Colman ( Reference Preston and Colman 2000 ) and Bendig ( Reference Bendig 1954 ) suggested longer scales (7 point to 9 point) are preferable for capturing respondents’ sentiments, with this benefit appearing to decrease with longer scales, such as 12-point scale (McRae, Reference McRae 1970 ).

3 Ln Grocery Expenditures, Store Trips, Restaurant Trips , and ShrPickup are potentially endogenous decisions to probability of choosing to shop online in June and the number of times shopped online in June 2020. Results were validated by estimating the models without these variables and the estimates appeared to be robust. These models are not presented in the interest of parsimony; however, they are available from the authors upon request.

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• Volume 53, Issue 3
• Kimberly L. Jensen (a1) , Jackie Yenerall (a1) , Xuqi Chen (a1) and T. Edward Yu (a1)
• DOI: https://doi.org/10.1017/aae.2021.15

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ORIGINAL RESEARCH article

Changing trends of consumers' online buying behavior during covid-19 pandemic with moderating role of payment mode and gender.

• 1 Management Studies Department, Bahria University, Karachi, Pakistan
• 2 Faculty of Engineering Sciences and Technology, Hamdard University, Karachi, Pakistan

It was not long ago when technological emergence fundamentally changed the landscape of global businesses. Following that, business operations started shifting away from traditional to advance digitalized processes. These digitalized processes gave a further boost to the e-commerce industry, making the online environment more competitive. Despite the growing trend, there has always been a consumer market that is not involved in online shopping, and this gap is huge when it comes to consumers from developing countries, specifically Pakistan. On contrary, the recent COVID-19 pandemic has brought drastic changes to the way consumers used to form their intention and behave toward digitalized solutions in pre COVID-19 times. Evidence shows that the global e-commerce industry has touched phenomenal growth during COVID-19, whereas Pakistan's e-commerce industry still holds a huge potential and has not fully boomed yet. These facts pave new avenues for marketers to cater to this consumer market for long-term growth. Hence, the study provides insights into how consumers' online buying behavior has transformed during the COVID-19 pandemic in the context of Pakistan. The study presents a framework based on the Technology Acceptance Model (TAM) and Theory of Planned Behavior (TPB). Furthermore, the moderating role of gender and payment mode has also been examined. For the analysis of variables, the partial least squares (PLS) method was used to conduct structural equation modeling (SEM) by collecting data from 266 respondents. The results show a significant and positive impact of perceived benefits, perceived ease of use, perceived enjoyment, and social influence on consumers' intention, but they also show an insignificant impact of gender and payment mode as a moderating variable on PEOU-BI and BI-AB, respectively. The results are of utmost significance for Pakistani businesses, marketers, and e-traders to streamline their business practices accordingly. Lastly, the proposed framework demonstrates new directions for future research to work upon.

Introduction

In developing countries like Pakistan, the Internet brought much convenience to businesses, specifically in the twenty-first century. Due to the current COVID 19 pandemic, there has been a drastic change in the way consumers have shifted toward online buying. It is evident that post-COVID circumstances have left a significant impact on the e-commerce industry ( Rashid et al., 2022 ). This has caused global e-commerce sales projection to reach $7.4 trillion by 2025 ( Statista, 2022 ). On the contrary, South Asian countries show a low share of just 1.4% in global e-commerce business compared to their population share in the world.

Pakistan's e-business has shown drastic improvements ever since the pandemic struck. As per SBP (FY20), registered e-commerce merchants have increased, and markets have expanded to Rs. 234.6 billion with 55.5% yearly. These situations have raised doubts about Pakistan's digital connectivity, which shows a huge potential for growth and untapped areas for e-traders in Pakistan. Despite these statistics, the e-commerce market in Pakistan is still in its infancy stage. It has been evident that there are a whole lot of consumer bases that are not involved in online shopping ( Ahmed et al., 2017 ). The reason for this lack of involvement in online shopping is unknown. Domestically, there is a research and development gap that causes inconsistency in theoretical and empirical evidence on factors that may shape an individual's online buying behavior in the Pakistani market. Therefore, it is pertinent to fill this gap by examining Pakistani consumers' psychological and behavioral beliefs. Globally, there has been plenty of research studies conducted proposing valuable conceptually, theoretically, and empirically tested frameworks that intend to explain antecedents of consumers' intentions toward online buying behavior.

These studies examined the online behavior of consumers in numerous dimensions and postulated perceptions behind online shopping behavior and attributes ( Jarvenpaa and Todd, 1996 ; Chang and Kannan, 2006 ), consumer information process styles, online store layouts, ( Park and Kim, 2003 ), behavioral and normative beliefs about technology adoption ( Karahanna et al., 1999 ; Limayem et al., 2001 ; Foucault and Scheufele, 2002 ) risks related to online shopping ( Jarvenpaa et al., 1999 ; Akhlaq and Ahmed, 2015 ; Haider and Nasir, 2016 ; Pappas, 2016 ), and technology-oriented factors affecting online purchase intention ( van der Heijden et al., 2003 ; Prashar et al., 2015 ).

Overall, the connotations of previous studies tended toward two dimensions: (a) “product and shopping attributes” that are customer-specific and (b) “technological attributes” that are website-/technology-specific. None of the available studies has covered integrated attributes of both dimensions “customer-specific” and “technology-specific” in a single framework to study insights of consumers' behaviors for online buying. Hence, there is a need to address underlying factors that may shape consumers' intention and actual behavior to opt for online purchases. Based on these arguments, the present study proposes a comprehensive framework comprised of factors impacting consumers' online buying behavior during the pandemic.

In this regard, the theoretical foundation of this study is built upon the Technology Acceptance Model ( Davis, 1989 ) (TAM), which is an extension of the Theory of Planned Behavior (TPB) ( Ajzen, 1985 ). TAM is a widely used and highly influential model of user's acceptance of “technology.” As the present study examines the buying behavior of an online consumer, it tends to predict how consumers' perceived benefits, perceived ease of use, perceived enjoyment, and social influence have an impact to form consumers' intention and behavior to purchase online.

The results of this study would be of interest to a diverse research audience, including the academia, marketers, advertisers, policymakers, governments, and businesses. For the academia, new theoretical literature has been presented with the inclusion of potent constructs obtained from the technological model (TAM), psychological, and behavioral model (TPB) along with the normative notion of social influence on consumers' behavioral intention and behavior. Marketers may devise strategies to encourage their consumers to opt for online purchases, whereas advertisers may use appealing and creative content to promote them. In addition, policymakers may enact laws to encourage e-trading, and the government may facilitate Pakistani e-traders by releasing funds to build and maintain an advanced IT infrastructure. Lastly, the study would be of optimum significance for businesses that may work on their website designs and processes and maintain a website infrastructure to aid consumers according to their changing shopping preferences.

Literature Review

Perceived benefits and behavioral intentions.

Previous studies have provided many findings and devoted considerably to delivering benefits to consumers to stimulate their shopping intentions. Research has clearly defined the concept of consumer benefits and the significance of hedonic and utilitarian benefits for them ( Babin et al., 1994 ; Holbrook, 1994 ; Jones et al., 2006 ; Wang et al., 2013 ). Consumers derive practical benefits from the performance of a product or a service after achieving a task ( Kim, 2002 ). Furthermore, recent studies conducted by Widyastuti et al. (2020) stated the perception of perceived benefits, whereas Yew and Kamarulzaman (2020) and Bangkit et al. (2022) found a significant positive impact of perceived benefits on online consumer behavior. In the same line, a study conducted by Jeong et al. (2003) on “online shoppers of the hotel industry” found that for customers, the most critical factor that influences their “behavior intention” is the satisfaction level of available information, dimensions, and attributes provided by a website. Chang and Kannan (2006) stated in their study that website quality has positively influenced consumers' purchase intention. Bai et al. (2008) found significantly positive empirical results in online usability, functionality, customer satisfaction, and behavior intentions. The study further stated that consumers perceive all these dimensions as valued, increasing their purchase intentions. As Babin and Babin (2001) stated that consumers who efficiently complete shopping tasks would show stronger repeated purchase intentions.

In addition, Teo (2002) , Xia et al. (2008) , Nazir et al. (2012) , and Manu and Fuad (2022) shared similar findings where consumers derive attributes of perceived benefits through online shopping; it provides the required information on a product or a service, saves time, low prices, and convenience in the availability of products that are not locally available. Online shopping is getting popular in Pakistan because of its ease of use and the comfort it brings to consumers without much effort ( Iqbal and Hunjra, 2012 ). Furthermore, research highlights that consumers seek internet shopping valuable for price reviews and comparisons, search and deal evaluation convenience, low prices, selection variety, information on product features, latest awareness of brands and fashion trends ( Sorce et al., 2005 ; Zhou and Zhang, 2007 ; Jiang et al., 2013 ; Jhamb and Gupta, 2016 ). Teo (2006) indicates that consumers expect benefits like sufficient product information, convenience, online security, and easy contact with vendors. Moreover, while shopping online, consumers also expect prompt delivery of a product, a reliable supply chain, and return transaction policies ( Dawn and Kar, 2011 ).

H1: Perceived benefits significantly impact the behavioral intention for online purchases.

Moderating Role of Gender

In various marketing and consumer behaviors, demographic variables, specifically the impact of gender, have been taken into different contexts. In some studies, overall demographics are used as antecedents of TAM variables ( Porter and Donthu, 2006 ). Others have used them to moderate the effect of the predictor and criterion relationship in technological acceptance ( Chang and Kannan, 2006 ). Previously, research studies have accepted that there is a significant role of gender in technology acceptance ( Yousafzai and Yani-de-Soriano, 2012 ); a study further shows that men have a more strong and significant impact on perceived usefulness and behavioral intention in relation to technology acceptance and women have more impact on perceived ease of use and behavioral intention. This study is in line with Davis (1989) , Clegg and Trayhurn (2000) , and Venkatesh et al. (2003) . In conclusion, it has been argued that men are more tech-savvy, task-oriented and adopt technology to avail themselves benefits of online shopping. However, for acceptance of technology, women tend to show more computer anxiety than men ( Venkatesh and Morris, 2000 ; Karavidas et al., 2005 ; Zhang, 2005 ).

H2: Gender moderates the effect of perceived benefits on the behavioral intention for online purchase.

H3: Gender moderates the effect of perceived ease of use on the behavioral intention for online purchase.

Perceived Ease of Use (PEOU) and Behavioral Intention (BI)

Perceived ease of use is best defined by Davis (1989 , 1993) as one of TAM's basic constructs. PEOU is defined as a degree to which a person believes using a particular system is effortless ( Davis, 1989 ). Al-Azzam and Fattah (2014) postulated that perceived ease of use refers to a consumer who believes that using the Internet for shopping is free of effort and involves minimal friction in using and handling websites. Apart from the vital role of “ease of use” in technology acceptance, it has also been proposed for website usability and efficiency while shopping online ( Monsuwe et al., 2004 ). Considering these findings, it can be claimed that if there is an ease in usage and effortlessness in handling technology, consumers are more likely to adopt a system while purchasing online. Hence, one's intention to purchase online increases ( Venkatesh, 2000 ; Xia et al., 2008 ). Many other researchers have confirmed a strong sign and a direct relationship between perceived ease of use and the behavioral intention of a person ( Teo et al., 1999 ; Venkatesh and Bala, 2008 ; Ingham et al., 2015 ).

The study further implies that if a consumer has an increased experience, they adjust themselves to system-specific ease of use and reflect on their interaction with repeated usage of the system, which influences the behavioral intention to shop online. Few latent dimensions merely shape “ease of use” including site characteristics, navigation, and download speed ( Zeithmal et al., 2002 ). However, the most significant role in shaping “ease of use” is played by two dimensions elaborated by Venkatesh (2000) ; these include computer self-efficacy, computer anxiety, and computer playfulness; “computer self-efficacy” relates to the general use of computer or skills needed to operate a system; “computer anxiety” refers to a person's fear of using a computer when required, whereas “computer playfulness” is a degree to which a consumer's cognitive ability makes them feel less effortful and underestimates the complexity of system usage for online interaction. Increased usage experience contributes to unique attributes of perceived enjoyment concerning user system specification; it makes a more enjoyable experience for users. ( Venkatesh, 2000 ; Monsuwe et al., 2004 ).

H4: PEOU significantly impacts behavioral intention for online purchase.

Perceived Enjoyment (PE) and Behavioral Intention (BI)

Researchers have explained enjoyment as how online shopping is perceived to be enjoyable or fun for a consumer. Various researchers have theoretically and empirically proved the role of intrinsic motivation in online shopping ( Davis et al., 1992 ; Venkatesh and Speier, 1999 ; Venkatesh, xbib2000 ). Intrinsic motivation has been taken as a construct of perceived enjoyment in many studies ( Monsuwe et al., 2004 ). Davis et al. (1992) introduced the third belief in TAM, perceived enjoyment. He proposed that perceived enjoyment directly impacts the behavioral intention of an online consumer. In addition, studies conducted in the past two decades have shed some light to state the role of perceived enjoyment in the behavioral intention of a consumer ( Koufaris, 2002 ; Cyr et al., 2006 ; Chang and Chen, 2008 ; Marza et al., 2019 ; Bangkit et al., 2022 ). Triandis (1980) reports that emotions like fun, joy, and pleasure influences human behavior. According to self-determination theory ( Deci, 1975 ), if a person is intrinsically involved in online shopping and personally determined, they enjoy doing it. Kuswanto et al. (2019) investigated variables impacting the online behavior of university students in Indonesia and highlighted that the online shopping behavior of consumers significantly gets influenced by enjoyment, social influence, and perceived risk.

Furthermore, a study conducted by Akhlaq and Ahmed (2015) has also proposed perceived enjoyment as a significant construct backed by an intrinsic motivation that positively impacts consumers' intention to shop online. Findings on Pakistani consumers reported by Cheema et al. (2013) show that perceived enjoyment has a significant and positive impact on online shopping intention and holds a 42% contribution to the model. Apart from intrinsic motivations, another latent dimension, exploration and curiosity to use a system, is also prominent in investigating the online shopping context. The empirical evidence reported by Teo (2002) shows that interest in online browsing is related to curiosity about knowing various products and brands available to purchase online. According to Teo's study, around 50% of the respondents browsed even if they did not intend to purchase.

H5: Perceived enjoyment mediates the relationship between perceived ease of use and behavioral intention for online purchase.

Causal Nature of Perceived Ease of Use (PEOU) and Perceived Enjoyment (PE)

There are differences in research findings that confirm the causal relationship between perceived ease of use and perceived enjoyment ( Sun and Zhang, 2006a , b ). In some studies, perceived enjoyment has been considered as an antecedent of perceived ease of use ( Venkatesh, 1999 , 2000 ; Agarwal and Karahanna, 2000 ; Venkatesh et al., 2002 ). In other studies, it has been confirmed as a consequence of perceived ease of use ( Deci, 1975 ; Davis et al., 1992 ; Teo et al., 1999 ; van der Heijden et al., 2003 ). It has been claimed that an easier-to-use system is more enjoyable ( Igbaria et al., 1995 ). For an empirical discussion of this inconsistent argument regarding the causal relationship between perceived ease of use and enjoyment, Sun and Zhang (2006a , b) conducted information system-based research in a utilitarian context using a covariance-based statistical method to find a causal relationship. They concluded that perceived enjoyment and perceived ease of use have overall dominance in the model in a utilitarian system environment. The present study aims to confirm this causal relationship by considering perceived enjoyment due to perceived ease of use. The study tends to measure a consumer's buying behavior via technology ( Davis et al., 1992 ; van der Heijden et al., 2003 ).

H6: Perceived ease of use significantly impacts perceived enjoyment for online purchase.

Social Influence (SI) and Behavioral Intention (BI)

“Social influence” (SI), an antecedent of the subjective norm (SN), is a crucial construct of TPB and TAM ( Davis, 1989 ) that has originated from the Theory of Reasoned Action (TRA) ( Fishbein and Ajzen, 1975 ). TRA states that a person's behavioral intention (BI) has a significant and positive relationship with subjective norms ( Karahanna et al., 1999 ). One's social circle may influence a person to behave in a particular manner ( Ajzen, 1985 ). According to classic internalization studies, when someone incorporates the referent's influence in adopting a system, the person perceives the referent's belief as their own belief ( Kelman, 1958 ; Warshaw, 1980 ). Wei et al. (2009) mentioned in their study Rogers (1995) ' proposition of social influence; he stated that social influence can be defined as two forms: mass media and interpersonal influence. Mass media or external influence includes newspapers, reports, academic journals, published articles, magazines, television, radio, and other applicable mediums, whereas interpersonal influence comes from family, peers, friends, social networks, and electronic word of mouth (EWOM) ( Bhattacherjee, 2000 ; LaRose and Eastin, 2002 ; Rao and Troshani, 2007 ; Pietro et al., 2012 ).

Venkatesh and Davis (2000) stated that people incorporate social influence to gain status and acceptance in their social setting. Studies by Ketabi et al. (2014) and Kuswanto et al. (2019) further highlighted the role of social norms and social influence on consumers, respectively; it has been stated that in certain situations the reference group of a person, specifically “friends,” strongly influences the behavior of an individual. In their qualitative study, Wani et al. (2016) also identified “social influence” and “e-word of mouth” as critical factors. Their study further elaborates that the opinions of consumers, peers, friends, and colleagues matter a lot while purchasing online. Even in the last shopping stage, just before check-out, if a consumer reads any comment about a product or a service, it undoubtedly impacts one's decision ( Park et al., 2011 ).

H7: Social influence significantly impacts behavioral intention for online purchase.

Behavioral Intention (BI) and Actual Behavior (AB)

Plenty of studies have used TPB and TAM to determine an individual's intention to engage in a particular behavior ( Ajzen, 1985 ; Pavlou and Marshall, 2002 ; Delafrooz et al., 2010 ; Tsai et al., 2011 ; Zaidi et al., 2014 ; Bauerová and Klepek, 2018 ). Behavioral intention is the focal point of TRA, TPB, and TAM. According to the extended TAM model postulated by Venkatesh and Davis (2000) , the relationship between intention to use and actual usage was significant and strongly mediated the effect of perceived benefits, perceived ease of use, and subjective norms on actual usage of an online consumer. Limayem and Hirt (2003) elaborated in their study that there are some “facilitating conditions” ( Triandis, 1979 ) that moderate the relationship between behavioral intention and actual behavior. Even if a person intends to act in a certain way, one cannot without those facilitating conditions. These conditions align with Ajzen's “perceived behavioral conditions,” but the point of difference is that Ajzen's perceived behavioral control is subjective, whereas Triandis' facilitating conditions are objective. In the present study, the behavioral intention was considered to examine the significance of perceived intentions of a person on actual buying behavior while purchasing online.

Payment Mode

According to a report by Yousuf (2018) [Asian Development Bank (ADB)], 95% of Pakistan's e-commerce transactions come from cash on delivery (COD), and the remaining 5% comes from electronic payment with debit/credit cards. Another study states that only 31% of Pakistani tend to pay online for shopping and that cybercrimes and lack of trust in payment systems are the main reasons for their choice. ( CIGI, 2017 ). An increase in the online payment rate includes uncertain security and privacy issues that may influence consumers' buying behavior in e-markets ( Pang et al., 2016 ). As Valois et al. (1988) stated, some factors may affect the strength of the relationship between intention and actual behavior. However, in the current perspective of the COVID-19 pandemic, it has been stated by Pollak et al. (2022) that the market has become adaptable to non-standard situations within a short period. This implies that there have been specific changes observed in consumers' behaviors and preferences during crisis times. Considering the facts and figures on the “payment mode” of Pakistan's e-commerce and the overall concept of “facilitating conditions” from Triandis' theory, the study tends to imply that the “online payment method” (OPM) is a moderator to determine the impact of payment method on the relationship between intentions and actual behavior.

H8: Payment mode moderates the effect of behavioral intention on actual behavior for online purchase.

H9: Behavioral intention mediates the impact of perceived benefits, perceived ease of use, social influence, and perceived enjoyment on actual behavior for online purchase.

Based on hypotheses this study builds the framework to study four variables namely, perceived benefits, perceived ease of use, perceived enjoyment, and social influence along with mediating role of behavioral intention, whereas moderating role of Gender and payment mode is also under examination for the study ( Figure 1 ).

Figure 1 . Theoretical framework.

Methodology

Instrument development.

This study conducted an online survey for data collection. In this regard, an adapted instrument from previous studies was used and tested for reliability and validation. Questionnaires were sent to respondents for collection of their responses on a five-point Likert scale, which ranged from 1 (Strongly Disagree) to 5 (Strongly Agree). It is in line with previous studies conducted in the context of online consumer buying behavior ( Davis et al., 1992 ; van der Heijden et al., 2003 ; Sorce et al., 2005 ; Yang et al., 2015 ).

Furthermore, for unit analysis, eight items of perceived benefits were adapted from Teo (2002) , Swinyard and Smith (2003) , Sorce et al. (2005) , and Forsythe et al. (2006) . Five items of perceived ease of use were adapted from Gefen et al. (2003) and Cheema et al. (2013) . Four items of perceived enjoyment were adapted from Teo (2002) and Cheema et al. (2013) . Five items of social influence were adapted from Davis (1985) and Karaiskos et al. (2012) . Three items of behavior intention were adapted from Limayem and Hirt (2003) ) and Karaiskos et al. (2012) . In addition, three items of actual behavior were adapted from Karaiskos et al. (2012) . Lastly, the items of payment mode were adapted from Hasan and Gupta (2020) .

Furthermore, the questionnaires contain two sections; the first section contains demographic variables of an individual including age, gender, and education level, whereas the second section contains the independent, moderating, and mediating variables of the study.

Sample and Procedures

For data collection of the present study, 350 questionnaires were sent out to respondents who were online buyers. The questionnaires were developed with questions regarding whether respondents are online buyers, how long they have been into online buying, and what is the occurrence of their buying patterns. In addition, the questionnaire link shared with the respondents included a note stating that this study seeks responses from online buyers only and that respondents who were not online buyers were not required to record responses.

A self-administered questionnaire was sent out using “an online survey”. A questionnaire link was sent out to the respondents via social media platforms and email. Out of 350, a total of 266 responses were received, and no data were missing from the 266 responses as the questionnaires were designed by utilizing close-ended questions to choose from the list, and fields were marked required. According to the gender category, of those who participated in the research, 51.5% were men and 48.5% were women. The remaining demographic details are shown in Table 1 .

Table 1 . Respondents' profile.

Evaluation Method

The partial least squares (PLS) method was used to conduct the structural equation modeling (SEM) approach to evaluate the present study. Hair et al. (2012) stated that PLS is a second-generation evaluation technique that measures and tests structural modeling, component factor analysis (CFA), and regression. Thus, extensive pre-analysis and data validation were conducted using Smart-PLS for the present study.

Result Analysis

Common method bias.

Kock (2015) stated that the occurrence of variance inflator factor (VIF) should be less than or equal to 3.3. For this study, all VIF values were in the range of 1.67- 2.6, showing that the model was considered free of common method bias because no such thing was observed. To further testify the model, Harman's single-factor test ( Podsakoff and Lee, 2003 ) was conducted to examine if the model was free from common method biases. According to the requirement, if the total variance extracted by one factor exceeds 50%, this shows the presence of common method biases in the study. However, the present study shows that the total variance extracted by one aspect is 27.288, less than 50%. Also, the inter-correlation of all the constructs of this study is less than 0.9 ( Pavlou and El Sawy, 2006 ). Hence, the outcomes indicate that common method bias is not an issue in this study.

Measurement Model

An assessment of reliability and validity was conducted to evaluate and reduce measurement errors. It has been stated as a required test to reduce measurement errors while assessing for internal consistency, discriminant, and convergent validities ( Hair et al., 2012 ). Furthermore, these tests have been evaluated by assessing the values of Cronbach's alpha (α), factor loadings, average variance extracted (AVE), and composite reliability (CR). The acceptable value of CFA should be 0.7 at minimum ( Hair et al., 2012 ). Along similar lines, the present study shows that the CFA values are above 0.7 and are acceptable to show the internal consistency of the data ( Table 2 ). Furthermore, for all the constructs, the values of AVE and CR are above 0.5 and 0.8, respectively ( Fornell and Larcker, 1981 ); these values show acceptable convergent validity. Table 2 shows that Cronbach's alpha, CR, and AVE of actual behavior are 0.789, 0.875, and 0.701, respectively. The alpha (α), CR, and AVE values of behavioral intentions are reported as 0.752, 0.858, and 0.668. Perceived benefits are 0.809, 0.867 and 0.568. In addition perceived ease of use-values are 0.838, 0.885, and 0.606.; perceived enjoyment values are 0.756, 0.845, and 0.577. Lastly, the three items of social influence show Cronbach α, CR, and AVE values of 0.757, 0.861, and 0.673, respectively. Furthermore, all the hypotheses (except for the moderator payment mode) show that their discriminant validity ( Table 3 ) meets the requirement suggested by Fornell and Larcker (1981) ; that is, the square root of each construct's AVE should be higher than its correlation with the remaining constructs.

Table 2 . Convergent validity of measurement model.

Table 3 . Measurement model and discriminant validity.

Structural Model

To assess the results, the estimated path coefficient of the structural model is analyzed. The results of variables and constructs are shown in Figure 2 . The analysis shows that there is a positive and significant impact of perceived behavior, perceived ease of use, perceived enjoyment, and social influence on behavioral intention for online shopping, as their values are H 1 : β = 0.32, p < 0; H 4 : β = 0.156, p < 0.001; H 5 : β = 0.217, p < 0.001; H 7 : β = 0.217, p < 0.001, respectively. However, perceived ease of use also shows a significant and positive impact on perceived enjoyment given that H 6 : β = 0.595, p < 0.001.

Figure 2 . Structural model.

PLS has been used to test moderating and mediating impacts, and special consideration has been given to assess relevant effects in a single model; PLS made it more sophisticated and allowed not to follow a causal step approach to evaluate mediating and moderating effects, whereas considering mediating and moderating effects with PLS is straightforward, and the outcomes give deep insights into advanced mediation and moderation analyses more accurately ( Chin, 2010 ; Streukens et al., 2010 ; Nitzl et al., 2016 ). Hence, relevant effects have been assessed overall in a single model. To discuss the moderating roles of the model, it is evident from the results that gender shows significant moderation in perceived behavior and behavioral intention relationship: H 2 : β = −0.164, p = 0.006. In contrast, there is an insignificant moderation impact of gender on perceived ease of use and behavioral intention relationship: H 3 : β = 0.036, p = 0.501.

In addition, the moderation impact of payment mode also shows insignificance on behavioral intention and actual behavior relationship: H 8 : β = 0.033, p = 0.247). Lastly, the model has demonstrated a significant and positive impact of behavioral intention on the actual buying behavior of online consumers: H 9 : β = 0.604, p < 0.001). Therefore, H 1 , H 2 , H 4 , H 5 , H 6 , H 7 , and H 9 are supported, whereas H 3 and H 8 are rejected based on the results. Detailed findings are shown in Table 4 .

Table 4 . Structural model results (hypothesis testing).

According to the criteria, the value of R 2 must be greater than 0.2, as proposed by Hair et al. (2016) . The present study shows an acceptable value of R 2 , which is 0.554. Furthermore, the value of Q 2 has also been examined using Stone-Geisser's blindfold technique; this technique can be used to examine function fitting and cross-validation. However, this procedure is stated as a sample reuse procedure by Mikalef et al. (2017) . If the value of Q 2> 0, it implies that the model has a predictive relevance ( Hair et al., 2012 ). The Analysis shows that the behavioral intention (Q 2 =0.371), perceived enjoyment (Q 2 = 0.2), and actual behavior (Q 2 = 0.376) variables show reasonable predictive relevance, demonstrating that their values are above 0.

Discussions and Implications

The recent COVID-19 pandemic has changed the landscape of business processes and how they used to function. Prolonged lockdowns resulted in responses to the pandemic causing closures of several companies. However, it brought a new wave of online shopping all over the global market. Interestingly, when businesses went bankrupt and started the closure of their processes, the online market thrived and expanded by over 30–50% ( Financial Times, 2021 ). This shifted the relevance and significance of the research domain once more toward examining key components shaping one's intentions and behavior in a certain way. Thus, the present study seeks to investigate determinants impacting, moderating, and mediating consumers' online buying behavior during the COVID-19 pandemic. The findings suggest several contributions in consumer behavior, advertising, social media, digitalized marketing, academia, and practical aspects of consumers' intention and behavior.

Theoretical Implications

The present study has examined and concluded the determinants impacting the way consumers' online behavior has changed during COVID-19 in the context of Pakistan. For this purpose, the study has developed an integrated model based on the foundation of the well-established Technology Acceptance Model and Theory of planned behavior. First, the results of this research have validated the established scales of measuring consumers' online behavior in South Asian countries, specifically Pakistan. Second, the significant impact of perceived benefits, perceived enjoyment, ease of use, and social influence shows the generalizability and predictive power of TAM and TPB to measure consumers' behavior during the COVID-19 pandemic. This contributes to the academia and research and development in the stated domain so that further research could be carried out with the inclusion of constructs obtained from the technological model (TAM), psychological, and behavioral model (TPB) along with the other notion of social influence on consumers' behavioral intention leading to shaping ones' actual technology usage behavior. The study holds novelty as the context is different from that of routine consumers' online behavior; this implies insights into how consumers' intentions have changed drastically to opt for online buying. Interestingly, according to pre-COVID times, some consumers showed reluctance to go for online buying considering facilitating conditions, i.e., payment mode ( Triandis, 1977 , 1980 ; Pang et al., 2016 ). However, during the COVID-19 pandemic, the same broader consumer base shifted drastically to opt for online buying. The study reveals a new research realm to extend relevant theoretical paradigms to examine the impact of the external environment on consumers' buying intention and behavior.

Second, the integrated model with the role of mediation and moderation implies that theory predicts consumers' intention across situations; the present study has shown its generalizability during the time of a pandemic. This paves the way for further theoretical contribution in “crisis times” by introducing key determinants in cross-cultural and longitudinal analyses.

Practical Implications

Based on the results of this study, the following practical implications have been proposed:

First, the study provides supporting evidence of perceived benefits sought by consumers when buying online. It implies that when a consumer enjoys buying online, it influences their intention to choose purchase behavior in the long run. As consumers find it convenient, businesses need to work on enhancing website design and logistics systems to make shopping more user-friendly and prompt. Interactive and appealing website designs will make one's online experience enjoyable by providing superior images and photos of products/services, proper availability of product/service descriptions, and previous reviews on the same or related products. On the contrary, a complicated website and delays in distribution and logistics will obliterate the purpose of the “convenience” sought by consumers.

Second, perceived ease of use has shown a significant positive impact on perceived enjoyment and intention, indicating that perceived enjoyment mediates the effect of perceived ease of use on behavioral intention. It reveals that a consumer enjoys more when there is more ease for them to use technology. Hence, it stimulates one's intention toward online buying. Therefore, businesses need to work on their online service portals, availability of mobile phone website options, online check-out counters, guest check-out counter chatbots, and advanced navigation options from one product to another to make it effortless and user-friendly to enhance consumers' shopping experience.

Third, the role of gender has been studied widely to understand how gender as a moderator plays its role specifically while managing or using tech-oriented systems ( Venkatesh et al., 2003 ; Yousafzai and Yani-de-Soriano, 2012 ). In the present study, in Pakistan, it is evident from the results that gender plays a significant role in the relationship between perceived benefits and behavioral intention, whereas it has an insignificant role in the relationship between perceived ease of use and behavioral intention. For the moderating role of gender in the relationship between perceived benefits and behavioral intention relationship, the coefficient is negatively significant, which means that although the relationship shows significance, it is weaker in nature.

The insignificant and weak moderating role of gender in the relationship between perceived ease of use and behavioral intention, and in that between perceived benefits and behavioral intention, reveals that the studied relationships are not affected by the gender of a consumer. One's perceived ease of use and perceived benefits may tend toward forming online intention regardless of what gender the person belongs to. Businesses must employ strategies considering gender-neutral online portals, website designs, and online shopping services regarding technology's ease of use and perceived benefits.

In addition, findings of payment mode have shown an interesting insight that payment mode has no impact on consumers' online buying. According to previous studies, the trust factor related to online privacy had been a vital issue, and consumers did not want to shop online because of fraudulent cases, specifically in developing countries. However, the present study reveals that COVID-19 circumstances left consumers with no choice but to adhere. This shift brought a considerable consumer market to the e-commerce sector, which was first-time online buyers ( Statista, 2022 ). When consumers start trusting online payment structures in Pakistan, businesses need to make sure they take payment security as a priority, develop transaction systems, and secure their electronic payments via enhanced “secure electronic transaction” (SET) protocol. Lastly, social influence has also shown a key finding to positively impact buying intention. Businesses and marketers need to utilize such behaviors by involving more powerful bloggers/influencers who are famous among the public. These influencers may use social-friendly content in effortless ways to buy online. In this way, businesses may move from a more traditional way to a more personal level with their consumers. This may also include the creation of personal blogs, putting up fewer formal posts on social media handles, and decreasing the gap between brands and consumers by going through the personalization process.

Limitations and Future Study

There is a significant scope to examine and investigate the external factors that impact consumers' shift toward online buying, specifically during crises like pandemics. Data have been collected from educated online users who are tech-savvy. However, education level and how users are learning technologies are constantly changing. Future research in this domain may be conducted with larger populations regardless of their educational status. Additionally, payment mode was one of the external factors used as a moderator to investigate its impact on online buying behavior; future research may include longitudinal studies to see if consumers' behavior persists across situations for payment mode or changes with difficult times like the COVID-19 pandemic. It will be significant to investigate diverse external factors that moderate one's intention-behavior relationship in particular times and changes over the period.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics Statement

Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent from the participants was not required to participate in this study in accordance with the national legislation and the institutional requirements.

Author Contributions

All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Agarwal, R., and Karahanna, E. (2000). Time flies when you're having fun: cognitive absorption and beliefs about information technology usage. MIS Q. 4, 665–694. doi: 10.2307/3250951

CrossRef Full Text | Google Scholar

Ahmed, Z., Su, L., Rafique, K., Khan, S. Z., and Jamil, S. (2017). A study on the factors affecting consumer buying behavior towards online shopping in Pakistan. J. Asian Bus. Strateg. 7, 44–56. doi: 10.18488/journal.1006/2017.7.2/1006.2.44.56

Ajzen, I. (1985). “From intentions to actions: a theory of planned behavior, in Action Control: From Cognition to Behavior , eds Kuhl, J. and J. Beckmann (New York, NY: Springer Verlag).

Google Scholar

Akhlaq, A., and Ahmed, E. (2015). Digital commerce in emerging economies. Int. J. Emerg. Mark. 10, 634–647. doi: 10.1108/IJoEM-01-2014-0051

Al-Azzam, A. F. M., and Fattah, M. (2014). Evaluating effect of social factors affecting. consumer behavior in purchasing home furnishing products in Jordan. Br. J. Market. Stud. 2, 80–94. Available online at: https://www.eajournals.org/wp-content/uploads/Evaluating-effect-of-social-factors-affecting-consumer-behavior-in-purchasing-home-furnishing-products-in-Jordan.pdf

Babin, B. J., and Babin, L. (2001). “Seeking something different? a model of schema typicality, consumer affect, purchase intentions and perceived shopping value.” J. Bus. Res. 54, 89–96. doi: 10.1016/S0148-2963(99)00095-8

Babin, B. J., Darden, W. R., and Griffin, M. (1994). Work and/or fun: Measuring hedonic and utilitarian shopping value. J. Consum. Res. 20, 644–656. doi: 10.1086/209376

Bai, B., Law, R., and Wen, I. (2008). The impact of website quality on customer satisfaction and purchase intentions: evidence from Chinese online visitors. Int. J. Hospital. Manage . 27, 391–402. doi: 10.1016/j.ijhm.2007.10.008

Bangkit, J., Tumbuan, W., and Tielung, M. (2022). Analysis of perceived risk and perceived benefit influencing online shopping behavior in Canada. Manajemen, bisnisdanakuntansi 10, 1. doi: 10.35794/emba.v10i1.38238

Bauerová, R., and Klepek, M. (2018). Technology acceptance as a determinant of online grocery shopping adoption. ActaUniversitatisAgriculturae et SilviculturaeMendelianaeBrunensis 66, 737–746. doi: 10.11118/actaun201866030737

Bhattacherjee, A. (2000). Acceptance of e-commerce services: the case of electronic brokerages. IEEE Transact. Syst. Man Cybernetic. 30, 411–420. doi: 10.1109/3468.852435

Chang, A., and Kannan, P. (2006). “Employee technology readiness and adoption of wireless technology and services,” in Proceedings of the 39th HICSS , pp. 1–9.

Chang, H. H., and Chen, S. W. (2008). The impact of online store environment cues on purchase intention: Trust and perceived risk as a mediator. Online Infm. Rev. 32, 818–841. doi: 10.1108/14684520810923953#

Cheema, U., Rizwan, M., Jalal, R., Durrani, F., and Sohail, N. (2013). The trend of online shopping in 21st century: Impact of enjoyment in TAM Model. Asian J. Empir. Res. 3, 131–141. Available online at: http://aessweb.com/journal-detail.php?id=5004

Chin, W. W. (2010). “How to write up and report PLS analyses,” in Handbook of Partial Least Squares: Concepts, Methods and Applications (New York, NY: Springer), pp. 655–690.

CIGI, Ipsos (2017). Global Survey on Internet Security and Trust Centre for International Governance Innovation . Available online at: https://www.cigionline.org/cigi-ipsos-global-survey-internet-security-and-trust/ (accessed April 12, 2022).

Clegg, S., and Trayhurn, D. (2000). Not just for men: a case study of teaching and learning of information technology. Higher Educ. 40, 123–145. doi: 10.1023/A:1004093116051

Cyr, D., Head, M., and Ivanov, A. (2006). Design aesthetics leading to m-loyalty in mobile commerce. Infm. Manag. 43, 950–963. doi: 10.1016/j.im.2006.08.009

Davis, F. D. (1985). A technology acceptance model for empirically testing new end-user information systems: Theory and results (Doctoral dissertation). Massachusetts Institute of Technology, Cambridge, MA, United States.

Davis, F. D. (1989). Perceived usefulness, perceived ease of use and user acceptance of information technology. MIS Q. 13, 319–340. doi: 10.2307/249008

Davis, F. D. (1993). User acceptance of information technology: system characteristics, user perceptions and behavioral impacts. Int. J. Man-Machine Stud. 38, 475–487. doi: 10.1006/imms.1993.1022

Davis, F. D., Bagozzi, R. P., and Warshaw, P. R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace. J. Appl. Soc. Psychol. 22, 1111–1132. doi: 10.1111/j.1559-1816.1992.tb00945.x

Dawn, S. K., and Kar, U. (2011). E-tailing in India- its issues, opportunities and effective strategies for growth and development. Int. J. Multidisciplin. Res. 1, 101–115.

Deci, E. L. (1975). Intrinsic Motivation . New York, NY: Plenum Press.

Delafrooz, N., Paim, L. H., and Khatibi, A. (2010). Students online shopping behavior: An empirical study. J. Am. Sci. 6, 137–147. Available online at: http://www.jofamericanscience.org/journals/am-sci/am0601/18_1101_student_online_pub_am0601.pdf

Financial Times (2021). Prospering in the Pandemic: 2020's Top 100 Companies . Available online at: https://www.ft.com/content/f8251e5f-10a7-4f7a-9047-b438e4d7f83a (accessed April 12, 2022).

Fishbein, M., and Ajzen, I. (1975). Belief, Attitude, Intention and Behavior: An Introduction to theory and Research. Reading, MA: Addison-Wesley.

Fornell, C., and Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error: algebra and statistics. J. Market. Research 18, 382–388. doi: 10.1177/002224378101800313

Forsythe, S., Liu, C., Shannon, D., and Gardner, L.C. (2006). Development of a scale to measure the perceived benefits and risks of online shopping. J. Interactive Mark . 20, 55–75. doi: 10.1002/dir.20061

Foucault, B. E., and Scheufele, D. A. (2002). Web vs campus store? Why students buy textbooks online. J. Consum. Market. 19, 409–423. doi: 10.1108/07363760210437632

Gefen, D., Karahanna, E., and Straub, D. W. (2003). Trust and TAM in online shopping: An integrated model. MIS Q. 27, 51–90. doi: 10.2307/30036519

Haider, A., and Nasir, N. (2016). Factors affecting online shopping behavior of consumers in Lahore, Pakistan. J. Manag. Eng. Infm. Technol. 3, 9–14. Available online at: http://www.jmeit.com/JMEIT_Vol_3_Issue_6_Dec_2016/JMEITDEC0306003.pdf

Hair, J., Sarstedt, M., Matthews, L., and Ringle, C. (2016). Identifying and treating unobserved heterogeneity with FIMIX-PLS: part I—method. Euro. Bus. Rev. 28, 63–76. doi: 10.1108/EBR-09-2015-0094

Hair, J. F., Sarstedt, M., and Ringle, C. M. (2012). An assessment of the use of partial least squares structural equation modeling in marketing research. J. Acad. Mark. Sci. 40, 414–433. doi: 10.1007/s11747-011-0261-6

Hasan, A., and Gupta, S. K. (2020). Exploring tourists' behavioural intentions towards use of select mobile wallets for digital payments. Paradigm. 24, 177–194. doi: 10.1177/0971890720959519

Holbrook, M. B. (1994). “The nature of customer's value: an axiology of service in consumption experience,” in Service Quality: New Directions in Theory and Practice , eds Rust, R.T. and Oliver, R.L., (Thousand Oaks: Sage), p. 21–71.

Igbaria, M., Iivari, J., and Maragahh, H. (1995). Why do individuals use computer technology ? a finnish case study. Inform. Manage. 29, 227–238. doi: 10.1016/0378-7206(95)00031-0

Ingham, J., Cadieux, J., and Berrada, A. M. (2015). e-Shopping acceptance: A qualitative and meta-analytic review. Infm. Mang. 52, 44–60. doi: 10.1016/j.im.2014.10.002

Iqbal, S., and Hunjra, A. I. (2012). Consumer intention to shop online: B2C E-commerce in developing countries. Middle East J. Scientific Res. 12, 424–443

Jarvenpaa, S. L., and Todd, P. A. (1996). Consumer reactions to electronic shopping on the world wide web. Int. J. Elect. Commer. 1, 59–88. doi: 10.1080/10864415.1996.11518283

Jarvenpaa, S. L., Tractinsky, N., and Saarinen, L. (1999). Consumer trust in an internet store: A cross-cultural validation. J. Comput. Mediat. Commun. 5, JCMC526. doi: 10.1111/j.1083-6101.1999.tb00337.x

Jeong, M., Oh, H., and Gregoire, M. (2003). Conceptualizing web site quality and its consequences in the lodging industry. Int. J. Hospital. Manage. 22, 161–175. doi: 10.1016/S0278-4319(03)00016-1

Jhamb, D. D., and Gupta, S. (2016). Antecedents of Online Shopping Attractiveness: The Youngster Perspective Determinants of Online Shopping Attractiveness: The Youngster Perspective .

Jiang, L., Yang, Z., and Jun, M. (2013). Measuring consumer perceptions of online shopping convenience. J. Serv. Manage. 24, 191–214. doi: 10.1108/09564231311323962

Jones, M. A., Reynolds, K. E., and Arnold, M. J. (2006). Hedonic and utilitarian shopping value: investigating differential effects on retail outcomes. Research 6, 974–981. doi: 10.1016/j.jbusres.2006.03.006

CrossRef Full Text

Karahanna, E., Straub, D. W., and Chervany, N. L. (1999). Information technology adoption across time : a cross-sectional comparison of pre-adoption and post-adoption beliefs. MIS Q. 23, 183–213. doi: 10.2307/249751

Karaiskos, D. C., Drossos, D. A., Tsiaousis, A. S., Giaglis, G. M., and andFouskas, K. G. (2012). Affective and social determinants of mobile data services adoption. Behav. Infm. Technol. , 31, 209–219. doi: 10.1080/0144929X.2011.563792

Karavidas, M., Lim, N. K., and Katsikas, S. L. (2005). The effects of computers on older adult users. Comput. Hum. Behav. 21, 193–201. doi: 10.1016/j.chb.2004.03.012

Kelman, H. C. (1958). Compliance, identification, and internalization: three processes of attitude change. J. Con?ict Resolut. 2, 51–60. doi: 10.1177/002200275800200106

PubMed Abstract | CrossRef Full Text | Google Scholar

Ketabi, S. N., Ranjbarian, B., and Ansari, A. (2014). Analysis of the effective factors on online purchase intention through theory of planned behavior. Int. J. Acad. Res. Bus. Soc. Sci. 4, 808. doi: 10.6007/IJARBSS/v4-i4/808

Kim, Y. K. (2002). Consumer value: an application to mall and Internet shopping. Int. J. Retail Distrib. Manage. 30, 595–602. doi: 10.1108/09590550210453075

Kock, N. (2015). Common method bias in PLS-SEM. Int. J. E-Collaborat. 11, 1–10. doi: 10.4018/ijec.2015100101

Koufaris, M. (2002). Applying the technology acceptance model and flow theory to online consumer behavior. Inform. Syst. Res. 13, 205–223. doi: 10.1287/isre.13.2.205.83

Kuswanto, H., Pratama, W. B. H., Ahmad, I. S., and Salamah, M. (2019). Analysis of students' online shopping behavior using a partial least squares approach: case study of Indonesian students. Cogent Bus. Manage. 6, 1. doi: 10.1080/23311975.2019.1699283

LaRose, R., and Eastin, M. S. (2002). Is online buying out of control? electronic commerce and consumer self- regulation. J. Broadcast. Electron. Media 46, 549–564. doi: 10.1207/s15506878jobem4604_4

Limayem, M., and Hirt, S. G. (2003). Force of habit and is usage: theory and initial validation. J. Assoc. Inform. Syst. 4, 65–97. doi: 10.17705/1jais.00030

Limayem, M., Hirt, S. G., and Chin, W. W. (2001). Intention does not always matter: the contingent role of habit on it usage behavior,” in Proceedings of the 9th European Conference on Information Systems , pp. 27–29.

Manu, C. D., and Fuad, F. (2022). Relationships between website quality and trust dimensions in online shopping: an empirical study in Indonesia. Int. J. Bus. Inform. Syst. 39, 400–423.

Marza, S., Idris, I., and Abror, A. (2019). The Influence of convenience, enjoyment, perceived risk, and trust on the attitude toward online shopping. Adv. Econ. Bus. Manage. Res. 64, 589–598. doi: 10.2991/piceeba2-18.2019.40

Mikalef, P., Michail, N., Giannakos, I., and Pappas, O. (2017). Designing social commerce platforms based on consumers' intentions. Behav. Inform. Technol. 36, 1308–1327. doi: 10.1080/0144929X.2017.1386713

Monsuwe, T. P., Dellaert, B. G. C., and de Ruyter, K. (2004). What drives consumers to shop online? A literature review. Int. J. Serv. Indust. Manag. 15, 102–121. doi: 10.1108/09564230410523358

Nazir, S., Tayyab, A., Sajid, A., Rashid, H., and Javed, I. (2012). How online shopping is affecting consumers buying behavior in Pakistan. Int. J. Comput. Sci. Issue 9, 486.

Nitzl, C., Roldán, J., and Cepeda-Carrion, G. (2016). Mediation analysis in partial least squares path modeling: helping researchers discuss more sophisticated models. Industr. Manage. Data Syst . 116. 1849–1864. doi: 10.1108/IMDS-07-2015-0302

Pang, A., Tan, E. Y., Qi Lim, R. S., Kwan, T. Y. -M., and Lakhanpal, P. B. (2016). Building effective relations with social media influencers in Singapore. Media Asia. 43, 56–68. doi: 10.1080/01296612.2016.117796

Pappas, N. (2016). Marketing strategies, perceived risks, and consumer trust in online buying behavior. J. Retail Consum. Serv. 29, 92–103. doi: 10.1016/j.jretconser.2015.11.007

Park, C., and Kim, Y. (2003). Identifying key factors affecting consumer purchase behavior in an online shopping context. Int. J. Retail Distr. Manag. 31, 16–29. doi: 10.1108/09590550310457818

Park, C., Wang, Y., Yao, Y., and Kang, Y. R. (2011). Factors influencing e-WOM effects: using experience, credibility, and susceptibility. Int. J. Soc. Sci. Human. 1, 74–79. doi: 10.7763/IJSSH.2011.V1.13

Pavlou, P. A., and El Sawy, O. A. (2006). From IT leveraging competence to competitive advantage in turbulent environments: the case of new product development. Inf. Syst. Res . 17, 198–227. doi: 10.1287/isre.1060.0094

Pavlou, P. A., and Marshall, L. C. (2002). J. Electron. Commer. Res. 3, 4.

Pietro, D. L., Virgilio, D. F., and Pantano, E. (2012). Social network for the choice of tourist destination: attitude and behavioral intention. J. Hospital. Tour. Technol. 3, 60–76. doi: 10.1108/17579881211206543

Podsakoff, M., and Lee, P. (2003). Common method biases in behavioral research: a critical review of the literature and recommended remedies. J. Appl. Psychol . 88, 879–903. doi: 10.1037/0021-9010.88.5.879

Pollak, F., Markovic, P., Vachal, J., and Vavrek, R. (2022). “Analysis of e-consumer behavior during the COVID-19 pandemic,” in Intelligent Processing Practices and Tools for E-Commerce Data, Information, and Knowledge. EAI/Springer Innovations in Communication and Computing , eds Gao, H., Kim, J.Y., Hussain, W., Iqbal, M., Duan, Y. (Cham: Springer). 6.

PubMed Abstract | Google Scholar

Porter, E., and Donthu, N. (2006). Using the technology acceptance model to explain how attitudes determine internet usage. J. Bus. Res. 59, 999–1007. doi: 10.1016/j.jbusres.2006.06.003

Prashar, S., Vijay, T. S., and Parsad, C. (2015). Antecedents to online shopping: Factors influencing the selection of web portal. Int. J. E-Bus. Res. 11, 35–55.

Rao, A., and Troshani, I. (2007). A conceptual framework and propositions for the acceptance of mobile services. J. Theoretic. Appl. Electron. Commer. Res. 2, 61–73. doi: 10.3390/jtaer2020014

Rashid, R. M., Pitafi, A. H., Qureshi, M. A., and Sharma, A. (2022). Role of social commerce constructs and social presence as moderator on consumers' buying intentions during COVID-19. Front. Psychol. 13, 772028. doi: 10.3389/fpsyg.2022.772028

Rogers, E. M. (1995). Diffusion of Innovations. New York, NY: The Free Press.

Sorce, P., Perotti, V., and Widrick, W. (2005). Attitude and age differences in online buying. Int. J. Retail Distrib. Manage. 33, 122–132. doi: 10.1108/09590550510581458

Statista (2022). Global: consumers new to eCommerce 2020 Available online at: https://www.statista.com/statistics/1192388/first-time-online-shoppers-since-Covid-19/ (accessed April 12, 2022).

Streukens, S., Wetzels, M., Daryanto, A., and de Ruyter, K. (2010). “Analyzing factorial data using PLS: application in an online complaining context,” in Handbook of Partial Least Squares: Concepts, Methods and Applications, Springer Handbooks of Computational Statistics Series , eds Esposito Vinzi, V. Chin W.W. Henseler J. Wang, H. (New York, NY: Springer), pp. 567–587.

Sun, H., and Zhang, P. (2006a). “The role of affect in information systems research,” in HCI in MIS (I): Foundations , eds P. Zhang and D. Galletta (Armonk, NY: M.E. Sharpe. Causal Relations), 618–645.

Sun, H., and Zhang, P. (2006b). The role of moderating factors in user technology acceptance. Int. J. Hum. Comput. Studies 64, 53–78. doi: 10.1016/j.ijhcs.2005.04.013

Swinyard, W. R., and Smith, S. M. (2003). Why people (don't) shop online: A lifestyle study of the internet consumer. Psychol. Market. 20, 567–597. doi: 10.1002/mar.10087

Teo, S. H. T. (2002). Attitudes toward online shopping and the internet. Behav. Inform. Technol. 21, 259±271. doi: 10.1080/0144929021000018342

Teo, S. H. T. (2006). To buy or not to buy online: adopters and non-adopters of online shopping in Singapore. Behav. Inform. Technol. 25, 497–509. doi: 10.1080/01449290500256155

Teo, T. S. H., Lim, V. K. G., and Lai, R. Y. C. (1999). Intrinsic and extrinsic motivation in Internet usage. Omega 27, 25–37. doi: 10.1016/S0305-0483(98)00028-0

Triandis, H. (1979). Values, Attitudes, and Interpersonal Behavior Nebraska Symposium on Motivation, 1979: Beliefs, Attitudes, and Values. Lincoln, NE: University of Nebraska Press. p. 195–259.

Triandis, H. C. (1977). Interpersonal behavior Monterey . CA: Cole: Brooks.

Triandis, H. C. (1980). Values, attitudes, and interpersonal behavior. Nebraska Symposium Motiv. 27, 195–259.

Tsai, M. T., Cheng, N. C., and Chen, K. S. (2011). Understanding online group buying intention: the roles of sense of virtual community and technology acceptance factors. Total Qual. Manage. Bus. Excell. 22, 1091–1104. doi: 10.1080/14783363.2011.614870

Valois, P., Desharnais, R., and Godin, G. (1988). A comparison of the Fishbein and Ajzen and the Triandis attitudinal models for the prediction of exercise intention and behavior. J. Behav. Med. 11, 459–472. doi: 10.1007/BF00844839

van der Heijden, H., Verhagen, Y., and Creemers, M. (2003). Understanding online purchase intentions: contributions from technology and trust perspectives. Eur. J. Infm. Syst . 12, 41–48. doi: 10.1057/palgrave.ejis.3000445

Venkatesh, V. (1999). Creation of favorable user perceptions: Exploring the role of intrinsic motivation. MIS Q. 23, 239. doi: 10.2307/249753

Venkatesh, V. (2000). Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model.

Venkatesh, V. (2000). Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Infm. Syst. Res. 11, 342–365. Available online at: https://www.jstor.org/stable/23011042

Venkatesh, V., and Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decis. Sci. 39, 273–315. doi: 10.1111/j.1540-5915.2008.00192.x

Venkatesh, V., C Speier, T., and Morris, M. G. (2002). User acceptance enablers in individual decision making about technology: Toward an integrated model . Decis. Sci . 33, 297. doi: 10.1111/j.1540-5915.2002.tb01646.x

Venkatesh, V., and Davis, F. D. (2000). A theoretical extension of the technology acceptance model: four longitudinal field studies. Manag. Sci. 46, 186–204. doi: 10.1287/mnsc.46.2.186.11926

Venkatesh, V., and Morris, G. (2000). Why don't men ever stop to ask for directions? gender, social in?uence, and their role in technology acceptance. MIS Q. 24, 115–139. doi: 10.2307/3250981

Venkatesh, V., Morris, M. G., Davis, G. B., and Davis, F. D. (2003). User acceptance of information technology: toward a unified view. MIS Q. 27, 425–478. doi: 10.2307/30036540

Venkatesh, V., and Speier, C. (1999). Computer technology training in the workplace: A longitudinal investigation of the effect of mood. Organ. Behav. Hum. Decis. Process. 79, 1–28. doi: 10.1006/obhd.1999.2837

Wang, Y. -S., Yeh, C. -H., and Liao, Y. -W. (2013). What drives purchase intention in the context of online content services? The moderating role of ethical self-efficacy for online piracy. Int. J. Infm. Manag. 33, 199-208. doi: 10.1016/j.ijinfomgt.2012.09.004

Wani, T. A., Ali, S. W., and Farooq, T. (2016). Determinants of online purchase intentions: a study of indian buyers. Amity J. Manag. Res. 1, 94–109. Available online at: https://amity.edu/UserFiles/admaa/233Paper%207.pdf

Warshaw, P. R. (1980). A new model for predicting behavioral intentions: an alternative to Fishbein. J. Marketing Res. 17, 153–172. doi: 10.1177/002224378001700201

Wei, T. T., Marthandan, G., Loong, A. C., and BoonOoi, K. (2009). What drives Malaysian m-commerce adoption? an empirical analysis. Industr. Manage. Data Syst. 109, 370–388. doi: 10.1108/02635570910939399

Widyastuti, W., Hartini, S., and Artanti, Y. (2020). Revisiting the online shopper's behavior in Indonesia: the role of trust and perceived benefit. Int. J. Industr. Eng. Manage. 11, 226–236. doi: 10.24867/IJIEM-2020-4-267

Xia, L., Mengqiao, H., Fang, G., and Peihong, X. (2008). An empirical study of online shopping customer satisfaction in China: a holistic perspective. Int. J. Retail Distrib. Manage. 36, 919–940. doi: 10.1108/09590550810911683

Yang, K., Li, X., Kim, H., and Kim, Y. H. (2015). Social shopping website quality attributes increasing consumer participation, positive eWOM, and co-shopping: The reciprocating role of participation. J. Retail Consum. Serv. 24, 1–9. doi: 10.1016/j.jretconser.2015.01.008

Yew, J. L. K., and Kamarulzaman, Y. (2020). Effects of personal factors, perceived benefits and shopping orientation on online shopping behavior in Malaysia. Int. J. Econ. Manage. Account. 28, 327–360.

Yousafzai, S., and Yani-de-Soriano, M. (2012). Understanding customer-specific factors underpinning internet banking adoption. Int. J. Bank. Market. 30, 60-81. doi: 10.1108/02652321211195703

Yousuf, A. (2018). Asian Development Bank (ADB) . Pro Pakistani. Available online at: https://propakistani.pk/2018/06/28/95-of-e-commerce-transactions-in-pakistan-are-cash-on-delivery-report/ (accessed November 12, 2018).

Zaidi, S. D. I., Gondal, B. J., and Yasmin, A. (2014). Antecedents of online shopping intention: a study held in Pakistan. J. Sociologic. Res . 5, 6564. doi: 10.5296/jsr.v5i1.6564

Zeithmal, V. A., Parasuraman, A., and Malhotra, A. (2002). Service quality delivered through web sites: A critical review of extant knowledge. J. Acad. Mark. Sci. 30, 362. doi: 10.1177/009207002236911

Zhang, Y. (2005). Age, gender, and Internet attitudes among employees in the business world. Comput. Hum. behav. 21, 1–10. doi: 10.1016/j.chb.2004.02.006

Zhou, L., and Zhang, D. (2007). Online shopping acceptance model-a critical survey of consumer factors in online shopping. J. Electron. Commer. Res. 8, 41.

Keywords: perceived benefits, perceived ease of use, perceived enjoyment, social influence, behavioral intention, actual behavior, gender, payment mode

Citation: Sajid S, Rashid RM and Haider W (2022) Changing Trends of Consumers' Online Buying Behavior During COVID-19 Pandemic With Moderating Role of Payment Mode and Gender. Front. Psychol. 13:919334. doi: 10.3389/fpsyg.2022.919334

Received: 13 April 2022; Accepted: 24 June 2022; Published: 10 August 2022.

Reviewed by:

Copyright © 2022 Sajid, Rashid and Haider. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Sana Sajid, sanasajid2010@yahoo.com

† These authors have contributed equally to this work

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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Impact of COVID Pandemic on eCommerce

The COVID pandemic crisis has forced many small businesses to reassess the decades-old traditional business models or face closing permanently. New and existing technologies are thrust to the forefront of every business toolkit, and forward-looking businesses are addressing talent questions that arise from these new digital business skillsets.

A Global Post-Crisis Bounce in eCommerce Sales?

Risk of further business closures from COVID-related disruptions, in addition to the inherent financial fragility of business, paints a grim forecast for many businesses still open. Or is this just an opinion based on a lack of data?

In the chart below we see a distinct upward jog in total global retail sales from 2019-2020, giving a strong boost to a steady 8% growth in retail ecommerce sales worldwide forecast through 2024 .This shows us an increase in online retail sales as a result of the paradigm shift that COVID disruptions have brought to business.

Pandemic Impact to Worldwide Consumer Behavior

As various pandemic-related business restrictions that prevented in-person activities crept across the world’s regions, business turned to the pandemic-proof ecommerce sales channels for basic survival. Online, global consumers could not stop purchasing through their favorite websites (44% of global digital purchases) and online marketplaces (47% of global digital purchases). In response to this consumer migration to digital, Brazil , Spain , Japan saw the largest increase in number of businesses selling online as a reaction to the pandemic.

  In the chart below we see a forecast increase of 19% n worldwide ecommerce revenue between pre-and-post COVID-19 timeframes in 2020. Food & Personal Care products show the most growth with a forecast increase of 26% of revenue as a result of consumer transition to online sales channels.

Pandemic Impact to Global Small B2B

The COVID pandemic has impacted business countries around the globe differently, creating opportunities for some where business was once lost. Small B2B companies in the United Kingdom and Brazil for example had significant increases in online revenue from their pre-COVID online sales figures.

Boosted by Pandemic, Cross-Border eCommerce Continues to Grow

The data tells us that COVID pandemic-related business restrictions have forced a global business paradigm shift towards the digital economy, which has negatively impacted traditional business models while also creating opportunity through sales diversification online.

This chart shows us clearly the impact to global ecommerce revenues the pandemic has had, adding an additional 19% sales growth for 2020, and additional 22% sales growth to the existing 9% and 12% regular forecast sales growth rates, respectively.

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COVID-19 has changed online shopping forever, survey shows

The pandemic has accelerated the shift towards a more digital world and triggered changes in online shopping behaviours that are likely to have lasting effects.

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The COVID-19 pandemic has forever changed online shopping behaviours, according to a survey of about 3,700 consumers in nine emerging and developed economies.

The survey, entitled “ COVID-19 and E-commerce ”, examined how the pandemic has changed the way consumers use e-commerce and digital solutions. It covered Brazil, China, Germany, Italy, the Republic of Korea, Russian Federation, South Africa, Switzerland and Turkey.

Following the pandemic, more than half of the survey’s respondents now shop online more frequently and rely on the internet more for news, health-related information and digital entertainment.

Consumers in emerging economies have made the greatest shift to online shopping, the survey shows.

“The COVID-19 pandemic has accelerated the shift towards a more digital world. The changes we make now will have lasting effects as the world economy begins to recover,” said UNCTAD Secretary-General Mukhisa Kituyi.

He said the acceleration of online shopping globally underscores the urgency of ensuring all countries can seize the opportunities offered by digitalization as the world moves from pandemic response to recovery.

Online purchases rise but consumer spending falls

The survey conducted by UNCTAD and Netcomm Suisse eCommerce Association, in collaboration with the Brazilian Network Information Center (NIC.br) and Inveon, shows that online purchases have increased by 6 to 10 percentage points across most product categories.

The biggest gainers are ICT/electronics, gardening/do-it-yourself, pharmaceuticals, education, furniture/household products and cosmetics/personal care categories ( Figure 1 ).

Figure 1: Percentage of online shoppers making at least one online purchase every two months

However, average online monthly spending per shopper has dropped markedly ( Figure 2). Consumers in both emerging and developed economies have postponed larger expenditures, with those in emerging economies focusing more on essential products.

Tourism and travel sectors have suffered the strongest decline, with average spending per online shopper dropping by 75%.

Figure 2: Fall of average online spending per month since COVID-19, per product category

“During the pandemic, online consumption habits in Brazil have changed significantly, with a greater proportion of internet users buying essential products, such as food and beverages, cosmetics and medicines,” said Alexandre Barbosa, manager of the Regional Center of Studies on the Development of Information Society (Cetic.br) at the Brazilian Network Information Center (NIC.br).

Increases in online shopping during COVID-19 differ between countries, with the strongest rise noted in China and Turkey and the weakest in Switzerland and Germany, where more people were already engaging in e-commerce.

The survey found that women and people with tertiary education increased their online purchases more than others. People aged 25 to 44 reported a stronger increase compared with younger ones. In the case of Brazil, the increase was highest among the most vulnerable population and women.

Also, according to survey responses, small merchants in China were most equipped to sell their products online and those in South Africa were least prepared.

“Companies that put e-commerce at the heart of their business strategies are prepared for the post-COVID-19 era,” said Yomi Kastro, founder and CEO of Inveon. “There is an enormous opportunity for industries that are still more used to physical shopping, such as fast-moving consumer goods and pharmaceuticals.”

“In the post-COVID-19 world, the unparalleled growth of e-commerce will disrupt national and international retail frameworks,” said Carlo Terreni, President, NetComm Suisse eCommerce Association.

“This is why policymakers should adopt concrete measures to facilitate e-commerce adoption among small and medium enterprises, create specialized talent pools and attract international e-commerce investors.”

Digital giants grow stronger

According to the survey, the most used communication platforms are WhatsApp, Instagram and Facebook Messenger, all owned by Facebook.

However, Zoom and Microsoft Teams have benefitted the most from increases in the use of video calling applications in workplaces.

In China, the top communication platforms are WeChat, DingTalk and Tencent Conference, the survey shows.

Changes are here to stay

The survey results suggest that changes in online activities are likely to outlast the COVID-19 pandemic.

Most respondents, especially those in China and Turkey, said they’d continue shopping online and focusing on essential products in the future.

They’d also continue to travel more locally, suggesting a lasting impact on international tourism.

The Perks of Online Selling: Shared Experiences and Defying Challenges

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Citation Count

Introduction to research in education

Understanding information systems continuance: an expectation-confirmation model, designing qualitative research, advances in social media research: past, present and future, online purchasing tickets for low cost carriers: an application of the unified theory of acceptance and use of technology (utaut) model, related papers (5), trending questions (3).

- Additional profit and widened market horizons for online sellers. - Source of fun, income, and opportunity to test skills.

Online sellers face challenges like dealing with buyers, stock unpredictability, and market competition. They find online selling a source of income, fun, and a platform to expand their market reach.

Online sellers face challenges such as dealing with impatient buyers, fluctuating stocks and prices, market competition, and uncertain product quality due to limited experience in entrepreneurship and online commerce.

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Research: Smaller, More Precise Discounts Could Increase Your Sales

• Dinesh Gauri,
• Abhijit Guha,
• Abhijit Biswas,
• Subhash Jha

Why bigger discounts don’t necessarily attract more customers.

Retailers might think that bigger discounts attract more customers. But new research suggests that’s not always true. Sometimes, a smaller discount that looks more precise — say 6.8% as compared to 7% — can make people think the deal won’t last long, and they’ll buy more. In a series of nine experimental studies involving around 2,000 individuals considering online or retail purchases of a variety of products, the authors found precise discount depths — the difference between the original and sale price — can increase purchase intentions by up to 21%.

Discounts are an important promotional tactic retailers use to drive sales. So much so that discounts were a major factor for three out of four U.S. online shoppers in 2023 , luring consumers away from shopping at other retailers, getting them to increase their basket size, and convincing them to make purchases they otherwise wouldn’t. Discounts have a particularly strong impact on food purchases, where 90% of consumers reported stocking up on groceries when they were on sale .

• DG Dinesh Gauri is a professor and Walmart chair in the department of marketing at the Sam M. Walton College of Business at the University of Arkansas. He is also the executive director of retail information at the Walton College. His research and teaching interests include retailing, pricing, marketing analytics, retail media, e-commerce and social media marketing. He advises for various companies in these areas and is a recognized leader in marketing.
• AG Abhijit Guha is an associate professor in the department of marketing at the Darla Moore School of Business at the University of South Carolina. His research and teaching interests include retailing, pricing, and artificial intelligence.
• AB Abhijit Biswas is the Kmart endowed chair and professor of marketing, chair of the department of marketing, and distinguished faculty fellow at the Mike Ilitch School of Business, Wayne State University. His research and teaching interests include retailing, pricing and advertising. He has published over a hundred articles, majority of which are in academic journals including the Journal of Marketing , Journal of Marketing Research , etc.
• SJ Subhash Jha is an associate professor of marketing at the Fogelman College of Business & Economics at the University of Memphis. His research and teaching interests include retailing, pricing, online reviews and role of haptic cues.

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What is Driving Widening Racial Disparities in Life Expectancy?

Latoya Hill and Samantha Artiga Published: May 23, 2023

Introduction

Amid the COVID-19 pandemic, life expectancy in the U.S. declined 2.7 years between 2019 and 2021, from 78.8 years to 76.1 years, marking the largest two-year decline in life expectancy since the 1920’s. This decline further widened the existing gap in life expectancy between the U.S. and other comparably large and wealthy countries. It also exacerbated longstanding racial disparities in life expectancy and mortality within the U.S., contributing to excess deaths and increased costs . This analysis examines trends in life expectancy and leading causes of death by race and ethnicity and discusses the factors that contribute to racial disparities in life expectancy. In sum, it finds:

• There was a sharp drop-off in life expectancy between 2019 and 2021, with particularly large declines among some groups. American Indian and Alaska Native (AIAN) people experienced the largest decline in life expectancy of 6.6 years during this time, followed by Hispanic and Black people (4.2 and 4.0 years, respectively).
• Reflecting these declines, provisional data for 2021 show that life expectancy was lowest for AIAN people at 65.2 years, followed by Black people, whose expectancy was 70.8 years, compared with 76.4 years for White people and 77.7 years for Hispanic people. It was highest for Asian people at 83.5 years. Data were not reported for Native Hawaiian and Other Pacific Islander (NHOPI) people.
• These declines were largely due to COVID-19 deaths and reflect the disproportionate burden of excess deaths, including premature excess deaths (before age 75), among people of color during the pandemic. Although COVID-19 mortality was a primary contributor to the recent decrease in life expectancy across groups, leading causes of death vary by race and ethnicity.

These recent stark declines and widening racial disparities in life expectancy amplify the importance of addressing underlying drivers of these disparities, including inequities in health insurance coverage and access to care and social and economic factors that drive health.

Trends in Life Expectancy by Race/Ethnicity

Life expectancy at birth represents the average number of years a group of infants would live if they were to experience throughout life the age-specific death rates prevailing during a specified period. Life expectancy is one of the most used measures of population health, enabling comparisons in health status between countries, states, local communities, and demographic groups. Differences in life expectancy occur across a broad range of dimensions which often intersect with each other, including race, socioeconomic status, gender, geography, and other characteristics. For example, In the U.S. and all other comparable countries, men tend to have shorter life expectancy at birth than women. In 2021, life expectancy for women in the U.S. was 5.9 years higher than for men (79.1 years vs. 73.2 year, respectively), and similar gender disparities persisted within racial and ethnic groups. This analysis focuses on differences in life expectancy by race and ethnicity overall, but within racial and ethnic groups there is variation by these other factors, such as gender.

Prior to 2015, there were relatively steady increases in life expectancy in the U.S., but racial disparities persisted. Before 2015, life expectancy in the U.S. steadily increased with an overall gain of about 10 years between 1960 and 2015 from 69.7 years to 79.4 years. While there have been large gains in life expectancy across racial and ethnic groups, racial disparities have been longstanding and persisted over time. Black people have consistently had lower life expectancy than White people, while, conversely, Hispanic people have consistently had longer life expectancy compared to White people. When life expectancy reached its peak in 2014, life expectancy for Black people was more than three years shorter than White people (75.3 vs. 78.8 years), and Hispanic people had a longer life expectancy at 82.1 years (Figure 1 and Appendix Table 1 ). (Data were not available for other groups.)

Causes of Recent Life Expectancy Declines

The declines in life expectancy between 2019 and 2021 largely reflect an increase in excess deaths amid the COVID-19 pandemic, which disproportionately impacted Black, Hispanic, and AIAN people . KFF analysis finds the pandemic was associated with faster rises in premature mortality rates and resulted in more excess years of life lost for people of color compared to their White counterparts, with people of color accounting for 59% of excess years of life lost while making up 40% of the population. Other analysis further finds that COVID-19 mortality had the largest contribution to the decline in life expectancy between 2020 and 2021 among AIAN, Black and White people, accounting for 21.4%, 35.0%, and 54.1% of their declines, respectively. Among Hispanic and Asian people, COVID-19 had the second largest contribution to the decline in life expectancy, accounting for 25.5% and 16.6% of their declines, respectively. The largest contributor to the decline for Hispanic people was an increase in mortality due to unintentional injuries, while growth in cancer deaths was the primary contributor to the decline for Asian people between 2020 and 2021.

Although COVID-19 mortality was a primary contributor to the recent decrease in life expectancy across groups, leading causes of death varied by race and ethnicity. Overall, COVID-19 was the third leading cause of death in 2021, after heart disease and cancer. However, COVID-19 was the top leading cause of death for Hispanic and AIAN people, followed by heart disease and cancer (Figure 3). Among Black, Asian, and White people, COVID-19 was the third leading cause of death, outranked by heart disease and cancer.

Provisional data from 2022 show that overall mortality declined 5.3% between 2021 and 2022, and that, in 2022, the three leading causes of death were heart disease, cancer, and unintentional injuries. During this time, COVID-19 deaths declined almost 50% overall and across all racial and ethnic groups, dropping to the fourth leading cause of death. Despite these declines in COVID-19 deaths, AIAN and Black people continued to have higher COVID-19 death rates compared to White people. Declining death rates from COVID-19 may improve life expectancy overall, however racial gaps will likely persist given the continued disparities in COVID-19 and other leading causes of death.

Factors Contributing to Racial Life Expectancy Disparities

Research suggests that the factors driving disparities in life expectancy are complex and multifactorial. They include differences in health insurance coverage and access to care, social and economic factors, and health behaviors that are rooted in structural and systemic racism and discrimination (Figure 4).

Figure 4: Health Disparities are Driven by Social and Economic Inequities​

People of color are more likely than their White counterparts to be uninsured and to face other barriers to accessing health care that may contribute to shorter life expectancy. Data show that people of color are less likely to have health insurance and more likely to face barriers to accessing care, such as not having a usual source of care. Among AIAN people, chronic underfunding of the Indian Health Service further contributes to barriers to health care. Research shows that, overall, uninsured people are more likely than those with insurance to go without needed medical care due to cost and less likely to receive preventive care and services. Research further shows that uninsured people have higher mortality rates and lower survival rates than people with insurance.

Underlying social and economic inequities also drive disparities in mortality and life expectancy. Hispanic, AIAN, and Black people are more likely to have lower incomes and educational attainment levels compared to White people, and studies find that people with higher incomes and more education live longer lives. Other social and economic factors may also affect life expectancy. For example, historic housing policies, including redlining, and ongoing economic inequities have resulted in residential segregation that pushed many low-income people and people of color into segregated urban neighborhoods.   Research finds that living in racially segregated neighborhoods is associated with shorter life expectancy and higher mortality rates for Black people.

Social and economic factors can also shape health behaviors and exposure to health risks that influence life expectancy ,  For example, Black and AIAN people  have higher rates of smoking , substance and alcohol use disorders , and obesity compared to White people. Research suggests that eliminating smoking and obesity would greatly narrow disparities in life expectancy between Black and White people. People of color are also disproportionately affected by violence, including police and gun-related violence. Research shows African American and AIAN men and women and Latino men are at increased risk of being killed by police compared to their White peers. Black and Hispanic adults also are more likely than White adults to worry about gun violence according to  2023 KFF survey data . Other KFF  analysis  shows that firearm death rates increased sharply among Black and Hispanic youth during the pandemic driven primarily by gun assaults and suicide by firearm.

Research also highlights the role of racism and discrimination in driving racial disparities in mortality . Many of the inequities described above are rooted in racism and discrimination. Racism also contributes to lower quality of care among people of color. For example, a KFF/The Undefeated survey  found that most Black adults believe the health care system treats people unfairly based on their race, and one in five Black and Hispanic adults report they were personally treated unfairly because of their race or ethnicity while getting health care in the past year. Beyond driving structural inequities and differences in experiences obtaining health care, research also demonstrates that racism and discrimination have direct negative impacts on health. For example, research finds that the cumulative effects of exposure to racism and chronic stress , referred to as allostatic load , may contribute to a more rapid decline in health and higher mortality among Black people. The health of AIAN people has also been negatively affected by ongoing racism and discrimination , and intergenerational trauma stemming from historical actions and policies, including genocide, removal from native lands, and assimilation efforts, including Indian boarding schools.

Some life expectancy patterns are not fully understood or observable in the data presented. Notably, Hispanic people have longer life expectancy than their White counterparts despite experiencing increased barriers to accessing health care and social and economic challenges typically associated with poorer health outcomes. Researchers have hypothesized that this finding, sometimes referred to as the Hispanic or Latino health paradox , in part, may stem from variation in outcomes among subgroups of Hispanic people by origin, nativity, and race, with better outcomes for some groups, particularly recent immigrants to the U.S. However, the findings still are not fully understood. Measures of life expectancy for Asian people as a broad group may mask underlying differences among subgroups of the population who vary across health access and social and economic factors. Research has shown variation in life expectancy among Asian subgroups, with Chinese people having the longest life expectancy and Vietnamese people having the shortest life expectancy, which may in part reflect differences in socioeconomic status . Additionally, data limitations for NHOPI people prevented the ability to include them in this analysis. Efforts to expand and improve data collection for NHOPI people will be important to gain a better understanding of their experiences, particularly since they suffered disproportionate impacts on mortality from COVID-19.

Overall, the data suggest that the COVID-19 pandemic exacerbated longstanding racial disparities in life expectancy. The recent declines and widening of disparities in life expectancy highlight the urgency and importance of addressing disparities in health broadly and increased attention to disparities in mortality and life expectancy specifically. Continued efforts within and beyond the health care system will be important to reduce ongoing racial disparities in life expectancy, many of which are rooted in systemic racism. Within the health care system, these may include ongoing efforts to reduce gaps in health insurance, increase access to care, and eliminate discrimination and bias. Beyond the health care system, addressing broader social and economic factors, including those that drive disparities in behavioral risks, will also be important.

• Racial Equity and Health Policy
• Race/Ethnicity
• American Indian/Alaska Native

news release

• Recent Widening of Racial Disparities in U.S. Life Expectancy Was Largely Driven by COVID-19 Mortality

Also of Interest

• Premature Mortality During COVID-19 in the U.S. and Peer Countries
• Key Data on Health and Health Care by Race and Ethnicity
• COVID-19 Cases, Deaths, and Vaccinations by Race/Ethnicity as of Winter 2022

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Online Consumer Satisfaction During COVID-19: Perspective of a Developing Country

Yonghui rao.

1 Antai College of Economics & Management, Shanghai Jiao Tong University, Shanghai, China

2 School of Management, Zhejiang Shuren University, Hangzhou, China

Aysha Saleem

3 Faculty of Management Sciences, Riphah International University, Faisalabad Campus, Punjab, Pakistan

Wizra Saeed

4 Department of Professional Psychology, Bahria University, Islamabad, Pakistan

Junaid Ul Haq

Associated data.

The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.

A conceptual model based on the antecedents and consequences of online consumer satisfaction has been proposed and empirically proved in this study. Data were collected during Smart Lockdown of COVID-19 from 800 respondents to observe the difference between perceived and actual, and direct and indirect e-stores. Confirmatory factor analysis was used to observe the validity of the data set. The structural equation modeling technique was used to test the hypotheses. The findings indicated that consumers feel more satisfied when they shop through direct e-store than indirect e-store, whereas their perception and actual experience are different. Implications have also been added to the study.

Introduction

Online shopping is the act of buying a product or service through any e-stores with the help of any website or app. Tarhini et al. ( 2021 ) stated that shopping through online channels is actively progressing due to the opportunity to save time and effort. Furthermore, online shopping varies from direct e-store and indirect e-store about their perception against the actual experience. Developing countries still face various conflicts and issues while promoting and utilizing e-commerce to the maximum compared with the developed countries (Rossolov et al., 2021 ). In the developing countries, the difference between the perception and actual experience of the consumers varies when buying from indirect e-store compared to the direct e-store. On the contrary, as the world has been suffering from the COVID-19 pandemic, it has brought drastic changes globally in many sectors, business being one of them. De Vos ( 2020 ) stated that a large-scale lockdown was imposed worldwide to prevent the virus from spreading.

To survive, switching traditional shopping or trade toward digital was one factor that captured the attention across the globe on a larger scale. In April 2020, Walmart reported a 74% increase in online sales even though they faced a low customer walk-in at stores (Nassauer, 2020 ; Redman, 2020 ). This upsurge of swift adoption of online channels has led this research to ask a few questions. First, what will be the difference between the perceived and the actual product purchased online? A recent study has documented that consumers bear actual risk after shopping through online channels (Yang et al., 2020 ). Research reported that 30% of the products through online channels get returned and are not according to their perception (Saleh, 2016 ). The same author also showed that the return and complaint rates are getting higher when consumers shop through an online channel.

Second, is there any difference between the perceived and the actual product purchase online from a direct e-store or an indirect e-store? Direct e-store means the online brand store, for example, Walmart, and indirect e-store means third-party stores such as Amazon, Alibaba, Jingdong (JD), and Daraz. The direct e-store strives hard to maintain a clear, potent perception in the mind of its buyer (Grewal et al., 2009 ). Kumar and Kim ( 2014 ) stated that a brand strengthening its relationship with its consumer satisfies its needs through the actual product or services. In the literature (Olotewo, 2017 ; Rossolov et al., 2021 ), it is stated that the shopping patterns of buyers from direct and indirect e-stores vary greatly, especially in the developing countries. In this way, when shopping through a direct e-store, consumers may easily recognize the difference in buying from a direct and indirect e-stores (Mendez et al., 2008 ).

Third, a conceptual framework from a consumer perspective, antecedents and consequences of customer satisfaction, has been proposed and empirically proved. The literature (Alharthey, 2020 ) discussed different risk types in online shopping. Three main types of risk, perceived uncertainty, perceived risk, and price, are addressed in this model. To the best of the knowledge of the authors, no such investigation directed specific circumstances, particularly in the developing countries. Therefore, it is necessary to look for the antecedents and consequences of customer satisfaction to promote online shopping in the developing countries. The degree of consumer satisfaction defines his/her experience and emotions about the product or service purchased through the online channel. Recent studies (Guzel et al., 2020 ; Mamuaya and Pandowo, 2020 ) stated that the intention of the consumers to repurchase and their electronic-word-of-mouth (e-WOM) depends on their degree of satisfaction. In light of these heavy investments in online shopping, there is an exciting yet unexplored opportunity to comprehend better how the purchasing experiences of consumers through online channels influence their satisfaction level.

The study contributed to the current marketing literature in several ways. First, this study has highlighted that the perceived risk is very high when shopping through online channels, mainly the indirect e-stores. Therefore, the managers should develop strategies that reduce the perceived risk for the online consumer to shop more. Second, the study also disclosed that the perceived uncertainty in shopping through the online channel is high. While shopping online, the website design, graphics, and color scheme make the product more attractive than the actual one. Therefore, the managers must balance the visual appearance of the product on the website with the actual appearance of the product. This would increase the confidence and satisfaction of the consumer. Third, this study has also revealed that people are more satisfied while shopping from direct e-stores than indirect e-stores. Because the focal brands officially sponsor the direct e-stores, they pay more attention to their quality to retain consumers and maintain their brand reputation. Fourth, an indirect e-store works as a third party or a retailer who does not own the reputation of the product. This study exhibited the difference between the perception of the consumer being very high and the actual experience of using that product being quite different when shopping from the indirect channel. Last but not the least, this study is the first to report pre- and post-purchase consumer behavior and confirmed the perceived and the actual quality of a product bought from (i) direct e-store and (ii) indirect e-store.

Literature Review

Theoretical review.

Literature shows that when consumers get influenced to buy a particular product or service, some underlying roots are based on their behavior (Wai et al., 2019 ). Appraisal theory significantly explains consumer behavior toward shopping and provides an opportunity to analyze the evaluation process (e.g., Roseman, 2013 ; Kähr et al., 2016 ; Moors et al., 2017 ; Ul Haq and Bonn, 2018 ). This research, aligned with the four dimensions of appraisal theory as the first stage, clearly defines the agency stage that either of the factors is responsible for customer satisfaction. The second stage explains that consumer's degree of satisfaction holds great importance and refers to novelty in the literature. The third stage of the model briefly explains the feelings and emotions of the consumers about the incident, aligning with the certainty phase. The last step explains whether the consumers have achieved their goal or are not aligned with the appetitive purpose.

Cognitive appraisal researchers stated that various emotions could be its root cause (Scherer, 1997 ); it could be the reaction to any stimulus or unconscious response. On the contrary, four dimensions of appraisal theory are discussed in this research (Ellsworth and Smith, 1988 ; Ma et al., 2013 ). Agency (considering themselves or objects are answerable for the result of the circumstance) (Smith and Ellswoth, 1985 ; Durmaz et al., 2020 ); novelty (assessing the difference between the perception of an individual and his actual experience) (Ma et al., 2013 ); certainty (analysis of the apparent probability of a specific outcome and its effect on the emotions of the buyer) (Roseman, 1984 ), and appetitive goal (judging the degree to what extent the goal has been achieved) (Hosany, 2012 ).

Hypotheses Development

Perceived risk and consumer satisfaction.

Perceived risk is the perception of shoppers having unpleasant results for buying any product or service (Gozukara et al., 2014 ). Consumers who buy a specific product or service strongly impact their degree of risk perception toward buying (Jain, 2021 ). Buyers who tend to indulge in buying through online channels face perceived risk characterized by their perception compared to the actual uncertainty involved in it (Kim et al., 2008 ). Literature (Ashoer and Said, 2016 ; Ishfaq et al., 2020 ) showed that as the risk of buying is getting higher, it influences the degree of consumers about information about their buying, either purchasing from the direct or indirect e-shop. Johnson et al. ( 2008 ) stated that consumer judgment that appears due to their experience strongly impacts their satisfaction level. Jin et al. ( 2016 ) said that as the ratio of risk perception of their consumer decreases, it enhances customer satisfaction. Thus, from the above arguments, it is hypothesized as follows:

• H 1 : Perceived risk has a significant negative impact on consumer satisfaction—direct vs. indirect e-store; perceived vs. actual experience .

Perceived Uncertainty and Consumer Satisfaction

Uncertainty is defined as a time that occurs in the future that comprises the predictable situation due to the asymmetry nature of data (Salancik and Pfeffer, 1978 ). Consumers may not expect the outcome of any type of exchange conducted as far as the retailer and product-oriented elements are concerned (Pavlou et al., 2007 ). Therefore, uncertainty initiates that retailers may not be completely predictable; on the contrary, consumers tend to analyze and understand their actions about decision making (Tzeng et al., 2021 ). Thus, the degree of uncertainty involved in buying through online channels influences that degree of customer satisfaction. In addition, when the performance of any particular product or service matches the degree of expectations, he gets satisfied and, hence, repeats his decision of buying (Taylor and Baker, 1994 ). But if the product quality fails to meet the requirements, it negatively affects the degree of satisfaction (Cai and Chi, 2018 ).

• H 2 : Perceived uncertainty has a significant negative impact on consumer satisfaction—direct vs. indirect e-store; perceived vs. actual experience .

Price Value and Consumer Satisfaction

Oliver and DeSarbo ( 1988 ) suggested that the price value is the proportion of the result of the buyer to the input of the retailer. It is defined as an exchange of products/services based on their quality against a price that is to be paid (Dodds et al., 1991 ). Consumers look for a higher value in return; consumers are willing to pay a higher price (Pandey et al., 2020 ). Yet, it leads to higher dissatisfaction when they receive a lower degree of profitable products. Besides, the buyers associate such type of product/service they use as less favorable or not according to their needs and desires. Hence, the buyers regret their decision-making degree for choosing that particular product (Zeelenberg and Pieters, 2007 ). Aslam et al. ( 2018 ) indicated that a product/service price influences the satisfaction of a buyer. Afzal et al. ( 2013 ) recommended that the price is among those factors that hold great significance for the degree of satisfaction of the consumer. If the price value of any product/service differs from consumer to consumer, consumers tend to switch brands. Hence, it is hypothesized that:

• H3 : Price value has a significant positive impact on consumer satisfaction—direct vs. indirect e-store; perceived vs. actual experience .

Consumer Satisfaction With Consumer Delight, Consumer Regret, and Outrage

Satisfaction is defined as how a consumer is pleased with a particular brand or view about a product/service that matches requirements. It is an essential factor that triggers when the product or service performance exceeds the expectation and perception of the customers (Woodside et al., 1989 ). The decision of the buyer significantly affects their satisfaction toward the product or service (Park et al., 2010 ). If buyers are satisfied with the product/service they purchased online, this degree of satisfaction significantly affects their repurchase intention and WOM (Butt et al., 2017 ). Tandon ( 2021 ) stated that a consumer satisfied with the product/service would get delighted. Consumer satisfaction, when exceeding the expectations, leads to consumer delight (Mikulić et al., 2021 ). Mattila and Ro ( 2008 ) recommended that the buyer gets disappointed by anger, regret, and outrage. It also defines that negative emotions have a significant effect on the purchasing intention of the consumers. Oliver ( 1989 ) stated that unsatisfied buyers or products that do not fulfill the needs of the customers can create negative emotions. Sometimes, their feelings get stronger and result in sadness and outrage. Bechwati and Xia ( 2003 ) recommended that the satisfaction of the consumers influences their behavior to repurchase; outraged consumers due to dissatisfaction sometimes want to hurt the company. Besides deciding to purchase, consumers mostly regret their choices compared to other existing choices (Rizal et al., 2018 ). Hechler and Kessler ( 2018 ) investigated that consumers who are outraged in nature actively want to hurt or harm the company or brand from which they got dissatisfied or hurt. Thus, it is proposed that:

• H 4 : Consumer satisfaction has a significant negative impact on (a) consumer delight, (b) consumer regret, (c) consumer outrage—direct vs. indirect e-store; perceived vs. actual experience .

Consumer Delight and E-WOM

Oliver et al. ( 1997 ) recommended that a degree of delight in a buyer is termed as a positive emotion. Consumers purchase a product/service that raises their degree of expectation and gets them delighted (Crotts and Magnini, 2011 ). Delighted buyers are involved in sharing their experiences with their friends and family and spreading positive WOM to others (Parasuraman et al., 2020 ). Happy buyers generally share their opinions while posting positive feedback through social media platforms globally (Zhang, 2017 ). A positive WOM of the buyer acts as a fundamental factor in spreading awareness about the product/service and strongly impacts other buyers regarding buying it (Rahmadini and Halim, 2018 ). Thus, it is proposed that:

• H5 : Consumer delight has a significant positive impact on E-WOM—direct vs. indirect e-store; perceived vs. actual experience .

Consumer Delight and Repurchase Intention

Delighted consumers tend toward brand loyalty; thus, they increase their buying intention of the service or product (Ludwig et al., 2017 ; Ahmad et al., 2021 ). Customers can understand the objective of loyalty in purchasing a similar product or a new one from the same company. Delighted consumers tend to indulge in a higher degree of an emotional state that leads them to higher purchase intentions; it eliminates the switching of brands (Parasuraman et al., 2020 ). Kim et al. ( 2015 ) stated that consumers delighted with a product or service of a brand become loyal to it, and the possibility of switching brands gets very low. Research (Loureiro and Kastenholz, 2011 ; Tandon et al., 2020 ) shows that delighted consumers are more eager to purchase the same product again. Hence, it is proposed that:

• H6 : Consumer delight has a significant positive impact on his repurchase intention—direct Vs. indirect e-store; Perceived Vs. actual experience

Consumer Regret and E-WOM

Regret is considered a negative emotion in reaction to an earlier experience or action (Tsiros and Mittal, 2000 ; Kumar et al., 2020 ). Regret is when individuals frequently feel pity, disgrace, shame, or humiliation after acting in a particular manner and afterward try to amend their possible actions or decisions (Westbrook and Oliver, 1991 ; Tsiros and Mittal, 2000 ). Regret is that specific negative emotion the buyers feel while making a bad decision that hurts them; their confidence level is badly affected. They blame themselves for choosing or creating a terrible decision (Lee and Cotte, 2009 ). Li et al. ( 2010 ) suggested that buyers quickly start regretting and find their way to express their negative emotions. When they feel betrayed, they tend to spread negative WOM (NWOM) as a response to their anxiety or anger. Jalonen and Jussila ( 2016 ) suggested that buyers who get dissatisfied with their selections get involved in negative e-WOM about that particular brand/company. Earlier research says that buyers suffering from failure to buy any product/services tend to participate actively and play a role in spreading NWOM due to the degree of regret after making bad choices. Whelan and Dawar ( 2014 ) suggested that consumers sense that business has treated them unreasonably, and many consumers complain about their experience, resulting in e-WOM that may reduce consumer repurchase intention. Thus, it can be stated that:

• H7 : Consumer regret has a significant negative impact on e-WOM—direct vs. indirect e-store; perceived vs. actual experience .

Consumer Regret and Repurchase Intention

Regret has a substantial influence on the intentions of the consumers to not entirely be measured by their degree of happiness (Thibaut and Kelley, 2017 ). Results may not be evaluated by matching the structured degree of expectation but are also linked to alternatives reachable in the market. Therefore, such sort of evaluation and assessments will probably influence repurchase intention. For example, suppose the skipped reserve overtakes the picked alternative. In that case, the customer might change the replacement for the future purchase, regardless of whether the individual is profoundly happy with the picked option (Liao et al., 2017 ). According to the researchers, there is a negative relationship between regret and consumer repurchase intention (Liao et al., 2017 ; Durmaz et al., 2020 ). Furthermore, Unal and Aydin ( 2016 ) stated that perceived risk negatively impacts regret, influencing the repurchase intention of the consumers. Thus, it can be stated that:

• H8 : Customer's regret has a significantly negative influence on his repurchase intention—direct vs. indirect e-store; perceived vs. actual experience .

Consumer Outrage and E-WOM

The disappointment of the consumers is a negative response to a product or a service (Anderson and Sullivan, 1993 ). Outrage is the negative emotion a consumer experience when he purchases something totally against his requirements (Lindenmeier et al., 2012 ). Besides, when the perception of the buyer is infringed, such behaviors occur. According to Torres et al. ( 2019 ), enraged consumers get involved in communicating their outrage through e-WOM. Outraged consumers actively hurt the firm or brand from which they got hurt (Hechler and Kessler, 2018 ). Consumers give e-WOM online reviews to decrease the negative emotions from the experiences of the consumer and re-establish a calm mental state to equilibrium (Filieri et al., 2021 ). Thus, such consumers tend to give negative comments about the brand or product, which failed to match their expectations. NWOM has been characterized as negative reviews shared among people or a type of interpersonal communication among buyers concerning their experiences with a particular brand or service provider (Balaji et al., 2016 ). Hence, it is hypothesized that:

• H9 : Consumer outrage has a significant negative impact on e-WOM—direct vs. indirect e-store; perceived vs. actual experience .

Consumer Outrage and Repurchase Intentions

Repurchase intentions are characterized as the expressed trust of a buyer that they will or will not purchase a specific product and service again in the future (Malhotra et al., 2006 ). Establishing relations with buyers should result in the repurchase. Negative disconfirmation ensues dissatisfaction or a higher level of outrage (Escobar-Sierra et al., 2021 ). When a service/product fails and is not correctly addressed, the negative appraisal is overstated. Hence, “it may be more difficult to recover from feelings of victimization than to recover from irritation or annoyance” typically associated with dissatisfaction (Schneider and Bowen, 1999 , p. 36). Therefore, consumers get outraged from buying such a product that fails to match their perception. When the experience of a consumer prompts a negative disconfirmation, the purchaser will also have a higher urging level through outrage. Therefore, consumers will probably have negative intentions to repurchase and do not want to indgule in making the same decision repeatedly (Wang and Mattila, 2011 ; Tarofder et al., 2016 ). Therefore, it is proposed that:

• H10 : Consumer outrage has a significant negative impact on repurchase intention—direct vs. indirect e-store; perceived vs. actual experience .

Methodology

This research explores the difference between the perception of the consumers and the actual online shopping experience through direct and indirect e-stores. It was an experimental design in which online shopping was studied in the developing countries. Data were collected from those individuals who shop from online channels; direct e-store and indirect e-store. Taking care of COVID-19 standard operating procedures, only 50 respondents were gathered two times, every time in a university auditorium after obtaining the permission from the administration. The total capacity of the auditorium was 500, as the lockdown restrictions were lifted after the first wave of the coronavirus.

Data Collection Tool

A questionnaire was used for the survey. The questionnaire was adapted in English to guarantee that the respondents quickly understood the questions used. A cross-sectional study technique was used for this research. A cross-sectional study helps in gathering the data immediately and collects data from a large sample size. The total number of distributed questionnaires was 1,250, out of which 800 were received in the usable form: 197 incomplete, 226 incorrect, and dubious responses, and 27 were eliminated. Thus, a 64% response rate was reported. Research showed that a 1:10 ratio is accepted (Hair et al., 1998 ) as far as the data collection is concerned; for that instance, this study data fell in the acceptable range.

Indirect E-Store

Consumers who prefer to shop through online channels were gathered in an auditorium of an institute. Only those consumers were eligible for this experiment, who themselves buy through e-stores. A few products were brought from an indirect e-store, and later on, those products were shown to the respondents from the website of that indirect e-store. After showing products, we asked the respondents to fill the survey as per their perception of the product. Then we asked them to fill out another questionnaire to ascertain the difference between the perception and actual experience when purchasing from an indirect e-store. Once all the respondents completed the survey, we have shown them the actual products they have selected by seeing the website of the indirect e-store.

Direct E-Store

The second experiment was carried out on those consumers who shop from direct e-stores. For that purpose, a few popular reviewed clothing articles were purchased from the e-store. As in the case of an indirect e-store, respondents were also shown these articles from the websites of these direct e-stores. We then asked the respondents to fill the survey to confirm their perception of the products. Once all the respondents completed the survey, we showed them the actual product and asked them to fill out another questionnaire according to their actual purchasing experience from the direct e-store. The primary purpose of this experiment was to compare buying from direct e-store and indirect e-store.

Construct Instruments

The total number of items was 34, which were added in the earlier section of the questionnaire. These items were evaluated with the help of using a five-point Likert scale that falls from strongly disagree (1) to strongly agree (5). The items used in the study were empirically validated. Table 2 carries the details of the items of the questionnaire. The price value was evaluated using three items used by Venkatesh et al. ( 2012 ). The perceived uncertainty was one of the independent variables that carry four items derived from Pavlou et al. ( 2007 ). Perceived risk was the third independent variable used, held three items; thus, its scale was derived from Shim et al. ( 2001 ). Wang ( 2011 ) validated consumer satisfaction carrying three items; consumer delight was measured by a 3-item scale proposed by Finn ( 2012 ); consumer regret was measured by the scale proposed by Wu and Wang ( 2017 ). It carries a three-item scale. Consumer outrage was measured by Liu et al. ( 2015 ); it has six items. Repurchase intention was measured through a scale adapted from Zeithaml et al. ( 1996 ), which carries four items. e-WOM was validated by the scale adapted from Goyette et al. ( 2010 ); it has five items.

Demographics of the Respondents

A total of 800 questionnaires were filled, and the respondents expressed their perception and actual experience from direct e-store and indirect e-store. Respondents belonged to different age groups from 18 to 50 years and above. There were 49% women and 51% men who took part in filling this survey. The income level of the respondents was grouped in different categories from “above 10,000 to above 50,000. The majority (56%) of the respondents were single, and 44% were married (Details can be viewed in Figure 1 ; Table 1 ). Data for both direct and indirect e-store was collected equally; 50% each to compare each category better.

Proposed conceptual framework.

Demographics of the respondents.

Reliability and Validity

Reliability evaluates with the help of composite reliability (CR). All CR values fall into the range of 0.7–0.9, which is acceptable (Hair et al., 2011 ). Convergent and discriminant validity has been observed through confirmatory factor analysis as recommended by some researchers (Fornell and Larcker, 1981 ; Hair et al., 2010 ).

Convergent Validity

Convergent validity is evaluated with the help of two standards mentioned in the literature earlier, factor loading and average variance extracted (AVE), both the values should be >0.5 (Yap and Khong, 2006 ). The values are mentioned in Table 2 .

Reliability and convergent validity.

Discriminant Validity

Discriminant validity is evaluated based on two conditions that are required to evaluate it. First, the correlation between the conceptual model variables should be <0.85 (Kline, 2005 ). Second, the AVE square value must be less than the value of the conceptual model (Fornell and Larcker, 1981 ). Table 3 depicts the discriminant validity of the construct of the study.

Discriminant validity.

All diagonal bold values are square root of AVE .

Multi-Group Invariance Tests

Multi-group confirmatory factor analysis was conducted as the pre-requisites for the measurement model. The multi-group analysis was used to investigate a variety of invariance tests. Different invariance tests were performed to guarantee the items working precisely in the same manner in all the groups. In this research, the following are the model fit indexes, that is, CMIN/dF =2.992 CFI = 0.915, TLI = 0.906, and RMSEA = 0.071. Byrne ( 2010 ) and Teo et al. ( 2009 ) stated that CFI gives more accurate results, especially when comparing variables in different groups.

Hypotheses Testing

Scanning electron microscope technique was used to run and test the proposed hypotheses for the conceptual model. First, all the hypotheses proposed were checked, from which eight were initially accepted. Later, the multi-group test was utilized to test the proposed hypotheses and compare the shopping experience from direct e-store with indirect e-store and consumer perception with actual experience. Table 4 explains this in detail.

Hypotheses results.

Discussion and Implications

This research offers a remarkable number of facts for practitioners. This study can benefit marketing strategists by reducing the perceived risk, decreasing the intensity of perceived uncertainty, stabilizing the price, enhancing consumer satisfaction, promoting delighting consumers, accepting the negative behavior of the consumers, consumer retention, and establishing a positive e-WOM.

Reducing Risks

Certain factors play a role in antecedents of consumer satisfaction; they are particularly those that resist consumers to shop from any online channel, neither direct e-store nor indirect e-store. Perceived risk, perceived uncertainty, and the price are some of those antecedents that play a significant role in affecting the degree of satisfaction of the consumers, resulting in either to retain a consumer or to outrage a consumer. This study aligns with the existing literature. Tandon et al. ( 2016 ); Bonnin ( 2020 ) and Pandey et al. ( 2020 ) showed that consumers seek to shop from an e-store without bearing any risk. Consumers feel more confident about an e-store when the perceived risk is less than shopping from traditional ones as consumers want to feel optimistic about their decision. Second, e-vendors should ensure that the quality of a product is up to the mark and according to the consumer needs. Therefore, vendors should offer complete details about the product/service and its risks to the consumers. Moreover, this study suggests that e-stores must align the visuals of a product with its actual appearance. This would help them to increase customer satisfaction and confidence in the e-store.

Focus on Consumer Satisfaction

Consumer satisfaction is the deal-breaker factor in the online sector. Literature (Shamsudin et al., 2018 ; Hassan et al., 2019 ) showed that organizations prioritize their consumers by fulfilling their requirements and required assistance. As a result, consumers are more confident and become satisfied consumers in the long run. This study adds to the literature that the degree of satisfaction of the consumers plays an essential role in shopping from an e-store. Consumers feel more confident in shopping from a direct e-store than an indirect e-store as the difference in the perception of consumers and the actual experience varies. Therefore, online vendors should focus on satisfying their consumers as it plays a remarkable role in retaining consumers.

Value Consumer Emotions

Online, retaining, and satisfying consumers are the most vital factor that directly affects the organization. This research aligns with the existing literature (Jalonen and Jussila, 2016 ; Hechler and Kessler, 2018 ; Coetzee and Coetzee, 2019 ); when the retailer successfully fulfills its requirements, the consumer gets delighted repeating his choice to repurchase. On the other hand, if the online retailer fails to serve the consumer, the consumer regrets and, in extreme cases, becomes outraged about his decision. The negative emotions of the consumers threaten the company from many perspectives, as the company loses its consumer and its reputation in the market is affected. Therefore, first, market practitioners should avoid ignoring the requirements of consumers. Second, online vendors should pay special attention to the feedback of the consumers and assure them that they are valued.

Consumer Retention

The ultimate goal is to retain its consumers, but e-vendors should make proper strategies to satisfy their consumers as far as the online sector is concerned. The earlier studies of Zhang et al. ( 2015 ) and Ariffin et al. ( 2016 ) contributed to the literature that consumer satisfaction is a significant aspect in retaining a consumer. This research has also suggested that the satisfaction of the consumers plays a vital role in retaining them. Moreover, online shoppers provide the fastest spread of the right WOM about the product/ service. Second, consumers should feel valued and committed to vendors.

Pre- and Post-buying Behavior

This study contributed to a conceptual model that deals with consumer pre- and post-purchase behavior from the direct and indirect e-stores. With the help of experimental design, this study has reported its finding, highlighted how a satisfied customer is delightful and shares e-WOM, and showed repurchase intention. However, if the customer is not satisfied with the flip of a coin, he may feel regretted or outraged and cannot share e-WOM or have a repurchase intention.

Conclusions

This research concludes that online shopping has boomed during this COVID-19 pandemic period, as the lockdown prolonged in both the developed and the developing countries. The study further supports the difference between shopping from a direct e-store and an indirect e-store. The perception of the consumers shopping from direct e-store is more confident, and their degree of satisfaction is much higher, as the actual experience of the consumers aligns with their perceptions. Instead, consumers feel dissatisfied or outraged to choose an indirect e-store for shopping. Indirect e-store makes false promises and guarantees to its buyers, and eventually, when the consumers experience the product, it is against their perception.

This research fills the literature gap about the antecedents that lead to online shopping growth in the developing countries. This study aligns with Hechler and Kessler's ( 2018 ) earlier research, which stated that dissatisfied consumers threaten the reputation of the organization. Furthermore, Klaus and Maklan ( 2013 ), Lemon and Verhoef ( 2016 ) suggested that handling the experience and satisfaction of the buyers plays a significant role in surviving among its competitors. Grange et al. ( 2019 ) recommended that e-commerce develops and attracts consumers by fulfilling their needs and requirements quickly. This study aligned with the existing literature by adding factors influencing the shopping preferences of the consumers from an e-store.

Limitations and Future Research

Despite its significant findings, this research has some limitations and scope for future research. First, this research only examined a few risks involved in online shopping. Future research studies should analyze other risks, for example, quality risk and privacy risk. Second, this study focused on shopping through direct e-stores and indirect e-stores. Future research can implement a conceptual model of a specific brand. Third, this study can be implemented in other sectors, for example, tourism, and hospitality. Fourth, it may be fascinating to look at other fundamentals, such as age, gender, education, relation with the retailer, or the degree of involvement with online shopping to differentiate other factors.

The proposed framework can be utilized in other developing countries, as every country faces different problems according to its growth and development. The model can be examined among specific direct e-stores to compare new customers and loyal customers. Future studies can explore indirect relationships along with adding mediators and moderators in the proposed model.

Data Availability Statement

Ethics statement.

The studies involving human participants were reviewed and approved by This study involving human participants was reviewed and approved by the Ethics Committee of the Department of Management Sciences, Riphah International University, Faisalabad Campus, Faisalabad, Pakistan. The participants provided their written informed consent to participate in this study. The patients/participants provided their written informed consent to participate in this study.

Author Contributions

AS contributed to the conceptualization and writing the first draft of the research. JU contributed to visualizing and supervising the research. All authors who contributed to the manuscript read and approved the submitted version.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

• Afzal S., Chandio A. K., Shaikh S., Bhand M., Ghumro B. A. (2013). Factors behind brand switching in cellular networks . Int. J. Asian Soc. Science 3 , 299–307. [ Google Scholar ]
• Ahmad W., Kim W. G., Choi H. M., Ul Haq J. (2021). Modeling behavioral intention to use travel reservation apps: a cross-cultural examination between US and China . J. Retail. Consum. Serv. 63 :102689. 10.1016/j.jretconser.2021.102689 [ CrossRef ] [ Google Scholar ]
• Alharthey B. (2020). The role of online trust in forming online shopping intentions . Int. J. Online Market. 10 , 32–57. 10.4018/IJOM.2020010103 [ CrossRef ] [ Google Scholar ]
• Anderson E. W., Sullivan M. W. (1993). The antecedents and consequences of customer satisfaction for firms . Market. Sci. 12 , 125–143. 10.1287/mksc.12.2.125 [ CrossRef ] [ Google Scholar ]
• Ariffin S., Yusof J. M., Putit L., Shah M. I. A. (2016). Factors influencing perceived quality and repurchase intention towards green products . Proc. Econ. Finan. 37 , 391–396. 10.1016/S2212-5671(16)30142-3 [ CrossRef ] [ Google Scholar ]
• Ashoer M., Said S. (2016). “The impact of perceived risk on consumer purchase intention in Indonesia; a social commerce study,” in Proceeding of the International Conference on Accounting, Management, Economics and Social Sciences . 1–13. [ Google Scholar ]
• Aslam W., Arif I., Farhat K., Khursheed M. (2018). The role of customer trust, service quality and value dimensions in determining satisfaction and loyalty: an Empirical study of mobile telecommunication industry in Pakistan . Market-TrŽište 30 , 177–194. 10.22598/mt/2018.30.2.177 [ CrossRef ] [ Google Scholar ]
• Balaji M. S., Khong K. W., Chong A. Y. L. (2016). Determinants of negative word-of-mouth communication using social networking sites . Inform. Manage. 53 , 528–540. 10.1016/j.im.2015.12.002 [ CrossRef ] [ Google Scholar ]
• Bechwati N. N., Xia L. (2003). Do computers sweat? the impact of perceived effort of online decision aids on consumers' satisfaction with the decision process . J. Consum. Psychol. 13 , 139–148. 10.1207/S15327663JCP13-1andamp;2_12 [ CrossRef ] [ Google Scholar ]
• Bonnin G. (2020). The roles of perceived risk, attractiveness of the online store and familiarity with AR in the influence of AR on patronage intention . J. Retail. Consum. Serv. 52 :101938. 10.1016/j.jretconser.2019.101938 [ CrossRef ] [ Google Scholar ]
• Butt M. M., Rose S., Wilkins S., Haq J. U. (2017). MNCs and religious influences in global markets: drivers of consumer-based halal brand equity . Int. Market. Rev . 12 :277. 10.1108/IMR-12-2015-0277 [ CrossRef ] [ Google Scholar ]
• Byrne B. M. (2010). Structural Equation Modeling With AMOS: Basic Concepts, Applications, and Programming , 2nd Edn. New York, NY: Routledge. [ Google Scholar ]
• Cai R., Chi C. G. Q. (2018). The impacts of complaint efforts on customer satisfaction and loyalty . Serv. Industr. J. 38 , 1095–1115. 10.1080/02642069.2018.1429415 [ CrossRef ] [ Google Scholar ]
• Coetzee A., Coetzee J. (2019). Service quality and attitudinal loyalty: the mediating effect of delight on retail banking relationships . Glob. Bus. Econ. Rev. 21 , 120–138. 10.1504/GBER.2019.096856 [ CrossRef ] [ Google Scholar ]
• Crotts J. C., Magnini V. P. (2011). The customer delight construct: is surprise essential? Ann. Tourism Res. 38 , 719–722. 10.1016/j.annals.2010.03.004 [ CrossRef ] [ Google Scholar ]
• De Vos J. (2020). The effect of COVID-19 and subsequent social distancing on travel behavior . Transport. Res. Interdisciplin. Perspect. 5 :100121. 10.1016/j.trip.2020.100121 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Dodds W. B., Monroe K. B., Grewal D. (1991). Effects of price, brand, and store information on buyers' product evaluations . J. Market. Res. 28 , 307–319. 10.1177/002224379102800305 [ CrossRef ] [ Google Scholar ]
• Durmaz Y., Demira,g B., Çavuşoglu S. (2020). Influence of regret and regret reversing effort on dissatisfaction and repurchase intention after purchasing fashion products . Preprints. 10.20944/preprints202003.0280.v1 [ CrossRef ] [ Google Scholar ]
• Ellsworth P. C., Smith C. A. (1988). Shades of joy: patterns of appraisal differentiating pleasant emotions . Cogn. Emot. 2 , 301–331. 10.1080/02699938808412702 [ CrossRef ] [ Google Scholar ]
• Escobar-Sierra M., García-Cardona A., Vera Acevedo L. D. (2021). How moral outrage affects consumer's perceived values of socially irresponsible companies . Cogent Bus. Manage. 8 :1888668. 10.1080/23311975.2021.1888668 [ CrossRef ] [ Google Scholar ]
• Filieri R., Galati F., Raguseo E. (2021). The impact of service attributes and category on eWOM helpfulness: an investigation of extremely negative and positive ratings using latent semantic analytics and regression analysis . Comput. Human Behav. 114 :106527. 10.1016/j.chb.2020.106527 [ CrossRef ] [ Google Scholar ]
• Finn A. (2012). Customer delight: distinct construct or zone of nonlinear response to customer satisfaction? J. Serv. Res. 15 , 99–110. 10.1177/1094670511425698 [ CrossRef ] [ Google Scholar ]
• Fornell C., Larcker D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error . J. Market. Res. 18 , 39–50. 10.1177/002224378101800104 [ CrossRef ] [ Google Scholar ]
• Goyette I., Ricard L., Bergeron J., Marticotte F. (2010). e-WOM Scale: word-of-mouth measurement scale for e-services context . Can. J. Admin. Sci. 27 , 5–23. 10.1002/cjas.129 [ CrossRef ] [ Google Scholar ]
• Gozukara E., Ozyer Y., Kocoglu I. (2014). The moderating effects of perceived use and perceived risk in online shopping . J. Glob. Strateg. Manage. 16 , 67–81. 10.20460/JGSM.2014815643 [ CrossRef ] [ Google Scholar ]
• Grange C., Benbasat I., Burton-Jones A. (2019). With a little help from my friends: Cultivating serendipity in online shopping environments . Inf. Manage . 56 , 225–235. [ Google Scholar ]
• Grewal D., Levy M., Kumar V. (2009). Customer experience management in retailing: An organizing framework . J. Retail. 85 , 1–14. 10.1016/j.jretai.2009.01.001 [ CrossRef ] [ Google Scholar ]
• Guzel M., Sezen B., Alniacik U. (2020). Drivers and consequences of customer participation into value co-creation: a field experiment . J. Product Brand Manage. 10.1108/JPBM-04-2020-2847 [ CrossRef ] [ Google Scholar ]
• Hair J. F., Anderson R. E., Tatham R. L., Black W. C. (1998). Multivariate Data Analysis . 5th ed., Hoboken, NJ: Prentice-Hall. [ Google Scholar ]
• Hair J. F., Celsi M., Ortinau D. J., Bush R. P. (2010). Essentials of Marketing Research , Vol. 2 . New York, NY: McGraw-Hill/Irwin. [ Google Scholar ]
• Hair J. F., Ringle C. M., Sarstedt M. (2011). PLS-SEM: indeed a silver bullet . J. Market. Theor. Pract. 19 , 139–152. 10.2753/MTP1069-6679190202 [ CrossRef ] [ Google Scholar ]
• Hassan S., Shamsudin M. F., Mustapha I. (2019). The effect of service quality and corporate image on student satisfaction and loyalty in TVET higher learning institutes (HLIs) . J. Tech. Educ. Train. 11 :4. [ Google Scholar ]
• Hechler S., Kessler T. (2018). On the difference between moral outrage and empathic anger: anger about wrongful deeds or harmful consequences . J. Exp. Soc. Psychol. 76 , 270–282. 10.1016/j.jesp.2018.03.005 [ CrossRef ] [ Google Scholar ]
• Hosany S. (2012). Appraisal determinants of tourist emotional responses . J. Trav. Res. 51 , 303–314. 10.1177/0047287511410320 [ CrossRef ] [ Google Scholar ]
• Ishfaq M., Nazir M. S., Qamar M. A. J., Usman M. (2020). Cognitive bias and the Extraversion personality shaping the behavior of investors . Front. Psychol. 11 :556506. 10.3389/fpsyg.2020.556506 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Jain S. (2021). Examining the moderating role of perceived risk and web atmospherics in online luxury purchase intention . J. Fash. Market. Manage. Int. J. 05 :89. 10.1108/JFMM-05-2020-0089 [ CrossRef ] [ Google Scholar ]
• Jalonen H., Jussila J. (2016). “Developing a conceptual model for the relationship between social media behavior, negative consumer emotions and brand disloyalty,” in Conference on e-Business, e-Services and e-Society (Cham: Springer; ), 134–145. 10.1007/978-3-319-45234-0_13 [ CrossRef ] [ Google Scholar ]
• Jin N., Line N. D., Merkebu J. (2016). The impact of brand prestige on trust, perceived risk, satisfaction, and loyalty in upscale restaurants . J. Hospital. Market. Manage. 25 , 523–546. 10.1080/19368623.2015.1063469 [ CrossRef ] [ Google Scholar ]
• Johnson M. S., Sivadas E., Garbarino E. (2008). Customer satisfaction, perceived risk and affective commitment: an investigation of directions of influence . J. Serv. Market. 5 :120. 10.1108/08876040810889120 [ CrossRef ] [ Google Scholar ]
• Kähr A., Nyffenegger B., Krohmer H., Hoyer W. D. (2016). When hostile consumers wreak havoc on your brand: the phenomenon of consumer brand sabotage . J. Mark. 80 , 25–41. 10.1509/jm.15.0006 [ CrossRef ] [ Google Scholar ]
• Kim D. J., Ferrin D. L., Rao H. R. (2008). A trust-based consumer decision-making model in electronic commerce: the role of trust, perceived risk, and their antecedents . Decis. Support Syst. 44 , 544–564. 10.1016/j.dss.2007.07.001 [ CrossRef ] [ Google Scholar ]
• Kim M., Vogt C. A., Knutson B. J. (2015). Relationships among customer satisfaction, delight, and loyalty in the hospitality industry . J. Hospital. Tourism Res. 39 , 170–197. 10.1177/1096348012471376 [ CrossRef ] [ Google Scholar ]
• Klaus P. P., Maklan S. (2013). Towards a better measure of customer experience . Int. J. Market Res. 55 , 227–246. 10.2501/IJMR-2013-021 [ CrossRef ] [ Google Scholar ]
• Kline R. B. (2005). Principles and Practice of Structural Equation Modeling , 2nd Edn. New York, NY: Guilford Press. [ Google Scholar ]
• Kumar A., Chaudhuri D., Bhardwaj D., Mishra P. (2020). Impulse buying and post-purchase regret: a study of shopping behaviour for the purchase of grocery products . Int. J. Manage. 11 :57. 10.34218/IJM.11.12.2020.057 [ CrossRef ] [ Google Scholar ]
• Kumar A., Kim Y. K. (2014). The store-as-a-brand strategy: the effect of store environment on customer responses . J. Retail. Consum. Serv. 21 , 685–695. 10.1016/j.jretconser.2014.04.008 [ CrossRef ] [ Google Scholar ]
• Lee S. H., Cotte J. (2009). Post-purchase Consumer Regret: Conceptualization and Development of the PPCR Scale . ACR North American Advances. [ Google Scholar ]
• Lemon K. N., Verhoef P. C. (2016). Understanding customer experience throughout the customer journey . J. Market. 80 , 69–96. 10.1509/jm.15.0420 [ CrossRef ] [ Google Scholar ]
• Li S., Zhou K., Sun Y., Rao L. L., Zheng R., Liang Z. Y. (2010). Anticipated regret, risk perception, or both: which is most likely responsible for our intention to gamble? J. Gambl. Stud. 26 , 105–116. 10.1007/s10899-009-9149-5 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Liao C., Lin H. N., Luo M. M., Chea S. (2017). Factors influencing online shoppers' repurchase intentions: the roles of satisfaction and regret . Inform. Manage. 54 , 651–668. 10.1016/j.im.2016.12.005 [ CrossRef ] [ Google Scholar ]
• Lindenmeier J., Schleer C., Pricl D. (2012). Consumer outrage: emotional reactions to unethical corporate behavior . J. Bus. Res. 65 , 1364–1373. 10.1016/j.jbusres.2011.09.022 [ CrossRef ] [ Google Scholar ]
• Liu M. W., Keh H. T. (2015). Consumer delight and outrage: scale development and validation . J. Serv. Theory Pract . 25 , 680–699. 10.1108/JSTP-08-2014-0178 [ CrossRef ] [ Google Scholar ]
• Loureiro S. M. C., Kastenholz E. (2011). Corporate reputation, satisfaction, delight, and loyalty towards rural lodging units in Portugal . Int. J. Hospital. Manage. 30 , 575–583. 10.1016/j.ijhm.2010.10.007 [ CrossRef ] [ Google Scholar ]
• Ludwig N. L., Heidenreich S., Kraemer T., Gouthier M. (2017). Customer delight: universal remedy or a double-edged sword? J. Serv. Theory Pract . 8 :197. 10.1108/JSTP-08-2015-0197 [ CrossRef ] [ Google Scholar ]
• Ma J., Gao J., Scott N., Ding P. (2013). Customer delight from theme park experiences: the antecedents of delight based on cognitive appraisal theory . Ann. Tour. Res. 42 , 359–381. 10.1016/j.annals.2013.02.018 [ CrossRef ] [ Google Scholar ]
• Malhotra N., Hall J., Shaw M., Oppenheim P. (2006). Marketing Research: An Applied Orientation . Melbourne, VC: Pearson Education Australia. [ Google Scholar ]
• Mamuaya N. C., Pandowo A. (2020). Determinants of customer satisfaction and its implications on word of mouth in e-commerce industry: case study in Indonesia . Asia Pacific J. Manage. Educ. 3 , 16–27. 10.32535/apjme.v3i1.740 [ CrossRef ] [ Google Scholar ]
• Mattila A. S., Ro H. (2008). Discrete negative emotions and customer dissatisfaction responses in a casual restaurant setting . J. Hosp. Tour. Res. 32 , 89–107. 10.1177/1096348007309570 [ CrossRef ] [ Google Scholar ]
• Mendez J. L., Oubina J., Rubio N. (2008). Expert quality evaluation and price of store vs. manufacturer brands: an analysis of the Spanish mass market . J. Retail. Consum. Serv. 15 , 144–155. 10.1016/j.jretconser.2007.11.003 [ CrossRef ] [ Google Scholar ]
• Mikulić J., Kreši,ć D., Šerić M. (2021). The factor structure of medical tourist satisfaction: exploring key drivers of choice, delight, and frustration . J. Hospital. Tour. Res. 1177 :1096348020987273. 10.1177/1096348020987273 [ CrossRef ] [ Google Scholar ]
• Moors A., Boddez Y., De Houwer J. (2017). The power of goal-directed processes in the causation of emotional and other actions . Emot. Rev. 9 , 310–318. 10.1177/1754073916669595 [ CrossRef ] [ Google Scholar ]
• Nassauer S. (2020). Walmart sales surge as Coronavirus drives Americans to stockpile. Wall Street J . Availale online at: https://www.wsj.com/articles/walmart-sales-surge-as-coronavirus-drivesamericans-to-stockpile-11589888464?mod=hp_lead_pos5 (accessed on May 18, 2020).
• Oliver R. L. (1989). Processing of the satisfaction response in consumption . J. Consum. Satisfact. Dissatisfact. Complain. Behav. 2 , 1–26. [ Google Scholar ]
• Oliver R. L., DeSarbo W. S. (1988). Response determinants in satisfaction judgments . J. Consum. Res. 14 , 495–507. 10.1086/209131 [ CrossRef ] [ Google Scholar ]
• Oliver R. L., Rust R. T., Varki S. (1997). Customer delight: foundations, findings, and managerial insight . J. Retail. 73 :311. 10.1016/S0022-4359(97)90021-X [ CrossRef ] [ Google Scholar ]
• Olotewo J. (2017). Examining the antecedents of in-store and online purchasing behavior: a case of Nigeria . J. Market. Res. Case Stud. 15 , 1–16. 10.5171/2017.668316 [ CrossRef ] [ Google Scholar ]
• Pandey N., Tripathi A., Jain D., Roy S. (2020). Does price tolerance depend upon the type of product in e-retailing? role of customer satisfaction, trust, loyalty, and perceived value . J. Strateg. Market. 28 , 522–541. 10.1080/0965254X.2019.1569109 [ CrossRef ] [ Google Scholar ]
• Parasuraman A., Ball J., Aksoy L., Keiningham T. L., Zaki M. (2020). More than a feeling? toward a theory of customer delight . J. Serv. Manage . 3 :34. 10.1108/JOSM-03-2019-0094 [ CrossRef ] [ Google Scholar ]
• Park E. O., Chung K. H., Shin J. I. (2010). The relationship among internal marketing, internal customer satisfaction, organizational commitment and performance . Product. Rev. 24 , 199–232. 10.15843/kpapr.24.2.201006.199 [ CrossRef ] [ Google Scholar ]
• Pavlou P. A., Liang H., Xue Y. (2007). Understanding and mitigating uncertainty in online exchange relationships: a principal-agent perspective . MIS Q. 105–136. 10.2307/25148783 [ CrossRef ] [ Google Scholar ]
• Rahmadini Y., Halim R. E. (2018). The “Influence of social media towards emotions, brand relationship quality, and word of Mouth (WOM) on Concert's Attendees in Indonesia,” in MATEC Web of Conferences (EDP Sciences) , 05058. [ Google Scholar ]
• Redman R. (2020). “Online grocery sales to grow 40% in 2020,” in Supermarket News . Available online at: https://www.supermarketnews.com/onlineretail/online-grocery-sales-grow-40-2020 (accessed on May 21, 2020).
• Rizal H., Yussof S., Amin H., Chen-Jung K. (2018). EWOM towards homestays lodging: extending the information system success model . J. Hosp. Tour. Technol. 10.1108/JHTT-12-2016-0084 [ CrossRef ] [ Google Scholar ]
• Roseman I. J. (1984). Cognitive determinants of emotion: a structural theory . Rev. Person. Soc. Psychol. 5 , 11–36. [ Google Scholar ]
• Roseman I. J. (2013). Author reply: on the frontiers of appraisal theory . Emot. Rev. 5 , 187–188. 10.1177/1754073912469592 [ CrossRef ] [ Google Scholar ]
• Rossolov A., Rossolova H., Holguín-Veras J. (2021). Online and in-store purchase behavior: shopping channel choice in a developing economy . Transportation 20 , 1–37. 10.1007/s11116-020-10163-3 [ CrossRef ] [ Google Scholar ]
• Salancik G. R., Pfeffer J. (1978). Uncertainty, secrecy, and the choice of similar others . Soc. Psychol. 23 , 246–255. 10.2307/3033561 [ CrossRef ] [ Google Scholar ]
• Saleh M. A. H. (2016). Website design, technological expertise, demographics, and consumer's e-purchase transactions . Int. J. Market. Stud. 8 , 125–138. 10.5539/ijms.v8n1p125 [ CrossRef ] [ Google Scholar ]
• Scherer K. R. (1997). The role of culture in emotion-antecedent appraisal . J. Pers. Soc. Psychol. 73 :902. 10.1037/0022-3514.73.5.902 [ CrossRef ] [ Google Scholar ]
• Schneider B., Bowen D. E. (1999). Understanding customer delight and outrage . Sloan Manage. Rev. 41 , 35–45. 10.1016/S0022-4359(01)00035-45 [ CrossRef ] [ Google Scholar ]
• Shamsudin M. F., Razak A. A., Salem M. A. (2018). The role of customer interactions towards customer satisfaction in theme parks experience . Opcion 34 , 546–558. [ Google Scholar ]
• Shim S., Eastlick M. A., Lotz S. L., Warrington P. (2001). An online prepurchase intentions model: the role of intention to search: best overall paper award—the Sixth Triennial AMS/ACRA Retailing Conference, 2000? J. Retail. 77 , 397–416. 10.1016/S0022-4359(01)00051-3 [ CrossRef ] [ Google Scholar ]
• Smith C. A., Ellsworth P. C. (1985). Patterns of cognitive appraisal in emotion . J. Pers. Soc. Psychol . 48 :813. [ PubMed ] [ Google Scholar ]
• Tandon A., Aakash A., Aggarwal A. G. (2020). Impact of EWOM, website quality, and product satisfaction on customer satisfaction and repurchase intention: moderating role of shipping and handling . Int. J. Syst. Assur. Eng. Manage. 54 , 1–8. 10.1007/s13198-020-00954-3 [ CrossRef ] [ Google Scholar ]
• Tandon U. (2021). Predictors of online shopping in India: an empirical investigation . J. Market. Anal. 9 , 65–79. 10.1057/s41270-020-00084-6 [ CrossRef ] [ Google Scholar ]
• Tandon U., Kiran R., Sah A. N. (2016). Understanding online shopping adoption in India: unified theory of acceptance and use of technology 2 (UTAUT2) with perceived risk application . Serv. Sci. 8 , 420–437. 10.1287/serv.2016.0154 [ CrossRef ] [ Google Scholar ]
• Tarhini A., Alalwan A. A., Al-Qirim N., Algharabat R. (2021). “An analysis of the factors influencing the adoption of online shopping,” in Research Anthology on E-Commerce Adoption, Models, and Applications for Modern Business (Pennsylvania: IGI Global; ), 363–384. [ Google Scholar ]
• Tarofder A. K., Nikhashemi S. R., Azam S. F., Selvantharan P., Haque A. (2016). The mediating influence of service failure explanation on customer repurchase intention through customers satisfaction . Int. J. Qual. Serv. Sci. 4 :44. 10.1108/IJQSS-04-2015-0044 [ CrossRef ] [ Google Scholar ]
• Taylor S. A., Baker T. L. (1994). An assessment of the relationship between service quality and customer satisfaction in the formation of consumers' purchase intentions . J. Retail. 70 , 163–178. 10.1016/0022-4359(94)90013-2 [ CrossRef ] [ Google Scholar ]
• Teo T., Lee C. B., Chai C. S., Wong S. L. (2009). Assessing the intention to use technology among preservice teachers in Singapore and Malaysia: a multigroup invariance analysis of the technology acceptance model (TAM) . Comput. Educ. 53 , 1000–1009. 10.1016/j.compedu.2009.05.017 [ CrossRef ] [ Google Scholar ]
• Thibaut J. W., Kelley H. H. (2017). The Social Psychology of Groups . Routledge. 10.4324/9781315135007 [ CrossRef ] [ Google Scholar ]
• Torres E. N., Milman A., Park S. (2019). Customer delight and outrage in theme parks: a roller coaster of emotions . Int. J. Hospital. Tour. Administr. 16 , 1–23. 10.1080/15256480.2019.1641455 [ CrossRef ] [ Google Scholar ]
• Tsiros M., Mittal V. (2000). Regret: a model of its antecedents and consequences in consumer decision making . J. Consum. Res. 26 , 401–417. 10.1086/209571 [ CrossRef ] [ Google Scholar ]
• Tzeng S. Y., Ertz M., Jo M. S., Sarigöll,ü E. (2021). Factors affecting customer satisfaction on online shopping holiday . Market. Intell. Plann. 8 :346. 10.1108/MIP-08-2020-0346 [ CrossRef ] [ Google Scholar ]
• Ul Haq J., Bonn M. A. (2018). Understanding millennial perceptions of human and nonhuman brands . Int. Hospital. Rev . 9 :14. 10.1108/IHR-09-2018-0014 [ CrossRef ] [ Google Scholar ]
• Unal S., Aydin H. (2016). Evaluation of consumer regret in terms of perceived risk and repurchase intention . J. Glob. Strateg. Manage. 2 , 31–31. 10.20460/JGSM.20161024354 [ CrossRef ] [ Google Scholar ]
• Venkatesh V., Thong J. Y., Xu X. (2012). Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology . MIS Q. 157–178. 10.2307/41410412 [ CrossRef ] [ Google Scholar ]
• Wai K., Dastane O., Johari Z., Ismail N.B. (2019). Perceived risk factors affecting consumers' online shopping behaviour . J. Asian Financ. Econ. Bus. 6 , 246–260. 10.13106/jafeb.2019.vol6.no4.249 [ CrossRef ] [ Google Scholar ]
• Wang C. Y., Mattila A. S. (2011). A cross-cultural comparison of perceived informational fairness with service failure explanations . J. Serv. Market . 25 , 429–439. 10.1108/08876041111161023 [ CrossRef ] [ Google Scholar ]
• Wang X. (2011). The effect of unrelated supporting service quality on consumer delight, satisfaction, and repurchase intentions . J. Serv. Res. 14 , 149–163. 10.1177/1094670511400722 [ CrossRef ] [ Google Scholar ]
• Westbrook R. A., Oliver R. L. (1991). The dimensionality of consumption emotion patterns and consumer satisfaction . J. Consum. Res. 18 , 84–91. 10.1086/209243 [ CrossRef ] [ Google Scholar ]
• Whelan J., Dawar N. (2014). Attributions of blame following a product-harm crisis depend on consumers' attachment styles . Mark. Lett. 27 , 285–294. 10.1007/s11002-014-9340-z [ CrossRef ] [ Google Scholar ]
• Woodside A. G., Frey L. L., Daly R. T. (1989). Linking service quality, customer satisfaction, and behavio . Mark. Health Serv. 9 :5. 10.1016/S0022-4359(01)0009-5 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Wu R., Wang C. L. (2017). The asymmetric impact of other-blame regret versus self-blame regret on negative word of mouth: empirical evidence from China . Eur. J. Market. 10.1108/EJM-06-2015-0322 [ CrossRef ] [ Google Scholar ]
• Yang Y., Gong Y., Land L. P. W., Chesney T. (2020). Understanding the effects of physical experience and information integration on consumer use of online to offline commerce . Int. J. Inf. Manage. 51 :102046. 10.1016/j.ijinfomgt.2019.102046 [ CrossRef ] [ Google Scholar ]
• Yap B. W., Khong K. W. (2006). Examining the effects of customer service management (CSM) on perceived business performance via structural equation modelling . Appl. Stochast. Models Bus. Indus. 22 , 587–605. 10.1002/asmb.648 [ CrossRef ] [ Google Scholar ]
• Zeelenberg M., Pieters R. (2007). A theory of regret regulation 1.0 . J. Consum. Psychol. 17 , 3–18. 10.1207/s15327663jcp1701_3 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Zeithaml V. A., Berry L. L., Parasuraman A. (1996). The behavioral consequences of service quality . J. Market. 60 , 31–46. 10.1177/002224299606000203 [ CrossRef ] [ Google Scholar ]
• Zhang H. (2017). Understanding the Consumption Experience of Chinese Tourists: Assessing the Effect of Audience Involvement, Flow and Delight on Electronic Word-of-mouth (eWOM) (Doctoral dissertation). [ Google Scholar ]
• Zhang Z., Ye Q., Song H., Liu T. (2015). The structure of customer satisfaction with cruise-line services: an empirical investigation based on online word of mouth . Curr. Issues Tourism . 18 , 450–464. [ Google Scholar ]

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Costco's Gold Bars Keep Selling Out. Should You Buy?

Published on June 19, 2024

By: Emma Newbery

• Costco's gold bars cost around $2,000 and they're stirring up a storm.
• Gold is a very specific investment that can work as part of a diversified portfolio.
• Physical gold can be expensive to store and insure.

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Costco started selling gold bars online last year and they proved a hit with customers. Soon after the retail giant started gold sales, Richard Galanti (then Costco CFO) told investors that the one-ounce gold bars were typically gone within a few hours of posting on the site.

Is gold a good investment?

If you're considering buying gold bars from Costco, think of it as an investment. An investment is an asset like stocks, bonds, real estate, and other commodities that can help you build long-term wealth. Sure, you can't put stocks in your Costco shopping cart. And you can earn Costco credit card rewards if you buy gold ingots, which is rarely possible with stocks. Even so, this is money you're investing for your future.

As such, research how gold might perform in comparison to other assets and consider how it fits in with your investment goals. As an investment, gold can be a way to diversify your portfolio. A lot of people view gold as a good store of value in turbulent times, particularly as it often performs better than stocks during recessions.

Some also see it as a hedge against inflation. It may hold its value even when the money in your bank account is losing spending power. For example, if you lived in a country like Venezuela (which saw inflation of almost 1,000,000% in 2018), gold would almost certainly feel like a safer way to hold your money.

But owning gold is also more complicated than having money in the bank, or stocks in a brokerage account, for that matter. For starters, if you buy physical gold, you'll need somewhere to keep it. You'll probably want to insure it. When you want to spend it, it won't be as easy as making a bank transfer. You'll have to first find somewhere to sell the gold. You'll probably lose money in commissions and spreads.

Finally, that gold won't be sitting in a safe producing little gold babies. Stocks might pay dividends and money in a savings account will earn interest. Your gold will only generate returns if you can sell it at a higher price than you bought it.

On which note, gold prices will go both up and down. Historically, the price of gold has trended upward, but with prices at all-time highs, there are no guarantees. It's also worth mentioning that the S&P 500 has performed better over long periods. Gold prices often go up in periods of economic uncertainty, but if you're a long-term investor, putting money into the stock market will often be a better bet.

What's the best way to buy gold?

If you decide there's a place for gold in your portfolio, think carefully about how you want to buy it. Costco has made gold bars convenient, but spending around $2,000 on a physical ingot is a lot of money. Costco's gold can only be bought online, and only by members.

There's a certain attraction to owning actual gold that you can touch. You might also own gold jewelry or coins, though you need to have a good understanding of the market. Ultimately, unless you're Gollum guarding your precious gold in The Lord of the Rings , holding physical gold as an investment can lose its shine.

If you don't want to worry about storage, insurance, and the hassle of resale, consider instead buying stocks in a gold-mining company. You might also invest in a gold ETF or mutual fund. Some will give you exposure to a mix of gold companies, while others hold physical gold. There will almost certainly be fees involved, but it is much easier to buy and sell stocks than gold bars.

Bottom line

There are many ways to save money by shopping at Costco . However, when viewed as an investment, Costco's gold bars will only make sense for a limited number of people. Even if you want to add gold to your portfolio alongside a mix of other investments, owning physical gold is a difficult way to build wealth.

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Emma owns the English-language newspaper The Bogota Post. She began her editorial career at a financial website in the U.K. over 20 years ago and has been contributing to The Ascent since 2019.

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Can Women Benefit From Viagra?

Research on how the drug affects female arousal is sparse. But doctors have been prescribing creams and pills anyway.

By Alisha Haridasani Gupta

Could the drug used to treat erectile dysfunction help women who have difficulties with arousal?

It’s a question that sexual medicine researchers have puzzled over since at least the late 1990s, when the Food and Drug Administration approved sildenafil, known as Viagra, for men.

Doctors prescribe sildenafil off-label to some female patients, either in the form of low doses of the pill or as a topical cream made by compounding pharmacies. Telehealth companies, like Alloy and Wisp, sell the creams on their platforms. Daré, a pharmaceutical company, is in the process of seeking approval from the F.D.A. for its topical sildenafil cream, which it plans to market specifically to women. Yet the research, including a study from Daré out today , suggests that if the drug is at all effective at increasing arousal in women, it’s only likely to do so for a small subset.

So should women experiencing sexual difficulties consider trying sildenafil? Here’s what experts advised.

Female Sexual Arousal Disorder and the Promise of Viagra

Research over the years, including the new study funded by Daré, has suggested that sildenafil might help women who have female sexual arousal disorder. This is the inability to attain or maintain sexual excitement , often including a lack of lubrication or genital swelling, to the extent that a person experiences distress as a result.

F.S.A.D. is technically separate from having low sexual desire, though in many cases, the two conditions overlap. It is sometimes a side effect of selective serotonin reuptake inhibitor antidepressants, and can also occur alongside other conditions that disrupt blood flow or nerve function, including diabetes and spinal cord injuries.

Difficulty with physical arousal in women is akin to erectile dysfunction in that it can come down to blood flow, said Dr. Lauren Streicher, a clinical professor of obstetrics and gynecology at Northwestern University, who has prescribed sildenafil for her female patients for almost a decade. Increased blood flow, particularly to the clitoris, heightens nerve sensitivity and triggers lubrication. Sildenafil dilates blood vessels, which makes it easier for blood to flow through them.

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