Online consumer resilience during a pandemic: An exploratory study of e-commerce behavior before, during and after a COVID-19 lockdown

Developing customer convenience and experience through increased competency and efficiency: A strategic approach to retail operations mastery

Retailers who are part of supply chains are crucial in attracting customers to physically visit their stores and thereby increase supply chain revenue. Retailers in supply chains are responsible for handling a high volume of transactions and high customer contact, thus better management of retail operations is necessary to provide greater convenience and experience for customers. In this regard, retail operations must be properly managed, particularly during times of crisis like COVID-19, to ensure that customers purchase their basic needs promptly and safely. Since customer contact is high, the retail operations can have a greater say in improving the customer convenience and experience but this perspective has not been specifically emphasized both in research and practice. Thus, the current study attempted to evaluate how well retail operations can improve customer convenience and experience so that retailers can do well even during times of uncertainty. The study, which is grounded in the resource-based view theory, thus looks at how well retailers' competency and efficiency in running the operations help in achieving better customer convenience and experience. In this regard, sample data from 416 practitioners belonging to the Indian retail industry has been selected to examine the mediating role of retailers' efficiency to retailers' competence in achieving better customer convenience and experience. This study importantly confirms that while more competence can help retailers operate more efficiently, it cannot, by itself, provide customers with greater convenience or a better shopping experience. This study found that retailers concentrate on streamlining their operations in order to overcome capacity limits because it is expensive and limited. The empirical evidence shows that retail operations have significance say in offering better convenience and experience customers.

Internet addiction and its relationship with food choice motives and the risk of eating disorders among young adults in Malaysia

The COVID-19 lockdown measures have dramatically altered the daily routines of young adults. Therefore, this study aims to elucidate the relationships between internet addiction, food choice motives and the risk of eating disorders in young adults during the transition to the endemic phase of COVID-19. The Internet Addiction Test was utilised to evaluate the presence and severity of internet addiction among young adults. The Food Choice Questionnaire was employed to assess the food choice motives of young adults, while the risk of eating disorders was determined using the Eating Attitude Test-26. The relationships between internet addiction, food choice motives, and the risk of eating disorders were assessed using model 4 of the PROCESS macro for SPSS. The findings indicated that 29.0% of young adults experienced moderate-to-severe internet addiction, whereas 32.6% were at risk of eating disorders. Young adults were highly valued for the price, sensory appeal, and mood when deciding food choices. The relationship between internet addiction and the risk of eating disorders was partially mediated by convenience (b = - 0.211, SE = 0.140, - 0.548 to - 0.016) and familiarity (b = 0.219, SE = 0.122, 0.019 to 0.494). A significant direct effect was also observed between internet addiction and the risk of eating disorders (B = 0.793, p = 0.017). There is an urgent need to implement intervention strategies aimed at reducing problematic internet use, promoting healthier food choices, and fostering healthy eating habits among young adults.

The demand for online grocery shopping: COVID-induced changes in grocery shopping behavior of Canadian consumers

The COVID-19 pandemic has had a lasting impact on many economies around the globe. One area where significant changes have been documented is consumer behavior. A questionnaire survey was carried out to understand the impact of COVID-19 on grocery purchase behavior of Canadian consumers and evaluate the permanence of these effects. With a focus on online grocery shopping, this work integrates multiple existing theories of consumer behavior to explore the influence of different factors on consumers' adoption of online mode of grocery shopping during the pandemic and their intentions to continue the use of this mode in the post-pandemic world. A total of more than 600 usable survey responses were analyzed using statistical analysis and a Logit econometrics technique. The results reveal that 72% of the survey participants had to alter their grocery shopping habits as a result of the COVID-19 pandemic; 63% of these consumers claim that the changes that occurred would prevail in the future, with no return to the "pre-COVID normal". The results also show that the pandemic resulted in significant proliferation of online grocery shopping among Canadian consumers. Further, the findings show that the important factors that explain adoption of online grocery shopping and the shift towards higher reliance on online grocery purchases in the future include the perceived threat of COVID, pre-COVID shopping habits, socio-demographic characteristics, and the variables that capture technological opportunities and abilities.

Increasing exercise participation during the COVID-19 pandemic: the buffering role of nostalgia

Introduction

Due to the coronavirus disease 2019 (COVID-19) pandemic, people faced difficulties engaging in exercise activities as usual. As a result, there has been an increase in the demand for home exercises and online sales. However, there is little research on individuals' buying and exercise behaviors during the pandemic. Thus, this study investigated how the perceived threat of COVID-19 influences exercise participants' compensatory consumption and exercise intention through emotional responses, such as feelings of solastalgia and nostalgia.

Methods

A total of 488 responses were collected from Generation Y, as individuals belonging to Generation Y are more prone to impulsive buying compared to other generations and, importantly, consider exercise a crucial component of their general well-being. Data were examined using a three-step method that involved the use of SPSS 26.0 and EQS 6.4 software.

Results

Results showed that perception of COVID-19 positively influenced solastalgia and negatively affected nostalgia. Also, solastalgia had positive effects on nostalgia and online browsing, and nostalgia positively affected online browsing. Finally, this study found that online browsing positively influenced impulse buying and exercise intention, while impulse buying did not significantly affect exercise intention.

Conclusion

This study contributes to identifying the crucial influence of emotions in decision-making and increasing the understanding of the connection between nostalgia and cognitive and emotional responses amid the COVID-19 pandemic.

What motivates customers to repurchase online under social distancing?

Global e-commerce is growing rapidly during the COVID pandemic. Previous research on customers' online shopping decisions rarely considered social distancing. To investigate customers' continued intention toward online purchases while socially isolated, we propose a framework based on the UTAUT model. A survey of 330 valid samples was collected through an online survey among internet users during a period of social distancing in Indonesia. Hypotheses were validated using a structural equation modeling approach. The results showed that social contingency is the most influential factor on customers' intention to repurchase online under social restriction conditions, followed by customer perceived value and other significant factors. The findings contribute to providing a new understanding of customers' online repurchase intentions when they are in a contingency situation.

How has COVID-19 impacted the economic resilience of retail clusters?: Examining the difference between neighborhood-level and district-level retail clusters

The spread of COVID-19 greatly restricted physical and economic activities in global cities due to growing fears of infection and lockdown policies. Accordingly, the transfer of shopping activities from traditional markets to e-commerce has accelerated. Urban retail has substantially declined by competing with these disrupting factors, but resilience capabilities are known to vary by scale and regional characteristics. This study identifies which types of retail clusters were more resilient to the economic shock caused by the pandemic. This research focuses on the changes in online and in-store sales and how retailers recovered differently at the neighborhood and district levels in 2019 and 2020. This research compares the resilience of two types of retail clusters by calculating the loss of resilience and online adaptivity. The findings suggest that neighborhood-level retail areas were more resilient and bounced back in sales at a faster rate than district-level retail areas during the pandemic. These findings suggest that retail clusters are more resilient if they have steady populations and low-rent loads and can accommodate online shopping. The study contributes theoretical insights into how sales have changed and how e-commerce has increased the resilience of retail clusters throughout the pandemic by examining in-store and online sales.

Online listing data and their interaction with market dynamics: evidence from Singapore during COVID-19

With the emergence of Property Technology, online listing data have drawn increasing interest in the field of real estate-related big data research. Scraped from the online platforms for property search and marketing, these data reflect real-time information on housing supply and potential demand before actual transaction data are released. This paper analyzes the interactions between the keywords of online home listings and actual market dynamics. To do so, we link the listing data from the major online platform in Singapore with the universal transaction data of resale public housing. We consider the COVID-19 outbreak as a natural shock that brought a significant change to work modes and mobility and, in turn, consumer preference changes for home purchases. Using the Difference-in-Difference approach, we first find that housing units with a higher floor level and more rooms have experienced a significant increase in transaction prices while close proximity to public transportation and the central business district (CBD) led to a reduction in the price premium after COVID-19. Our text analysis results, using the natural language processing, suggest that the online listing keywords have consistently captured these trends and provide qualitative insights (e.g. view becoming increasingly popular) that could not be uncovered from the conventional database. Relevant keywords reveal trends earlier than transaction-based data, or at least in a timely manner. We demonstrate that big data analytics could effectively be applied to emerging social science research such as online listing research and provide useful information to forecast future market trends and household demand.

Does the threat of COVID-19 modulate automatic imitation?

The tendency to involuntarily imitate the actions of others (automatic imitation) can be modulated by social affiliative cues. Here, we explored whether the disruption to our social lives caused by the COVID-19 pandemic may subsequently influence automatic imitation. Three groups were initially presented a sentence comprehension task that featured either neutral (control), safe or unsafe primes to COVID-19 infection. They then completed an automatic imitation task, where a numeric cue was presented alongside apparent motion of an index or middle finger, which was either compatible or incompatible with the required response. Reaction times were longer for the incompatible compared to compatible trials, and thus demonstrated automatic imitation. However, there was no influence of the primes indicating that automatic imitation was unaffected by the risk of COVID-19. The potential theoretical explanations and practical implications of pathogen avoidance and social bonding incentives are discussed with reference to pandemic events.

Effects of COVID-19 outbreak in pricing and collaboration of a health-social dual-channel supply chain

The coronavirus pandemic has affected most aspects of product supply and consumer behaviors and led to transformations in the supply chain. The COVID-19 pandemic and the requirements to reduce its prevalence have led many people to shop online and encouraged many manufacturers to sell their products online. In this study, a manufacturer, who intends to possess an online sales channel, and a retailer, who has an in-person sales channel, are considered. Then, pricing strategies and collaboration mechanisms between them in the health-social dual-channel supply chain are investigated. This study is developed in three models, including centralized, decentralized, and collaborated under Stackelberg game, whereas the optimal price of products in each channel, level of implementation of health and safety protocols in retailers, advertising level, and status of online shopping performance are obtained for improving customer trust. Moreover, the demand is represented as a function of selling prices of products in online and in-person shops, compliance level of health protocols, level of online shopping performance, and advertising in health during the COVID-19 pandemic. Although the centralized model provides more profit for the manufacturer, the collaborated model provides the highest profit for the retailer. Thus, since the supply chain profit of centralized and collaborated models is close, the collaboration model is the best option for members in this situation. Sensitivity analysis is finally performed to evaluate the impact of key parameters, and then according to obtained results, some management insights are suggested for the dual-channel supply chain during the COVID-19 pandemic.

Assessing last-mile distribution resilience under demand disruptions

The COVID-19 pandemic led to a significant breakdown of the traditional retail sector resulting in an unprecedented surge in e-commerce demand for the delivery of essential goods. Consequently, the pandemic raised concerns pertaining to e-retailers' ability to maintain and efficiently restore level of service in the event of such low-probability high-severity market disruptions. Thus, considering e-retailers' role in the supply of essential goods, this study assesses the resilience of last-mile distribution operations under disruptions by integrating a Continuous Approximation (CA) based last-mile distribution model, the resilience triangle concept, and the Robustness, Redundancy, Resourcefulness, and Rapidity (R4) resilience framework. The proposed R4 Last Mile Distribution Resilience Triangle Framework is a novel performance-based qualitative-cum-quantitative domain-agnostic framework. Through a set of empirical analyses, this study highlights the opportunities and challenges of different distribution/outsourcing strategies to cope with disruption. In particular, the authors analyzed the use of an independent crowdsourced fleet (flexible service contingent on driver availability); the use of collection-point pickup (unconstrained downstream capacity contingent on customer willingness to self-collect); and integration with a logistics service provider (reliable service with high distribution costs). Overall, this work recommends the e-retailers to create a suitable platform to ensure reliable crowdsourced deliveries, position sufficient collection-points to ensure customer willingness to self-collect, and negotiate contracts with several logistics service providers to ensure adequate backup distribution.

Design and Implementation of a Recommendation System for Buying Fresh Foods Online Based on Web Crawling

As shopping patterns have gradually shifted from offline to online mode, and with recent lockdowns during the coronavirus disease 2019 (COVID-19) pandemic restricting foreign trade and accelerating the growth of the domestic economy, digital transformation has become a major strategy for many retailers to support and expand their businesses. With the pandemic becoming a turning point, the business of major e-commerce companies in Taiwan in the retail of dry goods has grown significantly, and it has driven the online sales of fresh products as well. In this era of fierce competition, it is especially important to find a way that enables consumers to quickly find ideal fresh products on multiple platforms, shortens the time for price comparison, and improves the efficiency of online shopping. This study uses the Python programming language to write a web crawler program that captures product information from fresh food e-commerce platforms, including product introduction, price, origin, and sales volume, and then defines the relevant status of the product, such as product popularity. Accordingly, through Chinese text segmentation and term-frequency calculation, it aims to classify the product names and introductions into frequently occurring words and use them as product-related labels. Finally, the program combines the product information processing results and product-related labels to construct an online fresh food recommendation system. The results of the proposed system show that it reduces the time and energy spent comparing prices. It can also guide consumers to browse products that may be of interest using relevant tags and increase consumption efficiency by helping them find the ideal item when shopping.

Increasing Couriers' Job Satisfaction through Social-Sustainability Practices: Perceived Fairness and Psychological-Safety Perspectives

Due to the spike in online-retail demand during the pandemic, couriers confront increased workload and safety concerns, posing significant social-sustainability challenges for courier companies. This study explores the impact of social-sustainability practices on couriers' job satisfaction in the context of the COVID-19 pandemic. We designed the research model from the theoretical lens of Maslow's hierarchy of needs, equity, and psychological-safety theories. We collected the views of 428 couriers from the Chinese market, where there is a developed e-commerce industry. The structural-equation-model analysis results found that social-sustainability practices such as working environment, working conditions, health and safety, education, and training positively affected the job satisfaction of couriers during the pandemic through the mediators (psychological safety and perceived fairness). These findings provide empirical recommendations for improving employees' job satisfaction in courier companies during COVID-19 and addressing the social-sustainability issues of courier companies.

Influence of COVID-19 on online shopping behaviour, leisure and socialisation

Purpose

The purpose of this study is to analyse the effect of health risk perceptions on the degree of decisiveness of a person towards e-commerce (e-commerce acceptance), self-awareness towards leisure and self-awareness towards socialisation in pandemic contexts. The identified gap was related to the COVID-19 pandemic, where the development of studies inherent to this context is becoming urgent. Thus, it was necessary to understand how the health risks perceived by individuals influenced their choice regarding online shopping and, simultaneously, socialising and leisure activities.

Design/methodology/approach

To reach this goal, a conceptual model was developed based on the self-determination theory, and data were collected from two countries (Portugal and Croatia). The model was estimated using the structural equation modelling (SEM).

Findings

The results showed that the perceptions regarding health risks have a positive effect on the degree of decisiveness of a person towards e-commerce acceptance and self-awareness towards socialisation. In turn, the perceptions of health risks have a positive effect on self-awareness towards leisure, not supporting the model conceptualisation.

Originality/value

The paper contributes to fill a gap in literature by analysing the simultaneous effect of individuals’ e-commerce motivations for leisure and socialising activities within a pandemic context. These results have significant managerial implications for several stakeholders that must communicate to the specific public regarding this type of events.

Has Covid-19 permanently changed online purchasing behavior?

This study examines how the COVID-19 pandemic has affected online purchasing behavior using data from a major online shopping platform in Japan. We focus on the effect of two measures of the pandemic, i.e., the number of positive COVID-19 cases and state declarations of emergency to mitigate the pandemic. We find that both measures promoted online purchases at the beginning of the pandemic, but in later periods, their effect faded. In addition, online purchases returned to normal after states of emergency ended, and the overall time trend in online purchases excluding the effects of the two measures was stable during the first two years of the pandemic. These results suggest that the effect of the pandemic on online purchasing behavior is temporary and will not persist after the pandemic.

Supplementary Information

The online version contains supplementary material available at 10.1140/epjds/s13688-022-00375-1.

Class balancing framework for credit card fraud detection based on clustering and similarity-based selection (SBS)

Credit card fraud is a growing problem nowadays and it has escalated during COVID-19 due to the authorities in many countries requiring people to use cashless transactions. Every year, billions of Euros are lost due to credit card fraud transactions, therefore, fraud detection systems are essential for financial institutions. As the classes' distribution is not equally represented in the credit card dataset, the machine learning trains the model according to the majority class which leads to inaccurate fraud predictions. For that, in this research, we mainly focus on processing unbalanced data by using an under-sampling technique to get more accurate and better results with different machine learning algorithms. We propose a framework that is based on clustering the dataset using fuzzy C-means and selecting similar fraud and normal instances that have the same features, which guarantees the integrity between the data features.

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

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.

Exploring customer concerns on service quality under the COVID-19 crisis: A social media analytics study from the retail industry

The COVID-19 pandemic has triggered a set of government policies and supermarket regulations, which affects customers' grocery shopping behaviours. However, the specific impact of COVID-19 on retailers at the customer end has not yet been addressed. Using text-mining techniques (i.e., sentiment analysis, topic modelling) and time series analysis, we analyse 161,921 tweets from leading UK supermarkets during the first COVID-19 lockdown. The results show the causes of sentiment change in each time series and how customer perception changes according to supermarkets’ response actions. Drawing on the social media crisis communication framework and Situational Crisis Communication theory, this study investigates whether responding to a crisis helps retail managers better understand their customers. The results uncover that customers experiencing certain social media interactions may evaluate attributes differently, resulting in varying levels of customer information collection, and grocery companies could benefit from engaging in social media crisis communication with customers. As new variants of COVID-19 keep appearing, emerging managerial problems put businesses at risk for the next crisis. Based on the results of text-mining analysis of consumer perceptions, this study identifies emerging topics in the UK grocery sector in the context of COVID-19 crisis communication and develop the sub-dimensions of service quality assessment into four categories: physical aspects, reliability, personal interaction, and policies. This study reveals how supermarkets could use social media data to better analyse customer behaviour during a pandemic and sustain competitiveness by upgrading their crisis strategies and service provision. It also sheds light on how future researchers can leverage the power of social media data with multiple text-mining methodologies.

The impact of COVID-19 pandemic restrictions on offline and online grocery shopping: New normal or old habits?

The COVID-19 pandemic is changing future trends in retailing and e-commerce immensely. Recent research revealed a considerable increase in online grocery shopping (OGS) since the COVID-19 pandemic started. In addition, current statistics indicate a steady increase in OGS over the coming years. Despite this, less is known about whether consumers’ behavior is evolving to a ‘new normal’ or returning to ‘old habits’ after pandemic restrictions are withdrawn. To address this research gap, we operationalize and empirically analyze offline and online purchasing behavior before, during, and after pandemic restrictions. To this end, we use an extensive household panel dataset of 17,766 households reporting their purchases before, during and after the first lockdown in Germany in 2020. Our findings on offline purchase patterns show that while more than 10% of the consumers avoided brick-and-mortar retail during the lockdown, almost all of them returned afterwards. Looking at online purchase patterns, we find high volatility in OGS for both separate and combined purchase patterns. The combined analysis of purchase patterns (online and offline), reveal that households that avoided brick-and-mortar stores during the lockdown did not switch (completely) to the online channel. Based on our findings that consumers are still in reach of brick-and-mortar retailers we suggest offline retailers act now to retain their customers, e.g., by offering competitive benefits in their stores. OGS operators should urgently analyze the customer churn revealed in this analysis and derive measures to retain them. They do not seem to have succeeded in retaining their customers and keeping them loyal to the online channel during the entire observation period. Even worse, they also failed to convince consumers to use OGS who stayed at home due to the lockdown. The fact that a total of 96.75% of the observed consumers did not practice OGS at all shows that OGS in Germany was in 2020 still in its infancy. However, as current statistics forecast a further substantial increase in OGS over the coming years, our results are increasingly relevant for brand managers, brick-and-mortar retailers and OGS providers in Germany and beyond.

Exploring the Social Trend Indications of Utilizing E-Commerce during and after COVID-19's Hit

COVID-19 is a major global crisis affecter, changing global norms and societal behavioral models. Many companies have faced existential crises, but on the other hand, businesses that were and are helping others to boost digitalization, ICT and software solutions deployment, remote communications integration, e-commerce & e-services, and so on, have boosted their businesses, as people shifted online during the global lockdown and international travel restrictions. Our work explores the trend of e-commerce and e-services utilization during the ease of restrictions and the social distancing period to forecast the trend continuation patterns after the pandemic. An online survey was conducted and targeted individuals in Saudi Arabia and Egypt, resulting in 155 participants. The data were analyzed from four perspectives: demographics, COVID-19 health impact, trend analysis, and regression analysis. The results indicate heavy utilization of e-commerce and e-services during the global movement restrictions and travel bans. This trend has, however, significantly reduced during the ease of restrictions and social distancing period. Utilizing e-commerce and e-services in Saudi Arabia and Egypt, based on the research data, is positively correlated to the outbreak conditions. On the other hand, current data still does not give clear indications, and this pattern is going to be mostly, partly, or not at all permanent now as societies are returning to mostly a free movement of people and marginally restricted social distancing times.

Experts' opinions about lasting innovative technologies in City Logistics

The COVID-19 pandemic has highlighted the relevance of goods delivery in urban areas. However, this activity often generates negative environmental impact and several technologies have been proposed in recent years to reduce it, thus forming a complex innovation landscape characterized by different levels of maturity and effects on the City Logistics (CL) system. This complexity causes a deep uncertainty over the future of CL. This paper aims to tackle this uncertainty by forecasting the future of a set of CL technologies. A Delphi survey has been submitted to experts of this field to achieve a stable consensus over 33 projections related to 7 CL technologies for the year 2030. Results show that real-time data collection will help the coordination process between stakeholders, engendering an increased awareness over the value of using logistics data as well as its potential drawbacks. Moreover, experts share a positive attitude towards the expansion of Parcel Lockers, which should be monitored by public authorities to avoid a negative impact on land use. Finally, technologies such as drones and crowd-logistics have drawn the lowest level of consensus due to their lower level of maturity, which arouse the necessity to further explore several issues such as legal and technical barriers.

What Factors Determine the Online Consumer Behavior in This Digitalized World? A Systematic Literature

This review study envisioned to address the basic objective that is to investigate determinants of online consumer behavior. A conventional review strategy was used to address the objective raised above, i.e., systematic strategy, and also, the obtained data were analyzed via content analysis. In addition to the above, the study also employed descriptive research design to present the obtained result descriptively. According to the generated findings, purchase intention is the most studied area, which is followed by adoption, and conversely, continuance or repurchase stage of online consumer behavior is the most underresearched area. Perceived usefulness, perceived risk, attitude, perceived ease of use, trust, social influence, subjective norms, perceived enjoyment, security, perceived behavioral control, web design quality, privacy and security concerns, demographic factors (e.g., age, gender, occupation, education, and income), perceived value, service quality, perceived satisfaction, psychological factors (e.g., relative advantage), facilitating conditions, and consumers’ experience are the most influential factors significantly affecting online consumer behavior at large. Therefore, it is advised that industries those are experienced or newcomers in the market to work on the identified factors determining the online consumer behavior, to sustain and achieve success in this dynamic world.

Design is More Than Looks: Research on the Affordance of Review Components on Consumer Loyalty

BACKGROUND

Online review system contains multiple components, such as ratings, review text, product pictures, and video uploads, that could affect consumer loyalty. However, how the affordance of such components influences perceptions and behaviors of consumers remains unclear. We extend stimulus-organism-response (S-O-R) theory to the online review system. Specifically, we combine affordance theory and the technology acceptance model (TAM) to investigate the relations among the affordance of review systems, consumers' perceived beliefs, and their loyalty.

METHODS

We surveyed 320 customers on their online shopping experiences in China. We tested our hypotheses using the partial least squares path structural equation modeling (PLS-SEM) method. We report the direct effect of affordances of review components on consumer loyalty and its indirect effects on consumer loyalty through perceived beliefs.

RESULTS

Our results show that integrity and social interaction affordance of review components have significant relations with perceived ease of use, perceived usefulness, and perceived enjoyment. Intelligent topic mining reveals a positive relation on perceived enjoyment. Operability has a positive relation with perceived ease of use and perceived usefulness. These three consumer-perceived beliefs can mediate, to different degrees, the relationship between affordance of review components and consumer loyalty.

CONCLUSION

This research takes an innovative approach to offer insights into the relationships between IT affordances and consumer perceptions. We examine S-O-R theory through the lens of information technology and extend S-O-R theory by integrating IT affordances. Our research findings pave the way for businesses to design and implement more effective online review systems.

The impact of COVID-19 on the evolution of online retail: The pandemic as a window of opportunity

Pandemic-related shocks have induced an unexpected volatility into the evolution of online sales, making it difficult for retailers to cope with frequently occurring, drastic changes in demand. Relying on a socio-technical approach, the purpose of this paper is to (a) offer a deeper insight into the driving forces of online sales during the pandemic, and (b) investigate whether pandemic-related shocks accelerate the long-term growth of online retail. Novel, high-frequency data on GPS-based population mobility and government stringency is used to demonstrate how time spent in residential areas and governmental restrictions drive the monthly evolution of online sales in 23 countries. We deconstruct these effects into three main phases: lure-in, lock-in, and phase-out. Lastly, using time series analysis, we show that the pandemic has induced a level shift into the long-term growth trend of the online retail sector in the majority of countries investigated.

IMPACT OF SOCIAL MEDIA ENGAGEMENT AND ARTIFICIAL INTELLIGENCE ON ONLINE BUYING BEHAVIOUR

It has been observed that the Artificial intelligence (AI) has significantly transformed the business strategies as well as consumer behaviour very rapidly. The present study proposes a framework for understanding the impact of AI involving intelligence and deliberate social media engagements on consumers' online purchase behaviour. The analysed primary data have been collected randomly from 577 respondents through a self-administered questionnaire. Both online and interview methods have been adopted for the collection of these data. The findings of the study provide an empirical evidence on how the use of artificial intelligence helps to improve the consumer purchase frequency and spending patterns. More surprisingly, the study has revealed that consumers who spend more time on shopping apps are less likely to spend more money as their purchase frequency is low. On the other hand, social media engagements and the average time spent on social media apps are observed to have a significant and positive impact on online purchase behaviour.

Enablers of post-COVID-19 customer demand resilience: evidence for fast-fashion MSMEs

Purpose

This study aims to analyse the resilience of customer demand management post-coronavirus disease 2019, using fast fashion as an example. The paper provides insights for potential applications to micro-, small and medium enterprises (MSMEs).

Design/methodology/approach

Based on the qualitative analysis and an integrated Plan-Do-Check-Act (PDCA)-decision making trial and evaluation laboratory (DEMATEL)-fuzzy technique for order of preference by similarity to the ideal solution (TOPSIS) methodology of fuzzy multi-criteria decision-making, we explored and prioritised the enablers of resilience management for fast-fashion MSMEs.

Findings

The results reveal that the highest priority enabler is maintaining customer loyalty. Other enablers are associated with e-commerce endorsement, a customer-focussed assortment of items and flexible store operations.

Research limitations/implications

The study findings will enable fast-fashion MSMEs to develop effective actions and priorities in operations efforts to promote post-pandemic recovery.

Originality/value

Despite the importance of the resilience project and the changing fast-fashion customer patterns, only a handful of studies have explored how resilience can be managed in this field. Thus, the findings can contribute to closing this gap in the context of operations resilience research as well as MSME operations.

Extending the Frontiers of Electronic Commerce Knowledge through Cybersecurity

As technology becomes more sophisticated so do cyber-attacks. The resilience of electronic commerce organisations represents a critical point nowadays because it influences consumer and digital business behaviour. In this context, the cybersecurity and electronic commerce knowledge were reviewed as a unit. The main aim of this paper is to support researchers and managers in understanding the theoretical framework and to provide a knowledge-based model. To achieve this aim, the authors performed an analysis of 14,585 papers from the Web of Science Core Collection that generated two visualized networks, analyzed with the metrics mean silhouette, modularity, betweenness centrality, and citation bursts in the context of digital resilience. The mapping process results show that the human factor represents the central junction with the fear of cyber-attacks and the perception of online shopping as risky. The adoption of electronic commerce and mobile commerce are two challenging research lines in the global economic resilience. Their adoption enabled by big data, artificial intelligence, machine learning, and even blockchain technology can strengthen resilience even when cybersecurity education is needed.

Why is the generation of packaging waste from express deliveries a major problem?

The rapid growth of the express delivery industry resulted in the explosion of packaging waste with a wide range of direct and indirect environmental consequences. Until recently, little attention was given to this topic, but e-commerce and the associated growth of packaging waste exploded with the COVID-19 pandemic. Even though the phenomenon is only little to moderately documented, the waste problems in express delivery are recognized and caused by excessive packaging, lack of recyclability of the packaging material, and the low motivation of consumers to recycle. Key research, policy, and educational actions to reduce the environmental impacts of the fast-growing express delivery industry are discussed in this short discussion article.

Impacts of COVID-19 on the post-pandemic behaviour: The role of mortality threats and religiosity

This study explores the influence of intra-pandemic perceptions on travellers' post-pandemic hotel booking behaviour among crisis-resistant travellers and crisis-sensitive groups. It also examines the moderating role of mortality threats and religiosity on these behaviours. We collected quantitative data utilising survey method via questionnaires to address various levels of the research. We used PLS-SEM to evaluate our proposed model. We collected data from 1580 who had booked hotels in Egypt. Our study indicated that intra-pandemic perception has a stronger effect on travellers’ post-pandemic hotel booking behaviours if the travellers are less religious and feel deeply threatened by the idea of their own level of mortality. Moreover, it revealed that intra-pandemic perceptions had a stronger association with post-pandemic planned behaviour for travellers who chose to cancel their hotel booking plans. Our study also indicated that emergency public information plays a critical role in influencing post-pandemic planned behaviour. Our study offers effective strategies to aid hospitality and tourism practitioners when risky and threating situations such as COVID-19 arise, specifically in the period of response and recovery.

Enablers for resilience and pandemic preparedness in food supply chain

The recent COVID-19 pandemic has caused enormous disruptions to supply chain (SCs). Border restrictions forced countless businesses to close either permanently or temporarily. However, the food industry is an essential sector that needs to be operational during a pandemic. Although the food industry has proactively worked towards fulfilling human needs, the food supply chain (FSC) faced numerous challenges, forcing SC managers to rethink their business strategy to cater to consumer demands effectively. In a pandemic situation, manufacturing operations need to repurpose and adapt to produce different high-demand products. Resilience initiatives help fight disruption phases in an uncertain environment by building capacity to resist and recover to a better position. This study identifies 14 key enablers to develop a resilient FSC and reveals the most significant enablers in India. We used a hybrid Delphi-interpretive structural modeling (ISM) and Fuzzy decision-making trial and evaluation laboratory (Fuzzy-DEMATEL) methodology to achieve these goals. The Delphi technique identified essential enablers, while the ISM analyzed the interrelationship among enablers and level of importance in a hierarchical structural model. Finally, the Fuzzy-DEMATEL categorized the enablers into the cause-effect group. This study helps SC decision-makers recognize the enablers and the contextual and causal relationships to improve resilience initiatives. It also helps them repurpose their manufacturing operations and shift to other highly required and high-demand production.

Using Lean to Improve Operational Performance in a Retail Store and E-Commerce Service: A Portuguese Case Study

Ensuring on-shelf availability is essential for retailers to maintain high service levels for both in-store and E-Commerce consumers. The performance of this indicator largely depends on reorder planning decisions, as well as on the effectiveness of the replenishment process. This paper presents a case study that involved two Lean initiatives, which together have contributed to a significantly reduction in the number of out-of-stock events incurred by a retail store and an increase in the order fulfilment rate accomplished by the online commerce service. In the first initiative, a value stream management (VSM) methodology was adopted to redesign the existing replenishment process in the most relevant fresh food market: fruits and vegetables. The second initiative involved the implementation of a simple, but effective visual inventory management system in the warehouse of the E-Commerce division, where a wide set of fast-moving consumer goods (FMCG) is stored using kanban cards. This paper hence demonstrates, through practical application, that Lean tools can be employed to improve operational processes with positive impacts on both the physical store performance and on results regarding the online commerce business.

Impact of COVID-19 on changing consumer behaviour: Lessons from an emerging economy

The present study investigates the impact of COVID-19 on Consumers' changing way of life and buying behaviour based on their socio-economic backgrounds. A questionnaire survey was carried out to understand the impact of COVID-19 on consumers' affordability, lifestyle, and health awareness and how these effects influenced their buying behaviour. A total of 425 usable responses were analysed using the structural equation modelling considering Consumers' socio-economic background as exogenous variables and Consumers' changing way of life and Adaptation in consumers' buying behaviour as endogenous variables. The study reveals that COVID-19 has affected the consumers in the unorganised sectors more than others and induced an increase in the demand for affordable substitutes for daily necessities. The demand for wellness and entertainment products is found to depend upon the occupation and family earning status of consumers which is jointly mediated by affordability and lifestyle changes. Further, the findings show that the demand for health and hygiene products depends on the current employment status and family earning status of consumers which is jointly mediated by affordability and awareness towards health and hygiene. The model developed in the present study allows the decision-makers to identify which segments of the population with certain socio-economic backgrounds could be targeted for wellness products and which ones could be targeted for health and hygiene products. In addition, the model provides rich insights to the managers as to what kind of product substitution would be viable in the market during the pandemic.

Psychological and Behavior Changes of Consumer Preferences During COVID-19 Pandemic Times: An Application of GLM Regression Model

The life we considered normal was disrupted due to measures taken to limit the spread of the novel coronavirus. Quarantine, isolation, social distancing, and community containment have influenced consumer behavior and contributed to the rapid development of e-commerce. In pandemic times, even those unfamiliar with the online environment have had to adapt and make acquisitions in this new manner. Hence, we focused our research on measuring the perception of consumers on how the restrictive measures imposed to limit the spread of the COVID-19 virus had influenced their decision to buy a product or service from the online environment, given that purchases are highly subjective and influenced by cumulative effects of economic, social, psychological and behavioral factors. Our paper comes with additional insights from the literature. It adds empirical evidence that reveals that the number of transactions and the value per transaction increased during the COVID-19 pandemic and highlights that online purchases will continue as such even after the pandemic.

Changes in consumption patterns during the COVID-19 pandemic: Analyzing the revenge spending motivations of different emotional groups

People tend to alleviate their negative emotions by shopping. Considering the change of shopping behavior during COVID-19 outbreak, negative emotions are the key contributors to this change. In this light, this study aims to investigate how negative emotions caused by COVID-19 affect shopping behaviors. This study classified consumer groups based on their perceived negative emotions (i.e., anxiety, fear, depression, anger, and boredom). By clustering analysis, four groups (i.e., group of anxiety, depression, anger, and indifference) were derived. Then, this study examined how each of the emotional groups differently affect the shopping-related motivations (i.e., mood alleviation, shopping enjoyment, socialization seeking, and self-control seeking) and shopping behaviors (i.e., shopping for high-priced goods and buying of bulk goods). Results revealed all emotional groups affect socialization seeking and influence high-priced shopping intentions. However, depression and indifference are positively associated with socialization seeking and influence bulk shopping intentions. In addition, other emotions except for anxiety affect mood alleviation and influence high-priced shopping intentions. Finally, anger is associated with self-control seeking and affects bulk shopping intentions. This study enables practitioners and researchers to better understand how people control negative emotions by shopping in pandemic situations such as the current COVID-19 crisis.

The Impact of COVID-19 on Food Consumption and Dietary Quality of Rural Households in China

The COVID-19 pandemic disrupted the food supply chain and thus threatened the food security of many people, while the impact of the pandemic on food consumption of people living in rural areas is still unknown. This study filled in the research gaps by employing a three-wave food consumption survey from 2019 to 2021 conducted in rural China. We adopted a random effect model and Poisson regression to quantify the short-run and long-run impacts of COVID-19 on rural households' food consumption and dietary quality. We found that rural households increased the consumption of vegetables, aquaculture products and legumes in the short-run, and these changes in consumption behavior even lasted 1 year after lockdown was lifted. However, the positive impact was much smaller in households not engaged in agricultural production. In addition, our results showed that COVID-19 decreased dietary diversity but increased dietary quality for households still engaged in food-related agriculture production. Our study indicated that COVID-19 did not threaten the food security status of rural families in China. On the contrary, rural families, particularly those still engaged in agricultural production, increased the consumption of several foods to strengthen their resistance against the virus.

A Cluster Analysis Concerning the Behavior of Enterprises with E-Commerce Activity in the Context of the COVID-19 Pandemic

The planning of activities of e-commerce enterprises and their behavior has been influenced by the emergence of the COVID-19 pandemic. The behavior of e-commerce enterprises has been highlighted at the level of EU countries through an analysis elaborated on four variables: the value of e-commerce sales, cloud computing services, enterprises that have provided training to develop/upgrade the ICT skills of their personnel, e-commerce, customer relationship management (CRM) and secure transactions. Using the hierarchical clustering method, analysis was carried out on these variables to identify certain economic and behavioral patterns of e-commerce activity from 2018 and 2020. The study of the relationships involved in the e-commerce activity of these enterprises is reflected in models of the economic behavior of 31 European states in relation to the targeted variables. The results show that the impacts of the COVID-19 pandemic are strongly manifested in the direction of the evolution of each indicator but differ from one country to another. The trends depend on the level of development and the particularities of each country’s economy in adapting to the repercussions reported in relation to the level of impact of the COVID-19 pandemic. This is highlighted by the significant regrouping of countries in 2020 compared with 2018 in relation to the average values of the indicators. The results show that, in 2020, the most significant percentages of the value of e-commerce sales were recorded in Belgium, Ireland and Czechia, as in 2018. In e-commerce, customer relationship management and secure transactions, Denmark and Sweden were superior in 2020 to the countries mentioned above, which were dominant in 2018. For the other two indicators, Finland and Norway were the top countries included in the analysis in both years. The conclusion supports the continuous model of e-commerce enterprise behavior in order to meet the requirements of online customers.

Exploring Chinese Consumers' Online Purchase Intentions toward Certified Food Products during the COVID-19 Pandemic

The outbreak of COVID-19 has significantly increased consumers' demands for online groceries, as well as healthy, safe, and better-quality food products. In China, certified food products are commonly perceived as safe and good-quality products. Therefore, this study investigated potential factors that influenced Chinese consumers' online shopping intentions toward certified food during the COVID-19 crisis. An integrated model was proposed by combining the technology acceptance model (TAM) and the theory of planned behaviour (TPB) with the impact of COVID-19 (IOC). The empirical results of structural equation modelling analysis with 491 usable responses revealed that the proposed model showed a good model fit and satisfactory explanatory power (R2 = 53%) regarding consumers' certified online food shopping intentions during the pandemic. The path analysis demonstrated that attitude, perceived behavioural control, perceived usefulness (PU), and IOC significantly affected consumers' online purchase intentions of certified food. PU and perceived ease of use (PEOU) were important drivers of attitudes, and PEOU significantly influenced PU. Moreover, the IOC was significantly related to most factors, except subjective norms. These findings can be useful for detecting changes in consumer behaviour, and providing suitable strategic implications for stakeholders in the Chinese certified food sector during the current and post-pandemic eras.

Impact of the COVID-19 Pandemic on Online Consumer Purchasing Behavior

With the spread of the COVID-19 pandemic and the increasing importance of e-commerce, the study of online consumer behavior is of particular relevance. The purpose of this study was to form a methodological approach to assess the relationships and the level of influence of the factors activating the purchasing behavior of online consumers against the background of the COVID-19 pandemic. The research methodology was based on the transformation of Cattell’s questionnaire and the implementation of correlation analysis. To determine the predisposition of online consumer behavior at the time of making a purchase decision, this study used the questionnaire method. The survey was conducted among online shoppers in the top 10 countries in terms of e-commerce market growth. The scientific contribution is the proposed methodological toolkit to assess the purchasing behavior of online consumers, which identifies the most influential factors in their purchasing behavior and provides an opportunity to assess the dynamics of their activity during the study period, to identify key trends and determine changes in their behavior. The research revealed what changes in online consumer buying behavior are typical in the COVID-19 pandemic. The impact of consumer awareness and experience has increased. Online consumers have become more experienced, which has influenced the activity of their buying behavior. This study proved the shifting influence of online consumer purchasing behavior factors during the pandemic. The increasing importance of the speed of decision making by consumers when purchasing goods and services online was determined.

Consumer Privacy Protection With the Growth of AI-Empowered Online Shopping Based on the Evolutionary Game Model

Social distancing due to the COVID-19 pandemic has driven some consumers to online shopping, and concerns about pandemic risks and personal hygiene have increased the demand for e-commerce. Providing personalized recommendations seems quite profitable for e-commerce platforms, and consumers also benefit from personalized content with the advancement of AI technologies. However, this possible win-win situation is marred by the increase in consumers' privacy concerns. Technical solutions have been widely studied to protect consumer privacy, while few analyses have been conducted from the perspective of psychological and behavioral implications. In this paper, an evolutionary game model of privacy protection between e-commerce platforms and consumers is established to determine the mechanisms by which various factors exert influence, and evolutionary stable strategies are obtained from equilibrium points. Then, the strategy selections are simulated with MATLAB 2020 software. Based on the results, the following conclusions are drawn: (1) the application of AI technologies in e-commerce will fundamentally benefit consumers, which makes them actively share personal information with e-commerce platforms with incentives for generous rewards; (2) it is profitable for e-commerce platforms to conduct data mining by improving the ability to use AI technologies and making efforts to reduce technical costs; and (3) regulators should improve the level of supervision instead of imposing a large penalty to enhance consumer trust, which could effectively increase the profits of e-commerce platforms and protect consumers' privacy.