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Generation Z will be the leading consumer group in the future. Using convenience stores, the study provides an in-depth analysis on Gen Z’s current experience and future expectations from retail stores. The study further highlights the differences between Gen Z’s perception of convenience stores across three different regions – the USA, South Korea and Japan.

This study conducted a series of in-depth, semi-structured interviews with 36 Gen Z participants from the USA (12), South Korea (11) and Japan (13). All interviews were first coded based on a preselected list of themes and were further coded with new themes that emerged from exploratory coding.

Each regional cohort varied in terms of how they experienced and what they expected from convenience stores. US participants showed negative or utilitarian attitudes toward convenience stores, whereas South Korean participants had a positive, personal attachment to them. In comparison, Japanese participants had a relatively neutral attitude. However, all three groups showed a common preference for smart technology and health concerns surrounding convenience store foods.

Convenience store chains should consider the cultural nuances when designing future services. The chains should further strive to remove the health concerns about the foods provided at the stores and design smart technologies that enhance user experience.

The present study broadens the knowledge in this budding consumer segment where current research is limited. It further sheds light on the variance among Gen Zers across different cultural contexts.

Industry 4.0 has put forward a smart perspective on managing supply chain networks and their operations. The current manufacturing system is primarily data-driven. Industries are deploying new emerging technologies in their operations to build a competitive edge in the business environment; however, the true potential of smart manufacturing has not yet been fully unveiled. This research aims to extensively analyse emerging technologies and their interconnection with smart manufacturing in developing smarter supply chains.

This research endeavours to establish a conceptual framework for a smart supply chain. A real case study on a smart factory is conducted to demonstrate the validity of this framework for building smarter supply chains. A comparative analysis is carried out between conventional and smart supply chains to ascertain the advantages of smart supply chains. In addition, a thorough investigation of the several factors needed to transition from smart to smarter supply chains is undertaken.

The integration of smart technology exemplifies the ability to improve the efficiency of supply chain operations. Research findings indicate that transitioning to a smart factory radically enhances productivity, quality assurance, data privacy and labour efficiency. The outcomes of this research will help academic and industrial sectors critically comprehend technological breakthroughs and their applications in smart supply chains.

This study highlights the implications of incorporating smart technologies into supply chain operations, specifically in smart purchasing, smart factory operations, smart warehousing and smart customer performance. A paradigm transition from conventional, smart to smarter supply chains offers a comprehensive perspective on the evolving dynamics in automation, optimisation and manufacturing technology domains, ultimately leading to the emergence of Industry 5.0.

The rise of artificial intelligence (AI) applications has driven enterprises to provide many intelligent services to consumers. For instance, customers can use chatbots to make relevant inquiries and seek solutions to their problems. Despite the development of customer service chatbots years ago, they require significant improvements for market recognition. Many customers have reported negative experiences with customer service chatbots, contributing to resistance toward their use. Therefore, this study adopts the innovation resistance theory (IRT) perspective to understand customers’ resistance to using chatbots. It aims to integrate customers’ negative emotions into a predictive behavior model and examine users’ functional and psychological barriers.

In this study, we collected data from 419 valid individuals and used structural equation modeling to analyze the relationships between resistance factors and negative emotions.

The results confirmed that barrier factors affect negative emotions and amplify chatbot resistance influence. We discovered that value and risk barriers directly influence consumer use. Moreover, both functional and psychological barriers positively impact negative emotions.

This study adopts the innovation resistance theory perspective to understand customer resistance to using chatbots, integrates customer negative emotions to construct a predictive behavior model and explores users’ functional and psychological barriers. It can help in developing online customer service chatbots for e-commerce.

This study aims to examine the influence of the derived attributes of embedded artificial intelligence-mobile smart speech recognition (AI-MSSR) technology, namely perceived usefulness, perceived ease of use (PEOU) and perceived enjoyment (PE) on consumer purchase intention (PI) through the chain relationships of attitudes to AI and consumer smart experience, with the moderating effect of consumer innovativeness and Generation (Gen) X and Gen Y in fashion retail.

The study employed a quantitative survey strategy, drawing a sample of 836 respondents from Sri Lanka and India representing Gen X and Gen Y. The data analysis was carried out using smart partial least squares structural equation modelling (PLS-SEM).

The findings show a positive relationship between the perceived attributes of MSSR and consumer PI via attitudes towards AI (AAI) and smart consumer experiences. In addition, consumer innovativeness and Generations X and Y have a moderating impact on the aforementioned relationship. The theoretical and managerial implications of the study are discussed with a note on the research limitations and further research directions.

To multiply the effects of embedded AI-MSSR and consumer PI in fashion retail marketing, managers can develop strategies that strengthen the links between awareness, knowledge of the derived attributes of embedded AI-MSSR and PI by encouraging innovative consumers, especially Gen Y consumers, to engage with embedded AI-MSSR.

This study advances the literature on embedded AI-MSSR and consumer PI in fashion retail marketing by providing an integrated view of the technology acceptance model (TAM), the diffusion of innovation (DOI) theory and the generational cohort perspective in predicting PI.

This research aims to propose the development of a model that identifies, in essential services, the determining factors affecting the technological advances offered by different smart technologies in supermarket retail channels that influence citizens' quality of life, amidst the coronavirus disease 2019 (COVID-19) pandemic.

The data were collected using a cross-sectional questionnaire survey (n = 469). The authors applied the structural equation modeling (SEM) technique to test the hypotheses, along with the partial least squares (PLS) method for estimating latent variables and combining with the necessary condition analysis (NCA) method.

According to the results of the NCA method, the results were adequate, and more attention should be paid to the quality of life construct after finding the bottleneck point of 50%. In this sense, adaptive resilience was characterized as the main necessary predictor construct for quality of life. In addition, Generation Z and Millennials have the highest frequency of use in all smart technologies, with “assisted purchase” being the most widely used.

Finally, the effect of the pandemic changed the consumption routine with supermarkets, not being a mere option but a necessity in the context of a smart city.

As a result, the proposed model was consistent, showing that all direct and indirect SEM paths were validated, highlighting data security and privacy and resilience issues. In addition, the NCA method complemented the procedures performed in the SEM phase.

The purpose of this paper is to investigate the effects of anthropomorphism and identify factors related to adopting voice shopping on smart speakers.

Progress in partial least squares structural equation modeling (PLS-SEM) approach is used to test the proposed research framework regarding anthropomorphism and user perceptions on voice shopping via smart speakers. Individuals' responses to questions about attitude and intention to use voice shopping via smart speakers were collected and analyzed.

The results showed that anthropomorphism had a positive influence on satisfaction, which, in turn, had a positive impact on intention to adopt voice shopping, and customers had positive opinions regarding smart speakers.

This study only reflects a younger perspective on smart speaker voice shopping. This study identified the characteristics of smart speakers that increase customers' intention to purchase, which can be used to formulate sales strategies and management guidelines.

This research provided a new perspective to enable practitioners to promote smart speakers for voice shopping. Smart speaker manufacturers can utilize the findings of this research to improve the system design of smart speakers to further facilitate voice shopping.

Unlike previous studies, which focused on product attributes of smart speakers or voice shopping experiences, this study provided a clear picture of how the anthropomorphic feature of smart speakers affects customers' intention to adopt voice shopping.

Digitalization is revolutionizing the retail sector as today's consumers prefer a seamlessly integrated, fluid and irritation-free shopping experience enhanced with artificial intelligence (AI)-powered technologies. Literature highlights gaps in the understanding of the shopping experience in an omnichannel context, involving aesthetic, cognitive and affective experience dimensions. This research highlights the direct effects and the mechanism triggered in the presence of such device.

A sample of 259 consumers was interviewed at the point of sale. Data have been collected after a shopping experience in two concept stores belonging to the same fashion brand: (1) not equipped with AI-powered technology and (2) equipped with these tools. The measurement scales were validated through ANCOVA analysis and causal relationship analysis with structural equation modeling.

The results show that the presence of an in-store AI-powered technologies in a connected store generates a higher aesthetic reaction when visiting the store, a higher absorption when shopping through the flow and a higher intent to purchase. The authors further investigate the underlying mechanism triggered by the presence of this technology, which enables the authors to outline their consequences regarding purchase intention.

The study, conducted within an actual connected store in France, explores the impact of AI technology in connected retail environments on consumer responses. It is an early research in this field, shedding light on a rarely explored area. The authors’ research addresses a significant gap, providing insights into AI-driven retail experiences.

This research aims (i) to assess the level of customer satisfaction and loyalty to self-service technologies and Auchan Retail Portugal, (ii) to identify the determinants of customer satisfaction and loyalty to these technologies and also, (iii) to identify their influence on customer loyalty to this modern distribution retailer operating in Portugal.

A conceptual model was defined to meet the research objectives and to carry out the quantitative analysis applied to the random sample (n = 483) of customers. The data used where gathered via an online questionnaire survey, which covered all dimensions of the conceptual model, applied in 2021 in Portugal. To validate the hypotheses, Cronbach’s alpha and multiple linear regression models were used.

The results reveal that customer satisfaction with self-service technologies has a direct and positive effect on customer loyalty to Auchan Retail Portugal. Furthermore, results reveal that the technology utility factors significantly influence the customer technology experience which has an impact on perceived service quality and perceived risk. The findings of this research provide data on how to improve customer adoption and satisfaction with self-service technology and highlight that these technologies should be part of firm’s competitive strategy.

This study presents itself as a novelty for science, while granting important contributions to the retailer. It presents an innovative conceptual model that delivers to Auchan the basis for it to move toward smart retail technologies, aiming at the market trend of personalization. For future research, this study can be used as an instrument to evaluate the customer experience with self-service technology and to examine the determinants and effects of self-service technology separately.

The purpose of this paper is to give an overview of artificial intelligence (AI) and other AI-enabled technologies and to describe how COVID-19 affects various industries such as health care, manufacturing, retail, food services, education, media and entertainment, banking and insurance, travel and tourism. Furthermore, the authors discuss the tactics in which information technology is used to implement business strategies to transform businesses and to incentivise the implementation of these technologies in current or future emergency situations.

The review provides the rapidly growing literature on the use of smart technology during the current COVID-19 pandemic.

The 127 empirical articles the authors have identified suggest that 39 forms of smart technologies have been used, ranging from artificial intelligence to computer vision technology. Eight different industries have been identified that are using these technologies, primarily food services and manufacturing. Further, the authors list 40 generalised types of activities that are involved including providing health services, data analysis and communication. To prevent the spread of illness, robots with artificial intelligence are being used to examine patients and give drugs to them. The online execution of teaching practices and simulators have replaced the classroom mode of teaching due to the epidemic. The AI-based Blue-dot algorithm aids in the detection of early warning indications. The AI model detects a patient in respiratory distress based on face detection, face recognition, facial action unit detection, expression recognition, posture, extremity movement analysis, visitation frequency detection, sound pressure detection and light level detection. The above and various other applications are listed throughout the paper.

Research is largely delimited to the area of COVID-19-related studies. Also, bias of selective assessment may be present. In Indian context, advanced technology is yet to be harnessed to its full extent. Also, educational system is yet to be upgraded to add these technologies potential benefits on wider basis.

First, leveraging of insights across various industry sectors to battle the global threat, and smart technology is one of the key takeaways in this field. Second, an integrated framework is recommended for policy making in this area. Lastly, the authors recommend that an internet-based repository should be developed, keeping all the ideas, databases, best practices, dashboard and real-time statistical data.

As the COVID-19 is a relatively recent phenomenon, such a comprehensive review does not exist in the extant literature to the best of the authors’ knowledge. The review is rapidly emerging literature on smart technology use during the current COVID-19 pandemic.

Suning Group launched Suning.com when its chain stores were developing at the highest speed, realizing the transformation to an Internet retailer. Suning continued to follow the growth strategy of “Technological transformation and Smart Services”, and was renamed Suning Commerce Co. Ltd. It launched a business model of “e-commerce + stores + retail service providers”. Riding on the brand new O2O business model, Suning is thinking and practicing from simple donation to actual implementation, from constructing public welfare network to extending CSR ecosystem in a bid to advance towards deeper and more extensive Internet economy, and to create greater social value.