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Many off-line retailers have experienced a slump in sales and have the potential risk of overstock or understock. To overcome these problems, retailers have applied data mining techniques, such as association rule mining or sequential association rule mining, to increase sales and predict product demand. However, because these techniques cannot generate shopper-centric rules, many off-line shoppers are often inconvenienced after writing their shopping lists carefully and comprehensively. Therefore, the purpose of this paper is to propose a personalized recommendation methodology for off-line grocery shoppers.

This paper employs a Markov chain model to generate recommendations for the shopper’s next shopping basket. The proposed methodology is based on the knowledge of both purchased products and purchase sequences. This paper compares the proposed methodology with a traditional collaborative filtering (CF)-based system, a bestseller-based system and a Markov-chain-based system as benchmark systems.

The proposed methodology achieves improvements of 15.87, 14.06 and 37.74 percent with respect to the CF-, Markov chain-, and best-seller-based benchmark systems, respectively, meaning that not only the purchased products but also the purchase sequences are important elements in the personalization of grocery recommendations.

Most of the previous studies on this topic have proposed on-line recommendation methodologies. However, because off-line stores collect transaction data from point-of-sale devices, this research proposes a methodology based on purchased products and purchase patterns for off-line grocery recommendations. In practice, this study implies that both purchased products and purchase sequences are viable elements in off-line grocery recommendations.

This study defines a three-angled research plan to intensify the knowledge and development undergoing in the retail sector. It proposes a theoretical framework of the customer journey to explain the customers' intent to adopt artificial intelligence (AI) and machine learning (ML) as a protective measure for interaction between the customer and the brand.

This study presents a research agenda from three-dimensional online search, ML and AI algorithms. This paper enhances the readers' understanding by reviewing the literature present in utilizing AI in the customer journey and presenting a theoretical framework.

Using AI tools like Chatbots, Recommenders, Virtual Assistance and Interactive Voice Recognition (IVR) helps create improved brand awareness, better customer relationships marketing and personalized product modification.

This study intends to identify a research plan based on investigating customer journey trends in today's changing times with AI incorporation. The research provides a novel model framework of the customer journey by directing customers into different stages and providing different touchpoints in each stage, all supported with AI and ML.

Taking into account the importance of e‐commerce and the current applications of AI techniques in this area, this research aims to adequate the design of a multi‐agent system for the provisioning of e‐services in u‐commerce environments. This proposal is centred on the methods of evaluation in a u‐e‐commerce environment.

The multi‐agent systems (MAS) approach is based on an MAS model developed for AmI that has been redesigned to support u‐commerce. The use of a recommendation system, previously developed by the research group, is suggested for this MAS. The methodological proposal centres on the evaluation of this type of system.

The evaluation of this type of system is the principal problem of current research. Therefore, this is the main contribution of the paper.

The different evaluation methods that are proposed, whether qualitative or quantitative, offer the possibility of measuring the added value that the context can give to the use of e‐services in different domains of application. Qualitative evaluation should consider the customer as a central piece in the system. In addition, quantitative methods should objectively evaluate the contribution of context to the application.

At present, there is no single method for evaluating the benefits of different u‐commerce systems, so a new method needs to be found based on these techniques.

The research proposes an MAS designed for u‐commerce domains, analyzes the capacity of trust management techniques in this environment, and proposes several evaluation methods to show the benefits of context information in the use of e‐services. Several real developments are described to show the different applications of MAS in u‐commerce and how evaluation is carried out.

Online fresh food shopping has become increasingly popular in recent years, especially during the COVID-19 crisis. Online fresh food shopping provides consumers with an alternative to shopping in a traditional market, while also enabling procurement of such goods at a reduced risk of infection. The purpose of this study is to investigate whether online fresh food shopping behaviors change during public health emergency periods.

The data were collected through a web-based survey (508 respondents in China). Descriptive analysis, ordinal logistic regression analysis, and the Apriori algorithm were employed to explore what characteristics influence purchase frequency as well as food and delivery time preferences among different customer groups.

Based on the survey data, this study found that purchase frequency grew 71.2% during the COVID-19 crisis. City type and online shopping frequency of respondents are positively correlated with purchase frequency in normal and COVID-19 crisis periods. Number of daily hours worked by respondents only showed a significant impact for the normal period. People perceiving the risk of infection from going out are more willing to purchase fresh food online.

This is the first study to explore and compare online fresh food shopping behaviors during normal and COVID-19 crisis periods with a sample from China. The findings indicate a key role that online fresh food shopping can perform during a crisis and contribute to our understanding of fresh food online shopping behaviors during other possible public health emergency scenarios.

The purpose of this paper is to investigate e‐tail store attributes that develop customers' positive perceptions of e‐tail store image, and determines whether or not they develop a sense of loyalty to an e‐tailer.

Acknowledging the importance of customer retention, this paper is designed to examine e‐customer loyalty intentions toward the e‐tailer. To understand the concept of loyalty toward an e‐tailer, this study focuses on the importance of the final stage of the customer decision‐making process: post‐purchase evaluation. This paper develops a model that describes the extent to which e‐tail store image (derived from a set of e‐tail store attributes) indicates patronage intentions and finally predicts customer loyalty. We use the structural equation modeling to test the model and hypotheses.

Results in this paper indicate that e‐tail store image is derived from e‐merchandise, e‐service, and e‐shopping atmosphere attributes, all of which support the way consumers shop. A favorable e‐tail store image positively influences e‐patronage intentions, which thus leads to e‐loyalty.

The research in this paper provides a conceptual model that will help e‐retailers better articulate how and why consumers may be e‐loyal shoppers. Second, the research identifies attributes, unique to online shopping that serve as the basis for conceptualizing e‐tail image as a second order factor.