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Since their inception, which took place more than two decades ago, product recommendation agents (RAs) still attract very few consumers. Notably, most of academic work in the field had an empirical quantitative structure. In addition, no research has developed a comprehensive model to explain the adoption and usage of commercial RAs. The purpose of this paper is to follow a qualitative approach to investigate the factors behind the adoption and usage of commercial RAs, explore the effect of user age, and deduce the success factors of these RAs.

This research followed a qualitative approach. Qualitative research aims to form an in‐depth understanding of human behavior. It is essential for building grounded theory and for proposing comprehensive models for future examination. As such, in four discussion groups, participants provided their input following the shopping trial for a product using a factual RA (MyProductAdvisor.com). Discussion groups were used because they outline an important aspect of qualitative research and because they are ideal for both the inception and development of products and services.

Underlying the major themes, the analysis first provides insight in consumers' RA use and the products consumers regard as adequate to be offered using a commercial RA. The analysis then delineates some important factors that can be considered by developers to enhance the usability and trustworthiness of commercial RAs. Further, the analysis suggested four higher‐order factors that can be considered the success factors of a commercial RA: users appear to require a commercial RA that is friendly, smart, trusted, and informational. The themes that emerged from participants in the youth and the older discussion groups were rather invariant.

This is one of the few qualitative studies that focused on commercial RAs. The commercial RA success factors and their determinants are summarized in the form of a general framework to guide future work. This qualitative work provides a cornerstone that is of importance to theory development in the field of intelligent RAs and assistive technology. The results have important implications for RAs' developers and researchers.

The purpose of this study is to develop a synthesized conceptual framework for artificial intelligence (AI) adoption in the field of business-to-business (B2B) marketing.

A conceptual development approach has been adopted, based on a content analysis of 59 papers in peer-reviewed academic journals, to identify drivers, barriers, practices and consequences of AI adoption in B2B marketing. Based on these analyses and findings, a conceptual model is developed.

This paper identifies the following two key drivers of AI adoption: the shortcomings of current marketing activities and the external pressure imposed by informatization. Seven outcomes are identified, namely, efficiency improvements, accuracy improvements, better decision-making, customer relationship improvements, sales increases, cost reductions and risk reductions. Based on information processing theory and organizational learning theory (OLT), an integrated conceptual framework is developed to explain the relationship between each construct of AI adoption in B2B marketing.

This study is the first conceptual paper that synthesizes drivers, barriers and outcomes of AI adoption in B2B marketing. The conceptual model derived from the combination of information processing theory and OLT provides a comprehensive framework for future work and opens avenues of research on this topic. This paper contributes to both AI literature and B2B literature.

This chapter describes how conversational computer agents have been used in collaborative problem-solving environments. These agent-based systems are designed to (a) assess the students’ knowledge, skills, actions, and various other psychological states on the basis of the students’ actions and the conversational interactions, (b) generate discourse moves that are sensitive to the psychological states and the problem states, and (c) advance a solution to the problem. We describe how this was accomplished in the Programme for International Student Assessment (PISA) for Collaborative Problem Solving (CPS) in 2015. In the PISA CPS 2015 assessment, a single human test taker (15-year-old student) interacts with one, two, or three agents that stage a series of assessment episodes. This chapter proposes that this PISA framework could be extended to accommodate more open-ended natural language interaction for those languages that have developed technologies for automated computational linguistics and discourse. Two examples support this suggestion, with associated relevant empirical support. First, there is AutoTutor, an agent that collaboratively helps the student answer difficult questions and solve problems. Second, there is CPS in the context of a multi-party simulation called Land Science in which the system tracks progress and knowledge states of small groups of 3–4 students. Human mentors or computer agents prompt them to perform actions and exchange open-ended chat in a collaborative learning and problem-solving environment.

With the developments of e-commerce markets, novel recommendation technologies are becoming an essential part of many online retailers’ economic models to help drive online sales. Initially, the purpose of this paper is to undertake an investigation of apparel recommendations in the commercial market in order to verify the research value and significance. Then, this paper reviews apparel recommendation techniques and systems through academic research, aiming to acquaint apparel recommendation context, summarize the pros and cons of various research methods, identify research gaps and eventually propose new research solutions to benefit apparel retailing market.

This study utilizes empirical research drawing on 130 academic publications indexed from online databases. The authors introduce a three-layer descriptor for searching articles, and analyse retrieval results via distribution graphics of years, publications and keywords.

This study classified high-tech integrated apparel systems into 3D CAD systems, personalised design systems and recommendation systems. The authors’ research interest is focussed on recommendation system. Four types of models were found, namely clothes searching/retrieval, wardrobe recommendation, fashion coordination and intelligent recommendation systems. The forth type, smart systems, has raised more awareness in apparel research as it is equipped with advanced functions and application scenarios to satisfy customers. Despite various computational algorithms tested in system modelling, existing research is lacking in terms of apparel and users profiles research. Thus, from the review, the authors have identified and proposed a more complete set of key features for describing both apparel and users profiles in a recommendation system.

Based on previous studies, this is the first review paper on this topic in this subject field. The summarised work and the proposed new research will inspire future researchers with various knowledge backgrounds, especially, from a design perspective.

In the context of a profound digital transformation, the need for social interactivity is becoming fundamental for consumers on e-commerce sites. It allows them to interact with the company in the same way as with salespeople in physical stores. Among the different existing virtual agents used by companies to offer online solid interaction, this study focuses on virtual recommendation agents (VRAs). The purpose of this paper is to investigate the effectiveness of VRA on consumers’ psychological states and online impulse buying.

An experimental website was designed for this study. After interacting with VRA, respondents had to take part in a survey. The questionnaire included measures of perception of the VRA, perceived enjoyment, online impulse buying and perceived risk. Structural equation modelling was used to test the research model.

The results confirm the positive influence of the VRA on perceived enjoyment, which is positively associated with online impulse buying. The effect of the VRA’s presence on perceived enjoyment is moderated by gender.

Only one product category was studied, for which the advice of VRAs is undoubtedly essential. However, this could also be valid for other products, such as technological products, where the consumer’s level of expertise may be low. Hence, the authors propose to extend this study to various products for a better generalization of the results.

This study provides practitioners with relevant findings on the efficiency of VRAs and offers them guidelines to design more interactive commercial websites with higher levels of social interactions. Such interactions should reduce perceived risks and make visitors more confident. This can encourage more traffic and sales, which implies growth in incomes and revenues.

Through this technology, VRAs can create more humanized links between consumers and companies.

Working on VRAs is original as they represent the technology that can replace salespeople. In addition, to the best of the authors’ knowledge, this research is the first to test the impact of VRA on online impulse buying. By examining the VRA’s set of fundamental capabilities, this study contributes to existing research on how companies should integrate digital technologies in their sales interactions with consumers, which to date has focused on other sales channels such as social media platforms.

Intelligent software agents offer the potential to unleash the power of the Internet to optimise the process of supplier sourcing. Aims to promote an awareness in the buyer/supplier community of the potential for using intelligent software agents over the Internet for this purpose. The traditional relationship between suppliers and buyers is contrasted with that of Internet‐based trading. This comparison confirms that trust is a critical component in the traditional relationship and that an understanding of the nature of trust is necessary in revealing where and how agent technology will have maximum benefit for both buyers and suppliers. It is shown that the technology that can enable this form of sourcing is already well established, and a range of conceptual agents are described. Finally, identifies the benefits that will accrue to organisations that adopt agent technology as part of their supplier‐sourcing portfolio, and recommendations for further work are made.

The purpose of this paper is to present a brief outline of the potential applications of intelligent agent technology in libraries with a list of illustrative examples.

A definition and the attributes of intelligent agents are presented, along with different types of agents and their wide‐ranging applications in various contexts. Illustrative examples of intelligent agents are presented, with some interesting case studies from different domains.

The role of intelligent agents in modern libraries is highlighted from various perspectives, such as electronic information services, collection development and acquisition, classification and organization of library materials, indexing and abstracting, issuing collection, and reference services.

The authors do not attempt to devise an intelligent agent to test these applications in the libraries where they work, although they wish to pursue it as a future course of action.

With libraries dealing with patrons who never disclose fully their information needs and research interests to library staff, libraries have to resort to newer methods of feedback collection, and intelligent agents provide viable options to improve information organization and refine library services.

The paper, being very precise and covering the latest developments, offers new insight into the field of intelligent agents and their pragmatic applications in libraries to interested professionals.

The importance of artificial intelligence in human resource management has grown substantially. Previous literature discusses the advantages of AI implementation at a workplace and its various consequences, often hostile, for employees. However, there is little empirical research on the topic. The authors address this gap by studying if individuals oppose biased algorithm recommendations regarding disciplinary actions in an organisation.

The authors conducted an exploratory experiment in which the authors evaluated 76 subjects over a set of 5 scenarios in which a biased algorithm gave strict recommendations regarding disciplinary actions at a workplace.

The authors’ results suggest that biased suggestions from intelligent agents can influence individuals who make disciplinary decisions.

The authors’ results contribute to the ongoing debate on applying AI solutions to HR problems. The authors demonstrate that biased algorithms may substantially change how employees are treated and show that human conformity towards intelligent decision support systems is broader than expected.

The authors’ paper is among the first to show that people may accept recommendations that provoke moral dilemmas, bring adverse outcomes, or harm employees. The authors introduce the problem of “algorithmic conformism” and discuss its consequences for HRM.

The purpose of this paper is to describe UWIRS (Ubiquitous Web Information Retrieval Solution), an agent‐based Web Information Retrieval (WIR) solution designed taking into account the unique features of the World Wide Web (WWW) and the limitations of existing WIR solutions for ubiquitous environments.

UWIRS can offer recommendation services by using the Multi‐Agent Vizier Recommendation Framework (Vizier). Vizier was designed under a generic approach and therefore can provide services to information retrieval applications so these may offer product recommendations that consider several adaptation/personalization dimensions (e.g. user dimension, context, among others).

Overall, the main challenge resides on: location, retrieval, integration and presentation of information from the WWW, quickly and accurately, to satisfy a user's singular information needs.

In UWIRS, agents cooperate in order to retrieve personalized information, considering user needs, goals, preferences and contextual features. UWIRS's agents are responsible for: interpreting user input and adding adaptation information by means of a query enrichment process; identifying and selecting the appropriate data sources taking into consideration the Profile Set (composed of User, Device and Information‐Provider Profiles); executing query routing and the information retrieval process; integrating and filtering the retrieved results; and lastly, coherent presentation of quality and relevant ubiquitous information (anytime, anywhere and anyhow) that satisfies the user's particular information needs and constraints associated to his/her access device.

The purpose of this article is to provide a comprehensive literature review on the utilisation of intelligent agent technology in the library environment.

Research papers since 1990 on the use of various intelligent agent technologies in libraries are divided into two main application areas: digital library (DL), including agent‐based DL projects, multi‐agent architecture for DLs, intelligent agents for distributed heterogeneous information retrieval and agent support to information search process in DLs; and services in traditional libraries, including user interface for library information systems, automatic reference services and multi‐agent architecture for library services. For each paper on the topic, its new ideas or models, referred work, analyses, experiments, findings and conclusions are addressed.

The majority of the literature covers DLs and there have been fewer studies about services in traditional libraries. A variety of architecture, framework and models integrating agent technology in library systems or services are proposed, but only a few have been implemented in the practical environment. The application of agent technology is still at the research and experimentation stage. Agent technology has great potential in many areas in the library context; however it presents challenges to libraries that want to be involved in its adoption.

The survey has practical implications for libraries, librarians and computer professionals in developing projects that employ intelligent agent technology to meet end‐users' expectations as well as to improve information services within limited resources in library settings.

The paper provides a comprehensive survey on the development and research of intelligent agents in libraries in literature.