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In this study, the authors examine artificial knowledge as a fundamental stream of knowledge management for sustainable and resilient business models in supply chain management (SCM). The study aims to provide a comprehensive overview of artificial knowledge and digitalization as key enablers of the improvement of SCM accountability and sustainable performance towards the UN 2030 Agenda.

Using the SCOPUS database and Google Scholar, the authors analyzed 135 English-language publications from 1990 to 2022 to chart the pattern of knowledge production and dissemination in the literature. The data were collected, reviewed and peer-reviewed before conducting bibliometric analysis and a systematic literature review to support future research agenda.

The results highlight that artificial knowledge and digitalization are linked to the UN 2030 Agenda. The analysis further identifies the main issues in achieving sustainable and resilient SCM business models. Based on the results, the authors develop a conceptual framework for artificial knowledge and digitalization in SCM to increase accountability and sustainable performance, especially in times of sudden crises when business resilience is imperative.

The study results add to the extant literature by examining artificial knowledge and digitalization from the resilience theory perspective. The authors suggest that different strategic perspectives significantly promote resilience for SCM digitization and sustainable development. Notably, fostering diverse peer exchange relationships can help stimulate peer knowledge and act as a palliative mechanism that builds digital knowledge to strengthen and drive future possibilities.

This research offers valuable guidance to supply chain practitioners, managers and policymakers in re-thinking, re-formulating and re-shaping organizational processes to meet the UN 2030 Agenda, mainly by introducing artificial knowledge in digital transformation training and education programs. In doing so, firms should focus not simply on digital transformation but also on cultural transformation to enhance SCM accountability and sustainable performance in resilient business models.

This study is, to the authors' best knowledge, among the first to conceptualize artificial knowledge and digitalization issues in SCM. It further integrates resilience theory with institutional theory, legitimacy theory and stakeholder theory as the theoretical foundations of artificial knowledge in SCM, based on firms' responsibility to fulfill the sustainable development goals under the UN's 2030 Agenda.

The purpose of this paper is to explain the technological phenomenon artificial intelligence (AI) and how it can contribute to knowledge-based marketing in B2B. Specifically, this paper describes the foundational building blocks of any artificial intelligence system and their interrelationships. This paper also discusses the implications of the different building blocks with respect to market knowledge in B2B marketing and outlines avenues for future research.

The paper is conceptual and proposes a framework to explicate the phenomenon AI and its building blocks. It further provides a structured discussion of how AI can contribute to different types of market knowledge critical for B2B marketing: customer knowledge, user knowledge and external market knowledge.

The paper explains AI from an input–processes–output lens and explicates the six foundational building blocks of any AI system. It also discussed how the combination of the building blocks transforms data into information and knowledge.

Aimed at general marketing executives, rather than AI specialists, this paper explains the phenomenon artificial intelligence, how it works and its relevance for the knowledge-based marketing in B2B firms. The paper highlights illustrative use cases to show how AI can impact B2B marketing functions.

The study conceptualizes the technological phenomenon artificial intelligence from a knowledge management perspective and contributes to the literature on knowledge management in the era of big data. It addresses calls for more scholarly research on AI and B2B marketing.

At this time, not all of the versions and definitions of the concept of knowledge have been compiled within the scope of science. It is not the object of knowledge but rather the subject that knows, or in other words, the cognitive ability of the subject The process is as follows: to produce information, and on the basis of that information, to produce knowledge, and on the basis of that knowledge, wisdom. In the context of globalisation of information, the memory was the method of knowing. The organisation, processing and retrieval of information tend to take on a structure and design according to the information‐processing model of the human mind. This discontinuity is made up of the new forms of artificial knowledge and artificial wisdom. The pillars of the new society of knowledge are settling on the basis of productivity and utility, and this has turned knowledge into nothing more than productive information.

Purpose: In the contemporary knowledge economy, organisations mainly derive a competitive advantage by leveraging their intangible assets. Competent and motivated employees are the primary strategic resources to attain innovation and business continuity. Consequently, workplace learning and development (L&D) is at the forefront of the human resource management (HRM) discipline. At the same time, with the changing technology landscape, organisations are transforming their L&D function to be sustainable. Against this backdrop, the main objective of this chapter is to illustrate how artificial intelligence (AI) contributes to a specific HRM sub-function, that is, workplace L&D.

Design/Methodology/Approach: Grounded on intense scrutiny of literature, this chapter construes AI as intelligent machines that think and work like humans and have the potential for enhancing learning processes. Different themes have been presented, which suggest the capabilities of AI systems to fuel employee learning at the workplace.

Findings: Findings demonstrate that AI-enabled workplace learning is rooted in improved knowledge management (KM) capabilities, developmental feedback, personalised education, learning for a diverse pool of learners, virtual mentoring, and chatbot-based learning.

Research Limitations/Implications: This conceptual study suffers from a lack of empirical support.

Practical Implications: This chapter contributes to expanding scholarship on integrating AI and the HRM domain, particularly L&D. Further, it highlights how L&D professionals should integrate AI into employee learning journeys to evoke effective learning outcomes.

Originality/Value: This chapter provides a gestalt approach to integrating AI with employee L&D

According to many authors, differences in firm performances are increasingly attributed to tacit knowledge that cannot easily be transmitted or imitated. On the other hand, current quality management models knowledge typically relates only to people. Situations, in which knowledge that is related to people is not available, sufficient, reliable or lucrative for application, are not considered. This paper aims to investigate how to overcome this gap.

Based on the adopted classification, types of knowledge typically present in an organisation are identified, and are discussed. Techniques for acquiring and formalising tacit knowledge are explored, and related criteria are defined. Particular attention is shown to knowledge management and artificial intelligence techniques.

A new approach to quality management called deep quality concept (DQC) is conceptualised, and mechanisms and concepts needed to acquire and integrate formalised knowledge into quality systems are identified. Other concepts that need to be incorporated are also identified. Finally, a new quality management model based on the DQC is developed.

In further research the main points of the presented theoretical framework need to be validated through real examples from practice, and the resulting quality standard, i.e. award criteria, as well as the related handbooks completed and formalised.

Knowledge‐related and other relevant concepts need to be incorporated into contemporary quality management systems, as systematically and carefully as conventional quality management concepts. Knowledge of methods and tools suitable for that also needs to be assimilated.

In the paper a novel knowledge‐focused approach to quality management is presented. For this reason the paper is of great value for quality management theory and practice.

This study aims to explore the associations linking employees’ challenge-hindrance appraisals toward artificial intelligence (AI) to service performance while considering the dual mediating roles of job crafting and job insecurity, as well as the moderating role of AI knowledge.

A survey was administered to a sample of 297 service industry employees. This study examined all the hypotheses with Mplus 8.0.

This study confirms that challenge appraisal toward AI has an indirect positive influence on service performance via job crafting (motivation process), whereas hindrance appraisal toward AI has an indirect negative influence on service performance via job insecurity (strain process). Meanwhile, AI knowledge, serving as a key personal resource, could strengthen the positive impacts of challenge appraisal toward AI on job crafting and of hindrance appraisal toward AI on job insecurity.

Organizational decision-makers should first survey employees’ appraisals toward AI and then adopt targeted managerial strategies. From the perspective of service industry employees, employees should adopt proactive coping strategies and enrich their knowledge of AI to meet the challenges brought by this technology.

The primary contribution of this study is that we enrich the literature on AI by exploring the dual mediators (i.e. job crafting and job insecurity) through which AI awareness affects service performance. Moreover, this study advances our understanding of when appraisals toward AI influence job outcomes by identifying the moderating role of AI knowledge.

Government agencies carry out many events each year designed to determine future requirements and capabilities. These events include field experiments, surveys, interviews, simulations and workshops. Similar themes are evident across many of these events. Unfortunately, mechanisms for passing information from one event to the next, or for developing bodies of knowledge in the topical areas they address, have yet to be fully developed. The task is difficult on two fronts. In response to this need a knowledge management capability was developed to help provide structure for dynamic and static data and thereby, aid in the analysis of complex experimentation. The system warehouses qualitative and quantitative data and supports mining operations through a number of traditional and artificial intelligence‐based techniques. Described are the information architecture of the system, the knowledge processing methodologies, and the structure of the thematic data sets that form the knowledge ontologies.

This paper aims to provide and empirically test a conceptual model in which artificial intelligence (AI), knowledge management processes (KMPs) and supply chain resilience (SCR) are simultaneously considered in terms of their reciprocal relationships and impact on manufacturing firm performance (MFP).

In the study, six hypotheses have been developed and tested through an empirical survey administered to 120 senior executives of Italian manufacturing firms. The data analysis has been carried out via the partial least squares structural equation modelling approach, using the Advanced Analysis for Composites 2.0 variance-based software program.

Using a conceptual model validated using an empirical survey, the study sheds light on the relationships between AI, KMPs and SCR, as well as their impacts on MFP. In particular, the authors show the positive effects of the adoption of AI on KMPs, as well as the influence of KMPs on SCR and MFP. Finally, the authors demonstrate that KMPs act as a mediator through which AI affects SCR and MFP.

This study highlights the critical role of KMPs for manufacturing firms that can deploy AI to stimulate KMPs and through attaining a high level of the latter might succeed in enhancing both their SCR and MFP.

This study demonstrates that manufacturing firms interested in properly applying AI to ameliorate their performance and resilience must carefully consider KMPs as a mediator mechanism.

The purpose of this article is to give a non‐technical overview of some of the technical progress made recently on tackling three fundamental problems in the area of formal knowledge representation/artificial intelligence. These are the Frame Problem, the Ramification Problem, and the Qualification Problem. The article aims to describe the development of two logic‐based languages, the Event Calculus and Modular‐E, to address various aspects of these issues. The article also aims to set this work in the wider context of contemporary developments in applied logic, non‐monotonic reasoning and formal theories of common sense.

The study applies symbolic logic to model aspects of human knowledge and reasoning.

The article finds that there are fundamental interdependencies between the three problems mentioned above. The conceptual framework shared by the Event Calculus and Modular‐E is appropriate for providing principled solutions to them.

This article provides an overview of an important approach to dealing with three fundamental issues in artificial intelligence.

This paper describes an architecture for the usage of Instructional Design (ID) knowledge in intelligent instructional systems. In contrast with other architectures, ontologies are used to represent ID knowledge about both what to teach and how to teach. Moreover, set‐theoretic reasoning is used for the provision of inferential services. In particular, the paper shows how set‐theoretic deductions can be applied (i) to support the modelling of ID knowledge bases, (ii) to retrieve suitable teaching methods from them, and (iii) to detect errors in a training design. The intelligent knowledge management environment CONCEPTOOL is used to demonstrate the benefits of the proposed architecture.