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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.

A globalized world demands proactive tactics from organizational supply chains. Companies should be capable of mitigating the impacts of natural and manmade disasters, which requires that they understand their stages of maturity and resilience. This study develops a theoretical model of the relationship between maturity and resilience, seeking to guide decision-making about aligning these two concepts.

A systematic literature review was conducted to identify the constructs that form the basis for our proposed maturity and resilience model.

The authors identified the key constructs related to maturity and resilience by analyzing the existing literature and selected 13 constructs and 3 maturity stages to construct our maturity and resilience model.

This research contributes to the supply chain management literature, especially that involving the themes of maturity and resilience. It can encourage research to develop future empirical research in the field to validate and overcome the limitations of the initial model the authors propose.

The authors’ proposed model supports supply chain managers in establishing strategies to increase resilience based on the maturity of the chains they manage, enabling them to face crises such as the coronavirus disease 2019 (COVID-19) pandemic.

The model presents a holistic view of maturity and resilience in supply chains contributing to supply chain theory by examining the alignment between the two themes.

The purpose of this study is to identify the most vulnerable households and districts in Northwest Ethiopia and help decision-makers in developing and prioritising effective adaptive strategies and actions.

A multi-scale analytical tool and hazard-generic socio-economic indicators were developed to identify and prioritise the most vulnerable households and districts in Northwest Ethiopia. Categorical principal component analysis with 36 indicators was used to develop weights for different indicators and construct a household intrinsic vulnerability index. Data were collected through key information interviews, focus group discussions and a household survey with 1,602 randomly selected households in three districts of Northwest Ethiopia.

Drawing on intrinsic vulnerability index computation, this study highlights that low levels of education, low access to climate information and credit services, long distance travelled to fetch water and frequent food shortages are the dominant factors contributing to high levels of intrinsic vulnerability at district level, while lack of livelihood support and income diversification are the key drivers of vulnerability at household level. The findings of this study further show that the majority of households (78.01%) falls within the very high to moderately high vulnerable category. Disaggregating the data according to agro-climatic zones highlights that the prevalence of high intrinsic vulnerability is most widespread in the lowland agro-climatic zone (82.64%), followed by the highland (81.97%) and midland zones (69.40%).

From a policy intervention vantage point, addressing the drivers of vulnerability provides a reliable approach to reduce the current vulnerability level and manage potential climate change-induced risks of a system. Specifically, reliable information on inherent vulnerability will assist policymakers in developing policies and prioritising actions aimed at reducing vulnerability and assisting in the rational distribution of resources among households at a local level.

This study contributes to the existing vulnerability literature by showing how hazard-generic socio-economic indicators in the vulnerability assessment adopted by the IPCC (2014) are important to identify drives of vulnerability which ultimately may feed into a more fundamental treatment of vulnerability.

Academics and practitioners have long acknowledged the potential of multiagent systems (MAS) to automate and autonomize decision-making in logistics and supply chain networks. Despite the manifold promises of MAS, industry adoption is lagging behind, and the exact benefits of these systems remain unclear. This study aims to fill this knowledge gap by analyzing 11 specific MAS use cases, highlighting their benefits, clarifying how they can help enhance logistics network resilience and identifying existing barriers.

A three-stage Delphi study was conducted with 18 industry experts. In the first round, these experts identified 11 use cases of MAS and their potential benefits, as well as any barriers that could hinder their adoption. In the second round, they assessed the identified use cases with regard to their potential to enhance logistics network resilience and improve organizational productivity. Furthermore, they estimated the complexity of MAS implementation. In the third round, the experts reassessed their evaluations in light of the evaluations of the other study participants.

This study proposes 11 specific MAS use cases and illustrates their potential for increasing logistics network resilience and enhancing organizational performance due to autonomous decision-making in informational processes. Furthermore, this study discusses important barriers for MAS, such as lack of standardization, insufficient technological maturity, soaring costs, complex change management and a lack of existing use cases. From a theoretical perspective, it is shown how MAS can contribute to resilience research in supply chain management.

The identification and assessment of diverse MAS use cases informs managers about the potential of this technology and the barriers that need to be overcome.

This study fills a gap in the literature by providing a thorough and up-to-date assessment of the potential of MAS for logistics and supply chain management. To the best of the authors’ knowledge, this is the first study to investigate the relevance of MAS for logistics network resilience using the Delphi method.

This study aims to assess the associations of early marriage and spousal age difference (independent of early marriage) with reproductive and sexual health and autonomy in decision-making among married women before conception.

The present study was a part of a three-year community intervention to improve the preconception health of young married women (20–35 years) in the West Delhi district of India. The six key outcomes assessed were: knowledge of reproductive health, discussions related to sexual health, history of anemia, use of contraceptives by women, frequency of consumption of meals per day and the autonomy in decision-making for household expenditures. Unadjusted and adjusted logistic regression models were used to explore the associations between the two key predictors (early marriage or spousal age difference), sociodemographic variables and six outcomes. The results were expressed as odds ratio (OR) and 95% confidence interval (CI). A total of 2,324 women, enrolled from four wards in the district using cluster-based sampling, were interviewed.

Around 17% of women were married by the exact age of 18, and 20% were elder or just one year younger than their husbands. Women who were married early had low reproductive health knowledge (OR (95% CI): 0.48 (0.38–0.60)) and a lower probability of expressing autonomy (OR (95% CI): 0.78 (0.62–0.97)). However, women older than men or younger by just one year in the married relationship had higher reproductive health knowledge (OR (95% CI): 1.25 (1.01–1.54)) than women younger than men more than two years.

Under the umbrella of the preconception care domain, frontline workers should emphasize counseling girls and young women to marry late and delay the first pregnancy.

This study aims to identify the past, present and future lines of research in the supply chain resilience (SCRES) literature and to answer the question of whether resilience is “just another” supply chain (SC) concept or if it constitutes its own theory.

This study involves a bibliometric analysis of 1,596 research articles published in the Web of Science Core Collection™ database between 2004 and 2021, with a focus on: publications and citations per year, co-citations of articles and authors, co-occurrences of the keywords used and thematic development. Seven SCRES experts have also analysed and discussed the research results.

The results show how resilience has become one of the main thematic blocks of SC research in the aftermath of the COVID-19 pandemic, with a 62.04% increase in academic publications. Despite the abundant literature, there is still no overarching theory of SCRES. One possible hindrance to the creation of such a theory is the variety of existing definitions and models of SCRES. This research identifies this as a shortcoming that future studies should address. As the results show, the combination of SCRES with other fields, such as engineering and ecology, can help in constructing a global theory of SCRES.

Bibliometric analysis is used to confirm the SCRES literature gap in the development of an overarching theory, although resilience constitutes one of the main thematic blocks of the SC literature.