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Drawing on the theory of contingency, the aim of this work is to understand how supply chain-related contingencies, arising from climate change, are related to changes in the organisational structure of firms. Further, the authors explore how this relationship influences the perception of sustainability managers on the adoption of low-carbon operations management practices and their related benefits.

To achieve this goal, this research uses NVivo software to gather evidence from interviews conducted with ten high-level managers in sustainability and related areas from seven leading companies located in Brazil.

The authors present four primary results: a proposal of an original framework to understand the relationship between contingency theory, changes in organisational structure to embrace low-carbon management, adoption of low-carbon operations practices and benefits from this process; the discovery that an adequate low-carbon management structure is vital to improve the organisations’ perceptions of potential benefits from a low-carbon strategy; low-carbon management initiatives tend to emerge from an organisation’s existing environmental management systems; and controlling and monitoring climate contingencies at the supply chain level should be permanent and systematic.

Based on the knowledge of the authors, to date, this work is the first piece of research that deals with the complexity of putting together contingency theory, climate-change contingencies at the supply chain level, organisational structure for low-carbon management and low-carbon operations management practices and benefits. This research also highlights evidence from an emerging economy and registers future research propositions.

The purpose of this article is to address the prioritisation and focus of supply chain managers subsequent to coronavirus disease 2019 (COVID-19)/severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the great lockdown of 2020.

In this article, concepts and trends on resilient and sustainable supply chains are systematized. Main trends in sustainability of supply chains in the wake of COVID-19 are presented.

Guidelines on building smarter and more resilient supply chains are provided and future tendencies, which includes the increase of a sustainable consumption perspective, are highlighted.

This is a conceptual article blended with a practical approach aiming to propose guidelines for managers and scholars on how to address supply chain management challenges after the coronavirus pandemic.

Congruent with the world-wide call to combat global warming concerns within the context of advancements in smart technology, artificial intelligence, robotics, algorithms (STARA), and digitalisation, organisational leaders are being pressured to ensure that talented employees are effectively managed (nurtured and retained) to curb the potential risk of staff turnover. By managing such talent(s), organisations may be able to not only retain them, but consequently foster environmental sustainability too. Equally, recent debates encourage the need for teams to work digitally and interdependently on set tasks, and for leaders to cultivate competencies fundamental to STARA, as this may further help reduce staff turnover intention and catalyse green initiatives. However, it is unclear how such turnover intention may be impacted by these actions. This paper therefore, seeks to investigate the predictive roles of green hard and soft talent management (TM), leader STARA competence (LSC) and digital task interdependence (DTI) on turnover intention.

The authors used a cross-sectional data collection technique to obtain 372 useable samples from 49 manufacturing organisations in Nigeria.

Findings indicate that green hard and soft TM and LSC positively predict turnover intention. While LSC amplifies the negative influence of green soft TM on turnover intention, LSC and DTI dampen the positive influence of green hard TM on turnover intention.

Our study offers novel insights into how emerging concepts like LSC, DTI, and green hard and soft TM simultaneously act to predict turnover intention.