Purpose: This paper aims to evaluate the impacts of the COVID-19 crisis on startups performance and the moderating effects played by several resilience-related startup characteristics during times of crisis.Design/methodology/approach: To achieve this, 94 Brazil-based startups were surveyed, and multivariate data techniques (PLS-SEM) were applied.Findings: The results show that despite the startups performance having been affected by the pandemic crisis, the response measures, when influenced by the resilience characteristics of these companies, moderated this effect. Furthermore, our findings suggest the future challenges to be faced by these organisations in the post-pandemic period.Research limitations/implications: Proposing a framework, our survey research contributes to the dynamic capabilities theory by showing that startups resilience is linked to the micro-foundations of sensing (e.g. innovation systems, resilience culture, pivoting practices, innovativeness products), seizing (e.g. leadership/focused skills, people development and selection, agility, clear vision of business process) and reconfiguring capabilities.Practical implications: Not only for theory, but this paper also contributes insights and guidelines for business practice in the face of challenges arising from times of crisis. By demonstrating the positive effect of early response measures based on resilience, our findings provide genuine managerial input that can help managers, funders and decision-makers in these companies operations against turbulent crises early on, thereby supporting the traction phase and sustaining their performance.Originality/value: Previous research has examined the effects of the COVID-19 crisis in several sectors and perspectives. However, this study is the first to empirically test and clarify how the resilience and singularities of these new business models based on innovation could react to the changes caused by the pandemic.

This paper aims to assess the relationship between Industry 4.0 (I4.0) and the circular economy that could contribute to supply chain management performance. To achieve this, a combination of the interpretative structural modelling (ISM) and (cross-impact matrix multiplication applied to classification) MICMAC approach was used to establish the interrelationships between these topics. The developed analysis reveals that there are 19 constructs capable of elucidating this relationship and that there is a hierarchy between these constructs, which are presented in a structural model. Further, the different levels of dependency and driving power are compared in a cluster diagram. As the main result, it was found that there is a strong mutual relationship between the basic technologies. The use of Internet of Things and cloud computing technologies influences the collection of large amounts of data, leading to big data, which in turn influence the use of data analytics tools to obtain competitive advantages. These outcomes may contribute to managers’ more assertive decision-making regarding the selection, implementation, and evaluation of projects adopting Industry 4.0 technologies and circular economy approaches in supply chains. Moreover, our study could be the basis for future empirical research to investigate how companies incorporate Industry 4.0 technologies into their processes and how this influences the quest for sustainable supply chains.

Purpose: This paper aims to identify the interrelationships among critical factors for digital servitization (DS) implementation. Design/methodology/approach: A multi-method research was used. Critical factors for a successful DS implementation were identified using a systematic literature review and expert interviews. The interpretive structural modeling (ISM) method was used to develop a hierarchical model of the identified factors, followed by the fuzzy Matrice d’Impacts Croisés Multiplication Appliqués à un Classement (MICMAC) analysis to assess their dependence and driving powers. Findings: A total of 23 factors for DS implementation were identified, and the ISM model was developed. Based on MICMAC analysis, the factors were also grouped under four categories (dependent, driving, autonomous and linkage). A conceptual framework is proposed, highlighting that DS implementation relies on three main layers of critical factors: crafting alignment, scaling the change and achieving results. Originality/value: The ISM and fuzzy MICMAC methods used in this study provided valuable insights into the interrelationship among the identified DS factors through a conceptual framework. To the best of the authors’ knowledge, the study is one of the first to identify critical factors influencing DS implementation and develop hierarchical relationships among them.