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Analytics thrives in navigating emergency situations. Emergency operations management needs to develop analytics empowerment capability (ANEC) to prepare for uncertainty, support continuity and tackle any disruptions. However, there is limited knowledge on ANEC and its effects on strategic emergency service agility (SESA) and emergency service adaptation (ESAD) in such contexts. Drawing on the dynamic capability (DC) theory, we address this research gap by developing an ANEC model. We also model the effects of ANEC on SESA and ESAD using SESA as a mediator. We also assess the moderating and quadratic effects of ANEC on two higher-order DCs (i.e. SESA and ESAD).

Drawing on the literature on big data, empowerment and DC, we develop and validate an ANEC model using data from 245 service systems managers in Australia. The study uses the partial least squares-based structural equation modelling (PLS-SEM) to prove the research model. The predictive power of the research model is validated through PLSpredict (k = 10) using a training sample (n = 220) and a holdout sample (n = 25).

The findings show that analytics climate, technological enablement, information access, knowledge and skills, training and development and decision-making ability are the significant components of ANEC. The findings confirm strategic emergency service agility as a significant partial mediator between ANEC and emergency service adaptation. The findings also discuss the moderating and quadratic effects of ANEC on outcome constructs. We discuss the implications of our findings for emergency situations with limitations and future research directions.

The findings show that building ANEC plays a fundamental role in developing strategic agility and service adaptation in emergency situations to prepare for disruptions, mitigate risks and continue operations.

Drawing on a dynamic capability view, this study develops a decision support model that determines the most suitable configuration of strategies and challenges to adopt additive manufacturing (AM) to expedite digital transformation and performance improvement of the surgical and medical device (SMD) supply chain.

To investigate the research objective, a multi-method and multi-study research design was deployed using quality function deployment and fuzzy set qualitative comparative analysis.

The study finds that only resilience strategies or negation (i.e. minimisation) of challenges are not enough; instead, a configuration of resilience strategies and negation of challenges is highly significant in enhancing performance.

SMD supply chain decision-makers will find the decision support model presented in this study as beneficial to be resilient against various challenges in the digital transformation of service delivery process.

This study builds new knowledge of the adoption of AM technology in the SMD supply chain. The decision support model developed in this study is unique and highly effective for fostering digital transformation and enhancing SMD supply chain performance.

Sharing economy-based service platforms are a relatively new way of delivering services that have received increasing attention from both practitioners and researchers. However, current research in the area is still developing in terms of offering practical insight in conjunction with a decision model that may help to determine optimal strategies for efficient service design in the sharing economy from a service triad perspective. Exploring this gap in the literature, this paper aims to develop and apply a decision model that enables managers to identify and prioritise the efficiency attributes of sharing economy-based services. It also aids in designing optimal strategies to enhance efficiency over time based on the insights obtained from users (buyers and sellers) and platform providers.

This study adopts a mixed-methods approach. The qualitative approach comprised an extensive literature review followed by in-depth interviews, and the quantitative approach adopted the quality function deployment (QFD) integrated optimisation technique to design and prioritise the most optimal strategy emanating from the application of a decision model.

The findings revealed that establishing global distribution, continued technological research and development (R&D) and enhancing the transactional platform are the most important strategies in the context of sharing economy platform providers (e.g. accommodation-based-platform service providers). This study also revealed that as the importance weights of the efficiency attributes changed over time, so too did the portfolio of strategies used to attain an optimal efficiency level.

The decision model brings a richer conceptual understanding of the dynamic changes over time that occur in the business ecosystem. It also allows managers of sharing economy-based platforms to select optimal strategies and make astute decisions towards achieving efficient service design.

This study aims to investigate the conditional direct and indirect effects of supply chain relational capital (RC) on supply chain sustainability via sustainability governance.

In line with the study’s aims, a quantitative survey-based approach was adopted. This study uses a random sample of 272 manufacturing firms from the apparel industry in Bangladesh. This study assesses the measurement model using partial least square-based structural equation modelling and test the proposed hypotheses using the Hayes PROCESS.

The results reveal that the indirect effect of supply chain RC on supply chain sustainability via sustainability governance is significant. While at low levels of network complexity (NC), the conditional indirect effect of supply chain RC on supply chain sustainability via sustainability governance is significant, this study finds that such indirect effects are insignificant at high levels of NC. This study further shows that NC positively moderates the relationship between supply chain RC and supply chain sustainability.

While previous studies have demonstrated the role of RC in adopting sustainability practice, this study explores this link further by investigating the conditional direct and indirect effects of supply chain relational capital on supply chain sustainability via sustainability governance.