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The impact of extreme heat on prisons and carceral facilities is becoming increasingly visible, yet remains overlooked by scholars, practitioners and policymakers. Prisons are a unique type of infrastructure designed to severely limit and control the movement of hundreds and even thousands of individuals as a form of punishment. This leads to many significant challenges to mitigating the risk of heat-related illness in prisons and other carceral spaces that have remained overlooked across many disciplines including emergency management, disasters, corrections and public health.

For this study, we analyzed 192 surveys from incarcerated persons in state prisons throughout Texas to understand how incarceration and the punitive prison environment create challenges to managing extreme heat in prisons.

We found that characteristics of modern incarceration, including communal distribution of resources, crowded conditions and a lack of agency for incarcerated people, create barriers to accessing resources during periods of extreme heat. Furthermore, the punitive nature of the prison environment as manifested in the relationship between staff and incarcerated persons and certain prison policies also create barriers to incarcerated persons accessing resources to reduce their risk of heat-related illness and death.

These issues are particularly relevant to the health and safety of incarcerated persons during periods of extreme temperatures but also speak broadly to the implications of incarceration, disaster risk, and the advancement of human rights for incarcerated people.

This article addresses a gap in the literature by including the perspectives of persons incarcerated in Texas prisons experiencing extreme heat and implicates the characteristics of incarceration and punishment in the production of disaster risk.

To determine how to best deploy the Triple-A supply chain (SC) capabilities (AAA-agility, adaptability and alignment) to improve competitive advantage (CA) by identifying the Triple-A SC model with the highest CA predictive capability.

Assessment of in-sample and out-of-sample predictive capacity of Triple-A-CA models (considering AAA as individual constructs) to find which has the highest CA predictive capacity. BIC, BIC-Akaike weights and PLSpredict are used in a multi-country, multi-informant, multi-sector 304 plant sample.

Greater direct relationship model (DRM) in-sample and out-of-sample CA predictive capacity suggests DRM's greater likelihood of achieving a higher CA predictive capacity than mediated relationship model (MRM). So, DRM can be considered a benchmark for research/practice and the Triple-A SC capabilities as independent levers of performance/CA.

DRM emerges as a reference for analysing how to trigger the three Triple-A SC levers for better performance/CA predictive capacity. Therefore, MRM proposals should be compared to DRM to determine whether their performance is significantly better considering the study's aim.

Results with our sample justify how managers can suitably deploy the Triple-A SC capabilities to improve CA by implementing AAA as independent levers. Single capability deployment does not require levels to be reached in others.

First research considering Triple-A SC capability deployment to better improve performance/CA focusing on model's predictive capability (essential for decision-making), further highlighting the lack of theory and contrasted models for Lee's Triple-A framework.