From data to readiness: how digital technologies and memory can shape humanitarian supply chains

We are living in an era marked by the increasing frequency of natural disasters, exacerbated by human activities affecting the environment (Benevolenza and DeRigne, 2019; Frame et al., 2020). In 2024 alone, 20,738 people have lost their lives due to such events (Link to the cited article). In the countries most represented in our data set, such as the UK and the USA, last year’s examples include Storm Bert, which struck the UK and claimed at least five lives, and Hurricane Helene, which affected Florida, Georgia, North Carolina, South Carolina, Tennessee, Kentucky, Indiana and Virginia, causing at least 250 fatalities (Meng et al., 2025; Suri et al., 2024). Given these figures and the trend of intensifying disasters, the effective management of humanitarian supply chains becomes even more critical, as their primary mission is to save lives and alleviate the suffering of disaster-affected communities (Thomas and Kopczak, 2005).

Uncertainty is always present in the management of humanitarian supply chains, as they often do not know when or where a disaster will occur, what will be needed, in what quantity, where resources will come from or how frequently support will be required (Van Wassenhove, 2006; Yang et al., 2026). Although digital technologies are widely recognized as promising tools for addressing uncertainty through disaster mitigation, preparedness, response and recovery (Fischer-Preßler et al., 2024; Ismail et al., 2025), empirical research in humanitarian supply chains remains fragmented (Nunes and Pereira, 2021). Most studies examine single technologies individually (Marić et al., 2022), and only a few attempts have integrated multiple technologies into a single construct – yet these have not addressed readiness as the focal outcome (Akhtar et al., 2025; Bag et al., 2023; Dubey, 2023; Singh, 2025; Tiwari et al., 2024). Because digital technologies primarily enhance supply chains’ ability to process information under uncertainty, readiness emerges as a critical capability for efficient and effective humanitarian relief response to natural disasters (Stumpf et al., 2023).

Moreover, the literature has paid limited attention to the internal mechanisms that convert technology use into disaster management capabilities. In particular, the role of supply chain memory, although established in commercial supply chains (Alvarenga et al., 2023a; Ayad et al., 2025), has been largely overlooked as a mediator between technology use for information processing and readiness in humanitarian supply chains. Investigating mediators is crucial because they explain how and why technologies generate capabilities, rather than merely whether they have an effect (Hayes, 2022). Therefore, this study seeks to answer the following research questions (RQ):

To contribute to this discussion, this study integrates several digital technologies (Internet of Things [IoT], drones, blockchain, virtual reality (VR), artificial intelligence (AI), machine learning and 3D printing) and theoretically proposes and empirically tests their impact on the readiness of humanitarian supply chains to respond to natural disasters. We argue that this impact occurs through the ability of digital technologies to support the generation, storage and retrieval of knowledge (Alvarenga et al., 2023b), thereby strengthening supply chain memory and enhancing decision-making in the face of future disruptions.

In addition, because learning from past experiences is essential for preparing for future disruptions (Pettit et al., 2019; Rankin et al., 2014), we argue that such learning only becomes actionable when embedded in memory – the repository where knowledge is stored, assimilated and retrieved to inform future actions (Antunes and Pinheiro, 2020). Thus, the supply chain’s capability to process information through technology shapes not only how it learns from real events but also how it develops knowledge without direct exposure; ultimately enhancing its readiness to respond effectively to future disasters (Alvarenga et al., 2023b; Tortorella et al., 2020).

This work contributes to humanitarian supply chain management theory and government practice in several ways. Through a quantitative study involving professionals engaged in humanitarian efforts, our study confirmed that a set of digital technologies, including less explored tools in the context of disruption and natural disaster management, such as 3D printing, machine learning and robotics (Ismail et al., 2025), acts as a driver of readiness within humanitarian supply chains. We also found memory as a full mediator that directs the impact of digital technologies on readiness. In addition, this study provides a deeper understanding of the importance of learning from past events and transforming such experiences into actionable knowledge to prepare for future disruptions, extending the findings of Alvarenga et al. (2023b, 2023a) from the context of commercial supply chains to humanitarian supply chains. Finally, it highlights the critical role of digital technologies in knowledge development even in the absence of direct exposure to disaster scenarios, as mere experience with frequent natural disasters does not automatically translate into readiness, showing how the usage of digital technologies and memory can shape humanitarian supply chain management and public governance.

The remainder of the paper is structured as follows: following this introduction, Section 2 presents the theoretical background, the development of hypotheses and the research model. Section 3, Methodology, outlines the methodological procedures adopted, including data collection, respondent profile, data analysis techniques and robustness checks of the structural model. The subsequent section presents and discusses the results, emphasizing both theoretical and practical implications. The final section offers concluding remarks, addressing the study’s limitations and providing directions for future research.

A disaster is an event that disrupts the whole system and puts its goals at risk (Van Wassenhove, 2006). These events can be natural, the focus of this article or man-made (Van Wassenhove, 2006; Zibulewsky, 2001). Among natural disasters, some occur suddenly, such as cyclones, hurricanes, volcanic eruptions, earthquakes, tornados and floods, as well as those that develop gradually, such as droughts, poverty and famine (Van Wassenhove, 2006; Zibulewsky, 2001). It is important to note that although they are not initially classified as man-made disasters, many natural disasters can be triggered or have their effects amplified by human actions on the environment (Kovács and Spens, 2009). In other words, phenomena like floods and droughts can be consequences of climate change induced by human activity (Aalst, 2006; Benevolenza and DeRigne, 2019).

The ability of humanitarian supply chains to be ready to deal with various types of events is a critical factor for ensuring a rapid and effective response (El Baz et al., 2024; Chowdhury and Quaddus, 2016). Readiness refers to a state of preparedness and can be considered a predisaster capability that enables supply chains to anticipate potential events and develop the necessary actions, resources and capabilities to mitigate their impacts (El Baz et al., 2024; Chowdhury and Quaddus, 2016; Ruel and El Baz, 2021), being a key component of viable supply chains (Padovano and Ivanov, 2025). When disasters are accurately mapped in real time, resource mobilization becomes more efficient, facilitating a more coordinated response and ultimately saving more lives (Jahre and Jahre, 2018; Renkli and Duran, 2015; Roh et al., 2015). Moreover, Stumpf et al. (2023) found that investing in preparedness – especially for humanitarian supply chains operating in high-uncertainty environments – leads to better overall performance in disaster response.

Digital technologies, which connect the real world to the virtual one (Frank et al., 2019), are fundamental for supply chain management as they enhance visibility, collaboration, flexibility and agility in decision-making (Ismail et al., 2025). In humanitarian supply chains, which aim to prevent and monitor disasters, alleviate suffering and minimize their impacts, these technologies are extremely valuable as well (Zekhnini et al., 2024). Events can be anticipated through IoT sensors, drones can be used for area mapping and VR technologies enable the planning and training of actions before they take place. As a result, the digitalization of humanitarian supply chains promotes both operational outcomes and improvements in beneficiary and donor satisfaction (Akhtar et al., 2025).

In this regard, the paper examines the principal digital technologies highlighted in the literature as critical for disruption management: AI and machine learning, IoT, 3D printing, drones, blockchain and VR (Ismail et al., 2025). Each of these technologies contributes uniquely to enhancing humanitarian supply chain readiness, and their specific features and benefits are discussed in the following subsections. Building on this foundation, we now introduce the first research hypothesis. Accordingly, we state that:

AI refers to systems capable of acting like humans, thinking like humans, reasoning logically and behaving rationally (Russell and Norvig, 1995). In their literature review, Zaoui et al. (2025) highlighted that the use of AI enhances various aspects of supply chains, including resilience, transparency, performance, adaptability, collaboration, innovation, sustainability, optimization and efficiency. By analyzing both historical and real-time data, AI provides critical support for managerial decision-making (Karuppiah et al., 2025; Rodríguez-Espíndola et al., 2020), providing timely responses to natural disasters by humanitarian supply chains (Shrivastav and Sareen, 2024).

For instance, AI and machine learning algorithms can be used to predict and map flood routes, thereby facilitating the relocation of populations affected by such events (Bagatur and Onen, 2018; Rodríguez-Espíndola et al., 2020). Sun et al. (2020) examined the applications of AI in disaster management, and their literature review indicates that various methods, including supervised and unsupervised learning models, deep learning, reinforcement learning and optimization techniques, have been applied throughout all phases of disaster management. These approaches support key activities such as risk forecasting, early warning, resource allocation, understanding population needs and damage assessment (Sun et al., 2020).

The IoT refers to the digital interconnection of physical assets, allowing continuous sensing, monitoring and interaction both within firms and across their supply chains (Ben-Daya et al., 2019). By enabling real-time data collection, transfer and sharing, IoT enhances collaboration, transparency and flexibility among supply chain partners, capabilities that are essential for managing disruptions (Ben-Daya et al., 2019; Birkel and Hartmann, 2020; Rebelo et al., 2022). Birkel and Hartmann (2020) show that the IoT supports the detection of rare but high-impact disruptions and enables faster proactive and reactive responses. Moreover, Alvarenga et al. (2023b) demonstrated that the IoT is one of the key Industry 4.0 technologies that fosters the development of prior knowledge to deal with disruptions, thereby enhancing the robustness and resilience of supply chains.

3D printing, also known as additive manufacturing, involves the manufacturing of components or final products from digitally predesigned files through the successive addition of material, turning digital representations into physical objects (Shahrubudin et al., 2019). The use of 3D printing in manufacturing processes offers greater flexibility, reduced production complexity, opportunities for training, job creation, waste reduction and shorter lead times, ultimately leading to improved operational, environmental and social performance (Chan et al., 2018; Li et al., 2025). Given the urgency of responding to natural disasters, and considering that transportation systems often face severe disruptions during crises (Marcillo-Delgado et al., 2025). The use of 3D printing emerges as a way to overcome challenges related to material distribution, inventory management and lead times, providing a rapid, customized and efficient response to the contextual demands (Corsini et al., 2022; Zekhnini et al., 2024). During the COVID-19 pandemic, for instance, masks were internally produced by a department of a Swedish public agency to supply other departments that required greater protection to carry out their work (Cedergren and Hassel, 2024). Overall, 3D printing can be applied to produce essential relief items, build shelter components and restore critical infrastructure (Marcillo-Delgado et al., 2025).

VR technologies enable users to be virtually present in locations they are not physically in, allowing them to interact with simulated environments and experience immersive scenarios (Wohlgenannt et al., 2020). Within organizations, VR is used for a variety of purposes, including optimizing the distribution of materials in situations that are difficult to replicate in real-life training, conducting personnel recruitment and supporting supply chain management education (Saldanha et al., 2025; Wohlgenannt et al., 2020). In the context of humanitarian operations, the primary role of VR in disaster readiness is to offer realistic, hands-on training that is more resource- and time-efficient than traditional approaches such as online modules or tabletop exercises (Hsu et al., 2013). Andreatta et al. (2010) demonstrated, for example, that VR provides an effective alternative for training emergency management personnel in mass disaster triage.

A blockchain is a shared digital record where transactions are saved in blocks that are secure and cannot be changed, creating a permanent history (Seebacher and Schüritz, 2017). Its benefits for supply chain management are diverse; primarily related to the transparency and trust it can provide to supply chain members (Dubey et al., 2020; Manupati et al., 2022). The main drivers of its adoption lie in its ability to increase visibility within these supply chains and improve accountability, while limited resources for implementing blockchain-based systems and financial constraints remain the primary barriers to its use (Baharmand et al., 2021). Research shows that the use of blockchain technology by humanitarian supply chains can enhance donor confidence, facilitating accountability, resource mobilization and proper allocation of resources (Hunt et al., 2022). Furthermore, humanitarian supply chains, often rapidly formed and composed of multiple, potentially conflicting actors in response to urgent needs (Charles et al., 2010), rely on blockchain to foster swift trust (Dubey et al., 2020).

Drones are vehicles that can be either autonomous or remotely controlled, featuring various sizes, flight capabilities and applications (Hassanalian and Abdelkefi, 2017; Watts et al., 2012). Initially developed for military purposes due to their effectiveness in identifying and eliminating targets (Dworkin, 2013), technological advancements have expanded their use beyond the military. Drones are now beneficial across numerous fields, including agriculture, medical services, the film industry and entertainment (Ayamga et al., 2021; Javaid et al., 2022). A notable example is Amazon Air, which uses drones to enhance e-commerce by optimizing last-mile deliveries, especially in congested urban areas and remote regions with limited infrastructure (Straubinger et al., 2023).

Drones also offer numerous benefits to humanitarian supply chains, focusing on four key functions: (1) damage assessment and mapping, (2) search and rescue operations, (3) supply transportation and (4) training (Mohd Daud et al., 2022). For instance, Greenwood et al. (2020) demonstrated that drones significantly enhanced damage assessment after the 2017 hurricanes in Florida and Texas by capturing high-resolution images. In the 2024 Rio Grande do Sul floods (Marengo et al., 2025). Government agencies used drones to map the affected areas and plan their reconstruction. Moreover, drones improve search and rescue missions and efficiently deliver supplies to affected areas (Chowdhury et al., 2017). However, quantitative studies have specifically focused on identifying the main barriers to drone adoption and their relationships (Edwards et al., 2024; Kamat et al., 2022) or the profile of drone users (Sopha et al., 2024). Overall, drones provide valuable support across all phases of disaster management: before, during and after an event (Restas, 2015).

We can observe from these examples that using digital technologies enhances a critical aspect of making effective decisions regarding disruption management: the development, storage and retrieval of knowledge – whether acquired prior to or in real time – regarding natural disasters (Alvarenga et al., 2023a; Ayad et al., 2025). The accumulated knowledge, derived from past experiences or training, forms organizational memory, a repository of learned knowledge that can be accessed to inform future decision-making (Anand et al., 1998; Hult et al., 2004; Walsh and Ungson, 1991). This knowledge provides essential information for managing such events (Ayad et al., 2025; Fernandez-Pacheco et al., 2017; Kusumastuti et al., 2021).

Thus, while digital technologies may directly enhance disaster response efforts – whether by assisting in victim search operations (Albanese et al., 2022) or manufacturing and delivering critical relief items (Dukkanci et al., 2023; Ruggiero et al., 2021) – digitalization’s most significant impact on perceived readiness likely lies in the memory they help build and activate. Digital technologies can monitor geological changes and weather conditions, supporting actions to address landslides or track rising river levels and flooding caused by heavy rainfall (Chen, 2024). They also contribute to postdisaster assessments by capturing footage that identifies areas for improvement in humanitarian network coordination (Fernandez-Pacheco et al., 2017) and enable near real-time mapping of areas to create virtual representations that support operational alignment among response teams (Cheng et al., 2022). Therefore, digital technologies improve humanitarian supply chain memory by facilitating the systematic collection, storage and retrieval of critical information. As a result, the second hypothesis arises:

Recent research shows that supply chains that leverage past experiences are better prepared to manage future disruptions (Alvarenga et al., 2023a; Ayad et al., 2025; Roh et al., 2022). Alvarenga et al. (2023b, 2023a) demonstrated, for instance, that supply chains capable of developing knowledge before disruption and using technology to learn how to manage such events are better positioned to balance capabilities and vulnerabilities, enabling them to sustain operations more efficiently during a disruptive event or recover more quickly afterward. Ayad et al. (2025) further expanded these findings by showing that network prominence moderates the relationship between supply chain memory and both response and recovery.

Furthermore, a well-developed memory allows organizations to establish recoverable patterns of action to address future disasters, whether through procedures, routines, culture or other organizational mechanisms (Cohen and Bacdayan, 1994; Foroughi et al., 2020; Walsh and Ungson, 1991). The importance of prior knowledge was highlighted by Kusumastuti et al. (2021), who reported that the Lombok community in Indonesia experienced an increase in knowledge sharing and generation after the mid-2018 earthquake, which enabled them to respond more effectively to the earthquake in early 2019. Prior disaster experience was also reported as an important mechanism through which risks are understood and effectively managed (Wight et al., 2025). Therefore, the third hypothesis arises:

Figure 1 presents the research model, which aims to theoretically explore and empirically test how digital technologies affect the readiness of humanitarian supply chains to respond to natural disasters. The model is grounded in organizational information processing theory (OIPT), which posits that greater uncertainty increases the need for information processing to enable effective decision-making (Galbraith, 1973, 1974). In disaster scenarios, OIPT provides a suitable perspective for analyzing the complex aspects of the humanitarian supply chains (Dubey et al., 2022; Tiwari et al., 2024).

Humanitarian supply chains are composed of multiple actors, often with conflicting objectives, and are rapidly formed to address dynamic and uncertain disaster contexts. Such complexity and uncertainty intensify the need for effective information processing to enable coordination and decision-making (Van Wassenhove, 2006; Dubey et al., 2022). While alternative perspectives such as the resource-based view (RBV) (Barney et al., 2001) or dynamic capabilities theory (Teece et al., 1997) emphasize valuable resources and adaptive capabilities, they do not explicitly theorize uncertainty in terms of information-processing requirements nor specify how technology mitigates coordination complexity under extreme information asymmetry. In contrast, OIPT offers a more precise explanatory lens for examining how digital technologies address disaster-induced uncertainty through enhanced information processing, making it more suitable for this study.

Therefore, the rationale for adopting OIPT resides in its ability to explain how organizations must process information in uncertain and complex environments to generate effective results (Galbraith, 1973). As noted by Srinivasan and Swink (2018), the execution of complex tasks such as the coordination and action among members of humanitarian supply chains requires effective organization of information. Within this perspective, digital technologies function as mechanisms that expand the supply chain’s information processing capacity (Dalenogare et al., 2018; Tortorella et al., 2020), providing a continuous flow of data on natural disasters and enabling their transformation into actionable knowledge (Alvarenga et al., 2023a; Fischer-Preßler et al., 2024).

At the same time, supply chain memory serves as the repository that retains and makes available organized, accumulated knowledge for decision-making (Alvarenga et al., 2023a; Walsh and Ungson, 1991). Building on this, the model proposes that integrating digital technologies and supply chain memory enhances readiness, equipping humanitarian supply chains to confront natural disasters. In this way, OIPT directly supports the research questions and hypotheses, as it explains why digital technologies expand information processing capacity and supply chain memory, serving as the mediating mechanism between digital technology utilization and humanitarian supply chain readiness.

Moreover, the model includes the frequency of natural disasters as a control variable to account for contextual variation in the results. The inclusion of natural disaster frequency as a control variable is justified by the fact that memory is largely shaped by prior experience. To accurately assess the real impact of digital technologies on memory building, it is therefore necessary to account for the role of past disaster exposure. Controlling for disaster frequency allows us to disentangle whether it is the combination of digital technologies and memory built from prior disasters, rather than past experience alone, that enhances the readiness of humanitarian supply chains to respond to future events.

We recruited participants via Prolific, a widely recognized market research platform known for producing valid and ethically sound research outcomes (Palan and Schitter, 2018; Queiroz et al., 2022). We adopted purposive sampling to ensure that respondents possessed knowledge of humanitarian operations and experience with digital technologies. Within Prolific, we applied a professional area filter and included a screening question to select only participants who had used emerging technologies at least once in the past year. We collected data in October 2024 through an online questionnaire distributed to professionals engaged in humanitarian operations. We received a total of 277 responses. To enhance data quality, we included two instructed response items in the questionnaire to detect inattentive participants prone to rapid or pattern-based answering (Gummer et al., 2021; Kung et al., 2018). We removed individuals who failed these items from the analysis, which led to the exclusion of 22 responses. The final sample comprised 255 participants.

Table 1 presents the respondents’ profile.

We used the readiness scale developed by Ruel and El Baz (2021) and adapted the scale from Hult et al. (2006), as applied by Alvarenga et al. (2023b, 2023a) and Ayad et al. (2025), to measure humanitarian supply chain memory in managing natural disasters. Both constructs – memory and readiness – were coded on a seven-point Likert scale ranging from 1 (“strongly disagree”) to 7 (“strongly agree”), with respondents asked: “To what extent do you agree with the statements about your organization’s network when dealing with a disaster?” Readiness items capture the ability to detect disruptions rapidly, train employees to respond to crises, mobilize resources during emergencies, establish early warning signals and use forecasting to anticipate disasters – together providing a robust measure of preparedness in humanitarian supply chains.

In contrast, the memory scale emphasizes the accumulation of knowledge and expertise over time, encompassing dimensions such as knowledge, experience, familiarity and investment in research and development. Finally, natural disaster frequency was measured on a seven-point Likert scale ranging from 1 (“very low”) to 7 (“very high”). Respondents were asked: “Please indicate the extent to which the following statement reflects your organization’s network environment.”

The readiness and memory scales were modeled as reflective constructs, and their reliability and validity were assessed using SmartPLS 4 software (Ringle et al., 2014). Table 2 displays the loadings, average variance extracted and composite reliability of the constructs. Discriminant validity was assessed through the heterotrait–monotrait ratio, with results shown in Table 3 (Hair et al., 2019). All values are within the thresholds recommended in the literature, confirming the adequacy of the measurement model.

We specified the digital technologies construct as formative, unlike readiness and memory. We used direct questions to capture the application of these technologies, asking respondents to indicate the frequency of use of IoT, blockchain, 3D printing, VR, drones and AI/machine learning in dealing with natural disasters. We coded responses on a seven-point Likert scale ranging from 1 (“never”) to 7 (“always”). We evaluated the construct by testing collinearity among indicators and examining their significance and relevance (Cenfetelli and Bassellier, 2009). As shown in Table 4, no collinearity issues were detected because all variance inflation factor (VIF) values were below the recommended threshold of five (Hair et al., 2021). Concerning indicator significance and relevance, 3D printing for shelter and relief, AI and machine learning, blockchain for disaster response and, IoT sensors were found to be nonsignificant; however, they are absolute important to the construct given that their loadings exceed 0.5. Consequently, these results do not indicate a weakness in the measurement model (Cenfetelli and Bassellier, 2009; Hair et al., 2021).

Common method variance was carefully addressed and checked to ensure it was not a concern in the study. To mitigate potential variance associated with the method, we adopted the procedures recommended by Podsakoff et al. (2003). First, respondent anonymity was ensured. Second, the questionnaire was designed with clear and straightforward items. Third, the order of questions and indicators was randomized for each participant. Finally, the respondents were humanitarian actors with experience in the use of emerging technologies for disaster management, thereby ensuring adequate expertise to provide informed responses to the questionnaire. The results from the full collinearity VIF test showed that all values were below the 3.3 threshold, confirming that common method bias does not pose a threat to the findings (Kock, 2015).

The hypotheses were examined using structural equation modeling (SEM) with a partial least squares (PLS) estimator. We selected PLS-SEM as the analytical technique because it is well-suited for exploratory research, allows us to estimate complex models with multiple latent constructs and mediating relationships and does not require multivariate normality. Furthermore, given the sample characteristics, we ensured robust results by applying bootstrapping procedures, which guaranteed the reliability of the statistical inferences (Hair et al., 2019). As stated by Hair et al. (2009), SEM allows for the efficient estimation of multiple regression equations simultaneously. To assess the statistical significance of the relationships, a bootstrapping procedure with 5,000 subsamples was performed. In addition, collinearity among the predictor constructs was assessed using the VIF, with no issues identified as all VIF values were well below the threshold of five.

The results are presented in Table 5.

The results showed that the use of digital technologies has a significant positive impact on humanitarian supply chain memory (path coefficient = 0.462, p < 0.001), confirming H2, which, in turn, strongly influences readiness to respond to natural disasters (path coefficient = 0.817, p < 0.001), confirming H3. However, the direct effect of the use of digital technologies on readiness was not statistically significant (p > 0.05), rejecting H1. These findings indicate that the impact of digital technologies usage on readiness is fully mediated by its effect on the humanitarian supply chain memory. The indirect effect of digital technologies on readiness via memory was 0.377 (p < 0.001).

While previous research, such as Alvarenga et al. (2023b), has highlighted the mediating role of memory in the relationship between digital technologies use and the ability to maintain performance or recover quickly from disruptions in manufacturing organizations, our study expands these findings by examining digital technologies use and memory in the humanitarian context, with a particular focus on readiness in humanitarian supply chains.

In addition, the control variable significantly affected the memory of humanitarian supply chains (path coefficient = 0.232, p < 0.001), but its impact on readiness was not confirmed. These results will be further explored in the discussion section to clarify their theoretical and practical implications. Overall, the model explained 37.21% of the variance in humanitarian supply chains’ memory and 72.07% in humanitarian supply chains’ readiness to deal with natural disasters.

We conducted robustness checks to address normality and endogeneity (Vaithilingam et al., 2024). We performed a Kolmogorov–Smirnov test with Lilliefors correction on the standardized composite scores for digital technologies, memory and readiness to evaluate normality. The results suggested that the data did not follow a normal distribution. Consequently, to investigate potential endogeneity, we used the Gaussian copula approach (Becker et al., 2022; Vaithilingam et al., 2024) in SmartPLS 4 and found no evidence of endogeneity issues.

Table 6 presents the results of the endogeneity test. Model 1 examines the relationship between digital technologies, memory and readiness while accounting only for the Gaussian copula correction applied to the memory construct. Model 2, in turn, includes only the Gaussian copula correction for the digital technologies construct. Model 3 incorporates the Gaussian copula corrections for both constructs simultaneously. Model 4 incorporates the correction for the digital technologies, but with memory as the dependent construct. Finally, Model 5 approaches the full model with all copulas. This stepwise approach allows for the assessment of potential endogeneity issues associated with each construct individually, as well as in combination (Hult et al., 2018; Sarstedt et al., 2020).

To situate our results within the broader literature, the following table summarizes prior quantitative studies that have measured at least three digital technologies and their key findings.

The findings of this study provide novel insights into the role of digital technologies in humanitarian supply chains by advancing the discussion beyond the dominant focus of previous research. For instance, earlier studies have primarily examined direct effects of digital technologies for humanitarian response (e.g. Bag et al., 2023; Akhtar et al., 2025) or moderation effects such as environmental dynamism (Tiwari et al., 2024) and crisis leadership (Dubey, 2023), without testing mediating mechanisms. Moreover, most of this literature has emphasized postdisaster outcomes – such as resilience, antifragility, efficiency of aid deliveries or success – rather than the predisaster capabilities that enable supply chains to be ready for the next natural disaster (Akhtar et al., 2025; Bag et al., 2023; Singh, 2025).

While previous high-impact studies listed in Table 7 emphasize direct technological impacts on postdisaster outcomes, our findings challenge this direct-link assumption by proving a full mediation effect, suggesting that digital technologies are only effective if they successfully build a robust supply chain memory (Akhtar et al., 2025; Bag et al., 2023; Singh, 2025). By integrating multiple digital technologies into a unified construct and controlling for disaster frequency, the study disentangles the effects of prior experience from the role of technology-enabled memory building and demonstrates that combining supply chain memory with digital technologies can shape humanitarian supply chain management.

Another important distinction is that prior studies have typically examined established digital technologies such as AI, blockchain and the IoT (Akhtar et al., 2025; Bag et al., 2023; Dubey, 2023; Singh, 2025; Tiwari et al., 2024). On the other hand, our study incorporates emerging technologies that have seen limited attention in previously quantitative humanitarian research – specifically 3D printing and VR – into a unified construct. This broader technological scope advances the literature by showing that readiness is strengthened not only through widely studied technologies but also through innovative solutions that expand cognitive capabilities, thereby offering a more comprehensive understanding of how humanitarian supply chains can be prepared for natural disasters. Accordingly, the study offers theoretical, practical and policymaking implications for advancing humanitarian supply chain theory.

By adopting information processing theory as the foundation of our theoretical model, this study contributes to theory, practice and policymaking in humanitarian supply chain management by empirically examining how digital technologies impact disaster readiness. Digital technologies serve as mechanisms for information processing (Dalenogare et al., 2018; Tortorella et al., 2020), yet our findings demonstrate that their use alone does not yield readiness outcomes. For readiness to emerge, the knowledge generated must be stored and retrievable for future decision-making, which highlights the role of supply chain memory. The central function of these technologies is the generation, storage and retrieval of knowledge, thereby enhancing learning processes in humanitarian supply chains (Alvarenga et al., 2023a). This strengthens their knowledge, experience and familiarity with disaster response, ultimately increasing their perceived readiness to handle such events.

In advancing the application of information processing theory, our study shows that supply chain memory is a critical complement to information processing capabilities. That is, while the theory traditionally emphasizes the alignment between information processing needs and organizational processing capacity as a driver of results (Galbraith, 1973, 1974), our findings suggest that such alignment produces enduring benefits only when processed information is retained and retrievable.

In this vein, supply chain memory enriches this theoretical perspective by functioning as the repository of accumulated knowledge, ensuring that information processing does not remain transient but instead becomes a durable foundation for readiness (Antunes and Pinheiro, 2020). Thus, our study extends information processing theory to humanitarian supply chains, highlighting that readiness emerges not merely from the expansion of processing capacity through digital technologies but from their integration with memory, which serves as a cognitive mechanism that sustains organized, retrievable knowledge over time.

Our study also provides valuable insights into the role of experience – specifically, the frequency of natural disasters – in shaping memory and its effects on readiness. As noted by Rice and Jahn (2020), it is crucial to remember past disasters and lessons learned to improve readiness. The findings reveal that experience alone does not directly impact the readiness of humanitarian supply chains to respond to natural disasters. Instead, the knowledge generated from these events, embedded in memory, enhances readiness.

The aforementioned finding aligns with Singh and Singh (2019), who highlighted that past experiences are only transformed into resilience through information processing and knowledge generation within organizations that embrace data-driven decision-making cultures. Furthermore, consistent with Alvarenga et al. (2023b), our study highlights that memory, while rooted in accumulated knowledge from past events, can also be developed through technologies. These tools, like drones, AI, metaverse, VR, 3D printing and blockchain, can build, improve and make sense of supply chain memory without experiential learning (Ben-Daya et al., 2019; Queiroz et al., 2023; Zouari et al., 2020).

For practitioners, the research underscores the critical role of information processing in the learning process and the necessity of generating knowledge from past events to improve disaster readiness. These findings suggest that humanitarian supply chains should develop knowledge management systems that enhance memory and learning – leveraging digital technologies to strengthen readiness. Successful crisis management relies on rapid coordination, accurate information sharing and the use of appropriate knowledge to save lives (Dorasamy et al., 2013), making the integration of digital tools and organizational learning a strategic priority.

Despite their importance, all the digital technologies examined in this study show low adoption rates. The most widely used technologies, AI and machine learning, scored only 3.08 on a 1-to-7 scale. These findings reinforce the need to increase the use of such technologies, including end-user-centric AI applications to improve decision-making processes during crises. Moreover, a key challenge lies in translating their use into actionable knowledge that enhances disaster response. Achieving this requires a deeper understanding of the main barriers to digital technology adoption and the challenges associated with their implementation, including technological, legal, regulatory, social and cultural aspects (Kamat et al., 2022; Karuppiah et al., 2025; Moraes et al., 2025).

The results also reveal critical factors for governmental actions in managing natural disasters. As noted by Zhou et al. (2024), the emergency performance of public investments remains weak, largely due to resource misallocation and the population’s lack of preventive awareness. Considering that memory is a strategic resource that strengthens the link between resilience and robustness, allowing these capabilities to translate into improved operational performance (Alvarenga et al., 2023a), public policies aimed at building and preserving disaster memory, supported by the strategic use of digital technologies such as those discussed in this study, can profoundly transform how governments address natural disasters.

To achieve this goal, tools such as virtual and augmented reality are envisioned for use in learning programs with the population. This is particularly relevant for enhancing coordination among entities during crisis situations (Yang et al., 2024), thereby reducing material, human and nonhuman losses, including impacts on local biodiversity. Developing contingency plans to foster community resilience, along with training programs and the deliberate preservation of painful memories to keep them alive (Alvarenga et al., 2023a; Crawford et al., 2022) are effective strategies to enhance public awareness and strengthen the integration between governments and society in disaster response efforts. Therefore, governments should institutionalize “Digital Disaster Memory Protocols”, which use IoT and blockchain to ensure that lessons learned in one disaster are not lost due to turnover or political changes, but are systematically integrated into national readiness frameworks.

Our paper is not without limitations. Although it has provided important insights into the significance of using digital technologies in humanitarian operations, we did not examine which technology is most important in different phases of natural disaster management. Furthermore, we did not explore the need for interaction between the technologies to achieve effective results in generating, storing, retrieving and sharing knowledge prior to a natural disaster. Therefore, future studies could investigate how these technologies interact and which are necessary or sufficient to generate knowledge from past experiences, or even without going through a real-life experience.

In recent years, the use of digital technologies for disaster prevention, response and recovery – aimed at saving more lives and delivering supplies more efficiently – has gained prominence in the literature on humanitarian supply chains (Fischer-Preßler et al., 2024; Singh, 2025). This study examined how digital technologies, a promising strategy across various organizational contexts, can support readiness to deal with natural disasters. By proposing and testing a theoretical model developed based on the literature and practical cases, the study found that the use of digital technologies helps to develop and enhance the memory of humanitarian supply chains regarding how to deal with natural disasters, resulting in improved perceptions of readiness. Furthermore, the findings suggest that experience alone is insufficient to perceive readiness. Instead, the knowledge generated, maintained and retrieved from past disasters enables supply chains to be ready to face the uncertain future brought about by climate change.

In conclusion, we indicated that investments in digital technologies, while necessary, are insufficient on their own to achieve disaster readiness in humanitarian supply chains. Our study contributes to OIPT and humanitarian supply chain management by showing that simply using technologies to process information does not automatically generate readiness. The critical factor lies in the generation, storage and retrieval of knowledge through supply chain memory, which transforms processed information into actionable readiness. Digital technologies – ranging from AI and machine learning to drones, virtual and augmented reality, 3D printing and blockchain – serve as mechanisms to enhance this memory, ensuring that knowledge is organized, accessible and retrievable for future decision-making, ultimately strengthening learning, coordination, and the capacity to respond effectively to natural disasters.