# Unfolding Digital Resilience in Organizations: A Systematic Review and Research Agenda on Digital Resilience in Organization

Jean-Julien Dupin, Amandine Pascal, Cécile Godé

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Jean-Julien Dupin, Amandine Pascal, Cécile Godé. Unfolding Digital Resilience in Organizations: A Systematic Review and Research Agenda on Digital Resilience in Organization. Maria João Ferreira; Filomena Castro Lopes; Isabel Seruca. Leveraging Technology for Organizational Adaptability, IGI Global, pp.81-116, 2025, Advances in Business Information Systems and Analytics, 9798369360453. 10.4018/979-8-3693-6045-3.ch006 . halshs-04882735

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# Unfolding Digital Resilience in Organizations:
A Systematic Review and Research Agenda on Digital Resilience
in Organizations

Jean-Julien DUPIN

Aix-Marseille Université

Amandine Pascal

Aix-Marseille University

Cécile Godé

Aix-Marseille University

## ABSTRACT

In today's rapidly evolving digital world, the concept of digital resilience (DR) in organizations has emerged as a critical research area. However, the field has grown rapidly, leading to unclear boundaries. In this study, the authors employed a systematic method, analyzing 54 empirical articles to foster a comprehensive understanding of DR. The authors addressed disparities in the field and synthesized current knowledge on DR, highlighting its evolution, terminologies, methodologies, theoretical lenses, types of adversity, and digital aspects. The researchers provided reflections on the findings and directions for future research within a research agenda. This review contributes to the development of a more structured field and expands current understanding of DR.

Keywords: Digital resilience, Organizations, Organizational resilience, Digital resilience in organizations, Digital technologies, Systematic review, Descriptive review, Research Agenda.
# Unfolding Digital Resilience in Organizations:
A Systematic Review and Research Agenda on Digital Resilience in Organizations

## Introduction

Throughout history, the world has endured numerous crises that have had devastating effects on societies and organizations. Events such as the Plague of Athens in 430 BCE, the Black Death in 1347, and the Spanish Flu in 1918 profoundly disrupted social and economic structures (Snowden, 2019). Similarly, major geopolitical conflicts (e.g., Lindström et al., 2023) and natural disasters (e.g., Chewning et al., 2013), have significantly impacted organizations. As a result, organizations have consistently needed to adapt to disruptions, with their ability to do so being crucial for survival. While these historical crises were devastating, the complexity and frequency of disruptions in the modern era have significantly increased due to rapid technological advancements, environmental instability, and interconnected global systems (Heeks & Ospina, 2019). The convergence of such events not only impedes global development but also contributes to a perpetuating cycle of crises (Tim & Leidner, 2023). In this context, organizations must develop relevant strategies to survive and grow (Xie et al., 2022).

In response to these contemporary challenges, digital technologies have emerged as a critical enabler of resilience, particularly during the COVID-19 pandemic, which demonstrated their essential role in allowing organizations to adapt and survive in times of crisis (Rai, 2020; He et al., 2021). This real-world example underscores the broader importance of digital technologies enabled resilience, leading to a surge of interest in both industry and academia<fnref n="1" />. Therefore, it is imperative to explore further the contributions of digital technologies to resilience (Heeks & Ospina, 2019; Sein, 2021).

This chapter refers to the concept of digital resilience (DR), defined as “the capabilities developed through the use of digital technologies to absorb major shocks, adapt to disruptions, and transform to a new stable state” (Boh et al., 2023, p. 344). As the field of DR rapidly evolves, the increasing interest from both academia and industry has led to a proliferation of research and initiatives. As a result, DR knowledge tend to present unclear boundaries, and would benefit from a structured literature overview.

The motivations behind this review are numerous:

1. Researchers have studied DR from various perspectives and theoretical frameworks, leading to a blurred understanding of the phenomenon (Neumannova et al., 2023).

<footnote n="1">¹ For example, the Digital Operational Resilience Act (European Union Regulation 2022), Digital Resilience Programs in consulting firms (e.g., Business Finland, 2024); MIS Quarterly Special Issue on Digital Resilience, and the “IS for Resilience” track at the International Conference of Information Systems 2021.</footnote>
2. The term DR is used in different academic disciplines, such as educational science (Weller & Anderson, 2013) and finance (Regulation European Union, 2022), which has led to question the term's relevance in the IS discipline (Boh et al., 2021).

3. The types of disruptions on which IS scholars have built DR knowledge have evolved since 2020: From “major exogenous shocks” (Boh et al., 2020) to “major shocks” (Boh et al., 2023) and “exogenous shocks” (Tim & Leidner, 2023), indicating a lack of consensus on the nature of DR adversity.

4. Scholars have understood the “digital aspect” within DR in various ways, such as an enabler of resilience (Boh et al., 2023) and a source of disruptions (Fleron et al., 2021). Therefore, there is a need to clarify what constitutes the “digital aspect” of DR (Kohn, 2023, p. 6437).

This review brings value and originality to the literature in DR in two ways. First, the IS discipline has examined the DR of different entities, namely, individuals, groups, and organizations (Boh et al., 2023, p. 343). Previous literature reviews did not specifically focus on these distinctions (Boh et al., 2023; Kohn, 2023; Tim & Leidner, 2023), even though strategies can differ significantly according to the entity building and developing DR. In this review, we focused on DR in organizations to provide a specified perspective on DR knowledge.

Second, existing literature reviews on DR are conceptual (Boh et al., 2023; Heeks & Ospina, 2019; Tim & Leidner, 2023), offering rich leads for the development of the DR field. However, their authors did not aim for comprehensiveness or produce a state-of-the-art review. This review distinguishes itself from previous works because we adopted a systematic and descriptive approach to study the extent to which DR supports or reveals any interpretable patterns or trends (Paré et al., 2023). To date, no literature reviews have systematically and comprehensively covered how DR in organizations has been studied and understood empirically.

In performing this comprehensive review, we aim to address the disparities in how DR has been conceptualized and understood across studies. By providing a systematic and descriptive analysis of the empirical literature, we seek to bring clarity, structure, and new insights to this rapidly evolving field. Given the nascent stage of DR research, we believe this work marks a key milestone in the development of a promising area within the IS community.

This prompts the following research question: How has DR been understood so far?

This systematic review is organized as follows. First, we anchor our work into existing literature reviews and seminal studies on DR, in the *Related Literature* section. Then, we address the disparities within the literature by the coding and analysis presented in the *Results* section. Precisely, we review the evolutions and trends in the DR field, focusing on terminologies used, methods, theoretical lenses, types of adversity, and digital technologies featured in DR literature. Lastly, in the *Discussion* section, we reflect on the main findings and suggest a research agenda; we identified eight current issues in DR literature and offer recommendations for each of them.

We contend that this review will foster a more current and expansive understanding of DR and help the development of a more structured literature.
# Related Literature

## Historical Background of DR

Before 2020, the impact of IS on resilience had not been a predominant focus in the IS literature (Heeks & Ospina, 2019). Yet, early efforts in this area, such as Riolli and Savicki's (2003) pioneering work, explored the links between IS and organizational resilience (OR). This nascent exploration was further advanced by a special issue of the *Journal of Information Technology* in 2007, which broadened the scope of research on DR. For instance, Cho et al. (2007) and Ignitiadis and Nandhakumar (2007) tended to explore technological innovation and its adoption in organizations as key challenges calling for resilience. Erol et al. (2010) offered a framework on how information technology (IT) assists capabilities underlying enterprise resilience, Sarkar et al. (2016) proposed models of resilient IS, while Morisse and Prigge (2017) examined the contribution of IS to business resilience. These early works highlighted the complexity and richness of the links between digital technologies and resilience. However, resilience in the IS literature has been explored from diverse angles and standpoints, making it challenging to streamline and propose generalized implications (Weber et al., 2021).

Building on these early studies, the global impact of COVID-19 starkly underscored the critical role digital technologies play in modern organizational adaptation, in line with Heeks and Ospina (2019)'s call to study the impact of technologies on the resilience of organizations. This catalyzed a renewed focus on how these technologies enable organizations to adapt to a rapidly changing environment (Boh et al., 2020). Current research has demonstrated that organizations with high levels of digitization exhibit stronger resilience (e.g., Isensee et al., 2023; Park et al., 2023). Emerging technologies, such as artificial intelligence (AI), 3D printing, and blockchain, have been highlighted as key tools in addressing the challenges posed by the pandemic (He et al., 2021). In addition, widely adopted solutions like mobile applications and videoconferencing platforms allowed organizations to maintain and adapt operations effectively (He et al., 2021). Authors also acknowledge that, for these technologies to be fully effective, they must be supported by enablers, such as a strong organizational culture (Isensee et al., 2023) or innovative business models (Eriksson et al., 2022). However, while empirical studies have highlighted the growing relevance of DR, many questions remain unanswered, warranting further exploration.

## Challenges in DR literature

By showing the various original definitions of DR, Table 1 highlights the disparities mentioned in the introduction. During the early stages of its development, the concept of DR was defined in varied and often inconsistent ways (cf. Dupin et al., 2022), with different authors using the term to refer to distinct phenomena (e.g., Schemmer et al., 2021; Fleron et al., 2021; Kohn, 2020a). However, the latest original definitions of DR appear to be more aligned with Boh et al. (2023), suggesting a progressive acceptance and stabilization of a particular understanding on DR.

## Table 1

*Definitions of DR in the literature*
<table><thead><tr><td>References</td><td>Definitions</td></tr></thead><tbody><tr><td>Heeks &amp; Ospina, 2019, p. 79</td><td>“The latter we called ‘e-resilience’ for short, being synonymous with RISOS [resilience of the IS output system]: The impact of [information and communication technology] (ICT)-based information systems on a wider system; in this case, the community.”</td></tr><tr><td>Kohn, 2020a, p. 3</td><td>“Digital security resilience is an employee’s ability to continuously deliver the intended outcome despite adverse cyber events.”</td></tr><tr><td>Kohn, 2020b, p. 5</td><td>“The IS-supported adaption to and recovery from short-term and long-term disruptions.”</td></tr><tr><td>Fleron et al., 2021, p. 2407</td><td>“Digital organizational resilience is a response mechanism to the continuous shocks, disruptions, and maladaptive tendencies that proceed from new information technologies.”</td></tr><tr><td>Schemmer et al., 2021, p. 5</td><td>“Digital resilience is the property of an IS to increase the resilience of IS output systems while satisfying a sufficient resilience on sub-systems.”</td></tr><tr><td>Tim et al., 2021, p. 343</td><td>“Digital resilience—the capacity of individuals, organizations and society to make effective use of digital technology to prevent, anticipate, absorb, and adapt to major challenges, and to continuously evolve and transform in a productive and sustainable manner.”</td></tr><tr><td>Boh et al., 2023, p. 344</td><td>“Digital resilience refers to the capabilities developed through the use of digital technologies to absorb major shocks, adapt to disruptions, and transform to a new stable state.”</td></tr><tr><td>Tim &amp; Leidner, 2023, p. 1185</td><td>“The capacity of individuals, organizations, and communities to recover from exogenous shocks, through the design, deployment, and use of IS.”</td></tr><tr><td>Tremblay et al., 2023, p. 426</td><td>“We define digital resilience as the dynamic capability of an organization to deploy data, technology, and analytics to anticipate, rebound, and learn from a shock.”</td></tr><tr><td>Neumannova et al., 2023, p. 3</td><td>“The capability of [unit relying on digital technologies] to swiftly apply [microfoundations] to overcome [internal or external driven] disruptions.”</td></tr></tbody></table>

Despite progress, understandings of DR still present disparities in several aspects. Notably, semantic inconsistencies persist within DR literature. For instance, Heeks and Ospina (2019) proposed the term “e-resilience,” which, despite the seminal nature of their article, has not gained subsequent adoption. Instead, the term “digital resilience” has gained significant popularity, perhaps excessively so, across various academic disciplines, as we mentioned above, raising concerns about its potential status as a “buzzword.” Indeed, DR is recognized as a complex umbrella concept (Tim & Leidner, 2023). As a result, existing conceptualizations of resilience often assign different meanings to this term (Fleron et al., 2021; Tim & Leidner, 2023), leading to what can be considered a jingle-jangle fallacy (Kelley, 1927; Raetze et al., 2022).
In addition to the widespread use of the DR label, there is the issue that knowledge on the phenomenon of DR may be labelled differently. The literature on IT-enabled-OR presents relevant insights for understanding DR. Consequently, the IS community has questioned the necessity and relevance of the term “digital resilience”. As Boh et al. (2021) asked,

Do we need a new term “digital resilience”? Is there something new here or are we simply rehashing existing terms in a new context? For example, are we merely examining digital transformation or digital platforms during the pandemic and labeling it “digital resilience”? (p. 4).

Besides, there are notable differences in definitions regarding the notion of adversity (i.e., shocks). Shocks manifest in various forms and severities, each presenting unique challenges to the resilience of organizations (Tremblay et al., 2023). However, authors propose different terms to describe this adversity, such as “major shocks” (Boh et al., 2023) or “exogenous shocks” (Tim & Leidner, 2023). Major shocks refer to “existential threats that pose continuous and long-term risks to different entities, from individuals, groups, organizations, and institutions, and the complex systems in which such entities live and function” (Boh et al., 2023, p. 343). Exogenous shocks refer to “the intense impact introduced by low-probability but high-consequence event(s)” and are argued to be a distinctive attribute of DR (Tim & Leidner, 2023, p. 1192). In their definition, Tremblay et al. (2023) chose to stay generic and simply use the term “shocks”. These differences highlight the need for an exploration of the empirical literature to gain an overview of the types of adversity studied to build knowledge on DR so far.

Lastly, there is a need to clarify the “digital aspect” within DR (Kohn, 2023, p. 6437). The definitions present different perspectives on this matter, ranging from the use of digital technologies to achieve resilient outcomes (Boh et al., 2023; Tim & Leidner, 2023), to a property of IS that enables OR (Schemmer et al., 2021), and to the dynamic capability for deploying data, technology, and analytics for resilience (Tremblay et al., 2023). In some cases, the digital aspect within DR is viewed as the source of the disruptions requiring resilience (Fleron et al., 2021). However, while digital adversity may occur, it is not a defining characteristic of DR, as Lee et al. (2022) emphasized; they specified “DR to technology-driven disruption” (p. 3), implying there are numerous types of disruptions for organizations to be digitally resilient to. However, the digital technologies used to transform operations into digital formats, especially when done urgently (e.g., during the pandemic), can lead to new vulnerabilities that organizations need to address (Lallie et al., 2021). Tim and Leidner (2023) suggested that disruptions resulting from technological development should be encompassed within the DR phenomenon, but imply different response mechanisms, as they directly threaten the digital technologies on which DR relies. To clarify the digital aspect within DR, this review aimed to identify which specific digital technologies had been studied in the literature on DR in organizations.

Hence, there was much to explore in DR literature. Specifically, in this study we comprehensively identified empirical articles on DR and posed the following research questions: How has DR in organizations been understood in the literature so far? And more precisely,

* How has research on DR evolved?

* How has the academic community referred to the phenomenon of DR?
* Which methods have been used to understand DR? Through which theoretical lenses?

* Regarding which contexts of adversity has DR been studied?

* Which digital technologies appear in DR literature?
# Methodology

## Identification of the Corpus

The field of DR is in recent development, and a search for “digital resilience” in an exploratory literature review yields only a few articles. We contend that the OR field offers valuable and relevant contributions to the emerging concept of DR. Hence, our goal was to collate peer-reviewed empirical papers that focus on IT-enabled-OR.

To manage the identification process of this review effectively, we used multiple databases (i.e., Business Source Complete and Web of Science) to ensure comprehensive coverage of the literature, as Bramer et al. (2017) suggested. We tailored our search strings to each database, taking into consideration their advanced search capabilities and the volume of references they contained (Table 2). To identify all relevant articles, we coupled keywords for “organizational resilience” and “digital technologies.” In this regard, we used the SMACIT acronym, representing a typology for digital technologies (Sebastian et al., 2020). We also introduced additional terms related to digital, such as AI and distributed ledger technology (DLT). Upon conducting forward and backward searches, we recognized that the use of the keyword “organizational resilience” was overly restrictive. Consequently, we expanded our search to include “enterprise resilience” and “firm resilience,” resulting in the identification of a few more articles (n=76). Additionally, we conducted a targeted search within the Basket of Eleven journals (i.e., highest ranked journals in IS discipline), focusing on the keyword “resilien*” (593 results) to make sure we did not miss any foundational articles. To streamline the management of our references, we utilized the bibliography software Zotero. We completed the identification process in June 2024.

<caption>**Table 2**</caption>

*Search Strings*

<table><thead><tr><th>Scientific databases</th><th>Search strings</th></tr></thead><tbody><tr><td>Basket of eleven</td><td>• (ALL FIELD) “resilien*”</td></tr><tr><td>Web of Science and Business Source Complete</td><td>• “digital resilien*”<br>• “organization* resilien*” AND “digital technolog*” OR “emerg* technolog*” OR “social media” OR “IT” OR “informat* technolog*” OR “AI” OR “artificial intelligence” OR “IOT” OR “internet of things*” OR “big data” OR “cloud” OR “DLT” OR “distribut* ledger technolog*”<br>• “firm resilien*” AND TS= “digital technolog*” etc.<br>• “enterprise resilien*” AND “digital technolog*” etc.</td></tr></tbody></table>

*Note. Internet of things (IoT).*

Following established methods and protocols (Paul & Criado, 2020), we ensured clarity and replicability of our methodology, proceeding with a step-by-step approach (Figure 1). The initial phase involved a format screening to exclude non-peer-reviewed articles, using advanced
search feature of the databases (n=1749). We then conducted a content screening based on the titles and abstracts of the remaining articles (n=195). After removing duplicates, we eliminated papers from conferences and from publishers considered as “predatory<fnref n="2" />”. Lastly, we conducted a detailed examination of the full texts, engaging in discussions between two researchers to decide on the inclusion or exclusion of each article (Table 3). Along these steps, we put aside the literature reviews on DR to be used only in the *Related Literature* and *Discussion* sections. The systematic identification process led to the selection of 54 relevant, peer-reviewed, and empirical articles (Figure 1).

<caption>**Figure 1**</caption>

*Systematic Review Flow Diagram*

<figure><img src="image_2.png" /><figcaption>**Table 3**</figcaption></figure>

*Inclusion Criteria*

<table><thead><tr><th>Identification steps</th><th>Inclusion criteria</th></tr></thead><tbody><tr><td>Format screening</td><td><ul><li>Peer-reviewed articles.</li><li>Empirical articles.</li></ul></td></tr></tbody></table>

<footnote n="2">² Predatory journals are publications that prioritize profit over academic integrity. They exploit the open-access publishing model, charging exorbitant fees without providing the necessary quality control or peer review. These journals often make false promises, claiming to be reputable and indexed in legitimate databases, but they lack the rigorous editorial standards and ethical guidelines expected of credible scientific publications (see https://predatoryjournals.org/)</footnote>
<table><thead><tr><td>Identification steps</td><td>Inclusion criteria</td></tr></thead><tbody><tr><td>Content screening on title/abstract</td><td><ul><li>The term “resilience” or constructs very close to resilience (e.g., “coping with crisis” and “adapting to change”) must appear in the title or in the abstract.</li><li>Must adopt an organizational<fnref n="3" /> point of view.</li><li>The link between resilience and digital IS or digital technologies must be well established.</li></ul></td></tr><tr><td>Content screening on full text</td><td><ul><li>In-depth reading led to the exclusion the papers considered as “not falling into the scope of the study.” The elimination of these papers had to be discussed and validated by a duo of researchers.</li></ul></td></tr></tbody></table>

## Coding and Analysis

Following existing guidelines on descriptive systematic review (Paré et al., 2023), we performed the coding process using the software N’vivo 12 (Table 4).

<caption>**Table 4**</caption>

*Details of Nodes*

<table><thead><tr><td>Nodes</td><td>Description</td></tr></thead><tbody><tr><td>Data collection and methods</td><td>Longitudinal or cross-sectional study; primary or only secondary data.</td></tr><tr><td>Label used for DR in organizations</td><td>We distinguish articles using labels “digital resilience,” “organizational resilience,” and all “other labels.”</td></tr><tr><td>Theoretical lens</td><td>Theoretical lens, when explicitly mentioned by the authors.</td></tr><tr><td>Type of adversity</td><td>Organizations are only resilient regarding some type of adversity (or “shocks,” Boh et al., 2023). We coded all these adversities inductively and refined them into bigger categories.</td></tr><tr><td>Type of digital technologies</td><td>Considering existing typology of digital technologies (Sebastian et al., 2020).</td></tr></tbody></table>

We identified multiple disparities within DR literature, including labels used to refer to DR (Boh et al., 2021), theoretical lenses (Neumannova et al., 2023), types of adversity (Tim & Leidner, 2023), and the “digital aspect” (Kohn, 2023).

To address these disparities, in this review we examined the evolution of DR labels, data collection methods, theoretical lenses, types of adversity, and types of digital technologies. We

<footnote n="3">³ Thus, the corpus includes only empirical articles on DR within organizations. It is important to note that mentions to "DR literature" in the following section pertain solely to the selected corpus, and do not apply to DR in other contexts, such as individuals or communities.</footnote>
used acknowledged typology for digital technologies (Sebastian et al., 2020). For labels, data
collection and methods, theoretical lenses, and types of adversity, we coded each article word-for-
word and refined the codes into larger categories using N'Vivo 12.
## Results

This section presents the results of the analysis, the evolution of the volume of publications in DR literature, methodologies and data collection scholars used to conduct research on DR, theoretical lenses they mobilized to understand DR, the types of adversity they studied in DR literature, and, lastly, the types of digital technologies mentioned in DR literature. Thus, this section provides an overview of the representation of these elements in DR's developing field and yields new insights and implications.

### Evolution of Publications on DR

In this subsection, we review the evolution of the volume of publications on the phenomenon of DR. We distinguished between the various labels scholars used to refer to the phenomenon of DR in organizations to control the acceptance of the “digital resilience” label.

<caption>Figure 2</caption>

*Evolution in the Volume of Publications on DR by Year and by Label Used (June 2024)*

<figure><img src="image_3.png" /></figure>

Figure 2 depicts the evolution in the volume of communications regarding the phenomenon of DR categorized by publication year and labels. As we previously explained, our systematic identification process aimed to include the full breadth of knowledge about the phenomenon of DR, which had been represented under various labels. The coding of the pool of 54 papers led to the identification of three primary categories of labels referring to the phenomenon of DR in organizations: “Digital resilience,” “organizational resilience”, and “other labels”. The “other labels” category encompasses all marginal labels referring to DR in organizations that are neither “DR” nor “OR”, for example, “firm resilience” (Cui et al., 2023; Ge et al., 2022) and “corporate resilience” (Bughin, 2022).

Figure 2 yields several results. First, it reveals a significant increase in contributions since 2020, indicating a rapid growth in interest in DR (i.e., curve “all labels” combined in Figure 2),
which can logically be attributed to the COVID-19 pandemic. The pandemic not only provided a unique context to scrutinize DR, but also accentuated the urgency for comprehensive DR knowledge to navigate future large-scale disruptions. Consequently, numerous calls for research have spurred academic exploration, culminating in a variety of contributions at scholarly conferences and journal publications.

Second, knowledge about DR was rather marginal before 2021. Early empirical articles approached the topic from varied perspectives. For example, early works have studied “organizational resilience and the use of ICT to rebuild communication structures” (Chewning, 2013, p. 237), “the impact of enterprise systems on organizational resilience” (Ignatiadis & Nandhakumar, 2007, p. 36).

Third, the labels used to refer to DR in organizations still demonstrate some variability in the literature. It is noteworthy that the label “e-resilience” as proposed in Heeks and Ospina’s (2019) argument was not adopted subsequently within the corpus, despite the popularity of their article. Rather, the label “digital resilience” found a relative success (Figure 2). More than a third (35%) of the corpus is labeled as “digital resilience,” representing 19 out of the 54 articles and suggesting that a search limited to this term would overlook much of the existing DR in organizations’ knowledge. The introduction of this specific term offers a dual advantage: First, it proposes a distinct label to posit the concept of DR in the literature as distinct from OR, and, second, it encourages the generation of knowledge within this domain. Regarding this observation, we posited that the choices in using DR or OR labels would be linked to publishing journals strategies. The analysis demonstrated a clear separation between journals (see Appendix 2), with only three exceptions out of 37 different journals. For example, *Information System Frontiers* features one article referenced as DR (Paulus et al., 2022) and one article referenced as OR (Ye et al., 2022). Although we observed a clear separation between journals and labels used, we found no explainable pattern underlying this separation. For example, there seem to be no correlations between labels and area of expertise of each journal (Appendix 2).

Fourth, the adoption of the DR label has not been unanimous since 2021. For example, Nielsen et al. (2023) referenced publications using the label DR (Boh et al., 2020; Tremblay et al., 2023), but deliberately chose to not use the term DR in their article (e.g., “Organizational resilience and digital resources: Evidence from responding to exogenous shock by going virtual”, Nielsen et al., 2023, p. 1). This result shows that authors do not systematically choose to reference their work under the label DR, even though they are aware of this developing field. However, most of the articles in the corpus referenced under OR do not mention articles labelled as DR, which implies they could be unaware of the field of DR (e.g., Forliano et al., 2023; Wided, 2023). In both cases, the fact that authors intentionally or not do not include the full breadth of knowledge on DR due to label variations is a problematic issue for the development of the field. For example, being unaware of the existence of DR might cause scholars to only rely on literature on IT enabled OR, thereby missing 35% (i.e., DR label distribution among corpus) of potentially relevant knowledge.

This particular result raises questions about the better label to reference knowledge on DR in organizations. In sum, there does not yet seem to be a consensus on how the literature refers to this concept, which arguably brings fuzziness to this developing field.
## Research Approach, Methods, and Data Used in DR in organizations' Literature

We investigated the research approaches, methods, and data collection aspects used in empirical studies of DR (Table 5).

<caption>**Table 5**</caption>

*Distribution of Data Collection and Methods in DR in organizations' Literature (N=54)*

<table><thead><tr><th>Research approach</th><th>Articles</th><th>%</th></tr></thead><tbody><tr><td>Quantitative</td><td>32</td><td>59</td></tr><tr><td>Qualitative</td><td>20</td><td>37</td></tr><tr><td>Mixed</td><td>2</td><td>4</td></tr><tr><th colspan="3">Method</th></tr><tr><td>Single case study</td><td>9</td><td>17</td></tr><tr><td>Multiple case study</td><td>6</td><td>11</td></tr><tr><td>Discourse analysis</td><td>2</td><td>4</td></tr><tr><td>Grounded theory</td><td>1</td><td>2</td></tr><tr><td>Survey</td><td>25</td><td>46</td></tr><tr><td>Event study</td><td>6</td><td>11</td></tr><tr><td>Design and action research</td><td>2</td><td>4</td></tr><tr><td>Survey and interviews (mixed)</td><td>1</td><td>2</td></tr><tr><td>Interviews and social network analysis (mixed)</td><td>1</td><td>2</td></tr><tr><th colspan="3">Data types and collection</th></tr><tr><td rowspan="2">Primary or (only) secondary data</td><td>Pri</td><td>45</td><td>83</td></tr><tr><td>Sec</td><td>9</td><td>17</td></tr><tr><td rowspan="2">Longitudinal or cross-sectional</td><td>Lon</td><td>5</td><td>9</td></tr><tr><td>Cro</td><td>49</td><td>91</td></tr></tbody></table>

Table 5 offers an overview of the variety of methodological approaches in the literature. Surveys, accounting for 46%, emerge as the predominant method, reflecting a preference for structured data collection conducive to statistical analysis in DR (in most cases, structural equation modeling). Such methods propose a conceptual model and offer to measure the relationships between DR and related constructs. For example, on one hand, Chatterjee et al. (2023) emphasized on the positive impact of coordination and business-to-business cooperation for DR outcomes (here measured as financial and operational performances during COVID-19). On the other hand,
Park et al. (2023) measured DR as the satisfaction of students towards online course during the pandemic. These differences in DR measurements highlight a lack of consensus regarding DR operationalization and raise questions about the aggregability of these validated conceptual quantitative models.

Single case studies and multiple case studies, representing collectively 28% of the corpus, form a significant portion of the qualitative methods. Such methods have a potential for exploring and enhancing in-depth understanding of DR. Rather than measuring DR outcomes, they tend to highlight the processes of building or developing DR. For example, Lee et al. (2024) revealed four patterns of digital resource orchestrations that translate into a model of DR “in the making”, while Khurana et al. (2022) highlight the interrelations between entrepreneurial, organizational and ecosystem digital resilience. This emphasis highlights the value of detailed and context-specific inquiry in understanding DR.

Event study methods, which are quantitative analysis of a disruptive event (e.g., observation of financial impacts), account for 11% of the corpus (Wang et al., 2024). Interestingly, all event study articles were research conducted in China. These methods have the potential to present strong evidence because of their large and reliable sample and tend to demonstrate the existence of relations between constructs, such as digital resilience positive effect on internationalization (Li et al., 2023) and the negative impact of restrictive policies on firms’ revenue, mediated by work-from home (Ge et al., 2022).

Design and action research integrate conceptual insights into practice, aiding the development of practical DR frameworks. Longitudinal studies, which track changes over time, are recognized for their depth, but are less common (9%) due to their complexity and extended duration. Given DR’s relatively recent emergence, we posited that many longitudinal studies might currently be underway, poised to contribute to future literature.

In sum, DR in organizations’ literature demonstrates a variety of methodological approaches, with surveys being the most common, followed by qualitative case studies, and event studies. We could consider that the current distribution of methods would arguably be evolving in the coming years with a bigger representation of time-consuming data collection methods, such as longitudinal studies and design and action research.

## Theoretical Lenses Adopted by DR Literature

In the next subsection, we provide an overview of how DR has been theorized so far (Table 6).

<caption>**Table 6**</caption>

*Distribution of Theoretical Lenses in DR Literature (N=32/54)*

<table><thead><tr><th>Perspectives</th><th>n</th><th>Theories</th><th>n</th><th>Examples from corpus</th></tr></thead><tbody><tr><td rowspan="2">Resources</td><td></td><td>Dynamic capabilities</td><td>12</td><td>Forliano et al., 2023</td></tr><tr><td></td><td>Resource-based view</td><td>5</td><td>Aghazadeh et al., 2023</td></tr></tbody></table>
<table><thead><tr><th>Perspectives</th><th>n</th><th>Theories</th><th>n</th><th>Examples from corpus</th></tr></thead><tbody><tr><td rowspan="4">Knowledge</td><td rowspan="4">18</td><td>Resource orchestration view</td><td>1</td><td>Lee et al., 2024</td></tr><tr><td>Knowledge-based</td><td>2</td><td>Li et al., 2024</td></tr><tr><td>Absorptive capacity</td><td>1</td><td>Yuan et al., 2021</td></tr><tr><td>Organizational learning</td><td>3</td><td>Yang et al., 2023</td></tr><tr><td rowspan="2">Information</td><td rowspan="2">7</td><td>Intellectual capital</td><td>1</td><td>Wang et al., 2024</td></tr><tr><td>Organizational information processing</td><td>3</td><td>Cui et al., 2023</td></tr><tr><td rowspan="4">Human-technology interactions</td><td rowspan="2">4</td><td>Sensemaking</td><td>1</td><td>Paulus et al., 2022</td></tr><tr><td>Situational awareness</td><td>1</td><td>Paulus et al., 2022</td></tr><tr><td rowspan="2">2</td><td>Modernity</td><td>1</td><td>Igniatiadis &amp; Nandhakumar, 2007</td></tr><tr><td>Technology in practice</td><td>1</td><td>Chewning et al., 2013</td></tr><tr><td rowspan="2">Relationships</td><td rowspan="2">3</td><td>Dialectics</td><td>1</td><td>Cho et al., 2007</td></tr><tr><td>Social capital</td><td>1</td><td>Kumar et al., 2023</td></tr><tr><td rowspan="5">Used as variables for the quantitative model</td><td rowspan="5">1</td><td>Social network</td><td>1</td><td>Chatterjee et al., 2023</td></tr><tr><td>Information asymmetry; transaction cost; resource allocation; division of labor; organizational learning; industrial economics; enterprise competition.</td><td>1</td><td>Yang et al., 2023</td></tr><tr><td>Contingency</td><td>2</td><td>Benitez et al., 2023</td></tr><tr><td>Disruptive innovation</td><td>1</td><td>Al-Omoush et al., 2023</td></tr><tr><td>Social entrepreneurship</td><td>1</td><td>Al-Omoush et al., 2023</td></tr></tbody></table>

Table 6 details our review of the theoretical lenses employed in building DR knowledge. Of the 54 articles we surveyed, 32 explicitly used one theory or more. Authors explicitly presented 19 distinct theories. Notably, some studies integrated multiple theoretical approaches: For instance, Benitez et al. (2023) melded a resource-based view with a contingency theory perspective, while Kumar et al. (2023) drew on organizational learning, social capital, and knowledge-based theory.

From the 19 distinct theories identified, we categorized six overarching theoretical perspectives to understand DR. Theories in management provide different lenses to understand and improve management practices and ultimately create value (i.e., in this review, achieve
resilient outcomes). In the corpus, the theoretical perspectives condition the manner digital technologies are viewed for DR building. For example, some scholars viewed digital technologies as a resource to own to build DR (e.g., Aghazadeh et al., 2023) while others viewed them as a critical support for knowledge building and sharing that leads to resilient outcomes (e.g., Li et al., 2024). We develop on these perspectives below.

The most represented category is the resources perspective (appearing in 18 out of 54 empirical articles), which features resource-based view and its extended theories, such as resources orchestration and dynamic capabilities. The assumptions underlying this approach is that owning, managing, building, and reconfiguring effectively organizational resources and competencies contribute to DR. For example, Forliano et al. (2023) posited that technology opportunism is the firms' ability to understand, acquire, and assimilate internal and external knowledge about new technological development, and they considered this ability as a dynamic capability of DR.

The perspective based on knowledge management (7 out of 54) encompasses theories such as knowledge-based theory, absorptive capacity, organizational learning, and intellectual capital. This perspective assumes that the ability of an organization to acquire, assimilate, transform, and exploit knowledge is critical for achieving resilience. For example, Yang et al. (2023) emphasized the role of organizational learning in building DR, while Li et al. (2024) highlighted the importance of knowledge integration.

The perspective based on information (4 out of 54) is represented by theories such as organizational information processing theory, sensemaking, and situational awareness. These theories emphasize the critical role of information in resilient decision-making in uncertain environments. For example, Paulus et al. (2022) examined how data and cognitive bias negatively affect DR.

The approach on interactions between human and technology (2 out of 54) includes modernity theory and technology in practice. This perspective highlights the intertwined relationship between social and material aspects within organizations. For example, Chewning et al. (2013) showed how organizations enacted a variety of resilient behaviors through adaptive ICT use, including information sharing, (re)connection, and resource acquisition.

The perspective on relationships (3 out of 54) involves theories such as dialectics and social capital theory. This approach underscores the significance of relational dynamics and networks in fostering DR (Chatterjee et al., 2023), but also how these relationships condition DR (Cho et al., 2007). Lastly, some authors mentioned using multiple theories (Al-Omoush et al., 2023; Yang et al., 2023). These theories were used to add complementary implications to a model (e.g., contingency theory, Al-Omoush et al., 2023) or to integrate varied constructs into a proposed theoretical model (e.g., Yang et al., 2023).

In sum:

1. The resource approach, and particularly the dynamic capability theory, is prevalent in DR literature.

2. There is a relative diversity in theoretical lenses applied to DR, demonstrating the richness of perspectives possibilities to understand DR.
3. The relevance of such variety of theoretical lenses illustrates the complexity within the concept of DR.

4. However, not all articles adopt an explicit theoretical standpoint for conducting research.

## Types of Adversity in DR Literature

In this subsection, we present and analyze various types of adversity documented in the empirical literature on DR (Table 7).

<caption>**Table 7**</caption>

Types of Adversity in DR Literature (N=54)

<table><thead><tr><th>Types of adversity</th><th>n</th><th>Nature</th><th>n</th></tr></thead><tbody><tr><td>Implementing innovation and transforming digitally:<br>“Organizations demonstrated apparent resilience in adopting the telehealth innovation” (Cho et al., 2007, p. 24).</td><td>2</td><td>Endogenous shocks</td><td>5</td></tr><tr><td>Internationalization:<br>“This study focuses on how [small and medium-sized enterprises] SMEs with international growth strategies innovate their business models to increase their organizational resilience” (Eriksson et al., 2022, p. 205).</td><td>2</td><td></td><td></td></tr><tr><td>Operations-related disruptions:<br>“When someone was forced to leave a critical process because of, for example, sickness, or wanted to take care of several simultaneously running processes, then they would be more confident knowing that their autonomous digital representatives were as capable and predictable as their exact personal “copy”” (Golovianko et al., 2022, p. 1).</td><td>1</td><td></td><td></td></tr><tr><td>Dynamic business environment:<br>“This study examines the triggering effects of external turbulent environments on firm DT [digital transformation] from the dimensions of technological and market environmental turbulence” (Li et al., 2024, p. 1).</td><td>4</td><td>Exogenous shocks</td><td>42</td></tr><tr><td>Epidemic:<br>“Using data collected at the peak of the COVID-19 pandemic from owners and C-level executives from critical infrastructure sectors in the United Kingdom, this research analyzed core constructs that contribute to the</td><td>32</td><td></td><td></td></tr></tbody></table>
<table><thead><tr><th>Types of adversity</th><th>n</th><th>Nature</th><th>n</th></tr></thead><tbody><tr><td>required transformative, adaptive, and absorptive capacities for health systems digital resilience.” (Garcia-Perez et al., 2023, p. 1).</td><td></td><td></td><td></td></tr><tr><td>Post-COVID economy:</td><td>3</td><td></td><td></td></tr><tr><td>“My firm will survive the current economic crisis. My firm possesses the ability to withstand the challenges of the current economic crisis. My firm is in a good position to address the slowdown in business activity currently being experienced as a result of the economic crisis. Sales volume has decreased in the last three months as a result of the economic crisis, but sales will rebound back to the pre-crisis level” (Kyrdoda et al., 2023, p. 5).</td><td></td><td></td><td></td></tr><tr><td>Natural disaster:</td><td>2</td><td></td><td></td></tr><tr><td>“This study employs the perspective of organizational resilience to examine how information and communication technologies (ICTs) were used by organizations to aid in their recovery after Hurricane Katrina” (Chewning et al., 2013, p. 237).</td><td></td><td></td><td></td></tr><tr><td>Adverse cyber-event:</td><td>1</td><td></td><td></td></tr><tr><td>“In this study, we have developed a digital resilience framework to mitigate these cybersecurity issues” (Mahmood et al., 2024, p. 1).</td><td></td><td></td><td></td></tr><tr><td>Not applicable:</td><td>7</td><td>Not applicable</td><td>7</td></tr><tr><td>“Encouraged and supported to adapt behaviors and, where possible, to reduce future harm” (Aghazadeh et al., 2023, p. 11).</td><td></td><td></td><td></td></tr></tbody></table>

Resilience, by definition, implies the presence of adversity (cf. Tim & Leidner, 2023). The analysis reveals two categorizations (i.e., thematic and the “nature of the shocks:” Endogenous or exogenous, which appeared as a valuable complementary insight, cf. Tim & Leidner, 2023, p. 1192). Results highlight a significant diversity in terms of types of adversity within the DR literature.

The first category involves the challenges related to implementing innovation and transforming digitally (Cho et al., 2007). This perspective on shocks is consistent with early works on DR (Fleron et al., 2021) that posited that DR is being resilient to maladaptive tendencies that proceed from digital development. However, a critical aspect that distinguish digitally transforming from other technological shocks (e.g., dynamic technological environment and cyber-attacks) is its endogenous nature, which makes it predictable. Other endogenous shocks feature internationalization (e.g., international growth strategies calling for business model
innovation, Eriksson et al., 2023) and operations-related disruptions (developing digital twin to face resource shortage such as work overload, Golovianko et al., 2022).

Post-COVID economy and dynamic business environment feature exogenous shocks referring to changed or rapidly changing organizational environments. However, unlike “major shocks” (Boh et al., 2023), which do not have simple and predictable effects, these types of shocks can have global impacts, but belong to a category that organizations are familiar with and for which they prepare.

Epidemics, mostly the COVID-19 pandemic, but also Ebola epidemic (Paulus et al., 2022) and Opiöid crisis (Tremblay et al., 2023), account for 32 out of 54 articles within the corpus and has been a predominant focus in DR literature. The high number of articles studying COVID-19-related DR raise an additional concern: When facing the same shock, organizations operate in various contexts and experience DR in different ways. For example, SME in India (Khurana et al., 2022) have been differently impacted by COVID-19 than universities in the USA (Park et al., 2023). These variations yield different insights in the literature, calling for caution in generalization. For example, Khurana et al. (2022) emphasized on the key role of the entrepreneur towards building DR, while Park et al. (2023) demonstrated the value of centralized IT governance and investments. Hence, this observation suggests that DR solutions to cope to COVID-19 may vary across organizational contexts.

Categories such as natural disasters (e.g., tropical cyclones in Fiji, Moller et al., 2018) (2 out of 54) or adverse cyber-events (1 out of 54) represent other exogenous critical adversities for organizations; yet, they account for only three out of 54 articles in the DR literature. Lastly, generic disruptive events, coded as “not applicable” (7 out of 54), encompass a broad range of potential disruptions that require organizations to adapt and maintain resilience. These studies do not refer to specific disruptions, but rather mention “future harm” (Aghazadeh et al., 2023, p. 11), “crises such as natural hazards, financial crises, pandemics or regulatory issues” (He et al., 2021, p. 11) or “unexpected events” (Trieu et al., 2023, p. 549). This category, featured in survey studies (Table 7), suggests that their results may vary upon the respondent’s representations of future harm or crises.

The results highlight several insights:

1. This variety suggests that the construction of DR knowledge extends beyond the scope of major exogenous shocks, which was initially posited in the initial definition of DR (Boh et al., 2020). This observation could prompt existing conceptualizations to reevaluate the range of adversities relevant to DR research, encompassing “major exogenous shocks” (Boh et al., 2020), “major shocks” (Boh et al., 2023), “exogenous shocks” (Tim & Leidner, 2023), to “shocks” in general (Tremblay et al., 2023). These findings indicate that DR is a pertinent topic across varied adverse contexts, since organizations will inevitably face challenging conditions.

2. The results highlight that the type of adversity faced is an important parameter to consider when studying and building knowledge on DR. However, we argue that these considerations should be completed with other contextual parameters to embrace a deeper understanding of DR subtilities (e.g., the specific impact of the shock on an organization, economic, cultural, and legal environment). This demonstrate the richness and complexity
of DR regarding contextual factors and suggest considering only the adverse context from an external standpoint is arguably a limited approach.

3. The categorization of adversities highlights that the empirical literature on DR tend to build knowledge regarding a unique, specific, and isolated shock (mostly, the COVID-19 pandemic). Only in one article (Tremblay et al., 2023) the authors observed two shocks in the life of a governmental agency (opioid crisis and COVID-19 pandemic) and few articles explored different types of major exogenous shock.

In sum, DR research is well-positioned to encompass a broader spectrum of adversities particularly as the immediate impact of the COVID-19 pandemic recedes in upcoming studies.

## Digital Technologies in DR Literature

Lastly, we are interested in enlightening the digital aspect in DR, which contributes additional value beyond the established knowledge on OR (Kohn, 2023, p. 6437). Through inductive coding, we examined all empirical papers (n=54) to identify digital aspects playing a role towards DR. This process led to the identification of 25 “digital aspects” (i.e., what is digital within DR studies), including digital technologies as such and broader concepts as well, such as “automation,” “technological intensity,” “remote work,” and “Industry 4.0.”

To provide more structure to this digital aspect, we used SMACIT, the typology for digital technologies presented in the methodology section (Sebastian et al., 2020). We performed a textual search for each keyword in N'Vivo and assessed the number of occurrences (i.e., how many papers in the corpus mentioned them at least once and three times) (Table 8).

<caption>**Table 8**</caption>

*Types of Digital Technologies (N=54)*

<table><thead><tr><td>Digital Technologies</td><td>Articles</td><td>(&gt; three times)</td><td>Examples</td></tr></thead><tbody><tr><td>Social media</td><td>33</td><td>12</td><td>Al-Omoush et al., 2023; Andrade et al., 2023; Da Silva Giordani et al., 2023; Garrido-Moreno et al., 2024; Lee et al., 2024; Martin-Rojas et al., 2023; Moller et al., 2018.</td></tr><tr><td>IoT</td><td>24</td><td>7</td><td>Cui et al., 2023; Marcucci et al., 2021.</td></tr><tr><td>Mobile</td><td>25</td><td>7</td><td>Mangalaraj et al., 2022.</td></tr><tr><td>Platform</td><td>42</td><td>21</td><td>Aghazadeh et al., 2023; Floetgen et al., 2021; Liu et al., 2023; Yuan et al., 2023.</td></tr><tr><td>Analytics</td><td>33</td><td>17</td><td>Cui et al., 2023; Tremblay et al., 2023; Wided, 2023.</td></tr><tr><td>Cloud</td><td>23</td><td>10</td><td>Cui et al., 2023.</td></tr><tr><td>Blockchain or DLT</td><td>22</td><td>6</td><td></td></tr></tbody></table>
<table><tr><td>AI</td><td>30</td><td>16</td><td>Bughin, 2022; Ewertowski et al., 2023; Golovianko et al., 2022; Yang & Deng, 2023; Yu et al., 2024.</td></tr></table>

Table 8 provides an overview of the representation of specific technologies in DR literature. It aims to provide an overview of the representations of each category of digital technology in DR literature. Platforms, particularly e-commerce platforms, are frequently mentioned, illustrating how they have enabled organizations to withstand and adapt during crises (Floetgen et al., 2021; Ye et al., 2022). Analytics, referring to big data analytics and business analytics, are also well-represented in the DR literature. These articles underscore the significance of data as a condition for DR, as it enables analytics, and consequently, cost-saving and value creation (Tremblay et al., 2023), identification of opportunities and threats, and strategic flexibility (Wided, 2022). Social media account for an important topic in DR literature. Da Silva-Giordani et al. (2023) showed start-ups' social media use had a positive impact on their resilience and innovation during COVID-19. On the same topic, Moller et al. (2018) explored how the role of social media in the adaptation of the tourism industry to tropical cyclones in Fidji. Lastly, Yu et al. (2024) showed the positive impact of AI-driven process automation and cognitive engagement on resilience, and Golovianko et al. (2022) explored the potential of AI-digital clones to address work overload for decision-maker in times of crisis. These results highlight the varied applications of digital technologies across different phases of DR. For instance, big data analytics and AI play critical roles in the anticipation phase, enabling organizations to foresee disruptions, while mobile and social media technologies seem more impactful during the adaptation phase, facilitating real-time communication and flexible responses and experimentations.

While established technologies like mobile platforms and social media are well-represented, there is a noticeable gap in the literature concerning emerging digital technologies, such as blockchain and IoT. This may suggest either an undervaluation of these technologies' potential contributions to DR or that they have not yet been fully integrated into DR initiatives. Nonetheless, the few studies that have focused on these emerging technologies report significant positive effects on DR. For example, Cui et al. (2023) highlighted the positive impact of cloud computing and IoT on the resilience of Chinese manufacturing firms, particularly when information complexity was high during the pandemic. Similarly, Ye et al. (2024), in a case study on a Chinese ride-sharing platform, demonstrated the potential of blockchain-based technologies for enhancing visibility, traceability, and security. These technologies enabled transparent operational processes and partial resource sharing for partners. Their visibility and traceability features facilitated quick tracking, continuously improving the customer experience and strengthening the platform's resilience (Ye et al., 2024). The contributions of such studies suggest that cutting-edge technologies in relation to DR may have been underestimated in the existing literature.

These results reveal another important aspect of DR. Indeed, the most adaptive applications within DR are not necessarily centered on emerging technologies like IoT or blockchain. Instead, they often involve innovative uses of established technologies, such as platforms and social media (e.g., remote work), which significantly contribute to DR. Likewise, "dual purposing existing IS", as noted by Lee et al. (2024), illustrates how repurposing widely available technologies can enhance DR. This indicates that organizations must balance the integration of innovative technologies, which open new technical opportunities, with the use of established technologies
that are more widely distributed and accessible. This “balanced” approach (Lee et al., 2024), leveraging both cutting-edge and existing technologies, can help organizations build their DR in a relevant manner.

These findings indicate that the digital aspect within DR (Kohn, 2023) cannot always be neatly categorized as a single type. Rather, digital technologies often involve a combination of information, computing, communication, and connectivity technologies (Bharadwaj et al., 2013). Furthermore, the digital aspect is multifaceted and extends beyond the mere implementation of technology. Successful DR strategies depend on the interactions between social and technical aspects within the organization. In the literature, it has been viewed through various lenses, such as how ICT is enacted (Chewning et al., 2013), IT capabilities (Wided, 2022), or the effective use of digital technologies (Tim et al., 2021). Drawing from the initial inductive coding of digital aspects in DR literature, we categorize these aspects into five dimensions: digital technologies, organizational conditions, resilient characteristics of digital technologies, resilient uses of digital technologies, and related challenges to DR development. Table 9 illustrates how these aspects interact to achieve DR outcomes, using the example of remote work (e.g., Yuan et al., 2023).

<caption>**Table 9**</caption>

*Unfolding the “Digital Aspect” (With the Example of Remote Work)*

<table><thead><tr><td>Digital technologies (cf. SMACIT)</td><td>Organizational conditions</td><td>Resilient characteristics</td><td>Resilient uses <sup>4</sup></td><td>Related challenges</td></tr></thead><tbody><tr><td>Mobile, platform, cloud, social, among others.</td><td>Positive mindset and adaptive culture</td><td>Ubiquity and accessibility</td><td>Remote work</td><td>New cyber vulnerability and loss of social activities.</td></tr></tbody></table>

One prominent example of this complexity is remote work (Table 9), which, although not a specific digital technology, illustrates how various technologies and organizational conditions interact to enhance DR. Many scholars have studied remote work in relation to DR, noting that while it is not a specific digital technology (cf. SMACIT), it can significantly enhance resilience when supported by certain digital technologies and organizational conditions (Table 9). Remote work involves a complex combination of social, mobile, analytics, cloud, and IoT technologies, which vary depending on the activity performed remotely. While digital technologies provide the technical infrastructure for DR, their success is closely tied to supportive organizational conditions. For instance, an adaptive or innovation-oriented culture is essential to fully leverage the potential of these technologies. In their study on resilient SMEs and start-ups, Isensee et al. (2023) underscore the role of entrepreneurs in cultivating such a culture, recommending that organizations actively co-create resilient cultures to strengthen their anticipation and digitalization capabilities. Likewise, organizations must anticipate and address emerging challenges and issues related to the implementation of DR solutions, such as increased cyber vulnerability (Mahmood et al., 2024) or the potential reduction in social activities within the organization (Yuan et al., 2023).

<footnote n="4">⁴ Resilient characteristics and resilient uses of digital technologies</footnote>
Hence, while digital technologies provide technical opportunities for DR, these opportunities
cannot be realized without considering the social dynamics that enable their effective use.

In summary, this subsection provided an overview of the specific digital technologies studied in DR literature (Table 8) and proposed a framework for illustrating the complexity of the "digital aspect" of DR (Table 9).
## Discussion

Digital resilience of organizations has the potential to serve as a new lens for IS scholars to examine an emergent phenomenon within varied crisis contexts. DR broadens our current explorations of the effective design, deployment, and use of IS beyond the confines of business value creation to the multifaceted issue of resilience building at an organizational level (Tim & Leidner, 2023). However, to become a new lens for the IS community, DR must first evolve into a more integrative concept and clarify its numerous ambiguous aspects. In this review, we addressed these ambiguous aspects by showing what the empirical literature had done so far. Reflecting on the main results, below we highlight the current issues the field of DR is facing and provide recommendations to conduct future research.

## Current Issues and Recommendations

### Ensuring a Sustained Development of DR Knowledge

The results highlighted the evolution of interest in DR, particularly its rapid development since 2020. However, it is challenging to predict the future trajectory of DR literature over such a short period. As the consequences of COVID-19 gradually diminish and new issues arise, the importance of continuing to generate lasting knowledge on DR cannot be underestimated or forgotten. Research on DR should not be a fleeting trend within the IS community that fades away in the coming years. Instead, this is only the beginning of a journey toward a better understanding of a phenomenon critical to resilience in a digitized and uncertain world. Sustained knowledge growth in DR is essential, and this can be achieved through initiatives such as special issues, conferences, and seminars.

### Towards a Unified Terminology for the Phenomenon of DR

Our analysis revealed a variety of labels used to describe the phenomenon of DR, accompanied by a clear distinction in the associated knowledge (cf. results - evolution of labels in DR literature). Reflecting on Boh et al.'s (2021) query "do we need a new term for digital resilience?", we believe in the relevance of this effort. Developing a distinct term could enhance the understanding of DR and emphasize its growing significance in addressing future challenges. However, the current fragmentation in DR literature has notable implications.

To address this, we propose several approaches. First, researchers might consider adopting more comprehensive literature search strategies that extend beyond the DR or the OR label. For example, scholars could utilize the same search strings we employed in our systematic identification process or adapt this process to better align with their specific focus on DR. Second, we recommend that IS journals and conferences could work towards a unified terminology for the phenomenon of DR. Addressing these issues may help alleviate perceptions of digital resilience as a "buzzword" within the IS community and promote broader acceptance among scholars and publishers. As the term gains clarity and legitimacy, its adoption is likely to be viewed as less risky by journals and conferences, ultimately facilitating unanimous acceptance.

### Broadening the Relevance of DR: Beyond COVID-19 Contexts
Our study highlighted the significant emphasis on COVID-19 in DR research, with 32 out of 54 articles focusing on the pandemic. While this focus is understandable given the pandemic's global impact, it raises concerns about the long-term development of knowledge in the field. Specific shocks require unique resilience mechanisms (Tremblay et al., 2023), and, for DR to become a robust and enduring concept, it should demonstrate relevance across diverse crisis contexts (Tim & Leidner, 2023). The current literature's concentration on COVID-19 may risk narrowing our understanding of DR to pandemics, limiting its applicability to other types of crises.

To address this issue, we propose two potential avenues for future DR research. First, while our study revealed some diversity in the types of shocks examined, there is an opportunity to explore a broader range of disruptions. Given the rapidly evolving nature of global challenges, expanding knowledge on DR across varied contexts may enhance its holistic application. Future research could investigate underexplored adverse situations, such as political turbulence, war zones, natural disasters or any other disruptions scholars deem relevant.

Second, we suggest that more longitudinal studies could significantly contribute to sustainable knowledge of DR, as they currently represent only 9% of the literature. These studies might ideally track the same organizations through multiple perturbations to reflect real-world experiences. For example, Tremblay et al. (2023) examined a government organization's data exploitation strategy during both the opioid crisis and the COVID-19 pandemic, noting significant progression in DR between these events. Although these studies involved different perturbations, the shocks shared similar adaptation logics. We believe that studies of this nature can uncover the underlying mechanisms that resilient organizations share, regardless of the types of shocks they encounter.

## A More Nuanced Understanding of Disruptive Contexts

Our review of DR literature examined the distribution of shocks studied, highlighting the importance of distinguishing between the shocks themselves and their impact on organizations. Since these shocks affect each organization differently, understanding this distinction is crucial for advancing DR research. It may be beneficial to analyze shocks not only from an external perspective, but also from within the organization by asking questions such as “How did this specific shock affect this organization?” and “Which specific operations were threatened?”

While different shocks may require different resilience mechanisms (Tremblay et al., 2023), it is equally important to recognize that the same shock can disrupt organizations in various ways. This recognition has implications for the generalization of DR knowledge, particularly in comparative studies. For example, one organization might display greater resilience than another despite lower performance results if it was more severely impacted by the shock (Tim & Leidner, 2023). In this context, an in-depth understanding of each case can help qualitative researchers appreciate the specific impact of a shock. Quantitative researchers might also consider including a mediating variable for the impact of the shock in their models to better capture this complexity. By doing so, future research could provide a more nuanced understanding of how shocks affect DR.

## Advancing Measurement in DR Research: Addressing Current Limitations
Our review highlighted the predominance of qualitative studies (59%) in DR literature, raising questions about the measurement and operationalization of DR. This concern, as Kohn (2023) previously noted, underscores the challenges in drawing definitive conclusions about DR, particularly ex-ante, because authors cannot know if all consequences of a specific shock have been fully addressed at a given moment. Current measures of DR often use proxies, such as student satisfaction (Park et al., 2023) and financial performance (Tremblay et al., 2023), or rely on self-evaluation by organization members (Garrido-Moreno, 2024; Yu et al., 2024).

Although these measures are conducted rigorously and provide valuable insights, they do not encompass the full range of resilience outcomes as conceptualized in DR. This limitation reflects broader issues in resilience research (e.g., Sevilla et al., 2023). Therefore, while acknowledging the contributions of existing DR measurements, we suggest exercising caution in generalizing findings across different contexts. Future research could benefit from exploring alternative methods and metrics that more comprehensively capture the complexity of DR. By addressing these measurement challenges, scholars can advance the field's understanding of OR.

## Navigating Technological Shocks: Toward a Balanced Approach to DR

Our study allowed to identify that technological developments can serve as a source of shocks in DR literature (Table 7). These technological shocks include cyberattacks (Mahmood et al., 2024), maladaptive tendencies arising from digital transformation (Fleron et al., 2021), and challenges related to the implementation of innovations (Cho et al., 2007). As Tim and Leidner (2023) suggested, these shocks form a distinct category because they directly impact the digital aspects of DR, which are fundamental to its functioning. There is an opportunity to further explore DR mechanisms specific to these technological disruptions.

This raises intriguing questions about the self-disruptive nature of DR. For example, DR solutions can be related to new challenges for the organization (Table 9). To what extent can the development of digital solutions for resilience be a source of new disruptions for the organization? How might these disruptions be mitigated? Given the nascent state of the concept, there are currently few answers, but this opens the way to explore and build knowledge on "sustainable DR" (Tim et al., 2021). This notion suggests a "balanced" approach (Lee et al., 2024): How can organizations develop their DR without compromising their future resilience? Schemmer et al. (2021) advocated for "satisfying a sufficient resilience of the subsystems" (p. 5) (i.e., cyber-resilience and personal resilience), implying that resilience to its own disruptions could be a prerequisite for DR. We propose that further exploration of this topic presents a significant opportunity for scholars to adopt a more integrative perspective on DR, incorporating knowledge on cyber-resilience for example. This exploration is essential for the development of DR knowledge, as these concerns cannot be underestimated.

## Harnessing Digital Technologies for DR: Understanding the Balance Between Innovation and Adaptation

Our analysis of DR literature revealed an unequal distribution of specific digital technologies, which carries several implications for future research. Firstly, the low representation of "cutting-edge" technologies, such as blockchain or the IoT, suggests promising avenues for
further investigation. This under-exploration presents an opportunity for researchers to examine
the role of these emerging technologies in enhancing resilience (e.g., Zirar et al., 2024).

Secondly, the observation that DR often involves the innovative use of well-established
technologies, rather than the development of cutting-edge ones, also offers possibilities for study.
Future research could explore the circumstances under which organizations repurpose existing
technologies vs. those where they develop new technologies or applications to enhance their DR.
A deeper understanding of contextual factors, such as the resources available to the organization,
its culture and motivations, and environmental influences, could provide valuable insights into
these decisions. By examining these aspects, scholars can contribute to a more comprehensive
understanding of how various digital technologies can be leveraged to strengthen resilience. This
argument also suggests that there is no such thing as "one best way" for DR (Burnes, 1996), rather,
each organization must develop its own tailored approach to achieving DR.

Leveraging Theoretical Frameworks to Conceptualize the Digital Aspects Within DR

Our review highlighted a concerning absence of theoretical frameworks in 22 out of 54 DR
studies. This lack is especially notable in quantitative research that tests relationships between
recognized constructs, such as transformation management intensity and resilience (He et al.,
2021), without a robust theoretical foundation to support their hypotheses. Reflecting on this
finding, we suggest that the absence of theoretical frameworks contributes to ambiguity around
the digital aspect of DR. We believe that the perspectives through which the digital aspect is
understood are strongly influenced by the theoretical lens employed. For instance, resource-based
approaches often identify the digital aspect as a property, such as resources that need to be properly
orchestrated (Lee et al., 2024), IT capabilities (Wided, 2023) or dynamic capabilities (Tremblay
et al., 2023). Alternatively, the digital aspect can be seen as a support for the management and
transfer of information (Paulus et al., 2022) or knowledge (Wang et al., 2024), as well as through
practices (Chewning et al., 2013) or relationships (Kumar et al., 2023).

This observation has three major implications for DR research. First, IS scholars might
benefit from using adapted theoretical frameworks to clarify their positioning on how they view
the digital aspect within DR. Boh et al.'s (2023) framework, which distinguishes between the
resilient characteristics of digital technologies and the organizational conditions for effectively
exploiting them, offers a valuable path for representing these perspectives. However, as we
suggested in the *Results* section, in-depth explorations of how these aspects translate into resilient
practices, how these practices and heuristics become ingrained in the organization, and what new
related issues arise from these changes could further enrich this framework.

Second, further conceptual research should more extensively explore the contributions
each theoretical lens brings to DR and how these perspectives can be integrated into a
comprehensive theory of DR. Such exploration could lead to a more nuanced understanding of the
digital aspect within DR and strengthen the theoretical foundations of the field.

Third, the variety of theoretical lenses employed, combined with the complexity of DR—
which spans interrelated levels of analysis (e.g., individual, technical, organizational,
environmental, Schemmer et al., 2021)—highlights the need for a multidisciplinary approach to
fully understand DR. For example, resilience has its origins in diverse fields such as ecology (e.g.,
Holling, 1973), engineering (e.g., Hollnagel et al., 2006), and psychology (e.g., Richardson, 2002). The absence of a solid theoretical framework has led researchers to draw from various disciplines to study DR, including psychology (e.g., Kohn, 2020), biology (e.g., Lee et al., 2022), education (Weller & Anderson, 2013), and management (e.g., Tremblay et al., 2023). These perspectives have collectively contributed to advancing knowledge and understanding of DR. Therefore, we encourage scholars from all disciplines to continue this theoretical development and emphasize the importance of multidisciplinary approaches in building robust theoretical foundations for DR.

## Research Agenda

Eventually, we synthesize each discussed issue and related recommendations into a research agenda (Table 10). We believe our recommendations will help a more structured development of DR knowledge and contribute to the acceptance of DR as a new essential lens in the IS community.

<caption>**Table 10**</caption>

*Research Agenda for DR*

<table><thead><tr><td>Current issues in DR literature</td><td>Recommendations</td></tr></thead><tbody><tr><td>Understanding DR is critical to ensure resilience to future crises; yet, the evolution of the field is still unpredictable.</td><td>DR knowledge creation efforts need to be encouraged and supported, to last, by knowledge growth through initiatives such as special issues, conferences, and seminars is essential.</td></tr><tr><td>DR knowledge is disseminated across different labels.</td><td>Scholars interested in DR should perform comprehensive literature identification to not miss any key article (e.g., borrowing or adapting our search strings).<br/>The IS community should gather to agree a unique label to refer to DR phenomena.</td></tr><tr><td>The overwhelming focus of DR current literature on COVID-19 risks limiting our understanding of DR to similar future pandemics.</td><td>There is an opportunity to study DR regarding a broader range of disruptions. Longitudinal studies, which explore DR in the face of multiple disruptions over time, are promising.</td></tr><tr><td>Most studies do not control the impact of the disruptions on the organization, which weakens quantitative models and comparative approaches.</td><td>In-depth understanding of the context offers materials for scholars to acknowledge and justify the relevance of DR cases.<br/>Adding a mediating variable for the impact of a shock under study in quantitative models is helpful.</td></tr><tr><td>There are limitations regarding DR measurement, despite the</td><td>DR knowledge generalization must be conducted with caution.</td></tr></tbody></table>
<table><thead><tr><td>Current issues in DR literature</td><td>Recommendations</td></tr></thead><tbody><tr><td>predominance of quantitative methods.</td><td>Unified measurement for DR could help the comparability of existing quantitative models.</td></tr><tr><td>Technological shocks are still marginal, despite the self-disruptive nature of DR development.</td><td>It is necessary to integrate knowledge and theories on cyber-resilience to DR existing perspectives, and to identify the enablers of balanced and cautious development of DR.</td></tr><tr><td>There is unequal distribution of specific digital technologies in DR literature.</td><td>Specialized scholars should conduct conceptual and empirical research to highlight the potential of underexplored technologies regarding DR.<br/>Contextual studies could allow to explore when and why organizations rely on specific technologies for building DR.</td></tr><tr><td>The absence of theoretical frameworks and how it links to the fuzziness around the digital aspect within DR need attention.</td><td>Theories offer relevant frame to unfold these aspects with rigor.<br/>We suggest that mobilizing and integrating these theoretical lenses can help completing current DR frameworks.<br/>Perspectives from various disciplines on DR, and their integration, can reveal new insights.</td></tr></tbody></table>
**Conclusion**

This systematic review synthesizes current knowledge on the DR, highlighting its evolution, terminologies, methodologies, theoretical lenses, types of adversity, and digital aspects. We reflect on these findings and provide recommendations for future research to help develop a more structured field and expand our understanding of DR.

We believe that exploring the proposed research avenues will contribute to creating a more holistic and integrative perspective on DR. These avenues could pave the way for constructing and developing a new central lens in management to address contemporary challenges, encouraging organizations to view their activities not only through the lens of performance but also through the lens of DR.

**Competing Interests**

The authors of this publication declare there are no competing interests.
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# Appendix

## Corpus (54 empirical articles)

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Tremblay, M. C., Kohli, R., & Rivero, C. (2023). Data Is the New Protein: How the Commonwealth of Virginia Built Digital Resilience Muscle and Rebounded from Opioid and Covid Shocks. *MIS Quarterly, 47*(1), 423-449.

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Wang, Y., Chen, S., & Hu, Y. (2024). Internationalization and Organizational Resilience to COVID-19 Crisis: The Moderating Effect of Digitalization. *SAGE OPEN, 14*(1), 21582440241229561.

Wided, R. (2023). IT Capabilities, Strategic Flexibility and Organizational Resilience in SMEs Post-COVID-19: A Mediating and Moderating Role of Big Data Analytics Capabilities. *Global Journal of Flexible Systems Management, 24*(1), 123-142.

Xie, X., Wu, Y., Palacios-Marqués, D., & Ribeiro-Navarrete, S. (2022). Business networks and organizational resilience capacity in the digital age during COVID-19: A perspective utilizing organizational information processing theory. *Technological Forecasting and Social Change, 177*, 121548.

Yang, G., & Deng, F. (2023). Can digitalization improve enterprise sustainability?-Evidence from the resilience perspective of Chinese firms. *HELIYON, 9*(3), e14607.

Ye, D., Liu, M. J., Luo, J., & Yannopoulou, N. (2024). How to achieve swift resilience: the role of digital innovation enabled mindfulness. *Information Systems Frontiers, 26*(2), 551-573.

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Yuan, R., Luo, J., Liu, M. J., & Yu, J. (2022). Understanding organizational resilience in a platform-based sharing business: The role of absorptive capacity. *Journal of Business Research, 141*, 85-99.
<caption>**Table 11**</caption>

*Distribution of labels among publishing journals*

<table><thead><tr><th>Journal</th><th>DR</th><th>OR</th><th>Other</th></tr></thead><tbody><tr><td>Annals Of Operations Research</td><td></td><td></td><td>1</td></tr><tr><td>Asia Pacific Management Review</td><td></td><td>1</td><td></td></tr><tr><td>Central European Journal Of Operations Research</td><td></td><td>1</td><td></td></tr><tr><td>Electronic Commerce Research</td><td>1</td><td></td><td></td></tr><tr><td>Engineering, Construction And Architectural Management</td><td></td><td></td><td>1</td></tr><tr><td>European Journal Of Information Systems</td><td></td><td>1</td><td>1</td></tr><tr><td>European Journal Of Innovation Management</td><td></td><td>1</td><td></td></tr><tr><td>Global Journal Of Flexible Systems Management</td><td></td><td>1</td><td></td></tr><tr><td>Heliyon</td><td></td><td></td><td>1</td></tr><tr><td>Information & Management</td><td>1</td><td></td><td></td></tr><tr><td>Information Systems Frontiers</td><td>1</td><td>1</td><td></td></tr><tr><td>Information Systems Journal</td><td>2</td><td></td><td></td></tr><tr><td>International Journal Of Information Management</td><td></td><td>2</td><td></td></tr><tr><td>International Journal Of Production Economics</td><td></td><td>1</td><td></td></tr><tr><td>Journal Of Business Research</td><td></td><td>1</td><td>1</td></tr><tr><td>Journal Of Computer Information Systems</td><td>1</td><td></td><td></td></tr><tr><td>Journal Of Contingencies And Crisis Management</td><td>1</td><td></td><td></td></tr><tr><td>Journal Of Enterprise Information Management</td><td>6</td><td></td><td></td></tr><tr><td>Journal Of Information Technology</td><td></td><td>2</td><td></td></tr><tr><td>Journal Of Operations Management</td><td></td><td></td><td>1</td></tr><tr><td>Journal Of Service Management</td><td></td><td>1</td><td></td></tr><tr><td>Journal Of Services Marketing</td><td></td><td></td><td>1</td></tr><tr><td>Management Communication Quarterly</td><td></td><td>1</td><td></td></tr><tr><td>Management Decision</td><td></td><td>2</td><td></td></tr><tr><td>Managerial & Decision Economics</td><td></td><td></td><td>1</td></tr><tr><td>Mis Quarterly</td><td>2</td><td></td><td></td></tr><tr><td>Production Planning & Control</td><td></td><td>1</td><td></td></tr><tr><td>Public Management Review</td><td></td><td>1</td><td></td></tr><tr><td>Ram Mackenzie Management Review</td><td></td><td>1</td><td></td></tr><tr><td>Regepe Entrepreneurship & Small Business Journal</td><td></td><td>1</td><td></td></tr><tr><td>Sage Open</td><td></td><td>1</td><td></td></tr><tr><td>Small Enterprise Research</td><td></td><td>1</td><td></td></tr><tr><td>Strategic Entrepreneurship Journal</td><td></td><td>1</td><td></td></tr><tr><td>Supply Chain Management: An International Journal</td><td></td><td></td><td>1</td></tr><tr><td>Technological Forecasting And Social Change</td><td></td><td>4</td><td></td></tr><tr><td>Technology In Society</td><td></td><td></td><td>1</td></tr><tr><td>Technovation</td><td>1</td><td></td><td></td></tr><tr><td>Tourism Management</td><td></td><td>1</td><td></td></tr></tbody></table>
**Key terms and Definitions**

*Digital Resilience* is the capability developed through digital technology use, enabling an entity to withstand significant disruptions, adjust to changes, and transition to a new stable state.

*Organizations* are defined as groups with a distinct structure, hierarchy, rules, communication systems, and coordination mechanisms; they operate continuously in a specific environment to achieve goals, generating outcomes for both members and society.

*Organizational Resilience* encompasses the organization's capability to withstand significant disruptions, adjust to changes, and transition to a new stable state.

*Digital resilience in organizations* refers to the organizations' capabilities developed through digital technologies use to absorb major shocks, adapt to disruptions, and transform to a new stable state.

*Digital Technologies* combine various tools, including information, computing, communication, and connectivity technologies.

*A Systematic Review* represents a structured approach to reviewing literature that enhances trustworthiness, reproducibility, and potential impact.

*A Descriptive Review* aims to uncover patterns or trends within a collection of studies in a specific field, aligning with existing theories, propositions, or methodological guidelines.

*A Research Agenda* highlights existing research gaps and provides clear, actionable recommendations for future studies to address these gaps. It is tightly connected to the findings.