Firms’ DARQ readiness and marketing implications: a systematic literature review

DARQ technologies have a profound and transformative influence on a broad spectrum of businesses (Davenport et al., 2020). DARQ is an acronym that comprises a suite of cutting-edge and data-driven technologies: artificial intelligence (AI), distributed ledger technology (DLT), extended reality (XR) and quantum computing (QC). While many organisations currently adopt two or three DARQ technologies in isolation (such as implementing AI for customer service or blockchain for supply chain traceability), there is a growing strategic shift towards integrated or bundled adoption. Deloitte (2023) highlights that over 40% of digitally mature companies plan to integrate AI with blockchain or XR technologies, indicating a shift towards a multi-platform approach. Furthermore, Market Research Future (2023) projects that the global DARQ technology market will grow from US$3.5bn in 2023 to more than US$11.2bn by 2032, at a compound annual growth rate of 13.2%. This uptake is driven by the complementary nature of DARQ technologies. For example, blockchain can enhance AI through greater security and transparency, AI can enrich XR experiences with data-driven insights and QC can boost data processing across both. Together, these technologies enable greater innovation and operational efficiency (Anirudhan et al., 2022). Rather than being linked by their relative novelty, these technologies converge around advanced data processing, automation and trust-enhancing functionality (Accenture, 2019a), potentially unlocking new layers of business value when combined (Akter et al., 2022).

Indeed, DARQ technologies provide several benefits, including improved operational efficiency (Mithas et al., 2022), customer experience (Cui et al., 2022), transparency (Arrieta et al., 2020) and optimised portfolios (Ban et al., 2018). McKinsey&Company (2022, 2023) surveys show that over 50% of companies have adopted at least one DARQ technology in 2023. However, most companies struggle to generate a substantive return on investment from these technologies (Ransbotham et al., 2019). Companies often fail to capitalise on DARQ technologies owing to a lack of preparedness (Johnk et al., 2021), fuelling a growing body of research on firms’ readiness for these technologies (e.g. Hradecky et al., 2022; Issa et al., 2022).

Considerable research confirms the significant influence of technology readiness on diverse aspects of firm performance, including financial performance (Richey et al., 2007), organisational innovation (Zhen et al., 2021), strategic goal achievement (Vize et al., 2013), firm growth (Jafari-Sadeghi et al., 2021) and enhanced customer relationship management (Rahman et al., 2023). While research on DARQ technologies is proliferating, the concept of DARQ readiness lacks consistent definitions and frameworks across disciplines. Studies vary in terminology and focus, with some emphasising psychological and behavioural readiness (e.g. Weiner, 2009) while others stress infrastructural capabilities and strategic alignment (e.g. Lokuge et al., 2019). Also, as studies in this field mushroom, different terminologies have emerged to capture the concept of organisational readiness. For example, authors have used terms such as “preparedness”, “acceptance”, “commitment”, and “willingness” to refer to the same notion of organisational readiness (Gagnon et al., 2014).

Despite these growing research efforts (and perhaps partly because of them), the DARQ readiness literature remains fragmented and lacks cohesion. Conceptual ambiguity persists, with terms for readiness constructs used inconsistently and interchangeably. Moreover, integration across disciplines such as engineering, health sciences and marketing is limited. These shortcomings highlight the need for systematic review efforts to consolidate, clarify and ultimately advance our understanding of organisational DARQ readiness. To help tackle these issues, we present a systematic review of the subject. More specifically, we review findings from marketing (e.g. Baabdullah et al., 2021), management (e.g. Patil et al., 2024), information systems (e.g. Papagiannidis et al., 2023) and other relevant fields to develop a cohesive understanding of DARQ readiness and highlight areas where further research is needed to propose an integrated framework.

Given the nascent and fragmented state of DARQ readiness research, our review adopts a “miner” approach (see Breslin and Gatrell, 2020). In this metaphor, miner reviews play an important role in emerging fields through the systematic synthesis and categorisation of prior knowledge, the establishment of foundational clarity and the identification of conceptual gaps. This groundwork enables novel theorising to advance the field, often beyond its current disciplinary silos. By organising dispersed studies across marketing, management, information systems and related domains, our review addresses the critical need for an integrated understanding of DARQ readiness and its implications for organisations.

While each DARQ technology has distinct characteristics and applications, they share foundational qualities that justify their examination as a bundle (Alt, 2021). Collectively, these technologies represent a frontier in digital transformation, with each offering the potential for significant disruption and value creation across industries, yet their impact is magnified when converged, unlocking synergies that drive innovation (Accenture, 2019b).

Yet, as noted, despite their transformative potential, DARQ technologies remain in the early stages of enterprise adoption, with limited conceptual clarity and integrative understanding. This gap highlights the need for a structured exploration of foundational theories and empirical insights. To help address this issue, we ask two research questions: (1) What are the most relevant theoretical frameworks applied to conceptualise and explore the DARQ readiness construct? (2) What are the reported dimensions, antecedents and organisational outcomes of DARQ readiness? To answer these questions, we follow established guidelines for conducting systematic literature reviews (SLRs) (e.g. Tranfield et al., 2003; Littell et al., 2008) and explore studies from a broad range of related disciplines, including marketing, information systems, management and health science. Most review-centric work acknowledges the interpretive nature of synthesising studies, especially in nascent domains like DARQ, where definitional precision, boundary conditions and conceptual ordering are still evolving (Hoon and Baluch, 2020). Our SLR, too, supports such theoretical refinement and practical insights for organisations seeking to understand and prepare for DARQ-driven transformation.

More specifically, our study makes four key contributions. Firstly, our study advances the technology adoption literature by drawing together insights from marketing, management, information systems and health-care management to deepen our understanding of DARQ readiness. Each of these fields provides unique perspectives: marketing sheds light on customer-facing applications and adoption behaviours (Lin et al., 2007; Vize et al., 2013; Mullins and Agnihotri, 2022); management offers organisational and strategic frameworks (Lokuge et al., 2019); and information systems addresses the technical and infrastructural complexities of implementing DARQ technologies (Patil, 2021; Hradecky et al., 2022). Our literature review uncovers a burgeoning of studies across these disciplines, especially in journals focused on the societal impact of technology, engineering and construction. This finding underscores the critical need for scholars from diverse areas, such as marketing, health care and IS, to expand their contributions to this field by applying and evolving relevant theories and perspectives. We also explored the marketing implications of DARQ readiness across three key domains: internal marketing, external marketing and interactive marketing.

Secondly, our study examines various frameworks that can help enhance our understanding of DARQ readiness and its marketing implications. We bridge studies from multiple disciplines and advocate for applying diverse theories to deepen insights into DARQ readiness. Specifically, our analysis identifies nine different theories and models for evaluating DARQ readiness, with the most commonly used being the technology-organisation-environment (TOE) framework (Tornatzky et al., 1990), followed by the technology acceptance model (TAM) (Davis, 1989) and the technology readiness index (TRI) (Parasuraman, 2000).

Thirdly, our findings reveal a significant gap in the DARQ readiness literature, particularly an imbalance in prior work’s focus. While some topics, such as the link between organisational readiness and firm performance (e.g. Denicolai et al., 2021; Bag et al., 2022), have been extensively explored, other crucial areas – such as the antecedents and broader outcomes of DARQ readiness – have been somewhat neglected.

Finally, we analyse prior studies on DARQ readiness to uncover promising directions for future research. Key avenues include empirically identifying the factors that drive DARQ readiness, adapting DARQ frameworks to diverse industries and contexts and developing and validating measurement scales for firms’ DARQ readiness.

DARQ readiness refers to an organisation’s preparedness to adopt and integrate emerging DARQ technologies – DLT, AI, XR and QC. The concept of readiness, initially introduced by Jacobson in 1957, gained prominence in the 1990s when it was explored at the firm level as organisations sought to assess their capability for change, particularly technology adoption. A noteworthy study by Armenakis et al. (1993) played a key role in this development, as it expanded the notion of readiness to include members’ beliefs, attitudes and intentions to adapt to change. Over time, as new technologies became more complex and pervasive, the definition of readiness has expanded to include not only technological infrastructure but also organisational culture, governance and stakeholder alignment (Weiner, 2009; Hradecky et al., 2022).

More recent conceptualisations of technological readiness, especially in digital transformation contexts, go beyond traditional structural factors to include digital ethics, trust in automation and psychological openness to human–machine collaboration (Pant et al., 2024; Issa et al., 2022). As DARQ technologies introduce not just technical shifts but paradigm-altering disruptions, these behavioural and psychological dimensions become more critical. There is now much literature on organisational readiness in various disciplines, such as marketing (Lin et al., 2007; Mullins and Agnihotri, 2022), information systems (Zhu et al., 2003; Robey et al., 2008), management (Weeks et al., 2004; Jones et al., 2005) and health science (Fuller et al., 2007; Saldana et al., 2007). In this context, DARQ readiness represents a holistic approach to preparedness, addressing technical, human and strategic dimensions specific to DLT, AI, XR and QC.

In marketing, readiness assessments have become essential for gauging the potential success of digital transformation (Lamberton and Stephen, 2016), customer relationship management initiatives (Payne and Frow, 2005) and data-driven strategies (Wedel and Kannan, 2016). If organisations are indeed ready, it means they use these platforms to align their resources with market demands, driving more effective consumer engagement and analytics-based decision-making. Similarly, in health sciences, readiness has been evaluated for adopting evidence-based practices (Saldana et al., 2007), technology-based treatments (Lehman et al., 2002) and knowledge translation (Attieh et al., 2013). Management disciplines have examined readiness for digital innovation (Lokuge et al., 2019), new initiatives (Eby et al., 2000) and technology implementation (Abdinnour-Helm et al., 2003), providing insight into organisational change processes across fields.

Many studies on technology readiness build on the theory of organisational readiness for change (ORC). Weiner’s (2009)ORC theory conceives readiness for change through three dimensions: change valence, change efficacy and contextual factors. Change valence refers to the collective level of commitment among members of an organisation towards a proposed change, originating from the degree to which they value the change. Change efficacy is defined as the overall evaluation of an organisation’s potential capability to successfully carry out a change and achieve its goal (Gist and Mitchell, 1992). As Weiner (2009) argues, change efficacy depends on several factors, such as understanding the required actions and resources, time frame and sequence of activities for accomplishing a change. Hence, evaluating change efficacy involves assessing resources, including but not limited to financial, human and informational resources required for effective change implementation. In addition, experts argue that broader contextual factors such as organisational culture (Ingersoll et al., 2000; Chonko et al., 2002), policies and procedures (Eby et al., 2000; Hallinan et al., 2019) and past experiences (Armenakis et al., 1993) can influence an organisation’s readiness for change.

This study explores organisational readiness for DARQ technologies, a cluster of innovative technologies expected to shape the future of business, comprising AI, blockchain, XR and QC. AI is usually defined as “the use of computational machinery to emulate capabilities inherent in humans” (Huang and Rust, 2021, p. 31). Although AI is not a recent development and has been created alongside the first computers, its applications have gained significant popularity in the past decade. This is primarily attributed to advancements in computer capabilities, including natural language processing, machine learning and sensors (Bornet et al., 2021). DLT or blockchain, refers to a shared digital platform for various applications, such as financial transactions, that provides append-only and highly available distributed databases. DLT is maintained by multiple decentralised and separated devices called nodes (Kannengießer et al., 2020).

XR is a term used to describe a range of immersive technologies such as virtual reality, augmented reality and mixed reality (Fast-Berglund et al., 2018; Kwok and Koh, 2021). Through visual and audio, and at times, haptic touch cues, XR technologies enable the integration of the real world with virtual environments (Alizadehsalehi et al., 2020). XR technologies are quickly becoming one of the most prominent new developments in management, marketing and information systems (Kim and Hall, 2019; Xi and Hamari, 2021). QC involves using high-performance, non-binary computers that operate on wave-based systems to tackle highly intricate problems (Gupta et al., 2023). Focusing on these specific technologies, this study aims to explore how organisations can effectively prepare to integrate and leverage them for their benefit. This review explores various dimensions of DARQ readiness, its contributing factors and its consequences. Ultimately, this study aims to offer insights for organisations to enhance adaptability in the face of disruptive technologies and provide recommendations for advancing future research in this field.

Organisational readiness is inherently dynamic. As technologies evolve and digital maturity progresses, the factors that define readiness also shift. For example, in the early stages of AI adoption, the emphasis is typically on establishing foundational infrastructure and acquiring skilled talent (Chittipaka et al., 2022; Balasubramanian et al., 2021). In contrast, later stages prioritise seamless integration, robust governance and ethical considerations (Pant et al., 2024; Du and Xie, 2021). Consequently, DARQ readiness should ideally be conceptualised by future research as an evolving process that adapts over time. Table 1 provides a snapshot of how the organisational readiness construct transforms across different stages of technological adoption.

Our review method follows the systematic review framework outlined by Tranfield et al. (2003) and Denyer and Tranfield (2006), which is widely used for organising and analysing literature in management and related fields. We chose this approach to ensure the results’ reliability, verifiability and reproducibility (Booth et al., 2021). Following Tranfield et al. (2003), we structured our review into three phases: planning, conducting and reporting. In the planning phase, we identified the scope and research questions by reviewing foundational studies on DARQ readiness. We applied rigorous inclusion and exclusion criteria during the conducting phase, ensuring consistent screening across databases and data extraction processes. Finally, in the reporting phase, we synthesised findings to reveal patterns, gaps and themes in DARQ readiness research to inform future scholarship in marketing and management.

Keywords were developed through an iterative process informed by:

• expert consultation with scholars in marketing and information systems;

• pilot searches; and

• benchmarking against similar SLRs in technology adoption and readiness (e.g. Kumar et al., 2023; Blut and Wang, 2020).

The final keyword string included variations of terms related to each DARQ technology, paired with readiness-related constructs (e.g. “preparedness”, “adoption”, “maturity” and “acceptance”).

To compile a comprehensive collection of scholarly articles, following recommendations from SLR theorists (e.g. Gusenbauer and Haddaway, 2020), multiple databases were chosen as primary sources: Web of Science, EBSCOhost Academic Research Complete and ABI/INFORM ProQuest. The choice of databases was based on their multidisciplinary coverage, indexing quality and alignment with the fields of marketing, management and information systems (Mongeon and Paul-Hus, 2016; Gusenbauer and Haddaway, 2020). These databases are consistently used in recent systematic reviews on interdisciplinary topics, including AI and blockchain research, owing to their extensive coverage of peer-reviewed journals (e.g. Hentzen et al., 2022; Mohiuddin Babu et al., 2022; Mariani et al., 2023). Each database is benchmarked for its topical breadth and accessibility to high-quality studies in marketing, management, information systems and engineering – fields critical to understanding DARQ readiness comprehensively. Although Scopus is another popular database, it was not used in this study owing to its high overlap with Web of Science in terms of indexed business and technology journals (Gusenbauer and Haddaway, 2020). Also, Scopus is generally considered an alternative to Web of Science, although it is comparatively less commonly used in organisational studies (Zupic and Čater, 2015).

We conducted a query in Web of Science using a combination of keywords, as demonstrated in Table 2, in the “title”, “abstract” and “keywords” fields. The search was limited to the title, abstract and keywords fields in Web of Science to ensure relevance and avoid excessive noise. A full-text search was not conducted (following other SLRs in the field, such as Zhu et al., 2024; Khan and Pandey, 2023), as it would have retrieved articles mentioning DARQ-related or readiness-related terms incidentally without focusing on them substantively, potentially compromising the precision of the search (Booth et al., 2021). The search yielded 1911 studies, excluding proceedings papers, book chapters, editorials, letters, data papers, notes, reprints and retracted publications. We considered literature published until 24 October 2024 and established an alert to receive newly published papers after that date. In EBSCOhost Academic Research Complete, the same search formula was applied, targeting abstracts of articles. This approach yielded 117 additional studies. Among those, 39 articles were identified as identical to those already retrieved from the Web of Science and were thus removed, resulting in a final count of 78 unique studies. The duplicates were automatically identified by the EndNote 21 programme. Similarly, in ABI/INFORM ProQuest, identical keywords were used, leading to a total of 678 studies. Among those, 554 studies were distinct from the results obtained from the other two databases, while the remaining were found to be duplicates. Consequently, a combined count of 2,493 studies was obtained (updated to 2,543 studies in the revision process) by aggregating the findings from all three databases. The pertinent information from these articles, such as the journal, authors, title, year and abstract, was transferred and recorded in an MS Excel spreadsheet.

The article selection process consisted of two phases. We established a set of exclusion criteria for the screening process (refer to Table 2). To ensure rigour in the selection process, we adhered to established protocols for systematic reviews, including PRISMA guidelines (Page et al., 2021). Our inclusion criteria focused on peer-reviewed journal articles in English that specifically addressed DARQ technologies in an organisational context. Exclusion criteria include non-peer-reviewed content, such as book chapters, editorials, letters and articles outside the scope of business and technology readiness. Keywords were selected based on a preliminary review of DARQ literature and expert recommendations, as recommended by Tranfield et al. (2003). The search timeframe was up to 24 October 2024 (updated in the revision process), ensuring coverage of the most recent publications in the rapidly evolving field of DARQ technologies. To maintain accuracy across platforms, we used consistent keyword strings and conducted cross-validation to identify overlapping articles across Web of Science, EBSCOhost and ProQuest. Articles meeting initial inclusion were further reviewed in two phases to confirm alignment with the scope and research questions, following PRISMA-based verification protocols for consistency and rigour.

In the first phase, screening involved reading the abstracts of identified articles and evaluating them based on the inclusion/exclusion criteria. Two reviewers independently screened articles to identify studies lacking alignment with the research topic or review scope, failing to meet inclusion criteria or having other issues related to exclusion criteria. The researchers achieved a high inter-rater agreement (82%), as recommended by Landis and Koch (1977), and resolved disagreements. Most exclusions, as shown in the review process flowchart (Figure 1), came from studies with very weak alignment with the research topic (e.g. Zhukovsky et al., 2011; Williams et al., 2022), followed by studies with low relevancy to the study’s scope or research questions, albeit in the business realm (e.g. Ivashchenko et al., 2019; Singh and Kumar, 2022). One hundred and ten papers were excluded owing to being technical, medical or law papers (e.g. Ibanez and Moccia, 2020; Mallon et al., 2021; Belchior et al., 2022). We also eliminated studies that evaluated DARQ readiness but not at an organisational level (e.g. de Kervenoael et al., 2020; Jellason et al., 2021) or were conducted in a non-commercial context (e.g. Dey et al., 2020; Loiacono and Rulli, 2022). Some studies identified through our keywords, like AI or machine learning, used these technologies as research tools, lacking relevance to our study. Consequently, these studies were identified and excluded (e.g. Hamzehi and Hosseini, 2022; Tatoli et al., 2022). Finally, several exclusions were made owing to studies that, even in organisational contexts, treated “readiness” (and similar terms like preparedness) as mere adjectives describing concepts unrelated to technology adoption (e.g. Morelot et al., 2021; Mystakidis et al., 2021). Through this process, 2,166 publications were excluded for not meeting the criteria, resulting in a refined pool of 327 articles that progressed to the next screening phase.

In the second phase, full papers underwent a comprehensive review for relevancy and quality, considering factors such as relevance to the scope of the study, providing answers to our review questions and journal quality operationalised as SCImago Journal Rank Q1 and Q2 journals (Tranfield et al., 2003). Specifically, studies were included if they provided a conceptual, empirical or theoretical contribution to organisational DARQ readiness. Inclusion required the study to meet the criteria on a non-numeric binary decision rule (yes/no), such as direct relevance to organisational DARQ readiness (i.e. the study addressed readiness for at least one DARQ technology within an organisational context and at the organisational level). Studies failing to meet the criteria were excluded, regardless of publication outlet or citation count. In cases where a study’s relevance was uncertain, the article was flagged and discussed between the two independent reviewers to reach a consensus. The application of these criteria ensured that the synthesis remained focused and aligned with the review’s objectives.

Following this evaluation, as illustrated in Figure 1, 85 papers meeting the criteria were selected for the systematic review and synthesis. The inter-rater agreement of 84% was achieved in the second phase of screening, which is acceptable (Landis and Koch, 1977). To broaden the evidence base and ensure saturation, we conducted forward and backward citation searches using Google Scholar after the second stage of screening. This step was intentionally conducted at the end of the core screening process once we had established a high-quality set of eligible articles. The rationale was to use this refined pool of 85 studies as a seed set for identifying influential, recent or adjacent works that may not have appeared in our original database queries owing to indexing lag, database-specific coverage or terminology differences. The search criteria for this phase involved manually reviewing the reference lists of the selected 85 papers (backward citation search), as well as identifying studies that cited these papers (forward citation search) using Google Scholar’s “cited by” function. We then screened these additional sources using the same inclusion/exclusion criteria applied in earlier stages. Only those that met our conceptual focus (i.e. organisational-level DARQ readiness across DARQ technologies) were included. This process resulted in the addition of three conceptually aligned studies, enhancing completeness without compromising relevance. This process increased the final number of papers for the review to a total of 88.

In summary, we validated the search process through backward and forward citation checks on a seed set of highly cited DARQ readiness articles and conducted inter-rater screening at both the abstract and full-text levels to ensure conceptual alignment. The inclusion and exclusion criteria were calibrated using predefined relevance guidelines and refined based on pilot discrepancies between two independent reviewers.

In the next section, we synthesise the literature to establish a comprehensive understanding of organisational DARQ readiness. Following established review guidelines (e.g. Booth et al., 2021), we conducted a thematic analysis of the selected articles, coding concepts related to the dimensions, antecedents and consequences of organisational DARQ readiness (Cassell and Bishop, 2019). In addition, we explored the used theoretical frameworks. Thematic analysis was conducted following an abductive approach (Fereday and Muir-Cochrane, 2006; Cassell and Bishop, 2019), combining deductive coding based on established readiness constructs (e.g. organisational, technological and environmental factors) with inductive identification of themes. Initial coding units were derived from the research questions, focusing on extracting and coding the concepts related to our research questions including:

RQ1.Theoretical foundations: Organisational theories used to develop the construct of DARQ readiness or to explore its relationships with other constructs.

RQ2.Dimensions: Factors or components that collectively shape the mentioned construct in different contexts.

RQ3.Antecedents: Concepts used to predict organisational DARQ readiness.

RQ4.Consequences: Outcomes resulting from the focal construct.

Second and following the deductive phase, we conducted an inductive analysis of textual data associated with these factors, integrating them to derive comprehensive insights. Through iterative coding cycles, we clustered conceptually similar factors into broader categories, ensuring consistency and traceability. We drew on Webster and Watson’s (2002) approach for conceptual literature reviews, which emphasises identifying core themes and organising them into a coherent framework. This method guided our categorisation of DARQ readiness dimensions, antecedents, outcomes, theoretical foundations and marketing implications. In so doing, we ensured that the review provides a cumulative, organised synthesis of DARQ readiness across disciplines, highlighting both established themes and underexplored areas in the literature.

To classify the identified DARQ readiness dimensions into the organisational, technological and environmental categories, we used a structured categorisation process grounded in TOE’s definitional boundaries (Tornatzky et al., 1990). Specifically, factors were assigned to:

• Organisational domain if they reflected internal firm attributes (e.g. resources, culture, leadership and employee capabilities).

• Technological domain if they pertained to the intrinsic characteristics of DARQ technologies (e.g. complexity, compatibility and data requirements).

• Environmental domain if they originated from external forces such as competitive pressure, regulatory frameworks or ecosystem partnerships.

Where prior studies (e.g. Chittipaka et al., 2022; Chatterjee et al., 2021) had explicitly categorised factors using TOE or similar structures, we adopted or adapted their classifications while still prioritising the above systematic logic for classification. For factors lacking clear precedent, we applied a conceptual mapping logic based on locus of control (internal vs external) and factor nature (technological attribute vs organisational capability). This synthesis process was iterative and aligned with Harvey’s (2014) model of creative synthesis, ensuring a coherent and theory-driven classification across the data set.

Drawing from the pool of 84 research articles on organisational DARQ readiness published up to 2023, we present our systematic review findings in line with the proposed research questions and various descriptive analyses.

The descriptive analysis in this review is a foundational step to map and contextualise the field of organisational DARQ readiness. According to Tranfield et al. (2003), a rigorous systematic review report should begin with a comprehensive descriptive analysis of the literature, enabling researchers and practitioners to understand the field’s breadth, evolution and characteristics. This stage involves simple but essential categorisations, such as publication year, disciplinary focus and outlet analysis, to identify how the research domain has developed, where it is concentrated and where gaps may exist.

In the context of DARQ readiness, this descriptive analysis fulfils two purposes. Firstly, it provides a longitudinal perspective on the field’s maturity, signalling whether DARQ readiness is an emerging, nascent or established research stream. Secondly, it highlights the interdisciplinary nature of DARQ technologies by revealing where research efforts are concentrated and where there are underexplored areas. For instance, a concentration of DARQ readiness studies in technology-focused outlets but sparse coverage in marketing journals would indicate an opportunity for marketing scholars to contribute new perspectives. These insights not only frame the relevance and timeliness of this systematic review but also guide future scholarly contributions by identifying neglected perspectives or research opportunities.

We observed a clear trend in the number of publications on organisational DARQ readiness research, providing insight into the field’s growth trajectory. Following Tranfield et al. (2003), this temporal mapping helps assess whether the topic is emerging, nascent or established, thereby underscoring our SLR’s urgency and relevance. Although we did not limit our research to a specific time frame, the first publication on DARQ readiness dates to 2018. We observed an increase in published articles, starting from two in 2018, peaking at 27 in 2022, with 13 in 2023 and 2024. Most of these studies (85.2% of 88 articles) have emerged from 2021 onward.

Several reasons contribute to this growth. Firstly, the increasing adoption of AI and other DARQ technologies across diverse industries, along with the surge in digital data, has fuelled demand for research in this field. Moreover, technology providers are actively developing and marketing more effective forms of DARQ technologies, such as human-like chatbots, digital assistants and blockchain-based smart contracts. Secondly, the COVID-19 pandemic has likely accelerated the overall adoption of digital technologies, including DARQ technologies, as organisations embrace digital transformation at a faster pace (Donthu and Gustafsson, 2020), sparking additional research interest. Thirdly, the affordability of implementing DARQ technologies has played a role in their increasing popularity. Cost-effective, high-performance QC resources have made AI and blockchain more accessible. In addition, advancements in cloud computing platforms and specialised hardware, such as graphics processing units and tensor processing units, have made it easier and less expensive for organisations to train and deploy AI models. Furthermore, the rise in special issues in business journals dedicated to DARQ technologies has encouraged researchers to contribute to this area, leading to an increase in publications.

Our review revealed that research on DARQ readiness has been published in journals in a range of fields comprising business and management journals (e.g. Journal of Business Research, Annals of Operations Research), information systems (e.g. Information Systems Frontiers, Business Information Systems Engineering), construction (e.g. Buildings), marketing (e.g. Industrial Marketing Management), engineering (e.g. IEEE Transactions on Engineering Management), society and social science (e.g. Technology in Society, Technological Forecasting and Social Change), computer (e.g. Applied Soft Computing, Computers in Industry) and health and medicine (e.g. Journal of the American Medical Informatics Association). Technological Forecasting and Social Change journal leads with seven articles, reflecting its focus on the interplay of social, environmental and technological factors, along with a significant number of special issues. In 2022, the journal published 26 technology-related special issues, covering topics like “Artificial Intelligence as an Enabler for Innovation”. Our analysis indicates that most studies in the field are published in journals with a primary focus on the impact of technology in society, engineering management, construction, business, production and operations and information management. This finding highlights the necessity for further research on DARQ readiness, particularly exploring perspectives in marketing, health and information systems.

Figure 2 categorises DARQ readiness studies based on their technological focus, revealing imbalances in research attention: 32 articles focused on AI, 29 articles on blockchain, 1 article on QC, 3 articles on XR and 23 articles explored readiness for a combination of these technologies without specific emphasis on any single one. AI and blockchain-focused readiness studies are much more prevalent than XR and QC. The relative dearth of studies in QC is probably related to the on-the-ground nature preventing companies from using it (Anirudhan et al., 2022).

The reviewed articles use various theoretical foundations and frameworks, with the most frequently used theories, in order of usage, being: TOE, TAM, TRI, diffusion of innovation (DOI), resource-based view (RBV), dynamic capabilities view (DCV), ORC, unified theory of acceptance and use of technology (UTAUT) and theory of planned behaviour.

The TOE framework is the most cited theory (used in 26 articles) in the reviewed literature, owing to its holistic perspective, considering the interplay between technology, the organisation and the external environment (Tornatzky et al., 1990). Its comprehensive nature allows researchers to analyse multifaceted dynamics influencing DARQ readiness. In addition, the TOE framework provides a systematic approach, covering various components from other theories. For example, it includes characteristics of the technology itself, such as usefulness (TAM; Davis, 1989) and compatibility (DOI; Rogers, 2003), organisational factors like leadership support (ORC; Weiner, 2009) and resources (RBV; Barney, 1991) and external factors such as market dynamics (DCV; Teece et al., 1997) and social influence (UTAUT; Venkatesh et al., 2003). Several studies integrate TOE with other theories for enhanced analysis, such as combining it with TAM to explore blockchain adoption (Wang et al., 2022) or with TRI theory for investigating AI readiness (Hradecky et al., 2022). Table 3 offers succinct descriptions of the theories and exemplary papers from our review using these theories.

In the subsequent sections, we present a comprehensive DARQ readiness framework derived from the literature. The framework comprises four components:

1. Dimensions of readiness, outlining factors necessary for firms to prepare for adopting DARQ technologies.

2. Antecedents of readiness, explaining contributing factors to such readiness.

3. Marketing outcomes of readiness, explaining marketing implication of DARQ readiness.

4. Performance consequences of readiness, including performance impacts stemming from DARQ readiness.

We categorised dimensions into three groups: organisational, technological and environmental factors, aligning with the TOE framework (Tornatzky et al., 1990). The decision to organise the identified dimensions using the TOE framework was based on its widespread applicability in technology adoption research across multiple domains, including emerging technologies (e.g. Chatterjee et al., 2021; Chittipaka et al., 2022; Wael AL-khatib, 2023). The TOE framework offers a holistic yet parsimonious lens to categorise readiness factors into three core domains (i.e. technology, organisation and environment), providing clarity and comparability across studies. This choice is consistent with Harvey’s (2014) proposition that frameworks serve as shared conceptual maps that enable meaningful integration of diverse inputs, fostering more robust and actionable insights. Thus, the realignment into TOE was a deliberate and theory-informed synthesis step, not merely a convenience, aimed at enhancing the coherence and applicability of our findings. Table 4 summarises all the extracted concepts and constructs. The following sub-sections offer a detailed delineation of these categories.

One dimension of DARQ readiness is the role of employees. Several factors related to employees have been found to significantly influence such readiness, including their interaction with DARQ technologies, trust in these systems and knowledge and competence in using DARQ technologies.

The interaction between employees and AI is crucial in shaping organisational readiness. In the energy sector, Papagiannidis et al. (2023) emphasised the importance of fostering positive interaction channels between employees and AI systems, such as developing a monitoring dashboard to track actions and outcomes. Trust in technology advances is also a fundamental dimension, characterised by the assessment of the cost-benefit balance regarding the potential risks of adopting new technologies (Nam et al., 2021). Chittipaka et al. (2022) examined firms’ adoption of blockchain in emerging markets and discovered that employees’ trust significantly influences a firm’s blockchain readiness. Finally, employees’ knowledge and competence in using technologies are key. In a study on blockchain adoption in agricultural supply chains, Zkik et al. (2022) found that employees’ skills and capabilities in using blockchain are crucial for firms’ readiness and overall performance.

An important finding from the SLR underscores the significance of training as a dimension of DARQ readiness. Specifically, initiatives focusing on AI education, upskilling, raising AI awareness and providing AI ethics training play crucial roles in enhancing organisational readiness. AI education for employees is pivotal in preparing organisations for DARQ technology use, offering comprehensive training on AI concepts, applications and potential benefits. This equips employees with the necessary knowledge to engage effectively with AI and avoid misinterpretations. Papagiannidis et al. (2023) found that providing AI education to employees enhances firms’ AI readiness and governance through on-board training and educating employees about AI capabilities and limitations. In a qualitative study, Johnk et al. (2021) emphasise the significance of employee training in achieving AI readiness, particularly by enhancing awareness of AI fundamentals such as machine learning. This helps employees develop a sufficient understanding and realistic expectations for AI capabilities. Furthermore, the authors underscore the importance of upskilling and AI ethics training. The former enables employees to acquire AI skills, while the latter includes measures to prevent biases, safety breaches or discriminatory outcomes in AI processes.

The impact of organisational processes and operations on DARQ technology readiness reveals several key factors. These include business model readiness, governance practices, information processing management, pipeline evaluation and dynamic model selection. Tjebane et al. (2022) posit that organisations need to optimise information processing capabilities to handle the vast amounts of data generated (and used) by AI. This involves establishing effective data governance frameworks. Similarly, Setyowati et al. (2023) highlight the importance of a suitable governance framework for successful blockchain implementation, outlining the responsibilities of each party, approval mechanisms, error rectification and legal considerations.

Also, Setyowati et al. (2023) emphasise the importance of business model readiness in adapting processes, resources and value propositions to DARQ technologies. In addition, Papagiannidis et al. (2023) stress the need for pipeline evaluation and dynamic model selection to ensure ongoing DARQ readiness. This allows organisations to adapt business processes flexibly by assessing technology pipelines and selecting appropriate models for specific tasks. Proactively monitoring and evaluating emerging technologies enables organisations to stay at the forefront of readiness and integrate DARQ technologies effectively into operations.

The SLR highlights the influence of organisational culture on DARQ technology readiness, revealing key factors such as ORC, innovativeness, collaboration between employees and human–machine collaboration.

Organisational readiness, defined as a firm’s capability and willingness to embrace and adopt innovation or change (Li et al., 2022), is a significant factor influencing readiness for DARQ technologies, such as blockchain (Setyowati et al., 2023). Li et al. (2022) provide quantitative evidence supporting the impact of organisational readiness on the intention to use blockchain, indicating that firms prioritising continuous improvement and adaptability are more likely to embrace DARQ technologies effectively. Also, wael AL-khatib (2023) highlights the significance of organisational readiness in the implementation of generative AI.

Innovativeness, a component of TRI theory, plays a crucial role in organisational culture, impacting DARQ readiness. Abu Salim et al. (2022) found that innovativeness significantly influences the intention to adopt blockchain. As they explain, innovators, driven by their strong innovativeness trait, exhibit enthusiasm, share experiences and provide recommendations, influencing others to embrace new technologies.

In addition, Tjebane et al. (2022) highlight the importance of a collaborative organisational culture in fostering innovation, promoting shared understanding and commitment and ultimately enhancing DARQ readiness. Moreover, cultivating an environment where employees and technologies can effectively collaborate and complement each other’s strengths enhances the organisation’s DARQ readiness. Similarly, Issa et al. (2022) identify the human–machine collaboration mindset as one of the top three dimensions of AI readiness in the agriculture industry.

The next finding revolves around the role of leadership, management and strategy in shaping DARQ readiness. Factors such as top management support (Bag et al., 2022; Li et al., 2022; Somya et al., 2022), leadership skills and commitment (Frick et al., 2021; Kar et al., 2021), managers’ innovativeness and optimism (Hradecky et al., 2022), developing a problem-oriented AI strategy and communicating its goals (Papagiannidis et al., 2023) and efficient change management (Johnk et al., 2021) shape the organisation’s DARQ readiness. Cultivating effective leadership, formulating a comprehensive technology strategy aligned with organisational objectives and communicating relevant goals and challenges to employees contribute to managing the change process and facilitating DARQ adoption.

Unsurprisingly, the type and extent of resources available influence a firm’s DARQ readiness. Talent, for example, including domain experts and skilled employees proficient with DARQ technologies, holds a crucial position (Balasubramanian et al., 2021; Johnk et al., 2021; Papagiannidis et al., 2023). Robust IT infrastructure, including hardware, software, networking and computing capacity and data storage infrastructure, is essential for the seamless integration of DARQ technologies (Chittipaka et al., 2022). Moreover, financial resources significantly affect readiness, with substantial investment required for the development and utilisation of DARQ technologies (Alzahrani et al., 2022; Sarkhosh and Akhavan, 2023). Time is also critical as these technologies are not likely to bring short-term success (Tjebane et al., 2022). It necessitates time allocation for planning, implementation, adaptation, training, testing and troubleshooting (Behl et al., 2022a; Tjebane et al., 2022).

Data-related readiness is fundamental to DARQ readiness. Studies emphasise key considerations for data, including security (Setyowati et al., 2023), quality, marked by accuracy and reliability (Seethamraju and Hecimovic, 2022; Tehrani et al., 2024) and availability and accessibility (Johnk et al., 2021; Uren and Edwards, 2023). Furthermore, proficient data management guarantees the quality, privacy and security of information, thereby promoting governance, integration, scalability and efficiency in the deployment of AI (Hradecky et al., 2022; Issa et al., 2022).

Characteristics of DARQ technologies significantly affect firms’ readiness. Setyowati et al. (2023) assert that the design and architecture of blockchain systems are of utmost importance as they can minimise complexity and enhance user understanding by making technologies more explainable. Compatibility and relative advantage are highlighted as vital dimensions for firms’ readiness. Compatibility refers to the seamless integration of DARQ technologies with existing systems and workflows, facilitating adoption without disrupting established practices (Bag et al., 2022; Chittipaka et al., 2022). Firms are also more likely to invest in technologies perceived to offer a relative advantage, delivering higher efficiency, productivity, cost savings or improved outcomes (Li et al., 2022; Somya et al., 2022).

Moreover, security and complexity are additional key characteristics influencing readiness. Lu et al. (2021) found that information security influences blockchain adoption in the elderly care industry. They explain that information security involves protecting sensitive data from unauthorised access and interception. A higher level of information security is reported to positively correlate with the likelihood of managers adopting and implementing the technology. Conversely, system complexity poses challenges to DARQ readiness by increasing implementation difficulties, maintenance requirements and the need for skilled experts to work effectively with these technologies (Bag et al., 2022; Somya et al., 2022).

The first environmental factor revolves around the involvement of various stakeholders in DARQ technology readiness, including authorities, partners and customers. Customer readiness hinges on educating customers about DARQ systems, aligning their expectations and emphasising the perceived usefulness of those systems (Pumplun et al., 2019; Johnk et al., 2021).

Collaborating with strategic partners, including research institutions and supply chain partners, aids readiness by facilitating knowledge expansion and technology integration (Chatterjee et al., 2021). Ghobakhloo and Iranmanesh (2021) emphasise the significance of partners in firms’ Industry 4.0 readiness, stressing the need for digital transformation at both the micro-level (individual manufacturers) and macro-level (business partners).

Finally, engaging with regulatory bodies and government agencies is crucial for DARQ readiness. Collaborating with relevant authorities ensures compliance with regulations, standards and legal requirements, mitigating potential legal and regulatory risks. Studies emphasise the influence of regulatory support in establishing trust during the adoption of DARQ technologies, such as blockchain in Indonesia (Setyowati et al., 2023) and the elderly care industry (Lu et al., 2021). The reviewed articles highlight additional regulatory requirements, including regulations on data ownership (Papagiannidis et al., 2023), permissions and consents (Setyowati et al., 2023), as well as other legislations and strategies such as the Made in India initiative, Platform Industrie 4.0 and Made in China 2025 (Bag et al., 2022).

Competitive pressure and market dynamics significantly affect a firm’s DARQ readiness, according to our systematic review. Competitive pressure, driven by the competitive environment within an industry and characterised by rival firms adopting similar technologies, compels organisations to embrace DARQ technologies proactively to stay ahead and retain market share. Some scholars, including Chandra and Kumar (2018) and Wang et al. (2022), highlight the influence of competitive pressure on technology adoption, such as augmented reality in e-commerce and blockchain in the construction industry.

In addition, market dynamics, representing the continuous evolution of a complex and competitive market (Somya et al., 2022), are crucial to DARQ readiness. Organisations actively evaluating and strategically responding to market conditions demonstrate greater readiness for implementing AI technologies, as shown in studies like Somya et al. (2022) in the Indian insurance industry.

The research on the antecedents of DARQ readiness is limited, with a few identified and reviewed studies. These studies explore external factors such as environmental uncertainty and supply chain collaboration, along with internal organisational factors like firm size and technology trust. While some of these factors have been recognised as dimensions of DARQ readiness in other studies, the following research specifically investigates their impacts on DARQ readiness and adoption as antecedents.

Firm size is an internal factor explored by five studies as an antecedent of technology readiness, including AI readiness in the exhibition (Hradecky et al., 2022), blockchain readiness in health care (Balasubramanian et al., 2021), blockchain adoption in eight industry sectors in Ireland (Clohessy and Acton, 2019), Industry 4 readiness in Turkish manufacturing industry (Sari et al., 2020) and in supply chain in Australia (Hopkins, 2021). These studies suggest that larger firms (with superior access to resources, networks and technology maturity) demonstrate enhanced DARQ readiness. Lu et al. (2021) also identified technology trust and information security as key antecedents influencing blockchain readiness in the elderly care industry.

A few external antecedents have been reported in the literature. Wang and Pan (2022) found that supply chain collaboration and environmental uncertainty significantly influence firms’ AI adoption. They explain that collaborative sharing within the supply chain can lead to information overload, prompting firms to adopt AI for effective analysis and meaningful insights. They propose that heightened environmental uncertainty and frequent changes drive firms to adopt new technologies, including AI, to sustain competitiveness and enhance operational performance. Lu et al. (2021) also found that competitive pressure and government support positively influence top management support in adopting blockchain technology. While these factors are distinct components within the TOE framework of blockchain readiness, the study also investigates how these environmental factors act as antecedents of organisational factors such as top management support.

The literature highlights many marketing-related outcomes of DARQ readiness, which we categorise into three main areas: internal, external and interactive (see Figure 3). These categories shed light on how readiness for DARQ technologies, particularly AI and blockchain, affects organisational marketing capabilities and effectiveness.

DARQ readiness influences internal marketing outcomes by enhancing communication and organisational governance. Trocin et al. (2021) report that DARQ readiness, especially AI integration within organisations, improves internal communication, enabling more efficient information flow from management to employees and vice versa. Transparent feedback, also highlighted by Trocin et al. (2021), has been found to facilitate open communication channels within organisations. In addition, Agarwal (2023) identifies improvements in HR system effectiveness, suggesting that AI readiness leads to optimising internal processes and resource management. Furthermore, Baabdullah et al. (2021) found that relational governance structures, particularly in B2B small and medium-sized enterprises (SMEs), benefit from DARQ readiness, strengthening organisational cohesion and trust.

In the context of external marketing, DARQ readiness is associated with enhanced customer satisfaction and competitive positioning. Papagiannidis et al. (2023) report that readiness for DARQ technologies, especially AI, improves customer satisfaction by enabling more tailored customer interactions. The literature suggests that DARQ readiness strengthens competitive advantage, as firms in the energy sector, for instance, leverage AI to differentiate themselves and enhance market positioning (Papagiannidis et al., 2023). In addition, Trocin et al. (2021) note that DARQ readiness aids in market gap identification, allowing firms to respond proactively to unmet customer needs. As reported by AI-focused technology companies, improved value creation is another outcome (Adiguzel et al., 2024). DARQ readiness also supports refined segmentation practices (Trocin et al., 2021), reduces time-to-market (Bhasin and Tripathi, 2021) and facilitates successful new product development (Zhang et al., 2021).

DARQ readiness affects interactive marketing outcomes by enhancing trust, communication and collaboration with external stakeholders. AL-Ashmori et al. (2023) found that readiness for blockchain adoption in the software sector enhances trust in interactions between customers and frontline employees, which is essential for fostering reliable business relationships. Trocin et al. (2021) highlight the role of improved external communication facilitated by DARQ readiness in improving transparency in organisational interactions. They also found that DARQ readiness supports customers’ perceived fairness and unbiased decision-making in interaction with the organisations, further enhancing their trust and satisfaction. Enhanced relationship performance has been reported in B2B firms using AI technologies (Rahman et al., 2023), while Baabdullah et al. (2021) note improvements in customer engagement and service experience among B2B SMEs adopting blockchain. Finally, Hopkins (2021) and Patil et al. (2024) emphasise the role of DARQ readiness in facilitating supply chain collaboration, highlighting their value in maintaining efficient and trustworthy relationships across organisational boundaries.

A prominent outcome of DARQ readiness, reported in many studies, has been increased adoption of DARQ technologies. This outcome is evidenced through empirical exploration of the adoption intention of specific technologies, such as blockchain (Wong et al., 2020; Chittipaka et al., 2022; Deng et al., 2022; Li et al., 2022; Guan et al., 2023), AI (Baabdullah et al., 2021; Zhang et al., 2021; Behl et al., 2022a; Chen et al., 2022; Issa et al., 2022; Agarwal, 2023), augmented reality (Chandra and Kumar, 2018), big data analytics (Maroufkhani et al., 2023) and intelligent contract (McNamara and Sepasgozar, 2020). These studies provide insights into the impact of DARQ readiness on the adoption intentions of these technologies.

The analysed studies highlight enhanced firm performance as a significant outcome of organisational DARQ readiness. Firm performance has been reported in several studies as follows: general performance as a result of the blockchain (Chittipaka et al., 2022) and AI readiness (Samadhiya et al., 2023), market and financial performance as a consequence of blockchain readiness (Bag et al., 2022), international performance as an outcome of AI readiness (Denicolai et al., 2021), supply chain performance resulting from blockchain readiness (Nandi et al., 2020; Behl et al., 2022b) and AI readiness (Wang and Pan, 2022), AI performance (Papagiannidis et al., 2023) and relationship management performance resulting from AI readiness (Rahman et al., 2023). These studies highlight how organisational DARQ readiness influences various aspects of firm performance.

To further elaborate on the relationships between antecedents, readiness dimensions and outcomes, we propose the following conceptual linkage: antecedents, which include external pressures (e.g. competitive dynamics, government support) and internal organisational characteristics (e.g. firm size, technology trust), influence the development of organisational, technological and environmental readiness dimensions. These dimensions collectively represent the firm’s overall DARQ readiness capability. In turn, DARQ readiness facilitates positive outcomes such as external, internal and interactive marketing outcomes, increased technology adoption and ultimately improved firm performance. For example, a firm’s readiness dimension of “data security” is shaped by antecedents like firm size and government regulatory support (such as compulsory general data protection regulation compliance) (Setyowati et al., 2023), which subsequently enhance adoption outcomes. Setyowati et al. (2023) explain that larger organisations typically have more complex processes for implementing new technologies, which helps them ensure stronger data security. This chain of influence provides a cohesive understanding of how readiness components interact to drive organisational outcomes. Figure 4 illustrates a framework for DARQ readiness dimensions along with its antecedents and consequences.

This SLR on organisational DARQ readiness has shed light on the dimensions, antecedents and consequences of readiness in organisational settings. Despite the valuable insights from prior studies, several gaps and inconsistencies remain in the DARQ readiness literature. Notably, there is a marked imbalance in the attention given to individual DARQ technologies. AI and blockchain are relatively well-researched, while XR and QC readiness remain underexplored. This imbalance suggests that readiness frameworks may not fully capture the unique challenges of each technology, potentially limiting their applicability across industries. In addition, inconsistencies in terminology and readiness metrics complicate cross-study comparisons, as various studies emphasise different readiness dimensions, from psychological preparedness to infrastructural capabilities (Hradecky et al., 2022; Wang et al., 2022). Addressing these gaps requires a more standardised approach to defining and measuring DARQ readiness, as well as further empirical studies across diverse industry contexts to validate and refine these frameworks. Future research should address these gaps by developing industry-specific readiness frameworks and expanding the focus to underrepresented DARQ technologies such as QC and XR. Further empirical studies are needed to validate readiness frameworks across diverse contexts, providing a more cohesive and practical understanding of DARQ readiness in real-world settings.

Moreover, while providing a holistic view of organisations, the TOE framework displays considerable flexibility, resulting in diverse conceptualisations of DARQ readiness by researchers. For example, environmental dimensions vary, with Hradecky et al. (2022) emphasising data management and privacy, while Wang et al. (2022) investigate competitive pressure and government policies. This lack of consensus challenges researchers and practitioners, highlighting the need for a more specified framework, which warrants future endeavours.

Furthermore, as empirical research on DARQ readiness gains prominence, there is a crucial demand for developing robust measurement scales for assessing DARQ readiness. Despite beneficial efforts in developing scales for concepts related to DARQ technologies (e.g. AI service agents’ service quality; Noor et al., 2022), there is a lack of DARQ readiness scales. This need is particularly pronounced for AI and DLT, which constitute around 69% of the research focus on DARQ readiness in this study (Figure 2). Moreover, Figure 2 indicates a notable dearth of research on readiness for QC and XR, underscoring areas that warrant further exploration.

Moreover, future research should explore the interplay among DARQ readiness dimensions, such as the combined impact of employee factors (e.g. skills, knowledge and adoption behaviour) and training initiatives. In addition, investigating how organisational culture and leadership interact with various readiness dimensions can enhance our understanding of their collective impact on DARQ readiness.

Future research should explore contextual factors and boundary conditions influencing DARQ readiness. Investigating the impact of industry-specific regulations, diverse regulatory environments and the readiness processes associated with them can aid organisations in overcoming adoption challenges. Moreover, exploring DARQ readiness across various firm types (e.g. multinational corporations, SMEs) and markets (e.g. developing countries, emerging markets) would provide valuable insights for both research and practice. We recognise the limitations inherent in examining DARQ technologies as a bundled set. While they share common characteristics, each technology has unique readiness requirements and challenges. For instance, the boundary conditions of readiness factors for AI, with its reliance on data processing and machine learning, may differ significantly from those for QC, which requires specialised hardware and knowledge. Future research could benefit from disaggregating DARQ technologies to explore specific boundary conditions, allowing for a more granular understanding of how organisations can tailor their readiness strategies to each technology. Finally, ample opportunities for further research exist within the reported dimensions of DARQ readiness. The provided table in the supplementary document (Table A) compiles potential research questions related to these research directions.

We also encourage future scholars to adopt a longitudinal lens to examine how firms’ readiness evolves. Tracking readiness factors over time and across different technology life cycles, industries and market conditions could provide a richer, more nuanced and dynamic understanding of how organisations adopt and fare with various technologies. Such research could explore temporal shifts in readiness priorities, for instance, how early-stage readiness emphasises infrastructural capabilities while later stages focus on governance, integration and ethical considerations (Setyowati et al., 2023; Pant et al., 2024).

DARQ technologies offer much transformative potential for organisations, with AI alone projected to experience exponential growth (Davenport et al., 2020). Nevertheless, many firms hesitate when it comes to adopting DARQ technologies. A closer examination of this phenomenon reveals a range of trade-offs with which organisations must grapple, such as managers’ and employees’ preference for the status quo (Kim and Kankanhalli, 2009), training and upskilling requirements (Papagiannidis et al., 2023) and ethical considerations surrounding responsible use of DARQ technologies (Du and Xie, 2021). These factors can make managers feel ill-prepared to navigate the challenges associated with adopting DARQ technologies, potentially resulting in missed opportunities and lost investments. To tackle this concern, organisations should focus on improving their DARQ readiness before implementation.

Our systematic review explores organisational DARQ readiness, synthesising and analysing a comprehensive body of research. Through this exploration, several key themes and insights have emerged, shedding light on the dimensions of DARQ readiness across three main categories: organisational, technological and environmental factors. In addition, we examine the antecedents and consequences of DARQ readiness, providing valuable insights into the broader implications of DARQ readiness. We conclude with a roadmap for future research endeavours.

A key insight from this review is the disproportionate focus of DARQ readiness research on adoption and performance outcomes, with less attention given to marketing-specific implications, such as customer engagement, brand trust and digital relationship management. This gap is surprising given the growing recognition of technologies like AI and blockchain in shaping digital marketing strategies (e.g. Mahmoud et al., 2025; Mouritzen et al., 2024). Furthermore, while the TOE framework is frequently used to categorise readiness dimensions, there is a lack of empirical studies examining how these three dimensions and their sub-dimensions interact.

Another noteworthy observation is the inconsistency in how readiness is conceptualised across disciplines. For instance, while information systems and operations research tend to emphasise infrastructural and technical capabilities (e.g. Chittipaka et al., 2022), marketing and management studies often highlight softer dimensions like employees’ attitudes and motivation (Behl et al., 2022b). This divergence points to a need for more integrative and cross-disciplinary approaches in DARQ readiness research. The review also reveals a lack of longitudinal perspectives on DARQ readiness. Given the rapid evolution of these technologies, what qualifies as readiness today may differ in a few years, yet temporal dynamics remain, for now, underexplored.

Moreover, our review highlights that organisational readiness for DARQ technologies is not solely dependent on technical infrastructure but is equally influenced by organisational culture, leadership commitment and employee engagement. This finding aligns with the broader literature on technology adoption and readiness, emphasising the need for a holistic approach to readiness (Issa et al., 2022; Papagiannidis et al., 2023). Furthermore, while AI and blockchain readiness are relatively well-studied, with established integration frameworks, research on XR and QC readiness is still in its infancy, indicating that these areas require further exploration.

From a managerial perspective, identifying nuanced readiness dimensions provides organisations with a structured lens to evaluate and enhance their preparedness for DARQ technologies. Specifically, understanding how organisational, technological and environmental readiness factors interact could enable firms to allocate resources more strategically, design targeted capability-building initiatives and align internal processes with external market demands. Strengthening these readiness dimensions is not merely an operational imperative but a strategic necessity for firms seeking to harness DARQ technologies to improve customer engagement, achieve process efficiencies and sustain competitive differentiation in increasingly digital markets. The following subsections provide a more detailed discussion of theoretical and practical implications.

Our study takes an interdisciplinary approach, examining readiness across interconnected domains, including marketing, management and information systems. Doing so provides a holistic understanding of DARQ readiness, as these fields often overlap and share a common focus on implementing advanced technologies. By integrating insights from each discipline, our study complements prior domain-specific reviews on AI readiness (e.g. Budhwar et al., 2022; Enholm et al., 2022) and promotes cross-fertilisation between fields. This cross-disciplinary view is also key to deepening our understanding of DARQ readiness, as each domain addresses similar challenges from unique vantage points.

Firstly, our analysis reveals several frequently applied theories and models, including the TOE framework (Tornatzky et al., 1990), the TAM (Davis, 1989) and the TRI (Parasuraman, 2000), among others, that can help foster a metatheoretical lens through which to explore DARQ. In so doing, we can integrate organisational and individual as well as cross-disciplinary perspectives (which rarely intersect) to deepen our understanding of DARQ adoption and use.

Secondly, our study addresses critical gaps in the literature regarding the outcomes and antecedents of organisations’ DARQ readiness. Specifically, our findings reveal an imbalance in the focus of existing research: while specific outcomes, such as the impact of DARQ readiness on firm performance (e.g. Denicolai et al., 2021; Bag et al., 2022) and adoption intention (e.g. Deng et al., 2022; Guan et al., 2023), have received substantial attention, other key outcomes – such as organisational resilience, marketing agility and innovation capability – remain underexplored. Also, our analysis identifies a research gap on the antecedents of DARQ readiness. Although some studies have begun to address these factors (e.g. Hradecky et al., 2022; Wang and Pan, 2022), a more systematic investigation is needed. Addressing these limitations, our study provides a basis for future research to broaden its examination of outcomes and antecedents, deepening our understanding of DARQ readiness.

Our work reveals several future research avenues that can further advance our understanding of organisational DARQ readiness (see also Section 5 for future research directions). One key area here is the empirical exploration of the effects of various stakeholders, such as customers, employees and business partners, on firms’ DARQ readiness and the mechanisms through which these effects occur. To enable more rigorous empirical research in the field, developing and validating measures of organisational readiness for various DARQ technologies would also be helpful. In short, our study furnishes a list of research questions as a starting point for future investigations aimed at advancing our understanding of DARQ readiness and exploring the combined application of these technologies.

Our SLR offers several practical insights for organisations striving to enhance their DARQ readiness. Given the multidimensional nature of DARQ technologies, organisations should adopt a holistic readiness strategy that considers technical infrastructure, organisational culture, employee engagement and leadership commitment. DARQ technologies each bring unique capabilities and challenges; hence, organisations should avoid siloed readiness strategies. Instead, they should develop an integrated readiness approach that accommodates DARQ technologies (and other emerging technologies) within a cohesive framework (Ghobakhloo and Iranmanesh, 2021). Such an approach entails creating flexible, adaptable policies and resources that can be applied to adopting multiple technologies, allowing organisations to respond to evolving digital trends. For example, establishing cross-functional DARQ teams can facilitate knowledge-sharing and foster a collaborative environment that embraces multiple technological domains.

Moreover, to enhance DARQ readiness, organisations should prioritise building a skilled workforce equipped to operate and manage these advanced technologies. Targeted training in DARQ technologies is essential to cultivate employees’ technical competencies and foster digital fluency (Johnk et al., 2021). For instance, AI ethics training can prepare employees to address potential biases (Papagiannidis et al., 2023; Pant et al., 2024), while upskilling initiatives in blockchain can build trust in decentralised systems (Pólvora et al., 2020). Furthermore, employee engagement programmes, including workshops on the strategic value of DARQ, can encourage buy-in and reduce resistance to technology adoption (Trocin et al., 2021).

DARQ technologies are highly data-driven, requiring robust data governance frameworks to ensure privacy, security and implementation quality. Organisations should establish comprehensive data governance policies, especially when dealing with sensitive customer or organisational data (Seethamraju and Hecimovic, 2022). Investing in data governance not only protects the organisation but also builds trust among stakeholders, supporting smoother adoption of DARQ technologies. Also, as our findings revealed, collaboration with external stakeholders, including technology vendors, research institutions and industry partners, is necessary for DARQ readiness. Partnerships with technology providers can facilitate access to resources, training and technical expertise, enhancing readiness without incurring excessive costs (Chatterjee et al., 2021; Ghobakhloo and Iranmanesh, 2021). In addition, organisations can benefit from alliances within their industry to share best practices, accelerate adoption and keep abreast of regulatory changes relevant to DARQ technologies (Hopkins, 2021).

Finally, with the rapid evolution of DARQ technologies, it is key for organisations to continuously assess and update their readiness strategies. Ongoing evaluations of technology readiness, coupled with agile adaptation processes, enable managers to stay responsive to technological shifts (Tjebane et al., 2022). By setting clear metrics to track progress and pinpointing areas for improvement, organisations can align their DARQ readiness efforts with the pace of technological advancements.

A limitation of this review is the inherent temporal variability of organisational readiness factors. Readiness enablers, drivers and barriers are often context- and time-sensitive, evolving alongside technological advancements and market dynamics. For instance, factors like data availability, which were significant barriers in early AI adoption (e.g. Johnk et al., 2021), might have become less critical owing to cloud computing and big data platforms, while new challenges such as algorithmic transparency and ethical governance have emerged (Setyowati et al., 2023; Pant et al., 2024). While acknowledging that readiness factors evolve, this review is designed to synthesise the extant literature, providing a state-of-the-art snapshot, not a temporal analysis. Longitudinal shifts in readiness factors are best examined through empirical studies specifically designed to capture such dynamics, which fall beyond the scope of this review.

Moreover, while aggregating AI, DLT, XR and QC is a key feature of our work, it also represents a limitation. This approach (while valuable for capturing collective readiness for DARQ-driven digital transformation) may inadvertently overlook critical differences in technological maturity, adoption rates and technology-specific readiness nuances. For example, infrastructure demands, regulatory considerations and market acceptance can vary significantly across these technologies, potentially influencing their individual readiness trajectories. Although our integrative perspective effectively highlights the broad capabilities needed for leveraging DARQ technologies, we recognise that certain fine-grained distinctions (such as sector-specific challenges or regional adoption barriers) may not be fully captured in our work. Addressing these nuances could further enrich understanding and strategic planning for DARQ-enabled innovations.

Finally, although our review explores organisational readiness for DARQ technologies across contexts, we did not conduct a sector-specific analysis to determine which industries are leading or lagging in adoption. Future research could build on this foundation to explore industry-level readiness trends and sector-specific barriers or enablers.

This study reviews the growing yet fragmented literature on firms’ DARQ readiness, shedding light on how organisations prepare for and respond to the disruptive potential of DLT, AI, XR and QC. Our review reveals that organisational readiness for DARQ extends well beyond technical infrastructure, involving strategic, cultural and human dimensions that shape firms’ ability to capitalise on emerging technologies. More specifically, we delineate how DARQ readiness underpins three core dimensions of marketing: internal, external and interactive marketing. The review contributes to conceptual clarity and theoretical integration in a field marked by definitional ambiguity and disciplinary silos. Our findings also highlight a pronounced imbalance in the existing literature: while AI and blockchain readiness are relatively well established, XR and QC remain underexplored and the interactions among technological, organisational and environmental factors are insufficiently theorised, offering fertile ground for future research.

The supplementary material for this article can be found online.