Dynamic Capabilities and Digital Agility: A Framework for Strategic Adaptation

“Dynamic Capabilities and Digital Agility: A Framework for Strategic Adaptation”

Manisha P A, Prof Ramya H P

Department of Management Studies, Dayananda Sagar College of Engineering, Bangalore

DOI: https://doi.org/10.51584/IJRIAS.2025.100600105

Received: 18 June 2025; Accepted: 21 June 2025; Published: 15 July 2025

INTRODUCTION

In today’s business environment, marked by volatility, uncertainty, complexity, and ambiguity (VUCA), organizations face immense pressure to continuously adapt and evolve in order to remain competitive and sustainable. The VUCA paradigm, popularized by the U.S. Army War College to describe the post-Cold War world, has since been widely adopted in management literature to represent the dynamic challenges businesses encounter in the 21st century. Organizations now operate in environments where technological disruptions, regulatory changes, shifting consumer preferences, geopolitical tensions, and unexpected crises like the COVID-19 pandemic can rapidly alter market dynamics (Bennett & Lemoine, 2014). In such an environment, agility—defined as the ability to sense, respond, and adapt quickly to change—has become a crucial organizational capability. However, agility alone is not sufficient; it must be anchored in strategic foresight and enabled through dynamic capabilities and digital infrastructure. This necessitates a deeper understanding of how firms can develop and deploy digital agility in tandem with dynamic capabilities to achieve sustained strategic adaptation.

Despite widespread digital transformation initiatives across industries, many organizations continue to struggle with translating these investments into sustained strategic advantage. While investments in cloud computing, artificial intelligence (AI), data analytics, and digital platforms have increased, the ability to effectively integrate and align these technologies with strategic goals remains inconsistent (Fitzgerald et al., 2013). This points to a significant research and practice gap: a lack of understanding about how digital tools and capabilities can be operationalized to enhance strategic agility rather than merely automate existing functions. Studies suggest that digital transformation initiatives often remain siloed within IT departments or focus on operational efficiency rather than business model innovation or long-term competitiveness (Westerman et al., 2014). Consequently, organizations fail to fully realize the transformative potential of their digital investments.

Moreover, the dynamic capabilities framework (Teece, Pisano, & Shuen, 1997)—which highlights an organization’s capacity to sense opportunities and threats, seize them, and transform resources accordingly—offers a robust lens through which to analyze strategic adaptation. Yet, while the framework has been widely applied in strategic management research, its integration with emerging constructs like digital agility remains underexplored. Digital agility refers to an organization’s ability to rapidly reconfigure its digital resources, processes, and competencies in response to internal and external changes (Lu & Ramamurthy, 2011). The intersection between dynamic capabilities and digital agility, therefore, presents a promising area for inquiry. Understanding how these two constructs interact can offer valuable insights into how firms can enhance their adaptability in an increasingly digital and disruptive world.

The primary purpose of this chapter is to explore how dynamic capabilities and digital agility intersect to foster strategic adaptation in VUCA environments. By drawing upon both theoretical insights and real-world case examples, the chapter aims to provide a conceptual framework that bridges the gap between digital transformation efforts and strategic management practices. It examines how firms can build sensing, seizing, and transforming capabilities through digitally agile infrastructures and cultures. Furthermore, it considers the role of leadership, organizational learning, and data-driven decision-making in operationalizing these capabilities. The chapter also seeks to provide actionable insights for managers and policymakers looking to align technological investments with long-term strategic goals. Ultimately, the objective is to contribute to the growing body of literature on organizational agility and strategic renewal, while offering a timely response to the challenges posed by ongoing digital disruption.

The structure of the chapter is designed to systematically build an understanding of the key constructs and their interlinkages. The next section provides a comprehensive literature review on dynamic capabilities and digital agility, identifying key themes, definitions, and theoretical frameworks. This is followed by a detailed analysis of the intersection between these constructs, highlighting how digital tools can be embedded within the dynamic capabilities framework to enhance strategic responsiveness. The chapter then presents several real-world case studies from industries such as healthcare, retail, and manufacturing to illustrate how leading firms are leveraging this intersection to navigate uncertainty and maintain competitiveness. In the subsequent section, a conceptual framework is proposed, outlining the antecedents, enablers, and outcomes of integrating digital agility with dynamic capabilities. This includes a discussion of organizational culture, leadership styles, governance mechanisms, and talent development strategies that support such integration. Finally, the chapter concludes by reflecting on managerial implications, research limitations, and avenues for future inquiry.

In synthesizing the existing research, this chapter draws upon interdisciplinary literature from strategic management, information systems, organizational behavior, and innovation studies. It adopts a holistic view that situates digital agility not merely as a technical or operational function, but as a strategic imperative that must be integrated into the core decision-making processes of the firm. As recent studies have shown, organizations that exhibit both high digital maturity and dynamic capability development are better positioned to thrive in uncertain environments (Warner & Wäger, 2019). These organizations are not only faster at responding to market shifts but also more innovative in reimagining their value propositions. Thus, this chapter aims to offer both academic and practical contributions by providing a framework for understanding how digital agility, when embedded within the dynamic capabilities paradigm, can serve as a powerful lever for strategic adaptation in the digital age.

Theoretical Foundations

The concept of dynamic capabilities has emerged as a central pillar in the field of strategic management, particularly in the context of rapidly changing and unpredictable environments. Originally introduced by Teece, Pisano, and Shuen (1997), the dynamic capabilities framework offers a lens through which to understand how firms can achieve and sustain competitive advantage not through static resource endowments, but through the ability to renew, integrate, and reconfigure internal and external competencies. At its core, the framework identifies three essential capacities: sensing opportunities and threats, seizing opportunities through resource mobilization and investment, and transforming or reconfiguring the organization’s resource base to maintain relevance and competitiveness over time. This dynamic approach contrasts with earlier resource-based views that emphasized the rarity and inimitability of assets, and instead focuses on continuous evolution and strategic responsiveness.

In the digital era, the relevance of dynamic capabilities has only intensified. With industries being disrupted by digital innovations such as artificial intelligence, blockchain, the Internet of Things (IoT), and advanced analytics, organizations must move beyond one-time transformations to embrace continuous change (Teece, 2018). The “sensing” capability now includes sophisticated data-driven forecasting, real-time market scanning, and leveraging digital channels for customer insights. “Seizing” involves not only launching new products or services but also implementing digital business models and monetizing data assets. “Transforming” has expanded into areas such as re-skilling the workforce, building platform-based ecosystems, and redesigning organizational architectures to support digital collaboration and agility. In essence, the dynamic capabilities framework remains foundational but must be adapted and expanded to incorporate the new digital imperatives of speed, experimentation, and scalability (Warner & Wäger, 2019).

Digital agility is a closely related and increasingly critical construct that refers to an organization’s ability to rapidly adapt its digital infrastructure, processes, workforce competencies, and culture in response to changes in the external environment. Unlike traditional agility, which might focus solely on structural flexibility or process reengineering, digital agility encompasses technological adaptability (e.g., cloud computing, modular IT systems), agile processes (e.g., DevOps, design thinking, lean start-up methodologies), empowered and digitally literate teams, and an organizational culture that promotes innovation, transparency, and rapid decision-making (Ravichandran, 2018). These elements work in concert to enable organizations to sense opportunities more quickly and respond more effectively through digital means.

For example, digital agility in technology refers to the deployment of scalable and modular IT architectures that can support fast experimentation and low-latency operations. Process agility involves the ability to quickly redesign workflows or pivot operational models in response to emerging customer needs. People agility is about equipping employees with digital skills and decision-making autonomy, while cultural agility supports a mindset of continuous learning, calculated risk-taking, and cross-functional collaboration. Together, these dimensions enable firms to thrive in environments characterized by rapid technological advancement and shifting consumer expectations (Lu & Ramamurthy, 2011). Importantly, digital agility is not an end in itself but serves as an enabler of broader strategic objectives, such as innovation, market responsiveness, and value creation.

In the context of a VUCA world, the interplay between dynamic capabilities and digital agility becomes especially salient. Strategic adaptation—the ongoing process through which firms align their strategies, structures, and capabilities with a changing environment—requires more than just the ability to anticipate or react to change. It requires building resilient systems that can proactively shape the future. Organizations that demonstrate high levels of agility and dynamic capabilities are better equipped to navigate shocks, seize fleeting opportunities, and experiment with new business models under conditions of uncertainty (Doz & Kosonen, 2010). For instance, during the COVID-19 pandemic, companies that rapidly shifted to digital channels, reconfigured supply chains, and empowered teams to act autonomously were able to outperform less agile counterparts (Verhoef et al., 2021). Thus, strategic adaptation is not merely reactive but involves intentional and continuous renewal processes supported by both digital and organizational agility.

The integration of digital agility into the dynamic capabilities framework offers a dual-capability perspective that addresses current gaps in both theory and practice. While dynamic capabilities provide a high-level strategic understanding of how firms adapt and evolve, digital agility offers the practical, operational mechanisms through which this adaptation can occur in real-time. This synthesis helps bridge the traditional divide between strategic intent and execution by emphasizing that the sensing, seizing, and transforming capabilities of an organization are increasingly mediated by its digital competencies (Teece, Peteraf, & Leih, 2016). For example, the ability to sense market shifts today often depends on big data analytics and AI-driven customer insight tools. Seizing opportunities may involve leveraging digital platforms to rapidly test and scale new offerings. Transforming, in turn, requires not only structural flexibility but also digital fluency and change-ready leadership.

This dual-capability perspective underscores the importance of investing in both strategy and technology—not as separate initiatives but as mutually reinforcing dimensions of organizational adaptation. It also suggests that firms must develop integrated capabilities that are flexible, fast, and future-oriented. As organizations become more embedded in digital ecosystems and reliant on intangible assets like data and algorithms, the convergence of digital agility and dynamic capabilities will define the next frontier of competitive advantage. Future research must continue to explore how firms can institutionalize these capabilities, measure them effectively, and align them with long-term strategic vision in a way that is sustainable, ethical, and inclusive.

Developing digital agility requires a robust ecosystem of organizational enablers that go beyond adopting advanced technologies. These enablers serve as foundational elements that collectively empower organizations to sense shifts in the environment, respond quickly, and continuously adapt in the face of uncertainty. Drawing from recent scholarly work and industry best practices, four key enablers stand out as critical to building digital agility: a data-driven organizational culture, technological infrastructure and digital tools, agile leadership and cross-functional teams, and systems that foster learning and innovation. These enablers are deeply interrelated and must function in harmony to ensure an organization’s capacity for sustained strategic adaptation.

A data-driven organizational culture is central to enabling digital agility. This involves cultivating a mindset and set of practices that prioritize data as a strategic asset and embed data analytics into all levels of decision-making. In digitally agile organizations, decisions are not based merely on intuition or hierarchy but are informed by real-time data, predictive analytics, and evidence-based insights (McAfee & Brynjolfsson, 2012). Embedding analytics into daily operations allows companies to sense market changes, customer preferences, and operational inefficiencies swiftly, thereby enabling faster and more accurate strategic responses. However, one of the most persistent challenges in building such a culture is the existence of data silos—fragmented systems and departments that restrict data sharing and integration. Breaking down these silos requires investments in data governance, interoperable platforms, and a shared data vision across departments (Davenport & Bean, 2018). Moreover, democratizing access to data—ensuring that not just data scientists but all relevant employees have the tools and training to interpret and use data—fosters a culture of empowerment and rapid action.

Equally important is the presence of a modern technological infrastructure and a suite of digital tools that provide the necessary capabilities to act on insights quickly and effectively. Foundational technologies such as cloud computing enable scalability and flexibility, allowing organizations to experiment and deploy solutions without the burden of heavy upfront investments in IT hardware. Artificial Intelligence (AI) and Machine Learning (ML) further augment an organization’s ability to derive insights, automate decision-making, and personalize customer experiences (Westerman et al., 2014). Real-time data platforms and digital twins enable simulations and scenario planning that enhance forecasting and strategic planning capabilities. These tools, when integrated into a cohesive IT architecture, allow for the rapid prototyping, testing, and scaling of digital solutions. Importantly, the effectiveness of these technologies depends not only on their availability but also on their integration across functions and alignment with strategic objectives (Verhoef et al., 2021). Hence, technological infrastructure must be purpose-built to support experimentation, data fluidity, and cross-functional collaboration.

Another critical enabler of digital agility is agile leadership combined with empowered, cross-functional teams. In a VUCA environment, traditional hierarchical decision-making structures often prove too slow and rigid. Agile organizations require leaders who can operate in high-trust environments, foster psychological safety, and support distributed decision-making (Denning, 2018). Such leaders emphasize collaboration over control, experimentation over certainty, and learning over perfection. They model adaptability and provide clear strategic direction while empowering teams to take initiative. Cross-functional teams—comprising members from various departments such as IT, marketing, operations, and finance—bring diverse perspectives and capabilities, allowing for rapid ideation and implementation of solutions. Agile methodologies such as Scrum and Kanban support iterative development and fast feedback cycles, enabling these teams to respond to change incrementally and effectively. Decentralized governance structures further enhance agility by allowing frontline teams to make real-time decisions without waiting for top-down approvals, thus accelerating the pace of innovation.

Finally, learning and innovation systems are indispensable for cultivating an environment where digital agility can thrive. Organizations must institutionalize mechanisms that encourage continuous learning, knowledge sharing, and iterative improvement. This includes implementing feedback loops, conducting retrospectives, and using performance metrics to evaluate and refine strategies. Learning organizations promote experimentation, tolerate failure, and incentivize the discovery of new ideas (Garvin, 1993). Digital platforms such as knowledge management systems, collaboration tools, and intranet portals facilitate the sharing of insights and best practices across teams and geographies. Moreover, structured learning interventions such as digital training programs, hackathons, and innovation labs foster a culture of curiosity and capability-building. In the digital context, learning must be fast, scalable, and embedded in day-to-day operations to ensure that insights are quickly translated into action. The presence of adaptive learning systems, supported by AI-driven personalization, further ensures that employees are equipped with the most relevant skills and knowledge to respond to emerging challenges (Brynjolfsson & McAfee, 2014).

In sum, the development of digital agility is a multifaceted endeavor that requires a deliberate focus on both technological and human capabilities. A data-driven culture ensures that organizations are guided by real-time insights; a modern digital infrastructure provides the tools to act swiftly; agile leadership and empowered teams enable rapid decision-making; and learning systems institutionalize the capacity to evolve continuously. These enablers, when synergized, transform digital agility from a theoretical aspiration into a practical capability that drives resilience, adaptability, and sustained strategic performance in the digital age.

Operationalizing Dynamic Capabilities in Practice

The dynamic capabilities framework, proposed by Teece et al. (1997), identifies three key clusters of organizational activity—sensing, seizing, and transforming—which together enable firms to adapt to and shape their environments. In today’s digitally accelerated landscape, operationalizing these capabilities requires the integration of advanced technologies, adaptive processes, and proactive leadership. Rather than remaining abstract strategic concepts, dynamic capabilities must be embedded into daily routines, decision-making systems, and organizational structures. This section examines how firms can concretely enact these capabilities through digital tools and practices, thereby fostering long-term resilience, innovation, and competitive advantage.

Sensing: Scanning the Digital Horizon

Sensing refers to an organization’s capacity to detect, interpret, and anticipate shifts in markets, technologies, customer preferences, and competitive dynamics. In the digital era, sensing has evolved from intuition and periodic market research into a technology-enabled, real-time capability. Organizations are increasingly leveraging Artificial Intelligence (AI), Internet of Things (IoT) devices, and machine learning algorithms to perform continuous environmental scanning. These tools can monitor vast amounts of structured and unstructured data, identifying weak signals of change or emerging trends that might elude traditional methods (Teece, Peteraf, & Leih, 2016).

For example, AI-powered natural language processing can track social media conversations, customer reviews, and online forums to uncover early signs of shifting consumer sentiment. IoT sensors embedded in products or supply chains generate real-time operational data that can signal inefficiencies, demand surges, or emerging maintenance needs. Furthermore, firms are employing market intelligence platforms and predictive analytics to simulate various business scenarios and forecast future market trajectories (Bughin et al., 2017). By systematizing the sensing process through digital technologies, organizations not only enhance their foresight but also improve the accuracy and speed of strategic decision-making. However, effective sensing also requires strategic intent and organizational openness to external signals—fostering partnerships, participating in innovation ecosystems, and encouraging internal feedback loops are all crucial to building this capability.

Seizing: Capturing Strategic Opportunities

Once opportunities are identified, seizing refers to the firm’s ability to act upon them effectively—often by reconfiguring existing assets, creating new capabilities, or adjusting strategic focus. In the digital context, this involves rapid business model adaptation, value proposition redesign, and deployment of digital innovations that align with market shifts. Organizations must make bold investment decisions, build scalable platforms, and mobilize resources swiftly to capture the value of new opportunities (Teece, 2018).

Digital-first companies like Amazon, Netflix, and Alibaba exemplify high-performing seizing capabilities. For instance, Netflix successfully transitioned from DVD rental to streaming and later to content creation—each phase requiring the redesign of its value proposition and operational model. These transitions were enabled by leveraging data insights and building scalable digital infrastructure. Similarly, incumbent firms in traditional industries are using platform-based business models, subscription services, and ecosystem partnerships to innovate their offerings and meet evolving customer demands.

Moreover, seizing in practice requires cross-functional alignment, where marketing, product development, finance, and operations converge to develop agile go-to-market strategies. It also involves resource orchestration, including reallocation of capital and human resources toward high-potential initiatives. Decision frameworks like real options logic can help firms manage risk while pursuing multiple opportunities in parallel (Sirmon, Hitt, & Ireland, 2007). Thus, seizing is not merely about acting fast—it is about acting wisely, based on robust data and cross-functional collaboration.

Transforming: Organizational Reconfiguration

Transformation is the most complex but critical aspect of dynamic capabilities. It involves ongoing adjustments to an organization’s structure, culture, capabilities, and business processes to align with the external environment. In a digitally agile organization, transformation is not a one-off event but a continuous process enabled by talent transformation, digital process redesign, and change management.

Talent transformation involves equipping employees with new skills—especially in data literacy, digital tools, and collaborative working models. Upskilling, reskilling, and recruiting for emerging digital roles (e.g., data scientists, automation engineers, product managers) are vital for embedding digital fluency throughout the organization (Brynjolfsson & McElheran, 2016). Leading firms also promote a growth mindset, where learning from failure and iterative development are normalized as part of innovation culture.

Digital process redesign is another key pillar. Legacy systems and siloed workflows often hinder responsiveness and innovation. Agile firms adopt modular architectures, cloud-native systems, and automated workflows to enhance scalability and flexibility (Kane et al., 2015). Robotic Process Automation (RPA), AI-driven customer service, and digital supply chains are examples of how business processes are being reinvented for real-time responsiveness and efficiency.

Finally, effective transformation requires structured change management strategies to overcome resistance and align stakeholders. This includes clear communication from leadership, participatory change processes, and incentives aligned with desired behaviors. Tools like the ADKAR model or Kotter’s 8-step change process can guide the human and cultural dimensions of transformation. In digital enterprises, change becomes a core competency—continuous and embedded in everyday operations.

Collectively, these three dimensions of dynamic capabilities—sensing, seizing, and transforming—must be tightly integrated and institutionalized across all levels of the organization. Firms that operationalize these capabilities through digital technologies, empowered teams, and adaptive leadership are more resilient and better positioned to thrive in volatile and complex environments. This alignment between strategic agility and digital enablement offers a compelling model for sustained competitive advantage in the digital age.

Integrative Strategic Framework: Uniting Digital Agility and Dynamic Capabilities for Strategic Adaptation

In a business environment characterized by rapid change and heightened complexity, the integration of digital agility and dynamic capabilities emerges as a strategic imperative for organizations aiming to achieve sustained competitive advantage. While each concept individually contributes to organizational responsiveness, their combined application creates a synergistic force that enables firms to sense, seize, and transform in ways that are digitally empowered, data-driven, and strategically aligned. This integrative framework proposes a holistic model in which digital agility operates as the operational enabler of the dynamic capabilities framework, embedding technological tools and adaptive processes within the strategic cycles of the firm.

At the center of this integrative model is the Digital Agility × Dynamic Capabilities (DA × DC) Framework, which positions digital agility as the execution engine and dynamic capabilities as the strategic compass. The synergy between the two is illustrated through a visual model—a concentric cycle with three dynamic capabilities (sensing, seizing, transforming) forming the outer strategic layer, while the inner core consists of digital agility elements such as real-time data, modular technologies, empowered teams, and iterative processes. These layers are interconnected by continuous feedback loops, ensuring that each capability cycle is powered by digitally agile inputs (Teece, Peteraf, & Leih, 2016). For instance, AI tools enable faster sensing, cloud-based platforms support rapid seizing, and agile methodologies guide transformation. This cyclical, real-time model promotes continuous learning and responsiveness.

The operationalization of this framework begins with embedding digital tools into strategic planning and decision-making cycles. Traditional annual strategic planning cycles are being replaced by more agile, iterative planning processes supported by digital technologies. Tools such as predictive analytics, AI-driven dashboards, and scenario modeling software enable organizations to continuously assess internal capabilities, external threats, and emerging opportunities (Bughin et al., 2017). Predictive analytics allow firms to model demand shifts or potential disruptions, while AI-supported decision engines recommend optimized courses of action based on pattern recognition from vast data sets (Davenport & Bean, 2018). For example, in supply chain management, AI can forecast material shortages, enabling proactive procurement strategies that align with broader organizational goals. Agile planning cycles, often structured around quarterly objectives and key results (OKRs), allow teams to adjust priorities and reallocate resources based on real-time data and evolving market conditions.

The effectiveness of the DA × DC framework depends heavily on leadership and governance structures that support cross-functional collaboration, data transparency, and rapid decision-making. Digital leadership involves not only technical fluency but also the ability to lead change, foster innovation, and build a culture of continuous learning (Kane et al., 2019). Leaders must embody digital-first mindsets, promote psychological safety, and encourage experimentation. Models such as distributed leadership and network-based governance are increasingly replacing hierarchical command-and-control systems. In digitally agile organizations, decision rights are often delegated closer to the front lines, allowing teams to respond to localized conditions while remaining aligned with strategic goals.

Equally important is the presence of robust data governance frameworks, which ensure that data quality, security, and ethical use are maintained across the organization. As data becomes central to sensing and decision-making activities, governance must balance access with control. Organizations are adopting data stewardship models in which roles and responsibilities for data quality, lineage, and compliance are clearly defined. Frameworks like the FAIR principles (Findable, Accessible, Interoperable, Reusable) are being used to guide the responsible management of data assets (Wilkinson et al., 2016). Additionally, as AI becomes embedded in decision-making processes, governance mechanisms are needed to ensure algorithmic transparency, bias mitigation, and accountability (Gasser & Almeida, 2017).

The integrated strategic framework also has a cultural component. High-performing organizations intentionally develop cultures that reinforce agility and learning. Through mechanisms such as continuous feedback loops, real-time performance dashboards, and internal innovation hubs, firms embed agility into their DNA. This cultural shift is supported by internal branding and storytelling that celebrates agility, responsiveness, and innovation as core organizational values.

Taken together, this integrative framework redefines strategic adaptation in the digital age. It demonstrates that the path to long-term competitiveness lies not in choosing between digital transformation and strategic agility, but in orchestrating both as a unified system. By embedding digital agility within the dynamic capabilities paradigm, firms can ensure that they are not only reacting to change but actively shaping their environments in a way that is fast, flexible, and forward-thinking. Future research and practice must focus on refining this integration, measuring its outcomes, and building the leadership and governance structures that can sustain it in a VUCA world.

CASE STUDY ANALYSIS: Operationalizing Digital Agility and Dynamic Capabilities

To understand the real-world application of the integrated Digital Agility × Dynamic Capabilities (DA × DC) framework, it is essential to examine how leading firms across sectors have successfully deployed these capabilities to adapt, innovate, and compete in volatile environments. This section presents three case studies—Amazon, DBS Bank, and Siemens—each representing a distinct industry, followed by a comparative analysis that distills cross-case insights and strategic implications. These examples highlight how sensing, seizing, and transforming are enabled by digital tools, cultural adaptability, and strategic foresight.

Amazon (E-commerce/Technology): Anticipatory Capabilities and Platform Agility

Amazon exemplifies the deep integration of digital agility with dynamic capabilities, making it one of the most adaptive and dominant players in global e-commerce and cloud services. At the heart of Amazon’s success is its data-driven logistics system, powered by machine learning algorithms, real-time data platforms, and predictive analytics. Amazon’s sensing capability is built through massive data collection across customer interactions, browsing behavior, purchase patterns, and supply chain nodes. Its patented “anticipatory shipping” model uses predictive analytics to pre-ship products to regional warehouses before a customer places an order (Patent No. US8615473B2), minimizing delivery time and increasing customer satisfaction.

Amazon’s ability to seize opportunities lies in its relentless expansion and platform-based innovation. It has continuously redefined its value proposition, moving from an online bookstore to a cloud computing giant (Amazon Web Services), media producer (Prime Video), and logistics powerhouse. Its cloud-based infrastructure, APIs, and modular architecture enable the company to test, scale, and integrate new services rapidly, showcasing its platform agility (Dastin, 2018). Meanwhile, the company’s transformation capability is supported by a strong innovation culture, decentralization, and leadership principles that empower teams to “think big” and “invent and simplify.” Amazon’s two-pizza team rule—keeping teams small and autonomous—enhances cross-functional collaboration and decision-making speed (Denning, 2018).

DBS Bank (Finance): Agile Transformation and Digital Leadership

DBS Bank, headquartered in Singapore, offers a compelling case of agile transformation in the financial sector, a traditionally risk-averse and highly regulated industry. Faced with fintech disruption and changing customer expectations, DBS undertook a radical digital transformation initiative under CEO Piyush Gupta’s leadership. The bank integrated dynamic capabilities by embedding sensing mechanisms such as customer journey mapping, digital sentiment analysis, and real-time feedback systems to anticipate market trends and behavioral shifts (Chong et al., 2020).

To seize these opportunities, DBS shifted from product-centric to customer-centric service delivery through agile squads, design thinking, and rapid prototyping. The bank institutionalized “platform thinking,” leveraging APIs to co-create with fintech startups and partners. It also embraced digital experimentation by adopting a “fail fast, learn fast” culture, running over 1,000 experiments annually within its innovation group (DBS, 2021). Transformation was guided by strong digital leadership, investments in AI and cloud computing, and a cultural shift encapsulated in its vision to be “the best bank for a better world.” The organizational redesign included flattening hierarchies, reskilling employees, and embedding digital KPIs into performance evaluations, reflecting a comprehensive change management approach.

Siemens (Industrial/Technology): Modular Innovation and Ecosystem Agility

Siemens, a global industrial technology leader, has successfully blended traditional engineering excellence with cutting-edge digital innovation. In the context of Industry 4.0, Siemens operationalizes dynamic capabilities through modular product design, digital twins, and ecosystem collaboration. Its sensing capability is exemplified by its MindSphere platform—an open, cloud-based IoT operating system that aggregates sensor data from industrial equipment to detect performance anomalies and predict maintenance needs (Siemens, 2021).

The company’s seizing capability is visible in its proactive shift from selling standalone hardware to providing integrated digital services. Siemens continuously redesigns its value proposition to offer predictive maintenance, smart factory solutions, and energy optimization tools, often through co-innovation with startups, governments, and research institutions. It has established Siemens Next47, a global innovation and venture arm, to incubate emerging technologies and new business models.

Transformation at Siemens is driven by talent upskilling, a commitment to digital engineering, and agile governance structures. The company has restructured its internal divisions to support digital businesses and invested in developing new digital competencies among its workforce. Furthermore, Siemens’ strategic partnerships with players like SAP and IBM enable ecosystem agility, allowing it to leverage external capabilities to accelerate its transformation (Ghosh, 2020).

Comparative Insights: Cross-Case Analysis and Strategic Learning

A comparative analysis of Amazon, DBS Bank, and Siemens reveals several converging themes and strategic takeaways:

1. Digital Infrastructure as an Enabler: All three firms have invested significantly in foundational digital infrastructure—cloud computing (Amazon, DBS), IoT and AI platforms (Siemens)—that supports scalability and real-time responsiveness.
2. Leadership and Culture as Catalysts: Each organization highlights the pivotal role of visionary leadership and cultural transformation. Whether it is Jeff Bezos’s long-term orientation, Piyush Gupta’s agile leadership, or Siemens’ CEO Roland Busch’s digital-first mandate, leadership commitment is critical to embedding agility and dynamic capabilities.
3. Experimentation and Modular Design: Agile experimentation is central to seizing opportunities, seen in Amazon’s continuous innovation cycles, DBS’s test-and-learn environments, and Siemens’ modular engineering. Modular design reduces risk and speeds up transformation.
4. Cross-Functional Teams and Governance Flexibility: All cases showcase how cross-functional collaboration and decentralized governance enhance agility. Teams are empowered to act autonomously while aligned to organizational strategy.
5. Sustainability and Ecosystem Engagement: Particularly in the case of Siemens, ecosystem partnerships and sustainability agendas are becoming vital components of transformation strategies, signaling an evolution from internal agility to ecosystem-level agility.

These insights reinforce the value of an integrated DA × DC framework in guiding strategic decision-making. While contexts differ, the underlying mechanisms—data-driven sensing, agile seizing, and transformational adaptability—remain universally relevant across industries. Future-ready organizations will be those that not only digitize but institutionalize digital agility within their dynamic capabilities portfolio.

Challenges and Organizational Barriers in Implementing Digital Agility and Dynamic Capabilities

Despite the clear strategic benefits of integrating digital agility with dynamic capabilities, many organizations face persistent internal and external barriers that hinder successful implementation. These challenges often stem from entrenched systems, outdated organizational practices, and the complexity of digital transformation itself. This section explores four key categories of challenges: legacy system constraints, talent and capability gaps, cultural resistance, and data security and ethical governance. Understanding and addressing these barriers is critical for firms seeking to operationalize agility in a sustainable and scalable manner.

Legacy System Constraints

One of the most prominent obstacles to digital agility is the burden of legacy IT systems. These outdated and often monolithic systems—common in large, established firms—lack the flexibility required to support modularity, real-time data access, and scalable integration with modern digital tools (Westerman et al., 2014). The technical debt accumulated over decades makes it challenging to connect new cloud-based applications, AI systems, or Internet of Things (IoT) devices to existing enterprise resource planning (ERP) systems or on-premises databases. These integration hurdles not only slow down digital innovation but also increase operational risk and IT maintenance costs (Brenner & Uebernickel, 2016).

Furthermore, reliance on legacy infrastructure limits the organization’s sensing capability, as such systems often lack the ability to collect and analyze real-time data. This weakens responsiveness and undermines the feedback loops necessary for transformation. Organizations that attempt to “bolt on” new technologies without re-architecting their systems often encounter performance bottlenecks, data silos, and operational inconsistencies. A phased migration to microservices architecture, supported by robust change management, is often necessary to address these constraints (Fitzgerald & Stol, 2017).

Talent and Capability Gaps

A second significant challenge is the persistent skills mismatch between existing workforce competencies and the capabilities required in a digitally agile environment. As organizations adopt advanced technologies—ranging from cloud infrastructure and data analytics to AI and cybersecurity—the demand for digital fluency far outpaces supply. According to a McKinsey Global Survey (2021), nearly 87% of executives reported current or anticipated skill gaps in their workforce, particularly in areas like analytics, user experience design, and agile project management.

Workforce readiness also involves mindset adaptation, not just technical upskilling. Employees trained in legacy systems or traditional project management models often struggle with iterative methodologies like Scrum, design thinking, or lean start-up. The lack of digital talent inhibits the organization’s ability to seize emerging opportunities, build innovative products, or execute transformation strategies effectively. Addressing this gap requires comprehensive learning and development programs, partnerships with educational institutions, and internal mobility pathways that allow employees to transition into digital roles (Brynjolfsson & McElheran, 2016).

Cultural Resistance and Hierarchical Structures

Even with the right technology and skills, organizations may fail to achieve agility due to cultural resistance and entrenched hierarchical structures. Traditional corporate cultures often emphasize control, predictability, and risk aversion—values that conflict with the experimentation, speed, and adaptability required for digital success. Organizational inertia manifests as resistance to change, slow decision-making, and reluctance to abandon legacy practices (Kotter, 2012).

Hierarchical structures exacerbate this problem by centralizing decision-making and discouraging cross-functional collaboration. This siloes innovation efforts, limits responsiveness, and prevents the decentralized decision-making that characterizes digitally agile firms. Furthermore, middle management layers may feel threatened by changes that shift authority toward autonomous teams. Overcoming this challenge involves fostering a growth mindset, promoting psychological safety, and redesigning leadership models to prioritize empowerment and transparency (Denning, 2018).

Change management frameworks like Kotter’s 8-Step Process or Prosci’s ADKAR model can be instrumental in guiding cultural transformation. However, these initiatives must be owned and role-modeled by top leadership and supported by systems of rewards, recognition, and accountability aligned with agile values.

Data Security and Ethical Considerations

As firms digitize operations and embed AI into decision-making, data governance and ethical concerns become critical challenges. With vast volumes of data being collected, stored, and analyzed, organizations face increasing scrutiny over how personal and sensitive information is used. Regulatory frameworks such as the General Data Protection Regulation (GDPR) in Europe and the emerging AI Act place strict obligations on data protection, transparency, and algorithmic accountability (European Commission, 2021).

Poor data governance can lead to breaches, compliance violations, and reputational damage, undermining both customer trust and regulatory standing. Furthermore, the use of AI and machine learning raises concerns about bias, fairness, and explainability. If not properly managed, these tools can amplify existing social and organizational biases, leading to discriminatory outcomes or opaque decision-making (Gasser & Almeida, 2017).

To build trust and legitimacy, organizations must develop ethical AI policies, establish cross-functional data governance councils, and implement privacy-by-design principles. Cybersecurity capabilities must also be enhanced to protect against evolving digital threats, especially as firms move toward open ecosystems and platform-based models. Embedding these safeguards is not merely a compliance exercise—it is essential for sustaining dynamic capabilities in an increasingly transparent and accountable digital economy.

In conclusion, the road to digital agility and dynamic capabilities is fraught with structural, cultural, and ethical challenges. Legacy infrastructure constrains flexibility, talent gaps limit execution, organizational cultures resist change, and regulatory pressures demand responsible data use. These barriers are interdependent and must be addressed through a holistic transformation agenda that spans systems, skills, governance, and culture. Only by acknowledging and systematically overcoming these challenges can firms fully realize the potential of strategic agility in the digital era.

Managerial Implications and Strategic Recommendations

The convergence of digital agility and dynamic capabilities presents not only a theoretical construct but a practical roadmap for organizations seeking to remain competitive in volatile, uncertain, complex, and ambiguous (VUCA) environments. However, the realization of this integrative framework requires coordinated efforts from diverse stakeholders—senior executives, digital transformation leaders, and policymakers/educators. This section outlines tailored strategic recommendations for each stakeholder group, with actionable insights that bridge the gap between concept and execution.

Implications for Senior Executives: Strategic Prioritization and Performance Metrics

For senior executives—CEOs, CFOs, and members of the board—the primary implication is the need to recalibrate investment priorities toward initiatives that simultaneously build digital agility and dynamic capabilities. This involves allocating resources not just to infrastructure upgrades, but also to culture-building, upskilling, and organizational redesign. Investment decisions should favor scalable, interoperable technologies (e.g., cloud-native platforms, AI/ML models, low-code tools) that support rapid sensing, seizing, and transforming (Teece, Peteraf, & Leih, 2016).

Equally important is the establishment of agility-centric performance metrics. Traditional KPIs such as return on investment (ROI) and quarterly profit must be complemented by agility indicators like time-to-market, innovation velocity, rate of digital adoption, cross-functional team throughput, and customer feedback loops (Rigby, Sutherland, & Noble, 2018). Incorporating these metrics into enterprise dashboards and executive scorecards can help ensure alignment between corporate strategy and agile execution.

Executives must also set the tone from the top by championing digital culture, communicating a compelling vision for change, and modeling adaptive behaviors. Transformational leadership—characterized by vision articulation, inspirational motivation, and individualized consideration—can significantly enhance organizational readiness for digital adaptation (Bass & Riggio, 2006).

Implications for Digital Transformation Leaders: Implementation Roadmaps and Agile Change Strategies

For Chief Digital Officers (CDOs), CIOs, and heads of transformation, the key challenge is operationalizing the DA × DC framework through actionable roadmaps that balance short-term wins with long-term capability building. A successful digital transformation roadmap must be modular, allowing for parallel experimentation and phased implementation across business units.

An effective roadmap begins with capability audits to assess the current state of digital maturity, followed by the identification of strategic use cases aligned with dynamic capabilities—such as predictive maintenance, personalized customer journeys, or data-driven decision engines. Each initiative should be structured around agile change strategies, including cross-functional agile squads, iterative sprints, user feedback integration, and minimum viable product (MVP) development (Westerman et al., 2014).

Change management must be embedded in the transformation process. This includes structured interventions such as digital bootcamps, internal evangelism networks, and communities of practice that promote digital fluency and organizational buy-in. Tools like the Scaled Agile Framework (SAFe) can be used to manage agile delivery at enterprise scale, ensuring that strategy and execution remain tightly coupled (Knaster & Leffingwell, 2020).

Furthermore, transformation leaders must ensure data governance and security integration within all digital projects. The deployment of digital tools should comply with ethical AI guidelines and data privacy regulations, ensuring trust among customers, employees, and regulators (Gasser & Almeida, 2017).

Implications for Policymakers and Educators: Digital Readiness and Talent Development

The successful deployment of digital agility and dynamic capabilities at scale also depends on the broader socioeconomic and educational ecosystem. For policymakers and educators, the central implication is the need to design and support digital readiness frameworks that can foster a digitally competent workforce and resilient digital infrastructure.

Policymakers should promote public-private partnerships to accelerate digital skilling, especially in underserved and rural populations. Initiatives like India’s Digital India mission or the European Commission’s Digital Decade policy provide blueprints for building foundational digital literacy and access to technology (European Commission, 2021). Beyond infrastructure, there is a need for regulatory agility—frameworks that promote innovation (e.g., AI sandboxes) while ensuring responsible use of data and algorithms (Wirtz et al., 2021).

Educators and academic institutions play a pivotal role in developing future-ready talent. Curricula must evolve to integrate interdisciplinary skills such as computational thinking, data interpretation, ethical reasoning, and agile management practices. Experiential learning models—such as project-based learning, simulations, and industry collaborations—can enhance students’ ability to apply dynamic capabilities in real-world settings (Brynjolfsson & McAfee, 2014).

Additionally, business schools and executive education programs should offer continuous learning pathways for mid-career professionals, equipping them to lead digital transformation initiatives. Certifications in agile leadership, design thinking, AI ethics, and digital strategy are increasingly valuable in building a future-oriented managerial workforce.

CONCLUSION

The integrative framework of digital agility and dynamic capabilities offers a powerful lens for understanding strategic adaptation in the digital era. For senior executives, it calls for bold leadership, investment in agility metrics, and vision-driven transformation. For digital leaders, it demands structured roadmaps, cross-functional agility, and disciplined change management. For policymakers and educators, it emphasizes building the institutional and human capital infrastructure necessary to support enterprise-level agility. Only through this ecosystemic collaboration can organizations, industries, and societies thrive in an increasingly complex and digitally driven world.

Future Research Directions: Advancing the Understanding of Digital Agility and Dynamic Capabilities

As digital transformation accelerates, the interplay between digital agility and dynamic capabilities continues to evolve, influenced by technological advances, sectoral dynamics, and global developments. While this chapter presents a conceptual and empirical foundation for understanding their integration, several gaps and opportunities remain for future research. This section outlines three promising directions for scholarly exploration: the strategic fit of emerging technologies, the development of standardized agility measurement frameworks, and the need for broader cross-sectoral and international comparative studies. Addressing these areas will enhance the theoretical robustness and practical applicability of the Digital Agility × Dynamic Capabilities (DA × DC) framework.

Emerging Technologies and Strategic Fit

One of the most pressing areas for future research is the exploration of how emerging technologies such as quantum computing, blockchain, and edge AI can be strategically aligned with dynamic capabilities. While current research has largely focused on established technologies like cloud computing, big data, and machine learning, the next wave of digital disruption will likely be driven by these advanced innovations.

Quantum computing, for instance, offers unparalleled computational power that can transform sensing capabilities by enabling complex simulations, real-time risk assessments, and optimization at unprecedented scales. However, its integration into organizational processes remains nascent, requiring new organizational routines and cognitive frameworks (Preskill, 2018).

Similarly, blockchain technology provides a decentralized and immutable data infrastructure that enhances transparency and trust in value chains. Its potential in transforming business models—especially through tokenization and smart contracts—aligns closely with the ‘seizing’ function of dynamic capabilities. Yet, issues related to interoperability, regulatory compliance, and scalability must be further examined (Tapscott & Tapscott, 2016).

Edge AI—the deployment of artificial intelligence models on edge devices—enables real-time decision-making at the data source, thereby enhancing responsiveness and agility in sectors such as healthcare, manufacturing, and logistics. Future research should explore how edge computing redefines agility in decentralized organizational contexts and supports local-level transformation (Shi et al., 2016).

These technologies challenge traditional assumptions about organizational boundaries, governance, and value creation. Thus, research must focus on identifying strategic fit models that map specific technologies to appropriate organizational capabilities, industry characteristics, and competitive contexts.

Measuring Digital Agility and Capabilities

Despite growing interest in digital agility, a lack of standardized metrics continues to hinder empirical research and benchmarking across firms and industries. Existing frameworks often rely on qualitative assessments or firm-specific indicators, leading to inconsistencies and limited comparability (Chen et al., 2021). There is a pressing need to develop validated measurement instruments that quantify digital agility, dynamic capabilities, and their intersection.

Researchers could contribute by developing multi-dimensional agility indices that capture the speed, flexibility, responsiveness, and scalability of organizational processes. These indices should include inputs (e.g., digital maturity, leadership orientation), processes (e.g., innovation cycles, data velocity), and outcomes (e.g., time-to-market, customer adaptation rate). The Balanced Scorecard approach may be adapted to integrate agility-focused dimensions alongside financial and customer metrics (Kaplan & Norton, 1996).

In parallel, more rigorous instruments are needed to assess dynamic capabilities in practice. Teece’s framework—sensing, seizing, transforming—should be operationalized into measurable constructs through validated survey tools and case-based scoring systems. Longitudinal studies using these tools could reveal the causal relationships between capability development and organizational performance.

Furthermore, with the rise of AI and digital twins, real-time agility tracking systems using telemetry and system logs could be explored as digital proxies for agility. Such metrics would allow firms to continuously monitor their adaptability and recalibrate strategies in dynamic environments.

Sectoral and Global Perspectives

Lastly, the context-specific nature of digital transformation necessitates comparative research across sectors and geographies. To date, much of the literature on digital agility and dynamic capabilities has been concentrated in advanced economies and technology-intensive sectors such as IT, finance, and e-commerce. There is limited understanding of how these concepts translate into resource-constrained or heavily regulated environments.

Future research should explore sectoral dynamics, such as how agility manifests differently in capital-intensive industries like energy and infrastructure compared to service-oriented industries like education or healthcare. Similarly, comparative studies between startups and legacy firms can illuminate differences in capability development pathways.

From a geographical lens, cross-national studies can shed light on how institutional frameworks, regulatory environments, and cultural values affect digital transformation trajectories. For instance, firms in data-sovereign regions like the European Union face different agility constraints compared to those in the U.S. or Southeast Asia. Research can also examine how national digital readiness indices correlate with organizational agility and innovation outcomes (Chakravorti et al., 2022).

Additionally, there is room for exploration into emerging markets where leapfrogging through mobile, AI, or fintech solutions is common. These contexts offer rich insights into how agility and dynamic capabilities can be developed under conditions of volatility, uncertainty, and limited resources.

Conclusion

In the face of unprecedented technological disruption, market volatility, and socio-economic transformation, organizations are compelled to rethink how they create, deliver, and sustain value. This chapter has synthesized the integration of digital agility and dynamic capabilities as a strategic framework for organizational adaptation in a VUCA (volatile, uncertain, complex, and ambiguous) environment. By combining the sensing, seizing, and transforming mechanisms of dynamic capabilities with the responsiveness, speed, and flexibility offered by digital agility, firms can not only survive disruption but shape it to their advantage.

The analysis throughout the chapter has demonstrated that digital agility acts as the operational catalyst for dynamic capabilities. While dynamic capabilities provide the strategic logic for adaptation—enabling firms to detect environmental shifts, capitalize on emerging opportunities, and reconfigure resources—digital agility facilitates the execution of these capabilities through real-time data usage, modular technologies, empowered teams, and iterative processes (Teece, Peteraf, & Leih, 2016). For instance, sensing is enhanced through predictive analytics and IoT-driven data capture, while seizing benefits from platform-based business models and rapid MVP development. Transformation, meanwhile, is accelerated through agile methodologies, talent reskilling, and digital process redesign.

The chapter has also highlighted key organizational enablers such as data-driven culture, robust technological infrastructure, agile leadership, and continuous learning systems, all of which contribute to embedding digital agility as a core capability. Through detailed case studies of Amazon, DBS Bank, and Siemens, we observed how these principles translate into practice across diverse industries. Each case illustrated how organizations can align their digital investments with strategic intent, leverage cross-functional teams for innovation, and integrate digital governance into broader transformation efforts.

Despite these advancements, several barriers and challenges remain. Legacy systems, talent deficits, hierarchical resistance, and ethical concerns surrounding data and AI can obstruct agility and strategic responsiveness. These hurdles must be systematically addressed through integrated transformation strategies, involving coordinated leadership, upskilling initiatives, and ethical governance frameworks (Gasser & Almeida, 2017; Westerman et al., 2014). Strategic adaptation is not a linear journey but a dynamic, iterative process that requires a culture of experimentation and learning.

From a managerial standpoint, the implications are clear. Senior executives must lead with agility metrics and adaptive mindsets, transformation leaders must deploy scalable roadmaps and agile governance models, and educators and policymakers must create inclusive digital ecosystems that prepare the workforce for continuous innovation. As organizations move toward increasingly digitized and interconnected business environments, building these capabilities is no longer optional but existential.

Looking forward, future research must address emerging questions related to new technologies such as quantum computing, blockchain, and edge AI, while also focusing on sectoral variances and developing standardized metrics to assess agility and capability maturity. Interdisciplinary collaboration will be essential to expanding the theoretical and empirical understanding of these constructs, particularly in underrepresented contexts such as developing economies, public sector organizations, and non-tech-intensive industries.

In final reflection, the need for continuous reinvention and resilient strategic thinking has never been more critical. The integration of digital agility and dynamic capabilities offers a powerful and flexible framework to navigate this complexity. It encourages organizations to not merely react to change, but to anticipate, design, and lead it. In doing so, they transform uncertainty into opportunity, instability into innovation, and disruption into strategic growth.

REFERENCES

1. Bennett, N., & Lemoine, G. J. (2014). What VUCA really means for you. Harvard Business Review, 92(1/2), 27.
2. Fitzgerald, M., Kruschwitz, N., Bonnet, D., & Welch, M. (2013). Embracing digital technology: A new strategic imperative. MIT Sloan Management Review, 55(2), 1.
3. Lu, Y., & Ramamurthy, K. (2011). Understanding the link between information technology capability and organizational agility: An empirical examination. MIS Quarterly, 35(4), 931–954. https://doi.org/10.2307/41409967
4. Teece, D. J., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18(7), 509–533. https://doi.org/10.1002/(SICI)1097-0266(199708)18:7<509::AID-SMJ882>3.0.CO;2-Z
5. Warner, K. S. R., & Wäger, M. (2019). Building dynamic capabilities for digital transformation: An ongoing process of strategic renewal. Long Range Planning, 52(3), 326–349. https://doi.org/10.1016/j.lrp.2018.12.001
6. Westerman, G., Bonnet, D., & McAfee, A. (2014). Leading digital: Turning technology into business transformation. Harvard Business Review Press.
7. Doz, Y., & Kosonen, M. (2010). Embedding strategic agility: A leadership agenda for accelerating business model renewal. Long Range Planning, 43(2–3), 370–382. https://doi.org/10.1016/j.lrp.2009.07.006
8. Lu, Y., & Ramamurthy, K. (2011). Understanding the link between information technology capability and organizational agility: An empirical examination. MIS Quarterly, 35(4), 931–954. https://doi.org/10.2307/41409967
9. Ravichandran, T. (2018). Exploring the relationships between IT competence, innovation capacity and organizational agility. The Journal of Strategic Information Systems, 27(1), 22–42. https://doi.org/10.1016/j.jsis.2017.07.002
10. Teece, D. J. (2018). Business models and dynamic capabilities. Long Range Planning, 51(1), 40–49. https://doi.org/10.1016/j.lrp.2017.06.007
11. Teece, D. J., Peteraf, M., & Leih, S. (2016). Dynamic capabilities and organizational agility: Risk, uncertainty, and strategy in the innovation economy. California Management Review, 58(4), 13–35. https://doi.org/10.1525/cmr.2016.58.4.13
12. Teece, D. J., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18(7), 509–533. https://doi.org/10.1002/(SICI)1097-0266(199708)18:7<509::AID-SMJ882>3.0.CO;2-Z
13. Verhoef, P. C., Broekhuizen, T., Bart, Y., Bhattacharya, A., Qi Dong, J., Fabian, N., & Haenlein, M. (2021). Digital transformation: A multidisciplinary reflection and research agenda. Journal of Business Research, 122, 889–901. https://doi.org/10.1016/j.jbusres.2019.09.022
14. Warner, K. S. R., & Wäger, M. (2019). Building dynamic capabilities for digital transformation: An ongoing process of strategic renewal. Long Range Planning, 52(3), 326–349. https://doi.org/10.1016/j.lrp.2018.12.001
15. Brynjolfsson, E., & McAfee, A. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. W. W. Norton & Company.
16. Davenport, T. H., & Bean, R. (2018). Big companies are embracing analytics, but most still don’t have a data-driven culture. Harvard Business Review. https://hbr.org/2018/02/big-companies-are-embracing-analytics-but-most-still-dont-have-a-data-driven-culture
17. Denning, S. (2018). The age of agile: How smart companies are transforming the way work gets done. AMACOM.
18. Garvin, D. A. (1993). Building a learning organization. Harvard Business Review, 71(4), 78–91.
19. McAfee, A., & Brynjolfsson, E. (2012). Big data: The management revolution. Harvard Business Review, 90(10), 60–68.
20. Verhoef, P. C., Broekhuizen, T., Bart, Y., Bhattacharya, A., Dong, J. Q., Fabian, N., & Haenlein, M. (2021). Digital transformation: A multidisciplinary reflection and research agenda. Journal of Business Research, 122, 889–901. https://doi.org/10.1016/j.jbusres.2019.09.022
21. Westerman, G., Bonnet, D., & McAfee, A. (2014). Leading digital: Turning technology into business transformation. Harvard Business Review Press.
22. Brynjolfsson, E., & McElheran, K. (2016). The rapid adoption of data-driven decision-making. American Economic Review, 106(5), 133–139. https://doi.org/10.1257/aer.p20161016
23. Bughin, J., Hazan, E., Ramaswamy, S., Chui, M., Allas, T., Dahlström, P., Henke, N., & Trench, M. (2017). Artificial intelligence: The next digital frontier? McKinsey Global Institute. https://www.mckinsey.com
24. Kane, G. C., Palmer, D., Phillips, A. N., Kiron, D., & Buckley, N. (2015). Strategy, not technology, drives digital transformation. MIT Sloan Management Review and Deloitte University Press.
25. Sirmon, D. G., Hitt, M. A., & Ireland, R. D. (2007). Managing firm resources in dynamic environments to create value: Looking inside the black box. Academy of Management Review, 32(1), 273–292. https://doi.org/10.5465/amr.2007.23466005
26. Teece, D. J. (2018). Business models and dynamic capabilities. Long Range Planning, 51(1), 40–49. https://doi.org/10.1016/j.lrp.2017.06.007
27. Teece, D. J., Peteraf, M., & Leih, S. (2016). Dynamic capabilities and organizational agility: Risk, uncertainty, and strategy in the innovation economy. California Management Review, 58(4), 13–35. https://doi.org/10.1525/cmr.2016.58.4.13
28. Teece, D. J., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18(7), 509–533. https://doi.org/10.1002/(SICI)1097-0266(199708)18:7<509::AID-SMJ882>3.0.CO;2-Z
29. Bughin, J., Hazan, E., Ramaswamy, S., Chui, M., Allas, T., Dahlström, P., Henke, N., & Trench, M. (2017). Artificial intelligence: The next digital frontier? McKinsey Global Institute. https://www.mckinsey.com
30. Davenport, T. H., & Bean, R. (2018). Big companies are embracing analytics, but most still don’t have a data-driven culture. Harvard Business Review. https://hbr.org/2018/02/big-companies-are-embracing-analytics-but-most-still-dont-have-a-data-driven-culture
31. Gasser, U., & Almeida, V. A. F. (2017). A layered model for AI governance. IEEE Internet Computing, 21(6), 58–62. https://doi.org/10.1109/MIC.2017.4180835
32. Kane, G. C., Palmer, D., Phillips, A. N., Kiron, D., & Buckley, N. (2019). Accelerating digital innovation inside and out: Agile teams, ecosystems, and ethics. MIT Sloan Management Review and Deloitte Insights. https://sloanreview.mit.edu
33. Teece, D. J., Peteraf, M., & Leih, S. (2016). Dynamic capabilities and organizational agility: Risk, uncertainty, and strategy in the innovation economy. California Management Review, 58(4), 13–35. https://doi.org/10.1525/cmr.2016.58.4.13
34. Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M., Baak, A., … & Mons, B. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data, 3, 160018. https://doi.org/10.1038/sdata.2016.18
35. Chong, A., Hui, Y. Y., & Tan, C. S. (2020). DBS Bank: Digital transformation to ‘Make Banking Joyful’. Asian Case Research Journal, 24(1), 1–30. https://doi.org/10.1142/S0218927520500010
36. Dastin, J. (2018, July 25). Amazon’s AI predicts what you want before you buy it. Reuters. https://www.reuters.com/article/us-amazon-ai/amazons-ai-predicts-what-you-want-before-you-buy-it-idUSKBN1KF28D
37. DBS. (2021). Annual Report 2020: Succeeding in a digital world. DBS Bank. https://www.dbs.com/investor
38. Denning, S. (2018). The Age of Agile: How Smart Companies Are Transforming the Way Work Gets Done. AMACOM.
39. Ghosh, P. (2020). Siemens’ digital twin strategy. Forbes. https://www.forbes.com/sites/prakashghosh/2020/11/20/siemens-digital-twin-strategy/
40. (2021). Digital Industries: Driving the Digital Enterprise. Siemens AG. https://new.siemens.com/global/en/products/automation/topic-areas/digital-enterprise.html
41. Teece, D. J., Peteraf, M. A., & Leih, S. (2016). Dynamic capabilities and organizational agility: Risk, uncertainty, and strategy in the innovation economy. California Management Review, 58(4), 13–35. https://doi.org/10.1525/cmr.2016.58.4.13
42. Brenner, W., & Uebernickel, F. (2016). Design thinking for innovation: Research and practice. Springer.
43. Brynjolfsson, E., & McElheran, K. (2016). The rapid adoption of data-driven decision-making. American Economic Review, 106(5), 133–139. https://doi.org/10.1257/aer.p20161016
44. Denning, S. (2018). The Age of Agile: How Smart Companies Are Transforming the Way Work Gets Done. AMACOM.
45. European Commission. (2021). Proposal for a Regulation laying down harmonised rules on artificial intelligence (Artificial Intelligence Act). https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:52021PC0206
46. Fitzgerald, B., & Stol, K. J. (2017). Continuous software engineering: A roadmap and agenda. Journal of Systems and Software, 123, 176–189. https://doi.org/10.1016/j.jss.2015.06.063
47. Gasser, U., & Almeida, V. A. F. (2017). A layered model for AI governance. IEEE Internet Computing, 21(6), 58–62. https://doi.org/10.1109/MIC.2017.4180835
48. Kotter, J. P. (2012). Leading change. Harvard Business Review Press.
49. McKinsey & Company. (2021). The future of work after COVID-19. https://www.mckinsey.com/featured-insights/future-of-work/the-future-of-work-after-covid-19
50. Westerman, G., Bonnet, D., & McAfee, A. (2014). Leading digital: Turning technology into business transformation. Harvard Business Review Press.
51. Bass, B. M., & Riggio, R. E. (2006). Transformational leadership (2nd ed.). Psychology Press.
52. Brynjolfsson, E., & McAfee, A. (2014). The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. W. W. Norton & Company.
53. European Commission. (2021). 2030 Digital Compass: The European way for the Digital Decade. https://digital-strategy.ec.europa.eu/en/policies/digital-compass
54. Gasser, U., & Almeida, V. A. F. (2017). A layered model for AI governance. IEEE Internet Computing, 21(6), 58–62. https://doi.org/10.1109/MIC.2017.4180835
55. Knaster, R., & Leffingwell, D. (2020). SAFe 5.0 Distilled: Achieving Business Agility with the Scaled Agile Framework. Addison-Wesley Professional.
56. Rigby, D. K., Sutherland, J., & Noble, A. (2018). Agile at scale. Harvard Business Review, 96(3), 88–96. https://hbr.org/2018/05/agile-at-scale
57. Teece, D. J., Peteraf, M., & Leih, S. (2016). Dynamic capabilities and organizational agility: Risk, uncertainty, and strategy in the innovation economy. California Management Review, 58(4), 13–35. https://doi.org/10.1525/cmr.2016.58.4.13
58. Westerman, G., Bonnet, D., & McAfee, A. (2014). Leading Digital: Turning Technology into Business Transformation. Harvard Business Review Press.
59. Wirtz, B. W., Weyerer, J. C., & Geyer, C. (2021). Artificial intelligence and the public sector—Applications and challenges. International Journal of Public Administration, 44(6), 488–499. https://doi.org/10.1080/01900692.2021.1871579
60. Chakravorti, B., Bhalla, A., Chaturvedi, R., & Murtagh, S. (2022). Digital Planet 2022: How competitiveness and trust in digital economies will shape the future. The Fletcher School, Tufts University. https://digitalplanet.tufts.edu/
61. Chen, J., Liang, L., & Wang, Y. (2021). Digital agility: A new dynamic capability for achieving digital transformation. Journal of Business Research, 134, 548–559. https://doi.org/10.1016/j.jbusres.2021.05.006
62. Kaplan, R. S., & Norton, D. P. (1996). The Balanced Scorecard: Translating Strategy into Action. Harvard Business Press.
63. Preskill, J. (2018). Quantum computing in the NISQ era and beyond. Quantum, 2, 79. https://doi.org/10.22331/q-2018-08-06-79
64. Shi, W., Cao, J., Zhang, Q., Li, Y., & Xu, L. (2016). Edge computing: Vision and challenges. IEEE Internet of Things Journal, 3(5), 637–646. https://doi.org/10.1109/JIOT.2016.2579198
65. Tapscott, D., & Tapscott, A. (2016). Blockchain revolution: How the technology behind bitcoin is changing money, business, and the world. Portfolio.
66. Gasser, U., & Almeida, V. A. F. (2017). A layered model for AI governance. IEEE Internet Computing, 21(6), 58–62. https://doi.org/10.1109/MIC.2017.4180835
67. Teece, D. J., Peteraf, M., & Leih, S. (2016). Dynamic capabilities and organizational agility: Risk, uncertainty, and strategy in the innovation economy. California Management Review, 58(4), 13–35. https://doi.org/10.1525/cmr.2016.58.4.13
68. Westerman, G., Bonnet, D., & McAfee, A. (2014). Leading Digital: Turning Technology into Business Transformation. Harvard Business Review Press.