Long Range Planning 52 (2019) 326–349

Contents lists available at ScienceDirect

Long Range Planning

journal homepage: www.elsevier.com/locate/lrp

Building dynamic capabilities for digital transformation: An

T
ongoing process of strategic renewal
Karl S.R. Warnera,∗, Maximilian Wägerb
a
Edinburgh Napier University, Room 2/44 Craiglockhart Campus, The Business School, Edinburgh, Scotland, EH14 1DJ, UK
b
The Nunatak Group, Schackstrasse 3, 80539, München, Germany

ARTICLE INFO ABSTRACT

Keywords: In this qualitative study, we explore how incumbent firms in traditional industries build dynamic
Dynamic capabilities capabilities for digital transformation. Digital transformation has been defined as the use of new
Digital transformation digital technologies, such as mobile, artificial intelligence, cloud, blockchain, and the Internet of
Digitalization things (IoT) technologies, to enable major business improvements to augment customer experience,
Microfoundations
streamline operations, or create new business models. In making sense of digital transformation,
Qualitative interpretive research
Strategic agility
we discovered that leaders in various industry circles use the term inconsistently to describe
Strategic renewal various strategizing and organizing activities; in addition, the term has gained limited scholarly
Technology and innovation management attention as a context for study of strategic change. Drawing on senior executives' experiences
with leading digitalization projects at incumbent firms, we propose a process model comprising
of nine microfoundations to reveal the generic contingency factors that trigger, enable, and
hinder the building of dynamic capabilities for digital transformation. Our findings reveal that
digital transformation is an ongoing process of using new digital technologies in everyday or-
ganizational life, which recognizes agility as the core mechanism for the strategic renewal of an
organization's (1) business model, (2) collaborative approach, and eventually the (3) culture.

Introduction

Digital transformation has become a strategic imperative on leadership agendas (Fitzgerald et al., 2014; Hess et al., 2016; Singh
and Hess, 2017), but there is little conceptual or empirical research that examines how organizations are digitally transformed.
Fitzgerald et al. (2014: 2) define digital transformation as, “the use of new digital technologies (social media, mobile, analytics or
embedded devices) to enable major business improvements such as enhancing customer experience, streamlining operations, or
creating new business models.” Liu et al. (2011: 1730) argue that digital transformation is “as an organizational transformation that
integrates digital technologies and business processes in a digital economy.” Relatedly, Singh and Hess (2017:124) suggest the term
“transformation” rather than “change” emphasizes that an organization's digital transformation goes far beyond functional thinking
and holistically considers the “comprehensiveness of actions” that must be taken to exploit the opportunities or avoid the threats that
stem from digital technologies. Rogers (2016: 308) argues that “digital transformation is fundamentally not about technology, but
about strategy,” meaning that senior leadership teams must find ways to capitalize on new and unexpected business model in-
novations that optimize customer needs and experiences.
Hess et al. (2016) report that incumbent firms face significant challenges even if senior leadership teams are internally motivated
to support the digital transformation of business models, structures, and processes. A major challenge incumbents face is the

∗
Corresponding author.
E-mail addresses: k.warner@napier.ac.uk (K.S.R. Warner), maximilian.waeger@nunatak.com (M. Wäger).

https://doi.org/10.1016/j.lrp.2018.12.001
Received 15 September 2017; Received in revised form 19 September 2018; Accepted 16 December 2018
Available online 19 December 2018
0024-6301/ © 2018 Elsevier Ltd. All rights reserved.
K.S.R. Warner, M. Wäger Long Range Planning 52 (2019) 326–349

competing concern of needing to balance the exploitation of existing capabilities while also building new digital capabilities that are
compatible with the path dependencies of the past (Svahn et al., 2017). However, despite these tensions, the building of capabilities
for digital transformation has received limited scholarly attention and is now an essential context for the study of strategic change.
In contrast, the dynamic capabilities framework has become one of the most active research streams in the strategic management
literature because this area of study explains how firms respond to rapid technological and market change (Eisenhardt and Martin,
2000; Di Stefano et al., 2014; Helfat et al., 2007; Teece, 2007; Teece et al., 1997). Dynamic capabilities are innovation based and
provide the capacity to create, extend, and modify a firm's resource base (Helfat et al., 2007). Teece (2007) argues that dynamic
capabilities consist of three broad clusters: (1) sensing opportunities (and threats), (2) seizing opportunities, and (3) transforming the
organization's business model and wider resource base. Given the disruptive nature of digitalization, we posit that the dynamic
capabilities framework is a powerful lens for examining the digital transformation of incumbent firms in traditional industries. In
particular, it has been reported that firms need to build strong dynamic capabilities to rapidly create, implement, and transform
business models to remain relevant in the emergent digital economy (Achtenhagen et al., 2013; Karimi and Walter, 2015, 2016;
Teece, 2018; Teece and Linden, 2017; Velu, 2017). However, despite some microfoundations research on building dynamic cap-
abilities for strategic change (Bendig et al., 2018; Dixon et al., 2014; Helfat and Peteraf, 2015; Prange et al., 2017; Teece, 2007), there
is scant research that examines how organizations build dynamic capabilities for digital transformation.
To explore these microfoundations, we ask: How do incumbent firms in traditional industries build dynamic capabilities for digital
transformation? To answer this research question, we present multiple case study research on the digital transformation of seven
incumbent firms that are headquartered in Germany and that dominate traditional industries (Eisenhardt and Graebner, 2007). We
took a novel approach because we combined a wide scope of qualitative data to draw on senior executives' experiences with leading
digitalization projects in both strategy consultancies and incumbent firms. Early on in our research project, we conducted interviews
with the senior partners of strategy consultancies who are authors of published industry reports and used these data as a platform to
examine the digital transformation of incumbent firms. Therefore, we focus on the senior leader's perspective (Chia and MacKay,
2007; Jarzabkowski, 2004; Regnér, 2008; Whittington, 2006), emphasizing the individual leader's point of view concerning the
practice of strategic activities for digital transformation. This fine-grained analysis allowed us to make sense of digital transformation
by bringing participant voices to the forefront (Gioia et al., 2013).
Our study makes two contributions to the literature. First, we conceptualize and define the scope of the digital transformation
process. Our fieldwork revealed that the term “digital transformation” is inconsistently used by leaders within and across industry
contexts to describe various strategizing and organizing activities. Using Agarwal and Helfat's (2009) strategic renewal perspective,
we provide an empirically grounded definition of digital transformation. We argue that the scope of each digital transformation is
contingent on the strategic renewal of an organization's (1) business model, (2) collaborative approach, and eventually the (3)
culture. We report on a wide sample of case firms at early stages that have experimented with digitizing business models and at
advanced stages that have used digital technologies to replace collaborative approaches or refresh organizational cultures. In line
with Yoo (2010), our findings emphasize that genuine digital transformations are an ongoing process of using digital technologies in
everyday organizational life. We contribute to the business model and digital transformation literature and provide a rich context for
the study of strategic change.
Second, we contribute to the organizational capability literature and provide empirical insights into what types of digitally based
dynamic capabilities might be required for digital transformation. In the past decade, disruptive digital technologies, unexpected
consumer behaviors, and disruptive competition have accelerated an unprecedented level of change for incumbents. To address these
rapidly changing environmental conditions, we develop a process model that identifies nine digitally based microfoundations (e.g.,
subcapabilities) that underpin the building of digital sensing, digital seizing, and digital transforming capabilities. Echoing Velu's (2017)
research, we report on several capabilities that, in isolation, could apply to nondigital-based change, but as a system of capabilities,
reflect the industry's current view of what types of dynamic capabilities are needed to pursue a digital transformation.
In contrast to nondigital-based strategic change, we argue that the ubiquity of new digital technologies is changing the very
nature and purpose of dynamic capabilities. First, we found new digital technologies such as blockchain, cloud, and IoT platforms are
changing the nature of dynamic capabilities because organizations can now scale up or scale down their operations at a speed, ease,
and cost that was not possible only a decade ago. Second, the convergence and generativity of these pervasive digital technologies
means that the purpose of building dynamic capabilities is now paramount for a wider range of organizations. In line with recent
research (Autio et al., 2018), we found that digitalization is forcing incumbent firms to be more entrepreneurial when given the
strategic imperative to build a system of digital capabilities that address the unprecedented threats associated with the decoupling
and disintermediation of existing value chains.
We report that digital sensing consists of subcapabilities relating to (1) digital scouting, (2) digital scenario planning, and (3)
digital mindset crafting. Digital sensing capabilities advance the existing research on dynamic capabilities because we found evidence
of incumbents using disruptive technologies, such as artificial intelligence, analytics, and IoT platforms, to make sense of big data that
were previously hidden. We found that digital seizing consists of subcapabilities relating to (1) strategic agility, (2) rapid prototyping,
and (3) balancing digital portfolios. Given the disruptive nature of digitalization, we found that incumbents are experimenting with
entrepreneurial methods to build digital seizing capabilities that strengthen strategic agility for rapid responses to unexpected op-
portunities and threats. As a result, we argue that given their potential to shorten new product launches, maximize customer cen-
tricity, and rapidly scale at a marginally negligible cost, new digital technologies such as cloud computing and social media are
changing the very nature of seizing capabilities.
Digital transforming capabilities consists of microfoundations relating to (1) navigating innovation ecosystems, (2) redesigning
internal structures, and (3) improving digital maturity. For incumbents, we found that the core purpose of digital transforming

327
K.S.R. Warner, M. Wäger Long Range Planning 52 (2019) 326–349

Table 1
A digital technology perspective.
Concepts Definition Examples (alphabetical)

Digital technologies “Digital technologies (viewed as combinations of information, computing, communication, and • Analytics
connectivity technologies) are fundamentally transforming business strategies, business processes, • Artificial intelligence
firm capabilities, products and services, and key interfirm relationships in extended business • Blockchain
networks” (Bharadwaj et al., 2013: 471). • Cloud
• IoT
• Mobile
• Social media
Digitization “The encoding of analog information into digital format. Digitization makes physical products • Books
[e.g. artifacts] programmable, addressable, sensible, communicable, memorable, traceable and • Clothing
associable” (Yoo et al., 2010: 725). • Home appliances
• Music
Digital innovation “Digital innovation as the carrying out of new combinations of digital and physical components • Amazon Kindle
[in a layered modular architecture] to produce novel products” (Yoo et al., 2010: 725). • Apple iPhone
• Nest Thermostat
• Nike + Sensor
Digital artifact “A digital component, application, or media content that is part of a new product (or service) and • Computer files
offers a specific functionality or value to the end-user” (Nambisan, 2017: 1031). • Internet
• SaaS
• Wiki
Digital platform “A digital product platform typically encompasses a particular range of layers (e.g., content and • Autodesk Forge
service layers) that can function as a new product, but simultaneously enable others to innovate • Ethereum
upon using firm-controlled platform resources (e.g., SDKs and APIs)” (Yoo et al., 2010: 729). • Ford SYNC 3
• GE Predix
• Google Android
Digital infrastructure “Digital technology tools and systems (e.g., cloud computing, data analytics, online communities, • Amazon Web Services
social media, 3D printing, digital makerspaces, etc.) that offer communication, collaboration, • Intel Inside
• Microsoft
®

and/or computing capabilities to support innovation and entrepreneurship” (Nambisan, 2017: Azure
1032). • SAP Analytics
• Alibaba
®

Digitalization “The digitalization of these four dimensions of human experience [time-space-artifact-actor] Group
forms the basis of experiential computing” (Yoo, 2010: 220). • Alphabet Inc.
“A sociotechnical process of applying digitizing techniques to broader social and institutional • Amazon.com
contexts that render digital technologies infrastructural” (Tilson et al., 2010: 749). • Apple Inc.
“Digitalization creates potent digital affordances that likely have a transformative effect upon the • Facebook Inc.
organization of economic activity by supporting radical business model innovation” (Autio et al., • Microsoft Corp.
2018: 76). • NetEase Inc.
• Paycom Software Inc.
• Tencent Holdings Ltd.
• Tesla Inc.
• Uber Technologies Inc.
Digital transformation “Digital transformation is concerned with the changes digital technologies can bring about in a • General Electric Company
company's business model, which result in changed products or organizational structures or in the • ING Group
automation of processes” (Hess et al., 2016: 124). • ProSiebenSat.1
(P7S1)
Media SE

• Volvo Group
capabilities is to manage a wide range of tensions that relate to balancing internal and external collaboration, redesigning flexible and
manageable governance structures, and improving the digital maturity of an externally recruited and internally promoted workforce.
Therefore, our findings on digital transforming capabilities emphasize the need for digital technologies that provide rapid responses
to technological and market change (Eisenhardt and Martin, 2000; Teece et al., 1997). Overall, we therefore contribute to Peteraf
et al.'s (2013) call for contingency-based research to help theoretically and empirically integrate the dynamic capabilities field, and in
doing so, we provide new insights into digital transformation as a context for strategic change.

Theoretical overview

Why digital technology is changing strategizing

To explain why digital technology is changing the nature of strategizing, we focus on the digital innovation perspective, which has
its roots in the information systems (IS) literature (Nambisan et al., 2017; Tilson et al., 2010; Yoo et al., 2010). Over the past two
decades, IS scholars have examined the role of digital technology on firms' strategies (Bharadwaj et al., 2013; Hess et al., 2016;
Sambamurthy et al., 2003), innovation (Henfridsson et al., 2014; Yoo et al., 2012), and business models (Al-Debei and Avison, 2010;
El Sawy and Pereira, 2013; Timmers, 1998) by explaining their disruptive impact on organizations. In Table 1, we start by defining
the key digital concepts and introduce why new and pervasive digital technologies are changing the very nature of strategizing.
Yoo et al. (2012: 1398) explain that “a defining characteristic of pervasive digital technology is the incorporation of digital
capabilities into objects that previously had pure physical materiality.” Digital innovation is said to stem from the digitization of

328
K.S.R. Warner, M. Wäger Long Range Planning 52 (2019) 326–349

everyday physical products—such as adding software applications to books, clothing, home appliances, cars, and so forth—providing
novel functions that dramatically improve product design, production, distribution, and use (Yoo et al., 2010). Moreover, Yoo et al.
(2010) explain how the unique characteristics of digital innovation are rooted in the (1) reprogrammability, (2) data homogenization,
and (3) self-referential nature of digital technology. Therefore, digital technologies with these three characteristics pave the way for a
layered modular architecture that creates opportunities for embedding digital components into physical products, representing a
strategic choice for firms seeking digital innovation (Yoo et al., 2010). Notably, the organizing logic of the layered modular archi-
tectures of digital technologies is that digitized products can simultaneously be a product and platform—for example, Apple's iPad
and Amazon's Kindle—which results in strategic choices at different layers because two firms can compete on one layer, such as
devices, and collaborate on other layers, such as content or services (Henfridsson et al., 2014; Yoo et al., 2010).
This means a layered modular architecture is a hybrid technology that embeds the layered architecture of digital technology into
the modular architecture of a physical product, which can generate profound changes in a firm's organizing logic and innovation (Yoo
et al., 2010). The sheer reprogrammability and data homogenization of digital technologies has enabled the convergence and gen-
erativity of digital innovations that have led to new products, services, business models, strategies, and organizational forms (Autio
et al., 2018; Tilson et al., 2010; Yoo et al., 2010). For strategy researchers and practitioners, the convergence and generativity of
digital technologies are apparent in the form of three distinct yet related elements: (1) digital artifacts, (2) digital platforms, and (3)
digital infrastructures (Nambisan, 2017) —which we define in Table 1.
First, because digital artifacts such as the software or hardware components on a physical device are programmable, addressable,
sensible, communicable, memorable, traceable, and associable (Yoo, 2010), this enables the separation of form and function, where
new functionalities can be rapidly added to a wide range of digital products at a marginally negligible cost (Huang at al., 2017).
Second, digital platforms (and associated ecosystems) are often orchestrated by platform leaders (e.g., Apple iOS or Google Android),
meaning outsider firms face tough strategic choices about joining or building platforms, both of which contain risks and unintended
consequences (Dattée et al., 2018; Nambisan et al., 2017; Zahra and Nambisan, 2012). Third, digital infrastructures such as social
media, data analytics, cloud computing, and 3D printing have provided young digital firms with new tools for rapid scaling (Huang
et al., 2017) and accelerating rapid multinationalization (Monaghan and Tippmann, 2018). In recent years, “born digital” pioneers
such as Google, Amazon, and Facebook have grown to become powerful “behemoths” and created a “new generation of competition”
that has put the survival of incumbent firms under threat (Sebastian et al., 2017; Teece and Linden, 2017).
A defining characteristic of the above digital enterprises is that they have deeply engaged and accelerated the process of digi-
talization. In Weiser's (1991: 94) seminal article, he introduces ubiquitous computing as an environment where “the most profound
technologies are those that disappear. They weave themselves into the fabric of everyday life until they are indistinguishable from it.”
A decade later, Lyytinen and Yoo (2002) were predicting that ubiquitous computing would proliferate as mobile computing in-
tegrates with pervasive computing, which has the capability to embed our natural movements and interactions with our en-
vironments—both physically and socially. Since then, digitalization has been reported to form the basis of everyday experiential
computing (Yoo, 2010) and is regarded as a sociotechnical process (Tilson et al., 2010) that involves using advances in digital
infrastructures to analyze, interpret, and shape changes in social, institutional, and increasingly cognitive contexts. This has meant
digitalization is increasingly viewed as an entrepreneurial process (Autio et al., 2018; Henfridsson and Yoo, 2013; Huang et al., 2017;
Nambisan, 2017), where new business model innovations have eroded incumbent advantages and resulted in the world's most
valuable and/or fastest growing companies, as shown in Table 1.

Digital transformation of incumbent firms

The digital transformation of big and old firms has become a strategic imperative on leadership agendas to protect incumbent
advantages in traditional industries (Berman, 2012; Fitzgerald et al., 2014; Gray et al., 2013; Hess et al., 2016; Sebastian et al., 2017;
Svahn et al., 2017). Digital transformation differs from the traditional forms of strategic change on the basis that digital technologies
have accelerated the speed of change, resulting in much more environmental volatility, complexity, and uncertainty (Matt et al.,
2015; Loonam et al., 2018). Hess et al. (2016: 124) explain that a digital transformation must consider how advances in digital
technologies can bring about changes in a company's business model, organizational structures, and processes. However, as Sebastian
et al. (2017: 198) report, “Most leaders of big old companies believe their companies can retain leadership positions by taking
advantage of both their existing strengths and the capabilities offered by digital technologies”; and later add, this is without fully
appreciating the strategic challenges of digital transformation.
Digitalization has opened a wide range of possibilities for firms to interact with customers, which has led to new and unexpected
business model innovations (Amit and Zott, 2001; Aspara et al., 2013; Chesbrough, 2010; Khanagha et al., 2014; Wirtz et al., 2010).
Magretta (2002: 89) highlights that the term “business model” came into widespread use in the 1990s with the advent of personal
computers and spreadsheets because strategic planners were then able to test a wide range of assumptions to predict the profitability
of generating new revenue streams and changing cost structures. This has meant the “business model” particularly in a digital context
has become a new unit of analysis (Zott et al., 2011) that examines how a firm creates and delivers value to its customers and captures
profits from managing a system of networked activities (Amit and Zott, 2012; Zott and Amit, 2010). Velu (2017: 605) emphasizes that
this system of activities “need[s] to be aligned with one another in order to develop an efficient mechanism to create superior
performance for the firm while delivering value for the customer.” As a result, Teece (2018: 40) notes that a business model “de-
scribes an architecture for a how a firm creates and delivers value to customers, and mechanisms employed to capture a share of that
value. It's a matched set of elements encompassing the flows of costs, revenues and profits.”
In Zott et al.'s (2011) review of the business model literature, the authors report that the study of e-business—involving e-

329
K.S.R. Warner, M. Wäger Long Range Planning 52 (2019) 326–349

commerce, e-markets, and Internet-based businesses where buyers and/or suppliers conduct commercial transactions over the In-
ternet—has attracted the most research attention. Conversely, digitalization has empowered customers with more choice, expecta-
tions, and demands, which has forced firms to reevaluate or supplement traditional transactional customer value propositions with
new relational or multisided value propositions1 (Aversa et al., 2017; Baden-Fuller and Haefliger, 2013; Casadesus-Masanell and
Ricart, 2010; Teece, 2010; Weill and Woerner, 2013). Therefore, pervasive digital technologies challenge the logic of traditional
business models because growing customer expectations for free products or services often hinder the capacity to generate revenues
and earn profits (El Sawy and Pereria, 2013; Iansiti and Lakhani, 2017; McGrath, 2010; Teece, 2010). In response to these customer
demands, Teece and Linden (2017) emphasize that many Internet-based businesses have tended to pursue customer growth (i.e.,
value creation) ahead of profits (i.e., value capture), often resulting in flawed business models with unclear paths to profitability.
Hence, incumbent firms pursuing digital transformation are likely to encounter significant barriers for business model innovation
(Aspara et al., 2013; Berman, 2012; Demil and Lecocq, 2010; Iansiti and Lakhani, 2017; Kim and Min, 2015; Markides, 2006; Nylén
and Holmström, 2015; Velu and Stiles, 2013; Weill and Woerner, 2015). In Foss and Saebi's (2017: 201) systematic review of the
business model innovation (BMI) literature, they report that Teece's (2010: 172) definition of a business model as the “design or
architecture of the value creation, delivery and capture mechanisms” of a firm is widely adopted, whereas consensus on business
model innovation is less well understood. Relatedly, Foss and Saebi (2018) report that the BMI literature includes numerous definitions
associated with business model dynamics or change, such as business model “learning” (Teece, 2010), “evolution” (Demil and Lecocq,
2010; Velu, 2017), “replication” (Dunford et al., 2010) “reconfiguration” (Calia et al., 2007), “renewal” (Doz and Kosonen, 2010),
“transformation” (Aspara et al., 2013), and “innovation” (Chesbrough, 2010; Khanagha et al., 2014). A common thread connecting
the BMI literature is incumbents facing a range of tensions between traditional and new ways of working (Chesbrough, 2010). This
means that leaders face the “strategic paradox” of having to find new ways to balance the contradictory demands of managing
complex business models that are likely to change over time (Smith et al., 2010; Velu and Stiles, 2013).
A major barrier incumbents face with changing business models is that managers are often unwilling to experiment with new
business model archetypes (Amit and Zott, 2001; Chesbrough, 2010; Ritter and Lettl, 2018; Sosna et al., 2010). Baden-Fuller and
Morgan (2010) note business model archetypes describe well-known business logics that explain value creation, delivery, and capture
mechanisms across industry contexts. For incumbents, the root of this tension is that managers are likely to favor established models
with higher gross margins and use rules, norms, and metrics (e.g., gross margins must be at 40%) to protect the status quo and resist
experiments that might threaten the profitability of existing business models (Chesbrough, 2010; Johnson et al., 2008). For example,
the wide adoption of product business models is often a major barrier that hinders experimentation with other business model
archetypes. These archetypes include “servitization” (e.g., Rolls Royce power by the hour), “bundling” (e.g., Gillette's razor and
blade), “subscription” (e.g., Netflix's monthly rental fee), and “matchmaking” (e.g., eBay's online marketplace) models that have been
replicated across industries (Aversa et al., 2017; Martinez et al., 2017, McGrath, 2010; Teece, 2010; Ritter and Lettl, 2018).
Digitalization is reported to provide a source of experimentation for incumbents that seek to “reinvent” their business model
(Westerman et al., 2014; Westerman and Bonnet, 2015). Westerman et al. (2014: 78–92) explain that digital technology has driven
five broad archetypes of business model reinvention: (1) reinventing industries (e.g., Uber's reshaping of the industry structure); (2)
substituting products and services (e.g., Amazon's Kindle substitution of physical books); (3) creating new digital businesses (e.g.,
Nike + Sensor connectivity to Apple devices); (4) reconfiguring value delivery models (e.g., Volvo's embedding of digital artifacts
into cars); and (5) rethinking value propositions (e.g., Entravision's advanced use of data analytics to target unmet customer needs).
Overall, this shows that incumbents face the tension of balancing existing performance with new learning, which is a significant
barrier for business model adaptation (Itami and Nishino, 2010).
Path dependencies are major barriers for business model transformation, meaning established business models can become inert
and erode over time (Cavalcante et al., 2011; Doz and Kosonen, 2010; McGrath, 2010). Laudien and Daxböck (2016) argue that path-
breaking mechanisms such as exogenous and endogenous shocks are often needed to trigger changes in traditional product-based
logics. However, for most incumbents, the creation of new disruptive business models is unlikely because they are more likely to use
digital technologies to extend, revise, or terminate existing activities in an evolutionary manner (Cavalcante et al., 2011; Foss and
Saebi, 2018; Kim and Min, 2015). Because the digital transformation of an incumbent's business model is a highly complex change
process that combines the corporation's business model and the business models of its various business units, research has shown this
requires a series of calculated and interdependent strategic decisions (Aspara et al., 2013; Velu and Stiles, 2013).
To reduce complexity, Laudien and Daxböck (2016) note that managers tend to make use of prior experience, favoring strategic
choices they are familiar with (Gavetti and Levinthal, 2000) over unfamiliar options that could achieve transformational change.
Here, “managing the mismatch” between the new economic reality and managers' existing cognitive perceptions of old established
business models is often a challenge (Velu, 2017: 605). For example, Weill and Woerner (2013: 71–72) emphasize that a digital
business model challenges a physical model in three main areas: (1) internal power, where the “owner” of the customer's experience
often changes, requiring a business unit to manage multiproduct customer experiences; (2) business processes, which require
seamless integration across channels; and (3) customer data, which become an enterprise-wide resource. Weill and Woerner (2015)
therefore argue that incumbents are less likely to adopt a digital business model compared with smaller, younger enterprises because
of the economic and cognitive path dependencies brought about by legacy systems, global operations, work silos, and organizational
politics.

1
Baden-Fuller and his colleagues have established the Business Model Zoo™ (www.businessmodelzoo.com), an online resource that provides case
overviews of different business model pathways for developing a business.

330
K.S.R. Warner, M. Wäger Long Range Planning 52 (2019) 326–349

Senior leadership teams without digitalization experience are another significant barrier for business model transformation (El
Sawy et al., 2016). Bouchikhi and Kimberly (2003) note that senior leaders often struggle to radically transform the organization's
business model when these teams fail to escape the “identity trap” that intimately ties the organization's core competence to its
values, history, collective memory, politics, habits, and emotions; the authors note that Polaroid's failure to escape its “instant
photography” identity despite the digitalization of the imaging industry illustrates the urgency for a responsive leadership team.
Interestingly, Doz and Kosonen (2010) propose a wide repertoire of concrete actions senior leaders can take regarding (1) heigh-
tening strategic sensitivity, (2) fostering leadership unity, and (3) making resources more fluid for embedding strategic agility into
large organizations to accelerate the renewal and transformation of business models.
The management of conflicting demands is another major barrier for the transformation of complex business models (Smith et al.,
2010; Svahn et al., 2017; Velu and Stiles, 2013). Smith et al. (2010) emphasize that the contradictions and tensions inherent in
complex business models exert significant pressures on senior leaders and their teams, requiring a significant range of capabilities to
manage conflicting demands. This includes being able to balance agility and stability (Doz and Kosonen, 2010), capture profits from
investments in previous learning (Itami and Nishino, 2010), and ensuring there is “dynamic consistency” for business model evo-
lution (Demil and Lecocq, 2010). Interestingly, research has shown that senior leaders have used a staged approach to balance the
procedural rationality and politics of strategic decision making for resolving the paradoxes of running an established and new
disruptive business model in parallel (Velu and Stiles, 2013). Relatedly, Velu (2017: 606) emphasizes that firms need dynamic
capabilities to carefully achieve a balance between the efficiency and flexibility required to ensure business model evolution.
However, research on the dynamic capabilities for digital transformation is still at a nascent stage.

Building dynamic capabilities for digital transformation

The dynamic capabilities framework provides a powerful lens for studying strategic change in organizations (Barreto, 2010;
Peteraf et al., 2013; Schilke et al., 2018). Dynamic capabilities describe a company's capacity “(a) to sense and shape opportunities
and threats, (b) to seize opportunities, and (c) to maintain competitiveness through enhancing, combining, protecting, and, when
necessary, reconfiguring the business enterprise's intangible and tangible assets” (Teece, 2007: 1319). Dynamic capabilities are
innovation based and distinguishable from a firm's operational capabilities (Ambrosini and Bowman, 2009; Helfat and Winter, 2011;
Pavlou and El Sawy, 2011; Teece, 2014; Zahra et al., 2006). Helfat and Winter (2011: 1244) note that operational capabilities are
ordinary in the sense they help a firm in the present by maintaining the status quo, but this leaves the firm vulnerable to environ-
mental change.
Teece (2014:18–19) explains that although ordinary capabilities enable the firm to perform operational tasks, ordinary cap-
abilities in functions such as accounting, human resources management, and sales are now easily replicable because they can be
outsourced to the “cloud” and no longer support a durable competitive advantage. In contrast, dynamic capabilities govern the rate of
change in a firm's ordinary capabilities (Teece, 2007), are harder to replicate (Teece, 2014), and support evolutionary fitness (Helfat
et al., 2007), enabling a firm to alter “how it currently makes a living” (Helfat and Winter, 2011: 1244). Teece and Leih (2016: 7)
stress that dynamic capabilities must be built rather than bought because “ordinary capabilities are about doing things right, dynamic
capabilities are about doing the right things.”
The literature clarifies the relationship between dynamic capabilities, strategy, and business models (Achtenhagen et al., 2013;
DaSilva and Trkman, 2014; Teece, 2018; Velu, 2017). In advancing Casadesus-Masanell and Ricart's (2010:195) argument that a
business model is “a reflection of the firm's realized strategy,” DaSilva and Trkman (2014: 383) argue that “strategy is about building
dynamic capabilities aimed at responding efficiently to future and existing contingencies.” The authors argue the following:
Strategy (a long-term perspective) sets up dynamic capabilities (a medium-term perspective) which then constrain possible
business models (present or short-term perspective) to face either upcoming or existing contingencies. Thus, strategy entails
devising dynamic capabilities able to respond to contingencies through the organization's business model. Business models are
then bounded by the firm's dynamic capabilities. (p. 383)
Given that “every organization has a business model […but] not every organization has a strategy,” it is apparent that strategy is a
contingent plan of the competitive action on which business model to use (Casadesus-Masanell and Ricart, 2010: 200–203). In other
words, dynamic capabilities represent the intermediary between strategy and business models, ensuring the strategic renewal of
organizations (Agarwal and Helfat, 2009). Velu (2017) emphasizes that firms require a system of dynamic capabilities to orchestrate
resources and evolve the business model. Velu (2017) finds that (1) balanced redundancy, (2) requisite variety, and (3) cognitive
discretion were the dynamic capabilities that supported the evolution of a new firm's digital business model. For incumbent firms,
Teece (2018: 44) argues, “In many cases corporate strategy dictates business model design. At times however, the arrival of a new
general-purpose technology (e.g., the Internet) opens opportunities for radically new business models to which corporate strategy
must then respond.” Building sensing, seizing, and transforming capabilities thus allows a firm to craft future strategy that designs,
creates, and refines a defensible business model, guides organizational transformation, and provides a durable source for obtaining a
competitive advantage (Teece, 2018).

Sensing capabilities
Incumbents require sensing capabilities to scan the external environment for unexpected trends that could disrupt the organi-
zation (Birkinshaw et al., 2016; Day and Schoemaker, 2016; Dong et al., 2016; Giudici et al., 2018; Helfat and Raubitschek, 2018;
Loebbecke and Picot, 2015). Teece (2007: 1322) notes that “sensing (and shaping) new opportunities [and threats] is very much a

331
K.S.R. Warner, M. Wäger Long Range Planning 52 (2019) 326–349

scanning, creation, learning, and interpretative activity” that analyzes diverse information about trends in the business ecosystem.
Therefore, sensing should take place at all levels of the organization, with lower levels helping to provide information about and
insights into external trends to middle and top managers (Teece and Linden, 2017).
However, incumbents face significant challenges in building sensing capabilities that can predict the latest digitalization trends
(El Sawy et al., 2016; Fitzgerald et al., 2014; Hansen and Sia, 2015; Matt et al., 2015). Yoo et al. (2012) explain that building
platforms of digital capabilities rather than single products places reliance on complementors to distribute innovation, which in-
creases the risk of systematic failures and unintended consequences. The convergence of bringing unconnected user experiences and
unconnected industries together through platforms of “smart products,” for example, have resulted in new business model innova-
tions that have been very difficult for incumbents to predict (Sebastian et al., 2017). Conversely, Yoo et al. (2010) argue that
generativity has produced digital innovations that exceed original design intents and resulted in products that competitors, con-
sumers, and even the original innovator did not expect because various actors tend to pursue their own strategies.
Dynamic managerial capabilities (Helfat and Peteraf, 2015) and business units dedicated to scenario planning (Helfat and
Raubitschek, 2018; Teece et al., 2016) are important for sensing unexpected trends, but both have analytical limits. This is apparent
in a “big” or “smart” data context, where organizations are using digital infrastructures such as the IoT platforms to collect and
analyze large-scale and real-time granular data to predict and capitalize on human behavior (George et al., 2014; Ross et al., 2017).
Given the advances in computational speed, data storage, data retrieval, sensors, and algorithms, this has dramatically reduced the
cost of machine-learning-based predictions, meaning some firms are turning to artificial intelligence to anticipate new trends and
circumvent cognitive limits (Agrawal et al., 2017). However, Agrawal et al. (2017: 26) stress the following:
The key [AI] challenges for executives will be (1) shifting the training of employees from a focus on prediction-related skills to
judgment-related ones; (2) assessing the rate and direction of the adoption of AI technologies to properly time the shifting of
workforce training (not too early, yet not too late); and (3) developing management processes that build the most effective teams
of judgment focused humans and prediction-focused AI agents.
Dong et al. (2016) argue firms need to build generative sensing capabilities that use technologies to generate and test multiple
hypotheses in an abductive way to help managers explain surprising or anomalous events and judge the impact of unexpected trends.
Relatedly, some studies report that firms in pursuit of digital innovation require sensing capabilities in digital evolution scanning to
gather information through new digital devices, channels (e.g., software platforms, operating systems, and web services), and
emerging user behaviors across contexts and markets (Nylén and Holmström, 2015). Here, sensing capabilities do not reside ex-
clusively inside firms but can be co-created relationally with other parties in the business ecosystem (Giudici et al., 2018). Teece
(2018: 45) highlights, “It takes time for business model innovation to catch up with technological possibilities, perhaps because
business models are more context-dependent than technology,” which heightens the urgency for building seizing capabilities.

Seizing capabilities
To address opportunities or neutralize threats, incumbents require seizing capabilities that ensure leaders avoid hubris, deception,
bias, and delusion and that allow firms to experiment with decentralized boundaries, digital platforms, and new business models
(Teece, 2007). Day and Schoemaker (2016) report that “seizing” is an experimental capability that supports action and commitment
by using techniques such as rapid prototyping and real options logic to effectively balance risk and reward. Digitalization has
encouraged entrepreneurial firms to seize opportunities by experimenting with the (1) decoupling (i.e., regulating asset specificity of
power relationships), (2) disintermediation (i.e., reducing the power of established intermediaries), and (3) the generativity (i.e.,
unprompted innovative outputs) of existing value chains, which has created radical business model innovations (Autio et al., 2018).
However, Teece (2007: 1327) notes even if an incumbent enterprise senses an opportunity, one should not be surprised if it fails to
invest in this opportunity because path dependencies “tend to eschew radical competency-destroying innovation, in favor of more
incremental competency-enhancing improvements.” To overcome such inertia, incumbents in traditional industries are starting to
experiment with agility—originally a software methodology—to seize new opportunities, but execution is more difficult and less
transferable in practice (Birkinshaw, 2018; Sambamurthy et al., 2003; Pavlou and El Sawy, 2010; Rigby et al., 2016; Weber and
Tarba, 2014).
Teece et al. (2016: 17) define agility as “the capacity of an organization to efficiently and effectively redeploy/redirect its
resources to value creating and value protecting (and capturing) higher-yield activities as internal and external circumstances
warrant.” Sambamurthy et al. (2003) propose that firms should use information technology (IT) infrastructures to build three in-
terrelated capabilities consisting of (1) customer agility (e.g., co-creating user experiences), (2) partnering agility (e.g., orchestrating
an ecosystem of external partners), and (3) operational agility (e.g., achieving speed, accuracy, and cost efficiency) to enhance
financial performance. Indeed, the building of IT leveraging competencies has been reported to enable a variety of agile responses,
ranging from complex moves such as embarking on new corporate ventures for new product development to simple moves such as
adjusting existing production processes and resource utilization (Overby et al., 2006; Pavlou and El Sawy, 2006). Rigby et al. (2016)
note this has meant agile methods are now spreading beyond IT to other functions and industry contexts, yet incumbent projects tend
to be ineffective because leaders fail to understand the conditions on which agile does or does not work; this indicates flexible
sourcing arrangements, building organizational slack, and adopting open innovation processes are ways to preserve and enhance
agility (Teece et al., 2016). However, for incumbents, real agile action tends to require a deeper and broader digital transformation
(Birkinshaw, 2018; Hess et al., 2016; Sebastian et al., 2017; Svahn et al., 2017).

332
K.S.R. Warner, M. Wäger Long Range Planning 52 (2019) 326–349

Transforming capabilities
Sensing and seizing capabilities help create and discover opportunities, but to execute a digital strategy, firms need transforming
capabilities to realize the full potential of strategic change (Bharadwaj et al., 2013; Karimi and Walter, 2015; Teece and Linden,
2017). Day and Schoemaker (2016: 65) report that an organization with “transforming capabilities is one where agile, en-
trepreneurial mindset is actively cultivated within, with a broad expansive approach to external network-building as well.” Thus,
transforming capabilities supports incumbents with the continuous strategic renewal of assets and organizational structures to ensure
responsiveness in fast-changing environments (Agarwal and Helfat, 2009; Teece, 2014).
However, Svahn et al. (2017) report that digital transformation is more challenging because incumbents face four broad com-
peting concerns, even if they are willing to embrace digital innovation. Incumbents must try to balance (1) building innovation
capabilities alongside existing product innovation practices; (2) process and product innovations; (3) collaborative tensions between
employees and external partners; and (4) governance structures that ensure flexibility and control (Svahn et al., 2017: 239–240).
Most incumbents' digital transformations are hence still at an early stage because most revenues derive from traditional products and
services (Sebastian et al., 2017). Therefore, incumbents need to craft digital strategies that leverage digital technologies for rapid
innovation and responsiveness to inspire new value propositions and operational excellence (Hess et al., 2016). However, under-
standing how organizations build dynamic capabilities for digital transformation is a paramount strategic question that is yet to be
fully understood, providing the research focus for the remainder of the current study.

Research methodology

Research design and context

Our research design is based on multiple case studies that examine how incumbent firms in traditional industries build dynamic
capabilities for digital transformation (Eisenhardt and Graebner, 2007). As a participant in digital strategy consultancy projects, the
second author observed that industry-wide views on the term “digital transformation” are diverse, and the term is often used as a
buzzword to represent various strategizing and organizing activities. Therefore, we used qualitative methods to collect and analyze a
broad scope of data to make sense of digital transformation; we took a novel approach by drawing on senior executives' experiences
with leading digitalization projects in a wide range of industry contexts (Pettigrew, 1987). We started gathering insights from the
senior partners of German multinational corporation (MNC) subsidiaries in the global strategy consulting industry, each of whom had
substantial knowledge and experience with guiding client firms with digital transformation (Klarner et al., 2013). As our research

Table 2
Purposeful sample and scope of data collection.
Motion AG Powerhouse AG Balance AG Voice AG Energy AG Drive AG Media Inc.

Case overview
Industry Automotive Industrial Banking Telecoms Energy Automotive Media Publisher
Size (employees) +100.000 +100,000 1000–5000 +100,000 5000 - 10,000 +100,000 1000–5000
Revenues (2017) €25–50 billion €50–100 billion €1–5 billion €50–100 billion €1–5 billion €100–200 €1–5 billion
billion
Founded Late 1800s Late 1800s Early 1900s 1950s Late 1800s Early 1900s Early 1900s
Firm type Parent Parent Subsidiary Subsidiary Parent Parent Subsidiary
Market focus Global Global Europe Global Europe Global Global
Scope of case study data
Interviews 2 2 2 2 1 1 1
Position Director of digital Vice president of Director of Head of digital Director of Director of Chief digital officer
Transformation strategy digital banking consulting sales digital strategy
Leadership 15 years 10 years 15 years 11 years 7 years 17 years 13 years
experience
Education Diploma MSc Diploma MSc MSc MSc Diploma
News reports 11 20 11 15 12 21 15
Annual reports 2000–2017 2007–2016 Private 2000–2017 2007–2016 2006–2017 Private
Scope of consultancy data
Position Leadership Education Major industry Other industry Published Participant
experience exposure exposure reports observation
Build ltd. 1 senior partner 8 years PhD Automotive Mobility 3
Connect ltd. 1 senior manager 5 years MSc Retail Automotive 1
Digitize ltd. 1 mgt director 8 years MSc Finance Automotive 1 10 weeks
Redesign ltd. 1 senior partner 5 years MSc Automotive Telecoms 2
Seize ltd. 1 principal 8 years MSc Media Automotive 2
Navigate ltd. 2 senior partners 12/10 years MPA/MBA Automotive Finance 8

Totals/summary 18 in-depth 144 years Diploma-PhD Experience 105 company 80 published 10 weeks of
interviews combined education across 8 industry news reports reports participant
leadership contexts observation
experience

333
K.S.R. Warner, M. Wäger Long Range Planning 52 (2019) 326–349

project unfolded, we analyzed several incumbent firms located in Germany that are undergoing a digital transformation across a wide
range of industries. Table 2 presents our purposeful sample and allocates pseudonyms to maintain the confidentiality of each par-
ticipating firm. All experts— with at least five years of senior leadership experience and being well educated—shared the important
characteristic of being senior executives. Their human capital represents their deep knowledge of, and experience with digital
transformation (Helfat and Peteraf, 2015).

Data collection

Over a 21-month period, we collected a wide range of data, including observation, semistructured interviews, and industry
documents. At the start of the research project, the second author worked as a trainee consultant at one of the case consultancies
(Digitize Ltd.) from October 2016 to December 2016. This internship role provided opportunities to interact with senior consultants
who had digitalization experience and gain access to MNCs that are incumbent firms undergoing digital transformation. Following
this experience, we collected and analyzed 17 strategy consultancy reports on digital transformation. Each report ranged between six
and 96 pages and was published between 2013 and 2016. As a novel approach, we then contacted the authors of the strategy
consultancy reports when possible (five of nine senior consultants) and requested interviews to discuss the findings in more detail.
The managing partners from each strategy consultancy agreed to participate in the research.
Face-to-face interviews were chosen to collect data and enable personal contact (Alvesson, 2003), and they took place in June and
July 2017. In the interviews, we asked the senior consultants to share their personal views of and lived experiences with digital
transformation. To guide the discussion, we introduced the participants to a practitioner conceptualization of the dynamic cap-
abilities framework (Day and Schoemaker, 2016: 60); this interactive exercise provided participants with the opportunity to use the
dynamic capabilities framework to openly discuss their knowledge of and experiences with guiding client firms through a digital
transformation. After a preliminary analysis of the consultant interviews, we returned to the field and conducted repeat interviews
with the same senior executives of incumbent firms in Germany between August 2017 and January 2018. By this point, we had
started to develop a process model and asked participants to apply our model to obtain a breadth of contextuality on digital
transformation across automotive, banking, manufacturing, and telecommunications industries. This interactive exercise was useful
for refining our model and identifying which types of dynamic capabilities are built for digital transformation.
As a last step of data collection, we collected all publicly available annual reports and recent news reports on each firm's digital
transformation. We then triangulated our findings by conducting two more interviews in April 2018 with the founding partners of
Navigate Ltd.—a boutique digital strategy consultancy—which has extensive experience working on digitalization projects with our
purposeful sample of incumbent firms. In these two interviews, the senior partners agreed with the representation of our case findings
and offered new snowball sampling opportunities to connect with another three senior digital directors of incumbents in the media,
energy, and automotive industries. Therefore, this further data collection allowed us to triangulate our findings by asking the par-
ticipants to use our process model and verify the reliability of our case findings. The interviews ranged from 30 min to two hours and
were tape-recorded and transcribed.

Data analysis

Our data analysis was focused on making sense of digital transformation (Gioia et al., 2013). Our participant observation triggered this
sensemaking and led to a finer qualitative analysis, which allowed us to explore the microfoundations of building dynamic capabilities for
digital transformation. By initially conducting a summative content analysis (Hsieh and Shannon, 2005; Schreier, 2014) of strategy con-
sultancy reports, we identified the broad inconsistencies on what digital transformation really means and the ambiguities centering on the
processes and outcomes. The interviews with the senior consultants allowed us to discuss these inconsistencies and clarify how they guide
MNC problem solving regarding digital transformation (Gioia et al., 2013; Tippmann et al., 2012). A summative content analysis involves
“counting and comparisons, usually of keywords or content, followed by the interpretation of the underlying context” (Hsieh and Shannon,
2005: 1277); this analytical process was particularly effective because the reports provided a breadth of understanding for sensemaking,
which subsequently allowed us to direct our analysis and interpretation of the interviews to topics that required a deeper explanation.
We used NVivo© 11 software to identify the in-case conceptual patterns across the primary and secondary data through coding
and organizing the data into fragments (Miles and Huberman, 1994). Second, we aggregated the in-case similarities and differences
into broader categories through data reduction (Miles and Huberman, 1994). Based on this aggregation and data reduction in
Appendix 1, we conducted the cross-case analysis in the third step to look for similarities and differences across the cases (Eisenhardt
and Graebner, 2007). We derived first-order concepts representing strategic activities and directly connecting to the overarching
dimensions of the dynamic capabilities framework. We combined similar first-order concepts across cases into a set of second-order
themes (Gioia et al., 2013). Finally, we triangulated the second-order themes and first-order concepts with supplementary interviews
to strengthen the contextuality of our process model.
Fig. 1 represents our final data structure. The data structure not only helps to configure the data into a sensible visual aid, but it
also represents a graphic illustration of our analysis from raw data to concepts and themes (Gioia et al., 2013). The final data
structure in Fig. 1 summarizes the interrelations of first-order concepts, second-order themes, and aggregate dimensions on which we
propose a process model. The first-order concepts incorporate the language of the participants and describe the key activities for
digital transformation. Finally, we developed aggregate dimensions from the second-order themes that represent capability micro-
foundations, along with the contextual factors that relate to the triggers, enablers, and barriers of building dynamic capabilities for
digital transformation.

334
K.S.R. Warner, M. Wäger Long Range Planning 52 (2019) 326–349

Fig. 1. Data structure.

Emergent findings

Making sense of digital transformation

Early on in our research project, it was evident that the participants had different perspectives on what digital transformation
represents and entails. This was apparent by the fact that the senior strategy consultants used different terminology to discuss a wide
range of themes relating to how incumbents build new awareness, preparedness, and responsiveness to address the “digital re-
volution” (Connect-Q-1) or “digital age” (Seize-Q-1) context. For example, one consultant felt the meaning of digital transformation
can vary from an organizational and individual perspective:
I will start with our consultancy's view on digital transformation: It is the reaction to the digital revolution. We support our clients in order
to react faster in rapid changing environments. As you know, our consultancy is very technology oriented, which means that our approach
to digital transformation is also oriented toward tools, technologies, processes, and how do we make a company more digital.

My personal definition is a little bit different. I am personally not a digital person, I don't use Facebook or Twitter. For me, it is more a
mindset. Digital transformation for me means that I have to react faster, to be flexible and agile, and to broaden my perspective. The most
important thing is to take more things into account and to react to these trends. (Connect-Q-1)

In addition to technology adoption, the above quotation indicates that digital transformation also consists of a cultural orientation
(e.g., a specific mindset) that recognizes the importance of fast and flexible decision making for competing in an uncertain context.

335
K.S.R. Warner, M. Wäger Long Range Planning 52 (2019) 326–349

Awareness of advances in digital technologies and preparedness for changes in consumer behaviors were also common themes within
our data. As another consultant put it:
Digital transformation is not about technology or about acquiring new technological skills. It is in fact about getting new awareness and
bringing new ideas on how to think and how to act into a classical organization. […] So improving in the tactical or implementation skill set
on how fast, how customer centric, how agile these decisions are put into practice. (Redesign-Q-1)
In addition to these points, the digital transformation of an incumbent's business model was another prominent theme. For
example, another consultant discussed helping incumbents design new business models by identifying new forms of vertical and
horizontal collaboration while involving a wider ecosystem of external partners (Balance-Q-1). Interestingly, digital transformation
was also described as a capability-building process, as another consultant summarized:
Digital transformation describes a journey of a company trying to be equipped for the digital age […]. I believe there are different
capabilities needed to survive and succeed as a company in the digital age. Building and deploying these capabilities means digital
transformation for me. (Seize-Q-1)

Building dynamic capabilities for digital transformation: a process model

In this section, we present a process model that explains how incumbents build dynamic capabilities for digital transformation.
Our model, as shown in Fig. 2, is grounded in senior executives' experiences with leading digital transformation projects. The starting
point of our model is represented by external triggers, including disruptive digital competitors, changing consumer behaviors, and
disruptive digital technologies, which trigger the building of dynamic capabilities for digital transformation. Our model specifies
three core enablers—cross-functional teams, fast decision making, and executive support—along with three core barriers—rigid
strategic planning, change resistances, and a high level of hierarchy, which influence the building of dynamic capabilities for digital
transformation.

Building digital sensing capabilities

When presenting the “sensing cluster,” all participants highlighted that advances in digital technologies now challenge the tra-
ditional approach to strategizing. All consultants spoke about the need to develop new capabilities in digital scenario planning and
digital scouting to pinpoint new technological, customer, and competitor-based trends. All consultants talked about the use of informal
and formal networks in the world's technology hubs to identify technological trends; they also emphasized the importance of big data
analytics and artificial intelligence to sense new customer-centric trends that are hard for strategic planners to predict. As one senior
partner put it:
At no single time in the past, either in the industrial revolution or the revolution with the first computers, nothing has changed so fun-
damentally in such a short time. In the past, the products, customers, and sales channels remained the same. Now, you are in a situation

Fig. 2. Building dynamic capabilities for digital transformation: A process model.

336
K.S.R. Warner, M. Wäger Long Range Planning 52 (2019) 326–349

where customers basically change. […] At the same time, you have new competitors moving in, who are entirely new. Let's take the
automotive sector, where competitors were absolutely known by the firms. The incumbents knew exactly what the rivals will do in some
years. Now, you have a Tesla with a different product, totally different processes, so it is totally unpredictable. (Build-Q-1)
To build sensing capabilities in digital scouting and digital scenario planning, a common theme across all interviews and industry
reports was that capability development is contingent on digital mindset crafting. All senior consultants emphasized that strategizing in
a digital context must be based on crafting a strong digitally oriented culture. They talked about the importance of establishing a
long-term digital vision while promoting an entrepreneurial and digital mindset within large established companies. As one con-
sultant put it:
Firms can spend millions of dollars in their digital transformation, but if they don't have a digital vision, then nothing will actually change.
(Connect-Q-2)
Within our consultancy data, digital mindset crafting was a prominent theme and was regarded by the consultants as an essential
capability for starting an incumbent's digital transformation. As the managing director of a digital consultancy division explained:
You need to understand how exponential technologies evolve and translate that into what does it mean from economic value perspective for
the business. You also need to understand what kind of people capabilities that will require. The promotion of a digital culture will
accelerate the company's digital transformation. (Digitize-Q-1)
Our consultancy data raise three central issues that incumbents face in traditional industries for building sensing capabilities in
the digital age. First, our data show that informal and formal scouting networks need to be incorporated into incumbent firms' formal
strategic planning process. Second, our data indicate that traditional formal strategic planning mechanisms have yet to catch up with
the advent of disruptive digital technologies, such as analytics and artificial intelligence, which can enhance strategic decision
making. Third, our data show that crafting a digital mindset and culture throughout the entire organization is essential for building
sensing capabilities that will allow incumbents to seize on the latest unexpected trends.

Building digital seizing capabilities

When reviewing the “seizing cluster,” the participants stressed that business model innovation was an essential component of an
incumbent's digital transformation. The interviews and consultancy reports reveal that the strategy consultancies often work with
traditional organizations to incorporate strategic agility into their business model thinking to quickly exploit technological and market
opportunities. For example, one senior consultant highlighted the central role of continuous redirection and speed in rapidly de-
veloping a client's digital business model:
You need the capability to implement or rather work toward such a target picture in a very agile way with fast and short sprints. And being
willing and able to recalibrate your cores after these sprints. So it is all about being flexile, agile, dynamic. But at the same time under-
standing what is the bigger picture and underlying things that you will not change. (Build-Q-2)
To strengthen strategic agility, the consultants emphasized the importance of rapid prototyping, providing examples of recent
strategic decisions to build “digital innovation labs” to experiment with minimum viable products. The consultants explained that
digital innovation labs allowed incumbents to gather customer feedback in almost real time and use this feedback to respond to new
customer-centric trends. As one consultant highlighted:
This is about the capability to really put things into practice very fast, to understand and elaborate on specific solutions, to scale up digital
products to get something going. (Redesign-Q-2)
For balancing digital portfolios, the senior consultants emphasized that new business model innovations such as servitization or
subscription models, must be balanced with existing product-based business models. The consultants explained that investments in
digital infrastructures for rapid scaling, open innovation techniques, and technology acquisitions all help reach an appropriate speed
of execution. For example, the managing director of a digital strategy boutique discussed the importance of using digital scenario
planning capabilities to ensure that incumbents strengthen their strategic agility while balancing digital portfolios to enhance traditional
product offerings:
Traditional strategic planning doesn't address the disruptions caused by digital technologies, changing consumer behaviors or economic
volatility. Rather than tying corporate strategies on rigid three–five-year plans, you need to systematically manage critical uncertainties
with scenario planning. You need several possible future scenarios and continuous iteration if you are to develop strategic agility in this
unpredictable, digital environment. (Navigate-Q-1)
A prominent theme is that strategic agility is the critical dynamic capability for incumbents to seize on the latest trends and avoid
potential existential threats. We found a consensus that incumbent firms must learn to specialize in pacing strategic actions and
accept the reality that no firm can win every competitive race. In other words, constant redirection is likely to become commonplace
for even the most traditional firms. Second, our consultancy data emphasize that dynamic capabilities in rapid prototyping provide
the opportunity to accelerate an incumbent's digital transformation. The consultants emphasized this requires decentralized in-
novation labs or new subsidiaries with a blank canvas to ensure ongoing digital mindset crafting. Third, our consultancy data show that
balancing digital portfolios provide incumbents with the capability to scale up or down on business model innovations that have the
potential to enhance existing customer needs and demands. Together, we found this system of capabilities represent the industries

337
K.S.R. Warner, M. Wäger Long Range Planning 52 (2019) 326–349

current view on what is required to seize on the latest digitalization opportunities.

Building digital transforming capabilities

An introduction to the “transforming capabilities” cluster expectedly led to a rich discussion about digital transformation. The
participants talked about a wide range of strategic issues relating to organizational culture, organizational redesign, and managing
innovation ecosystems. For example, one consultant emphasized that improving digital maturity was essential for incumbents to pursue
digital transformation:
It is really about thinking how do we get people to think about the same stuff with a different lens instead of thinking with the same lens
about different things. Culture is so important in digital transformation, because if you don't get the cultural stuff right, if you don't get the
wow effect, what are your agile processes helping with? When you compare LinkedIn profiles from Kaufhof [traditional German retailer]
and Amazon managers, the job description may be the same, but they have a completely different understanding of how to interpret their
job. (Redesign-Q-3)
To improve the digital maturity of the workforce, our data underscore the importance of involving younger “digital natives” in the
capability-building process of traditional organizations. The senior consultants emphasized the need to strike a balance between
improving the digital maturity of an externally appointed and internally promoted workforce. In the interviews, the participants
talked about the challenges of building the capabilities for redesigning internal structures and argued that incumbents must build a
leadership team, strategy, and business model that adopt a digital focus. For example, one participant drew on a recent digital
consultancy project to illustrate the strategic leadership required to redesign management structures:
What was very interesting about our [automotive client] is they did not care about money. The senior management were more concerned
about losing power and wanted to give decision-making power away. This highlights that digital transformation is also about culture and
leadership change. The senior leaders realized it was essential to change the structure and the resources of the company. They didn't want
financial or company boundaries issues, just a new [digital] mindset of the people. (Connect-Q-3)
The above quote implies that transformational leadership and decentralization are integral to renew strategies and business
models. Interestingly, the participants talked about decentralization in the context of navigating innovation ecosystems. This discussion
included the need for traditional firms to build or join a digital ecosystem to work with new partners on “co-creation” and “co-
opetition” activities, which help redefine the speed of collaborative behaviors and invent new business models. Moreover, “un-
learning” existing practices was a common theme noted as an important skill for effectively interacting and collaborating with a wide
range of new partners in digital ecosystems; this requires transformational leadership. One consultant emphasized the importance of
leadership style for navigating digital ecosystems:
Maybe your ecosystem is changing every couple of years, maybe your platform has to be rebuilt four times within the next 15 years. And
this is also why the leadership style becomes so decisive nowadays, it is driven not by hierarchy but by purpose. (Digitize-Q-2)
This system of capabilities provides insight into the wide range of complexities currently associated with digital transformation.
What is clear from our data is that improving digital maturity of the workforce is a fundamental capability for the ongoing digital
transformation of incumbent firms. Our findings highlight that incumbents must work toward redesigning internal structures, and this
can be achieved through the decentralization of business units and establishment of independent subsidiaries. In addition, our data
show that navigating innovation ecosystems can radically address customer needs compared with firms that maintain traditional
product-based business models. These strategic activities help with improving the digital maturity of the workforce and support an
incumbent's ongoing digital transformation.

The digital transformation of incumbent firms—an ongoing process of strategic renewal

To gain deeper and broader insights into digital transformation, we interviewed senior executives of incumbent firms in a wide
range of traditional industries facing disruptive competition. In these interviews, we asked the executives to apply our model as a tool
to openly discuss each firm's digital transformation journey. This helped us improve the contingency power of our model, provide as
much context as possible, and verify the reliability of our findings. Table 3 outlines the strategic change of each incumbent firm,
demonstrating that although each digital transformation is contextually unique, we found three broad forms relating to the strategic
renewal of the firm's business model, collaborative approach, and eventually the culture.
Using Agarwal and Helfat's (2009) strategic renewal perspective, Table 3 shows that the scope of digital transformation varies
because some firms have refreshed their traditional business models (e.g., Drive), while others have used digital technologies to
replace their traditional operations with new collaborative approaches (e.g., Motion). In some cases, the ongoing strategic renewal of
business models and collaborative approaches have refreshed traditional organizational cultures with a new digital mindsets and
cultures (e.g., Balance and Media). Our findings reveal that digital transformation often starts with the strategic renewal of the
incumbent's business model, and changes in business models tend to lead to wider changes in the firm's collaborative approach,
which, if executed correctly, will eventually lead to deeper changes in organizational culture. As a result, our findings show that the
scope of digital transformation varies on these three dimensions.
Table 4 provides the context for the unique digital transformation journey of each incumbent firm, as outlined in the following
sections. In Table 4, we outline the major triggers, the scope of digital transformation, the main dynamic capabilities discussed, and
the key successes of the existing digital initiatives. Our cross-case findings reveal that disruptive competition, changes in consumer

338
K.S.R. Warner, M. Wäger Long Range Planning 52 (2019) 326–349

Table 3
Three forms of digital transformation.
Incumbent firm Renewal of business model Renewal of collaborative approach Renewal of culture

Motion AG Prior business model: Automotive multiproduct Prior collaboration: Limited internal Prior culture: Known as a traditional
supplier. Sale of standardized products created collaboration because of silo mentality automotive and transportation original
transactional value propositions. between departments. equipment manufacturer (OEM) supplier.
Replaced business model with new Replaced collaboration with an Refreshed culture to become a
digitalization logic. The sale of tailored services enterprise social network to remove work technology leader and pioneering
using mobility services, IoT, cloud, and artificial silos and facilitate company-wide software company most attractive for its
intelligence is creating relational and multisided knowledge sharing. people.
value propositions.
Powerhouse AG Prior business model: Industrial equipment Prior collaboration: Limited initiatives Prior culture: Traditional corporate
multiproduct manufacturer. Sale of standardized in place to support knowledge-sharing for culture based on efficiency, profit
products created transactional value propositions. internal or external collaboration. maximization, and compliance.
Replaced business model with new Refreshed collaboration with cross- Refreshed culture with a new digital
digitalization services logic. Sale of new digitized functional “working out loud” approach vision and innovation-driven ownership
products is creating relational and multisided to co-create products across silos and with culture.
value propositions. ecosystem partners.
Balance AG Prior business model: Financial provider of Prior collaboration: Strong focus on Prior culture: A traditional and formal
physical banking services. The sale of internal initiatives with almost no banking culture with a strong focus on
standardized products created transactional value connection to an external ecosystem for personal banking advice.
propositions. knowledge sharing.
Replaced business model with digital banking. Replaced collaboration with external Refreshed culture with digital banking
The sale of personal services and digitized collaboration in a wide ecosystem of while protecting the bank's cultural
products is creating relational value propositions. young and disruptive Fintech players. heritage as the “family bankers.”
Voice AG Prior business model: Telecommunications Prior collaboration: Strong focus on Prior culture: A traditional mindset to be
provider. The sale of standardized products internal R&D activities to protect market a dominant telecommunications provider.
created transactional value propositions. leadership.
Replaced business model with a digitalization Refreshed collaboration with open Refreshed culture by establishing a new
services logic. The sale of tailored services using innovation activities across the whole subsidiary to create a digital innovation
Cloud, IoT, and big data are creating transactional company. culture.
and multisided value propositions.
Energy AG Prior business model: Vertically integrated Prior collaboration: Limited initiatives Prior culture: Known as a traditional
energy provider. The sale of standardized in place to support knowledge sharing for energy provider with a formal corporate
commodity products created transactional value internal or external collaboration. culture.
propositions.
Replaced business model with a digitalization Replaced collaboration with the Refreshed culture by establishing a
“prosumer” logic. The sale of tailored services establishment of a digital joint venture to digital joint venture to start creating
using predictive maintenance, cloud, and IoT is facilitate internal and external openness and a digital innovation culture.
creating multisided value propositions. collaboration to work with “prosumers.”
Drive AG Prior business model: Automotive multiproduct Prior collaboration: Strong focus on Prior culture: Traditional corporate
manufacturer. The sale of standardized products internal R&D activities to protect culture based on responsibility, trust, and
created transactional value propositions. innovation power from competitors. first-class performance.
Refreshed business model with digitalization Replaced collaboration with a radical Refreshed culture by weaving new
services logic. The additional sale of rental company-wide approach to co-create new digital innovations into existing corporate
services using digital platforms and cloud is products with ecosystem players. culture.
creating multisided value propositions.
Media AG Prior business model: Print media multiproduct Prior collaboration: Limited initiatives Prior culture: Traditional and formal
content publisher. The sale of standardized in place to support knowledge sharing for corporate culture with a strong focus on
products created transactional value propositions. internal or external collaboration. marketing and brand management.
Replaced business model with digitalization Replaced collaboration with a radical Refreshed culture by reinventing the
services logic. The sale of new mass media, data company-wide approach to co-create new organization as a mass media company in
analytics, and advertising on a digital platform is products with ecosystem players. pursuit of new digital innovations.
creating new multisided value propositions.

demands, and advances in digital technologies are major triggers for strategic change, driving the creation of dynamic capabilities for
digital transformation.

Strategic renewal of business models

Table 4 shows that all the incumbents' digital transformations involved the strategic renewal of business models. In all cases, the
incumbent firms had started to formulate strategies to renew established product-based business models with digitalization logics.
This was intended to limit the reliance on transactional value propositions and capture value from new relational or multisided value
propositions. The increasing threat of disruptive competition from new entrants in adjacent industries was a major trigger for the
creation and delivery of new value propositions. However, our cross-case analysis shows that the incumbents built different types of
dynamic capabilities for the strategic renewal of business models.
An interesting example of an incumbent firm that refreshed its business model is Drive AG—a global German automotive mul-
tiproduct manufacturer. Drive refreshed its traditional product business model with a digitalization services logic. During interviews,

339
 K.S.R. Warner, M. Wäger

Table 4
Digital transformation as a context for strategic change.
Incumbent firm Major trigger of digital transformation Scope of digital transformation Main dynamic capabilities discussed Key successes (so far)

Motion AG New digital technologies Organizational wide Sensing: Digital mindset crafting Launched digital solutions monitoring platform
Seizing: Strategic agility Replaced product logic with a digital services model
Transforming: Improving digital maturity Launched digital culture training program
Powerhouse AG New digital technologies Organizational wide Sensing: Digital scouting Replaced product logic with a digital services model
Seizing: Balancing digital portfolios Global launch of Industry 4.0 product portfolio
Transforming: Navigating innovation ecosystems Digital products account for 13% of global revenues
Balance AG Changing consumer demands Business model & collaborative approach Sensing: Digital scenario planning Refreshed business model with “blended” logic
Seizing: Balancing digital portfolios German excellence award for “strategy and innovation”
Transforming: Navigating innovation ecosystems Award for being Germany's most customer-centric bank

340
Voice AG Changing consumer demands Business model & collaborative approach Sensing: Digital scouting Founded scouting networks in world's technology hubs
Seizing: Rapid prototyping Founded digital lab to launch MVPs
Transforming: Navigating innovation ecosystems New R&D software alliances are replacing product logics
Energy AG Disruptive competition Business model & collaborative approach Sensing: Digital scouting Replaced product logic with a “prosumer” model
Seizing: Rapid prototyping Founded digital joint venture to attract entrepreneurial
Transforming: Redesigning internal structures talent and to enhance internal and external collaboration
Drive AG Disruptive competition Business model & collaborative approach Sensing: Digital scenario planning Launched digital corporate strategy
Seizing: Balancing digital portfolios Refreshed business model with “digitalization” logic by
Transforming: Improving digital maturity balancing traditional ownership with mobility services
Media AG Changing consumer demands Organizational wide Sensing: Digital mindset crafting Appointed senior digital leadership team
Seizing: Strategic agility Founded digital strategy division to start transformation
Transforming: Redesigning internal structures Redesign of traditional business units
Long Range Planning 52 (2019) 326–349
K.S.R. Warner, M. Wäger Long Range Planning 52 (2019) 326–349

the global head of digital strategy explained that Drive's digital transformation began at the end of the 1990s when SIM cards were
integrated into cars to provide basic mobile connectivity. The participant told us that Drive's digital transformation has evolved
considerably since then because the company is now testing the embedding of advanced digital capabilities into fully autonomous
vehicles. When applying our model, the executive stressed the importance of digital scenario planning for the launch of its digital
corporate strategy:
We reframed our strategic direction in [201X] and made digitalization as a big component of this new strategy. Our board has chosen a
company-wide digital strategy to become more and more a tech company. We see ourselves no longer as a pure car manufacturer, we try to
become a mobility provider. (Drive-Q-1)
To become a technology company, Drive refreshed its existing business model by using digital platforms and mobile technology to
sell car-sharing services, which has created a supplementary multisided value proposition. Drive's strategic decision to enter the
innovative and fast-growing mobility services segment identified new revenue streams, promoted the new electric ideology, and
strengthened its strategic position against new digital competitors in the market. By balancing digital portfolios, the incumbent en-
hanced its traditional ownership model with a mobility services offering. As the senior executive explained:
Our current business model is still grounded in an ownership model, but with digitalization, we are shifting more and more to a service-
oriented business model. Of course, this is highly dependent on the region, but in certain areas in the world, owning a car is no longer a
status symbol as it has been some years ago. Instead it has become trendier to use a car, so we try to make use of the sharing economy and
test out new digital services like payment per hour. (Drive-Q-2)
We found that Drive's refreshment of its product business model is replacing its traditional collaborative approach. The senior
executive explained that Drive and its competitors in the automotive industry have historically focused on internal research and
development (R&D) activities. However, the threat of disruptive competition encouraged Drive to use digital platforms to openly
collaborate with an innovation ecosystem of direct rivals and start-ups to co-create and co-deliver new offerings. The participant
stressed that this new and open collaborative approach will be instrumental for improving the digital maturity of the workforce:
We must speed up, become more agile in our daily life, and open our company boundaries to the outside world. (Drive-Q-3)
On the other hand, Powerhouse AG—a global industrial equipment manufacturer—is an exemplar of an organization-wide digital
transformation that started by replacing the firm's traditional multiproduct business model with a servitization logic by digitizing
industrial products to create a range of new relational and multisided value propositions. This includes a new IoT platform that
provides advanced analytics of real-time data for customers in several industries and digital consulting services that already account
for 13% of its global revenues. However, despite Powerhouse's corporate strategy to become an industrial leader in digitalization, the
incumbent has witnessed unexpected competition when trying to defend its new business model. In the interviews, we asked the vice
president of strategy to apply our model, which led the participant to discuss a range of initiatives that Powerhouse have taken to
build sensing capabilities. He explained that the company had created specialized market intelligence departments to build new
digital scouting and digital scenario planning capabilities to predict the emergence of future competitors from adjacent industries. But
even with these advanced capabilities, he felt the company's strategizing was still insufficient in today's digital age:
Our strategy process is yearly based. How fast does a digital world change? Probably faster. We need to be more flexible and agile in future.
For sure, our yearly budget-planning processes are potentially challenging for this digital space. (Powerhouse-Q-1)
Discussing how Powerhouse seizes on new opportunities, the participant focused on the importance of building strategic agility and
balancing digital portfolios. The participant emphasized that strategic agility was required to rapidly reallocate resources to new
internal projects, such sensors and autonomous robots, and external projects, such as acquiring proprietary technologies (e.g., smart
data and analytics), that protect Powerhouse's strategic position in the industrial equipment and services industry. As a result, he
emphasized that navigating innovation ecosystems was the essential capability to ensure that Powerhouse continues to strategically
renew its business model:
From a product perspective, the more digital you get, the more customer co-creation you do. You need to be fast in understanding and
integrating customer requirements in these development processes. If you are not agile with your customer, you will not succeed.
(Powerhouse-Q-2)
These themes were also apparent in Balance AG—an old private bank headquartered in Germany—that transformed its business
model and collaborative approach to become a financial leader in digitalization. The managing director of digital banking explained
the bank was slow to respond to the emergence of new digital competitors such as financial technology start-ups (e.g., FinTech's) and
Internet players from adjacent industries that offer customer-centric banking services such as mobile payments or peer-to-peer
lending. New disruptive competitors and the erosion of market share forced Balance to replace its traditional product logic with a
business model that blends personal services and digital products to create and deliver a customized relational value proposition.
As a starting point, the participant explained that Balance had to build new sensing capabilities in digital scenario planning to
improve the bank's understanding of customer needs. This encouraged the bank to “follow” customers down new digital channels.
When asked to apply our model to describe the bank's digital transformation, the managing director initially stated:
As a starting point, you not only have to look at digitalization, but you also have to look on the whole business model you have. What must
be digitized and what doesn't have to be digitized? Once you do a full analysis of the business model, you come to the point how does my

341
K.S.R. Warner, M. Wäger Long Range Planning 52 (2019) 326–349

future business model have to look to have a sustainable future? (Balance-Q-1)

When asked how the firm seized on this opportunity, the participant emphasized that balancing digital portfolios was necessary to
ensure that digitalization did not encroach on the personal values and cultural heritage of the organization. The executive explained
that Balance was known as a private and personalized bank, meaning that new digital products had to be blended with the firm's
existing capabilities in personal banking. In describing the incumbent's digital transformation, the participant stressed the following:
You need to create a certain urgency without creating panic. Banks for the past 500 years have thought about the same stuff in an analogue
way and were not digitized. Managers need to get all the people involved in digitalization. This is about changing a culture. This is the
biggest challenge you have to do on a management level; you have to do a very delicate change process. (Balance-Q-2)
The participant explained that dramatic changes in consumer demand (e.g., “mobile-first” expectations) triggered this urgency for
change and provided the executive team with enough support to replace the firm's existing business model. The sale of standardized
products was replaced with a blended logic that created a relational value proposition that used digital products to deliver more
customized financial services. The participant highlighted that to replace the business model, the firm had to build new capabilities in
navigating innovation ecosystems to actively collaborate with external partners, which changed cost structures and drove new business
model innovation:
We cooperate with FinTechs to insert the culture of digital innovation in our company and bringing people up to speed. Everybody has its
own world in this ecosystem. Banks have to deal with structures and regulatory, and FinTechs are faster and more technology focused.
Getting these two worlds together and finding the reality somewhere in between is how you embrace the power of such an ecosystem.
(Balance-Q-3)

Strategic renewal of collaborative approach

We found that the strategic renewal of an organization's collaborative approach extends the digital transformation of business
models, incorporating a wider range of organizational activities. All the participants noted that digitalization has forced incumbents
to change the way in which people collaborate across departments and divisions and to do so with a wider ecosystem of external
partners. Table 3 shows how most incumbents have used digital technologies to refresh or replace existing internal and external
collaboration activities.
Voice AG—a big and old telecommunications provider—is one organization that has digitally transformed its collaborative ap-
proach because they have refreshed existing internal R&D activities with new external R&D activities. During the interviews, the
senior executive explained that in 2007, the subsidiary looked over the competitive threat (and opportunity) of IT infrastructure
providers moving toward cloud-based computing. By 2012, advances in cloud computing had led to new digital competitors (e.g.,
Amazon and Microsoft), creating unprecedented customer demands for digital infrastructures not governed by this established player.
The participant explained this new customer demand for new digital technologies had gradually “cannibalized” some of Voice's core
IT infrastructure products, which was the major trigger to refresh its collaborative approach toward R&D. In the interviews, the
participant identified that Voice has built strong sensing capabilities in digital scouting. When asking the participant to apply our
model, he highlighted:
This [sensing] is happening in our scouting network. We currently have a trend team meeting that are looking for the latest trends in
digitizing the food chain. What is happening for instance in blockchain to track ingredients, and circumstances in the food chain. There is
an existing network throughout [Voice plc] and in [our subsidiary] which is very important. We have guys scouting around Silicon Valley,
in Israel, and in Berlin looking out for the next big trends. So it is a network of people engaged in interesting and important developments
regarding digitization. That is the scouting network we have deployed. (Voice-Q-1)
When asked how Voice tried to seize on new technological opportunities and respond to disruptive competition, the participant
discussed the growing importance of external collaboration with players in “adjacent industries.” For example, the participant
emphasized that Voice had built new capabilities in rapid prototyping, which has been one of the organization's primary means for
balancing digital portfolios and improving strategic agility. He explained:
We have a CEO workshop between [Voice], and [a large insurance group] to work out what we can do jointly in the digital area. And
within a digital innovation lab environment, we have gone from a series of ideation workshops to a capability implementation, to now go to
market, and launch of new products, such as a B2C security product that couples the capabilities from [Voice] and capabilities from [a
large insurance group]. So that is a concrete example of how to speed up board discussions on what could we do to actually accelerate the
whole process of a joint go to market. So, that was a process of ideation to product launch in 6 months. (Voice-Q-2)
Finally, the participant identified that navigating innovation ecosystems was an essential capability for supporting Voice's digital
transformation. The participant explained that Voice had made the strategic decision to pursue R&D through open and multiplex
forms of external collaboration. The participant explained this new R&D approach had helped the firm replace its business model
with a digitalization services logic. This created a suite of customer-centric digital products and refreshed the company-wide ap-
proach to business collaboration:
One of our top strategies is to win with partners. To deliver new digital experience to a customer nowadays, means to fully involve
competitors from adjacent industries and universities for co-creation. This is where innovation truly happens. Open innovation, working

342
K.S.R. Warner, M. Wäger Long Range Planning 52 (2019) 326–349

with many partners and working with universities to transfer new business model thinking is one of the most important factors to become a
digital organization. (Voice-Q-3)
In contrast, Energy AG—a traditional German vertically integrated energy provider—is replacing its company-wide approach to
business collaboration. In 1998, the liberalization of the German energy market created a strategic challenge for Energy when the
firm encountered stagnating revenues in its main business of commodity goods because of an influx of new competition. In addition,
the rise of Internet comparison portals such as “Check24.de” or “Verivox.de” increased the price sensitivity of consumers and put
further pressure on Energy's existing business model. When applying our process model, the director of sales explained how Energy's
digital scouting network recognized the need to replace the traditional, commodity product business model with an emerging digi-
talization “prosumer” logic:
We observed our market for new revenue streams. We recognized that the whole energy industry offers huge potential to collect data and
identify big data opportunities. Consumers turn into prosumers and provide valuable insights for predictive maintenance of home appliances
and technical equipment. It is notable that none of the big Internet companies such as Google or Amazon have dared to enter the energy
market to collect data so far. (Energy-Q-1)
The digital scouting for technological trends and competitor intelligence gathering triggered the strategic decision to engage in
rapid prototyping. Instead of developing this capability within the old legacy structures of the firm, the incumbent decided to establish
a joint venture with other German digital players outside the company boundaries. Because prior collaboration was limited to a few
knowledge-sharing initiatives, this joint venture replaced Energy's previous collaborative approach, as follows:
This change was not incremental, it was radical. The joint venture was provided with different working conditions, very loose structures and
a start-up atmosphere. People got empowered in working with agile methods like SCRUM or Kanban and directly testing ideas with MVPs.
(Energy-Q-2)
The joint venture was an important step for improving the digital maturity of the workforce. Energy's employees were empowered
with the opportunity to work in a “digital innovation hub” environment and experience new forms of internal and external colla-
boration. Finally, redesigning internal structures provided Energy with a starting point to refresh its formal corporate culture with a new
digital innovation corporate culture.

Strategic renewal of organizational culture

Our cross-case findings show that the strategic renewal of organizational culture is more apparent at an advanced stage of digital
transformation. Transforming the business model and/or collaborative approach serves as preconditions to trigger deeper changes in
corporate culture. Table 3 highlights that all incumbents have attempted to refresh their corporate culture with a wide range of digital
initiatives. Interestingly, there was agreement among all executives that digitalization should not replace historic values but should
rather continue to refresh the roots of corporate culture.
Motion AG—a global automotive supplier—is one organization that has replaced its business model and collaborative approach
and is now at an advanced stage of digital transformation, having refreshed its corporate culture. In the interviews, the digital
transformation manager emphasized that Motion had experimented with the creation and adoption of internal innovation methods to
foster idea sharing and encourage internal collaborations. This refreshment of organizational culture was triggered in 2010 by the
awareness of a competition race among Motion's competitors:
Our culture was being a fast follower. We screened the market and realized that in a digital world, there is just one market leader, and the
distance to the followers is enormous. With our digital change initiative [“Dynamic Digital”], we transformed our organization from being a
fast follower to being most progressive with technology and attractive for people. (Motion-Q-1)
When applying our model, the executive focused on digital mindset crafting for sensing new opportunities for “remaining relevant”
for both customers and employees in the digital age. The participant explained that the “Dynamic Digital” campaign was a change
management initiative that communicated four company values and drove the corporate strategy and long-term vision. Interestingly,
the four company values were promoted to encourage strategic agility in the workplace. The “Dynamic Digital” campaign incorporated
the introduction of mobile work, flexi-time work, and sabbaticals to improve strategic agility. The executive explained that the heart
of this change management initiative was facilitated by the construction of a digital enterprise social network platform:
We recognized that we had to introduce an enterprise social network, that served as a vehicle for cultural development. So everybody was
able to participate in organizational change projects across functions and hierarchies. (Motion-Q-3)
Returning to our model, the executive explained this social network platform was instrumental for redesigning internal structures
and improving the digital maturity of the workforce. Moreover, the enterprise's social network allowed the workforce to participate in
ideation processes and collaborate in virtual teams across the organization. This helped Motion leverage digital knowledge inside the
firm and connect the global workforce in unexpected ways across functions, hierarchies and locations. This digital platform and social
media technology continue to play a significant role in the ongoing refreshment of Motion's organizational culture.
However, Media Inc.—a global mass media company with a strong presence in Germany—was slow to replace its magazine print
media business model with a mass media business model. Despite owning some of the world's most elite magazine brands, this
incumbent had historically prioritized print advertising over online advertising. With the rise of disruptive competition from a new

343
K.S.R. Warner, M. Wäger Long Range Planning 52 (2019) 326–349

generation of Internet media companies and social media platforms, the failure to adapt to the digital age had triggered an urgency
for the incumbent's rapid digital transformation. An early step in Media's digital transformation journey was to appoint chief digital
officers in international regions to expand the company's digital product portfolio. This included digital media, data analytics, and a
digital platform that scaled the generation of online advertising revenues across all of the company's elite brands. In the interview, the
chief digital officer of Europe emphasized the significance of digital mindset crafting and strategic agility to support the incumbent's
rapid digital transformation:
We accelerated our digital transformation considerably by starting to build a fully autonomous digital company within the company. This
strategic move implied a change of team structures, internal collaboration, and the digital innovation unit became a part of the organization
with its own logic. (Media-Q-1)
The digital innovation division provided Media with the opportunity to experiment with rapid prototyping to test out early-stage
digital media offerings and seize new revenue streams. In this role, the chief digital officer established cross-functional teams to
collaborate on “building” a company-wide digital strategy. This involved creating a “radical” company-wide approach to colla-
boration that supported the co-creation of new products with ecosystem players that included consumers and external business
partners. The executive explained that redesigning internal structures was essential to accelerate this digital transformation:
Our primary intention to create a digital lab was to shape future business models, especially in the field of digital advertising. But then we
realized that such an agile and open space environment is a unique channel to educate employees in new ways of collaboration and
empower them to develop a real digital mindset. (Media-Q-2)
Here, the establishment of a decentralized digital innovation division had contributed to improving the digital maturity of the
incumbent's workforce. Therefore, the creation of a “real digital mindset” is strategic activity that ensures the ongoing refreshment of
a traditional corporate culture that was slow to adapt to the digital age.

Discussion

Conceptualizing digital transformation

In the present qualitative study, we examined how incumbent firms in traditional industries build dynamic capabilities for digital
transformation, and our findings have important implications for strategy research in fast-changing environments. During our re-
search project, we aimed to make sense of digital transformation by drawing on senior executives' lived experiences with digitali-
zation projects (Chia and MacKay, 2007; Gioia et al., 2013). Throughout our fieldwork, we found that leaders in various industry
circles inconsistently used the term “digital transformation” to describe various strategizing and organizing activities. For example,
we observed that leaders would draw on strategic activities such as experimenting with rapid prototyping, moving operations to
cloud computing, or simply establishing a digital marketing team as concrete examples of digital transformation. Indeed, by en-
countering such divergent opinions on what a digital transformation really means, our industry-wide data show there is a general
misalignment in strategic thinking, planning, and action on this topic. This is problematic, especially for incumbent firms because
digital transformation is now a strategic imperative on most senior leadership agendas (Fitzgerald et al., 2014; Hess et al., 2016;
Sebastian et al., 2017; Svahn et al., 2017).
A central contribution of the current study is the examination of the ongoing digital transformation of incumbent firms in a wide
range of traditional industries. To explain this digital context, we have drawn on the IS literature to describe why the convergence
and generativity of pervasive digital technologies are changing the “rules of the game” and creating unexpected strategic choices at
product and platform levels (Nambisan et al., 2017; Tilson et al., 2010; Yoo et al., 2010). Consequently, the sheer reprogrammability
and data homogenization of digital technologies have enabled the convergence and generativity of new digital products, services,
business models, strategies, and organizational forms (Yoo et al., 2012). Because digitalization accelerates organizational change and
amplifies environmental complexity, volatility, and uncertainty (Autio et al., 2018; Bharadwaj et al., 2013; Dattée et al., 2018), the
digital transformation of incumbent firms is now an essential context for the study of strategic change.
We reported that digital transformation often starts with the strategic renewal of the incumbent's business model and changes in
business models, tending to lead to wider changes in the firm's collaborative approach, which, if executed correctly, will eventually
lead to deeper changes in organizational culture. Therefore, our findings align with perspectives that recognize strategic change as an
ongoing process (Agarwal and Helfat, 2009; Tsoukas and Chia, 2002).
Indeed, since Weiser's (1991) seminal article, IS scholars have been predicting early on that digitalization would accelerate
everyday experiential computing, where new digitized products would shape changes in our social, institutional, and cognitive
contexts (Lyytinen and Yoo, 2002; Tilson et al., 2010; Yoo, 2010). Our research provides further context on situations where digi-
talization is driving changes in corporate strategies that aim to digitally transform an incumbent's business models, organizational
structures, and processes. Bringing these theoretical perspectives together, we argue that digital transformation is an ongoing process
of using new digital technologies in everyday organizational life, which recognizes agility as a core mechanism for strategic renewal.
As a result, we contribute to the literature by providing an empirically grounded definition that conceptualizes the scope of digital
transformation, as follows:
Digital transformation is an ongoing process of strategic renewal that uses advances in digital technologies to build capabilities
that refresh or replace an organization's business model, collaborative approach, and culture.

344
K.S.R. Warner, M. Wäger Long Range Planning 52 (2019) 326–349

Yoo et al. (2010: 731) emphasize that we “need new theoretical frameworks for competitive strategy and for the development of
digitized products that are based on dynamic and fluid views on products.” As outlined in Fig. 2, our process model conceptualizes
digital transformation as a process of building dynamic capabilities for the ongoing strategic renewal of an organization's business
model, collaborative approach, and eventually the culture. Our findings show that the scope of digital transformation varies on these
three dimensions, where some incumbents such as Drive are at an earlier stage in refreshing business models, while other firms such
as Balance, Motion, or Media are at an advanced stage, having replaced collaborative approaches and refreshed traditional cultures.
Our conceptualization of digital transformation serves to provide scholars and practitioners with a holistic framework that can be
used to examine the strategic renewal of business models but that also considers wider organizational behaviors such as how digital
technologies replace the way people work and how corporate cultures can be refreshed in the long term.

Building dynamic capabilities for digital transformation—a contingency approach

To explore digital transformation, we took a novel methodological approach to develop a breadth and depth of contextuality
(Gioia et al., 2013). We collected and analyzed a wide range of case data that span several industries. We interviewed the authors of
published strategy consultancy reports on digitalization and combined this with case study data that drew on senior executives'
experiences with leading incumbents' digital transformation. Therefore, we focused on the senior executive's perspective (Chia and
MacKay, 2007; Jarzabkowski, 2004; Regnér, 2008; Whittington, 2006), emphasizing the individual practitioner's point of view
concerning the practice of strategic activities for digital transformation.
By asking senior executives to use a dynamic capabilities lens to share their lived experiences, we identify nine digitally grounded
microfoundations (e.g., subcapabilities) that underpin the building of dynamic capabilities for digital transformation. In line with
Velu (2017), we found that incumbents must build a system of dynamic capabilities for digital transformation. Indeed, although
activities in isolation such as “creating minimal viable products” and “analyzing scouted signals” are examples of nondigital change,
in combination with digital activities such as “formulating digital strategies,” “promoting digital mindsets,” and “hiring a chief digital
officer” highlight an essential context for the study of strategic change. In contrast to nondigital-based strategic change, we argue that
the ubiquity of new digital technologies is changing the very nature and purpose of dynamic capabilities. In particular, the con-
vergence and generativity of digital technologies means that building dynamic capabilities is now a strategic imperative for in-
cumbents to ensure survival in the digital age.
Our process model reveals the generic contingency factors that trigger, enable, and hinder the building of dynamic capabilities for
digital transformation. By revealing these contingency factors, our model shows that building dynamic capabilities is idiosyncratic to
each digital transformation, which involves ongoing refreshments and replacements of business models, collaborative approaches,
and cultures. Our model reveals that as soon as organizations construct a pathway to digital transformation, new external triggers
arise, recalibrating the need for sensing and seizing new opportunities.
Our findings on building digital sensing capabilities contributes to the dynamic capabilities literature. Interestingly, the literature
has yet to fully report on the fact that organizations need to build sensing capabilities that are increasingly digitized (Nambisan et al.,
2017; Sebastian et al., 2017). In a digital age of information abundance (Bharadwaj et al., 2013), our data emphasize that building
new capabilities in digital scouting and digital scenario planning are essential for quickly making sense of unexpected trends in fast-
changing environments. Our findings resonate with recent research that reports on organizations that have built scouting (e.g.,
Monteiro and Birkinshaw, 2017) and strategic planning capabilities (Dong et al., 2016; Matt et al., 2015) to better equip themselves
for a digital age. However, in addition to these strategic functions, our data emphasize that digital sensing capabilities require digital
mindset crafting, which can be enabled through cross-functional teams and hindered by rigid forms of strategic planning. Digital
mindset crafting brings the classical principals of strategic thinking to the fore (Mintzberg, 1994), supporting recent research that
shows new forms of strategic thinking are required to avoid disruptive threats such as the “digital war” for new talent (Kane et al.,
2017).
Our findings contribute to the research on strategic agility because we argue this is a critical dynamic capability for seizing new
digital trends. Our data show that fast decision making is central to seizing technological opportunities and aligns with existing
research that strategic agility is central for operating in conditions of deep uncertainty (Doz and Kosonen, 2010; Rigby et al., 2016;
Teece et al., 2016). Our findings emphasize that building digital seizing capabilities are contingent on pacing strategic actions, which
aligns with dynamic capabilities research in hypercompetitive contexts (Eisenhardt and Martin, 2000; Peteraf et al., 2013;
Sambamurthy et al., 2003). Eisenhardt and Martin (2000: 1115–1116) argue that the evolution of dynamic capabilities involves the
pacing of experience and time-pacing skills for creating profitable product development cycles. Interestingly, our data indicate that
within a digital context, organizations must specialize in pacing strategic actions, which, again, is a mindset that accepts the reality
that failed sprints, constant redirection, and temporary advantages are common ways of life in the digital age. In other words, our
data align with existing research that strategic agility is the driving force for ongoing business model innovation (Teece et al., 2016;
Volberda et al., 2018; Sebastian et al., 2017).
Finally, our findings emphasize that digital transformation involves the ongoing strategic renewal of an organization's colla-
borative approach and eventually the culture. Unlike recent research that concentrates on technology adoption in digital transfor-
mations (Karimi and Walter, 2016; Li, 2017), our findings show that improving the digital maturity of the workforce is a fundamental
dynamic capability for ongoing digital transformations. Our findings resonate with the research on management innovations, such as
the building of new digital governance capabilities to digitally transform internal collaborative approaches (Birkinshaw, 2018; Singh
and Hess, 2017). Moreover, our findings show the importance of navigating innovation ecosystems, which is reported to be an emerging
form of radical business model innovation (Autio et al., 2018; Dattée et al., 2018; Nambisan et al., 2017; Zahra and Nambisan, 2012).

345
K.S.R. Warner, M. Wäger Long Range Planning 52 (2019) 326–349

Conclusion

In the current paper, we examined how incumbent firms in traditional industries build dynamic capabilities for digital trans-
formation and conceptualized digital transformation as a process of building dynamic capabilities for ongoing strategic renewal.
Therefore, we contribute to Peteraf et al.'s (2013) call for contingency-based research by revealing digital transformation as a context
for strategic change. However, as with all exploratory research, the current study is not without its limitations. One limitation
concerns the transferability of our findings to wider research contexts because our model is grounded in observations directly related
to digital transformation. It is unclear if our model is applicable to a broader population of firms in fast-changing or moderately
dynamic environments. Our focus was on a practitioner's perspective, meaning we used qualitative methods to extract meaning from
processes rather than used quantitative methods to measure the effects of digital transformation on variables such as organizational
survival, growth, and performance. To move this agenda forward, we suggest survey research that operationalizes our framework to
provide new insights into the long-term organizational effects of digital transformation.
To advance this work, future research could explore how ordinary capabilities interact with dynamic capabilities for digital
transformation over time (Teece, 2014). Further studies are also needed to assess the relevance of new ventures in their building of
dynamic capabilities for digitalization (Autio et al., 2018; Huang et al., 2017), which would contribute to the debate of their role and
intended purpose (Barreto, 2010). Further research could also compare if there is a difference in building dynamic capabilities guided
by consultants versus a process without consultancy. Finally, in a world of disruptive competition, pervasive digital technologies, and
changing consumer behaviors, the sources of a competitive advantage are increasingly unknown (McGrath, 2013). Hence, more
research needs to explore the temporal role that digital transformation plays in the maintenance of transient competitive advantage.

Appendix 1. Paths to second-order themes development

Key activities Build ltd. Connect ltd. Digitize ltd. Redesign ltd. Seize ltd.

Report Interview Report Interview Report Interview Report Interview Report Interview

Subcapabilities
Scanning for technological trends ○ ○ ○ ○ ○ ○ ○ ○
Screening of digital competitors ○ ○ ○ ○ ○ ○
Sensing customer-centric trends ○ ○ ○ ○
Digital scouting ● ● ● ● ● ● ● ● ● ●
Analyzing scouted signals ○ ○ ○ ○
Interpreting digital future scenarios ○ ○ ○ ○
Formulating digital strategies ○ ○ ○ ○ ○ ○ ○ ○ ○
Digital scenario planning ● ● ● ● ● ● ● ● ●
Establishing a long-term digital vision ○ ○ ○ ○ ○ ○ ○
Enabling an entrepreneurial mindset ○ ○ ○ ○ ○ ○
Promotion a digital mindset ○ ○ ○ ○ ○
Digital mindset crafting ● ● ● ● ● ● ● ● ●
Creating minimum viable products ○ ○ ○ ○ ○ ○
Considering a lean start-up methodology ○ ○ ○ ○ ○
Using a digital innovation lab ○ ○ ○ ○ ○
Rapid prototyping ● ● ● ● ● ● ● ●
Balancing internal and external options ○ ○ ○ ○ ○
Scaling up innovative business models ○ ○ ○ ○ ○
Setting an appropriate speed of execution ○ ○ ○ ○ ○
Balancing digital portfolios ● ● ● ● ● ● ● ● ●
Pacing strategic responses ○ ○ ○ ○ ○
Rapidly reallocating resources ○ ○ ○ ○ ○ ○
Accepting redirection and change ○ ○ ○ ○ ○ ○
Strategic agility ● ● ● ● ● ● ● ● ●
Joining a digital ecosystem ○ ○ ○ ○ ○ ○ ○ ○
Interacting with multiple external partners ○ ○ ○ ○ ○ ○ ○ ○ ○
Exploiting new ecosystem capabilities ○ ○ ○ ○
Navigating innovation ecosystems ● ● ● ● ● ● ● ● ● ●
Hiring a chief digital officer ○ ○ ○ ○ ○ ○ ○
Digitalization of business models ○ ○ ○ ○ ○ ○
Designing team-based structures ○ ○ ○ ○ ○
Redesigning internal structures ● ● ● ● ● ● ●
Identifying digital workforce maturity ○ ○ ○ ○ ○ ○
External recruiting of digital talent ○ ○ ○
Leveraging digital knowledge inside firm ○ ○ ○ ○
Improving digital maturity ● ● ● ● ● ● ● ●
Disruptive digital competitors ○ ○ ○ ○ ○ ○ ○ ○
Changing consumer behaviors ○ ○ ○ ○ ○
Disruptive digital technologies ○ ○ ○ ○ ○ ○ ○ ○
External triggers ● ● ● ● ● ● ● ● ●
Cross-functional teams ○ ○ ○ ○ ○ ○

346
K.S.R. Warner, M. Wäger Long Range Planning 52 (2019) 326–349

Fast decision making ○ ○ ○ ○ ○ ○ ○ ○

Executive support ○ ○ ○ ○ ○ ○ ○
Internal enablers ● ● ● ● ● ● ● ● ● ●
Rigid strategic planning ○ ○ ○ ○ ○ ○ ○ ○
Change resistances ○ ○ ○ ○ ○ ○ ○ ○
High level of hierarchy ○ ○ ○ ○ ○
Internal barriers ● ● ● ● ● ● ● ● ● ●

Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.lrp.2018.12.001.

References

Achtenhagen, L., Melin, L., Naldi, L., 2013. Dynamics of business models–strategizing, critical capabilities and activities for sustained value creation. Long. Range Plan.
46 (6), 427–442.
Agarwal, R., Helfat, C.E., 2009. Strategic renewal of organizations. Organ. Sci. 20 (2), 281–293.
Agrawal, A.K., Gans, J.S., Goldfarb, A., 2017. What to expect from artificial intelligence. MIT Sloan Manag. Rev. 58 (3), 23.
Al-Debei, M.M., Avison, D., 2010. Developing a unified framework of the business model concept. Eur. J. Inf. Syst. 19 (3), 359–376.
Alvesson, M., 2003. Beyond neopositivists, romantics, and localists: a reflexive approach to interviews in organizational research. Acad. Manag. Rev. 28 (1), 13–33.
Ambrosini, V., Bowman, C., 2009. What are dynamic capabilities and are they a useful construct in strategic management? Int. J. Manag. Rev. 11 (1), 29–49.
Amit, R., Zott, C., 2001. Value creation in e-business. Strat. Manag. J. 22 (6-7), 493–520.
Amit, R., Zott, C., 2012. Creating value through business model innovation. MIT Sloan Manag. Rev. 53 (3), 41.
Aspara, J., Lamberg, J.A., Laukia, A., Tikkanen, H., 2013. Corporate business model transformation and inter-organizational cognition: the case of Nokia. Long. Range
Plan. 46 (6), 459–474.
Autio, E., Nambisan, S., Thomas, L.D., Wright, M., 2018. Digital affordances, spatial affordances, and the genesis of entrepreneurial ecosystems. Strat. Entrepreneur. J.
12 (1), 72–95.
Aversa, P., Haefliger, S., Reza, D.G., 2017. Building a winning business model portfolio. MIT Sloan Manag. Rev. 58 (4), 49–54.
Baden-Fuller, C., Haefliger, S., 2013. Business models and technological innovation. Long. Range Plan. 46 (6), 419–426.
Baden-Fuller, C., Morgan, M.S., 2010. Business models as models. Long. Range Plan. 43 (2–3), 156–171.
Barreto, I., 2010. Dynamic capabilities: a review of past research and an agenda for the future. J. Manag. 36 (1), 256–280.
Bendig, D., Strese, S., Flatten, T.C., da Costa, M.E.S., Brettel, M., 2018. On micro-foundations of dynamic capabilities: a multi-level perspective based on CEO
personality and knowledge-based capital. Long. Range Plan. 51 (6), 797–814.
Berman, S.J., 2012. Digital transformation: opportunities to create new business models. Strat. Leader. 40 (2), 16–24.
Bharadwaj, A., El Sawy, O.A., Pavlou, P.A., Venkatraman, N.V., 2013. Digital business strategy: toward a next generation of insights. MIS Q. 37 (2), 471–482.
Birkinshaw, J., 2018. What to expect from agile. MIT Sloan Manag. Rev. 59 (2), 39–42.
Birkinshaw, J., Zimmermann, A., Raisch, S., 2016. How do firms adapt to discontinuous change? Bridging the dynamic capabilities and ambidexterity perspectives.
Calif. Manag. Rev. 58 (4), 36–58.
Bouchikhi, H., Kimberly, J.R., 2003. Escaping the identity trap. MIT Sloan Manag. Rev. 44 (3), 20.
Calia, R.C., Guerrini, F.M., Moura, G.L., 2007. Innovation networks: from technological development to business model reconfiguration. Technovation 27 (8),
426–432.
Casadesus-Masanell, R., Ricart, J.E., 2010. From strategy to business models and onto tactics. Long. Range Plan. 43 (2–3), 195–215.
Cavalcante, S., Kesting, P., Ulhøi, J., 2011. Business model dynamics and innovation: (re)establishing the missing linkages. Manag. Decis. 49 (8), 1327–1342.
Chesbrough, H.W., 2010. Business model innovation: opportunities and barriers. Long. Range Plan. 43 (2–3), 354–363.
Chia, R., MacKay, B., 2007. Post-processual challenges for the emerging strategy-as-practice perspective: discovering strategy in the logic of practice. Hum. Relat. 60
(1), 217–242.
DaSilva, C.M., Trkman, P., 2014. Business model: what it is and what it is not. Long. Range Plan. 47 (6), 379–389.
Dattée, B., Alexy, O., Autio, E., 2018. Maneuvering in poor visibility: how firms play the ecosystem game when uncertainty is high. Acad. Manag. J. 61 (2), 466–498.
Day, G.S., Schoemaker, P.J., 2016. Adapting to fast-changing markets and technologies. Calif. Manag. Rev. 58 (4), 59–77.
Demil, B., Lecocq, X., 2010. Business model evolution: in search of dynamic consistency. Long. Range Plan. 43 (2–3), 227–246.
Di Stefano, G., Peteraf, M., Verona, G., 2014. The organizational drivetrain: a road to integration of dynamic capabilities research. Acad. Manag. Perspect. 28 (4),
307–327.
Dixon, S., Meyer, K., Day, M., 2014. Building dynamic capabilities of adaptation and innovation: a study of micro-foundations in a transition economy. Long. Range
Plan. 47 (4), 186–205.
Dong, A., Garbuio, M., Lovallo, D., 2016. Generative sensing: a design perspective on the microfoundations of sensing capabilities. Calif. Manag. Rev. 58 (4), 97–117.
Doz, Y.L., Kosonen, M., 2010. Embedding strategic agility. Long. Range Plan. 43 (2–3), 370–382.
Dunford, R., Palmer, I., Benveniste, J., 2010. Business model replication for early and rapid internationalisation: the ING direct experience. Long. Range Plan. 43 (5–6),
655–674.
Eisenhardt, K.M., Graebner, M.E., 2007. Theory building from cases: opportunities and challenges. Acad. Manag. J. 50 (1), 25–32.
Eisenhardt, K.M., Martin, J.A., 2000. Dynamic capabilities: what are they? Strat. Manag. J. 21 (10–11), 1105–1121.
El Sawy, O.A., Kræmmergaard, P., Amsinck, H., Vinther, A.L., 2016. How LEGO built the foundations and enterprise capabilities for digital leadership. MIS Q. Exec.
15 (2).
El Sawy, O.A., Pereira, F., 2013. Business Modelling in the Dynamic Digital Space: an Ecosystem Approach. Springer, Heidelberg.
Fitzgerald, M., Kruschwitz, N., Bonnet, D., Welch, M., 2014. Embracing digital technology: a new strategic imperative. MIT Sloan Manag. Rev. 55 (2), 1.
Foss, N.J., Saebi, T., 2017. Fifteen years of research on business model innovation: how far have we come, and where should we go? J. Manag. 43 (1), 200–227.
Foss, N.J., Saebi, T., 2018. Business models and business model innovation: between wicked and paradigmatic problems. Long. Range Plan. 51 (1), 9–21.
Gavetti, G., Levinthal, D., 2000. Looking forward and looking backward: cognitive and experiential search. Adm. Sci. Q. 45 (1), 113–137.
George, G., Haas, M.R., Pentland, A., 2014. Big data and management. Acad. Manag. J. 57 (2), 321–326.
Gioia, D.A., Corley, K.G., Hamilton, A.L., 2013. Seeking qualitative rigor in inductive research. Organ. Res. Methods 16 (1), 15–31.
Giudici, A., Reinmoeller, P., Ravasi, D., 2018. Open-system orchestration as a relational source of sensing capabilities: evidence from a venture association. Acad.
Manag. J. 61 (4), 1369–1402.
Gray, P., El Sawy, O.A., Asper, G., Thordarson, M., 2013. Realizing strategic value through center-edge digital transformation in consumer-centric industries. MIS Q.
Exec. 12 (1).
Hansen, R., Sia, S.K., 2015. Hummel's digital transformation toward omnichannel retailing: key lessons learned. MIS Q. Exec. 14 (2).

347
K.S.R. Warner, M. Wäger Long Range Planning 52 (2019) 326–349

Helfat, C.E., Finkelstein, S., Mitchell, W., Peteraf, M.A., Singh, H., Teece, D.J., Winter, S.G., 2007. Dynamic Capabilities: Understanding Strategic Change in
Organizations. Blackwell, Oxford.
Helfat, C.E., Raubitschek, R.S., 2018. Dynamic and integrative capabilities for profiting from innovation in digital platform-based ecosystems. Res. Pol. 47 (8),
1391–1399.
Helfat, C.E., Winter, S.G., 2011. Untangling dynamic and operational capabilities: strategy for the (N) ever-changing world. Strat. Manag. J. 32 (11), 1243–1250.
Helfat, C.E., Peteraf, M.A., 2015. Managerial cognitive capabilities and the microfoundations of dynamic capabilities. Strat. Manag. J. 36 (6), 831–850.
Henfridsson, O., Mathiassen, L., Svahn, F., 2014. Managing technological change in the digital age: the role of architectural frames. J. Inf. Technol. 29 (1), 27–43.
Henfridsson, O., Yoo, Y., 2013. The liminality of trajectory shifts in institutional entrepreneurship. Organ. Sci. 25 (3), 932–950.
Hess, T., Matt, C., Benlian, A., Wiesböck, F., 2016. Options for formulating a digital transformation strategy. MIS Q. Exec. 15 (2).
Hsieh, H.F., Shannon, S.E., 2005. Three approaches to qualitative content analysis. Qual. Health Res. 15 (9), 1277–1288.
Huang, J., Henfridsson, O., Liu, M.J., Newell, S., 2017. Growing on steroids: rapidly scaling the user base of digital ventures through digital innovation. MIS Q. 41 (1).
Iansiti, M., Lakhani, K.R., 2017. The truth about blockchain. Harv. Bus. Rev. 95 (1), 118–127.
Itami, H., Nishino, K., 2010. Killing two birds with one stone: profit for now and learning for the future. Long. Range Plan. 43 (2–3), 364–369.
Jarzabkowski, P., 2004. Strategy as practice: recursiveness, adaptation, and practices-in-use. Organ. Stud. 25 (4), 529–560.
Johnson, M.W., Christensen, C.M., Kagermann, H., 2008. Reinventing your business model. Harv. Bus. Rev. 86 (12), 57–68.
Kane, G.C., Palmer, D., Phillips, A.N., Kiron, D., 2017. Winning the digital war for talent. MIT Sloan Manag. Rev. 58 (2), 16–19.
Karimi, J., Walter, Z., 2015. The role of dynamic capabilities in responding to digital disruption: a factor-based study of the newspaper industry. J. Manag. Inf. Syst. 32
(1), 39–81.
Karimi, J., Walter, Z., 2016. Corporate entrepreneurship, disruptive business model innovation adoption, and its performance: the case of the newspaper industry.
Long. Range Plan. 49 (3), 342–360.
Khanagha, S., Volberda, H., Oshri, I., 2014. Business model renewal and ambidexterity: structural alteration and strategy formation process during transition to a
Cloud business model. R D Manag. 44 (3), 322–340.
Klarner, P., Sarstedt, M., Hoeck, M., Ringle, C.M., 2013. Disentangling the effects of team competences, team adaptability, and client communication on the per-
formance of management consulting teams. Long. Range Plan. 46 (3), 258–286.
Kim, S.K., Min, S., 2015. Business model innovation performance: when does adding a new business model benefit an incumbent? Strat. Entrepren. J. 9 (1), 34–57.
Laudien, S.M., Daxböck, B., 2016. The influence of the industrial internet of things on business model design: a qualitative-empirical analysis. Int. J. Innovat. Manag.
20 (08), 1640014.
Li, F., 2017. The Digital Transformation of Business Models in the Creative Industries: a Holistic Framework and Emerging Trends. Technovation, pp. 1–10.
Liu, D.-Y., Chen, S.-W., Chou, T.-C., 2011. Resource fit in digital transformation. Manag. Decis. 49 (10), 1728–1742.
Loebbecke, C., Picot, A., 2015. Reflections on societal and business model transformation arising from digitization and big data analytics: a research agenda. J. Strat.
Inf. Syst. 24 (3), 149–157.
Loonam, J., Eaves, S., Kumar, V., Parry, G., 2018. Towards digital transformation: lessons learned from traditional organizations. Strat. Change 27 (2), 101–109.
Lyytinen, K., Yoo, Y., 2002. Research commentary: the next wave of nomadic computing. Inf. Syst. Res. 13 (4), 377–388.
Magretta, J., 2002. Why business models matter. Harv. Bus. Rev. 80 (5), 86–92.
Markides, C., 2006. Disruptive innovation: in need of better theory. J. Prod. Innovat. Manag. 23 (1), 19–25.
Martinez, V., Neely, A., Velu, C., Leinster-Evans, S., Bisessar, D., 2017. Exploring the journey to services. Int. J. Prod. Econ. 192, 66–80.
Matt, C., Hess, T., Benlian, A., 2015. Digital transformation strategies. Business Inf. Syst. Eng. 57 (5), 339–343.
McGrath, R.G., 2010. Business models: a discovery driven approach. Long. Range Plan. 43 (2–3), 247–261.
McGrath, R.G., 2013. The End of Competitive Advantage: How to Keep Your Strategy Moving as Fast as Your Business. Harvard Business Review Press, Boston.
Miles, M.B., Huberman, A.M., 1994. Qualitative Data Analysis: an Expanded Sourcebook, second ed. Sage, Thousand Oaks.
Mintzberg, H., 1994. The fall and rise of strategic planning. Harv. Bus. Rev. 72 (1), 107–114.
Monaghan, S., Tippmann, E., 2018. Becoming a multinational enterprise: using industry recipes to achieve rapid multinationalization. J. Int. Bus. Stud. 49 (4),
473–495.
Monteiro, F., Birkinshaw, J., 2017. The external knowledge sourcing process in multinational corporations. Strat. Manag. J. 38 (2), 342–362.
Nambisan, S., 2017. Digital entrepreneurship: toward a digital technology perspective of entrepreneurship. Enterpren. Theor. Pract. 41 (6), 1029–1055.
Nambisan, S., Lyytinen, K., Majchrzak, A., Song, M., 2017. Digital innovation management: reinventing innovation management research in a digital world. MIS Q. 41
(1), 223–238.
Nylén, D., Holmström, J., 2015. Digital innovation strategy: a framework for diagnosing and improving digital product and service innovation. Bus. Horiz. 58 (1),
57–67.
Overby, E., Bharadwaj, A., Sambamurthy, V., 2006. Enterprise agility and the enabling role of information technology. Eur. J. Inf. Syst. 15 (2), 120–131.
Pavlou, P.A., El Sawy, O.A., 2011. Understanding the elusive black box of dynamic capabilities. Decis. Sci. J. 42 (1), 239–273.
Pavlou, P.A., El Sawy, O.A., 2006. From IT leveraging competence to competitive advantage in turbulent environments: the case of new product development. Inf. Syst.
Res. 17 (3), 198–227.
Pavlou, P.A., El Sawy, O.A., 2010. The ‘third hand’: IT-enabled competitive advantage in turbulence through improvisational capabilities. Inf. Syst. Res. 21 (3),
443–471.
Peteraf, M., Di Stefano, G., Verona, G., 2013. The elephant in the room of dynamic capabilities: bringing two diverging conversations together. Strat. Manag. J. 34 (12),
1389–1410.
Pettigrew, A.M., 1987. Context and action in the transformation of the firm. J. Manag. Stud. 24 (6), 649–670.
Prange, C., Bruyaka, O., Marmenout, K., 2017. Investigating the Transformation and Transition Processes between Dynamic Capabilities: Evidence from DHL.
Organization Studies, pp. 1–27.
Regnér, P., 2008. Strategy-as-practice and dynamic capabilities: steps towards a dynamic view of strategy. Hum. Relat. 61 (4), 565–588.
Rigby, D.K., Sutherland, J., Takeuchi, H., 2016. Embracing agile. Harv. Bus. Rev. 94 (5), 40–50.
Ritter, T., Lettl, C., 2018. The wider implications of business-model research. Long. Range Plan. 51 (1), 1–8.
Rogers, D., 2016. The Digital Transformation Playbook: Rethink Your Business for the Digital Age. Columbia University Press, New York.
Ross, J.W., Beath, C.M., Sebastian, I.M., 2017. How to develop a great digital strategy. MIT Sloan Manag. Rev. 58 (2), 6–9.
Sambamurthy, V., Bharadwaj, A., Grover, V., 2003. Shaping agility through digital options: reconceptualizing the role of information technology in contemporary
firms. MIS Q. 237–263.
Schilke, O., Hu, S., Helfat, C.E., 2018. Quo vadis, dynamic capabilities? A content-analytic review of the current state of knowledge and recommendations for future
research. Acad. Manag. Ann. 12 (1), 390–439.
Schreier, M., 2014. Qualitative content analysis. In: Flick, U. (Ed.), The SAGE Handbook of Qualitative Data Analysis. Sage, London, pp. 170–183.
Sebastian, I.M., Ross, J.W., Beath, C., Mocker, M., Moloney, K.G., Fonstad, N.O., 2017. How big old companies navigate digital transformation. MIS Q. Exec. 197–213.
Singh, A., Hess, T., 2017. How chief digital officers promote the digital transformation of their companies. MIS Q. Exec. 16 (1).
Smith, W.K., Binns, A., Tushman, M.L., 2010. Complex business models: managing strategic paradoxes simultaneously. Long. Range Plan. 43 (2–3), 448–461.
Sosna, M., Trevinyo-Rodríguez, R.N., Velamuri, S.R., 2010. Business model innovation through trial-and-error learning: the Naturhouse case. Long. Range Plan. 43
(2–3), 383–407.
Svahn, F., Mathiassen, L., Lindgren, R., 2017. Embracing digital innovation in incumbent firms: how Volvo cars managed competing concerns. MIS Q. 41 (1), 239–254.
Teece, D.J., 2007. Explicating dynamic capabilities: the nature and microfoundations of (sustainable) enterprise performance. Strat. Manag. J. 28 (13), 1319–1350.
Teece, D.J., 2010. Business models, business strategy and innovation. Long. Range Plan. 43 (2–3), 172–194.
Teece, D.J., 2014. The foundations of enterprise performance: dynamic and ordinary capabilities in an (economic) theory of firms. Acad. Manag. Perspect. 28 (4),

348
K.S.R. Warner, M. Wäger Long Range Planning 52 (2019) 326–349

328–352.
Teece, D.J., 2018. Business models and dynamic capabilities. Long. Range Plan. 51 (1), 40–49.
Teece, D.J., Pisano, G., Shuen, A., 1997. Dynamic capabilities and strategic management. Strat. Manag. J. 18 (7), 509–533.
Teece, D., Leih, S., 2016. Uncertainty, innovation, and dynamic capabilities: an introduction. Calif. Manag. Rev. 58 (4), 5–12.
Teece, D., Peteraf, M., Leih, S., 2016. Dynamic capabilities and organizational agility: risk, uncertainty, and strategy in the innovation economy. Calif. Manag. Rev. 58
(4), 13–35.
Teece, D.J., Linden, G., 2017. Business models, value capture, and the digital enterprise. J. Org. Des. 6 (1), 1–14.
Tilson, D., Lyytinen, K., Sørensen, C., 2010. Research commentary—digital infrastructures: the missing IS research agenda. Inf. Syst. Res. 21 (4), 748–759.
Timmers, P., 1998. Business models for electronic markets. Electron. Mark. 8 (2), 3–8.
Tippmann, E., Scott, P.S., Mangematin, V., 2012. Problem solving in MNCs: how local and global solutions are (and are not) created. J. Int. Bus. Stud. 43 (8), 746–771.
Tsoukas, H., Chia, R., 2002. On organizational becoming: rethinking organizational change. Organ. Sci. 13 (5), 567–582.
Velu, C., 2017. A systems perspective on business model evolution: the case of an agricultural information service provider in India. Long. Range Plan. 50 (5), 603–620.
Velu, C., Stiles, P., 2013. Managing decision-making and cannibalization for parallel business models. Long. Range Plan. 46 (6), 443–458.
Volberda, H., Van den Bosch, F., Heij, K., 2018. Reinventing Business Models: How Firms Cope with Disruption. Oxford University Press, Oxford.
Weber, Y., Tarba, S.Y., 2014. Strategic agility: a state of the art introduction to the special section on strategic agility. Calif. Manag. Rev. 56 (3), 5–12.
Weill, P., Woerner, S.L., 2013. Optimizing your digital business model. MIT Sloan Manag. Rev. 54 (3), 71.
Weill, P., Woerner, S.L., 2015. Thriving in an increasingly digital ecosystem. MIT Sloan Manag. Rev. 56 (4), 27.
Weiser, M., 1991. The computer for the 21st century. Sci. Am. 265 (3), 94–105.
Westerman, G., Bonnet, D., 2015. Revamping your business through digital transformation. MIT Sloan Manag. Rev. 56 (3), 10.
Westerman, G., Bonnet, D., McAfee, A., 2014. Leading Digital: Turning Technology into Business Transformation. Harvard Business Press, Cambridge.
Whittington, R., 2006. Completing the practice turn in strategy research. Organ. Stud. 27 (5), 613–634.
Wirtz, B.W., Schilke, O., Ullrich, S., 2010. Strategic development of business models: implications of the Web 2.0 for creating value on the internet. Long. Range Plan.
43 (2–3), 272–290.
Yoo, Y., 2010. Computing in everyday life: a call for research on experiential computing. MIS Q. 213–231.
Yoo, Y., Boland Jr., R.J., Lyytinen, K., Majchrzak, A., 2012. Organizing for innovation in the digitized world. Organ. Sci. 23 (5), 1398–1408.
Yoo, Y., Henfridsson, O., Lyytinen, K., 2010. Research commentary—the new organizing logic of digital innovation: an agenda for information systems research. Inf.
Syst. Res. 21 (4), 724–735.
Zahra, S.A., Nambisan, S., 2012. Entrepreneurship and strategic thinking in business ecosystems. Bus. Horiz. 55 (3), 219–229.
Zahra, S.A., Sapienza, H.J., Davidsson, P., 2006. Entrepreneurship and dynamic capabilities: a review, model and research agenda. J. Manag. Stud. 43 (4), 917–955.
Zott, C., Amit, R., 2010. Business model design: an activity system perspective. Long. Range Plan. 43 (2–3), 216–226.
Zott, C., Amit, R., Massa, L., 2011. The business model: recent developments and future research. J. Manag. 37 (4), 1019–1042.

Karl Warner is a Lecturer in Strategy and International Business at Edinburgh Napier University, UK. His research lies at the intersection
of strategy, internationalization and entrepreneurship, with a processual focus on capability and network building for firm survival and
growth. He holds the Adam Smith Business School 2015 prize (Management) for PhD excellence at the University of Glasgow, UK. E-
mail: k.warner@napier.ac.uk

Maximilian Wäger is a Consultant for Digital Business at The Nunatak Group, a Munich-based strategy consulting company. He supports
clients with digital transformations in sectors ranging from media, financial services and automotive to conventional industry. His
research interests lie at the intersection of strategy, innovation management and entrepreneurship. E-mail: maximilian.waeger@
nunatak.com

349