© 2022 The Authors. R&D Management published by RADMA and John Wiley & Sons Ltd. 1
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Digital transformation, for better 
or worse: a critical multi- level 
research agenda
Justyna Dąbrowska1,* , Argyro Almpanopoulou2 , 
Alexander Brem3,4 , Henry Chesbrough5,6, 
Valentina Cucino7 , Alberto Di Minin7 ,  
Ferran Giones3 , Henri Hakala2 ,  
Cristina Marullo7 , Anne- Laure Mention8,9,10,11 , 
Letizia Mortara12 , Sladjana Nørskov13 ,  
Petra A. Nylund3,14 , Calogero Maria Oddo14 , 
Agnieszka Radziwon5,13  and Paavo Ritala2
1 School of Management, College of Business and Law, RMIT University, 445 Swanston Street, 
Melbourne, Victoria 3000, Australia. justyna.dabrowska@rmit.edu.au
2 School of Business and Management, LUT University, P.O. Box 20, Lappeenranta, FI- 53851, Finland, 
argyro.almpanopoulou@lut.fi, henri.hakala@lut.fi paavo.ritala@lut.fi
3 Institute of Entrepreneurship and Innovation Science, University of Stuttgart, Stuttgart, 70569, 
Germany, alexander.brem@eni.uni-stuttgart.de, ferran.giones@eni.uni-stuttgart.de, petra.nylund@eni.
uni-stuttgart.de
4 The Mads Clausen Institute, University of Southern Denmark, Sønderborg, 6400, Denmark. alexander.
brem@eni.uni-stuttgart.de
5 Haas School of Business, University of California, Berkeley, California 94720, USA, chesbrou@
berkeley.edu, agra@btech.au.dk
6 Maire Tecnimont Professor of Open Innovation, Luiss University, Rome, 00197, Italy. chesbrou@
berkeley.edu
7 Institute of Management and EMBEDS Department, Sant’Anna School of Advanced Studies, Pisa, 
56127, Italy, valentina.cucino@santannapisa.it, alberto.diminin@santannapisa.it, cristina.marullo@
santannapisa.it
8 College of Business, RMIT University, Melbourne, 3000, Australia. 
9 Tampere University, Visiting Professor, Tampere, 33100, Finland. 
10 Singapore University of Social Sciences, Research Fellow, 599494, Singapore. 
11 INESC TEC, Visiting Scholar, Porto, 4200- 465, Portugal. anne-laure.mention@rmit.edu.au
12 Centre for Technology Management, Institute for Manufacturing, University of Cambridge, 
Cambridge, CB3 0FS, UK. lm367@cam.ac.uk
13 Business Development and Technology, Aarhus BSS, Aarhus University, Herning, 7400, Denmark, 
norskov@btech.au.dk, agra@btech.au.dk
14 BioRobotics Institute, Sant’Anna School of Advanced Studies, Pontedera, Pisa, 56025, Italy, petra.
nylund@eni.uni-stuttgart.de, calogero.oddo@santannapisa.it
© 2022 The Authors. R&D Management published by RADMA and John Wiley & Sons Ltd.
DĄBROWSKA ET AL
2 R&D Management 2022
For better or worse, digital technologies are reshaping everything, from customer behaviors 
and expectations to organizational and manufacturing systems, business models, markets, 
and ultimately society. To understand this overarching transformation, this paper extends 
the previous literature which has focused mostly on the organizational level by developing 
a multi- level research agenda for digital transformation (DT). In this regard, we propose 
an extended definition of DT as “a socioeconomic change across individuals, organizations, 
ecosystems, and societies that are shaped by the adoption and utilization of digital technolo-
gies.” We suggest four lenses to interpret the DT phenomenon: individuals (utilizing and 
adopting digital technologies), organizations (strategizing and coordinating both internal 
and external transformation), ecosystems (harnessing digital technologies in governance 
and co- producing value propositions), and geopolitical frameworks (regulating the environ-
ments in which individuals and organizations are embedded). Based on these lenses, we 
build a multi- level research agenda at the intersection between the bright and dark sides of 
DT and introduce the PIAI framework, which captures a process of perception, interpreta-
tion, and action that ultimately leads to possible impact. The PIAI framework identifies a 
critical research agenda consisting of a non- exhaustive list of topics that can assist research-
ers to deepen their understanding of the DT phenomenon and provide guidance to manag-
ers and policymakers when making strategic decisions that seek to shape and guide the DT.
1.  Introduction
Digitalization – that is, the implementation of digital technologies (Setia et al., 2013) – has 
provided both major opportunities and significant 
challenges to individuals, organizations, ecosystems, 
and entire societies. At the core of such transforma-
tive trends are digital technologies, broadly defined as 
combinations of “information, computing, commu-
nication, and connectivity technologies” (Bharadwaj 
et al., 2013, p. 471) or so- called SMACIT tech-
nologies (social, mobile, analytics, cloud, Internet 
of Things; Sebastian et al., 2017). Despite major 
 advances in digital technologies, the complexity of 
implementing those technologies and the implica-
tions they have for many aspects of social life are not 
yet fully understood. To better understand such com-
plexity across different levels of analysis, the aim 
of this paper is to develop a framework and multi- 
level research agenda for digital transformation (DT) 
which can guide scholars and practitioners.
Digital technologies involve unique features for 
individuals and organizations: re- programmability, 
homogenization of data, and a self- referential nature 
(Yoo et al., 2010). In addition, they include new 
properties that make them generative, malleable, 
and combinatorial (Kallinikos et al., 2013; Hanelt 
et al., 2020; Kostis and Ritala, 2020), blurring the 
boundaries between the physical and digital worlds 
and enabling both flexibility and scalability. Most 
recently, the rapid development of digital technol-
ogies, coupled with the coronavirus disease 2019 
(COVID- 19) pandemic, has impacted all businesses 
and societies (Brem et al., 2020; Carnevale and 
Hatak, 2020; Kudyba, 2020; Soto- Acosta, 2020). 
Digital technologies are, for better or worse, reshap-
ing the workplace (Marsh et al., 2021), organizational 
and manufacturing systems (Rauch et al., 2020), 
customer expectations and behaviors (Manyika 
et al., 2013; Coad et al., 2021), business models 
(Nambisan, 2017; Song, 2019), value creation and 
capture (Lanzolla et al., 2020), and markets (Diaz- 
Rainey et al., 2015; Autio et al., 2018).
The DT concept has been widely used to describe 
the adoption of digital technologies and the replace-
ment of non- digital processes with digital ones, lead-
ing to organization- wide changes and the emergence 
of new business models (Radziwon et al., 2021; 
Verhoef et al., 2021) or the modification of exist-
ing ones (Dąbrowska et al., 2019). At its inception, 
DT was predominantly discussed in the information 
systems literature (Vial, 2019; Nadkarni and Prügl, 
2020), with a focus on its technological aspects 
such as optimization of operational processes within 
organizations (Vial, 2019). More recently, increas-
ing attention has been paid by management scholars 
(Hanelt et al., 2020) and multidisciplinary research-
ers (e.g., Verhoef et al., 2021), who emphasize DT’s 
strategic, managerial, and organizational implications 
(Hanelt et al., 2020; Nadkarni and Prügl, 2020).1
In contrast to IT- enabled organizational transfor-
mation, DT transcends organizational boundaries 
(Nadkarni and Prügl, 2020), since it (re)defines an 
organization’s value propositions and business mod-
els, and can even imply the development of new orga-
nizational identities (Wessel et al., 2020). Moreover, 
© 2022 The Authors. R&D Management published by RADMA and John Wiley & Sons Ltd.
Digital transformation, for better or worse
R&D Management 2022 3
DT is expected to have both positive and negative 
implications that go beyond the organization’s imme-
diate remit and affect individuals both within and 
outside companies, along with organizational busi-
ness models, platforms and ecosystems, and whole 
industries (Autio et al., 2018; Vial, 2019). Still, the 
majority of studies in the management field (e.g., 
Hess et al., 2016; Singh et al., 2020) tend to focus 
on DT at the organizational level, which is reflected 
in DT definitions that specifically highlight “organi-
zational change” (see, e.g., Hess et al., 2016; Hanelt 
et al., 2020; Nadkarni and Prügl, 2020). Crucially, 
such perspectives overlook other important levels of 
analysis: individual, ecosystem, and geopolitical, and 
their interplay.
We argue that, for better or worse, DT eventually 
leads to sociotechnical change (Geels and Schot, 
2007) or, more broadly, socioeconomic change 
(Breslin, 2011; Ekbia et al., 2015). This change not 
only relates to organizations but also involves the 
individuals who use and adopt digital technologies, 
participants in the ecosystems who are co- creating 
their value propositions, and geopolitical frameworks 
that regulate the industries in which organizations 
and individuals are embedded. Hence, we define DT 
as a socioeconomic change across individuals, orga-
nizations, ecosystems, and societies that is shaped by 
the adoption and utilization of digital technologies. 
In this definition, the key elements are: “socioeco-
nomic change” (expressing the multi- level nature of 
the phenomenon), “shaped” (referring to the overar-
ching role of DT beyond the mere triggering role), 
and “digital technologies” (which can relate to the 
causes, contingencies, and outcomes of the socio-
technical change). We advocate four lenses through 
which DT can be viewed: individual, organizational, 
ecosystem, and geopolitical. Each level conditions 
and influences the other levels while providing a 
unique perspective on the processes and outcomes of 
DT. Importantly, regardless of the level, DT does not 
always lead to positive outcomes. It may also trig-
ger conflicting interpretations, contradictions, and 
tensions, for which there is no single best solution 
but rather various solutions that may be good for 
some but worse for others (see, e.g., Selander and 
Jarvenpaa, 2020).
We conduct a design- oriented research synthesis 
focusing on the gaps and challenges of DT at the dif-
ferent levels of analysis and their relationships (see 
e.g., Bogers et al., 2017). Differently from positivist 
approaches (e.g., systematic literature reviews) aim-
ing at summarizing literature by merging thematically 
similar studies, design- oriented approaches are suit-
able for exploratory conceptualization, as they serve 
to identify mechanisms within different studies and 
to assess the context in which such mechanisms pro-
duce their outcomes (Denyer et al., 2008). They have 
proven useful in management literature to understand 
and integrate different theories (Ferras- Hernandez 
and Nylund, 2019) or strands of research (Van Burg 
and Romme, 2014) into broader frameworks. In this 
study, we took a collaborative approach to the pro-
cess of collection, selection, and interpretation of rel-
evant literature (see, e.g., Bogers et al., 2017; Beck 
et al., 2020, 2021). For each of the four levels through 
which DT processes and outcomes can be analyzed, 
authors formed self- organizing teams collecting and 
interpreting salient contributions.
In the following section, we provide a brief over-
view of the gaps and challenges of DT based on the 
DTs’ research synthesis at the individual, organiza-
tional, ecosystem, and geopolitical levels of analysis. 
Next, we develop a multi- level research agenda at 
the intersection between the bright and dark sides of 
DT, in which we view DT as a process of perception, 
interpretation, and action that eventually leads to a 
broader socioeconomic impact (PIAI framework). 
Our critical approach contributes to the DT literature 
by providing a holistic and pragmatic understanding 
of DT. By doing so, we embed practical and pol-
icy implications throughout the entire multi- level 
research agenda to guide companies and policymak-
ers when making strategic decisions on the direction 
of DT.
2.  Four levels of digital transformation
2.1.  The individual- level digital 
transformation
Although digital technologies have a major impact 
on individuals, organizations implementing DT often 
lack an understanding of its human side (Davenport 
and Redman, 2020; Frankiewicz and Chamorro- 
Premuzic, 2020). The current emerging body of 
knowledge on the human side of DT can be divided 
into two groups. The first one focuses on employees 
or top management teams (TMTs) and point out that 
the determinants of success or failure in DT lie in 
an organization’s ability to configure the right mix 
of talent (Karimi and Walter, 2015; Davenport and 
Redman, 2020) or in the skills, abilities, and ori-
entations of employees and managers (e.g., Ritala 
et al., 2021). The second one offers a complementary 
view with an in- depth discussion of the co- existence 
and interdependence of humans and digital tech-
nologies (such as robots and artificial intelligence 
[AI]), along with considerations of their emotional, 
social, and moral implications (Pagani and Pardo, 
© 2022 The Authors. R&D Management published by RADMA and John Wiley & Sons Ltd.
DĄBROWSKA ET AL
4 R&D Management 2022
2017; Amabile, 2019; Wang and Siau, 2019; Baptista 
et al., 2020; Solberg et al., 2020; Ulhøi and Nørskov, 
2020).
2.1.1.  Behaviors, perceptions, emotions, and their 
effect on digital transformation
Affect and emotions are central to change accep-
tance, resistance, and disengagement (Oreg 
et al., 2018). Employees’ acceptance of or resis-
tance to DT is influenced by their mindsets and 
cognitive processes, which reflect their self- and 
situation- oriented beliefs (Solberg et al., 2020). 
Many employees envisage digital technologies as 
a job destroyer, which amplify their resistance to 
change (Cortellazzo et al., 2019). These fears are 
justified when DT leads to replacing some work-
force with AI, robotics, and virtual agents (Verhoef 
et al., 2021). Other documented resistance factors 
relate to employees’ skeptical attitudes toward 
automation and efficiency promises and the loss 
of competence and autonomy associated with the 
fear of digital technologies’ surveillance potential 
(Hirsch- Kreinsen, 2014), as well as a more general 
anxiety created by their use (Kummer et al., 2017). 
However, while uncertainty may inhibit the adop-
tion of digital technologies, it may also motivate 
people to work harder to find solutions that are 
beneficial for them (Cacciotti et al., 2016).
2.1.2.  Skills, capabilities, and the emergence of new 
jobs
DT is reshaping the labor market. This happens glob-
ally in a differentiated manner that depends heav-
ily on the nature of the work, its predictability, and 
its complexity (Brynjolfsson and Mitchell, 2017), 
along with its routinization and transactional nature 
(Cortellazzo et al., 2019). New technologies simul-
taneously destroy and create jobs and induce signif-
icant and irreversible changes to the nature of work. 
Increasingly, job descriptions sit at the intersection 
of previously distinct disciplines: for example, smart 
healthcare specialists who master biomedical exper-
tise with (big) data analysis, or accountants with 
knowledge of blockchains and smart contracts. In 
turn, demand is rising for skills related to data ana-
lytics, effective use, and interpretation of visualiza-
tion and simulation systems, and interaction with 
objects and machines (Dougherty and Dunne, 2012; 
De Mauro et al., 2018).
This skills gap can be met by hiring new tech- savvy 
staff to complement in- house expertise. However, 
the skill gap may also create tension between exist-
ing employees with institutional memory and the 
new breed of workers, resulting in cultural conflict 
and suboptimal organizational outcomes (Kohli and 
Johnson, 2011). These conflicts can be mitigated if 
employee upskilling raises the aptitude of existing 
employees close to recent recruits (Cortellazzo et al., 
2019). In addition, novel technical solutions, such as 
robot programming by gesture or demonstration (see, 
e.g., Kostis and Ritala, 2020), may allow workers to 
take care of machine reprogramming tasks without 
requiring them to have frontier educational back-
grounds. Finally, digital know- how is increasingly 
required in top management positions, and new roles 
like chief digital officers are being created to facili-
tate the adoption of digital technologies (Hess et al., 
2016).
2.1.3.  TMTs and leadership
The complexity of DT requires TMTs to not only 
recognize the need for DT and coordinate its imple-
mentation but also to willingly take on the role of DT 
change agents (Cortellazzo et al., 2019). However, 
some DT processes fail because of a lack of mal-
leable leadership skills within TMTs, such as DT 
awareness, acceleration, and harmonization (Hanelt 
et al., 2020). Solberg et al. (2020) found that TMT 
members responsible for DT can negatively impact 
employees’ acceptance of DT based on their own 
attitudes, styles of communication, and understand-
ing of the DT paradigm and the process through 
which it is achieved.
Proponents of a top- down approach to DT (e.g., 
Frankiewicz and Chamorro- Premuzic, 2020) con-
sider it a prerequisite for the efficient adoption and 
acceptance of digital technologies. Likewise, several 
authors have argued that the successful adoption of 
digital technologies is contingent on the leadership 
support from TMTs (Karimi and Walter, 2015).
2.1.4.  The co- existence and interdependence of 
human and digital: emotional, social, and 
moral implications
As organizations increasingly rely on digital tech-
nologies, managers need to balance the goals of effi-
ciency and human wellbeing (Nørskov and Nørskov, 
2020). One prominent example is AI’s duality, as 
both a complementary enhancement to individual 
capabilities and a potential replacement for human 
cognition (Amabile, 2019; Wang and Siau, 2019). 
Similarly, the three- dimensional presence of social 
robots and their “human social” abilities imposes rad-
ically different types of perceptions of, reactions to, 
and interactions with these robots (Fong et al., 2003; 
Cross et al., 2012; Saygin et al., 2012; Dumouchel 
and Damiano, 2017). Likewise, with the recent work- 
from- home experiment due to COVID- 19, the shift in 
workstyle intensified the utilization and adoption of 
digital technologies, yet it also uncovered unintended 
dark side effects in relation to employees’ wellbeing 
(Marsh et al., 2021).
© 2022 The Authors. R&D Management published by RADMA and John Wiley & Sons Ltd.
Digital transformation, for better or worse
R&D Management 2022 5
For technologies such as AI and social robotics, 
the crucial question is how organizations can leverage 
such technologies based on the principle of cooper-
ation with rather than replacement of humans (Seibt 
et al., 2018). This is known as the “non- replacement 
maxim” (Seibt et al., 2018, p. 37), which argues that 
the process of research, development, and design of 
robotics should include value- sensitive social inter-
actions (Friedman, 1996). These novel interactions 
will alter work processes, practices, occupations, 
and challenge the psycho- social contingencies in the 
workplace (Faraj et al., 2018; Beane, 2019; Ulhøi 
and Nørskov, 2020). For instance, new research is 
emerging on how AI- related algorithms are used in 
decision- making (Lindebaum et al., 2020), how it 
augments individual and team creativity (Amabile, 
2019), or how DTs affect employees’ well- being 
and performance in the digital workplace environ-
ment (Marsh et al., 2021). Yet, the consequences of 
augmenting individuals’ capabilities via AI remain 
insufficiently explored (Longin and Deroy, 2022). 
Furthermore, scholars have begun to examine the 
positive effects of robots as facilitators of group 
processes (Sebo et al., 2020) and how human- robot 
dyads can boost human creativity (e.g., Kahn et al., 
2016; Alves- Oliveira et al., 2020).
2.2.  The organizational- level digital 
transformation
At the organizational level, DT involves various 
changes such as changes to the company’s strategy, 
legacy, governance, structure, resources, processes, 
competencies, culture, or leadership (Orlikowski, 
1996; Cennamo et al., 2020; Hanelt et al., 2020). 
Successful DT involves the implementation and 
understanding of technology not only at an individ-
ual level but also at the organizational level and in the 
overarching strategy (Rogers, 2016; Mention, 2019; 
Nadkarni and Prügl, 2020).
2.2.1.  Strategy and strategic responses to digital 
transformation
DT requires a significant departure from existing 
culture, work practices, and organizational routines, 
and a proactive exploration of new possibilities while 
generating organizational support for them (Garud 
and Karunakaran, 2018). As DT is triggered by the 
implementation of digital technologies and has the 
potential for pervasive use and impact on existing 
economic structures, DT cannot be conceived as a 
single process. DT simultaneously boosts organi-
zational efficiency and increased responsiveness to 
the core legacy products and requires new ways of 
organizing value chains and interfirm relationships 
(Chesbrough, 2020). Indeed, as value creation 
shifts from single products to platform ecosystems 
(i.e., integrated offerings spanning multiple prod-
ucts and markets), the dynamics of the competition 
itself are profoundly altered (Cennamo et al., 2020). 
Relatedly, new ways of combining core competen-
cies with digital innovations also require intensified 
inter- organizational collaboration (Chesbrough et al., 
2018; Enkel et al., 2020).
2.2.2.  Change and organizational design
Established organizational structures are often ill- 
suited to the uncertain outcomes of the DT process. 
This exacerbates the inherent ambiguity of innova-
tion processes (Garud et al., 2013) with the addi-
tional complexity of digital innovation (Yoo et al., 
2010). There are, however, at least two pathways 
that can alleviate such discontinuities: (a) enabling 
organizational support for the development of novel 
ideas (change from inside) and (b) introducing and 
adopting new organizational structures and forms 
(Lanzolla et al., 2020).
A positive perception of DT- related ambigu-
ity can be recast as an enabler of interpretation of 
what DT means for the organization. The organiza-
tion’s employees may generate novel ideas that give 
meaning to the ambiguities by building narratives 
on their organizational experiences and expectations 
(March, 2010; Garud et al., 2011). This encourages 
individual- level behaviors that contribute to the con-
textual ambidexterity of DT (Gibson and Birkinshaw, 
2004). Organizational design can be both a driver and 
a subject of change (see also Lanzolla et al., 2020). 
The new structures can embrace organizing logics 
and mechanisms that facilitate collaboration, inter-
action, and coordination for digital innovation. For 
instance, organizations might benefit from estab-
lishing cross- functional teams (Dremel et al., 2017) 
and DT offices or units (Singh et al., 2020). In addi-
tion, the introduction of new TMT functions like the 
chief digital officer, as discussed above, can promote 
a digital perspective inside the organization (Singh 
and Hess, 2017; Tumbas et al., 2018; Kunisch et al., 
2020).
2.2.3.  Building (digital) capabilities to support 
decision- making
DT also opens up discussion on new capabilities 
that could enhance (or constrain) the organization. 
These capabilities are often augmented by a vari-
ety of AI technologies that enable firms to improve 
their customer offerings by learning from the 
accumulated data and effectively generating “data 
network effects” that aim to constantly improve 
customer value (Gregory et al., 2020). These new 
capabilities build on new pools of structured and 
© 2022 The Authors. R&D Management published by RADMA and John Wiley & Sons Ltd.
DĄBROWSKA ET AL
6 R&D Management 2022
unstructured data, integrating it with the data gen-
erated by machines while controlling for AI’s own 
inbuilt flaws and biases (Hakala and Vuorinen, 
2020). The implications are broad: in socially facil-
itated planning (e.g., road- mapping) contexts (e.g., 
Kerr et al., 2013), digital technologies could mod-
ify the dynamics by which managers analyze and 
make sense of current and future trends and plan 
around them. Enhancing sensemaking from com-
plex datasets (An et al., 2018) may allow current 
processes to increase the innovation capability of 
firms (Mention et al., 2019) and generate a broader 
societal impact (Wang and Siau, 2019).
2.2.4.  Changes in value creation and capture logics
Finally, as digital technologies are constantly 
evolving, DT can bring enormous long- term ben-
efits to businesses able to recast their external 
relationships and interdependencies and embed 
them into new and more flexible business mod-
els. First, DT requires companies to establish and 
manage multiple modalities of value generation 
and delivery, to structure collective action at the 
field and ecosystem level (Alaimo, 2021). Second, 
it requires finding an optimal business model that 
leverages a company’s skills and resources through 
data generated by digital technologies (Björkdahl, 
2020). This effort should include (a) leveraging 
data- driven processes by focusing on monitoring, 
optimization, and organizational responsiveness, 
(b) approaching business model transformation 
that exploits the interconnection and interdepen-
dence between actors, and (c) taking advantage of 
platform marketplaces that render product- market 
boundaries irrelevant to define the type and inten-
sity of competition (Cennamo et al., 2020). These 
insights show that the DT challenges at the orga-
nizational level cannot be assessed properly with-
out considering the ecosystem perspective (Hanelt 
et al., 2020).
2.3.  The ecosystem- level digital 
transformation
The management literature has recognized the 
importance of ecosystems in which numerous 
actors interact to collectively define and deliver 
an ecosystem- level output that aims at meet-
ing both shared and individual goals (Radziwon 
et al., 2017; Dattée et al., 2018; Jacobides et al., 
2018). Ecosystems offer unique access to diverse 
resources, including knowledge, expertise, and 
technologies (Aarikka- Stenroos and Ritala, 2017; 
Cobben et al., 2021). Increasingly, digital technolo-
gies and interfaces are used to bundle actors’ inputs 
in ecosystems (Thomas et al., 2014; Cusumano 
et al., 2019; Gawer, 2020).
2.3.1.  Digital affordances
Digital affordances refer to all types of activities 
made possible for ecosystem actors using digital 
technologies and infrastructures (Autio et al., 2018). 
Thus, diverse actors can co- create value across a 
particular field or domain, which can increasingly 
span different geographical regions by virtue of 
digital connectivity. Examples include open- source 
software development, which is (self- )organized 
into heterogeneous ecosystems that link together in 
digital forums and platforms (Fjeldstad et al., 2012; 
Mäenpää et al., 2018) and the global ecosystems 
operated by giant platform leaders like Amazon and 
Google Android.
Ecosystems evolve dynamically over time as their 
actors and relationships change (Rong et al., 2020). 
Their actors can utilize design artifacts that are con-
stantly being made and remade (O’Shea et al., 2019). 
In turn, these artifacts and cues (digital forums, col-
laboration spaces, application programming inter-
faces, etc.) are needed to establish trustworthiness 
and standardization in the ecosystem. Members of 
an ecosystem collaboratively design that system by 
co- intuiting, co- interpreting, and co- integrating what 
they imagine it to be (O’Shea et al., 2019). However, 
we still have only a limited understanding of how 
ecosystems negotiate their legitimacy with the sur-
rounding world and the various actors involved 
(Thomas and Ritala, 2021).
Digital technologies are not restricted by lim-
itations of the physical location and thus they 
fundamentally change the ability of organizations 
to decide whether to be part of a specific ecosys-
tem. Therefore, one of the key issues in the DT 
context is how organizations decide to form, join, 
remain in, or exit ecosystems and who manages 
those ecosystems. In this regard, we differentiate 
in the following subsection between two contrast-
ing views on ecosystems: orchestrator- centric and 
system- community.
2.3.2.  Orchestrator- centric view vs. systems- 
community view
The first view on ecosystems focuses on a power-
ful hub actor (i.e., an orchestrator) that organizes 
the ecosystem around a joint value proposition 
(Jacobides et al., 2018; Shipilov and Gawer, 2020; 
Thomas and Autio, 2020; Thomas and Ritala, 2021) 
– oftentimes delivered over a digital platform. An 
example of an orchestrated digital ecosystem is the 
Amazon Marketplace (Ritala et al., 2014), where 
the platform orchestrator (Amazon.com) bundles 
complementary inputs into continuously renewing 
© 2022 The Authors. R&D Management published by RADMA and John Wiley & Sons Ltd.
Digital transformation, for better or worse
R&D Management 2022 7
offerings for wide customer bases. Another example 
of DT that follows an orchestrator- centric view is 
AirAsia, a low- cost airline based in south- east Asia 
that designed a completely new business model for 
its ecosystem, which served as a growth infrastruc-
ture for its post- pandemic future (Radziwon et al., 
2021).
As much as success stories like Amazon.com and 
AirAsia demonstrate the potential of DT, similar 
changes and ecosystem initiatives are much more 
difficult to undertake in regulation- driven industries 
like finance and healthcare. This may explain why in 
recent years we have been witnessing a rapid devel-
opment of fintech start- ups that benefit extensively 
from open data regulations and regulatory sandboxes 
(Alaassar et al., 2020) and have been disrupting 
larger and extremely rigid organizations. This hap-
pens because those more established organizations 
failed to develop interfaces between the legacy sys-
tems and multiple bureaucratic structures that have 
become part of their organizational culture. Hence, 
ecosystem orchestrators face challenges that are not 
only of a technical but also of an organizational and 
institutional nature (Dattée et al., 2018; Järvi et al., 
2018). We still know very little about the complex 
nature of those challenges, how they interrelate or 
reinforce one another, or the mechanisms that could 
enable their resolution.
Whereas the orchestrator- centric view perceives 
ecosystems as something coordinated by a powerful 
focal or hub actor and directed toward particular goals 
(often set by the focal actor), the systems- community 
view is much more open- ended and incorporates an 
important but different role for other actors (Haarla 
et al., 2018; Hakala et al., 2020). According to this 
view, value creation, innovation, and entrepreneurial 
growth are both processes and outcomes of commu-
nities of actors concentrated around either a specific 
geographical region or joint knowledge, technology, 
or innovation challenges (e.g., van der Borgh et al., 
2012; Autio et al., 2018; Järvi et al., 2018). Without 
a central governing organization, what is perceived 
as an ecosystem in terms of resource dependencies 
is negotiated by its members and determined collec-
tively. This is in sharp contrast to the traditional pur-
chasing and distribution arrangements – or platform 
interfaces and standards – of a powerful ecosystem 
orchestrator that determines who is part of an ecosys-
tem and who is not. In the digital context, by contrast, 
even loosely coupled communities can form their 
own artifacts, institutions, and outputs, thus creating 
a digitally enabled organization. Furthermore, digital 
technologies increasingly allow also decentralized 
governance on platforms, as opposed to the classic 
orchestrator- led platform models (Chen et al., 2021).
2.4.  The geopolitical- level digital 
transformation
The geopolitical level is reflected in the sociotech-
nical systems view of management research as part 
of sociotechnical regimes and landscapes (Geels, 
2002). Sociotechnical landscapes can be perceived 
as broad business environments, while the sociotech-
nical regime consists of the set of institutions and 
rules that establish an ecosystem’s boundaries (Geels 
and Schot, 2007; Brem and Radziwon, 2017). In the 
past, it was primarily cultural differences (Nonaka 
and Takeuchi, 1995; Asheim and Coenen, 2005) that 
distinguished landscapes and regimes. Today, the 
perception of data (Lee, 2018), intellectual property 
rights (IPRs), appropriation regimes (Petricevic and 
Teece, 2019), and geopolitical strategies have all 
become conditions the use of digital technologies 
and data (Brem and Nylund, 2021).
2.4.1.  Data as the “new oil”
Data has become nowadays a key productive resource 
for companies, yet there are major differences in how 
it is used globally. In the United States, for exam-
ple, data are regarded as the property of the company 
that collects it. That firm has the right to aggregate, 
process, and sell data as it sees fit, with the notable 
exception of personal health data. In China, by con-
trast, data are in the service of the state; it must be 
shared with the government on request and stored 
inside China’s national boundaries.2 In the European 
Union, the rules governing data are different still: it 
is the right of the citizen to control and limit the use 
of her or his data, and companies that compile data 
must adhere to a number of legislative restrictions, 
including the General Data Protection Regulation 
(GDPR). Moreover, individuals in the European 
Union have the right to be “forgotten” (i.e., to have 
their data removed from a commercial database), but 
no such rights exist in the United States or China. 
Indeed, China has been developing a sophisticated 
Social Credit System based on extensive observa-
tion of citizen behavior in the digital domain (Liang 
et al., 2018).
2.4.2.  The geopolitical transformation
The geopolitical landscape has shifted markedly in 
the past 20 years. The hegemony of the United States 
and Europe is giving way to an Asian innovation 
resurgence led by China (Collinson and Liu, 2019), 
which is no longer a passive receiver of Western tech-
nologies but an important developer of innovation in 
its own right (Xu et al., 2018). It is also increasingly 
clear that China’s rise will not simply fold into the 
existing institutional arrangements of global trade or 
conform to Western notions of data privacy. There is 
© 2022 The Authors. R&D Management published by RADMA and John Wiley & Sons Ltd.
DĄBROWSKA ET AL
8 R&D Management 2022
a digital divide emerging in the quality of informa-
tion systems and data between China and the West 
(Lee, 2018) that is generating a “splinternet.” The 
Chinese innovation ecosystem relies heavily on gov-
ernmental support and investment and porous bound-
aries between enterprises and policymakers (Zhang 
and Merchant, 2020). There is an emerging environ-
ment of “open innovation with Chinese characteris-
tics” (Chesbrough et al., 2020), and the increasingly 
politicized nature of innovation has brought digital 
innovation to the forefront of the geopolitical agenda.
2.4.3.  The protection of IPRs
IPRs have become a flashpoint for competition 
(Petricevic and Teece, 2019) in the reshaped global 
economic order. High- fidelity replication and trans-
fer of innovations at little or no cost across firms and 
national boundaries are unique features of digital 
technologies, which are reshaping the protections 
that IPRs seek to enable. While global technology 
transfer, along with its appropriability regimes and 
transaction costs, has been discussed since 1980 (see 
Pisano and Teece, 1989), academics and organiza-
tions alike still face significant challenges in gov-
erning and measuring the technology flow in global 
systems of innovation. In addition, harmonizing DT 
policies through regulations, standards, procedures, 
and antitrust measures is a major challenge for pol-
icymakers across the globe. The large- scale produc-
tion and accumulation of highly portable data will 
require better data infrastructures, interfaces, and 
storage (Otto and Jarke, 2019), along with more 
robust governance structures, which will allow its 
regulation- compliant commercialization. Since data 
is by nature “nonrival”, it could generate a lot of 
value when shared widely; however, in practice data 
is often not shared due to competitive or legal con-
cerns (Jones and Tonetti, 2020).
2.4.4.  Digital competitive strategies
While most governments have embraced DT as 
imperative, their policies toward data have been 
extremely heterogeneous, at times even contradic-
tory. As a result of different strategies, countries must, 
therefore, advance DT in distinctive ways, as exem-
plified by different indexes of digital competitiveness 
(Chakravorti et al., 2017). Innovative collaborations 
often require large amounts of data, which they also 
generate (Del Vecchio et al., 2018). However, the 
rights and abilities of organizations to use, manage, 
and control data are conditioned by the underlying 
institutional requirements of each country or region 
(Balachandran and Hernandez, 2019). Excluding for-
eign companies like Google and Facebook from the 
Chinese market (or Huawei from the US market), a 
requirement for Chinese and American companies to 
be GDPR- compliant to operate within the EU, and the 
recent threat to shut down the Chinese- owned video- 
sharing social media platform TikTok in the United 
States (Zhai et al., 2020) are all examples of these 
geopolitical differences, which influence the ways 
companies can manage their data internationally.
3.  The multi- level research agenda
Based on the interpretive approach to reviewing 
DT literature, we now build a multi- level research 
agenda i.e., how it might lead to positive impact 
whilst acknowledging the less comfortable aspects 
of change it could bring. The PIAI framework (see 
Figure 1) encourages the reader to evaluate percep-
tion, interpretation, and action in DT and ultimately 
their impact across various levels. The PIAI frame-
work allows academics to move away from a single 
lens of analysis to leverage different (and adjacent) 
fields. While this might make the analysis more com-
plex, we believe it better captures the broad nature of 
the phenomenon. Our critical assessment of the DT 
literature reveals a highly fragmented understanding 
of this topic that leads to a disjointed discussion on 
the consequences and efficacy of DT. A broader and 
multi- level view is needed to map the landscape of 
the processes and outcomes of DT as a managerial 
and socioeconomic phenomenon.
The PIAI framework is partly built on the psy-
chological science literature. Psychologists have 
suggested that people perceive their environments 
in terms of their ability to act on them (Witt, 2011). 
Human behavior and personal and environmental 
factors are all intertwined, and learning – as a means 
to adapt to change – is affected as much by external 
as by internal reinforcement (Bandura, 1985). Since 
organizations, ecosystems, and countries are also 
made up of individual human beings, we extend the 
logic of perceiving, interpreting, and acting to induc-
ing change and generating impact, both individually 
and collectively. In doing so, we argue that deci-
sions on how to act on, adopt, and utilize DT will be 
determined based on perceptions and interpretations 
that are judgments resulting from the cognitive pro-
cessing of what is perceived (Bitektine, 2011). DT 
would then bring change and have an impact at the 
different levels at which individuals, organizations, 
ecosystems, politicians, and governments deal with 
the phenomenon.
Our PIAI framework provides a non- exhaustive 
list of themes that are relevant to explore in fur-
ther research; in the sub- sections below, we discuss 
the most critical research questions and direc-
tions across the four levels of analysis, thereby 
© 2022 The Authors. R&D Management published by RADMA and John Wiley & Sons Ltd.
Digital transformation, for better or worse
R&D Management 2022 9
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© 2022 The Authors. R&D Management published by RADMA and John Wiley & Sons Ltd.
DĄBROWSKA ET AL
10 R&D Management 2022
embedding their practical and policy implications. 
Furthermore, the PIAI framework highlights a set 
of opposing, paradoxical tensions (Schad et al., 
2016) that characterize the scope of the emerg-
ing managerial challenges that DT poses at mul-
tiple levels. Given the overarching nature of DT, 
we expect such tensions to arise as different actors 
have both positive as well as negative perceptions, 
interpretations, and actions DT. Together, the pro-
posed framework and accompanying questions can 
assist companies and policymakers in their strategic 
decision- making on the direction of DT, especially 
in turbulent environments, where the emergence of 
conflicting demands requires more rapid actions. 
We conclude with a summary table of exemplary 
research questions (Table 1).
3.1.  The individual- level research agenda
For individuals, DT can be perceived as an opportu-
nity to improve those aspects of work that are typi-
cally considered desirable: for instance, using digital 
technologies such as AI, robotics, or virtual collabo-
ration environments to facilitate individual creativity 
(Kahn et al., 2016), creative collaborations between 
humans (Amabile, 2019; Kostis and Ritala, 2020), or 
by relying on AI, automation, and robots in personnel 
selection to increase fairness (Konradt et al., 2013; 
Nørskov and Ulhøi, 2020). However, DT can also be 
perceived as a threat to employees’ current jobs (by 
replacing their skills) and to their social and emo-
tional wellbeing. If work tasks are largely based on 
interactions with digital technologies, this is likely 
to limit the opportunity for employees to engage in 
and benefit from the socioemotional aspects of work, 
which are known to positively affect employee per-
formance. To understand the effects of DT at the 
individual level, future research needs to differenti-
ate between various digital technologies and examine 
how each unique technology type may influence the 
perceived meaningfulness of work and the different 
aspects of employee well- being.
Interpretation refers to the way in which technol-
ogy is appropriated by users, or as “the sense- making 
activity of taking up technologies” (Kudina, 2019, 
p. 88). Because technologies act as “moral mediators” 
(Verbeek, 2015), they can shape and even fundamen-
tally change the way people interact. As a result, a 
more granular understanding of human- technology 
encounters is needed, particularly regarding how 
digital technologies are appropriated by organiza-
tional members and how this process alters human 
social norms and value spaces within organizations. 
Crucially, developing such an understanding will 
inform and support the design and implementation 
of digital technologies in more culturally sustain-
able ways and with empowerment rather than forced 
adoption in mind.
Acting upon DT will necessarily entail the devel-
opment of new practices, the changing of roles, and 
cognitive and social challenges for individuals. We 
offer some examples below.
First, physically embodied robots may have very 
different effects on human performance or creativ-
ity than virtual ones. Similarly, human- like robots 
that interact and behave according to human social 
norms and values (e.g., social robots) are likely to 
have different effects on human behavior and per-
formance than tool- like industrial robots. Current 
research has yet to identify and understand such 
practices and distinguish between those that lead to 
digital immersion and those that lead to resistance 
or avoidance. Second, “prediction machines” based 
on AI could increasingly generate insights to help 
decision- makers reduce uncertainty and develop 
strategic plans (Agarwal et al., 2018). Researchers 
need to understand how socially driven (e.g., 
scouting methods) and digitally driven insights are 
merged by individuals and explore the consequence 
that automated insight development will have on 
the people involved (Kellogg et al., 2020). There 
is still limited research on how managers perceive 
the support of these AI agents and integrate their 
outcomes with those of human intelligence in the 
course of their decision- making. A critical question 
concerns how credible AI agents are in the eyes 
of humans. While it is widely acknowledged that 
digital tools could help identify hidden trends and 
make sense of both structured (e.g., patents or aca-
demic papers) (An et al., 2018) and, increasingly, 
unstructured data (Lindebaum et al., 2020), the 
interface between human and digital intelligence 
is still largely unexplored (Amabile, 2019). Third, 
virtual and augmented reality tools might create 
more persuasive ways to translate insights into 
more tangible alternative and future economic real-
ities (Kostis and Ritala, 2020), as they could reduce 
some of the cognitive issues found in previous 
research (Kerr et al., 2012; Mortara, 2015). More 
work is needed to understand which configurations 
of AR and VR could help communicate insights.
The impact of designing and using digital 
technologies to stimulate certain desired human 
behaviors – with expectations of enhanced human 
creativity, engagement, problem- solving, and 
other performance goals – puts humans at risk of 
being treated merely as instruments that can be 
“tweaked” and “tuned” according to the needs 
of organizations (Kellogg et al., 2020; Nørskov, 
2021). A substantial managerial (and research) task 
© 2022 The Authors. R&D Management published by RADMA and John Wiley & Sons Ltd.
Digital transformation, for better or worse
R&D Management 2022 11
Ta
bl
e 
1.
 R
es
ea
rc
h 
ag
en
da
 –
  s
am
pl
e 
qu
es
tio
ns
Pe
rc
ep
tio
n
In
te
rp
re
ta
tio
n
A
ct
io
n
Im
pa
ct
W
ha
t a
re
 th
e 
bo
un
da
ri
es
 o
f 
D
T
?
H
ow
 c
ou
ld
 w
e 
be
tte
r 
un
de
rs
ta
nd
 w
ha
t i
s 
al
re
ad
y 
ha
pp
en
in
g 
an
d 
w
ha
t w
ill
 p
ot
en
-
tia
lly
 h
ap
pe
n 
at
 th
es
e 
di
ff
er
en
t l
ev
el
s?
H
ow
 d
o 
in
di
vi
du
al
s,
 o
rg
an
iz
at
io
ns
, 
ec
os
ys
te
m
s,
 a
nd
 g
ov
er
nm
en
ts
 a
ct
 to
 
su
pp
or
t o
r 
pr
ev
en
t D
T
?
W
ha
t a
re
 th
e 
(u
ni
nt
en
de
d)
 c
on
se
qu
en
ce
s 
of
 
D
T
 w
ith
in
 a
nd
 a
cr
os
s 
th
e 
di
ff
er
en
t l
ev
el
s 
of
 a
na
ly
si
s?
W
ha
t s
ho
ul
d 
re
m
ai
n 
un
ch
an
ge
d 
at
 th
e 
in
-
di
vi
du
al
, o
rg
an
iz
at
io
na
l, 
ec
os
ys
te
m
, a
nd
 
so
ci
et
al
 le
ve
ls
?
W
ill
 D
T
 e
xa
ce
rb
at
e 
in
eq
ua
lit
y 
in
 s
oc
ie
tie
s 
(a
t i
nd
iv
id
ua
l, 
or
ga
ni
za
tio
na
l a
nd
 g
eo
-
po
lit
ic
al
 le
ve
l)
, a
nd
 if
 s
o,
 h
ow
?
In
di
vi
du
al
 le
ve
l q
ue
st
io
ns
 to
 e
xp
lo
re
 th
e 
PI
A
I 
fr
am
ew
or
k 
pe
rs
pe
ct
iv
e:
• 
H
ow
 c
an
 h
um
an
s 
le
ve
ra
ge
 d
ig
ita
l t
ec
hn
ol
og
ie
s 
to
 a
ug
m
en
t t
he
ir
 c
og
ni
tiv
e 
ab
ili
tie
s,
 c
re
at
iv
ity
, a
nd
 le
ar
ni
ng
?
• 
H
ow
 is
 D
T
 c
ha
ng
in
g 
th
e 
na
tu
re
 o
f 
hu
m
an
 in
te
ra
ct
io
ns
? 
H
ow
 is
 in
te
rp
er
so
na
l c
om
m
un
ic
at
io
n 
af
fe
ct
ed
 b
y 
th
e 
ra
pi
d 
up
ta
ke
 o
f 
di
gi
ta
l t
ec
hn
ol
og
ie
s?
• 
H
ow
 d
o 
em
pl
oy
ee
s’
 e
m
ot
io
na
l f
ac
to
rs
, p
er
ce
pt
io
ns
, a
nd
 b
eh
av
io
rs
 a
ff
ec
t t
he
 a
cc
ep
ta
nc
e 
an
d 
su
cc
es
s 
of
 D
T
?
• 
H
ow
 d
o 
di
gi
ta
l t
ec
hn
ol
og
ie
s 
in
fl
ue
nc
e 
th
e 
pe
rc
ei
ve
d 
m
ea
ni
ng
fu
ln
es
s 
of
 w
or
k,
 a
nd
 h
ow
 d
o 
th
ey
 a
ff
ec
t t
he
 d
if
fe
re
nt
 a
sp
ec
ts
 o
f 
em
pl
oy
ee
 w
el
l-
 be
in
g?
• 
W
ha
t r
ol
e 
ca
n 
re
sp
on
si
bl
e 
re
se
ar
ch
 a
nd
 in
no
va
tio
n 
(R
R
I)
 p
la
y 
in
 f
os
te
ri
ng
 in
di
vi
du
al
s’
 a
cc
ep
ta
nc
e 
of
 D
T
?
• 
H
ow
 c
an
 m
an
ag
er
s 
m
ak
e 
re
sp
on
si
bl
e 
de
ci
si
on
s 
ab
ou
t w
hi
ch
 d
ig
ita
l t
ec
hn
ol
og
ie
s 
th
ei
r 
or
ga
ni
za
tio
ns
 w
ill
 a
do
pt
 to
 e
ns
ur
e 
th
e 
w
el
lb
ei
ng
 o
f 
em
pl
oy
ee
s,
 a
nd
 o
n 
w
ha
t c
ri
te
ri
a 
sh
ou
ld
 
su
ch
 d
ec
is
io
ns
 b
e 
ba
se
d?
• 
H
ow
 is
 A
I 
(a
nd
 o
th
er
 d
ig
ita
l t
ec
hn
ol
og
ie
s)
 u
se
d 
in
 d
ec
is
io
n-
 m
ak
in
g?
 H
ow
 d
oe
s 
it 
co
nt
ri
bu
te
 to
 b
ia
se
s 
re
du
ct
io
n 
or
 r
ei
nf
or
ce
m
en
t?
• 
H
ow
 c
an
 a
lte
rn
at
iv
e 
an
d 
vi
rt
ua
l r
ea
lit
y 
to
ol
s 
be
 u
se
d 
to
 im
pr
ov
e 
so
ci
al
 d
ec
is
io
n-
 m
ak
in
g 
pr
oc
es
se
s?
• 
H
ow
 c
an
 r
ob
ot
ic
s 
an
d 
A
I 
be
 u
se
d 
to
 a
ug
m
en
t a
nd
 f
ac
ili
ta
te
 in
di
vi
du
al
 a
nd
 te
am
 c
re
at
iv
ity
?
• 
H
ow
 c
an
 r
ob
ot
s 
be
 d
es
ig
ne
d 
(a
nd
 p
er
ce
iv
ed
) 
in
 c
ul
tu
ra
lly
 s
us
ta
in
ab
le
 w
ay
s?
• 
H
ow
 w
ill
 la
bo
r 
m
ar
ke
ts
 b
e 
(f
ur
th
er
) 
im
pa
ct
ed
 b
y 
D
T
?
O
rg
an
iz
at
io
na
l l
ev
el
 q
ue
st
io
ns
 to
 e
xp
lo
re
 th
e 
PI
A
I 
fr
am
ew
or
k 
pe
rs
pe
ct
iv
e:
• 
W
ha
t a
re
 th
e 
ne
w
 m
ec
ha
ni
sm
s 
en
ac
te
d 
by
 d
ig
ita
l t
ec
hn
ol
og
ie
s 
th
at
 a
ff
ec
t c
om
pa
ni
es
?
• 
W
ha
t a
re
 th
e 
di
ff
er
en
t a
nt
ec
ed
en
ts
, c
on
se
qu
en
ce
s,
 p
er
fo
rm
an
ce
 im
pl
ic
at
io
ns
, a
nd
 n
ua
nc
es
 o
f 
D
T
 a
nd
 d
ig
ita
l t
ec
hn
ol
og
ie
s 
on
 in
cu
m
be
nt
s,
 S
M
E
s,
 a
nd
 s
ta
rt
- u
ps
?
• 
W
ha
t a
re
 th
e 
fa
ct
or
s 
ac
ce
le
ra
tin
g 
or
 s
lo
w
in
g 
do
w
n 
D
T
 in
 m
at
ur
e 
in
du
st
ri
es
? 
H
ow
 c
an
 in
cu
m
be
nt
s 
th
at
 a
re
 la
gg
in
g 
in
 te
rm
s 
of
 d
ig
ita
l t
ec
hn
ol
og
y 
ad
op
tio
n 
re
ne
w
 th
em
se
lv
es
? 
W
ha
t i
s 
th
e 
ro
le
 in
 th
is
 p
ro
ce
ss
 f
or
 d
ig
ita
l s
ta
rt
- u
ps
?
• 
H
ow
 c
an
 th
e 
em
er
ge
nc
e 
of
 a
 n
ew
 o
rg
an
iz
at
io
na
l i
de
nt
ity
 b
e 
en
co
ur
ag
ed
 th
ro
ug
h 
D
T
?
• 
W
ha
t a
re
 th
e 
or
ga
ni
za
tio
na
l c
ap
ab
ili
tie
s,
 p
ro
ce
ss
es
, a
nd
 s
tr
uc
tu
re
s,
 b
ot
h 
fo
rm
al
 a
nd
 in
fo
rm
al
, o
f 
in
te
lli
ge
nc
e 
pr
ov
is
io
n 
an
d 
de
ci
si
on
- m
ak
in
g 
to
 s
up
po
rt
 D
T
?
• 
H
ow
 d
o 
de
ci
si
on
- m
ak
er
s 
so
lv
e 
pr
ob
le
m
s 
cr
ea
te
d 
by
 a
lg
or
ith
m
ic
 b
ia
se
s 
an
d 
ou
ts
ou
rc
ed
 ta
sk
s?
• 
H
ow
 c
re
di
bl
e 
ar
e 
A
I 
ag
en
ts
 in
 d
el
iv
er
in
g 
in
si
gh
t, 
an
d 
ho
w
 is
 th
ei
r 
le
ve
l o
f 
cr
ed
ib
ili
ty
 m
ea
su
re
d?
• 
W
ha
t k
in
d 
of
 d
ec
is
io
n-
 m
ak
in
g 
ca
n 
be
 le
ft
 to
 a
lg
or
ith
m
s 
an
d 
A
I,
 a
nd
 w
hi
ch
 d
ec
is
io
ns
 w
ill
 s
til
l r
eq
ui
re
 h
um
an
 in
te
rv
en
tio
n 
or
 c
on
tr
ol
?
• 
W
ha
t a
re
 th
e 
et
hi
ca
l i
m
pl
ic
at
io
ns
 o
f A
I-
  a
nd
 d
at
a-
 dr
iv
en
 o
rg
an
iz
at
io
ns
?
(C
on
tin
ue
s)
© 2022 The Authors. R&D Management published by RADMA and John Wiley & Sons Ltd.
DĄBROWSKA ET AL
12 R&D Management 2022
Pe
rc
ep
tio
n
In
te
rp
re
ta
tio
n
A
ct
io
n
Im
pa
ct
E
co
sy
st
em
 le
ve
l q
ue
st
io
ns
 to
 e
xp
lo
re
 th
e 
PI
A
I 
fr
am
ew
or
k 
pe
rs
pe
ct
iv
e:
• 
H
ow
 d
o 
or
ga
ni
za
tio
ns
 d
ec
id
e 
to
 f
or
m
, j
oi
n,
 s
ta
y 
in
, o
r 
ex
it 
di
gi
ta
l e
co
sy
st
em
s,
 a
nd
 w
ho
 m
an
ag
es
 th
em
?
• 
H
ow
 c
an
 e
xi
st
in
g 
or
 n
ew
 e
co
sy
st
em
s 
im
pl
em
en
t a
nd
 d
ep
lo
y 
di
gi
ta
l t
ec
hn
ol
og
ie
s 
to
 im
pr
ov
e 
an
d 
ex
pa
nd
 th
ei
r 
va
lu
e 
pr
op
os
iti
on
s 
an
d 
m
ob
ili
ze
 u
se
rs
 a
nd
 c
om
pl
em
en
to
rs
 a
ro
un
d 
th
os
e 
va
lu
e 
pr
op
os
iti
on
s?
• 
H
ow
 d
o 
di
gi
ta
l t
ec
hn
ol
og
ie
s 
af
fe
ct
 c
om
pe
tit
io
n 
an
d 
co
op
er
at
io
n 
dy
na
m
ic
s,
 a
lo
ng
 w
ith
 b
ar
ga
in
in
g 
po
w
er
 o
ve
r 
va
lu
e 
cr
ea
tio
n 
an
d 
ca
pt
ur
e,
 in
 e
co
sy
st
em
s?
• 
H
ow
 d
o 
ec
os
ys
te
m
 o
rc
he
st
ra
to
rs
 c
on
st
ru
ct
 le
gi
tim
ac
y 
an
d 
th
e 
re
la
te
d 
co
lle
ct
iv
e 
id
en
tit
y,
 a
nd
 h
ow
 c
an
 d
ig
ita
l t
ec
hn
ol
og
ie
s 
he
lp
 in
 th
es
e 
ta
sk
s?
• 
W
ha
t a
re
 th
e 
(d
ig
ita
l)
 o
rg
an
iz
in
g 
m
ec
ha
ni
sm
s 
an
d 
di
gi
ta
l a
rt
if
ac
ts
 th
at
 f
ac
ili
ta
te
 in
no
va
tio
n 
w
ith
in
 a
nd
 a
ro
un
d 
th
e 
ec
os
ys
te
m
?
• 
H
ow
 c
an
 n
ew
 e
co
sy
st
em
s 
be
 c
re
at
ed
 a
nd
 s
ca
le
d 
up
 u
si
ng
 p
la
tf
or
m
 g
ov
er
na
nc
e 
an
d 
di
gi
ta
l i
nt
er
fa
ce
s?
• 
H
ow
 c
an
 th
e 
op
en
ne
ss
 a
nd
 f
le
xi
bi
lit
y 
in
 d
ig
ita
l e
co
sy
st
em
s 
be
 b
al
an
ce
d 
w
ith
 th
e 
ne
ce
ss
ar
y 
le
ve
l o
f 
co
nt
ro
l a
nd
 o
ve
rs
ig
ht
? 
W
ha
t a
re
 th
e 
or
ga
ni
zi
ng
 e
le
m
en
ts
 n
ee
de
d 
to
 a
ch
ie
ve
 th
is
 
ba
la
nc
e?
• 
W
ha
t h
ap
pe
ns
 to
 th
e 
kn
ow
le
dg
e 
br
ok
er
s 
of
 th
e 
tr
ad
iti
on
al
 e
co
sy
st
em
s 
w
he
n 
an
d 
if
 d
at
a 
be
co
m
es
 d
ig
ita
lly
 a
nd
 o
pe
nl
y 
av
ai
la
bl
e 
fo
r 
ec
os
ys
te
m
 a
ct
or
s?
• 
W
ha
t a
re
 th
e 
ke
y 
go
ve
rn
an
ce
 p
ri
nc
ip
le
s 
of
 s
el
f-
 or
ga
ni
zi
ng
 d
ig
ita
l e
co
sy
st
em
s?
• 
H
ow
 c
an
 e
co
sy
st
em
s 
ha
rn
es
s 
da
ta
 f
ro
m
 u
se
rs
 a
nd
 o
th
er
 a
ct
or
s 
in
 a
 w
ay
 th
at
 is
 b
en
ef
ic
ia
l t
o 
bo
th
 th
e 
co
re
 v
al
ue
 p
ro
po
si
tio
n 
an
d 
th
e 
va
ri
ou
s 
ec
os
ys
te
m
 a
ct
or
s?
G
eo
po
lit
ic
al
 le
ve
l q
ue
st
io
ns
 to
 e
xp
lo
re
 th
e 
PI
A
I 
fr
am
ew
or
k 
pe
rs
pe
ct
iv
e:
• 
H
ow
 d
o 
co
un
tr
ie
s 
an
d 
go
ve
rn
m
en
ts
 d
ea
l w
ith
 th
e 
in
cr
ea
si
ng
 p
ow
er
 o
f 
m
aj
or
 d
ig
ita
l p
la
tf
or
m
s 
an
d 
e-
 co
m
m
er
ce
 g
ia
nt
s?
• 
W
ha
t a
re
 th
e 
sh
or
t-
  a
nd
 lo
ng
- t
er
m
 im
pl
ic
at
io
ns
 o
f 
D
T
 f
or
 d
if
fe
re
nt
 in
du
st
ri
es
 a
nd
 in
 d
if
fe
re
nt
 c
ul
tu
ra
l c
on
te
xt
s?
• 
H
ow
 d
o 
go
ve
rn
m
en
ts
 a
ct
 u
po
n 
th
e 
de
ve
lo
pm
en
t o
f 
ne
w
 te
ch
no
lo
gi
es
?
• 
H
ow
 a
re
 o
pe
n 
da
ta
 s
ha
ri
ng
 p
ra
ct
ic
es
 a
ff
ec
te
d,
 s
ha
pe
d,
 a
nd
 e
na
ct
ed
 b
y 
D
T
?
• 
H
ow
 c
an
 o
pe
nn
es
s 
(o
f 
da
ta
),
 tr
an
sp
ar
en
cy
 (
of
 g
ov
er
nm
en
ta
l i
nt
en
tio
ns
),
 a
nd
 f
ai
rn
es
s 
(o
f A
I)
 b
e 
ha
nd
le
d 
ef
fe
ct
iv
el
y?
• 
W
ha
t a
re
 th
e 
ri
sk
s,
 c
os
ts
, a
nd
 b
en
ef
its
 o
f 
op
en
ne
ss
 in
 s
ha
ri
ng
 d
at
a 
(o
r 
ac
ce
ss
 to
 d
at
a)
 a
s 
pa
rt
 o
f 
D
T
?
• 
W
ha
t l
eg
al
 r
am
if
ic
at
io
ns
 c
ou
ld
 a
ri
se
 f
ro
m
 th
e 
m
is
us
e 
of
 d
ig
ita
l t
ec
hn
ol
og
ie
s 
an
d 
op
en
 d
at
a?
• 
H
ow
 d
o 
di
ff
er
en
t d
at
a 
ow
ne
rs
hi
p 
fr
am
ew
or
ks
 a
ff
ec
t o
pe
n 
an
d 
co
lla
bo
ra
tiv
e 
di
gi
ta
l i
nn
ov
at
io
n?
• 
H
ow
 c
an
 I
PR
s 
an
d 
th
ei
r 
pr
ot
ec
tio
n 
be
 r
ec
on
ce
iv
ed
 in
 li
gh
t o
f 
th
e 
di
ff
er
en
t a
pp
ro
ac
he
s 
to
 D
T
 a
ro
un
d 
th
e 
gl
ob
e?
• 
H
ow
 c
an
 D
T
 e
na
bl
e 
gr
ea
te
r 
le
ve
ls
 o
f 
ci
vi
c 
en
ga
ge
m
en
t, 
pa
rt
ic
ip
at
io
n,
 a
nd
 d
el
ib
er
at
io
n,
 s
uc
h 
as
 th
ro
ug
h 
R
R
I?
Ta
bl
e 
1.
 
(C
on
tin
ue
d)
© 2022 The Authors. R&D Management published by RADMA and John Wiley & Sons Ltd.
Digital transformation, for better or worse
R&D Management 2022 13
thus awaits not only in determining how to design 
facilitative digital technologies but also in how to 
use them in culturally sustainable ways by ensuring 
that those technologies transform or disrupt work 
practices, values, and norms “in a way that leads to 
moral, social, and emotional upskilling or reskill-
ing rather than deskilling” of employees (Ulhøi 
and Nørskov, 2020, p. 96). How such a responsible 
approach can be designed, what criteria it should 
be built upon, and how it should enable socially 
and ethically robust organizational decision- 
making are all issues that require much more inves-
tigation. For this, tools that foster responsibility 
and the integration of external stakeholders – such 
as the Responsible Research and Innovation (RRI) 
approach – should be included more widely and at 
an earlier stage (Jirotka et al., 2017).
Furthermore, the introduction of digital tech-
nologies can lead to individuals feeling included, 
while others are excluded due to a lack of oppor-
tunities or competencies. This progression can be 
detrimental to innovation and diversity. Likewise, 
DT might lead to the dispersion of individuals to 
geographically distant locations, with interaction 
both facilitated and constrained by digital technolo-
gies. Changes triggered by technologies encompass 
not only skills but also workers’ jurisdictions since 
they alter the task domains of specialists and the 
division of labor (Barrett et al., 2012). For instance, 
whereas digital technologies may be able to pro-
mote learning (Belpaeme et al., 2018), engage-
ment (Traeger et al., 2020), and problem- solving 
(Tennent et al., 2019), they may also change the 
status and visibility of workers in the workplace 
and lead to the marginalization of certain work-
ers and occupations in favor of others. The open 
question is whether DT will lead to a more inclu-
sive model of working in which individuals have 
rich access to knowledge and to each other or to a 
model from which only certain individuals derive 
benefit. Furthermore, the predictability of a task 
determines its “robotification” and automatization 
potential (Ford, 2015). While removing mundane, 
repetitive tasks is typically viewed as desirable, not 
all unpredictable work is meaningful; nor is all pre-
dictable work dull.
3.2.  The organizational- level research 
agenda
At the organizational level, DT is perceived as 
either an enabler of organizational renewal that 
offers ample opportunities to recast how firms 
can best capture and create value (Bradley and 
O’Toole, 2016) or as an externally enforced driver 
that threatens a company’s survival (Vial, 2019). 
In addition, given new challenges with IPRs and 
appropriability brought by DT (Ilvonen et al., 
2018), DT demands the construction of an appro-
priation advantage (Di Minin and Faems, 2013) 
to create and capture value considering the inter-
dependencies enabled by digital technologies and 
data, both within and between organizations. In 
fact, since data are by nature a “nonrival good,” it 
can be used and reused with a near- zero marginal 
cost, highlighting both the value creation potential 
of data within and across organizations and the 
importance of capturing value from it (Jones and 
Tonetti, 2020, Alaimo, 2021).
DT can be interpreted as an opportunity to inno-
vate and transform organizational legacies, capa-
bilities, structures, processes, and business models 
(Cennamo et al., 2020; Lanzolla et al., 2020) or as 
a set of drastic changes that could disrupt and even 
cannibalize the core competencies of incumbent 
firms (O’Reilly and Tushman, 2016) or even entire 
industries. As with other technology- driven transfor-
mations in organizations, DT initiatives are known 
to be difficult to implement (Saldanha, 2019). As 
firms are confronted with discontinuous changes in 
their environment, they experience increasing ambi-
guity and issues of organizational identity (Tripsas, 
2009), especially compared to “born- digital” players 
like Amazon, Netflix, and Airbnb. Interestingly, the 
internal structure of established companies becomes 
a subject of change itself, with the blurring of bound-
aries between units allowing for broader and con-
tinuous adaptation without inertia (Hanelt et al., 
2020). This reflects an apparent paradox between 
the organizational intent of engaging in DT (and 
creating specific structures to support this change) 
and the inherent transformative properties of digital 
technologies that transcend existing structures and 
boundaries.
How companies act in response to DT will be 
determined by their perception and interpretation of 
disruptive events. A given company’s actions may be 
offensive (first mover, market leader) or defensive. 
Companies can, for example, exploit digital technol-
ogies to enter previously unconnected markets by 
reinventing their legacy value chains (Lanzolla et al., 
2020), to enter new markets created by technology 
diffusion, or to leverage digital technologies across 
various organizational units, whether to reduce costs, 
to optimize processes and production, or to make 
“smart” business decisions (Vial, 2019; Cennamo 
et al., 2020; Lanzolla et al., 2020). In the worst case, 
DT can result in inefficiencies in organizations, 
including dysfunctional information systems and 
interfaces and increasing coordination costs. Indeed, 
© 2022 The Authors. R&D Management published by RADMA and John Wiley & Sons Ltd.
DĄBROWSKA ET AL
14 R&D Management 2022
a key question is whether digital technology serves 
the needs of the organization or whether the organi-
zation finds itself shaped to serve the features of the 
technology. Organizations thus face an ambiguous 
challenge in the need to balance the new structures, 
business models, and ecosystems developed as part 
of DT and the ability to harness the potential of their 
existing structures and capabilities (Maijanen and 
Virta, 2017). The implementation of DT also pro-
foundly questions the soundness of existing growth 
strategies (Verhoef et al., 2021) and business models 
(De Marco et al., 2019).
The impact of organizational actions can affect 
not only companies’ success in terms of financial 
performance, innovation performance, or even sur-
vival but will also have positive or negative implica-
tions on the broader economic and social structure by 
differentiating DT champions and beneficiaries from 
digital laggards. This opens the door for multidisci-
plinary research on the different antecedents, conse-
quences, performance implications, and nuances of 
DT and digital technologies on incumbents, SMEs, 
and start- ups, as well as on the wider society.
Clearly, there are many research opportunities to 
investigate DT at the organizational level. For exam-
ple, we still know little about the new organizational 
principles, designs, and processes that are triggered 
by digital technologies (Lanzolla et al., 2020). 
Moreover, we are not yet fully aware of the different 
ways in which incumbents lagging in digital technol-
ogy adoption can renew themselves. For instance, 
technological collaborations with born- digital start- 
ups or more dominant born- digital players are likely 
avenues for renewal, but the effectiveness of these ini-
tiatives remains an open question. Furthermore, the 
consequences of new digital technologies on organi-
zational decision- making or innovation performance 
(Usai et al., 2021) are not well known. Likewise, the 
accelerated adoption of digital workplace technolo-
gies (Marsh et al., 2021) and the recently promoted 
future- of- work in a “Metaverse” have short- and 
long- term consequences yet to be explored.
3.3.  The ecosystem- level research agenda
At the ecosystem level, DT is perceived as either 
being embedded within the ecosystem itself (as in the 
case of digital platforms) or as a driver of transfor-
mation in existing ecosystems. We suspect that this 
difference in perception leads to significant differ-
ences in terms of organizational- and individual- level 
strategies that warrant research. For ecosystems, an 
interesting question is whether DT is perceived as 
serving the ecosystem members’ individual interests 
or promoting shared ideas and collective action to 
compete against other ecosystems that may be less 
digitally capable. Furthermore, easy- access member-
ship in many platform ecosystems invites more gen-
erativity, but might also spur opportunistic behavior 
(Karhu and Ritala, 2020). These challenges evoke the 
importance of the legitimacy of both the ecosystem 
and the legitimacy of its constituent organizations 
(Thomas and Ritala, 2021). The reality is that many 
(digital) ecosystems fail to attract enough valuable 
contributions and eventually die out. The role of dig-
ital technologies in securing and maintaining eco-
system health, renewal, and generativity (Kallinikos 
et al., 2013) is thus essential.
The interpretation of DT is similarly divided into 
fully digital organizing principles or introducing and 
implementing digital aspects in existing ecosystem 
governance. From the orchestrator- centric perspec-
tive, interesting aspects relate to how the orchestrator 
can mobilize ecosystem actors around a shared value 
proposition (Dattée et al., 2018) and the role played 
by digital technologies in this process. Further 
research could be devoted to the organizing princi-
ples that orchestrators can use to facilitate innovation 
within the ecosystem. Ecosystem orchestrators need 
to balance between several tensions, such as genera-
tivity as opposed to control (Cennamo and Santalo, 
2019) and openness and flexibility in value creation 
as opposed to tightly enforced value capture princi-
ples (Karhu and Ritala, 2020). From the community- 
system perspective, DT scholars could examine how 
digital artifacts and interfaces change the interaction 
dynamics between ecosystem actors and which actors 
are influential in such ecosystems. The wealth of dig-
itally available information amplifies misinformation 
and causes tensions on all levels, from the individual 
(e.g., cyberbullying, identity theft, or addictive use) 
to the political (Baccarella et al., 2018), on which the 
perceived fairness of the value appropriation within 
the ecosystem may vary and have implications at the 
individual (emotions), organizational (strategies), 
and geopolitical levels (regulations). These and other 
themes emerge as we try to understand the new pos-
sibilities of heterogeneous actors joining together to 
create value in distinctive types of ecosystems around 
various themes (Nylund et al., 2021).
The action that results is driven by the role of the 
digital ecosystem: it either ought to seek generativity 
and innovation through digital organizing (Cennamo 
and Santalo, 2019) or to facilitate interaction among 
ecosystem participants through digital tools and 
connectivity. While ecosystem relationships can be 
diverse and not always directly and immediately ben-
eficial, they ought to provide meaningful affordances 
for ecosystem members if they are to be sustained 
over time (Nambisan, 2017). Interorganizational 
© 2022 The Authors. R&D Management published by RADMA and John Wiley & Sons Ltd.
Digital transformation, for better or worse
R&D Management 2022 15
relationships within ecosystems are both enabled 
and constrained by digital technologies. For exam-
ple, digital technologies (such as platform interfaces) 
enable easier maintenance and augment interorgani-
zational relationships, but they may also compromise 
the quality of relationships.
In addition, more research is needed to understand 
what the (intentional and unintentional) impacts of 
digital technology- driven changes could have on 
ecosystems. All ecosystems are naturally heteroge-
neous in their actors, technologies, and institutional 
environments (Aarikka- Stenroos and Ritala, 2017, 
Cobben et al., 2021); the impact of DT will therefore 
differ for each ecosystem. Some ecosystems, such as 
Facebook, are fundamentally born digital; they were 
built on the organizing principles of platform mar-
kets (Cennamo et al., 2020). On the contrary, some 
ecosystems, such as those in sectors like energy and 
health, are organized around a value proposition 
that may not be delivered in a (fully) digital format. 
Ultimately, ecosystems that can harness digital tech-
nologies are seeing significant growth advantages 
compared to those that cannot. However, there is also 
a risk of some established ecosystems becoming too 
powerful, and with their strengths in proprietary data 
and information systems, those companies may be 
less vulnerable to disruption than previously (Bessen 
et al., 2020). This might suppress competition and 
concentrate markets among even fewer companies 
and platforms. The resulting impact might differ 
for participants to digital ecosystems. As such, DT 
may lead to a “beautiful” virtuous cycle of value co- 
creation and co- evolution in which different actors 
join, innovate, and collaborate, contributing to the 
renewal and ongoing competitiveness of the entire 
ecosystem. Indeed, such generativity is seen as an 
ideal feature of digital technologies and digital eco-
systems (Yoo et al., 2010; Cennamo and Santalo, 
2019). Conversely, DT could lead to an “ugly” long 
tail of ecosystem actors that fail to profit or benefit 
from the ecosystem, if value only migrates to the rare 
superstar complementors or actors.
3.4.  The geopolitical- level research agenda
At the geopolitical level, DT is perceived as a tool 
for market and even socio- political dominance. Only 
states and international organizations comprised of 
states have the resources and authority to balance the 
rights of individuals, organizations, the state, and soci-
ety. Therefore, in assessing the potential of data and 
more broadly of DT, national and geopolitical con-
texts are of major relevance, even if they are typically 
overlooked in the literature. Thus, we should consider 
the geopolitical dimension as an independent unit of 
observation. We also need to acknowledge that geopo-
litical dynamics significantly influence the individual, 
organizational, and ecosystem levels, along with the 
regional and national units of analysis.
The interpretation of DT does, however, vary 
greatly across states depending on whether personal 
data is viewed as an asset, a right, or a public good. 
Due to varying geopolitical perceptions of data, the 
consequences of non- transparent data handling differ 
around the world. For example, the StudiVZ plat-
form started in Germany in 2005 as an online social 
network for students and young people; it achieved 
a user base of over six million in German- speaking 
countries by 2009. This number fell to around 
600,000 in 2016 before the company declared bank-
ruptcy in 2017. A significant contributor to this col-
lapse was the criticisms the company received for 
data exploitation, which resulted in bad press and 
a loss of public trust (Fuchs, 2010). At the same 
time, Facebook continues to grow, with over 2.2 bil-
lion users (more than the population of any single 
nation), and still leads the market, despite even more 
concerning allegations around personal data misuse. 
Combined, these examples show how questions of 
data, regulation, and market competition are often 
unevenly distributed and may cause unintended and 
sometimes harmful consequences.
The transformation is acted on because of these 
considerations. In the United States, where data is 
seen as an asset, the government promotes the eco-
nomic utilization of these assets by organizations. 
Platforms are thus encouraged to profit from data 
through business model innovation (Cusumano 
et al., 2019). In China, where data are a public good, 
platforms are asked to collect data that serves the 
state, and information sometimes triumphs over 
profit. In Europe, where data is an individual right 
whose protection is paramount, innovation becomes 
more defensive and reactive to regulation at every 
step. These approaches are all consistent with the 
prominent values of their respective regions. Future 
research should investigate when and how different 
approaches to data and geopolitical tensions influ-
ence innovation ecosystems and open innovation. 
Furthermore, ecosystems and platforms that grow 
too large or powerful may also be perceived as a 
threat to the power of governments, both democratic 
and autocratic, hence causing geopolitical responses 
aimed at their control. At the same time, DT –  
especially regarding data ownership issues – has also 
fueled the re- emergence of geopolitical blocs. While 
geopolitics is currently more concerned with the 
race for the ownership of as- yet unexploited natural 
resources, the discussions on data may further accen-
tuate this trend.
© 2022 The Authors. R&D Management published by RADMA and John Wiley & Sons Ltd.
DĄBROWSKA ET AL
16 R&D Management 2022
However, DT means that actions transcend bor-
ders to a far larger extent, and the impact of DT on 
innovation is not only influenced by the approach 
of each nation but by the clashes between these 
approaches. As digital platforms change the socio-
technical landscape across the globe (Martin, 2016), 
they are becoming geopolitical tools (Andersson 
Schwarz, 2017), allowing multinationals and gov-
ernments access to user data and the possibility of 
managing user interactions to such an extent that 
other nations may be excluded from market access. 
Another regulatory issue that emerges from DT is 
the “Uberization” of societies (Hill, 2015), which 
refers to the freedom of choice in capitalizing on 
one’s tangible assets (cars in the case of Uber, real 
estate in the case of Airbnb) as sources of short- term 
income. However, the regulatory aspect of this free-
dom is often a grey area, so the long- term picture for 
technology- enabled access to services is not fully 
clear. If nothing changes, there may be negative con-
sequences related to a lack of pension contributions 
for gig workers and issues with access to healthcare 
benefits, which are not a public good in many coun-
tries. Moreover, the apparent autonomy associated 
with embracing a sharing economy model (largely 
enhanced by digital businesses) may have serious 
implications both for individuals who are forced to 
accept precarious employment structures and for 
organizations tainted by a hyper- distrust of techno-
logical surveillance (Fleming, 2017; Zuboff, 2020). 
Similar challenges relate to the so- called “platform 
work” where individuals act as entrepreneurs in digi-
tal platforms, such as those focusing on food delivery.
In addition, large platforms in Europe may also 
face a series of non- regulatory issues, such as the 
need to offer access and service in local or minority 
languages. Platforms like Facebook and Twitter 
from the United States or TikTok and WeChat from 
China initially tap into far larger local markets 
broadly united by one language, which provides an 
edge over their multilingual European counterparts. 
However, network effects and other ecosystem- 
specific advantages of digital platforms are often 
not bound to a location (Nambisan et al., 2019), 
meaning that platforms can take advantage of their 
network effects even when they are late entrants 
into a particular geographical market. Moreover, 
as English- , Chinese- , or Korean- speaking com-
munities are relatively well represented across the 
globe, their various diasporas could play an instru-
mental role in introducing locally popular products 
into completely different markets. Entrepreneurs 
on these platforms become dependent on the type 
of business dynamics that dominate such platforms 
(Cutolo and Kenney, 2019). These dynamics are 
in turn shaped by infrastructures, norms, and poli-
cies that shape the platform economy (Kenney and 
Zysman, 2016).
Recent technological developments in AI, cloud 
computing, 5G, and Web3 all call for either more 
regulatory actions, which may involve laws, regula-
tions, and antitrust initiatives, or more international 
data standards, better data architecture, and greater 
interoperability of data through better interfaces. 
Some still unanswered research questions focus on 
the ways in which open data sharing practices are 
affected and shaped by DT, and how DT and new 
digital technologies can enact open data sharing 
practices. More broadly, we need to learn more about 
different ways of handling openness, transparency, 
and fairness (see, e.g., European Commission, 2018). 
Researchers could also look further into questions 
such as what are the risks and costs of openness in 
sharing data (or access to data) as part of DT, and 
what are the short- and long- term implications for 
different industries and in different cultural contexts?
4.  Conclusion
This paper makes two key contributions. First, we 
propose an extended definition of DT that goes 
beyond capturing change at the organizational level. 
Second, given the broader socioeconomic and soci-
otechnical transformation (Geels and Schot, 2007) 
related to DT, we provide a foundation for advancing 
our understanding of DT across multiple levels of 
analysis by developing a critical, multi- level research 
agenda at the intersection between the bright and 
dark sides of DT. In doing so, we aim to answer the 
call of Urbinati et al. (2020) and Yoo et al. (2010), 
which invite us to provide strategic and innovation 
frameworks in a digital technology context. More 
concretely, we approach DT from the tensions and 
paradoxes perspective (Schad et al., 2016), and 
through the prism of our proposed PIAI framework 
(perception, interpretation, action, and impact). This 
framework provides a balanced way to approach the 
overarching transformation brought along by DT in 
multiple levels, inviting scholars and practitioners to 
embrace not only the best practices or benefits but 
different challenges and downsides as well.
Our contributions provide insights to R&D and 
innovation management, calling managers to make 
balanced decisions on the overarching, and some-
times the disruptive effect of digital technology. The 
multi- level framework allows managers to consider 
how actions at individual, organizational, ecosystem, 
and geopolitical levels are contributing (or not) to 
accelerate DT. Similarly, by connecting perceptions, 
© 2022 The Authors. R&D Management published by RADMA and John Wiley & Sons Ltd.
Digital transformation, for better or worse
R&D Management 2022 17
interpretation, and specific actions we draw a path 
they can follow to decipher what impact means in this 
context, and its consequences. DT is inevitable, but it 
is not deterministic, since individuals, organizations, 
ecosystems, and governments affect – whether inten-
tionally or not – how it evolves and shapes the world.
In this study, we pursued not to simplify, but to 
embrace the complexity of DT, which provides a lot 
of future research opportunities. As the DT phenom-
enon evolves and permeates across and beyond the 
analysis dimensions of our framework, we encourage 
future research to further unpack and look inside the 
transformed or new processes, taking a closer look at 
different aspects of this phenomenon. We hope our 
work will be helpful in stimulating fruitful discus-
sions, debates, and future research.
Acknowledgments
This research has received funding from the EU’s 
Horizon2020 research and innovation program 
under grant agreement no. 956745, EINST4INE: 
The European Training Network for Industry Digital 
Transformation across Innovation Ecosystems. The 
content of this publication does not reflect the official 
opinion of the EU. Responsibility for the information 
and views expressed in the publication lies entirely 
with the authors.
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Notes
1 A Scopus search (December 17, 2021) reveals that over 
40% of scientific papers that refer to “digital transfor-
mation” in the title, abstract, or keywords were pub-
lished in the computer science and engineering fields, 
followed by business and management, social sciences, 
and decision sciences.
2 The second draft of China’s first comprehensive law on 
the protection of personal data, known as the Personal 
Data Protection Law, was released on 29 April 2021.
Justyna Dąbrowska is a Vice- Chancellor’s 
Postdoctoral Research Fellow at RMIT University, 
Australia. She conducts research in innovation 
management with a focus on organizational re-
newal, developing open innovation capabilities, 
and micro- foundations of open innovation in the 
digital era. Her additional research interests in-
clude collaboration between mature companies 
and startups, innovation ecosystems, leadership, 
entrepreneurship. She has gained industry experi-
ence from European Headquarters in automotive 
and marketing & advertising industries and has 
wide experience in managing large international 
research projects.
Argyro Almpanopoulou is a Postdoctoral 
Researcher at the School of Business and 
Management at LUT University, Finland. Her re-
search focuses on the factors and processes that 
shape ecosystem emergence. In particular, she is 
interested in the ways ecosystems are organized 
to create relevant knowledge and solutions for re-
solving societal challenges especially in sectors 
like energy and healthcare. Methodologically, her 
primary expertise lies in qualitative methods. She 
publishes in top- tier academic outlets, such as 
Research Policy, Technovation, and Technological 
Forecasting and Social Change.
Alexander Brem is Endowed Chaired Professor 
and Institute Head at the University of Stuttgart, 
Germany. In addition, he is an Honorary Professor 
at the University of Southern Denmark. His re-
search focus is on technological innovation and 
entrepreneurship.
Henry Chesbrough is best known as “the fa-
ther of Open Innovation.” He teaches at the Haas 
School of Business at the University of California- 
Berkeley. He is also Maire Tecnimont Professor 
of Open Innovation at Luiss University in Rome. 
He has written books such as Open Innovation, 
Open Business Models, Open Services Innovation, 
and Open Innovation Results. He has been rec-
ognized four times as one of the leading business 
thinkers by Thinkers50. He received an Innovation 
Luminary award from the European Commission 
in 2014, the Industrial Research Institute Medal 
of Achievement in 2017, the PICMET Medal of 
Achievement in 2019, and holds two honorary 
doctorates.
© 2022 The Authors. R&D Management published by RADMA and John Wiley & Sons Ltd.
DĄBROWSKA ET AL
24 R&D Management 2022
Valentina Cucino is a Postdoctoral Scholar at the 
Institute of Management of the Sant’Anna School 
of Advanced Studies, Pisa, Italy. She holds a PhD 
in Management Innovation, Sustainability, and 
Healthcare. Her research interest and teaching mainly 
deals with innovation management, purpose- driven 
innovation and entrepreneurship and university- 
industry technology transfer. Her works have been 
published in journals such as R&D Management, 
Journal of Knowledge Management, and European 
Journal of Innovation Management.
Alberto Di Minin is a Full Professor of 
Management at the Institute of Management 
Sant’Anna School of Advanced Studies, Pisa, Italy. 
Alberto is also a Research Fellow with the Berkeley 
Roundtable on the International Economy (BRIE), 
University of California – Berkeley, and Social 
Innovation Fellow with the Meridian International 
Center of Washington, DC. Alberto deals with 
Open Innovation, appropriation of innovation, 
and science and technology policy. He also works 
on technology transfer, intellectual property, and 
R&D management.
Ferran Giones is an Associate Professor 
(Akademischer Rat) at the University of Stuttgart and 
Deputy Director at the Institute of Entrepreneurship 
and Innovation Science. Before joining academia, he 
worked strategy consulting and international proj-
ect management. He has been an assistant professor 
for Technology Entrepreneurship at the University of 
Southern Denmark. His research and teaching areas are 
technology entrepreneurship, science commercializa-
tion, technology innovation, and industry emergence.
Henri Hakala is a Professor of Entrepreneurship 
at LUT University, School of Business and 
Management, Finland. His research interests focus 
on learning, development, and decision making in 
small and medium- sized businesses as well as strat-
egy, sustainability, and entrepreneurial ecosystems.
Cristina Marullo is an Assistant Professor 
of Innovation Management at the Institute of 
Management, Sant’Anna School of Advanced 
Studies, Pisa, Italy. Her research focuses on R&D 
and innovation management in the field of entrepre-
neurship, with a special emphasis on performance 
determinants and key managerial challenges of col-
laborative innovation strategies.
Anne- Laure Mention is a Professor and the 
Director for Global Business Innovation Enabling 
Capability Platform, RMIT University, Australia. 
Anne- Laure is a world- renowned innovation 
scholar. She holds several visiting positions across 
Europe and Asia and is one of the founding edi-
tors of the Journal of Innovation Management. Her 
research focuses on open and collaborative inno-
vation, innovation in business to business services, 
with a particular focus on financial industry and 
fintech, technology management, and business 
venturing.
Letizia Mortara is a Lecturer in Technology 
Management at the University of Cambridge and a 
Senior Fellow at Newnham College, Cambridge. She 
is also an Associate Editor for the R&D Management 
journal and the Head of the R&D Management 
Conference. At the Centre for Technology 
Management at the Institute for Manufacturing, 
she researches Technology intelligence (i.e. activity 
set- up in order to keep abreast with the latest devel-
opments in technology) Open Innovation, and the 
advent of Digital Fabrication technologies and infra-
structure in manufacturing and their implications for 
business.
Sladjana Nørskov is an Associate Professor at the 
School of Business and Social Sciences at Aarhus 
University. Her research focuses on innovation man-
agement, organizational behavior, and social robot-
ics. She investigates how alternative organizational 
structures promote innovation, and the role that so-
cial robots play in problem- solving and creative col-
laborations with humans.
Petra A. Nylund is a Researcher at the University 
of Stuttgart. She holds a Ph.D. in Management from 
IESE and an M.Sc. in Engineering and Business 
Management from KTH, Stockholm. She is currently 
enthusiastic about the development of innovation 
platforms and ecosystems and is also an expert in the 
econometric analysis of panel data. She has created 
strategies in the telecoms industry of Africa, Latin 
America, and Europe.
Calogero Maria Oddo is an Associate Professor of 
Bioengineering at Sant’Anna School of Advanced 
Studies, Pisa, Italy, and Head of the Neuro- Robotic 
Touch Laboratory at The BioRobotics Institute, coor-
dinating a team of about 20 research fellows. He also 
serves as deputy coordinator of the PhD program 
in Biorobotics, one of the largest doctoral schools 
worldwide in robotics and biomedical engineering, 
with more than 100 PhD students enrolled. His main 
research interests are in the Neuro- Robotics Area: 
specific research topics include medical devices, tac-
tile sensing, and artificial skins for bionic systems, 
and safe human- machine integration in the work-
place. He has over 90 publications in Scopus, with 
h- index 21 and 2300+ citations.
© 2022 The Authors. R&D Management published by RADMA and John Wiley & Sons Ltd.
Digital transformation, for better or worse
R&D Management 2022 25
Agnieszka Radziwon is an Associate Professor 
of Innovation Management at Aarhus University, 
Department of Business Development and 
Technology, also affiliated with University of 
California Berkeley, Garwood Center for Corporate 
Innovation. She obtained her Ph.D. degree in 
Product Design and Innovation from the University 
of Southern Denmark. Her main interests center on 
the antecedents and consequences of organizational 
collaboration. She is interested in the ways how 
knowledge is transferred within and across organi-
zations, and in its role in the process of facilitating 
and hindering innovation. Her research has been pub-
lished in journals such as Technological Forecasting 
& Social Change, R&D Management, Industry and 
Innovation, and Journal of Business Research.
Paavo Ritala is a Professor of Strategy and Innovation 
at the School of Business and Management at LUT 
University, Finland. His main research themes in-
clude ecosystems and platforms, the role of data and 
digital technologies in organizations, collaborative 
innovation, sustainable business models, and circular 
economy. His research has been published in journals 
such as Journal of Management, Research Policy, 
Journal of Product Innovation Management, R&D 
Management, Technovation, Long Range Planning, 
Industrial and Corporate Change, California 
Management Review, Technological Forecasting & 
Social Change, British Journal of Management, and 
Industrial Marketing Management. He is closely 
involved with business practice through company- 
funded research projects, executive and professional 
education programs, and in speaker and advisory 
roles. Prof. Ritala is the Co- Editor- in- Chief of R&D 
Management and he serves the editorial board of 
Journal of Product Innovation Management.