Summary Articles
Digital Transformation Strategy
Week 1: Introduction to Digital Transformation Strategy
• Adner, R., Puranam, P., & Zhu, F. 2019. What is different about digital strategy? From quanti-
tative to qualitative change. Strategy Science, 4(4): 253–261.
• Nambisan, S., Wright, M., & Feldman, M. 2019. The digital transformation of innovation and
entrepreneurship: Progress, challenges and key themes. Research Policy, 48(8): 103773.
• Verhoef, P. C., Broekhuizen, T., Bart, Y., Bhattacharya, A., Qi Dong, J., Fabian, N., & Haenlein,
M. 2021. Digital transformation: A multidisciplinary reflection and research agenda. Journal of
Business Research, 122: 889–901.
Week 2: Strategies for Digital Platforms and Ecosystems
• Cennamo, C., & Santalo, J. 2013. Platform competition: Strategic trade-offs in platform markets.
Strategic Management Journal, 34(11): 1331–1350.
• Shipilov, A., & Gawer, A. 2020. Integrating research on interorganizational networks and eco-
systems. Academy of Management Annals, 14(1): 92–121.
• Zhu, F., & Iansiti, M. 2012. Entry into platform-based markets. Strategic Management Journal,
33(1): 88–106.
Week 3: Digital Governance of Interorganizational Relationships
• Hanisch, M., & Goldsby, C. M. 2021. The boon and bane of blockchain: Getting the governance
right. California Management Review, forthcoming.
• Lumineau, F., Wang, W., & Schilke, O. 2021. Blockchain governance—A new way of organizing
collaborations? Organization Science, 32(2): 500–521.
Week 4: Big Data and Business Analytics
• Chen, Chiang, & Storey. 2012. Business intelligence and analytics: From big data to big impact.
MIS Quarterly, 36(4): 1165–1188.
• Davenport, T. H. Competing on analytics. Harvard Business Review, 84(1): 98– 107.
• McAfee Andrew, & Brynjolfsson Erik. 2012. Big data: The management revolution. Harvard
Business Review, 90(10): 60–66.
Week 5: Artificial Intelligence and Automation
• Agrawal, A., Gans, J., & Goldfarb, A. 2019. Prediction, judgment and complexity: A theory of
decision making and artificial intelligence. In A. Agrawal, J. Gans & A. Goldfarb (Eds.), The
economics of artificial intelligence: An agenda. Chicago, London: The University of Chicago
Press.
• Berente, N., Gu, B., & Santhanam, R. 2021. Managing artificial intelligence. MIS Quarterly,
45(3): 1433–1450.
• Dixon, J., Hong, B., & Wu, L. 2021. The robot revolution: Managerial and employment conse-
quences for firms. Management Science, 67(9): 5586–5605.
Week 6: Digital Market Regulation
• Martens, B. 2018. The impact of data access regimes on artificial intelligence and machine
learning. JRC Digital Economy Working Paper.
• Parker, G., Petropoulos, G., & van Alstyne, M. W. 2020. Digital Platforms and Antitrust. SSRN
Electronic Journal.
• Tirole, J. 2020. Competition and the Industrial Challenge for the Digital Age.
,Week 1: Introduction to Digital Transformation Strategy
Adner, R., Puranam, P., & Zhu, F. 2019. What is different about digital strategy? From quantitative
to qualitative change. Strategy Science, 4(4): 253–261.
Abstract
The recent attention paid to the challenge of digital transformation signals an inflection point in the im-
pact of digital technology on the competitive landscape. We suggest that this transition can be under-
stood as a shift from the quantitative advances that have historically characterized digital progress (e.g.,
Moore’s law, Metcalf’s law) to qualitative changes embodied in three core processes underlying modern
digital transformation: representation, connectivity, and aggregation. We consider the implications for
firm strategy and raise questions for future strategy research.
1. Introduction
Digitization has accelerated in the postwar era. However, even as the exponential growth rate of pro-
cessing capacity relative to cost predicted by Moore’s law has assumed an almost taken-for-granted
status since its first articulation in 1965 (Moore 1965), something dramatic has changed in recent years.
We suggest that this “something” can be understood as a transition from quantitative improvements to
qualitative changes. Therefore, we focus on the qualitative changes that interact to produce truly novel
outcomes. We posit that the changes we highlight demand a re-examination and expansion of the strat-
egy principles that have guided the field’s approach to technological transitions thus far.
Example of recorded digital music:
• First major digital transition: from analog to digital formats (from LPs and cassette tapes to CDs)
– mainly a shift in representation, from the capture of physical markers to digital markers.
o Characterized by utilizing tools of traditional competitive and technology strategy.
• Second transition: from physical format to downloadable formats – essentially a shift in con-
nectivity, which enabled music content to be accessed through a digital network, with implica-
tions for access, governance, and form.
o Required the addition of new concepts, particularly surrounding economies of scale and
network effects in network environments.
• Third transition: exemplified by services like Spotify, shift from requested content to suggested
content. – shift primarily in aggregation.
o Requires the addition of a new conceptual apparatus, which will broaden the scope of
inquiry that researchers can pursue, and educators can deploy.
Our article focuses on the qualitative shifts and interactions embodied in the three core processes un-
derlying digital transformation: representation, connectivity, and aggregation. We suggest that the inter-
actions among these processes have important implications for a number of central strategy concerns,
including the resource-based view.
2. Digital Foundations
We identify three foundational processes that, in our view, explain much of the variety of phenomena
that are subsumed under the rubric of “digital transformation.” We propose that any example of contem-
porary strategic interest can be usefully deconstructed into these core components.
2.1 Representation
Digital transformation begins with digitization. It is the digital representation of information that enables
analysis and algorithmic manipulation. In order to appreciate the scope of what digital representation
has evolved into, consider its roots. A qualitative shift occurred when aspects of reality that were not
considered data in the past—the location of people and cars; the on/off status of a living room light
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,switch—were captured, digitized, and incorporated as inputs into algorithmic processes that produce
predictions regarding traffic patterns or electricity consumption.
The resulting deluge of data would be more of a hindrance than help if we only dealt with it using human-
bounded rationality; however, it is now possible to represent large volumes of data and the actionable
insights they contain in the forms of algorithms. The ability to represent data algorithmically rather than
in a human-guided form (as in traditional descriptive statistics or statistical modeling for hypothesis test-
ing) is qualitatively distinct in terms of what it implies, both for human-bounded rationality and in terms
of raising the intriguing question of how to approach the potential for competence without comprehen-
sion.
2.2 Connectivity
Digitization creates new connections and enhances existing connections among objects, individuals,
and organizations. The sheer size and density of the network of connections as well as the range and
number of new actors who are part of the network of connectivity are the first major effects of digitization.
Greater network density has generally followed Metcalfe’s law in yielding exponentially greater network
value. The quantitative explosion of connected points has enabled the emergence of completely new
business and organizational models, some of which have cannibalized their nondigital equivalents.
However, the shift from connectivity-on-demand to connectivity-by-default has resulted in a qualitative
change that goes beyond quantitative increases in network density. As products and services become
more digitized, every product or service can be used to facilitate connections. This transition to always-
on connectedness enables revolutions in search, monitoring, and control.
As the digital revolution has shattered the constraints of information search and availability, it has height-
ened the constraints of deliberation and choice. Thus, how firms allocate their attention has become a
more important strategic decision than ever.
2.3 Aggregation
A qualitative shift arises from the ability to combine previously disjoint data (e.g., location, search query,
and social network) to answer questions that were formerly impossible to address.
Combining data related to human resources with traditional supply chain data provides managers an
unprecedented opportunity to understand their internal organization and its constituents. Enhancing
such synergies explains the drive toward diversification and the blurring of boundaries at firms such as
Oracle and SAP. While that is an energizing vision for many, it has a few dystopian shades as well.
• Governments can now have more information regarding their citizens than they ever could in
the past, raising a specter of Orwellian observation and control.
• Similar concerns could apply to the relationship between corporations and their employees.
2.4 Interactions
While each of these effects of digitization is significant, truly dramatic changes become visible when
they interact and reinforce each other. A combination of enhanced connectivity and data aggregation
has produced new functionality and opportunities for value creation and capture.
• Advances in connectivity and representation produced intelligent social media platforms such
as Facebook, WeChat, and LinkedIn.
• Combinations of representation and aggregation form the backbone of the dramatic increase in
consumer analytics (including credit scoring) as well as the burgeoning field of organizational
analytics.
• When connectivity, aggregation, and representation all come together, we see developments
such as self-driving cars, the Internet of things, Spotify, and the Chinese state’s social credit
system.
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, To provide a concrete example, “digital twins” currently enable physical processes to be represented
digitally in the form of a simulation model. Such a model derives its predictive power from the dynamic
connectivity to the actual engine being modeled, aggregation of data across similar engines in other
planes, and the use of algorithms to extract predictive insights from these data. Crucially, the resulting
model enables not just preventive maintenance but may also enable virtual experimentation for design
improvements.
There may also be strong complementarities between these processes, as the development in one
increases the value of the other. Aggregation enables potentially better connectivity, just as more con-
nectivity produces data that can benefit from being aggregated.
Put simply, as connectivity and aggregation erode transaction costs (and in turn accelerate as transac-
tion costs erode), the resulting increase in transactions enhances the potential for new and more kinds
of data; consequently, advances in data representation become ever more valuable in the effort to pro-
cess these data and mitigate the constraints of human-bounded rationality. In turn, this spurs further
investments in connectivity and aggregation, driving a positive feedback loop. The increasing velocity of
these mutually reinforcing changes, driven by the underlying complementarities between these pro-
cesses, may account for the distinct sense that digitization is creating a dramatic set of recent changes.
3. Implications
3.1 Resource-Based View (RBV): Data and Algorithms as Self-Generating Resources
Fundamental to the question of what digital transformation means for strategy is an understanding of
the characteristics of data as a strategic resource.
In order to understand the complexities that arise from how data are generated and consumed, con-
sider a particularly interesting new form of digital data creation that can be described as “autogenic.”
This arises when the very act of engaging with data creates new data—for example, the act of requesting
a search and reading its results itself creates new data about the requester, his or her interests and
habits, and so forth.
The fungibility of a resource is defined in terms of low decline in value when a resource is applied in its
second-best use relative to its first-best use. A smaller decline indicates higher fungibility. Data are
always scale-free (non-subtractable or non-rivalrous, e.g., brand), although their fungibility may vary.
Data instantiate the point that fungibility depends not just on the target-use case but also on the other
data sources alongside it in the aggregation pool.
If data and algorithms are resources, their replication may be a qualitatively distinct phenomenon from
knowledge transfer among humans. On the one hand, issues of stickiness and causal ambiguity might
appear less relevant in replicating digital content. On the other hand, stickiness may be even more
important in the use by humans of the insights generated algorithmically from data.
3.2 Data, Ownership, and Factor Markets
Simultaneously, it is also rather unclear who should own these data. Does the keyboard user even know
that such data are being collected? Is their consent required? If so, should this consent be required for
ongoing collection or each successive reuse?
With enhanced connectivity, it is also no longer easy to curtail information flow at the legal boundaries
of the firm, thereby creating new opportunities and challenges. Creating differential pathways for infor-
mation of different kinds—where to block its flow and where to enable it—is likely to become a more
important managerial challenge than has traditionally been the case.
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