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What Is Different About Digital Strategy? From
Quantitative to Qualitative Change
Ron Adner, Phanish Puranam, Feng Zhu
To cite this article:
Ron Adner, Phanish Puranam, Feng Zhu (2019) What Is Different About Digital Strategy? From Quantitative to Qualitative
Change. Strategy Science 4(4):253-261. https://doi.org/10.1287/stsc.2019.0099
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, STRATEGY SCIENCE
Vol. 4, No. 4, December 2019, pp. 253–261
http://pubsonline.informs.org/journal/stsc ISSN 2333-2050 (print), ISSN 2333-2077 (online)
What Is Different About Digital Strategy? From Quantitative to
Qualitative Change
Ron Adner,a Phanish Puranam,b Feng Zhuc
a
Tuck School of Business, Dartmouth College, Hanover, New Hampshire 03755; b INSEAD, 138676 Singapore; c Harvard Business School,
Boston, Massachusetts 02163
Contact: ron.adner@dartmouth.edu, https://orcid.org/0000-0003-1238-2248 (RA); phanish.puranam@insead.edu,
https://orcid.org/0000-0002-0032-8538 (PP); fzhu@hbs.edu, https://orcid.org/0000-0002-3034-6876 (FZ)
Received: October 11, 2019 Abstract. The recent attention paid to the challenge of digital transformation signals an
Accepted: November 3, 2019 inflection point in the impact of digital technology on the competitive landscape. We
Published Online in Articles in Advance: suggest that this transition can be understood as a shift from the quantitative advances that
December 10, 2019 have historically characterized digital progress (e.g., Moore’s law, Metcalf’s law) to quali-
https://doi.org/10.1287/stsc.2019.0099
tative changes embodied in three core processes underlying modern digital transformation:
representation, connectivity, and aggregation. We consider the implications for firm strategy
Copyright: © 2019 INFORMS and raise questions for future strategy research.
Keywords: digital transformation • digital technology • representation • connectivity • aggregation • firm strategy • digital strategy
1. Introduction formats—the shift from CDs to MP3 files distributed
Digitization has accelerated in the postwar era. How- not through physical means but through platforms
ever, even as the exponential growth rate of processing like Napster and iTunes. This was essentially a shift
capacity relative to cost predicted by Moore’s law has in connectivity, which enabled music content to be
assumed an almost taken-for-granted status since accessed through a digital network, with implica-
its first articulation in 1965 (Moore 1965), something tions for access (any song posted on the network was
dramatic has changed in recent years. We suggest now available to all network members), governance
that this “something” can be understood as a tran- (redefining the rules of behavior and legality), and
sition from quantitative improvements to qualitative form (the unbundling of albums into individual songs).
changes. While we do not minimize the miracles that The third and current transition, exemplified by ser-
have led to, and been enabled by, the exponential vices like Spotify, entails a shift from requested con-
improvement in processing power, storage capacity, tent to suggested content. More than a change from
bandwidth, and their associated costs, we suggest downloads to streaming, this is a shift primarily in
that their impact has been well accommodated within aggregation. By combining and analyzing the past
the existing strategy canon until recently. Therefore, we content requests of numerous other users as well
focus on the qualitative changes that interact to pro- as rating and other usage data regarding the focal
duce truly novel outcomes. We posit that the changes user, it is now possible to proactively customize sug-
we highlight demand a re-examination and expansion gestions for a specific user and even to predict the
of the strategy principles that have guided the field’s likelihood that the user will follow the suggestion. This
approach to technological transitions thus far. This shift from responsive to predictive streaming changes
is a conversation we hope others will swiftly join—to the relationship between producers and consumers
challenge, complement, and ultimately improve our and impacts the very nature of consumer demand,
collective understanding of firm strategy in the dig- choice, and preference.
ital era. The first transition from tapes to CDs could be well
Consider the example of recorded digital music: characterized by utilizing tools of traditional com-
The first major digital transition was from analog to petitive and technology strategy (e.g., Abernathy and
digital formats (from LPs and cassette tapes to CDs). Utterback 1978; Porter 1980, 1985; Tushman and
This was mainly a shift in representation, from the Anderson 1986; Prahalad and Hamel 1990). Under-
capture of physical markers (grooves in a record; standing the second transition required the addition
magnetic distributions on a tape) to digital markers of new concepts, particularly surrounding econo-
(ones and zeros on a CD), with implications for fi- mies of scale and network effects in network envi-
delity and replicability of the information and tech- ronments (e.g., Katz and Shapiro 1994, Shapiro and
nical capabilities of industry participants. The second Varian 1999, Rangan and Adner 2001). This enriched
transition was from physical format to downloadable the strategy lexicon and opened up new subfields
253
, Adner, Puranam, and Zhu: What Is Different About Digital Strategy?
254 Strategy Science, 2019, vol. 4, no. 4, pp. 253–261, © 2019 INFORMS
for study. We suggest that understanding the cur- 2.1. Representation
rent transition—to hypercustomized, predictive, self- Digital transformation begins with digitization. It is
improving technologies—similarly requires the ad- the digital representation of information that enables
dition of a new conceptual apparatus, which will analysis and algorithmic manipulation. It has become
broaden the scope of inquiry that researchers can a truism to state that data are the new oil, the key input
pursue and educators can deploy. to the engine of the information age. However, the
Our article focuses on the qualitative shifts and explosion in the quantity of data available has been
interactions embodied in the three core processes accompanied by qualitative revolution in the represen-
underlying digital transformation: representation, tation of these data that underlie digital transformation.
connectivity, and aggregation. We suggest that the In order to appreciate the scope of what digital
interactions among these processes have important representation has evolved into, consider its roots.
implications for a number of central strategy concerns, Early digital logic was famously used by militaries to
including the resource-based view (Wernerfelt 1984, compute ballistic trajectories more rapidly than hu-
Barney 1986)—the analysis of data and algorithms man calculators. This is an example of converting in-
as resources; the behavioral theory of the firm (March formation from one logical form (analog tables and
and Simon 1958, Cyert and March 1963) in terms of written equations) to another (data and programs) to
the impact of algorithmic decision making on bounded generate digital data and insight. Further, the transi-
rationality and organizational learning; transaction tion from LPs to CDs is an example of converting
cost economics (Coase 1937; Williamson 1975, 1985) analog information into digital form. The early phases
in terms of the decline in search and contracting costs; of the digital revolution were characterized by such
diversification (Chandler 1962, Rumelt 1982)—for conversions, as paper ledgers transitioned to digital
example, understanding the nature of relatedness spreadsheets. A qualitative shift occurred when as-
and the choice of corporate scope based on data; or- pects of reality that were not considered data in
ganizational design (Simon 1947), such as organizing the past—the location of people and cars; the on/off
without hierarchy and designing human–algorithm status of a living room light switch—were captured,
collaboration; and technology evolution (Abernathy digitized, and incorporated as inputs into algorithmic
and Utterback 1978)—for example, the impact of processes that produce predictions regarding traffic
artificial intelligence (AI) and autogenic data on how patterns or electricity consumption.
organizations work and, ultimately, the possibility of The growing ubiquity of sensor technology has
new business models and the very nature of com- created new variants of digital fodder and expanded
petitive advantage (Agrawal et al. 2018). the “on ramp” onto the digital transformation pro-
cess; simultaneously, there has also been rapid de-
2. Digital Foundations velopment of the “off ramp” that involves the trans-
Digitization does not require us to abandon the basic formation from the digital back to the physical
conceptualizations of the economic phenomena we world. This is a mirror process—also characterized
are familiar with. Transaction costs (Coase 1937) by quantitative acceleration—in which digital signals
and bounded rationality (Simon 1957) as conceptual are transformed into analog actions. Automation,
building blocks and resource (Barney 1986) and in- robotics, and 3D printing are the most visible mani-
dustry analysis (Porter 1980) as analytical tools re- festations of this process, which is foundational to
main important guideposts on the journey. At the the idea of the “fourth industrial revolution.” The
same time, it is critical to recognize the need for resulting deluge of data would be more of a hin-
new additional tools and conceptualizations (see drance than help if we only dealt with it using human-
also Levin 2011, Goldfarb and Tucker 2019). In this bounded rationality; however, it is now possible to
spirit, we identify three foundational processes that, represent large volumes of data and the actionable
in our view, explain much of the variety of phe- insights they contain in the forms of algorithms. Ma-
nomena that are subsumed under the rubric of “digital chine learning is essentially a form of function ap-
transformation.” We propose that any example of proximation (Abu-Mostafa et al. 2012, Varian 2014).
contemporary strategic interest—whether it be Ali- Critically, there is limited need for human guid-
baba’s e-commerce platforms, Instagram’s appar- ance in functional form selection, and the resulting
ently unlimited appeal to teenagers, Tesla’s efforts function is not always easy to interpret for humans
in autonomous driving, or the startling popular- (Mullainathan and Spiess 2017). This ability to rep-
ity of multiplayer online gaming as a spectator resent data algorithmically rather than in a human-
sport—can be usefully deconstructed into these core guided form (as in traditional descriptive statis-
components. tics or statistical modeling for hypothesis testing) is
, Adner, Puranam, and Zhu: What Is Different About Digital Strategy?
Strategy Science, 2019, vol. 4, no. 4, pp. 253–261, © 2019 INFORMS 255
qualitatively distinct in terms of what it implies, important strategic decision than ever (e.g., Ocasio
both for human-bounded rationality and in terms of 1997, Piezunka and Dahlander 2019).
raising the intriguing question of how to approach the
2.3. Aggregation
potential for competence without comprehension.
Finally, beyond the quantitative growth in data stor-
age capacity and reduction in storage costs is a third
2.2. Connectivity
qualitative shift—that of data aggregation. A quali-
Digitization creates new connections and enhances
tative shift arises from the ability to combine pre-
existing connections among objects, individuals, and
viously disjoint data (e.g., location, search query, and
organizations (e.g., Siggelkow and Terwiesch 2019).
social network) to answer questions that were for-
From the one-to-one connectivity of email or text
merly impossible to address.
messaging to the many-to-many connectivity of social
For example, combining multiple types of data on
media, e-commerce platforms and sensor-embedded
individuals changes what we can say about their
production lines today instantiate the enormous in-
health risks or their financial soundness. Combining
crease in potential connections among economic actors
data related to human resources with traditional
and inputs into economic decision making. The sheer
supply chain data provides managers an unprece-
size and density of the network of connections as well
dented opportunity to understand their internal or-
as the range and number of new actors who are part
ganization and its constituents. Enhancing such syn-
of the network of connectivity are the first major ef-
ergies explains the drive toward diversification and
fects of digitization. Greater network density has
the blurring of boundaries at firms such as Oracle
generally followed Metcalfe’s law in yielding expo-
and SAP. While that is an energizing vision for many,
nentially greater network value (e.g., Metcalfe 2013).
it has a few dystopian shades as well. Governments
The quantitative explosion of connected points has
can now have more information regarding their citi-
enabled the emergence of completely new business
zens than they ever could in the past, raising a specter
and organizational models, some of which have can-
of Orwellian observation and control. Similar con-
nibalized their nondigital equivalents.
cerns could apply to the relationship between cor-
However, the shift from connectivity-on-demand
porations and their employees. The new corollary to
to connectivity-by-default has resulted in a qualita-
Star Trek’s Borg mantra of “you will be assimilated”
tive change that goes beyond quantitative increases
may be “your data will be aggregated.”
in network density. As products and services become
more digitized, every product or service can be used 2.4. Interactions
to facilitate connections. This transition to always-on While each of these effects of digitization is signifi-
connectedness enables revolutions in search, moni- cant, truly dramatic changes become visible when
toring, and control. For example, whereas the success they interact and reinforce each other. For example,
of a search used to be assessed in terms of accuracy connectivity and aggregation, in conjunction, un-
and comprehensiveness of results (whether in the derlie a host of new business models such as Trip-
search engine battles between Google and Yahoo or Advisor, Napster, Groupon, Yelp, and the iTunes
the knowledge management system quest for infor- store. In each of these instances, a combination of
mation retrieval), search success is now assessed in enhanced connectivity and data aggregation has
terms of context-specific relevance—“is it right” ver- produced new functionality and opportunities for
sus “is it right for me, right here, right now.” Whether value creation and capture. Advances in connectiv-
from the perspective of a consumer engaged in in- ity and representation produced intelligent social
formation search or a producer engaged in information media platforms such as Facebook, WeChat, and
targeting, the challenge has shifted from broadening LinkedIn, where recommendations are made for
the search space to assure more comprehensiveness potentially useful connections among individuals
to an ever greater urgency to winnow down infor- who may not even be aware of each other’s exis-
mation and choices into manageable sets. tence. Within organizations, messaging platforms
Indeed, as the digital revolution has shattered the such as Yammer and Slack attempt to bring the same
constraints of information search and availability, it benefits. Combinations of representation and aggre-
has heightened the constraints of deliberation and gation form the backbone of the dramatic increase in
choice (Rangan 2000). Questions of “what do I erase” consumer analytics (including credit scoring) as well
and “what do I ignore” have become critical. It is as the burgeoning field of organizational analytics.
perhaps not coincidental that the phrase “TMI,” or When connectivity, aggregation, and representation
“too much information,” made its first appearance all come together, we see developments such as self-
in the Oxford English Dictionary in 2009. Thus, how driving cars, the Internet of things, Spotify, and the
firms allocate their attention has become a more Chinese state’s social credit system.
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