Reading 1................................................................................. 3
Adner, R., Puranam, P., & Zhu, F. 2019. What is different about digital
strategy? From quantitative to qualitative change. Strategy Science,
4(4): 253–261..................................................................................3
Reading 2................................................................................. 6
Hanelt, A., Bohnsack, R., Marz, D., & Antunes Marante, C. 2021. A
systematic review of the literature on digital transformation: Insights
and implications for strategy and organizational change. Journal of
Management Studies, 58(5), 1159–1197...........................................6
Reading 3................................................................................. 9
Hanisch, M., & Goldsby, C. 2025. Digital platform strategies: Strategic
positioning and competitive dynamics in network-based markets. In
V. Slettli & S. Baumann (Eds.), Value Creation in Digital Platforms and
Business Ecosystems: Networks, Relationships and the Platform
Markets. Routledge, forthcoming.....................................................9
Reading 4............................................................................... 14
Zhu, F., & Iansiti, M. 2019. Why some platforms thrive and others
don’t. Harvard Business Review, 97(1): 118–125.............................14
Reading 5............................................................................... 16
Hanisch, M., Goldsby, C. M., Fabian, N. E., & Oehmichen, J.
2023. Digital governance: A conceptual framework and research
agenda. Journal of Business Research, 162: 1–13.............................16
Reading 6............................................................................... 20
Hanisch, M., Goldsby, C. M., Fortin, M., & Rogerson, M.
2025. Governing Within Semirigid Limits: Navigating the
Centralization–Decentralization Paradox in Blockchain‐Based
Platforms. Journal of Operations Management, 71(7): 917–938.........20
Reading 7............................................................................... 23
Muñoz-Flores, C. H., & Olivella-Nadal, J. 2021. Enablers and inhibitors
for IoT implementation. Internet of Things: Cases and Studies, 25-48.
....................................................................................................23
Reading 8............................................................................... 30
DalleMule, L. & Davenport, T.H. 2017. What’s your data strategy.
Harvard Business Review, 95(3): 112-121.......................................30
Reading 9............................................................................... 34
Hutzschenreuter, T., & Lämmermann, T. 2024. What is your AI
strategy? Systematically integrating self-learning technologies into
your business strategy. Academy of Management...........................34
Perspectives, (in-Press).................................................................34
Reading 10.............................................................................39
, Kemp, A. 2024. Competitive advantage through artificial intelligence:
Toward a theory of situated AI. Academy of Management Review,
49(3): 618-635...............................................................................39
Case study.............................................................................. 43
Austin, R.D. & Pelow, G. (2019). Digital transformation at GE: What
went wrong? Harvard Business School............................................43
,Reading 1
Adner, R., Puranam, P., & Zhu, F. 2019. What is different about
digital strategy? From quantitative to qualitative change. Strategy
Science, 4(4): 253–261.
Introduction
The paper addresses the way digital technology reshapes the competitive landscape.
Traditionally, digital progress has been understood through quantitative advances such as
Moore’s Law (exponential growth in processing power) and Metcalfe’s Law (value of
networks grows exponentially with number of users). The authors argue that the current
phase of digital transformation is characterized by a qualitative shift, fundamentally altering
how firms create and capture value.
They identify three core processes that embody these qualitative changes:
Representation: how information and data are digitally encoded.
Connectivity: how digital technologies create and enhance connections.
Aggregation: how diverse data are combined to generate new insights.
These processes interact to produce novel strategic challenges and opportunities, requiring a
re-examination of existing strategic frameworks.
Core Digital Processes
Representation
The shift from analogue to digital representation enables information to be encoded in binary
formats (e.g., CDs replacing tapes).
Modern representation includes the digitization of previously non-data phenomena, such as
the on/off status of devices or geolocation data.
Machine learning exemplifies a new form of representation where algorithms approximate
functions without explicit human guidance, sometimes resulting in insights that are difficult
for humans to interpret.
Machine learning = a form of function approximation that enables representing large volumes
of data and the actionable insights they contain in algorithmic forms without the need for
human-guided selection of functional forms.
This shift challenges traditional human-bounded rationality and raises questions about
managing "competence without comprehension".
Connectivity
Digital transformation massively expands the number and density of connections among
people, devices, and organizations.
The move from connectivity-on-demand to connectivity-by-default means products and
services are always connected. This shift allows for revolutions in search, monitoring, and
, control, changing how information is accessed and utilized. For example, search success has
shifted from broad comprehensiveness to context-specific relevance, emphasizing “is it right
for me, right here, right now” rather than just “is it right.” This always-on connectedness also
creates new challenges, such as managing information overload and deciding what
information to ignore or erase.
This enables revolutions in search (context-specific relevance), monitoring, and control, but
also intensifies the challenge of information overload and choice complexity.
Firms must now strategically manage attention allocation amid "too much information"
(TMI).
Aggregation
Aggregation refers to combining disparate data types (e.g., location, social networks, search
queries) to answer previously impossible questions. This enables richer analytics, such as
health risk assessments or credit profiling.
Aggregation drives blurring boundaries between firms and sectors, raising issues related to
privacy, data ownership, and surveillance.
The interaction of aggregation with connectivity and representation underpins many
transformative business models (e.g., Spotify, social media platforms).
Interactions Among the Three Processes
The interplay among representation, connectivity, and aggregation creates powerful positive
feedback loops. For example, digital twins in manufacturing use real-time connectivity,
aggregated data from multiple sources, and algorithmic representation to enable predictive
maintenance and virtual experimentation. These complementarities accelerate digital
transformation far beyond incremental improvements.
Processes combined:
Connectivity and aggregation together underpin new business models such as
TripAdvisor, Napster, Groupon, Yelp, and iTunes, where enhanced connectivity
allows for widespread sharing and aggregation combines previously disjoint data to
generate useful insights.
Representation combined with connectivity has produced intelligent social media
platforms like Facebook, WeChat, and LinkedIn, where algorithmic recommendations
help users connect with others they might not have known.
Representation and aggregation together form the basis for increased consumer
analytics and organizational analytics, such as credit scoring.
When all three come together, they enable advanced developments like self-driving
cars, the Internet of Things, Spotify’s recommendation services, and the Chinese
social credit system.
Digital twins = digital representations or simulation models of physical processes or objects,
such as the wear and tear of a jet engine.
Strategic Implications
Resource-Based View (RBV): Data and Algorithms as Strategic Resources
Adner, R., Puranam, P., & Zhu, F. 2019. What is different about digital
strategy? From quantitative to qualitative change. Strategy Science,
4(4): 253–261..................................................................................3
Reading 2................................................................................. 6
Hanelt, A., Bohnsack, R., Marz, D., & Antunes Marante, C. 2021. A
systematic review of the literature on digital transformation: Insights
and implications for strategy and organizational change. Journal of
Management Studies, 58(5), 1159–1197...........................................6
Reading 3................................................................................. 9
Hanisch, M., & Goldsby, C. 2025. Digital platform strategies: Strategic
positioning and competitive dynamics in network-based markets. In
V. Slettli & S. Baumann (Eds.), Value Creation in Digital Platforms and
Business Ecosystems: Networks, Relationships and the Platform
Markets. Routledge, forthcoming.....................................................9
Reading 4............................................................................... 14
Zhu, F., & Iansiti, M. 2019. Why some platforms thrive and others
don’t. Harvard Business Review, 97(1): 118–125.............................14
Reading 5............................................................................... 16
Hanisch, M., Goldsby, C. M., Fabian, N. E., & Oehmichen, J.
2023. Digital governance: A conceptual framework and research
agenda. Journal of Business Research, 162: 1–13.............................16
Reading 6............................................................................... 20
Hanisch, M., Goldsby, C. M., Fortin, M., & Rogerson, M.
2025. Governing Within Semirigid Limits: Navigating the
Centralization–Decentralization Paradox in Blockchain‐Based
Platforms. Journal of Operations Management, 71(7): 917–938.........20
Reading 7............................................................................... 23
Muñoz-Flores, C. H., & Olivella-Nadal, J. 2021. Enablers and inhibitors
for IoT implementation. Internet of Things: Cases and Studies, 25-48.
....................................................................................................23
Reading 8............................................................................... 30
DalleMule, L. & Davenport, T.H. 2017. What’s your data strategy.
Harvard Business Review, 95(3): 112-121.......................................30
Reading 9............................................................................... 34
Hutzschenreuter, T., & Lämmermann, T. 2024. What is your AI
strategy? Systematically integrating self-learning technologies into
your business strategy. Academy of Management...........................34
Perspectives, (in-Press).................................................................34
Reading 10.............................................................................39
, Kemp, A. 2024. Competitive advantage through artificial intelligence:
Toward a theory of situated AI. Academy of Management Review,
49(3): 618-635...............................................................................39
Case study.............................................................................. 43
Austin, R.D. & Pelow, G. (2019). Digital transformation at GE: What
went wrong? Harvard Business School............................................43
,Reading 1
Adner, R., Puranam, P., & Zhu, F. 2019. What is different about
digital strategy? From quantitative to qualitative change. Strategy
Science, 4(4): 253–261.
Introduction
The paper addresses the way digital technology reshapes the competitive landscape.
Traditionally, digital progress has been understood through quantitative advances such as
Moore’s Law (exponential growth in processing power) and Metcalfe’s Law (value of
networks grows exponentially with number of users). The authors argue that the current
phase of digital transformation is characterized by a qualitative shift, fundamentally altering
how firms create and capture value.
They identify three core processes that embody these qualitative changes:
Representation: how information and data are digitally encoded.
Connectivity: how digital technologies create and enhance connections.
Aggregation: how diverse data are combined to generate new insights.
These processes interact to produce novel strategic challenges and opportunities, requiring a
re-examination of existing strategic frameworks.
Core Digital Processes
Representation
The shift from analogue to digital representation enables information to be encoded in binary
formats (e.g., CDs replacing tapes).
Modern representation includes the digitization of previously non-data phenomena, such as
the on/off status of devices or geolocation data.
Machine learning exemplifies a new form of representation where algorithms approximate
functions without explicit human guidance, sometimes resulting in insights that are difficult
for humans to interpret.
Machine learning = a form of function approximation that enables representing large volumes
of data and the actionable insights they contain in algorithmic forms without the need for
human-guided selection of functional forms.
This shift challenges traditional human-bounded rationality and raises questions about
managing "competence without comprehension".
Connectivity
Digital transformation massively expands the number and density of connections among
people, devices, and organizations.
The move from connectivity-on-demand to connectivity-by-default means products and
services are always connected. This shift allows for revolutions in search, monitoring, and
, control, changing how information is accessed and utilized. For example, search success has
shifted from broad comprehensiveness to context-specific relevance, emphasizing “is it right
for me, right here, right now” rather than just “is it right.” This always-on connectedness also
creates new challenges, such as managing information overload and deciding what
information to ignore or erase.
This enables revolutions in search (context-specific relevance), monitoring, and control, but
also intensifies the challenge of information overload and choice complexity.
Firms must now strategically manage attention allocation amid "too much information"
(TMI).
Aggregation
Aggregation refers to combining disparate data types (e.g., location, social networks, search
queries) to answer previously impossible questions. This enables richer analytics, such as
health risk assessments or credit profiling.
Aggregation drives blurring boundaries between firms and sectors, raising issues related to
privacy, data ownership, and surveillance.
The interaction of aggregation with connectivity and representation underpins many
transformative business models (e.g., Spotify, social media platforms).
Interactions Among the Three Processes
The interplay among representation, connectivity, and aggregation creates powerful positive
feedback loops. For example, digital twins in manufacturing use real-time connectivity,
aggregated data from multiple sources, and algorithmic representation to enable predictive
maintenance and virtual experimentation. These complementarities accelerate digital
transformation far beyond incremental improvements.
Processes combined:
Connectivity and aggregation together underpin new business models such as
TripAdvisor, Napster, Groupon, Yelp, and iTunes, where enhanced connectivity
allows for widespread sharing and aggregation combines previously disjoint data to
generate useful insights.
Representation combined with connectivity has produced intelligent social media
platforms like Facebook, WeChat, and LinkedIn, where algorithmic recommendations
help users connect with others they might not have known.
Representation and aggregation together form the basis for increased consumer
analytics and organizational analytics, such as credit scoring.
When all three come together, they enable advanced developments like self-driving
cars, the Internet of Things, Spotify’s recommendation services, and the Chinese
social credit system.
Digital twins = digital representations or simulation models of physical processes or objects,
such as the wear and tear of a jet engine.
Strategic Implications
Resource-Based View (RBV): Data and Algorithms as Strategic Resources
