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Summary all mandatory articles Digital Transformation Strategy
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Summary of 31 pages for the course Digital Transformation Strategies at RuG (-)
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Summary Digital Transformation StrategiesYear: 2023 – 2024
W1: 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.
- 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.
W2: 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.
- Zhu, F., & Iansiti, M. 2012. Entry into platform-based markets. Strategic Management Journal,
33(1): 88–106.
W3: Big Data and Business Analytics
- DalleMule, L. & Davenport, T.H. 2017. What’s your data strategy. Harvard Business Review,
95(3): 112-121.
- McAfee Andrew, & Brynjolfsson Erik. 2012. Big data: The management revolution. Harvard
Business Review, 90(10): 60–66.
W4: Digital Governance
- Hanisch, M., & Goldsby, C. M. 2022. The boon and bane of blockchain: Getting the governance
right. California Management Review, 64(3): 141–168.
- 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.
W5: Artificial Intelligence and Automation
- Brynjolfsson, E. & Mitchell, T. 2017. What can machine learning do? Workforce implications.
Science, 358(6370): 1530-1534.
- Stadler, C., & Reeves, M. 2023. Chatting about strategy. BCG Henderson Institute. Retrieved
from https://bcghendersoninstitute.com/chatting-about-strategy
W6: Digital Organizational Design and Change Management
- Muñoz-Flores, C. H., & Olivella-Nadal, J. (2021). Enablers and inhibitors for IoT implementation.
Internet of Things: Cases and Studies, 25-48.
- Austin, R.D. & Pelow, G. (2019). Digital transformation at GE: What went wrong? Harvard
Business School
,W1: 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.
The authors propose that digitization shifted from quantitative improvements to qualitative changes,
requiring new strategic principles for technology transitions.
Quantitative acceleration = rapid increase in the volume and speed of data processing and transfer
enabled by digital technologies. It encompasses the exponential growth in digital capabilities, such as
processing power, data storage, and network bandwidth, that facilitate faster and more efficient
handling of large amounts of data.
The authors identify 3 foundational processes underlying digital transformation:
• Representation: Digitization enables the algorithmic manipulation of digital information. The
qualitative revolution in data representation is evident from converting physical data into
digital format. The ubiquity of sensor technology expands digital representation, while
machine learning represents data algorithmically, posing challenges for human-guided
interpretation and bounded rationality.
• Connectivity: Digitization creates new connections and enhances existing connections among
objects, individuals, and organizations, impacting network density and value. The transition
from connectivity-on-demand to connectivity-by-default represents a qualitative change. This
shift facilitates revolutions in search, monitoring, and control, moving from broadening search
space to context-specific relevance and intensifying the constraints of deliberation and choice.
How firms allocate their attention has become a crucial strategic decision.
• Aggregation: Beyond quantitative data growth, this qualitative shift arises from the ability to
combine previously disjoint data to answer questions that were formerly impossible to
address. This capability enables unique assessments in areas like health risks and financial
soundness, influencing organizational understanding and management, while also raising
concerns about privacy and control.
Interaction:
“As connectivity and aggregation erode transaction costs (and in turn accelerate as transaction 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 process these
data and mitigate the constraints of human-bounded rationality.”
There may also be strong complementarities between these processes, as the development in one
increases the value of the other. Dramatic changes occur when representation, connectivity, and
aggregation interact, forming the basis of new business models and value creation.
Implications:
• RBV: data and algorithms as self-generating resources:
Data and algorithms as self-generating resources are considered ultimate scale-free resources.
Autogenic data generation: where interaction with data creates new data and challenges
traditional resource value, rarity, and substitutability criteria.
Fungibility (= replaceable) of data: A smaller decline in value indicates higher fungibility.
• Data, ownership, and factor markets: “Who should own the data? Ownership of data
generated by digital devices, like cars, raises questions about consent and control. Enhanced
connectivity challenges traditional boundaries of information flow and IP protection,
underscoring the managerial challenges of differential information pathways (where to block
, its flow and where to enable it). Contracting challenges since data can be reused, repackaged,
and resold ad infinitum.
• Digitization, replication, and super-scalable business models: Digital format allows error-free,
costless replication, leading to scalable business models. Connectivity and aggregation
enhance scalability, as seen in smart speakers that leverage user data to improve functionality
and create barriers to entry. As you accumulate more data from each user, the product
becomes more intelligent and attracts more users, enjoys higher scalability, this is results in a
positive feedback loop.
• Digital transformation of firm scope: The digital transformation of firm scope, driven by the
fungibility of digital assets like software capability and data analytics, creates opportunities
across multiple markets, leading to blurred industry boundaries. This challenges traditional
diversification theories and requires a re-examination of relatedness concepts, as firms face
competition from outside their industries and leverage digital asymmetries. Corporate strategy
researchers are encouraged to develop and test theories that reflect the extreme fungibility of
data due to aggregation and algorithmic representation.
• Digital transformation and internal organization of firms:
Digital transformation creates opportunities for "algorithmic management," augmenting or
automating managerial work. Digital transformation challenges hierarchical control, as
connectivity implies managers cannot rely solely on information access for authority, but on
abilities to lead and manage. Extreme connectivity fosters alternatives to hierarchical
organizing, like online communities for innovation, showing the potential of purely algorithmic
solutions for organizational problems like division of labor and effort integration.
• Organizational sensemaking in an algorithmic world:
Algorithmic extraction of actionable predictions represents a major shift in data use, moving
from enhancing human perception to prediction, which may not involve human
comprehension. Managers might need to prioritize predictive needs over understanding,
balancing this against ethical concerns and regulatory constraints. There is an increased need
for clear narratives in an environment of complex algorithmic decision-making but ultimate
human responsibility. These developments raise philosophical and ethical issues, such as the
implications of using algorithms for hiring, retention, and promotion, and the risks of
institutionalized discrimination and coercion.
Conclusion:
The article concludes that while traditional strategic concepts remain valuable, a new framework is
needed to understand digital transformation's impact. This includes a focus on representation,
connectivity, and aggregation processes and their interactions, which are pushing firms to innovate in
value creation, business models, and organizational management.
The article highlights the nuanced shifts in digital strategy, emphasizing the qualitative changes brought
about by digitization and its implications on firm strategy and organization. The interactions among
representation, connectivity, and aggregation form the foundation of these strategic shifts, suggesting
a need for a re-evaluation of traditional strategic principles in the digital era.
, 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.
Abstract:
In this article, we provide a systematic review of the extensive yet diverse and fragmented literature
on digital transformation (DT), with the goal of clarifying boundary conditions to investigate the
phenomenon from the perspective of organizational change. On the basis of 279 articles, we provide
a multi-dimensional framework synthesizing what is known about DT and discern two important
thematical patterns:
DT is moving firms to malleable organizational designs that enable continuous adaptation;
and this move is embedded in and driven by digital business ecosystems.
From these two patterns, (with the dimensions of context and process) we derive four perspectives
on the phenomenon of DT:
technology impact, compartmentalized adaptation, systemic shift, and holistic co-evolution.
Linking our findings and interpretations to existing work, we find that the nature of DT is only partially
covered by conventional frameworks on organizational change. On the basis of this analysis, we derive
a research agenda and provide managerial implications for strategy and organizational change.
We define DT as: “Organizational change that is triggered and shaped by the wide- spread diffusion of
digital technologies.”
DT differs from IT:
1. Technologies involved, such as big data analytics, social media, mobile technology, or cloud
computing, seem very different from earlier IT.
2. Many digital technologies cannot be restricted to the boundaries of specific firms or industries
but involve a wider ecosystem and the demand side, so not just companies but by anyone.
3. The consequences of DT – such as the emergence of new digital business models even in non-
digital industries – seem to extend beyond those of previous phases of IT-enabled change,
which were usually related to the practice level and rather incremental change within firms.
DT seems to have a more intricate and encompassing connection to the topic of organizational change,
requiring a broader view of and comparison with the literature on organizational change and
innovation.
Findings are particularly part of the distinction in organizational change literature between episodic &
continuous change perspectives:
o Episodic position: relates to infrequent and intentional organizational change.
o Continuous position: assumes ‘ongoing, evolving and cumulative’ change.
Although DT leads to an overall shift towards continuous change, this shift can be triggered and shaped
by episodic bursts.
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