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Summary MTC: Managing Technological Change
- Instelling
- Rijksuniversiteit Groningen (RuG)
Alle research papers samengevat voor de toets op 26 maart 2024.
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Lecture 1 IntroductionCompulsory literature:
Kapoor, R., & Klueter, T. (2021). Unbundling and managing uncertainty
surrounding emerging technologies Strategy Science (1), 62-74.
In this article, we view emerging technologies through the lens of innovating firms expanding
significant resources to develop and commercialize those technologies.
Although there is widespread recognition that emerging technologies are characterized by a
high degree of uncertainty as to whether, when, and how they will evolve, we lack a
framework with which to characterize the sources of that uncertainty and their implications
for firms and their strategies. Extant literature has generally considered uncertainty as an
aggregate feature of the technological context. But, when simply treating uncertainty as high
or characterized by many unknowns, we limit the role that managers can play with respect to
sensing and responding to opportunities and threats and ensuring their firms’
competitiveness in the face of technological change
Definition of uncertain surroundings.
We define uncertainty surrounding the emergence of a new technology as a general lack of
knowledge regarding that technology`value creation.
However, when it comes to managing uncertainties, firms face a “knowledge problem,” which
needs to be addressed by developing specific types of dynamic capabilities These include
sensing capabilities that facilitate the identification of new opportunities, while recognizing
the underlying challenges, and seizing capabilities that facilitate resource-allocation
decisions and strategic commitments toward the emerging technology.
Emerging technologies
Represent novel technological innovations with the potential to become dominant in their
respective industries. An important feature of emerging technologies is that there is
significant uncertainty, which stems from a lack of knowledge regarding the economic
potential of the new technology.
For an emerging technology, the value creation begins with the technology being deployed in
a given application domain for a set of users by focal firms using a specific business model.
In addition to focal firms, several actors in the ecosystem contribute to the technology’s
value creation. Each of these drivers of value creation can also represent an important
source of uncertainty.
Dynamic capabilities
Which is premised on managers having superior capabilities with respect to sensing the
threats and opportunities, and seizing on the opportunities.
3.1. Focal Technology
During the emergence of a technology, it is often challenging to understand how the
technology will perform and to accurately assess the effort and time necessary to achieve a
technological advance. Multiple alternative technologies and the industry’s prevailing
technologies may evolve at the same time. This makes it very difficult to predict how the
,technology would improve over time as a function of the effort expended and whether those
improvements would be adequate to generate performance superiority over prevailing
technologies.
3.2. Application
Another important source of uncertainty underlying a technology’s emergence is associated
with a lack of information about possible applications where the new technology can be
successfully deployed. Emerging technologies often have properties whose utility may not
be immediately obvious, making it difficult to determine in which application a technology will
create the most value.
General purpose technologies (GPTs)
Are an important example of emerging technologies, which have the potential to serve
multiple applications in a wide range of market sectors.
“enabling technologies” or “general technologies”
Highlight technologies that may not have as wide an economic impact as GPTs, but can still
be used across several applications
For such technologies, the value-creation potential within an application is typically not
established ex ante, but requires substantial efforts over time.
3.3. User
Within a given market application, user uncertainty results from a lack of information
regarding users’ preferences and the way users will adopt an emerging technology. It is
often unclear how potential users perceive the benefits associated with emerging
technologies and if they are willing to incur the cost of adopting that technology and when
they will decide to adopt the technology.
S-Shape: (Was in the lecture)
A pattern observed for new technologies is that when the number of cumulative users of a
new technology is plotted versus time, the curve typically resembles an S-shape, with a slow
initial rate of adoption by a small set of users, which could then accelerate to entail a large
set of users before gradually slowing as the market becomes saturated
3.4. Ecosystem
Beyond the industry participants originating and developing an emerging technology, a range
of actors in the ecosystem contribute to an emerging technology’s value creation.
Ecosystem uncertainty is rooted in whether and how the set of actors and the associated
activities in the ecosystem can contribute to the technology’s value proposition.
In many cases, the commercialization of an emerging technology is dependent on innovation
by other actors in the ecosystem. These
may be suppliers, whose components need to be integrated in a focal offer underlying an
emerging technology, or complementors, who provide additional products and services to
increase the focal offer’s value creation. Suppliers and complementors may need to innovate
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,with respect to the technologies (e.g., improving the performance of batteries for electric
cars) or the business model (e.g., developing a service plan for charging infrastructure).
Finally, ecosystem participants may also need to agree on new technological standards to
bring about the emerging technology’s value proposition. In the absence of these
innovations, bottlenecks may arise, constraining the commercial viability of an emerging
technology.
3.5. Business Model
Business model uncertainty stems from the question of how firms appropriate value for their
products, or the “profit equation,” which will be used to commercialize an emerging
technology. The business model specifies the logic of how the business surrounding the
emerging technology creates and delivers value to customers and outlines the breakdown of
revenues, costs, and profits associated with delivering that value. In many cases, the
prevailing business model within an industry may not be viable, and new business models
may have to be developed that may entail changes to who performs the underlying activities
or how and when users pay for the products and services associated with the emerging
technology
4. Interactions Among Different Sources of Uncertainty
Sources of uncertainty may interact.
Explicating the interactions among different sources of uncertainties can help shed light on
how and when the overall uncertainty surrounding the emerging technology may be resolved
Pooled interactions are present when any two sources of uncertainty are relatively
independent of each other—that is, the resolution of uncertainty in one source has little or no
effect on the resolution of uncertainty in another source. Although the sources of uncertainty
are independent of each other, they have an aggregate effect on the overall likelihood that
an emerging technology gets commercialized, as each source of uncertainty may
independently limit commercialization.
For pooled uncertainty, the sources of uncertainty are independent of one another, so each
can be addressed individually. However, the challenge of pooled uncertainty is that all
sources of uncertainty may matter for value creation for the emerging technology. Using a
probabilistic notion, pooled interactions result in a multiplicative effect, wherein each source
of uncertainty can affect the likelihood of emerging technology’s commercialization
Sequential interactions point to interdependence between two tasks, in which one is an
input for the other— that is, one task must be completed before the other can be undertaken.
Applying this lens to sources of uncertainty underlying an emerging technology suggests
that, for sequential interactions, one uncertainty needs to be resolved prior to the resolution
of another
In the case of sequential interaction, reducing uncertainty in one source can directly impact
the subsequent resolution of another source of uncertainty. In probabilistic terms, there is
conditional dependence between the two sources of uncertainty that corresponds to a
Bayesian updating process
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, Reciprocal interactions
When the resolution of one source of uncertainty impacts the resolution of another source
and vice versa, we consider this to be a reciprocal interaction. Reciprocal interactions create
feedback loops. Addressing one source of uncertainty shapes the resolution of another
source of uncertainty, which then feeds back to the original source of uncertainty.
When sources of uncertainties interact in a reciprocal way, it is challenging to know and
anticipate when one source of uncertainty has sufficiently been resolved. There is no easy
calculus that allows for incorporating such dynamic models of feedback cycles, and,
accordingly, such interactions may be most difficult to manage
5. Management of Uncertainties
In terms of managing uncertainties, however, there is little information available regarding
alternatives and outcomes, and the strategic-planning process is much less reliable.
Accordingly, superior capabilities with respect to sensing and seizing of opportunities are of
fundamental importance.
Sensing
“filtering of technological, market, and competitive information from both inside and outside
the enterprise, making sense of it, and figuring out implications for action”
Once sensed, firms need to seize the opportunities presented by emerging technologies and
make strategic commitments under uncertainty.
Seizing
requires taking actions such as the commitment and allocation of resources and
organizational efforts toward emerging opportunities and the timing of such actions.
Managers are constrained in terms of attention, and their perception, reasoning, and
problem solving tend to be biased by prior experiences and prevailing business models.
Prior literature has highlighted the significant challenges for firms in perceiving the threats
and the opportunities surrounding emerging technologies and undertaking strategic
commitments. Underlying these challenges are managerial cognitive processes that relate to
attention, perception, reasoning, and problem solving. Managers are constrained in terms of
attention, and their perception, reasoning, and problem solving tend to be biased by prior
experiences and prevailing business models.
The proposed structured approach to unbundling the different sources of uncertainty and
recognizing the interactions among them can also be integrated with prominent managerial
practices for managing uncertainty and provide firms with a basis for improving their sensing
and seizing capabilities.
Scenario planning
is a popular practice, in which managers identify the most relevant uncertainties in contexts
when predicting the future is not possible Rather than describe all possibilities, the
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