Organizing for innovation 2025 - 2026
Lecture 1
How do organizations influence information processing:
Organizations are goal-oriented (shared goals)
Organizational roles and culture
Division of labor and integration of effort
Information flows and knowledge transfer
Incentivicing collaboration
Coordination of activities via routines and capabilities
Organizations = systems of coordinated action among individuals and groups whose
preferences, information, interests or knowledge differ. E.g.: universities, companies,
government agencies, non-profits, open source software development.
Organizations can be designed
Organizations can be designed by adjusting design elements(a.k.a. Managerial levers) such as
structure(hierarchy), centralization of decision making, formalization of processes, task
structures and task allocation(modularity and interdependencies), incentives, cultures, etc.
All these elements can be changed, some more easily than others.
Organization design influences performance and how well organizations function.
Organization > innovation
How do these organization design choices influence innovation outcomes? 3 outcomes
- Innovation outcomes: quantity, quality, speed, efficiency.
- Types of innovation: incremental, radical, novel, breakthrough, explorative, exploitative,
systemic.
- Why some organizational design choices are better or worse for specific innovation
outcomes under certain conditions(contingencies)?
Article 1
Decomposability
Means how much a system (here, a firm’s knowledge base) can be divided into independent
parts (modules or components).
- High decomposability = components interact little → easier to change one part without
breaking others.
- Low decomposability = components interact a lot → tightly coupled system.
In knowledge terms:
, - Highly decomposable = knowledge domains are separate (e.g., software and hardware
engineers rarely need to coordinate deeply).
- Low decomposable = domains are interdependent (e.g., chemistry and materials science
deeply intertwined).
Knowledge-base malleability = How easily a firm can reconfigure and combine its existing
knowledge to create new inventions. A more malleable knowledge base → easier future
innovation.
We noemen het vermogen van een kennisbasis om te veranderen kneedbaarheid (malleability),
waarvan wij stellen dat dit wordt bepaald door de decomposabiliteit.
High decomposability = high malleability > supports exploration
Low decomposability = low malleability > supports exploitation
There is an inverted-U relationship between decomposability and invention usefulness.
→ Moderate decomposability leads to inventions that are more widely cited (more valuable).
Too low decomposability → too rigid, hard to recombine → low usefulness.
exploitation-dominant (deep integration, efficiency, but rigid).
Too high decomposability → too modular, little synergy → also low usefulness.
exploration-dominant (flexibility, experimentation, but less coherence).
Moderate decomposability → best balance of flexibility and integration → highest usefulness.
optimal balance, enabling firms to integrate diverse domains (exploitation) while still allowing
recombination (exploration).
There is a positive relationship between decomposability and knowledge-base malleability.
→ The more decomposable the knowledge base, the easier it is for the firm to reconfigure and
innovate in new directions.
Firms with high decomposability → more adaptable in the future (malleable) but produce less
immediately useful inventions.
Firms with moderate decomposability → produce highly useful inventions but are less flexible
later.
→ There’s a tension between short-term usefulness and long-term adaptability.
Define decomposability and explain why it matters for innovation
Decomposability refers to the extent to which a system’s components can be separated and
modified independently without disrupting the functioning of the whole system.
In the context of organizational knowledge, it reflects how interdependent or modular different
knowledge domains are within a firm’s knowledge base.
Yayavaram & Ahuja argue that decomposability shapes innovation because it determines how
easily firms can combine, recombine, or modify their knowledge to create new inventions.
,Highly decomposable knowledge bases enable flexibility and experimentation (easy to change
parts).
Low decomposability makes change difficult because components are tightly coupled.
Moderate decomposability balances both—allowing integration across domains while retaining
adaptability.
Yayavaram & Ahuja identify a fundamental trade-off:
Moderate decomposability → produces the most useful inventions but limits future adaptability
(less malleable).
High decomposability → increases malleability (flexibility for future innovation) but yields less
useful current inventions.
This means firms must choose strategically:
Focus on current innovation impact (exploit integration) or;
Prioritize future adaptability (explore modular recombination).
Fundamental trade-off is: You cannot simultaneously maximize short-term invention
usefulness and long-term adaptability. Firms must choose or balance between producing
immediately impactful innovations and maintaining a knowledge structure that allows flexibility
for future innovation.
Knowledge bases that are nearly decomposable (Simon, 1962), relative to more integrated or
fully decomposable knowledge bases, should lead to outcomes such as enhanced innovation
quality.
There are important differences between how decomposability affects products and
organizations and how it affects knowledge structures. For instance, in products,
decomposability is used to buffer one module from another and to eliminate ripple effects.
Eliminating such ripple effects in products ensures several benefits, such as stability of product
design, more reliable performance, and enabling the repair of individual modules.
Specialization fosters deep understanding of a specific area, ease of use of repeated application
of a few elements, and superior knowledge of the interconnections between a set of elements,
as well as of the problems in connecting the elements to each other. Broad exploration provides
exposure to new ideas, innovative applications, and distinctive new variations and combinations
of a given set of elements.
Extremely low levels of decomposability will limit search breadth and make effective
recombination of any newly identified elements into successful inventions more complex and
difficult. Moderate levels of decomposability should correct these deficiencies by allowing
enhanced exploration and providing mechanisms to link the new knowledge discovered through
broad search with the expertise born of specialization; they should also simplify the invention
task, making effective combinations possible. But extremely high levels of decomposability,
while introducing sufficient exploration, would provide no integration mechanisms to link the
, results of this exploration across clusters, limiting the likelihood of successful recombination and
invention.
Complex systems become progressively more difficult to improve upon as the number of
interdependent elements increases.
Relative to integrated knowledge bases, nearly decomposable knowledge bases can therefore
lead to improvements in both specialization and knowledge depth, as well as broader search
and enhanced exploration. Nearly decomposable knowledge bases let each part specialize and
develop deep expertise (because components are independent) while still being loosely
connected, which allows the firm to explore new combinations and search broadly without
disrupting the whole system.
In fully decomposable knowledge bases, clusters are independent with no integration, so
changes in one cluster may affect others without staff in other clusters being aware, leading to
missed innovation opportunities, vulnerability to harmful changes, and lack of coordinated
problem-solving. In contrast, nearly decomposable knowledge bases maintain integrative links
that enable knowledge transfer, joint search across clusters, and allow decision-makers to
balance or mitigate negative effects, combining flexibility with enough integration for effective
innovation.
Nearly decomposable knowledge bases that permit both breadth and depth in search, and thus
provide the benefits of both exploration within a cluster and exploration across clusters, highly
integrated knowledge bases limit exploration of new configurations or breadth of search, while
highly modular structures permit depth in individual clusters but fail to
provide a mechanism for linking knowledge across clusters. provide a mechanism for linking
knowledge across clusters. If the knowledge bases of all firms have a nearly decomposable
structure, and if all achieve the optimal level of decomposability, then there would be no
performance differences between firms with respect to their technological search. But firms are
likely to err on both sides and choose a level of decomposability that is higher or lower than the
optimal level.
In firms with highly integrated or non-decomposable knowledge bases, exploration is limited
because the threshold of accepting change is high. Further, the high level of coupling between
knowledge elements also suggests that there may be few conduits for receiving external and
new knowledge. Given this lack of exploration, firms are not required to, and are unable to,
update their beliefs about interdependencies. They can continue their technology search with
their current set of couplings, and thus a highly integrated knowledge base is likely to result in
little change in the knowledge-base structure.
Highly decomposable knowledge bases also have their limitations in exploration: Modularity
promotes deep search within a cluster, but cross-cluster exploration often occurs with
incomplete knowledge of dependencies. Discovering new dependencies alone does not
Lecture 1
How do organizations influence information processing:
Organizations are goal-oriented (shared goals)
Organizational roles and culture
Division of labor and integration of effort
Information flows and knowledge transfer
Incentivicing collaboration
Coordination of activities via routines and capabilities
Organizations = systems of coordinated action among individuals and groups whose
preferences, information, interests or knowledge differ. E.g.: universities, companies,
government agencies, non-profits, open source software development.
Organizations can be designed
Organizations can be designed by adjusting design elements(a.k.a. Managerial levers) such as
structure(hierarchy), centralization of decision making, formalization of processes, task
structures and task allocation(modularity and interdependencies), incentives, cultures, etc.
All these elements can be changed, some more easily than others.
Organization design influences performance and how well organizations function.
Organization > innovation
How do these organization design choices influence innovation outcomes? 3 outcomes
- Innovation outcomes: quantity, quality, speed, efficiency.
- Types of innovation: incremental, radical, novel, breakthrough, explorative, exploitative,
systemic.
- Why some organizational design choices are better or worse for specific innovation
outcomes under certain conditions(contingencies)?
Article 1
Decomposability
Means how much a system (here, a firm’s knowledge base) can be divided into independent
parts (modules or components).
- High decomposability = components interact little → easier to change one part without
breaking others.
- Low decomposability = components interact a lot → tightly coupled system.
In knowledge terms:
, - Highly decomposable = knowledge domains are separate (e.g., software and hardware
engineers rarely need to coordinate deeply).
- Low decomposable = domains are interdependent (e.g., chemistry and materials science
deeply intertwined).
Knowledge-base malleability = How easily a firm can reconfigure and combine its existing
knowledge to create new inventions. A more malleable knowledge base → easier future
innovation.
We noemen het vermogen van een kennisbasis om te veranderen kneedbaarheid (malleability),
waarvan wij stellen dat dit wordt bepaald door de decomposabiliteit.
High decomposability = high malleability > supports exploration
Low decomposability = low malleability > supports exploitation
There is an inverted-U relationship between decomposability and invention usefulness.
→ Moderate decomposability leads to inventions that are more widely cited (more valuable).
Too low decomposability → too rigid, hard to recombine → low usefulness.
exploitation-dominant (deep integration, efficiency, but rigid).
Too high decomposability → too modular, little synergy → also low usefulness.
exploration-dominant (flexibility, experimentation, but less coherence).
Moderate decomposability → best balance of flexibility and integration → highest usefulness.
optimal balance, enabling firms to integrate diverse domains (exploitation) while still allowing
recombination (exploration).
There is a positive relationship between decomposability and knowledge-base malleability.
→ The more decomposable the knowledge base, the easier it is for the firm to reconfigure and
innovate in new directions.
Firms with high decomposability → more adaptable in the future (malleable) but produce less
immediately useful inventions.
Firms with moderate decomposability → produce highly useful inventions but are less flexible
later.
→ There’s a tension between short-term usefulness and long-term adaptability.
Define decomposability and explain why it matters for innovation
Decomposability refers to the extent to which a system’s components can be separated and
modified independently without disrupting the functioning of the whole system.
In the context of organizational knowledge, it reflects how interdependent or modular different
knowledge domains are within a firm’s knowledge base.
Yayavaram & Ahuja argue that decomposability shapes innovation because it determines how
easily firms can combine, recombine, or modify their knowledge to create new inventions.
,Highly decomposable knowledge bases enable flexibility and experimentation (easy to change
parts).
Low decomposability makes change difficult because components are tightly coupled.
Moderate decomposability balances both—allowing integration across domains while retaining
adaptability.
Yayavaram & Ahuja identify a fundamental trade-off:
Moderate decomposability → produces the most useful inventions but limits future adaptability
(less malleable).
High decomposability → increases malleability (flexibility for future innovation) but yields less
useful current inventions.
This means firms must choose strategically:
Focus on current innovation impact (exploit integration) or;
Prioritize future adaptability (explore modular recombination).
Fundamental trade-off is: You cannot simultaneously maximize short-term invention
usefulness and long-term adaptability. Firms must choose or balance between producing
immediately impactful innovations and maintaining a knowledge structure that allows flexibility
for future innovation.
Knowledge bases that are nearly decomposable (Simon, 1962), relative to more integrated or
fully decomposable knowledge bases, should lead to outcomes such as enhanced innovation
quality.
There are important differences between how decomposability affects products and
organizations and how it affects knowledge structures. For instance, in products,
decomposability is used to buffer one module from another and to eliminate ripple effects.
Eliminating such ripple effects in products ensures several benefits, such as stability of product
design, more reliable performance, and enabling the repair of individual modules.
Specialization fosters deep understanding of a specific area, ease of use of repeated application
of a few elements, and superior knowledge of the interconnections between a set of elements,
as well as of the problems in connecting the elements to each other. Broad exploration provides
exposure to new ideas, innovative applications, and distinctive new variations and combinations
of a given set of elements.
Extremely low levels of decomposability will limit search breadth and make effective
recombination of any newly identified elements into successful inventions more complex and
difficult. Moderate levels of decomposability should correct these deficiencies by allowing
enhanced exploration and providing mechanisms to link the new knowledge discovered through
broad search with the expertise born of specialization; they should also simplify the invention
task, making effective combinations possible. But extremely high levels of decomposability,
while introducing sufficient exploration, would provide no integration mechanisms to link the
, results of this exploration across clusters, limiting the likelihood of successful recombination and
invention.
Complex systems become progressively more difficult to improve upon as the number of
interdependent elements increases.
Relative to integrated knowledge bases, nearly decomposable knowledge bases can therefore
lead to improvements in both specialization and knowledge depth, as well as broader search
and enhanced exploration. Nearly decomposable knowledge bases let each part specialize and
develop deep expertise (because components are independent) while still being loosely
connected, which allows the firm to explore new combinations and search broadly without
disrupting the whole system.
In fully decomposable knowledge bases, clusters are independent with no integration, so
changes in one cluster may affect others without staff in other clusters being aware, leading to
missed innovation opportunities, vulnerability to harmful changes, and lack of coordinated
problem-solving. In contrast, nearly decomposable knowledge bases maintain integrative links
that enable knowledge transfer, joint search across clusters, and allow decision-makers to
balance or mitigate negative effects, combining flexibility with enough integration for effective
innovation.
Nearly decomposable knowledge bases that permit both breadth and depth in search, and thus
provide the benefits of both exploration within a cluster and exploration across clusters, highly
integrated knowledge bases limit exploration of new configurations or breadth of search, while
highly modular structures permit depth in individual clusters but fail to
provide a mechanism for linking knowledge across clusters. provide a mechanism for linking
knowledge across clusters. If the knowledge bases of all firms have a nearly decomposable
structure, and if all achieve the optimal level of decomposability, then there would be no
performance differences between firms with respect to their technological search. But firms are
likely to err on both sides and choose a level of decomposability that is higher or lower than the
optimal level.
In firms with highly integrated or non-decomposable knowledge bases, exploration is limited
because the threshold of accepting change is high. Further, the high level of coupling between
knowledge elements also suggests that there may be few conduits for receiving external and
new knowledge. Given this lack of exploration, firms are not required to, and are unable to,
update their beliefs about interdependencies. They can continue their technology search with
their current set of couplings, and thus a highly integrated knowledge base is likely to result in
little change in the knowledge-base structure.
Highly decomposable knowledge bases also have their limitations in exploration: Modularity
promotes deep search within a cluster, but cross-cluster exploration often occurs with
incomplete knowledge of dependencies. Discovering new dependencies alone does not
