OG articles summaries
W1: Introduction
− Yayavaram, S., & Ahuja, G. 2008. Decomposability in Knowledge Structures and Its
Impact on the Usefulness of Inventions and Knowledge-base Malleability.
Main findings: Knowledge-based structures range from highly-decomposable/modular to
non-decomposable/integrated, with near-decomposable structures in between. First, the paper
aims to examine how these structures influence the usefulness of inventions by analyzing the
inventor’s depth, breadth and integrative mechanisms. Near-decomposable structures balance
depth and breadth, resulting in highly useful innovations. Secondly, the paper explores how
structural decomposability affects knowledge malleability, able to promote exploration and
absorptive capacity. Near-decomposable structures by having cross-cluster integration
encourage exploration, and its integrative ties allow it to span across clusters. Results confirm a
U-shaped relationship between structural decomposability and both usefulness of inventions and
knowledge malleability.
, Summary
Theory
organizational variations in coupling patterns between knowledge elements can be reflected in a
spectrum of knowledge-base structures—varying from fully decomposable (the knowledge base
is composed of distinct clusters of knowledge elements coupled together with no significant ties
between clusters) through nearly decomposable (knowledge clusters are discernable but are
connected through cross- cluster couplings) to non-decomposable (no knowledge clusters
emerge, as the couplings are pervasively distributed)—and that organizations may differ in the
way they use their knowledge because of variations in their knowledge-base structure, rather
than because of differences in the knowledge elements themselves. Results show that a nearly
decomposable knowledge base increases the use- fulness of the inventions generated from it, as
measured by patent citations, and also the knowledge base’s malleability or capacity for change.
Previous literature has made the connection between:
● size of a knowledge base to create organization’s innovative productivity
● the degree of overlap between different organizational knowledge bases has been
related to the ability of an organization to absorb external knowledge from its geograph-
ic or technological neighbors
This paper: how the structure by which different knowledge elements are coupled together or
isolated from each other in different clusters will affect the organization’s ability to combine
knowledge elements for innovation.
● maintaining a balance between depth and breadth is critical to successful invention
● The set of couplings or ties and the strength of the ties constitute the structure of a firm’s
knowledge base.
Coupling can be seen as a tie or relationship between two knowledge elements or consider
elements uncoupled. Couplings thus reflect an organization’s revealed beliefs about which
elements of knowledge are most likely to work well together. These can be strong to weak to
non-existent.
Within a knowledge base, couplings can be distributed in several possible patterns that together
delineate a continuum of possible structures, from highly decomposable to non- decomposable.
● A modular, clustered, or highly decomposable knowledge base, the couplings will be
extremely dense within some clusters of knowledge elements but non- existent between
the groups of elements.
● Nearly decomposable, would be one in which some elements are more densely
connected with each other than they are with other elements, thus allowing clusters to
be distinguished, but at the same time, there are some couplings that connect clusters to
one another.
●
, ● Non-decomposable or integrated structure, the couplings in a knowledge base can be
pervasive and indiscriminately distributed across elements, such that no groups of
elements can be distinguished as a cluster because the density of ties between groups of
elements is not any lower than within any groups of elements.
Og’s with same knowledge base may still differ in ability to use said knowledge.
Coupling vs Interdependence is the degree to which two elements are related to each other in
the natural world and is not known a priori. Coupling is the extent to which search across two
elements is combined by an entity.
Decomposability in Knowledge Structure
★ Couplings represent organizational decisions rather than a state of nature, coupling
choices can vary across firms even when the set of underlying interdependencies is
common to all firms.
★ Nearly decomposable enhance innovation quality
★ Variations in coupling = variations in structure of firm’s knowledge bases.
★ Coupling patterns must change to reflect new knowledge. Dissolve old ties.
★ A knowledge base’s capacity for change is its malleability, which we argue should be
determined by its decomposability.
★ Nearly decomposable facilitates change to break old ties and form new ones.
★ Effect of the decomposability of a knowledge base’s structure on the quality of inventions
generated from it and in making the structure itself malleable.
Search for invention and the structure of knowledge base
In products, decomposability is used to buffer one module from another and to eliminate ripple
effects. In contrast, in knowledge structures, ripple effects may actually be desirable, as they
can lead to exploratory search across modules.
● “knowledge and information-processing structure come to mirror the internal structure
of the product they are designing”
● A technological invention can be seen as the outcome of a recombination of existing
knowledge elements.
Combinatorial Explosion Problem: the process of generating new technological inventions can
be recast as the computationally complex problem of searching through the vast problem space
of possible recombinations.
Inventions being useful would be in terms of economic wel- fare generated or their frequency of
usage in subsequent problem solving (within the technological landscape). the commercial value
that is generated from an invention depends beyond its technological usefulness, such as firm
ability to market the invention and then protect and appropriate the value that is generated.
, Finding combinations with superior utility can be abstracted as assembling configurations of
elements, evaluating their utility, and then changing the states of one or more elements of a
given configuration to create a new configuration.
Both local and distant search can be visualized. Local search implies a change of state in a few
elements of a given configuration, while distant search is the change of state for many elements
in a given configuration.
Solution for the Combinatorial Explosion Problem:
Couple or link together groups of elements and thus reduce the number of combinations to be
considered.
(a) Decision driven by its current understanding of the interdependencies between those
knowledge elements.
(b) Best guesses vary across organizations or across time, they should lead to
cross-sectional and intertemporal variations in coupling choices.
Firms’ best guesses on interdependence differ in the first place.
(a) A firm’s knowledge structure resides in its routines, communication patterns, beliefs, and
organizational structure. These pathways of information in any organization affect how
knowledge is understood
(b) what knowledge is focused on, what is ignored, what ties represent legitimate
connections, and what ties are ill-considered or illegitimate.
(c) Types of routines of organization
(d) SOP’s
(e) Communication patterns: reflected in the patterns of couplings of knowledge elements in
which the individuals or the research units have expertise.
(f) Further, firm’s beliefs: about perceived interdependencies.
(g) the organizational structure of the research units can have a direct effect on how
knowledge is structured. M-form vs Modular
Organization knowledge base elements = By modifying the communication patterns, beliefs, routines,
and organizational structure, a firm can modify the structure of its knowledge bases and its level of
decomposability.
