736 research outputs found
Ambidexterity in Service Innovation Research: A Systematic Literature Review
Increased interconnectedness of multiple actors and digital resources in service eco-systems offer new opportunities for service innovation. In digitally transforming eco-systems, organizations need to explore and exploit innovation simultaneously, which is defined as ambidexterity. However, research on ambidextrous service innovation is scarce. We provide a systematic literature review based on the concepts of ambidexterity, offering two contributions. First, research strands are disconnected, emphasizing either exploration or exploitation of service innovation, despite an organizations’ need to accelerate innovation cycles of exploring and exploiting services. Second, a new framework for ambidextrous service innovation is provided, inspired by the dynamism and generative mechanisms of the ontologically related concept of organizational routines. The framework adopts the perspective of a mutually constitutive relationship between exploring new and exploiting current resources, activities, and knowledge. The findings remedy the scattered literature through a coherent perspective on service innovation that responds to organizations’ needs and guides future research
Lumping of Degree-Based Mean Field and Pair Approximation Equations for Multi-State Contact Processes
Contact processes form a large and highly interesting class of dynamic
processes on networks, including epidemic and information spreading. While
devising stochastic models of such processes is relatively easy, analyzing them
is very challenging from a computational point of view, particularly for large
networks appearing in real applications. One strategy to reduce the complexity
of their analysis is to rely on approximations, often in terms of a set of
differential equations capturing the evolution of a random node, distinguishing
nodes with different topological contexts (i.e., different degrees of different
neighborhoods), like degree-based mean field (DBMF), approximate master
equation (AME), or pair approximation (PA). The number of differential
equations so obtained is typically proportional to the maximum degree kmax of
the network, which is much smaller than the size of the master equation of the
underlying stochastic model, yet numerically solving these equations can still
be problematic for large kmax. In this paper, we extend AME and PA, which has
been proposed only for the binary state case, to a multi-state setting and
provide an aggregation procedure that clusters together nodes having similar
degrees, treating those in the same cluster as indistinguishable, thus reducing
the number of equations while preserving an accurate description of global
observables of interest. We also provide an automatic way to build such
equations and to identify a small number of degree clusters that give accurate
results. The method is tested on several case studies, where it shows a high
level of compression and a reduction of computational time of several orders of
magnitude for large networks, with minimal loss in accuracy.Comment: 16 pages with the Appendi
Bounding the Equilibrium Distribution of Markov Population Models
Arguing about the equilibrium distribution of continuous-time Markov chains
can be vital for showing properties about the underlying systems. For example
in biological systems, bistability of a chemical reaction network can hint at
its function as a biological switch. Unfortunately, the state space of these
systems is infinite in most cases, preventing the use of traditional steady
state solution techniques. In this paper we develop a new approach to tackle
this problem by first retrieving geometric bounds enclosing a major part of the
steady state probability mass, followed by a more detailed analysis revealing
state-wise bounds.Comment: 4 page
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