Dealing with Complexity: Integrated vs. Chunky Search Processes
Author(s) -
Oliver Baumann,
Nicolaj Siggelkow
Publication year - 2012
Publication title -
organization science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.96
H-Index - 238
eISSN - 1526-5455
pISSN - 1047-7039
DOI - 10.1287/orsc.1110.0729
Subject(s) - problem solver , interdependence , computer science , process (computing) , domain (mathematical analysis) , solver , complex system , search algorithm , search problem , artificial intelligence , mathematics , algorithm , software engineering , mathematical analysis , programming language , operating system , political science , law
Organizations are frequently faced with high levels of complexity. While the importance of search for dealing with complex systems is widely acknowledged, how organizations should structure their search processes remains largely unexplored. This paper starts to address basic questions: How much of the entire system, and thus complexity, should be taken into consideration at any given time during a search process? Should a problem solver pursue an integrated search and be concerned with the whole system right from the start, or should a problem solver incrementally expand the “search domain,” i.e., the subset of system elements and interdependencies that are included in the search efforts? If the latter, how “chunky” should these steps be? Our analysis of a simulation model yields four insights: 1 expanding the search domain in smaller steps can yield a distinct advantage in final system performance, 2 following a completely incremental expansion pattern is not necessary as long as larger chunks are added early on in the process, 3 the value of chunky search is particularly high if highly influential system elements are considered first and highly dependent elements are added later, and 4 under time pressure, chunky search can lose its performance advantage over more integrated search processes. We discuss the implications of our findings for managing organizational search and complex systems more broadly.
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