Managing science and research requires a unique skill set that are not the same as general management skills required for other types of businesses. General management theory is applicable to science and research management, but not sufficient to cater for the specific requirements of science and research management. For that purpose we assume in this article that the reader is already familiar with general management principles and approaches. Our focus here is to look at the specific requirements of science and research management.
An important aspect is understanding what would constitute good science and how to create an environment that would allow the knowledge generation aspect of science and research to flourish. Important aspects that differ from general management principles are:
Quality assurance often supersedes the process-focused approach in organization generally. Especially where the problems are not standard and therefore require unique approaches to be solved, it is very difficult to provide consistent quality assurance and performance indicators.
Science and research management requires a careful balance between investment and creating utility for current use. Unless a considerable effort is made to constantly invest in more capabilities and growth of existing capabilities, management of science and research finds itself over the medium term with an increasingly stale and unproductive scientific research capability. This requires a financial management approach that does not optimise for short term profit only, but also caters for the capability building of ongoing the investment.
The people performing the science and research work are usually a scarce commodity, and replacing them require considerable investment of both time and money. For this reason retention and ongoing development of existing experts needs to be a focus in the business model (this is true for all knowledge-intensive innovative environments).
The work environment need to enable innovative and creative work, and facilitate and value team work. The performance indicators for these are often difficult to define (they might even be intangible). But giving attention to them and getting them right for the specific type of science and research work is very important for a successful science and research capability.
In addition to all of this there is the aspect of “managing science where it happens”, namely to ensure the scientific work itself is of a good quality and make the best use of the available capabilities. Usually this is catered for by the various conventions that scientists and researchers of specific disciplines adhere to professionally.
However, the various sciences have a number of differences and commonalities that make maintaining the scientific rigour when work is done in more than one of the major branches of science very difficult. For this reasons many research capabilities either restrict themselves to only selected branches of science, or they retain the barriers between the various sciences and never really get to an integrated scientific capability that spans across the boundaries of the sciences. In the complex and highly connected societies we live in that is becoming an increasingly untenable situation. We need to be able to integrate the sciences to be able to provide relevant and useful new knowledge, utilising the best that science offers.
Using science in an integrated way unlocks most value in situations like this. We need to keep in mind that
All the sciences share a common goal to search for the “truth”, or “facts”, or “evidence. This common goal provides the background against which we are able to identify a number of similarities.
There are some legitimate differences between the sciences that we cannot remove by forcing one approach on all the branches of science.
Accomplishing this is not easy. However, there are two sets of features that are common to all branches of the sciences. They can be used in all branches of science to ensure that we are able to integrate our scientific work across the traditional branches of the sciences. They are
The scientific productiveness features: These are the features of science that facilitate its success in knowledge generation. Knowledge can be generated in a number of ways, but these science has illustrated over the centuries that where these features are present and used appropriately they facilitate a level of success that is not otherwise possible.
The Scientific Capability Features: These are the features that describe the way to go about knowledge generation utilising the scientific productivity features.
We have used these two for integrated scientific work in a number of cross-disciplinary applications (mostly to solve complex real life problems in strategic management decision making). They have proven themselves to add value in the rigor, quality and relevance of cross-disciplinary scientific work.
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