Estimating the Value of Knowledge Management in the Context of Agricultural Research Priority Setting
Decision makers may be reluctant to invest in knowledge management (KM) without understanding the economic value of such investments. KM creates economic value through two direct pathways: (i) reducing the time and other costs of obtaining knowledge needed for decision making; and (ii) reducing the uncertainty associated with decision options (or policy alternatives for policy decision making). The first pathway is relatively obvious, but calculating the value of saved time in large institutions can be difficult.
The second pathway creates the main source of value of KM; knowledge and access to it reduces the probability that an improper (wrong) decision is taken. There is a large literature on the value of information in decision making and the main source of economic value comes through reduction in uncertainty about the returns to a specific decision.
Measurement of value is complicated because KM impacts can be diffuse and easily confounded. While impacts of knowledge transmission to end users such as farmers can be measured using experimental techniques such as randomized delivery of the message (Larochelle et al. 2017), effects of non-randomized knowledge transmission to decisionmakers within an institution are difficult to measure because of the many confounders. These include differences in unobserved individual characteristics that may affect both the decision to use the system and the decision to make a policy change. As a result, studies of KM in complex organizations have relied on corporate results measurement where a package of factors including organizational responsibilities, management styles, etc. also have influence.
This course takes the learner through a process of thinking about these sources of value and understanding challenges to measurement of the value of KM. It begins with the underlying premise that investments in KM imply costs to the institution or individual making these investments and net returns (benefits minus costs) to the investments should be compared to alternative uses of institutional or individual resources. Adoption of KM is partly an economic decision requiring some knowledge of its benefits relative to costs.
Simple economic principles can inform the design of a KM system. Increased benefits from KM will emerge if the system enables greater time savings for its users—to know this, information is needed on which types of knowledge search involve significant time use for many people in the organization. This information can be gained ex-ante through a needs assessment at the beginning stages of system development and can be used to determine what knowledge should be included and how the system will make knowledge more accessible and easier to obtain. Alternatively, patterns of time use following system implementation can be compared to use prior to the system (the counterfactual) in an ex-post analysis.
Decisions about what knowledge to include in the KM system will benefit from consideration of the second direct pathway. Three basic principles emerge here. First, decisions for which the consequences are large should be prioritized for inclusion in the KM. Identification of these decisions will require structured dialog with intended users to obtain estimates of the value of correct information based on the economic size of the sector/decision domain of users. For example, if sorghum is planted over a large area in a country, information on sorghum should be prioritized over information about a much less-prominent crop. Or if a high-value crop has the same planted area as a low-value crop, the information on the high-value crop should be prioritized, all other factors held equal.
Second, the system should contain information on decisions for which the decision maker can have an impact. If it is not possible to alter a decision, then there is no value to the information.
The third principle governing the uncertainty pathway is that the value of KM is determined by the degree of certainty about the current practice versus the practice (change) recommended in the KM system. If the optimality of a decision is considered to be highly uncertain, then additional information provided by the KM system will have higher value. This decision uncertainty is difficult to measure but alternative means exist of eliciting it from the users of the information.