Number/percentage of the intended users who recommend a KM output to a colleague

Indicator Number: 


Logic Model Component: 

Data Type(s): 
Count, proportion
Short Definition: 
Measures how many intended users recommend a KM output to a colleague
Definition and Explanation (Long): 
This indicator measures how many intended users recommend a KM output to a colleague. A “recommendation” is an endorsement of the output, indicating the recommender’s judgment that the output is a suitable resource for a particular purpose. The term “colleague” indicates a professional relationship.
Data Requirements: 
Quantitative data from self-reported information on recommendations received
Data Sources: 
Feedback forms, user surveys (print, online, email, telephone), evaluations of extended professional networks, if feasible
Frequency of Data Collection: 
The decision to recommend a KM output reflects a user’s assessment of its quality, relevance, and value (which can be captured by indicators 26 and 27. Recommendations also provide evidence that user-driven sharing is exposing a wider professional network to the KM output. Frequent recommendations may speak to the overall success of the KM output.
Issues and Challenges: 
It may be useful to distinguish a recommendation from a referral. A referral may reflect a judgment of relevance, but it can be quite casual; the referrer may know little about the KM output beyond its topic. A recommendation implies a judgment of quality. Both recommendations and referrals are worth tracking, as they can indicate secondary distribution. In data collection instruments, “recommending” needs to be clearly defined and distinguished from simple “referral” or “sharing.”
Sample Topics and Questions for Data Collection Instruments: 
To approximately how many colleagues or co-workers have you recommended the [Web product] or its resources? (Fill in the blank.) _________colleagues
Pages in the Guide: 

Published Year: 

  • 2013
Last Updated Date: 
Wednesday, September 6, 2017