I have written earlier about a research study in which we are looking at developing interventions (approaches, tools) for supporting knowledge work and learning in the workplace.
As part of the study we conducted a survey (n=462) and semi-structured interviews (n=29) exploring current learning and knowledge sharing practices utilised by workers in a major multinational company. The survey aims to elucidate, among other questions, the types of knowledge work that individuals carry out. To this end, we are using Davenport’s classification structure for knowledge-intensive processes.
For the survey, we translated each of the four subcategories of Davenport’s typology into a set of options. Respondents were asked to choose as many options as applied in describing their current job.
The initial analysis of the survey results shows that only a very small proportion of individuals characterised their job as neatly fitting into one specific model. Instead, the majority of knowledge worker jobs fit two or more categories spanning across the four models.
These findings suggest not only that, as Davenport himself admits, knowledge work is too complex to be reduced to two dimensions, but also may point to the possibility that the categories in these typology are not quite correct. Many knowledge-intensive jobs, even if they are primarily routine, may require some degree of collaboration and personal interpretation or judgement.
Of course one has to take into consideration that our data is based on respondents’ self-reports, and we do not verify through parallel measures the extent to which these individuals’ categorisations of their work tasks are objective. Also, we cannot determine the extent to which the meaning of the options was interpreted uniformly across the sample (a common problem for surveys).
***** UPDATE on Oct 2, 2011: This study has now been published in the Journal of Knowledge Management. The full reference is:
Margaryan, A., Milligan, C., & Littlejohn, A. (2011). Validation of Davenport’s Classification Structure of Knowledge-intensive Processes. Journal of Knowledge Management, 15(4), 568-581.