A scientific commentary on the question: Quo Vadis Knowledge
Management?
Dr. Karsten Ehms
Knowledge management, like GfWM, can look back on over 20 years as its own discipline. For a large part of this time it has been declared dead repeatedly! I remember a quote by Dave Snowden from 2005: "KM is dead, now they are selling it to the government." Now there is nothing special about management fads having an expiration date, the "laws" of connected markets already demand that. What is remarkable about our topic is that it can still attract at least enough attention to be rhetorically promoted to the afterlife anew every year.
As an advisory board member of the GfWM institution, can you use the word "bullshit" constructive reflection? I hesitated after opting for a toned-down "cut the jargon, learn the concepts" in my candidacy. The surprising death a few weeks ago of David Graeber, author of "Bullshit Jobs: A Theory," among others, makes Zero Bullshit Knowledge Management (#0BSKM) seem the order of the day. Graeber has passionately and astutely addressed the bureaucratic rigidity of institutions in the face of breakthrough innovation, and in this context describes how long-term change transforms what was originally functional into bullshit. It is in this sense that I would like strong language to be understood, and I abbreviate it to make it suitable for our anniversary purpose.
0BSKM can be described as my personal extensive attempt:
This has resulted in the framework of thinking shown in Figure 1 with currently 3x3 ideas, which are described below.
Figure 1: Categories of 0BSKM
Among many short-term oriented pragmatists, knowledge management is considered a failure (cf. Introduction) because it has generally (and in the hands of novices) failed to deliver short-term results, or the "quick-wins" could not be stabilized and developed further. I would cite, almost inadmissibly condensing, three bundles of reasons why WM initiatives fail again and again
Knowledge, no matter how precisely defined, always has a non-material character and behaves fundamentally differently to interventions than material goods. The success story of management in the outgoing industrial society, however, is based precisely on the optimization of physical and not mental labor. In my opinion, this fact, which is actually easy to understand, is the reason for the frequent sigh: "They just don't get it".
Since the change just described is not a "pragmatic switch" or "shift", knowledge professionals always have to deal with the long term, which challenge precise calculation and attribution in the short term. Especially when the "calculation" in the sense of an ROI is demanded even before the implementation. But also due to the intended(!) distribution of value creation across the cooperation, the measurement becomes a seemingly precise illusion of control.
The discipline of knowledge management is inherently caught between two (or more) stools. On the one hand, it is a theory-practice hybrid; on the other hand, it is ideally based on very different basic disciplines. These include psychology, computer science, sociology, organization and strategy (business administration), pedagogy and didactics. A particular challenge is that these basic disciplines imply different conceptions of truth, i.e. different claims as to which theories and concepts are considered valid. This quickly leads to intense discussions between practitioners from diverse backgrounds. An unavoidable and actually positive circumstance! What should be avoided is the reluctance of the German university system to design fundamentally interdisciplinary courses of study. Here we are structurally "too disciplined" compared to Anglo-Saxon approaches.
I have repeatedly encountered the following aspects in my work and have gained the impression that they create unnecessary complexity. In contrast to the challenges described last, they are usually relatively easy to "sort out", provided one accepts their derivation.
For about 15 years I was and still am active in teaching at
different institutions and in this role I have been challenged to
convey the secured knowledge about KM. Repeatedly, I have been
confronted with sources that deliberately ignored the
fundameltals, passed over them for reasons of "pragmatics", or
simply did not take into account findings and corrections that had
been available for decades.
To illustrate such "original sin" (cf. Schneider, 2001), consider
the famous SECI model from Nonaka and Takeuchi from the mid 90th,
which was corrected by the authors themselves after only four
years, but is still recommended as a viable basis for scientific
backing today (Ehms 2010). Without better scientific practice,
dissertations will probably still be supervised and written on
this basis 10 years from now. So called practice improves upon the
findings inscience, at least on the long run. In that sense,
potential of existing scientific knowledge has been given away for
years. It helps to consider sociological wisdom that science also
follows its own institutional rules. Which is of course not a call
to negate the value of scientific knowledge per se but to unite
„behind“ scientific knowledge.
We should have learned in over 20 years of knowledge management that the relationship of people to tools is a much more complex one than the conference mantra of "people at the center" versus "it's just technology tools" conveys. The popularity of codification and distribution by IT tools seems to be unbroken. What has been the „knowledge database“ decades ago finds its revenants in the current hype about supposedly intelligent chatbots. The fact that there is a tilt (unbalancing) of proposed solutions toward codified knowledge is known in expert circles as the "codification trap." Less common is the idea that basicallly all knowledge management challenges are about a balance between "documented information" and time-synchronized dialog between people. The focus on people described above, and the devaluation of technology often associated with it, significantly fails to recognize the everyday transformational impact of digital tools. They have always been a necessary and never a sufficient condition for successful knowledge management.
Where there is complexity, snake oil and silver bulltets are close. And so should critical thinking and „Digital Enlightment“ as a shortcut to regain orientation in world deservedly described as VUCA. It starts by educating about the interest-driven coloring of "solution concepts". The critical handling of promises of salvation and their backgrounds (cui bono?) must not only be generally proclaimed, as here, but practiced. A simple form of snake oil consists in the pure relabeling of well researched challenges in order to sell (apparently!) new products. Unfortunately with considerable side effects for education and knowledge communication.
Now, it would be naive to claim that the difficulties of shaping knowledge transfer in the long term consist only of the basic challenges (section 1) and inefficient learning delays (section 2). Of course, especially technical developments in the last 20 years have triggered changes that also affect our area of interest. In 0BSKM, I would currently like to pick out three topics.
The first factor, which massively influences the way knowledge is handled, is the ubiquitous availability and use of digital devices and services. Initially laptops instead of desktops, from around 2008 in the massive spread of smartphones as a universal digital infrastructure. These tools are always also(!) knowledge and learning tools. At least on a personal level.
While in the early phase of knowledge management (around the year 2000) the effort of codification has often been emphasized. Writing something down digitally or creating interactive visualizations of has been an additional acctivity that hat to be – and rarely got – integrated into daily routines. 20 years later almost all information is initially created digitally. More laborious than "mere codification" appears the sharp selection (filtering) that is now necessary in the information overload and the curation of granular fragments of information into larger units of meaning.
No reflection and no outlook can currently do without a reference to the developments that are summarized under the catchword of artificial intelligence. Undoubtedly, artificial intelligence techniques, especially in the area of machine learning / connectionism, create further possibilities for filtering and condensing the constantly growing amounts of information. The lack of information has long since turned into abundance (cf. Nefiodow 1995) and thus the use of flexibly automated symbol and data processing seems to be without alternative. However, already the selection of the appropriate approach generates new challenges which are important as orientation knowledge for knowledge experts.
Where is knowledge management heading to? This essay consists more of a programmatic review than an attempt to sketch realistic future scenarios. Nevertheless, by condensing it into long-term aspects, theses can be derived.