A common complaint from anyone working in universities (or any large organisation) is the impact of organisational silos. Since the 1950s most organisations have been structured hierarchically (Cilliers & Greyvenstein, 2012). Inherent to any hierarchical organisation is the vertical and horizontal clustering of people based on function (Cilliers & Greyvenstein, 2012). On the surface, the clustering of people affords a practical way for organisations to operate efficiently, whereby accountabilities and responsibilities are clearly delineated and communicated. However, in reality, these organisational delineations can become “tribal”, whereby people identify with and feel comfortable and secure with “their community”, which leads to a culture or mindset that inhibits cross-boundary collaboration and cooperation (Cilliers & Greyvenstein, 2012). Silos are barriers to achieving organisational goals and can often pose a threat to internal cooperation (Bento et al., 2020).
In my experience, one of the more debilitating examples of organisational silos is the separation of education and technology that is common with university organisational structures. The educators are typically clustered into faculties, disciplines and programs, while the IT folk will most often sit within a separate branch of the organisational hierarchy. Each of these departments will have different reporting lines, performance indicators, goals, perspectives on problems, problem-solving methods and so forth. Over time, this separation becomes a cultural norm that becomes deeply entrenched in our mental models; it becomes what we perceive as normal, and we accept it as such. I often see the problems caused by this arbitrary separation whereby it manifests as day-to-day decision-making that is either heavily biased towards technology considerations or heavily biased towards education considerations. In either case, internal politics, power games and inter-silo warfare are often evident, resulting in decision-making that more heavily biases the perspective of one party over the other.
However, no matter which party happens to dominate at a point in time, this is where the Dunning-Kruger effect can be seen in action. The Dunning-Kruger effect is a cognitive bias in which people with limited competence in a particular domain overestimate their knowledge and abilities. Perhaps a more brutal explanation of the Dunning-Kruger effect:
“The rationally ignorant fail to overcome their ignorance not just because they face steep costs and lack incentives to improve, but because they are unaware that they are relatively ignorant.”
(Dunning, 2011)
In other words, either the technology folk are overestimating their knowledge of learning and teaching or are dismissive of the educator perspective, or the educators are ignorant of, or dismissive of the impact of technology on learning and teaching. From my (limited) perspective, I tend to see the pendulum sway either way rather than straddle the “compromise” space in between, which is where I think it needs to be.
Tim Fawnes puts forward the idea of entangled pedagogy where he suggests that pedagogy is not just methods, and technology is not just a vehicle for implementing these methods, but they are entangled and interdependent (Fawns, 2022). Neil Selwyn reinforces this perspective by stating that technologies can influence the way that people do things while at the same time are profoundly shaped by those who design, develop, implement and use them (Selwyn, 2019). Jon Dron presents the idea of orchestration where “it is the ways that the machine is orchestrated by humans, with humans, and for humans that makes it educational” (Dron, 2021, p. 10). Whichever theoretical or philosophical lens is preferred, it is clear that education (pedagogy) and technology are symbiotic and mutually influencing, and the arbitrary and active maintenance of their separation is fundamentally flawed.
There is a wealth of research around organisational silos and silo-busting strategies – ironically, the same genre of literature that gave us organisational silos in the first place. Unsurprisingly, much of the research around silo-busting tends to focus on things like shared organisational values, collaborative operating models, collaborative environments, leadership, reward and development (Bento et al., 2020; Cilliers & Greyvenstein, 2012; de Waal et al., 2019). Much of this is what my more cynical colleagues would label the “yay team” approach used by HR departments when they try to shift the staff culture. While I am likely exhibiting the same ignorance mentioned above in association with the Dunning-Kruger effect, I wonder if there are other ways that we could think about organisational performance.
Our organisation’s departments are driven to achieve predetermined and measurable goals that are meticulously aligned with the university’s strategic plan. Each department’s operational plan includes measurable outcomes that funnel upward to the university’s operational plan, something we’re all likely very familiar with. I think this is where the wheels start to fall off; it is too prescriptive. One such example relates to student retention, an ever-present and growing problem here in Australia. I haven’t seen the IT department’s operational plan, but I doubt that it includes student retention, even though almost every aspect of the student experience is mediated by technology to varying degrees. Likewise, my department, an academic department, does not have a performance metric around cybersecurity, when it is without question a shared responsibility.
I do not believe that the major problems faced by contemporary universities (like those the separation of educators and technologists creates) can be solved through administrative leadership. Our formal structures are not designed to foster the internal collaboration and innovation required to solve problems that are increasingly complex and entangled (Uhl-Bien et al., 2007). I would argue that some of the problems facing universities today have been created by or at least sustained by the inflexibility inherent in our formal organisational structures. Perhaps more accurately, it is the rigidity of our thinking about these structures that is the bigger problem. I think there are some clues in the complexity leadership literature, but shifting our thinking, shifting from long-entrenched ideas of what is normal is going to be the bigger challenge. I don’t have the answers, but the following are some ideas I came across while thinking about this post:
- Decentralise decision-making
- Decentralise problem identification and rectification approaches
- Flatten the organisational structure
- Create more project teams and cross-functional teams centred around initiatives or problems. Allow these teams to develop organically
- Focus on skills and expertise, not titles or formal positions
- Promote a learning environment where knowledge is shared
- Encourage innovation and experimentation outside of formal structures
- Shift from episodic performance evaluation (reports) to continuous and transparent assessment
- Shift from leaders as managers to leaders as facilitators or coaches
- Recognise that leadership exists at all levels of the organisation
- Accept ability and uncertainty
- Rely on values to guide decisions rather than policy
References
Bento, F., Tagliabue, M., & Lorenzo, F. (2020). Organizational silos: a scoping review informed by a behavioural perspective on systems and networks. Societies, 10(3), 56.
Cilliers, F., & Greyvenstein, H. (2012). The impact of silo mentality on team identity: An organisational case study [Systems psychodynamics; physical environment; structure; intragroup relations; experiences of management; intergroup relations]. 2012, 38(2). https://doi.org/10.4102/sajip.v38i2.993
de Waal, A., Weaver, M., Day, T., & van der Heijden, B. (2019). Silo-busting: overcoming the greatest threat to organizational performance. Sustainability, 11(23), 6860.
Dron, J. (2021). Educational technology: what it is and how it works. AI & SOCIETY. https://doi.org/10.1007/s00146-021-01195-z
Dunning, D. (2011). The Dunning–Kruger effect: On being ignorant of one’s own ignorance. In Advances in experimental social psychology (Vol. 44, pp. 247-296). Elsevier.
Fawns, T. (2022). An Entangled Pedagogy: Looking Beyond the Pedagogy—Technology Dichotomy. Postdigital Science and Education. https://doi.org/10.1007/s42438-022-00302-7
Selwyn, N. (2019). What’s the Problem with Learning Analytics? Journal of Learning Analytics, 6(3), 11–19-11–19.
Uhl-Bien, M., Marion, R., & McKelvey, B. (2007). Complexity leadership theory: Shifting leadership from the industrial age to the knowledge era. The Leadership Quarterly, 18, 298-318. https://doi.org/10.1016/j.leaqua.2007.04.002
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