BACKGROUND Changing clinical practice is a difficult process, best illustrated by the time lag between evidence and use in practice and the extensive use of low-value care. Existing models mostly focus on the barriers to learning and implementing new knowledge. Changing clinical practice, however, includes not only the learning of new practices but also unlearning old and outmoded knowledge. Th...

This paper identifies mechanisms that affected over 200 Information System Process Innovation (ISPI) unlearning decisions in three organisational environments over a period that spanned four decades. The analysis is based on previous unlearning studies. Four distinct generations analysed are early computing (1954-1965); main frame era (1965-1983); office computing era (1983-1991), and distribut...

Accumulating evidence indicates the key role of alpha-calcium/calmodulin-dependent protein kinase II (alphaCaMKII) in synaptic plasticity and learning, but it remains unclear how this kinase participates in the processing of memory extinction. Here, we investigated the mechanism by which alphaCaMKII may mediate extinction by using heterozygous knock-in mice with a targeted T286A mutation that p...

Organizations need to remain competitive in today’s marketplace. Technology change impacts knowledge competencies that require alteration quickly, to reduce operating costs, and eliminate human errors. Updating computer system documentation procedures require unlearning to maintain competency. Physician end-users possess specialized competencies, or knowledge base in documentation of patient da...

Many studies uphold market orientation as a key factor in creating and sustaining a firm’s competitive advantage. The present research model explores this topic further by including within the model the links between organizations’ innovation outcomes, its process of organizational unlearning and business performance. In particular, the model empirically tests the mediating role of innovation o...

The textured images’ classification assumes to consider the images in terms of area with the same texture. In uncertain environment, it could be better to take an imprecise decision or to reject the area corresponding to an unlearning class. Moreover, on the areas that are the classification units, we can have more than one texture. These considerations allows us to develop a belief decision mo...

We introduce into the classical Perceptron algorithm with margin a mechanism of unlearning which in the course of the regular update allows for a reduction of possible contributions from “very well classified” patterns to the weight vector. The resulting incremental classification algorithm, called Margin Perceptron with Unlearning (MPU), provably converges in a finite number of updates to any ...