Access to this document was granted through an Emerald subscription provided by emerald-srm:198285 [] For AuthorsIf you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation. AbstractPurpose -The purpose of this research is to help knowledge managers systematically grasp ''knowledge about management knowledge'' and get a ''deep and full'' understanding of the nature, scope and methodologies of knowledge management.Design/methodology/approach -Through presenting a variety of perspectives on knowledge, management, and knowledge management, the article explores the essence of knowledge management in organizations from a perspective of critical systems thinking.

To better understand Nonaka’s SECI model of knowledge creation and its constraints, we revisit the fundamental points of tacit knowledge in the model and provide a critical review on the role of tacit knowledge in business organization. First, the “tacitness” of knowledge is broken down into two parts: implicitness and real tacitness. We argue that the tacit dimension of knowledge in the context of the model is different from that in Polanyi’s original context; it actually includes considerable “implicitness” idiosyncratic in Japanese context. The separation of implicitness from real tacitness suggests carefully considering the potentialities of “unveiling” the secrets of tacit knowledge in different contexts. Second, considering most cases for the model mainly came from certain Japanese manufacturing companies that more or less relates to assemble lines, it is necessary to be cautious when the model is extended for a broader application.

KM is increasingly imperative as it is regarded as the key determinant of a firm, industry or country for survival and growth in knowledge era. Varieties of disciplines have made contributions to knowledge and knowledge management. Research focuses on one or more specific fields, but to understand which levels of knowledge processes knowledge management should concentrate on, should be more fundamental than advocacy of knowledge management. Knowledge-related matters were examined from the viewpoint of systems science. Using critical systems thinking, soft systems thinking etc., a new systematic perspective on knowledge was proposed, aiming to provide a new way of thinking and a useful toolbox on different levels and phases of knowledge management for practical knowledge users.

Personnel selection and improvements in system and task design can potentially mitigate boredom. However, more work is needed to develop and evaluate other potential interventions.

The proximal gradient algorithm has been popularly used for convex optimization. Recently, it has also been extended for nonconvex problems, and the current state-of-the-art is the nonmonotone accelerated proximal gradient algorithm. However, it typically requires two exact proximal steps in each iteration, and can be inefficient when the proximal step is expensive. In this paper, we propose an efficient proximal gradient algorithm that requires only one inexact (and thus less expensive) proximal step in each iteration. Convergence to a critical point is still guaranteed and has a O(1/k) convergence rate, which is the best rate for nonconvex problems with first-order methods. Experiments on a number of problems demonstrate that the proposed algorithm has comparable performance as the state-of-the-art, but is much faster.