Picking Investments in Knowledge Management

{Excerpt} What can be measured is not necessarily important and what is important cannot always be measured. When prioritizing investments in knowledge management, common traps lie waiting. They are delaying rewards for quick wins, using too many metrics, implementing metrics that are hard to control, and focusing on metrics that tear people away from business goals. How can investments in knowledge management be picked? This is no easy matter. What can be measured is not necessarily important and what is important cannot always be measured. Not surprisingly, despite the wide implementation of knowledge management initiatives, a systematic and comprehensive assessment tool to prioritize investments in knowledge management in terms of return on investment is not available. This owes to the difficulty of demonstrating direct linkages between investments in knowledge management and organizational performance, most of which can only be inferred, and the fact that the miscellany of possible knowledge management initiatives calls for both quantitative and qualitative approaches. This is indeed the rationale behind the Balanced Scorecard introduced by Robert Kaplan and David Norton in 1992, whose qualities make it quite useful as a knowledge management metric

Measuring the Performance of Corporate Knowledge Management Systems

Whereas knowledge management systems (KMS) continues to gain popularity as a corporate most advanced information systems, the acceptance of standardized KMS assessment approaches has logged. Developing metrics to assess a corporate KMS is inherently problematic due to the intangible nature of knowledge-based resources, and for the fact that measurement is a precursor to improvement. This is true for knowledge management capabilities of an organization. Nonetheless, assessment is of vital importance for valuation purposes as well as to help managers determine whether particular KMS are effective working. The main focus of this paper is to explain the value of knowledge management and provide a general overview of measurement approaches. Finally, developing an improved measurement system for corporate KMS is considered the key to the competitive success of the organization.Corporate Knowledge, Knowledge Management Systems, Measuring the Performance

Private Foundations, Business and Developing a Post - 2015 Framework

Global debates about what might replace the Millennium Development Goals (MDGs) after their 2015 deadline are currently underway. Philanthropic foundations and businesses need to be integrated into these discussions alongside civil society, national governments and multilateral organisations. This could be achieved by encouraging cooperation on individual MDGs; transferring project ownership and management to private actors where it is deserved; improving knowledge transfer and decreasing project duplication; and creating a common set of performance metrics

Why do research on commercial property management? Somebody HAS to!

This paper identifies some significant gaps in our knowledge of the configuration and performance of the property asset management sector. It is argued that, as many leading academic property researchers have focussed on financial vehicles and modelling, in-depth analysis of property assets and their management has been neglected. In terms of potential for future in-depth research, three key broad preliminary research themes or questions are identified. First, how do the active management opportunities presented, costs of management and the key management tasks vary with market conditions, asset type and life-cycle stage? Second, how is property asset management delivered and what are the main costs and benefits of different models of procurement? Finally, what are the appropriate metrics for measuring the performance of different property managers and approaches to property management? It is concluded that the lack of published materials addressing these issues has implications for educating property students

Knowledge Management Concepts and Models Applicable in Regional Development

The paper discusses the relationship between the knowledge management models, the knowledge transfer processes, and the intellectual capital of an organization. The field of knowledge, and knowledge management, is approached both historically, and from a synchronous perspective, by presenting theoretical models of knowledge management and IC management, and by examining business clusters, as a source of competitive advantage. Some metrics for regional intellectual capital are also proposed.clusters, intellectual capital, knowledge management.

Developments in Practice XVII: A Framework for KM Evaluation

Demonstrating the value of knowledge management (KM) to the organization represents an elusive challenge. In part, this challenge is due to the nature of knowledge management itself and the difficulty in creating direct linkages between knowledge sharing and sales growth or productivity. But it is also undoubtedly due to misaligned KM activities. This paper first reviews the current state of metrics in KM and presents six principles of measurement immediately applicable to the practice of KM. It then outlines a framework for KM evaluation using four key approaches: balanced scorecard; strategic imperatives; capabilities assessment; and measurement matrix. The paper concludes by presenting a number of strategies for improving KM metrics

A Project To Improve Advanced Practice Provider Financial Metrics Through A Practice Management Program

A Project to Improve Advanced Practice Provider Financial Metrics Through a Practice Management Program This DNP project developed a practice management program for ambulatory Advanced Practice Providers (APPs) practicing in a large academic healthcare system with the goal to improve financial metrics. In 2020, Centers for Medicare and Medicaid Services (CMS) reported $25.74 billion in incorrect payments citing documentation errors and insufficiency as the common cause. The growth of the APP workforce necessitates APP practice management knowledge to avoid significant revenue loss since APPs collectively report lack of healthcare business knowledge. Twenty ambulatory APPs participated in a 12-week practice management program focused on visit code assignment, global procedural period, modifiers, charge capture, and revenue cycle management. A 10-minute podcast lecture for each concept was sent to participants’ mobile phones via text message every 2-weeks. Participants completed a pre- and post-program practice management knowledge assessment and a perceived self-efficacy survey. The participants received monthly productivity metrics. Average work relative value units (wRVUs) per session benchmarks for each participant were established and monitored during and for 2-months after the program. There was a highly significant improvement post -program in average total perceived self-efficacy of (t = 4.8695, p \u3c 0.0001) and average total knowledge acquisition of (t = 2.579, p = 0.014). Areas within these domains also demonstrated significant trends in improvement. Mean wRVUs per session during implementation was found to be statistically significant (t = 2.63, p = 0.017). at 0.60 above benchmark. In conclusion, a short, focused practice management program improved APP practice management confidence and knowledge and increased in APP estimated financial productivity

A Primer on Intellectual Capital

{Excerpt} Born of the information revolution, knowledge management has arisen in response to the belated understanding that intellectual capital is a core asset of organizations and that it should be circumscribed better. From this perspective, it is the growing body of tools, methods, and approaches, inevitably underpinned by values, by means of which organizations can bring about and maximize a return on knowledge assets, aka intellectual capital. That, Thomas Stewart explained pithily (yet broadly) is organized knowledge that can be used to generate wealth. (Conversely, it also helps to think of what intellectual capital is not, that is, monetary or physical resources.) More specifically, aggregated intellectual capital comprises • Human capital—the cumulative capabilities and engagement of an organization\u27s personnel, rooted in tacit and explicit knowledge, that can be invested to serve the joint purpose. • Relational (or customer) capital—the formal and informal external relationships, counting the information flows across and knowledge partnerships in them, that an organization devises with clients, audiences, and partners to co-create products and services, expressed in terms of width (coverage), channels (distribution), depth (penetration), and attachment (loyalty). • Structural (or organizational) capital—the collective capabilities of an organization—any of them codified, packaged, and systematized, including its governance, values, culture, management philosophy, business processes, practices, research and development, intellectual property, performance metrics, and information systems, as well as the systems for leveraging them

Technical Report: A Trace-Based Performance Study of Autoscaling Workloads of Workflows in Datacenters

To improve customer experience, datacenter operators offer support for simplifying application and resource management. For example, running workloads of workflows on behalf of customers is desirable, but requires increasingly more sophisticated autoscaling policies, that is, policies that dynamically provision resources for the customer. Although selecting and tuning autoscaling policies is a challenging task for datacenter operators, so far relatively few studies investigate the performance of autoscaling for workloads of workflows. Complementing previous knowledge, in this work we propose the first comprehensive performance study in the field. Using trace-based simulation, we compare state-of-the-art autoscaling policies across multiple application domains, workload arrival patterns (e.g., burstiness), and system utilization levels. We further investigate the interplay between autoscaling and regular allocation policies, and the complexity cost of autoscaling. Our quantitative study focuses not only on traditional performance metrics and on state-of-the-art elasticity metrics, but also on time- and memory-related autoscaling-complexity metrics. Our main results give strong and quantitative evidence about previously unreported operational behavior, for example, that autoscaling policies perform differently across application domains and by how much they differ.Comment: Technical Report for the CCGrid 2018 submission "A Trace-Based Performance Study of Autoscaling Workloads of Workflows in Datacenters