Purpose -The paper seeks to develop a list of operational knowledge assets as antecedents to a validated common taxonomy of intangible strategic value drivers. Design/methodology/approach -Qualitative data from focus groups was collected to validate a theory-based list. The list contains the value generation activities (i.e. performance focus areas) and their respective critical success factors emerging from the interaction of eight validated intangible value drivers. Two primary questions were addressed at the focus groups: What performance focus areas does the organization need to focus on?; and What knowledge do employees need to leverage within each focus area? Data were analyzed using template analysis. Structural equation modeling using PLS was performed to develop a theory about the impact of the list of operational knowledge assets on intellectual capital and business performance measures. Findings -A validated list of knowledge assets called the list of operational knowledge assets (LOKA) was developed comprising 31 categories (i.e. critical success factors) grouped into seven value generating activities (i.e. performance focus areas). The thematic dimensions of each category surface internal views of a business that define the strategic and operational value drivers that are aligned with organizational performance.Research limitations/implications -The list is not an exhaustive one but rather a template that can be adapted based on the industry investigated. The scope of the study was focused on the high-tech federal contractors in the USA. Therefore the results and related models of this study are generic in nature. Future research could focus applying the study on different environments (i.e. organizational, cultural, type of knowledge workers) and compare results. Practical implications -The paper could improve managers' understanding of the human capital drivers on performance and thus facilitate better resource allocation on human capital management practices, technology investments, process improvements and business intelligence functions. Originality/value -The list of operational knowledge assets presents a first attempt in addressing the lack of understanding of the knowledge assets that managers need to leverage at the lowest level of operational granularity.

Purpose -Proposes that one should take a few steps back to engineer and understand one's knowledge blueprint, so that one can understand better and have more certainty in one's outcome of knowledge valuation. Design/methodology/approach -This paper discusses knowledge valuation. Findings -Businesses are chasing "knowledge" and implementing "knowledge" solutions, with not a clue as to whether the knowledge they are chasing or using to solve business problems is achieving the outcome towards which they are driving. In other words, there is no clear starting-point (baseline measures) that can be monitored and tracked over time to ensure that the goal is being achieved, to encourage abandonment if the results are not favorable, or to revise the goal if the outcome is favorable. Notwithstanding, there are success stories in leveraging knowledge, but have businesses in the knowledge era truly institutionalized the identification, capture and leveraging of knowledge to adequately manage and control the intangible assets that contribute 70 percent of the value of a business? Originality/value -Contains useful information on knowledge valuation.

Purpose-This study investigates the adequacy of existing intangible asset models and defines and codifies common principal valuation drivers of intangible assets for use in enterprise balanced scorecard valuation practices of information technology (IT) firms. Design/methodology/approach-Existing intangible asset balance scorecard valuation models and value chain models are evaluated to extract their value components and align them with performance-based activities of the business enterprise to define a common taxonomy of value drivers of intangible assets. Chief executive officers (CEOs), chief finance officers (CFOs) and "other executives" of IT firms validate the taxonomy. Findings-IT firms that use a standard and consistent taxonomy of intangible assets could increase its ability to identify and account for more intangible assets for measurement and valuation. Research limitations/implications-This study is limited to the Washington Metropolitan Area, is a single sector study (IT firms), the target audience is CEOs and CFOs; and emphasis is on the Score Card (SC) type model as classified by Sveiby. Future studies could expand the geographic circumference, the scope to other industry sectors, and the target audience to other decision makers Practical implications-The framework of intangible valuation areas (FIVA) allows a business to identify and link performance measurements/indicators to its intangible value drivers. It supports the capture and subsequent evaluation of leading and lagging indicators in the achievement of a knowledge management strategy. Originality/value-FIVA provides a framework to have command of and access to effective utilization of business resources and knowledge, to develop, sustain and enhance its mission effectiveness and/or competitive advantage.

Purpose -This paper proposes a logical approach to valuing knowledge within the context of the business enterprise. Design/methodology/approach -The methodology or approach to knowledge valuation is derived from empirical research based on a framework of intangible valuation areas (FIVA). The key valuation components of FIVA are used as the basis for the evolution of an enterprise knowledge valuation system (KVS). Findings -A conceptual model provides the foundation a business needs to construct a KVS that aligns with business performance. This aids businesses in modeling their business intelligence and identifying intelligent behavior that significantly contributes to the decision-making process of stakeholders in today's business enterprises. Originality/value -Businesses enterprises are challenged with the development and use of knowledge within the business to positively affect the performance and market valuation of a business enterprise. The conceptual model presented in this paper expands on existing components of intangible asset and value chain models to aid business stakeholders in a method that organizes and structures enterprise knowledge such that they understand what their enterprise knowledge is and the value of their enterprise knowledge.

Purpose -The purpose of this paper is to propose a logical approach to identifying and modeling business intelligence from corporate information. Design/methodology/approach -The methodology or approach to identifying business intelligence is based on the cross-pollination of eight business value drivers. Modeling business intelligence from the hybrid blends of value sources provides a view of business intelligence in more realist dimensions. Findings -Modeling business intelligence from the hybrid blends of value sources provides a view of business intelligence in more realist dimensions. Originality/value -Business enterprises are challenged with identifying the primitive components that construct business intelligence within the business. The conceptual model presented in this paper decomposes to the primitive level of information or meta-data needed to model business intelligence. The primitive level defines the business objects and provides a map to defining the actual business information to capture.