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The purpose of this paper is to propose a logical approach to identifying and modeling business intelligence from corporate information.

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.

Modeling business intelligence from the hybrid blends of value sources provides a view of business intelligence in more realist dimensions.

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.

The purpose of this paper is to present the culmination of an engineering concept of a knowledge valuation system that identifies the name of intangible assets.

The paper employs the methodology or approach of identifying or naming intangible assets within the business environment.

The paper finds that knowing what to capture is fundamental to alignment with measures and indicators inherent in a business operational data.

The paper tackles the elusiveness of intangibles and attempts to bring structure and identification to a complex and confusing discipline. The decomposition of an intangible assets system that names intangibles could be the first step to measuring, valuing and managing them.

The paper seeks to develop a list of operational knowledge assets as antecedents to a validated common taxonomy of intangible strategic value drivers.

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.

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.

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.

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.

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.

Sustainability has become a topical issue in many countries, with emphasis on green buildings. Though Ghana has recently adopted green buildings, there is lack of its speedy implementation. There is little literature on the adoption of green certification of buildings, especially in a developing country like Ghana. The purpose of this paper is to identify the factors that drive the adoption of green certification of buildings in Ghana.

The study adopts face-to-face and telephone interviews, using a semi-structured interview guide among ten built environment professionals. Qualitative responses to the interview are thematically analysed using Nvivo 11 Pro analysis application software.

The findings suggest that “observability of the benefits of green certified buildings”, “commitment of Government to green building initiatives”, “incorporating green certification of buildings into the code of practice of professional bodies”, “green building certification incentives”, “public acknowledgement of the green building concept”, “policies and regulations to enforce the adoption of the concept” and “effective communication and source of information on the concept” are the factors that drive the adoption of green certification of buildings in Ghana.

The paper contributes to the understanding of the factors that drive the adoption of green building certification in Ghana. With these findings, stakeholders and industry practitioners can make informed decisions regarding how they can put in place strategies to ensure the effective adoption of green certification of buildings. Though this study was conducted within the context of Ghana, its findings and implications can be useful to policy makers, stakeholders and practitioners in other developing countries.

The purpose of this paper is to present valuation, economic, and corporate management aspects related to the design and implementation of intangible asset valuation in common business language.

The methodology or approach to identifying or naming intangible assets within the business environment was used.

The purpose of intangible asset valuation is to understand what the intangible asset is and how it affects the bottom line of the business. Understanding the reason for the intangible asset valuation, whether for tax purposes, corporate planning, or dispute resolution, is paramount when considering the nature of the intangible asset to be valued.

Intangible assets are generally not included in active company management. Many companies do not recognize or investigate ways to maximize the income to be derived from intangible assets or other benefits of a centralized intangible asset management program. Fundamental to valuing intangible assets are their identification and subsequent representation. As presented in this paper, the three categories of intelligence identify intangible assets by the business value drivers that comprise them.

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.

This paper discusses knowledge valuation.

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?

Contains useful information on knowledge valuation.

This paper proposes a logical starting point to valuing knowledge within the context of the business enterprise.

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).

The findings of the paper are that, fundamental to implementing a KVS is the leveraging of the current enterprise environment, to uncover the intelligence that currently exists so as to make decisions and act on those decisions. This paper presents the first layer of a conceptual model that supports the development of a KVS that leverages the current enterprise environment and integrates existing systems development methods and techniques in its approach to KVS creation and implementation.

The conceptual model – KVS Building Blocks – integrates fundamental types of models with each other to provide a holistic approach to implementing knowledge valuation in an enterprise environment. The first block in the KVS Building Blocks establishes a journey to identifying the data and information enterprise stakeholders need represented in a language they understand, to build a foundation that supports the capability of enterprise stakeholders to value knowledge within their enterprise.

This paper proposes a logical approach to valuing knowledge within the context of the business enterprise.

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).

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.

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.

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.

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.

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.

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

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.

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.