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There is a dramatic increase in the number of companies whose value lies largely in their intangible assets; with relatively little or no value associated with their tangible assets. Traditional methods of valuation, based on accounting principles, where the value of the firm’s assets is a portion of the value, have systematically undervalued companies such as these. This article discusses the problem of valuing intangibles companies and suggests two approaches to determining their value. It also describes two common circumstances where company value is desired and discusses how value may be determined using a non‐traditional perspective on the company along with traditional methods for valuation. The two circumstances examined are the going‐concern value and the value under merger or acquisition circumstances (recognizing that these two circumstances produce very different valuations for the corporation).

This paper summarizes a study, undertaken by Arthur Andersen’s Intellectual Property Group in London, to consider the economic and financial issues, principally as they affect the valuation of intellectual property and its suitability as security. The study encompasses a review of available literature, interviews and discussions, and an analysis of the results of a questionnaire which was distributed to owners and managers of intellectual property. Views were canvassed across industries, of both borrowers and lenders, and also of lawyers and other advisers experienced in the transactions involving intellectual property.

At The Dow Chemical Company, Intellectual Assets are regarded as knowledge with a value and are a key factor in creating wealth for the company. These Intellectual Assets include patents, trade secrets, trademarks and know‐how. The company has devised a six‐step Intellectual Asset model which involves strategy setting; identification and classification of existing Intellectual Assets; valuing these assets; deciding whether to invest in new knowledge through the development or purchase of technology and skills; and assembling the Intellectual Assets into Knowledge Portfolios for each global Dow business. This process is repeated on a regular basis. This paper discusses how the methodology has been developed and implemented at Dow, including the company’s approach to creating a Global Intellectual Asset Technology Centre and multi‐functional and multi‐geographic Intellectual Asset teams. Tools and techniques developed for measuring the company’s Intellectual Assets are generic and may be appropriately applied to any organization.

This chapter makes a case for extending institutional preservation strategies to the entire landscape of knowledge capital. First, the authors define the three primary types of capital – physical, financial, and knowledge. Knowledge capital is further broken down into three categories – human, structural, and relational. The individual types of knowledge capital are defined, along with their variant economic properties and behaviors. The challenges these variations present for preservation are discussed. The authors also highlight these assets’ significant opportunities for curating new knowledge. Each type of knowledge capital is described, along with the preservation challenges and the curation opportunities.

Publishes some of the findings of a research project that examined whether valuers and valuations have a role in the provision of more strategic property advice to business occupiers. The research consisted of a postal questionnaire survey of over 250 businesses that examined the role of property and the use of valuations in strategic business decisions. The survey was supported by the findings from 18 structured interviews and an analysis of over 70 sets of company accounts. The results revealed that, despite valuers becoming increasingly involved in measuring corporate efficiency and valuations being used for this purpose, business occupiers do not recognise valuers in a strategic role. Instead, many firms see valuers as providing a single valuation service, the estimation of market value for purchase/sale decisions and corporate disclosure. The research suggests that valuations do have a role to play in the provision of more strategic business advice but the valuer will need to understand the client’s wider business needs and how property plays a part in the client’s business. Valuers need to convince clients that they are not overly technical in their outlook, have broad business skills that include strategic thinking and an awareness of business issues.

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.

The paper seeks to develop a business model that shows the impact of operational knowledge assets on intellectual capital (IC) components and business performance and use the model to show how knowledge assets can be prioritized in driving resource allocation decisions.

Quantitative data were collected from 84 high‐tech federal contractors in the Washington DC metro area. Respondents in the target population were middle‐level and operations managers of business sectors holding positions as presidents, vice‐presidents, directors, engineering managers, operations managers, and analysts. Partial least squares (PLS) analysis was performed to develop a structural model between operational knowledge assets and IC components that maximizes explained variance for business performance. Operational assets were specified as formative constructs and IC and business performance were specified as reflective constructs.

A parsimonious conceptually sound model with significant measured variables and path coefficients was developed that explains almost 40 percent of the variance in business performance. The model shows both the interrelationships between the IC components that drive performance and the operational assets as levers for each IC component, respectively.

The scope of the study was focused on the high‐tech federal contractors in the USA. However, the model can be applied and tested in different industry sectors. This would provide evidence of the different operational knowledge assets used as levers in different industry sectors.

Senior executives and chief financial officers in particular are constantly challenged with making the optimum investment decisions given their budget constraints. The model offers a tool for developing and evaluating different resource allocation decisions based on an organization's strategic intent. In addition, the model can be useful in evaluating merger and acquisition decisions. In evaluating target companies the model can be used to identify the core capabilities or competency areas that the target company is leveraging and assess the impact or integration potential for the acquiring company.

This is the first study in the field of IC that has adopted the use of formative indicators in specifying operational knowledge asset constructs. Previous research has focused on developing models with the use of proxy measures as reflective indicators. Therefore the emphasis so far has been on scale development. The use of formative items in this study fills both the business need and theory gap to understand better the causal relationships that exist between work and knowledge assets.

The purpose of this paper is to explore the interrelationships of intangible assets to business performance. The paper reports an empirical evidence for the impact of three elements of intangible assets: learning and growth, internal business process, and external structure on the business performance of the firm. The linkages between intangible asset elements and business performance are investigated in companies of various business sizes, business sectors and establishment ages.

The proposed model was adapted from the balanced scorecard strategy map. The primary data for analyzing and investigating the interrelationships between intangible assets and business performance were gathered by subjective opinion survey questionnaire. In all, 3,084 questionnaires were distributed to the top management. The numbers of qualified responses were 304 and the data were analyzed using the structural equation modeling technique.

The commonly assumed causal relationships are confirmed, i.e. the element of learning and growth has influence on internal business process, the element of internal process has effect on external structure, and the element of external structure, in turn, has effect on business performance.

Following the research findings, top management in companies of different sizes, business sectors, and establishment ages should understand the nature of interrelationships and recognize the importance of intangible assets. These findings will enable top management to realize the impact of intangible asset elements on business performance so that long‐term strategies for effective intangible asset management may be emphasized for sustainable competitive advantage of the firm.

The purpose of this paper is to present the strategic intangible process assets characterization (SIPAC) methodology illustrated by an example of its application to the field of information technology (IT). This is a pioneering methodology for characterizing the impact and quality of intangible process assets and intellectual capital as levers to achieve organizational objectives. This strategic intellectual capital approach will help to identify both intangible assets and indicators geared to meeting organizational objectives. This is of vital importance since the success of an organization can be construed in terms of goal achievement.

The paper illustrates an example of the step-by-step application of the proposed methodology at an IT company. The aim is to describe its use in a real case so that other companies can benefit from the replication of the methodology used.

The proposed methodology (SIPAC) that the authors have designed and applied has been found to be useful and provide an insightful new point of view for strategic decision making in the IT industry taking into account intangible process assets.

The proposed methodology has been exemplified in a real case. This should help organizations to use the methodology to replicate the results.

Each and every organization has know-how represented by intangible assets. This paper meets an identified need to use intangible process assets as levers to help organizations achieve their business goals.

The purpose of this paper is to analyse current challenges and to articulate the preconditions for data-driven, fact-based product portfolio management (PPM) based on commercial and technical product structures, critical business processes, corporate business IT and company data assets. Here, data assets were classified from a PPM perspective in terms of (product/customer/supplier) master data, transaction data and Internet of Things data. The study also addresses the supporting role of corporate-level data governance.

The study combines a literature review and qualitative analysis of empirical data collected from eight international companies of varying size.

Companies’ current inability to analyse products effectively based on existing data is surprising. The present findings identify a number of preconditions for data-driven, fact-based PPM, including mutual understanding of company products (to establish a consistent commercial and technical product structure), product classification as strategic, supportive or non-strategic (to link commercial and technical product structures with product strategy) and a holistic, corporate-level data model for adjusting the company’s business IT (to support product portfolio visualisation).

The findings provide a logical and empirical basis for fact-based, product-level analysis of product profitability and analysis of the product portfolio over the product life cycle, supporting a data-driven approach to the optimisation of commercial and technical product structure, business IT systems and company product strategy. As a virtual representation of reality, the company data model facilitates product visualisation. The findings are of great practical value, as they demonstrate the significance of corporate-level data assets, data governance and business-critical data for managing a company’s products and portfolio.

The study contributes to the existing literature by specifying the preconditions for data-driven, fact-based PPM as a basis for product-level analysis and decision making, emphasising the role of company data assets and clarifying the links between business processes, information systems and data assets for PPM.