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The paper discusses the role of measurement and classification in intellectual capital research. Its purpose is to develop an alternative role for measure. The aim is to explore how measurement may be important even if it does not pretend to accurately represent and underlying reality.

The paper is an essay on the difficulties of measuring intellectual capital. It is a conceptual, analytical paper which proposes a new approach to analyzing the roles and effects of measurement in the area of intellectual capital.

The analysis suggests that measurement of intellectual capital is difficult because it is not possible to copy its properties in a number; yet it is necessary because it allows intervention to happen since it develops a wholly new set of dimensions to manage. This dual conclusion both problematic because the search for a definitive measure of the size, value and effects of intellectual capital is impossible, yet it is also comforting because measurement helps develop the actions that can be made in the name of intellectual capital.

The central implication is that research should be explicit about the status of measurement in intellectual capital research since measurement does not primarily represent an underlying reality to be reported by intellectual capital. This directs attention to different purposes of measurement.

This paper explores alternative roles for measurement and suggests that it is necessary to study how measurement is input to management decision making more than a representation of an underlying reality.

The purpose of this paper is to investigate the measurement and the management of the dynamic aspects of intellectual capital through the use of causal mapping.

The paper details the methods utilized in a single in-depth case study of a network-based business model.

The paper illustrates how causal mapping can be used to understand how intellectual capital really works in the specific business context in which it is deployed. Moreover, exploiting the causal map as a platform for extracting a set of indicators can provide information on the length of the lag and the persistence of the effects of managerial actions. In addition, it can signal when and how to refine and update the causal map. The combination of these factors can potentially support the dynamic measurement and management of intellectual capital.

The paper presented has two main limitations. First, the use of a single case study to provide in-depth and rich data limits the generalizability of the observations. Second, the proposed approach has not been implemented in practice. Future research opportunities include interventionist-type case studies that put the causal mapping approach into practice.

The paper highlights the need to build causal maps to enhance the measurement and management of intellectual capital, which is dynamic in nature. As a consequence, this tool can be useful for monitoring the intangibles of companies and networks and to better understand the contribution their intellectual capital makes to the value creation process.

The paper openly questions the measurement of the fluid and dynamic aspects of intellectual capital. It proposes a tool for governing these aspects and it suggests that even the existing intellectual capital measurement systems can improve their usefulness by including these dimensions. So, a shift in intellectual capital measurement is prescribed.

Measurement of intellectual capital is important, but not only for descriptive purposes. It is important because it enables intervention. If intervention and measurement are coupled, then measurement is an input rather than an output, and then measurement is not to be evaluated on its reflection of reality but rather on its ability to help actors transform their reality. This is particularly true for intellectual capital, which is widely accepted as part of an agenda for transformation and growth – it is a strategic/political agenda. To arrive at this conclusion, the paper discusses relationships between measurement and intervention comparing conventional financial statements with intellectual capital statements.

Intellectual capital, as used here, is a concept that deals with intellectual property concepts, like patents and licences, but also includes less tangible assets like know‐how, skills and information systems. The need to measure the amount of intellectual capital in an organization has grown in importance. The worth or value of an organization cannot be given by the values in the balance sheet alone. Just as the use of discounted cash‐flow analysis is being accepted as a more reliable measure of value and will eventually replace other accounting‐based valuations, the value of intellectual capital will need to be measured and determined. Intellectual capital will also be a useful concept for setting corporate goals and strategies. Describes some current conceptions of intellectual capital and looks at ways of measuring intellectual capital. Compares intellectual capital valuation with financial cash flow valuation. Suggests that additional studies regarding the measurement of intellectual capital are still needed, and recommends, when good measures of intellectual capital are not available, the use of indicators as a means of indicating that intellectual capital is present or growing.

The purpose of this paper is to define the elements of intellectual capital of firms in Turkey and to empirically investigate the relationship between intellectual capital and market value of firms in Istanbul Stock Exchange. To create a suitable intellectual capital measurement model for this study, a wide literature research was made. In almost every created intellectual capital measurement model, elements of intellectual capital are defined in three dimensions; i.e. human capital, structural capital and relation capital. For the research, an intellectual capital measurement model is created and four hypotheses are defined. The main conclusions from this study are that: human capital and relation capital of firms have a positive relationship with market/book value of firms in Turkey; and structural capital of firms in Turkey has a correlation with human and relation capital.

Developing intellectual capital and knowledge management measuring systems are two fast growing research areas. Many companies are striving to be known as knowledge organizations and have started measuring and analyzing organizational intellectual capital indicators based on what has been reported in the literature. Very little effort has been made to standardize the measurement and reporting of these indicators with most organizations using very general components. This article discusses a research study that sets out to gain an understanding of management attitudes to the measurement of intellectual capital. The study was conducted with 36 top management participants with at least 20 years of experience in a service‐related industry in Australia. This research investigates the relationship between the perception of the importance of measuring intellectual capital indicators and the level of understanding of these indicators. The strategic implications of understanding the measurements are also discussed within the context of the attitude of top management.

This study seeks to provide a broad ranging review that identifies, summarises and integrates the different multi-level approaches contributing to advances in research on measuring IC. This furthermore sets out and highlights an agenda for future research.

Deploying a systematic and thorough review of the literature, the authors were able to identify 60 articles and identify the main theories applied and the respective methodological orientations of these articles across three levels of analysis: micro, meso and macro.

The study's findings suggest that the literature on measuring IC has approached the theme across three different levels –micro, meso and macro. In addition, the results enable the identification of seven dimensions to IC: competitive advantage, economic value generated, resources and capacities, corporate governance, IC components, innovation management and the dissemination of IC.

The mixed-methods approach, which combines a traditional systematic literature review with ontological thematic analysis, casts light on the core aspects that led to the identification of a new approach in the academic literature on measuring IC.

This study puts forward a holistic model with measurements for IC across the three levels of analysis as well as the respective criteria for choosing the variables.

This study represents a first attempt to analyse the emerging literature on IC measurement through a multi-level approach; integrating and extracting the potential theoretical contributions in this field of knowledge; proposing an integrated model as well as a theoretically relevant and innovative research agenda that opens up paths to future research projects.

The literature shows several intellectual capital models. Nevertheless, there is little empirical evidence about the building blocks that form intellectual capital in practice. The purpose of this paper is to test the widespread categorization of human capital, structural capital, and relational capital with a survey applied to high‐technology firms from Spain.

Factor analysis was conducted with a sample of 49 firms (larger than 50 employees).

The results indeed demonstrate the existence of three main components of intellectual capital that, in general, fit the dominant structure proposed by other authors.

Before moving into an internationally accepted system for classification and measurement of intellectual capital, future research should seek a geographical and industrial agreement about the main components of this construct. In that direction, our empirical evidence provides only the experience of Spanish high‐tech firms; this experience could be different in other countries or industries.

In this paper, managers interested in the field can find a useful guidance for structuring an intellectual capital balance sheet, taking the three proposed components as main dimensions, and the items of the survey as a measurement tool for analyzing the intellectual strengths and weaknesses of their firms.

Academics can also benefit from this research, taking it as a basis for replication studies about intellectual capital in other countries and/or industries. This article presents one of the first empirical tests of the theoretically accepted components of intellectual capital.

The purpose of this paper is to demonstrate how the ICS intellectual capital methodology was developed starting from the underpinning academic theory.

The approach is founded upon a number of theoretical strands. The basic intellectual capital approach is based on a development of the resource based theory of the firm. Most intellectual capital approaches have problems with meaningful measurement. ICS addresses the valuation of intellectual capital resources by using axiology and multi‐attribute value theory to produce a valuation framework and measurement theory to ensure that the results are reliable.

The ICS intellectual capital approach generates navigators (maps) of how resources are used in companies which have proven to be very useful. It has also demonstrated the value of deeper analysis of the intellectual capital resources. The measurement part, which is often used independently (known as the Conjoint Value Hierarchy (CVH), is shown as a powerful aid to decision making as well as to more straightforward valuation.

The limitations are that the navigator and its associated analyses are non‐rigorous while the CVH is rigorous, transparent and auditable. This mismatch can lead to problem and the challenge is to integrate them.

While parts have been reported previously, this paper is the first integrated review of ICS’ methodology.

The purpose of this paper is to describe the development and validation of an instrument for measuring intellectual capital in the academic research context. The current research context describes a new paradigm of scientific production characterized by interdisciplinarity, heterogeneity and the intensification of the relations between the generators of knowledge. In this scenario, traditional measures of intellectual capital do not capture all the variables that make up the environment in which the research activities are carried out. This transformation of research processes suggests the need to bring theories of organizational behavior, more appropriate to an organizational context, to the study of scientific context. Thus, the paper contextualizes the intellectual capital approach, thereby explaining how the different attributes that build it influence scientific productivity and providing a measurement instrument to evaluate relative levels of intellectual capital in an academic research context.

The scale was designed through a double qualitative–quantitative scale development process. The literature on intellectual capital does not provide strong theoretical support for the definition of a specific set of items to be applied in the specific academic research context. Consequently, the scale constructs and observable variables were initially conceptualized through a Delphi panel. This initial set of indicators was empirically validated through a second quantitative stage to a sample of 1,798 Spanish academics. Given that no prior published studies have examined the construct validity of the proposed scale, and the proposed scale is not based on other previously validated scales, the authors used exploratory and confirmatory factor analysis to assess the internal consistency, using Cronbach’s α to determine reliability.

Drawing on the evidence obtained from a double qualitative–quantitative process, a scale consisting of 47 items was proposed to measure the three dimensions of intellectual capital, namely, the researcher’s human capital, as well as the nature of the social capital and organizational capital of the team in which the scholar is integrated. The process of identifying and validating indicators of intellectual capital allowed the authors to identify certain intangible elements that are key in the research process and that, therefore, determine scientific productivity. Thus, the proposed scale contributes by conceptualizing new variables that could be used to deepen and broaden the study of the determinants of research performance. The contextualization of intellectual capital approach can also help to assess the value of intangibles, offering an external reporting tool and making universities’ social contributions more visible to public and private stakeholders, justifying the efforts made by societies in the generation of academic knowledge.

The empirical analysis was carried out with an initial sample of 1,798 Spanish scholars. The validation of the scale should therefore be confirmed in different national contexts, with larger data sets. Likewise, the use of longitudinal data sets could help to study the effects of intellectual capital in academic research, thereby contributing to the ongoing debate on the determinants of research performance.

From a practical perspective, the instrument could be considered both as a management and an external reporting tool, providing a self-assessment instrument of the levels of intellectual capital. As a management tool, a specific measure of intellectual capital in an academic context could help to identify training needs, the implementation of practices that encourage the capability for building research networks and the development of reports with intellectual capital-related inputs for the justification of the resources received. At an institutional level, the proposed set of indicators also identifies the attributes of scholars linked to higher scientific performance, and the scale could be used as an instrument for selection processes in academic institutions, to develop practices related to the distribution of workload or the publication of intellectual capital indicators of its researchers in a healthy exercise of transparency.