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The purpose of this paper is to provide reinforcement for ITIL V 2.0 implementation process through knowledge management principles embedded in enterprise management‐engineering framework (EMEF).

EMEF has been amended to include knowledge management (KM) activities that are imperative for a melioration of ITIL implementation. The framework of four domains has been documented in detail. Additionally, the three major amendments of structure, architecture, and context have been suggested for a configuration management database (CMDB) to comply with KM principles.

There are strong indications that implementing ITIL by following the system‐thinking approach may add and sustain competitive advantage. This may be achieved through the leveraging of knowledge, improvement of core competencies, and fostering a customer‐consciousness approach. The apprehension of knowledge continuum components, and the differentiation between knowledge types, are critical for fortifying the ITIL process path and supporting the decision‐making process throughout ITIL implementation. The four layers of the integrative management domain will significantly contribute to the tuning of operational misalignment between IT and business, and the betterment of the employee and processes effectiveness. The similarities found between ontology objects and CMDB configuration items will raise CMDB information to a higher level of conceptualization.

This paper will be valuable for ITIL customers, decision makers, and implementers by providing a more complete framework allowing organizations to attain effectiveness, efficiency and innovation throughout ITIL implementation.

The purpose of the article is to identify and define an internal communications plan that may assist in the successful deployment of a knowledge management system.

This article focuses on communications strategies that may be adopted by organizations deploying knowledge management systems where resistance to the implementation of such systems may exist. Such resistance may be a result of issues such as trust, organizational culture and misunderstandings as to the nature of knowledge management and knowledge management systems. An internal communications plan is proposed with a focus on knowledge management systems which details target audiences, timing strategies and suggests possible media solutions including traditional collaterals and conversational support technologies to facilitate improved deployments of such systems.

The article proposes a framework of communications methodologies, timings and target audiences to be considered during the deployment, pilot and pre‐pilot phases of a KM system implementation. It concludes by discussing the opportunities to use conversational technologies in deploying KM systems to knowledge workers.

The deployment issues raised in this article are expected to be of value to KM system developers, project managers, systems analysts, business executives and academic researchers.

The aim of this paper is to find out how the productivity of public services can be measured in the operative level of organisations. In particular, the role of different output elements (tangible and intangible) is examined.

A qualitative research approach is used. The current knowledge of the issue is presented by examining literature on public service productivity, intellectual capital and performance measurement. In the empirical part, the findings of the literature review are applied and further examined in the context of two services of the City of Helsinki, Finland.

The paper identifies tangible and intangible output factors in two case services. It also illustrates how the factors can be measured in practice. The empirical examination suggests that the challenges in the operative level productivity measurement relate especially to defining measures. Identifying of different output factors is an easier task.

The study presents and applies a novel approach of designing productivity measures for public services. More empirical studies using the approach are called for.

The measurement approach presented here can be utilised as a basis for designing sophisticated productivity measures of public services.

A key challenge in examining public service productivity relates to the intangible nature of services. Despite the great potential and practical relevance of the topic there seems to be no understanding of how to capture this feature in order to design valid productivity measures. The paper adds to the current knowledge by describing the process of designing measures for disaggregated components of productivity, shares practical experiences related to the design process and highlights the main challenges.

This paper aims to define a theoretical model that assesses and measures the intangible asset contribution to company performance. The model keeps in focus the most meaningful elements that reflect the success factors, crucial to company business strategy and value creation.

The model adopts a hierarchical structure. The strategic intangible assets of the company have been divided into value drivers; a series of measurement indicators have been selected to describe the characteristics of each aspect of the intangible company performance. The measurements obtained from numerical indicators, which express the totality of the results achieved by the organizational strategies, are combined to create the hierarchical assessment index (HAI), by assessing quantitative and qualitative company features, through the analytic hierarchy process (AHP).

The HAI identifies the sources of added value and competitive advantage in each business context; it traces the subordination of every element on company performance, and singles out those intangible assets that improve the company performance, at every level of the hierarchy.

The HAI provides guidelines to understand what are the key intangible factors to create the value of the company and suggests the implementation of corrective strategies.

The HAI is the expression of the combination of the objective measurements of intangible assets with the subjective contributions by the managers. In fact, besides the numerical results of the performance of every element in the hierarchical structure, the managers' opinions about the significance of their performance are also considered. Thus, objective and subjective evaluations jointly contribute to suggest the way to achieving the expected objectives.

The aim of this paper is to understand how many and what intangible assets firms from two different contexts disclose in order to comprehend whether an accounting harmonization is actually reached in practice and what are the eventual hurdles to surmount in order to reach it.

A qualitative and quantitative analysis of the purchase analyses disclosed by the Swedish and Italian listed companies in their financial statements refering to the first year of application of the IFRS3 is conducted.

The main findings are the following. First, firms do not disclose intangible assets in the same way. Second, contracts become a useful tool to make it possible to account for IC. Third, the disclosure of labels shows a variety. Fourth, differences in behavior are seen.

The main limitation is that only a sample of firms (the listed ones in the SSE and MTA/MTAX) that apply IFRS3 is investigated. The main implication is that the disclosing of IC in financial statements is problematic and makes harmonization difficult to achieve. The empirical deepening of these two conclusions represents opportunities for future researchers.

The research is an investigation of the first year of application of a new accounting principle from an inter‐country comparison considering it as an opportunity to disclose more IC and consequently to contribute to the debate about how and what IC should be disclosed.

This study seeks to examine how the quantitative semantics of the learning curve phenomenon can be employed in order to derive monetary information for team learning observed within knowledge‐intensive production environments.

Software development is selected as an identical example of a team‐based, knowledge‐intensive production environment. The interaction of learning rate of the developer teams and the improvements on their average solving time (i.e. productivity) is modelled as a Lotka‐Volterra predator‐prey interacting populations system establishing a causal relationship between the human capital (HC) of organizational teams and the observed learning curve effects on their performance. In addition, empirical evidence illustrates that the estimated learning rates capture the entire range of team learning effects on performance fluctuations caused by the HC.

The fluctuations on the learning rates can be interpreted as a result of the HC variability across the population of developer teams. Hence, the cost implications of the HC within knowledge‐intensive production environments can be rationalised using the quantitative semantics of the learning curve phenomenon

The learning curve is associated with the cost side of the organizational income‐generating process limiting its potential valuation applications for team learning observed within the context of the production environments.

The study offers a theoretical justification, supported by empirical evidence, for employing the mathematical expression of the learning curve paradigm to rationalize the financial consequences of team learning observed within production environments.

Intellectual capital theory and practice predominantly focus on measuring and managing intangible assets. However, if one wants to balance the intellectual capital books, one should recognise both intellectual assets and intellectual liabilities. Therefore, the purpose of this article is to present a theoretical framework for measuring intellectual liabilities.

Identifying intangible liabilities is identifying the risk of the decline and fall of organisations. One of the first extensive studies related to the causes of decline and fall is Gibbon's The Decline and Fall of the Roman Empire. It seems as if the main lessons that were drawn from the study are also applicable to today's business environment. Therefore, the framework that is developed here is based not only on intellectual capital literature, but also on Gibbon's study into the causes of the decline and fall of the Roman Empire.

The findings are combined in a framework for measuring intellectual liabilities. The main distinction within the proposed framework is the distinction between internal and external liabilities. Internal liabilities refer to the causes of deterioration that arise from the sources of value creation within the organisation. External liabilities refer to the causes of deterioration that come from outside and are beyond the control of the organisation.

The article explores a relatively new topic (intellectual liabilities) from a perspective (historical sciences) that is rarely used in management science.

The aim of this paper is to propose an integrated model for intangible resources, which will contribute to breaking down the old paradigm, and bring it to managers' attention that the best returns are likely to come from intangible resources.

The theoretical approach is based on the concept of core competencies as defined by Hamel and Prahalad, and further developed in Andriessen's Making Sense of Intellectual Capital. It also explores the benchmarking concept presented in Viedma's “Intellectual capital benchmarking system”.

Business practitioners and scholars have been developing new methods and models to understand intellectual capital. Unfortunately, these methods and models present a view on both tangible and intangible resources of an organisation that is non‐integrated and reinforces the traditional management focus on tangible assets only.

The method proposed here is being prepared to be tested in a large energy company under a three‐phase project (the object of future papers).

The paper describes a model and introduces a method based on it.

This article aims to show the application of scientometrics and patent bibliometrics in remaining useful life (RUL) analysis for evaluating the value of intangible assets.

Technology innovation management is strictly related to the RUL. The RUL concept is defined as the time remaining until the reliability drops below a defined minimal operating threshold. The RUL analysis of certain intangible assets (patents and know‐how licence agreements, industrial designs, trade marks, logos, customer base) is done through different methodologies and various different approaches. The key subject in all these methodologies is the life cycle of the technology. The analyst tries to approach the foresight of the life cycle of technology to establish its value in use. Different life measure systems are considered in RUL analysis depending on different typologies of technology life: statutory, contract, judicial, economic and functional. Data used in life cycle estimation may be used in RUL analysis. Typically, these data include scientific articles, registration documents (patent applications, trade marks and copyright applications), commercial contracts, judicial orders, financial statements and technology data.

The analysis of the life cycle allows the incorporation of qualitative considerations (legal, contractual, physical, technical know‐how, functional, economic) related to the conduct of future technologies. But technology development is conditioned by trends in scientific research and by the changes in the marketing dynamic, today and in the future. Qualitative methods provide a valuable information service that relates to the intangible assets over time. The “typical survivor curve” shows the released, remaining and probable life span of a certain technology by taking into account factors such as technological changes, marketing acceptance, and other exogenous and endogenous factors. Quantitative analysis of scientific production, applications for patents, industrial designs and trade marks, developed in scientometrics and bibliometrics, provide an unbiased guide to R&D and business trends.

The original purpose of the paper is to emphasise how the technology life cycle is influenced by changes in technology but also in scientific research evolution. Scientific research life analysis must examine the historical emergence or decay of a certain intellectual interest in the scientific community through the study of what is and what is not published in scientific journals.