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The purpose of this paper is to illustrate how the emergent properties perspective from the field of biology can be applied to the field of intellectual capital. Much attention has in recent years been directed towards problems of accounting for intellectual capital and how the value of intellectual capital at one level of an organization influences the value of intellectual capital at higher or lower levels of the organization.

Through a literature review an emergent properties framework was developed. This framework was applied to an intellectual capital perspective with empirical illustrations.

In this paper, it is argued that the inherent difficulties of understanding the interdependencies of intellectual capital across different levels of an organization can be traced to a lack of understanding of the differences between synergetic effects, causal relationships and emergent properties. The paper illustrates through examples how an emergent properties perspective can be mobilized in relation to the field of intellectual capital.

The paper is conceptual in nature and reports the theoretical propositions from an ongoing empirical project. The lack of empirical content limits the strength of the conclusions.

The paper is a step in the direction of enhancing the understanding of the different levels of intellectual capital by applying the biological perspective of emergent properties. Although this perspective in itself may not constitute a general testable theory of intellectual capital, it is the hope, at least, that it may provoke thought, development and further research in this field.

Purpose – To question the coupling of “intellectual” with “capital” and the assumption that such a coupling legitimises measurement. Design/methodology/approach – The contestability and multiple meanings of intellectual capital are revealed using rational and non‐rational management perspectives as examples. A process is presented through a metalectic framework that exposes a variety of attitudes of mind so that the integration of rational and non‐rational management perspectives becomes a possibility. Findings – Intellectual capital cannot be reduced to a calculable number that establishes whether an organisation's intellectual capital has increased or diminished. Intellectual capital can only indicate a direction when imagination, creativity and learning are at work. Research limitations/implications – Without a critical approach that provides an insight into the way different perspectives are promoted and what their promoters gain from their use it is not possible to make sense of intellectual capital. Practical implications – The realisation of the potential of intellectual capital requires a fundamental change in the assumptions of what management is about and that forcing the thinking about intellectual capital into existing working frameworks will not bring about a change in the attitude of mind of managers or workers. Originality/value – This paper offers an alternative thinking that provides a richer and broader meaning for intellectual capital by locating different perspectives on their strengths and by giving equal importance to them whilst endlessly remaining critical of them. This way of thinking is more appropriate than what is currently on offer if intellectual capital is to become more meaningful.

This paper compares balanced scorecard and intellectual capital and finds important differences between their theoretical underpinnings, which suggest that the breath of indicators will work differently in organisations.

Analysing texts about balanced scorecard and intellectual capital, the paper discusses not the obvious similarities – that they are both integrated performance management systems – but four more aspects: strategy, organisation, management, and indicators. Comparing these four dimensions the paper discusses the differences arising from the very different theories of strategy that they presuppose: competitive advantage versus competency strategy.

The paper suggests that the very different notions of strategy that underpin the balanced scorecard and the intellectual capital approach make such comprehensive performance management systems behave in very different ways – the difference between a tightly coupled and a loosely coupled system accounts for this.

The main limitation is that the paper is primarily a literature study and therefore it is not certain that in practical situations companies will necessarily adopt the theoretical perspectives mobilised behind balance scorecard and intellectual capital.

The usefulness of that paper is that practitioners may understand the breath of implications of a shift in strategic focus and realise the various organisational conditions that can help mobilise the use of indicators in different ways.

The paper's analysis shows how the two models assume how indicators work in an organisational systems and concludes that the differences are significant and that therefore there are considerable differences in how a system of indicators may work in the context of balanced scorecard compared with the context of intellectual capital.

The purpose of the paper is to develop a concept of organization of the system of science and education in the form 4.0 based on human and artificial intellectual capital.

The methods of regression and correlation analysis are used. The role of human and artificial intellectual capital for provision of effectiveness and competitiveness of the system of science and education is determined, as well as its correspondence to the modern challenges. Analysis of perspectives and limitations of automatization of the system of science and education based on AI is conducted; a model of organization of the system of science and education in the form 4.0 based on human and artificial intellectual capital is compiled.

It is established that in the system of science and education, the decisive production factor is intellectual capital, but human intellect does not necessarily have to dominate in its structure. AI is one of the most popular technologies of Industry 4.0 in the system of science and education, which has wide perspectives of practical implementation. As experience of the leading world universities, which had the highest level of digitization in 2018, showed, foundation on non-breakthrough digital technologies (computer equipment and Internet) does not allow opening the potential of increase of indicators of effectiveness and competitiveness of the system of science and education and bringing it in correspondence to the modern challenges based on digitization. However, correlation of activity of application of artificial intellectual capital with these indicators is four times higher (0.2), as compared to correlation of these indicators with activity of application of human intellectual capital (0.05). This shows demand for digitization of the system of science and education, but based on breakthrough digital technologies, of which AI should become the key one.

It is substantiated that higher education could be automatized based on AI by 85% and science by 63%. Therefore, on the whole in the system of science and education, the share of AI in the structure of intellectual capital could reach 74% ((85 + 63)/2). It is recommended to use the developed model of organization of the system of science and education in the form 4.0 based on human and artificial intellectual capital.

As the carrier of knowledge, intellectual capital plays a crucial role in technology capability. However, most of the previous studies focus on technological capability from a static perspective, rather than take dynamic technology capability into consideration. Based on this research gap, the purpose of this paper is to investigate the effects of intellectual capital and its sub-dimensions on dynamic technology capability, measuring by the factor scores of five technological input and output variables.

The authors combine the system dynamic method and empirical study to guarantee the internal and external validity. Specifically, the authors design the system dynamic model and simulation to analyze the system mechanism of intellectual capital and its sub-dimensions on dynamic technology capabilities from four cause and effect feedback loops. Then, the authors propose eight hypotheses based on this system dynamic model. In the empirical test phase, the authors employed a panel data set pertaining to Chinese manufacturing firms from 2007 to 2017, and adopted the fixed effect panel model according to Hausman test.

The authors find that intellectual capital efficiency (ICE) and its sub-dimensions (i.e. human capital efficiency, organizational capital efficiency and capital employed efficiency (CEE) have significantly positive impacts on dynamic technology capability. The results also show that the positive effects of ICE and OC on dynamic technology capability would be strengthened in state-owned enterprises compared with non-state-owned enterprises, while this moderation effect is weakened on the relationship between CEE and dynamic technology capability.

In this study, the authors first introduce the system dynamic method to explore the relationship of intellectual capital and dynamic technology capability, which is a valuable trial on combining system science and empirical study. Additionally, the authors continue to expand the dynamic technology capability from the intellectual capital perspective, and also find the moderating effect from the ownership aspect. It is beneficial to the theoretical development of intellectual capital and dynamic technology capability. Furthermore, the authors provide significant inspirations and implications for enterprise’s managers.

The purpose of this study is to explore the knowledge network-based intellectual capital of corporate universities and its co-evolution process with knowledge management activities.

An exploratory and multiple case study was conducted, investigating three Chinese corporate universities. Grounded theory was used for data analysis.

This paper finds that the intellectual capital of corporate universities comprises teacher network capital, knowledge process capital and knowledge ba. The steering wheel model is established through the synergistic interaction and promotion among these three types of intellectual capital. The interaction between intellectual capital and the knowledge network within corporate universities constructs the intellectual capital network, which plays the roles of coordinator, knowledge gatekeeper and innovation bridge. The intellectual capital of corporate universities is characterized by sequential inertia. Moreover, the intellectual capital and intellectual capital network are aligned with knowledge management activities at each stage of corporate university development, interacting and following the principles of ladder evolution.

A significant contribution of this paper lies in applying the concept of intellectual capital within the fourth-stage ecosystem to a broader range of knowledge networks. By exploring the dynamics and network of intellectual capital in corporate universities, especially the role of intellectual capital networks and the synergy between intellectual capital and knowledge management activities, this study enriches the existing research on knowledge management and intellectual capital of corporate universities. Furthermore, it advances the development of knowledge management promotion in corporate universities from a new perspective of intellectual capital.

The measurement and reporting of intellectual capital has recently attracted a growing interest from accounting researchers, promoting a lively and far‐reaching debate. Two related issues have informed this debate. It is possible to identify these issues as exemplifying financial reporting and management accounting perspectives on the emergence of intellectual capital. Provides a commentary on the progress of the debate to date, while also attempting to contextualise some of the issues it entails in both earlier and wider debates. In an effort to progress the project of accounting for intellectual capital, suggests the adoption of a critical accounting perspective. This would entail exploring the possibilities of intellectual capital providing its own accounts, rather than remaining imprisoned within accounts devised by others.

The purpose of this paper is to apply the concept of Intellectual Capital to assess intangible resources as crucial for the quality of educational processes. It identifies patterns of interdependence between drivers of Intellectual Capital and generic processes of educational institutions as a prototype study in Austria. Four levels of education are analysed and related to each other in order to identify the need to differentiate drivers of Intellectual Capital.

Educational institutions are typically regulated by governmental procedures and do not follow entrepreneurial management models. This paper suggests the assumption of a value chain of schools with the student as a significant customer as well as the object of intervention. Experiences from the application of Intellectual Capital assessment in value chains of the automotive industry are transferred to education. The methodology of “Wissensbilanz – made in Germany” is applied in action research-oriented prototypes.

Prioritizing scarce resources and systematically monitoring intangible assets in public as well as privately management educational intuitions contributes to economic improvement and better accomplishment of strategic objectives.

Applying methodologies for Intellectual Capital reporting in educational institutions and integrating the management of these resources along the value chain of education seems to be very beneficial not only for ministries of education but for de-central decision makers as well.

Austria officially implemented the legal obligation for Intellectual Capital reporting for universities in 2007. Up to date, no test on the usefulness of a comprehensive Intellectual Capital assessment in educational institutions was reported. Based on data from 12 case studies collected over two years, preliminary insights are reported.

The purpose of this paper was to present the stages of the Brazilian startups' organizational life cycle (OLC), identifying and describing the main factors related to the entrepreneurial and organizational perspectives, as well as the factors related to external relationship agents and the associated risks at each OLC stage. In addition, the variables that comprised the three perspectives above were used for a descriptive reading of the evolutionary process from the perspective of intellectual capital during the stages.

The type of research used was quantitative with a descriptive character. For data collection, the authors have used the survey method and two complementary questionnaires were used as research tools. The measurement scale used in these instruments was the 11-point Likert scale. In total, 100 Brazilian startups registered in the STARTUPBASE and residents in São Paulo comprised the sample of this research. For the data processing, it was used the exploratory factor analysis techniques, to identify the analysis factors, and the cluster analysis, to identify the OLC stages.

Based on the results obtained, four stages were identified and described to build the OLC model of this research: Stage I – conception and development; Stage II – organization and traction; Stage III – growth and scale; and Stage IV – consolidation and transition. In addition, the authors described the main lines of evidence found at each stage.

This research contributes to academic studies of the OLC in startups and the evolution of risks that originate from the relationship between external agents in the business environment and startups. Thus, a management map is built, which helps entrepreneurs and managers construct these businesses since such a management map allows identifying the risks and challenges that a startup needs to overcome to grow and develop.

The originality of this research lies in the adaptation of the OLC approach, which is widely used in studies analyzing the growth and development of mature organizations. The authors used this adaptation to analyze the growth and development of startups in Brazil. In addition, the identification and analysis of external agents that make up the business environment, as well as the analysis of the risks, originated from the relationships between the startup and these agents, brought original and essential results and discussions, both for OLC studies and for risk analysis studies in startups.