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Managing a project involves taking a number of critical decisions that can have a crucial impact on the success or failure of the initiative. The analytical definition and visualization of the main components of a project can support project managers engaged to address the right issues at the right time. This article aims to identify crucial crossroads in the management of a project and to provide a visual representation of knowledge involved into a system of project components and decisions.

A design science process is adopted to define the initial goals and requirements and to develop the knowledge visualization framework. Expert feedback is also gathered to obtain a preliminary validation of the framework.

Moving from a system view of project dimensions, we identify eight types of strategic decisions, i.e. growth, problem shifting, goals balancing, escalation, rewarding, resource allocation, problem fixing and cooperation. We then present a visualization map of project decision making addressing six categories of knowledge (i.e. “what-knowledge”, “how-knowledge”, “who-knowledge”, “why-knowledge”, “what for-knowledge”, “when-knowledge”).

The framework needs further theoretical refinement in terms of more fine-grained decision types, other determinants and the reciprocal influence in the management of project activities.

The article can support project managers attempting to build a comprehensive view of project decisions, and it can be a basis to develop novel types of knowledge management systems for project-related applications.

The article proposes a new approach to sustain strategic decision making in project management by adopting a knowledge visualization view. Moreover, it provides an operational tool for managers and analysts at different levels engaged into the management of a project.

Although new forms of innovation such as open innovation, user innovation or crowdsourcing have been intensively discussed in the past decade, there is little systematic exploration of their wider positive and negative effects on economy, society and environment. Based on the recent debate in the literature and findings from a European foresight project, this paper aims to discuss the critical aspects of new forms of innovation such as increased participation, the use of information technologies and the increased pace of innovation and their challenges for innovation policy.

Based on a collection of international practice examples from industry and society, innovation visions have been generated and assessed by different experts across whole Europe.

A generic trend identified can be best described as open, distributed and networked innovation process. Although many new innovation models accelerate the innovation process, there are also some counter trends which in some fields may slow down the innovation process. In addition, the increased use of web-based tools, algorithms and information technologies raises new questions concerning the protection of intellectual property and data security. This reveals new questions for policymaking, which have not gained much attention on the European level so far.

Although there is an established discourse around potentially negative impacts of the outcomes of the innovation processes notably in the field of technology assessment, innovation capacity is usually seen as a desirable characteristic of innovation systems. In this paper, the possible negative aspects of new innovation models, an issue hardly addressed in the innovation literature so far, are discussed.

The purpose of this paper is to identify whether the prior industry experience (IE) or industry leadership experience (ILE) of the head might influence the department’s publication output, the ability to acquire external research funds or its entrepreneurial activities (e.g. the commercialization of research results through patents).

The study is based on data from 208 Austrian university departments and combines data from different sources (CVs of the heads of departments, commercial register, funding data and publication data).

The results show a positive relationship between ILE and the patent output of the departments as one indicator for the commercialization of research activities. Low positive effects of IE on the extent of third-party funding were also found. Furthermore, the scientific experience of the head of department has a positive influence on the publication output of the whole department.

The findings suggest that the scientific ability of researchers should be key when selecting the head of a department, due to the fact that scientific performance is still essential for most of these units. However, when universities seek to focus more strongly on other, for example, entrepreneurial activities, then additional competencies come into play. As the actual focus of universities is currently subject to change, former IE and ILE will become increasingly more important and the heads of departments will play a decisive role in the transition toward becoming an entrepreneurial university. Therefore, universities are well advised to integrate these experiences in the job specifications and to establish processes that facilitate the change from an industrial to a university job or which allow “double lives” in university and industry.

Previous studies have mostly investigated the role of the scientific experience of academic leaders in the research performance of their institution in later decades. This study examines the actual relevance of previous entrepreneurial experiences of heads of departments to the departments’ research performance, the ability to acquire external research funds or their entrepreneurial activities.

The public sector is one of the least addressed areas of intellectual capital (IC) research. Universities are an interesting area of investigation because they are considered critical players in the knowledge-based society. The purpose of this paper is to develop a more general, flexible and comprehensive “IC Maturity Model” for Universities (ICMM), a framework for defining and implementing IC measurement and management approaches, as part of the whole strategic management of universities. Thus, the ICMM proposes a staged framework to initiate a step-by-step change within a university based upon its current level of IC management maturity. The different steps of maturity might be an answer to cope with the huge diversity of European universities, some of which have strong managerial orientation, while others follow collegial forms of governance.

The research approach is based on what has been called the “third stage” of IC research (Dumay and Garanina, 2013), focused on the practices of IC approaches rather than on its theoretical conceptualisation. The ICMM has been developed under the “Quality Assurance in Higher Education through Habilitation and Auditing” project framework, initiated by the Executive Agency for Higher Education and Research Funding of Romania (EUFISCDI). Three Mutual Learning Workshops (MLWs) were organised as a mean to bring together 15 international experts and practitioners to share their views and experience on IC reporting and setting up task forces.

An ICMM, which is a flexible model of implementing IC approaches within public universities, is developed. The ICMM provides a theoretical continuum along which the process of maturity can be developed incrementally from one level to the next, moving from IC data collection, awareness of IC, adjustment of IC specific indicators, measurement of IC, reporting of IC, interpretation and decision making, strategy and planning.

Future research needs to conduct empirical studies in universities to generalise the effectiveness of the ICMM model and guidelines for implementation.

The ICMM provides a staged framework to initiate a step-by-step change within a university based upon its current level of IC management maturity and its IC value creation dynamics. It allows universities to follow different paths, not necessarily a linear sequence.

Although several methods for IC measurement and management exist, most of these cannot accommodate the trade-off between the comparability aims and the efforts to capture the institution’s uniqueness when designing an IC model.

Within the context of intellectual capital (IC) reporting, social network analysis (SNA) is applied to identify driving factors, which allows it to control the development of intellectual capital. Hence, the aim of the paper is to address an important weakness of established methods for IC management, i.e. their inability to cope with interdependencies between different elements.

The paper describes the method of SNA and its ability to identify IC value drivers. Thereby, the relationships between different key success factors and organisational results are represented as a graph and their interactions are analysed by specific SNA indicators with the aim of identifying appropriate IC control factors. The experience with the proposed new method gained within a vocational education and training centre in Austria during the implementation of an intellectual capital report is presented to illustrate the new method.

The newly introduced approach delivers new kinds of information for management control in an organisation and leads to different selection of indicators than only applying the classical sensitivity model.

The paper introduces a new management method for practitioners. Compared to traditional methods such as strategy maps, managers are able to consider interdependencies systematic and indirect effects between factors when selecting control measures.

To the authors' knowledge it is the first time that the SNA technique has been used for selecting management control factors based on identified cause‐effect relationships.

The purpose of this paper is to study the nature of the strategy formation and its impact on firm performance in relation to market development and product innovation.

The paper is based on an empirical study of 91 Austrian SMEs which covers a time period of ten years. Strategy formation was captured by an analysis of strategic intentions and corresponding actions in two surveys carried out in 1995 and 2003.

The study finds no direct association between strategy formation and performance, though, emergent strategists had less often a growth orientation. Taking into account industry dynamics, shows, contrary to our expectations, that companies which employed an emergent market development strategy achieved higher sales growth in stable than in dynamic industries.

The question of the superiority of planned vs emergent strategies has a long debate in strategy formation literature. The authors contribute to this question by investigating the role of different information sources for the formation of market and product innovation strategies and its impact on the performance in different environments over a ten-year time period.

The purpose of this paper is to examine the intermingling of new product development and strategy making which are interpreted as co-evolutionary processes where self-organisation and emergence are significant phenomena.

The paper is based on an empirical study of 50 major Austrian innovations in various manufacturing industries developed by small and large firms in the 1980s and 1990s. The theoretical arguments for studying the innovation and strategy process are based on the findings of the complexity science.

The paper shows that emerging opportunities, self-organisation and strategic intentions are equally important for the development of new major product innovations as deliberate search processes and rational decision making. The author identifies three strategy paths concerning the innovation and strategy process which are described as “strategically managed innovations”, “strategically enabled self-organized innovations” and “purely self-organised innovations”.

While empirical studies investigating the emergent nature of strategy and innovation have so far mostly been analysed for very specific industries and firm types, this paper aims to deliver a broader empirical base for the question as to how strategy enables and guides the emergence of product innovations and how the development of new products contributes to the formation of innovation strategies.

The purpose of this paper is to demonstrate the usefulness of data envelopment analysis (DEA) as a consulting and management tool that fulfils the requirements of quantitatively and comprehensively evaluating and benchmarking the efficiency of intellectual capital (IC).

DEA is applied for a sample of input and output data of all technical and natural science departments of Austrian universities. Correlation and factor analyses are carried out to select appropriate variables of the sample. DEA estimates the production function of the units under evaluation in relation to peer units, which are identified as fully efficient.

Results illustrate the existence of scale efficiencies of Austrian university departments and show a large heterogeneity within and among universities as well as between different fields of study with respect to their efficiency.

DEA is mainly appropriate for larger samples inside an organisation or among different organisations. The method can be easily transferred to similar management situations in other types of organisations or industries, where the efficiency of IC should be assessed.

The results reveal detailed improvement or reduction amounts of each input and output of the evaluated organisational units and indicate areas for managerial action at Austrian universities.

For the first time DEA is applied for evaluating and benchmarking IC of Austrian universities. DEA is proposed as consulting and management tool for evaluation IC performance.

With intellectual capital and intangible assets high on the agenda of executives around the world, and little practical evidence of good practice in measuring and managing these assets, there is a great need for help. This editorial to a special issue on the topic introduces the problem and highlights key issues. The special issue provides an overview of how management consulting companies acting in this space suggest tackling the problem. The purpose is therefore to bring together the approaches of different management consulting firms and to make their differences explicit.

All major general management consulting firms as well as specialist consulting firms focusing in the area of intellectual capital and intangible assets were directly invited to submit a paper for this special issue. The call for papers was also made publicly available in the journal and through e‐mail campaigns by Emerald. All submissions underwent a double‐blind refereed selection process.

Even though many submissions were received for this special issue, most of the authors were not able to demonstrate a sufficient understanding of the constructs nor were they able to justify the tools and methodologies developed. Reviewers were made aware of the practical background of many of the authors and it was ensured that sufficient and constructive feedback was provided. Even with various rounds of reviews many papers had to be rejected as they resembled marketing brochures rather then logical discussions. This unfortunately shows that there still is a massive skills gap in the industry and companies should be careful before they engage with any management consulting firm to help them measuring or managing their intangibles.

The focus of potential papers was not academic rigor (as opposed to the Special Issue Vol. 5 No 2) but the provision of an overview of the state of the art in intellectual capital consulting practice. The papers therefore provide practitioners with good insights into current practice.

This special issue is the first to bring together in a structured and rigorous format different management consulting approaches to the measurement and management of intellectual capital and intangible assets.