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Climate change, poverty and pandemics are some of the complex real-world problems that are increasingly challenging higher education institutions (HEIs) to equip future graduates with meta-competences that have hitherto not been demanded. These graduates need to be able to capture and operate within complex systems and relationships. By focusing on complex real-world problems, this study aims to systematically review competences and frame meta-competences supporting curricula development in HEIs.

This study applies a systematic literature review according to the review protocol of Preferred Reporting Items for Systematic Reviews and Meta-Analyses. The review process resulted in the selection of 39 articles, which were subjected to qualitative synthesis to identify competences for tackling complex real-world problems. These competences were grouped into meta-competences and aligned with the Sustainable Development Goals (SDGs).

Scientific disciplines commonly describe four competences for tackling complex real-world problems including domain-specific competence, inter-relation competence, intrapersonal competence and normative competence.

This study found that inter-relation competence is in line with all the SDGs, which is considered important for tackling complex real-world problems across disciplines.

A study in a survey design across disciplines and a weighting of these competences in respect to complex real-world problems aligned with the SDGs could contribute to a more consolidated and common understanding of the meta-competences identified.

Intraorganizational knowledge and information sharing are important steps toward more-accessible organizational knowledge. The aim of this study is to qualitatively explore factors that contribute to employees' motivations for sustaining intraorganizational knowledge-sharing behaviors and to examine the impact of these factors in a quantitative study with the Austrian Federal Ministry of Defense. This ministry faces a retirement wave in the next 5–10 years. Intraorganizational knowledge sharing before, during, and following this wave will play a decisive role for the organization in the near future.

An exploratory sequential mixed-methods study was conducted. The study design involved a qualitative study phase with expert interviews and stakeholder workshops (n = 9) and a quantitative study phase based on a cross-sectional online survey with an implicit association test on intraorganizational knowledge sharing (n = 59).

In the qualitative study phase, three main research topics regarding intraorganizational knowledge sharing were identified: employee attitude, organizational support, and specific relational aspects of knowledge transfer, such as reciprocal relationships among employees and opportunities for knowledge exchange. A hierarchical multiple regression analysis revealed that perceived organizational support was the only factor that was a significant predictor of motivation for engaging in knowledge sharing. We also analyzed the data for moderation effects and demonstrated that sociopsychological factors (e.g., the engagement or openness of colleagues to share their knowledge) further strengthened the positive relationship between employees' perceived support and personal willingness to share knowledge.

We conclude that an organizational culture that supports knowledge sharing within the organization is highly relevant for motivating employees to share their knowledge. Practitioners will also benefit from the insights of the various dimensions of employees' willingness to engage in knowledge-sharing behaviors to better design further interventions in organizations.

In accordance with an exploratory sequential mixed-methods approach, we followed a transdisciplinary process in which scientific and practical experiences and knowledge were integrated. For this purpose, interviews and workshops with experts and stakeholders in the organization were conducted. The qualitative findings were incorporated into a quantitative survey and an implicit association test for the employees of the organization. This approach demonstrates a different and more holistic approach to analyzing a real-world problem in the context of a governmental agency in order to investigate the multidimensional and complex topic of intraorganizational knowledge sharing.

The dynamics of modern life lead to societal changes that affect innovation systems. Entrepreneurship is an important driver for fostering adaptive capacities of innovation systems in such uncertain and complex environments. This study aims to gain a detailed understanding of how (innovative) entrepreneurship can promote innovation systems, leading to more sustainable societies. A particular focus is placed on migrant entrepreneurship in the digital economy, as a concrete implication of innovative entrepreneurship, and its role within the Austrian innovation system.

In order to develop a shared system understanding from a scientific and practical perspective, transdisciplinary multistage system modeling was applied. The transdisciplinary discourse involved 14 experts, and several system models were iteratively co-created during the course of the research.

The main result demonstrates the interrelationship between the innovation system and migrant entrepreneurship in the digital economy, which includes six core reinforcing loops: (1) the mindsets of entrepreneurs, (2) the role of international collaboration, (3) the role of entrepreneurial education, the financial sphere in regard to (4) government and (5) private funding, as well as (6) the impact of formal procedures.

The authors present and discuss the relational dynamics of this complex phenomenon as well as the applied transdisciplinary approach, with the aim of identifying a potential way to improve the sustainable impact of (migrant) entrepreneurship considering the case of the Austrian innovation system.

To establish a statistical formulation of robust design optimization and to develop a fast optimization algorithm for the solution of the statistical design problem.

Existing formulations and methods for statistical robust design are reviewed and compared. A consistent problem formulation in terms of statistical parameters of the involved variables is introduced. A novel algorithm for statistical optimization is developed. It is based on the unscented transformation, a fast method for the propagation of random variables through nonlinear functions. The prediction performance of the unscented transformation is demonstrated and compared with other methods by means of an analytical test function. The validity of the proposed approach is shown through the design of the superconducting magnetic energy storage device of the TEAM workshop problem 22.

Provides a consistent formulation of statistical robust design optimization and an efficient and accurate method for the solution of practical problems.

The proposed approach can be applied to all kinds of design problems and allows to account for the inevitable effects of tolerances and parameter variations occuring in practical realizations of designed devices.

The purpose of this paper is to investigate the achievable improvement in reconstruction accuracy in electrical tomography through the incorporation of physical bound constraints as prior knowledge in the inverse problem solution.

The structure of the nonlinear least squares inverse problem formulation and the importance of prior knowledge are addressed. Several different methods for the incorporation of bound constraints are discussed. The methods are compared by means of reconstructions from simulated and measured data and the computational demands.

The inclusion of bound constraints on the material values in the inverse problem solution results in a considerable improvement of the reconstructions. The occurrence of artefacts and blurring can be reduced. Among the investigated constraint handling methods, the logarithmic parameter reconstruction approach can be implemented with minimal additional computational effort.

The study is performed with discrete two‐phase material distributions as occurring in industrial problems. A further step would be the extension to multiple phases.

The logarithmic transform method is a novel approach for the incorporation of bound constraints in tomography. It outperforms other constraint handling approaches and may be of interest for electrical tomography systems in various applications.

Uncertainties in the design variables of non‐linear engineering optimization problems are often neglected. That could result in considerable deterioration of the target function value of an implemented design compared with the computed optimal solution. This effect can be reduced with robust optimization, where it is tried to achieve robust designs by actively embedding the uncertainties and robustness measures in the optimization process. A methodology for robust optimization of non‐linear problems is presented, including practical methods for the solution of such programs. The benefits of the approach are discussed in a numerical field calculation example.

The investigation of the influence of the modeling error on the solution of the inverse problem given uncertain measured data in electrical capacitance tomography (ECT).

The solution of the nonlinear inverse problem in ECT and hence, the obtainable accuracy of the reconstruction result, highly depends on the numerical modeling of the forward map and on the required regularization. The inherent discretization error propagates through the forward map, the solution of the inverse problem, the subsequent calculation of process parameters and properties and may lead to a substantial estimation error. Within this work different finite element meshes are compared in terms of obtainable reconstruction accuracy. In order to characterize the reconstruction results, two error measures are introduced, a relative integral error and the relative error in material fraction. In addition, the influence of the measurement noise given different meshes is investigated from the statistical point of view using repeated measurements.

The modeling error, the degree of regularization, and measurement uncertainties are the determining and limiting factors for the obtainable reconstruction accuracy of electrical tomography systems. The impact of these key influence factors on the calculation of process properties given both synthetic as well as measured data is quantified. Practical implications – The obtained results show that especially for measured data, the variability in calculated parameters strongly depends on the efforts put on the forward modeling, i.e. on an appropriate finite element mesh size. Hence, an investigation of the modeling error is highly recommended when real‐world tomography problems have to be solved.

The results presented in this work clearly show how the modeling error as well as inherent measurement uncertainties influence the solution of the inverse problem and the posterior calculation of certain parameters like void fraction in process tomography.

The purpose of this paper is to present how the Regional Centres of Expertise (RCE) Graz‐Styria as well as RCEs as instruments can contribute to regional development. The RCE Graz‐Styria is representing a case study of Central European RCEs.

The paper describes the development process of RCE Graz‐Styria. Regional challenges to strengthen sustainable regional development have been investigated by literature review and interviews with regional actors.

The paper discusses challenges of establishing an RCE and describes how an RCE can face regional and global challenges by innovative actions.

The paper provides an insight of the establishment of the RCE Graz‐Styria. Readers who would like to establish an RCE in their own region can learn from the process of RCE Graz‐Styria. The reader has to take into account that the regional challenges might be different, and thus the RCE Graz‐Styria is a role model regarding its management structure for RCEs based at universities.

By 2008 and since 2006, every year more than ten new RCEs have been officially acknowledged by the United Nations University. Specially for those new RCEs coming in the next years, which can learn from reading about different management structures and different challenges experienced by different RCEs. For already established RCEs, RCE case studies are useful not only for learning from other RCEs but also for getting in contact with RCEs with common interests and initiating common projects.

The purpose of this paper is to point out the necessity of implementing more appropriate approaches instead of the traditional single disciplinary approaches, in order to be able to cope with the ill‐defined, highly complex problem of sustainable development in systems such as organizations or regions.

Based on empirical data concerning expert and stakeholder preferences, it is argued that research and teaching on innovation for sustainability need to be both inter‐ and transdisciplinary.

Here, the approach of transdisciplinary case studies, developed at the Swiss Federal Institute of Technology (ETH) in Zurich, allows appropriate integration of research and teaching activities and thus leads to mutual learning between the case study actors.

In the second part of the paper, these conceptual considerations are illustrated with the so‐called Erzherzog Johann case study, an integrative research and teaching project at the University of Graz.

In the paper the very complex task to integrate research and teaching on sustainability‐related innovation is described and illustrated with the first transdisciplinary case‐study conducted in Austria according to the ETH approach.

The main focus of this paper is to discuss appropriate forms of higher education for building up students' competence for working on complex real‐world problems.

Within this paper the Harvard approach is accurately compared with the ETH approach by discussing theoretical and practical implications as well.

It is argued that the Harvard case study approach is a sensible approach to bridging the gap between the academic and the practical world, but it has important limits in preparing students to cope with complex real‐world problems. In some important respects, the ETH case study approach goes further by exposing students directly to the multi‐faceted and complex character of real‐world problems.

The ETH approach puts additional demands on students and teachers to bridge the gap between university and society with a high degree of responsibility. Consequently, a combination of both the Harvard and the ETH approach might be interesting.

The comparison of the Harvard case study approach with the ETH case study approach is novel. The discussion of educational together with practical implications provides insight to the peculiarities of each single approach together with an orientation for their implementation within higher education. Guidance is given to universities who are deciding what educational means have to be implemented in order to prepare their students for the task of solving complex real‐world problems in an inter but also transdisciplinary manner.