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This study aims to discuss science governance in Europe and the network of associated nonprofit institutions. The authors posit that this network, which comprises both (partial) learning organizations and non-learning organizations, has been observed to postpone taking up “responsibility” as an issue in science governance and funding decisions.

This paper discusses the challenge of learning and policy implementation within the European science governance system. By exploring how learning on responsible innovation (RI) in this governance system can be provoked, it addresses the question how Senge’s insights in organizational learning can clarify discourses on and practices of RI and responsibility in research. This study explores the potential of a new organizational form, that of Social Labs, to support learning on Responsible Research and Innovation (RRI) in standing governance organizations.

This study concludes that Social Labs are a suitable format for enacting the five disciplines as identified by Senge, and a Social Lab may turn into a learning organization, be it a temporary one. Responsibility in research and innovation is conducive for learning in the setting of a Social Lab, and Social Labs act as intermediary organizations, which not merely pass on information among actors but also actively give substantive shape to what they convey from a practice-informed, normative orientation.

This empirical work on RRI-oriented Social Labs therefore suggests that Social Lab–oriented temporary, intermediary learning organizations present a promising form for implementing complex normative policies in a networked, nonhierarchical governance setting.

Based on this research funding and governance organizations in research, policy-makers in other domains may take up and create such intermediary organizations to aid learning in (science) governance.

This research suggests that RRI-oriented Social Labs present a promising form for implementing complex normative policies, thus integrate learning on and by responsible practices in various governance settings.

European science governance is characterized by a network of partial Learning Organization (LOs) and Non-Learning Organization (nLOs) who postpone decision-making on topics around “responsibility” and “solving societal challenges” or delegate authority to reviewers and individual actors, filtering possibilities for collaborative transformation toward RRI. social lab (SLs) are spaces that can address social problems or social challenges in an open, action-oriented and creative manner. As such, they may function as temporary, intermediary LOs bringing together diverse actors from a specific context to work on and learn about issues of science and society where standing organizations avoid doing so. Taken together, SLs may offer temporary organizational structures and spaces to move beyond top-down exercise of power or lack of real change to more open, deliberative and creative forms of sociopolitical coordination between multiple actors cutting across realms of state, practitioners of research and innovation and civil society. By taking the role of temporary LOs, they may support existing research and innovation organizations and research governance to become more flexible and adaptive.

This paper aims to present an approach based on electrical impedance tomography spectroscopy (EITS) for the determination of water and ice fraction in low-power applications such as autarkic wireless sensors, which require a low computational complexity reconstruction approach and a low number of electrodes. This paper also investigates how the electrode design can affect the reconstruction results in tomography.

EITS is performed by using a non-iterative method called optimal first order approximation. In addition to that, a planar electrode geometry is used instead of the traditional circular electrode geometry. Such a structure allows the system to identify materials placed on the region above the sensor, which do not need to be confined in a pipe. For the optimization, the mean squared error (MSE) between the reference images and the obtained reconstructed images was calculated.

The authors demonstrate that even with a low number of four electrodes and a low complexity reconstruction algorithm, a reasonable reconstruction of water and ice fractions is possible. Furthermore, it is shown that an optimal distribution of the sensor electrodes can help to reduce the MSE without any costs in terms of computational complexity or power consumption.

This paper shows through simulations that the reconstruction of ice and water mixtures is possible and that the electrode design is a topic of great importance, as they can significantly affect the reconstruction results.

The purpose of this paper is to explore how the graduate assistants (GAs) are inducted into the system and ethos of the institutions of higher education (IHE) in Eritrea. The paper serves in the purpose of creating more conducive and supportive work environment in IHE facilitating the socialization of junior faculty members to the culture, standards and system of the institutions.

The research adhered a combined approach of quantitative and qualitative research methods. Data were gathered through a Likert scale questionnaire and in-depth interviews. The study was conducted in seven IHE involving 165 participants.

The GAs’ knowledge of job description, access to institutional information, sharing of resources, the quality of guidance and support provided, supervised teaching and feedback are discussed in detail. Results revealed that the GAs shoulder vital responsibilities but they receive poor induction at individual and institutional levels. GAs complain for lack of job description clarity and lack of transparent institutional communication at work. Holding first degree, GAs teach senior courses without any prior induction, pedagogic trainings and unsupervised. The GAs are recruited on the basis of the colleges’ long-term staff development plan, but little is done.

Despite their academic rank, the GAs represent 64 percent of the national academic staff (ADF, 2010). Creating conducive work atmosphere for the junior faculty members in the institutions is a long-term investment on institutional capacity building and quality assurance of the institutions’ performance.

Induction of the newly recruited junior faculty members to the social, professional and the institutional ethos is a socialization process that would minimize the professional isolation and inefficiency of new recruits.

The authors aimed to identify the opinions of young adult consumers regarding food processing companies’ (FPCs) credibility in terms of food safety (FS).

The authors surveyed Generation Z (GenZ) consumers. The authors assessed the reliability of the research questionnaire using Cronbach’s alpha statistics. The authors used descriptive statistics and one-way ANOVA analysis of variance in the data analysis to determine intergroup variability. The authors performed statistical analyses using IBM SPSS Statistics. 27.

The most valued determinants for consumers were competence and skills, and the most valued family members’ opinions on FS, followed by experts’ opinions. FS concerns are more associated with FPCs than with farmers. The ethics of conduct and moral responsibility play an important role in assessing the FPCs’ credibility.

The questionnaire did not focus on specific food industries, such as fruit and vegetables, fish, meat, dairy, etc. In the future, a similar survey on producers’ credibility should consider the issue of FS risks associated with the specifics of a particular industry.

The authors proposed a set of factors that may determine young adult consumers’ perception of the FPCs’ credibility, which they may use for research within other consumer groups.

This paper aims to explore how the cross-fertilization of knowledge and technologies in EU-funded research projects, including serious games and gamification, is influenced by the following variables: multidisciplinarity, knowledge base and organizations (number and diversity). The interrelation of actors and projects form a network of innovation. The largest contribution to cross-fertilization comes from the multidisciplinary nature of projects and the previous knowledge and technology of actors. The analysis draws on the understanding of how consortia perform as an innovation network, what their outcomes are and what capabilities are needed to reap value.

All the research projects including serious games and/or gamification, funded by the EU-Horizon 2020 work programme, have been analyzed to test the hypotheses in this paper. The study sample covers the period between 2014 and 2016 (June), selecting the 87 research projects that comprised 519 organizations as coordinators and participants, and 597 observations – because more organizations participate in more than one project. The data were complemented by documentary and external database analysis.

To create cross-fertilization of knowledge and technologies, the following emphasis should be placed on projects: partners concern various disciplines; partners have an extensive knowledge base for generating novel combinations and added-value technologies; there is a diverse typology of partners with unique knowledge and skills; and there is a limited number of organizations not too closely connected to provide cross-fertilization.

First, the database sample covers a period of 30 months. The authors’ attention was focused on this period because H2020 prioritized for the first time the serious games and gamification with two specific calls (ICT-21–14 and ICT-24–16) and, second, for the explosion of projects including these technologies in the past years (Adkins, 2017). These facts can be understood as a way to push the research to higher technology readiness levels (TRLs) and introducing the end-user in the co-creation and co-development along the value chain. Second, an additional limitation makes reference to the European focus of the projects, missing strong regional initiatives not identified and studied.

This paper has attempted to explore and define theoretically and empirically the characteristics found in the cross-fertilization of collaborative research projects, emphasizing which variables, and how, need to be stimulated to benefit more multidisciplinary consortia and accelerate the process of innovation.

This paper aims to determine the influence of various corporate characteristics such as total assets (TA), total sales (TSE), return on assets (ROA), return on sales (ROS), liquidity and age on leverage of the listed non-financial companies in the Dhaka Stock Exchange (DSE).

A non-probability sampling technique has been used in this study, and the leverage of 106 companies listed in the DSE has been examined for the time period 2011-2015. Multiple regression models are used to estimate the influence of corporate characteristics on leverage and leverage is measured by the debt ratio, that is, total liabilities divided by total assets (TA).

The results obtained from the regression models show that TA, ROA and age are negatively and significantly related to the leverage of companies.

Considering only non-financial companies as the sample is a limitation. Hence, the results may not extend across all listed companies in Bangladesh. The study explores only six corporate characteristics variables; other factors influencing the leverage of the firm such as the number of foreign shareholders, ownership structure and auditors’ opinion could be explored in further studies.

The finding of this study contributes to the regulators and enforcement agencies such as Institute of Cost and Management Accountants of Bangladesh (ICMAB), Institute of Chartered Accountants of Bangladesh (ICAB), the Securities and Exchange Commission (SEC) and the DSE. It will enable the regulatory agencies to aim at greater compliance with the local and international standards and also enforce penalties for non-compliance.

There has been an increase in research examining whether and how companies disclose climate change impacts and how these disclosures influence capital structure strategies in recent years. However, prior literature has generally focused on developed countries. This paper proposes to examine the impact of voluntary climate change disclosures on corporate financing decisions in an emerging economy.

The dataset includes 335 firm-year observations listed in the Borsa Istanbul (BIST) 100 manufacturing industry firms that participated in the Carbon Disclosure Project (CDP) questionnaire from 2016 to 2020, characterized by high public awareness of greenhouse gas emissions in Turkey. To accomplish this aim, two models have been constructed that link capital structure strategies with voluntary corporate climate change disclosures while controlling for firm-level attributes in terms of size, profitability, market value and free float ratio (FFR).

The significant and negative relationship between the voluntary disclosure of climate-related activities and long-term borrowing is consistent with the arguments that companies with high commitments are unlikely to reduce default risk in emerging markets. This paper also provides empirical evidence that the high size and the level of low profitability magnify this relationship between CDP and financial leverage.

The Paris Agreement seems to be a significant point where corporate lenders have become aware of the commitment of policymakers to fight climate change. The results have significant implications for both managerial strategies and environmental regulatory policy-making issues. In addition, the findings shed light on the strategic behavior of managers in the consideration of climate change risks and related transparency.

This study aims to propose a solution to accelerate financing support low carbon (circular economy) transition. The authors developed a sustainability governance (SGOV) model and a sustainability governance (SGOV) index as a proxy for the diffusion of sustainability innovation. This study investigates the effect of SGOV practices on profitability with the mediating role of operational efficiency.

The SGOV index consists of 32 and 122 sub-items, constructed using content analysis of annual and sustainability reports published by banks listed on the Indonesia Stock Exchange (IDX) from 2010 to 2020 (404 bank-year observations).

Banks are at a moderate level of sustainability innovation. They are prioritizing the balance aspects of financial, social and environmental. SGOV practice negatively affects profitability. However, operational efficiency plays a positive mediating role that is robust.

The measurement of the SGOV index uses criteria that have not been tested in previous studies. There is the potential subjectivity in interpreting qualitative data, although this has been minimized by cross-checking the analysis of five raters.

This study gives feedback for the Indonesia sustainable finance (SF) journey phase I to proceed into SF journey phase II.

The SGOV model can be applied in other industry sectors to know the readiness for entering low carbon (circular economy) transition.

The uniqueness of the scoring technique assuming a step-by-step innovation model to sustainable finance.

The objective of the presented pilot study was to test the applicability of a metric to specifically measure performance improvement via a hands-on workshop about collaborative robotics.

Candidates interested in acquiring basic practical skills in working with a collaborative robot completed a distance learning exercise in preparation for a hands-on training workshop. The candidates executed a test before and after the workshop for recording the parameters compiled in the tested performance index (PI).

The results reflected the potential of the tested PI for applications in detecting improvement in practical skill acquisition and revealed potential opportunities for integrating additional performance factors.

The low number of candidates available limited in-depth analyses of the learning outcomes.

The study outcomes provide the basis for follow-up projects with larger cohorts of candidates and control groups in order to expedite the development of technology-assisted performance measurements.

The study contributes to research on performance improvement and prediction of learning outcomes, which is imperative to this emerging field in learning analytics.

The development of the presented PI addresses a scientific gap in learning analytics, i.e. the objective measurement of performance improvement and prediction along skill-intensive training courses. This paper presents an improved version of the PI, which was published at the 12th Conference on Learning Factories, Singapore, April 2022.

This paper aims to describe the results of applying ethical AI requirements to a healthcare use case. The purpose of this study is to investigate the effectiveness of using open educational resources for Trustworthy AI to provide recommendations to an AI solution within the healthcare domain.

This study utilizes the Hackathon method as its research methodology. Hackathons are short events where participants share a common goal. The purpose of this to determine the efficacy of the educational resources provided to the students. To achieve this objective, eight teams of students and faculty members participated in the Hackathon. The teams made suggestions for healthcare use case based on the knowledge acquired from educational resources. A research team based at the university hosting the Hackathon devised the use case. The healthcare research team participated in the Hackathon by presenting the use case and subsequently analysing and evaluating the utility of the outcomes.

The Hackathon produced a framework of proposed recommendations for the introduced healthcare use case, in accordance with the EU's requirements for Trustworthy AI.

The educational resources have been applied to one use-case.

This is the first time that open educational resources for Trustworthy AI have been utilized in higher education, making this a novel study. The university hosting the Hackathon has been the coordinator for the Trustworthy AI Hackathon (as partner to Trustworthy AI project).