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The purpose of this paper is to understand how the creativity of pupils and teachers is nurtured through the use of a virtual world (VW) within a sociotechnical network affecting pupils’ learning in a pilot secondary school.

The analysis is the result of a pluri-disciplinary systemic analysis involving didactics, sociology, psychology and management science on an individual, collective and systemic scale. This participatory action research is based on interviews and systematic observations in class, in-world and in the global ecosystem. Linguistic and multimodal analysis is applied to the data, through teacher monographs that hint at the teachers’ activity.

Pupils’ and teachers’ creativity appeared to be anchored within four main interdependent nurturing conditions the personal inclinations and professional interactions in the sociotechnical network sustaining the VW; a creative regulation allowing compromises with the institutional constraints of pedagogical control; avatars and 3 D boundary objects that act as a motor of teachers-pupils inquiry and creativity; the sociotechnical network that contributes, through the actors’ play, to bringing the organisational rules of the school towards an innovation trajectory, that in turns mediates success in the use and the adoption of the new technology.

Although this is a study within a specific school, the findings can be put to use by other pedagogical teams who would wish to integrate a VW to re-engage pupils.

The participatory design processes taking place within a sociotechnical network support teachers in the building of Virtual World scenarios negotiated with researchers and start-up developers.

The pedagogical use of a virtual world opens new learning engagement opportunities for the pupils through enhanced experiential learning and sustains the transformation of teachers’ professionality.

The authors’ approach differs from the previous educational VW literature, in that they integrate the teachers’ creativity and their pedagogical scripts into their study, within a systemic approach, thus requiring a wider theoretical framework, necessary for understanding the building of strategies and knowledge that foster teachers’ and pupils’ creativity in educational settings using a VW.

This study aims to investigate the organisational structure to exploit data analytics in the educational sector. The paper proposes three different organisational configurations, which describe the connections among educational actors in a national system. The ultimate goal is to provide insights about alternative organisational settings for the adoption of data analytics in education.

The paper is based on a participant observation approach applied in the Italian educational system. The study is based on four research projects that involved teachers, school principals and governmental organisations over the period 2017–2020.

As a result, the centralised, the decentralised and the network-based configurations are presented and discussed according to three organisational dimensions of analysis (organisational layers, roles and data management). The network-based configuration suggests the presence of a network educational data scientist that may represent a concrete solution to foster more efficient and effective use of educational data analytics.

The value of this study relies on its systemic approach to educational data analytics from an organisational perspective, which unfolds the roles of schools and central administration. The analysis of the alternative organisational configuration allows moving a step forward towards a structured, effective and efficient system for the use of data in the educational sector.

Describes the parameter estimation procedures for the non‐linear finite element analysis using the hierarchical neural network. These procedures can be classified as the neural network based inverse analysis, which has been investigated by the authors. The optimum values of the parameters involved in the non‐linear finite element analysis are generally dependent on the configuration of the analysis model, the initial condition, the boundary condition, etc., and have been determined in a heuristic manner. The procedures to estimate such multiple parameters consist of the following three steps: a set of training data, which is produced over a number of non‐linear finite element computations, is prepared; a neural network is trained using the data set; the neural network is used as a tool for searching the appropriate values of multiple parameters of the non‐linear finite element analysis. The present procedures were tested for the parameter estimation of the augmented Lagrangian method for the steady‐state incompressible viscous flow analysis and the time step evaluation of the pseudo time‐dependent stress analysis for the incompressible inelastic structure.

The purpose of this paper is to establish a connection between the business model (BM) and B2B marketing research by developing a new approach to the BM analysis and improvement, which is based on inter-organizational networks and value chains.

The methodology is based on mutual enrichment of methods and results of BM and business-to-business marketing studies that are relatively isolated from each other, and on integration of them to the unified structured approach that could be applied to analyze both BM and a set of interfirm relationships of networked market players. This paper is based on extensive literature review in the areas of BM and relationship/industrial marketing. The review is based on most cited and contemporary articles found in the Scopus and EBSCO databases.

This paper formulates the key BM research directions and visualizes their development over time. It is demonstrated in the paper that currently, there is little involvement of marketing researchers in the study of BM; however, there are multiple touchpoints between these areas of knowledge, which can help in further developing BM studies. On the basis of these findings, a conceptual model and new network-based approach to BM analysis is offered, which allows addressing the complex nature of networked interaction among BM participants. The approach includes stepwise algorithm for BM analysis designed for business practitioners.

The proposed approach can be applied by business practitioners to analyze and improve their BM via managing the interactions of inter-organizational network participants with a focus on customer interests. While the approach is of a universal character, the specific tools for evaluating BM on each stage may vary across different markets.

This research contributes to the current conceptual knowledge on BM studies development and their relationship with marketing. It also contributes to theory and practice by the development of a new marketing-based approach to the BM analysis focused on managing business relationships, which allows evaluation of the current state of a BM and provides directions for its improvement. This approach evaluates the alignment of interfirm relationships along the value chain and orients it toward the final consumer.

A novel asynchronous network-based model is proposed in this paper for the sentiment analysis of online public opinions. This new model provides a new approach to analyze the evolution characteristics of online public opinion sentiments in complex environment.

Firstly, a new sentiment analysis model is proposed based on the asynchronous network theory. Then the graphical evaluation and review technique is employed and extended to design the model-based sentiment analysis algorithms. Finally, simulations and real-world case studies are given to show the effectiveness of the proposed model.

The dynamics of online public opinion sentiments are determined by both personal preferences to certain topics and the complex interactive influences of environmental factors. The application of appropriate quantitative models can improve the prediction of public opinion sentiment.

The proposed model-based algorithms provide simple but effective ways to explore the complex dynamics of online public opinions. Case studies highlight the role of government agencies in shaping sentiments of public opinions on social topics.

This paper proposes a new asynchronous network model for the dynamic sentiment analysis of online public opinions. It extends the previous static models and provides a new way to extract opinion evolution patterns in complex environment. Applications of the proposed model provide some new insights into the online public opinion management.

The aim of this article is to examine network‐based learning and discuss how participation in network can enhance organisational learning.

In recent years, companies have increased their collaboration with other organisations, suppliers, customers, etc., in order to meet challenges from a globalised market. They form and participate in a variety of different networks in order to improve innovation, organisational learning and effectiveness. These networks can be considered as one context for learning parallel to training institutions and workplaces. In this paper characteristics of and preconditions for network‐based learning are analysed and compared with school‐based learning and workplace learning. The analysis draws on experiences from an action research project aiming at improving the occupational health and safety work within 11 Danish enterprises forming a network.

Network learning might hold potential for forming an important supplement to school‐based learning and workplace learning. Some of the difficulties related to these two learning arenas can be reduced in a network setting, and in that way network‐based learning can help building bridges between the two “traditional” learning arenas.

Examines network‐based learning and discusses how participation in network can enhance organisational learning.

This chapter reviews three analytical perspectives – ‘structural’, ‘network’ and ‘cultural’ – on the study of power and their implications for theorizing elites. It builds on this initial theoretical review by developing a critical realist approach to the study of organizational elites out of the structurally based perspective identified in the first section of the chapter. The explanatory potential of this critical realist approach is then illustrated through two case studies of ruling elites embedded in contrasting historical, political and social contexts. The final section of the chapter provides a discussion of the wider implications of these case study analyses for understanding and explaining the ‘new feudalism’ which is emerging in advanced political economies and societies.

The purpose of this paper is to propose new directions for human resource management (HRM) research by drawing attention to online data as a complementary data source to traditional quantitative and qualitative data, and introducing network text analysis as a method for large quantities of textual material.

The paper first presents the added value and potential challenges of utilising online data in HRM research, and then proposes a four-step process for analysing online data with network text analysis.

Online data represent a naturally occuring source of real-time behavioural data that do not suffer from researcher intervention or hindsight bias. The authors argue that as such, this type of data provides a promising yet currently largely untapped empirical context for HRM research that is particularly suited for examining discourses and behavioural and social patterns over time.

While online data hold promise for many novel research questions, it is less appropriate for research questions that seek to establish causality between variables. When using online data, particular attention must be paid to ethical considerations, as well as the validity and representativeness of the sample.

The authors introduce online data and network text analysis as a new avenue for HRM research, with potential to address novel research questions at micro-, meso- and macro-levels of analysis.

This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies.

This briefing is prepared by an independent writer who adds his/her own impartial comments and places the articles in context.

The growth and popularity of social media and other online platforms offer a potentially rich data source that can enhance human resource management. Effective acquisition and analysis permit researchers to identify attitudes, developments, and trends within the sector. Attention must be paid to various ethical factors when capturing and using such data, which is naturally occurring and unstructured.

The briefing saves busy executives and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.

This paper aims to shed light on the linkage between research and development (R&D) networks and public funding presented in a given period by using network-based evaluation tools as a means of exploring the relational dimension in public projects designed to foster technology R&D activities.

This research uses co-occurrence network analysis of relevant public projects to assess how technological associations might occur within the R&D activities of given publicly funded projects as well as conducts correlation analysis to understand the extent to which linkages of R&D activity in technology fields are related to public expenditure.

Core technology fields, regarded as eligible to receive continued public funding, are critical for enhancing competitiveness and sustainable growth at the nationally strategic technology level. Thus, the relationship between R&D and the level of government funding for these fields is generally perceived as strong. However, a few technology fields, which did not actively form specific network relationships with other technology fields, are considered to exceptionally drive the largest government support. This trend indicates that the government-funded R&D should be designed and managed not only to curb the inefficiencies existing in the current funding programs but also to achieve the appropriateness for further technology development.

Despite the comprehensive findings, this study has several limitations. First, it is difficult to control any confounding factors, such as the determinants and constraints of the government budget allocation and expenditure decisions over S&T areas, strategic frameworks for public investment and evolving policy landscapes in technology sectors, which lead to bias in the study results. Second, this study is based on a narrow, single-year data set of a specific field of projects supported by the Korean government’s R&D program. Therefore, the generalization of findings may be limited. The authors assumed that influences caused by confounding variables during the initial phase of the public funding schemes would not be significant, but they did not take into account possible factors that might arise coincident with the subsequent phase changes. As such, the issue of confounding variables needs to be carefully considered in research design to provide alternative explanations for the results that have been ruled out. The limitations of this study, therefore, could be overcome by comparing the outcome difference between subsidized and non-subsidized R&D projects or evaluating targeted funding schemes or tax incentives that support and promote various areas of R&D with sufficiently large, evidence-based data sets. Also, future research must identify and analyze the R&D activities concerning public support programs performed in other countries associated with strategic priorities to provide more profound insight into how they differ. Third, there are some drawbacks to using these principal investigators-provided classification codes, such as subjectivity, inaccuracy and non-representation. These limitations may be addressed by using content-based representations of the projects rather than using pre-defined codes. Finally, the role that government investment in R&D has played in developing new science and manufacturing technologies of materials and components through network relationships could be better examined using longitudinal analysis. Furthermore, the findings suggest the need for further research to integrate econometric models of performance outcomes such as input–output relations into the network analysis for analyzing the flow of resources and activities between R&D sectors in a national economy. Therefore, future research would be helpful in developing a methodological strategy that could analyze temporal trends in the identification of the effects of public funding on the performance of R&D activity and demand.

Public funding schemes and their intended R&D relationships still depend on a framework to generate the right circumstances for leading and promoting coordinated R&D activities while strengthening research capacity to enhance the competitiveness of technologies. Each technology field has a relatively important role in R&D development that should be effectively managed and supervised to accomplish its intended goals of R&D budgeting. Thus, when designing and managing R&D funding schemes and strategy-driven R&D relations, potential benefits and costs of using resources from each technology field should be defined and measured. In this regard, government-funded R&D activities should be designed to develop or accommodate a coordinated program evaluation, to be able to examine the extent to which public funding is achieving its objectives of fostering R&D networks, balancing the purpose of government funding against the needs of researchers and technology sectors. In this sense, the examination of public R&D relations provides a platform for discussion of relational network structures characterizing R&D activities, the strategic direction and priorities for budget allocation of the R&D projects. It also indicates the methodological basis for addressing the impact of public funding for R&D activities on the overall performance of technology fields.

The value of this work lies in a preliminary exploratory analysis that provides a high-level snapshot of the areas of metallurgy, polymers/chemistry/fibers and ceramics, funded by the Korean Government in 2016 to promote technological competitiveness by encouraging industries to maintain and expand their competencies.