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This study aims to analyse the systemic risk emitted by all publicly listed commercial banks in a key emerging economy, India.

The study makes use of the Tobias and Brunnermeier (2016) estimator to quantify the systemic risk (ΔCoVaR) that banks contribute to the system. The methodology addresses a classification problem based on the probability that a particular bank will emit high systemic risk or moderate systemic risk. The study applies machine learning models such as logistic regression, random forest (RF), neural networks and gradient boosting machine (GBM) and addresses the issue of imbalanced data sets to investigate bank’s balance sheet features and bank’s stock features which may potentially determine the factors of systemic risk emission.

The study reports that across various performance matrices, the authors find that two specifications are preferred: RF and GBM. The study identifies lag of the estimator of systemic risk, stock beta, stock volatility and return on equity as important features to explain emission of systemic risk.

The findings will help banks and regulators with the key features that can be used to formulate the policy decisions.

This study contributes to the existing literature by suggesting classification algorithms that can be used to model the probability of systemic risk emission in a classification problem setting. Further, the study identifies the features responsible for the likelihood of systemic risk.

This paper aims to explore FinTech and its dynamic transitions in the banking industry. In particular, the study analyses the systemic innovation nature of FinTech-based innovations. The main contribution of this research study is the development of systemic innovation model which can be used as a dynamic tool to track the progress and pattern of technology development and diffusion. The research also discusses the latest financial innovation of PromptPay FinTech – the e-payment system in Thailand.

This research uses the case study approach to analyse the systemic innovation characteristics of FinTech-based innovations. This research offers a new systemic innovation model which is developed and can be used as a dynamic tool to track the progress and pattern of technology development and diffusion. The study uses FinTech-based innovations as case study samples to gain a better understanding concerning the systemic characteristics and the pattern of technology diffusion under the analytical framework of systemic innovation model. This research involves qualitative interviews with five major commercial banks in the financial services industry of Thailand.

The analyses of findings show the systemic characteristics of FinTech-based innovations in the banking industry, both at a global scale and Thailand case. The analyses have shown that systemic characteristics of the innovation process are the outcome of interactions between the complexity of the innovation and the capabilities of innovators in managing the innovation. The insightful implications on the systemic nature of innovation give the trend and direction of FinTech-based innovation development in the banking industry.

The main contribution which shows originality and value of this paper is the development of systemic innovation model. This research study develops a systemic innovation model to analyse the systemic characteristics which can be applied to all innovations in any industry. The model can also help track the progress and pattern of technology development and diffusion. Therefore, the model can be used to project the trend and diffusion of innovation competition in the banking industry.

The purpose of this paper is to address the development of a conceptual framework to drive and assess the quality of software production in the digital ecosystem domain.

This research used action research paradigm, the soft methodology SSM and the framework PRO2PI-MFMOD. The methodologies were applied at Brazilian Public Software Ecosystem.

The results of this research shows: the dimension of the capacity, as suggested by ISO/IEC 15504, is insufficient for quality treatment in certain domains; SSM methodology is suitable for scope and domain clarification in digital ecosystems; and PRO2PI-MFMOD framework is suitable to create a reference model process for digital ecosystems software production.

A complete SSM cycle was conducted, but with only one research cycle. In that sense, the results obtained in this research can be interpreted as boundaries to reflections regarding the learning occurred in the system.

The research offers an empirical contribution, mapping and defining maturity framework elements that can be used by the software production digital ecosystems, aiming the description and understanding of the phenomenon through the theoretical views of complexity theory, systemic thinking, digital ecosystems and maturity models.

The contributions can be summarized in the following aspects: the maturity models evolution, shifting from command and control basis to cooperation and connection basis; the maturity models scope, drifting from organizational units to collaborative networks of software production; the assimilation and broadcast of digital ecosystems concept by MCTI; and the maturity levels introduced in the research can be used as a particular case of capacity dimension of the further ISO/IEC 33000 standards.

Through the results obtained with systematic revision of the literature, one can notice the absence of publications that approach all the four axes that ground this research simultaneously. The proposed subject is original and relevant to the software community in the matter of software process improvement and to the current and further public digital ecosystems of software development.

Science of systems requires a specific and constructive mathematical model and language, which describe jointly such systemic categories as adaptation, self‐organization, complexity, evolution, and bring the applied tools for building a system model for each specific object of a diverse nature. This formalism should be connected directly with a world of information and computer applications of systemic model, developed for a particular object. The considered information systems theory (IST) is aimed at building a bridge between the mathematical systemic formalism and information technologies to develop a constructive systemic model of revealing information regularities and specific information code for each object.

To fulfill this goal and the considered systems' definition, the IST joins two main concepts: unified information description of interacted flows, initiated by the sources of different nature, with common information language and systems modeling methodology, applied to distinct interdisciplinary objects; general system's information formalism for building the model, which allows expressing mathematically the system's regularities and main systemic mechanisms.

The formalism of informational macrodynamics (IMD), based of the minimax variational principle, reveals the system model's main layers: microlevel stochastics, macrolevel dynamics, hierarchical dynamic network (IN) of information structures, its minimal logic, and optimal code of communication language, generated by the IN hierarchy, dynamics, and geometry. The system's complex dynamics originate information geometry and evolution with the functional information mechanisms of ordering, cooperation, mutation, stability, diversity, adaptation, self‐organization, and the double helix's genetic code.

The developed IMD's theoretical computer‐based methodology and the software has been applied to such areas as technology, communications, computer science, intelligent processes, biology, economy, management, and other nonphysical and physical subjects.

The IMD's macrodynamics of uncertainties connect randomness and regularities, stochastic and determinism, reversibility and irreversibility, symmetry and asymmetry, stability and instability, structurization and stochastization, order and disorder, as a result of micro‐macrolevel's interactions for an open system, when the external environment can change the model's structure.

The purpose of this paper is to introduce a new business model logic, highlighting value processes in and properties of platform business models to inform business model thinking from a systemic and dynamic perspective. It challenges the idea of firms managing, influencing and controlling entire activity systems.

The study traces the evolution of different approaches to business models and assesses theories that explain value cocreation and systemic value capture to develop a new business model logic.

Business model thinking has evolved away from Porter’s value chain to a new logic based on open networks and platforms. This study develops a framework for understanding platform business models from a systemic perspective. Derived from service-dominant logic, this new business model logic responds to phenomena in contemporary business environments characterized by increasing connectivity and sociality among actors.

The framework, developed from an extensive body of business model literature, has yet to be subjected to empirical investigation. Future research may involve the exploration of business model design processes and business model innovation from a systemic perspective.

Managers who aim to design their business models based on the logic of platform businesses require an understanding of their organization’s collaboration potential, technological interfaces and potential to leverage network relationships. This research guides start-ups and incumbents to evaluate their platform potential.

This study systematically emancipates the business model logic from a firm-centered, inside-out perspective, focuses on network relationships beyond the customer–firm dyad, explains value processes beyond organizational borders and rethinks value capture from a systemic perspective.

The systemic sciences, be they politico‐socio‐econometric, medico‐systemic, psycho‐neurological, or ecologico‐environmental, must be founded on a computer science that permits not only the expeditious and conscientious modelling of collections of natural phenomena, but also a methodology which ensures that computerized simulation models are stringently verified and assiduously validated by both their authors and their critics. The paper delineates the operational procedure by which simulation models can be well‐written, yet, despite their complexity, subjected to the scrutiny of the entire scientific community in order that they be deemed credible mimics of the complex systems that they describe. The important aspects and evolutionary development of computational linguistics are placed in the context of providing ad hoc simulation languages especially amenable to the systemic scientist. In this context, the role, and value of the use of, the telecommunications technology to the verification and validation of the computerized models of the systemic scientists are delineated.

With the diversification of modelling activities encouraged by versatile modelling tools, handling their datasets has become a formidable problem. A further impetus stems from the emergence of the real‐time forecasting culture, transforming data embedded in computer programs of one‐off modelling activities of the 1970s‐1980s into dataset assets, an important feature of modelling since the 1990s, where modelling has emerged as a practice with a pivotal role to data transactions. The scope for data is now vast but in legacy data management practices datasets are fragmented, not transparent outside their native software systems, and normally “monolithic”. Emerging initiatives on published interfaces will make datasets transparent outside their native systems but will not solve the fragmentation and monolithic problems. These problems signify a lack of science base in data management and as such it is necessary to unravel inherent generic structures in data. This paper outlines root causes for these problems and presents a tentative solution referred to as “systemic data management”, which is capable of solving the above problems through the assemblage of packaged data. Categorisation is presented as a packaging methodology and the various sources contributing to the generic structure of data are outlined, e.g. modelling techniques, modelling problems, application areas and application problems. The opportunities offered by systemic data management include: promoting transparency among datasets of different software systems; exploiting inherent synergies within data; and treating data as assets with a long‐term view on reuse of these assets in an integrated capability.

This study aims to explore an exemplar of the design and application of a systemic framework for higher education. The field of application is in the social sciences and the perspective long-term, covering three generations of faculty and many generations of students.

This study is exploratory. It contains a conceptual component and an empirical component with a long-term case study from a European university.

A cybersystemic approach to higher education has been shown, at the focal university, to be a powerful amplifier of individual and institutional capabilities, and it still has great potential. The crucial prerequisite is that the approach is virtuously designed and implemented.

A case study ranging over 50 years is presented. The respective university has been a role model for other educational institutions for many years. Its influence in the German-speaking countries, and more recently also internationally, has become significant.

Education is a key institution of modern society, long recognized for its central role in the reproduction of inequities and with the potential to challenge them. Schools behave as their systems are designed. Achieving equity and excellence is not possible through attempts to fix “the school” or educators. Principles of systemic design that incorporate equity and excellence are needed to increase the likelihood of desirable outcomes.

Using the social contract as a design principle, this paper systematically builds an empirical model of school provision aimed at equitable excellence.

Equitable excellence in school provision is possible if choice is available across geolocation and socio-educational (dis)advantage, schools have autonomy over fiscal, personnel and curricular matters, public accountability is linked to academic outcomes and social impact, all moderated by the quality of teaching.

Data-driven empirical modelling is particularly attractive to policy makers, systemic authorities and researchers when theory (of all varieties) does not yield the necessary insights to support the functionality and effectiveness of systems to deliver equitable outcomes at scale. Empirical examples can be used to test the explanatory power of the novel model – and refine it when necessary.

The empirical model and threshold question are the genesis of a common language for assessing relevant costs and benefits of initiatives for government and system designers. Significantly, establishing a threshold question and tests of legitimacy and strength to accompany the novel model provides a more principled way of prioritizing the competing demands on public investment in education.

Establishing a threshold question and tests for legitimacy and strength to accompany the novel model provides a more principled way of prioritizing the competing demands to accompany.

Lessons from the most recent financial crisis show specific vulnerabilities of financial markets due to weaknesses in the structure of the financial system (structural fragility). As the literature points out, the impact of systemic risk can be closely related to issues of concentration (“too big to fail”) and dependency (“too connected to fail”). However, different structural variables are emphasized in various ways, and most authors analyze each variable separately. This raises the questions of how structural fragility, as a cause of systemic distress, can be assessed more comprehensively and consistently, and what the implications are for modeling it within an integrated systemic risk framework. This paper seeks to address these issues.

On the basis of theoretical considerations and in the light of current transformations in financial markets, this paper explores elements of structural fragility and the requirements for modeling them.

The paper suggests an extended approach for conceptualizing structural fragility, evaluates directions for quantifying structural issues in early warning systems (EWSs) for systemic crises, and lays a theoretical groundwork for further empirical studies.

The need for supervisory actions to prevent crises is urgent, as is the need for integrating structural aspects into EWSs for systemic financial crises. Since a significant aspect of a financial firm's risk comes from outside the firm, individual institutions should understand and monitor the structural aspects of the various risk networks they are in.