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This study, a conceptual paper, aims an answer the question, how significant cluster ambidexterity is for the resilience of individual clusters.

The authors draw up an abductive synopsis of empirical information and relevant theoretical sources. A case study is used to illustrate some of the findings.

The results of the analysis show that the ambidexterity of a cluster can contribute to its resilience when adverse external developments arise. Ambidexterity proves to be simultaneously a common strategy of key cluster actors and a mechanism for coping with critical situations and developments that can be activated by the cluster actors and may – eventually – lead to cluster resilience. While ambidexterity does not guarantee cluster survival, it can contribute significantly to their economic resilience under adverse conditions.

The concept is developed on a limited empirical basis and would need to be tested and deepened by comparing a wide range of case studies from different clusters.

A better understanding of the importance of ambidexterity for the development of industrial clusters contributes to a better fine-tuning of cluster support policies.

Ambidexterity as a concept originating from business administration has so far only been rudimentarily tapped for empirical and theoretical cluster research. The paper identifies and develops a path how this could be accomplished to a greater extent in the future.

The Models of Child Health Appraised (MOCHA) project identified the different models of primary care that exist for children, examined the particular attributes that might be different from those directed at adults and considered how these models might be appraised. The project took the multiple and interrelated dimensions of primary care and simplified them into a conceptual framework for appraisal. A general description of the models in existence in all 30 countries of the EU and EEA countries, focusing on lead practitioner, financial and regulatory and service provision classifications, was created. We then used the WHO ‘building blocks’ for high-performing health systems as a starting point for identifying a good system for children. The building blocks encompass safe and good quality services from an educated and empowered workforce, providing good data systems, access to all necessary medical products, prevention and treatments, and a service that is adequately financed and well led. An extensive search of the literature failed to identify a suitable appraisal framework for MOCHA, because none of the frameworks focused on child primary care in its own right. This led the research team to devise an alternative conceptualisation, at the heart of which is the core theme of child centricity and ecology, and the need to focus on delivery to the child through the life course. The MOCHA model also focuses on the primary care team and the societal and environmental context of the primary care system.

The 30 MOCHA (Models of Child Health Appraised) countries are diverse socially, culturally and economically, and differences exist in their healthcare systems and in the scope and role of primary care. An economic analysis was undertaken that sought to explain differences in child health outcomes between countries. The conceptual framework was that of a production function for health, whereby health outputs (or outcomes) are assumed affected by several ‘inputs’. In the case of health, inputs include personal (genes, health behaviours) and socio-economic (income, living standards) factors and the structure, organisation and workforce of the healthcare system. Random effects regression modelling was used, based on countries as the unit of analysis, with data from 2004 to 2016 from international sources and published categorisations of healthcare system. The chapter describes the data deficiencies and measurement conundrums faced, and how these were addressed. In the absence of consistent indicators of child health outcomes across countries, five mortality measures were used: neonatal, infant, under five years, diabetes (0–19 years) and epilepsy (0–19 years). Factors found associated with reductions in mortality were as follows: gross domestic product per capita growth (neonatal, infant, under five years), higher density of paediatricians (neonatal, infant, under five years), less out-of-pocket expenditure (neonatal, diabetes 0–19), state-based service provision (epilepsy 0–19) and lower proportions of children in the population, a proxy for family size (all outcomes). Findings should be interpreted with caution due to the ecological nature of the analysis and the limitations presented by the data and measures employed.

Impact evaluations of cluster programs at firm level are still scarce in the literature. The available evidence on the effectiveness of such programs based on rigorous quantitative impact evaluations is mixed. The purpose of this paper is to contribute to the body of literature the evaluates quantitatively the impact of cluster programs in emerging economies on firms’ performance. In particular, the authors evaluate the impact of a cluster program in Uruguay on firms’ sales and exports.

The authors use state-of-the-art impact evaluation methods to evaluate the impact of the program. In particular, difference in differences and matching methods

There is very strong evidence that the program had a positive impact on exports and the propensity to export of firms. However, the evidence of a positive impact on sales is weak. The evidence suggests that the maximum effect of the program can be found in the fourth or fifth year after the intervention.

The contribution of this paper to the literature is fourfold. First, this paper adds to the scarce body of literature evaluating the effects of cluster development programs with state-of-the-art impact evaluation methods. Second, it adds evidence for Latin America, a region that has implement a number of cluster policies (Maffioli et al., 2016) and where, as far as the authors know, there is only one additional paper evaluating rigorously the impacts of them (Figal-Garone et al., 2015). In addition, the authors provide evidence about the timing of the effects after the implementation of a cluster policy, an important issue that is mostly overlooked in the existent literature. Finally, the paper focus its attention on the impacts on exports and the propensity to export of firms, key elements for small open economies in Latin America that are heavily reliant on foreign currency inflows.

This chapter discusses the major steps and issues related to the inclusion of public services in inequality research. Empirically, it investigates how the income distribution in countries changes when the value of publicly provided services to households is included. The authors consider five major categories of public services: education, health care, social housing, childcare and elderly care. On average across OECD countries, spending on these ‘in-kind’ benefits accounts for about 13% of GDP, slightly more than the spending on cash transfers – but with considerable cross-country variation. Broadening the income concept to account for in-kind benefits considerably increases households’ economic resources. But public services also contribute to reducing income inequality, by between one-fifth and one-third depending on the inequality measure. This chapter suggests that publicly provided services fulfil an important direct redistributive role in OECD countries.

This paper aims to explore the nature of Industry 4.0 (I4.0) cluster, to establish and empirically verify in the pilot study the role of clusters in developing the fourth industrial revolution. It aims to find out if the cluster can provide a conducive knowledge environment fostering the advancement of I4.0; simplify the implementation of I4.0 by making it faster, easier, and cheaper and finally be applied as policy tool organising the development of I4.0.

This paper is based on the narrative literature review mapping the cluster’s nature with I4.0 features. It adopts the case study approach and uses simple statistical analysis to explore the basic characteristics of I4.0 clusters combined with a survey technique – short questionnaire organised in three major blocks.

Drawing on German pilot study it can be confirmed that clusters offer conducive environment facilitating the emergence, testing and development of I4.0 specific solutions. They provide favourable knowledge environment, simplify and increase the efficiency of the business processes and organise the policymaking in this area.

The results of presented pilot study rooted in Germany – a country seen as the front-runner in implementing the solutions of the fourth industrial revolution – can add a certain value to the emerging research on unearthed linkages between clusters and I4.0. This paper might be seen as a contribution to the emerging literature on the spatial dimension of I4.0. It expands previous research on cluster channels’ likely impact on I4.0.

This study aims to analyze the value-based health-care model in defining a strategy to guide the evolution of health-care organizations toward a value-oriented model. To improve the quality of care by ensuring economic sustainability, it is necessary to redefine the concept of competition in healthcare and align it with the concept of maximizing value for patients.

Performance measurement is a crucial aspect of the analysis of health-care organizations. Porter developed an effective analytical technique and presented the measurement of health-care outcomes based on health conditions, the efficiency of health-care organizations and the type of service provided.

Clinical outcomes and data on the costs of care of each patient are essential to evaluate improvement in treatment value over time. Engaging in the evaluation of what happens to patients in their course of care enables the expansion of the measurement of outcomes because it measures all the health services related to it.

Building a health-care system based on the value and continuous improvement of care and services provided is a goal shared by many countries and international organizations. Today, the analysis of outcomes is important for making informed decisions, directing and planning clinical and organizational changes by improving the quality of care and services.