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The aim of this paper is to provide an approach to analyze the performance of TV programs and to identify what can be done to improve them.

The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), the Ng-model, Grey relational analysis (GRA), and principal component analysis (PCA) were applied to evaluate the programs, using audience, share, and duration as the performance criteria.

By comparing TOPSIS to the Ng-model, PCA, and GRA, we verified that SVD and bootstrap SVD TOPSIS provide a good balance between equal-weights TOPSIS and the other models. This is because SVD and bootstrap SVD TOPSIS break down the data to a higher degree, but are less impacted by outliers compared to the long tail models.

To determine which TV programs should be replaced or modified is a complex decision that has not been addressed in the literature. The advantage of using a multi-criteria decision-making (MCDM) approach is that analysts can choose as many criteria as they want to rank TV programs, rather than relying on a single criterion (e.g., audience, share, target rating point).

This work represents the first time that robust MCDM methodology is applied to an audience data set to analyze the performance of TV programs and to identify what can be done to improve them. This study shows the application of a detailed methodology that is useful for the improvement of TV programs and other entertainment industry content.

In recent decades, childhood in Japan has undergone significant transformations. Government policies geared at boosting women's labor force participation, a declining fertility rate, rising costs of having children on the one hand, and increased spending on public childcare and support measures for families, on the other hand, contribute to these ongoing changes. Having only one child is becoming the norm while mothers' role in society is shifting. The traditional family structure is moving from the previously predominant male breadwinner model to more dual-earner families. Children now spend significant amounts of time in care and education institutions.

In this chapter, we analyze current configurations of early childhood in institutions and the family from a policy perspective and regarding children's predominant education and care arrangements. Drawing on various survey data sets and evidence from demographic statistics to pedagogical ethnographies, we look at how childcare policies and families reshape the organization of children's lives and outline how institutions and educators create learning experiences aligned with the values of a collectivist society. However, despite being deeply rooted in traditional child-rearing goals, many parents also subscribe to rigorous educational arrangements from early childhood onwards to prepare children for success in a competitive education system. The chapter finishes with an outlook on future directions of how policymakers and the ongoing institutionalization of childhood continue to change children's lives.

This paper aims to validate an initially developed e-Health readiness assessment model.

The authors thematically analysed an initial qualitative data collected and used the outcome to develop survey instruments for this study. To collect the quantitative data, the authors used the drop and collect survey approach given the research setting. The quantitative data was analysed using factor and regression analyses of SPSS 23 in which hypotheses formulated were tested.

The results suggest that the model [R² = 0.971; F (5, 214) = 1414.303], which is made up of readiness assessment factors (constructs) and measuring tools explain about 97% of the variance of the overall health information technology/e-Health adoption readiness at Komfo Anokye Teaching Hospital. The measuring tools were reliable for assessing the composite variables (constructs): technology readiness; operational resource readiness; organizational and cultural readiness; regulatory and policy readiness; and core readiness, which have significant influence on eHealth adoption readiness assessment..

This study has successfully validated empirically developed eHealth readiness assessment model with complete reliable indicators given that existing eHealth readiness assessment models have not been effective due to a general lack of standard indicators for measuring assessment factors. The study also contributes to the growing research on the adoption of information technology/systems in health-care environment using the Technology–Organization–Environment framework.

Progress in theory building in the field of collaborative networks in manufacturing is preponderantly seen in contributions from disciplines outside manufacturing science. Interdisciplinary research is one way of accelerating the development of appropriate theory for this emerging domain where industrial practice has moved beyond the state of the art of scientific knowledge for establishing workable, competitive solutions. The purpose of this paper is to examine to what extent interdisciplinary research has contributed to a better understanding of collaborative (manufacturing) networks.

To find out more about provenances of on‐going studies, to identify clusters of contributions and to provide direction for future work of researchers in this domain, publications of the past 22 years have been evaluated. To retrieve these contributions, a structured literature review has been undertaken by applying keywords to selected databases and using a strictly defined stepwise procedure. In total, 202 publications of all kinds have been evaluated.

From the analysis of the results, it appears that most interdisciplinary contributions to collaborative (manufacturing) networks rely on one original outside discipline for either developing solutions or advancing theoretical insight. Consequently, and after further analysis, it seems that researchers in collaborative networks hardly resort to multi‐disciplinary approaches, unless “natural”; further advances might arrive from stimulating these multi‐disciplinary avenues rather than sticking to more mono‐disciplinary, and less risky, takes on both applications and theoretical insight. A more detailed investigation of the value of contributions reveals that efforts to make interdisciplinary advances are either difficult or limited. Also, the findings indicate that researchers tend to follow a more “technical” approach to decision making by actors in networks rather than searching for a shift in paradigm.

While setting out these directions for future research and guiding research, this first‐of‐its‐kind review introduces the collaboration model as a systematic approach to collaborative (manufacturing) networks. This model might serve as a reference model to integrate disciplines for addressing the characteristics of Collaborative Networks. Its use in the review led to the finding that typical traits of networks, such as changeability, supplementary assets and decentralisation of decision making, are under‐researched.