100 key research questions for the post‐2015 development agenda

The Sustainable Development Goals (SDGs) herald a new phase for international development. This article presents the results of a consultative exercise to collaboratively identify 100 research questions of critical importance for the post‐2015 international development agenda. The final shortlist is grouped into nine thematic areas and was selected by 21 representatives of international and non‐governmental organisations and consultancies, and 14 academics with diverse disciplinary expertise from an initial pool of 704 questions submitted by 110 organisations based in 34 countries. The shortlist includes questions addressing long‐standing problems, new challenges and broader issues related to development policies, practices and institutions. Collectively, these questions are relevant for future development‐related research priorities of governmental and non‐governmental organisations worldwide and could act as focal points for transdisciplinary research collaborations.     

Inferactive data analysis

We describe inferactive data analysis, so-named to denote an interactive approach to data analysis with an emphasis on inference after data analysis. Our approach is a compromise between Tukey's exploratory and confirmatory data analysis allowing also for Bayesian data analysis. We see this as a useful step in concrete providing tools (with statistical guarantees) for current data scientists. The basis of inference we use is (a conditional approach to) selective inference, in particular its randomized form. The relevant reference distributions are constructed from what we call a DAG-DAG—a Data Analysis Generative DAG, and a selective change of variables formula is crucial to any practical implementation of inferactive data analysis via sampling these distributions. We discuss a canonical example of an incomplete cross-validation test statistic to discriminate between black box models, and a real HIV dataset example to illustrate inference after making multiple queries on data.     

Varieties of liberalism: A comparative analysis of family policy and poverty outcomes across the 50 United States

The United States is generally recognised as an exemplar of liberal policy regime types. However, given evidence of state-level divergence in social policy, this article investigates to what extent such variation is present and relevant in state-level family policy. To this end, we pose a primary and secondary research question. Our primary question is whether varieties of liberalism exist across the 50 states in the United States. Our secondary question is whether these varieties of liberalism correspond to variety in social indicators of families' economic well-being. To answer our primary question, we first construct a family policy index that scores the relative generosity and coverage of state-administered social programmes that contribute to the (de)commodification and (de)familialisation of households with children. We then perform a cluster analysis to group states by shared decommodifying and defamilialising features. To answer our secondary question, we investigate how our family-policy clusters are associated with social indicators of families' financial security. Our findings suggest that varieties of liberalism do, indeed, exist within the United States. With the exception of Vermont, which stands out with respect to its comparatively generous family policies, we classify states into two groups: those with ‘soft’ liberal family policy in states featuring relatively generous and accessible policies and ‘hard’ liberal family policy in states achieving little of either. We find that these differences help explain state-level heterogeneity in levels of economic security among households with children, particularly single-mother households.     

A knowledge chain management framework to support integrated decisions in global supply chains

Knowledge management has been identified as a key enabler to achieve organisation’s value chain competitiveness. It, however, has been facing fresh challenges in a global supply chain setting. This paper proposes a global knowledge chain management (GKCM) framework that identifies and prioritises critical knowledge that a global supply chain can focus on to support integrated decisions. The framework explores three types of global context knowledge, namely global market knowledge, global capacity knowledge and global supply network configuration knowledge. Empirical study has been undertaken within the manufacturing industry to evaluate the GKCM framework. Analytic network process has been explored as a key method to assess the importance of the global knowledge constructs from supply chain managers’ perspectives. A key contribution of the paper is that it advances existing knowledge chain management approaches within one organisation and its local supply chain to include the global context knowledge applicable to global manufacturing settings, and highlights how the GKCM framework can support global supply chain integrated decisions.     

The Value of Information in Decision‐Analytic Modeling for Malaria Vector Control in East Africa

Decision analysis tools and mathematical modeling are increasingly emphasized in malaria control programs worldwide to improve resource allocation and address ongoing challenges with sustainability. However, such tools require substantial scientific evidence, which is costly to acquire. The value of information (VOI) has been proposed as a metric for gauging the value of reduced model uncertainty. We apply this concept to an evidenced‐based Malaria Decision Analysis Support Tool (MDAST) designed for application in East Africa. In developing MDAST, substantial gaps in the scientific evidence base were identified regarding insecticide resistance in malaria vector control and the effectiveness of alternative mosquito control approaches, including larviciding. We identify four entomological parameters in the model (two for insecticide resistance and two for larviciding) that involve high levels of uncertainty and to which outputs in MDAST are sensitive. We estimate and compare a VOI for combinations of these parameters in evaluating three policy alternatives relative to a status quo policy. We find having perfect information on the uncertain parameters could improve program net benefits by up to 5–21%, with the highest VOI associated with jointly eliminating uncertainty about reproductive speed of malaria‐transmitting mosquitoes and initial efficacy of larviciding at reducing the emergence of new adult mosquitoes. Future research on parameter uncertainty in decision analysis of malaria control policy should investigate the VOI with respect to other aspects of malaria transmission (such as antimalarial resistance), the costs of reducing uncertainty in these parameters, and the extent to which imperfect information about these parameters can improve payoffs.     

The implementation of digital technologies for operations management: a case study for manufacturing apps

Abstract

Manufacturing applications address business to business (B2B) with highly customised applications developed for specific requirements, offering highly specialised solution-oriented and service-based software components, systems, and digital tools that aim at a fast and accurate decision-making support system. The purpose of this paper is to describe the implementation of digital technologies for operations management using manufacturing or engineering apps (eApps), for product design and manufacturing processes. In particular, starting from the specific needs of two companies from mature European industries as automotive and food, this work depicts how this kind of solutions can support companies and improve their operations. In particular, related benefits and challenges faced for the full implementation of the developed tools are highlighted. Moreover a business model to exploit the manufacturing apps is also proposed. The business model proposed for the exploitation of the eApps supports the commercialisation of all the revenue streams offered by this rapidly growing sector taking into account the specific needs of the concerned stakeholders through a diversified value proposition.     

Key to Effective Organizational Performance Management Lies at the Intersection of Paradox Theory and Stakeholder Theory

One of the fundamental and recurring issues in performance management is the adoption of a simplistic, short‐term, narrow, metrics‐oriented approach, which often results in unintended negative outcomes, some of which could be disastrous. This paper makes the case that the key to preventing this syndrome lies at the intersection of paradox and stakeholder theories. Both theories encourage a more complex, long‐term, holistic, balanced approach to management. Stakeholder theory focuses on addressing the many (sometimes conflicting) goals of multiple stakeholders, and paradox theory provides insights into how this challenging task (i.e. of simultaneously addressing multiple conflicting priorities) can be accomplished. Thus, the former provides the ‘what’ and the latter the ‘how’ of effective organizational performance management. Accordingly, the literature at the intersection of both theories (composed of 69 scholarly outputs), was reviewed, and in so doing, identified seven domain areas and 21 constructs, all of which implicitly deal with either performance management or its communication, thereby lending support to this paper's thesis. The implications of this review for both theory and practice, including the role of paradoxical cognitive mechanisms, is discussed.     

Toward Probabilistic Prediction of Flash Flood Human Impacts

This article focuses on conceptual and methodological developments allowing the integration of physical and social dynamics leading to model forecasts of circumstance‐specific human losses during a flash flood. To reach this objective, a random forest classifier is applied to assess the likelihood of fatality occurrence for a given circumstance as a function of representative indicators. Here, vehicle‐related circumstance is chosen as the literature indicates that most fatalities from flash flooding fall in this category. A database of flash flood events, with and without human losses from 2001 to 2011 in the United States, is supplemented with other variables describing the storm event, the spatial distribution of the sensitive characteristics of the exposed population, and built environment at the county level. The catastrophic flash floods of May 2015 in the states of Texas and Oklahoma are used as a case study to map the dynamics of the estimated probabilistic human risk on a daily scale. The results indicate the importance of time‐ and space‐dependent human vulnerability and risk assessment for short‐fuse flood events. The need for more systematic human impact data collection is also highlighted to advance impact‐based predictive models for flash flood casualties using machine‐learning approaches in the future.     

Consumption Over the Life Cycle

This paper estimates a structural model of optimal life‐cycle consumption expenditures in the presence of realistic labor income uncertainty. We employ synthetic cohort techniques and Consumer Expenditure Survey data to construct average age‐profiles of consumption and income over the working lives of typical households across different education and occupation groups. The model fits the profiles quite well. In addition to providing reasonable estimates of the discount rate and risk aversion, we find that consumer behavior changes strikingly over the life cycle. Young consumers behave as buffer‐stock agents. Around age 40, the typical household starts accumulating liquid assets for retirement and its behavior mimics more closely that of a certainty equivalent consumer. Our methodology provides a natural decomposition of saving and wealth into its precautionary and life‐cycle components.     

Technology,Geography, and Trade

We develop a Ricardian trade model that incorporates realistic geographic features into general equilibrium. It delivers simple structural equations for bilateral trade with parameters relating to absolute advantage, to comparative advantage (promoting trade), and to geographic barriers (resisting it). We estimate the parameters with data on bilateral trade in manufactures, prices, and geography from 19 OECD countries in 1990. We use the model to explore various issues such as the gains from trade, the role of trade in spreading the benefits of new technology, and the effects of tariff reduction.