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Data analytics projects can be like throwing darts in the dark. Problem-centric thinking is vital, argue Vincent Charles, Ali Emrouznejad, Tatiana Gherman, and James Cochran

The results-based budgeting (RBB) framework is a public management strategy in which economic resources are allocated to certain budget programs, oriented towards delivering specific products and results to the population. The present paper analyzes the regional governments’ efficiency in using their economic resources, under the RBB framework, with an application to the Peruvian context. To this end, we employ a data envelopment analysis (DEA) model with bootstrapping. In the first stage, different sectors of the regional governments are considered individually: education, security, health, sanitation, transportation, and recreation. In the second stage, the overall efficiency index is calculated using the sectoral indices obtained in the first stage. Finally, the factors or determinants influencing the level of efficiency are analyzed. The results show improvements in efficiency levels in the areas of health and sanitation, to the detriment of the rest of the sectors. The average overall efficiency level over the period 2013–2016 remains in the range of 0.25–0.30, which indicates an inefficiency level of 70%. Finally, the variables fiscal autonomy, capital stock, and population density show a positive relationship with respect to the overall efficiency index.

This study addresses a capacity expansion problem (CEP). A typical CEP model usually focuses on addressing demand fluctuation for cost minimization and assumes a constant marginal productivity, which may overestimate the capacity level, thus leading to an infeasible capacity plan. However, the marginal productivity theory has some merits which can complement the CEP; for example, a production function estimates the production possibility set limiting the production behavior and characterizes the law of diminishing marginal returns (DMR). To integrate the perspective of marginal productivity factors in the CEP model, we propose a two-stage model to solve the CEP. The first stage estimates the production possibility set and finds the directional marginal productivity (DMP) towards marginal profit maximization. The second stage, which addresses demand fluctuation, develops the minimax regret model, balancing capacity shortage and capacity surplus to build a robust capacity plan. The results of a numerical illustration validate the robust decision generated by the proposed model and correct a typical CEP model without considering marginal productivity, where the major factor affecting capacity decisions is the ability to raise/leverage resource for marginal productivity rather than demand variation and cost structure.

Linear programming (LP) has long proved its merit as the most flexible and most widely used technique for resource allocation problems in various fields. To solve an LP problem, we have traditionally considered crisp values for the parameters, which are unrealistic in real-world decision-making under uncertainty. The fuzzy set theory has been used to model the imprecise parameter values in LP problems to overcome this shortcoming, resulting in a fuzzy LP (FLP) problem. This paper proposes a new method for solving fuzzy variable linear programming (FVLP) problems in which the decision variables and resource vectors are fuzzy numbers. We show how to use the standard simplex algorithm to solve this problem by converting the fuzzy problem into a crisp one once a linear ranking function is chosen. The novelty of the proposed model resides in that it requires less effort on fuzzy computations as opposed to the existing fuzzy methods. Furthermore, to solve the FVLP problem using the existing methods, fuzzy arithmetic operations and the solution to fuzzy systems of equations are required. By contrast, only arithmetic operations of real numbers and the solution to crisp systems of equations are required to solve the same problem with the method proposed in this study. Finally, a transportation case study in the coal industry is presented to demonstrate the applicability of the proposed algorithm.

Louis Sébastien Mercier abhorre la satire personnelle, tandis que les antiphilosophes l’emploient abondamment, contre lui notamment, avant la Révolution. Cependant, il utilise dans son Tableau de Paris le style satirique au milieu de nombreux autres, pour dénoncer les travers de groupes sociaux. Il semble alors à mi-chemin entre les moralistes classiques et la sociologie moderne, l’observation rigoureuse et la fiction piquante. Contre le rire satirique, il sent poindre une inquiétude prérévolutionnaire.

This document contains the protocol for our narrative review, "Models of children's social care delivery". The preregistration is dated June 2023.

Healthcare systems are operating under substantial pressures. These pressures include staff shortages and increasing numbers of elderly patients and patients with multiple chronic conditions. Staff, teams and managers are constantly making adaptions in response to these pressures, to minimise the risks to patients and to maintain the quality and safety of care within the available constraints. We will carry out a scoping review, which aims to identify papers containing strategies used by frontline clinicians and managers to adapt to and manage pressures in the healthcare system. We will do this by examining empirical studies from within the resilient healthcare literature that contain findings that are relevant to understanding strategies healthcare professionals and organisations use to adapt under pressure. We aim to create a taxonomy of strategies which are useful to frontline clinicians and managers.

PurposeThis study investigates the interrelationships between efficiency, competition and risk in the Chinese banking industry.Design/methodology/approachParametric stochastic frontier analysis is used to estimate bank efficiency; the Lerner index is used as the competition indicator; accounting ratios and a translog function are used to measure different types of risk and finally, the three-stage least square estimator is used to investigate the interrelationships.FindingsThe results of this study show that the impact of competition on different types of risk is significant and positive, while there is a significant and positive impact of credit risk, liquidity risk and capital risk on bank competition. In addition, the findings demonstrate that the interrelationships between efficiency and competition are significant and negative. The authors do not find any robust interrelationships between different types of risk and different types of efficiency; the authors find that diversification and higher levels of profitability reduce bank credit risk. The results suggest that a higher developed banking sector reduces the level of bank competition in China.Originality/valueThis is the first piece of research that comprehensively investigates the interrelationships between different types of risk, competition and different efficiencies in China.

Combining scholarship on norms and trust in International Relations, this article puts forward the concept of entrusted norms as a novel means to understand certain dynamics of cooperation and conflict in international politics. Entrusted norms differ from non-entrusted norms both in the manner that they are policed and in the reaction to their infringement. In the first case, there are few formal hedging mechanisms taken against potential defection. In the second case, when broken, they result in a betrayal reaction where a return to the behavioral status quo is insufficient to return to the political status quo. We illustrate the analytical usefulness of entrusted norms through an examination of the established norms of diplomacy within the Gulf Cooperation Council, paying particular attention to interactions between Saudi Arabia and Qatar in the post-Arab Spring period. We argue that the perception of Qatar’s defection from an entrusted norm, the preservation of individual and collective dignity, contributed to the 2014 diplomatic rupture between these two states and set in motion a betrayal/attempted reconciliation cycle, where even Qatar’s attempts to move back to the behavioral status quo prior to the fallout have been insufficient to fully repair the relationship. In addition to providing a novel interpretation to this case, this paper highlights the need for further theoretical consideration of the severity and duration of punishment after norm transgression within social constructivism, reinforces the theoretical connection between social structures and emotions, and advocates for an expansion in the domains of trust that we study.

Every contact centre engages in some form of Call Quality Monitoring in order to improve agent performance and customer satisfaction. Call centres have traditionally used a manual process to sort, select, and analyse a representative sample of interactions for evaluation purposes. Unfortunately, such a process is marked by subjectivity, which in turn results in a distorted picture of agent performance. To address the challenge of identifying and removing subjectivity, empirical research is required. In this paper, we introduce an evidence-based, machine learning-driven framework for the automatic detection of subjective calls. We analyse a corpus of seven hours of recorded calls from a real-estate call centre using Deep Neural Network (DNN) for a multi-classification problem. The study establishes the first baseline for subjectivity detection, with an accuracy of 75%, which is comparable to relevant speech studies in emotional recognition and performance classification. We conclude, among other things, that in order to achieve the best performance evaluation, subjective calls should be removed from the evaluation process or subjective scores deducted from the overall results.

Many of the healthcare tasks now carried out by patients and family carers were once solely the responsibility of healthcare professionals. The care provided by parents for children with chronic and life limiting conditions can be especially complex. One example is gastrostomy care. A gastrostomy is a surgically inserted device which allows a child to be fed directly into their stomach. The quality of training parents receive for gastrostomy care is highly variable. When the quality of training is poor, the consequences for the child can be serious. This study tests ways to improve the information giving and training parents receive leveraging findings from cognitive psychology. In previous experiments we found the recall and schema-enhanced training did not significantly improve memory for new information. We are now exploring whether videos can improve memory for the information. According to dual-coding theory, information that is presented alongside complementary images is better remembered than when text is presented on its own (Paivio, 1990). Multiple representations of the same information enhances memory for the information provided the accompanying visual representations are informative and carefully constructed (Mayer, Mayer, & Gallini, 1990). The visual representations can be videos, animations, charts etc. (Mayer, 2002). Visual aids can serve multiple functions, including making the information more memorable, making the text easier to understand or providing a structural framework for the text (Carney & Levin, 2002). We have co-produced a set of videos on gastrostomy care with clinicians and parents of children with gastrostomies to be used in this experiment. Participants will be a sample of the general public recruited via standard methods for online experimentation. Participants will be asked to study the information and will be tested on it in a delayed memory test at one week. The aim of this study is to determine whether video-enhanced training improves memory for information about a complex medical task (gastrostomy care).

We employ Data Envelopment Analysis to estimate the new technical, new cost, and new profit efficiency of Indian banks over the period 2008–2018. Then, we use Random Forest Regression to examine the impact of corporate governance (Board Size, Board Independence, Duality, Gender Diversity, and Board Meetings), bank characteristics (Return on Assets, Size, and Equity to Total Assets), and other characteristics (Ownership and Years) on bank efficiency. Among others, we found that board characteristics play a significant role particularly in new profit efficiency; therefore, policymakers and regulators should consider Board Size, Board Independence, Board Meetings, and Duality while framing guidelines for enhancing bank new profit efficiency. We also found that Board Independence plays a vital role in bank new cost efficiency, while Gender Diversity contributes to both new technical and new cost efficiency. This study makes methodological contributions by employing Machine Learning based Random Forest Regression in tandem with Data Envelopment Analysis under a two-phase model to examine corporate governance and bank efficiency, which is a pioneering attempt.

When it comes to performance evaluation, productivity analytics, and benchmarking, Data Envelopment Analysis (DEA) is the most widely used tool. In particular, DEA is data-enabled analytics (Zhu, 2...

This paper provides a comprehensive review of the applications of data science techniques and methodologies in productivity. The paper is structured as a combination of a bibliometric analysis and ...

The salience of the debate over Russian political interference in the USA has increased significantly during the Trump administration. However, there is no consensus over how to respond to the interference of this openly illiberal power. This paper argues that we need to reconceptualize our understanding of Russian influence to understand this high level of contestation. While the current understanding of Russian influence is characterized as a problem of unwanted information flows, we argue that we also need to take seriously the ideological influence that the Russia regime has in the USA—that the appeal of the Russian regime’s conservative and populist ideology can help to explain this division. By taking ideological attraction seriously, we can explain why it is currently difficult for the USA to respond to this threat and why solutions to Russian interference based on the information flow models need to be reconsidered.

Total Quality Management represents a management approach to long-term success used by organisations worldwide to improve their products and processes and achieve customer satisfaction, being perce...

Regular network data envelopment analysis (DEA) models deal with evaluating the performance of a set of decision-making units with a two-stage construction in the context of a deterministic data se...

Studies have shown that the sensible operation of big data may yield pow- erful insights that can improve the organisations’ strategic decision-making process and contribute to achieving an enhanced competitive advantage. In this manuscript, we explore the promise of big data in redefining strategy in service operations man- agement (SOM) by means of investigating a rich range of bibliographic material. The SOM field has a plethora of research opportunities to capitalise on, which are enhanced by the presence of big data. SOM research in the big data age implies a shift in attention from being increasingly integrative across themes to being integra- tive across multiple disciplines, requiring the expertise of and tuning between dif- ferent actors and expertise domains. Our aim is to stimulate debate in the field and set out a renewed research agenda by means of calling for additional considerations of strategic aspects, namely technology, people, and ethics, that can help guide and move the field forward.

In this paper, we study the agency problem in an organisation within a Markovian framework. More specifically, the paper presents the case of a principal imposing an incentive-control structure upon an agent to force him to follow the principal’s interests for which he was hired, against the tendency of the agent to follow his own interests. Findings point toward the principal’s difficulty in controlling the behaviour of the agent through incentives and monitoring; instead, best results are obtained when hiring agents who care for their reputation and refrain from unprofessional behaviours. The implication is that if we consider that it might be difficult to identify this characteristic at the time of the agent’s hiring, the best criterion will be to look for low levels of greed in the agent. This conclusion goes in some way against current practices of looking for aggressive agents for the generation of higher profits. Nevertheless, it should be noted that these potential benefits might actually fade away if the agent follows his own interests, instead of the principal’s. Another interesting result points to the restricted, although necessary, role of monitoring to control the agent’s behaviour, a result that goes against current research interests on measures of corporate governance. The paper is a contribution to expert decision-making.

Data envelopment analysis (DEA) is a technique for identifying the best practices of a given set of decision-making units (DMUs) whose performance is categorized by multiple performance metrics that are classified as inputs and outputs. Although DEA is regarded as non-parametric, the sample size can be an issue of great importance in determining the efficiency scores for the evaluated units, empirically, when the use of too many inputs and outputs may result in a significant number of DMUs being rated as efficient. In the DEA literature, empirical rules have been established to avoid too many DMUs being rated as efficient. These empirical thresholds relate the number of variables with the number of observations. When the number of DMUs is below the empirical threshold levels, the discriminatory power among the DMUs may weaken, which leads to the data set not being suitable to apply traditional DEA models. In the literature, the lack of discrimination is often referred to as the “curse of dimensionality”. To overcome this drawback, we provide a simple approach to increase the discriminatory power between efficient and inefficient DMUs using the well-known pure DEA model, which considers either inputs only or outputs only. Three real cases, namely printed circuit boards, Greek banks, and quality of life in Fortune’s best cities, have been discussed to illustrate the proposed approach.

PurposeThe purpose of this paper is to investigate the mediator role that knowledge sharing plays between organisational culture, organisational structure, and technology infrastructure and process improvement in a knowledge management context in manufacturing enterprises operating in the food, beverage and textile industry.Design/methodology/approachAn empirical study is conducted with a sample of 200 food, beverage and textile companies. Data are obtained by means of a survey questionnaire applied to general managers in each of the sample firms. The impact of the factors organisational culture, organisational structure and technology infrastructure on process improvement via knowledge sharing is assessed. Structural equation modelling and maximum likelihood estimation are applied to find the direction and strength of the relationships.FindingsThe main findings indicate the significant relationships between knowledge sharing and process improvement, between organisational culture and knowledge sharing, and between organisational structure and knowledge sharing. The relationship between technology infrastructure and knowledge sharing is found not to be significant.Research limitations/implicationsThe findings of the present study are limited to the food, beverage and textile industry. Future research could incorporate data from other manufacturing sectors or service companies.Practical implicationsThis study provides practical guidance for general managers who wish to implement process improvement programmes.Originality/valueSeveral authors have noted that there are few research studies concerning the interaction between each phase of knowledge management and total quality management practices. This study is interested in knowledge sharing and its impact on process improvement in a knowledge management context.

The 2014–15 Ebola epidemic in West Africa highlighted the significance of trust between the public and public health authorities in the mitigation of health crises. Since the end of the epidemic, there has been a focus amongst scholars and practitioners on building resilient health systems, which many see as an important precondition for successfully combatting future outbreaks. While trust has been acknowledged as a relevant component of health system resilience, we argue for a more sustained theoretical engagement with underlying models of trust in the literature. This article takes a first step in showing the importance of theoretical engagement by focusing on the appeal to rational models of trust in particular in the health system resilience literature, and how currently unconsidered assumptions in this model cast doubt on the effectiveness of strategies to generate trust, and therein resilience, during acute public health emergencies.

Uncertainty is unavoidable and addressing the same is inevitable. That everything is available at our doorstep is due to a well-managed modern global supply chain, which takes place despite its efficiency and effectiveness being threatened by various sources of uncertainty originating from the demand side, supply side, manufacturing process, and planning and control systems. This paper addresses the demand- and supply-rooted uncertainty. In order to cope with uncertainty within the constrained multi-objective supply chain network, this paper develops a fuzzy goal programming methodology, with solution procedures. The probabilistic fuzzy goal multi-objective supply chain network (PFG-MOSCN) problem is thus formulated and then solved by three different approaches, namely, simple additive goal programming approach, weighted goal programming approach, and pre-emptive goal programming approach, to obtain the optimal solution. The proposed work links fuzziness in transportation cost and delivery time with randomness in demand and supply parameters. The results may prove to be important for operational managers in manufacturing units, interested in optimizing transportation costs and delivery time, and implicitly, in optimizing profits. A numerical example is provided to illustrate the proposed model.