Evaluation Review最新文献

Maternal, neonatal, and child health play crucial roles in achieving the objectives of Sustainable Development Goal (SDG) 2030, particularly in promoting health and wellbeing. However, maternal, neonatal, and child services in Moroccan public hospitals face challenges, particularly concerning mortality rates and inefficient resource allocation, which hinder optimal outcomes. This study aimed to evaluate the operational effectiveness of 76 neonatal and child health services networks (MNCSN) within Moroccan public hospitals. Using Data Envelopment Analysis (DEA), we assessed technical efficiency (TE) employing both Variable Returns to Scale for inputs (VRS-I) and outputs (VRS-O) orientation. Additionally, the Tobit method (TM) was utilized to explore factors influencing inefficiency, with hospital, doctor, and paramedical staff considered as inputs, and admissions, cesarean interventions, functional capacity, and hospitalization days as outputs. Our findings revealed that VRS-I exhibited a higher average TE score of 0.76 compared to VRS-O (0.23). Notably, the Casablanca-Anfa MNCSN received the highest referrals (30) under VRS-I, followed by the Khemisset MNCSN (24). In contrast, under VRS-O, Ben Msick, Rabat, and Mediouna MNCSN each had three peers, with 71, 22, and 17 references, respectively. Moreover, the average Malmquist Index under VRS-I indicated a 7.7% increase in productivity over the 9-year study period, while under VRS-O, the average Malmquist Index decreased by 8.7%. Furthermore, doctors and functional bed capacity received the highest Tobit model score of 0.01, followed by hospitalization days and cesarean sections. This study underscores the imperative for policymakers to strategically prioritize input factors to enhance efficiency and ensure optimal maternal, neonatal, and child healthcare outcomes.

Cooperation between employees in a company is an important input to firm performance. This study examines how a manager's cooperative behavior and the visibility of this behavior affect the cooperation amongst employees, and subsequently firm performance. To do so, we conducted a field experiment with managers and their employees from 320 Vietnamese small and micro firms to determine the impact of a manager's leading by example (LBE) on employees' behavior, corporate culture, and firm performance. Both managers and employees participated in a Public Good experiment which aimed to elicit an individual cooperative behavior. Noteworthy is that the decision made by a manager in the experiment was given as an example to employees before they made decision in that same experiment. We considered that the example of cooperation by managers in the Public Good experiment communicated a powerful signal to the employees regarding the importance of fostering cooperation in the workplace. Such a signal by the manager, who is at the top in the organizational hierarchy, would impact their employees' behavior in the workplace and firm's outcomes beyond the experiment. Interestingly, we found that concealing a manager's identity from their employees enhances the impacts of LBE.

As found in behavioral decision theory, venture capitalists (VCs) rely on heuristics and bias, owing to their bounded rationality, either by limited alternatives or information and resources. India's booming startup scene challenges VCs in decision-making owing to information overload from numerous evolving ventures, which hinders informed judgment. VC investment behavior, due diligence, and cognitive factors related to decision-making have always drawn the attention of researchers. We provide an alternative approach for an optimal decision by VCs by identifying the attributes that influence investment or funding decisions at an early stage of a venture in tech-based industries. Through a literature review, we identify eight attributes, both on internal and external criteria, that venture investors consider when making investment decisions. Based on interviews with 20 experts, we further identify eight key tech-based sectors. Using grey system theory, we then determine the rankings of eight tech startups for investors' early-stage investment decisions. This study presents a linguistic variable-based approach of grey numbers to decide weights and ratings, the grey possibility degree to compare and rank different tech startups, and based on the results, suggests the ideal tech startup. We find that agritech ranks first; thus, investors should prefer venturing into such startups for early-stage investment. E-commerce and edutech ranked second and third, respectively, followed by electric vehicle infrastructure, insurtech, fintech, space tech, and software as a service.

Gamified Flipped Classrooms (GFC) are increasingly implemented in teaching and have become a trending research subject. Gamification and Flipped Classroom (FC) literature expresses a clear need for a theoretical foundation that positively affects motivation, behavioral change, and learning. However, this requires an overview of the current theoretical foundations of GFC research. Therefore, this study conducted a systematic mixed studies review (SMSR) of the current theoretical foundations of GFC research and learning effects. Sixty-nine studies were identified after applying rigorously defined inclusion and exclusion criteria. The review revealed that a significant number of studies (25 of 69) lacked a theoretical foundation. In studies supported by theory, Self-Determination Theory (SDT) was most frequently employed. Moreover, only three studies used theories to design both gamification and FC components. While GFC research generally shows positive effects on learning, studies with a theoretical background reported a higher rate of positive GFC outcomes (56%), compared to only 29% for studies lacking a theoretical foundation. Future research should explore and attempt to link SDT and other theories to learning impacts.

Most existing solutions to the current replication crisis in science address only the factors stemming from specific poor research practices. We introduce a novel mechanism that leverages the experts' predictive abilities to analyze the root causes of replication failures. It is backed by the principle that the most accurate predictor is the most qualified expert. This mechanism can be seamlessly integrated into the existing replication prediction market framework with minimal implementation costs. It relies on an objective rather than subjective process and unstructured expert opinions to effectively identify various influences contributing to the replication crisis.

The impact of sustainable mobility may be considered through the mode of non-motorization, which may vary by spatial environmental conditions, especially accessibility by active mobility in a necessarily safe, comfortable, and pleasant atmosphere. Sustainable mobility comprises willingness to allow commuting behaviors on non-motorized travel for safety and green ethics. These chiefly participate to create livable places, improve health and well-being, and promote quality of life and economic growth. By contrast, an impoverished street environment decreases the possibility of active mobility and relies on motorization. It is essential to explore the perception of road users through diverse environmental features of street networks and sidewalks directly affecting road user behavior and preferences about motorization and non-motorization. This study evaluated multi-indicators of sustainable urban mobility (SUM) for a group of road users, with data gathered by questionnaire-based survey. 1,998 Pathum Thani Province inhabitants in Thailand were asked to evaluate their neighborhood characteristics by SUM indicators. In addition to different social dimensions and socioeconomic backgrounds, transportation characteristics among diverse trip makers and different perceptions of mobility indicators were classified by factor analysis using principal components and spatial visualization. Results were that traffic management, including intersection controls and signage, as well as information, sidewalk, management, and personal mobility choices, is a key strategy to improve sustainable mobility. This is achieved by tailoring solutions to achieve a modal shift prioritized over other supportive facilities.

When individuals are released from prison, they typically enter a period of post confinement community supervision. While under community supervision, their behaviors are subject to special conditions requiring them to report to supervisors and prohibiting certain behaviors such as drug and alcohol use. Many supervisees are returned to prison because they violate those special conditions, or because they commit minor crimes that would not result in prison were they not being supervised. But others are returned to prison for serious new crimes. We distinguish the two as nuisance behaviors (the former) and pernicious behaviors (the latter). Our research applies competing events survival analysis to distinguish a structural model that accounts for nuisance behaviors from a structural model that accounts for pernicious behaviors. We demonstrate that returning offenders to prison for technical violations and minor crimes may reduce the incidence of major crimes because the occurrence of nuisance behaviors and pernicious behaviors are highly correlated. Our findings support the theory that nuisance behaviors signal the likelihood of pernicious behaviors.

Randomized experiments involving education interventions are typically implemented as cluster randomized trials, with schools serving as clusters. To design such a study, it is critical to understand the degree to which learning outcomes vary between versus within clusters (schools), specifically the intraclass correlation coefficient. It is also helpful to anticipate the benefits, in terms of statistical power, of collecting household data, testing students at baseline, or relying on administrative data on previous cohorts from the same school. We use data from multiple cluster-randomized trials in four Latin American countries to provide information on the intraclass correlations in early grade literacy outcomes. We also describe the proportion of variance explained by different types of covariates. These parameters will help future researchers conduct statistical power analysis, estimate the required sample size, and determine the necessity of collecting different types of baseline data such as child assessments, administrative data at the school level, or household surveys.

This article describes a conceptual and empirical approach for estimating a human capital production function of child development that incorporates mother- or child-fixed effects. The use of mother- or child-fixed effects is common in this applied economics literature, but its application is often inconsistent with human capital theory. We outline the problem and demonstrate its empirical importance with an analysis of the effect of Head Start and preschool on child and adult outcomes. The empirical specification we develop has broad implications for a variety of applied microeconomic analyses beyond our specific application. Results of our analysis indicate that attending Head Start or preschool had no economically or statistically significant effect on child or adult outcomes.

In this paper, based on the established Vitae Researcher Development Framework (RDF), we introduce a new framework, tailor-made specifically for Vietnamese educational researchers, namely, Vietnam's Framework for Educational Researchers (VFER). VFER is expected to serve as a tool for Vietnamese educational researchers to self-evaluate their skills and support them in developing their career qualities from junior to senior career ladders. The framework includes four domains with ten subdomains and 28 indicators. To date, VFER has been implemented in some Vietnamese universities of pedagogical education. We expect that other research fields will look to VFER as a reference to build their own research capacity framework.