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While there exist many surveys on the use stochastic frontier analysis (SFA), many important issues and techniques in SFA were not well elaborated in the previous surveys, namely, regular models, copula modeling, nonparametric estimation by Grenander’s method of sieves, empirical likelihood and causality issues in SFA using regression discontinuity design (RDD) (sharp and fuzzy RDD). The purpose of this paper is to encourage more research in these directions.

A literature survey.

While there are many useful applications of SFA to econometrics, there are also many important open problems.

This is the first survey of SFA in econometrics that emphasizes important issues and techniques such as copulas.

This study investigates the impact of political alignment on intergovernmental transfers to the Brazilian unified health system (SUS). The authors analyzed both automatic transfers based on pre-established rules and discretionary transfers, using two criteria of political alignment between mayors and the central government.

For the empirical analysis the authors used regression-discontinuity design (RDD) and the outcomes of close elections between 2001 and 2017.

The results indicate positive and statistically significant effects of party alignment on the two transfer categories, especially discretionary transfers, but also on transfers based on pre-established rules. The effect of direct party alignment, when mayors and the president are from the same party, is greater than that resulting from coalitions established in municipal and federal elections.

The positive effect of party alignment was found both in discretionary transfers (those that do not have previously established rules) and some non-discretionary transfers (although they have previously defined regulations). A part of these regulations depends on production capacity and on taking part in programs promoted by the central government, which may produce entropy in the financing system, and a margin to benefit political allies. In the case of the SUS system, it is possible that this entropy is greater in the basic health care category than in the moderate and high complexity one, allowing a higher margin for discretion in transfers allocated to the former. Stricter rules associated to basic health care transfers would be desirable.

In Brazil, stricter rules and monitoring associated to basic healthcare intergovernmental transfers would be desirable.

The results may inspire some improvement in the mechanisms that govern the distribution of resources to basic healthcare in Brazil, improving social welfare by improving social justice in the distribution of resources to basic healthcare.

The authors does not know any other study about the impact of party alignment on the distribution of intergovernmental transfers to the Brazilian unified health system.

The purpose of this paper is to evaluate an English Proficiency (EP) programme and its efficacy with respect to students' academic performance in a university within a multi-lingual context, as the programme had been in effect for some years.

A quasi-experimental approach was used to study the efficacy of an EP programme in a university within a multilingual context. Data across two academic years were used, along with regression discontinuity design.

Results suggest that the EP programme had a significant and positive intervention effect on students' initial semester grade point average. The programme effect size was found to be medium to large.

It might be useful to extend the study for one more year for more concrete conclusions. As the study was anchored upon the structure of the 2016 EP programme, any major curricular/structural change to the programme warrants another study.

This study demonstrated that the implementation of EP programmes in higher education institutions is essential not only for international students who are foreign language speakers of English but also for domestic students in English-speaking countries, especially for bi/multilingual speakers.

Previous studies related to the efficacy of EP within higher education have focused on international students who speak English as an additional/foreign language. Further, most studies have focussed on students' self-reported experiences and have yielded disparate findings. This study contributes to scholarship as it addresses the under-researched area related to domestic students who speak English as the first language in a bi/multi-lingual context.

The purpose of this study is to develop a method to assess social performance. Traditionally, environment, social and governance (ESG) rating providers use subjectively weighted arithmetic averages to combine a set of social performance (SP) indicators into one single rating. To overcome this problem, this study investigates the preconditions for a new methodology for rating the SP component of the ESG by applying machine learning (ML) and artificial intelligence (AI) anchored to social controversies.

This study proposes the use of a data-driven rating methodology that derives the relative importance of SP features from their contribution to the prediction of social controversies. The authors use the proposed methodology to solve the weighting problem with overall ESG ratings and further investigate whether prediction is possible.

The authors find that ML models are able to predict controversies with high predictive performance and validity. The findings indicate that the weighting problem with the ESG ratings can be addressed with a data-driven approach. The decisive prerequisite, however, for the proposed rating methodology is that social controversies are predicted by a broad set of SP indicators. The results also suggest that predictively valid ratings can be developed with this ML-based AI method.

This study offers practical solutions to ESG rating problems that have implications for investors, ESG raters and socially responsible investments.

The proposed ML-based AI method can help to achieve better ESG ratings, which will in turn help to improve SP, which has implications for organizations and societies through sustainable development.

To the best of the authors’ knowledge, this research is one of the first studies that offers a unique method to address the ESG rating problem and improve sustainability by focusing on SP indicators.

This study aims to explore how to overcome and address the loss of exploratory innovation, thereby achieving greater success in exploratory innovation. This phenomenon of loss occurs when enterprises decrease their investment in and engagement with exploratory innovation, ultimately leading to an insufficient amount of such innovation efforts. Drawing on dynamic capabilities, this study investigates the relationship between organizational foresight and exploratory innovation and examines the moderating role of breakthrough orientation/financial orientation.

This study used survey data collected from 296 Chinese high-tech companies in multiple industries and sectors.

The evidence produced by this study reveals that three elements of organizational foresight (i.e. environmental scanning capabilities, strategic selection capabilities and integrating capabilities) positively influence exploratory innovation. Furthermore, this positive effect is strengthened in the context of a high-breakthrough orientation. Moreover, the relationships among environmental scanning capabilities, strategic selection capabilities and exploratory innovation become weaker as an enterprise’s financial orientation increases, whereas a strong financial orientation does not affect the relationship between integrating capabilities and exploratory innovation.

Ambidexterity is key to successful enterprise innovation. Compared with exploitative innovation, it is by no means easy to engage in exploratory innovation, which is especially important in high-tech companies. While the loss of exploratory innovation has been observed, few empirical studies have explored ways to promote exploratory innovation more effectively. A key research implication of this study pertains to the role of organizational foresight in the improvement of exploratory innovation in the context of high-tech companies.

This paper contributes to the broader literature on exploratory innovation and organizational foresight and provides practical guidance for high-tech companies regarding ways of avoiding the loss of exploratory innovation and becoming more successful at exploratory innovation.

Ground Penetrating Radar is a multidisciplinary Nondestructive Evaluation technique that requires knowledge of electromagnetic wave propagation, material properties and antenna theory. Under some circumstances this tool may require auxiliary algorithms to improve the interpretation of the collected data. Detection, location and definition of target’s geometrical and physical properties with a low false alarm rate are the objectives of these signal post-processing methods. Basic approaches are focused in the first two objectives while more robust and complex techniques deal with all objectives at once. This work reviews the use of Artificial Neural Networks and Machine Learning for data interpretation of Ground Penetrating Radar surveys. We show that these computational techniques have progressed GPR forward from locating and testing to imaging and diagnosis approaches.