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The development of information and communication technologies accompanied by the exponential growth of electronic scholarly publications and computer archives has led, on the one hand, to the development of techniques for the systematic analysis of published literature and, on the other hand, to a significant growth in the number of literature reviews conducted and published. A systematic literature review seeks to comprehensively identify and synthesize all studies conducted on a given topic. However, how does one ensure that the entire state of the art on a given topic has been covered? How does one collect and select a large number of publications on a given topic, and how does one analyze the knowledge extracted from these publications? In other words, how do we use systematic literature analysis to produce new knowledge from existing knowledge? To make the best use of the various tools available today for conducting systematic reviews, it is necessary to be aware of their limitations. In the paper, we provide illustrations, examples, and detailed information on the various alternatives for conducting systematic reviews, presenting a review of available sources and tools. We report an analysis of the articles published in the journal International Transactions in Operational Research to show the potential and limitations of systematic review versus domain expert‐based selection. Finally, we show that a good systematic review relies on the intelligent combination of the latest tools available for conducting systematic research with the knowledge of domain experts. [ABSTRACT FROM AUTHOR]

One of the most significant problems in the development of quantitative models to assess the performance of decision‐making units (DMUs) is the availability of information coded into variables and indicators that are proxies for the relevant dimensions of the models: resources or inputs, products or services or outputs, and heterogeneous contextual factors including environmental factors. Data envelopment analysis (DEA) and balanced scorecard (BSC) are two of the best‐known and applied tools to model and measure the performance of DMUs. Within the information set requested to model the performance, the most critical and sensitive variables and indicators related to the intangible capital of organizations, which includes with a primary role the intellectual capital (IC). Although they are very different, DEA (based on linear programming) and BSC (including a set of indicators along four dimensions) have recently been combined to try to address the problem indicated earlier, namely, having a set of variables and indicators available to better measure the performance of DMUs. We apply a three‐level methodology combining (i) a series of systematic reviews, (ii) a bibliometric analysis of all the published works found, and (iii) an analysis of the so‐called grey literature contained in the reports of knowledge‐based organizations. The main results obtained are (1) a comprehensive survey and mapping of all scientific works combining DEA, BSC, and IC including the gender dimension; (2) an integral and inclusive list integrating all indicators found in both published works and reports, reclassified according to the main dimensions of the IC. [ABSTRACT FROM AUTHOR]

We estimate a dynamic network technology where new knowledge in the form of publications in STEM (science, technology, engineering, and mathematics) is an intermediate product. Knowledge is produced in the first stage of production and is used in the second stage of production to produce a final output of real consumption, which equals gross domestic product minus investment spending on physical capital minus research and development expenditures. Knowledge also spills over between producers as it becomes disseminated. The two stages of production are linked between periods as investments in research capital and physical capital enhance future production possibilities. Our model combines several theories of production: dynamic data envelopment analysis (DEA) and two‐stage network DEA. Using pooled data on 53 countries during 1999–2012, the model estimates indicate that dynamic efficiency averages about 70%. Countries could increase final consumption by about 25% via greater technical efficiency in production and by another 5% via an optimal intertemporal reallocation of investment spending. [ABSTRACT FROM AUTHOR]

This contribution is the first attempt to systematically review all empirical surveys that so far have been made available in the broad field of efficiency and productivity analysis using frontier estimation methodologies. We provide a systematic bibliometric review on the many empirical surveys in the field of efficiency and productivity analysis, the most relevant concepts, areas, overlaps, and potentials to explore from its introduction to the most recent surveys. We combine the United Nations' International Standard Industrial Classification (ISIC) taxonomy for the economic activity with the Journal of Economic Literature [ABSTRACT FROM AUTHOR]

The software available to implement and carry out efficiency analysis is crucial for the diffusion of efficiency frontier techniques among applied researchers and policy makers. The implementation of up‐to‐date productivity and efficiency analysis is indeed important to advance our knowledge in many fields, ranging from the public and regulated sectors to the private ones. This contribution fills a gap in the existing literature and surveys the currently available options to estimate a variety of frontier methodologies using either general or dedicated programs. We outline directions for future research. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses articles in the issue on topics including higher education policy, public spending and technological changes on the research environment.

Abstract: This paper aims at being a comprehensive reference for stakeholders, policy makers, and scholars interested in analyzing the problem of efficiency, effectiveness, and impacts of rail transport systems in a sound empirical way, paying specific attention to passenger transport services. The paper combines different analytical frameworks (engineering, economics, impacts), systematic review techniques, and advanced mappings. Framing economic efficiency studies into a transport planning perspective permits to move from efficiency to effectiveness issues. In addition, including impacts offers a critical discussion of the existing empirical studies, relating them to the main methodological approaches used. This analysis can be useful for those interested in developing new techniques for the evaluation of this sector. The critical analysis developed in this paper provides a catalog of inputs, outputs, external factors, possible impacts to account for, data, and approaches, which allows us to identify areas in which new methodological developments, new approaches, are needed to address the relevant societal challenges of the rail transport sector. [ABSTRACT FROM AUTHOR]

The need for new indicators on universities is growing enormously. Governments and decision makers at all levels are faced with the huge opportunities generated by the availability of new knowledge and information and, simultaneously, are pressed by tight budget constraints. University rankings, in particular, are attracting policy and media attention, but at the same time receive harsh methodological criticism. After summarizing the main criticisms of rankings, we describe 2 trends in the user requirements for indicators; namely, granularity and cross-referencing. We then suggest that a change in the paradigm of the design and production of indicators is needed. The traditional approach is one that not only leverages on the existing data but also suggests heavy investment to integrate existing databases and to build up tailored indicators. We show, based on the European universities case, how the intelligent integration of existing data may lead to an open-linked data platform which permits the construction of new indicators. The power of the approach derives from the ability to combine heterogeneous sources of data to generate indicators that address a variety of user requirements without the need to design indicators on a custom basis. [ABSTRACT FROM AUTHOR]