After 40 years of research with thousands of application-oriented scientific papers, empirical evidence that data envelopment analysis (DEA) has really improved the practice of performance measurement and benchmarking in real-life non-production contexts is rare. The main reason for this deficit may be that DEA is founded on the concepts of production theory such as production possibility set or returns to scale. These concepts can hardly be applied to pure multiple-criteria evaluation problems, which are often attempted to be solved using DEA. This paper systematically investigates strengths and weaknesses of DEA in the exemplary case of welfare evaluation using real data on 27 countries of the European Union. We analyze and explain the differences in the results of various frequently used DEA models for two different, but strongly connected sets of welfare indicators, thereby demonstrating the pitfalls, which often arise in the application of DEA, as well as some approaches for avoiding them.
The development of alternative indicators (altmetrics) can be traced back to a discussion a few years ago where the central question was: does the focus on classical bibliometric indicators still adequately reflect the scientific and social significance of scientific work in the Internet age? In the course of this discussion, the term "altmetrics" was introduced as a collective term for all those indicators that contain previously unnoticed information from the Internet-especially concerning social media. Altmetrics shed light on the reception of scientific publications in news websites as well as in scientific blogs, policy papers, and other web-based content. This chapter deals with the current state of the art of altmetrics, focusing on the present discussion about the informative value of altmetrics. Furthermore, we investigate to what extent altmetrics can be used in scientific evaluations. We conclude our chapter with an outlook on the potential prospects for success of altmetrics in different fields of application.
To date, there has not been any definitive statement for bibliometric analyses from the scientific community as to how long to make the period for analysis in order to guarantee the maximum possible validity of citation analyses. Up to now, it has been particularly unclear as to whether different disciplines and citation indicators have any effect on what period for analysis to select. This is why this study aims to provide answers to these issues. Therefore, publications in the fields of business studies, biology, and medicine from 2007 to 2009 are surveyed. The citations from ten separate consecutive citation periods from 2009 up to 2018 are generated for these publications from Web of Science and selected citation indicators are calculated for them. The results show that the fundamental validity of citation indicators increases with time. However, if the goal of the analyses is to establish a ranking order, then even short citation periods are informative. Our results are stable in terms of the disciplines, although differences do occur in the citation indicators under consideration.
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