Rigorous evidence identification is essential for systematic reviews and meta‐analyses (evidence syntheses) because the sample selection of relevant studies determines a review's outcome, validity, and explanatory power. Yet, the search systems allowing access to this evidence provide varying levels of precision, recall, and reproducibility and also demand different levels of effort. To date, it remains unclear which search systems are most appropriate for evidence synthesis and why. Advice on which search engines and bibliographic databases to choose for systematic searches is limited and lacking systematic, empirical performance assessments. This study investigates and compares the systematic search qualities of 28 widely used academic search systems, including Google Scholar, PubMed, and Web of Science. A novel, query‐based method tests how well users are able to interact and retrieve records with each system. The study is the first to show the extent to which search systems can effectively and efficiently perform (Boolean) searches with regards to precision, recall, and reproducibility. We found substantial differences in the performance of search systems, meaning that their usability in systematic searches varies. Indeed, only half of the search systems analyzed and only a few Open Access databases can be recommended for evidence syntheses without adding substantial caveats. Particularly, our findings demonstrate why Google Scholar is inappropriate as principal search system. We call for database owners to recognize the requirements of evidence synthesis and for academic journals to reassess quality requirements for systematic reviews. Our findings aim to support researchers in conducting better searches for better evidence synthesis.

Information on the size of academic search engines and bibliographic databases (ASEBDs) is often outdated or entirely unavailable. Hence, it is difficult to assess the scope of specific databases, such as Google Scholar. While scientometric studies have estimated ASEBD sizes before, the methods employed were able to compare only a few databases. Consequently, there is no up-to-date comparative information on the sizes of popular ASEBDs. This study aims to fill this blind spot by providing a comparative picture of 12 of the most commonly used ASEBDs. In doing so, we build on and refine previous scientometric research by counting query hit data as an indicator of the number of accessible records. Iterative query optimization makes it possible to identify a maximum number of hits for most ASEBDs. The results were validated in terms of their capacity to assess database size by comparing them with official information on database sizes or previous scientometric studies. The queries used here are replicable, so size information can be updated quickly. The findings provide first-time size estimates of ProQuest and EbscoHost and indicate that Google Scholar's size might have been underestimated so far by more than 50%. By our estimation Google Scholar, with 389 million records, is currently the most comprehensive academic search engine.

Innovation offshoring (IO) has become a widespread management practice. Yet, evidence on the performance implications is inconsistent, and scattered across disciplines and contexts. We argue that the benefits firms can derive from IO depend on the institutional environment at home. Drawing on recent work on institutional theory in international business, we explore institutions that facilitate reverse knowledge transfer and/or institutional arbitrage with respect to innovation‐related activities. The results of our meta‐analysis that synthesizes evidence from 48 samples show that IO is related positively to innovation performance. As predicted, this relationship is moderated by differences in the institutional environments across countries. Specifically, when national innovation systems are weak at home, IO appears to enable institutional arbitrage strategy whereas Confucian cultures enable more effective reverse knowledge transfer. However, contrary to our expectations, the beneficial effects of IO appear to have diminished over time.

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This paper introduces a novel scientometrics method and applies it to estimate the subject coverages of many of the popular English-focused bibliographic databases in academia. The method uses query results as a common denominator to compare a wide variety of search engines, repositories, digital libraries, and other bibliographic databases. The method extends existing sampling-based approaches that analyze smaller sets of database coverages. The findings show the relative and absolute subject coverages of 56 databases—information that has often not been available before. Knowing the databases’ absolute subject coverage allows the selection of the most comprehensive databases for searches requiring high recall/sensitivity, particularly relevant in lookup or exploratory searches. Knowing the databases’ relative subject coverage allows the selection of specialized databases for searches requiring high precision/specificity, particularly relevant in systematic searches. The findings illustrate not only differences in the disciplinary coverage of Google Scholar, Scopus, or Web of Science, but also of less frequently analyzed databases. For example, researchers might be surprised how Meta (discontinued), Embase, or Europe PMC are found to cover more records than PubMed in Medicine and other health subjects. These findings should encourage researchers to re-evaluate their go-to databases, also against newly introduced options. Searching with more comprehensive databases can improve finding, particularly when selecting the most fitting databases needs particular thought, such as in systematic reviews and meta-analyses. This comparison can also help librarians and other information experts re-evaluate expensive database procurement strategies. Researchers without institutional access learn which open databases are likely most comprehensive in their disciplines.