Department of Strategic Management, Marketing and Tourism, University of Innsbruck, Innsbruck, Austria.
Chair for Strategy and Organization, Technical University of Munich, Munich, Germany.
Res Synth Methods. 2021 Mar;12(2):136-147. doi: 10.1002/jrsm.1457. Epub 2020 Oct 8.
We researchers have taken searching for information for granted for far too long. The COVID-19 pandemic shows us the boundaries of academic searching capabilities, both in terms of our know-how and of the systems we have. With hundreds of studies published daily on COVID-19, for example, we struggle to find, stay up-to-date, and synthesize information-all hampering evidence-informed decision making. This COVID-19 information crisis is indicative of the broader problem of information overloaded academic research. To improve our finding capabilities, we urgently need to improve how we search and the systems we use. We respond to Klopfenstein and Dampier (Res Syn Meth. 2020) who commented on our 2020 paper and proposed a way of improving PubMed's and Google Scholar's search functionalities. Our response puts their commentary in a larger frame and suggests how we can improve academic searching altogether. We urge that researchers need to understand that search skills require dedicated education and training. Better and more efficient searching requires an initial understanding of the different goals that define the way searching needs to be conducted. We explain the main types of searching that we academics routinely engage in; distinguishing lookup, exploratory, and systematic searching. These three types must be conducted using different search methods (heuristics) and using search systems with specific capabilities. To improve academic searching, we introduce the "Search Triangle" model emphasizing the importance of matching goals, heuristics, and systems. Further, we suggest an urgently needed agenda toward search literacy as the norm in academic research and fit-for-purpose search systems.
我们研究人员在搜索信息方面一直过于依赖,这一问题已经存在了很长时间。COVID-19 大流行让我们认识到了学术搜索能力的局限性,无论是在我们的专业知识还是我们所拥有的系统方面。例如,每天都有数百篇关于 COVID-19 的研究发表,我们在寻找、更新和综合信息方面都存在困难,所有这些都妨碍了循证决策。这场 COVID-19 信息危机表明了学术研究中信息过载这一更为广泛的问题。为了提高我们的搜索能力,我们迫切需要改进我们的搜索方式和使用的系统。我们对 Klopfenstein 和 Dampier(Res Syn Meth. 2020)的评论做出了回应,他们对我们 2020 年的论文发表了评论,并提出了一种改进 PubMed 和 Google Scholar 搜索功能的方法。我们的回应将他们的评论置于更广泛的框架内,并提出了如何全面改进学术搜索的建议。我们强烈认为,研究人员需要认识到,搜索技能需要专门的教育和培训。更好、更高效的搜索需要对确定搜索方式的不同目标有初步的理解。我们解释了我们这些学者通常会进行的主要搜索类型;区分查找、探索和系统搜索。这三种类型必须使用不同的搜索方法(启发式)和具有特定功能的搜索系统来进行。为了改进学术搜索,我们引入了“搜索三角”模型,强调了目标、启发式和系统匹配的重要性。此外,我们建议将搜索素养作为学术研究的规范,并开发适合用途的搜索系统,这是当务之急。