Webinar: Build Search Strings in Mendeley and Google Scholar for Research Literature

The core takeaway is that MLA Desktop can turn literature review work from a manual, paper-by-paper scavenger hunt into a targeted search process—while Google Scholar query operators (like intitle and intext) help narrow results to the right kind of evidence. Together, these tools let researchers quickly locate definitions, limitations, theories, and measurement instruments inside large collections of PDFs, then pull out quotable text with proper MLA-style citation.

After downloading and installing Mendeley (the transcript refers to “MLA Desktop,” but the workflow is clearly Mendeley Desktop), the first practical step is organizing papers into dedicated folders for each research stream. The presenter recommends creating separate folders by research topic (e.g., “servant leadership and organizational performance”) rather than splitting by individual variables, because relationship-focused searching becomes harder when concepts are scattered. Papers are then imported into the folder, and Mendeley’s in-library search is used to filter documents by author names, titles, publication details, and—most importantly—text inside the PDFs.

Simple searches work as keyword filters, but phrase accuracy depends on how the query is entered. Without quotation marks, searching for “internal marketing” can return documents containing the individual terms; with the phrase properly specified, the search narrows to occurrences of the exact wording. A common workflow is to search for a related concept (for example, searching “customer service” within an internal marketing folder) to avoid opening hundreds of papers just to find relevant passages. When a promising match appears, the text can be copied into a Word document, then paired with a formatted citation copied directly from Mendeley (“copy as formatted citation”). The presenter emphasizes that copied text must be formatted and attributed, with in-text citation and a corresponding entry in the references list.

The transcript also addresses a major literature review obstacle: concepts with multiple labels. For internal marketing, the same idea may appear as “internal customer orientation” or related variants. Mendeley searches can handle this by using wildcard characters (e.g., an asterisk) to capture term variations in one query. The same strategy extends beyond terminology—researchers can search for definitional language by combining concept terms with definition cues like “state*,” “defined,” or “refer*,” then narrow further by adding the concept itself. That approach can surface a small number of papers that are explicitly focused on definitions, saving time that would otherwise be spent scanning PDFs.

Gap spotting and theory selection are treated similarly. To find limitations or scarcity claims, researchers search for limitation-related terms such as “limited*” or “scarcity of research” within their recent corpus, then extract the exact limitation wording and cite it properly. For theory usage, searching for “theory” helps identify which papers rely on theoretical framing to connect constructs; reading those high-signal papers shows how theories are used to explain relationships rather than merely named.

Finally, Google Scholar is presented as a complementary search engine when Mendeley’s library is incomplete. The transcript stresses using query operators to control where terms appear: intitle restricts matches to titles, intext restricts matches to the body text, and combining operators (e.g., intitle + intext) targets papers that link two variables across different parts of the record. Using these operators can quickly reveal whether an area has substantial work or only a handful of studies—information that directly informs what to cite and where future research opportunities likely sit.