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#Mendeley - Software for #Literature Review thumbnail

#Mendeley - Software for #Literature Review

Research With Fawad·
5 min read

Based on Research With Fawad's video on YouTube. If you like this content, support the original creators by watching, liking and subscribing to their content.

TL;DR

Create a dedicated Mendeley folder for each research theme so searches run across a focused set of PDFs.

Briefing

Mendeley can be used as more than a citation manager: it can rapidly surface the exact passages needed for a literature review—definitions, theoretical framings, research gaps, and even measurement instruments—by searching within a curated library of PDFs. The core workflow is to download and install Mendeley Desktop, register, then build a structured set of folders and import relevant papers so targeted searches can filter hundreds of documents down to a handful of useful sources.

After creating a dedicated folder for a research theme (for example, internal marketing), the next step is importing PDFs into that folder using Mendeley’s add files function. With a large library in place, Mendeley’s search bar becomes the engine for narrowing results. A plain keyword search like “internal marketing” initially highlights only single-word matches (e.g., “internal”) unless the search is treated as a phrase. When the search is refined to the right phrase—such as “internal marketing” together with a related concept like “customer service”—the results shrink dramatically, letting researchers avoid opening every paper just to find whether a specific relationship is discussed.

A practical detail matters here: Mendeley may treat quoted text as an exact phrase, and removing quotation marks can switch the search back to matching the intended wording across documents. Once a relevant passage is found, the workflow supports direct extraction into writing. Researchers can right-click and copy text, then paste it into Word, but they must format and cite it properly. For citations, Mendeley offers “copy as formatted citation,” and the citation needs to be placed both in-text and in the reference list, with formatting adjusted to match MLA-style expectations (e.g., using last names in the in-text citation).

The transcript also emphasizes that concepts often appear under multiple names. Internal marketing, for instance, can be described as internal customer orientation, internal customer orientation, or internal marketing variants. Mendeley search strings can use wildcard-like patterns (e.g., adding an asterisk) to capture these terminological variations in one sweep. The same approach extends beyond internal marketing: corporate social responsibility may appear as business social responsibility, corporate sustainability, or social responsibility.

Beyond finding relationships, Mendeley can locate the building blocks of a literature review. For definitions, researchers can search for definitional verbs and variants—such as “state,” “stated,” “define,” “defined,” or “referred”—and combine them with the target concept to isolate papers that explicitly conceptualize the term. For gaps, searches for limitation-related language (“limited,” “scarce,” “scarcity of research”) can identify which recent papers acknowledge under-researched areas, reducing the need to manually scan every PDF.

Theory selection is treated similarly: searching for “theory” reveals which papers use theoretical lenses to connect constructs, and then reading the relevant sections shows how those theories are applied. Measurement discovery is also supported by searching for “scale” or “measures,” enabling researchers to find instruments like “social climate scale” when they exist in the library.

Finally, the transcript highlights a real-world use case: when reviewers demand more on a specific link (e.g., internal marketing to knowledge management), a simple keyword search across the imported internal marketing corpus can quickly pull the relevant passages and citations, avoiding a time-consuming manual review of every paper. In short, Mendeley’s strength is turning literature review research from document-by-document searching into targeted retrieval of the exact evidence needed for writing.

Cornell Notes

Mendeley Desktop can streamline literature reviews by letting researchers search inside imported PDFs for the exact language needed for writing. The process starts with installing Mendeley, creating a dedicated folder for a research topic, and importing all relevant papers. From there, keyword and phrase searches narrow hundreds of documents to the ones containing specific relationships, definitions, limitations, theories, or measurement instruments. Using search variants and wildcard-style patterns helps capture concepts described under multiple names (e.g., internal marketing vs internal customer orientation). Once relevant text is found, researchers can copy passages and use Mendeley’s “copy as formatted citation” to place in-text citations and reference-list entries correctly.

How does a researcher set up Mendeley so searches are efficient for a literature review?

The workflow begins with downloading and installing Mendeley Desktop, registering, and then creating a folder for a specific research focus (e.g., a folder for “internal marketing”). After that, PDFs are imported into the selected folder using the add files/plus option. With papers organized in one folder, Mendeley’s internal search can filter results across all imported documents, avoiding manual scanning of each PDF.

Why might a search for “customer service” return “no match,” and how can it be fixed?

The transcript describes a situation where searching with quotation marks causes Mendeley to look for an exact quoted phrase, leading to “no match.” Removing the quotes changes the search behavior so Mendeley searches for the intended wording across the papers. The practical takeaway is to adjust quotation usage when results look empty despite related content being present.

How can Mendeley handle concepts that appear under multiple terminologies?

The transcript gives internal marketing as an example: it can be described as internal customer orientation and other variants. By using a wildcard-style search string (e.g., adding an asterisk after a shared stem), Mendeley can retrieve papers containing any of the related forms (internal marketing, internal marketing orientation, internal customer orientation). This same strategy applies to other concept clusters like corporate social responsibility variants.

What search strategy helps find papers that contain definitions of a concept?

Instead of searching only for the concept name, the transcript recommends searching for definitional language plus the concept. Common definitional verbs include “state,” “stated,” “define,” “defined,” and “referred,” with variants captured via suffixes (e.g., using “stat*” to catch state/state* forms). Combining these with the target concept (e.g., internal marketing) isolates papers that explicitly define or conceptualize the term.

How can Mendeley help identify research gaps and limitations without reading every paper end-to-end?

Researchers can search within the imported corpus for limitation-related wording such as “limited,” “scarce,” or “scarcity of research.” The transcript notes that this can surface papers that explicitly acknowledge limited research or scarcity, producing a manageable set of sources for gap-writing. The key is choosing the right phrase to match how authors describe limitations in their text.

How can Mendeley assist with theory selection and measurement instrument discovery?

For theory, searching for “theory” within the folder highlights papers that use theoretical framing; reading the relevant sections shows how the theory explains relationships between constructs. For measurement, searching for “scale” (and related terms like “measures”) helps locate papers containing specific instruments, such as a “social climate scale,” so researchers can identify tools used in prior studies.

Review Questions

  1. When would you remove quotation marks from a Mendeley search string, and what does that change about matching behavior?
  2. What definitional verbs and variants (e.g., state/define/refer) would you combine with a target concept to find definition-focused papers?
  3. How would you design a search string to capture multiple names for the same concept (e.g., internal marketing vs internal customer orientation)?

Key Points

  1. 1

    Create a dedicated Mendeley folder for each research theme so searches run across a focused set of PDFs.

  2. 2

    Import all relevant PDFs into the folder before searching, using the add files/plus workflow.

  3. 3

    Use phrase vs keyword search carefully; removing quotation marks can resolve “no match” results when exact-phrase matching fails.

  4. 4

    Capture terminological variants with wildcard-style search strings so one search retrieves concept synonyms (e.g., internal marketing and internal customer orientation).

  5. 5

    Find definition-focused papers by combining the target concept with definitional verbs such as state/defined/referred (including variants).

  6. 6

    Surface research gaps by searching for limitation language like limited/scarce/scarcity of research rather than reading every paper manually.

  7. 7

    Use “theory” and “scale” searches to locate papers that apply theoretical lenses or include measurement instruments, then extract the relevant passages with proper citation formatting.

Highlights

Mendeley can filter hundreds of PDFs down to the ones containing a specific relationship or phrase, reducing the need to open every document.
Search strings can be tuned to handle exact-phrase issues—quotation marks can cause “no match,” while removing them can restore broader matching.
Wildcard-style searches help when a concept is described under multiple names, such as internal marketing and internal customer orientation.
Searching for definitional verbs (state/define/refer with variants) can quickly identify papers that explicitly define a concept.
Keyword searches can help respond to reviewer demands (e.g., internal marketing to knowledge management) by pulling relevant passages across an imported corpus.

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