Variable Discussion in the Literature Review and using Google Scholar for Search

A strong literature review doesn’t treat each variable as a single paragraph of background. Instead, it breaks variables into a structured set of elements—conceptualization, definitions, evolution over time, key characteristics, agreement or disagreement in the literature, and the variable’s organizational importance—then searches each element deliberately to build evidence.

When discussing a variable individually, the first job is to define what the variable means. That starts with conceptualization: what the variable is, how it’s defined in general, and how that definition shifts when attention narrows to a specific sector (for example, defining servant leadership in higher education rather than in all contexts). The next layer is historical: definitions and concepts often evolve, so the literature review should track how the concept has changed over time, not just list current wording.

From there, the literature review should extract the variable’s “facets”—the traits, characteristics, and traceable features that repeatedly appear across definitions. A useful way to write this section is to compare whether scholars converge on the same core elements or diverge into competing interpretations. Agreement and disagreement matter because they shape how confidently a study can operationalize the variable and justify its measurement approach.

Finally, the variable needs a clear “why it matters” section. The review should explain the variable’s importance in organizations in general, and then connect that importance to the study’s specific industry scope—making the case that the concept is not just theoretically interesting but relevant to the field being studied.

To support these six elements, the transcript recommends using Google Scholar as a targeted search tool rather than a one-shot keyword hunt. The workflow is built around six separate search strings—one for each ingredient of the variable discussion. For definitions, the approach is to search within text using flexible phrasing that captures common definitional language. For example, searching for “servant leadership” alongside terms like “defined,” “definition,” or “refers” helps surface papers where the concept is explicitly defined.

For sector-specific conceptualization, the search string adds the sector context—such as “servant leadership” plus “higher education”—then opens relevant papers to locate definitions used in that domain. To capture evolution over time, the search string swaps in terms that signal change (e.g., “development” or “time”), then scans the retrieved text for statements about how the concept has shifted.

To identify key characteristics, the search string focuses on trait-like language (e.g., “primary characteristics” or similar descriptors) so the results highlight recurring facets. To assess agreement or disagreement, the search is used to see whether multiple authors align on the same defining features. For importance, the search string targets value and impact language (e.g., “importance” or related terms) to gather evidence about why the variable matters.

Overall, the method turns variable discussion into a repeatable research process: define the variable, locate sector-specific definitions, track conceptual change, extract characteristics, check scholarly consensus, and document organizational value—using Google Scholar with purpose-built queries for each step.