Visualizing marketing knowledge via main path analysis (MPA): an exposition via the salesperson performance literature
本文介绍了主路径分析（MPA）这一高级分析技术，用于绘制特定领域知识演化图谱，并以销售人员绩效文献为例，展示了如何通过MPA识别知识路径、关键转折点和数据选择的影响。
Purpose The purpose of this study is to present main path analysis (MPA) as an advanced analytic technique for mapping the evolution of knowledge within a specified domain. Going beyond citation counts and traditional bibliometric methods, MPA provides a distinctive lens for visualizing how ideas propagate through a citation network, highlighting influential contributions that shape a field’s development. Its value is illustrated using a representative marketing domain. Design/methodology/approach This study offers a step-by-step best practice approach for implementing MPA, including data selection, preprocessing and traversal weight calculations that influence the resulting knowledge structure. Using the salesperson performance literature as an illustrative case, the authors demonstrate how MPA enables scholars to identify knowledge pathways, critical inflection points and the methodological consequences of data inclusion choices. Findings Through the analysis of three MPAs based on variations of the same data set, this study demonstrates the method’s structured approach to mapping knowledge evolution. It identifies milestone articles, thematic clusters and intellectual transitions, and, to the best of the authors’ knowledge, offers the first systematic assessment of how input data selection influences the granularity of MPA results. Research limitations/implications MPA enables marketing scholars to systematically trace knowledge progression, identify foundational contributions and detect research gaps. However, decisions regarding data inclusion can influence MPA results, underscoring the need for thoughtful data set construction. Practical implications Practitioners can leverage MPA to identify emerging trends that inform strategic decision-making. Originality/value This study offers the first methodologically centered introduction to MPA and assesses how input data choices affect its outcomes, providing a replicable framework for scholars to trace knowledge development.