Mapping the Landscape of Algorithmic Management: Insights from Bibliometrics Using Citespace

Abstract

Algorithmic management is a revolutionary method for organizational governance, marked by the incorporation of algorithms and data-centric techniques into management decision-making. This study intends to conduct an extensive bibliometric analysis of the algorithmic management research area utilizing CiteSpace, software tailored for visualizing bibliometric data. The dataset, encompassing 475 articles sourced from the Web of Science database spanning the years 2001 to 2024, underwent analysis to explore publication trends, co-citation networks related to journal, author, reference, and keyword co-occurrence networks. The findings indicate a substantial increase in research output on algorithmic management, particularly evident from 2010 onwards, with notable peaks in publication activity in recent years (2021-2023). Furthermore, the analysis uncovers emerging research hotspots and trends, providing insights into the evolving landscape of algorithmic management. The use of CiteSpace's visualization capabilities has enabled the identification of pivotal research clusters and influential scholars, enhancing the understanding of the algorithmic management. In this study, the current state of algorithmic management research is visualized and used to make helpful suggestion for research directions in the future. Understanding the dynamics of this research domain, researchers and practitioners can navigate the complexities of algorithmic management and realizes its potential for improving organizational efficiency and innovation.

Keywords: algorithmic management, bibliometric analysis, citespace, publication trends, research hotsports