A Topical Exploration of the Intellectual Development of Decision Sciences 1975–2016. (20th July 2018)

• Record Type: Journal Article
• Authors: Chae, Bongsug (Kevin); Olson, David
• Is Part Of: Decision sciences. Volume 52:Number 3(2021)
• Page Start: 543
• Page End: 566
• Publication Date: 2018-07-20
• Subjects: Topic Modeling -- Text Analytics -- Decision Sciences -- Data Analytics; Decision making -- Periodicals; Policy sciences -- Periodicals; 658.40305
• DOI: 10.1111/deci.12326
• Languages: English
• ISSNs: 0011-7315

Abstract

ABSTRACT: This article reviews Decision Sciences journal articles and metadata to analyze its intellectual tradition. Text analytics is used with probabilistic topic modeling. The topical structure of the journal is reviewed by topic definition and popularity, with correlations. Fifty research topics or themes involving a wide range of quantitative methods and decision‐making practice were selected. Functional areas were also examined. The evolution of topics since 1975 is noted. There is clear indication that journal coverage has evolved. In early years, emphasis was on quantitative modeling methods and relevant methodologies. More recently new research areas to include supply chain management, marketing, service management, and health care are more noted. Some topics are highly correlated. We find that this evolution reflects the changes occurring in business and decision‐making environment. The article discusses external and internal factors important in shaping the journal's topical trajectory. Unique challenges in analyzing text data are discussed. Latent Dirichlet Allocation, an unsupervised Bayesian approach for statistical topic modeling, is applied to 1, 698 research articles from Decision Sciences over the period from 1975 to 2016. This approach is found to be useful to discover journal topical trends. Potential for other applications in decision sciences is discussed.