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Machine Learning and Expert Judgement: Analyzing Emerging Topics in Accounting and Finance Research in the Asia–Pacific
Author(s) -
Cai Cynthia W.,
Linnenluecke Martina K.,
Marrone Mauricio,
Singh Abhay K.
Publication year - 2019
Publication title -
abacus
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.632
H-Index - 45
eISSN - 1467-6281
pISSN - 0001-3072
DOI - 10.1111/abac.12179
Subject(s) - judgement , computer science , strengths and weaknesses , data science , context (archaeology) , robustness (evolution) , variety (cybernetics) , systematic review , management science , accounting , artificial intelligence , political science , psychology , engineering , business , social psychology , paleontology , biochemistry , chemistry , medline , gene , law , biology
In this paper, we focus on the question to what extent machine learning (ML) tools can be used to support systematic literature reviews. We apply a ML approach for topic detection to analyze emerging topics in the literature—our context is accounting and finance research in the Asia–Pacific region. To evaluate the robustness of the approach, we compare findings from the automated ML approach with the results from a manual analysis of the literature. The automated approach uses a keyword algorithm detection mechanism whereby the manual analysis uses common techniques for qualitative data analysis, that is, triangulation between researchers (expert judgement). From our paper, we conclude that both methods have strengths and weaknesses. The automated analysis works well for large corpora of text and provides a very standardized and non‐biased way of analyzing the literature. However, the human researcher is potentially better equipped to evaluate current issues and future trends in the literature. Overall, the best results might be achieved when a variety of tools are used together.