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Determining Optimal Skillsets for Business Managers Based on Local and Global Job Markets: A Text Analytics Approach
Author(s) -
Nasir Murtaza,
Dag Ali,
Young William A.,
Delen Dursun
Publication year - 2020
Publication title -
decision sciences journal of innovative education
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.52
H-Index - 19
eISSN - 1540-4609
pISSN - 1540-4595
DOI - 10.1111/dsji.12212
Subject(s) - business intelligence , analytics , data science , computer science , business analytics , extant taxon , intuition , knowledge management , marketing , business , business model , psychology , business analysis , evolutionary biology , biology , cognitive science
We are experiencing a significant shift in management practices—moving from intuition, experience, and gut‐feeling driven decision‐making to one that is driven by data, evidence, and computational sciences. This shift, which is often called the analytics revolution , is not only changing the business landscape from a practical perspective but also redefining the white‐collar job market. The goal of this study is to employ a data‐ and analytics‐driven approach to analyze a large and feature‐rich dataset (which is composed of the recent job postings and the extant published literature) to characterize the state of the current business job market, especially to magnify the analytics related features and expectations including the geographic (i.e., local vs. global) differentiators. Using several graphical and tabular representations, in this paper we report on our thought‐provoking findings that collectively illustrate the changing face of knowledge, skills, and abilities required by the current local and global business job markets.