COVID-19 Pandemic in the New Era of Big Data Analytics: Methodological Innovations and Future Research Directions

被引:137
|
作者
Sheng, Jie [1 ]
Amankwah-Amoah, Joseph [2 ]
Khan, Zaheer [3 ]
Wang, Xiaojun [1 ]
机构
[1] Univ Bristol, Sch Management, Howard House,Queens Ave, Bristol BS8 1SD, Avon, England
[2] Univ Kent, Kent Business Sch, Chatham ME4 4TE, Kent, England
[3] Univ Aberdeen, Business Sch, Aberdeen AB24 3QY, Scotland
关键词
SUPPLY CHAIN MANAGEMENT; PREDICTIVE ANALYTICS; PRESCRIPTIVE ANALYTICS; ARTIFICIAL-INTELLIGENCE; CHURN PREDICTION; TEXT ANALYTICS; MODEL; TIME; BEHAVIOR; NETWORK;
D O I
10.1111/1467-8551.12441
中图分类号
F [经济];
学科分类号
02 ;
摘要
Although scholars in management recognize the value of harnessing big data to understand, predict and respond to future events, there remains little or very limited overview of how various analytics techniques can be harnessed to provide the basis for guiding scholars in studying contemporary management topics and global grand challenges raised by the COVID-19 pandemic. In this Methodology Corner, we present a review of the methodological innovations in studying big data analytics and how they can be better utilized to examine contemporary organizational issues. We provide insights on methods in descriptive/diagnostic, predictive and prescriptive analytics, and how they can be leveraged to study 'black swan' events such as the COVID-19-related global crisis and its aftermath's implications for managers and policymakers.
引用
收藏
页码:1164 / 1183
页数:20
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