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Data analytic trends and training in strategic management
Author(s) -
Shook Christopher L.,
Ketchen David J.,
Cycyota Cynthia S.,
Crockett Dilene
Publication year - 2003
Publication title -
strategic management journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 11.035
H-Index - 286
eISSN - 1097-0266
pISSN - 0143-2095
DOI - 10.1002/smj.352
Subject(s) - bridging (networking) , field (mathematics) , computer science , regression analysis , longitudinal data , survey data collection , strategic planning , management science , data science , operations research , management , economics , engineering , mathematics , statistics , data mining , computer network , machine learning , pure mathematics
Data analysis is a key element of the research process. Accordingly, appropriate doctoral training in data analysis is vital to the strategic management field's future. We used a two‐study design to evaluate quantitative data analysis trends and doctoral training. An analysis of Strategic Management Journal articles from 1980 to 2001 reveals that, contrary to some predictions, the use of general linear model techniques such as regression has increased over time. However, the use of more specialized techniques, including those suitable for examining longitudinal data, discrete events, and causal structure, has also grown substantially. A survey of recent doctoral graduates shows that, although skilled with general linear models, many are ill prepared to use specialized techniques. Based on our findings, we offer suggestions aimed at bridging gaps between what doctoral students (and other researchers) know and what they need to know about data analysis. Copyright © 2003 John Wiley & Sons, Ltd.