Big Data Methods: Leveraging Modern Data Analytic Techniques to Build Organizational Science. (July 2018)

• Record Type: Journal Article
• Authors: Tonidandel, Scott; King, Eden B.; Cortina, Jose M.
• Other Names: LeBreton James M. guest-editor.; Meade Adam W. guest-editor.
• Is Part Of: Organizational research methods. Volume 21:Number 3(2018:Jul.)
• Page Start: 525
• Page End: 547
• Publication Date: 2018-07
• Subjects: quantitative research -- quantitative -- multivariate analysis -- philosophy of science -- big data -- data analytics -- data science; Organization -- Research -- Methodology -- Periodicals; Organizational sociology -- Research -- Methodology -- Periodicals; Management -- Research -- Methodology -- Periodicals; 302.350721
• DOI: 10.1177/1094428116677299
• Languages: English
• ISSNs: 1094-4281

Abstract

Advances in data science, such as data mining, data visualization, and machine learning, are extremely well-suited to address numerous questions in the organizational sciences given the explosion of available data. Despite these opportunities, few scholars in our field have discussed the specific ways in which the lens of our science should be brought to bear on the topic of big data and big data's reciprocal impact on our science. The purpose of this paper is to provide an overview of the big data phenomenon and its potential for impacting organizational science in both positive and negative ways. We identifying the biggest opportunities afforded by big data along with the biggest obstacles, and we discuss specifically how we think our methods will be most impacted by the data analytics movement. We also provide a list of resources to help interested readers incorporate big data methods into their existing research. Our hope is that we stimulate interest in big data, motivate future research using big data sources, and encourage the application of associated data science techniques more broadly in the organizational sciences.