Tuesday, April 14, 2015

Big Data's Applications in HRM

Publications / Journal Articles 
a. Bosco, F. A., Aguinis, H. Singh, K., Field, J. G., & Pierce, C. A. 2015. Correlational effect size benchmarks. Journal of Applied Psychology, 100: 431-449. 
- Herman Aguinis (co-author)
b. George, G., Haas, M., & Pentland, A. (2014). Big data and management. Academy of Management Journal, 57, 321-326. doi: 10.5465/amj.2014.4002 
- George Banks
c. Guzzo, Nalbantian & Parra. (2014). A Big Data, Say-Do Approach to Climate and Culture. In Schneider & Barbera (Ed.), Oxford Handbook of Organizational Climate and Culture. Oxford
The chapter summarizes longitudinal research across 34 organizations on the relationship between compensation and voluntary turnover and their relationships with climate and culture. 
- Banjamin Schneider
d. Vern Glaser's recent dissertation on his ethnography of a law enforcement organization, and specifically how the organization used algorithms (part of 'big data/data analytics') in their work. http://www.vernglaser.com/ 
- Emily Heaphy

Big Data Research Project
An “open science” and “big data” project: You’re either omnibus or off the bus (personally I found this project very interesting)
- Frank Bosco (co-founder), Michael A McDaniel

Roughly, there are four major types of big data knowledge (I like this idea a great deal!)
a. Big Data research methods, for the automated extraction of large quantities of information from non-optimally formatted sources, usually using programming languages like Python, Ruby, or even homegrown ones; might or might not include data mining/machine learning, which is often Bayesian.
b. Big Data storage techniques, to reasonably hold, store, and access Big Data – the most common system these days is probably Apache Hadoop, although vanilla SQL is common too
c. Big Data analytic techniques, for the analysis of datasets that won’t be analyzed easily in SAS/SPSS/Minitab/whatever, typically either R or Python
d. Big Data visualization techniques, for the display of millions of cases in meaningful tables and figures, also often done in R or Python
- Richard Landers

Company Applications
a. Google’s people analytics group case:  “Google's Project Oxygen started with a fundamental question raised by executives in the early 2000s: do managers matter?” (Available on Harvard Business School Press website)
The topic generated a multi-year research project that ultimately led to a comprehensive program, built around eight key management attributes, designed to help Google employees become better managers. By November 2012, the program had been in place for several years, and the company could point to statistically significant improvements in managerial effectiveness and performance. Now executives were wondering: how could Google build on the success of this project, extending it to senior leaders, teams, and other constituencies while striving to create truly amazing managers?”
- Heidi Gardner
b. Workday is a company which offers Big Data analytics services to their clients for tracking and predicting various HR-related outcomes, such as employee performance and retention. One of their claims is that they can help predict which high-performing employees are likely to leave a company in the next year. 
- Harry Joo

Books / Book Chapters
Kitchin, R. (2014). The data revolution: Big data, open data, data infrastructures and their consequences. Sage.
- James Field
Hausknecht, J., & Li, H. (In press, 2015). Big Data in Turnover/Retention. In Tonidandel, King, & Cortina (Eds.), Big Data at Work -SIOP Frontiers Book Series. 
- Huisi (Jessica) Li (co-author)

Theory Development
Davis, G. F. (2010). Do theories of organizations progress?. Organizational Research Methods, 13(4), 690-70.
"It is safe to say that theory in astronomy improved with the invention of the telescope and that theory in biology was enhanced by the availability of the microscope. Has organization theory improved as a result of this new avalanche of data? Sadly, and surprisingly, no."  
- Alan Silva

Recent Workshops / Symposiums
SIOP, April 23-25 Philadelphia
- Richard Landers
Cheng, M., Hackett, RD., (2015). Big Data Analytics in Human Resources Management: Early Adoptions, Promising Applications, and Caveats. Symposium to be presented at the 75th Annual Meeting of the Academy of Management in Vancouver, BC

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