1932

Abstract

Big data and artificial intelligence (AI) have become quite compelling—and relevant, ideally—to organizations and the consulting services that help manage them. Researchers and practitioners in industrial-organizational psychology (IOP) and human resource management (HRM) can add significant value to big data and AI by offering their substantive expertise in how workforce-relevant data are measured and analyzed and how big data results are professionally, legally, and ethically interpreted and implemented by organizational decision makers, employees, policymakers, and other stakeholders in the employment arena. This article provides a perspective and framework for big data relevant to IOP and HRM that include both micro issues (e.g., linking data sources, decisions about which data to include, big data analytics) and macro issues (e.g., changing nature of big data, developing big data teams, educating professionals and graduate students, ethical and legal considerations). Ultimately, we strongly believe that IOP and HRM researchers and practitioners will become increasingly valuable for their contributions to the substance, technologies, algorithms, and communities that address big data, AI, and machine learning problems and applications in organizations relevant to their expertise.

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2020-01-21
2024-04-24
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Literature Cited

  1. Aguinis H, Pierce CA, Bosco FA, Muslin IS 2009. First decade of organizational research methods: trends in design, measurement, and data-analysis topics. Organ. Res. Methods 12:69–112
    [Google Scholar]
  2. Aiken LS, West SG, Millsap RE 2008. Graduate training in statistics, measurement, and methodology in psychology: replication and extension of Aiken, West, Sechrest, and Reno's 1990 survey of PhD programs in North America. Am. Psychol. 63:32–50
    [Google Scholar]
  3. Al-Kassab J, Ouertani ZM, Schiuma G, Neely A 2014. Information visualization to support management decisions. Int. J. Inf. Technol. Decis. Mak. 13:407–28
    [Google Scholar]
  4. Angrave D, Charlwood A, Kirkpatrick I, Lawrence M, Stuart M 2016. HR and analytics: why HR is set to fail the big data challenge. Hum. Resour. Manag. J. 26:1–11
    [Google Scholar]
  5. APA (Am. Psychol. Assoc.) 2017. Ethical Principles of Psychologists and Code of Conduct Washington, DC: Am. Psychol. Assoc.
  6. Austin JT, Scherbaum CA, Mahlman RA 2002. History of research methods in industrial and organizational psychology: measurement, design, analysis. Handbook of Research Methods in Industrial and Organizational Psychology SG Rogelberg 1–33 Oxford, UK: Blackwell
    [Google Scholar]
  7. Bair E, Hastie T, Debashis P, Tibshirani R 2006. Prediction by supervised principal components. J. Am. Stat. Assoc. 101:119–37
    [Google Scholar]
  8. Bal K. 2016. Building an HR analytics team. Human Resource Executive March 16. http://hrearchive.lrp.com/HRE/print.jhtml?id=534360037
    [Google Scholar]
  9. Berk RA. 2006. An introduction to ensemble methods for data analysis. Sociol. Methods Res. 34:263–95
    [Google Scholar]
  10. Berman JJ. 2013. Principles of Big Data: Preparing, Sharing, and Analyzing Complex Information Waltham, MA: Morgan Kaufmann
  11. Bien J, Taylor J, Tibshirani R 2013. A lasso for hierarchal interactions. Ann. Stat. 41:1111–41
    [Google Scholar]
  12. Blei DM, Ng AY, Jordan MI 2003. Latent Dirichlet allocation. J. Mach. Learn. Res. 3:993–1022
    [Google Scholar]
  13. Bosco FA, Uggerslev K, Steel P 2014. Scientific findings as big data for research synthesis: the metaBUS Project. 2014 IEEE International Conference on Big Data18–22 New York: IEEE
    [Google Scholar]
  14. Boudreau JW, Jesuthasan R. 2011. Transformative HR: How Great Companies Use Evidence-Based Change for Sustainable Advantage San Francisco, CA: Jossey-Bass
  15. Braun MT, Kuljanin G, DeShon RP 2017. Special considerations for the acquisition and wrangling of big data. Organ. Res. Methods 21:633–59
    [Google Scholar]
  16. Breiman L. 2001a. Random forests. Mach. Learn. 45:5–32
    [Google Scholar]
  17. Breiman L. 2001b. Statistical modeling: the two cultures. Stat. Sci. 16:199–215
    [Google Scholar]
  18. Breiman L, Friedman JH, Olshen RA, Stone CJ 1984. Classification and Regression Trees New York: Chapman & Hall
  19. Burnham KP, Anderson DR. 2002. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach New York: Springer. , 2nd ed..
  20. Campbell DT, Fiske DW. 1959. Convergent and discriminant validation by the multitrait-multimethod matrix. Psychol. Bull. 56:281–105
    [Google Scholar]
  21. Campbell JP, Wilmot MP. 2018. The functioning of theory in IWOP. Handbook of Industrial, Work, and Organizational (IWOP) Psychology, Vol. 1: Personnel Psychology N Anderson, DS Ones, HK Sinangil, C Viswesvaran 3–37 London: SAGE. , 2nd ed..
    [Google Scholar]
  22. Campion MC, Campion MA, Campion ED, Reider MH 2016. Initial investigation into computer scoring of candidate essays for personnel selection. J. Appl. Psychol. 101:958–75
    [Google Scholar]
  23. Cascio W, Boudreau J, Fink A 2019. Investing in People: Financial Impact of Human Resource Initiatives Alexandria, VA: Soc. Hum. Resour. Manag. , 3rd ed..
  24. Chaffin D, Heidl R, Hollenbeck JR, Howe M, Yu A et al. 2017. The promise and perils of wearable sensors in organizational research. Organ. Res. Methods 20:3–31
    [Google Scholar]
  25. Chamorro-Premuzic T, Winsborough D, Sherman RA, Hogan R 2016. New talent signals: shiny new objects or a brave new world?. Ind. Organ. Psychol. 9:621–40
    [Google Scholar]
  26. Chatfield C. 1995. Model uncertainty, data mining, and statistical inference. J. R. Stat. Soc. A 158:419–66
    [Google Scholar]
  27. Chen EE, Wojcik SP. 2016. A practical guide to big data research in psychology. Psychol. Methods 21:458–74
    [Google Scholar]
  28. Connelly BS, Ones DS. 2010. An other perspective on personality: meta-analytic integration of observers’ accuracy and predictive validity. Psychol. Bull. 6:1092–122
    [Google Scholar]
  29. Cronbach LJ, Meehl PE. 1955. Construct validity in psychological tests. Psychol. Bull. 52:281–302
    [Google Scholar]
  30. da Silva N, Cook D, Lee E-K 2017. Interactive graphics for visually diagnosing forest classifiers in R. arXiv:1704.02502 [stat.ML ]
  31. Domingos P. 2012. A few useful things to know about machine learning. Commun. ACM 55:78–87
    [Google Scholar]
  32. Douthitt EA, Aiello JR. 2001. The role of participation and control in the effects of computer monitoring on fairness perceptions, task satisfaction, and performance. J. Appl. Psychol. 86:867–74
    [Google Scholar]
  33. Ducey AJ, Guenole N, Weiner SP, Herleman HA, Gibby RE, Delany T 2015. I-Os in the vanguard of big data analytics and privacy. Ind. Organ. Psychol. 8:555–63
    [Google Scholar]
  34. Elith J, Leathwick JR, Hastie T 2008. A working guide to boosted regression trees. J. Anim. Ecol. 77:802–13
    [Google Scholar]
  35. Fiske ST, Hauser RM. 2014. Protecting human research participants in the age of big data. PNAS 111:3813675–76
    [Google Scholar]
  36. Friedman J. 1991. Multivariate adaptive regression splines. Ann. Stat. 19:1–67
    [Google Scholar]
  37. Friedman J. 2001. Greedy function approximation: a gradient boosting machine. Ann. Stat. 29:1189–232
    [Google Scholar]
  38. Friedman J. 2002. Stochastic gradient boosting. Comput. Stat. Data Anal. 38:367–78
    [Google Scholar]
  39. Gandomi A, Haider M. 2015. Beyond the hype: big data concepts, methods, and analytics. Int. J. Inf. Manag. 35:137–44
    [Google Scholar]
  40. Goodfellow I, Bengio Y, Courville A 2016. Deep Learning Cambridge, MA: MIT Press
  41. Grand JA, Braun MT, Kuljanin G, Kozlowski SWJ, Chao GT 2016. The dynamics of team cognition: a process-oriented theory of knowledge emergence in teams. J. Appl. Psychol. 101:1353–85
    [Google Scholar]
  42. Hambrick DC. 2007. The field of management's devotion to theory: too much of a good thing. ? Acad. Manag. J. 50:1346–52
    [Google Scholar]
  43. Harv. Bus. Rev. Anal. Serv 2014. HR joins the analytics revolution Rep., Harvard Bus. Sch. Publ Boston:
  44. Hastie T, Tibshirani R. 1990. Generalized Additive Models London: Chapman and Hall
  45. Hastie T, Tibshirani R, Friedman J 2009. The Elements of Statistical Learning: Data Mining, Inference, and Prediction New York: Springer. , 2nd ed..
  46. Hoeting JA, Madigan D, Raferty AE, Volinksy CT 1999. Bayesian model averaging: a tutorial. Stat. Sci. 14:382–417
    [Google Scholar]
  47. Hunter JE, Schmidt FL. 2015. Methods of Meta-Analysis: Correcting Error and Bias in Research Findings Thousand Oaks, CA: SAGE. , 3rd ed..
  48. James G, Witten D, Hastie T, Tibshirani R 2013. An Introduction to Statistical Learning with Applications in R New York: Springer
  49. Kern ML, Park G, Eischstaedt JC, Schwartz HA, Sap M et al. 2016. Gaining insights from social media language: methodologies and challenges. Psychol. Methods 21:507–25
    [Google Scholar]
  50. King EB, Tonidandel S, Cortina JM, Fink AA 2016. Building understanding of the data science revolution and IO psychology. Big Data at Work: The Data Science Revolution and Organizational Psychology S Tonidandel, EB King, JM Cortina 1–15 New York: Routledge
    [Google Scholar]
  51. Kosinski M, Matz SC, Gosling SD, Popov V, Stillwell D 2015. Facebook as a research tool for the social sciences: opportunities, challenges, ethical considerations, and practical guidelines. Am. Psychol. 70:543–56
    [Google Scholar]
  52. Kosinski M, Wang Y, Lakkaraju H, Leskovec J 2016. Mining big data to extract patterns and predict real-life outcomes. Psychol. Methods 21:493–506
    [Google Scholar]
  53. Kozlowski SWJ, Chao GT. 2018. Unpacking team process dynamics and emergent phenomena: challenges, conceptual advances, and innovative methods. Am. Psychol. 73:576–92
    [Google Scholar]
  54. Kozlowski SWJ, Chao GT, Grand JA, Braun MT, Kuljanin G 2016. Capturing the multilevel dynamics of emergence: computational modeling, simulation, and virtual experimentation. Organ. Psychol. Rev. 6:3–33
    [Google Scholar]
  55. Kuhn M, Johnson K. 2013. Applied Predictive Modeling New York: Springer
  56. Landers RN. 2014. Developing a theory of gamified learning: linking serious games and gamification of learning. Simul. Gaming 45:752–68
    [Google Scholar]
  57. Landers RN. 2017. A crash course in natural language processing. Ind. Psychol. 54:5–16
    [Google Scholar]
  58. Landers RN, Brusso RC, Cavanaugh KJ, Collmus AB 2016. A primer on theory-driven web-scraping: automatic extraction of big data from the Internet for use in psychological research. Psychol. Methods 21:475–92
    [Google Scholar]
  59. Lee D, Lee W, Lee Y, Pawitan Y 2011. Sparse partial least-squares regression and its applications to high-throughput data analysis. Chemom. Intell. Lab. Syst. 109:1–8
    [Google Scholar]
  60. Locke EA. 2007. The case for inductive theory building. J. Manag. 33:867–90
    [Google Scholar]
  61. Lohr S. 2014. For big-data scientists, ‘janitor work’ is key hurdle to insights. New York Times Aug. 17. https://www.nytimes.com/2014/08/18/technology/for-big-data-scientists-hurdle-to-insights-is-janitor-work.html
    [Google Scholar]
  62. MacCallum RC, Wegener DT, Uchino BN, Fabrigar LR 1993. The problem of equivalent models in applications of covariance structure analysis. Psychol. Bull. 114:185–99
    [Google Scholar]
  63. Martinsson PG, Rokhlin V, Tygert M 2011. A randomized algorithm for the decomposition of matrices. Appl. Comput. Harmonic Anal. 30:47–68
    [Google Scholar]
  64. McLean S, Stakim C, Timner H, Lyon C 2016. Big data and human resources—letting the computer decide. ? Scitech Lawyer 12:20–23
    [Google Scholar]
  65. Merenda PF. 2007. Psychometrics and psychometricians in the 20th and 21st centuries: how it was in the 20th century and how it is now. Percept. Motor Skills 104:3–20
    [Google Scholar]
  66. Miller PJ, Lubke GH, McArtor DB, Bergeman CS 2016. Finding structure in data using multivariate tree boosting. Psychol. Methods 21:583–602
    [Google Scholar]
  67. Mitroff SR, Biggs AT, Adamo SH, Dowd EW, Winkle J, Clark K 2015. What can 1 billion trials tell us about visual search?. J. Exp. Psychol. Hum. Percept. Perform. 41:1–5
    [Google Scholar]
  68. Molloy JC, Ployhart RE, Wright PM 2011. The myth of “the” micro-macro divide: bridging system-level and disciplinary divides. J. Manag. 37:581–609
    [Google Scholar]
  69. Muñoz C, Smith M, Patil DJ 2016. Big data: a report on algorithmic systems, opportunity, and civil rights Rep., Exec. Office Pres Washington, DC: https://obamawhitehouse.archives.gov/sites/default/files/microsites/ostp/2016_0504_data_discrimination.pdf
  70. Murgia M. 2019. Who's using your face? The ugly truth about facial recognition. Financial Times April 19. https://www.ft.com/content/cf19b956-60a2-11e9-b285-3acd5d43599e
    [Google Scholar]
  71. Naim I, Tanveer MI, Gildea D, Hoque ME 2016. Automated analysis and prediction of job interview performance. IEEE Trans. Affect. Comput. 99:191–204
    [Google Scholar]
  72. Olguín Olguín D, Waber BN, Kim T, Mohan A, Ara K, Pentland A 2009. Sensible organizations: technology and methodology for automatically measuring organizational behavior. IEEE Trans. Syst. Man Cybern. B Cybern. 39:43–55
    [Google Scholar]
  73. Oswald FL, Putka DJ. 2016. Statistical methods for big data: a scenic tour. Big Data at Work: The Data Science Revolution and Organizational Psychology S Tonidandel, EB King, JM Cortina 43–63 New York: Routledge
    [Google Scholar]
  74. Oswald FL, Putka DJ. 2017. Big data methods in the social sciences. Curr. Opin. Behav. Sci. 18:103–6
    [Google Scholar]
  75. Park G, Schwartz HA, Eichstaedt JC, Kern ML, Kosinski M et al. 2015. Automatic personality assessment through social media language. J. Personal. Soc. Psychol. 108:934–52
    [Google Scholar]
  76. Pennebaker JW, Mehl MR, Niederhoffer KG 2003. Psychological aspects of natural language use: our words, our selves. Annu. Rev. Psychol. 54:547–77
    [Google Scholar]
  77. Preacher KJ, Merkle EC. 2012. The problem of model selection uncertainty in structural equation modeling. Psychol. Methods 17:1–14
    [Google Scholar]
  78. Press G. 2016. Cleaning big data: most time-consuming, least enjoyable data science task, survey says. Fortune March 23. https://www.forbes.com/sites/gilpress/2016/03/23/data-preparation-most-time-consuming-least-enjoyable-data-science-task-survey-says/#7b9080bc6f63
    [Google Scholar]
  79. PriceWaterhouseCoopers 2015. Innovating for tomorrow's workforce: transformation enabled by HR in the Cloud Survey, PriceWaterhouseCoopers London: https://roubler.com/au/wp-content/uploads/sites/9/2017/02/hr-tech-survey.pdf
  80. Putka DJ, Beatty AS, Reeder MC 2018. Modern prediction methods: new perspectives on a common problem. Organ. Res. Methods 21:689–732
    [Google Scholar]
  81. Ribiero MT, Singh S, Guestrin C 2016. Local interpretable model-agnostic explanations (LIME): an introduction. O'Reilly Aug. 12. https://www.oreilly.com/learning/introduction-to-local-interpretable-model-agnostic-explanations-lime
    [Google Scholar]
  82. Rocher L, Hendrickx JM, de Montjoye Y-A 2019. Estimating the success of re-identifications in incomplete datasets using generative models. Nat. Commun. 10:3069
    [Google Scholar]
  83. Rotolo CT, Church AH. 2015. Big data recommendations for industrial-organizational psychology: Are we in Whoville?. Ind. Organ. Psychol. 8:515–20
    [Google Scholar]
  84. Rousseau V, Aubé C, Savoie A 2006. Teamwork behaviors: a review and integration of frameworks. Small Group Res 37:540–70
    [Google Scholar]
  85. Ryan J, Herleman H. 2015. A big data platform for workforce analytics. Big Data at Work: The Data Science Revolution and Organizational Psychology S Tonidandel, EB King, JM Cortina 19–42 New York: Routledge
    [Google Scholar]
  86. Shaffer T. 2017. The 42 V's of big data and data science. KDnuggets April. https://www.kdnuggets.com/2017/04/42-vs-big-data-data-science.html
    [Google Scholar]
  87. Sharpe D. 2013. Why the resistance to statistical innovations? Bridging the communication gap. Psychol. Methods 18:572–82
    [Google Scholar]
  88. Shen W, Kiger TB, Davies SE, Rasch RL, Simon KM, Ones DS 2011. Samples in applied psychology: over a decade of research in review. J. Appl. Psychol. 96:1055–64
    [Google Scholar]
  89. Short JC, McKenny AF, Reid SW 2018. More than words? Computer-aided text analysis in organizational behavior and psychology research. Annu. Rev. Organ. Psychol. Organ. Behav. 5:415–35
    [Google Scholar]
  90. SHRM (Soc. Hum. Resour. Manag.) Found 2016. Use of Workforce Analytics for Competitive Advantage New York: The Econ. Intell. Unit
  91. Sinar EF. 2015. Data visualization. Big Data at Work: The Data Science Revolution and Organizational Psychology S Tonidandel, EB King, JM Cortina 115–57 New York: Routledge
    [Google Scholar]
  92. SIOP (Soc. Ind. Organ. Psychol.) 2018. Principles for the validation and use of personnel selection procedures. Ind. Organ. Psychol. 11:Suppl. S11–97
    [Google Scholar]
  93. Spector PE, Rogelberg SG, Ryan AM, Schmitt N, Zedeck S 2014. Moving the pendulum back to the middle: reflections on and introduction to the inductive research special issue of Journal of Business and Psychology. J. Bus. Psychol 29:499–502
    [Google Scholar]
  94. Strobl C, Malley J, Tutz G 2009. An introduction to recursive partitioning: rationale, application, and characteristics of classification and regression trees, bagging, and random forests. Psychol. Methods 14:323–48
    [Google Scholar]
  95. Sutton CD. 2005. Classification and regression trees, bagging, and boosting. Handb. Stat. 24:303–29
    [Google Scholar]
  96. Tay L, Parrigon S, Huang Q, LeBreton JM 2016. Graphical descriptives: a way to improve data transparency and methodological rigor in psychology. Perspect. Psychol. Sci. 11:5692–701
    [Google Scholar]
  97. Tett RP, Walser B, Brown C, Simonet DV, Tonidandel S 2013. The 2011 SIOP I-O Psychology Graduate Program Benchmarking Survey Part 3: curriculum and competencies. Ind.-Organ. Psychol. 50:69–89
    [Google Scholar]
  98. Tibshirani R. 1996. Regression shrinkage and selection via the lasso. J. R. Stat. Soc. B 58:267–88
    [Google Scholar]
  99. Tikkinen-Piri C, Rohunen A, Markkula J 2018. EU General Data Protection Regulation: changes and implications for personal data collecting companies. Comput. Law Secur. Rev. 34:134–53
    [Google Scholar]
  100. Tonidandel S, King EB, Cortina JM 2018. Big data methods: leveraging modern data analytic techniques to build organizational science. Organ. Res. Methods 21:525–47
    [Google Scholar]
  101. Trzesniewski KH, Donnellan MB, Lucas RE 2011. Secondary Data Analysis: An Introduction for Psychologists Washington, DC: Am. Psychol. Assoc.
  102. Vapnik V. 1998. Statistical Learning Theory New York: Wiley & Sons
  103. Wanberg CR, Banas JT. 2000. Predictors and outcomes of openness to changes in a reorganizing workplace. J. Appl. Psychol. 85:132–42
    [Google Scholar]
  104. Wax A, Asencio R, Carter DR 2015. Thinking big about big data. Ind. Organ. Psychol. 8:545–50
    [Google Scholar]
  105. Whelan TJ, DuVernet AM. 2015. The big duplicity of big data. Ind. Organ. Psychol. 8:509–15
    [Google Scholar]
  106. Yost A, Behrend TS, Howardson GN, Darrow JB, Jensen J 2018. Reactance to electronic surveillance: a test of antecedents and outcomes. J. Bus. Psychol. 34:71–86
    [Google Scholar]
  107. Youyou W, Kosinski M, Stillwell D 2015. Computer-based personality judgments are more accurate than those made by humans. PNAS 112:1036–40
    [Google Scholar]
  108. Zou H, Hastie T. 2005. Regularization and variable selection via the elastic net. J. R. Stat. Soc. B 67:301–20
    [Google Scholar]
  109. Zyphur MJ, Oswald FL, Rupp DE 2016. Rendezvous overdue: Bayes analysis meets organizational research. J. Manag. 41:387–89
    [Google Scholar]
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