Inductive reasoning: The promise of big data

Human Resource Management Review

Volumn 27, Issue 2, 2017, Pages 277-290

  McAbee, Samuel T ^a   Landis, Ronald S ^a   Burke, Maura I ^a  

^a ILLINOIS INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

Big data; Empiricism; Inductive reasoning; Theory

Indexed keywords

EID: 84995969786     PISSN: 10534822     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.hrmr.2016.08.005     Document Type: Article

References (112)

1. Ackerman, P.L., Heggestad, E.D., Intelligence, personality, and interests: Evidence for overlapping traits. Psychological Bulletin 121 (1997), 219–245.
2. Aguinis, H., Forcum, L.E., Joo, H., Using market basket analysis in management research. Journal of Management 7 (2013), 1799–1824.
3. Aiken, J.R., Hanges, P.J., Teach an I-O to fish: Integrating data science into I-O graduate education. Industrial and Organizational Psychology: Perspectives on Science and Practice 8 (2015), 539–544.
4. Anderson, C., The end of theory: The data deluge makes the scientific method obsolete. 2008, Wired (Retrieved from: http://archive.wired.com/science/discoveries/magazine/16-07/pb_theory).
5. Baldwin, T.T., Ford, J.K., Transfer of training: A review and directions for future research. Personnel Psychology 41 (1988), 63–105.
6. Beal, D.J., ESM 2.0: State of the art and future potential of experience sampling methods in organizational research. Annual Review of Organizational Psychology and Organizational Behavior 2 (2015), 383–407.
7. Beal, D.J., Weiss, H.M., Methods of ecological momentary assessment in organizational research. Organizational Research Methods 6 (2003), 440–464.
8. Behrend, T.S., Sharek, D.J., Meade, A.W., Wiebe, E.N., The viability of crowdsourcing for survey research. Behavior Research Methods 43 (2011), 800–813.
9. Berka, G., Olien, J., Rogelberg, S., Rupp, D., Thornton, M., An inductive exploration of manuscript quality and publication success in small research teams. Journal of Business Psychology 29 (2014), 725–731.
10. Bollier, D., The promise and peril of big data. 2010, The Aspen Institute, Washington, DC.
11. Bosco, F.A., Aguinis, H., Field, J.G., Pierce, C.A., Dalton, D.R., HARKing's threat to organizational research: Evidence from primary and meta-analytic sources. Personnel Psychology 69 (2016), 709–750.
12. Botsford Morgan, W., Dunleavy, E., DeVries, P.D., Using big data to create diversity and inclusion in organizations. Tonidandel, S., King, E., Cortina, J., (eds.) Big data at work: The data science revolution and organizational psychology, 2015, Routledge, New York, 310–335.
13. Braun, M.T., Kuljanin, G., Big data and the challenge of construct validity. Industrial and Organizational Psychology: Perspectives on Science and Practice 8 (2015), 521–527.
14. Brown, V.R., Vaughn, E.D., The writing on the (Facebook) wall: The use of social networking sites in hiring decisions. Journal of Business and Psychology 26 (2011), 219–225.
15. Chang, W., Madera, J.M., Using social network sites for selection purposes: An investigation of hospitality recruiters. Journal of Human Resources in Hospitality & Tourism 11 (2012), 183–196.
16. Chen, C., Chen, Y., Du, X., Li, C., Lu, J., Zhao, S., Zhou, X., Big data challenge: A data management perspective. Frontiers of Computer Science 7 (2013), 157–164.
17. Coronel, C., Morris, S., Rob, P., Database systems: Design, implementation, and management. 10th ed., 2013, Cengage Learning, Boston.
18. Cortina, J.M., Dunlap, W.P., On the logic and purpose of significance testing. Psychological Methods 2 (1997), 161–172.
19. Cortina, J.M., Landis, R.S., The earth is not round (p = .00). Organizational Research Methods 14 (2011), 332–349.
20. Cronbach, L.J., The two disciplines of scientific psychology. American Psychologist 12 (1957), 173–196.
21. Cucina, J.M., Hayes, T.L., Walmsley, P.T., Martin, N.R., It is time to get medieval on the overproduction of pseudotheory: How Bacon (1267) and Alhazen (1021) can save industrial-organizational psychology. Industrial and Organizational Psychology: Perspectives on Science and Practice 7 (2014), 356–364.
22. Daft, R.L., Lengel, R.H., Information richness: A new approach to managerial behavior and organizational design. Research in Organizational Behavior 6 (1984), 191–233.
23. Daft, R.L., Lengel, R.H., A proposed integration among organizational information requirements, media richness, and structural design. Management Science 32 (1986), 554–571.
24. Davenport, T.H., Big data at work: Dispelling the myths, uncovering the opportunities. 2014, Harvard Business School, USA.
25. Denison, D.R., What is the difference between organizational culture and organizational climate? A native's point of view on a decade of paradigm wars. Academy of Management Review 21 (1996), 619–654.
26. DeRue, D.S., Nahrgang, J.D., Ashford, S.J., Interpersonal perceptions and the emergence of leadership structures in groups: A network perspective. Organization Science 26 (2015), 1192–1209.
27. Eisenhardt, K., Graebner, M., Theory building from cases: Opportunities and challenges. Academy of Management Journal 50 (2007), 25–32.
28. Fayyad, U., Piatetsky-Shapiro, G., Smyth, P., From data mining to knowledge discovery in databases. AI Magazine 17 (1996), 37–54.
29. Feldman, R., Techniques and applications for sentiment analysis. Communications of the ACM 56 (2013), 82–89.
30. Gabel, T.J., Tokarski, C., Big data and organizational design: Key challenges await the survey research firm. Journal of Organizational Design 3 (2014), 37–45.
31. Gandomi, A., Haider, M., Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management 35 (2015), 137–144.
32. George, G., Haas, M.R., Pentland, A., Big data and management. Academy of Management Journal 57 (2014), 321–326.
33. Glaser, B.G., Strauss, A.L., The discovery of grounded theory. 1967, Aldine De Gruyter, Hawthorne, NY.
34. Griffiths, T.L., Manifesto for a new (computational) cognitive revolution. Cognition 135 (2015), 21–23.
35. Gully, S., Chen, G., Individual differences, attribute-treatment interactions, and training outcomes. Kozlowski, S.W.J., Salas, E., (eds.) Learning, training, and development in organizations, 2010, Routledge/Taylor & Francis Group, New York, 3–64.
36. Guo, S.Y., Ding, L.Y., Luo, H.B., Jiang, X.Y., A big-data-based platform of workers’ behavior: Observations from the field. Accident Analysis and Prevention 93 (2016), 299–309, 10.1016/j.aap.2015.09.024.
37. Guzzo, R.A., Fink, A., King, E.B., Tonidandel, S., Landis, R.S., Big data recommendations for industrial-organizational psychology. Industrial and Organizational Psychology: Perspectives on Science and Practice 8 (2015), 491–508.
38. Guzzo, R.A., Nalbantian, H.N., Parra, L.F., A big data, say-do approach to climate and culture: A consulting perspective. Schneider, B., Barbera, K., (eds.) Oxford handbook of climate and culture, 2014, Oxford University Press, New York, 197–211.
39. Hambrick, D.C., The field of management's devotion to theory: Too much of a good thing?. Academy of Management Journal 50 (2007), 1346–1352.
40. Harrison, S., Rouse, E., An inductive study of feedback interactions over the course of creative projects. Academy of Management Journal 58 (2015), 375–404.
41. Hendricks, C.A., Robie, C., Oswald, F.L., Web-based multisource reference checking: An investigation of psychometric integrity and applied benefits. International Journal of Selection and Assessment 21 (2013), 99–110.
42. Hendrickson, A.R., Human resource information systems: Backbone technology of contemporary human resources. Journal of Labor Research 24 (2003), 381–394.
43. Hibbert, P., Sillince, J., Diefenbach, T., Cunliffe, A.L., Relationally reflexive practice: A generative approach to theory development. Organizational Research Methods 17 (2014), 278–298.
44. Hunter, R.F., Hunter, J.E., Validity and utility of alternative predictors of job performance. Psychological Bulletin 96 (1984), 72–98.
45. Iliinsky, N., Steele, J., Designing data visualizations. 2011, O'Reilly, Sebastopol, CA.
46. Illingworth, A.J., Big data in I-O psychology: Privacy considerations and discriminatory algorithms. Industrial and Organizational Psychology: Perspectives on Science and Practice 8 (2015), 567–575.
47. Illingworth, A.J., Lippstreu, M., Deprez-Sims, A.S., “Big data” in talent selection and assessment. Tonidandel, S., King, E., Cortina, J., (eds.) Big data at work: The data science revolution and organizational psychology, 2015, Routledge, New York, 213–249.
48. James, G., Witten, D., Hastie, T., Tibshirani, R., An introduction to statistical learning with applications in R. 2013, Springer, New York.
49. Kepes, S., McDaniel, M.A., How trustworthy is the scientific literature in I-O psychology?. Industrial and Organizational Psychology: Perspectives on Science and Practice 6 (2013), 252–268.
50. Kerr, N., HARKing: Hypothesizing after the results are known. Personality and Social Psychology Review 2 (1998), 196–217.
51. Ketokivi, M., Mantere, S., Two strategies for inductive reasoning in organizational research. Academy of Management Review 35 (2010), 315–333.
52. Kluemper, D.H., Rosen, P.A., Future employment selection methods: Evaluating social networking web sites. Journal of Managerial Psychology 24 (2009), 567–580.
53. Kluemper, D.H., Rosen, P.A., Mossholder, K.W., Social networking websites, personality ratings, and the organizational context: More than meets the eye?. Journal of Applied Social Psychology 42 (2012), 1143–1172.
54. Kosinski, M., Bachrach, Y., Kohli, P., Stillwell, D., Graepel, T., Manifestations of user personality in website choice and behavior on online social networks. Machine Learning 95 (2014), 357–380.
55. Kosinski, M., Stillwell, D., Graepel, T., Private traits and attributes are predictable from digital records of human behavior. Proceedings of the National Academy of Sciences 110 (2013), 5802–5805.
56. Kozlowski, S.W.J., Chao, G.T., Chang, C.-H.D., Fernandez, R., Team dynamics: Using “big data” to advance the science of team effectiveness. Tonidandel, S., King, E., Cortina, J., (eds.) Big data at work: The data science revolution and organizational psychology, 2015, Routledge, New York, 272–309.
57. Landis, R.S., Rogelberg, S.G., Our scholarly practices are derailing our progress: The importance of “nothing” in the organizational sciences. Industrial and Organizational Psychology: Perspectives on Science and Practice 6 (2013), 299–302.
58. Landis, R.S., James, L.R., Lance, C.E., Pierce, C.A., Rogelberg, S.G., When is nothing something? Editorial for the null results special issue of Journal of Business and Psychology. Journal of Business and Psychology 29 (2014), 163–167.
59. Laney, D., 3D data management: Controlling data volume, velocity, and variety. Application Delivery Strategies, file 949. 2001, Meta Group, Inc., Stamford, CT (Retrieved from: http://blogs.gartner.com/doug-laney/files/2012/01/ad949-3D-Data-Management-Controlling-Data-Volume-Velocity-and-Variety.pdf).
60. Lazer, D., Kennedy, R., King, G., Vespignani, A., The parable of Google flu: traps in big data analysis. Science 343 (2014), 1203–1205.
61. Leung, K., Presenting post hoc hypotheses as a priori: Ethical and theoretical issues. Management and Organization Review 7 (2011), 471–479.
62. Liu, B., Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies 5 (2012), 1–167.
63. Locke, E.A., The case for inductive theory building. Journal of Management 33 (2007), 867–890.
64. Mantere, S., Ketokivi, M., Reasoning in organization science. Academy of Management Review 38 (2013), 70–89.
65. McAfee, A., Brynjolfsson, E., Big data: The management revolution. Harvard Business Review 90 (2012), 60–68.
66. Mercereau, L., Data: The art of the possible – How to create value from your different data sources. 2015, Rosslyn Analytics (Retrieved from http://www.rosslynanalytics.com/blog/2015/01/27/data-the-art-of-the-possible-how-to-create-value-from-your-different-data-sources/).
67. Messick, S., Validity of psychological assessment: Validation of inferences from persons’ responses and performances as scientific inquiry into score meaning. American Psychologist 50 (1995), 741–749.
68. Miller, T.M., Modeling techniques in predictive analytics: Business problems and solutions with R. 2014, Pearson Education, Upper Saddle River, NJ.
69. Minbashian, A., Bright, J.E.H., Bird, K.D., A comparison of artificial neural networks and multiple regression in the context of research on personality and work performance. Organizational Research Methods 13 (2010), 540–561.
70. Miner, J.B., Organizational behavior: From theory to practice. 1997, M. E. Shape, Armonk, NY.
71. Morrison, J.D. Jr., Abraham, J.D., Reasons for enthusiasm and caution regarding big data in applied selection research. The Industrial-Organizational Psychologist 52 (2015), 134–139.
72. Mumford, M.D., Owens, W.A., Methodology review: Principles, procedures, and findings in the application of background data measures. Applied Psychological Measurement 11 (1987), 1–31.
73. Nickerson, R.S., Null hypothesis significance testing: A review of an old and continuing controversy. Psychological Methods 5 (2000), 241–301.
74. Noyes, K.N., For the airline industry, big data is cleared for take-off. 2014, Fortune (Retrieved from http://Fortune.com/2014/06/19/big-data-airline-industry/).
75. O'Reilly, K., Paper, D., Marx, S., Demystifying grounded theory for business research. Organizational Research Methods 15 (2012), 247–262.
76. Oswald, F.L., Putka, D., Statistical methods for big data: A scenic tour. Tonidandel, S., King, E., Cortina, J., (eds.) Big data at work: The data science revolution and organizational psychology, 2015, Routledge, New York, 43–63.
77. Pang, B., Lee, L., Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval 2:1–2 (2008), 1–135.
78. Parks, M.R., Big data in communication research: Its contents and discontents. Journal of Communication 64 (2014), 355–360.
79. Power, D.J., Using ‘big data’ for analytics and decision support. Journal of Decision Systems 23 (2014), 222–228.
80. Provost, F., Fawcett, T., Data science and its relationship to big data and data-driven decision making. Big Data 1 (2013), 51–59, 10.1089/big.2013.1508.
81. Putka, D.J., Oswald, F.L., Implications of the big data movement for the advancement of I-O science and practice. Tonidandel, S., King, E., Cortina, J., (eds.) Big data at work: The data science revolution and organizational psychology, 2015, Routledge, New York, 181–212.
82. Putka, D.J., Beatty, A.S., Reeder, M.C., Modern prediction methods: New perspectives on a common problem. Organizational Research Methods, 2016 (in press).
83. Roulin, N., Bangerter, A., Social network websites in personnel selection: A signaling perspective on recruiters’ and applicants’ perceptions. Journal of Personnel Psychology 12 (2013), 143–151.
84. Rupp, D.E., Ethical issues faced by editors and reviewers. Management and Organization Review 7 (2011), 481–493.
85. Ryan, A.M., Ployhart, R.E., A century of selection. Annual Review of Psychology 65 (2014), 693–717.
86. Savage, M., Burrows, R., The coming crisis of empirical sociology. Sociology 41 (2007), 885–899.
87. Savage, M., Burrows, R., Some further reflections on the coming crisis of empirical sociology. Sociology 43 (2009), 762–772.
88. Shadish, W.R., Cook, T.D., Campbell, D.T., Experimental and quasi-experimental designs for generalized causal inference. 2002, Houghton Mifflin, Boston.
89. Silver, N., The signal and the noise: Why so many predictions fail-but some don't. 2012, The Penguin Press, New York.
90. Sinar, E.F., Data visualization. Tonidandel, S., King, E., Cortina, J., (eds.) Big data at work: The data science revolution and organizational psychology, 2015, Routledge, New York, 115–157.
91. Spector, P.E., Rogelberg, S.G., Ryan, A.M., Schmitt, N., Zedeck, S., Moving the pendulum back to the middle: Reflections on and introduction to the inductive research special issue of Journal of Business and Psychology. Journal of Business and Psychology 49 (2014), 499–502.
92. Stam, H.J., Pythagoreanism, meaning and the appeal to number. New Ideas in Psychology 24 (2006), 240–251.
93. Stanton, J.M., Data mining: A practical introduction for organizational researchers. Cortina, J.M., Landis, R.S., (eds.) Modern research methods for the study of behavior in organizations, 2013, Routledge Academic, New York.
94. Stanton, J.M., Sensing big data: Multimodal information interfaces for exploration of large data sets. Tonidandel, S., King, E., Cortina, J., (eds.) Big data at work: The data science revolution and organizational psychology, 2015, Routledge, New York, 158–177.
95. Stone-Romero, E.F., Research strategies in industrial and organizational psychology: Nonexperimental, quasi-experiment, and randomized experimental research in special purpose and nonspecial purpose settings. Zedeck, S., (eds.) APA handbook of industrial and organizational psychology, Vol. 1: Building and developing the organization, 2011, American Psychological Association, Washington, DC, 37–72.
96. Suddaby, R., What grounded theory is not. Academy of Management Journal 49 (2006), 633–642.
97. Suddaby, R., Why theory?. Academy of Management Journal 39 (2014), 407–411.
98. Sutton, R.I., Staw, B.M., What theory is not. Administrative Science Quarterly 40 (1995), 371–384.
99. Swan, M., The quantified self: Fundamental disruption in big data science and biological discovery. Big Data 1 (2013), 85–99, 10.1089/big.2012.0002.
100. Sweeney, L., Simple demographics often identify people uniquely. 2000, Carnegie Mellon University (Retrieved from http://dataprivacylab.org/projects/identifiability/paper1.pdf).
101. Tannenbaum, S.I., Human resource information systems: User group implications. Journal of Systems Management 41 (1990), 27–32.
102. Telea, A.C., Data visualization: Principles and practice. 2nd ed., 2015, CRC Press, Boca Raton, FL.
103. Tibshirani, R., Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society. Series B (Methodological) 58 (1996), 267–288.
104. Tichy, N.M., Tushman, M.L., Fombrun, C., Social network analysis for organizations. Academy of Management Review 4 (1979), 507–519.
105. Van Maanen, J., Some notes on the importance of writing in organization studies. Harvard Business School research colloquium, 1989, Harvard Business School, Boston, 27–33.
106. Wernimont, P.F., Campbell, J.P., Signs, samples, and criteria. Journal of Applied Psychology 52 (1968), 372–376.
107. Whelan, T.J., DuVernet, A.M., The big duplicity of big data. Industrial and Organizational Psychology: Perspectives on Science and Practice 8 (2015), 509–515.
108. Wickham, H., ggplot2. Wiley Interdisciplinary Reviews: Computational Statistics 3:2 (2011), 180–185.
109. Youyou, W., Kosinski, M., Stillwell, D., Computer-based personality judgments are more accurate than those made by humans. Proceedings of the National Academy of Sciences 112 (2015), 1036–1040.
110. Yu, Y., Wang, X., World Cup 2014 in the Twitter world: A big data analysis of sentiments in U.S. sports fans’ tweets. Computers in Human Behavior 48 (2015), 392–400.
111. Yu, Y., Duan, W., Cao, Q., The impact of social and conventional media on firm equity value: A sentiment analysis approach. Decision Support Systems 55 (2013), 919–926.
112. Zickar, M.J., Carter, N.T., Reconnecting with the spirit of workplace ethnography: A historical and methodological review. Organizational Research Methods 13 (2010), 304–319.