References
- Baesens B, Bapna R, Marsden JR, Vanthienen J, Zhao JL. Transformational issues of big data and analytics in networked business. MIS Q. 2016;40(4):807–18. doi:https://doi.org/10.25300/MISQ/2016/40:4.03.
- McAfee A, Brynjolfsson E. Big data: the management revolution. Harverd Bus Rev. 2012;90:61–68.
- Torres R, Sidorova A, Jones MC. Enabling firm performance through business intelligence and analytics: a dynamic capabilities perspective. Inf Manag. 2018;55:822–39. doi:https://doi.org/10.1016/j.im.2018.03.010.
- Sharma R, Reynolds P, Scheepers R, Seddon PB, ShanksG G. Business analytics and competitive advantage: a review and a research agenda. In: DSS.2010. p. 187–98.
- Krishnamoorthi S, Mathew SK. Business analytics and business value: a comparative case study. Inf Manag. 2018;55:643–66. doi:https://doi.org/10.1016/j.im.2018.01.005.
- Jennex ME. Big data, the internet of things, and the revised knowledge pyramid. Data Base Adv Inf Syst. 2017;48(4):69–79. doi:https://doi.org/10.1145/3158421.3158427.
- Côrte-Real N, Ruivo P, Oliveira T. Leveraging internet of things and big data analytics initiatives in European and American firms: is data quality a way to extract business value? Inf Manag. 2019;57(1):paper number 103141.
- Kwon O, Lee N, Shin B. Data quality management, data usage experience and acquisition intention of big data analytics. Int J Inf Manage. 2014;34(3):387–94. doi:https://doi.org/10.1016/j.ijinfomgt.2014.02.002.
- Huang TCK, Liu CC, Chang DC. An empirical investigation of factors influencing the adoption of data mining tools. Int J Inf Manage. 2012;32(3):257–70. doi:https://doi.org/10.1016/j.ijinfomgt.2011.11.006.
- Todd P, Benbasat I. The use of information in decision making: an experimental investigation of the impact of computer-based decision aids. MIS Q. 1992;16(3):373–93. doi:https://doi.org/10.2307/249534.
- Mount M, Johnson E. Relationship of personality traits and counterproductive work behaviors: the mediating effects of job satisfaction. Pers Psychol. 2006;59(3):591–622. doi:https://doi.org/10.1111/j.1744-6570.2006.00048.x.
- Judge TA, Zapata CP. The person–situation debate revisited: effect of situation strength and trait activation on the validity of the big five personality traits in predicting job performance. Acad Manag J. 2015;58(4):1149–79. doi:https://doi.org/10.5465/amj.2010.0837.
- Hull CL. Principles of behavior: an introduction to behavior theory. Oxford (England): Appleton-Century; 1943.
- Simon HA. A behavioral model of rational choice. Q J Econ. 1955;69(1):99–118. doi:https://doi.org/10.2307/1884852.
- Kahneman D. Thinking, fast and slow. New York (USA): Farrar, Straus and Groux; 2011.
- Payne JW. Contingent decision behavior. Psychol Bull. 1982;92(2):382–402. doi:https://doi.org/10.1037/0033-2909.92.2.382.
- Kool W, McGuire JT, Rosen ZB, Botvinick MM. Decision making and the avoidance of cognitive demand. J Exp Psychol Gen. 2010;139(4):665–82. doi:https://doi.org/10.1037/a0020198.
- Kool W, Botvinick M. A labor/leisure tradoff in cognitive control. J Exp Psychol Gen. 2014;143(1):131–41. doi:https://doi.org/10.1037/a0031048.
- Todd P, Benbasat I. Inducing compensatory information processing through decision aids that facilitate effort reduction: an experimental assessment. J Behav Decis Mak. 2000;13(1):91–106. doi:https://doi.org/10.1002/(SICI)1099-0771(200001/03)13:1<91::AID-BDM345>3.0.CO;2-A.
- Todd P, Benbasat I. Evaluating the Impact of DSS, cognitive effort, and incentives on strategy selection. Inf Syst Res. 1999;10:356–74.
- Todd P, Benbasat I. An experimental investigation of the impact of computer based decision aids on decision making strategies. Inf Syst Res. 1991;2(2):87–115. doi:https://doi.org/10.1287/isre.2.2.87.
- Todd P, Benbasat I. An experimental investigation of the relationship between decision makers, decision aids and decision making effort. INFOR. 1993;31:80–100.
- Aksoy L, Bloom PN, Lurie NH, Cooil B. Should recommendation agents think like people? J Serv Res. 2006;8(4):297–315. doi:https://doi.org/10.1177/1094670506286326.
- Lak P, Turetken O. The Impact of sentiment analysis output on decision outcomes: an empirical evaluation. AIS Trans Human-Computer Interact. 2017;9(1):1. doi:https://doi.org/10.17705/1thci.00086.
- Xu DJ, Benbasat I, Cenfetelli RT. A two-stage model of generating product advice: proposing and testing the complementarity principle. J Manag Inf Syst. 2017;34(3):826–62. doi:https://doi.org/10.1080/07421222.2017.1373011.
- Tan C-H, Teo -H-H, Benbasat I. Assessing screening and evaluation decision support systems: a resource-matching approach. Inf Syst Res. 2010;21(2):305–26. doi:https://doi.org/10.1287/isre.1080.0232.
- Azevedo A, Santos MF 2008. KDD, SEMMA and CRISP-DM: a parallel overview. IADIS Eur Conf. Data Min, 182–85. Amsterdam, The Nerthlands.
- Torres R, Sidorova A. Reconceptualizing information quality as effective use in the context of business intelligence and analytics. Int J Inf Manage. 2019;49:316–29. doi:https://doi.org/10.1016/j.ijinfomgt.2019.05.028.
- Aytug H. Feature selection for support vector machines using generalized benders decomposition. Eur J Oper Res. 2015;244(1):210–18. doi:https://doi.org/10.1016/j.ejor.2015.01.006.
- Lismont J, Vanthienen J, Baesens B, Lemahieu W. Defining analytics maturity indicators: a survey approach. Int J Inf Manage. 2017;37(3):114–24. doi:https://doi.org/10.1016/j.ijinfomgt.2016.12.003.
- Turban E, Sharda R, Delen D, King D. Business intelligence: a managerial approach. 2nd. Upper Saddle River (NJ): Pearson Education, Inc; 2011.
- Rexer K, Gearan P, Allen H 2015. Rexer Analytic 2015 Data Science Survey.
- SAS Inc. 2017. Getting Started with SAS® Enterprise Miner 14.3. Cary (NC): SAS Institute Inc.
- Yeh I-C, Lien C. The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients. Expert Syst Appl. 2009;36(2):2473–80. doi:https://doi.org/10.1016/j.eswa.2007.12.020.
- Sarma KS. Predicitve modeling with SAS enterprise miner: practical solutions for business applications. Cary (NC): SAS Institute Inc; 2007.
- Rattenbury T, Hellerstein JM, Heer J, Kandel S, Carreras C 2017. Principles of Data Wrangling
- Hohman F, Wongsuphasawat K, Kery MB, Patel K 2020. Understanding and visualizing data iteration in machine learning. Conf Hum Factors Comput Syst - Proc.
- Sambasivan N, Kapania S, Highfill H, Akrong D, Paritosh PK, Aroyo LM 2021. ‘Everyone wants to do the model work, not the data work’: data cascades in high-stakes AI CHI ’21. Proc. SIGCHI Conf. Hum. Factors Comput. Syst.; Yokohama, Japan.
- Pal M, Mather PM. An assessment of the effectiveness of decision tree methods for land cover classification. Remote Sens Environ. 2003;86(4):554–65. doi:https://doi.org/10.1016/S0034-4257(03)00132-9.
- Géron A. Hands-on machine learning with scikit-learn, keras, and tensorflow: concepts, tools, and techniques to build intelligent system. Sebastopol (CA): O'Reilly Media, Inc; 2019.
- Kahneman D, Tversky A 1984. Choices, Values, and Frames.
- Kember D. Interpreting student workload and the factors which shape students’ perceptions of their workload. Stud High Educ. 2004;29(2):165–84. doi:https://doi.org/10.1080/0307507042000190778.
- McGrath JE. Dilemmatics: the study of research choices and dilemmas. Am Behav Sci. 1981;25(2):179–210. doi:https://doi.org/10.1177/000276428102500205.
- Briggs SR. Assessing the five-factor model of personality description. J Pers. 1992;60(2):253–93. doi:https://doi.org/10.1111/j.1467-6494.1992.tb00974.x.
- Goldberg LR. The structure of phenotypic personality traits. Am Psychol. 1993;48(12):1303–04. doi:https://doi.org/10.1037/0003-066X.48.12.1303.
- John OP, Srivastava S. The big five trait taxonomy: history, measurement, and theoretical perspectives. Handb Personal Theory Res. 1999;2:102–38.
- Davis FD, Bagozzi RP, Warshaw PR. Extrinsic and intrinsic motivation to use computers in the workplace. J Appl Soc Psychol. 1992;22(14):1111–32. doi:https://doi.org/10.1111/j.1559-1816.1992.tb00945.x.
- Ghani JA, Deshpande SP. Task characteristics and the experience of optimal flow in human-computer interaction. J Psychol. 1994;128(4):381–91. doi:https://doi.org/10.1080/00223980.1994.9712742.
- Luce MF, Bettman JR, Payne JW. Choice processing in emotionally difficult decisions. J Exp Psychol Learn Mem Cogn. 1997;23(2):384–405. doi:https://doi.org/10.1037/0278-7393.23.2.384.
- Barnett T, Pearson AW, Pearson R, Kellermanns FW. Five-factor model personality traits as predictors of perceived and actual usage of technology. Eur J Inf Syst. 2015;24(4):374–90. doi:https://doi.org/10.1057/ejis.2014.10.
- Burton-Jones A, Grange C. From use to effective use: a representation theory perspective. Inf Syst Res. 2013;24(3):632–58. doi:https://doi.org/10.1287/isre.1120.0444.
- Soto CJ, John OP, Gosling SD, Potter J. Age differences in personality traits from 10 to 65: big five domains and facets in a large cross-sectional sample. J Pers Soc Psychol. 2011;100(2):330–48. doi:https://doi.org/10.1037/a0021717.
- Gagné M, Deci EL. Self-determination theory and work motivation. J Organ Behav. 2005;26(4):331–62. doi:https://doi.org/10.1002/job.322.
- Glover SM, Prawitt DF, Spilker BC. The influence of decision aids on user behavior: implications for knowledge acquisition and inappropriate reliance. Organ Behav Hum Decis Process. 1997;72(2):232–55. doi:https://doi.org/10.1006/obhd.1997.2735.
- Buhl HU. IT as curse and blessing. Bus Inf Syst Eng. 2013;5(6):377–81. doi:https://doi.org/10.1007/s12599-013-0292-2.
- Wixom BH, Ariyachandra T, Douglas D, Goul M, Gupta B, Iyer L, Kulkarni U, Mooney JG, Phillips-Wren G, Turetken O. The current state of business intelligence in academia: the arrival of big data. Commun Assoc Inf Syst. 2014;34:1–13.