Acknowledgement
This article is financially supported by the College of Public Policy at Korea University.
References
- Jianqing Fan, Fang Han, Han Liu, Challenges of Big Data analysis, National Science Review, Volume 1, Issue 2, June 2014, Pages 293-314. https://doi.org/10.1093/nsr/nwt032
- Patrick Mikalef, John Krogstie, Ilias O. Pappas, Paul Pavlou, Exploring the relationship between big data analytics capability and competitive performance: The mediating roles of dynamic and operational capabilities, Information & Management, Volume 57, Issue 2, 2020, 103169.
- Fan C, Chen M, Wang X, Wang J and Huang B (2021) A Review on Data Preprocessing Techniques Toward Efficient and Reliable Knowledge Discovery From Building Operational Data. Front. Energy Res. 9:652801.
- John Qi Dong, Chia-Han Yang, Business value of big data analytics: A systems-theoretic approach and empirical test, Information & Management, Volume 57, Issue 1, 2020, 103124.
- Sunil Erevelles, Nobuyuki Fukawa, Linda Swayne, Big Data consumer analytics and the transformation of marketing, Journal of Business Research, Volume 69, Issue 2, 2016, Pages 897-904. https://doi.org/10.1016/j.jbusres.2015.07.001
- Rangaswamy, E., Nawaz, N. & Changzhuang, Z. The impact of digital technology on changing consumer behaviours with special reference to the home furnishing sector in Singapore. Humanit Soc Sci Commun 9, 83 (2022).
- White, K., Habib, R., & Hardisty, D. J. (2019). How to SHIFT Consumer Behaviors to be More Sustainable: A Literature Review and Guiding Framework. Journal of Marketing, 83(3), 22-49. https://doi.org/10.1177/0022242919825649
- Emmanuel, T., Maupong, T., Mpoeleng, D. et al. A survey on missing data in machine learning. J Big Data 8, 140 (2021).
- Uthayasankar Sivarajah, Muhammad Mustafa Kamal, Zahir Irani, Vishanth Weerakkody, Critical analysis of Big Data challenges and analytical methods, Journal of Business Research, Volume 70, 2017, Pages 263-286. https://doi.org/10.1016/j.jbusres.2016.08.001
- Desamparados Blazquez, Josep Domenech, Big Data sources and methods for social and economic analyses, Technological Forecasting and Social Change, Volume 130, 2018, Pages 99-113. https://doi.org/10.1016/j.techfore.2017.07.027
- Goldstein M, Uchida S (2016) A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data. PLOS ONE 11(4): e0152173.
- Panjei, E., Gruenwald, L., Leal, E. et al. A survey on outlier explanations. The VLDB Journal 31, 977-1008 (2022). https://doi.org/10.1007/s00778-021-00721-1
- Kean Ming Tan, Daniela Witten, Ali Shojaie, The cluster graphical lasso for improved estimation of Gaussian graphical models, Computational Statistics & Data Analysis, Volume 85, 2015, Pages 23-36. https://doi.org/10.1016/j.csda.2014.11.015
- Jain R, Xu W (2021) HDSI: High dimensional selection with interactions algorithm on feature selection and testing. PLOS ONE 16(2): e0246159.
- Friedman, Jerome, Trevor Hastie, and Robert Tibshirani. 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent." Journal of Statistical Software, Articles 33 (1): 1-22. https://doi.org/10.18637/jss.v033.i01
- Morris, T.P., White, I.R. & Royston, P. Tuning multiple imputation by predictive mean matching and local residual draws. BMC Med Res Methodol 14, 75 (2014).
- ur Rehman, A., Belhaouari, S.B. Unsupervised outlier detection in multidimensional data. J Big Data 8, 80 (2021).
- Yusuke Hara, Junpei Suzuki, Masao Kuwahara, Network-wide traffic state estimation using a mixture Gaussian graphical model and graphical lasso, Transportation Research Part C: Emerging Technologies, Volume 86, 2018, Pages 622-638. https://doi.org/10.1016/j.trc.2017.12.007
- Andres Martinez, Claudia Schmuck, Sergiy Pereverzyev, Clemens Pirker, Markus Haltmeier, A machine learning framework for customer purchase prediction in the non-contractual setting, European Journal of Operational Research, Volume 281, Issue 3, 2020, Pages 588-596. https://doi.org/10.1016/j.ejor.2018.04.034
- Nicholas P. Danks, Pratyush N. Sharma, Marko Sarstedt, Model selection uncertainty and multimodel inference in partial least squares structural equation modeling (PLS-SEM), Journal of Business Research, Volume 113, 2020, Pages 13-24. https://doi.org/10.1016/j.jbusres.2020.03.019
- Mohammad Zoynul Abedin, Petr Hajek, Taimur Sharif, Md. Shahriare Satu, Md. Imran Khan, Modelling bank customer behaviour using feature engineering and classification techniques, Research in International Business and Finance, Volume 65, 2023, 101913.
- C.L. Philip Chen, Chun-Yang Zhang, Data-intensive applications, challenges, techniques and technologies: A survey on Big Data, Information Sciences, Volume 275, 2014, Pages 314-347. https://doi.org/10.1016/j.ins.2014.01.015
- Sarker, I.H. Data Science and Analytics: An Overview from Data-Driven Smart Computing, Decision-Making and Applications Perspective. SN COMPUT. SCI. 2, 377 (2021).
- Bickley, S.J., Chan, H.F. & Torgler, B. Artificial intelligence in the field of economics. Scientometrics 127, 2055-2084 (2022). https://doi.org/10.1007/s11192-022-04294-w
- Federico Battiston, Giulia Cencetti, Iacopo Iacopini, Vito Latora, Maxime Lucas, Alice Patania, Jean-Gabriel Young, Giovanni Petri, Networks beyond pairwise interactions: Structure and dynamics, Physics Reports, Volume 874, 2020, Pages 1-92. https://doi.org/10.1016/j.physrep.2020.05.004
- Douglas A. Luke and Jenine K. Harris, Network Analysis in Public Health: History, Methods, and Applications, Annual Review of Public Health 2007 28:1, 69-93. https://doi.org/10.1146/annurev.publhealth.28.021406.144132
- Marko Sarstedt, Christian M. Ringle, Denis Iuklanov, Antecedents and consequences of corporate reputation: A dataset, Data in Brief, Volume 48, 2023, 109079.
- Batko K, Slezak A. The use of Big Data Analytics in healthcare. J Big Data. 2022;9(1):3