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MODELING NON-LINEAR ASSOCIATIONS BETWEEN INDEPENDENT AND DEPENDENT VARIABLES USING ARTIFICIAL NEURAL NETWORKS IN PYTHON

Mr. Nachiket Talwar

First Published December 27,2022

Authors
  1. Mr. Nachiket Talwar
Affiliation
  • Student, Department of Computer Science and Engineering, Vellore Institute of Technology, Vellore
Abstract
The purpose of this paper is to present a method of data analysis that can analyze both linear and
non-linear associations between independent and dependent variables. The program is developed
to help researchers examine the behaviors of Asian consumers to help firms understand the factors
influencing their buying behavior.
Keywords

Artificial neural network, dependent variable, independent variable, Python, structural equation modeling

References
  1. Talwar, S., Srivastava, S., Sakashita, M., Islam, N., & Dhir, A. (2021). Personality and travel intentions during and after the COVID-19 pandemic: An artificial neural network (ANN) approach. Journal of Business Research. doi:10.1016/j.jbusres.2021.12.002
  2. Talwar, S., Talwar, M., Kaur, P., Singh, G., & Dhir, A. (2021). Why have consumers opposed, postponed, and rejected Innovations during a pandemic? A Study of mobile payment Innovations. Australasian Journal of Information Systems, 25. https://doi.org/ 10.3127/ ajis.v25i0.3201
  3. Talwar, S., Talwar, M., Tarjanne, V., & Dhir, A. (2021). Why retail investors traded equity during the pandemic? An application of artificial neural networks to examine behavioral biases. Psychology & Marketing, 38(11), 2142–2163. doi:10.1002/mar.21550
  4. Talwar, M., Talwar, S., Kaur, P., Tripathy, N., & Dhir, A. (2021). Has financial attitude impacted the trading activity of retail investors during the COVID-19 pandemic? Journal of Retailing and Consumer Services, 58, 102341. doi: 10.1016/j.jretconser.2020.102341
  5. Khan, M., Ajmal, M., Jabeen, F., Talwar, S., & Dhir, A. (2022). Green supply chain management in manufacturing firms: A resource􀀀based viewpoint. Business Strategy And The Environment. https://doi.org/10.1002/bse.3207
  6. Malik, S., Arshad, M., Amjad, Z., & Bokhari, A. (2022). An empirical estimation of determining factors influencing public willingness to pay for better air quality. Journal Of Cleaner Production, 133574. https://doi.org/10.1016/j.jclepro.2022.133574
  7. Hallikainen, H., Luongo, M., Dhir, A., & Laukkanen, T. (2022). Consequences of personalized product recommendations and price promotions in online grocery shopping. Journal Of Retailing And Consumer Services, 69, 103088. https://doi.org/10.1016/j.jretconser.2022.103088
  8. Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411-423. https://doi.org/10.1037/0033-2909.103.3.411
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