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Data science in sustainable entrepreneurship: A multidisciplinary field of applications

Author

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  • Gupta, Brij B.
  • Gaurav, Akshat
  • Arya, Varsha
  • Alhalabi, Wadee

Abstract

Defined as the merging of social and environmental sustainability into corporate operations, sustainable entrepreneurship has embraced data science more and more to improve operational effectiveness and decision-making. Using statistics, machine learning, and computer science to uncover insights from challenging datasets, this interdisciplinary method blends the ideas of sustainability with sophisticated data analysis approaches. Our research supports the choice of this issue by stressing the urgent requirement of sophisticated analytical instruments to negotiate the complexity of sustainable business practices. We compare our proposed model against Logistic Regression, Feedforward Neural Networks, and Support Vector Machines (SVMs). This not only shows how better CNN models are for certain uses but also highlights the general possibilities of data science in promoting sustainability in business. Our results highlight the transforming ability of sophisticated machine learning methods in promoting informed, sustainable decision-making and supporting the more general conversation on sustainable business.

Suggested Citation

  • Gupta, Brij B. & Gaurav, Akshat & Arya, Varsha & Alhalabi, Wadee, 2024. "Data science in sustainable entrepreneurship: A multidisciplinary field of applications," Technological Forecasting and Social Change, Elsevier, vol. 209(C).
    • Handle: RePEc:eee:tefoso:v:209:y:2024:i:c:s0040162524005961
    DOI: 10.1016/j.techfore.2024.123798
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