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How to Make Indians Happy? Using Explainable AI to Identify Happiness Indicators
Manohar Kapse and MA Sanjeev
Jaipuria Institute of Management - Indore Campus, Indore, India
Vinod Sharma and Yogesh Mahajan
Symbiosis Centre for Management and Human Resource Development (SCMHRD), Symbiosis International (Deemed University), Pune, India
Volume 17: 2024, pp. 55-62; ABSTRACT
India, the world's fastest-growing major economy, remains a bright spot amid global recession concerns. However, despite its economic success,
the country faces challenges in enhancing the happiness of its over 1.4 billion citizens, ranking a low 126th out of 146 nations in the recent
World Happiness Report (WHR). Although the central and state governments have introduced various happiness measurement and promotion schemes,
achieving significant improvement in WHR rankings remains elusive. The WHR data offers valuable insights for policymakers to understand the
factors affecting happiness, its cross-cultural differences, and impact on productivity. Governments and organizations can use such insights
to develop both global and localized strategies to improve citizen well-being and improve productivity. This case examines how explainable AI
(XAI) and machine learning (ML) can be leveraged to identify key happiness indicators and integrate it with organizational behaviour principles
to leverage employee performance. Students are tasked with analysing global and local drivers of happiness and developing predictive models using AI and ML tools.
Keywords: subjective well-being, organization behaviour, machine learning, explainable AI (XAI), support vector machine, random forest, gradient boosting regressor, decision tree, multiple perceptron neural network.
The Americas + Rest of World
Minimum Order Value
6 copies:
Minimum Order Value
6 copies:
Minimum Order Value
6 copies:
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