How to make Indians happy? : using explainable AI to identify happiness indicators / Manohar Kapse, MA Sanjeev, Vinod Sharma, Yogesh Mahajan.

Format:

Book

Author/Creator:

Kapse, Manohar, author.

Sanjeev, MA, author.

Sharma, Vinod Kr, author.

Mahajan, Yogesh, author.

Series:

SAGE business cases.

SAGE business cases

Language:

English

Subjects (All):

Artificial intelligence--Business applications--Case studies.

Artificial intelligence.

Personnel management--Case studies.

Personnel management.

Happiness--Case studies.

Happiness.

Physical Description:

1 online resource : illustrations.

Place of Publication:

London : NeilsonJournals Publishing, 2024.

Summary:

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.

Notes:

Description based on XML content.

ISBN:

979-83-488-5427-0

OCLC:

1569208705

Publisher Number:

T301250