2005/01/01 by R. P. W. Havers · 1 citation
Social Sciences · Arts and Humanities · #South Asian Studies and Conflicts #Indian History and Philosophy #Colonial History and Postcolonial Studies #Independence (probability theory) #War of independence #Surrender #Indian nationalism #Politics #Mutiny #League #Nationalism #British Empire #Spanish Civil War #Ancient history #History #Political science #Law #Economic history #Empire #Military service
paper · doi:10.1057/9780230522459_6
openalex publication_date 2005/01/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/31
The Indian National Army (INA) was an Indian military formation that fought on the side of the Japanese, against the British, between 1942 and 1945. The INA was the result of an informal alliance between the radical expatriate Indian political leaders of the Indian Independence League (IIL) and the Imperial Japanese Army (IJA). The INA existed in two distinct incarnations. It was raised and led initially by a disillusioned British-Indian Army Officer, Captain Mohan Singh, who had been captured in the opening stages of the Japanese invasion of Malaya. After the British surrender at Singapore in February 1942, Singh recruited Indian troops of the British-Army from Japanese Prisoner of War (POW) camps with a view to eventually fighting the British in India. Disagreements between Singh and the Japanese, over both the intended size and the specific role of the INA, led to Singh’s dismissal. His replacement in June 1943 was a well-known political figure in the Indian independence movement, Subhas Chandra Bose. This second life of the INA proved to be far more robust and substantial than the first. Pre-war, Bose had established an international reputation as a nationalist politician, although his belief in the best way to achieve independence increasingly brought him into conflict with other leaders of the Congress Party. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.