Abstract

ABSTRACT Working on the interface between wildlife and communities, forests guards in Indian tiger reserves are responsible not only for the protection of forests, but also for implementing development initiatives in forest villages. Their job profile entails living in isolated environments with multiple threats to their safety, and relatively little data is available about their psychological well-being. This paper presents findings from a study of forest guards’ experience with their work environment, work satisfaction and psychological well-being. A randomized sample of 242 forest guards from six tiger reserves in Madhya Pradesh, India, responded to questionnaires measuring employee satisfaction and motivation, and completed implicit association tests. The machine learning algorithms and mean comparisons that were carried out suggest that workers from Kanha, Bandhavgarh and Panna Tiger Reserves form a group who have high association with their work indicative of high performance but low work satisfaction and well-being. Whereas workers from Pench, Satpura and Sanjay-Dubri Reserves form another group whose members have high employee engagement along with high work satisfaction and well-being but low association with work. Machine learning-based classification and regression tree (CART) analysis suggested that feedback, task identity, skill variety and organizational commitment are some of the important factors affecting work motivation.

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