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In the present era of rapid economic development, successive governments in India have placed priority on ‘inclusive economic growth and social justice’. After independence, India chose to adopt mixed economy model and central role for development planning and resource allocation across sectors have been done by the State's planning machinery. It has been the resolve of the Government of India to achieve development and welfare of all social groups including scheduled tribes in the country. In this pursuit, several tribal development programmes have been undertaken from time to time entailing enormous human, financial and material resources. However, data driven planning involving data analytics has not been in use for overall planning process for tribal development instead traditional statistical approach has been mostly in practice. The aim of this paper is to bring to the fore the usefulness of data driven gap based planning using data analytics tools. The endeavour is to put in place a scientific mechanism to identify and prioritize the infrastructural gaps in tribal dominated areas for the State of Telangana using twenty-two different parameters. Expert workshop conducted to cluster twenty-two parameters into seven broad clusters viz Finance Infrastructure, Village Material Infrastructure, Household Material Infrastructure, Information Infrastructure, Social Infrastructure, Education Infrastructure, Health Infrastructure. Moreover, the paper aims to connect the smart and sustainable village development considering various parameters such Information and Social Infrastructure. AHP and TOPSIS multi-criteria approaches have been applied to identify and prioritize the infrastructural gap areas for all nine districts and 1663 tribal dominated villages of Telangana. Analysis reveals that Mahbubnagar being the top ranked district while Adilabad being lowest ranked district in the Telangana in terms of gaps in infrastructure facilities. Villages in all nine districts have been clustered into three categories viz. Good, Fair and Poor. Findings and recommendations have been provided in the paper.

The importance of text mining is increasing in services management as the access to big data is increasing across digital platforms enabling such services. This study adopts a systematic literature review on the application of text mining in services management. First, we analyzed the literature on which has used text mining methods like Sentiment Analysis, Topic Modeling, and Natural language Processing (NLP) in reputed business management journals. Further, we applied visualization tools for text mining and the topic association to understand the dominant themes and relationships. The analysis highlighted that social media analysis, market analysis, competitive intelligence are the most dominant themes while other themes like risk management and fake content detection are also explored. Further, based on the analysis, future research agenda in the field of text mining in services management has been indicated.