Artificial intelligence divulges effective tactics of top management institutes of India

Abstract

Purpose

The performance analysis of top 50 management institutions of India is conducted to understand their efficiency in utilizing available resources. The importance of different indicators is investigated to identify most preferred strategies of top management institutions in the country in order to meet the expectations of all stakeholders. Artificial neural networks models are applied for pattern recognition and classification purpose using self-organized map algorithms. A huge reservoir of young generation is being trained every year to meet the demand of business in different sectors of economies. It becomes a matter of concern to know the performance of the management institutes to ensure the overall national progress, which can be done by enabling organizations to improve their efficiency and effectiveness, provided the right information and skills are served. Data envelopment analysis (DEA) and self-organizing maps are utilized together to take advantages of optimization and prediction capabilities inherent in each method, and they may be beneficial to assess institution’s competitive position and design their own strategies in order to improve. The paper aims to discuss these issues.

Design/methodology/approach

The DEA is used to understand the utilization of resources by institutions on the bases of efficiency scores. Due to a greater flexibility and adaptability, neural technique, i.e. self-organized map, which is an artificial intelligence-based technique, a popular unsupervised learning model with a capability to capture patterns from data sets, is used. In this study, various parameters like qualification of faculty, research output of faculty members, expenditure made for functioning of the institution, etc., are considered. These academic and operational indicators are investigated in relation to the rank score and the efficiency score of top management institutions, and different strategies as a combination of input as well as output indicators are identified.

Findings

In the analysis, three types of strategies are identified. At present, the focus on salary packages of graduates seems the most utilized strategy. It is also observed that the strategy of having good performance, in terms of consultancy, peer and employer perception, has the highest success rate (in terms of score used for ranking). Results obtained using both techniques shows that due to high deviation and less explored research publications and sponsored research project is an opportunity that institutions can work upon to have maximum output. But to maintain consistency in terms of the high rank score and efficiency score, management institutions need to focus on consultancy, peer and employer perception.

Practical implications

This research identifies the different parameters categorized into various inputs and outputs for the management institutions in India for the benchmarking. It studies the importance of identified parameters in terms of success (rank score and efficiency score). Further investigation of relationship between parameters and success is conducted. Different strategies as a combination of parameters are identified. The current choice of top management institutions is revealed in terms of their preference and effectiveness of strategy. This research also provides some insight about long-term and short-term strategies, which may be beneficial to education managers or decision makers.

Originality/value

It is one of the rare papers in terms of performance measurement through data envelopment method and identification of strategy using artificial intelligence. This paper utilized a hybrid methodology that integrates these two data analytic methods to capture an innovative performance and strategies prediction in education system.

Citation

Kumar, S. (2019), "Artificial intelligence divulges effective tactics of top management institutes of India", Benchmarking: An International Journal, Vol. 26 No. 7, pp. 2188-2204. https://doi.org/10.1108/BIJ-08-2018-0251