Browsing by Author "Nagpal, Ankita"

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Challenges in Adoption of Business Analytics by Small Retailers: An Empirical Study in the Indian Context
(ACM Digital Library, 2023-01) Nagpal, Gaurav; Nagpal, Ankita; Jasti, Naga Vamsi Krishna
Business analytics are transforming every industry in modern times, and the retail sector is not an exception to this. However, the adoption of analytics among small and medium enterprises is quite limited. There is a decent amount of research that has been done in the recent past on identifying the barriers of digitalization for small and medium enterprises. But any such work is missing in the case of analytics. Also, the small retailer, in particular, has been left untouched by the existing research studies when it comes to identifying what prohibits him from the use of analytics for making business decisions. Using a scientific way of data collection from small and mid-sized retailers and from retail industry experts, this study explores why the local retailers are reluctant to use analytics in their work and what can be done to make them more comfortable with the use of analytics. The study also proposes a novel framework for adoption of data analytics and titles it “SPEFSERT Framework.”
Conjoint Analysis based Predictive Analytics to study the employee attitude towards virtual and digital work practices in IoT work environments
(IEEE, 2023-02) Nagpal, Gaurav; Nagpal, Ankita; Jasti, Naga Vamsi Krishna
Covid-19 has been one of the most disruptive pandemics to date. Among the other aspects of disruption, it also disrupted the way people work in organizations. Many of the organizations surrendered their offices for good. However, there are many ill effects of these unconventional work practices also. This research study aims to explore the perception of the employees towards the adoption of Virtual and flexible work practices. The study uses a conjoint analysis approach on different possible Work Practice Profiles, that specify the nature of work (Virtual, offline, or hybrid), nature of work schedule (flexible, or fixed), nature of ownership (individual, or team), and length of working hours (8.5 hours, or 9.5 hours or 10.5 hours). The study finds that the number of working hours is the most important criterion for the employees followed by mode of work, responsibility, and work schedule.
Influence of Time on Efficiency of Indian Capital Markets: Proposition of a Recommendation Engine
(IEEE, 2022) Nagpal, Gaurav; Nagpal, Ankita
This study aims to investigate the influence of the time on the efficiency of Indian capital markets and to propose a recommendation engine that can assist as a feedback system for better investment actions and an efficient capital market. It uses the data comprising fifty stocks listed on the National Stock Exchange over a period from Jan'2015 to Dec'2020 to determine the capital market efficiency and its trends using three techniques: correlation test, residuals test, and runs test. The proposed recommendation engine can generate the implications of contemporary events on the stock prices which can further bring the pricing errors to the surface faster. It can also assist in enhancing the capital market's efficiency which leads to a reduction of arbitrage opportunities and faster removal of pricing anomalies in the market.
Ore Grade Estimation in Mining Industry from petro-physical data using neural networks
(ACM Digital Library, 2022) Nagpal, Gaurav; Nagpal, Ankita; Jasti, Naga Vamsi Krishna
The grade of the ore in mining industry plays a very important role. From the petro-physical data, the grade of the ore can be predicted with reasonable accuracy. However, the existing literature is silent on the techniques of data analytics that can be used for ore-grade estimation with the help of data. The study uses multi-layer neural network perceptron model and neural network regression models for predicting the grade on the basis of Petro-physical data that was collected by doing borehole geophysical survey capturing twenty-one properties of the ore. The research study is able to estimate the grade of the ore with reasonable accuracy using the data.
Predictive Analytics based Modeling of the purchase intention of electric vehicles, and understanding the drivers and risks in their adoption for the people of Tamil Nadu in India
(IEEE, 2023) Jasti, Naga Vamsi Krishna; Nagpal, Gaurav; Nagpal, Ankita
While the adoption of electric vehicles by the World is very important to address the issues of climate change, the rate of adoption of EVs is substantially low due to some of the barriers in their adoption by the population. While several studies have been done in the past using different theories to measure the perception of people towards the electric vehicles in the context of several countries including India, there is no such study for a specific Indian state. Since India is the land of diversity where people with different lifestyles and values co-exist, the state-specific studies can generate more insights for the policy makers, manufacturers and marketers of EVs. Therefore, this study measures the perception of people of Tamil Nadu, one of the prominent automotive manufacturing hubs in the country, towards the adoption of electric vehicles through a structured primary survey. A logistic regression model has also been developed to measure the purchase intention of the potential adopters. The important findings of the study and managerial implications have also been discussed.