Indian Institute of Management Bangalore

About: Indian Institute of Management Bangalore is a education organization based out in Bengaluru, Karnataka, India. It is known for research contribution in the topics: Emerging markets & Context (language use). The organization has 491 authors who have published 1254 publications receiving 23853 citations. The organization is also known as: IIMB.

Employment Transitions of Women in India: A Panel Analysis

Abstract: This study analyses employment transitions of working-age women in India. The puzzling issue of low labour force participation despite substantial economic growth, strong fertility decline and expanding female education in India has been studied in the recent literature. However, no study so far has looked into the dynamics of employment in terms of labour force entry and exit in this context. Using a nationally representative panel dataset, we show that women are not only participating less in the labour force, but also dropping out at an alarming rate. We estimate an endogenous switching model that corrects for selection bias due to initial employment and panel attrition, to investigate the determinants of women’s entry into and exit from employment. We find that an increase in income of other members of the household leads to lower entry and higher exit probabilities of women. This income effect persists even after controlling for the dynamics of asset holding of the household. Along with the effects of caste and religion, this result reveals the importance of cultural and economic factors in explaining the declining workforce participation of women in India. We also explore other individual and household level determinants of women’s employment transitions. Moreover, we find that a large public workfare program significantly reduces women’s exit from the labour force.

Attitudes towards Marketing Practices, Consumerism and Government Regulations: An Exploratory Survey of Consumers in India:

Abstract: The myriad facets of consumerism have been examined by several authors during the last two decades in the context of industrialized nations. However, there is a dearth of research on consumerism in...

Dense Vector Embedding Based Approach to Identify Prominent Disseminators From Twitter Data Amid COVID-19 Outbreak

Abstract: The unprecedented transmission of the Coronavirus COVID-19 across the globe has grown to be a matter of prime concern for researchers, authorities, and healthcare professionals alike. Owing to the unavailability of vaccination, educating people is reckoned to be of utmost importance to mitigate the risk. With a plethora of unstructured data available on social media, it becomes crucial to comprehend information and use it effectively to combat COVID-19. A fine-grained knowledge base could be advantageous in developing a reliable social network for pandemic situations. However, there has been no prior finding related to the identification of disseminators forCOVID-19 and hence, there is a need to build a computationally intelligent system that utilizes the potential of a massive amount of data to disseminate information more effectively. In this work, we gathered Twitter data of 3.2 million unique users, consisting of over 12 million tweets. We divided our work into four parts. Firstly, by employing dense vector embedding, one of the techniques of the neural network, to generate semantically similar keywords. Secondly, we classified the collected data into three awareness categories i.e., information, prevention, and action. Thereafter, we used the statistical physics of complex networks to recognize prominent disseminators w.r.t. the identified categories. Finally, we sub-categorized the prominent disseminators into media, people, and organizations based on their profile information. From the result, we concluded that data generated broadly fall into information and prevention categories, whereas the print media, politicians, and health organizations are the forerunners of the selected prominent disseminators.

An optimal data-splitting algorithm for aircraft sequencing on two runways

Abstract: We study the static aircraft sequencing and scheduling problem (during peak hour) on a two independent runway system both under arrivals only and mixed mode of operations. This problem is formulated as a 0–1 mixed-integer program with the objective of maximizing the total throughput of both runways, taking into account several realistic constraints including safety separation standards, wide time-windows, and constrained position shifting. This NP-hard problem is computationally harder than its single runway counterpart due to the additional runway allocation decisions. Recognising the intractability of peak-traffic instances of this problem by direct application of the MIP formulation, a novel application of data-splitting algorithm (DS-ASP) is proposed to the case of two runways scenario. DS-ASP divides the given set of flights into several disjoint subsets, and then optimises each of them using 0–1 MIP while ensuring the optimality of the entire set. Computational results show a significant reduction in average solution time (by more than 92% in some scenarios) compared to direct use of a commercial solver while achieving optimality in all of the instances. Capable of producing real-time solutions for various peak-traffic instances even with sequential implementation, pleasingly parallel structure further enhances its efficiency and scalability.