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The purpose of this paper is to investigate the impact of Covid-19 on female health workers (FHWs) in Odisha (India). Here, this study discusses the economic hardship of the FHWs during the pandemic time. Additionally, this study also identifies the various socio-economic factors responsible for the increasing mental stress level of FHWs in Odisha.

The study is based on a telephonic survey method. Overall the study has collected 80 samples of FHWs from Odisha (India). Additionally, the study uses a logistic regression model to measure the impact of various socio-economic factors affecting the mental health status of FHWs during the pandemic time.

The findings of this study depict that FHWs working as a Covid warrior during the pandemic time are facing severe economic vulnerability in their life. It is found that though the FHWs have worked tirelessly to save millions of human lives, but irregularity in salary payment and temporary jobs position have caused more mental distress to them. Moreover, the empirical findings have shown that factors such as joint family, social isolation, having an elderly member in their family, duty in Covid ward, staying in staff quarter and temporary job position are responsible for increasing the mental stress of FHWs in Odisha during the Covid-19 pandemic time.

This study is a first attempt to explain the life struggle, economic vulnerability and mental stress of Indian (Odisha) FHWs working during the Covid-19 pandemic period to save millions of human lives.

The purpose of this study is to measure the availability, accessibility and usability of financial products and services in both rural and urban India from 1991 to 2014.

This paper uses principal component analysis (PCA) method to construct financial inclusion index that serves as a proxy variable for indicating the inclusiveness of financial products and services among the rural and urban people. To fulfill this objective, the study proposes separate indexes of financial inclusion for both rural and urban India from 1991 to 2014. The paper uses annual time series data from 1991 to 2014 to construct the rural-urban financial inclusion index. The used data have been collected from the basic statistical returns of Reserve Bank of India and Economic Political Weekly research foundation.

The study inferences that though there is a remarkable increase in financial inclusion in India from 1991 onwards, it does not result in sizeable growth of financial access to rural masses in comparison to urban masses. The rural India does not substantiate an equivalent growth to that of urban India, contrasting a perceptible increase in financial inclusion. The finding of this study will help the researchers and policymakers to understand the status of financial inclusion in the context of both rural and urban India. Furthermore, policymakers can take appropriate policy initiatives to fulfill the financial inclusion gap that exists between rural and urban people. Additionally, the proposed index is easy to compute and can be used to make comparison across countries for further studies.

The present paper attempts to include all possible dimensions (and indicators within a dimension) that have been considered so far by various authors. Therefore, the authors hope that this index will be more indicative and accurate than previous index. Again, the authors propose to use PCA for the first time to assign the weight of factors in the financial inclusion index for rural and urban India separately.

The main purpose of this controller is to carryout irrigation by the farmers with renewable energy resources.

The proposed design includes the Deep learning based intelligent stand-alone energy management system used for irrigation purpose. The deep algorithm applied here is Radial basis function neural network which tracks the maximum power, maintains the battery as well as load system.

The Radial Basis Function Neural Network algorithm is used for carrying out the training process. In comparison with other conventional algorithms, this algorithm outperforms by higher efficiency and lower tracking time without oscillation.

It is little complex to implement the hardware setup of neural network in terms of training process but the work is under progress.

The practical hardware implementation is under progress.

If controller are implemented in a real-time environment, definitely it helps the human-less farming and irrigation process.

If this system is implemented in real-time environment, every farmer gets benefitted.

This case can be used to teach students how to analyze innovative business models, as well as to trace their reasons for success and failure. The following objectives also align with categories in Bloom’s taxonomy (Forehand, 2010), consistent with the keywords underlined. More specifically, this case will enable students to learn the following: First, to analyze the distinctive features of a social commerce business model, and how these differ from a traditional e-commerce model. This objective maps to Discussion Question No. 1. This objective helps students to understand the value proposition of an unfamiliar business model (social commerce platform) and compare it with that of a familiar business model (e-commerce platform). Second, racing the causes for success and failure of a venture, using frameworks from entrepreneurship and strategy. This relates to Discussion Question No. 2. This objective helps students analyze strategic decisions of an entrepreneur in light of available resource constraints and by applying appropriate conceptual frameworks. Third, developing recommendations to help a new venture sustain its business model in the face of severe challenges. Discussion Question No. 3 covers this objective. This objective enables students to debate possible paths that the startup could take. The discussion on possible paths naturally causes students to create sustainable or viable options.

The case describes the challenge facing Vidit Aatrey, the founder and chief executive of Meesho, a social commerce venture headquartered in Bangalore, India, in October of 2022. While Meesho recorded the second-highest sales (by order volume) during India’s festive season, it also recorded layoffs and business closures. While Meesho’s core business of getting resellers to sell through its online platform seemed to be working, its new business ventures, such as expanding into the grocery business and into Indonesia, had failed and resulted in more than 300 layoffs. Meesho was also pressed for funding: valued at US$4.9bn, the global market for venture capital funding had chilled and now demanded profitability, not growth-at-all-costs. Meesho’s cash burn rate was about $40m per month, and Aatrey was hard pressed to come up with options for profitable growth.

This case is intended for students of management at a master’s level in a course on entrepreneurship. At the authors’ institute, this case is used with MBA students in an elective course on entrepreneurship and also in an elective course in general management.

Teaching notes are available for educators only.

CCS 3: Entrepreneurship

The purpose of this paper is to assess the impact of India’s trade liberalization during the late 1990s and 2000s on productivity of manufacturing firms and verify whether the productivity-enhancing impact of reductions in input tariffs was greater than that of output tariff cuts, as found in some earlier studies.

Firm-level (company-level) data drawn from Prowess database are used for the estimation of total factor productivity (TFP) at the firm level, done by using the Levinsohn–Petrin methodology. Econometric models are estimated to explain firm-level TFP. The explanatory variables used are output and input tariff rates and quantitative restrictions on imports at the industry level and firm characteristics such as firm size, export intensity and import intensity. Firm-level panel data for 2002-2010 or for a longer period 1998-2010 are used for the estimation of econometric models. Model estimation is done by applying the fixed-effects model and IV-2SLS, 3SLS estimators and EC2SLS estimators.

Trade liberalization had a significant positive effect on the productivity of Indian manufacturing firms. The lowering of output tariff had a greater beneficial impact on TFP of Indian manufacturing firms than the lowering of tariff on intermediate inputs.

Good deal of care has been taken in the measurement of output and inputs for the purpose of TFP measurement. Two alternative frameworks, gross output and value added, are used. This helps in making a better estimate of the impact of trade liberalization on TFP.