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This study examines the performance effect of working capital for a large sample of Indian manufacturing firms in light of supply chain disruption, i.e. the COVID-19 pandemic.

This study is based on secondary data collected from the Prowess database on Indian manufacturing firms listed on the Bombay Stock Exchange (BSE) 500. Panel data regression analyses are used to estimate all models. Moreover, this study has employed robust standard errors to consider for heteroscedasticity concerns.

The results challenge the current notion of working capital investment and reveal that higher working capital has a positive and significant impact on firm performance. Further, it highlights that Indian manufacturing firms suffered financially post-COVID-19 as they significantly lack the working capital to run day-to-day operations.

This research contributes to the scant literature by examining the association between working capital financing and firm performance in light of the COVID-19 pandemic, representing typical developing economies like India.

The purpose of this paper is to identify critical Clean Development Mechanism (CDM) benefits and assess their relative significance in the context of developing economies (e.g. India).

This study has conducted face-to-face (offline/online) discussions with experts in order to identify appropriate criteria and related CDM benefits. Further, this study has used subsequently, using the analytic hierarchy process, a multi-criteria decision-making method and assess the relative significance of benefits of CDM projects.

The results reveal that knowledge and capacity building, technology transfer and social benefits are the most significant CDM benefits, respectively. It is because the knowledge and capacity building tends to disseminate the awareness on CDM benefits among policymakers and stakeholders, thereby, lead to efficient policy-making and encourage effective technology transfer in a way to achieve sustainable economic growth in the host country.

The literature is dominated by studies of CDM projects in Brazil, Mexico, Chile, Africa and China. Within the thousands of CDM projects globally, India only has 1,376 registered CDM projects. To the authors' knowledge, this is one of the first studies that highlight the relative significance of CDM benefits in the context of India. This study will enhance the implementation in the Indian scenario.

Project delivery organizations (PDO) have to develop competitive advantage against new entrants. This study aims to explore the knowledge conversion transactions proposed by Nonaka and Takeuchi (1995) in project phases through the interplay of dynamic and operational capabilities. This study is based on a case study for a PDO in the engineering industry.

This study proposes a model of dynamics between the constructs, and its illustration with a case study of a PDO. The research extends the socialization, externalization, combination and internalization (SECI) model of knowledge management (KM).

This study provides an overview of existing research related to the constructs like applicability of operational and dynamic capabilities, knowledge configuration and knowledge management processes to individual projects delivered by a PDO for its clients. Further, this study provides an overview of the knowledge configuration adopted by an organization and how it helps to build the competitive advantage of an organization.

This study proposes a model for applying the constructs to each of the phases of a project. It then illustrates the knowledge value chain in a PDO in the field of engineering projects with detailed insights into the steps of sensing, seizing and sharing knowledge across the project life cycle.

Project-based firms can use the learnings and create their own SECI model linking the conceptual model of KM and PDO and KM value chain.

In social projects implementation, this conceptual model and process will be helpful in building efficiency and effectiveness.

This case study presents the knowledge value chain in a PDO in the field of engineering projects with detailed insights into the steps of sensing, seizing and sharing knowledge across the project life cycle.

The current natural language processing algorithms are still lacking in judgment criteria, and these approaches often require deep knowledge of political or social contexts. Seeing the damage done by the spreading of fake news in various sectors have attracted the attention of several low-level regional communities. However, such methods are widely developed for English language and low-resource languages remain unfocused. This study aims to provide analysis of Hindi fake news and develop a referral system with advanced techniques to identify fake news in Hindi.

The technique deployed in this model uses bidirectional long short-term memory (B-LSTM) as compared with other models like naïve bayes, logistic regression, random forest, support vector machine, decision tree classifier, kth nearest neighbor, gated recurrent unit and long short-term models.

The deep learning model such as B-LSTM yields an accuracy of 95.01%.

This study anticipates that this model will be a beneficial resource for building technologies to prevent the spreading of fake news and contribute to research with low resource languages.