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The learning outcomes of this paper are follows: students should be able to understand the complexity related to the provision of safe drinking water for disaster-hit areas and effective solutions to overcome this problem. Also, students should be able to evaluate the need for awareness about post traumas mental health especially in case of disasters and identify how technology can provide answers to such critical issues.

The case represents a unique scenario where the head of an organization has moved away from the financial prospect and invested time and efforts for the provision of safe drinking water to the inaccessible areas and devise strategies for the improvement of disaster relief operations.

Undergraduate and post graduate students.

Teaching notes are available for educators only.

CSS 4: Environmental Management.

The purpose of this paper is to provide more accurate structure that allows the estimation of coronavirus (COVID-19) at a very early stage with ultra-low latency. The machine learning algorithms are used to evaluate the past medical details of the patients and forecast COVID-19 positive cases, which can aid in lowering costs and distinctively enhance the standard of treatment at hospitals.

In this paper, artificial intelligence (AI) and cloud/fog computing are integrated to strengthen COVID-19 patient prediction. A delay-sensitive efficient framework for the prediction of COVID-19 at an early stage is proposed. A novel similarity measure-based random forest classifier is proposed to increase the efficiency of the framework.

The performance of the framework is checked with various quality of service parameters such as delay, network usage, RAM usages and energy consumption, whereas classification accuracy, recall, precision, kappa static and root mean square error is used for the proposed classifier. Results show the effectiveness of the proposed framework.

AI and cloud/fog computing are integrated to strengthen COVID-19 patient prediction. A novel similarity measure-based random forest classifier with more than 80% accuracy is proposed to increase the efficiency of the framework.

The paper aims to study the impact of corporate governance variables on the adoption of accounting conservatism by S&P BSE 500 index firms.

The period for the study is from 2010–2018. The data has been extracted from the BSE website, annual reports of the sample companies and the Prowess IQ database. Panel data methodology has been used to analyse the impact of the corporate governance variables on accounting conservatism. Accounting conservatism is the dependent variable, which has been measured by using the CONACCR (negative accruals) measure and the independent variables include the characteristics of the board of directors and the audit committee.

Overall, the relationship between accounting conservatism and corporate governance indicates a significant impact of corporate governance variables, namely, characteristics of the board of directors and the audit committee, on the accounting conservatism policy of the firm.

This research explores the benefits of conservatism in resolving agency conflict. Very few studies have captured the relationship of individual components of corporate governance with accounting conservatism. Moreover, this study contributes to the literature regarding the influence of corporate governance variables on the extent of conservatism used in accounting records.

The purpose of this paper is to evaluate the satisfaction level of customers using third party logistics (3PL) services in the Indian capital region and its surrounding areas. The American Customer Satisfaction Index (ACSI) model is used as a framework to identify the major drivers of satisfaction and areas requiring immediate attention for provision of better services.

Present study includes an exhaustive review of literature for the identification of enablers for this model. Through iterative and structured discussions, variables related to process, service information and user’s expectations are identified, which are subsequently grouped into four dimensions. A survey method is used to get the primary data for this research from 3PL service users’ organizations in the Delhi and adjoining capital region. ACSI is used to prioritize the most preferred driver of satisfaction.

Enablers related to process involving order processing, order picking, order fulfillment and final decision making stand out as the winners and also other critical areas have been identified.

There is a gap between the services obtained and services expected and information-related complications which lead to unsatisfied customers. The 3PL service providers need to focus on these areas for better business performance and healthy and long-lasting business relationships.

The paper is an attempt to implement a satisfaction model for the 3PL sector from user’s perspective.