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This paper reviews the theoretical and empirical contributions to the proxy contest literature. The theoretical work, to date, suggests that (1) the use of the proxy contest as a method of taking over a corporation depends on its cost relative to the tender offer; (2) the security voting structure and the debt/equity ratio influence the outcome of the proxy contest; and, (3) the value of a proxy contest can be estimated using the principles of option pricing theory. A review of empirical research indicates that (1) firms which are inefficiently managed are more likely to become the target of a proxy fight; (2) wealth gains accrue to shareholders of contested firms during the contest period; and, (3) incumbent management is more likely to succeed in a proxy fight but not necessarily ‘win’.

Immunization is one of the most cost-effective ways to save lives while promoting good health and happiness. The coronavirus disease 2019 (COVID-19) pandemic has served as a stark reminder of vaccines' ability to prevent transmission, save lives, and have a healthier, safer and more prosperous future. This research investigates the sustainable development (SD) of the COVID-19 vaccine supply chain (VSC).

This study investigates the relationship between internal process, organizational growth, and its three pillars of SD environmental sustainability, economic sustainability and social sustainability. Survey-based research is carried out in the hospitals providing COVID-19 vaccines. Nine hypotheses are proposed for the study, and all the hypotheses got accepted. The survey was sent to 428 respondents and received 291 responses from health professionals with a response rate of 68%. For the study, the healthcare professionals working in both private and public hospitals across India were selected.

The structural equation modelling (SEM) approach is used to test the hypothesis. All nine hypotheses are supported. This study examines a link between internal processes and organizational learning and the three sustainability pillars (environmental sustainability, economic sustainability and social sustainability).

This study will help the management and the policymakers to think and adopt SD in the COVID-19 VSC. This paper also implies that robust immunization systems will be required in the future to ensure that people worldwide are protected from COVID-19 and other diseases.

This paper shows the relationship between organizational learning and internal process with environmental sustainability, economic sustainability and social sustainability for the COVID-19. Studies on VSC of COVID-19 are not evident in any previous literature.

The purpose of this study is to explore the impact of other customer's opinion on a service firm and its alliance on the evaluation of the airline by the focal customer by integrating qualitative and quantitative user-generated content. The study also explores the relative importance of core and peripheral attributes in consumer evaluations.

A text mining and natural language processing-based approach was followed to extract insights from the qualitative part of 18,457 consumer reviews, which were later analyzed along with the quantitative information obtained from the reviews using linear regression and logistic regression methods.

The authors found that customer satisfaction and recommendation behavior is formed by own and others' opinion about the airline and alliance. The relative importance of the core and peripheral attributes depends on the psychological distance from the evaluation of the attribute.

The theoretical contribution and managerial implications have been discussed in detail.

It helps in review management strategy, service design strategy and the alliance and partnership strategies of the airlines.

This is the first paper that explores the impact attribute-level evaluations found in prior reviews on the future reviews of customers. It also explores the effect of prior reviews in the context of a service business and its alliances.

Nonperforming assets in any banking system have stressed the economic health of nations. Resultantly, literature has given considerable impetus to predict failures and bankruptcy. Past studies have focused on the outcome of failures, while, there is a dearth of studies focusing on ongoing firms in bad shape. We plug this gap and attempt to identify underlying communication patterns for firms witnessing prolonged underperformance. Using text mining, we extract and analyze semantic, linguistic, emotional, and sentiment-based features in non-numeric communication channels of these poor-performing firms and their peers. These uncovered patterns highlight the use of vocabulary and tone of communication, in correspondence to their financial well-being. Furthermore, using such patterns, we deploy various Machine Learning algorithms to identify loser firm(s) way ahead in time. We observe promising accuracy over a time window of five years. Such early warning signals can be of critical importance to various stakeholders of a firm. Exploration of writing style-related features for any firm would help its investors, lending agencies to assess the likelihood of future underperformance. Firm management can use them to take suitable precautionary measures and preempt the future possibility of distress. While investors and lenders can be benefitted from this incremental information to identify the likelihood of future failures.

Behavioral finance literature has long claimed that internet stock message boards can move markets. In this chapter, the authors study more than 2,000 internet board messages posted across finance message boards in India (Chittorgarh, etc.) for 110 companies that went for initial public offering (IPO) in the last one year. This study has multi-fold objectives. First, the authors try to identify the factors which lead to a discussion on an IPO stock in the message board. Second, the authors identify the factors which differentiate a widely discussed stock from the less discussed one. Next, the authors apply advanced machine learning technique to identify the topics which are discussed in the message board through automatic topic modeling. The methodology used includes a logistic regression model for identifying firm characteristics which leads to a probability of getting stakeholders’ attention and hence more discussion. The authors also use advanced topic modeling techniques to identify topics of discussion on the message boards through machine learning. The authors find that larger sized firms, younger firms, firms with low leverage, and non-manufacturing firms get discussed more and the topics of discussion relate to their financial statements, trading strategies, stock behavior, and performance.

In the age of social media, when publishers are vying for consumer attention, click-baits have become very common. Not only viral websites but also mainstream publishers, such as news channels, use click-baits for generating traffic. Therefore, click-bait detection and prediction of click-bait virality have become important challenges for social media platforms to keep the platform click-bait free and give a better user experience. The purpose of this study is to try exploring how the contents of the social media posts and the article can be used to explain and predict social media posts and the virality of a click-bait.

This study has used 17,745 tweets from Twitter with 4,370 click-baits from top 27 publishers and applied econometric along with machine learning methods to explain and predict click-baitiness and click-bait virality.

This study finds that language formality, readability, sentiment scores and proper noun usage of social media posts and various parts of the target article plays differential and important roles in click-baitiness and click-bait virality.

The paper contributes toward the literature of dark behavior in social media at large and click-bait prediction and explanation in particular. It focuses on the differential roles of the social media post, the article shared and the source in explaining click-baitiness and click-bait virality via psycho-linguistic framework. The paper also provides explanability to the econometric and machine learning predictive models, thus performing methodological contribution too.

The paper helps social media managers create a mechanism to detect click-baits and also predict which ones of them can become viral so that corrective measures can be taken.

To the best of the authors’ knowledge, this is one of the first papers which focus on both explaining and predicting click-baitiness and click-bait virality.

The authors describe the rationale for key worker services for disabled children, factors to consider in developing such services, the role of key workers, how a key worker service is operating in one authority and the impact it has had for families who received it.

Scheduled rejection of lubricants in central lubrication systems often leads to loss of lubricants as these could be used for a further long period. To avoid such loss, periodic monitoring of lubricants is suggested so as to forecast the time when the lubricant in a particular system of equipment loses its properties and requires to be drained‐off and charged afresh. To assess the remaining useful life of lubricant (RULL) in use, a new method by fourier transform infrared (FTIR) spectroscopy is used. The work is supplemented by a case study on heavy earth moving machinery (HEMM) deployed in Indian mines.

The significant cutting parameters in turning EN 24 steel with carbide tools using MoS2 as a cutting fluid have been evaluated and an equation connecting the significant parameters with surface roughness of the job material has been determined. A typical nomogram has been constructed to determine easily the surface roughness produced on the job when the independent variables are known within the range of experimentation. A comparative study between ordinary cutting fluid and MoS2 as a cutting fluid has been shown. Optimum cutting combinations have been ascertained statistically by the steepest descent method.