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The primary teaching objective is to discuss the capital raising efforts of a firm under financial distress. It also provides supporting data to calculate cost of capital, DuPont/modified DuPont values and Altman’s Z-Score that can appropriately be incorporated into the discussion. Case-B provides information and data of the company’s recent performance and to changes in bankruptcy law in India. Overall, this case study provides ample scope to discuss, understand and provide the solution to the following key corporate finance themes as follows: 1. Analyzing accounting statements and examine potential earnings quality issue. 2. Predicting default and bankruptcy using qualitative analysis, financial ratios, traditional and modified DuPont models and Altman’s Z score model. 3. Examining the capital raising efforts of a distressed firm, which has already defaulted on borrowings. 4. To explore the impact of changes in regulation on the turnaround efforts of the firm as well as on the promoters of the firm.

Since 2005, Amtek Auto moved at a breathtaking speed with the goal of reaching $10bn in sales, from the current level of about $1.2bn. The group had acquired more than a dozen companies spending about Rs.5,000cr. ($850m) during this period primarily through borrowed funds. However, the market and business expansion was not happening as expected. The company’s capacity utilization was just about 40% (approx.) during much of this period. The mounting fixed costs of operation and debt servicing grew to the level of unsustainability, led the firm to default on its borrowing. Now the company had to quickly recapitalize itself to run its operations and retain the premier position in auto component industry. The company and its promoters were considering various methods of debt restructuring, asset sale and further equity infusion.

Introductory and elective level corporate finance.

Teaching notes are available for educators only.

CSS 1: Accounting and Finance.

This paper aims to analyse the effect of the investment duration, the overall market condition and the industry to which the investee firm belongs on exit returns realised by venture capital (VC) firms invested in Indian market, using hierarchical regression models.

The study examines the relationship that exist among the variables of interest by analysing all the 210 exits that happened in the Indian VC market over the period 2004–2017 by using analytical tools such as moving averages, hierarchical regressions and pooled ordinary least squares regression.

Exit return has an approximate U-shaped relationship with investment duration, and the turning point in the convex relationship happens around seven to eight years after investment. Returns are weakly related to the market condition, discarding the market timing hypothesis. Relationship patterns are found to be generally unvarying during the time period under study.

The results indicate VC funds in the Indian market tend to exit in a brief time span and gain substantial returns from the immediate exits beyond, which returns start dipping. This points to the illiquidity of the Indian VC market wherein the exits from “lemons” are quite tricky, which make them remain invested for longer durations and eroding the value substantially in the process. VC funds may make rational investment/exit decisions in the Indian market capitalising this knowledge.

This study empirically connects the value creating factors in a VC process to the established theories about the early stage investments and analyse the applicability and relevance of those theories in a market with high growth potential like India.

The rapid increase in analytics is playing an essential role in enlarging various practices related to the health sector. Big Data Analytics (BDA) provides multiple tools to store, maintain, and analyze large sets of data provided by different systems of health. It is essential to manage and analyze these data to get meaningful information. Pharmaceutical companies are accumulating their data in the medical databases, whereas the payers are digitalizing the records of patients. Biomedical research generates a significant amount of data. There has been a continuous improvement in the health sector for past decades. They have become more advanced by recording the patient’s data on the Internet of Things devices, Electronic Health Records efficiently. BD is undoubtedly going to enhance the productivity and performance of organizations in various fields. Still, there are several challenges associated with BD, such as storing, capturing, and analyzing data, and their subsequent application to a practical health sector.

Tremendous measure of data lakes with the exponential mounting rate is produced by the present healthcare sector. The information from differing sources like electronic wellbeing record, clinical information, streaming information from sensors, biomedical image data, biomedical signal information, lab data, and so on brand it substantial as well as mind-boggling as far as changing information positions, which have stressed the abilities of prevailing regular database frameworks in terms of scalability, storage of unstructured data, concurrency, and cost. Big data solutions step in the picture by harnessing these colossal, assorted, and multipart data indexes to accomplish progressively important and learned patterns. The reconciliation of multimodal information seeking after removing the relationship among the unstructured information types is a hotly debated issue these days. Big data energizes in triumphing the bits of knowledge from these immense expanses of information. Big data is a term which is required to take care of the issues of volume, velocity, and variety generally seated in the medicinal services data. This work plans to exhibit a survey of the writing of big data arrangements in the medicinal services part, the potential changes, challenges, and accessible stages and philosophies to execute enormous information investigation in the healthcare sector. The work categories the big healthcare data (BHD) applications in five broad categories, followed by a prolific review of each sphere, and also offers some practical available real-life applications of BHD solutions.

While Big Data offer marketing managers information that is high in volume, variety, velocity, and veracity (the 4Vs), these features wouldn’t necessarily improve their decision-making. Managers would still be vulnerable to confirmation bias, control illusions, communication problems, and confidence issues (the 4Cs). The authors argue that traditional remedies for such biases don’t go far enough and propose a lean start-up approach to data-based learning in marketing management. Specifically, they focus on the marketing analytics component of Big Data and how adaptations of the lean start-up methodology can be used in some combination with such analytics to help marketing managers improve their decision-making and innovation process. Beyond the often discussed technical obstacles and operational costs associated with handling Big Data, this chapter contributes by analyzing the various learning and decision-making problems that can emerge once the 4Vs of Big Data have materialized.

This study aims to investigate the influence of online opinion leadership and opinion seeking the intention to purchase and purchase behaviour of organic food in Saudi Arabia.

This study used an online questionnaire as a method to collect data from Instagram users in Saudi Arabia. Statistical analysis was performed using the SmartPLS to test the research model.

The study demonstrates the links between the perceived benevolence of the opinion leadership through Instagram and purchase behaviour of organic food and between opinion seeking and intention to purchase.

This study provides insights into the favourable impact of opinion leadership and opinion seeking on consumers’ intention to purchase and purchase behaviour for marketers, especially in the organic food sector of a Middle Eastern context.

Prior studies have investigated the impact of opinion leadership and opinion seeking on purchase behaviour, but not within the organic food sector. This study attempts to fill this gap in the literature by providing useful insights to enhance the understanding of the influence of online opinion leadership on purchase behaviour of organic food. This study also makes a valuable contribution to organic food research in Middle East countries, where there is a lack of research on organic food purchase behaviour.

In recent years, there has been a growing importance of sustainability communication and the role of the Internet in contemporary corporate communication that has allowed the diversification of information dissemination tools. Thus, the objective of this study is to determine the quantity and nature of the content of the information related to sustainability disclosed through the corporate website of Portuguese metal mould companies. The results obtained based on the content analysis seem to indicate that the number of metal mould companies that discloses sustainability information is quite low. Those who disclose information are in a very limited way whether in quantity or in relation to the type of information disclosed. Considering the various dimensions of sustainability, the information disclosed about environmental and social aspects is scarce. The focus is on aspects related to the economic dimension, particularly in the areas related to products and services and customers.

With the advent of Big Data, the ability to store and use the unprecedented amount of clinical information is now feasible via Electronic Health Records (EHRs). The massive collection of clinical data by health care systems and treatment canters can be productively used to perform predictive analytics on treatment plans to improve patient health outcomes. These massive data sets have stimulated opportunities to adapt computational algorithms to track and identify target areas for quality improvement in health care.

According to a report from Association of American Medical Colleges, there will be an alarming gap between demand and supply of health care work force in near future. The projections show that, by 2032 there is will be a shortfall of between 46,900 and 121,900 physicians in US (AAMC, 2019). Therefore, early prediction of health care risks is a demanding requirement to improve health care quality and reduce health care costs. Predictive analytics uses historical data and algorithms based on either statistics or machine learning to develop predictive models that capture important trends. These models have the ability to predict the likelihood of the future events. Predictive models developed using supervised machine learning approaches are commonly applied for various health care problems such as disease diagnosis, treatment selection, and treatment personalization.

This chapter provides an overview of various machine learning and statistical techniques for developing predictive models. Case examples from the extant literature are provided to illustrate the role of predictive modeling in health care research. Together with adaptation of these predictive modeling techniques with Big Data analytics underscores the need for standardization and transparency while recognizing the opportunities and challenges ahead.

Drawing on the resource-based theory and dynamic capability view, this paper aims to examine the mechanisms by which business analytics (BA) capabilities (i.e. the effective use of data aggregation, analytics and data interpretation tools) in healthcare units indirectly influence decision-making effectiveness through the mediating role of knowledge absorptive capacity.

Using a survey method, this study collected data from the hospitals in Taiwan. Of the 155 responses received, three were incomplete, giving a 35.84 per cent response rate with 152 valid data points. Structural equation modeling was used to test the hypotheses.

This study conceptualizes, operationalizes and measures the BA capability as a multi-dimensional construct that is formed by capturing the functionalities of BA systems in health care, leading to the conclusion that healthcare units are likely to obtain valuable knowledge through using the data analysis and interpretation tools effectively. The effective use of data analysis and interpretation tools in healthcare units indirectly influence decision-making effectiveness, an impact that is mediated by absorptive capacity.

This study adds values to the literature by conceptualizing BA capabilities in healthcare and demonstrating how knowledge absorption matters when implementing BA to the decision-making process. The mediating role of absorptive capacity not only provides a mechanism by which BA can contribute to decision-making practices but also offers a new solution to the puzzle of the IT productivity paradox in healthcare settings.

The proposed article is aimed at exploring the opportunities, challenges and possible outcomes of incorporating big data analytics (BDA) into health-care sector. The purpose of this study is to find the research gaps in the literature and to investigate the scope of incorporating new strategies in the health-care sector for increasing the efficiency of the system.

Fora state-of-the-art literature review, a systematic literature review has been carried out to find out research gaps in the field of healthcare using big data (BD) applications. A detailed research methodology including material collection, descriptive analysis and categorization is utilized to carry out the literature review.

BD analysis is rapidly being adopted in health-care sector for utilizing precious information available in terms of BD. However, it puts forth certain challenges that need to be focused upon. The article identifies and explains the challenges thoroughly.

The proposed study will provide useful guidance to the health-care sector professionals for managing health-care system. It will help academicians and physicians for evaluating, improving and benchmarking the health-care strategies through BDA in the health-care sector. One of the limitations of the study is that it is based on literature review and more in-depth studies may be carried out for the generalization of results.

There are certain effective tools available in the market today that are currently being used by both small and large businesses and corporations. One of them is BD, which may be very useful for health-care sector. A comprehensive literature review is carried out for research papers published between 1974 and 2021.