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This case study has been prepared after thoroughly studying the Indian wine industry. Secondary data for the value of wine imported in India has been taken from the website of the Ministry of Commerce and Industry, Government of India. Data for GDP per capita for India has been taken from World Development Indicators, World Bank. Data for revenue generation of top five countries have been sourced from Statista Report on Indian wine market. Fictitious names are used for the Winery (Romaniszyn) and the individual (Harish G.) working there.

This case revolves around accounting complexities for starting a winery in India. A new vineyard comes with the challenge of the gestation period (five years), during which it would not generate revenue. Harish G. was adept at business development and had experience working with an Italian winery. He was unaware of how difficult accounting decisions would be, including determining the quantum of funds required, ascertaining different financial options to purchase assets and meeting cash requirements. He knew he would not be able to generate any sales for five years at the newly formed winery; the grapevines imported from New Zealand took that long to reach maturity. Furthermore, given that Harish was starting a new brand of wine, he also faced constraints with attracting new customers.

This case can be used in teaching undergraduate and postgraduate students in the introductory session of a financial accounting course. It is designed to approach basic accounting concepts, permitting the class to discuss and focus on the principal thought process of starting a real business and accounting for the transactions as and when they occur rather than only on mathematical computations. The objective of this case study is attained by involving the students in recording various accounting transactions and decisions the winery’s management must take over the time from early plantation to bottling of the wine. Every decision assesses the students’ comprehension of accounting concepts by making them analyze and resolve the accounting issues raised.

The trend among the financial investors to integrate cryptocurrencies, the very first completely digital assets, in their investment portfolio, has increased during the last decade. Even though cryptocurrencies share certain common characteristics with other investment products, they have their own distinct characteristic features, and the behavior of this asset class is currently being studied by the research scholars interested in this domain.

Using the text mining approach, this article examines research trends in the field of cryptocurrencies to identify prospective research needs. To narrow down to ten topics, the abstracts and the indexed keywords of 1,387 research publications on cryptocurrency, blockchain and Bitcoins published between 2013 and 2022 were analyzed using the topic modeling technique and Latent Dirichlet allocation (LDA).

The findings show a wide range of study trends on various aspects of cryptocurrencies. In the recent years, there have been lots of research and publications on the topics such as cryptocurrency markets, cryptocurrency transactions and use of blockchain in transactions and security of Bitcoin. In comparison, topics such as use of blockchain in fintech, cryptocurrency regulations, blockchain smart contract protocols and legal issues in cryptocurrency have remained relatively underexplored. After using the LDA, this paper further analyzes the significance of each topic, future directions of individual topics and its popularity among researchers in the discussion section.

While similar studies exist, no other work has used topic modeling to comprehensively analyze the cryptocurrencies literature by considering diverse fields and domains.

The learning outcomes of this study are to gain an understanding of the banking regulations and their impact on banking performance, to understand the intermediation role of banks by channelizing depositors’ savings and providing loans to borrowers, to explain an impact of a recent regulatory change in the Indian banking that directly impacts their financial performance, to critically evaluate the different financial ratios to analyze the performance of a bank and to build a DuPont analysis framework for banks.

The case serves as a primer on banking regulations in India and provides insights into banking performance. Banking regulations play an important role in maintaining financial stability, specifically in emerging economies like India. The protagonist of the case is Salil Kumar who presented his internship project to the review committee of Stock Investment Company on April 16, 2021. However, he had to rework and present his final project within seven days on the basis of the feedback received from the committee. Kumar faced the dilemma of bringing together a comparative study across two banks, namely, Industrial Credit and Investment Corporation of India (ICICI Bank) and State Bank of India (SBI) and building a DuPont framework covering the different aspects of banking performance. The case exemplifies the intricate regulatory landscape in India within which banks operate and highlights the recent alterations introduced by the Reserve Bank of India. For instance, the framework for dealing with domestic systemically important banks (D-SIBs) was introduced in 2014 and subsequently adopted in August 2015. The D-SIB framework provides inherent guarantee to large banks such as ICICI Bank and SBI. This ensures government backup in the event of any failure, thereby securing financial stability. The case study is suitable for banking and financial accounting courses taught in postgraduate management programs. Once the case is studied, the students are expected to understand the basics of banking, regulations, impact of regulations on banking performance and financial measures.

The case provides valuable insights into the intricate dynamics of the banking industry, offering a critical perspective for analysis. A well-structured teaching note would serve as a valuable tool for instructors, allowing them to facilitate engaging classroom discussions and effectively guide students toward achieving the desired teaching objectives.

Teaching notes are available for educators only.

CSS 1: Accounting and Finance.

There is a need to predict whether the consumers liked the stay in the hotel rooms or not, and to remove the aspects the customers did not like. Many customers leave a review after staying in the hotel. These reviews are mostly given on the website used to book the hotel. These reviews can be considered as a valuable data, which can be analyzed to provide better services in the hotels. The purpose of this study is to use machine learning techniques for analyzing the given data to determine different sentiment polarities of the consumers.

Reviews given by hotel customers on the Tripadvisor website, which were made available publicly by Kaggle. Out of 10,000 reviews in the data, a sample of 3,000 negative polarity reviews (customers with bad experiences) in the hotel and 3,000 positive polarity reviews (customers with good experiences) in the hotel is taken to prepare data set. The two-stage feature selection was applied, which first involved greedy selection method and then wrapper method to generate 37 most relevant features. An improved stacked decision tree (ISD) classifier) is built, which is further compared with state-of-the-art machine learning algorithms. All the tests are done using R-Studio.

The results showed that the new model was satisfactory overall with 80.77% accuracy after doing in-depth study with 50–50 split, 80.74% accuracy for 66–34 split and 80.25% accuracy for 80–20 split, when predicting the nature of the customers’ experience in the hotel, i.e. whether they are positive or negative.

The implication of this research is to provide a showcase of how we can predict the polarity of potentially popular reviews. This helps the authors’ perspective to help the hotel industries to take corrective measures for the betterment of business and to promote useful positive reviews. This study also has some limitations like only English reviews are considered. This study was restricted to the data from trip-adviser website; however, a new data may be generated to test the credibility of the model. Only aspect-based sentiment classification is considered in this study.

Stacking machine learning techniques have been proposed. At first, state-of-the-art classifiers are tested on the given data, and then, three best performing classifiers (decision tree C5.0, random forest and support vector machine) are taken to build stack and to create ISD classifier.

Although the consumption of luxury brands is significantly on the rise in emerging markets, a significant impetus is needed to boost their growth to achieve desired revenues and profitability. This paper aims to consider social media marketing activities and consumer-brand engagement as key constructs to realize these desired objectives.

The empirical relationships among social media marketing activities, consumer-brand engagement and consumer response have been examined with a sample of 465 respondents.

Social media marketing activities foster engagement among luxury brand consumers, translating into a favorable response. Besides this, informational marketer generated content (MGC) moderates the relationship between social media marketing activities and consumer-brand engagement.

Marketers can use the outcomes of this study to better engage consumers of luxury brands on social media platforms to engender a favorable response.

This paper highlights how consumers of luxury brands are engaged through different marketing activities in the social media context. Also, the moderating role of MGC has been explored.

The present research paper is an attempt to study how COVID-19 can affect the global sourcing practices of various supply chain intermediaries across the demand chain. This study aims to explore and is an attempt to understand the overall impact of COVID-19 on the sustainable operations of the firm such as sourcing, procurement, economic performance, social responsibility, consumption and distributions.

This study uses a quantitative technique using data collected from 708 respondents. Structural equation modeling (SEM) has been applied to test the proposed model and hypothesis.

The findings of the study suggest that sourcing practices, distribution and sustainability considerations of manufacturers, suppliers, distributors and retailers are affected by COVID-19 to a great extent but the pandemic has also led to making supply chain intermediaries understand the changing dynamics of the business scenario which can help them in their own strategic and business evolution.

The current disruptions throughout global delivery chains caused by COVID-19 affect badly, the already poor-performing supply chains. Hence, the present study provides fresh insight on how organizations can limit the ill effects of COVID-19 by safeguarding some of their key sustainable operations in a post-pandemic business scenario.

The present study takes into consideration how core supply functions such as sourcing, distribution and manufacturing and various sustainable operations are disrupted by pandemic and its after-effects. This knowledge base can help business organizations to mitigate such problems/disruptions in the future.

There is a strong need for companies to monitor customer-generated content of social media, not only about themselves but also about competitors, to deal with competition and to assess competitive environment of the business. The purpose of this paper is to help companies with social media competitive analysis and transformation of social media data into knowledge creation for decision-makers, specifically for app-based food delivery companies.

Three online app-based food delivery companies, i.e. Swiggy, Zomato and UberEats, were considered in this study. Twitter was used as the data collection platform where customer’s tweets related to all three companies are fetched using R-Studio and Lexicon-based sentiment analysis method is applied on the tweets fetched for the companies. A descriptive analytical method is used to compute the score of different sentiments. A negative and positive sentiment word list is created to match the word present on the tweets and based on the matching positive, negative and neutral sentiments score are decided. The sentiment analysis is a best method to analyze consumer’s text sentiment. Lexicon-based sentiment classification is always preferable than machine learning or other model because it gives flexibility to make your own sentiment dictionary to classify emotions. To perform tweets sentiment analysis, lexicon-based classification method and text mining were performed on R-Studio platform.

Results suggest that Zomato (26% positive sentiments) has received more positive sentiments as compared to the other two companies (25% positive sentiments for Swiggy and 24% positive sentiments for UberEats). Negative sentiments for the Zomato was also low (12% negative sentiments) compared to Swiggy and UberEats (13% negative sentiments for both). Further, based on negative sentiments concerning all the three food delivery companies, tweets were analyzed and recommendations for business provided.

The results of this study reveal the value of social media competitive analysis and show the power of text mining and sentiment analysis in extracting business value and competitive advantage. Suggestions, business and research implications are also provided to help companies in developing a social media competitive analysis strategy.

Twitter analysis of food-based companies has been performed.

The COVID-19 pandemic has impacted 222 countries across the globe, with millions of people losing their lives. The threat from the virus may be assessed from the fact that most countries across the world have been forced to order partial or complete shutdown of their economies for a period of time to contain the spread of the virus. The fallout of this action manifested in loss of livelihood, migration of the labor force and severe impact on mental health due to the long duration of confinement to homes or residences.

The current study identifies the focus areas of the research conducted on the COVID-19 pandemic. Abstracts of papers on the subject were collated from the SCOPUS database for the period December 2019 to June 2020. The collected sample data (after preprocessing) was analyzed using Topic Modeling with Latent Dirichlet Allocation.

Based on the research papers published within the mentioned timeframe, the study identifies the 10 most prominent topics that formed the area of interest for the COVID-19 pandemic research.

While similar studies exist, no other work has used topic modeling to comprehensively analyze the COVID-19 literature by considering diverse fields and domains.

The circular economy (CE) promotes the recovery of value from waste while also working towards achieving long-term environmentally sustainable goals. The goal of this research is to explore the challenges, opportunities, future scope and green practices that small and medium-sized firms (SMEs) face as they move from the linear economy to the CE.

Based on a structured questionnaire, a survey was conducted with representatives from 163 SMEs in India. The data were analysed using co-variance based structural equation modelling technique.

This research identifies various challenges, including consumer acceptability, worries about awareness, recyclability issues, financial constraints and the absence of a defined management plan for SMEs in adopting the CE. Further, strong management will, innovation, technical up-gradation, training of employees, employee motivation and appropriate guidelines are recognized as essential possibilities for CE implementation.

While there has been some work on CE, no studies have directly compared these efforts with the goal of shifting from a linear economy to a CE. Given the transient nature of many archives, it is critical that the efforts put into them and the opinions of those who work in them be recorded so that communities can benefit from a shared linear economy in making decisions about their own history.

Damage induced during drilling of polymer matrix composites depends upon torque during drilling. Modeling of torque with feed rate and its control becomes imminent for damage free drilling of composite laminates. Therefore, the purpose of this paper is to construct a transfer function between drilling torque and feed rate based upon experiments. Thereafter, the torque is controlled by using PID controller.

This paper presents step-by-step procedure to capture complex drilling dynamics of polymer matrix composites in a mathematical model. A glass fiber reinforced plastic (GFRP) composite laminate is drilled at constant feed rate during experimentation. The corresponding time response of torque is recorded. First order, second order and third order transfer functions between torque and feed rate are identified using system identification toolbox of Matlab®. These transfer functions are then converted into state-space models. Experimental verification is performed on GFRP composite laminate. PID controller is designed using Simulink® to track a given reference torque during drilling of polymer matrix composite. The controller is then validated using different reference torque trajectories.

Good match is observed between torque response from state-space models and experiments. Error analysis based on integral absolute error and integral squared error on experimental and simulated response show that third-order system represents the complex drilling dynamics in a better way than first and second-order systems. PID controller effectively tracks given reference trajectories.

Third-order model between torque and feed rate for drilling of composites not available in literature has been presented. PID controller has previously been applied successfully for drilling of conventional materials, this paper extends implementation of PID torque control for drilling of composites.