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Business environment, international business management.

The case is suitable for students of the business environment, and of international business management.

The case revolves around the reaction of the Finance Ministry of India on Vodafone's tax case and its implications on FDI and the foreign investors who are investing in India. The core issue is the political risk(s) faced by Vodafone even after having won the tax case in the Supreme Court, the highest judiciary body in India. The Government of India has amended the law to bring the tax into retrospective mode and it signifies the impact of political decisions on business organizations.

The case can aid in understanding the effects of changes in a political system and legal framework on the efficacy of business entities; and the importance of, and intricacies involved in, the formulation of political risk mitigating strategies while entering into new markets. The key learning outcomes are: understanding various types of political risks faced by multinationals; assessing the political risks involved in foreign investments; and appreciating the possible mitigating strategies to handle such risks.

Teaching notes are available, please consult your librarian for access.

To make more smart health-care system, the health-care data should be shared in the secure manner, and it improves health-care service quality. This paper aims to implement a modern decentralized blockchain, safe and easy-to-use health-care technology application in the cloud.

On observing the graph, the convergence analysis of proposed Levy Flight-integrated moth flame optimization method at 80th iteration was 4.59%, 2.80%, 3.316%, 8.92% and 2.55% higher than the traditional models MFO, artificial bee colony (ABC), particle swarm optimization (PSO), moth search algorithm (MSA) and glow worm swarm optimization (GWSO), respectively, for Hungarian data set. Particularly, in best case scenario, the adopted method attains low cost value (5.672671) when compared to all other traditional models such as MFO (5.727314), ABC (5.711577), PSO (5.706499), MSA (5.764517) and GWSO (5.723353).

The proposed method achieved effective performance in terms of key sensitivity, sanitization effectiveness, restoration effectiveness, etc.

The purpose of this paper was to explore the possibility of utilization of Ocimum sanctum Linn (Tulsi) leaf extract as a natural preservative in muscle foods. The products incorporated with Ocimum sanctum leaf extract were assessed for various oxidative stability and storage quality parameters.

The Ocimum sanctum leaf extract was incorporated at 300 mg/kg level in the formulation. Chicken sausages incorporated with Ocimum sanctum leaf extract along with control samples were aerobically packaged in low-density polyethylene pouches and assessed for lipid oxidation, physicochemical, microbiological and sensory characteristics under refrigerated (4 ± 1°C) conditions.

Ocimum sanctum leaf extract showed a significant (p < 0.05) effect on the lipid stability, as the treated products exhibited significantly (p < 0.05) lower thiobarbituric acid reactive substances (mg malonaldehyde/kg) and free fatty acid (% oleic acid) values in comparison to control. A significant (p < 0.05) effect was also observed on the microbiological characteristics of the products, as the treated products showed significantly (p < 0.05) lower values for total plate count, psychrophilic count and yeast and mould count. Significantly, (p < 0.05) higher scores were also observed for various sensory parameters of the treated products.

The paper has demonstrated the use of Ocimum sanctum leaf extract as a potential natural preservative, as it successfully improved the oxidative stability and storage quality of the products during refrigerated (4 ± 1°C) storage and may be commercially exploited as a natural preservative in muscle foods.

This paper aims to evaluate the effect of different fiber sources and tert-butylhydroquinone (TBHQ) on the quality characteristics of chicken harrisa.

An attempt was made to develop fiber-enriched chicken harrisa, a meat-based product, by incorporating optimum level of different fiber sources, viz., oat bran (10 per cent) or wheat bran (10 per cent) or barley bran (5 per cent). The products developed with optimized level of fiber were further treated with TBHQ (200 ppm) and were aerobically packaged in low-density polyethylene pouches along with control and assessed for various storage quality parameters under refrigerated (4 ± 1°C) conditions. The products were evaluated for various physicochemical, microbiological and sensory parameters at regular intervals of 0, 7, 14 and 21 days.

A significant (p < 0.05) increase was observed in the fiber content of the products prepared with oat bran (10 per cent) or wheat bran (10 per cent) or barley bran (5 per cent). TBHQ showed a significant (p < 0.05) effect on the lipid oxidative stability, as the treated products exhibited significantly (p < 0.05) lower thiobarbituric acid-reactive substance (mg malonaldehyde/kg) values for the entire period of storage. No significant (p > 0.05) effect was observed on the microbiological characteristics of the products. Sensory parameters showed significant (p < 0.05) decreasing trend for control as well as TBHQ-treated products throughout the period of storage; however, significantly (p < 0.05) higher scores were observed for treated products in comparison to control.

Fiber-enriched chicken harrisa was developed by incorporating oat bran (10 per cent) or wheat bran (10 per cent) or barley bran (5 per cent) in the formulation. TBHQ successfully improved the lipid oxidative stability and storage quality of fiber-enriched chicken harrisa during refrigerated (4 ± 1°C) storage and may be commercially exploited to improve the storage quality of the product.

Budget hotels are becoming an emerging industry for convenience and affordability, where consumer sentiments are of paramount importance. Tourism has become increasingly dependent on social media and online platforms to gather travel-related information, purchase travel products, food, lodging, etc., and share views and experiences. The user-generated data helps companies make informed decisions through predictive and behavioural analytics.

Design/Methodology/Approach: This study uses text mining, deep learning, and machine learning techniques for data collection and sentiment analysis based on 117,151 online reviews of the customers posted on the TripAdvisor website from May 2004 to May 2019 from 197 hotels of five prominent budget hotel groups spread across India using Feedforward Neural Network along with Keras package and Softmax activation function.

Findings: The word-of-mouth turns into electronic word-of-mouth through social networking sites, with easy access to information that enables customers to pick a budget hotel. We identified 20 widely used words that most customers use in their reviews, which can help managers optimise operational efficiency by boosting consumer acceptability, satisfaction, positive experiences, and overcoming negative consumer perceptions.

Practical Implications: The analysis of the review patterns is based on real-time data, which is helpful to understand the customer’s requirements, particularly for budget hotels.

Originality/Value: We analysed TripAdvisor reviews posted over the last 16 years, excluding the Corona period due to industry crises. The findings reverberate in consonance with the performance improvement theory, which states feed-forward a neural network enhances organisational, process, and individual-level performance in the hospitality industry based on customer reviews.

Purpose: The main aim of the study is to provide a tool for non-financial information in decision-making. We analysed the non-financial data in the annual reports in order to show the usage of this information in financial decision processes.

Need for the Study: Main financial reports such as balance sheets and income statements can be analysed by statistical methods. However, an expanded financial reporting framework needs new analysing methods due to unstructured and big data. The study offers a solution to the analysis problem that comes with non-financial reporting, which is an essential communication tool in corporate reporting.

Methodology: Text mining analysis of annual reports is conducted using software named R. To simplify the problem, we try to predict the companies’ corporate governance qualifications using text mining. K Nearest Neighbor, Naive Bayes and Decision Tree machine learning algorithms were used.

Findings: Our analysis illustrates that K Nearest Neighbor has classified the highest number of correct classifications by 85%, compared to 50% for the random walk. The empirical evidence suggests that text mining can be used by all stakeholders as a financial analysis method.

Practical Implications: Combining financial statement analyses with financial reporting analyses will decrease the information asymmetry between the company and stakeholders. So stakeholders can make more accurate decisions. Analysis of non-financial data with text mining will provide a decisive competitive advantage, especially for investors to make the right decisions. This method will lead to allocating scarce resources more effectively. Another contribution of the study is that stakeholders can predict the corporate governance qualification of the company from the annual reports even if it does not include in the Corporate Governance Index (CGI).

The purpose of this paper is to describes the role of knowledge management at MindTree, a medium-sized Indian information technology firm that has featured among the Global Most Admired Knowledge Enterprises (MAKE) and the best companies to work for.

The paper investigates the relationship between knowledge management and innovation at the company and describes its various knowledge-management initiatives.

The paper explains that the company streamlined the process of discovering what knowledge was lying where and established various policies and processes to encourage information sharing and creativity.

It highlights the role of self-organized knowledge communities, various software tools and the prevailing organizational culture in promoting knowledge exchange and original thinking.

It hints that knowledge management will be an increasingly important source of competitive advantage in the years ahead.

It describes knowledge-management practices that can be used by other companies to foster innovation and business leadership among employees.

Nowadays, the speech emotion recognition (SER) model has enhanced as the main research topic in various fields including human–computer interaction as well as speech processing. Generally, it focuses on utilizing the models of machine learning for predicting the exact emotional status from speech. The advanced SER applications go successful in affective computing and human–computer interaction, which is making as the main component of computer system's next generation. This is because the natural human machine interface could grant the automatic service provisions, which need a better appreciation of user's emotional states.

This paper implements a new SER model that incorporates both gender and emotion recognition. Certain features are extracted and subjected for classification of emotions. For this, this paper uses deep belief network DBN model.

Through the performance analysis, it is observed that the developed method attains high accuracy rate (for best case) when compared to other methods, and it is 1.02% superior to whale optimization algorithm (WOA), 0.32% better from firefly (FF), 23.45% superior to particle swarm optimization (PSO) and 23.41% superior to genetic algorithm (GA). In case of worst scenario, the mean update of particle swarm and whale optimization (MUPW) in terms of accuracy is 15.63, 15.98, 16.06% and 16.03% superior to WOA, FF, PSO and GA, respectively. Under the mean case, the performance of MUPW is high, and it is 16.67, 10.38, 22.30 and 22.47% better from existing methods like WOA, FF, PSO, as well as GA, respectively.

This paper presents a new model for SER that aids both gender and emotion recognition. For the classification purpose, DBN is used and the weight of DBN is used and this is the first work uses MUPW algorithm for finding the optimal weight of DBN model.