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This paper aims to test the effectiveness of communication platforms in conveying the importance of sustainability messages focusing on electronic waste (e-waste) recycling. While corporate communication has been explored well, this research explores the influence of communication medium on the shift in attitude and behavioural intention of higher education students.

An experimental design approach was used with quantitative data analysis to address the research questions.

The results revealed that the text form of communication was more effective in conveying the e-waste recycling message. Students demonstrated a significant shift in attitude and call for action when they read the sustainability article instead of watching a video with the same message.

With several universities trying to integrate sustainability in their curriculum, this research provides guidelines on effective communication methods for students. It also sheds light on the choice of platforms that can be used by organisations to reach out to their employees to convey sustainability-related messages.

The paper addresses sustainability communication in a university by exploring the best method of communication. The results open up new conversations on the media richness theory in the context of sustainability.

Bottom of the Pyramid (BOP); social innovation and business modeling.

Undergraduate and graduate level management/business school students. It can be taught in marketing management and entrepreneurship/innovation courses.

LifeSpring Hospitals Pvt. Ltd is an expanding chain of hospitals that provide high quality health care to lower-income women and children across Andhra Pradesh. It is a 50-50 equity partnership between Hindustan Latex Ltd and the Acumen Fund. LifeSpring has demonstrated exceptional management principles, some of them being the most innovative and attractive ones. The entire focus of LifeSpring is on one particular niche: maternal care. Some argue about its strategy of not adopting diversification, but LifeSpring has proved its point by actually turning out to be a profitable business. The strategy of focusing on one niche has led to reduction in cost in terms of specialized doctors and the range of equipment needed to serve. Adding to the strategic strength of LifeSpring, its operations (management) is perfectly aligned with the organization's vision and quality is achieved via highly standardized procedures for maternal care service.

This case will cover two important aspects of BOP and social innovation. MBA students will investigate an innovative business model and apply their analytical skills to analyse the sustainability of the model.

Teaching notes and exercise for class-based discussion.

Strategic management and social innovation

Undergraduate and graduate level management/business school students. It can be taught in strategic management and social innovation courses.

GOONJ is a non–profit organization which has life and dignity for lakhs of people in India over the last decade. It aimed at bringing up clothing as one of the important aspects of human life and make it available for the needy keeping their dignity intact. The case begins with Anshu Gupta, founder of GOONJ thinking deeply about the high–priority meeting to take GOONJ to the next level and scale up the operations of his social innovation. It then tries to bring up the potential problem of clothing and menstrual hygiene in India followed by explanation of the present working model of GOONJ which allows them to manage the operations with 97 paisa per cloth. With the dream of taking GOONJ to the next level and converting it into a nation–wide phenomenon, will the present model work?

This case will cover two important aspects: social innovation process (themes, challenges and implications for practice); and strategic management concepts (stakeholder theory, internal–external factor evaluation).

Teaching notes.

The purpose of this paper is to examine the factors that significantly affect the prediction of fake news from the virality theory perspective. The paper looks at a mix of emotion-driven content, sentimental resonance, topic modeling and linguistic features of news articles to predict the probability of fake news.

A data set of over 12,000 articles was chosen to develop a model for fake news detection. Machine learning algorithms and natural language processing techniques were used to handle big data with efficiency. Lexicon-based emotion analysis provided eight kinds of emotions used in the article text. The cluster of topics was extracted using topic modeling (five topics), while sentiment analysis provided the resonance between the title and the text. Linguistic features were added to the coding outcomes to develop a logistic regression predictive model for testing the significant variables. Other machine learning algorithms were also executed and compared.

The results revealed that positive emotions in a text lower the probability of news being fake. It was also found that sensational content like illegal activities and crime-related content were associated with fake news. The news title and the text exhibiting similar sentiments were found to be having lower chances of being fake. News titles with more words and content with fewer words were found to impact fake news detection significantly.

Several systems and social media platforms today are trying to implement fake news detection methods to filter the content. This research provides exciting parameters from a viral theory perspective that could help develop automated fake news detectors.

While several studies have explored fake news detection, this study uses a new perspective on viral theory. It also introduces new parameters like sentimental resonance that could help predict fake news. This study deals with an extensive data set and uses advanced natural language processing to automate the coding techniques in developing the prediction model.

University selection in higher education is a complex task for aspirants from a decision-making perspective. This study first aims to understand the essential parameters that affect potential students' choice of higher education institutions. It then aims to explore how these parameters or priorities have changed given the impact of the COVID-19 pandemic. Learning about the differences in priorities for university selection pre- and post-COVID-19 pandemic might help higher education institutions focus on relevant parameters in the post-pandemic era.

This study uses a mixed-method approach, with primary and secondary data (university parameters from the website and LinkedIn Insights). We developed a university selector system by scraping LinkedIn education data of various universities and their alumni records. The final decision-making tool was hosted on the web to collect potential students' responses (primary data). Response data were analyzed via a multicriteria decision-making (MCDM) model. Portal-based data collection was conducted twice to understand the differences in university selection priorities pre- and post-COVID-19 pandemic. A one-way MANOVA was performed to find the differences in priorities related to the university decision-making process pre- and post-COVID-19.

This study considered eight parameters of the university selection process. MANOVA demonstrated a significant change in decision-making priorities of potential students between the pre- and post-COVID-19 phases. Four out of eight parameters showed significant differences in ranking and priority. Respondents made significant changes in their selection criteria on four parameters: cost (went high), ranking (went low), presence of e-learning mode (went high) and student life (went low).

The current COVID-19 pandemic poses many uncertainties for educational institutions in terms of mode of delivery, student experience, campus life and others. The study sheds light on the differences in priorities resulting from the pandemic. It attempts to show how social priorities change over time and influence the choices students make.

By the end of this chapter on minors, internet-enabled devices and online shopping behaviour, readers will be able to

• Identify fundamental benefits and harm engendered when minors have unlimited access to internet-enabled devices

• Locate the main catalysts of benefit and harm to minors due to internet usage

• Show how a priori studies have created a rich and balanced narrative in the field of benefits and harm of the internet to minors

• Argue how the benefits outweigh the harm (or vice versa) impacting on minors in unlimited use of the internet

• Develop strategies to enhance the benefits and limit the harm caused by unlimited access to the internet

Identify fundamental benefits and harm engendered when minors have unlimited access to internet-enabled devices

Locate the main catalysts of benefit and harm to minors due to internet usage

Show how a priori studies have created a rich and balanced narrative in the field of benefits and harm of the internet to minors

Argue how the benefits outweigh the harm (or vice versa) impacting on minors in unlimited use of the internet

Develop strategies to enhance the benefits and limit the harm caused by unlimited access to the internet

Technology advancement has changed how banks operate. Modernizing technology has, on the one hand, made it simpler for banks to do their daily business, but it has also increased cyberattacks. The purpose of the study is to to determine the factors that have the most effects on online fraud detection and to evaluate the advantages of AI and human psychology research in preventing online transaction fraud. Artificial intelligence has been used to create new techniques for both detecting and preventing cybercrimes. Fraud has also been facilitated in some organizations via employee participation.

The main objective of the research approach is to guide the researcher at every stage to realize the main objectives of the study. This quantitative study used a survey-based methodology. Because it allows for both unbiased analysis of the relationship between components and prediction, a quantitative approach was adopted. The study of the body of literature, the design of research questions and the development of instruments and procedures for data collection, analysis and modeling are all part of the research process. The study evaluated the data using Matlab and a structured model analysis method. For reliability analysis and descriptive statistics, IBM SPSS Statistics was used. Reliability and validity were assessed using the measurement model, and the postulated relationship was investigated using the structural model.

There is a risk in scaling at a fast pace, 3D secure is used payer authentication has a maximum mean of 3.830 with SD of 0.7587 and 0.7638, and (CE2).

This study focused on investigating the benefits of artificial intelligence and human personality study in online transaction fraud and to determine the factors that affect something most strongly on online fraud detection. Artificial intelligence and human personality in the Indian banking industry have been emphasized by the current research. The study revealed the benefits of artificial intelligence and human personality like awareness, subjective norms, faster and more efficient detection and cost-effectiveness significantly impact (accept) online fraud detection in the Indian banking industry. Also, security measures and better prediction do not significantly impact (reject) online fraud detection in the Indian banking industry.