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A secondary research method was used to collect data for this case. The authors have made use of newspaper articles and articles by experts published in the public domain.

This case discusses the dilemma faced by Amazon Prime Video in India regarding content. Amazon Prime Video attained success and rapid growth in India ever since its entry into the Indian over the top (OTT) market in 2016. However, the pursuit of attractive and bold content landed Amazon Prime Video in a legal tangle in India. Amazon Prime Video was accused of hurting the religious and political sentiments of Indians by broadcasting bold shows like Tandaav, Family Man, Mirzapur, Family Man 2, etc. Litigations against Amazon Prime Video were filed in the Indian courts by members of religious and political organizations. Protests and online campaigns on Twitter caught the attention of internet influencers in India. The key dilemma faced by the protagonist in this case is whether to continue streaming attractive content that may be controversial and may occasionally hurt the religious/political sentiments of some Indians or stream only safe content that may be deemed as boring by its young target audience.

Undergraduate and postgraduate students studying marketing management and international business courses in business management and commerce streams can use this case.

Alexa’s integration in rural primary schools has improved the pedagogy and has created an engaging and objective learning environment. This study investigates the integration, with a specific focus on exploring its various aspects. The impact of Alexa’s on students' English vocabulary, comprehension and public speaking are examined. This study aims to provide insights the teachers and highlight the potential of artificial intelligence (AI) in rural education.

This content analysis study explores the use of Alexa in primary education in rural areas of India. The study focuses on the types of the questions asked by the students and examines the pedagogical implications of these interactions. By analyzing the use of Alexa in rural educational settings, this study aims to contribute to our understanding of how voice assistants are utilized as educational tools in underprivileged areas.

Alexa significantly improved students' English vocabulary, comprehension and public speaking confidence. Alexa increased school enrollment and retention. Virtual voice assistants like Alexa may improve pedagogy and help India’s rural education. This study shows AI improves rural education.

The study only covers rural India. Self-reported data and observations may bias the study. The small sample size may underrepresent rural educational institutions in India.

Alexa is used to study rural India’s primary education. Voice assistants in rural education are understudied. The study examines Alexa’s classroom use, student questions, and policy and teacher education implications. AI’s education transformation potential addresses UNESCO’s teacher shortage. This novel study examines how AI can improve rural education outcomes and access.

In a rapidly shifting market, organizations seek more diverse and innovative employee development interventions. Yet, these initiatives may have limited impact without employees’ engagement. This conceptual paper aims to propose self-leadership as a value-added strategy for promoting both individual and organizational development.

The authors conducted a conceptual analysis with three case examples. The cases were purposefully selected, aiming to comprehend how the concept of self-leadership has been applied within organizations and to identify real-life examples where self-leadership has been adopted as an organizational strategy.

This study demonstrates that self-leadership plays a significant role in facilitating human resource development (HRD) initiatives. Specifically, the authors illustrate how self-leadership interventions in companies empower individuals to take charge of their development, aligning personal and organizational goals. When effectively applied, self-leadership strategies positively impact HRD practices in the areas of training and development, organization development and career development, yielding benefits for both employees and employers.

This study addresses knowledge gaps in the emerging field of self-leadership in HRD by providing three companies’ examples of how self-leadership can add value to HRD. The findings offer unique insights into the synergy between self-leadership and HRD, benefiting academics interested in this line of inquiry and HRD practitioners seeking innovative approaches to employee and organizational development.

This research analyses the impact of customers and government regulations on the implementation of socio-environmental practices in certifying organic agricultural products. It explores the dyad’s relationship between the focal company and its suppliers in the application of socio-environmental practices.

This study uses a quantitative methodology through a survey approach, with a sample of 206 agro-extractivists from the acai berry supply chain. The data are evaluated using regression analysis.

The main results reveal that customer pressure positively influences the implementation of social and environmental practices, but suggest a non-significant relationship between government regulations and the impact on environmental practices implementation. Social and environmental practices are positively related to operational performance. A moderating effect of organic certification is found in the relationship between customer pressure and the application of environmental practices.

The main contributions are exploring the use of socio-environmental practices in an emerging economy and organic certification as a moderating variable, revealing an “institutional void” that may hamper the enforcement of government regulations.

The paper argues that the classical Marxist theory of capitalist accumulation is inadequate to understand new forms of capitalism and their accumulation processes determined by “platforms” and “big data”. Big data platforms are shaping the processes of production, labour, the price of products and market conditions. “Digital platforms” and “big data” have become an integral part of the processes of production, distribution and exchange relations. These twin pillars are central to the capitalist accumulation processes. The article argues that the classical Marxist theory of capitalist accumulation is inadequate to understand new forms of capitalism and their accumulation processes determined by “platforms” and “big data”.

As a conceptual paper, this paper follows critical methodological lineages and traditions based on non-linear historical narratives around the conceptualisation, construction and transition of the “Marxist theory of capital accumulation” in the age of platform economy. This paper follows a discourse analysis (Fairclough, 2003) to locate the way in which an artificial intelligence (AI)-led platform economy helps identify and conceptualise new forms of capitalist accumulation. It engages with Jørgensen and Phillips' (2002) contextual and empirical discursive traditions to undertake a qualitative comparative analysis by exploring a broad range of complex factors with case studies and examples from leading firms within the platform economy. Finally, it adopts two steps of “Theory Synthesis and Theory Adaptation” as outlined by Jaakkola (2020) to synthesise, adopt and expand the Marxist theory of capital accumulation under platform capitalism.

This article identifies new trends and forms of data driven capitalist accumulation processes within the platform capitalism. The findings suggest that an AI led platform economy creates new forms of capitalist accumulation. The article helps to develop theoretical understanding and conceptual frameworks to understand and explain these new forms of capital accumulation.

This study builds upon the limited theorisation on the AI and new capitalist accumulation processes. This article identifies new trends and forms of data driven capitalist accumulation processes within platform capitalism. The article helps to understand digital and platform capitalisms in the lens of digital labour and expands the theory of capitalist accumulation and its new forms in the age of datafication. While critiquing the Marxist theory of capitalist accumulation, the article offers alternative approaches for the future.

In this chapter, we explore the ethical dilemmas commonly faced in community development projects, providing guidance for practitioners and policy makers. We delve into various challenges, from resource allocation to managing diverse stakeholder needs, using ethical theories and real-world case studies, including examples from the Ecuadorian Amazon Rainforest, Haiti Earthquake relief, and an Indigenous education program in Australia. We emphasize the importance of ethical decision-making, showcasing the potential impacts of choices on communities and individuals. Practical strategies are presented to maintain ethical integrity, such as transparent communication and accountability mechanisms, enabling stakeholders to navigate dilemmas with sensitivity and uphold ethical standards. This chapter serves as a valuable guide for those involved in community development, fostering sustainable and equitable initiatives that empower communities and drive positive transformation.

Given the critical role of user-generated content (UGC) in e-commerce, exploring various aspects of UGC can aid in understanding user purchase intention and commodity recommendation. Therefore, this study investigates the impact of UGC on purchase decisions and proposes new recommendation models based on sentiment analysis, which are verified in Douban, one of the most popular UGC websites in China.

After verifying the relationship between various factors and product sales, this study proposes two models, collaborative filtering recommendation model based on sentiment (SCF) and hidden factors topics recommendation model based on sentiment (SHFT), by combining traditional collaborative filtering model (CF) and hidden factors topics model (HFT) with sentiment analysis.

The results indicate that sentiment significantly influences purchase intention. Furthermore, the proposed sentiment-based recommendation models outperform traditional CF and HFT in terms of mean absolute error (MAE) and root mean square error (RMSE). Moreover, the two models yield different outcomes for various product categories, providing actionable insights for organizers to implement more precise recommendation strategies.

The findings of this study advocate the incorporation of UGC sentimental factors into websites to heighten recommendation accuracy. Additionally, different recommendation strategies can be employed for different products types.

This study introduces a novel perspective to the recommendation algorithm field. It not only validates the impact of UGC sentiment on purchase intention but also evaluates the proposed models with real-world data. The study provides valuable insights for managerial decision-making aimed at enhancing recommendation systems.

It is important to implement artificial intelligence (AI) because it can simplify and solve complex problems faster than humans. Because AI learns about people and their behavior from the first purchase, AI marketing can boost marketing efforts by leveraging data to target extremely precise consumer groups. There is a debate about the efficacy of AI marketing due to the constraints and limits imposed by the system's nature. This chapter presents insights from published studies regarding the relationship of AI with marketing and how AI can affect marketing. A real-world example of Netflix's usage of AI in marketing has been demonstrated. Then, consumer attitudes regarding AI were revealed. Then, several ethical considerations concerning AI were highlighted. Finally, the anticipated future of AI marketing was addressed. This chapter demonstrated the significance of firms implementing AI marketing to get a competitive advantage. Although some of the difficulties mentioned in this study need to be resolved, AI marketing has a bright future. There are ethical concerns about bias and privacy that should be addressed further. This chapter will encourage firms to use AI systems in marketing, and it will open the door to concerns that will need to be investigated academically in the future.

The main goal of this research work is the optimization of the purchasing business process in the Moroccan public sector in terms of transparency and budgetary optimization. The authors have worked on the public university as an implementation field.

The design of the research work followed the design science research (DSR) methodology for information systems. DSR is a research paradigm wherein a designer answers questions relevant to human problems through the creation of innovative artifacts, thereby contributing new knowledge to the body of scientific evidence. The authors have adopted a techno-functional approach. The technical part consists of the development of an intelligent recommendation system that supports the choice of optimal information technology (IT) equipment for decision-makers. This intelligent recommendation system relies on a set of functional and business concepts, namely the Moroccan normative laws and Control Objectives for Information and Related Technology's (COBIT) guidelines in information system governance.

The modeling of business processes in public universities is established using business process model and notation (BPMN) in accordance with official regulations. The set of BPMN models constitute a powerful repository not only for business process execution but also for further optimization. Governance generally aims to reduce budgetary wastes, and the authors' recommendation system demonstrates a technical and methodological approach enabling this feature. Implementation of artificial intelligence techniques can bring great value in terms of transparency and fluidity in purchasing business process execution.

Business limitations: First, the proposed system was modeled to handle one type products, which are computer-related equipment. Hence, the authors intend to extend the model to other types of products in future works. Conversely, the system proposes optimal purchasing order and assumes that decision makers will rely on this optimal purchasing order to choose between offers. In fact, as a perspective, the authors plan to work on a complete automation of the workflow to also include vendor selection and offer validation. Technical limitations: Natural language processing (NLP) is a widely used sentiment analysis (SA) technique that enabled the authors to validate the proposed system. Even working on samples of datasets, the authors noticed NLP dependency on huge computing power. The authors intend to experiment with learning and knowledge-based SA and assess the' computing power consumption and accuracy of the analysis compared to NLP. Another technical limitation is related to the web scraping technique; in fact, the users' reviews are crucial for the authors' system. To guarantee timeliness and reliable reviews, the system has to look automatically in websites, which confront the authors with the limitations of the web scraping like the permanent changing of website structure and scraping restrictions.

The modeling of business processes in public universities is established using BPMN in accordance with official regulations. The set of BPMN models constitute a powerful repository not only for business process execution but also for further optimization. Governance generally aims to reduce budgetary wastes, and the authors' recommendation system demonstrates a technical and methodological approach enabling this feature.

The adopted techno-functional approach enabled the authors to bring information system governance from a highly abstract level to a practical implementation where the theoretical best practices and guidelines are transformed to a tangible application.