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In recent years, decision-making regarding business growth has attracted the attention of many researchers. Also, considering the importance of scale in startups for their survival and the development of economies, investigating the scalability of startups in emerging markets that are booming, can be useful. Scaling for international business has taken on a new meaning: they must be leaders in both emerging as well as advanced markets; they must be responsive to customers in both departments, which require tremendous innovation and agility; they have to build the competency needed in designing, developing, and marketing the opposite for advanced world markets; and they need to demonstrate rapid decision-making, innovation, and opportunism in delivery to the cost-sensitive underdeveloped markets. As a result, decision-making about the scale of start-ups at the international level plays an important role. The internationalisation of start-up activities is an extremely important and attractive topic among researchers, entrepreneurs, and practitioners. But there is very little research and also projects on the internationalisation of start-up venture activities, particularly after the gain of scaling and exponential growth.

A growing body of literature suggests that creating a just sustainable society will require reorganizing economic arrangements and, in particular, rethinking work. Previous studies have recognized alternative organizations, such as cooperatives and intentional communities, as sites for building more democratic, sustainable models of work. This study contributes a description and analysis of work and sustainability at Twin Oaks Intentional Community, an 80-person income- and resource-sharing commune in Louisa, Virginia. Some measures show that Twin Oaks members live more sustainably in terms of energy consumption than the average US resident. In this article, I investigate the relationship between sustainability and work at Twin Oaks. I find that sustainable work is linked to the following key principles: broadening definitions of work, prioritizing community well-being, and democratizing decision making. In doing so, I contribute to previous literature on work, sustainability, and alternative organizations by suggesting that (1) sustainability in intentional communities is deeply intertwined with systems of work; (2) broadening definitions of work to include social reproductive labour contributes to sustainability; and (3) the democratization of work can further goals of sustainability.

Successful smart cities' implementation will require organisational leadership decision-making competences. The foundation of smart cities is digital technologies; many of these technologies are emerging technologies that require IT skills, which are scarce and will exacerbate the battle for talent between organisations. Filling the talent gap will necessitate global hiring, which has implications for organisational culture, cultural diversity and organisational leadership. Organisational cultural mix is an important contributor to leadership decision-making. However, decision-making is underpinned by trust. Blockchain is an emerging technology that has the potential to engender organisational trust in decision-making and, by extension, in the leadership with the ‘right’ organisational culture. Smart cities will be required to leverage emerging technologies to give business performance a competitive advantage and use emerging technologies’ applications to build a sustainable competitive advantage.

This research navigates the confluence of data analytics, machine learning, and artificial intelligence to revolutionize the management of urban services in smart cities. The study thoroughly investigated with advanced tools to scrutinize key performance indicators integral to the functioning of smart cities, thereby enhancing leadership and decision-making strategies. Our work involves the implementation of various machine learning models such as Logistic Regression, Support Vector Machine, Decision Tree, Naive Bayes, and Artificial Neural Networks (ANN), to the data. Notably, the Support Vector Machine and Bernoulli Naive Bayes models exhibit robust performance with an accuracy rate of 70% precision score. In particular, the study underscores the employment of an ANN model on our existing dataset, optimized using the Adam optimizer. Although the model yields an overall accuracy of 61% and a precision score of 58%, implying correct predictions for the positive class 58% of the time, a comprehensive performance assessment using the Area Under the Receiver Operating Characteristic Curve (AUC-ROC) metrics was necessary. This evaluation results in a score of 0.475 at a threshold of 0.5, indicating that there's room for model enhancement. These models and their performance metrics serve as a key cog in our data analytics pipeline, providing decision-makers and city leaders with actionable insights that can steer urban service management decisions. Through real-time data availability and intuitive visualization dashboards, these leaders can promptly comprehend the current state of their services, pinpoint areas requiring improvement, and make informed decisions to bolster these services. This research illuminates the potential for data analytics, machine learning, and AI to significantly upgrade urban service management in smart cities, fostering sustainable and livable communities. Moreover, our findings contribute valuable knowledge to other cities aiming to adopt similar strategies, thus aiding the continued development of smart cities globally.

Education transmits knowledge and abilities that are essential for any society’s socioeconomic progress. It improves the quality of life by providing both individuals and civilizations with several advantages. In this regard, women’s education is critical as they spend longer time with children than men. Despite the fact that education plays an immense role in overall wellbeing of society, in India, there are fewer educational opportunities available to young women than to young men. The gender bias that persists in Indian education and professional training is evident even in the richest countries in the world. In this regard, this study focuses on this intergenerational transmission of knowledge from the mother to her progenies in Karimganj District of Assam using the data from the household level primary survey. This study examines mothers’ educational profiles and ascertains the extent to which their education influence their children’s educational attainments. The results show that woman’s education has a direct role in enhancing the overall welfare of children and, thus, indirectly improves the economic status of the family. The results provide definite causal connections between a mother’s education and children’s educational attainment.

Introduction: Numerous decision-making situations are faced in education where Artificial Intelligence may be prevalent as a decision-making support tool to capture streams of learners’ behaviours.

Purpose: The purpose of the present study is to understand the role of AI in student performance assessment and explore the future role of AI in educational performance assessment.

Scope: The study tries to understand the adaptability of AI in the education sector for supporting the educator in automating assessment. It supports the educator to concentrate on core teaching-learning activities.

Objectives: To understand the AI adaption for educational assessment, the positives and negatives of confidential data collections, and challenges for implementation from the view of various stakeholders.

Methodology: The study is conceptual, and information has been collected from sources comprised of expert interactions, research publications, survey and Industry reports.

Findings: The use of AI in student performance assessment has helped in early predictions for the activities to be adopted by educators. Results of AI evaluations give the data that may be combined and understood to create visuals.

Research Implications: AI-based analytics helps in fast decision-making and adapting the teaching curriculum’s fast-changing industry needs. Students’ abilities, such as participation and resilience, and qualities, such as confidence and drive, may be appraised using AI assessment systems.

Theoretical Implication: Artificial intelligence-based evaluation gives instructors, students, and parents a continuous opinion on how students learn, the help they require, and their progress towards their learning objectives.

Contemporary organisations are data-driven with sophisticated and strong Information Technology (IT) supporting the Business Intelligence (BI) systems. Due to the Industrial Revolution 4.0, businesses are subjected to volatility, uncertainty, complexity, and ambiguity (VUCA). The accuracy and agility of decision making (DM) play a key role in the success of contemporary organisations. Traditional methods of DM, i.e. based on tacit knowledge, are no longer relevant in the constantly altering business scenarios. Innovations in the IT domain have accomplished systems to gather and process business data at an exponential speed. Context-driven analytics along with computation capability and performance-driven visualisation have become an implicit need for businesses. BI systems offer the capabilities of data-driven DM simultaneously allowing organisations to predict the future business scenarios. Qualitative research is conducted in this chapter. In the research, interviews, questionnaires, and secondary data from previous research are used as data source. Case studies are discussed to clarify the business use cases of BI systems and their impact on managerial DM. Theoretical foundations are stated basis a thorough literature review of the available body of knowledge. The current environment demands data-driven DM in an organisation at all levels, i.e. strategic, tactical, and operational. Heterogeneous data sources add unlimited value to the decision support systems (DSSs). The BI systems have become an integral part of the technology landscape and an essential element in managerial DM. Contemporary businesses have deployed BI systems in all the functions.

Purpose: The main aim is to explore and recognize productive ways to create human-centred designs (HCDs) for employee experience (EX). HCD is a concept that prioritizes the needs, preferences, and behaviours of humans using the product or service. EX refers to all interactions an employee has with their employment lifespan – from recruitment to retirement. By taking the HCD approach to EX design, companies can create a work environment tailored to their employees’ needs and preferences.

Design / Methodology: The explorative research design to apply journey maps. By mapping out the employee journey, designers can identify pain points and areas for improvement.

Findings: The findings highlight that artificial intelligence and robotics are core components of designing HCD and can be applied to EX design. By prioritizing EX, companies can attract and retain top talent, increase employee engagement and productivity, and gain a competitive advantage.

Research Limitations: The study is developing and involves detailed insights from different companies, making it difficult and time-consuming to prepare a comprehensive report.

Practical Implications: The findings of the study will add value to other organizations to follow and develop policies and practices that make the employees cherish their work.

Originality: The chapter’s originality lies in providing a comprehensive understanding of HCD and EX. It emphasizes leveraging the strengths of both humans and bots for enhanced workforce experience and business growth. Exploring future automation and technology integration trends adds depth to the chapter’s contribution.

In terms of having the general idea of what the whole book entails, the first chapter gave an insight into all the chapters and what details each chapter holds. This chapter began with an introduction of the book title in relation to the construction industry and sustainable practice in a collaborative manner. In addition, sustainable infrastructure management (SIM) was discussed in relation to construction Industry 4.0 and the management qualities of the technologies incorporated into construction activities. The objective of the book gave the need for SIM in construction with the view of achieving sustainable practices beneficial to both the client and the construction professional.

According to Global Healthcare Security Index 2021, India ranked 66 out of 195 countries, indicating the need and scope for improvement. The Cooperative healthcare system which has been rendering exemplary services is yet to gain visible recognition in India. Given the need for upgrading the health infrastructure in India and providing more affordable health services to the country’s growing population, it would help appreciate the large role that cooperative healthcare can play along with others. This study explores the structure, conduct, and performance of healthcare co-operatives in India, the factors contributing to their success and failure, and the challenges they face. The Health Cooperatives have a strong presence in Kerala and Karnataka and are also coming up in other parts of the country. However, a detailed database of them for public awareness is very limited. The cooperative hospitals can meet the basic requirements of curative treatment in rural and poorly-endowed urban areas. The democratic way in which they function makes them a destination for a financially weaker section. They must retain this feature. The study covers two successful cases which reveal that India needs a more dense healthcare cooperative network. Since cooperative hospitals in tune with the spirit of service run on the principle of being ‘Not-for-Profit’ they need to be supported by the governments more liberally, without however interfering with their governance and administration.