Search results

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.

In this study, we examine the impact of audit protection services on individual taxpayer decision making. Audit protection services provide additional support for taxpayers in the event of an audit including preparation and representation. While these services could provide taxpayers with additional confidence, such services could also foster greater reliance on tax software, possibly resulting in riskier tax decisions. Drawing on risk homeostasis theory, we investigate two factors that could affect taxpayer reliance: the amount of taxes owed and the extent of audit protection services. Our results indicate that taxpayers are more likely to rely on tax software prompts when there are full audit protection services and a greater amount of taxes owed. Further, we find that the provision of full audit protection services reduces the likelihood that taxpayers change their tax reporting behavior. Collectively, we provide evidence on taxpayer interactions with tax software.

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.

This final chapter delves into the future of digital supply chain management (DSCM) amid today's dynamic business environment, shaped by technological advancements and factors like automation, artificial intelligence (AI), sustainability, and resilience. It emphasizes the crucial role of digital technologies (DTs) such as AI, the Internet of Things (IoT), Industrial IoT, and the Internet of Everything (IoET), along with blockchain, in revolutionizing supply chain operations. These technologies enable agility, flexibility, efficiency, and responsiveness, crucial for supply chains to proactively adapt to market changes. The chapter explores the trends in DSCs, focusing on real-time data analytics, end-to-end visibility, sustainability, and resilience. It highlights the growing importance of transparency in supply chains, driven by consumer demand for sustainable practices and product origins. DSCM is identified as pivotal for prioritizing sustainability, leading organizations toward green practices. Despite the opportunities in DSCM, challenges like cybersecurity, data management complexities, geopolitical uncertainties, and talent shortages are acknowledged. To overcome these, the chapter stresses strategic foresight in DSCM and the importance of robust process management, risk management, and talent development. The future-readiness of supply chain professionals is discussed, highlighting the need for change management, development of social and deep work skills, collaboration, and ethical practices. The chapter concludes by underscoring the transformative potential of DTs in the digital era, urging organizations to embrace innovation, transparency, and sustainability in their supply chains, recognizing that the future of DSC is an imminent reality.

Industry 4.0 and the digital supply chain (DSC) are changing how things are made and moved around the world. This change is all about how smart technologies like the Internet of Things (IoT), artificial intelligence (AI), and blockchain are making supply chains work better. These tools help companies react faster and more clearly to what's needed. By using these new technologies, businesses can get better at guessing what customers want, keeping the right amount of stock, and quickly adjusting to new market trends. With these advanced technologies, companies can see big improvements, like being able to match supply with demand more closely and change their plans fast when things in the market change. It is really important for businesses to get how these tech tools work together as the world of making and selling things keeps changing. This chapter examines the convergence of traditional supply chain systems with Industry 4.0, focusing on the transformative impact of technologies such as the IoT, AI, and blockchain.

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.