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One of the most hyped concepts in the manufacturing industry is ‘Industry 4.0’. The ‘Industry 4.0’ concept is grabbing the attention of every manufacturing industry across the globe because of its immense applications. This phenomenon is an advanced version of Industry 3.0, combining manufacturing processes and the latest Internet of Things (IoT) technologies. The main advantage of this paradigm shift is efficiency and efficacy in the manufacturing process with the help of advanced automated technologies. The concept of ‘Industry 4.0’ is contemporary, so it falls under exploratory study. Therefore, the research methodology is thematic narration grounded on secondary data (online) analysis. In this light, this chapter aims to explain ‘Industry 4.0’ in terms of concepts, theories and models based on the Web of Science (WoS) database. The data include research manuscripts, book chapters, blogs, white papers, news items and proceedings. The study details the latest technologies behind the ‘Industry 4.0’ phenomenon, different business intelligence technologies and their practical implications in some manufacturing industries. This chapter mainly elaborates on Industry 4.0 frameworks designed by (1) PwC (2) IBM (3) Frost & Sullivan.

Purpose: This chapter aims to enlighten the stakeholders on the role and contribution and the issues and challenges of large-scale industries in the wake of the globally unified economies.

Need for the study: Large-scale industries are one of the pillars of any nation and can exercise an immense impact on the numerous facets of the economy of any country. Their role and contribution can benefit all the stakeholders, especially in today’s integrated and interdependent world economies. Hence, there is an absolute need to highlight the issues and challenges and suggest measures to overcome them to promote a resilient global economy.

Methodology: The study gathered data from secondary sources like textbooks, articles, and the internet.

Findings: The findings of the study state that large-scale industries are enormous contributors to employment creation, development of the economy, growth of revenue, research and development (R&D) and innovation, export promotion, and infrastructure. The significant challenges include regulatory compliance, workforce management, economic volatility, political instability, supply chain management, environmental compliance, and technology and infrastructure.

Protectionism, deregulation, public–private partnership, privatisation, and environmental regulation are significant government decisions that affect large-scale industries. The study identifies tax incentives, easy access to financing, and domestic and international trade policies to safeguard large-scale industries’ interests.

Practical implications: Large-scale industries contribute towards the growth of global economic resilience in terms of employment generation, technological advancements, and innovation, fostering international trade in today’s interconnected world.

The liberalization initiative commenced in India from 1991 onwards, replacing the four-decade long import substitution policy. The primary objective was to enhance the role of foreign and private investment, in line with the newly embraced outward-oriented growth model. The government had undertaken several policy initiatives since then, especially to strengthen the manufacturing sector which plays an important role in the economic development of any country. The current study evaluates the effects of the liberalization policy in India on industrial outcomes. Recent studies have found that when firm heterogeneity is present in trade models, reforms will lead to a decrease in the number of firms and a rise in their average size (Melitz, 2003). A dataset of 24 manufacturing industries had been used in the current study. We test empirically whether liberalization had led to a rise in the average size of establishments as stated in the literature. We also attempt to analyze the magnitude of trade costs in terms of the impact of reforms on wages and prices. The empirical analysis based on the difference-in-difference (DID) estimation method shows that on average, trade reforms do not lead to an increase in the real wages and average size of establishments. In addition, prices appear to increase in the long run due to liberalization, with potential ramifications.

As Czech export is widely considered the key to the economic development of Czechia, this chapter explores the relationship between microeconomic profitability among companies in selected TOP10 export industries and the macroeconomic development of the export itself. An investigation was carried out to compare the differences caused by the COVID-19 pandemic. In addition, the comparison is developed according to the size and concentration of ownership among exporting companies. Annual data are obtained from the Bureau van Dijk Orbis database to analyse profitability among 4,283 companies in 10 NACE industries from 2012 to 2021. We have obtained encouraging results, demonstrating that not only those less profitable companies affected export development. However, in general, our results emphasise the importance of those less profitable medium-sized companies for Czech export, within the manufacture of machinery and equipment, and the manufacture of motor vehicles in particular.

Artificial intelligence and augmented reality are two key tools gaining importance in the digital era due to their wide range of applications in different fields and sectors. Industry 4.0 lays emphasis principally on the technology used to help the business remain competitive and sustainable. Sustainable development goals are another important objective of the UN which has laid responsibility for every business to support addressing the global challenges. Purpose: This chapter essentially aims to present the standpoint of artificial intelligence and augmented reality in meeting the sustainability perspective of organizations. Information about the study is gathered through secondary approaches, critically reviewing published literature, scientific reports, and statistical data accessible through business reports, and corporate websites. Further analyzed to present the perspectives of the authors in the study. Globally artificial intelligence market size is predicted to reach $190 billion by 2025, while the funding for startups doubled during the period 2011–2020 globally. The investment in artificial intelligence is going to reach $500 by 2024 resulting in substantial revenue returns. The augmented reality market size could reach $97 billion by 2028. Artificial intelligence today is increasingly used in many fields and is attracting multiple applications in many sectors such as manufacturing, retail, education, IT, and health care and has also contributed to sustainable development the same time by providing energy conservation options, optimization, and reduction of resources, minimizing wastage, offering timely assistance on maintenance schedules, practices which are enabling organizations to reach closer to sustainability and transformation.

Growing environmental challenge awareness among consumers is today's business reality that pushes for sustainable product development. Governments, industries, and consumers' attention are significantly moved from traditional products to eco-friendly product development. Green product development is the future for manufacturing businesses' survival in most markets. Green product development is an emerging phenomenon and, unfortunately, lacks theoretical and empirical research regarding effective organizational policies and practices for green product development. This study aims at filling research gaps towards green product development by highlighting green employee aspects influenced by leadership for sustainable business growth. The study hypothesized relations between the green effect of transformational leadership on green product development as an outcome through green behaviour, green climate, and green innovative creativity. Data was collected from small and medium enterprises (SMEs) of Karachi through a self-administered survey questionnaire. Results revealed significant support for hypothesized relations through the partial least square statistical tool. This study contributes theoretical and empirical advancement in past literature wherein leadership style influences employee behaviour that leads to predict product development from an environmental perspective. Study inferences suggest for visionary green leadership style for sustainable business growth. Limitations of this study regarding other variable inclusiveness, sampling, and geography are potential extensions for further scholarly investigation.

Introduction: Organisations all over the world are experiencing skill gaps. One of the key factors contributing to the shortage of competent workers is the inability to find candidates that fit the profile. Most of the time, the market does not offer what organisations require.

Purpose: This research focuses on skill shortages and labour market rigidity in the information technology (IT) sector. It discusses the impact of labour shortage and strategies to overcome these challenges.

Need of the Study: The study is required to reduce the skill shortage in the IT sector and inflexibility in the labour market.

Methodology: The data are collected from secondary sources, that is, books, journals and other internet sources.

Findings: The labour market volatility is impacted by several external factors leading to rigidity and talent shortages. Different forecasts within the IT industry, manufacturing, media and telecommunications indicate large-scale labour shortages. The growing influence of digitalisation further creates challenges for organisations during the hiring process as the identified skill gaps for IT professionals are also identified.

Practical Implications: Labour market rigidity affects the labour market. Shifts in labour supply and demand do not always impact wages. Methods are suggested on how to reduce the rigidity in the labour market and, in turn, decrease the skill gaps.

Prior family business research has been dominated by an agency theory perspective, narrow definitions of what constitutes family wealth, and a preoccupation with business governance mechanisms to the exclusion of family governance mechanisms. This chapter presents the findings of examining the role of a broader range of governance mechanisms (for the business; for the family) in achieving more comprehensive wealth (economic and non-economic) family business goals in the Australian context. Based on survey responses from around 400 family businesses, the findings from this study show that both family and business governance mechanisms contribute significantly to achieving both the business’s financial performance and the achievement of family-centered goals that are important to the owning family. The results also suggest that the relationship between governance and performance in the family business context is much more complex than that acknowledged in prior research and has implications for both future research and practice.

This chapter of the book aims to introduce multiobjective linear programming (MLP) as an optimum tool to find the best quality engineering techniques (QET) in the main domains of supply chain management (SCM). The importance of finding the best quality techniques in SCM elements in the shortest possible time and at the least cost allows all organizations to increase the power of experts’ analysis in supply chain network (SCN) data under cost-effective conditions. In other words, this chapter aims to introduce an operations research model by presenting MLP for obtaining the best QET in the main domains of SCM. MLP is one of the most determinative tools in this chapter that can provide a competitive advantage. Under goal and system constraints, the most challenging task for decision-makers (DMs) is to decide which components to fund and at what levels. The definition of a comprehensive target value among the required goals and determining system constraints is the strength of this chapter. Therefore, this chapter can guide the readers to extract the best statistical and non-statistical techniques with the application of an operations research model through MLP in supply chain elements and shows a new innovation of the effective application of operations research approach in this field. The analytic hierarchy process (AHP) is a supplemental tool in this chapter to facilitate the relevant decision-making process.

The study's objective was to address the factors impacting the employability attributes of fresh graduates in the Kingdom of Bahrain while considering the various challenges. This study used a quantitative approach which employed the questionnaire tool, and data were collected by using a convenience sampling method. The study sample comprised n = 385 respondents from different industries, including manufacturing, banking and finance, hospitality, healthcare, oil and gas, and real estate sectors of Bahrain. Data gathered from questionnaire were analyzed using Statistical Package for the Social Sciences (SPSS), where descriptive and inferential statistics was used to analyze the data. The results of the study showed for the major Hypothesis 1 that the demographic variables have no significant statistical impact on employment attributes of the new fresh graduates. Moreover, findings suggest that null hypothesis for major Hypothesis 2 has been rejected as applied academic skills and critical thinking skills have no significant impact on employability attribute of fresh graduates in the Kingdom of Bahrain. Null hypothesis for major Hypothesis 3 has been accepted as findings suggest that technology use skills (β ₁ = 0.080), system thinking skills (β ₂ = 0.210), communication skills (β ₃ = 0.402), and information skills (β ₄ = −0.100) which are an antecedent of workplace skills, have significant statistical impact on employability attribute of fresh graduates in Kingdom of Bahrain. Lastly, null hypothesis for major Hypothesis 4 has been accepted as findings suggest that interpersonal skills (β ₅ = 0.229) which are an antecedent of effective relationship have significant statistical impact on employability attribute of fresh graduates in the Kingdom of Bahrain.