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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.

Large-scale farm management in China has developed rapidly in recent years. Large-scale farmers face substantial operating risks, requiring extensive price risk management. However, the agricultural insurance and futures markets in China are incomplete. This study aims to analyze the price-risk-management behaviors of large-scale farmers under incomplete market conditions, with a focus on the interconnections between large scale farmers' subjective preferences (risk preferences, time preferences), liquidity constraints and their price risk management.

The authors construct an analysis framework to reveal the impact of large-scale farmers' risk preferences, time preferences and liquidity conditions on their price-risk-management behaviors under incomplete market conditions. Using data from field surveys and subjective preference experiments involving 409 large-scale grain farmers in China, an empirical analysis was conducted using the bivariate probit model.

The results show that risk-averse farmers will use risk transfer (such as contract farming) and risk diversification (such as multi-period sales) to avoid price risk. However, farmers subject to liquidity constraints and strong time preferences will not choose risk diversification, and the interaction between time preferences and liquidity constraints will strengthen this decision. The larger the farm-management scale, the greater the impact.

The authors focus on rapidly developed large-scale farm management in China. Appropriate price risk management is required by large-scale farmers due to their substantial operating risks. Considering the incomplete conditions of agricultural insurance and futures markets, the results of this study will help identify behavioral characteristics of large-scale farmers and optimize their price-risk-management strategies, further stabilizing large-scale farm management.

This study investigates the impact of large-scale teaching in higher education on students’ preparedness for the workforce within the context of evolving labour market demands, the expansion of higher education and the application of high-impact teaching strategies. It synthesizes perspectives on employer work readiness, the challenges and opportunities of large-scale teaching and strategies for fostering a dynamic academia-industry feedback loop. This multifaceted approach ensures the relevance of curricula and graduates’ preparedness while addressing the skills gap through practical recommendations for aligning teaching methodologies with employer expectations.

The research methodically examines the multifaceted challenges and opportunities inherent in large-scale teaching. It focuses on sustaining student engagement, maintaining educational quality, personalizing learning experiences and cultivating essential soft skills in extensive student cohorts.

This study highlights the critical role of transversal skills in work readiness. It also uncovers that despite its challenges, large-scale teaching presents unique opportunities. The diversity of large student groups mirrors modern workplace complexities, and technological tools aid in personalizing learning experiences. Approaches like peer networking, innovative teaching methods, real-world simulations and collaborative resource utilization enrich education. The importance of experiential learning for augmenting large-scale teaching in honing soft skills is emphasized.

This manuscript contributes to the discourse on large-scale teaching, aligning it with employer expectations and the dynamic requirements of the job market. It offers a nuanced perspective on the challenges and opportunities this educational approach presents, providing insights for crafting engaging and effective learning experiences in large cohorts. The study uniquely integrates experiential learning, co-creation in education and industry-academia feedback loops, underscoring their importance in enhancing student work readiness in large-scale teaching.

Purpose – Chapter 3 analyses if Russia's current problems in agriculture, particularly the slow growth of labour productivity are due primarily to the weak property rights of shareholders stemming from the privatisation and therefore attributed to Russia's failure to implement the family farm project as supposed by the World Bank and many other international institution.

Methodology – To compare the development of labour productivity and farm structure in Russia and the Baltic countries after decollectivisation.

Findings – The comparisons show that the outcomes in Latvia and Lithuania are not in fact any better than in Russia, even though large-scale farms here have largely been replaced by individual farms. They also show that the most likely explanation for the extremely poor results in Lithuania lies in the overly strong property rights of shareholders. Estonia's success compared to Russia's failure cannot be explained away by stronger property rights or family farming, but the reasons lie in the country's more successful application of Soviet farming traditions, the capability of the middle class of former Soviet farms to maintain and modernise large-scale production in capitalist conditions.

Originality/value of chapter – It calls into question one of the basic interpretations presented by World Bank, IMF, OECD and EBRD.

The purpose of this study is to assess the use of digital collections created via the large-scale digitization of archival collections. The large-scale digitization method specifically examined is the reuse of archival description from finding aids to create digital collections that consist mainly of compound digital objects, equivalent to a folder of items, minimally described at the aggregate level. This paper compares Web analytics data for two large-scale digital collections and one digital collection with rich, item-level description.

This study analyzed one year of Web analytics for three digital collections. The main research question of this study is: Are digital collections of minimally described compound objects used less than digital collections of richly described single objects?

This study found that the large-scale digital collections analyzed received less use than the traditional item-level collection, when examined at the item level. At the object level, the large-scale collections did not always receive less use than the traditional item-level collection.

This study is limited to three different digital collections from one institution. Web analytics also represent a limited interpretation of “use.”

This study presents a method for other institutions to assess their own large-scale digitization efforts and contributes to the profession’s understanding of the impact of large-scale digitization.

This paper is unique because it uses Web analytics to compare the use of large-scale digital collections to the use of traditional boutique digital collections.

The Chinese Government relies heavily on increasing farming scale to address the new challenges facing China’s agricultural development. A basket of polices targeted at increasing farming scale has been issued in recent years thereby resulting to debates over large-scale farming in China. The purpose of this paper is to summarize and highlight the debate over China’s on-going large-scale farming.

This paper contributes to the policy debates over large-scale farming in China by reviewing the existing literature on the positive and negative impacts of large-scale farming on agricultural development, land productivity, and national food security. This paper overviews the different ideas and findings of studies associated with the topic.

The mainstream literature supports the idea that large-scale farming will address the challenges facing China’s agricultural development, while others argue that it crowds out the benefits of small farmers, and raises concerns over inequalities of resources allocation and income distribution, and undermines social stability. While proponents argue that large-scale farming may lead to rise in land productivity, opponents argue that it may result into decrease in land productivity and national grain output.

This paper is a policy review based on existing works. It organizes the different debates over large-scale farming in China, and highlights the key arguments put forward.

Large-scale projects are the typical delivery model in the engineering and construction industry, with their very own characteristics. Even though well established, only 1 in 1,000 large-scale projects is successful (Flyvbjerg, 2011). A lack of effective supply chain risk management (SCRM) has repeatedly been identified as one of the main causes. While the SCRM body of knowledge seems increasingly well established, a lack of effective methods meeting the specific requirements of large-scale projects can be observed.

This paper presents a structured and prioritized view on the supply chain risk portfolio in this sector: first, the authors identified and categorized the key supply chain risks in the recent literature. Next, the authors surveyed large-scale project managers across multiple industries, mainly coming from the domains of supply chain management and project management. Finally, the authors provide a contextualized risk taxonomy for engineering, procurement and construction (EPC) projects.

The identified risk portfolio deviates from generic projects significantly and shows a very high inherent risk exposure of large-scale projects. In particular, behavioral risks are identified as crucial. Additionally, a bias to considerably underestimate risks at project beginning is found.

The contextualized SCRM taxonomy offers a systematic and structured view on the key supply chain risks in EPC large-scale projects. The identified risks are considerably different in their characteristics compared to generic projects or classical SCRM approaches. The authors thus provide a new perspective on SCRM in this specific setting and complement traditional risk and project risk management techniques.

Barriers exist on large scale assessment when students are not able to perform at potential for a variety of reasons. Accommodations are mostly available for students who meet criteria for diagnosed disability or criteria for the identification of students who have English as a second language. However, knowing that students have diverse needs, accommodations for a few may not be providing appropriate access for all. Options for designing broader universal design for learning (UDL) on large scale assessment, through strategies that are typically restricted to special accommodations, increase access.

In recent decades, infrastructure has continued to develop as an important basis for social development and people's lives. Resource management of these large-scale projects has been immensely concerned because dozens of construction enterprises (CEs) often work together. In this situation, resource collaboration among enterprises has become a key measure to ensure project implementation. Thus, this study aims to propose a systematic multi-agent resource collaborative decision-making optimization model for large projects from a matching perspective.

The main contribution of this work was an advancement of the current research by: (1) generalizing the resource matching decision-making problem and quantifying the relationship between CEs. (2) Based on the matching domain, the resource input costs and benefits of each enterprise in the associated group were comprehensively analyzed to build the mathematical model, which also incorporated prospect theory to map more realistic decisions. (3) According to the influencing factors of resource decision-making, such as cost, benefit and attitude of decision-makers, determined the optimal resource input in different situations.

Numerical experiments were used to verify the effectiveness of the multi-agent resource matching decision (MARMD) method in this study. The results indicated that this model could provide guidance for optimal decision-making for each participating enterprise in the resource association group under different situations. And the results showed the psychological preference of decision-makers has an important influence on decision performance.

While the MARMD method has been proposed in this research, MARMD still has many limitations. A more detailed matching relationship between different resource types in CEs is still not fully analyzed, and relevant studies about more accurate parameters of decision-makers’ psychological preferences should be conducted in this area in the future.

Compared with traditional projects, large-scale engineering construction has the characteristics of huge resource consumption and more participants. While decision-makers can determine the matching relationship between related enterprises, this is ambiguous and the wider range will vary with more participants or complex environment. The MARMD method provided in this paper is an effective methodological tool with clearer decision-making positioning and stronger actual operability, which could provide references for large-scale project resource management.

Large-scale engineering is complex infrastructure projects that ensure national security, increase economic development, improve people's lives and promote social progress. During the implementation of large-scale projects, CEs realize value-added through resource exchange and integration. Studying the optimal collaborative decision of multi-agent resources from a matching perspective can realize the improvement of resource transformation efficiency and promote the development of large-scale engineering projects.

The current research on engineering resources decision-making lacks a matching relationship, which leads to unclear decision objectives, ambiguous decision processes and poor operability decision methods. To solve these issues, a novel approach was proposed to reveal the decision mechanism of multi-agent resource optimization in large-scale projects. This paper could bring inspiration to the research of large-scale project resource management.

Seeks to raise the question of why large‐scale technochange is difficult and often failure‐prone and to attempt to answer this question by viewing technochange as an instance of institutional change and design in which self‐destructive mechanisms are inherently embedded.

In order to explore the complex institutional dynamics of large‐scale technochange the paper uses the exploration/exploitation framework originally developed by March and extended by Lanzara to the study of institution‐building processes in the political domain. The argument is that problems in implementing large‐scale technochange stem from learning dilemmas in the inter‐temporal and inter‐group allocation of material and cognitive resources. The paper uses a case of large‐scale technology in a major US university system to illustrate the institutional perspective on technochange.

It is argued and illustrated that the development and redesign of large‐scale information systems involve both the exploration of alternative institutional arrangements and the exploitation of pre‐existing ones, such that a delicate balance must be struck to overcome incoherences and dilemmas between the two activities.

The proposed framework to understand large‐scale technochange is not examined empirically. The illustration of the framework relies on a single large‐scale system project of a non‐profit organization in the USA. Further empirical work and comparative research on multiple cases are needed.

The paper discusses some sources of the failures of large‐scale technochange and offers three interrelated mechanisms to counteract such failure sources, namely focal points, increasing returns, and bricolage. These counteracting mechanisms may help organizations to effectively deal with the dilemmas of exploration and exploitation in technochange.

This paper fills the gap in understanding the nature of large‐scale technochange, providing an explanation of why it is difficult and failure‐prone and offering some modest proposals for intervention in large‐scale system projects.