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This section describes sustainable development (SD) in relation to infrastructure projects and explains how to evaluate SD. SD is assessed as context-dependent, considering the project’s economic, social, and ecological context. Sustainable road infrastructure projects (SRIP) should encapsulate the complete life cycle from idea to development, functionality, and maintenance. SD should be considered as part of the evaluation process prior to project execution, but it can also serve other functions. Sustainability evaluation must start with project appraisal or evaluation and the earliest stages of project decision-making. Sustainable infrastructure projects (SIPs) are evaluated using a variety of techniques and models, such as cost-benefit analysis (CBA), multi-criteria techniques, ecological and societal impact assessments, ranking techniques, models, and evaluation guidelines. Established SD structures and modelling techniques for infrastructure projects are presented from an SD perspective, with the primary objective of investigating how they operate and determining whether existing models provide an effective method for applying the SD idea into infrastructure development. CBA is a widely used strategy for evaluating alternatives to maximize sociocultural well-being. It is based on the likelihood of costing customer advantages and negative impacts and has been discussed in scholarly articles. The multi-criteria decision analysis (MCDA) approach is an acceptable methodology for addressing complex matters involving high risk, conflicting objectives, different types of information and data, different concerns and points of view, and the representation of complex and evolving biological, ecological, and financial frameworks. It combines many methodologies and offers various advantages over more conventional ways of decision-making and plan development. It should be used to increase community participation and empower partner organizations and should apply several criteria at the same time, including those that are difficult to adjust and quantify. The key difficulty with this strategy is the usage of weightings, which has been sharply criticized by several researchers. Life-cycle assessment (LCA) is an adaptive tool used to assess the ecological effects of a particular action, task, or procedure. It is applied globally to decision-making in numerous fields, including transportation, energy, and water, and has become a typical tool for determining the ecological performance of infrastructure projects. However, it has a few flaws and could benefit from improvements to assess SD with greater precision. It is a fragmented mechanism for assessing the three components of SD, but its incorporation into other evaluation approaches is desirable. The evaluation of societal implications has been conducted using a variety of methods and techniques, but there is currently no standard method for assessing the communal and appropriation consequences of infrastructure initiatives. Social life-cycle assessments (SLCAs) are advancing, but consensus remains a challenge. The Evaluation Partnership and the Centre for European Policy Studies identified several obstacles and challenges to implementing an outstanding societal assessment, such as the term ‘societal impacts’ being potentially overbroad and not adequately defined, and the lack of a suitable method for quantitatively evaluating sociological effects. Additionally, a large section of societal assessments is biased and frequently inconsequential. The chapter discussed the theoretical and methodological stances on sustainable road infrastructure, using current SID concepts and evaluation techniques thoroughly.

Digital technologies (DTs) have emerged as a major driving force, transmuting the ways Supply Chains (SCs) are managed. The integration of DTs in supply chain management (SCM), Digital Supply Chain Management (DSCM), has fundamentally reshaped the SCM landscape, offering new opportunities and challenges for organizations. This chapter provides a comprehensive overview of modern DTs and the way they impact modern SCM. This chapter has twofold objectives. First, it illustrates the major changes that DTs have brought to the supply chain landscape, unraveling their multifaceted implications. Second, it offers readers a deeper and comprehensive understanding of the challenges and opportunities arising from the incorporation of DTs into supply chains. By going through the chapter, readers will be able to have a comprehensive grasp of how DTs are reshaping SCM and how organizations can survive and thrive in the digital age. This chapter commences by shedding light on how DTs have and continue to redefine SCM, improving supply chain resilience, visibility, and sustainability in an increasingly complex and interconnected world. It also highlights the role of DTs in enhancing SC visibility, agility, and customer-centricity. Furthermore, this chapter briefly highlights the challenges related to the adoption (pre and post) of DTs in SCM, elucidating on issues related to talent acquisition, data security, and regulatory compliance. It also highlights the ethical and societal implications of this digital transformation, emphasizing the significance of responsible and sustainable practices. This chapter, with the help of three cases, illustrates how the adoption of DTs in SC can impact the various SC performance indicators.

This chapter investigates the potential of integrating multiple criteria decision-making (MCDM) techniques with decision support systems of digital supply chain management (DSCM) to achieve optimal outcomes. Digital supply chain (DSC) employs digital technologies (DTs) such as artificial intelligence (AI), Internet of Things (IoT), and big data analytics to provide extensive datasets and valuable insights pertaining to supply chain operations. MCDM techniques employ these realizations to facilitate informed decision-making through the assessment of multiple competing criteria. Usually MCDM approaches are used in the academic research with comparatively lesser application in industry. We argue that MCDM methodologies can play an instrumental role in DSCM, specifically in the areas of supplier selection, demand forecasting, and inventory management. Nevertheless, the integration of MCDM like AHP, ANP, DEMATEL, etc., with decision support systems presents several challenges, including concerns regarding the quality of data and the intricate task of assigning weights to various factors.

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.

Climate change and digitisation are unquestionably the two defining features of this era. Both present immense challenges with unimaginable consequences for humankind while promising enormous rewards for those who can adequately address their adverse effects. These two critical factors must be considered while establishing strategies for the businesses' future operations. Hence, post-pandemic, especially with the rise of online commerce, packages and documents are delivered around the globe nearly every day, propelling the logistics industry's growth. This is not the critical challenge in logistics, the issue of sustainability, particularly as the returns are increasing exponentially, leading to a significant impact on transportation is and its reliance on fossil fuel has made it a prime target for society's growing environmental concerns. Thus, real-time visibility, collaboration and integration in reverse logistics (RL) are imperative for business sustainability. The most applicable Industry 4.0 technologies in RL are the Internet of Things (IoT), cloud computing, blockchain and digital twin that enable the defragmentation of the RL market.

This chapter analyses the technological impact of Industry 4.0 on RL. This research investigates the challenges faced by the logistics industry in the context of sustainability and how digital transformation can bring many potential benefits across the entire value chain. This chapter also presents a guidance for a framework based on the literature review that tends to favour the development of elastic logistics, implying improved company responsiveness to market conditions. The study contributes to the body of literature and the establishment of the framework for planning on the application of various Industry 4.0 technologies in developing eco-friendly and sustainable reverse logistics framework.

Collegiality is the modus operandi of universities. Collegiality is central to academic freedom and scientific quality. In this way, collegiality also contributes to the good functioning of universities’ contribution to society and democracy. In this concluding paper of the special issue on collegiality, we summarize the main findings and takeaways from our collective studies. We summarize the main challenges and contestations to collegiality and to universities, but also document lines of resistance, activation, and maintenance. We depict varieties of collegiality and conclude by emphasizing that future research needs to be based on an appreciation of this variation. We argue that it is essential to incorporate such a variation-sensitive perspective into discussions on academic freedom and scientific quality and highlight themes surfaced by the different studies that remain under-explored in extant literature: institutional trust, field-level studies of collegiality, and collegiality and communication. Finally, we offer some remarks on methodological and theoretical implications of this research and conclude by summarizing our research agenda in a list of themes.

Economic costs and benefits are at the core while taking decision to adopt digitalization in the supply chain. The present chapter provides an in-depth exploration of the economic dimensions of digital supply chain management (DSCM) adoption in a firm. Drawing from a diverse source of literature, this chapter discusses the effect of economic outlook on DSCM adoption, the economic benefits of DSCM adoption and costs associated with it, and economic analysis and evaluation methodologies. The chapter also shares the case studies illustrating the real-world implications of economic considerations within DSCM initiatives. The chapter highlights how changing international socioeconomic and political dynamics can influence businesses across the globe. By analyzing the impacts of evolving market trends, changing consumer preferences, and geopolitical tensions, organizations can considerably forecast the possible impacts of these macroeconomic forces adeptly. The chapter also undertakes discussion on the economic cost and benefits associated with DSCM adoption. The economic analysis helps understand that the expected benefits outweigh economic costs, substantiating the economic viability of DSCM projects. The chapter concludes by discussing the examples of some real-world companies, highlighting how organizations have successfully applied economic analyses to their DSCM initiatives. This also highlights as to how showcasing how detailed economic assessments can justify substantial investments, deliver operational efficiencies, and reshape industries.

The theory of third order inference is a theory of how cultural beliefs influence individuals' decisions under conditions of interdependence and uncertainty. In this study, I build on prior work extending the theory to the role of third order information on social trust in public goods dilemmas. Namely, I argue that when second order information on the beliefs of those relevant to the group task are present, this information should influence decision-making over first and third order.

I test this argument in an experimental public goods game. After measuring first order social trust, participants are randomly sorted into one of four conditions – two that pair third and second order information on social trust as parallel and two that pair them as in conflict.

The results suggest that in the presence of second order information on social trust, third order information doesn't have an effect on cooperation.

The study extends the theory of third order inference to understanding the role of social trust at the first, second, and third levels in public goods dilemmas. It puts second order information in competition with third order in predicting cooperation. It suggests that resolving the uncertainty over the second order beliefs of a collective is key to preventing inefficient equilibriums when second and third order beliefs conflict.

The globalisation of markets, emerging concepts of sustainable development, and circular economy have defined the boundaries within which organisations must compete and address the needs of key stakeholders. As circumstances change, boundaries are often replaced by the relationships between companies and the communities they serve. Consequently, strategy has become a central aspect of sustainable leadership and the foundation for implementing strategic management in a dynamic system of relationships. Every company is born and grows within social and economic ecosystems. Drawing on the metaphor of biology, ecosystems are described as dynamic interconnections among various elements that influence and foster entrepreneurship. Interconnections between players (such as marketplaces, organisations, governments, and universities) create a flow of expertise, abilities, knowledge, experience, and tangible resources. Economic and social ecosystems involve various actors and components that continuously coexist and interact, leading to the creation of numerous mutual relationships. Consequently, it is crucial for managers to gain a comprehensive understanding of the internal and external environments. Various decision-making tools and strategies can be used to achieve this goal. These tools were developed to assist managers, researchers, and consultants in making informed decisions under complex scenarios. This chapter presents several decision-making strategies and tools, including the Boston Consulting Group (BCG) matrix, General Electric (GE) matrix, Balanced Scorecard (BSC), PEST, PESTEL analysis, and SWOT analysis.

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.