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This chapter provides an overview of the potential use of Intelligent Transport Systems (ITS) and associated artificial intelligence (AI) techniques in the land transport sector in an attempt to achieve related United Nations Sustainable Development Goals (SDGs) targets. ITS applications that have now been extensively tested worldwide and have become part of the everyday transport toolkit available to practitioners have been discussed. AI techniques applied successfully in specific ITS applications such as automatic traffic control systems, real-time image processing, automatic incident detection, safety management, road condition assessment, asset management and traffic enforcement systems have been identified. These methods have helped to provide traffic engineers and transport planners with novel ways to improve safety, mobility, accessibility and efficiency in the sector and thus move closer to achieving the various SDG targets pertaining to transportation.

This chapter offers an overview of the applications of artificial intelligence (AI) in the textile industry and in particular, the textile colouration and finishing industry. The advent of new technologies such as AI and the Internet of Things (IoT) has changed many businesses and one area AI is seeing growth in is the textile industry. It is estimated that the AI software market shall reach a new high of over US$60 billion by 2022, and the largest increase is projected to be in the area of machine learning (ML). This is the area of AI where machines process and analyse vast amount of data they collect to perform tasks and processes. In the textile manufacturing industry, AI is applied to various areas such as colour matching, colour recipe formulation, pattern recognition, garment manufacture, process optimisation, quality control and supply chain management for enhanced productivity, product quality and competitiveness, reduced environmental impact and overall improved customer experience. The importance and success of AI is set to grow as ML algorithms become more sophisticated and smarter, and computing power increases.

Companies are constantly striving for superior customer service that meets consumers' expectations. Products that do not provide consumers with good service quality are unlikely to meet the expectations of consumers. The aim is to maximize customer satisfaction and achieve financial success by closing gaps to provide high-quality service to consumers. Customers use quality of service to choose a service provider. This does not only include the quality of products or service but also the quality of customer service. The five essential elements (5Es) – experience, emotions, exclusivity, execution and engagement – must be used by companies to ensure that their products and services meet defined standards or customer's expectations. The customer's opinion of a service is formed immediately, regardless of whether it is positive or negative. The result of a negative customer experience is negative word of mouth, which would cause loss in business from dissatisfied customers as well as from potential customers who will no longer use the services. Using the gap model for service quality, this study proposes the essential 5Es of service quality. As a result of the synthesis of this current research, the company's service delivery will be improved by identifying weaknesses. The use of these 5Es for the control of service quality and monitoring of quality defects leads to better understanding and reduction of cost.

Due to its high leverage nature, a bank suffers vitally from the credit risk it inherently bears. As a result, managing credit is the ultimate responsibility of a bank. In this chapter, we examine how efficiently banks manage their credit risk via a powerful tool used widely in the decision/management science area called data envelopment analysis (DEA). Among various existing versions, our DEA is a two-stage, dynamic model that captures how each bank performs relative to its peer banks in terms of value creation and credit risk control. Using data from the largest 22 banks in the United States over the period of 1996 till 2013, we have identified leading banks such as First Bank systems and Bank of New York Mellon before and after mergers and acquisitions, respectively. With the goal of preventing financial crises such as the one that occurred in 2008, a conceptual model of credit risk reduction and management (CRR&M) is proposed in the final section of this study. Discussions on strategy formulations at both the individual bank level and the national level are provided. With the help of our two-stage DEA-based decision support systems and CRR&M-driven strategies, policy/decision-makers in a banking sector can identify improvement opportunities regarding value creation and risk mitigation. The effective tool and procedures presented in this work will help banks worldwide manage the unknown and become more resilient to potential credit crises in the 21st century.

Road transport is an important sector of the economy, however, its negative impacts on the environment, human health and sustainability of potential economic growth are significant. Transport externalities tend to be neglected within the market process. The damage generated through significant externalities of road transport in Czechia was identified, based on unique data processing, to reach almost 3.5 billion € annually. This chapter presents an overview of internalization taxes and fees, their current rates and generated receipts. If excise duty is disregarded as fundamentally unsuitable for the internalization of externalities, then the receipts from other applied taxes and fees, particularly the road tax, seem to be insufficient. Although economic growth is encouraged, its form is not sustainable in view of the rising phase of the Kuznets curve and the related irreversible environmental impacts.

The purpose of this chapter is to review the accumulated research on a strategically important intangible asset – organizational reputation – and articulate promising research pathways forward. To do so, I first provide definitional clarity by comparing reputation to the related constructs of status, celebrity, legitimacy, and social approval and highlight the codifiable, cumulative, and beneficial nature of reputation. I then discuss three developments in reputation literature: (1) conceptualizing reputation as a multidimensional, rather than generalized, construct; (2) theorizing about reputational malleability rather than its path dependence; and (3) focusing on the costs of a high reputation in addition to its benefits. Based on these developments and the increasing role of social media in affecting and reflecting stakeholder perceptions, I discuss three pathways for future reputation research. Specifically, I focus on the decrease in credibility of powerful intermediaries and increase in stakeholder empowerment, conceptualization of reputation as a flow rather than a stock, and the role of strategic reticence. My hope is that this chapter will stimulate conceptual and empirical work on the role of reputation in the complex and dynamic era of social media.

An in-depth analysis of how senior managers in a large multinational corporation interpret their social and environmental responsibilities revealed that, notwithstanding formal corporate interpretations, discrepancies persisted in their interpretation of what was expected of them and how to implement it. Two fault lines emerged: (1) an instrumental versus a normative interpretation of corporate societal responsibilities, and (2) a focus on ‘doing less/no harm’ versus ‘doing more good’. This chapter introduces a theoretical framework that combines these fault lines to form four quadrants that each represent a different set of challenges managers face as they commit to improving their organisation’s impact on society. Rather than adjudicate between them, a holistic interpretation of corporate social responsibility (CSR) takes all four types into account. But the four types of challenges differ considerably in nature and thus in the strategic approach that is necessary to deal with them. In this chapter, each quadrant is discussed in detail. What characterises the issues in this quadrant, what mindset, and what strategy are necessary to address them? The chapter concludes with the observation that the framework, and the taxonomy of types of CSR challenges that it brings to the fore, creates greater awareness of how industries are confronted with different sets of challenges and thus need different strategic approaches. A better understanding of these differences may lead to more support, in particular for those managers who work in industries that face a disproportionate share of one particular type of challenges, the ‘nasty trade-offs’.