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Connected and autonomous mobility may be an imminent game-changing reality, still in its embryonic form, that is set to disrupt a century-long ‘driver-centric’ status quo and recalibrate transport in unprecedented and possibly entirely unexpected ways. Autonomous vehicles (AVs) may have among others, a major impact on sustainability which in an era where concerns about the urgency and magnitude of climate change threats are voiced more and louder than ever before, needs to be a positive one for helping societies to enjoy liveable futures. This might not be an easy task to accomplish, however. This chapter, using a thematically organised narrative review approach, tries to give a well-rounded answer on whether driverless technology can yield sustainability benefits (or not) by looking at all three spheres of sustainability referring to environmental, economic and social implications. Agendas like motor traffic, air pollution, energy consumption, employment dynamics, inclusion, cybersecurity and privacy are all explored, and a conclusion is derived highlighting the need to package automation with connectivity, alternative fuelling and multimodality and building it around public transport (and to a lesser extent sharing service) provision. The road to make driverless transport genuinely sustainable is ‘bumpy’ and ‘uphill’ and requires the development of an appetite not for technology excellence per se, but rather for travel behaviour change. Achieving this needs serious strategic and coordinated multi-stakeholder efforts in terms of pro-active policy reform, user (and transport provider) education and training initiatives, infrastructure investment, business plan development, and living lab experimentation.

The management of consumer privacy has become a critical concern for organizations in the age of artificial intelligence–powered marketing. The impact of data on the market environment has brought both benefits and challenges, with marketers gaining valuable insights but also raising privacy concerns. As artificial intelligence–powered marketing advances, consumer vulnerability increases due to the sensitivity of collected data. This vulnerability leads some consumers to resort to falsifying information, posing a significant threat to the digital economy. Privacy empowerment and customer control play a vital role in addressing these challenges. This chapter explores the influencing factors and ethical considerations surrounding data falsification. It also discusses strategies to mitigate perceived vulnerability through privacy controls and explores the consequences of data breaches and customer vulnerability. The chapter further emphasizes the need for organizations to balance benefits, risks, and customer trust while harnessing the value of customer data. An ethical framework for data privacy marketing audits is proposed to help organizations assess their data practices responsibly and competitively. By integrating personal data protection strategies within an ethical framework, organizations can protect consumer privacy, enhance customer trust, and maintain their competitive edge in the market.

This chapter aims to gain a deeper understanding of the phenomenon of an ultraconservative group’s crisis communication. It delves into the communication strategies and narratives of the Taliban as they interact with the international media, particularly in relation to the women residing in Afghanistan. A qualitative content analysis of the Taliban’s initial press conference, subsequent interviews and statements on the women in Afghanistan after the Kabul takeover in August 2021 was conducted to understand how the group constructed its narrative on women. The findings suggest that the Taliban adopt a coherent communication strategy. Overall, the group seems to construct a positive image of free women in Afghanistan under their governance by representing image repair strategies of denying disadvantages and positioning themselves as supportive of women’s rights, embedded in hero narratives. Through an analysis of the data employed in this research, it transpired that the Taliban lay a special emphasis on a promising future for their home country through the implementation of the principles of human rights, with a special focus on their commitment to women’s rights and the respect they accord to women. With regard to the imposition of restrictions on Afghan women, the group can be seen to adopt an image repair strategy, employed by evading responsibility, coupled with a narrative of blaming foreign forces.

The supply chain is undergoing a significant digital transformation to adapt to the increasingly digitalized and globalized business environment. To remain competitive in this evolving market, businesses must seamlessly integrate digital technologies throughout the supply chain, spanning all stages from procurement to distribution. This chapter delves into models and methodologies critical to digital supply chain (DSC) transformation, with a focus on advanced techniques such as the Internet of Things (IoT), artificial intelligence (AI), blockchain, and data analytics to boost the resilience and agility of supply chain operations. By leveraging practical examples and case studies, the chapter highlights the myriad enhancements digital transformation can introduce across diverse supply chain stages, including sourcing and after-sales service. Additionally, the chapter examines the complexities of cybersecurity, data integrity, and change management within the digital transformation framework, proposing strategies to address these challenges. The insights offered in this chapter will serve as a thorough guide for both practitioners and scholars in the supply chain field, equipping them to adeptly navigate the multifaceted arena of digital transformation.

This study investigates Rokkan's research programme in the light of the differences between case- and variables-based methodologies. Three phases of the research process are distinguished. Studying the way Rokkan actually proceeded in the research within his Europe project, we find that he follows the protocols of case-methodologies such as grounded theory. In the second phase of the research process, however, he constructs variables-based models as tools for his macro-historical comparisons. To get to variables from the sensitizing concepts coded in the first phase, Rokkan defines his variables as close to cases as possible: variables as nominal level typologies, types as variable values. He thus faces two interrelated dilemmas. First, a philosophy of science dissonance: he legitimates his research only with reference to a variable-methodology, while his research is thoroughly case based. Second, a paradox of double coding: using variable-based models in the second phase, the status of the knowledge available in the first phase memos is degraded. Rokkan cannot decide between the two main solutions to these dilemmas: The first solution is to discard his heterogeneous data, instead working only with homogeneous data that opens up to more consistently variables-oriented research. The second solution is to replace the notion of variables/variable values with typology/types, thereby returning to cases, pursuing comparative case reconstructions in the third phase of research. The study concludes in favour of the second solution.

Racism occurs in many ways and varies across countries, evolving and adapting to sociocultural history, as well as contemporary economic, political and technological changes. This chapter discusses the multilevel dimensions of racism and its diverse manifestations across multiracial societies. It examines how different aspects of racism are mediated interpersonally, and embedded in institutions, social structures and processes, that produce and sustain racial inequities in power, resources and lived experiences. Furthermore, this chapter explores the direct and indirect ways racism is expressed in online and offline platforms and details its impacts on various groups based on their intersecting social and cultural identities. Targets of racism are those who primarily bear the adverse effects. However, racism also affects its perpetrators in many ways, including by limiting their social relations and attachments, and by imposing social and economic costs. This chapter thus analyses the many aspects of racism both from targets and perpetrators' perspectives.

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.

The traditional predictor of technical inefficiency proposed by Jondrow, Lovell, Materov, and Schmidt (1982) is a conditional expectation. This chapter explores whether, and by how much, the predictor can be improved by using auxiliary information in the conditioning set. It considers two types of stochastic frontier models. The first type is a panel data model where composed errors from past and future time periods contain information about contemporaneous technical inefficiency. The second type is when the stochastic frontier model is augmented by input ratio equations in which allocative inefficiency is correlated with technical inefficiency. Compared to the standard kernel-smoothing estimator, a newer estimator based on a local linear random forest helps mitigate the curse of dimensionality when the conditioning set is large. Besides numerous simulations, there is an illustrative empirical example.