Search results

The relationship between airline and airport is complex, fascinating, and wide open for new research endeavors. In Volume 6 of the series, we conducted the analyses of risk-sharing contract between airline and airport from numerical risk balance assessment and incomplete contract theory perspectives based on an interesting real example of risk-sharing contracts, the Noto Airport Load Factor Guarantee Mechanism (LFGM) contract in Japan.

In this chapter, we further advance the analyses of risk-sharing contracts, based on the real example of Noto LFGM contract, from the perspectives of game theory and principal-agent theory. The risk-sharing arrangements, such as LFGM contract, are relevant to the rapidly changing business environment in Asia’s aviation industries.

We conduct a two-stage game analysis. The first phase is the contract negotiation phase and the second phase is the effort-making phase after signing the contract. We show that the two parties can attain a Pareto optimal utility level by bargaining a simple linear risk-sharing contract in the contract negotiation phase based on the equilibrium effort levels in the effort-making phase.

The relationship between airports and airlines is very interesting from an economics perspective, and analysis of this relationship is wide open for new research endeavors. For instance, airport and airline interactions can be viewed as a zero-sum game of deciding, say, airport landing charges, while at the same time both entities have an incentive making a joint effort to enhance their ability to generate passenger demand and to contribute to growing regional economies. Within this theoretical framework, their relationship consists of not only a binary choice of conflict or cooperation, but also suggests the possibility of complex mixtures of conflict and cooperation. While understanding the interdependence of airports and airlines is an important issue in transportation economics, research examining the complexity of airport and airline relationships is relatively new to the field. This chapter contributes to this research area, in part, by introducing one very interesting example of an airport and airline relationship that considers an element of conflict and cooperation. Specifically, this chapter examines the economic consequences of a risk sharing contract. Analysis of the risk sharing contract recognizes the relevance of microeconomic theories, such as contract theory and principal–agent theory and reveals how these concepts can be applied to traditional transport economics. Predictions of risk sharing between airlines and airports using these theories are derived using numerical examples. Findings reveal that the risk-sharing agreement based on the Noto Airport Load Factor Guarantee Mechanism (LFGM) contract enables the airport side and the airline side not only to share the monetary consequences of demand fluctuation, but also to secure air flights from a local airport to Tokyo, to jointly enhance their various demand-inducing efforts, and to increase their utilities in order to meet the common target they set in the contract. With the LFGM contract, both sides have consistently maintained the air transport network in a relatively low demand area for more than 10 years without significant outside financial assistance. The findings from this chapter also contribute to better understanding the complex relationships among aviation entities, to the recognition of importance and potential to design properly the airport and airline contract, and to the advancement of economic and public policy analysis of this sector.

Air cargo was traditionally considered as a by-product of passenger air transport. However, in the last decade a defined strategy for air cargo has gained a key position in the strategies of most combination airlines, contributing largely to the cash and profit levels of these airlines. The global air cargo industry is nowadays a mature industry with over 60 billion USD in direct revenues. The strategic context is, therefore, far beyond the basic entrepreneurial framework in which an emerging and young industry tends to operate. This chapter aims to gain an enhanced insight into the strategies of airlines that transport cargo, either in the bellies of passenger aircraft or in full-freighter aircraft. A Cluster Analysis generates a typology of seven representative clusters of air cargo operators’ strategy models. The typology proposes a spectrum of strategies for air cargo, ranging from the cluster group “Carpet Sellers” up to the “Cargo Stars” cluster. While the former tend to be the small airlines or all-cargo carriers which barely manage to cover their costs with their revenues, the latter are profitable, very large globally operating airlines that focus on both passengers and cargo with passenger and freighter aircraft. Within this spectrum there are five other main strategy groups: the “Basic Cargo Operators,” the “Strong Regionals,” the “Low Cost Low Yielder,” the “Large Passenger Wide-body Operators,” and the “Premium Cargo Operators.” Our findings suggest the existence of superior strategy models that could be defined as “winning strategies” that differ according to airline size.

Purpose: Green computing is a way of using the computer resource in an eco-friendly while maintaining and decreasing the harmful environmental impact. Minimising toxic materials and reducing energy usage can also be used to recycle the product.

Need for the Study: The motivation of the study is to use green computing resources to decrease carbon emissions and their adverse effect on the environment.

Methodology: The study uses a qualitative method of collecting resources and data to address the opportunities, challenges, and future trends in green computing for Sustainable Future Technologies. The study focusses on multiple kinds of cloud computing services collected and executed into single remote servers. The service demand processor offers these services to the client per their needs. The simultaneous requests to access the cloud services, processing and expertly managing these requests by the processors are discussed and analysed.

Findings: The findings suggest that green computing is an upcoming and most promising area. The number of resources employed for green computing can be beneficial for lowering E-waste so that computing can be environmentally friendly and self-sustainable.

Practical Implications: Green computing applies across all industries and service sectors like healthcare, entertainment, tourism, and education. The convergence of technologies like Cloud Computing, AI, and Internet of Things (IoT) is greatly impacting Green Supply Chain Management (GSCM) market.

Male identity and motor-mobility are deeply intertwined across much of the globe but nowhere is this relationship more strongly in evidence than in Africa. On the African continent, road transport work has always appeared, in essence, to be a masculinist domain: it is almost always men who are seen driving commercial vehicles, regulating loading activities in the lorry and bus parks (and now the motorcycle stages), undertaking roadside repairs, vulcanising tyres, and even serving fuel. This does not mean that women are entirely absent from the sector, but their place is commonly peripheral – constrained at least in part by hegemonic norms of femininity that shape women’s self-understandings. They typically supply cooked food, alcohol and sex to male road workers, or take on back-breaking work in the lowliest – and lowest paid – of porterage roles, head-loading goods along the road, carrying materials when assisting men making and mending roads, or loading vehicles. From time to time, women have aspired to infiltrate more lucrative areas of the sector, especially through ownership of commercial vehicles, but their closer engagement with the oily nuts and bolts of the road business remains rare.

This chapter draws on a wide range of published and grey literature and some personal ethnographic research from a diversity of African countries and contexts to examine women’s efforts at engagement in the sector. The discussion spans women’s employment in road transport services (porterage, ticket-selling, taxis, buses, Bus Rapid Transit [BRT] and commercial trucks) and the road construction that supports transport service operations (engineering, planning, contracting, and labouring). The authors pay particular attention to the factors that so often continue to impede women’s progress in these arenas. The concluding section first references COVID-19 and its detrimental impacts on women transport workers’ jobs, then considers the potential for overcoming current barriers and promoting a more central space for women in transport operations, a development that could provide significant benefits across the sector.

Over the past few years, mathematical and computational models of organizations have attracted a great deal of interest in various fields of scientific research (see Lin & Carley, 1993 for review). The mathematical models have focused on the problem of quantifying the structural (mis)match between organizations and their tasks. The notion of structural congruence has been generalized from the problem of optimizing distributed decision-making in structured decision networks (Pete, Pattipati, Levchuk, & Kleinman, 1998) to the multi-objective optimization problem of designing optimal organizational structures to complete a mission, while minimizing a set of criteria (Levchuk, Pattipati, Curry, & Shakeri, 1996, 1997, 1998). As computational models of decision-making in organizations began to emerge (see Carley & Svoboda, 1996; Carley, 1998; Vincke, 1992), the study of social networks (SSN) continued to focus on examining a network structure and its impact on individual, group, and organizational behavior (Wellman & Berkowitz, 1988). Most models, developed under the SSN, combined formal and informal structures when representing organizations as architectures (e.g., see Levitt et al., 1994; Carley & Svoboda, 1996). In addition, a large number of measures of structure and of the individual positions within the structure have been developed (Roberts, 1979; Scott, 1981; Wasserman & Faust, 1994; Wellman, 1991).

The diffusion and adoption (D&A) of innovation propels today's technological landscape. Crisis situations, real or perceived, motivate communities of people to take action to adopt and diffuse innovation. The D&A of innovation is an inherently human activity; yet, artificially intelligent techniques can assist humans in six different ways, especially when operating in fifth generation ecosystems that are emergent, complex, and adaptive in nature.

Humans can use artificial intelligence (AI) to match solutions to problems, design for diffusion, identify key roles in social networks, reveal unintended consequences, recommend pathways for scaling that include the effects of policy, and identify trends for fast-follower strategies. The stability of the data that artificially intelligent systems rely upon will challenge performance; nevertheless, the research in this area has positioned several promising techniques where classically narrow AI systems can assist humans. As a result, human and machine interaction can accelerate the D&A of technological innovation to respond to crisis situations.