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Purpose — The main objective of this survey is to collect data for the development of six models in a freight modeling framework. The framework aims to simulate the interactions between shippers and carriers in a freight market.

Methodology/approach — A web-based survey was designed using stated preference methods and experimental auctions, to collect information about shipper and carrier behavior when facing hypothetical situations. Hypothetical situations were constructed using information collected during the survey.

Findings — The modeling results are available for one model, the carrier selection model. In this model, data were collected using stated preference (SP) methods. Nine SP designs were developed using D-designs and an approach to minimize the nonattendance problem. A multinomial probit model was used. No bias was found due to the position of alternatives on the screen, signs of the parameters are as expected, and level of service attributes are relevant in the carrier selection process.

Research limitations/implications — The final response rate was small (about 9%) which is not uncommon in surveys with freight managers. This response rate might result in nonresponse bias of the estimates, which is the subject of future research.

Practical implications — Since freight transport is the output of a freight market, the application of the freight modeling framework presented in this chapter has potential to improve forecasts of freight flows.

Originality/value of chapter — To the best of our knowledge, the survey presented in this chapter consists of an innovative data collection procedure for the development of an original freight modeling framework.

Few industries may be better suited to study the effects of financial distress on managerial decision making than the airline industry. Economic recessions, natural catastrophes, and terrorist attacks are just some of the factors that frequently take a particularly heavy toll on the airline industry. Thus, coping with and overcoming financial distress is a critical aspect of airline management.

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.