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The purpose of this paper is to investigate whether factor sensitivities of margins of bank-issued warrants depend on issuers’ credit risk during the period of economic turmoil between January 2008 and June 2010.

Therefore, first, Fama–MacBeth estimations were applied and it was demonstrate that the sensitivities of margins in terms of time to maturity and moneyness vary substantially over time; the average outcomes are similar to the results of classical pooled estimations.

Then, time-series tests were used and it was found that the steepness of the issuers’ credit default swap (CDS) spread curves correlates negatively with the time-to-maturity sensitivities as well as with the explanatory power of Fama–MacBeth estimations.

These findings indicate that the life-cycle hypothesis is weakened when the issuers’ CDS spread curves become steeper.

Thus, this study offers a new approach to gain insights into the role of issuers’ credit risk on price setting behavior.

Destination recommender systems need to become truly human‐centric in their design and functionality. This requires a profound understanding of human interactions with technology as well as human behavior related to information search and decision‐making in the context of travel and tourism. This paper seeks to review relevant theories that can support the development and evaluation of destination recommender systems and to discuss how quantitative research can inform such theory building and testing.

Based on a review of information search and decision‐making literatures, a framework for the development of destination recommender systems is proposed and the implications for the design and evaluation of human‐centric recommender systems are discussed.

A variety of factors that influence the information search and processing strategies that influence interactions with a destination recommender system are identified. This reveals a great need for data‐driven models to inform recommender system processes.

The proposed framework provides a basis for future research and development in the area of destination recommender systems. The paper concludes that the success of a specific destination recommender system will depend largely on its ability to anticipate and respond creatively to transformations in the personal and situational needs of its users. Such system intelligence needs to be based on empirical data analyzed with sophisticated quantitative methods. The importance of recommender systems in tourism marketing is also discussed.