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In the digital era, major shipping lines are developing instant quote and online booking platforms. In one of the first attempts to investigate the post-event effects of this trend, we evaluate how a shipping line's online quote platform impacts its shipper portfolio and the ordered container volume. In order to control for unobserved, time-varying effects that could be correlated with the platform's implementation, we apply the regression discontinuity design (RDD) method to the import trade data from 2016 to 2019 of a top shipping line that released its platform in August 2018. We also adopt global polynomial regression and local liner regression so as to control for the effects of different polynomial time trends. We find the container volume assigned to the shipping line declined slightly after the online platform was launched. The container orders of small shippers increase by 3.97 TEU on average after online platform adoption. The volume of assigned containers from other shippersdeclines. These findings hold fruitful implications for shipping lines. A significant increase in the number of small shippers and their container volumes demonstrate administrative cost saving and risk mitigation for a shipping line. However, it also leads to possible loss of large customers. [ABSTRACT FROM AUTHOR]

This study takes stock of business and economics research on Artificial Intelligence (AI) and provides a dynamic panorama of the overall knowledge structure of this ever-growing body of work ever since its inception in 1966. Our bibliometric analysis based on the full archive of 1024 studies identifies the main trends of and the major intellectual contributors to the extant knowledge of AI in business and economics research. Specifically, our results show that (1) AI-focused business and economics research wintnessed growth over three stages, particularly with a sharp increase after 2017. (2) While this body of research has gained tremendous momentum across the globe, the United States is by far the center of knowledge generation. (3) Research collaborations are still limited in this area. (4) Research topics flourished, ranging from early decision support systems, neural networks, and scheduling methods to more recent machine learning, automation, and big data. This study also identifies fruitful avenues for further business and economics research with an AI focus. [ABSTRACT FROM AUTHOR]

To address cruise companies' revenue loss caused by over-reliance on indirect channels for ticket distribution, we design a dual-channel pricing policy that includes dynamic horizontal and vertical pricing regimes aiming to maximizing cruise companies' revenue. In the horizontal pricing regime, cruise companies and travel agencies offer uniform prices to consumers dynamically. In the vertical pricing regime, cruise companies provide dynamic commissions to travel agencies based on their sales performances. In view of the dynamic customer arrivals and uncertain demand, we model the dynamic dual-channel pricing decisions as a Markov decision process, and solve the model by means of a reinforcement learning method. Our computational results show that dual-channel pricing policies generated by our method outperform the classic Expected Marginal Seat Revenue method, which has been widely used in similar industries such as airlines for revenue management. In addition to the various features of dual-channel in ticket distribution, our model also takes into account customers' onboard consumptions, which is a critical source of revenue in cruise industry but has never been considered in the literature. As a result, our model and computational results provide insightful implications for cruise revenue management. [ABSTRACT FROM AUTHOR]