Using machine learning and big data for efficient forecasting of hotel booking cancellations

Cancellations are a key aspect of hotel revenue management because of their impact on room reservation systems. In fact, very little is known about the reasons that lead customers to cancel, or how it can be avoided. The aim of this paper is to propose a means of enabling the forecasting of hotel booking cancellations using only 13 independent variables, a reduced number in comparison with related research in the area, which in addition coincide with those that are most often requested by customers when they place a reservation. For this matter, machine-learning techniques, among other artificial neural networks optimised with genetic algorithms were applied achieving a cancellation rate of up to 98%. The proposed methodology allows us not only to know about cancellation rates, but also to identify which customer is likely to cancel. This approach would mean organisations could strengthen their action protocols regarding tourist arrivals.     

Efficiency evaluation of China's high-tech industry with a multi-activity network data envelopment analysis approach

This paper employs a Russell multi-activity network DEA model and divides the overall innovation process into the upstream Research and Development (R&D) process and the downstream commercialization process to appraise the innovation performance of China's high-tech industries from 2009 to 2013. This model can deal with the problems of intermediates, shared inputs and slack-based measure in a unified framework, and the result can provide policy makers with process-specific information on how to improve the innovation performance of China's high-tech industries. The main findings are presented as follows. First, the overall efficiency of China's high-tech industries still remains at a low level, which has its roots mainly in commercialization inefficiencies other than R&D inefficiencies. Second, for most provinces, their R&D efficiencies do not match up with their commercialization efficiencies. Finally, the innovative activities of China's high-tech industries should be driven by the market demand -oriented for the improvement of innovation efficiency.     

Bayesian selection of technology assumptions for the transformation from supply-use to input–output tables

ABSTRACT

In the construction of input–output models from supply-use tables, technology assumptions disambiguate how an industry uses inputs in the production recipe of multiple outputs. This paper uses Bayes' theorem to select technology assumptions, taking into account empirical observations. The paper presents a formulation to explore hybrids between product and industry technology assumptions in product-by-product tables. We then present Markov chain Monte-Carlo techniques to implement the Bayesian method for selecting technology assumptions. We apply the method in a case study using Eurostat supply-use tables of 2004 and 2005, exhibiting a volume of secondary products of less than 13%, and 59 products and industries per country. The results show that the choice of technology is not important, given that there is no strong evidence in favour of any of them.     

互联网视角下供应链金融的新发展

在这个网络社会愈发深化的时代,通过互联网科技完善和发展线上供应链金融成为必要的发展方向。本文首先梳理了传统供应链金融的基本模式,发现了其存在的不足;然后重点介绍了当前三种主要的线上供应链金融模式,总结了线上供应链金融的优势;最后,提出相关政策建议,认为金融机构应该不断升级线上供应链金融产品,逐步完善供应链金融线上操作系统,监管机构需要出台更加严格的监管规则对线上供应链金融进行有效监管。     

Diagnosing a healthcare cybersecurity crisis: The impact of IoMT advancements and 5G

Internet of Medical Things (IoMT) technology remains in early stages of adoption, but advancements and breakthroughs are quickly moving this process forward. There is a critical need for cybersecurity to be a priority in the development of these new tools, alongside design and utility. Given the rapid pace and potential magnitude of the coming advancements in IoMT, if privacy and security risks are neglected, a significant crisis could emerge in the form of more frequent cybersecurity breaches. This article examines the market opportunities and risks associated with IoMT and outlines a plan for proactively mitigating concerns and providing a platform to foster growth, to modify attitudes and behaviors, and to continue to build consumer confidence in the overall health system without sacrificing security.     

Consumers’ luxury restaurant reservation session abandonment behavior during the COVID-19 pandemic: The influence of luxury restaurant attachment,emotional ambivalence,and luxury consumption goals

The purpose of this research is to examine how consumers’ attachment to luxury restaurants and their emotional ambivalence contribute to their reservation session abandonment during the coronavirus disease 2019 (COVID-19) pandemic. In addition, the moderating effect of luxury consumption goals (e.g., self-presentation motives, status consumption, and need for uniqueness) is examined. A total of 408 participants completed questionnaires, and the results reveal that luxury restaurant attachment significantly influences consumers’ emotional ambivalence, which in turn causes them to not complete their reservation sessions. Furthermore, the moderating effects of status consumption and need for uniqueness are supported.     

Customer reactions to self-checkout discontinuance

Self-service technologies (SSTs) increasingly permeate retail space. Yet, sometimes retailers decide to revert to human-delivered service mode by discontinuing their incumbent SST. In this study, we examine how self-checkout (SCO) discontinuance affects customers’ perceptions of SCO technology and purchase behavior. We conduct a natural field experiment by surveying two groups of customers pre- and post- SCO discontinuance: treatment group (who experience discontinuance) and control group (who do not experience discontinuance). Leveraging difference-in-differences analyses, we find that SCO discontinuance results in decreases in customers’ satisfaction with technology, intentions to use technology, perceived simplicity of technology, and basket size. Our results inform managers of the potential downsides of discontinuing SST and provide corroborating evidence of the technology’s benefits.     

To look tasty,let's show the ingredients! Effects of ingredient images on implicit tasty–healthy associations for packaged products

This study investigates the effect of ingredient images on implicit tasty–healthy associations for packaged products. An implicit association test (IAT) with 106 respondents reveals the impact of repeating ingredient images on the implicit healthy = tasty intuition; fewer ingredient images are linked to a stronger intuition. This study also considers explicit product packaging preferences and the number of ingredient images depicted. The implicit intuitions affect explicit preferences, such that packages depicting few ingredient images are preferred over those depicting many ingredient images for healthy products, but no significant effects emerge for unhealthy products.     

Monitoring the foreign exchange rate benchmark fix

ABSTRACT

We develop a Manipulation Index (ManIx) that captures the potential manipulation intention of dealers during the World Markets/Reuters (WMR) benchmark (London Close) period at 4 pm London time through a unique algorithm and simulation. The application of this model (using a dataset with dealers’ identities) can identify banks that are prone to potential manipulative behavior. The results concerning the identified banks are validated by the regulatory investigations. Implementation of this algorithm allows regulators better direct their limited resources towards more targeted in-depth investigation.     

How markets will drive the transition to a low carbon economy

This paper investigates the driving factors behind the transition to a low carbon economy. Here, we offer a two-part analysis: First, we examine the factors leading to the current level of cleantech development. To do so, we examine the impact of country-level economic variables (real GDP, market return, and turnover) and country-level institutional variables on patent intensity. Results from this analysis show that cleantech patenting activity is fostered by a supportive institutional environment that promotes innovation and low-carbon development through carbon pricing policies, country-level public R&D expenditure and human capital. Second, we extend the notion of ‘path creation’ to map out different pathways for cleantech development on a country-level within a real options framework, and offer a corresponding valuation of cleantech patents. Our estimates of total wealth creation through the development of cleantech patents by 2050 range from US$10.16 to US$15.49 trillion dollars (13%–20% of the world GDP in 2017) with investment growth from US$2.93 to US$3.71 trillion (3.7%–4.7% of the world GDP in 2017). The results from our analysis suggest that market forces will drive the transition to a cleantech economy.