Customer purchase forecasting for online tourism: A data-driven method with multiplex behavior data

Online tourism has received increasing attention from scholars and practitioners due to its growing contribution to the economy. While related issues have been studied, research on forecasting customer purchases and the influence of forecasting variables, online tourism is still in its infancy. Therefore, this paper aims to develop a data-driven method to achieve two objectives: (1) provide an accurate purchase forecasting model for online tourism and (2) analyze the influence of behavior variables as predictors of online tourism purchases. Based on the real-world multiplex behavior data, the proposed method can predict online tourism purchases accurately by machine learning algorithms. As for the practical implications, the influence of behavior variables is ranked according to the predictive marginal value, and how these important variables affect the final purchase is discussed with the help of partial dependence plots. This research contributes to the purchase forecasting literature and has significant practical implications.     

Balancing risk with virtual private networking during a pandemic

When the pandemic struck and teaching went online worldwide, universities had to make pressing decisions that balanced cybersecurity against other factors, including health and safety, usability, and cost. One such challenge Indiana University (IU) faced was how to accommodate the secure telecommunications needs of 130,000 faculty, staff, and students who would now be teaching, learning, doing research, and working from home. Some universities reflexively promoted virtual private network (VPN) use for all activities. Such an approach would have been unsustainable at IU, however, owing both to the licenses and resources needed for the sheer number of users and to the high-throughput applications on which they rely. Perhaps even worse, it would have increased the chances that the VPN would be unavailable during a critical incident or other situation in which secure communications must be guaranteed. Instead, IU launched an awareness campaign demonstrating exactly when VPN use is and isn’t needed. In addition, network staff employed a VPN feature called split tunneling to reduce the load. This article discusses the advantages and disadvantages of this approach and how IU made the decision to balance both sides of the risk equation to ensure the continued advancement of its mission throughout the pandemic.     

Assessing strategic flight schedules at an airport using machine learning-based flight delay and cancellation predictions

To mitigate air traffic demand-capacity imbalances, large European airports implement strategic flight schedules, where flights are assigned arrival/departure slots several months prior to execution. We propose a generic assessment of such strategic schedules using predictions about arrival/departure flight delays and cancellations. We demonstrate our approach for strategic flight schedules in the period 2013–2018 at London Heathrow Airport. Together with the development of dedicated strategic flight schedule optimization models, our proposed approach supports an integrated strategic flight schedule assessment, where schedules are evaluated with respect to flight delays and cancellations.     

Unemployment and econometric learning

We apply well-known results of the econometric learning literature to the Mortensen and Pissarides real business cycle model. Agents can always learn the unique rational expectations equilibrium (REE), for all possible well-defined sets of parameter values, by using the minimum-state-variable solution to the model and decreasing gain learning. From this perspective the assumption of rational expectations in the model could be seen as reasonable. But using a parametrisation with UK data, simulations show that the speed of convergence to the REE is slow. This type of learning dampens the cyclical response of unemployment to small structural shocks.     

iSDS: a self-configurable software-defined storage system for enterprise

ABSTRACT

Storage is one of the most important aspects of IT infrastructure for various enterprises. But, enterprises are interested in more than just data storage; they are interested in such things as more reliable data protection, higher performance and reduced resource consumption. Traditional enterprise-grade storage satisfies these requirements at high cost. It is because traditional enterprise-grade storage is usually designed and constructed by customised field-programmable gate array to achieve high-end functionality. However, in this ever-changing environment, enterprises request storage with more flexible deployment and at lower cost. Moreover, the rise of new application fields, such as social media, big data, video streaming service etc., makes operational tasks for administrators more complex. In this article, a new storage system called intelligent software-defined storage (iSDS), based on software-defined storage, is described. More specifically, this approach advocates using software to replace features provided by traditional customised chips. To alleviate the management burden, it also advocates applying machine learning to automatically configure storage to meet dynamic requirements of workloads running on storage. This article focuses on the analysis feature of iSDS cluster by detailing its architecture and design.     

Street-level new public governances in integrated services-as-a-system

The paper explores the emergence of street-level public service integration in eight cities, in five countries using a new framework: services-as-a-system to explore new public governances in health and social care integration. Data is analysed from eight cities in six countries (Australia, Canada, The Netherlands, Spain, UK, and USA) gathered in over 100 semi-structured interviews with key agents. We show that whilst culture and context shape the form and processes of governances there are underlying processual drivers of new public governances, in particular where users are involved in the co-design and co-production of integrated services.     

External pressure or internal culture? An innovation diffusion theory account of small retail businesses’ social media use

COVID-19 put unprecedented external pressure on small businesses to adopt or increase use of social media while not all small businesses are internally ready for this rapid change. This study investigated the roles of external pressures and organizational culture of openness and learning in driving small retail business owners'/managers' social media use decisions by impacting their perceived usefulness and barriers, based on the innovation-decision process model from the diffusion of innovations theory and the theory of reasoned action. An online survey with structured measurements was administered to 411 U.S. small retail business owners/managers. Results from structural equation modeling revealed that external pressures positively influenced small business owners'/managers’ perceived usefulness and barriers and social media use intention. In addition, the culture of openness and learning positively influenced the perceived usefulness while mitigating the perceived barriers, thereby directly and indirectly influencing the social media use intention. Theoretical and managerial implications are discussed.     

Country Market Sequential Order-of-Entry: A Learning Effects Approach

ABSTRACT

In this article, we identify and describe the sequential order-of-entry problem. The sequential order-of-entry problem arises when, after having identified a desirable set of country markets to enter, firms are unable (because of budgetary constraints), or unwilling (because of possible learning or strategic issues), to enter all at once. The question then arises whether some sequences to entering the desirable set of country markets are better than others? Answering this question is shown to be nontrivial. The article outlines a number of arguments for the existence of superior sequences to entering a set of desirable country markets, and develops normative and dynamic frameworks to aid in the identification and evaluation of sequences.     

大学英语教学应激发学生的学习兴趣

学习兴趣是学生学习的动力。大学英语教学应通过培养良好的师生关系,根据学生差异,做到因材施教,努力创设语言环境,合理使用奖惩,激发学生的学习兴趣。