Search results

Increasing availability of the financial data has opened new opportunities for quantitative modeling. It has also exposed limitations of the existing frameworks, such as low accuracy of the simplified analytical models and insufficient interpretability and stability of the adaptive data-driven algorithms. I make the case that boosting (a novel, ensemble learning technique) can serve as a simple and robust framework for combining the best features of the analytical and data-driven models. Boosting-based frameworks for typical financial and econometric applications are outlined. The implementation of a standard boosting procedure is illustrated in the context of the problem of symbolic volatility forecasting for IBM stock time series. It is shown that the boosted collection of the generalized autoregressive conditional heteroskedastic (GARCH)-type models is systematically more accurate than both the best single model in the collection and the widely used GARCH(1,1) model.

Reducing food-related greenhouse gas emissions is one of the major tasks in the future, as food causes one-third of global emissions. Influencing customers' purchasing decisions towards low-carbon food is thus decisive. Nudging has been proven to be an adequate mechanism to influence people towards sustainable food choices. Another relatively new approach is boosting, which promotes people's education, inducing autonomous decision-making. In the context of sustainable food, research on nudging and boosting is still at the beginning. Therefore, this chapter conducts a systematic literature review to identify, classify and assess the potential of cognitively oriented nudges and boosts towards sustainable food choices. The sample consists of 217 English-speaking papers published between 2011 and 2021. After three filtering steps, 21 scientific journal publications remained in the data extraction form. All articles are field experiments, comprising descriptive labelling, evaluative labelling, and visibility enhancements. The analysis shows that menu restructurings (e.g. placing a vegetarian option on the top of the menu) in restaurants are the most effective intervention to reshape customers' demands. Evaluative labels (e.g. traffic-light labels on the menu or product packaging) are the second most effective measure. They help people understand eco-related information and thus make better decisions. The effect of descriptive labels seemed small, as they provide no meaningful frame assisting people in processing the data. In conclusion, the research recommends applying cognitively oriented nudges and boosts to promote sustainable food choices and deduces practical implications for appropriate implementation and marketing.

Across disciplines, researchers and practitioners employ decision tree ensembles such as random forests and XGBoost with great success. What explains their popularity? This chapter showcases how marketing scholars and decision-makers can harness the power of decision tree ensembles for academic and practical applications. The author discusses the origin of decision tree ensembles, explains their theoretical underpinnings, and illustrates them empirically using a real-world telemarketing case, with the objective of predicting customer conversions. Readers unfamiliar with decision tree ensembles will learn to appreciate them for their versatility, competitive accuracy, ease of application, and computational efficiency and will gain a comprehensive understanding why decision tree ensembles contribute to every data scientist's methodological toolbox.

The reputation has a significant impact on business, and it influences by causing an exponential positive or negative effect. There are many different issues that affect the level of reputation, and they are independent from the sector of the activity. If we focus on museums, previous studies mainly highlight the different strategies in order to manage and to increase their reputation. However, each research points out a specific aspect linked to the engagement, the use of digital technologies, or social media platforms. Our research aims to deep investigate the governance and leadership drivers in order to increase positive feeling within stakeholders also by using social media instruments. This research has indeed an exploratory nature as, to the best of our knowledge, no previous research focused on a similar strategy that characterizes for a “stellar” manager applied to a small museum in a small city.

The research uses a case study by observing the effects of selecting a well-known manager of the top ranked museum in the world (Louvre) in order to boost the reputation of a “startup” museum barely known (Pistoia Museums). After having analyzed the main drivers of reputation of the case, the study uses interview with the scientific director of the small museum.

The results contribute to the discussion about the drivers and the different strategies to boost reputation by showing how the management and leadership issues are able to increase it rapidly. However, findings also point out the need to have a clear overview about the digital tools applied to an adequate communication. The results show how the flexibility of a small growing museum can positively affect and boost reputation. This strategy can be applied to other small museums that aim to affirm their identity.

The tourism industry is seeing unprecedented growth due to the convergence of the internet and growing travel trends. E-tourism digitises the tourism business, including processes, value chains, infrastructure, services and auxiliary industries, to maximise efficiency and effectiveness. It is in response to the globalisation of the tourism sector, which has spurred the introduction of information and communications technologies (ICTs) during the past four decades. ICT can boost travellers' satisfaction by providing pre-trip information about tourism attractions and allowing them to contact vendors directly, obtain accurate information and get the lowest price.

ICT has made the global tourism sector more competitive by providing pre-trip information about tourism attractions, allowing tourists to contact vendors directly, obtain accurate information and get the lowest price. Search engines, transport capacity and network speeds have increased the number of international travellers who use technology to organise and document their trips. E-tourism reduces seasonality, improves consumer communication and boosts reservations and sales. ICT also affects customer behaviour management and future marketing models and supports tourism industry globalisation by providing practical tools to tourism agents to develop and disseminate their offers globally. Strategic behaviours significantly impact tourism, as eliminating intermediaries shortens the distance between client and provider. ICT is a new way to boost tourism demand, which attracts more tourists and generates more revenue while improving the sector.

Behavioral economics poses a challenge for the welfare evaluation of choices, particularly those that involve risk. It demands that we recognize that the descriptive account of behavior toward those choices might not be the ones we were all taught, and still teach, and that subjective risk perceptions might not accord with expert assessments of probabilities. In addition to these challenges, we are faced with the need to jettison naive notions of revealed preferences, according to which every choice by a subject expresses her objective function, as behavioral evidence forces us to confront pervasive inconsistencies and noise in a typical individual’s choice data. A principled account of errant choice must be built into models used for identification and estimation. These challenges demand close attention to the methodological claims often used to justify policy interventions. They also require, we argue, closer attention by economists to relevant contributions from cognitive science. We propose that a quantitative application of the “intentional stance” of Dennett provides a coherent, attractive and general approach to behavioral welfare economics.

Predicting a patient's length of stay (LOS) in a hospital setting has been widely researched. Accurately predicting an individual's LOS can have a significant impact on a healthcare provider's ability to care for individuals by allowing them to properly prepare and manage resources. A hospital's productivity requires a delicate balance of maintaining enough staffing and resources without being overly equipped or wasteful. This has become even more important in light of the current COVID-19 pandemic, during which emergency departments around the globe have been inundated with patients and are struggling to manage their resources.

In this study, the authors focus on the prediction of LOS at the time of admission in emergency departments at Rhode Island hospitals through discharge data obtained from the Rhode Island Department of Health over the time period of 2012 and 2013. This work also explores the distribution of discharge dispositions in an effort to better characterize the resources patients require upon leaving the emergency department.

Formal orientation programs play a potentially important role in newcomer adjustment, yet research aimed at understanding and improving the effects of these interventions has stagnated in recent years. The purpose of this chapter is to facilitate a redirection of researchers’ attention to such programs, and to suggest ways to integrate this body of research with recent developments in socialization and training literatures.