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This study aims to propose a risk-induced game theoretic forecasting model to predict average daily rate (ADR) under COVID-19, using an advanced recurrent neural network.

Using three data sets from upper-midscale hotels in three locations (i.e. urban, interstate and suburb), from January 1, 2018, to August 31, 2020, three long-term, short-term memory (LSTM) models were evaluated against five traditional forecasting models.

The models proposed in this study outperform traditional methods, such that the simplest LSTM model is more accurate than most of the benchmark models in two of the three tested hotels. In particular, the results show that traditional methods are inefficient in hotels with rapid fluctuations of demand and ADR, as observed during the pandemic. In contrast, LSTM models perform more accurately for these hotels.

This study is limited by its use of American data and data from midscale hotels as well as only predicting ADR.

This study produced a reliable, accurate forecasting model considering risk and competitor behavior.

This paper extends the application of game theory principles to ADR forecasting and combines it with the concept of risk for forecasting during uncertain times.

This study is the first study, to the best of the authors’ knowledge, to use actual hotel data from the COVID-19 pandemic to determine an appropriate neural network forecasting method for times of uncertainty. The application of Shapley value and operational risk obtained a game-theoretic property-level model, which fits best.

This study aimed to compare mortality rate, length of stay (LOS) and hospitalization costs at different priority levels for a patient admitted to an intensive care unit (ICU) at a public tertiary hospital in Qazvin, Iran. This study also aimed to predict influencing factors on patients’ mortality, ICU LOS and hospitalization costs in different admission groups.

The authors conducted a retrospective cohort study among patients who mainly suffered from internal diseases admitted to an ICU of a public hospital. This study was conducted among 127 patients admitted to ICU from July to September 2019. The authors categorized patients into four groups based on two crucial hemodynamic and respiratory status criteria. The authors used a logistic regression model to predict the likelihood of mortality in ICU admitted patients during hospitalizations for the four prioritization groups. Furthermore, the authors conducted a multivariate analysis using the “enter” method to identify risk factors for LOS.

Results showed a statistically significant relationship between the priority of being admitted to ICU and hospitalization costs. The authors’ findings revealed that age, LOS and levels of consciousness had a predictability role in determining in-hospital mortality. Besides, age, gender, consciousness level of patients and type of the disease were mentioned as affecting factors of LOS.

This study’s findings emphasize the necessity of categorizing patients according to specific criteria to efficiently use available resources to help health-care authorities reduce the costs and allocate the budget to different health sectors.

The purpose of this paper is to analyze how sustainability was operationalized in the Spanish universities through plans and actions that contribute actively to the achievement of the sustainable development goals (SDGs).

A systematic search and content analysis served to examine information available on websites belonging to the 76 universities listed in the Conference of Rectors of the Spanish Universities (CRUE).

The participation of Spanish universities on initiatives focused on sustainability is very limited, highlighting the negligible role of private institutions in which topics like sustainability and the 2030 Agenda/SDGs were scarcely addressed.

The study outlines the actual extent of the inclusion of sustainability in particular co-curricular actions toward the SDGs in the CRUE. The findings enable to define a long-term sustainability road map for the Spanish university system.

Teleworking, working from home and flexible work have gained popularity over the last few years. A shift in policies and practices in the workplace is required owing to the COVID-19 pandemic accelerating current trends in work-from-everywhere (WFE) research. This article presents a systematic literature review of WFE research from 1990 to early 2023 to understand the transformation of the field.

The Web of Science database was used to conduct this review based on rigorous bibliometric and network analysis techniques. The prominence of the research studied using SPAR-4-SLR and a collection of bibliometric techniques on selected journal articles, reviews and early access articles. Performance and keyword co-occurrence analysis form the premise of cluster analysis. The content analysis of recently published papers revealed the driving and restraining forces that help define and operationalize the concept of WFE.

The major findings indicate that the five established and accelerated trends from cluster analysis are COVID-19 and the pandemic, telework(ing), remote working, work from home and well-being and productivity. Driving and restraining forces identified through content analysis include technological breakthroughs, work–life integration challenges, inequality in the distribution of jobs, gender, shifts in industry and sector preferences, upskilling and reskilling and many more have been published post-COVID in the restraining forces category of WFE.

A key contribution of this pioneering study of “work from everywhere” is the linking of the bibliometric trends of the past three decades to the influencing and restraining factors during the pandemic. This study illustrates how WFE could be perceived differently post-COVID, which is of great concern to practitioners and future researchers.

A wide range of publications on WFE and multiple synonyms can create confusion if a systematic and effective system does not classify and associate them. This study uses both bibliometric and scientometric analyses in the context of WFE using systematic literature review (SLR) methods.