The Impact of Corporate Digital Responsibility (CDR) on Internal Stakeholders' Satisfaction in Hungarian Upscale Hotels

2022 ◽  
pp. 35-51
Author(s):  
Edit Kővári ◽  
Mohamad Saleh ◽  
Gyöngyi Steinbachné Hajmásy
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Digital Transformation ◽  
Technology Application ◽  
External Stakeholders ◽  
Hungarian Case ◽  
Hospitality Sector ◽  
Internal Stakeholders ◽  
The Impact

Digital transformation and artificial intelligence are considered among the most vital trending topics in the process of hospitality sector evolution. Many scholars found that digital transformation and artificial intelligence cause a massive shift in all aspects of the hospitality sector and digital technology application that impact the whole facet of internal and external stakeholders' lives. However, the adoption of digitalization and artificial intelligence is considered a strength. Corporate digital responsibility (CDR) is a strategy that enhances trust between the companies adopting digitalization and their primary stakeholders. Internal and external stakeholders' satisfaction develop contemporary social responsibility (CSR) challenges in the decision-making process in acquiring, analysing, implementing, and assessing for adopting digitalization in the hospitality sector. This chapter aims to give a literature review focusing on CDR and its relation analyses to hotel industry's internal stakeholders' satisfaction trough a Hungarian case study.

scholarly journals Decision-Making Processes in Controlling Exposure to Sunlight Supported by Simulation Tools: A Case Study in Warm Weather

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4100
Author(s):  
Mariana Huskinson ◽  
Antonio Galiano-Garrigós ◽  
Ángel Benigno González-Avilés ◽  
M. Isabel Pérez-Millán
Keyword(s):  
Decision Making ◽  
Global Analysis ◽  
Energy Performance ◽  
Information Modeling ◽  
The European Union ◽  
Evaluation Tool ◽  
Simulation Tools ◽  
Building Information ◽  
The Impact

Improving the energy performance of existing buildings is one of the main strategies defined by the European Union to reduce global energy costs. Amongst the actions to be carried out in buildings to achieve this objective is working with passive measures adapted to each type of climate. To assist designers in the process of finding appropriate solutions for each building and location, different tools have been developed and since the implementation of building information modeling (BIM), it has been possible to perform an analysis of a building’s life cycle from an energy perspective and other types of analysis such as a comfort analysis. In the case of Spain, the first BIM environment tool has been implemented that deals with the global analysis of a building’s behavior and serves as an alternative to previous methods characterized by their lack of both flexibility and information offered to designers. This paper evaluates and compares the official Spanish energy performance evaluation tool (Cypetherm) released in 2018 using a case study involving the installation of sunlight control devices as part of a building refurbishment. It is intended to determine how databases and simplifications affect the designer’s decision-making. Additionally, the yielded energy results are complemented by a comfort analysis to explore the impact of these improvements from a users’ wellbeing viewpoint. At the end of the process the yielded results still confirm that the simulation remains far from reality and that simulation tools can indeed influence the decision-making process.

scholarly journals The Role of Artificial Intelligence in Fighting the COVID-19 Pandemic

2021 ◽  
Author(s):  
Francesco Piccialli ◽  
Vincenzo Schiano di Cola ◽  
Fabio Giampaolo ◽  
Salvatore Cuomo
Keyword(s):  
Artificial Intelligence ◽  
Clinical Trials ◽  
Digital Transformation ◽  
Daily Activities ◽  
Commercial Vehicles ◽  
Human Skills ◽  
To Come ◽  
Complex Scenario ◽  
The Impact

AbstractThe first few months of 2020 have profoundly changed the way we live our lives and carry out our daily activities. Although the widespread use of futuristic robotaxis and self-driving commercial vehicles has not yet become a reality, the COVID-19 pandemic has dramatically accelerated the adoption of Artificial Intelligence (AI) in different fields. We have witnessed the equivalent of two years of digital transformation compressed into just a few months. Whether it is in tracing epidemiological peaks or in transacting contactless payments, the impact of these developments has been almost immediate, and a window has opened up on what is to come. Here we analyze and discuss how AI can support us in facing the ongoing pandemic. Despite the numerous and undeniable contributions of AI, clinical trials and human skills are still required. Even if different strategies have been developed in different states worldwide, the fight against the pandemic seems to have found everywhere a valuable ally in AI, a global and open-source tool capable of providing assistance in this health emergency. A careful AI application would enable us to operate within this complex scenario involving healthcare, society and research.

scholarly journals Impact of artificial intelligence support on accuracy and reading time in breast tomosynthesis image interpretation: a multi-reader multi-case study

2021 ◽  
Author(s):  
Suzanne L. van Winkel ◽  
Alejandro Rodríguez-Ruiz ◽  
Linda Appelman ◽  
Albert Gubern-Mérida ◽  
Nico Karssemeijer ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Artificial Intelligence ◽  
Cancer Detection ◽  
Support System ◽  
Reading Time ◽  
Digital Breast Tomosynthesis ◽  
Breast Tomosynthesis ◽  
Screening Programs ◽  
The Impact

Abstract Objectives Digital breast tomosynthesis (DBT) increases sensitivity of mammography and is increasingly implemented in breast cancer screening. However, the large volume of images increases the risk of reading errors and reading time. This study aims to investigate whether the accuracy of breast radiologists reading wide-angle DBT increases with the aid of an artificial intelligence (AI) support system. Also, the impact on reading time was assessed and the stand-alone performance of the AI system in the detection of malignancies was compared to the average radiologist. Methods A multi-reader multi-case study was performed with 240 bilateral DBT exams (71 breasts with cancer lesions, 70 breasts with benign findings, 339 normal breasts). Exams were interpreted by 18 radiologists, with and without AI support, providing cancer suspicion scores per breast. Using AI support, radiologists were shown examination-based and region-based cancer likelihood scores. Area under the receiver operating characteristic curve (AUC) and reading time per exam were compared between reading conditions using mixed-models analysis of variance. Results On average, the AUC was higher using AI support (0.863 vs 0.833; p = 0.0025). Using AI support, reading time per DBT exam was reduced (p < 0.001) from 41 (95% CI = 39–42 s) to 36 s (95% CI = 35– 37 s). The AUC of the stand-alone AI system was non-inferior to the AUC of the average radiologist (+0.007, p = 0.8115). Conclusions Radiologists improved their cancer detection and reduced reading time when evaluating DBT examinations using an AI reading support system. Key Points • Radiologists improved their cancer detection accuracy in digital breast tomosynthesis (DBT) when using an AI system for support, while simultaneously reducing reading time. • The stand-alone breast cancer detection performance of an AI system is non-inferior to the average performance of radiologists for reading digital breast tomosynthesis exams. • The use of an AI support system could make advanced and more reliable imaging techniques more accessible and could allow for more cost-effective breast screening programs with DBT.

scholarly journals The Impact of COVID-19 on Hospitality Industry in Greece and Its Treasured Santorini Island

2021 ◽  
Vol 13 (14) ◽  
pp. 7906
Author(s):  
Nikola Medová ◽  
Lucie Macková ◽  
Jaromir Harmacek
Keyword(s):  
Social Impact ◽  
Tourism Sector ◽  
Challenges And Opportunities ◽  
Multiple Variables ◽  
Hospitality Sector ◽  
Tourism And Hospitality ◽  
The Impact ◽  
Further Development

This paper focuses on the dynamic of the recent upheaval in the tourism and hospitality sector due to the COVID-19 epidemic in Greece and Santorini island. It uses the case study of a country one-fourth of whose GDP consists of tourism. We compare the available statistical data showing the change in variables in the previous years with 2020 and look into the new challenges and opportunities posed by the drop in the numbers of visitors and flights. We focus mainly on the economic and social impact on the destination and possible future scenarios for further development in the area. Data show a significant effect of the pandemic on multiple variables, such as the long-term trend of the importance of tourism sector in GDP in Greece, the number of flights and visitors to Greece and Santorini island, and the contribution of tourism and travel to GDP. Based on the available data, we also construct three foresight scenarios that describe the possible futures for Santorini island in terms of the pandemic evolution. These scenarios may help various stakeholders and policymakers to be better prepared for different developments that may appear.

Impact of CEDIT recommendations: An example of health technology assessment in a hospital network

2006 ◽  
Vol 22 (2) ◽  
pp. 161-168 ◽  
Author(s):  
Florence Bodeau-Livinec ◽  
Emmanuelle Simon ◽  
Catherine Montagnier-Petrissans ◽  
Marie-Eve Joël ◽  
Elisabeth Féry-Lemonnier
Keyword(s):  
Decision Making ◽  
Technology Assessment ◽  
Health Technology ◽  
Technological Innovations ◽  
Administrative Staff ◽  
Good Match ◽  
French Hospital ◽  
The Impact ◽  
Widespread Interest

Objectives: The objective of this study is to assess the impact of CEDIT (French Committee for the Assessment and Dissemination of Technological Innovations) recommendations on the introduction of technological innovations within the AP-HP (Assistance Publique–Hôpitaux de Paris), the French hospital network to which this body is attached.Methods: In 2002, a study based on semidirective interviews of fourteen people affected by these recommendations and a case study relating to thirteen recommendations issued between 1995 and 1998 were conducted.Results: The CEDIT is very scientifically reputable among interviewees. There is generally widespread interest for the recommendations. They are used as decision-making tools by administrative staff and as negotiating instruments by doctors in their dealings with management. Based on the case study, ten of thirteen recommendations had an impact on the introduction of the technology in health establishments. One recommendation appears not to have had an impact. Furthermore, the impact of two technologies was impossible to assess.Conclusions: This study highlights the significant impact of recommendations arising from a structure that is attached to a hospital network and the good match between CEDIT's objectives and its assignments.

Who’s making the decisions? How managers can harness artificial intelligence and remain in charge

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Pooya Tabesh
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Big Data ◽  
Digital Transformation ◽  
Decision Processes ◽  
Decision Makers ◽  
Content Type ◽  
Human Decision ◽  
Practical Implications

Purpose While it is evident that the introduction of machine learning and the availability of big data have revolutionized various organizational operations and processes, existing academic and practitioner research within decision process literature has mostly ignored the nuances of these influences on human decision-making. Building on existing research in this area, this paper aims to define these concepts from a decision-making perspective and elaborates on the influences of these emerging technologies on human analytical and intuitive decision-making processes. Design/methodology/approach The authors first provide a holistic understanding of important drivers of digital transformation. The authors then conceptualize the impact that analytics tools built on artificial intelligence (AI) and big data have on intuitive and analytical human decision processes in organizations. Findings The authors discuss similarities and differences between machine learning and two human decision processes, namely, analysis and intuition. While it is difficult to jump to any conclusions about the future of machine learning, human decision-makers seem to continue to monopolize the majority of intuitive decision tasks, which will help them keep the upper hand (vis-à-vis machines), at least in the near future. Research limitations/implications The work contributes to research on rational (analytical) and intuitive processes of decision-making at the individual, group and organization levels by theorizing about the way these processes are influenced by advanced AI algorithms such as machine learning. Practical implications Decisions are building blocks of organizational success. Therefore, a better understanding of the way human decision processes can be impacted by advanced technologies will prepare managers to better use these technologies and make better decisions. By clarifying the boundaries/overlaps among concepts such as AI, machine learning and big data, the authors contribute to their successful adoption by business practitioners. Social implications The work suggests that human decision-makers will not be replaced by machines if they continue to invest in what they do best: critical thinking, intuitive analysis and creative problem-solving. Originality/value The work elaborates on important drivers of digital transformation from a decision-making perspective and discusses their practical implications for managers.

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