Big Data in Cultural Tourism – Building Sustainability and Enhancing Competitiveness

2021 ◽  
Keyword(s):  
Big Data ◽  
Case Studies ◽  
Value Chain ◽  
New Technologies ◽  
Cultural Tourism ◽  
End Users ◽  
Ethical Implications ◽  
Tourism Sector ◽  
Cultural Experiences ◽  
Selection Of

The use of big data is becoming increasingly important across the tourism sector and the value chain. With this publication, UNWTO intends to provide a baseline research on using big data by tourism and culture stakeholders, in order to improve the competitiveness of cultural tourism and reinforce its sustainability. The study sets the basis to connect tourism, culture and new technologies for mutual benefits, while calling for a reflection on the ethical implications for policymakers, businesses and end-users. The selection of case studies illustrates the most frequent case-scenarios of the use of big data in cultural tourism within destinations, compiled during the research. As the new technologies are facing ever-evolving scenarios, their use will be harnessed by the tourism sector in its endeavour to innovate and provide new cultural experiences.

scholarly journals Demonstrating the full-value chain of climate services in Southern Africa: the FOCUS-Africa project

2021 ◽  
Author(s):  
Roberta Boscolo ◽  
Hamid Bastani ◽  
Asmerom Beraki ◽  
Nicolas Fournier ◽  
Raül Marcos-Matamoros ◽  
...  
Keyword(s):  
Food Security ◽  
Case Studies ◽  
Scientific Knowledge ◽  
Value Chain ◽  
Service Providers ◽  
End Users ◽  
Climate Change Scenarios ◽  
Climate Information ◽  
Climate Services ◽  
Wide Range

<p><strong><em>FOCUS-Africa</em></strong> is an EU Horizon 2020 project funded to co-develop tailored climate services in the Southern African Development Community (SADC) region. The project, led by the WMO and started in September 2020, gathers 16 partners across Africa and Europe jointly committed to addressing the value of climate services for key economic sectors in Africa: agriculture and food security, water, energy, and infrastructure.</p><p>The project is piloting eight case studies (CSs) in five different countries involving a wide range of end-users. New services derived from seasonal and decadal forecasts are applied for food security and crop production in South Africa, Malawi, Mozambique, and Tanzania. High-resolution climate projections, as well as historical climate reanalyses, are used to support planning and investment decisions for: a railway infrastructure and a mix of renewable energies in Tanzania, hydropower generation assessment under climate change scenarios in Malawi, and water resources management in Mauritius.</p><p>For all the FOCUS-Africa’s case studies, socio-economic impact assessment of the delivered climate services will be carried out in collaboration with the CS leaders, service providers, and end-users, by providing ex-ante and ex-post evaluations grounded in the Global Indicator Framework for the Sustainable Development Goals. The project will align the capacity development efforts with those promoted by WMO for enhancing the capabilities of the NMHSs to deliver climate services to users and will make sure that the project's innovative processes and tools will be part of the WMO training curricula.</p><p>FOCUS-Africa's expected impacts are:</p><ul><li>Build a strong link between the climate scientific community and stakeholders in the SADC region by leveraging the advanced scientific knowledge and strong networks of the implementing team, and by establishing dedicated channels of communications, so as to target the full value chain of our users, from the start of the project</li> <li>Advance the way in which climate information is developed by characterising end-use requirements through regular engagement</li> <li>Contribute to the advancement of the scientific knowledge in the region and strengthened support for international scientific assessments through publications and reports such as those relevant for the IPCC, through the innovative science developed by FOCUS-Africa</li> <li>Demonstrate the effectiveness of the climate information by strengthening the adaptive capacity of end-users by delivering tailored, actionable, and exploitable climate services and by estimating their socio-economic benefits across the full value chain.</li> <li>Enhance policy-making for climate adaptation in the project and other countries</li> <li>Increase women’s access to climate services</li> </ul>

scholarly journals Digitalization and Big Data in Smart Farming – Bibliometric and Systemic Analysis

2020 ◽  
Author(s):  
Jaqueline Iaksch ◽  
Ederson Fernandes ◽  
Milton Borsato
Keyword(s):  
Big Data ◽  
Value Chain ◽  
New Technologies ◽  
Research Area ◽  
Management Control ◽  
Digital Information ◽  
Agricultural Efficiency ◽  
Systemic Analysis ◽  
Smart Farming ◽  
Research Problems

Agriculture has always had a great significance in the civilization development. However, modern agriculture is facing increasing challenges due to population growth and environmental degradation. Commercially, farmers are looking for ways to improve profitability and agricultural efficiency to reduce costs. Smart Farming is enabling the use of detailed digital information to guide decisions along the agricultural value chain. Thus, better decisions and efficient management control are required through generated information and knowledge at any farm. New technologies and solutions have been applied to provide alternatives to assist in information gathering and processing, and thereby contribute to increased agricultural productivity. Therefore, this article aims to gain state-of-art insight and identify proposed solutions, trends and unfilled gaps regarding digitalization and Big Data applications in Smart Farming, through a literature review. The current study accomplished these goals through analyses based on ProKnow-C (Knowledge Development Process – Constructivist) methodology. A total of 2401 articles were found. Then, a quantitative analysis identified the most relevant ones among a total of 39 articles were included in a bibliometric and text mining analysis, which was performed to identify the most relevant journals and authors that stand out in the research area. A systemic analysis was also accomplished from these articles. Finally, research problems, solutions, opportunities, and new trends to be explored were identified.

Cross Reality (XR) in Deutschland

2021 ◽  
Author(s):  
Christian Zabel ◽  
Verena Telkmann ◽  
Gernot Heisenberg
Keyword(s):  
Case Studies ◽  
New Technologies ◽  
End Users ◽  
Representative Survey ◽  
Cluster Theory

This book examines the structure and development of the German XR industry. Where are the companies that produce VR, AR and MR located and why? What do they produce and for whom? Where do they see the potential of XR and the hurdles it presents, especially during the current coronavirus pandemic? Based on the first nationwide representative survey on this topic (carried out in 2020) and previous studies, the book examines findings from the perspective of cluster theory or agglomeration research. In addition, the authors analyse acceptance of these new technologies by users (end users and companies) through case studies of the adoption of XR in companies and a survey on VR usage by individuals.

DITA or Not?

2010 ◽  
Author(s):  
Lynne A. Price
Keyword(s):  
Case Studies ◽  
End Users ◽  
Anecdotal Evidence ◽  
Selection Of

Use of DITA has become so pervasive that some users assume that anyone who inquires about moving to an XML environment must use DITA. Often, the selection of DITA is independent of DITA's strengths such as ease of reuse, specialization, support of distributed authoring, and availability of the Open Toolkit. While numerous DITA case studies have been published, such reports tend to focus on what was accomplished rather than how the approach was chosen, and typically reflect successful implementations in large organizations. This study focusses on why end users, consultants, and tools have chosen to use or to avoid DITA. While this should not be considered an unbiased or scientifically balanced survey, anecdotal evidence such as this can be valuable to organizations faced with similar decisions.

Artificial intelligence and big data in tourism: a systematic literature review

2020 ◽  
Vol 11 (2) ◽  
pp. 343-367 ◽  
Author(s):  
Dimitra Samara ◽  
Ioannis Magnisalis ◽  
Vassilios Peristeras
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Literature Review ◽  
Systematic Literature Review ◽  
New Technologies ◽  
Recent Literature ◽  
Content Type ◽  
Analysis Methodology ◽  
Tourism Sector

Purpose This paper aims to research, identify and discuss the benefits and overall role of big data and artificial intelligence (BDAI) in the tourism sector, as this is depicted in recent literature. Design/methodology/approach A systematic literature review was conducted under the McKinsey’s Global Institute (Talwar and Koury, 2017) methodological perspective that identifies the four ways (i.e. project, produce, promote and provide) in which BDAI creates value. The authors enhanced this analysis methodology by depicting relevant challenges as well. Findings The findings imply that BDAI create value for the tourism sector through appropriately identified disseminations. The benefits of adopting BDAI strategies include increased efficiency, productivity and profitability for tourism suppliers combined with an extremely rich and personalized experience for travellers. The authors conclude that challenges can be bypassed by adopting a BDAI strategy. Such an adoption will stand critical for the competitiveness and resilience of existing established and new players in the tourism sector. Originality/value Besides identifying the benefits that BDAI brings in the tourism sector, the research proposes a guidebook to overcome challenges when introducing such new technologies. The exploration of the BDAI literature brings important implication for managers, academicians and consumers. This is the first systematic review in an area and contributes to the broader e-commerce marketing, retailing and e-tourism research.

scholarly journals Investigational Model to Generate Aggregate Value in Eco-sustainable Housing Development

2018 ◽  
Vol 14 (5) ◽  
pp. 72
Author(s):  
Ernesto Adrian Diaz Montagner ◽  
Juan Carlos Perez Garcia
Keyword(s):  
Value Chain ◽  
Construction Projects ◽  
New Technologies ◽  
Cost Savings ◽  
Added Value ◽  
Construction Company ◽  
Sustainable Housing ◽  
Sustainable Value ◽  
Selection Of

Purpose – Conventional materials and procedures in worldwide construction sector is a paradigm that requires change. Constructors usually do not take the adventure of testing novel techniques and materials in their construction projects and, as a result, profits are usually given in terms of time and cost savings. The purpose of this article is to introduce a model to add aggregate value in eco-sustainable dwelling construction through ecological sustainable value chain and hypothesis settlement. Design/methodology/approach - A review of the literature was done in databases using keywords. The selection of sources includes scientific articles and case studies based on different methods and contexts which were carried out. Findings - The findings suggest the implementation of the training of both the employee who works in the construction and the client regarding selfsustainable construction. A significant improvement was obtained based on the commercial relations of suppliers, the client, and also with the construction company. Finally, innovation in at least one of the six proposed aspects was carried out: new technologies, including eco-technologies, the quality of materials (including the eco-sustainable ones), the segmentation of the demand for eco-sustainable housing and its new ways of marketing and financing. They were, however, proposed for added value generation for the development of eco-sustainable housing. Originality/value - A model for the generation of added value in the construction of eco-sustainable housing was elaborated based on the relationships between human and relational capital, training, and the generation of aggregate value.

scholarly journals Innovative Tools for Tourism and Cultural Tourism Impact Assessment

2020 ◽  
Vol 12 (18) ◽  
pp. 7470
Author(s):  
Tarmo Kalvet ◽  
Maarja Olesk ◽  
Marek Tiits ◽  
Janika Raun
Keyword(s):  
Big Data ◽  
Business Models ◽  
Application Programming Interface ◽  
Cultural Tourism ◽  
Data Sources ◽  
Web Searches ◽  
Tourism Impact ◽  
Travel And Tourism ◽  
Mobile Positioning ◽  
Tourism Sector

The importance of data and evidence has increased considerably in policy planning, implementation, and evaluation. There is unprecedented availability of open and big data, and there are rapid developments in intelligence gathering and the application of analytical tools. While cultural heritage holds many tangible and intangible values for local communities and society in general, there is a knowledge gap regarding suitable methods and data sources to measure the impacts and develop data-driven policies of cultural tourism. In the tourism sector, rapid developments are particularly taking place around novel uses of mobile positioning data, web scraping, and open application programming interface (API) data, data on sharing, and collaborative economy and passenger data. Based on feedback from 15 European cultural tourism regions, recommendations are developed regarding the use of innovative tools and data sources in tourism management. In terms of potential analytical depth, it is especially advisable to explore the use of mobile positioning data. Yet, there are considerable barriers, especially in terms of privacy protection and ethics, in using such data. User-generated big data from social media, web searches, and website visits constitute another promising data source as it is often publicly available in real time and has low usage barriers. Due to the emergence of new platform-based business models in the travel and tourism sector, special attention should be paid to improving access and usage of data on sharing and collaborative economy.

scholarly journals Big Data Value Chain: Multiple Perspectives for the Built Environment

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4624
Author(s):  
Gema Hernández-Moral ◽  
Sofía Mulero-Palencia ◽  
Víctor Iván Serna-González ◽  
Carla Rodríguez-Alonso ◽  
Roberto Sanz-Jimeno ◽  
...  
Keyword(s):  
Big Data ◽  
Built Environment ◽  
Value Chain ◽  
Energy Demand ◽  
New Technologies ◽  
Real Life ◽  
Multiple Perspectives ◽  
Energy Aware ◽  
Building Stock ◽  
Data Value

Current climate change threats and increasing CO2 emissions, especially from the building stock, represent a context where action is required. It is necessary to provide efficient manners to manage energy demand in buildings and contribute to a decarbonised future. By combining new technologies, such as artificial intelligence, Internet of things, blockchain, and the exploitation of big data towards solving real life problems, the way could be paved towards smart and energy-aware buildings. In this context, the aim of this paper is to present a critical review and an in-detail definition of the big data value chain for the built environment in Europe, covering multiple needs and perspectives: “policy”, “technology” and “business”, in order to explore the main challenges and opportunities in this area.

Radiation Minimisation and Outcome Maximisation in Cardiac Computed Tomograpy

2010 ◽  
Vol 6 (1) ◽  
pp. 15
Author(s):  
James P Earls ◽  
Jonathon A Leipsic ◽  
◽  
Keyword(s):  
Radiation Dose ◽  
Dose Reduction ◽  
New Technologies ◽  
Cardiac Computed Tomography ◽  
Substantial Reduction ◽  
Radiation Doses ◽  
Tube Current ◽  
Prospective Triggering ◽  
Effective Radiation ◽  
Selection Of

Recent reports have raised general awareness that cardiac computed tomography (CT) has the potential for relatively high effective radiation doses. While the actual amount of risk this poses to the patient is controversial, the increasing concern has led to a great deal of research on new CT techniques capable of imaging the heart at substantially lower radiation doses than was available only a few years ago. Methods of dose reduction include optimised selection of user-defined parameters, such as tube current and voltage, as well as use of new technologies, such as prospective triggering and iterative reconstruction. These techniques have each been shown to lead to substantial reduction in radiation dose without loss of diagnostic accuracy. This article will review the most frequently used and widely available methods for radiation dose reduction in cardiac CT and give practical advice on their use and limitations.

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