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This study aims to propose a data-driven approach, based on open-source tools, that makes it possible to understand customer satisfaction of the accommodation offer of a whole country.

The method starts by extracting information from all hotels of Portugal available at TripAdvisor through Web scraping. Then, a support vector machine is adopted for modeling the TripAdvisor score, which is considered a proxy of customer satisfaction. Finally, knowledge extraction from the model is achieved using sensitivity analysis to unveil the influence of features on the score.

The model of the TripAdvisor score achieved a mean absolute percentage error of around 5 per cent, proving the value of modeling the extracted data. The number of rooms of the unit and the minimum price are the two most relevant features, showing that customers appreciate smaller and more expensive units, whereas the location of the hotel does not hold significant relevance.

National tourist offices can use the proposed approach to understand what drives tourists’ satisfaction, helping to shape a country’s strategy. For example, licensing new hotels may take into account the unit size and other characteristics that make it more attractive to tourists. Furthermore, the procedure can be replicated at any time and in any country, making it a valuable tool for data-driven decision support on a national scale.

Airbnb Experiences is a new type of service launched by Airbnb in November 2016, where users can offer travellers a wide range of activities. This study devotes attention to analysing customer feedback expressed in online reviews published in Airbnb to evaluate those experiences.

A total of 1,110 reviews were collected from 12 categories, including 111 experiences, resulting in 10 reviews per experience. First, the sentiment score was computed based on the text of the reviews. Second, 17 quantitative features encompassing user, Airbnb experience and review information were used to model the score through a support vector machine. Third, a sensitivity analysis was performed to extract knowledge on the most relevant features influencing the sentiment score.

Tourists writing online reviews are not only influenced by their tourist experience but also by their own online experience with the booking and online review platform. The number of reviews made by the user accounted for more than 20 per cent of relevance, while users with more reviews tended to grant more positive reviews.

Current literature is enhanced with a conceptual model grounded on existing studies that assess tourist satisfaction with tour services. Both services online visibility and user characteristics have shown significant importance to tourist satisfaction, adding to the existing body of knowledge.

Social media has become the main venue for users to express their opinions and feelings, generating a vast number of available and valuable data to be scrutinized by researchers and marketers. This paper aims to extend previous studies analyzing social media reviews through text mining and sentiment analysis to provide useful recommendations for management in the restaurant industry.

The Lexalytics, a text mining artificial intelligence tool, is applied to analyze the text of the online reviews of the restaurants in a touristic Dutch village extracted from the most frequently used social media platforms focusing on the four restaurant quality factors, namely, food and beverage, service, atmosphere and value.

The findings of this research are presented by the identified key themes with comparisons of the customers’ review sentiment between a selected restaurant, Zwaantje, vis-à-vis its bench-mark restaurants set by a specific approach under the abovementioned quality dimensions, in which the food and beverage and service are the most commented by customers. Results demonstrate that text mining can generate insights from different aspects and that the proposed approach is valuable to restaurant management.

The paper provides a relatively big scale in numbers and resources of social media reviews to further explore the most important service dimensions in the restaurant industry in a specific tourist area. It also offers a useful framework to apply the text mining business intelligence tool by comparison of peers for local small business restaurant practitioners to improve their management skills beyond manually reading social media reviews.

This study aims to present a model drawn on both the extension of the unified theory of acceptance and use of technology (UTAUT2) and the perceived value for explaining consumer behavior toward mobile hospitality services (MHS) from two perspectives: intention to use and recommendation.

The partial least square (PLS) was applied to data gathered from 348 validated responses to a survey to test a number of research hypotheses.

Results found that the proposed conceptual model explains 62 per cent of the intention to use of MHS and 51 per cent of the variation in the recommendation. Perceived value plays a role in explaining both the intention to use and recommend MHS, with both constructs also helping in explaining behavior intention, to which effort expectancy, facilitating conditions and performance expectancy also contribute.

This research goes beyond perceived value by combining it with a cornerstone model, UTAUT2, used in technology adoption studies. The paper addresses updated MHS that include but are not limited to mobile hotel reservations.

To describe a robot designed and built to operate in outdoor environments hostile to the human presence, such as debris resulting from the collapse of built structures, and targeted to the tele‐operated detection of potential survivors using a set of specific sensors whose information is transmitted to a remote human operator.

RAPOSA's mechanical structure is composed of a main body and a front body, whose locomotion is supported on tracked wheels, allowing motion even when the robot is upside down. The front body has variable tilting capabilities, providing means to overcome edges higher than the robot main body (e.g. when climbing a stair) and is also useful to grab the lower ground when only the main body has ground contact. This front body has one thermal camera and two webcameras installed. Additional sensors include gas, temperature and humidity sensors, web cams, light diodes, microphone and loudspeaker. The robot uses wireless communications, with an option for tethered operation.

The robot was tested in several scenarios of the Fire Fighters school. In this particular exercise, the robot reduced the inspection time down to 25 percent of the time that specialized firefighters teams would take to finish the exercise. This was due to the fact that the firefighters need to stabilize the environment in order to reduce live threats. In this case, as in many other similar situations, not only the robot provides a faster inspection method, but also a much safer one.

The tether carries both power and communications, with an access point on its end. Docking and undocking the robot to the tether is accomplished remotely by the operator with the help of a camera located inside the robot, and represents the most innovative feature of RAPOSA.

This paper seeks to discuss the process and results of a scenario exercise applied to the Portuguese tourism and hospitality industries with a 2020 horizon.

Through an intuitive‐logical process adapted to the specific purposes of the research, four alternative scenarios of the evolution of the industry were developed. The exercise involved three workshops, with the participation of a group of experts, academics, representatives from industry and local government.

Based on the nature and dynamics of the key forces and trends, the four scenarios identified are: southern experience; global emotions; “sin surprise”; and non‐charming Portugal. In all scenarios there is increased global competition amongst tourist destinations. A desire for authenticity opens the opportunity for Portugal as a tourist destination but it also poses challenges for managers and policy makers. These scenarios are of practical use for managers, policy makers and other practitioners as they provide a framework for the development and assessment of robust strategies and policies, i.e. strategies that will survive several kinds of external development.

The paper contributes to the knowledge of scenario methods in practice and to help policy planners and managers recognize, consider and reflect on uncertainties they are likely to face. The work is also valuable for the international foresight community by discussing the method and addressing some drawbacks of the scenario exercise and recommendations for practice.

The purpose of the research project was to examine the process of developing a data sharing framework between different public sector organisations.

A two-year case study of a data sharing project between a UK fire and rescue service, local council, NHS primary care trust and a police force was undertaken.

It is important to carefully determine the requirements for data sharing, to establish data sharing agreements, to have secure arrangements for data sharing, and to ensure compliance with data protection legislation.

Data sharing between public sector organisations can operate effectively if appropriate care is taken when creating data sharing agreements between partner organisations.

Data sharing can assist in reducing duplication of effort between public sector organisations and can reduce costs and enable more co-ordinated provision of public services.

The detailed analysis of a data sharing case study identified the need for a systematic data sharing framework. Such a framework is proposed and illustrated with practical examples of specification, implementation and evaluation.

The purpose of this paper is to describe the development and evaluation of a geographical information system (GIS) testing framework that was used to test a fire prevention support GIS.

A year‐long case study was undertaken concerning the testing of a fire prevention support GIS in a UK fire and rescue service.

The GIS testing framework developed involved testing the different components of a GIS, testing their interactions, and then testing the system as a whole. Since GISs contain different components such as spatial analyses and map‐based output, this supports the adoption of a different testing framework compared to existing types of information systems.

GISs will typically be used by organisations for decision making. Clearly if the information presented by a GIS is inaccurate, unrepresentative, or unreliable, then the decision‐making process can be undermined.

This is particularly important with regard to GISs used by emergency services (such as the fire and rescue service studied) where lives could potentially be put at risk by erroneous information provided by such systems.

Previous research had indicated that GISs may be inadequately tested. The framework developed for GISs testing provided a systematic testing approach, reducing the likelihood of errors in such systems.

The purpose of this paper is to present a lightweight approach to help diagnose and eliminate issues in existing business processes, which cause participants to resist following them as modelled. The analysis is made accessible by the use of drillable graphical dashboards.

Two action research cycles in two distinct industries were used to test and refine the approach, while also solving the specific organizational issues.

The approach was considered simple to use and proved capable of identifying pain points causing friction in the smooth operation of business processes. Various longer-term positive effects were reported by one of the organizations that is ISO 9001-certified.

This type of research benefits from experiments in new cases with different contexts that can challenge it. In particular, it would be interesting to evaluate the approach in an organization with a more ad hoc view of processes, as opposed to the more standards-based cases of this paper.

Using the proposed approach to tune the processes, so that participants are more willing to follow them, removes some inconsistency of operations and potential non-conformities in audits.

The proposed approach is aimed at the “social sustainability” of the business processes, as it seeks to eliminate people grievances with those processes and make them sustainable in the long term.

Although there is a lot written about process improvement, the literature is scarce in lightweight, pragmatic approaches to identify and resolve the social aspects that cause participants to deviate from the processes, or see them as a burden instead of valuable help for their everyday tasks.

To investigate the feasibility of using artificial neural networks for conceptual design of bracings systems for tall steel buildings.

Database of 234 design examples has been developed using commercially available detailed design software. These examples represent building up to 20 storeys. Feed forward back‐propagation neural network is trained on these examples. The results obtained from the artificial neural network are evaluated by re‐substitution, hold‐out and ten‐fold cross‐validation techniques.

Results indicate that artificial neural network would give a performance of 97.91 percent (ten‐fold cross‐validation). The performance of this system is benchmarked by developing a binary logistic regression model from the same data. Performance of the two models has been compared using McNemar's test and receiver operation characteristics curves. Artificial neural network shows a better performance. The difference is found to be statically significant.

The developed model is applicable only to steel building up to 20 storeys. The feasibility of using artificial neural networks for conceptual design of bracings systems for tall steel buildings more than 20 storeys has not been investigated.

Implementation of the broad methodology outlined for the use of neural networks can be accomplished by conducting short training courses. This will provide personnel with flexibility in addressing buildings‐specifics bracing conditions and limitations.

In tall building design a lot of progress has been made in the development of software tools for numerical intensive tasks of analysis, design and optimization, however, professional software tools are not available to help the designer to choose an optimum building configuration at the conceptual design stage. The presented research provides a methodology to investigate the feasibility of using artificial neural networks for conceptual design of bracings systems for tall buildings. It is found that this approach for the selection of bracings in tall buildings is a better and cost effective option compared with database generated on the basis of expert opinion. It also correctly classifies and recommends the type of trussed bracing system.