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Prof. Dr. Nuno Antonio
Nova IMS, Nova Information Management School, Universidade Nova de Lisboa Campus de Campolide

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Journal article
Published: 10 April 2021 in Applied Sciences
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The present COVID-19 pandemic is happening in a strongly interconnected world. This interconnection explains why it became universal in such a short period of time and why it stimulated the creation of a large amount of relevant open data. In this paper, we use data science tools to explore this open data from the moment the pandemic began and across the first 250 days of prevalence before vaccination started. The use of unsupervised machine learning techniques allowed us to identify three clusters of countries and territories with similar profiles of standardized COVID-19 time dynamics. Although countries and territories in the three clusters share some characteristics, their composition is not homogenous. All these clusters contain countries from different geographies and with different development levels. The use of descriptive statistics and data visualization techniques enabled the description and understanding of where and how COVID-19 was impacting. Some interesting extracted features are discussed and suggestions for future research in this area are also presented.

ACS Style

Nuno António; Paulo Rita; Pedro Saraiva. COVID-19: Worldwide Profiles during the First 250 Days. Applied Sciences 2021, 11, 3400 .

AMA Style

Nuno António, Paulo Rita, Pedro Saraiva. COVID-19: Worldwide Profiles during the First 250 Days. Applied Sciences. 2021; 11 (8):3400.

Chicago/Turabian Style

Nuno António; Paulo Rita; Pedro Saraiva. 2021. "COVID-19: Worldwide Profiles during the First 250 Days." Applied Sciences 11, no. 8: 3400.

Journal article
Published: 22 January 2021 in Information
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An accurate short-term load forecasting (STLF) is one of the most critical inputs for power plant units’ planning commitment. STLF reduces the overall planning uncertainty added by the intermittent production of renewable sources; thus, it helps to minimize the hydrothermal electricity production costs in a power grid. Although there is some research in the field and even several research applications, there is a continual need to improve forecasts. This research proposes a set of machine learning (ML) models to improve the accuracy of 168 h forecasts. The developed models employ features from multiple sources, such as historical load, weather, and holidays. Of the five ML models developed and tested in various load profile contexts, the Extreme Gradient Boosting Regressor (XGBoost) algorithm showed the best results, surpassing previous historical weekly predictions based on neural networks. Additionally, because XGBoost models are based on an ensemble of decision trees, it facilitated the model’s interpretation, which provided a relevant additional result, the features’ importance in the forecasting.

ACS Style

Ernesto Aguilar Madrid; Nuno Antonio. Short-Term Electricity Load Forecasting with Machine Learning. Information 2021, 12, 50 .

AMA Style

Ernesto Aguilar Madrid, Nuno Antonio. Short-Term Electricity Load Forecasting with Machine Learning. Information. 2021; 12 (2):50.

Chicago/Turabian Style

Ernesto Aguilar Madrid; Nuno Antonio. 2021. "Short-Term Electricity Load Forecasting with Machine Learning." Information 12, no. 2: 50.

Research article
Published: 25 December 2020 in Current Issues in Tourism
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Lessons from previous endemics/pandemics show which type/timing of public health measures had a significant influence on the impact of diseases. However, those show that public health measures and travel restrictions represent a significant burden on countries’ economies, especially in the tourism industry. This study aims to investigate whether a country’s dependence on tourism might influence the time/nature of pandemic mitigation measures and the impact of the pandemic on tourism, particularly in the hospitality sector. To achieve a comprehensive/multidimensional perspective, 12 European countries were studied based on the collection of data from 6 different sources: cases/deaths caused by the disease, economic indicators, public health measures, rooms supply/demand, reservation/cancellation rates, demographic and healthcare system characteristics. Using data science techniques/methods allowed to verify that the dependence of some countries on tourism did not make them to have a different behaviour in terms of the application of measures. Despite the differences in the timings/types of measures implemented, tourism was highly affected in all countries.

ACS Style

Nuno António; Paulo Rita. March 2020: 31 days that will reshape tourism. Current Issues in Tourism 2020, 1 -16.

AMA Style

Nuno António, Paulo Rita. March 2020: 31 days that will reshape tourism. Current Issues in Tourism. 2020; ():1-16.

Chicago/Turabian Style

Nuno António; Paulo Rita. 2020. "March 2020: 31 days that will reshape tourism." Current Issues in Tourism , no. : 1-16.

Data article
Published: 24 November 2020 in Data in Brief
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This data article describes a hotel customer dataset with 31 variables describing a total of 83,590 instances (customers). It comprehends three full years of customer behavioral data. In addition to personal and behavioral information, the dataset also contains demographic and geographical information. This dataset contributes to reducing the lack of real-world business data that can be used for educational and research purposes. The dataset can be used in data mining, machine learning, and other analytical field problems in the scope of data science. Due to its unit of analysis, it is a dataset especially suitable for building customer segmentation models, including clustering and RFM (Recency, Frequency, and Monetary value) models, but also be used in classification and regression problems.

ACS Style

Nuno Antonio; Ana de Almeida; Luís Nunes. A hotel's customers personal, behavioral, demographic, and geographic dataset from Lisbon, Portugal (2015–2018). Data in Brief 2020, 33, 106583 .

AMA Style

Nuno Antonio, Ana de Almeida, Luís Nunes. A hotel's customers personal, behavioral, demographic, and geographic dataset from Lisbon, Portugal (2015–2018). Data in Brief. 2020; 33 ():106583.

Chicago/Turabian Style

Nuno Antonio; Ana de Almeida; Luís Nunes. 2020. "A hotel's customers personal, behavioral, demographic, and geographic dataset from Lisbon, Portugal (2015–2018)." Data in Brief 33, no. : 106583.

Journal article
Published: 19 November 2020 in Sustainability
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This study explores two World Heritage Sites (WHS) as tourism destinations by applying several uncommon techniques in these settings: Smart Tourism Analytics, namely Text mining, Sentiment Analysis, and Market Basket Analysis, to highlight patterns according to attraction, nationality, and repeated visits. Salamanca (Spain) and Coimbra (Portugal) are analyzed and compared based on 8,638 online travel reviews (OTR), from TripAdvisor (2017–2018). Findings show that WHS reputation does not seem to be relevant to visitors-reviewers. Additionally, keyword extraction reveals that the reviews do not differ from language to language or from city to city, and it was also possible to identify several keywords related to history and heritage; in particular, architectural styles, names of kings, and places. The study identifies topics that could be used by destination management organizations to promote these cities, highlights the advantages of applying a data science approach, and confirms the rich information value of OTRs as a tool to (re)position the destination according to smart tourism design tenets.

ACS Style

Nuno Antonio; Marisol Correia; Filipa Ribeiro. Exploring User-Generated Content for Improving Destination Knowledge: The Case of Two World Heritage Cities. Sustainability 2020, 12, 9654 .

AMA Style

Nuno Antonio, Marisol Correia, Filipa Ribeiro. Exploring User-Generated Content for Improving Destination Knowledge: The Case of Two World Heritage Cities. Sustainability. 2020; 12 (22):9654.

Chicago/Turabian Style

Nuno Antonio; Marisol Correia; Filipa Ribeiro. 2020. "Exploring User-Generated Content for Improving Destination Knowledge: The Case of Two World Heritage Cities." Sustainability 12, no. 22: 9654.

Journal article
Published: 17 July 2019 in Journal of Travel Research
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This study examines the relationship between distance measures and a Portuguese data set consisting of 34,622 online hotel reviews extracted from Booking.com and TripAdvisor written in Portuguese, Spanish, and English. Based on the country of origin of each review author, a geographic and a psychic distance measure is calculated for Portugal. Data and text mining analysis provides additional insights into online hotel ratings. The authors confirm that online travelers’ evaluations are multifaceted constructs displaying varying patterns of rating behavior among the traveler base. By investigating the contemporary relevance of geographic and psychic distance, a key finding of this study is that travelers with less distance both in terms of psychic and geographic distance give a lower rating score than travelers with greater distance. The inclusion of psychic and geographic distance is advocated as a salient aspect for future researchers and for those practitioners who wish to enhance hotel product and service features.

ACS Style

Paul Phillips; Nuno Antonio; Ana Maria de Almeida; Luís Nunes. The Influence of Geographic and Psychic Distance on Online Hotel Ratings. Journal of Travel Research 2019, 59, 722 -741.

AMA Style

Paul Phillips, Nuno Antonio, Ana Maria de Almeida, Luís Nunes. The Influence of Geographic and Psychic Distance on Online Hotel Ratings. Journal of Travel Research. 2019; 59 (4):722-741.

Chicago/Turabian Style

Paul Phillips; Nuno Antonio; Ana Maria de Almeida; Luís Nunes. 2019. "The Influence of Geographic and Psychic Distance on Online Hotel Ratings." Journal of Travel Research 59, no. 4: 722-741.

Journal article
Published: 08 July 2019 in Data Science Journal
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ACS Style

Nuno Antonio; Ana De Almeida; Luis Nunes. An Automated Machine Learning Based Decision Support System to Predict Hotel Booking Cancellations. Data Science Journal 2019, 18, 1 .

AMA Style

Nuno Antonio, Ana De Almeida, Luis Nunes. An Automated Machine Learning Based Decision Support System to Predict Hotel Booking Cancellations. Data Science Journal. 2019; 18 (1):1.

Chicago/Turabian Style

Nuno Antonio; Ana De Almeida; Luis Nunes. 2019. "An Automated Machine Learning Based Decision Support System to Predict Hotel Booking Cancellations." Data Science Journal 18, no. 1: 1.

Research article
Published: 29 May 2019 in Cornell Hospitality Quarterly
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In the hospitality industry, demand forecast accuracy is highly impacted by booking cancellations, which makes demand-management decisions difficult and risky. In attempting to minimize losses, hotels tend to implement restrictive cancellation policies and employ overbooking tactics, which, in turn, reduce the number of bookings and reduce revenue. To tackle the uncertainty arising from booking cancellations, we combined the data from eight hotels’ property management systems with data from several sources (weather, holidays, events, social reputation, and online prices/inventory) and machine learning interpretable algorithms to develop booking cancellation prediction models for the hotels. In a real production environment, improvement of the forecast accuracy due to the use of these models could enable hoteliers to decrease the number of cancellations, thus, increasing confidence in demand-management decisions. Moreover, this work shows that improvement of the demand forecast would allow hoteliers to better understand their net demand, that is, current demand minus predicted cancellations. Simultaneously, by focusing not only on forecast accuracy but also on its explicability, this work illustrates one other advantage of the application of these types of techniques in forecasting: the interpretation of the predictions of the model. By exposing cancellation drivers, models help hoteliers to better understand booking cancellation patterns and enable the adjustment of a hotel’s cancellation policies and overbooking tactics according to the characteristics of its bookings.

ACS Style

Nuno Antonio; Ana Maria de Almeida; Luís Nunes. Big Data in Hotel Revenue Management: Exploring Cancellation Drivers to Gain Insights Into Booking Cancellation Behavior. Cornell Hospitality Quarterly 2019, 60, 298 -319.

AMA Style

Nuno Antonio, Ana Maria de Almeida, Luís Nunes. Big Data in Hotel Revenue Management: Exploring Cancellation Drivers to Gain Insights Into Booking Cancellation Behavior. Cornell Hospitality Quarterly. 2019; 60 (4):298-319.

Chicago/Turabian Style

Nuno Antonio; Ana Maria de Almeida; Luís Nunes. 2019. "Big Data in Hotel Revenue Management: Exploring Cancellation Drivers to Gain Insights Into Booking Cancellation Behavior." Cornell Hospitality Quarterly 60, no. 4: 298-319.

Journal article
Published: 31 January 2019 in Tourism & Management Studies
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ACS Style

Nuno Antonio; Ana Maria de Almeida; Luís Nunes; Portugal Instituto Universitário De Lisboa (Iscte-Iul). Predictive models for hotel booking cancellation: a semi-automated analysis of the literature. Tourism & Management Studies 2019, 15, 7 -21.

AMA Style

Nuno Antonio, Ana Maria de Almeida, Luís Nunes, Portugal Instituto Universitário De Lisboa (Iscte-Iul). Predictive models for hotel booking cancellation: a semi-automated analysis of the literature. Tourism & Management Studies. 2019; 15 (1):7-21.

Chicago/Turabian Style

Nuno Antonio; Ana Maria de Almeida; Luís Nunes; Portugal Instituto Universitário De Lisboa (Iscte-Iul). 2019. "Predictive models for hotel booking cancellation: a semi-automated analysis of the literature." Tourism & Management Studies 15, no. 1: 7-21.

Data article
Published: 29 November 2018 in Data in Brief
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This data article describes two datasets with hotel demand data. One of the hotels (H1) is a resort hotel and the other is a city hotel (H2). Both datasets share the same structure, with 31 variables describing the 40,060 observations of H1 and 79,330 observations of H2. Each observation represents a hotel booking. Both datasets comprehend bookings due to arrive between the 1st of July of 2015 and the 31st of August 2017, including bookings that effectively arrived and bookings that were canceled. Since this is hotel real data, all data elements pertaining hotel or costumer identification were deleted. Due to the scarcity of real business data for scientific and educational purposes, these datasets can have an important role for research and education in revenue management, machine learning, or data mining, as well as in other fields.

ACS Style

Nuno Antonio; Ana Maria de Almeida; Luís Nunes. Hotel booking demand datasets. Data in Brief 2018, 22, 41 -49.

AMA Style

Nuno Antonio, Ana Maria de Almeida, Luís Nunes. Hotel booking demand datasets. Data in Brief. 2018; 22 ():41-49.

Chicago/Turabian Style

Nuno Antonio; Ana Maria de Almeida; Luís Nunes. 2018. "Hotel booking demand datasets." Data in Brief 22, no. : 41-49.

Journal article
Published: 06 November 2018 in International Journal of Contemporary Hospitality Management
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Purpose This paper aims to develop a model to predict online review ratings from multiple sources, which can be used to detect fraudulent reviews and create proprietary rating indexes, or which can be used as a measure of selection in recommender systems. Design/methodology/approach This study applies machine learning and natural language processing approaches to combine features derived from the qualitative component of a review with the corresponding quantitative component and, therefore, generate a richer review rating. Findings Experiments were performed over a collection of hotel online reviews – written in English, Spanish and Portuguese – which shows a significant improvement over the previously reported results, and it not only demonstrates the scientific value of the approach but also strengthens the value of review prediction applications in the business environment. Originality/value This study shows the importance of building predictive models for revenue management and the application of the index generated by the model. It also demonstrates that, although difficult and challenging, it is possible to achieve valuable results in the application of text analysis across multiple languages.

ACS Style

Nuno Antonio; Ana Maria de Almeida; Luís Nunes; Fernando Batista; Ricardo Ribeiro. Hotel online reviews: creating a multi-source aggregated index. International Journal of Contemporary Hospitality Management 2018, 30, 3574 -3591.

AMA Style

Nuno Antonio, Ana Maria de Almeida, Luís Nunes, Fernando Batista, Ricardo Ribeiro. Hotel online reviews: creating a multi-source aggregated index. International Journal of Contemporary Hospitality Management. 2018; 30 (12):3574-3591.

Chicago/Turabian Style

Nuno Antonio; Ana Maria de Almeida; Luís Nunes; Fernando Batista; Ricardo Ribeiro. 2018. "Hotel online reviews: creating a multi-source aggregated index." International Journal of Contemporary Hospitality Management 30, no. 12: 3574-3591.

Journal article
Published: 31 August 2018 in Acta Médica Portuguesa
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ACS Style

Érica Rocha; Paulo Faria De Sousa; Nuno Antonio; Susana Medeiros; Miguel Julião. Carta ao Editor: O Conceito de Dignidade em Idosos Não-institucionalizados Seguidos em Cuidados de Saúde Primários: Um Modelo Empírico Preliminar. Acta Médica Portuguesa 2018, 31, 441 -442.

AMA Style

Érica Rocha, Paulo Faria De Sousa, Nuno Antonio, Susana Medeiros, Miguel Julião. Carta ao Editor: O Conceito de Dignidade em Idosos Não-institucionalizados Seguidos em Cuidados de Saúde Primários: Um Modelo Empírico Preliminar. Acta Médica Portuguesa. 2018; 31 (7-8):441-442.

Chicago/Turabian Style

Érica Rocha; Paulo Faria De Sousa; Nuno Antonio; Susana Medeiros; Miguel Julião. 2018. "Carta ao Editor: O Conceito de Dignidade em Idosos Não-institucionalizados Seguidos em Cuidados de Saúde Primários: Um Modelo Empírico Preliminar." Acta Médica Portuguesa 31, no. 7-8: 441-442.

Journal article
Published: 20 March 2018 in Tourism & Management Studies
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This study main objective was to assess the viability of development of a Performance Management (PM) system, delivered in the form of Software as a Service (SaaS), specific for the hospitality industry and to evaluate the benefits of its use. Software deployed in the cloud, delivered and licensed as a service, is becoming increasingly common and accepted in a business context. Although, Business Intelligence (BI) solutions are not usually distributed in the SaaS model, there are some examples that this is changing. To achieve the study objective, design science research methodology was employed in the development of a prototype. This prototype was deployed in four hotels and its results evaluated. Evaluation of the prototype was focused both on the system technical characteristics and business benefits. Results shown that hotels were very satisfied with the system and that building a prototype and making it available in the form of SaaS is a good solution to assess BI systems contribution to improve management performance.

ACS Style

Nuno António; Francisco Serra; Universidade do Algarve. Software as a Service: An effective platform to deliver holistic Hotel Performance Management systems. Tourism & Management Studies 2018, 14, 25 -35.

AMA Style

Nuno António, Francisco Serra, Universidade do Algarve. Software as a Service: An effective platform to deliver holistic Hotel Performance Management systems. Tourism & Management Studies. 2018; 14 (1):25-35.

Chicago/Turabian Style

Nuno António; Francisco Serra; Universidade do Algarve. 2018. "Software as a Service: An effective platform to deliver holistic Hotel Performance Management systems." Tourism & Management Studies 14, no. 1: 25-35.

Original research
Published: 08 March 2018 in Information Technology & Tourism
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Online reviews are one of the main influencers of hotel purchase decisions. This study performs an analysis of reviews extracted from well-known online review sources in combination with hotel sales data and concludes that ratings differ according to the language of reviews. Data science tools have been applied to English, Spanish, and Portuguese reviews, revealing that reviews written in English achieve higher ratings when compared with Spanish or Portuguese reviews. A new visualization method is proposed to quickly depict the sentiment of main topics mentioned in reviews, clearly revealing that not all customers are influenced by reviews in the same way or look for the same things in a hotel. This study has great implications for online reviews research and for hotel management as it clearly shows that language can be used to identify preferences of guests from different origins and because it gives hoteliers more information on how to provide a better service according to guests’ cultural background.

ACS Style

Nuno Antonio; Ana De Almeida; Luis Nunes; Fernando Batista; Ricardo Ribeiro. Hotel online reviews: different languages, different opinions. Information Technology & Tourism 2018, 18, 157 -185.

AMA Style

Nuno Antonio, Ana De Almeida, Luis Nunes, Fernando Batista, Ricardo Ribeiro. Hotel online reviews: different languages, different opinions. Information Technology & Tourism. 2018; 18 (1-4):157-185.

Chicago/Turabian Style

Nuno Antonio; Ana De Almeida; Luis Nunes; Fernando Batista; Ricardo Ribeiro. 2018. "Hotel online reviews: different languages, different opinions." Information Technology & Tourism 18, no. 1-4: 157-185.

Conference paper
Published: 01 December 2017 in 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA)
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Booking cancellations have significant impact on demand-management decisions in the hospitality industry. To mitigate the effect of cancellations, hotels implement rigid cancellation policies and overbooking tactics, which in turn can have a negative impact on revenue and on the hotel reputation. To reduce this impact, a machine learning based system prototype was developed. It makes use of the hotel's Property Management Systems data and trains a classification model every day to predict which bookings are “likely to cancel” and with that calculate net demand. This prototype, deployed in a production environment in two hotels, by enforcing A/B testing, also enables the measurement of the impact of actions taken to act upon bookings predicted as “likely to cancel”. Results indicate good prototype performance and provide important indications for research progress whilst evidencing that bookings contacted by hotels cancel less than bookings not contacted.

ACS Style

Nuno António; Ana De Almeida; Luis Nunes. Predicting Hotel Bookings Cancellation with a Machine Learning Classification Model. 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) 2017, 1049 -1054.

AMA Style

Nuno António, Ana De Almeida, Luis Nunes. Predicting Hotel Bookings Cancellation with a Machine Learning Classification Model. 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA). 2017; ():1049-1054.

Chicago/Turabian Style

Nuno António; Ana De Almeida; Luis Nunes. 2017. "Predicting Hotel Bookings Cancellation with a Machine Learning Classification Model." 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) , no. : 1049-1054.

Journal article
Published: 30 April 2017 in Tourism & Management Studies
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ACS Style

Nuno Antonio; Ana De Almeida; Luis Nunes. Predicting hotel booking cancellations to decrease uncertainty and increase revenue. Tourism & Management Studies 2017, 13, 25 -39.

AMA Style

Nuno Antonio, Ana De Almeida, Luis Nunes. Predicting hotel booking cancellations to decrease uncertainty and increase revenue. Tourism & Management Studies. 2017; 13 (2):25-39.

Chicago/Turabian Style

Nuno Antonio; Ana De Almeida; Luis Nunes. 2017. "Predicting hotel booking cancellations to decrease uncertainty and increase revenue." Tourism & Management Studies 13, no. 2: 25-39.

Chapter
Published: 01 January 2017 in Handbook of Research on Holistic Optimization Techniques in the Hospitality, Tourism, and Travel Industry
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Booking cancellations in the hospitality industry not only generate revenue loss and affect pricing and inventory allocation decisions, but they also, in overbooking situations, have the potential to affect the hotel's online social reputation. By employing data sets from four resort hotels and addressing this issue as a classification problem in the scope of data science, the authors demonstrate that it is possible to build models for predicting booking cancellations with accuracy results in excess of 90%. This research also demonstrates that despite what was alleged by Morales and Wang (2010), it is possible to predict with high accuracy whether a booking will be canceled. Results allow hotel managers to act on bookings with high cancellation probability and contain the associated revenue losses, produce better net demand forecasts, improve overbooking/cancellation policies, and have more assertive pricing and inventory allocation strategies.

ACS Style

Nuno António; Ana de Almeida; Luis M. M. Nunes. Using Data Science to Predict Hotel Booking Cancellations. Handbook of Research on Holistic Optimization Techniques in the Hospitality, Tourism, and Travel Industry 2017, 141 -167.

AMA Style

Nuno António, Ana de Almeida, Luis M. M. Nunes. Using Data Science to Predict Hotel Booking Cancellations. Handbook of Research on Holistic Optimization Techniques in the Hospitality, Tourism, and Travel Industry. 2017; ():141-167.

Chicago/Turabian Style

Nuno António; Ana de Almeida; Luis M. M. Nunes. 2017. "Using Data Science to Predict Hotel Booking Cancellations." Handbook of Research on Holistic Optimization Techniques in the Hospitality, Tourism, and Travel Industry , no. : 141-167.

Journal article
Published: 22 December 2015 in Dos Algarves: A Multidisciplinary e-Journal
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ACS Style

Nuno António; Francisco Serra. The use of design science research in the development of a performance management system for hospitality. Dos Algarves: A Multidisciplinary e-Journal 2015, 26, 23 -46.

AMA Style

Nuno António, Francisco Serra. The use of design science research in the development of a performance management system for hospitality. Dos Algarves: A Multidisciplinary e-Journal. 2015; 26 (2):23-46.

Chicago/Turabian Style

Nuno António; Francisco Serra. 2015. "The use of design science research in the development of a performance management system for hospitality." Dos Algarves: A Multidisciplinary e-Journal 26, no. 2: 23-46.