Ranking Hotels Research Articles

Measuring the efficiency of four star hotels in Makarska-Croatia using data envelopment analysis

This study explores the scale efficiency of four star hotels in a small tourist destination in Croatia. The number of overnight stays and the increase in hotel beds are two indicators of the development of a tourist destination. Among the accommodation facilities, hotels play a significant role in the development of a tourist destination, but they are increasingly facing a labor force crisis. Data envelopment analysis is used to rank hotels by efficiency coefficient. The aim of the paper is to investigate the efficiency of the hotel by taking certain inputs and outputs, which are explained in detail in the paper. The paper uses the CCR (Charnes, Cooper, and Rhodes) and BCC (Banker, Charnes, and Cooper) models to calculate hotel scale efficiency and also presents an overview of previous research around the world.

Clustering using ordered weighted averaging operator and 2-tuple linguistic model for hotel segmentation: The case of TripAdvisor

With the growth of online tourism, it is important to analyze the reviews left by numerous customers on social networks to improve the hotel's online reputation. Hotel segmentation based on online reviews has attracted an increasing interest from many academics. The problem is that many hotel segmentation models overlook the fact that some customers focus on positive reviews when choosing a hotel, while others focus on negative ones. To address this shortcoming, this paper develops a novel approach to classify hotels using the ordered weighted averaging (OWA) operator, the 2-tuple linguistic model, and K-means clustering. The proposed approach has been evaluated with a real dataset from TripAdvisor, which contains more than 50 million customer online reviews on eight aspects of the hotel. The results show that the proposed model can produce denser and more separated clusters than the model without linguistic quantifiers. From a business point of view, this model enables hotels to distinguish customers' perceptions (from the less demanding to the most demanding) about their eight aspects, allowing them to specify which of them need to be improved and develop strategies more quickly. At the same time, it introduces a new way of ranking hotels online, allowing customers to create personalized rankings of hotels based on their degree of demand for various hotel aspects (better location, cleaner rooms, etc.) rather than the average ratings, so that they can select the most suitable hotels more quickly.

Ranking hotels through multi-dimensional hotel information: a method considering travelers’ preferences and expectations

Ranking hotels through multi-dimensional hotel information: a method considering travelers’ preferences and expectations

Integrated Fuzzy AHP-TOPSIS Method to Analyze Green Management Practice in Hospitality Industry in the Sultanate of Oman

Climate change is the most serious threat that the modern world has ever faced. This has led to increasing attention from the government, industries, researchers, and practitioners on the theme of green practice. Due to the heightened awareness of climate change, the hospitality industry is under pressure to implement green practices and reduce the environmental impact of their operation. The research aims at understanding the indicators that define green practice in the hospitality industry and then developing a model that can be used to measure the green score. The research identifies twenty-six indicators of green practice in the hotel industry. These indicators were clustered into six different criteria. Based on the identified indicators and criteria, an integrated fuzzy AHP-TOPSIS method is proposed to calculate the green score. The fuzzy AHP method is used to calculate the weight of the criteria and indicators, while the fuzzy TOPSIS method is used to calculate the green score and rank hotels. The fuzzy AHP result shows that the criterion “Recycling and Reuse” has the highest weight among the identified criteria, while “Green Training and Incentives” has the lowest weight. The application of the proposed method is demonstrated by using a case study of hotels situated in the Sultanate of Oman. The result shows that the 4-star and 5-star hotels in the Sultanate have green scores between 0.56 and 0.641 out of 1.0 at a 95% confidence interval. The results further show that having a high star ranking hotel does not necessarily mean that the hotel is better in terms of green practice. The developed model helps the hotel industry to understand the indicator and criteria, as identified in this research work, they need to improve in order to improve their overall green management practice.

Combined Shannon's entropy and DEA: case of Indian hotel and restaurant sector

A non-parametric linear programming-based data envelopment analysis (DEA) has been broadly applied in many sectors to measure the performance of decision-making units (DMUs). Basic DEA models such as CCR and BCC can bifurcate the DMUs into efficient and inefficient sets but cannot further identify the most efficient DMUs. Numerous methods have been proposed to address this issue and allotted ranks to DMUs. The main objective of the current paper is to implement an integrated DEA with Shannon's entropy to measure efficiencies and to rank hotels and restaurants (H&R) operating in India. The present study is carried out by integrating an orientation independent CCR, orientation dependent BCC, and non-oriented SBM with Shannon's entropy. The combined DEA-Shannon's entropy method provides comprehensive efficiency scores (CES) to all DMUs. The adopted methods have been applied to the selected 45 H&R companies in India for the year 2019-2020.

An Effective Hotel Recommendation System through Processing Heterogeneous Data

Recommendation systems have recently gained a lot of popularity in various industries such as entertainment and tourism. They can act as filters of information by providing relevant suggestions to the users through processing heterogeneous data from different networks. Many travelers and tourists routinely rely on textual reviews, numerical ratings, and points of interest to select hotels in cities worldwide. To attract more customers, online hotel booking systems typically rank their hotels based on the recommendations from their customers. In this paper, we present a framework that can rank hotels by analyzing hotels’ customer reviews and nearby amenities. In addition, a framework is presented that combines the scores generated from user reviews and surrounding facilities. We perform experiments using datasets from online hotel booking platforms such as TripAdvisor and Booking to evaluate the effectiveness and applicability of the proposed framework. We first store the keywords extracted from reviews and assign weights to each considered unigram and bigram keywords and, then, we give a numerical score to each considered keyword. Finally, our proposed system aggregates the scores generated from the reviews and surrounding environments from different categories of the facilities. Experimental results confirm the effectiveness of the proposed recommendation framework.

Green performance evaluation and ranking hotels using fuzzy PROMETHEE

Green performance evaluation and ranking hotels using fuzzy PROMETHEE

Innovation radar in hospitality: a new procedure to evaluate the innovation in hotels

酒店服务创新雷达：论评估酒店创新的新流程研究目的本论文旨在提出一种新流程, 名为酒店服务创新雷达（IRH）, 其设计用来评估酒店的创新服务研究设计/方法/途径本论文通过系统文献综述的方法, 设计结构性问卷, 此问卷有31个问题, 其问题涉及酒店服务的12个维度：产品、平台、解决方案、顾客、顾客体验、价值获得、流程、组织、供应链、存在、网络、和品牌。本论文开发的IRH量表可以用来确认酒店服务业中五个创业阶段：基础运营、升级运营、基础创新者、中级创新者、和高级创新者。IRH在多个巴西酒店中得到了真实的测评研究结果IRH流程证明其是稳定的且能够评定酒店的创新级别。IRH流程将酒店体系地分在五个阶段。通过分析每个酒店, IRH流程显示酒店价格与IRH创新阶段之间并无偏差联系。研究实际意义IRH流程是自动结构性的量表, 用来衡量创新, 其中参考了消费者、平台、组织、研究人员、和政府等多重方面。研究原创性/价值本论文是首个科研项目, 提出这个结构性定量的流程, 以评估酒店的创新。IRH流程能够检测每个创新项目, 以作为酒店业中的创新预测指标。旅游业中IRH也可以作为很好的创新机制, 提供分析机制和评估监督公司。

Emotional intelligence or artificial intelligence– an employee perspective

ABSTRACTEmotional intelligence as personal intelligence and artificial intelligence as a machine intelligence have been popular in the relevant literature over the last two decades. The current study integrates these two concepts and explores how emotional and artificial intelligence influences employee retention and performance with a focus on service employees in the hotel industry. Employee performance is operationalised into internal and external dimensions that captures employees’ task efficiency over both internal and external service encounters with co-workers and customers respectively. The data were collected from a variety of different ranking hotels. The results show that emotional intelligence has a significant effect on employee retention and performance; whereas artificial intelligence plays a significant moderating role in employee performance. A discussion of the findings and implications concludes this paper.

Ranking hotels using aspect ratings based sentiment classification and interval-valued neutrosophic TOPSIS

Many a times, customers regret their decision when they book a hotel room purely on the basis of price or the hotel images available online. The customers look for additional information to substantiate their decision and this has led to the popularity of the usage of online feedbacks provided by guests towards various aspects of the hotel services. This feedback more appropriately called the electronic word-of-mouth is provided either in terms of some rating or textual comments. The numerical ratings of various service aspects of the hotels posted by guests provide a comprehensive evaluation of their sentiments and assessments on a standardized scale. Studying these sentiments is necessary in order to understand the customer needs and identify the improvement areas for hoteliers. Customers consider various alternatives and gather relevant aspect information before booking a hotel room. This involves evaluating the hotel alternatives on the basis of more than one hotel characteristics. This demands application of multi criteria decision making approach for ranking of hotels. The paper proposes a hotel ranking model based on the aspect ratings accessed from Tripadvisor website. The aspects play the role of criteria consisting of service, cleanliness, value, sleep quality, room, and location. These ratings are classified into positive, neutral, and negative sentiments, which are transformed to Neutrosophic numbers and results in the formation of interval-valued Neutrosophic decision matrix. Also, since the aspect weights are completely unknown, a non-linear programming model called maximizing deviation method is employed. Lastly, the aspect weights and decision matrix are combined to perform the procedure required for applying technique for order preferences by similarity to ideal solution method for ranking five alternative hotels. Future studies may extend the present model for various product selection problems for which product feature ratings are available.

An extended COPRAS model for multi-criteria decision-making problems and its application in web-based hotel evaluation and selection

Facilitation of suitable accommodation for different travellers is the prime concern of travel agencies. Travel agencies must keep themselves competitive and sustain a good pace of growth to continue raising profits by attracting and retaining as many tourists as possible through meeting their various prospective needs. To achieve this, the agencies must prepare well-organised data for hotels and destinations from a quality control perspective. Initially, the hotels are ranked and evaluated according to performance across several criteria from the tourists’ viewpoint. The relative importance of each criterion is mainly subjective and depends on the assessor’s judgement. Additionally, hotels’ rankings vary across different websites, resulting in inconsistencies. To handle such inconsistencies and subjectivity, this paper presents a collective decision-making evaluation framework by integrating a weighted interval rough number (WIRN) method and a WIRN-based complex proportional assessment (COPRAS) model to evaluate and rank hotels. An empirical example and a real-world case study from the Indian tourism industry are presented to validate the applicability of the proposed framework. Finally, a comparison and sensitivity analysis are performed to examine the validity and robustness of the proposed model.

RHALSA: Ranking Hotels using Aspect Level Sentiment Analysis

Opinion play a vital role which impacts the business in large extent. Users by-and-large depend on the reviews of others to avail the services. Online hotel bookings are becoming popular because of mobile applications being used on smart phones for placing the bookings from far away in advance along with the access to their reviews on different aspects. In this work, we present a data mining algorithm to rank the hotels based on the reviews provided by the users using aspect level sentiment analysis. The proposed Ranking Hotels using Aspect Level Sentiment Analysis (RHALSA) algorithm is evaluated conducting experiments on Tripadvisor data set to demonstrate its adequacy.

A New Method for Ranking Hotels Based on Group Decision Making in Honeybee Swarms

Currently, studies on group decision making are attracting considerable attention in many fields. With the increasing use of the Internet, there is an abundance of information available easily to us. However, it is difficult to evaluate accurately the reliability of such information, because much of it is colored by preferences and bias or opinion. It is therefore of interest to develop a group decisionmaking algorithm that can make appropriate decisions by processing this kind of information. Group decision-making algorithms are employed in many situations, a typical example being the evaluation of a hotel’s overall quality. However, current methods of evaluating hotels are not always reliable, because a guest may not be able to evaluate all hotels, and different guests have different preferences, or “inclinations.” As such, it is difficult to select the best hotel from a review of hotel rankings. In this paper, in an attempt to solve this problem, we focus on decision making that takes place in a honeybee swarm to select a nesting site. Although each bee has different inclinations, the honeybee swarm is capable of choosing the best nesting site. We applied this advantage to generate hotel rankings. We then conducted simulations that assumed practical application and confirmed that reviewers agreed on the top hotels, and the reliability of the top rankings improved, at the expense of decreased reliability of the lower rankings.

Analysis of Environmental Statements Issued by EMAS-Certified Spanish Hotels

In June 2005, Spain became the country with the second greatest number of sites and companies certified by the Ecomanagement and Audit Scheme (EMAS). Indeed, the Spanish hospitality industry led the way in certifications under this standard. An analysis of 115 environmental statements issued by EMAS-certified Spanish hotels finds that the data in the statements are difficult to compare. This study found that the current nature of the studies makes it unfeasible to rank hotels under this standard. The study found little difference in the release of environmental information according to certain specific hotel characteristics, including size, ownership, and location.

EVALUATING HOTEL ADVERTISEMENTS EFFICIENCY USING DATA ENVELOPMENT ANALYSIS

This paper introduces popular methods for ranking alternatives with multiple inputs and multiple outputs in the DEA context. The ranking methods are based on different criteria. Consequently, the ranking of the alternatives are not always the same, particularly as regards the best alternative. The decision maker, however, must make an absolute decision as to the most favored alternative. This study proposes a new ranking method, which is based on the average of the highly correlated ranking method. The new method is applied on a case study of ranking hotels in Israel.

Evaluating hotel advertisements efficiency using data envelopment analysis

Abstract This paper introduces popular methods for ranking alternatives with multiple inputs and multiple outputs in the DEA context. The ranking methods are based on different criteria. Consequently, the ranking of the alternatives are not always the same, particularly as regards the best alternative. The decision maker, however, must make an absolute decision as to the most favored alternative. This study proposes a new ranking method, which is based on the average of the highly correlated ranking method. The new method is applied on a case study of ranking hotels in Israel.

EVALUATING HOTEL ADVERTISEMENTS EFFICIENCY USING DATA ENVELOPMENT ANALYSIS

This paper introduces popular methods for ranking alternatives with multiple inputs and multiple outputs in the DEA context. The ranking methods are based on different criteria. Consequently, the ranking of the alternatives are not always the same, particularly as regards the best alternative. The decision maker, however, must make an absolute decision as to the most favored alternative. This study proposes a new ranking method, which is based on the average of the highly correlated ranking method. The new method is applied on a case study of ranking hotels in Israel.