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This study aims to identify the key hotel characteristics and facilities that significantly influence customer satisfaction based on Google review scores. By applying decision tree analysis, the research seeks to determine which aspects, such as service quality, hotel facilities and location, play pivotal roles in shaping customer experiences. The objective is to provide professional with practical recommendations to improve service quality and cultivate enduring customer loyalty.

The research used a data set collected from Hotels.com, featuring various characteristics of 802 hotels in Izmir Province. Decision tree analysis was conducted using Classification and Regression Tree algorithm to explore the relationship between hotel characteristics and facilities with customer satisfaction.

The analysis revealed that the number of rooms is the primary factor influencing hotel ratings, with proximity to the airport and hotel classification also being significant. Additional factors such as public transportation distance and laundry services were important, while facilities such as ATMs, beach access and spas showed no significant impact on customer satisfaction. These findings emphasize the importance of core facilities and accessibility.

This study contributes to the literature by offering a novel approach, using decision tree analysis to assess hotel customer satisfaction with structured data. It provides practical implications for hotel managers, enabling them to make data-driven improvements to achieve customer satisfaction. The integration rules created by the decision tree model into hotel management systems can enhance operational efficiency and competitive advantage in the hospitality industry.

Predicting customers’ purchase behaviors is a challenging task. The literature has introduced the individual-level and the segment-based predictive modeling approaches for this purpose. Each method has its own advantages and drawbacks, and performs in certain cases. The purpose of this paper is to propose a hybrid approach which predicts customers’ individual purchase behaviors and reduces the limitations of these two methods by combining the advantages of them.

The proposed hybrid approach is established based on individual-level and segment-based approaches and utilizes the historical transactional data and predictive algorithms to generate predictions. The effectiveness of the proposed approach is experimentally evaluated in the domain of supermarket shopping by using real-world data and using five popular machine learning classification algorithms including logistic regression, decision trees, support vector machines, neural networks and random forests.

A comparison of results shows that the proposed hybrid approach substantially outperforms the individual-level and the segment-based approaches in terms of prediction coverage while maintaining roughly comparable prediction accuracy to the individual-level method. Moreover, the experimental results demonstrate that logistic regression performs better than the other classifiers in predicting customer purchase behavior.

The study concludes that the proposed approach would be beneficial for enterprises in terms of designing customized services and one-to-one marketing strategies.

This study is the first attempt to adopt a hybrid approach combining individual-level and segment-based approaches to predict customers’ individual purchase behaviors.

The purpose of this study is to investigate and understand the performance of countries in individual Olympic Games, specifically focusing on the Tokyo 2020 Olympics. Employing cluster analysis and decision trees, the research aims to categorize countries based on their representation, participation and success.

This research employs a data-driven approach to comprehensively analyze and enhance understanding of countries' performances in individual Olympic Games. The methodology involves a two-stage clustering method and decision tree analysis to categorize countries and identify influential factors shaping their Olympic profiles.

The study, analyzing countries' performances in the Tokyo 2020 Olympics through cluster analysis and decision trees, identified five clusters with consistent profiles. Notably, China, Great Britain, Japan, Russian Olympic Committee and the United States formed a high-performing group, showcasing superior success, representation and participation. The analysis revealed a correlation between higher representation/participation and success in individual Olympic Games. Decision tree insights underscored the significance of population size, GDP per Capita and HALE index, indicating that countries with larger populations, better economic standing and higher health indices tended to perform better.

The study has several limitations that should be considered. Firstly, the findings are based on data exclusively from the Tokyo 2020 Olympics, which may limit the generalizability of the results to other editions.

The research offers practical implications for policymakers, governments and sports organizations seeking to enhance their country's performance in individual Olympic Games.

The research holds significant social implications by contributing insights that extend beyond the realm of sports.

The originality and value of this research lie in its holistic approach to analyzing countries' performances in individual Olympic Games, particularly using a two-stage clustering method and decision tree analysis.

The purpose of this paper is to propose a new RFM model called length, recency, frequency, monetary and periodicity (LRFMP) for classifying customers in the grocery retail industry; and to identify different customer segments in this industry based on the proposed model.

This study combines the LRFMP model and clustering for customer segmentation. Real-life data from a grocery chain operating in Turkey is used. Three cluster validation indices are used for optimizing the number of groups of customers and K-means algorithm is employed to cluster customers. First, attributes of the LRFMP model are extracted for each customer, and then based on LRFMP model features, customers are segmented into different customer groups. Finally, identified customer segments are profiled based on LRFMP characteristics and for each customer profile, unique CRM and marketing strategies are recommended.

The results show that there are five different customer groups and based on LRFMP characteristics, they are profiled as: “high-contribution loyal customers,” “low-contribution loyal customers,” “uncertain customers,” “high-spending lost customers” and “low-spending lost customers.”

This research may provide researchers and practitioners with a systematic guideline for effectively identifying different customer profiles based on the LRFMP model, give grocery companies useful insights about different customer profiles, and assist decision makers in developing effective customer relationships and unique marketing strategies, and further allocating resources efficiently.

This study contributes to prior literature by proposing a new RFM model, called LRFMP for the customer segmentation and providing useful insights about behaviors of different customer types in the Turkish grocery industry. It is also precious from the point of view that it is one of the first attempts in the literature which investigates the customer segmentation in the grocery retail industry.

In the recent years, the rapid growth of the grocery retailing industry has created a great heterogeneity in prices across sellers in the market. Online price comparison agents which are key mechanisms to solve this problem by providing prices from different sellers. However, there are many sellers in the grocery industry do not offer online service, and so it is impossible to automatically retrieve price information from such grocery stores. In this manner, crowdsourcing can become an essential source of information by collecting current price data from shoppers. Therefore, this paper aims to propose Kiyaslio, a gamified mobile crowdsourcing application that provides price information of products from different grocery markets.

Kiyaslio has been developed through leveraging the power of crowdsourcing technology. Game elements have also been used to increase the willingness of users to contribute on price data entries. The proposed application is implemented using design science methodology, and it has been evaluated through usability testing by two well-known techniques which are the system usability scale and the net promoter score.

The results of the usability tests indicate that participants find Kiyaslio as functional, useful and easy to use. These findings prove its applicability and user acceptability.

The proposed platform supports crowd sourced data collection and could be effectively used as a tool to support shoppers to easily access current market product prices.

This paper presents a mobile application platform for tracking current prices in the grocery retail market whose strength is based on the crowdsourcing concept and incorporation of game elements.

The purpose of this paper is to statistically explore the relationship between web usability and web presence of the universities. As a case study, five Turkish universities in different rankings which were selected from Webometrics rankings were evaluated and compared.

Two different methods were employed for performing usability evaluation of the selected universities: a user testing was used to measure the user performance on the selected tasks and a questionnaire to assess the user satisfaction on the website use. Both usability evaluation methods were applied on the pre-determined tasks for each university by participation of 20 subjects. After the usability evaluation, the universities were ranked in terms of usability results and finally, the relationship between web usability and web presence of universities was statistically investigated by using Kendall’s rank correlation.

Several common usability problems which were asserted by related previous studies were identified at the end of usability evaluation of university websites. The usability results also revealed that selected Turkish university websites suffer from numerous usability problems. Further, a strong positive correlation (p < 0.05) between the usability of the university websites and their web presences were identified. Hence, the participants showed a higher success and satisfaction while performing the tasks on the university websites which have strong web presences.

The findings from this study have practical implications for universities. Correlation results showed that universities can improve their web usability by giving importance to their web presence volumes. Universities can estimate their web usability levels by investigating their web presence rankings and they can also raise their rankings in Webometrics ranking system by improving the usability of their websites. Moreover, university web developers can design more usable and more user-friendly websites by avoiding usability and design problems identified through usability evaluation.

Different from the prior research efforts focussing on usability of educational web pages, this study contributes to the growing literature by statistically exploring the relationship between web presence and web usability of universities. This study is also precious from the point of view that it is one of the first attempts to evaluate and compare usability levels of a set of universities’ websites from Turkey.