Search results

This study aims to examine how dynamic pricing impacts customer perceptions of restaurants and sentiment toward prices via online reputation metrics. In addition, to deepen the debate on dynamic pricing, a novel definition is drawn by exploring the specific forms of discrimination that can manifest in different industries.

Leveraging a comprehensive data set of restaurant reviews sourced from TripAdvisor, the study focuses on restaurants affiliated with one of the largest groups of restaurants in Spain. We used a quasi-experimental method (difference-in-differences), to study how dynamic pricing strategies influence customers’ perceptions of value based on numerical ratings. Meanwhile, we used a Bidirectional Encoder Representations from Transformers model on the textual component of reviews to dissect the emotional nuances of dynamic pricing.

Results did not reveal a causal impact of dynamic pricing strategies on customers’ perceptions. Moreover, the sentiment analysis shows no heightened negative view after introducing dynamic pricing in restaurants compared to the control group. Contrary to what previous literature suggests, our findings indicate that implementing dynamic pricing does not adversely affect customers’ perceptions or sentiments regarding prices in restaurants.

The quasi-experimental setting of the study presents inherent challenges in establishing causality that require further investigation using controlled experimental settings. Nevertheless, our study reveals that restaurant customers do not perceive dynamic pricing as unfair. This finding is critical for restaurant managers when considering the implementation of dynamic pricing and revenue management strategies. In addition, our study highlights the importance of considering not only numerical ratings but customer sentiment analysis as well. This more holistic approach to assessing the impact of pricing strategies can give restaurant managers a deeper understanding of customer reactions. In addition, a more rigorous definition of dynamic pricing is provided, clarifying its nature and its distinction in using different price discrimination.

This study contributes to the evolving understanding of dynamic pricing strategies’ impact on customers’ perceptions and sentiments in the restaurant industry. It aims to fill the gap in understanding customer reactions to algorithmically determined prices (via revenue management systems such as DynamEat) in this industry. The combination of causal inference and sentiment analysis offers a novel perspective, shedding light on the nuanced connections between dynamic pricing implementation and customers’ emotions.

本研究考察动态定价如何通过在线声誉指标影响顾客对餐厅的感知和对价格的情绪。此外, 为了深化对动态定价的讨论, 通过探索不同行业中可能表现出的具体歧视形式, 提出了一个新的定义。

利用从TripAdvisor获取的餐厅评论的全面数据集, 研究聚焦于与西班牙最大的餐厅集团之一相关联的餐厅。我们采用了准实验方法（差异中的差异）, 研究动态定价策略如何根据数值评分影响顾客对价值的感知。同时, 我们运用BERT模型对评论的文本成分进行分析, 以解析动态定价的情感细微差别。

结果没有揭示动态定价策略对顾客感知产生因果影响。此外, 情绪分析显示, 在餐厅引入动态定价后, 与对照组相比, 没有增加消极观点。与以往文献所述相反, 我们的发现表明, 实施动态定价并不会对顾客对价格的感知或情绪产生负面影响。

研究的准实验设置存在确立因果关系的固有挑战, 需要通过控制实验设置进一步调查。尽管如此, 我们的研究揭示了餐厅顾客不认为动态定价不公平。这一发现对餐厅经理在考虑实施动态定价和收入管理策略时至关重要。此外, 我们的研究强调, 考虑顾客情绪分析和数值评分的重要性。这种更全面的方法评估定价策略的影响, 可以让餐厅经理更深入地理解顾客反应。此外, 提供了一个更严格的动态定价定义, 澄清了其性质及其在使用不同价格歧视中的区别。

本研究对于理解动态定价策略对餐厅行业顾客感知和情绪影响的不断发展有所贡献。它旨在填补对客户对算法确定的价格（通过收入管理系统（RMS）例如DynamEat）在此行业中反应的理解空白。因果推断与情绪分析的结合提供了新的视角, 揭示了动态定价实施与顾客情绪之间微妙的联系。

Este estudio examina cómo la fijación dinámica de precios impacta en las percepciones de los clientes de los restaurantes y en el sentimiento hacia los precios a través de métricas de reputación en línea. Además, para profundizar en el debate sobre la fijación dinámica de precios, se propone una definición novedosa explorando las formas específicas de discriminación que pueden manifestarse en diferentes industrias.

Utilizando un conjunto de datos exhaustivo de reseñas de restaurantes obtenidas de TripAdvisor, el estudio se centra en los restaurantes afiliados a uno de los mayores grupos de restaurantes en España. Empleamos un método cuasiexperimental (diferencias en diferencias) para estudiar cómo las estrategias de precios dinámicos influyen en las percepciones de valor de los clientes basándonos en las calificaciones numéricas. Mientras tanto, empleamos un modelo BERT en el componente textual de las reseñas para desentrañar los matices emocionales de la fijación dinámica de precios.

Los resultados no revelaron un impacto causal de las estrategias de precios dinámicos en las percepciones de los clientes. Además, el análisis de sentimiento no muestra una visión negativa aumentada después de introducir la fijación dinámica de precios en los restaurantes en comparación con el grupo de control. Contrariamente a lo que sugiere la literatura previa, nuestros hallazgos indican que la implementación de precios dinámicos no afecta negativamente las percepciones o los sentimientos de los clientes respecto a los precios en los restaurantes.

La configuración cuasiexperimental del estudio presenta desafíos inherentes para establecer la causalidad que requieren una investigación más profunda utilizando entornos experimentales controlados. Sin embargo, nuestro estudio revela que los clientes de restaurantes no perciben la fijación de precios dinámica como injusta. Este hallazgo es crítico para los gerentes de restaurantes al considerar la implementación de la fijación de precios dinámica y estrategias de gestión de ingresos. Además, nuestro estudio resalta la importancia de considerar no solo las calificaciones numéricas sino también el análisis del sentimiento del cliente. Este enfoque más holístico para evaluar el impacto de las estrategias de precios puede dar a los gerentes de restaurantes una comprensión más profunda de las reacciones de los clientes. Además, se proporciona una definición de fijación de precios dinámica más rigurosa, aclarando su naturaleza y su distinción en el uso de diferentes discriminaciones de precios.

Este estudio contribuye a la comprensión en evolución del impacto de las estrategias de fijación de precios dinámicos en las percepciones y sentimientos de los clientes en la industria restaurantera. Su objetivo es llenar el vacío en la comprensión de las reacciones de los clientes a los precios determinados algorítmicamente (a través de sistemas de gestión de ingresos (RMS) como DynamEat) en esta industria. La combinación de inferencia causal y análisis de sentimientos ofrece una perspectiva novedosa, arrojando luz sobre las conexiones matizadas entre la implementación de la fijación de precios dinámicos y las emociones de los clientes.

This study investigates consumer perspectives on cultural appropriation in the fashion industry by analysing user-generated content on Douyin.

Using text-mining techniques, including topic modeling and sentiment analysis, 6,584 comments related to the Dior midi-length skirt controversy were analyzed to identify key themes and sentiments.

The study reveals that cultural appropriation elicits polarized responses. Positive themes, such as cultural appreciation, innovation and global exposure, highlight consumer approval of respectful and creative integration of cultural elements. Negative themes, including cultural theft, historical inaccuracy and commercial exploitation, underscore concerns about commodification and misuse of cultural symbols.

Fashion brands should prioritize cultural sensitivity by collaborating with cultural communities, ensuring historical accuracy and promoting ethical engagement with cultural symbols. Proactively addressing these issues through real-time consumer feedback can help mitigate backlash, foster trust and enhance brand equity.

This study advances the discourse on cultural appropriation by leveraging real-time user-generated content to provide actionable insights for ethical practices in the fashion industry.

This study aims to explore the relationship between wine label content and consumer reviews on Vivino, focusing on how label information compares to consumer perceptions and purchase decisions.

The research uses text mining, image mining and topic modeling techniques to analyze a data set of 444 highly rated Portuguese wines and 993,945 consumer reviews from Vivino. It examines the overlap between topics discussed in consumer reviews and the information provided on wine labels.

The findings reveal that wine labels have a limited similarity with consumer reviews. While consumer reviews primarily emphasize sensory characteristics like fruitiness and tannins, wine labels focus more on descriptive and technical information.

This research provides new insights into the dynamic relationship between wine labeling and consumer behavior. By analyzing the alignment between traditional wine labels and online consumer reviews, the study offers valuable implications for wineries seeking to optimize label design to match consumer preferences, particularly sensory attributes better. This study extends existing research and highlights critical gaps in how wine labels convey essential sensory information to consumers.

This study aims to investigate the digital entrepreneurial intentions of Albanian youth, identify the obstacles they face in starting digital businesses and examine their preferences regarding the types of businesses they aspire to establish. The Theory of Planned Behavior (TPB) is used as a framework to analyze these factors.

Primary data were collected via questionnaires distributed in public and private universities. In a sample of 325 students, Structural Equation Modeling with Confirmatory Factor Analysis, path analysis and machine learning-based text analysis were used.

This study reveals significant impacts of innovativeness, attitude towards entrepreneurship, subjective norms, perceived behavioral control and self-efficacy on digital entrepreneurial intentions among Albanian students. Additionally, text mining highlights a strong preference for digital entrepreneurship.

The theoretical contributions of this study include applying Structural Equation Modeling to reveal insights into the impact of entrepreneurial factors and obstacles. The findings can inform policymakers and educators in designing targeted interventions to support student entrepreneurship. Meanwhile, the limitations of this study encompass a small sample size, lack of time series and panel data and the absence of an evaluation of the impact of education system practices, along with the need to investigate the effects of young population emigration from Albania to the EU.

This research contributes to the understanding of digital entrepreneurial intentions and behavior by using TPB in the Albanian context, offering access to a diverse dataset from Albanian universities, testing the direct impact of innovativeness on entrepreneurial behavior and pioneering the use of machine learning techniques for text analysis. Thus, it provides novel insights into the entrepreneurial landscape in Albania. In addition, this work can drive initiatives to support student entrepreneurship and bridge the gap between academia and industry in Albania.

This study systematically reviews the research landscape of humanitarian financing, identifying key themes and pathways to foster a resilient humanitarian value ecosystem.

A systematic review using BERTopic analysed 1,043 academic publications from the Web of Science database to uncover major themes in humanitarian financing literature.

Six core themes emerged from the study: Disaster Risk and Climate Change Resilience, Refugee Crises and Humanitarian Health Services, Preparedness and Response to Health Emergencies, Supply Chain and Logistics Fundraising, Food Security and Nutrition Insecurity and Global Health Challenges and Disease Control. The study highlights the role of financing in addressing short-term needs and promoting long-term systemic resilience, such as disaster preparedness, healthcare systems and food security.

This study provides a comprehensive overview of key research trends in humanitarian financing. However, the reliance on academic literature limits the inclusion of insights from grey literature and practitioner reports. Future studies should adopt broader data sources and interdisciplinary approaches.

Findings underscore the importance of integrating emergency funds with long-term resilience investments, enhancing financial transparency and advocating innovations to optimise humanitarian operations.

The study advocates for interdisciplinary approaches and policies connecting disaster risk reduction, healthcare, climate resilience and food security to build a sustainable humanitarian ecosystem.

This review proposes a novel conceptual framework based on a systematic review of literature using BERTopic, offering actionable insights into the role of humanitarian financing in addressing global crises and promoting resilience.

The deformation of the roadbed is easily influenced by the external environment to improve the accuracy of high-speed railway subgrade settlement prediction.

A high-speed railway subgrade settlement interval prediction method using the secretary bird optimization (SBOA) algorithm to optimize the BP neural network under the premise of gray relational analysis is proposed.

Using the SBOA algorithm to optimize the BP neural network, the optimal weights and thresholds are obtained, and the best parameter prediction model is combined. The data were collected from the sensors deployed through the subgrade settlement monitoring system, and the gray relational analysis is used to verify that all four influencing factors had a great correlation to the subgrade settlement, and the collected data are verified using the model.

The experimental results show that the SBOA-BP model has higher prediction accuracy than the BP model, and the SBOA-BP model has a wider range of prediction intervals for a given confidence level, which can provide higher guiding value for practical engineering applications.

This paper advances the understanding of how blockchain technology enhances the quality of information within the supply chain by employing the information-processing view as a theoretical lens.

Our research begins with a review of the existing literature and formulates a set of propositions. We then employ a positivist case study approach in various industries across Europe, the USA and Argentina, encompassing agriculture, fisheries, e-commerce and pharmaceuticals, to empirically investigate these propositions.

Our study confirms that blockchain enhances the dimensions of information quality by reinforcing traceability and transparency. Nevertheless, the degree of traceability and transparency depends on the supply chain’s internal processes, blockchain configurations and willingness to share information. Moreover, blockchain does not ensure perfect information security, lacking guaranteed immutability and decentralization in present supply chain applications. Furthermore, the integration of complementary technologies with blockchain to reconcile the divide between physical and digital realms remains largely untested, introducing uncertainty about its influence on information accuracy. Consequently, this study cautions against viewing blockchain as a wholesale replacement for inter-organizational trust.

Blockchain technology is widely regarded as having substantial potential to revolutionize supply chain information systems. However, only a limited number of studies have critically examined the value added by blockchain within an inter-organizational context.

In recent years, the development of metaheuristic algorithms for solving optimization problems within a reasonable timeframe has garnered significant attention from the global scientific community. In this work, a new metaheuristic algorithm inspired by the inflection mechanism of the avian influenza virus H5N1 in poultry and humans, taking into account its mutation mechanism, called H5N1.

This algorithm aims to explore optimal solutions for optimization problems by simulating the adaptive behavior and evolutionary process of the H5N1 virus, thereby enhancing the algorithm’s performance for all types of optimization problems. Additionally, a balanced stochastic probability mechanism derived from the infection probability is presented. Using this mechanism, the H5N1 algorithm can change its phrase, including exploitation and exploration phases. Two versions of H5N1, SH5N1 and MH5N1, are presented to solve single-objective optimization problems (SOPs) and multi-objective optimization problems (MOPs).

The performance of the algorithm is evaluated using a set of benchmark functions, including seven unimodal, six multimodal, ten fixed-dimension multimodal to solve SOPs, ZDT functions and CEC2009 has been used to demonstrate its superiority over other recent algorithms. Finally, six optimization engineering problems have been tested. The results obtained indicate that the proposed algorithm outperformed ten algorithms in SOPs and seven algorithms in MOPs.

The experimental findings demonstrate the outstanding convergence of the H5N1 algorithm and its ability to generate solutions of superior quality.

Various methods are used for cancer detection such as genetic tests, scanning, MRI, mammography, etc. These methods help collect data on patients, which can be utilized for comparing a new patient’s information with the aggregated data to detect cancer. The main step in this process is data classification. There are several cancer detection methods with their own disadvantages in flexibility, non-linear complexity and sensitive in imbalance data. In this paper, a new fuzzy bio-inspired based classification method is designed to classify the imbalance medical data.

In this paper, a new fuzzy bio-inspired-based classification method is designed to classify the imbalance of medical data. The method consists of a new fuzzy draft of the Cuckoo Optimization Algorithm (COA) and separating hyper-planes based on assigning binary codes to separated regions that are called Hyper-Planes Classifier (HPC). Based on the technical review is done in the paper, the HPC has a better structural superiority than the other classification algorithms. The Fuzzy Cuckoo Optimization Algorithm (FCOA), which fills up its challenge in proper tuning parameters, is proposed to optimize the weights of the separating hyper-planes with linear complexity time.

The experimental results were presented in five steps. Step1, the details of the average and the best results of the proposed methods were reported and compared. Step2, the quality of the detection methods with different numbers of hyper-planes were compared. The obtained weights of different numbers of hyper-planes were reported in Step3. Step4, the convergence process of the FCOA and the COA were shown. Step5, the best obtained results were compared with the best reported one in previous literature. The experimental results and the presented comparisons show that the proposed hybrid detection method is comparable to other methods and operates better than them in most cases.

A technical review has been done based on classifying the applied classification methods to cancer detection and analyzing advantages (+) and disadvantages (−) of the methods and their optimizer algorithms. A new fuzzy draft of COA has been designed to dynamically tuning the Egg Laying Radius based on a fuzzy inference system with four fuzzy rules. A novel hybridization of the hyper-planes classification method and the designed FCOA has been proposed to optimize the hyper-planes' weights. The effectiveness of the proposed hybridization has been examined in famous UCI cancer datasets based on one, two, three and four hyper-planes and compared with more than 30 previous researches.

In golf, strokes gained (SG) unify different shot types under a single system, including off the tee (SG: OTT), approach (SG: APP), around the green (SG: ARG) and putting (SG: PUTT). Despite SG having equal value across types, each shot type requires different skills and physical attributes. This paper analyzes how professional golfers leverage these diverse shot types to achieve wins and maximize their earnings.

This paper explores shot type consistency, demonstrating the superior consistency of the long game compared to the short game through regression and correlation analyses. These analyses are conducted season-to-season, tournament-to-tournament and round-to-round using seven seasons of professional golf data. Additionally, we examine the types of shots that contribute to improvements within the field.

Through analyzing average gains across evenly distributed groups and employing a regression model, we find that SG: APP is the most important type of shot, followed by SG: PUTT, SG: OTT and finally SG: ARG. Additionally, when considering both empirical earnings data and a regression model, we find that SG: PUTT becomes less important. Top players primarily win their earnings through SG: APP, followed by SG: OTT, SG: PUTT and finally SG: ARG shots. Next, we examine where strokes are gained at different levels of earnings and find that SG: APP makes up most of the SG increases across the field, followed by SG: PUTT, then SG: OTT and SG: ARG.

In this paper, we extend previous research by analyzing the entire professional golf association field rather than focusing solely on amateurs or small sections of the professional field; we also conduct our analysis using SG instead of traditional stats.