Search results

A visual sweetness scale with an arrow pointing to a specific sweetness level is now required on all labels of AOC Alsace. The sweetness scale makes it easier for consumers to understand what is in the bottle. What is less clear, however, is whether such labeling is always effective. To fill this gap, the current research paper aims to examine the positive and negative effects (double-edged effects) of a visual sweetness scale and identify the boundary condition.

Two studies were conducted using a 2 (cue type: scale vs text) by 2 (consumer type: novices vs experienced wine consumers) between-subjects, quasi-experimental design.

The double-edged effects are only significant among wine novices. Specifically, though wine novices are more likely to purchase wine with a sweetness scale (vs text) due to perceived diagnosticity (Study 1), they are unwilling to pay more due to low perceived quality (Study 2).

The study findings provide practical implications for wine producers, marketers and restaurants regarding when and how to use the sweetness scale on wine labels and wine service.

To the best of the authors’ knowledge, this research is the first to reveal the impact of visualizing wine style on wine labels. More importantly, while most previous research demonstrates the positive effects of using visual cues, this research sheds light on its drawbacks and examines the underlying mechanisms.

Hiring employees with criminal records has become a heightened topic of conversation for hospitality practitioners. The labor shortage in the hospitality industry has increased consideration for individuals impacted by the justice system, bolstered by programming such as second-chance vocational training programs. However, hospitality practitioners with second-chance employment practices have had challenges managing the multiple stakeholder relationships to employ and retain justice-impacted employees. The purpose of this paper is to aid practitioners in the hospitality industry with an innovative and inclusive hiring practice with timely implications in the United States.

This paper will emphasize the correctional system actors, community-based partners, and justice-impacted employees as salient stakeholders in such hiring contexts. Employing a stakeholder theoretical perspective, we outline a stakeholder map for hospitality practitioners with fair chance hiring practices to better understand second-chance employees.

Organizations do not sit on the periphery of a community; they are interconnected with the community in many direct and indirect ways. Past research has yet to identify a relationship between stakeholder theory and second-chance employment when the stakeholders involved in this employee population are arguably extended. By providing a stakeholder mapping process second-chance employment context, the interwoven actors’ needs can be more holistically assessed.

In America, individuals with a criminal record are often a forgotten and stigmatized labor pool. With this paper, we aim to break down barriers of bias while encouraging the narrative toward true Diversity, Equity, Inclusion, and Belonging (DEIB) hiring practices. Multiple stakeholder management is often an organizational challenge, and by providing this framework, we provide guidelines to practitioners while highlighting the opportunity for community leadership. To that end, we provide guidelines for hospitality practitioners intending to increase justice-involved employee retention through stakeholder relationship management.

The purpose of this study is to examine institutional quality’s absorptive capacity in African countries’ remittances-finance nexus.

A balanced panel data set of thirty African countries between 2000 and 2022 is used for the study. The study adopts an augmented mean group (AMG), method of moment quantile regression (MMQR) and two-step system generalized method of moment (2SGMM) as the estimation techniques due to the nature of the data set.

The findings of the direct effect reveal that remittances do not constitute the growth of financial development, while institutional quality promotes the growth of financial development in the long. The moderating effect of institutional quality in the linkages shows that the interactive term of institutional quality and remittances has a significant positive effect on financial development in the region. Hence, institutional quality moderates the impact of remittances. These results are robust to different proxies of financial development and estimates obtained from MMQR and 2SGMM.

This study, therefore, suggests that institutional quality is essential in the linkages between remittances and financial development. Hence, remittances should be seen as one of the instruments that can be used to develop the financial sector rather than survival mechanisms for households.

This study contributes to the literature by unearthing the absorptive capacity of institutional quality in the nexus between remittances and financial development in African countries, which extant studies have neglected.

There is widespread concern about low adherence to clinical practice guidelines (CPGs) and the low adoption of new medical technologies. To assist the regulatory response, we propose benchmarking clinical practice on the lower bound on the probability that a recommended treatment/new technology achieves a better outcome. This inequality–probability bound can be estimated from marginal outcome distributions. We illustrate the approach by comparing Swedish cardiologists' adoption of drug-eluting stents (DESs) with the inequality–probability bound on this technology improving outcomes. A substantial fraction of cardiologists are below the benchmark.

This article introduces SigBERT, a novel approach that fine-tunes bidirectional encoder representations from transformers (BERT) for the purpose of distinguishing between intact and impaired structures by analyzing vibration signals. Structural health monitoring (SHM) systems are crucial for identifying and locating damage in civil engineering structures. The proposed method aims to improve upon existing methods in terms of cost-effectiveness, accuracy and operational reliability.

SigBERT employs a fine-tuning process on the BERT model, leveraging its capabilities to effectively analyze time-series data from vibration signals to detect structural damage. This study compares SigBERT's performance with baseline models to demonstrate its superior accuracy and efficiency.

The experimental results, obtained through the Qatar University grandstand simulator, show that SigBERT outperforms existing models in terms of damage detection accuracy. The method is capable of handling environmental fluctuations and offers high reliability for non-destructive monitoring of structural health. The study mentions the quantifiable results of the study, such as achieving a 99% accuracy rate and an F-1 score of 0.99, to underline the effectiveness of the proposed model.

SigBERT presents a significant advancement in SHM by integrating deep learning with a robust transformer model. The method offers improved performance in both computational efficiency and diagnostic accuracy, making it suitable for real-world operational environments.

The purpose of this paper is to examine the relationship between wine tourism experiences (i.e. winescape, winery service quality, winery brand differentiation and tourist motivation) and tourist behavioral intentions (satisfaction, loyalty and revisit intention) from both demand and supply perspectives in Ningxia, China, by using the SERVQUAL model and the self-determination theory.

The research comprises two studies with an exploratory sequential mixed-methods approach. Study 1 adopted semi-structured interviews with winery owners and managers to explore the factors related to wine tourism experiences that influence tourists’ tourism experience. The results from the Study 1 leads to Study 2, which used a survey to explore wine tourist experiences and their behavioral intentions

This study provided important information regarding the factors that influence wine tourists experience in Ningxia wine region from the perspectives of supply and demands sides. Frist, this study confirmed the various factors influence wine tourism experiences in Ningxia based on the results from Study 1 (supply perspectives). Second, by further investigation in the Study 1, this study integrates the SERVQUAL model and the self-determination theory as fundamental theoretical frameworks in Study 2 to analysis the tourist perspectives. Third, as results, the authors finally confirmed the theoretical frameworks of wine tourism in Ningxia based on dual supply-demand perspectives.

The integration of multiple research approaches enriches the research findings and provides a more robust understanding of the wine tourism experiences in Ningxia.

This study aimed to identify both visitors’ and residents’ general wine consumer typology as well as their typology specific to a wine destination, Florida, and investigated the influences of these typologies on consumer motivation to visit a winery in Florida and their motivations’ influences on their satisfaction with the winery visit and loyalty in the way of likelihood to revisit.

Two studies were conducted to identify the wine consumer typology and its influences, one with residents of a wine destination and one with its visitors. In both studies, the same cross-sectional survey design was conducted. The survey was designed using the survey design tools of Qualtrics.

Study results showed similar general wine consumer typology for both visitors and residents but slight differences in the destination-specific wine consumer typology. Results also revealed differences in the influences of these typologies on motivation, with different influences on satisfaction and loyalty for the two segments.

Even though consumer typologies have been discussed in tourism destination decision-making and choice, consumer typology in wine destination consumption has been lacking in the literature. To the best of the authors’ knowledge, this is the first study to identify both visitors’ and residents’ general wine consumer typology as well as their typology specific to a wine destination, Florida, and to investigate the influences of these typologies on consumer motivation to visit a winery in Florida and their motivations’ influences on their satisfaction with the winery visit and likelihood to revisit.

此文章通过两项研究来探讨葡萄酒消费者类型及其影响, 一项针对葡萄酒目的地的居民, 另一项针对其游客。这两项研究采用了设计相同的调查问卷, 该调查问卷的设计使用了 Qualtrics 的调查设计工具。

该研究旨在归纳游客和居民的葡萄酒消费者类型以及他们在佛罗里达州葡萄酒目的地的特定类型, 并调查了这些类型对消费者参观佛罗里达州酒庄的动机的影响以及他们的动机对其满意度和再次参观酒庄意愿的影响。

研究结果显示, 游客和居民的葡萄酒消费者类型相似, 但特定目的地的葡萄酒消费者类型略有不同。 结果还揭示了这些类型对动机的影响存在差异, 对游客和居民的满意度和忠诚度的影响也不同。

尽管文献已经讨论了旅游目的地决策和选择背景下的消费者类型, 有关葡萄酒目的地消费的消费者类型的研究却相对缺乏。此文章旨在发掘游客和居民的葡萄酒消费者类型以及佛罗里达州葡萄酒目的地特有的消费者类型, 并调查这些类型对消费者参观佛罗里达州酒庄动机的影响以及此动机对酒庄参观的满意度和再次参观的可能性的影响。

Se realizaron dos estudios para identificar la Tipología del Consumidor de Vino y sus influencias, uno con residentes de un destino enoturístico y otro con sus visitantes. En ambos estudios se llevó a cabo el mismo diseño de encuesta transversal. La encuesta se diseñó utilizando las herramientas de diseño de encuestas de Qualtrics.

El estudio identificó la tipología general del consumidor de vino tanto de los visitantes, como de los residentes, así como su tipología específica para un destino vinícola, como el de Florida, e investigó las influencias de estas tipologías en la motivación del consumidor para visitar una bodega en Florida y las influencias de sus motivaciones en su satisfacción con la visita a la bodega y la probabilidad de volver a visitarla

Los resultados del estudio mostraron una tipología general del consumidor de vino similar tanto para los visitantes, como para los residentes, pero ligeras diferencias en la tipología del consumidor de vino específica del destino. Los resultados también revelaron diferencias en las influencias de estas tipologías sobre la motivación, con distintas influencias sobre la satisfacción y la fidelidad para los dos segmentos.

Aunque se ha hablado de tipologías de consumidores en la toma de decisiones y la elección de destinos turísticos, la tipología de consumidores en el consumo de destinos vinícolas ha estado ausente en la literatura. Éste es el primer estudio que identifica tanto la tipología general del consumidor de vino de los visitantes y residentes como su tipología específica para un destino vinícola como el de Florida, e investiga las influencias de estas tipologías en la motivación del consumidor para visitar una bodega en Florida y las influencias de sus motivaciones en su satisfacción con la visita a la bodega y la probabilidad de volver a visitarla.

This study aims to develop a robust long short-term memory (LSTM)-based forecasting model for daily international tourist arrivals at Incheon International Airport (ICN), incorporating multiple predictors including exchange rates, West Texas Intermediate (WTI) oil prices, Korea composite stock price index data and new COVID-19 cases. By leveraging deep learning techniques and diverse data sets, the research seeks to enhance the accuracy and reliability of tourism demand predictions, contributing significantly to both theoretical implications and practical applications in the field of hospitality and tourism.

This study introduces an innovative approach to forecasting international tourist arrivals by leveraging LSTM networks. This advanced methodology addresses complex managerial issues in tourism management by providing more accurate forecasts. The methodology comprises four key steps: collecting data sets; preprocessing the data; training the LSTM network; and forecasting future international tourist arrivals. The rest of this study is structured as follows: the subsequent sections detail the proposed LSTM model, present the empirical results and discuss the findings, conclusions and the theoretical and practical implications of the study in the field of hospitality and tourism.

This research pioneers the simultaneous use of big data encompassing five factors – international tourist arrivals, exchange rates, WTI oil prices, KOSPI data and new COVID-19 cases – for daily forecasting. The study reveals that integrating exchange rates, oil prices, stock market data and COVID-19 cases significantly enhances LSTM network forecasting precision. It addresses the narrow scope of existing research on predicting international tourist arrivals at ICN with these factors. Moreover, the study demonstrates LSTM networks’ capability to effectively handle multivariable time series prediction problems, providing a robust basis for their application in hospitality and tourism management.

This research pioneers the integration of international tourist arrivals, exchange rates, WTI oil prices, KOSPI data and new COVID-19 cases for forecasting daily international tourist arrivals. It bridges the gap in existing literature by proposing a comprehensive approach that considers multiple predictors simultaneously. Furthermore, it demonstrates the effectiveness of LSTM networks in handling multivariable time series forecasting problems, offering practical insights for enhancing tourism demand predictions. By addressing these critical factors and leveraging advanced deep learning techniques, this study contributes significantly to the advancement of forecasting methodologies in the tourism industry, aiding decision-makers in effective planning and resource allocation.

本研究旨在开发一种基于LSTM的强大预测模型, 用于预测仁川国际机场的日常国际游客抵达量, 结合多种预测因素, 包括汇率、WTI原油价格、韩国综合股价指数 (KOSPI) 数据和新冠疫情病例。通过利用深度学习技术和多样化数据集, 研究旨在提升旅游需求预测的准确性和可靠性, 对酒店与旅游领域的理论和实际应用有重要贡献。

本研究通过利用长短期记忆（LSTM）网络引入创新方法, 预测国际游客抵达量。这一先进方法解决了旅游管理中的复杂管理问题, 提供了更精确的预测。方法论包括四个关键步骤: (1) 收集数据集; (2) 数据预处理; (3) 训练LSTM网络; 以及 (4) 预测未来的国际游客抵达量。本文的其余部分结构如下：后续部分详细介绍了提出的LSTM模型, 呈现了实证结果, 并讨论了研究的发现、结论以及在酒店与旅游领域的理论和实际意义。

本研究首次同时使用包括国际游客抵达量、汇率、原油价格、股市数据和新冠疫情病例在内的大数据进行日常预测。研究显示, 整合汇率、原油价格、股市数据和新冠疫情病例显著增强了LSTM网络的预测精度。研究填补了现有研究在使用这些因素预测仁川国际机场国际游客抵达量的狭窄范围。此外, 研究证明了LSTM网络在处理多变量时间序列预测问题上的能力, 为其在酒店与旅游管理中的应用提供了坚实基础。

本研究首次将国际游客抵达量、汇率、WTI原油价格、KOSPI数据和新冠疫情病例整合到日常国际游客抵达量的预测中。它通过提出同时考虑多个预测因素的全面方法, 弥合了现有文献的差距。此外, 研究展示了LSTM网络在处理多变量时间序列预测问题方面的有效性, 为增强旅游需求预测提供了实用见解。通过处理这些关键因素并利用先进的深度学习技术, 本研究在旅游业预测方法的进步中做出了重要贡献, 帮助决策者进行有效的规划和资源配置。

Applying the stimulus-organism-response (S-O-R) theory in a wine museum context, this paper aims to examine how and why experience design characteristics influence visitor satisfaction, particularly investigating the role of epistemic (learning) and hedonic (having fun) values as the underlying mechanisms of this relationship.

The authors collected field survey data from 652 visitors at a world-leading wine museum. The authors tested the research model on ten modules of the museum using path analysis and a bootstrap approach; the authors further conducted mediation analyses to test how the design of the museum’s modules influenced perceived value and satisfaction.

Content comprehensibility and surprise, as well as interactivity and ease of use, are core design characteristics that drive visitor satisfaction. More significantly, hedonic and epistemic values play a significant mediating role in influencing the relationship between design characteristics and visitor satisfaction.

The authors provide clear and actionable recommendations to help managers design museums that provide educational, entertaining and satisfying visitor experiences.

To the best of the authors’ knowledge, this is the first study to apply the S-O-R theory in a wine museum context. The significance of this study lies in demonstrating how and why experience design characteristics support the creation of an edutainment visitor experience that drives visitor satisfaction.

The present study aimed to investigate the impact of social networks on the use of academic libraries by university students.

The method used in the present study was a survey. The statistical population included 461 university students. The data collection tool was a questionnaire. The result of the Cronbach test was equal to 0.726 indicating the acceptable reliability of the questionnaire. For data analysis, descriptive statistical methods and inferential statistical methods using SPSS 21 software were employed.

The findings showed that 243 of the participants used social networks for 4–6 h a day, 192 students never used university libraries and 229 used the university library only once in a month. Communication with friends was also reported to be one of the main goals in using social networks. The results of regression analysis also indicated that four predictor variables including information retrieval, social influence, trust and attractiveness of social networking environment were statistically able to explain the variance of reluctance to use university libraries.

The present study is one of the few studies that has examined the negative impact of social networks on visiting university libraries.