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The purpose of this paper is to examine two types of departmental interventions focused on creating healthier and more equitable academic departments as well as enhancing faculty members’ capacity for collective dialogue, goals and work. Both interventions were informed by the “dual-agenda” approach and focused on targeted academic units over a prolonged period.

This paper uses a variety of qualitative and quantitative data (including National Science Foundation (NSF) ADVANCE indicator data) to assess the potential of dual-agenda informed interventions in reducing gendered structures and gendered dynamics.

The authors outline essential components of a dual-agenda model for maximizing success in creating more gender equitable work organizations and discuss why the authors are more optimistic about the dual-agenda approaches than many past researchers have been in terms of the potential of the dual-agenda model for promoting more equal opportunities in work organizations.

Most previous dual-agenda projects referenced in the literature have been carried out in non-academic contexts. The projects examined here, however, were administered in the context of multiple academic departments at two medium-sized, public US universities. Although other NSF ADVANCE institutional transformation institutions have included extensive department-focused transformation efforts (e.g. Brown University, Purdue University and Syracuse University), the long-term benefits of these efforts are not yet fully understood; nor have systematic comparisons been made across institutions.

As our readers are well aware THE BRITISH FOOD JOURNAL has invariably supported any legitimate effort having for its object the improvement, in one form or another, of the national food supply, and so long as the methods adopted are fair and above‐board this journal will continue to support such efforts by whomsoever they may be made. Fair and proper methods, however, are not always adopted, and a circular has recently been forwarded to us which affords an illustration of the fact.

This article investigates which criteria and processes are used to identify global leadership potential (GLP) in multinational corporations.

First, the literature at the intersection between leadership potential and global leadership is reviewed to identify a set of criteria that can be used for assessing GLP. The findings are then validated in a qualitative study against a sample of nine global corporations.

Several traits (integrity and resilience), attitudes (learning orientation, motivation to lead, change orientation, drive for results, customer orientation and a global mindset) and competencies (cognitive complexity and intercultural, interpersonal, leadership, learning, change and business competencies) are associated with GLP. The core steps in the GLP identification process are nomination, assessment and confirmation. These steps can be complemented by a preassessment phase and a subsequent talent dialogue.

The results of this research can inform human resource (HR) management practitioners in their endeavor to successfully identify and assess potential future global leaders.

Prior research has focused either on defining global leadership or on assessing leadership potential in general, without a clear focus on identifying global leaders. In this article, the two concepts of global leadership and leadership potential are combined, thus providing an integrated content and process model that indicates how global corporations select their future global leaders.

A growing body of research outlines how to best facilitate and ensure methodological rigor when using dictionary-based computerized text analyses (DBCTA) in organizational research. However, these best practices are currently scattered across several methodological and empirical manuscripts, making it difficult for scholars new to the technique to implement DBCTA in their own research. To better equip researchers looking to leverage this technique, this methodological report consolidates current best practices for applying DBCTA into a single, practical guide. In doing so, we provide direction regarding how to make key design decisions and identify valuable resources to help researchers from the beginning of the research process through final publication. Consequently, we advance DBCTA methods research by providing a one-stop reference for novices and experts alike concerning current best practices and available resources.

A review of research on the use of documentary and human resources by journalists is framed in an Anglo‐Saxon professional culture of standardised assignments. The comparative fieldwork focused on journalists and communication officers in a medium‐developed small community in the Caribbean and a parochial community in Western Europe. The innovative professional activities of different personalities are important. Information technology leads to some ethical and social problems in mass communication.

Product returns are a major challenge for e-businesses as they involve huge logistical and operational costs. Therefore, it becomes crucial to predict returns in advance. This study aims to evaluate different genres of classifiers for product return chance prediction, and further optimizes the best performing model.

An e-commerce data set having categorical type attributes has been used for this study. Feature selection based on chi-square provides a selective features-set which is used as inputs for model building. Predictive models are attempted using individual classifiers, ensemble models and deep neural networks. For performance evaluation, 75:25 train/test split and 10-fold cross-validation strategies are used. To improve the predictability of the best performing classifier, hyperparameter tuning is performed using different optimization methods such as, random search, grid search, Bayesian approach and evolutionary models (genetic algorithm, differential evolution and particle swarm optimization).

A comparison of F1-scores revealed that the Bayesian approach outperformed all other optimization approaches in terms of accuracy. The predictability of the Bayesian-optimized model is further compared with that of other classifiers using experimental analysis. The Bayesian-optimized XGBoost model possessed superior performance, with accuracies of 77.80% and 70.35% for holdout and 10-fold cross-validation methods, respectively.

Given the anonymized data, the effects of individual attributes on outcomes could not be investigated in detail. The Bayesian-optimized predictive model may be used in decision support systems, enabling real-time prediction of returns and the implementation of preventive measures.

There are very few reported studies on predicting the chance of order return in e-businesses. To the best of the authors’ knowledge, this study is the first to compare different optimization methods and classifiers, demonstrating the superiority of the Bayesian-optimized XGBoost classification model for returns prediction.

Competitor analysis is a key component in operations management. Most business decisions are rooted in the analysis of rival products inferred from market structure. Relative to more traditional competitor analysis methods, the purpose of this paper is to provide operations managers with an innovative tool to monitor a firm’s market position and competitors in real time at higher resolution and lower cost than more traditional competitor analysis methods.

The authors combine the techniques of Web Crawler, Natural Language Processing and Machine Learning algorithms with data visualization to develop a big data competitor-analysis system that informs operations managers about competitors and meaningful relationships among them. The authors illustrate the approach using the fitness mobile app business.

The study shows that the system supports operational decision making both descriptively and prescriptively. In particular, the innovative probabilistic topic modeling algorithm combined with conventional multidimensional scaling, product feature comparison and market structure analyses reveal an app’s position in relation to its peers. The authors also develop a user segment overlapping index based on user’s social media data. The authors combine this new index with the product functionality similarity index to map indirect and direct competitors with and without user lock-in.

The approach improves on previous approaches by fully automating information extraction from multiple online sources. The authors believe this is the first system of its kind. With limited human intervention, the methodology can easily be adapted to different settings, giving quicker, more reliable real-time results. The approach is also cost effective for market analysis projects covering different data sources.

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.

Aficionados of wine festivals, a component of wine tourism experience, engage in vigorous online discussions that influence fellow travelers’ purchase behaviors. This study aims to delve into these overlooked discussions, identifying emotions, topics and assessing their usefulness in TripAdvisor’s Travel Forums for two US wine festivals: Taste of Yountville and Epcot International Food and Wine Festival, located in traditional and nontraditional wine tourism destinations.

The study uses state-of-art sentiment analysis and topic modeling methods to extract emotions and underlying latent topics in travel forum discussions. Drawing from information theory, two regression analyses are performed on 10,677 forum posts to examine how the extracted Ekman’s emotions and key underlying topics influence the helpfulness of wine forum posts for each festival.

While three topics were identified in Epcot and four in Yountville, both festival platforms highlight travelers’ common preferences for “culinary experience” and “planning” attributes but reveal notable differences in their utility. Other shared novel findings include the importance of “anger” and “surprise” emotions on the helpfulness of forum posts.

These findings enhance wine festival managers’ and destination planners’ understanding of online travelers’ preferences and cognitive evaluation of user-generated contents’ usefulness. This marketing intelligence informs strategies for boosting the wine destination’s economic development.

This research offers a novel comparative analysis of social media on wine festival tourism experiences in diverse regions. Unlike hotel reviews, typically posted after consumption, forums offer unique and broader perspectives on discussions before, during, and after experiencing the wine festival.