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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 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.

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.

Focus on Mental Health is a UK forum which was formed in 1993. It consists of representatives of leading UK mental health groups, who work together on common objectives. In 2000/2001, Focus was funded by the Department for Education and Employment to carry out a programme of work looking at issues around poverty and mental health. This paper will focus on the results of a survey of people's personal experiences of mental health problems and living on a low income led by a sub‐group of Focus members.

The purpose of this paper is to examine strengths and limitations that search engines (SEs) exhibit when responding to web search queries associated with the grade school curriculum

The authors employed a simulation-based experimental approach to conduct an in-depth empirical examination of SEs and used web search queries that capture information needs in different search scenarios.

Outcomes from this study highlight that child-oriented SEs are more effective than traditional ones when filtering inappropriate resources, but often fail to retrieve educational materials. All SEs examined offered resources at reading levels higher than that of the target audience and often prioritized resources with popular top-level domain (e.g. “.com”).

Findings have implications for human intervention, search literacy in schools, and the enhancement of existing SEs. Results shed light on the impact on children’s education that result from introducing misconception about SEs when these tools either retrieve no results or offer irrelevant resources, in response to web search queries pertinent to the grade school curriculum.

The authors examined child-oriented and popular SEs retrieval of resources aligning with task objectives and user capabilities–resources that match user reading skills, do not contain hate-speech and sexually-explicit content, are non-opinionated, and are curriculum-relevant. Findings identified limitations of existing SEs (both directly or indirectly supporting young users) and demonstrate the need to improve SE filtering and ranking algorithms.

Peer-to-peer (P2P) accommodation sharing has become a significant part of the travel and lodging industry, allowing homeowners to engage in entrepreneurial activity via sharing of resources. However, there is limited understanding of how hosts can use listing descriptions to better match their offerings to different consumer segments. The purpose of this paper is to understand the use of listing descriptions by Airbnb hosts and the impact of such descriptions on sales performance.

In this paper, a deep learning-based sentence-level aspect mining approach is used to extract various aspects from host-provided listing descriptions. Then a regression-based approach is used to understand the impact of various aspects of listing descriptions on listing performance.

It was found that aspects for which listing descriptions are the sole source of information have the greatest influence on listing performance. The authors also find that the impact of an aspect on listing performance varies by listing type, and that there is a mismatch between the most included aspects by hosts in their listing descriptions and the most influential aspects that impact sales.

The impact of consumer reviews in the context of Airbnb has been extensively studied. A novel aspect of this study is the exploration of P2P accommodations from a supplier perspective, by understanding the use and impact of host-provided textual descriptions on sales. The findings of this study can help better market properties from a practice perspective and better understand consumer information consumption from a theoretical perspective. The authors also demonstrate a new approach for exploring social phenomena by performing quantitative analysis on textual data using deep-learning and regression-based techniques.