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This research seeks to understand whether employer branding (EB) and internal marketing (IM) are fundamental to the challenge of attracting and retaining talent and how these strategies can help companies to overcome the difficulties related to the talent shortage, from the perspective of a SME Portuguese metallurgical industry.

The research resorts to a case study of a qualitative nature, through a semi-structured interview with the head of the human resources (HR) training and development area of the Navarra Group, and quantitative, through surveys to its employees. Based on the literature, a conceptual model was constructed, whose application allowed us to perceive the relationships between the practices of EB and IM; satisfaction, motivation and commitment; attraction and retention.

The exploratory interview concluded that organizations consider EB and IM essential for an effective talent management strategy. The quantitative results demonstrate that IM and EB practices implemented in the organization contribute to the satisfaction, motivation and involvement of employees, which results in a decrease in the intention to leave. It is also noted that these practices promote an increase in the perception of organizational attractiveness, which represents a positive impact on its ability to attract.

From a theoretical perspective, the research contributes to the development of knowledge about IM, EB and talent management, providing relevant data that can help define the best strategies for attraction and retention, from the point of view of IM and EB.

The research presents preliminary insights that can be an auxiliary tool for HR managers and professionals in the context of industrial SMEs.

This research explores why tourists are dissatisfied in places considered “wonders of the world”. The authors ask if the place does not match visitors' expectations or if other factors spoil the experience.

The authors analysed the lowest-rated reviews of these wonders on TripAdvisor. The authors identified the main causes of complaints and the problems tourists faced. The authors grouped the complaints into categories and used CoDa.

The results indicate that dissatisfaction does not stem from unmet expectations regarding the monument itself, but rather from other factors related to the quality of the tourist service.

The findings of this research can be implemented in those tourist spots that, despite their global popularity, have considerable proportions of unhappy visitors, not due to the attraction itself, but to shortcomings in its administration.

This study provides a deeper insight into the causes of complaints about some of the most renowned monuments, regarded as extraordinary places, where high satisfaction levels would be anticipated. It also contributes theoretically to the literature on customer complaints in tourist places.

The COVID-19 pandemic has induced tourism destinations to reconsider organisational aspects related to health safety measures, as perceptions of health safety may have become particularly important for tourists. Using data from summer 2020, the period immediately after the outbreak of the pandemic, we investigate the factors that affected tourist perceptions of health safety.

Data come from a survey of tourist-card holders in Trentino, an Italian mountain destination. Through regressions, we assess the conditional correlation between health safety measure evaluations following a holiday and a set of covariates related to the features of the tourist area and the tourists themselves, as well as COVID-19 incidence in their province of residence in the months before the holiday.

Tourist-related features seem not to impact on perceived health safety, whereas some destination- and accommodation-related elements do. In particular, the number of tourist beds affects it negatively, and staying at a hotel does it in a positive way. COVID-19 incidence in one’s home province does not affect perceptions of health safety measures, which suggests a possible sample selection effect and/or the need for more fine-grained data.

This paper is one of the few on the immediate effects of the COVID-19 pandemic using data from a large sample of actual tourists. Our findings point out the importance of the intrinsic features of some places and accommodation in influencing perceptions of safety. We discuss implications for scholars and destination managers.

Tourism in Coimbra today is influenced by the fact that the Univer(s)city was distinguished as a World Heritage Site in 2013. The number of visits has grown very significantly in recent years, but the diversification of the tourist offer is still weak and unable to take advantage of existing resources. This paper aims to present genealogy tourism as an alternative urban cultural tourism in Coimbra.

Methodology involved mapping the Jewish culture elements in the city of Coimbra, and a route was outlined and proposed.

Genealogy tourism resources are identified in the historic centre of the city. These alternative spaces need urban rehabilitation and (re)functionalisation, which allowed the authors to rethink tourism in Coimbra. They are the motivation to visit for all urban cultural tourists, especially Israelis/Jews, and provide contact with places where the experiences of ancestors combine with the history and memory of places, with recent discoveries and the elements of Jewish culture in the city.

It is concluded that the quantity, diversity, authenticity and singularity of the heritage resources that bear witness to the Jewish presence in Coimbra are sufficient assets to create a route, to enrich the tourist experience in the city and to include the destination in the Sephardic routes.

The tourism industry may result in positive as well as negative effects for local communities. Although it is generating economic growth and job creation in different parts of the world, it can impact on sociocultural and ecological dimensions of destinations. A high influx of tourists can disrupt the residents' attitudes, behaviors, and their quality of life. It can also lead to the degradation of natural environments, including land, air, water, plants, and animals. In this light, this research raises awareness about the importance of managing tourism in a sustainable and responsible manner. The researchers deliberate about the opportunities and challenges of tourism. Afterward, they focus on the implementation of sustainable tourism development activities in the South African context. In conclusion, they put forward their recommendations for policymakers.

The purpose of this paper is to explore the tension in brand management created through the rapid transformation of social media, mapping the maintenance of increasingly complex B2B relationship dynamics with key intermediaries.

In-depth interviews with 17 social media practitioners from leading fashion brands, agencies and platforms in the UK and Australia informed this study. Analysis used grounded theory, and results were interpreted through the lens of network and stakeholder theories.

Social media platforms have evolved into critical brand stakeholders, serving as gatekeepers in an increasingly unbalanced system between provider and marketer. The perpetuation of a hierarchical power dynamic affects the development of both practitioner and firm capabilities with negative implications and consequences for organisational control of branded social media communications. Three theoretical propositions are offered relating to the nature of platform hegemony, the notion of social media democratisation and the limiting impact of rapid change on the formation of relational ties.

This study extends the conceptualisation of communal custody of brands in social media settings to incorporate a growing number of commercial stakeholders, challenging the traditional dyadic consumer-brand relational paradigm. This study sheds new light on the impact of digital transformation on power distributions in social media communities not hitherto addressed.

The study's objective is to estimate the association of specific perceived employer-provided benefits on employees' intention to leave in different age cohorts during coronavirus disease 2019 (COVID-19). Informed by the psychological theories of ageing, the authors propose three age-cohort-specific hypotheses in three motivational domains: security and health benefits, flexible work arrangement and education-related benefits.

The authors use a large survey of employees in Estonia (n = 7,209) conducted in 2020 and test the association of specific benefits and their interactions with age on employees' intention to leave.

The results show that older cohorts are generally less prone to leave their jobs. Benefits that employers could use during the COVID-19 crisis generally had negative associations with the intention to leave, but age-specific differences were negligible; only the perceived provision of flexible work arrangements reduced the younger cohort's intention to leave relatively more.

This study is one of the few that allows us to make inferences regarding the benefits preferences amongst the working population during an unprecedented health crisis.

The research in competitive facility location (CFL) is quite dynamic, both from a problem formulation and an algorithmic point of view. Research direction has changed immensely over the years to address various competitive challenges. This study aims to explore CFL literature to highlight these research trends, important issues and future research opportunities.

This study utilises the Scopus database to search for related CFL models and adopts a five-step systematic approach for the review process. The five steps involve (1) Article Identification and keyword selection, (2) Selection criteria, (3) Literature review, (4) Literature analysis and (5) Research studies.

The paper presents a comprehensive review of CFL modelling efforts from 1981 to 2021 to provide a depth study of the research evolution in this area. The published articles are classified based on multiple characteristics, including the type of problem, type of competition, game-theoretical approaches, customer behaviour, decision space, type of demand, number of facilities, capacity and budget limitations. The review also highlights the popular problem areas and dedicated research in the respective domain. In addition, a second classification is also provided based on solution methods adopted to solve various CFL models and real-world case studies.

The paper covers 40 years of CFL literature from the perspective of the problem area, CFL characteristics and the solution approach. Additionally, it introduces characteristics such as capacity limit and budget constraint for the first time for classification purposes.

Incorporating dynamic spatial effects exhibits considerable potential in improving the accuracy of forecasting tourism demands. This study aims to propose an innovative deep learning model for capturing dynamic spatial effects.

A novel deep learning model founded on the transformer architecture, called the spatiotemporal transformer network, is presented. This model has three components: the temporal transformer, spatial transformer and spatiotemporal fusion modules. The dynamic temporal dependencies of each attraction are extracted efficiently by the temporal transformer module. The dynamic spatial correlations between attractions are extracted efficiently by the spatial transformer module. The extracted dynamic temporal and spatial features are fused in a learnable manner in the spatiotemporal fusion module. Convolutional operations are implemented to generate the final forecasts.

The results indicate that the proposed model performs better in forecasting accuracy than some popular benchmark models, demonstrating its significant forecasting performance. Incorporating dynamic spatiotemporal features is an effective strategy for improving forecasting. It can provide an important reference to related studies.

The proposed model leverages high-frequency data to achieve accurate predictions at the micro level by incorporating dynamic spatial effects. Destination managers should fully consider the dynamic spatial effects of attractions when planning and marketing to promote tourism resources.

This study incorporates dynamic spatial effects into tourism demand forecasting models by using a transformer neural network. It advances the development of methodologies in related fields.

纳入动态空间效应在提高旅游需求预测的准确性方面具有相当大的潜力。本研究提出了一种捕捉动态空间效应的创新型深度学习模型。

本研究提出了一种基于变压器架构的新型深度学习模型, 称为时空变压器网络。该模型由三个部分组成：时空转换器、空间转换器和时空融合模块。时空转换器模块可有效提取每个景点的动态时间依赖关系。空间转换器模块可有效提取景点之间的动态空间相关性。提取的动态时间和空间特征在时空融合模块中以可学习的方式进行融合。通过卷积运算生成最终预测结果。

结果表明, 与一些流行的基准模型相比, 所提出的模型在预测准确性方面表现更好, 证明了其显著的预测性能。纳入动态时空特征是改进预测的有效策略。它可为相关研究提供重要参考。

所提出的模型利用高频数据, 通过纳入动态空间效应, 在微观层面上实现了准确预测。旅游目的地管理者在规划和营销推广旅游资源时, 应充分考虑景点的动态空间效应。

本研究通过使用变压器神经网络, 将动态空间效应纳入旅游需求预测模型。它推动了相关领域方法论的发展。

La incorporación de efectos espaciales dinámicos ofrece un considerable potencial para mejorar la precisión de la previsión de la demanda turística. Este estudio propone un modelo innovador de aprendizaje profundo para capturar los efectos espaciales dinámicos.

Se presenta un novedoso modelo de aprendizaje profundo basado en la arquitectura transformadora, denominado red de transformador espaciotemporal. Este modelo tiene tres componentes: el transformador temporal, el transformador espacial y los módulos de fusión espaciotemporal. El módulo transformador temporal extrae de manera eficiente las dependencias temporales dinámicas de cada atracción. El módulo transformador espacial extrae eficientemente las correlaciones espaciales dinámicas entre las atracciones. Las características dinámicas temporales y espaciales extraídas se fusionan de manera que se puede aprender en el módulo de fusión espaciotemporal. Se aplican operaciones convolucionales para generar las previsiones finales.

Los resultados indican que el modelo propuesto obtiene mejores resultados en la precisión de las previsiones que algunos modelos de referencia conocidos, lo que demuestra su importante capacidad de previsión. La incorporación de características espaciotemporales dinámicas supone una estrategia eficaz para mejorar las previsiones. Esto puede proporcionar una referencia importante para estudios afines.

El modelo propuesto aprovecha los datos de alta frecuencia para lograr predicciones precisas a nivel micro incorporando efectos espaciales dinámicos. Los gestores de destinos deberían tener plenamente en cuenta los efectos espaciales dinámicos de las atracciones en la planificación y marketing para la promoción de los recursos turísticos.

Este estudio incorpora efectos espaciales dinámicos a los modelos de previsión de la demanda turística mediante el empleo de una red neuronal transformadora. Supone un avance en el desarrollo de metodologías en campos afines.