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Although previous studies have examined the influence of celebrity involvement in behavioural intentions, the role of celebrity dimensions such as attraction, self-expression and centrality in influencing tourists’ intention in the context of developing countries such as Tanzania remains largely unaddressed. This study, therefore, examined the relationship between celebrity involvement and domestic tourists' intentions to visit tourist attractions, attitude being the mediating variable.

A questionnaire was self-administered on a convenient sample of 279 domestic tourists in the Tanzania’s four largest regions, namely, Dar es Salaam, Mbeya, Arusha and Mwanza. Employing a quantitative research approach, structural equation modelling was performed to test the cause-and-effect relationships between celebrity involvement and tourists’ intentions before testing the mediating role of attitude in such a relationship. Confirmatory factor analysis was also performed to test the measurement models.

Attraction emerged to be the main determinant of the celebrity dimension that significantly influenced domestic tourists’ travel intentions, whereas attitude partially mediates such a relationship. Moreover, Bongo Fleva musicians, particularly Diamond Platnumz, one of the leading celebrities in this genre, were found to influence most of the respondents’ travel intentions – he posted a picture on his Instagram account of him touring the Serengeti National Park.

The study focused on domestic tourists residing in four of the Mainland Tanzania’s largest regions, hence excluding those residing on the islands of Unguja and Pemba. Due to cultural differences, including the islands not only could unleash new perspectives on celebrity involvement dimensions but also could have introduced new determinants of travel intentions.

This study offers guidance to tourism businesses on designing their marketing campaigns that they should harness celebrity’s attractive qualities effectively. The focus should be directed not only towards linking destinations with celebrities but also on stimulating positive perception of those destinations, aligning with the attitudes of their followers.

The study has set out a new perspective for researchers, practitioners and tourism businesses to refine their promotional strategies and for academicians to gain a deeper understanding of visitor behavioural intention dynamics.

This study has proposed and verified that attraction is a dominant determinant compared to self-expression and centrality in explaining tourists’ travel intentions and attitudes, which play a significant role in explaining such a relationship. Although the study employed a modified theory of planned behaviour in a celebrity involvement study, the findings have broadened the understanding and its applicability in the context of a developing country.

Scholars and practitioners alike wish to understand what makes workplace conflict beneficial or injurious to, for example, performance and satisfaction. The authors focus on parties’ personal experience of the conflict, which is complementary to studying conflict issues (i.e. task- or relationship-related conflict). Although many authors discuss the personal experience of conflict, which the authors will refer to as conflict personalization, different definitions are used, leading to conceptual vagueness. Therefore, the purpose of this paper is to develop an integrative definition of the concept of conflict personalization.

The authors conducted a systematic literature review to collect definitions and conceptualizations from 41 publications. The subsequent thematic analysis revealed four building blocks that were used to develop an integrative definition of conflict personalization.

The authors developed the following definition: Conflict personalization is the negative affective as well as cognitive reaction to the self being threatened and/or in danger as a result of a social interaction about perceived incompatibilities.

The integrative definition of this study enables the development of a measurement instrument to assess personalization during workplace conflict, paving the way for developing effective research-based interventions.

Conceptual vagueness hampers theoretical development, empirical research and the development of effective interventions. Although the importance of conflict personalization is mentioned within the field of workplace conflict, it has not been empirically studied yet. This paper can serve as the basis for future research in which conflict issue and personal experience are separated.

Clinical staff working in mental health services experience high levels of work-related stress, burnout and poor well-being. Increased levels of stress, burnout, depression and anxiety and poorer mental well-being among health-care workers are associated with more sick days, absenteeism, lower work satisfaction, increased staff turnover and reduced quality of patient care. Virtual reality (VR) relaxation is a technique whereby experiences of pleasant and calming environments are accessed through a head-mounted display to promote relaxation. The purpose of this paper is to describe the design of a study that assesses the feasibility and acceptability of implementing a multi-session VR relaxation intervention amongst mental health professionals, to improve their relaxation levels and mental well-being.

The study follows a pre–post-test design. Mental health staff will be recruited for five weeks of VR relaxation. The authors will measure the feasibility and acceptability of the VR relaxation intervention as primary outcomes, alongside secondary outcomes evaluating the benefits of VR relaxation for mental well-being.

The study aims to recruit 20–25 health-care professionals working in both inpatient and specialist community mental health settings.

Research indicates the potential of VR relaxation as a low-intensity intervention to promote relaxation and reduce stress in the workplace. If VR relaxation is shown to be feasible and acceptable, when delivered across multiple sessions, there would be scope for large-scale work to investigate its effectiveness as an approach to enable health-care professionals to de-stress, relax and optimise their mental well-being. In turn, this may consequently reduce turnover and improve stress-related sick leave across health-care services.

In fire resistance tests (FRTs) of building materials, a crucial criterion to pass the test procedure is to avoid the leakage of the hot flue gases caused by gaps and cracks occurring due to the thermal exposure. The present study's aim is to calculate the deformation of a steel door, which is embedded within a wall made of bricks, and qualitatively determine the flue gas leakage.

A computational fluid dynamics/finite element method (CFD/FEM) coupling was introduced representing an intermediate approach between a one-way and a full two-way coupling methodology, leading to a simplified two-way coupling (STWC). In contrast to a full two way-coupling, the heat transfer through the steel door was simulated based on a one-way approach. Subsequently, the predicted temperatures at the door from the one-way simulation were used in the following CFD/FEM simulation, where the fluid flow inside and outside the furnace as well as the deformation of the door were calculated simultaneously.

The simulation showed large gaps and flue gas leakage above the door lock and at the upper edge of the door, which was in close accordance to the experiment. Furthermore, it was found that STWC predicted similar deformations compared to the one-way coupling.

Since two-way coupling approaches for fluid/structure interaction in fire research are computationally demanding, the number of studies is low. Only a few are dealing with the flue gas exit from rooms due to destruction of solid components. Thus, the present study is the first two-way approach dealing with flue gas leakage due to gap formation.

The purpose of this paper is to review cases of artificial reefs built through additive manufacturing (AM) technologies and analyse their ecological goals, fabrication process, materials, structural design features and implementation location to determine predominant parameters, environmental impacts, advantages, and limitations.

The review analysed 16 cases of artificial reefs from both temperate and tropical regions. These were categorised based on the AM process used, the mortar material used (crucial for biological applications), the structural design features and the location of implementation. These parameters are assessed to determine how effectively the designs meet the stipulated ecological goals, how AM technologies demonstrate their potential in comparison to conventional methods and the preference locations of these implementations.

The overview revealed that the dominant artificial reef implementation occurs in the Mediterranean and Atlantic Seas, both accounting for 24%. The remaining cases were in the Australian Sea (20%), the South Asia Sea (12%), the Persian Gulf and the Pacific Ocean, both with 8%, and the Indian Sea with 4% of all the cases studied. It was concluded that fused filament fabrication, binder jetting and material extrusion represent the main AM processes used to build artificial reefs. Cementitious materials, ceramics, polymers and geopolymer formulations were used, incorporating aggregates from mineral residues, biological wastes and pozzolan materials, to reduce environmental impacts, promote the circular economy and be more beneficial for marine ecosystems. The evaluation ranking assessed how well their design and materials align with their ecological goals, demonstrating that five cases were ranked with high effectiveness, ten projects with moderate effectiveness and one case with low effectiveness.

AM represents an innovative method for marine restoration and management. It offers a rapid prototyping technique for design validation and enables the creation of highly complex shapes for habitat diversification while incorporating a diverse range of materials to benefit environmental and marine species’ habitats.

The purpose of our research is to better understand inquiry-based pedagogy in the context of leadership education. Specifically, we sought to learn about how leadership learning is characterized in an immersive inquiry course, and how inquiry-based pedagogy is experienced by students engaged in interdisciplinary leadership learning.

We used a case study approach as an overarching methodology. The research methods employed to collect data were World Cafe and episodic narrative interview. Further, we used collocation analysis and systematic text condensation as analytical strategies to interpret data.

Our findings led us to four primary conclusions: (1) inquiry-based learning helps to foster an inquiry mindset amongst leadership education students; (2) the challenges and tensions associated with inquiry-based learning are worth the learning gains for leadership students; (3) the opportunity to learn in relationship is beneficial for leadership development outcomes and (4) students’ experiences of inquiry-based learning in leadership education often included instances of transformation.

Limitations of the research were: (1) it is a case study situated within a unique, particular social and educational context; (2) demographic data were not collected from participants, so results cannot be disaggregated based on particular demographic markers and (3) the small sample size involved in the study makes it impossible to generalize across a broad population.

This research has enabled a deep understanding of structural and relational supports that can enable effective inquiry-based learning in leadership education. It also offers evidence to support institutional shifts to inquiry-based pedagogy in leadership education.

Our research demonstrates that use of inquiry-based pedagogy in leadership education has long-lasting positive effects on students' capacity for applied leadership practice. Consequently, participants in this type of leadership learning are better positioned to effectively lead social change that is pressing in our current global context.

There is scant (if any) published research that has focused on using inquiry-based pedagogies in leadership education. This research makes a significant contribution to the scholarship of leadership education.

Although the study of factors that promote employee well-being is not a new research area, the impact of managerial caring and perceived insider status on subjective employee well-being is a relatively new and unexplored area within the management literature. Therefore, this study examined the relationship between managerial caring and subjective employee well-being while considering the mediating effect of perceived insider status. Based on social identity theory, the study hypothesized that perceived insider status mediates the link between managerial caring and subjective employee well-being.

The study analyzed data from 193 employees working across various industries in the United Arab Emirates using Process Macro Model 4.

The findings revealed a positive relationship between managerial caring and employee well-being, which was influenced by the mediating effect of perceived insider status.

The study’s results, which shed light on the process by which managerial caring positively affects employee well-being, provide valuable insights for developing a caring workplace. The findings make a significant contribution to the literature on managerial caring by explaining how the mediating role of perceived insider status influences the relationship between managerial caring and subjective employee well-being.

This study aims to deepen the understanding of the challenges and implications entailed by deploying mobile clinics in conflict zones to reach populations affected by violence and cut off from health-care services.

This research combines an integrated literature review and an instrumental case study. The literature review comprises two targeted reviews to provide insights: one on conflict zones and one on mobile clinics. The case study describes the process and challenges faced throughout a mobile clinic deployment during and after the Iraq War. The data was gathered using mixed methods over a two-year period (2017–2018).

Armed conflicts directly impact the populations’ health and access to health care. Mobile clinic deployments are often used and recommended to provide health-care access to vulnerable populations cut off from health-care services. However, there is a dearth of peer-reviewed literature documenting decision support tools for mobile clinic deployments.

This study highlights the gaps in the literature and provides direction for future research to support the development of valuable insights and decision support tools for practitioners.

Vision, audition, olfactory, tactile and taste are five important senses that human uses to interact with the real world. As facing more and more complex environments, a sensing system is essential for intelligent robots with various types of sensors. To mimic human-like abilities, sensors similar to human perception capabilities are indispensable. However, most research only concentrated on analyzing literature on single-modal sensors and their robotics application.

This study presents a systematic review of five bioinspired senses, especially considering a brief introduction of multimodal sensing applications and predicting current trends and future directions of this field, which may have continuous enlightenments.

This review shows that bioinspired sensors can enable robots to better understand the environment, and multiple sensor combinations can support the robot’s ability to behave intelligently.

The review starts with a brief survey of the biological sensing mechanisms of the five senses, which are followed by their bioinspired electronic counterparts. Their applications in the robots are then reviewed as another emphasis, covering the main application scopes of localization and navigation, objection identification, dexterous manipulation, compliant interaction and so on. Finally, the trends, difficulties and challenges of this research were discussed to help guide future research on intelligent robot sensors.

This paper provides practical advice for archaeologists and heritage specialists wishing to use ML approaches to identify archaeological features in high-resolution satellite imagery (or other remotely sensed data sources). We seek to balance the disproportionately optimistic literature related to the application of ML to archaeological prospection through a discussion of limitations, challenges and other difficulties. We further seek to raise awareness among researchers of the time, effort, expertise and resources necessary to implement ML successfully, so that they can make an informed choice between ML and manual inspection approaches.

Automated object detection has been the holy grail of archaeological remote sensing for the last two decades. Machine learning (ML) models have proven able to detect uniform features across a consistent background, but more variegated imagery remains a challenge. We set out to detect burial mounds in satellite imagery from a diverse landscape in Central Bulgaria using a pre-trained Convolutional Neural Network (CNN) plus additional but low-touch training to improve performance. Training was accomplished using MOUND/NOT MOUND cutouts, and the model assessed arbitrary tiles of the same size from the image. Results were assessed using field data.

Validation of results against field data showed that self-reported success rates were misleadingly high, and that the model was misidentifying most features. Setting an identification threshold at 60% probability, and noting that we used an approach where the CNN assessed tiles of a fixed size, tile-based false negative rates were 95–96%, false positive rates were 87–95% of tagged tiles, while true positives were only 5–13%. Counterintuitively, the model provided with training data selected for highly visible mounds (rather than all mounds) performed worse. Development of the model, meanwhile, required approximately 135 person-hours of work.

Our attempt to deploy a pre-trained CNN demonstrates the limitations of this approach when it is used to detect varied features of different sizes within a heterogeneous landscape that contains confounding natural and modern features, such as roads, forests and field boundaries. The model has detected incidental features rather than the mounds themselves, making external validation with field data an essential part of CNN workflows. Correcting the model would require refining the training data as well as adopting different approaches to model choice and execution, raising the computational requirements beyond the level of most cultural heritage practitioners.

Improving the pre-trained model’s performance would require considerable time and resources, on top of the time already invested. The degree of manual intervention required – particularly around the subsetting and annotation of training data – is so significant that it raises the question of whether it would be more efficient to identify all of the mounds manually, either through brute-force inspection by experts or by crowdsourcing the analysis to trained – or even untrained – volunteers. Researchers and heritage specialists seeking efficient methods for extracting features from remotely sensed data should weigh the costs and benefits of ML versus manual approaches carefully.

Our literature review indicates that use of artificial intelligence (AI) and ML approaches to archaeological prospection have grown exponentially in the past decade, approaching adoption levels associated with “crossing the chasm” from innovators and early adopters to the majority of researchers. The literature itself, however, is overwhelmingly positive, reflecting some combination of publication bias and a rhetoric of unconditional success. This paper presents the failure of a good-faith attempt to utilise these approaches as a counterbalance and cautionary tale to potential adopters of the technology. Early-majority adopters may find ML difficult to implement effectively in real-life scenarios.

Unlike many high-profile reports from well-funded projects, our paper represents a serious but modestly resourced attempt to apply an ML approach to archaeological remote sensing, using techniques like transfer learning that are promoted as solutions to time and cost problems associated with, e.g. annotating and manipulating training data. While the majority of articles uncritically promote ML, or only discuss how challenges were overcome, our paper investigates how – despite reasonable self-reported scores – the model failed to locate the target features when compared to field data. We also present time, expertise and resourcing requirements, a rarity in ML-for-archaeology publications.