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The procurement reforms carried out by the Government of Ghana have led to the creation of the Public Procurement Authority and the passage of the Public Procurement (Amended) Act 2016. Nevertheless, many obstacles could prevent an open and fair competition during the procurement of works. This significantly affects the success of Ghanaian construction projects. Therefore, this paper aims to explore the factors to enhance the success of the contractor selection phase of the Ghanaian public labor-based construction project.

A quantitative research approach was adopted by this study. Data was collected through a self-administered questionnaire to 377 respondents across Ghana. The data was then analyzed using structural equation modeling.

This study revealed that external, project procedures and project management factors impact the Ghanaian labor-based contractor selection phase success.

This study is limited to the contractor selection phase of Ghanaian public labor-based projects and developing nations with similar socioeconomic characteristics.

Strict adherence to this study’s findings will enable the government to undertake more infrastructure projects to raise the standard of living for its populace.

Although several studies have been conducted to improve the success of construction projects in developing nations, very little focus has been placed on the success factors of the contractor selection phase of public construction projects. These findings are vital in discovering for the first-time factors influencing the success of the tender stage of public labor-based projects in Ghana and developing nations with similar socioeconomic characteristics.

Existing tourism review articles have limited review topics and cover a relatively short period. This review paper aims to extend the coverage of the previous literature and enhances the completeness of tourism-related studies to provide comprehensive tourism-related literature from 1945 (World War II onward) to 2022. Specifically, this paper reveals the major research themes present in published tourism research during this time period and highlights the evolution of tourism research from the preliminary phase, the transversal phase, to the growth phase.

The present study visualizes tourism research through networks of coauthors and their countries and regions, cocitation analysis of keywords and explores the thematic evolution of tourism research after the World War II (i.e., 1945–2022) from Web of Science and Google Scholar through bibliometric analysis.

Findings reveal that the themes of tourism research in the past years can be divided into seven major research themes. The tourism research evolution from World War II to 2022 can be categorized into three stages: preliminary (1945–1970), transversal (1971–2004) and growth (2005–2022). In addition, the research themes of tourism are not static but evolve according to the dynamics of the society and the industry, and that seven main research themes have been formed, namely, “heritage tourism,” “medical tourism,” “adventure tourism,” “dark tourism,” “sustainable tourism,” “rural tourism” and “smart tourism.”

The present study expands and refines the comprehensive literature in tourism research, as well as reveals the trends and dynamics in tourism research through network analysis and thematic evolution research methods.

现有的旅游评论文章在审查主题方面有限, 并且涵盖的时间相对较短。本综述文章扩展了先前文献的涵盖范围, 增强了与旅游相关研究的完整性, 提供了从1945年（第二次世界大战之后）到2022年的全面旅游相关文献。具体而言, 本文揭示了此期间发表的旅游研究中的主要研究主题, 并突出了旅游研究从初步阶段、横向阶段到增长阶段的演变。

本研究通过共同作者及其国家的网络、关键词的共同引用分析, 将旅游研究可视化, 并探索二战后旅游研究的主题演变。本研究通过文献计量学分析, 将 Web of Science (WoS) 和 Google Scholar 中的旅游研究（即 1945–2022 年）可视化。

研究结果显示, 过去几年的旅游研究主题可分为七大研究主题。从第二次世界大战到 2022 年的旅游研究演变可分为三个阶段：初步阶段（1945–1970 年）、横向阶段（1971–2004 年）和成长阶段2005–2022 年）。此外, 旅游的研究主题并不是静态的, 而是根据社会和行业的动态而演变, 形成了七个主要研究主题, 即“遗产旅游”、“医疗旅游”、“冒险旅游”、“黑暗旅游”、“可持续旅游”、“乡村旅游”和“智慧旅游”。

本研究通过网络分析和主题演变研究方法扩展和完善了旅游研究方面的综合文献, 并揭示了旅游研究的趋势和动态。

Los artículos de revisión existentes sobre turismo tienen temas de revisión limitados y cubren un periodo relativamente corto. Este artículo de revisión amplía la cobertura de la bibliografía anterior y mejora la exhaustividad de los estudios relacionados con el turismo para ofrecer una bibliografía exhaustiva sobre el turismo desde 1945 (Segunda Guerra Mundial en adelante) hasta 2022. En concreto, este documento revela los principales temas de investigación presentes en la investigación turística publicada durante este periodo de tiempo y destaca la evolución de la investigación turística desde la fase preliminar, la fase transversal, hasta la fase de crecimiento.

El presente estudio visualiza la investigación turística a través de redes de coautores y sus países y regiones, análisis de co-citación de palabras clave, y explora la evolución temática de la investigación turística después de la Segunda Guerra Mundial (es decir, 1945–2022) a partir de Web of Science y Google Scholar mediante análisis bibliométricos.

Los resultados revelan que los temas de la investigación turística de los últimos años pueden dividirse en siete grandes temas de investigación. La evolución de la investigación turística desde la Segunda Guerra Mundial hasta 2022 puede clasificarse en tres etapas: preliminar (1945–1970), transversal (1971–2004) y de crecimiento (2005–2022). Además, los temas de investigación del turismo no son estáticos, sino que evolucionan según la dinámica de la sociedad y de la industria, y que se han formado siete temas principales de investigación, a saber: “turismo patrimonial”, “turismo médico”, “turismo de aventura”, “turismo oscuro”, “turismo sostenible”, “turismo rural” y “turismo inteligente”.

El presente estudio amplía y perfecciona la amplia bibliografía existente en el campo de la investigación turística, además de revelar las tendencias y la dinámica de la investigación turística mediante el análisis de redes y los métodos de investigación de evolución temática.

In modern China, sports and nationalism always have close connection, and nationalism is the important reason for the promotion of Chinese sports. However, the relationship between Chinese sports and nationalism in globalised China could be much more examined by academics, as well as its influencing factors. This chapter selects the Beijing 2008 Olympic Games as the context and representative three Chinese sports heroes in the period of globalisation to study. The findings show that in some extent, Beijing 2008 Olympic Games and three Chinese sports heroes represent the national image of China in the globalised world, also bearing the burden of washing away historical humiliation and pursuing national glory. Furthermore, it is manifested that China have a complex nationalism in the process of hosting the 2008 Olympic Games. Under the influence of mass media, market economy and sports professionalisation, nationalism still exists in Chinese sports, but people gradually start to reflect on the ‘Juguo Tizhi’, the traditional Chinese sports system and the concept of ‘winning glory for the nation’. The relationship between Chinese nationalism and sports shows the important implications of rapid Chinese sports development.

Computer vision and deep learning (DL) methods have been investigated for personal protective equipment (PPE) monitoring and detection for construction workers’ safety. However, it is still challenging to implement automated safety monitoring methods in near real time or in a time-efficient manner in real construction practices. Therefore, this study developed a novel solution to enhance the time efficiency to achieve near-real-time safety glove detection and meanwhile preserve data privacy.

The developed method comprises two primary components: (1) transfer learning methods to detect safety gloves and (2) edge computing to improve time efficiency and data privacy. To compare the developed edge computing-based method with the currently widely used cloud computing-based methods, a comprehensive comparative analysis was conducted from both the implementation and theory perspectives, providing insights into the developed approach’s performance.

Three DL models achieved mean average precision (mAP) scores ranging from 74.92% to 84.31% for safety glove detection. The other two methods by combining object detection and classification achieved mAP as 89.91% for hand detection and 100% for glove classification. From both implementation and theory perspectives, the edge computing-based method detected gloves faster than the cloud computing-based method. The edge computing-based method achieved a detection latency of 36%–68% shorter than the cloud computing-based method in the implementation perspective. The findings highlight edge computing’s potential for near-real-time detection with improved data privacy.

This study implemented and evaluated DL-based safety monitoring methods on different computing infrastructures to investigate their time efficiency. This study contributes to existing knowledge by demonstrating how edge computing can be used with DL models (without sacrificing their performance) to improve PPE-glove monitoring in a time-efficient manner as well as maintain data privacy.

This study aims to investigate the effect of incorporating cobalt (Co) into Sn-58Bi alloy on its phase composition, tensile properties, hardness and thermal aging performances. The fracture morphologies of tensile-tested solders are also investigated to correlate the microstructural changes with tensile properties of the solder alloys. Then, the thermal aging performances of the solder alloys are investigated in terms of their intermetallic compound (IMC) layer morphology and thickness.

The Sn-58Bi and Sn-58Bi-xCo, where x = 1.0, 1.5 and 2.0 Wt.%, were prepared using the flux doping technique. X-ray diffraction (XRD) is used to study the phase composition of the solder alloys, whereas scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDX) are used to investigate the microstructure, fractography and compositions of the solders. Tensile properties such as ultimate tensile strength (UTS), Young’s modulus and elongation are tested using the tensile test, whereas the microhardness value is gained from the micro-Vickers hardness test. The morphology and thickness of the IMC layer at the solder’s joints are investigated by varying the thermally aging duration up to 56 days at 80°C.

XRD analysis shows the presence of Co₃Sn₂ phase and confirms that Co was successfully incorporated via the flux doping technique. The microstructure of all Sn-58Bi-xCo solders did not differ significantly from Sn-58Bi solders. Sn-58Bi-2.0Co solder exhibited optimum properties among all compositions, with the highest UTS (87.89 ± 2.55 MPa) at 0.01 s⁻¹ strain rate and the lowest IMC layer thickness at the interface after being thermally aged for 56 days (3.84 ± 0.67 µm).

The originality and value of this research lie in its novel exploration of the flux doping technique to introduce minor alloying of Co into Sn-58Bi solder alloys, providing new insights into enhancing the properties and performance of these solders. This new Sn-Bi-Co alloy has the potential to replace lead-containing solder alloy in low-temperature soldering.

The outbreak of the COVID-19 pandemic led to the rise of online learning in Hong Kong. Online learning was identified as the only solution to meet students’ learning needs in the higher education sector during this chaotic period. This research aims to explore students’ perceptions of online teaching and learning from home under a health emergency via a comparison of undergraduate and postgraduate students’ experiences.

A total of 174 postgraduate students and 286 undergraduate students in various universities and colleges in Hong Kong were surveyed in this study.

The results show that postgraduate students generally gave more positive feedback on individual and environmental prerequisites, alongside pedagogical and institutional support, and were more motivated in online classes as compared to undergraduate students. Undergraduate students considered the shift to online education in light of the COVID-19 pandemic to be timely and rated their level of discipline with respect to online learning higher than did postgraduate students.

This study enables educators to better understand the first-hand experiences of students across different levels of study in Hong Kong, as well as to examine the possibility of establishing online education as a more prevalent mode of study in the future. The COVID-19 pandemic has educated us on the significance of being well-prepared to ensure quality education continues when emergencies and disturbances arise.

This study aims to examine the use of the metaverse in tourism and hospitality to comprehend better how the technology might shape customer journey management, especially relative to information provision, experiences and customer benefits.

This explanatory research used a two-stage approach of media analysis and practitioner interviews to analyse the interactions among tourism information provision, customer experiences and customer benefits in the metaverse. It conceptualized and mapped the consumer journey of the emerging metaverse experience, focusing on the ideas and practices of metaverse design pioneers in tourism and hospitality.

Based on the media analysis and interviews with 27 designers, the metaverse – information – experiences – benefits (MIEB) model was proposed, containing three parts (information characteristics, customer experiences and customer benefits) and 31 supporting items grouped into nine components.

One of the unique contributions of this research is the MIEB model for applying the metaverse in customer journey management (pre-, during- and post-trip). The findings contribute to the current literature with this model based on the practical perspectives of metaverse designers and provide insights on how to incorporate the MIEB model in applying the metaverse in tourism and hospitality management. The findings also address existing literature gaps of insufficient research on metaverse management and design through all stages of the customer travel journey and by paying attention to stakeholders’ viewpoints, including the media and designers of metaverse applications. Engaging in semi-structured interviews with pioneers of the metaverse to gain insights into the design of tourism experiences was also different from other metaverse tourism research, although this is not claimed as a significant point of innovation.

Community detection is a key factor in analyzing the structural features of complex networks. However, traditional dynamic community detection methods often fail to effectively solve the problems of deep network information loss and computational complexity in hyperbolic space. To address this challenge, a hyperbolic space-based dynamic graph neural network community detection model (HSDCDM) is proposed.

HSDCDM first projects the node features into the hyperbolic space and then utilizes the hyperbolic graph convolution module on the Poincaré and Lorentz models to realize feature fusion and information transfer. In addition, the parallel optimized temporal memory module ensures fast and accurate capture of time domain information over extended periods. Finally, the community clustering module divides the community structure by combining the node characteristics of the space domain and the time domain. To evaluate the performance of HSDCDM, experiments are conducted on both artificial and real datasets.

Experimental results on complex networks demonstrate that HSDCDM significantly enhances the quality of community detection in hierarchical networks. It shows an average improvement of 7.29% in NMI and a 9.07% increase in ARI across datasets compared to traditional methods. For complex networks with non-Euclidean geometric structures, the HSDCDM model incorporating hyperbolic geometry can better handle the discontinuity of the metric space, provides a more compact embedding that preserves the data structure, and offers advantages over methods based on Euclidean geometry methods.

This model aggregates the potential information of nodes in space through manifold-preserving distribution mapping and hyperbolic graph topology modules. Moreover, it optimizes the Simple Recurrent Unit (SRU) on the hyperbolic space Lorentz model to effectively extract time series data in hyperbolic space, thereby enhancing computing efficiency by eliminating the reliance on tangent space.

This paper seeks to explore the influence of generative artificial intelligence (GenAI) on employee performance in the workplace, viewed from a managerial perspective. It concentrates on key elements such as employee engagement, trust in GenAI and attitudes toward its implementation. This exploration is motivated by the ongoing evolution of GenAI, which presents managers with the crucial task of understanding and integrating this technology into their strategic frameworks.

We collected 251 responses from managers and senior managers representing companies that have embraced GenAI in Spain. A hierarchical regression analysis was employed to examine the hypotheses. Subsequently, mediating effects and moderated mediation effects were scrutinized using the bias-corrected bootstrapping method.

The data analysis suggests a significant enhancement in employee engagement and performance from a managerial perspective, attributed to improved attitudes and trust toward the adoption of GenAI. This conclusion is drawn from our research conducted with samples collected in Spain. Notably, our findings indicate that while positive attitudes toward GenAI correlate with enhanced engagement and performance, there exists a weakening effect on the significant positive impact of GenAI adoption in the workplace. This suggests that GenAI is still in its early stages of adoption within these companies, necessitating additional time for managers to develop greater confidence in its efficacy.

This study represents one of the pioneering investigations centered on the implementation of GenAI within the workplace context. It contributes significantly to the existing body of literature concerning the stimulus-organism-response (S-O-R) model in technology innovation adoption within work environments.

This paper examines how different configurations of societal impact are pursued by purpose-driven organizations (PDOs) and how these configurations align with the application of varying supply chain design (SCD) practices.

This multi-method study uses quantitative data from 1588 B Corps and qualitative data from 316 B Corps to examine how PDOs align SCD with the pursuit of diverse types of societal impact. The authors first conduct a cluster analysis to group organizations based on the impact they create. Second, qualitative content analysis connects impact with enabling SCD elements.

The analysis of the five identified clusters provides detailed empirical insights on influencers, design decisions and building blocks adopted by PDOs to drive a range of societal impacts. Specifically, the nature of the impact pursued affects (1) whether a PDO will be more influenced by a need in the political environment or an opportunity in the industry environment, (2) the relative importance of the design of social flows versus material flows and (3) the need to develop new relational resources with beneficiaries versus leveraging existing capabilities to manage inter-firm processes.

This study responds to calls to disaggregate different dimensions of societal impact and examines the relationship between SCD and a breadth of sustainability impacts for different stakeholders. In doing so, the authors identify four SCD pathways organizations can follow to achieve specific societal impacts. This study is also the first to employ a supply chain perspective in the study of certified B Corps.