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This study aims to examine the influence of imposter syndrome on women’s careers in the hospitality and tourism industry drawing on the role congruity theory of prejudice.

Employing cross-sectional design, this research analyzed quantitative data from 574 women at various managerial levels using the Structural Equation Modeling (SEM) technique.

The results show that imposter syndrome significantly impacts women employees’ engagement and organizational commitment. The study found that higher levels of diversity management and female role models have a stronger moderating effect on the relationship between imposter syndrome, career expectations, work-family balance, employee engagement, career advancement and organizational commitment whilst lower levels of diversity management and female role models have a weaker moderating effect on these relationships.

This study presents a new theoretical perspective on female employees’ challenges to career advancement. It expands the knowledge of imposter phenomenon and role congruity theory of prejudice, illustrating how imposter syndrome can be influenced not only by individual traits but also by institutional, situational or stereotypical factors.

This work provides valuable insights for hotel policymakers and managers to implement policies that alleviate imposter syndrome and contribute to women’s career development and gender equality in workplaces.

This study can serve to raise awareness of women’s issues in the workplace and offer insightful guidance to organizations to promote diversity and women’s leadership. These pertain to how realistic human resources policies can be used to promote the well-being of hospitality and tourism women employees.

This study is dedicated to investigate why Mainland Chinese students pursuing higher education in Hong Kong are more likely to return to first-tier cities in Mainland China rather than stay in Hong Kong after graduation, despite Hong Kong’s increasing efforts to retain them as talent. By identifying the rationale that leads them to make such a choice, a critical gap in talent retention strategies for emerging education hubs can thus be addressed.

Through semi-structured interviews among 20 Mainland Chinese students have recently graduated from Hong Kong institutions, this study analyzes the two-way push and pull factors between Hong Kong and first-tier cities in China, elucidating how international students are influenced by various factors and make the common decisions to go to first-tier cities rather than Hong Kong after graduation.

The study reveals that while Hong Kong’s favorable visa policies and sector-specific employment opportunities serve as significant pull factors, they are outweighed by the comparative advantages of first-tier cities in mainland China. The comparative advantages of first-tier cities in China, including stronger economic incentives, better social integration prospects and more attractive talent recruitment policies, ultimately determine students' destination choices.

One major limitation lies in the relatively small and specific sample size, which may not fully capture the diversity of experiences among the targeted population. Moreover, the potential destinations of graduates are not limited to Hong Kong or the first-tier cities in China.

This research extends the traditional two-way push-pull theory by integrating comparative advantage analysis, offering a nuanced framework to evaluate international student mobility and talent retention dynamics. The research simultaneously provide insights for policymakers and higher education institutions to refine talent retention strategies, enhance the appeal of emerging education hubs and better align policies with global student mobility trends.

The psychological theory of self-determination postulated that gamification enhances learning engagement by intrinsically motivating learners to undertake tasks spontaneously. Gamification has then been integrated into adult learning as part of the initiative of learner-centred pedagogies to curb the low retention rates of adult learners who struggle with heavy work commitments, family obligations and financial pressure. Gamification, being one of the technological mediations, assumes the crucial role of engaging and retaining adult learners. Adult learners have received less attention in research when compared with conventional university students. The purpose of this study is to conduct a bibliographic analysis to assess the past, present and future publication trends of gamifying adult learning and to identify the research gap.

This study included publications related to gamification and adult learning from 2014 to 2022, extracted from Dimensions. A total of 79,864 publications were retrieved initially, and 3,469 publications were ultimately selected for final analysis after the refinement of the keyword search. VOSviewer was used for bibliographic coupling, keyword co-occurrence, clustering and co-citation analysis of countries.

The number of publications related to gamification in adult learning has decreased since its peak in 2020. The saturation is mainly concentrated in the USA, the UK and China, with similar levels of national income and technology advancement skills. However, gamification in adult learning remains a popular and growing research area in developing countries like Malaysia, which has huge potential due to government investments in education, technology and lifelong learning. There is also an evident research gap on gamification, adult learning and personality traits, which have not been covered in previous studies.

Prior research mostly focused on systematic literature reviews, while the use of bibliometric analysis could be a missing link in this research domain. This paper unveils the evolution of publications on this topic over time by scientifically analysing a large number of publications and rigorously identifying research gaps contributing to future research avenues.

This study aims to deeply understand customer experiences toward Internet of Things (IoT) applications in retail by developing machine learning models for aspect-based sentiment analysis (SA). It includes creating a related terms dictionary and proposing implications for retail businesses in Vietnam based on these analyses. The ultimate goal is to gain insights into customer opinions and assist administrators in formulating effective digital transformation and business strategies within the Vietnamese market.

Initially, this research uses qualitative methods to identify different aspects of customer experience at stores equipped with IoT applications. Then, quantitative methods were applied through classification machine learning models which were trained on the annotated data set to classify comments into aspects and sentiments. Finally, the classification results were analyzed and visualized to draw implications about customer opinions of these stores.

This study collected 77,042 customers’ comment from potential and actual customers who have ever shopped at retail stores with IoT applications deployed worldwide, identified ten new aspects of customer experience in this field and built a dictionary of related terms. Furthermore, this study contributed two efficient ensemble models with an accuracy of 81% and 89% for analyzing aspects and customer sentiments, respectively. This study also proposes implications for managers regarding the use of IoT technology in retail stores to improve shopping experiences for customers.

This study’s findings help managers develop appropriate digital transformation and business strategies for integrating IoT technology into retail stores, especially for retail businesses in the Vietnamese market based on the analysis results and proposed model.

The anti-friction and anti-wear properties of WS₂@GO composites on paraffin liquid were investigated with a four-ball tribometer.

A series of graphene oxide (GO) nano hybrid composites decorated with tungsten disulfide (WS₂) were prepared in-suit by hydrothermal strategy.

The results showed that compared to the virgin oil, friction coefficient and diameter of wear scare of lubricant oil containing W/G = 1:1 hybrid composite was reduced by 42.7% and 31.6%, respectively. At the microscopic, the excellent lubrication performance resulted from the tribo-chemical reaction on the sliding interface, which promotes the formation of tribo-film with a thickness of 8 nm. The carbonization compound, WO₃ and Fe₂O₃ in the tribo-film results from the tribo-chemical reactions at the sliding interface, which can improve the stability and strength of tribo-film. Thereby the metal surface was further protected from friction and wear.

A series of WS₂@GO composites were prepared in-suit by a hydrothermal strategy, and the tribo-film was analyzed by the transmission electron microscope and X-ray photoelectron spectrometer.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-10-2024-0397

Nano graphitic-carbon nitride (g-C₃N₄) is an emerging lubrication technology with excellent performance and significant potential for future applications. This study aims to investigate the effect of nano g-C₃N₄ as a lubricant additive on the wear performance of bearing steel disk.

Various mass fractions of g-C₃N₄ were introduced into the base oil. Combining tribological testing, rheological testing and surface analysis methods, the anti-wear properties and lubrication mechanisms were analyzed.

Transmission electron microscopy images revealed that the size of the nanoparticles of g-C₃N₄ ranges from 10 to 100 nm. Phase analysis of the g-C₃N₄ sample was conducted using X-ray diffraction. Further, 1.0% mass fraction of g-C₃N₄ in the base oil provides excellent anti-wear and friction-reducing performance. Compared to the base oil alone, it reduces the average friction coefficient by 63.8% and decreases the wear rate by 43.1%, significantly reducing the depth and width of the wear scar. Energy-dispersive X-ray spectroscopy and scanning electron microscope analysis revealed that the oil sample containing nano g-C₃N₄ can form a lubricating film on the sliding surface of bearing steel after wear, which enhances the lubricating properties of the base oil.

The synergistic effect of the base oil and nanoparticles reduces friction and wear and is expected to extend the service life of bearing steel. These findings suggest that incorporating nano g-C₃N₄ as a lubricant additive offers significant potential for improving the performance of mechanical components.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-12-2024-0456/

This study aims to test the relationship between virtual knowledge sharing (VKS) and team effectiveness (TE) during the COVID-19 pandemic. The study also explores the moderating role of collaborative technologies.

This is a cross-sectional study conducted in the service sector of India. A purposive sample of 321 knowledge workers from National Capital Region of India was used. Questionnaires were distributed to knowledge workers working in a virtual environment. The hypotheses were tested with confirmatory factor analysis and structural equation modeling (SEM) using partial least square-SEM.

The study reveals that, amid the COVID-19 pandemic, virtual knowledge sharing (VKS) positively affects team effectiveness (TE). Furthermore, the impact of VKS on TE is contingent upon the utilization of collaborative technologies.

The study contributes to the existing literature by exploring the impact of VKS on TE during the COVID-19 pandemic and the importance of collaborative technologies in facilitating virtual team collaboration, which has practical implications for organizations seeking to enhance TE in virtual environments.

The review paper aims to understand the development of workplace ostracism as a field in organizational studies from 2000 to the present. The study provides a comprehensive synthesis of the current state of the domain by exploring its antecedents, consequences, underlying mechanisms and buffering mechanisms.

The present study analyses 134 published peer-reviewed empirical and non-empirical articles retrieved from the Scopus database. A systematic literature review and bibliometric analyses (using VOS viewer) have been used to gain insights into the development and trends within the field. Bibliometric analyses involved science mapping techniques such as co-citation analysis, co-occurrence of keywords and bibliographic coupling. Combining these three techniques, the study aimed to provide a comprehensive overview of the workplace ostracism research domain's historical, current and future landscape.

In the present study, through descriptive analyses, the authors uncovered publishing trends, productive journals, countries and industries that contribute to this research field. The systematic review enabled the showcasing of the current landscape of workplace ostracism. The bibliometric analyses shed light on major authors, influential articles, prominent journals and significant keywords in workplace ostracism.

This study enriches the existing literature by offering a comprehensive research framework for workplace ostracism. It goes beyond that by presenting significant bibliographic insights by applying bibliometric analyses. Furthermore, this study identifies and emphasizes future research directions using the theory, characteristics, construct and methodologies framework, aiming to expand the knowledge base and understanding of this topic.

The purpose of this paper is to identify the key factors influencing the resilience of innovation ecosystems and propose strategies for proactively managing disruptions to ensure their continued viability. Enhancing resilience within innovation ecosystems is a fundamental prerequisite for ensuring their sustainable development. The resilience of such ecosystems is commonly associated with their capacity to recover from disturbances. Consequently, to ensure their continued viability, innovation ecosystems must proactively manage disruptions by identifying the factors that influence resilience.

Given the relatively limited attention afforded to indicators impacting the resilience of innovation ecosystems thus far, this article endeavors to present a framework for assessing resilience within such ecosystems, drawing upon the metaphorical understanding of resilience in natural ecosystems. To achieve this objective, the present research adopts the metaphor research method, which involves delineating the research problem and elucidating the origin of the metaphor.

Subsequently, through content analysis, the indicators for evaluating resilience in natural ecosystems are identified, and corresponding indicators and components are derived for the innovation ecosystem. These indicators are categorized into five dimensions, encompassing ecosystem capabilities, ecosystem interactions and structure, ecosystem status, ecosystem capacity and ecosystem environment.

This article endeavors to present a resilience framework for innovation ecosystems, drawing on the metaphorical concept of resilience evident in natural ecosystems. Through the method of metaphor research, the article first elucidates the research problem and selects ecology as the primary source of metaphor. Subsequently, evaluation indicators of resilience in natural ecosystems are determined using theme analysis.

Cracks are prevalent signs of pavement distress found on highways globally. The use of artificial intelligence (AI) and deep learning (DL) for crack detection is increasingly considered as an optimal solution. Consequently, this paper introduces a novel, fully connected, optimised convolutional neural network (CNN) model using feature selection algorithms for the purpose of detecting cracks in highway pavements.

To enhance the accuracy of the CNN model for crack detection, the authors employed a fully connected deep learning layers CNN model along with several optimisation techniques. Specifically, three optimisation algorithms, namely adaptive moment estimation (ADAM), stochastic gradient descent with momentum (SGDM), and RMSProp, were utilised to fine-tune the CNN model and enhance its overall performance. Subsequently, the authors implemented eight feature selection algorithms to further improve the accuracy of the optimised CNN model. These feature selection techniques were thoughtfully selected and systematically applied to identify the most relevant features contributing to crack detection in the given dataset. Finally, the authors subjected the proposed model to testing against seven pre-trained models.

The study's results show that the accuracy of the three optimisers (ADAM, SGDM, and RMSProp) with the five deep learning layers model is 97.4%, 98.2%, and 96.09%, respectively. Following this, eight feature selection algorithms were applied to the five deep learning layers to enhance accuracy, with particle swarm optimisation (PSO) achieving the highest F-score at 98.72. The model was then compared with other pre-trained models and exhibited the highest performance.

With an achieved precision of 98.19% and F-score of 98.72% using PSO, the developed model is highly accurate and effective in detecting and evaluating the condition of cracks in pavements. As a result, the model has the potential to significantly reduce the effort required for crack detection and evaluation.

The proposed method for enhancing CNN model accuracy in crack detection stands out for its unique combination of optimisation algorithms (ADAM, SGDM, and RMSProp) with systematic application of multiple feature selection techniques to identify relevant crack detection features and comparing results with existing pre-trained models.