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Although doctoral education has experienced substantial development in recent decades, it remains an elite, hence fragile, dimension of university policy and practice. This study aims to articulate perspectives to guide the next phase of strengthening and growth.

Working from theoretical and empirical research conducted in China, including scholarship on workforce ecosystems, education design and the student experience, this study contributes a framework with qualitative insights which clarify the goals and experiences of doctoral education in ways that will render it more relevant, effective and contributing.

The paper identifies areas for doctoral reform to ensure career readiness, including three distinctive outcomes and four indispensable experiences.

This study advances a doctoral design framework which can render transparent the substance of programs and prompt program coordinators to develop and ensure career relevance.

This study investigates the relationships among digital transformation, technological innovation, industry–university–research collaborations and labor income share in manufacturing firms.

The relationships are tested using an empirical method, constructing regression models, by collecting 1,240 manufacturing firms and 9,029 items listed on the A-share market in China from 2013 to 2020.

The results indicate that digital transformation has a positive effect on manufacturing companies’ labor income share. Technological innovation can mediate the effect of digital transformation on labor income share. Industry–university–research cooperation can positively moderate the promotion effect of digital transformation on labor income share but cannot moderate the mediating effect of technological innovation. Heterogeneity analysis also found that firms without service-based transformation and nonstate-owned firms are better able to increase their labor income share through digital transformation.

This study provides a new path to increase the labor income share of enterprises to achieve common prosperity, which is important for manufacturing enterprises to better transform and upgrade to achieve high-quality development.

Conflict and violence are the main drivers of globally escalating humanitarian needs. Local grassroots initiatives are pivotal in distributing humanitarian supplies in the acute response phase until more established humanitarian aid organizations can enter. Nevertheless, scant research exists regarding the role of grassroots associations in providing humanitarian assistance during a military conflict. The purpose of this paper is to understand the role of grassroots associations and identify important themes for effective operations.

This paper adopts a case-study approach of three Ukrainian grassroots associations that began operating in the immediate days of the full-scale invasion of Ukraine. The findings are based on analyzing primary sources, including interviews with Ukrainian volunteers, and are supported by secondary sources.

Grassroots associations have local contacts and a contextual understanding of population needs and can respond more rapidly and effectively than large intergovernmental agencies. Four critical themes regarding the operations of grassroots associations emerged: information management, inventory management, coordination and performance measurement. Grassroots humanitarian response operations during conflict are challenged by personal security risks, the unpredictability of unsolicited supplies, emerging volunteer roles, dynamic transportation routes and shifting demands.

Grassroots responses are central to humanitarian responses during the acute phase of a military conflict. By examining the operations of grassroots associations in the early months of the 2022 war in Ukraine, the authors provide a unique perspective on humanitarian logistics. Nonetheless, more inclusive models of humanitarian responses are needed to harness the capacities and resilience of grassroots operations in practice.

The study investigates the mediated moderation impact of Memorable Religious Experience (MRE) and Religiosity (REL) on the relationship between Memorable Tourism Experience (MTE) and Attitude towards Pilgrimage (ATT) finally driving Recommend Intention (RCI) of visitors to a religious site. It suggests visitors' incentive variable religiosity can influence their decision to recommend visiting a religious destination.

The research uses a quantitative cross-sectional approach wherein a self-administered survey was used for data collection from 223 pilgrims who visited a popular pilgrimage site. Partial least squares-structural equation modeling (PLS-SEM) was employed for analysis.

The results showed that MTE has a significant influence on ATT which further influences RCI (a dimension of behavioral intention-BI) of visitors towards a religious destination. Further, MRE mediates the relationship between MTE and ATT. Nevertheless, REL illustrated a significant moderation influence on the relationship between MRE and ATT, further verifying the mediated moderation impact of MRE and REL in the model.

Recommendation of existing customers is one of the most powerful indicators of customer loyalty and usually leads to revisit. The research provides destination managers/tourism planners of pilgrimage sites to formulate appropriate marketing strategies to develop RCI and sustainable branding.

This study adds to the empirical studies conducted on REL by constructing a composite picture of the memorable tourism experience within a pilgrimage tourism context. The uniqueness lies in the attempt to investigate the mediated moderation impact of MRE and REL using a symmetric (PLS-SEM) approach.

To support the advancement of an underrepresented category of research in the field of entrepreneurial teams, this study proposes and tests a novel empirical model that connects two team emergent states, namely team entrepreneurial passion (TEP) and transactive memory systems (TMSs), and their influence on team performance.

The data were gathered using an online questionnaire distributed to undergraduate students who had formed entrepreneurial teams as part of a course assignment. Two methods were executed on the obtained data, namely partial least-square structural equation modelling (PLS-SEM) and necessary condition analysis (NCA).

The results uphold the hypothesised mediation role of TMSs between TEP and team performance. Of the two direct relations in the model, only the necessary conditions were present for the effect of TEP on TMSs.

The issue of the small sample size, a common feature in entrepreneurial team research, as discussed in the methodical section of the paper, is sidestepped with the use of PLS-SEM tools. Nonetheless, a larger sample size could have increased confidence in the results' validity. In addition, a longitudinal approach to data collection and analysis could have been used to augment that confidence further.

Three practical implications stem from the empirical findings. First, it lends support for implementing teaching approaches and task designs that are envisaged to improve team functioning in university classrooms. Making a business plan boosts students' desire to exploit the received knowledge and find a venture, so the teaching effort in entrepreneurship courses can have real-world consequences.

By testing the mediation model, new insights are made into the associations between team emerging states and, subsequently, team performance. In addition, this study responds to recent calls in the literature to incorporate NCA in an entrepreneurial setting.

Despite the significance of a gender-diverse workforce, there is a lack of comprehensive review of gender diversity and women's career advancement literature. Moreover, past literature focuses on women-on-board and other subsets based on outcomes like firm financial and non-financial performance, corporate social performance and board interlocks. The purpose of this study is to examine the research on gender diversity and women's career advancement through an analysis of 143 articles published during past decade. Theoretical frameworks, contexts and constructs-based contribution to scholarship were reviewed. The authors attempt to highlight key theories, constructs and contexts and provide direction for future research.

A comprehensive systematic literature review of 143 articles spanning January 2008–March 2023 about gender diversity and women’s career advancement was conducted.

Majority of the past studies have focused on women on board and top management team, and most of them have been conducted in the context of the USA and China. There is no specific industry which has been covered extensively. Resource dependency, resource-based views and agency theories are the primary theoretical frameworks used in the past studies. Furthermore, these findings suggest the scope to further focus on women’s retention and career growth initiatives, especially at levels other than top levels, for a stronger leadership pipeline.

This study has been conducted with a focused analysis of the context, constructs and theoretical frameworks, enabling future researchers to decide how and where to focus, to now strengthen retention of women.

The metallic alloys and their components fabricated via laser powder bed fusion (LPBF) suffer from the microvoids formed inevitably due to the extreme solidification rate during fabrication, which are impossible to be removed by heat treatment. This paper aims to remove those microvoids in as-built AlSi10Mg alloys by hot forging and enhance their mechanical properties.

AlSi10Mg samples were built using prealloyed powder with a set of optimized LPBF parameters, viz. 350 W of laser power, 1,170 mm/s of scan speed, 50 µm of layer thickness and 0.24 mm of hatch spacing. As-built samples were preheated to 430°C followed by immediate pressing with two different thickness reductions of 10% and 35%. The effect of hot forging on the microstructure was analyzed by means of X-ray diffraction, scanning electron microscopy, electron backscattered diffraction and transmission electron microscopy. Tensile tests were performed to reveal the effect of hot forging on the mechanical properties.

By using hot forging, the large number of microvoids in both as-built and post heat-treated samples were mostly healed. Moreover, the Si particles were finer in forged condition (∼150 nm) compared with those in heat-treated condition (∼300 nm). Tensile tests showed that compared with heat treatment, the hot forging process could noticeably increase tensile strength at no expense of ductility. Consequently, the toughness (integration of tensile stress and strain) of forged alloy increased by ∼86% and ∼24% compared with as-built and heat-treated alloys, respectively.

Hot forging can effectively remove the inevitable microvoids in metals fabricated via LPBF, which is beneficial to the mechanical properties. These findings are inspiring for the evolution of the LPBF technique to eliminate the microvoids and boost the mechanical properties of metals fabricated via LPBF.

This paper aims to concentrate on recent innovations in flexible wearable sensor technology tailored for monitoring vital signals within the contexts of wearable sensors and systems developed specifically for monitoring health and fitness metrics.

In recent decades, wearable sensors for monitoring vital signals in sports and health have advanced greatly. Vital signals include electrocardiogram, electroencephalogram, electromyography, inertial data, body motions, cardiac rate and bodily fluids like blood and sweating, making them a good choice for sensing devices.

This report reviewed reputable journal articles on wearable sensors for vital signal monitoring, focusing on multimode and integrated multi-dimensional capabilities like structure, accuracy and nature of the devices, which may offer a more versatile and comprehensive solution.

The paper provides essential information on the present obstacles and challenges in this domain and provide a glimpse into the future directions of wearable sensors for the detection of these crucial signals. Importantly, it is evident that the integration of modern fabricating techniques, stretchable electronic devices, the Internet of Things and the application of artificial intelligence algorithms has significantly improved the capacity to efficiently monitor and leverage these signals for human health monitoring, including disease prediction.

Abnormal vibrations often occur in the liquid oxygen kerosene transmission pipelines of rocket engines, which seriously threaten their safety. Improper handling can result in failed rocket launches and significant economic losses. Therefore, this paper aims to examine vibrations in transmission pipelines.

In this study, a three-dimensional high-pressure pipeline model composed of corrugated pipes, multi-section bent pipes, and other auxiliary structures was established. The fluid–solid coupling method was used to analyse vibration characteristics of the pipeline under various external excitations. The simulation results were visualised using MATLAB, and their validity was verified via a thermal test.

In this study, the vibration mechanism of a complex high-pressure pipeline was examined via a visualisation method. The results showed that the low-frequency vibration of the pipe was caused by fluid self-excited pressure pulsation, whereas the vibration of the engine system caused a high-frequency vibration of the pipeline. The excitation of external pressure pulses did not significantly affect the vibrations of the pipelines. The visualisation results indicated that the severe vibration position of the pipeline thermal test is mainly concentrated between the inlet and outlet and between the two bellows.

The results of this study aid in understanding the causes of abnormal vibrations in rocket engine pipelines.

The causes of different vibration frequencies in the complex pipelines of rocket engines and the propagation characteristics of external vibration excitation were obtained.

The study aims to investigate the presence of herding behavior in the stock market of UK with a special emphasis on news sentiment regarding the economy. The authors focus on the news sentiment because in the current digital era, investors take their decision making on the basis of current trends projected by news and media platforms.

For empirical modeling, the authors use machine learning models to investigate the presence of herding behavior in UK stock market for the period starting from 2006 to 2021. The authors use support vector regression, single layer neural network and multilayer neural network models to predict the herding behavior in the stock market of the UK. The authors estimate the herding coefficients using all the models and compare the findings with the linear regression model.

The results show a strong evidence of herding behavior in the stock market of the UK during different time regimes. Furthermore, when the authors incorporate the economic uncertainty news sentiment in the model, the results show a significant improvement. The results of support vector regression, single layer perceptron and multilayer perceptron model show the evidence of herding behavior in UK stock market during global financial crises of 2007–08 and COVID’19 period. In addition, the authors compare the findings with the linear regression which provides no evidence of herding behavior in all the regimes except COVID’19. The results also provide deep insights for both individual investors and policy makers to construct efficient portfolios and avoid market crashes, respectively.

In the existing literature of herding behavior, news sentiment regarding economic uncertainty has not been used before. However, in the present era this parameter is quite critical in context of market anomalies hence and needs to be investigated. In addition, the literature exhibits varying results about the existence of herding behavior when different methodologies are used. In this context, the use of machine learning models is quite rare in the herding literature. The machine learning models are quite robust and provide accurate results. Therefore, this research study uses three different models, i.e. single layer perceptron model, multilayer perceptron model and support vector regression model to investigate the herding behavior in the stock market of the UK. A comparative analysis is also presented among the results of all the models. The study sheds light on the importance of economic uncertainty news sentiment to predict the herding behavior.