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In this systematic review of the literature on women’s entrepreneurship education, this paper aims to examine the current state of the field. The authors analyze publication trends, identify major themes and propose an agenda for future research.

The authors review 363 articles published between 1993 and 2023, to develop a synthesized overview of women’s entrepreneurship education, complete with insights into the journals that have provided the most coverage of this topic, as well as how it has emerged over time.

The authors tracked the evolution of research themes and collaboration networks over a 30-year period. Results show there has been significant growth in research on women’s entrepreneurship education, as evidenced by a surge of publications on the topic and the total number of citations.

The authors categorized and analyzed six thematic clusters within the literature: entrepreneurial intention, ethical perspectives, gender-specific barriers, gender stereotypes, rural entrepreneurship and entrepreneurial self-efficacy. Building on these thematic clusters, this study discusses future research directions to advance the field.

This paper aims to compare the surface roughness and hardness of three commercially available self-curing acrylic resins for dental use, under different polymerization conditions.

A comparative in vitro study was conducted using a convenience sample of 12 × 5 × 2 mm blocks with n = 40 for each material (Nic Tone, Arias Plus and Orthocryl®), with subgroups according to the polymerization method: conventional (C) and polymerization under ambient conditions (A). The surface roughness of the materials was measured using a profilometer; hardness was measured with a portable hardness tester. Additionally, surface morphology as well as particles size and morphology were evaluated with scanning electron microscopy.

There were significant differences in roughness and hardness values between the three self-curing acrylic resins (p < 0.05), as well as within each self-curing acrylic resin according to the polymerization method used (p < 0.05). The samples polymerized with the conventional method presented lower surface roughness and hardness values.

This study provides scientific evidence of values not provided by manufacturers in relation to the surface roughness and hardness of these materials, and all of them met the ideal minimum values of surface roughness, regardless of the polymerization technique used.

We investigate the joint impacts of three trust cues – content, sentiment and helpfulness votes – of online product reviews on the trust of reviews and attitude toward the product/service reviewed.

We performed three studies to test our research model, presenting participants with scenarios involving product reviews and prior users' helpful and unhelpful votes across experimental settings.

A high helpfulness ratio boosts users’ trust and influences behaviors in both positive and negative reviews. This effect is more pronounced in attribute-based reviews than emotion-based ones. Unlike the ratio effect, helpfulness magnitude significantly impacts only negative attribute-based reviews.

Future research should investigate voting systems in various online contexts, such as Facebook post likes, Twitter microblog thumb-ups and up-votes for article comments on platforms like The New York Times.

Our findings have significant implications for voting system-providers implementing information techniques on third-party review platforms, participatory sites emphasizing user-generated content and online retailers prioritizing product awareness and reputation.

This study addresses an identified need; that is, the helpfulness votes as an additional trust cue and the joint effects of three trust cues – content, sentiment and helpfulness votes – of online product reviews on the trust of customers in reviews and their consequential attitude toward the product/service reviewed.

This study aims to comprehensively investigate the electron beam powder bed fusion (EB-PBF) process for copper, offering validated estimations of melt pool temperature and morphology through numerical and analytical approaches. This work also assesses how process parameters influence the temperature fluctuations and the morphological changes of the melt pool.

Two distinct methods, an analytical model and a numerical simulation, were used to assess temperature profiles, melt pool morphology and associated heat transfer mechanisms, including conduction and keyhole mode. The analytical model considers conduction as the dominant heat transfer mechanism; the numerical model also includes convection and radiation, incorporating specific parameters such as beam power, scan speed, thermophysical material properties and powder interactions.

Both the analytical model and numerical simulations are highly correlated. Results indicated that the analytical model, emphasising material conduction, exhibited exceptional precision, although at substantially reduced cost. Statistical analysis of numerical outcomes underscored the substantial impact of beam power and scan speed on melt pool morphology and temperature in EB-PBF of copper.

This numerical simulation of copper in EB-PBF is the first high-fidelity model to consider the interaction between powder and substrate comprehensively. It accurately captures material properties, powder size distribution, thermal dynamics (including heat transfer between powder and substrate), phase changes and fluid dynamics. The model also integrates advanced computational methods such as computational fluid dynamics and discrete element method. The proposed model and simulation offer a valuable predictive tool for melt pool temperature, heat transfer processes and morphology. These insights are critical for ensuring the bonding quality of subsequent layers and, consequently, influencing the overall quality of the printed parts.

This study aims to develop a modelling framework of housing supply dynamics within the context of urban microsimulation systems. Housing markets have witnessed substantial investigation over recent decades, predominantly concerning residential demand. However, comparatively limited attention has been directed towards comprehending the housing supply dynamics. Housing policy disconnects with the developers’ market behaviours, which leads to significant mismatch between the housing construction and affordable housing needs of the population. Research attention should be made in comprehending the residential construction market activities. To address this gap, this study developed an autoregressive distributed lag (ARDL) model and analyzed the temporal evolution of housing construction.

An ARDL model was developed to address the issue of temporal modelling of the housing supply. An empirical study was conducted in the Greater Toronto and Hamilton Area (GTHA) based on a longitudinal housing starts data set from 1998 to 2020. The model integrates diverse variables, including macroeconomic conditions, property development costs, dwelling prices and opportunity costs. Notably, the model captures both the path-dependent effects stemming from supply market fluctuations and the temporal lag effect of influential factors.

The findings reveal that the supply-side’s responsiveness to market condition alterations may span up to 18 months. The model has reasonable and satisfying performance in fitting the observed starts. The methodological foundations laid will facilitate future modelling of housing supply dynamics.

This study innovatively separated the modelling of housing supply within the context of urban microsimulation, into two parts, the modelling of housing starts and completion. The housing starts are determined in a complex and regressive process influenced by both the micro-economic environment and the construction cost and housing market trends. Through the temporal modelling method, this study captures how long it would take for the housing supply to respond to multiple factors and provides insight for urban planners in regulating the housing market and leveraging various policies to influence the housing supply.

Unlocking the potential of Big Data Analytics (BDA) has proven to be a transformative factor for the Architecture, Engineering and Construction (AEC) industry. This has prompted researchers to focus attention on BDA in the AEC industry (BDA-in-AECI) in recent years, leading to a proliferation of relevant research. However, an in-depth exploration of the literature on BDA-in-AECI remains scarce. As a result, this study seeks to systematically explore the state-of-the-art review on BDA-in-AECI and identify research trends and gaps in knowledge to guide future research.

This state-of-the-art review was conducted using a mixed-method systematic review. Relevant publications were retrieved from Scopus and then subjected to inclusion and exclusion criteria. A quantitative bibliometric analysis was conducted using VOSviewer software and Gephi to reveal the status quo of research in the domain. A further qualitative analysis was performed on carefully screened articles. Based on this mixed-method systematic review, knowledge gaps were identified and future research agendas of BDA-in-AECI were proposed.

The results show that BDA has been adopted to support AEC decision-making, safety and risk assessment, structural health monitoring, damage detection, waste management, project management and facilities management. BDA also plays a major role in achieving construction 4.0 and Industry 4.0. The study further revealed that data mining, cloud computing, predictive analytics, machine learning and artificial intelligence methods, such as deep learning, natural language processing and computer vision, are the key methods used for BDA-in-AECI. Moreover, several data acquisition platforms and technologies were identified, including building information modeling, Internet of Things (IoT), social networking and blockchain. Further studies are needed to examine the synergies between BDA and AI, BDA and Digital twin and BDA and blockchain in the AEC industry.

The study contributes to the BDA-in-AECI body of knowledge by providing a comprehensive scope of understanding and revealing areas for future research directions beneficial to the stakeholders in the AEC industry.

The objective of this study is to investigate the interaction effect between incentive type (financial and compassionate incentives) and the ethicality of merchant strategy on consumer willingness to post positive reviews, while also examining potential variations in consumer responses based on consumption experience, shopping frequency and social class.

Building upon construal level theory, we hypothesized the moderating influence of the ethicality of merchant strategy and examined the three-way interaction among consumers’ demographic characteristics (i.e. consumption experience, shopping frequency and social class), incentive type and the ethicality of merchant strategy. To empirically test our hypotheses, we conducted four experiments and employed ANOVA for data analysis.

The ethicality of merchant strategies moderates the association between incentive type and consumer willingness to post positive reviews, with compassionate incentives eliciting more pronounced moral judgments toward merchant strategies compared to financial incentives. The moderating effect of the ethicality of merchant strategy on the relationship between incentive type and consumer willingness to post positive reviews is particularly strong among consumers who have favorable consumption experiences, engage in frequent shopping and belong to lower social classes.

This study contributes to the existing literature on online reviews by examining the impact of compassionate incentives on consumer review behaviors, analyzing the ethicality of merchant strategies within the realm of online reviews and investigating variations in consumer responses to merchant strategies regarding consumption experience, shopping frequency and social class.

This study is based on the enforcement record from Canada’s natural mutual fund regulator. This record documented a small subset of mutual fund dealers who had been disciplined for their misconduct from 2007 to 2014. The purpose of this paper is to determine what factors contribute to mutual fund dealers’ time to first financial fraud offense. The longer the time to fraud, the healthier the mutual fund industry and the better a mutual fund dealer’s career.

Based on the belief that adversity reveals true character, the study approaches a mutual dealer’s career success from human capital, socio-demographic and organizational sponsorship points of view by measuring dealers’ success as their time from career beginning to first instance of financial fraud. Ordinary least square regression analysis was used to identify if those factors, including provision of supervisor reminders, gender, position and penalties, are related to career success within the Canadian mutual fund regulatory framework. The research is based on a small sample of mutual fund dealers who had been disciplined for their misconduct from 2007 to 2014.

The study finds that a supervisor’s reminders positively contribute to the career success of a mutual fund dealer in the form of extending their time to fraud. As well, being female is an adverse factor to career success even when both female and male dealers received about the same level of supervisor reminders. It also finds that being in a management position has no association with time to fraud.

The study establishes the statistically significant positive relationship between time to fraud and supervisor’s reminders for mutual fund dealers. At the same time, it shows that human capital and access to organizational resources, measured by being in a management position, have no significant relation to when fraud is committed. This result indicates the value of continuing education for all mutual fund dealers, both inexperienced and experienced.

The purpose of this paper is to embark on evaluating the role of Chat Generative-Trained Transformer (ChatGPT) in personal knowledge management (PKM) practices of individual knowledge workers across varied disciplines.

The methodology involves four steps, i.e. literature search, screening and selection of relevant data, data analysis and data synthesis related to KM, PKM and generative artificial intelligence (AI) with a focus on ChatGPT. The findings are then synthesized to develop a viewpoint on the challenges and opportunities brought by ChatGPT for individual knowledge workers in enhancing their PKM capability.

This work highlights the prevailing challenges and opportunities experienced by knowledge workers while leveraging PKM through implying ChatGPT. It also encapsulates how some management theories back the cruciality of generative AI (specifically ChatGPT) for PKM.

This study identifies the challenges and opportunities. from existing studies and does not imply empirical data/result. The authors believe that findings can be adjusted to diverse domains regarding knowledge workers’ PKM endeavors. This paper draws some conclusions and calls for further empirical research.

ChatGPT’s capability to accelerate organizational performance compelled scholars to focus in this domain. The linkage of ChatGPT to Knowledge Management is an under-explored area specifically the role of ChatGPT on PKM hasn't been given attention in the existing work. This is one of the earliest studies to explore this context.

The purpose of this paper is to examine the relationship between Chinese female executives and corporate risk-taking the contingencies that affect this relationship.

A integrated theoretical framework was established, on the basis of which theoretical hypotheses were developed and tested using 20,315 firm-year observations collected from China’s publicly listed companies during the period 2005–2020. Data were collected from China's Shanghai and Shenzhen A-share Stock Exchanges and analyzed using a moderated regression analysis, PSM, 2SLS-IV and PSM-DID model.

The empirical results indicate a negative effect of the ratio of female executives in top management team on corporate risk-taking, and this negative effect can be weakened by the social capital of board directors and the regional marketization.

The paper contributes to research on the relationship between female executives and risk-taking by considering the effect of eastern culture on female executives’ business decision-making and examining the moderating factors inside and outside the firm.

The paper illustrates the active steps that corporations can take to enhance female executives' willingness and capacity to take firm-related risks so as to improve the firm value in the long run.

The paper explores how Chinese culture and Chinese traditional value affect female executives’ decision-making on risky projects or uncertain investments. In addition, our study for the first time examines the moderating effect of board social capital as an internal factor and marketization as an external one on the relationship between Chinese female executives and corporate risk taking. The research examines the gender inequality in the work and competitive environment facing female executives in the areas of different marketization level, which would affect female executives’ cognition and motivation in corporate risk taking.