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The purpose of the this study is to discover the impact of practices of knowledge management, intellectual property protection and management innovation on entrepreneurial leadership, which in turn leads to sustainable growth in small and medium-sized enterprises.

The data was collected from 292 small and medium-sized businesses (SMEs) in the USA using a cross-sectional survey. To evaluate the study hypotheses and analyse the data, structural equation modelling was used. SMART-PLS software was used for both confirmatory factor analysis and structural analysis.

The work has significantly contributed in revealing that knowledge management practices, management innovation and intellectual property protection have a substantial and constructive impact on entrepreneurial leadership, which in turn leads to sustainable growth in SMEs.

The study findings recommend that SMEs must focus on knowledge management practices, intellectual property protection and management innovation to nurture entrepreneurial leadership, which can lead to sustainable growth. SMEs can benefit from investing in knowledge management practices, protecting their intellectual property and innovating their management practices to achieve sustainable growth. Also, the absorptive capacity of an SME can help it to aggravate the impact of the above factors and lead them to sustainable growth faster.

The current work studies the association between knowledge management practices, intellectual property protection, management innovation, entrepreneurial leadership and sustainable growth in SMEs, thus contributing to the literature. The study provides insights into the factors that can nurture entrepreneurial leadership and contribute to sustainable growth in SMEs, which can inform policy and practice in the field of entrepreneurship.

Many countries have a significant vaccination hesitancy rate regardless of vaccine prosperity. This study aims to identify factors restricting hesitancy and fostering vaccination intention and uptake against coronavirus in Vietnam.

The study has proposed an extended COM-B model based on the Theoretical Domains Framework to explore critical factors influencing vaccination intention and uptake in Vietnam. A database was collected from 1,015 suitable respondents who had received at least one dose of the COVID-19 vaccine, and ten hypotheses were tested by the partial least squares structural equation model.

The findings showed that six factors, including knowledge, experience, resource, social influence, belief and reinforcement, have either direct or indirect positive effects on COVID-19 vaccine uptake behavior. The output also indicated that personal experience positively affects vaccination intention and uptake.

This study contributes to understanding COVID-19 vaccine uptake behavior by identifying several direct and indirect factors of the extended COM-B model that include “knowledge” and “reinforcement” in shaping behavior change. The study adds to the literature on COVID-19 vaccine uptake behavior and could help achieve higher vaccination rates, ultimately leading to better control of the pandemic.

Well-being is an important dimension of human life but plays a controversial role in organizations, especially in different working environments. This study aims to explore the role of well-being in work from home (WFH) context, and its consequence on job outcomes among accountants in a fast-developing country, Vietnam.

A quantitative study is conducted with the data from Vietnamese accountants who had to work from home during the pandemic time. Partial least squares structural equation modeling method is applied to analyze the data.

The results show a direct and significant relationship of WFH with the employees’ well-being and job satisfaction (JS). Well-being fully mediates the influence of home-based work and JS on job performance.

This study generates valuable knowledge and practical implication in organizational policies and initiatives that aim to enhance employees’ well-being and promote positive job outcomes in the context of remote work. Organization employers should take their employees’ well-being as a key factor and consider it in personnel policy to enhance their work performance.

The research aims to assess the relationship between ecological innovation, green productivity, sustainable development and agricultural productivity in Vietnam. The agricultural sector of Vietnam has been observed with new opportunities which have fostered its productivity and growth.

The study uses a range of methods where initially, the researcher used descriptive analysis, cointegration and unit root tests. Secondly, Quantile Autoregressive Distributed Lag (QARDL) is used to assess the short and long run effects. The QARDL methodology is employed to capture the relationship between variables. Through this approach, the researcher is able to examine the scale of the interaction between dependent and independent variables.

The unique findings drawn through statistical techniques are also a great addition to the context of literature related to Vietnam’s agricultural productivity. Practical insights can also not be denied as the study provides beneficial guidelines for Vietnam’s agricultural sector to refine agricultural productivity.

Scholars are advised to use strong literary techniques to overcome these limitations and give a more thorough investigation into the same ideas. The availability and dependability of data was one of the primary challenges in carrying out this study. Vietnam has made significant advances in the collection and documentation of agricultural data, but there might still be gaps in the availability of thorough and current data on ecological innovation, green production and sustainable development.

Vietnam’s unique socioeconomic, cultural and environmental features influence how ecological innovation, green productivity, sustainable development and agricultural production are interconnected. Consequently, consideration should be taken when applying the results to various scenarios.

The present study is performed to identify the propagation mechanism of the ripple effect as well as examine the simultaneous impact of risks on supply chain (SC) performance.

A theoretical framework with many hypotheses regarding the relationships between SC risk types and performance is established. The data are collected from a large-scale survey supported by a project of the Japanese government to promote sustainable socioeconomic development for the Association of Southeast Asian Nations (ASEAN) region, with the participation of 207 firms. Structural equation modeling (SEM) is used to test the hypotheses of the theoretical framework.

It is indicated that human-made risk causes operational risk, while natural risk causes both supply risk and operational risk. Furthermore, the impacts of human-made risk and natural risk on performance are amplified through operational risk.

This study is one of the first attempts that identifies the propagation mechanism of the ripple effect and examines the simultaneous impact of risks on performance in construction SCs.

Although many studies on risk management in construction SCs have been carried out, they mainly focus on risk identification or quantification of risk impact. It is observed that research on the ripple effect of disruptions has been very scarce.

The goal of the study is to offer important insights into the dynamics of the cryptocurrency market by analyzing pricing data for Bitcoin. Using quantitative analytic methods, the study makes use of a Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model and an Autoregressive Integrated Moving Average (ARIMA). The study looks at how predictable Bitcoin price swings and market volatility will be between 2021 and 2023.

The data used in this study are the daily closing prices of Bitcoin from Jan 17th, 2021 to Dec 17th, 2023, which corresponds to a total of 1065 observations. The estimation process is run using 3 years of data (2021–2023), while the remaining (Jan 1st 2024 to Jan 17th 2024) is used for forecasting. The ARIMA-GARCH method is a robust framework for forecasting time series data with non-seasonal components. The model was selected based on the Akaike Information Criteria corrected (AICc) minimum values and maximum log-likelihood. Model adequacy was checked using plots of residuals and the Ljung–Box test.

Using the Box–Jenkins method, various AR and MA lags were tested to determine the most optimal lags. ARIMA (12,1,12) is the most appropriate model obtained from the various models using AIC. As financial time series, such as Bitcoin returns, can be volatile, an attempt is made to model this volatility using GARCH (1,1).

The study used partially processed secondary data to fit for time series analysis using the ARIMA (12,1,12)-GARCH(1,1) model and hence reliable and conclusive results.

This study aims to explore the impact of social, financial and institutional factors on women’s entrepreneurship in Vietnam, emphasizing motivation’s role in addressing challenges. Women’s entrepreneurship holds economic significance, driving local economies and creating opportunities. Government efforts to support women entrepreneurs have increased, but research on this in developing economies, especially in Vietnam, is limited.

The paper investigates women’s entrepreneurship in Vietnam, examining social, financial and institutional influences and emphasizing motivation in overcoming challenges. Using a qualitative approach, it conducts in-depth interviews with 28 female entrepreneurs, analyzing data thematically. Methodologically, the study uses purposive sampling, triangulation and member checking to enhance credibility.

Findings reveal key motivations like financial incentives, self-achievement and social impact. These motivations empower women to overcome financial constraints, skill gaps, limited support and societal perceptions. This research guides women entrepreneurs to enhance success through learning, persistence, skill development and self-awareness.

This paper presents a novel exploration into women’s entrepreneurship in Vietnam, offering original insights into the interplay of social, financial and institutional factors, with a spotlight on motivational drivers. It provides unique perspectives on their motivations, challenges and support mechanisms. The study’s contribution lies in its comprehensive understanding of women’s entrepreneurship dynamics in a developing economy like Vietnam, offering valuable insights for policymakers, practitioners and academics alike. Its originality lies in its holistic approach and nuanced examination, enriching the discourse on women’s entrepreneurship in emerging

This study aims to investigate the relationship between corporate environmental responsibility (CER) and risk-taking in Chinese A-share listed companies from 2011 to 2020. It seeks to understand the influence of CER on risk-taking behavior and explore potential moderating factors.

A quantitative approach is used, using data from Chinese A-share listed companies over the specified period. Regression analysis is used to examine the relationship between CER and risk-taking, while considering moderating variables such as performance aspiration, environmental enrichment and contextual factors.

The findings indicate that CER positively influences corporate risk-taking, with significant impacts on information asymmetry and corporate reputation. Moreover, positive performance aspiration strengthens the effect of CER on risk-taking, while negative performance aspiration and environmental enrichment weaken this effect. Cross-sectional analysis shows that the positive association between CER and risk-taking is more prominent for firms located in areas with strict environmental regulation, for nonstate-owned firms, and for firms with higher levels of internal control.

This research contributes to the literature by providing insights into the dynamics between CER and risk-taking in the Chinese market context. It expands existing knowledge by considering the influence of performance aspiration on this relationship, offering practical implications for firms seeking to enhance corporate performance through strategic management of environmental responsibilities.

Citizen engagement (CE) in public policy is increasingly considered to be an important feature of governance worldwide. The aim of this study is to explore the impact of e-government usage (EGU) on citizens’ engagement. In addition, the study investigates the mediating effect of government reputation (GR) and the moderating role of digital divide (DD) in EGU and citizens' engagement relationship.

Data were collected from 938 respondents in Vietnam with a random method. This study used the partial least squares – structural equation modeling method to examine hypotheses.

The results show that e-government and GR are critical determinants of CE. Furthermore, GR plays a mediating role in the relationship between e-government and CE. The results also show how DD moderates this nexus.

The findings of this study provide valuable evidence and implications. Public officials must improve the GR and administrative capacity as the determination for the CE. Moreover, the government should continually carry out policies to reduce the DD.

By investigating the mediating and moderating effects of GR and DD, this study has significantly contributed to advancing the body knowledge of e-government.

The ability to measure cognitive load in the workplace provides several opportunities to improve workplace learning. In recent years, virtual reality (VR) has seen an increase in use for training and learning applications due to improvements in technology and reduced costs. This study aims to focus on the use of simulation task load index (SIM-TLX), a recently developed self-reported measure of cognitive load for virtual environments to measure cognitive load while undertaking tasks in different environments.

The authors conducted a within-subject design experiment involving 14 participants engaged in digit-recall n-back tasks (1-back and 2-back) in two VR environments: a neutral grey environment and a realistic industrial ozone facility. Cognitive load was then assessed using the SIM-TLX.

The findings revealed higher task difficulty for the 2-back task due to higher mental demand. Furthermore, a notable interaction emerged between cognitive load and different virtual environments.

This study relied solely on an n-back task and SIM-TLX self-report measure to assess cognitive load. Future studies should consider including ecologically valid tasks and physiological measurement tools such as eye-tracking to measure cognitive load.

Identifying cognitive workload sources during VR tasks, especially in complex work environments, is considered beneficial to the application of VR training aimed at improving workplace learning.

This study provides unique insights into measuring cognitive load from various sources as defined by the SIM-TLX sub-scales to investigate the impact of simulated workplace environments.