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We aim to advance our understanding by examining voices related to employees’ own interests and associated outcomes for employees and healthcare organizations. Patient safety reviews do not explore contextual factors such as organizational or professional cultures and regulatory environments in depth, and arguments for overcoming barriers to voice in health are underdeveloped. The research has largely developed in separate literature (various subdisciplines of management and the health field), and we outline the divergent emphases and opportunities for integration with the aim of investigating all relevant contextual factors and providing guidance on best practice informed by multiple disciplines.

A systematic approach was taken to gathering and reviewing articles, and coding and reporting are in line with the most recent Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement (Rethlefsen et al., 2021).

We identified a range of facilitators, barriers and outcomes of employee interest voice at different levels (organizational, leadership, team or individual) in a healthcare context. We identify various theoretical, methodological and knowledge gaps, and we suggest several ways in which these can be addressed in future research efforts.

We make multiple contributions to both theory and practice, including highlighting the importance and implications of integrating disciplinary approaches, broadening context, improving research design and exploring under-researched samples and topics. This information is essential in providing a more comprehensive model of healthcare voice and to shifting management focus to include all forms of employee voice in healthcare for the benefit of staff and patients.

We make multiple contributions to both theory and practice including highlighting the importance of integrating disciplinary approaches, broadening context to include employee interest issues, improving research design and exploring under-researched samples and topics. This information is essential in providing a more comprehensive model of health care voice and to shifting management focus to take a more inclusive view of employee voice in healthcare for the benefit of staff as well as patients.

The purpose of this study is to examine the propagation of consumer and business confidence in the euro area with a particular focus on the global financial crisis (GFC), the European sovereign debt crisis (ESDC) and the COVID-19-induced Great Lockdown.

The authors apply Diebold and Yilmaz’s connectedness framework and the improved method based on the time-varying parameter vector autoregressive model.

The authors find that although the evolution of business confidence marked the GFC and the ESDC the role of consumer confidence (mainly in those countries with stricter containment and closure measures) increased in the COVID-19-induced crisis.

The findings are related to the different origins of the examined crisis periods, and the analysis of their interrelationship is a very relevant topic for future research.

We argue this method of inquiry better represents the participants' learning, lives and experiences in the formal neoliberal education system prioritising performativity, categorising and ranking students.

The paper explores using poetry as a research method to reveal the learning experiences of adult learners, who have often had disruptive experiences of the formal schooling system and return to study in community-based education spaces. Inspired by Laurel Richardson’s transgressive technique of presenting sociological data through poetry as method, we use poetic representations of these learners' lives alongside case study research methodology. The research was conducted in conjunction with Neighbourhood Houses in Victoria, Australia. Qualitative data were generated through conducting multiple case studies of learners across various adult community education (ACE) sites. In this research, some case studies were presented in the traditional method of writing biography, others were written in the form of found poetry, which we refer to as data as poetry and text. The paper uses found poetry through participant-voiced poems written from interview transcripts. We argue this method of inquiry better represents the participants' learning, lives and experiences in the formal neoliberal education system prioritising performativity, categorising and ranking students. Our findings highlight the benefits of using poetry to communicate data in case study research as it effectively represents the experiences of adult learners' lives in a creative and concise form, transgressing normative practices of writing education research. These poetic representations of data reveal learner experiences in an embodied and agentic way while providing readers with a deep and rich understanding of these crucial adult learning spaces.

Our findings highlight the benefits of using poetry to communicate data in case study research as it effectively represents the experiences of adult learners' lives in a creative and concise form, transgressing normative practices of writing education research.

This research paper is empirical research and has not been submitted elsewhere for publication.

This study aims to investigate the impact of the 2022 collapse of the Terra-Luna ecosystem on volatility correlations among digital assets, including U.S. Terra, Luna, Bitcoin, Ether, a Decentralized Finance index and U.S.-sourced conventional assets stocks, bonds, oil, gold and the dollar index. The primary research question addresses whether correlations increased between digital and conventional assets during the collapse.

A dynamic conditional correlation generalized autoregressive conditional heteroskedasticity model was used to examine changes in volatility correlations during the market crash. Specifically, a data set of 1,442 close prices from 30-minute interval candles of digital and conventional asset prices are considered to provide a granular view of market dynamics during the sample period from January 3rd, 2022, to May 31st, 2022, including the crash event.

While the dynamic conditional correlation plots of the model indicate increased volatility, the results do not offer sufficient evidence to confirm an increase in correlations between digital and conventional assets during the Terra-Luna downfall. Furthermore, the authors confirm Bitcoin’s role as a diversifier with oil and observe the dollar index maintaining a negative correlation with Bitcoin during the crash, supporting Bitcoin’s function as a hedge against the U.S. dollar. However, the findings during the crash diverge from previous studies, reflecting shifts in correlation patterns in broader market downturns. Specifically, the authors identify the need for adaptive capital allocation strategies, as gold’s oscillation during the period suggests it may not serve as an effective hedge during black swan events.

The findings provide insights for investors, financial institutions and regulators to improve risk management, portfolio diversification, trading strategies and the formulation of consumer protection regulations. In addition, the results underscore the challenges of mitigating risks beyond regulatory measures and emphasize the importance of exercising caution for investors.

This study addresses the research gap in changes between conventional and digital asset volatility correlations during collapses in the digital asset space.

A novel psychology-based framework is proposed to investigate the causes of stock market crashes.

Our approach builds upon the dynamic capital mobility model, expanding it by incorporating bidirectional relationships between investor sentiment and the average stock market price level. Investor sentiment is measured in terms of jumping rates of investors’ attitudes to the stock market in our model. The role and impact of investor sentiment on the average stock market price level are further clarified across various scenarios through simulations.

Our model provides a direct quantification of the influence of investor sentiment on stock market price dynamics, shedding light on an alternative mechanism contributing to stock market crashes alongside fundamental changes. Our simulation results confirm that optimistic investor sentiment can mitigate the magnitude of negative stock market price movements triggered by adverse fundamental changes. Our model also indicates that the stock market price level may exhibit higher volatility than can be attributed to fundamental changes.

Our study only briefly touches on the origins of shifts in investor sentiment by introducing a self-fulfilling feedback loop from the average stock market price level to investor sentiment. In future research, we will identify other key factors contributing to shifts in investor sentiment and subsequently analyze their influence on investor sentiment.

Our findings carry practical implications for recent and future movements in the US stock market. For instance, despite the Federal Reserve’s continuous interest rate hikes since March 2022, the overall optimistic investor sentiment has helped mitigate a severe downward movement in the stock market. However, in the absence of clear indications of a loose monetary policy, a rapid shift of investor sentiment from optimism to pessimism in the US stock market could potentially lead to a historic stock market crash.

While the significance of investor sentiment in shaping stock market price dynamics, particularly regarding cross-sectional variations of stock returns, is widely recognized, quantifying directly its impact on the average stock market price level has remained a persistent challenge. Only very limited literature has delved into this subject, often hindered by a lack of theoretical support or yielding inconsistent results.

It has been proven that the characteristics of top management in an organization have an impact on the organization’s employee management strategies. In micro, small and medium-sized enterprises, the business owner’s influence is constructive. However, there has been little discussion about the influence of business owners’ personal characteristics on enterprises’ human resource management (HRM) strategies.

In this study, two complementary experimental designs – quantitative research and qualitative research – were adopted to verify the theoretical framework.

The results show that business owners with masculine characteristics tend to adopt the strategies of internal development and external acquisition, while those with feminine characteristics tend to adopt the strategies of internal development. Significant differences were found between the female business owner group and the male business owner group in terms of the relationship paths of gender role orientation, leadership style and gender role orientation and HRM strategies.

Firstly, this is the first time to discuss human resource management strategies from the perspective of gender roles. Secondly, the data of this research is collected from entrepreneurs, which is another advantage of this research. Finally, this research has obtained more valuable research findings. This study found that feminine bosses tend to adopt internally developed human resource strategies, while masculine bosses value internally developed human resource management strategies and also tend to adopt external acquisition strategies. Studies have shown that companies that adopt defensive strategies rely more on internally developed human resource management strategies, while those with exploratory strategies are more inclined to externally acquired human resource management strategies. So, does the boss’s gender role orientation match the organizational strategy, that is, feminine bosses adopt defensive strategies, while masculine bosses adopt defensive and exploratory organizational strategies? This point provides new research ideas for the development of private enterprises.

The purpose of this paper is to explore user engagement (UE) within the Metaverse (MV) environment, emphasising the crucial role of immersive experiences (IEs). This study aims to understand how IEs influence UE and the mediating effects of hedonic value (HV) and utilitarian value (UV) on this relationship. Additionally, the authors examine the moderating impacts of user perceptions (UPs) such as headset comfort, simulation sickness, prior knowledge and ease of use on the utilisation of the MV. This study seeks to elucidate the dynamics of virtual travel at a pre-experience stage, enhancing the comprehension of how digital platforms can revolutionise UE in travel and tourism.

This study used a triangulation methodology to provide a thorough investigation into the factors influencing UE in the MV. A systematic literature review (SLR) was conducted to frame the research context and identify relevant variables. To gather empirical data, 25 interviews were performed with active MV users, supplemented by a survey distributed to 118 participants. The data collected was analysed using structural equation modelling (SEM) to test the hypothesised relationships between IEs, UPs, HV and UV and their combined effect on UE within the MV.

The findings from the SEM indicate that engaging in the MV leads to a positive IE, which significantly enhances UE. Additionally, it was discovered that HV and UV play a mediating role in strengthening the link between IEs and UE. Furthermore, UPs, including headset comfort, simulation sickness, prior knowledge and ease of use, are significant moderators in the relationship between IEs and MV usage. These insights provide a nuanced understanding of the variables that contribute to and enhance UE in virtual environments.

This research contributes original insights into the burgeoning field of digital tourism by focusing on the MV, a rapidly evolving platform. It addresses the gap in the existing literature by delineating the complex interplay between IEs, UPs and value constructs within the MV. By using a mixed-method approach and advanced statistical analysis, this study provides a comprehensive model of UE specific to virtual travel platforms. The findings are particularly valuable for developers and marketers in the hospitality and tourism sectors seeking to capitalise on digital transformation and enhance UE through immersive technologies.

The study assessed the role of entrepreneurial self-efficacy (ESE) in the performance of graduate-owned small and medium-sized enterprises (SMEs). Specifically, the paper focused on how graduate owners’ confidence in identifying and commercializing new business opportunities and create new commodities according to the market demand influences both, the financial and non-financial performance of graduate-owned SMEs.

The study employed a cross-sectional survey design, in which 228 SME graduate owners majored in entrepreneurship were involved through online structured questionnaires. The relationship between the variables was determined by using partial least squares structured equation modeling (PLS-SEM).

The results indicate that entrepreneurial self-efficacy of graduate SME owners influence the financial and non-financial performance of their SMEs. Specifically, financial performance values have a ß-value of 0.576 and a p-value of <0.0001, non-financial performance values have a ß-value of 0.275 and a p-value of <0.0001.

In order to improve the performance of graduate-owned SMEs, universities should strive to equip graduates with self-confidence, which will enable them to utilize their entrepreneurial competences acquired through the entrepreneurship course programs. This will enable them to tackle business environmental challenges and be able to run successful businesses.

The results of this study extend the use of the self-efficacy theory in explaining the performance of graduate-owned SMEs in Tanzanian. Further, the study focused on ESE as a specific domain developed from entrepreneurship learning for entrepreneurial outcomes. Therefore, the study engaged graduate entrepreneurs who majored in entrepreneurship, which has not been the focus in most studies.

Under the digital wave and the new industrial competition pattern, the automobile industry is facing multiple challenges such as the redefinition of new technologies and supply chain changes. The purpose of this study is to link big data analytics and artificial intelligence (BDA-AI) with digital supply chain transformation (DSCT) by taking Chinese automobile industry firms as a sample and to consider the role of supply chain internal integration (SCII), supply chain external integration (SCEI) and supply chain agility (SCA) between them.

Data were collected from 192 Chinese firms in the automotive industry and analyzed using partial least squares structural equation modeling (PLS-SEM). Importance-performance map analysis is used to extend the standard results reporting of path coefficient estimates in PLS-SEM.

The results indicate that BDA-AI, SCII, SCEI and SCA positively influence DSCT. In addition, this study found that SCII, SCEI and SCA play an intermediary role in BDA-AI and DSCT.

The paper enriches the research on the mechanism of digital resources affecting DSCT and expands the research of organizational information processing theory in the context of digital transformation. The paper explores how the resources deployed by firms change the strategic measures of firms from the perspective of responsiveness. By exploring the positive impact of SCA as a response capability on the DSCT strategy and its intermediary role between digital resources and DSCT, which is helpful to the further theoretical development of logistics and supply chain disciplines.