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Blockchain’s potential is so significant that business activities across all industries can be drastically altered. Furthermore, the characteristics of blockchain appear to be well-suited to accounting requirements. However, accounting professionals’ attitude and intention toward blockchain adoption are not clear, particularly in India. Thus, this study aims to investigate and evaluate accountants’ intention to adopt blockchain technology in accounting activities.

This study examined and assessed accountants’ intention to use blockchain in accounting. To effectively measure usage intention, this study extended the unified theory of acceptance and use of technology (UTAUT) model by including context-specific constructs. To empirically test and validate the proposed model, data were collected from “369” professional accountants in India.

The findings revealed that facilitating conditions, performance expectancy and initial trust had a significant impact on adoption. Furthermore, the regulatory framework materially moderated the association between usage intention and its predictors.

These findings provide new empirical evidence about the impact of different predictors of usage intention by extending the UTAUT model. Relevant stakeholders can refer to this pioneering study to increase the adoption of blockchain as an efficient and trustworthy system among professional accountants, particularly in developing countries such as India.

This paper aims to examine the current quality of work-life (QWL) situation and the effectiveness of labor laws for promoting QWL in the context of Nepalese workplaces.

It uses a descriptive-interpretative-qualitative approach to analyze the responses. Information is gathered through discussions with 85 higher- and middle-level managers of large and medium-sized organizations.

The majority of Nepalese organizations accept safe and healthy working conditions, social relevance of work-life, social integration in the work organization, and work and total life space as the key aspects of QWL. They have become even more critical as a result of the COVID-19 pandemic. However, they face challenges in providing employees with opportunities for continued growth and security, immediate opportunity to use and develop human capacities, adequate and fair compensation and constitutionalism in the work organization. QWL-related provisions in Labour Act, 2017, play a vital role in promoting the QWL situation. The QWL programs offer many benefits to employees’ private and working lives. The lack of such programs would undoubtedly have negative consequences for Nepalese companies. Compliance with labor laws will promote a better QWL situation at Nepalese workplaces.

Only managerial perspectives are considered for examining the current situation of QWL and the effectiveness of QWL-related provisions of the Labour Act, 2017. It excludes the views of union leaders.

This paper indicates that labor laws’ QWL-related provisions are effective. It also provides several policy measures for promoting a better QWL in Nepalese workplaces.

This study presents QWL-related legal provisions and the actual situation at the workplaces of Nepal. It also presents the key aspects of QWL in the context of Nepal.

The present study used the job-demands and resources (JD-R) framework to understand how the training is transferred to an extreme working context through the analysis of job and personal resources (social support from the leader and colleagues and adaptability). Specifically, the authors tested the mediating role of motivation to transfer in the relationship (1) between the perceived support from the supervisor and colleagues and performance after training and (2) between adaptability and performance in an extreme context of the pandemic crisis – the first peak of COVID-19 in Portugal. Further, an inspection of the factors that predicted knowledge transfer and adaptability under an extreme context was carried out.

To do so, necessary training about the new safety rules regarding the pandemic crisis of COVID-19 was implemented in a healthcare institution as a strategy to help healthcare workers deal with the increasing uncertainty and complexity that was threatening their work. It consisted of three sessions (each with one hour of training) regarding procedures, rules and safety norms. The training occurred in May 2020. Overall, 291 healthcare workers participated in the study and answered one online questionnaire one week after training completion.

The results showed that the motivation to transfer had a significant indirect effect on the relationship between colleagues' and supervisors' support and performance and between adaptability and performance. Additionally, complementary analyses showed that the mediations depended on the levels of self-efficacy in such a way that the indirect relationships were stronger when self-efficacy was higher. Thus, adaptability and support, both from colleagues and the supervisor, are determining factors for knowledge transfer and resultant performance in extreme contexts, such as the COVID-19 pandemic crisis. Lastly, the results showed that the most significant predictors of transference were self-efficacy and the motivation to transfer the learned knowledge. On the other hand, self-efficacy, peer support and the opportunity to use the knowledge were the most significant predictors of adaptability.

These findings provide support for the role of employee motivation to transfer as a mechanism connecting both perceived support and adaptability to performance outcomes under extreme working contexts.

This study, conducted in the middle of the COVID-19 pandemic context – an extreme and uncertain working context – shows the relevance of both job and individual factors to predict employees' adaptability to such contexts.

The current gap between the required and available artificial intelligence (AI) professionals poses significant challenges for organisations and academia. Organisations are challenged to identify and secure the appropriate AI competencies. Simultaneously, academia is challenged to design, offer and quickly scale academic programmes in line with industry needs and train new generations of AI professionals. Therefore, identifying and structuring AI competencies is necessary to effectively overcome the AI competence shortage.

A probabilistic topic model was applied to explore the AI competence categories empirically. The authors analysed 1159 AI-related online job ads published on LinkedIn.

The authors identified five predominant competence categories: (1) Data Science, (2) AI Software Development, (3) AI Product Development and Management, (4) AI Client Servicing, and (5) AI Research. These five competence categories were summarised under the developed AI competence framework.

The AI competence framework contributes to clarifying and structuring the diverse AI landscape. These findings have the potential to aid various stakeholders involved in the process of training, recruiting and selecting AI professionals. They may guide organisations in constructing a complementary portfolio of AI competencies by helping users match the right competence requirements with an organisation's needs and business objectives. Similarly, they can support academia in designing academic programmes aligned with industry needs. Furthermore, while focusing on AI, this study contributes to the research stream of information technology (IT) competencies.

The study aims to investigate the nurse job enjoyment and their participation in medical affairs to enchance the quality of patient care in Saudi hospitals. In addition, this study also investigates the management team's role in patient care quality through the mediating effects of nurse job enjoyment and participation in medical affairs.

The present study used a self-administered survey questionnaire to collect data from registered nurses in Saudi hospitals. In this study, 600 survey questionnaires were distributed online (Google Forms) and received 266 valid responses (44.33% response rate). In addition, SmartPLS-4.0 was applied to validate the research constructs and test the hypotheses via partial least squares structural equation modelling (PLS-SEM).

The study's findings indicate that the job enjoyment of the nurses and participation in medical affairs have positive and significant effects on the quality of patient care. In addition, the research findings also suggest that the management team of the hospitals has a significant indirect influence on the quality of patient care through the medicating effects of nurse job enjoyment and participation in medical affairs.

The findings of this study also offer various practical implications. This study showed the direct impact of the management team on job enjoyment, medical affairs and patient care quality. Therefore, hospital authorities and policymakers may emphasise clear communication, collaboration, respect and trust for the effective management team in providing higher-quality patient care. The present study suggests that hospital policymakers should strive to create a positive work environment, provide adequate resources, foster team spirit, offer incentives and allow flexible scheduling to ensure higher job enjoyment and increase nurse participation in medical affairs.

This study adds to the growing body of knowledge by investigating the effects of the management team, job enjoyment and nurses' participation in medical affairs on patient care quality. This study also enhances the theoretical depth by exploring the mediating impact of job enjoyment in predicting the relationships between the management team and the quality of care provided to patients. The present study provides guidelines for healthcare service providers or practitioners to focus on the nurses' job enjoyment and their engagement in medical activities to continuously improve the quality of patient care in the hospitals.

The ever-demanding role of employees in the hospitality sector stimulates job crafting. This study examines the relationship between inclusive leadership and job-crafting dimensions under the mediating role of work engagement through the lens of conservation of resources (COR) theory. It also aims to analyse the boundary condition of job autonomy between inclusive leadership and work engagement.

The data were collected in 3-time lags from 319 front-line workers in the hospitality sector. The adopted and adapted questionnaires were executed through a deductive approach and an applied research method. The data were analysed through SmartPLS by applying the structural equation modelling (SEM) technique.

This study provides evidence for a predictive relationship between inclusive leadership and job-crafting dimensions under the mediating psychological mechanism of work engagement. Additionally, the moderating role of job autonomy is established in the unique context of the hospitality sector of an underdeveloped country, Pakistan.

Services-based organisations need to endure the inclusive leadership style by establishing work engagement practices. Engaged employees result in better job-crafting behaviours through better training and subsequent performance.

This study established that work engagement and job autonomy are imperative forces that impact the relationship between inclusive leadership and job-crafting dimensions. The research study has time-lagged data and conveys meaningful theoretical and practical implications.

The purpose of this study is to investigate the phenomenon of defensive biasing in work stress surveys, which occurs when employees trivialize potential stressors and strains due to fear of negative consequences from their supervisors or management. This study aims to better understand the factors that influence this behavior and to develop a scale to measure it.

The study used an online survey of 200 employees to investigate the factors influencing defensive biasing behavior. The researchers developed a scale for defensive biasing with the help of subject matter experts and derived possible factors from the literature. Participants were presented with a hypothetical scenario in which they imagined a work stress survey in their organization and were asked to answer related items. The data were analyzed using regression analysis.

The study found that defensive biasing behavior was significantly predicted by perceived anonymity and neuroticism. Participants who felt less anonymous and had higher levels of neuroticism were more likely to engage in defensive biasing. Job insecurity and trust in supervisors were not found to be significant predictors of defensive biasing.

This study contributes to the literature on work stress surveys by developing a scale for defensive biasing and investigating the factors that influence this behavior. The study highlights the importance of making the survey process more transparent to reduce defensive biasing and obtain trustworthy results.

This study aims to ascertain the intentions of risk managers to use artificial intelligence in performing their tasks by examining the factors affecting their motivation.

The study employs an integrated theoretical framework that merges the third version of the technology acceptance model 3 (TAM3) and the unified theory of acceptance and use of technology (UTAUT) based on the application of the structural equation model with partial least squares structural equation modeling (PLS-SEM) estimation on data gathered through a Likert-based questionnaire disseminated among Italian risk managers. The survey reached 782 people working as risk professionals, but only 208 provided full responses. The final response rate was 26.59%.

The findings show that social influence, perception of external control and risk perception are the main predictors of risk professionals' intention to use artificial intelligence. Moreover, performance expectancy (PE) and effort expectancy (EE) of risk professionals in relation to technology implementation and use also appear to be reasonably reliable predictors.

Thus, the study offers a precious contribution to the debate on the impact of automation and disruptive technologies in the risk management domain. It complements extant studies by tapping into cultural issues surrounding risk management and focuses on the mostly overlooked dimension of individuals.

Yet, thanks to its quite novel theoretical approach; it also extends the field of studies on artificial intelligence acceptance by offering fresh insights into the perceptions of risk professionals and valuable practical and policymaking implications.

The purpose of this study is to determine the enablers of the quality of work life (QWL) of employees working in the Garment industries.

The study was carried out in a fivefold step. In the first step, the enablers of QWL were identified through an exhaustive literature survey, in the second step identified vital few components through Pareto analysis. Then the third step was followed by exploratory factor analysis (EFA) to further, to identify the precise components and validate the same using confirmatory factor analysis in fourth step. The final step included interpretive structural modeling and Cross-Impact Matrix Multiplication Applied to Classification analysis to model the validated components and determine the interrelationships and linkages.

Predominant QWL enablers of employees working in the garment industries are training and development, satisfaction in job, compensation and rewards, relation and co-operation, grievance handling, work environment, job nature, job security and facilities.

In this study, the interpretive structural model is designed based on the opinion of the experts who are working in the garment industry considering the responses from employees in garment sectors. The framework can be extended further to the other sectors.

In future, the researchers in QWL may develop a model to quantify the level of employees’ QWL who are working in different sectors. Enablers of QWL are essential, and based on this further statistical analysis can be carried out. This study will provide limelight to the researchers in choosing the valid and reliable set of enablers for the empirical studies. Organizations can get benefit by implementing the outcome of this research for the enhancement of the QWL of employees.

The study was carried out in 133 garment industries where 851 workers constituted the final valid responses that were considered for analysis. The outcomes from the study help administrators, policy and decision-takers in taking decisions to enhance QWL.

The aim of this paper is to empirically investigate whether social contacts can mediate the way in which current unemployment impacts future unemployment.

We use 2006–2017 data from the Household Income and Labour Dynamics in Australia (HILDA) survey and a dynamic random-effects model to describe the evolution of individual unemployment status over time.

Once controlled for the local context where individuals live and create friendships, we find that above-average social contacts reduce unemployment persistence. However, social contacts seem to be slightly less effective in deprived neighborhoods. These findings are consistent with the idea that individuals obtain information about job opportunities through a network of social contacts, and unemployment may lead to a decay of social capital, making it more difficult to find employment in future periods. Our results also show that neighborhood deprivation increases individual unemployment risk, while above-average neighborhood cohesion reduces the probability of unemployment in deprived neighborhoods.

Although many studies have been published on unemployment persistence, to the best of the author's knowledge, this is the first study quantifying the impact of social contacts on unemployment persistence. The study also offers fresh empirical evidence on the impact of neighborhood characteristics on unemployment risk.