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This study aimed to examine the impact of learning organization (LO) on job satisfaction and individual performance in Vietnamese enterprises. The study further explores the mediating effect of job satisfaction on the relationship between learning organization and employee performance.

Data were collected from 653 employees from various types of organizations in Vietnam. Structural equation modeling was implemented to test the hypotheses.

The results revealed that the proposed research model was supported. Results indicated that LOs positively influenced employees’ job satisfaction and the broader range of their individual performance. In addition, employees’ job satisfaction motivated them to achieve higher performance levels. The study also found a mediating effect of job satisfaction on the relationship between LO and employee performance. The results underscore the importance of implementing an LO culture for individual outcomes such as job satisfaction and employee performance in the Vietnamese cultural context, which is based on socialism and Confucianism.

To the best of the authors’ knowledge, this is the first study to examine the relationships among LO, job satisfaction and individual employee performance in the Vietnamese context. The results offer a deeper understanding of the LO concept in the Vietnamese cultural context and highlight the cultural impact on the LO concept and its effects. The results suggest how the LO concept is applied in the Vietnamese context.

Based on the ability, motivation and opportunity (AMO) theory, this study aims to investigate the role of digital human resources management (digital-HRM) practices in influencing hotel employee behaviors, especially their adoption of work-at-home (WAH).

The study was conducted in two stages in hotels in Vietnam. Stage 1 used a mixed method to develop an instrument to measure digital-HRM practices. In Stage 2, through a survey of 303 respondents, the research investigated digital-HRM practices’ additive and interactive effects on WAH.

The study shows that digital-HRM comprised five factors. Except for digital recruitment, the other digital-HRM practices significantly affected WAH. In addition, the research suggests that digital training and employee involvement should be combined to enhance employee willingness for WAH.

Drawing on the AMO theory, this study constructs a digital-HRM measurement scale to study the antecedents and consequences of these practices to improve employees’ digital work efficiency. In addition, through both additive and combinative (a two-way interaction) models, the study enhances the HRM and hotel management theory by understanding why digital-HRM practices are essential to boost employees’ digital competencies to adopt remote working.

By investigating the role of digital-HRM practices in improving employees’ adoption of WAH, this study provides empirical implications for hotels to manage digital-HRM practices better and thus makes remote working effective.

The existing literature reveals the lack of a deep understanding of how HRM practices can promote digital devices and services and their influence on employee behaviors, especially in the hotel sector. To the best of the authors’ knowledge, this study is unique in extending the AMO theory into the digital context to illuminate components of digital-HRM practices and clarify how digitalizing HRM practices can motivate hotel employees to accept WAH.

The current study aims to investigate the impact of perceived blockchain-related information transparency on consumers’ intention to purchase organic food. This study examines how perceived blockchain- related information transparency, directly and indirectly, affects purchase intentions through attitudes, perceived behavioural control and subjective norms. Additionally, the study explores how blockchain-based trust moderates the influence of perceived blockchain-related information transparency on these factors and the intention to purchase organic food.

Based on the theory of planned behaviour framework and a sample of 5,326 consumers, this study uses partial least squares structural equation modelling to test the research model.

This study finds that perceived blockchain-related information transparency directly enhances consumers’ attitudes towards organic food purchase, perceived behavioural control, subjective norms and intention to purchase organic food. Additionally, perceived blockchain-related information transparency indirectly affects consumers’ intention to buy organic food through three antecedents of the theory of planned behaviour model. Notably, these indirect effects were moderated by consumers’ blockchain-based trust.

This study provides recommendations for leveraging blockchain to enhance transparency and build trust, which could boost consumer engagement and organic food purchases.

This research contributes to blockchain literature by empirically examining the role of perceived blockchain-related transparency and blockchain-based trust in consumers’ purchasing decisions regarding organic food. It provides valuable insights into the consumer-centric benefits of blockchain technology. Furthermore, this study also contributes to the literature on organic food, particularly its promotion through blockchain technology.

This study aims to explore key questions within the context of Asian countries: How do artificial intelligence (AI) and blockchain adoption in accounting influence enterprise risk management and environmental, social and governance (ESG) performance? What role does enterprise risk management have as a mediator in this relationship? In addition, how does environmental uncertainty shape the interplay between AI and blockchain adoption in accounting, enterprise risk management and ESG performance?

The authors collected data from Thomson Reuters Eikon Datastream, initially targeting the 20 Asian countries with the highest gross domestic product (GDP) per capita. Using stringent selection criteria, the research sample included 22,212 firms from these countries: Bahrain, China, Hong Kong, Indonesia, Israel, Japan, Jordan, Kazakhstan, South Korea, Kuwait, Lebanon, Malaysia, Oman, Qatar, Saudi Arabia, Singapore, Sri Lanka, Thailand, the United Arab Emirates and Vietnam. After a rigorous screening process, the final sample comprised 1,742 firms, representing 17,420 firm-year observations over the 2014–2023 period. This paper applied maximum likelihood structural equation modeling to analyze the data.

The findings reveal that both AI and blockchain adoption in accounting, along with enterprise risk management, positively impact ESG performance in the Asian context. Enterprise risk management serves as a mediating factor between AI and blockchain adoption in accounting and ESG performance. In addition, environmental uncertainty significantly moderates the relationships between AI and blockchain adoption in accounting and enterprise risk management, as well as between enterprise risk management and ESG performance.

This study uncovers the interplay between internal factors – such as AI and blockchain adoption in accounting and enterprise risk management – and external factors, notably environmental uncertainty, in fostering sustainable value for Asian firms. Internal factors enable firms to integrate ESG considerations into their operations, facilitating risk mitigation and enhancing ESG performance. Meanwhile, heightened environmental uncertainty drives the adoption of sustainable practices. Consequently, Asian Governments should prioritize the development of regions characterized by high environmental uncertainty to advance national sustainable development goals and encourage responsible business practices.

This study contributes to the existing literature by uncovering the combined effects of internal and external factors on ESG performance, offering empirical evidence from Asian countries with high GDP per capita. Specifically, it underscores the efficacy of AI and blockchain adoption in accounting and enterprise risk management, as well as the moderating role of environmental uncertainty, within the Asian context.

In recent years, the development of metaheuristic algorithms for solving optimization problems within a reasonable timeframe has garnered significant attention from the global scientific community. In this work, a new metaheuristic algorithm inspired by the inflection mechanism of the avian influenza virus H5N1 in poultry and humans, taking into account its mutation mechanism, called H5N1.

This algorithm aims to explore optimal solutions for optimization problems by simulating the adaptive behavior and evolutionary process of the H5N1 virus, thereby enhancing the algorithm’s performance for all types of optimization problems. Additionally, a balanced stochastic probability mechanism derived from the infection probability is presented. Using this mechanism, the H5N1 algorithm can change its phrase, including exploitation and exploration phases. Two versions of H5N1, SH5N1 and MH5N1, are presented to solve single-objective optimization problems (SOPs) and multi-objective optimization problems (MOPs).

The performance of the algorithm is evaluated using a set of benchmark functions, including seven unimodal, six multimodal, ten fixed-dimension multimodal to solve SOPs, ZDT functions and CEC2009 has been used to demonstrate its superiority over other recent algorithms. Finally, six optimization engineering problems have been tested. The results obtained indicate that the proposed algorithm outperformed ten algorithms in SOPs and seven algorithms in MOPs.

The experimental findings demonstrate the outstanding convergence of the H5N1 algorithm and its ability to generate solutions of superior quality.

The banking sector is one of the main drivers of any developed economy and the decisions of individual banks can have global consequences on markets. In recent decades, the European banking sector has undergone major transformations, including restructuring, mergers and changes in regulation and supervision. This has attracted the interest of academics and policymakers alike. The purpose of this paper is to analyze the efficiency in the European banking sector, but there is no consensus in the literature on which factors of banks affect their efficiency.

This study analyzes data from the consolidated financial statements of a sample of 471 banks over the period 2005–2022 from 39 countries. Two methodologies are applied: data envelopment analysis methodology for the calculation of efficiency, both input- and output-oriented, and Tobit regression model to determine which variables significantly affect banks’ efficiency scores.

The results show that the efficiency scores are similar in the input-oriented and output-oriented model. The Tobit model shows that the variables that positively affect efficiency are the ROA, size, capital ratio and liquidity. On the other hand, the variables that decrease the extent of efficiency are the provisions ratio and the fact of being a financial entity under the Single Supervisory Mechanism.

The main contribution of this study is a more comprehensive and global approach that includes aspects of the most important insights from the previous literature, over a very extended period and including bank and macroeconomic environment characteristics.

This study examines the efficiency dynamics of public health service providers in Colombia between 2010 and 2021. In order to achieve this objective, a data panel is utilized to assess the effectiveness of 841 institutions across three levels of complexity (1, 2 and 3) in providing patient care, taking into account user satisfaction from 2010 to 2021.

By employing growth mixture analysis (GMA) and other statistical techniques, we may identify unique efficiency profiles among hospitals with different levels of complexity.

The results demonstrate varied efficiency patterns, with certain profiles generally retaining or improving efficiency over time, while others observe fluctuations or reductions. Efficiency outcomes are significantly impacted by factors such as capital investment, operational costs and workforce mix. Efficiency levels can be improved by making strategic investments in capital infrastructure and implementing effective operational cost management.

Changes in the healthcare market conditions in recent years in Colombia have compelled healthcare providers (IPS) to transform themselves into sustainable organizations.

The purpose of this study is to improve the stability and dyeing properties of natural curcumin by adsorption packaging technology, and promote the clean dyeing technology of wool fabrics.

The response surface method was used to optimize the dyeing process of wool fabrics. The color fastnesses and the K/S value of the dyed wool fabrics were tested and analyzed, as well as the scanning electron microscopy (SEM) observation of wool fibers.

The mordant dyeing method was optimized using the response surface method under pH 3.5 and a 1:50 dye bath ratio. The results showed that the mordant dyeing method was one-bath, two-step post-mordant and the optimized dyeing process was as follows: dyeing time 70 min, dyeing temperature 70°C and the dosage of mordant was 2% and yielding a K/S value of 35.22. The dyed wool had excellent rub and wash fastness (grade 4+), but inadequate light fastness, to be improved later. The results of SEM demonstrated that the optimized dyeing processes had no adverse effects on wool fibers.

No comprehensive and systematic study reports have been conducted on the dyeing process of wool fabric using natural curcumin pigment, which is adsorbed and packaged by ZIF-8, and researchers have not used statistical analysis to optimize the dyeing process using response surface methodology.

Scaling up smart city infrastructure projects will require a large financial investment. Using public–private partnerships is one of the most effective ways to address budget constraints. Numerous factors have varying degrees of influence on the performance of Public private partnerships (PPP) projects; certain PPP factors are more crucial to the success of a smart city infrastructure project than others, and their influence can be greatly increased when they are fulfilled collectively. This study aims to find out what factors are unique to smart city PPP initiatives, as well as how these factors work together, so that successful smart city infrastructure PPP projects can be scaled up.

The methodology included three sequential stages: identifying the critical success factors (CSF) of PPP for smart cities based on an extensive literature review, collecting data from a sample of 90 PPP practitioners using a Likert scale questionnaire and estimating interrelationships among the CSF and their emergent clusters using structural equation modelling.

The best fit model developed in this study demonstrated the significance of each factor and their interrelationships within their categories in enhancing the performance of PPPs in smart city infrastructure projects. Five categories of critical success factors for PPPs in smart city infrastructure projects have been established: partnership and collaboration; financial sustainability; contractual duties and outsourcing; smart integration; and contract governance.

The proposed model represented the causal interrelationships among relevant critical success factors derived from literature, which may help in directing the organization’s attention and resources to more critical areas, leading to the effective fulfilment of the smart city infrastructure project’s objectives. In addition to the theoretical and methodological contributions, this study produced a usable and readily adaptable list and clusters of critical success factors for research in the area of the implementation of PPP in smart city infrastructure projects.

To the best of the authors’ knowledge, this is the first study to identify PPP critical success factors and their themed clusters for smart city infrastructure projects.

This paper aims to empirically examine the influence of macroeconomic and socioeconomic factors on improving financial inclusion in India, with a specific focus on two distinct indicators of financial inclusion.

This study has used a time-series data set covering the years 1996 to 2022, using a nonlinear autoregressive distributed lag methodology. This approach allows for the examination of both short- and long-run effects of key macroeconomic and socio-economic indicators, including GDP per capita growth, remittance inflows and the income share held by the lowest 20% of the population on the growth of two financial inclusion indicators: the number of commercial bank branches and ATMs per 100,000 adults.

Model-1 investigates how commercial bank branch growth affects financial inclusion. Positive remittance inflow growth and a rise in the income share of the bottom 20% both lead to increased financial inclusion in both the short and long term, with the effects being more pronounced in the long run. Conversely, negative effects of remittance inflow growth and a decline in GDP per capita growth lead to reduced financial inclusion, primarily affecting the long run. Focusing on ATM growth, Model-2 reveals that positive remittance inflow growth has the strongest impact on financial inclusion in the short term. While income share growth for the bottom 20% and GDP growth also positively influence financial inclusion, their effects become significant only in the long run. Conversely, a decline in GDP per capita growth hinders financial inclusion, primarily affecting the short run.

This study fills a gap in research on macroeconomic and socioeconomic factors influencing financial inclusion in India by examining the impact of GDP per capita growth, remittance inflows and the income share held by the lowest 20% of the population, an area relatively unexplored in the Indian context. Second, the study provides comprehensive distinct results for different financial inclusion indicators, offering valuable insights for policymakers. These findings are particularly relevant for policymakers working toward Sustainable Development Goal 8.10.1, as they can use the results to tailor policies that align with SDG objectives. Additionally, policymakers in other developing nations can benefit from this study’s findings to enhance financial inclusion in their respective countries.