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This study aims to identify and assess the factors challenging the integration of artificial intelligence (AI) technologies in healthcare workplaces.

The study utilized a mixed approach, that starts with a literature review, then developing and testing a questionnaire survey of the factors challenging the integration of AI technologies in healthcare workplaces. In total, 46 factors were identified and classified under 6 groups. These factors were assessed by four different stakeholder categories: facilities managers, medical staff, operational staff and patients/visitors. The evaluations gathered were examined to determine the relative importance index (RII), importance rating (IR) and ranking of each factor.

All 46 factors were assessed as “Very Important” through the overall assessment by the four stakeholder categories. The results indicated that the most important factors, across all groups, are “AI ability to learn from patient data”, “insufficient data privacy measures for patients”, “availability of technical support and maintenance services”, “physicians’ acceptance of AI in healthcare”, “reliability and uptime of AI systems” and “ability to reduce medical errors”.

Determining the importance ratings of the factors can lead to better resource allocation and the development of strategies to facilitate the adoption and implementation of these technologies, thus promoting the development of innovative solutions to improve healthcare practices.

This study contributes to the body of knowledge in the domain of technology adoption and implementation in the medical workplace, through improving stakeholders’ comprehension of the factors challenging the integration of AI technologies.

The purpose of this paper is to improve the performance of control loop suffering from control valve stiction. Control valve stiction is considered as of one of the main causes of oscillation in process variables, which require performing costly unplanned maintenance and process shutdown. An adaptive solution to handle valve stiction while maintaining safety and quality until next planned maintenance is highly desirable to save considerable cost and effort.

This paper implements a new stiction compensation method built using adaptive inverse model techniques and intelligent control theories. Finite impulse response (FIR) model, which is known to be robust, as a compensator for stiction. The parameters of FIR model are tuned in an adaptive way using differential evolution (DE) technique. The performance of proposed method is compared with other two compensation techniques.

The new method showed excellent performance of the DE–FIR compensator compared to other dynamic inversion methods in terms of minimizing process variability, energy saving and valve stem aggressiveness.

The compensation ability for all compensators reduces with the increase of stiction severity, thus the over shoot case always shows the worst result. In future works, other optimization techniques will be explored to find the appropriate technique that can extend the FIR model size with smallest computation time that can improve the performance of the compensator in over shoot case. In addition, the estimation of the valve residual life based on the level of stiction and effort required by the controller should be considered.

The presented approach represents an original contribution to the literature. It performs stiction compensation without a need for a prior knowledge on the process or the valve models and guarantees a smooth control of the stem movement with a low control effort. The proposed approach differs from previous adaptive methods as it uses stable FIR models and DE to find the appropriate parameters of the inverse model and handle nonlinear behavior of stiction.

Responding to the need for exploration of psychological predictors influencing innovation, this study aims to examine the impact of psychological ownership (PO) in employee innovation behavior (EIB) with a focus on psychological empowerment (PE) and employee resilience (ER) as mediators.

Data was collected from full-time employees working in travel agencies and five-star hotels in Egypt. A Partial Least Squares Structural Equation Modeling analysis was performed on 409 valid responses.

The findings of this study revealed PO has a positive effect on EIB. Such relationship is partially mediated by PE and ER.

Theoretically, this study offers valuable insights into the underlying mechanisms that elucidate how PO influences employees' innovation behavior and resilience, while aligning with the principles of self-determination theory.

This study aims to provide a comprehensive review of the existing literature on corporate governance (CG) aspects of the Malaysian market. It offers insights into the phases of Malaysian CG, identifies crucial gaps in the literature and outlines an agenda for impending research.

Following a systematic literature review approach, a final sample of 125 studies from Scopus and Web of Science databases was used in this study. These studies were selected based on quality assessment criteria. Then, the sample literature was evaluated in terms of journals, methodology, theories, modelling, research outcomes and CG characteristics.

The results show that there is a growing interest among researchers to further explore CG aspects in Malaysia due to the continuous development of the Malaysian CG codes. Likewise, the review reveals that the majority of prior studies are quantitative and were carried out using archived data from non-financial firms. Also, the existing literature has primarily focused on the outcomes of CG, especially firm performance.

Overall, the results show that there is ample room for future research. The present paper identifies a number of methodological problems and concerns, and discusses the implications of these problems, while also providing recommendations for future research. The main caveat is that the authors use scholarly papers published in academic journals only, but this approach offers them with opportunities for considerable further developments.

To the best of the authors’ knowledge, this study contributes to the literature by being the first of its kind to concentrate on the Malaysian context. It provides a comprehensive knowledge assessment of the Malaysian CG research and offers advice regarding improvements in research, policy and practice by identifying possible knowledge gaps. Consequently, this study provides a cohesive story of the past and a road map for future research on Malaysian CG.

The purpose of this study provide answers to the following research questions: Whether and to what extent money laundering affects the achievability and the trend of Sustainable Development Goals (SDGs)?; Does the influence of money laundering on the SDGs’ achievement differ from developing to developed countries?; How does the influence of money laundering vary among the 17 SDGs?

The paper’s analysis involves two key parts. In the first part, the authors perform a multivariate analysis to examine the influence of money laundering on the achievement of SDGs, and then in the second part, the authors make use of an ordered probit regression model to investigate the impact of money laundering on the trend of attaining each SDG.

Using a sample of 98 developed and developing countries, the regression results from multivariate analysis estimates show that money laundering has a strong inhibiting effect on the achievement of almost all the SDGs in the whole sample of countries and the sub-sample of developing countries, whereas no significant effect is observed for developed countries. However, for the SDG trends, the ordered probit estimates reveal that the harmful effect of money laundering occurs for all countries regardless their development level. In addition, perhaps surprisingly, the results from both the approaches yield also evidence advocating that money laundering activities might be associated with positive externalities on production and consumption. In fact, money laundering is found to have a significant positive influence on the achievement and the trend of SDG12 (Sustainable Consumption and Production). Overall, this study’s findings do have interesting policy implications, especially for developing countries. In these countries, prioritising the formulation and implementation of sound anti-money laundering policies is a necessary requirement for their progress towards achieving the SDGs.

The long-standing tradition of previous empirical studies examining the nexus between money laundering and sustainable development concentrates mainly on the economic dimension of sustainability (i.e. economic growth). However, little is known about the consequences of money laundering activities on the environment and the societies. Consequently, this study seeks to fill this gap by assessing the influence of money laundering on the achievement of the economic, environmental and social goals of sustainable development. To the best of the authors’ knowledge, this is the first integrated study to analyse the potential repercussions of money laundering on the SDGs’ achievement.