Assessing Mediating Effect of National Culture on the Relationship of Leadership Style with UAE Smart Government Organizational Performance

This study presents persuasive evidence that national culture acts as a mediator in the relationship between leadership style and organisational performance in the context of UAE Smart Government. By investigating these links, the study emphasises the importance of taking cultural aspects into account when evaluating the impact of leadership on organisational outcomes in the context of smart governance. Data collected through questionnaire survey was used to develop a PLS-SEM mediation model in SmartPLS software. The modelling analysis found that national culture partially mediates the leadership styles with the organisational performance. It further found that Abu Dhabi's smart government had low worker productivity requirements and lacked a strong national culture. Leadership has a major and positive impact on the organization's national culture. As a result, it is acceptable to claim that leadership style has a substantial impact on organisational procedures. According to studies, there is a link between national culture and organisational performance. The findings indicate a connection between leadership styles and the nation's commitment, cohesion, and mission. This implies that an organisational culture in which employees participate in important organisational decisions, that is consistent in its workplace practises, and that is motivated by a clear mission, ultimately affects the organisational performances of employees in such organisations

Modelling of Non-Financial Factors Affecting Yemen Small Medium Enterprises (SMEs) Performance Using AMOS

This study exhibits the construction of a structural relationship model of Non-Financial Factors that include innovation, training, human capital, and market orientation on the performance of Yemen\u27s Small Medium Enterprises (SMEs). The modelling was done using the AMOS-SEM software. Structural Equation Modelling (SEM), route analysis, and confirmatory factor analysis are all performed using the software. It is well-known for its visual approach, which enables users to graphically build models with basic sketching tools and analysts to participate in intricate statistical modelling. The modelling examines the specific effects of these factors and discloses key contributors to the success or failure of SMEs. Data used for modelling was collected from 350 valid responses of employees in the Yemeni manufacturing SME. The findings from the modelling highlight the dominant significance of innovation, as proved by its highest beta coefficient of 0.90, signifying its profound impact on SME performance. Following closely is training, with a coefficient of 0.3, further establishing its crucial role in influencing performance outcomes. Ultimately, this research concludes that the four factors—human capital, innovation, training, and market orientation—exhibit a statistically significant relationship with SME performance within manufacturing sector. The inferences of these findings are of paramount importance to policymakers and practitioners as it offers actionable insights for enhancing SME performance and driving economic growth within Yemen

Proposing A Predictive Model for Assessing the Influence of Employee Empowerment on Organizational Performance

Embracing employee empowerment in the UAE Federal National Council is an absolute necessity for achieving outstanding organizational performance. By granting employees freedom to make decisions, take initiative, and contribute their unique perspectives, it can unleash untapped potential and drive the council towards unparalleled success. Hence, the use of a novel multi-linear regression model in this study is an important step forward in understanding the relationship between employee empowerment and organisational performance. Unlike prior research, which may have ignored essential components, this model incorporates the independent variables meaningfulness, self-determination, impact, and competence. It examines how employee empowerment affects overall organisational performance by taking these essential factors into account. This novel methodology paves the way for new insights and potential breakthroughs in optimising workforce potential and attaining outstanding organisational success. The study used 115 respondents from the employees of UAE Federal National Council Organization to develop the model using multi-linear regression approach. The establish model can be used to predict the performance of UAE Federal National Council Organization by inserting the parameters of the Employee Empowerment which are Meaning; Competence; Impact; and Self-determination. Hopefully, the outcomes of this research contribute to the community of UAE Federal National Council Organization

Ranking of Leadership Styles and National Culture Factors Affecting Smart Government Organizational Performance

Investigating leadership style, organisational performance, and national culture indicators in Abu Dhabi, the smart-government capital of the UAE was carried out through quantitative method of research. Total 274 valid data samples were gathered and analysed with SPSS software to perform descriptive assessment. Reliability test revealed that all the indicators have Cronbach Alpha value above 0.7 confirming the validity of the data. Skeweness and Kutosis values of the parameters showed that the data follows normal distribution. Employee morale and satisfaction were cited as the most critical parameters for gauging organisational performance, while the study found that personal steadiness and stability is the most desired parameters of national culture. All four of the leadership styles that were studied were deemed significant. Based on responses, "my supervisor makes others feel good to be around him/her" is the most important aspect of a transformational leader, while "my supervisor tells others what to do if they want to be rewarded for their work" is the most important aspect of a transactional leader. Among the parameters studied, "As a rule, my supervisor allows me to appraise my own work" ranked highest in accordance with Laissez Faire Leadership, while "Do you agree that the Authoritative leadership style employed by your supervisor contributes to your feelings of insecurity in your work and the need for clear direction?" ranked highest in accordance with Authoritative Leadership. These finding pointed out that the designed parameters can be used for further study related to national culture, organizational performance and leadership styles adopted in UAE in relation with Smart government

Developing A Framework: Adoption of International Financial Reporting Standards (IFRS) and Its Implications on Organizational Performance

This study was aimed to develop a framework for adopting International Financial Reporting Standards (IFRS) and gauging its influence on Organizational Performance. The developed framework reveals that Organizational factors strongly align with Financial Reporting & Transparency and Economic & Industry Impact & Global Market Presence but not with Decision-Making & Governance. however, the accounting capabilities factors are predominantly linked to industry impact & global market presence, while top management support factors robustly connect with both industry impact & global market presence and subsequently, decision-making & governance. Conversely, readiness factors exhibit a compelling relationship across financial reporting & transparency, industry impact & global market presence, and decision-making & governance, highlighting their significant impact on organizational performance. This framework can be applicable in organizational contexts, providing guidance for strategic decision-making and performance enhancement in the adoption of IFRS. Leveraging the identified correlations, organizations can focus on strengthening specific factors, including Organizational Readiness, accounting capabilities, Top management support, and Readiness, to enhance financial reporting, transparency, industry impact, global market presence, and decision-making processes. Implementing targeted strategies based on these relationships may contribute to overall organizational success and sustainable growth

A Model of Factors Influencing the Implementation of Artificial Intelligent in Crisis Management: A Case Study of National Crisis and Emergency Management Authority (NCEMA)

This paper outlines the development of a structural equation model focusing on factors influencing the implementation of AI in crisis management within the UAE National Crisis and Emergency Management Authority. Literature has identified 28 factors which are categorized into seven domains that influencing the implementation of AI in crisis management for the model. The model was constructed and evaluated using SmartPLS software. The model was evaluated at its measurement and structural components. The results revealed that at the measurement component, the model met all evaluation criteria. While, at the structural component, the relationship between 'CoV' and 'CrM' was statistically significant (T-statistic = 2.633, P-value = 0.009), indicating a robust connection. However, the links between 'ReF' and 'CrM' and 'LSM' and 'CrM' were not statistically significant (P-values = 0.999 and 0.949, respectively), suggesting limited impact on 'CrM.' Relationships between 'RoB,' 'IoT,' 'DeL,' and 'NLP' with 'CrM' showed moderate evidence but lacked statistical significance, possibly due to data limitations. Furthermore, the model demonstrated a strong fit, with an R-squared (R²) value of 0.761, explaining approximately 76.1% of the variance in "CrM" with the seven independent variables. Lastly, for predictive relevance, the "CrM" as a dependent construct displayed a Q² value of 0.608, indicating that around 60.8% of the variation in "CrM" is explained by the model beyond random chance, confirming its strong predictive value

A Study of Factors Influencing the Adoption of Artificial Intelligence in Crisis Management

This paper presents a study on the Factors Influencing the Adoption of Artificial Intelligence (AI) in Crisis Management. The research identifies 28 AI usage factors categorized into seven groups: Large-Scale Machine Learning, Deep Learning, Reinforcement Learning, Robotics, Computer Vision, Natural Language Processing, and Internet of Things. The study conducted a questionnaire survey among 281 employees at the UAE National Crisis and Emergency Management Authority, using purposive sampling to assess their opinions regarding the impact of these usage factors on the adoption of AI in crisis management. The collected data underwent descriptive analysis to determine the ranking of AI usage factors within each of the seven groups. In terms of group rankings, Robotic emerged as the top-ranking factor, followed by Reinforcement Learning. Large-Scale Machine Learning occupied the next position, succeeded by Natural Language Processing, Deep Learning, Internet of Things, and Computer Vision, which held the lowest rank. Furthermore, when examining the correlation between these usage factor groups, it was discovered that most of them exhibited strong positive correlations, with correlation coefficients ranging from 0.634 to 0.934. This indicates that changes in one variable are associated with predictable changes in another variable. While this information can be instrumental in understanding relationships and making predictions, it does not establish a causal relationship

Evaluating the Mediating Effect of Employee Training on the Link between Employee Empowerment and Organizational Performance

Assessing employee training as a mediator on the relationship between employees' empowerment and organizational performance offering insights for effective training interventions to further enhance employee empowerment and overall organizational success. Hence, this paper discusses a study on assessing a mediation effect of employee training which act as mediator to the relationship between the employee empowerment constructs with organisational performance construct. Data used to develop this mediation model was from 115 employees of UAE Federal National Council Organization. The model was developed and assessed in SmartPLS software using the concept of PLS-SEM technique of model development. The model was assessed in three processes which are the PLS Algorithm; Blindfolding and Bootstrapping. The results of mediating modelling assessments found that there is no evidence of mediation effect of Employee Training on the relationships of Meaning and Organization Performance and also, between Competence and Organization Performance. However, there is a partial mediation effect of Employee Training on the relationship between Impact and Organization Performance, indicating that Employee Training partially mediates the relationship between Impact and Organization Performance. Additionally, Employee Training fully mediating the relationship between Self-determination and Organization Performance. The table provides insights into the complex relationships within the model, clarifying the role of mediation in explaining the associations between variables. The findings from this study contributes knowledge on the mediation model of employees’ empowerment on organisational performance

Service Quality Factors Influencing the Use of Artificial Intelligent Security Technology in UAE

This study assessed various factors related to service quality in influencing artificial intelligence security technology. It was carried out through quantitative approach where the data of respondents perceptions of UAE citizens towards the enhancing service quality in the UAE's AI security sector was derived through queationnaire survey. The factors were categorized in four groups which are performance expectancy group with seven factors;  effort expectancy group with seven factors; social influence group with four factors; and facilitates condition group with five factors. With these factors, the respondents were requested to gauge the degree of influence of each factor using 5-points likert scale. The questionnaire survey managed to secure 359 completed questionnaire forms. The data from these forms were analysed using descriptive statistic. It was found that facilitates condition group of factor is the most influencing category on service quality to adopt AI technology. It can be concluded that the overall results indicate that facilitates condition, effort expectancy and performance expectancy groups are reported having very high influencing categories while social influence group having high influencing ategory. It was also found that AI technologies are very frequently used technologies by the UAE citizens. Based on the study, it is deduced that service quality is a basic consideration as reported by the UAE personnel for adoption of any technology

Mediation Model of Service Quality and Behavioural Intention to Use of Artificial Intelligence Security Technology in UAE

This study created and evaluated a mediation model which allows the role of essential artificial intelligence (AI) in mediating the connection between service quality and behavioural intent to use AI security features in the United Arab Emirates. The primary objective is to improve the standards for customer service in the UAE's artificial intelligence security industry. The data to developed the model was derived from 389 valid questionnaires form the questionnaire survey. The data was screened and cleaned before uploaded in Smart-PLS software to developing and assessing the model. Based on the assessment on the model, it was found that the most fundamental form of artificial intelligence exerts a mediating effect to some extent, on the connection that exists between service quality and behavioural intention in terms of the application of Al security technology. The coefficient and t-value point to a substantial indirect relationship between the quality of the service and the intention to use artificial intelligence. This relationship is shown to be indirect rather than direct. It is possible to draw the conclusion that improved service quality raises people's likelihood of intending to use AI security technologies. This is due to the fact that the contribution of such technologies to improved job performance, as well as the convenience with which such technologies can be utilised, raises people's awareness of the perceived value of such technologies