The study's aims are to identify healthcare innovation variables, explore innovative work behavior’s (IWB's) influence on Saudi health sector companies and evaluate the mediating…
The study's aims are to identify healthcare innovation variables, explore innovative work behavior’s (IWB's) influence on Saudi health sector companies and evaluate the mediating function of transformational leadership in the link between IWB and healthcare organizations. In this backdrop, the purpose of the current research was to investigate the impact of creative work behavior on organizational performance and the role of transformational leadership in this process.
The objective of this quantitative cross-sectional study was to examine, according to 587 participants, the perceived elements of creative work behavior (RQ1). In various 10 departments of the 5 Dammam Health Network (DHN) in the Eastern Province of Saudi Arabia, online questionnaires were used to collect data. SmartPLS 3 software was used to analyze the data.
The findings indicated that healthcare professionals perceive the elements of autonomy, competence, relatedness, motivation and knowledge sharing as key features that influence high efficiency in organizational efficiency (p < 0.001). IWB also had a significant and direct positive influence on organizational performance (p < 0.001). Transformational leadership behavior had an insignificant negative effect on employees’ task performance when considering organizational performance (P = 0.122). Therefore, the mediation role did not affect the relationship with IWB concerning employees’ task performance, suggesting that transformational leadership behaviors did not have a mediating effect on the effectiveness of employees’ task performance.
This article contains original analysis and interpretation highlighting integrating IWB and transformational leadership into Saudi Arabia's national healthcare system that can help address specific difficulties facing healthcare practitioners.
There is a gap in knowledge about the Gulf Cooperation Council (GCC) because most studies are undertaken in countries outside the Gulf region – such as China, India, the US and…
There is a gap in knowledge about the Gulf Cooperation Council (GCC) because most studies are undertaken in countries outside the Gulf region – such as China, India, the US and Taiwan. The stock market contains rich, valuable and considerable data, and these data need careful analysis for good decisions to be made that can lead to increases in the efficiency of a business. Data mining techniques offer data processing tools and applications used to enhance decision-maker decisions. This study aims to predict the Kuwait stock market by applying big data mining.
The methodology used is quantitative techniques, which are mathematical and statistical models that describe a various array of the relationships of variables. Quantitative methods used to predict the direction of the stock market returns by using four techniques were implemented: logistic regression, decision trees, support vector machine and random forest.
The results are all variables statistically significant at the 5% level except gold price and oil price. Also, the variables that do not have an influence on the direction of the rate of return of Boursa Kuwait are money supply and gold price, unlike the Kuwait index, which has the highest coefficient. Furthermore, the height score of the variable that affects the direction of the rate of return is the firms, and the accuracy of the overall performance of the four models is nearly 50%.
Some of the limitations identified for this study are as follows: (1) location limitation: Kuwait Stock Exchange; (2) time limitation: the amount of time available to accomplish the study, where the period was completed within the academic year 2019-2020 and the academic year 2020-2021. During 2020, the coronavirus pandemic (COVID-19), which was a major obstacle, occurred during data collection and analysis; (3) data limitation: The Kuwait Stock Exchange data were collected from May 2019 to March 2020, while the factors affecting the stock exchange data were collected in July 2020 due to the corona pandemic.
The study used new titles, variables and techniques such as using data mining to predict the Kuwait stock market. There are no adequate studies that predict the stock market by data mining in the GCC, especially in Kuwait. There is a gap in knowledge in the GCC as most studies are in foreign countries, such as China, India, the US and Taiwan.