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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.

This study aims to provide a foundation for the development of subcontracts that suit takt production in construction.

This is a non-empiric conceptual study, which integrates takt production and general construction literature into new proposals for subcontract clauses suitable for takt production in construction. This study uses literature reviews, from which proposals regarding takt production viable subcontract clauses are conducted via logical reasoning.

A total of 13 proposals for takt production applicable subcontracts are provided in this study. The proposals emphasize detailed and collaborative planning, suitable payment methods and flexibility for takt plan modification.

Previous takt literature has not properly addressed the development of subcontracts for takt production, despite regular attempts to use subcontracting in takt production. This study aims to aid main contractors to create fair and suitable subcontracts, so that adhering to takt schedules could be more viable in practice. This study also acts as a foundation for further empirical studies regarding the subject.

This paper elucidates the role of anger in error management (EM) and organizational learning behaviors. The study explores how anger can catalyze learning, emphasizing its strategic implications.

A double-layered moderated-mediated model was developed and tested using data from 744 Chinese CEOs. The psychometric properties of the survey instrument were rigorously examined through structural equation modeling, and hypotheses were tested using Hayes's PROCESS macros.

The findings reveal that anger is a precursor for recognizing the value of significant errors, leading to a positive association with learning behavior among top management team members. Additionally, the study uncovers a triple interaction effect of anger, EM culture and supply chain disruptions on the value of learning from errors. Extensive experience and positive grieving strengthen the relationship between recognizing value from errors and learning behavior.

This study uniquely integrates affect-cognitive theory and organizational learning theory, examining anger in EM and learning. The authors provide empirical evidence that anger can drive error value recognition and learning. The authors incorporate a more fine-grained approach to leadership when including executive anger as a trigger to learning behavior. Factors like experience and positive grieving are explored, deepening the understanding of emotions in learning. The authors consider both negative and positive emotions to contribute to the complexity of organizational learning.