2692-2967ISSN (Online)
2770-0216ISSN (Print)
This study aims to investigate the connection between organizational culture and strategy formulation in several private colleges in Iraq, as organizational culture is a major factor in the success or failure of organizations, and it is a crucial element in organizational transformulation and growth, which is a characteristic of the modern age. This research seeks to explore the significance of organizational culture by looking at its resurgence, its cultural makeup, and the dimensions of strategy formulation in universities and private colleges. It will then examine the connection between organizational culture and strategy formulation among the study sample. The hypothesis is that there is no meaningful relationship between organizational culture and strategy formulation. The research sample of (100) lecturers from (10) private universities and colleges was surveyed using a questionnaire to assess the interest in organizational culture in the educational community. The results revealed that there is a relative interest in the culture, but it is not given an important role in formulating the strategy. It was suggested that mental and intellectual abilities and experiences should be harnessed through dialogue and direct training to transform them into a powerful tool for formulating educational strategy.
Read MoreDoi: https://doi.org/10.54216/AJBOR.110201
Vol. 11 Issue. 2 PP. 1-22, (2024)
As part of the scope of the Artificial Neural Network – Particle Swarm Optimization (ANN-PSO) notion, the computational capability of ANNs is integrated with the optimization potential of PSO. This method proves to be very effective in solving complex non-linear forecasting problems where traditional approaches would not be effective. The data interactions that exist are the ones that are modelled and captured by the ANN component. However, the PSO method is charged with the duty of minimizing the biases and weights used in the ANN to ensure that the model attains the global minimum without being trapped in tiny local minimum. The application of this framework can be extended to cash forecast used in business like the one above in which a days of cash requirement forecast is created based on experience and factors like holidays, pay check effects and working days. However, the given contribution of the PSO element in learning process is linked with continuous transformation of variables under the basic guidelines of swarming intelligence, it makes the learning session of ANN more efficient. Therefore, the degree of accuracy of forecasts that are given by such configurations is improved, especially in the conditions that are in a state of steady evolution. The ANN-PSO model mirrors similar attributes, including its ability to process data in parallel and furthermore, its high compatibility with large-scale data as well as it robustness when working with both non-linear and linear data set. Incorporating the PSO into a model enhances the range of possible solutions and given the peculiarity of the gradient-based approach, it reduces mistakes more effectively than the conventional techniques. They suggested that by applying ANN with PSO the framework act as an efficient tool for prediction and for solving various issues in several fields. In this case, the ANN-PSO strategy suggested here works out to an impressive overall accuracy of over 98% compared to the previous systems.
Read MoreDoi: https://doi.org/10.54216/AJBOR.110202
Vol. 11 Issue. 2 PP. 23-37, (2024)
In order to analyses the diffusion of new technological products in rapidly changing market environments, this paper presents two new stochastic diffusion models: SDM1 and SDM2. The two models also utilize stochastic market size function in capturing rather random growth of potential users, inherent in most real-world markets. SDM1 apply the exponential distribution to model the market growth rate to consider the cases characterized by the high increase, while SDM2 adapt the Erlang distribution to reflect the S-shape to consider the long-term adoptions. The presented models rely on stochastic differential equations with recourse to calculus, and they adopt stochastic geometric Brownian motion and logistic growth function for adoption rates. This makes it possible to capture effects of learning as well as the non-regularity of adoption over time. The empirical results of benchmark models by using Apple iPhones and Samsung Galaxy smartphones sales data show the better performance of SDM1 and SDM2. The performance of the methodologies is measured using parameters, the goodness-of-fit tests and the forecast accuracy that all show that the proposed methods are very efficient. These models have a rich theoretical background, which comprises the foundation for explaining adoption patterns, which in turn will facilitate the behaviour of managers and policymakers towards understanding consumers, controlling inventory, and designing significant marketing strategies for technology products in a stochastic world. Both SDM1 and SDM2, the suggested algorithms, outperform the state-of-the-art techniques in terms of accuracy. SDM1 outperforms the other models with an accuracy of 95.32 percent. SDM2's greater accuracy in forecasting is shown by its outperformance of all techniques, which stands at 97.3%.
Read MoreDoi: https://doi.org/10.54216/AJBOR.110203
Vol. 11 Issue. 2 PP. 38-52, (2024)
For the purposes of maintaining a healthy liquid balancing and maximizing cash flow, accurate cash forecasting is very necessary for banking operations. In order to overcome the shortcomings of conventional forecasting techniques, such as linear regression, which do not take into account dynamic elements like pay impacts and vacations, this research, offers a Cash Management Model (PSO-CMM) that is based on Particle Swarm Optimization. Taking into account a number of characteristics, such as working days, holiday impacts, and pay patterns, PSO-CMM improves its coefficients for cash prediction. This allows for both short-term and long-term predictions. By swarm intelligence, the model is able to improve the accuracy of its predictions, hence providing greater resilience to continuously modifying surroundings. In addition to the development of linear and hybrid models that combine PSO with artificial neural networks (ANNs), the incorporation of adaptive computing approaches to improve weights is one of the most important advances. Furthermore, in order to prevent local optimums and to promote universal convergence, erratic patterns were incorporated in the most sophisticated systems. The results of this evaluation revealed a significant rise in the accuracy of cash projections. This study presents a comprehensive methodology for predicting cash requirements, which makes it possible for micro financial organizations to get useful insights and improves their operating effectiveness in situations that are always changing. When compared with Normal Data, the suggested PSO-CMM method's overall accuracy is around 91%.
Read MoreDoi: https://doi.org/10.54216/AJBOR.110204
Vol. 11 Issue. 2 PP. 53-66, (2024)
The Artificial Neural Network-based Cash Forecasting Model (ANN-CFM) is introduced in this part as one way of mitigating the vices that are characterised with linear approach to financial management. This paradigm is quite helpful when the analysis is focused on non-linear and, generally, troublesome data. ANN-CFM, therefore, simultaneously takes both the linear and non-linear information for improving on the cash forecasting. Due to this fact, it is able to realise and leverage over advantage from the computational competence that neural systems provide. The hidden, output and input layers use randomised initial biased and weights. These include biases together with weighting that is altered regarding a standard basis with the use of a learning strategy to try to find the greatest cash needs. This design is actually composed of three various layering. This is exactly what the ANN-CFM is capable of dealing with and it accepts inputs, both for LT and ST forecasting. Among these inputs, you have factors of the working days, wages’ impact, and the impacts of holidays. The ANN-CFM is a system that revolutionises the way a human would perform his/her decisions and is a highly parallelized and efficient analytical tool for large data. As a result, this results in enhancement of precision to that which is predicted. The kind of architecture used in the system is feed forward neural network, which uses back propagation to help in reducing the numbers of errors that prevail at the time of prediction. In this part, extensive application of ANN, including its ability to learn in environments that may be constantly evolving is also highlighted. Thus, this innovative approach allows for sure receipt of accurate solutions for the management of these funds by companies operating in the financial industry. Comparing it with Normal Data, it is clear that the ANN-CFM technique proposed here provides an overall accuracy of approximately 95%.
Read MoreDoi: https://doi.org/10.54216/AJBOR.110205
Vol. 11 Issue. 2 PP. 67-81, (2024)