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<p>Recent organizations are works under extremely viable and dynamic environments. For that, efficient resource allocation and task scheduling strategies are needed to minimize total completion time and resource costs, while maximizing resource utilization and overall system performance. For achieving this, the integration of Resource-Based View (RBV), Task-Technology Fit (TTF), and advanced deep learning/optimization techniques are proposed for optimizing Organizational Performance. Initially, the diverse data from various sources are collected. This dataset offers a comprehensive foundation for analysing organizational dynamics. After this, raw input data is pre-processed using missing values replacement, outlier detection, and data normalization. Then, statistical and time-series features are extracted for productivity rates, task completion time, and quality scores. After this, proposed Twin Convolutional Neural Network (CNN) architecture integrated with Inception v3 is utilized to predict RBV and TTF data performance outcomes. To enhance model accuracy, the parameters are optimized using the Spider Wasp Optimizer (SWO). Finally, Task-resource matching is performed using a hybrid optimization approach that combines the Chimp Optimization Algorithm (ChOA) and the Parrot Optimization Algorithm (POA). The proposed model attains an accuracy of 0.9845, precision of 0.9615, recall of 0.9804, F1-score of 0.9708, and processing of 97.63&#xa0;s. By this, the proposed model optimizes resource allocation and task scheduling by minimizing total completion time and resource costs while maximizing resource utilization and overall performance.</p>

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Optimizing organizational performance through integration of RBV, and TTF: a deep learning and optimization approach

  • 期刊论文

  • 发表日期：

    2026年4月13日

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  • Lamia Alhazmi

摘要

Recent organizations are works under extremely viable and dynamic environments. For that, efficient resource allocation and task scheduling strategies are needed to minimize total completion time and resource costs, while maximizing resource utilization and overall system performance. For achieving this, the integration of Resource-Based View (RBV), Task-Technology Fit (TTF), and advanced deep learning/optimization techniques are proposed for optimizing Organizational Performance. Initially, the diverse data from various sources are collected. This dataset offers a comprehensive foundation for analysing organizational dynamics. After this, raw input data is pre-processed using missing values replacement, outlier detection, and data normalization. Then, statistical and time-series features are extracted for productivity rates, task completion time, and quality scores. After this, proposed Twin Convolutional Neural Network (CNN) architecture integrated with Inception v3 is utilized to predict RBV and TTF data performance outcomes. To enhance model accuracy, the parameters are optimized using the Spider Wasp Optimizer (SWO). Finally, Task-resource matching is performed using a hybrid optimization approach that combines the Chimp Optimization Algorithm (ChOA) and the Parrot Optimization Algorithm (POA). The proposed model attains an accuracy of 0.9845, precision of 0.9615, recall of 0.9804, F1-score of 0.9708, and processing of 97.63 s. By this, the proposed model optimizes resource allocation and task scheduling by minimizing total completion time and resource costs while maximizing resource utilization and overall performance.