Exploring the influence of management information systems on strategic planning: The mediating role of business intelligence

This quantitative study investigates the relationships between Management Information Systems (MIS), Business Intelligence (BI), and Strategic Planning (SP) within Jordanian Public Listed Companies, with a focus on the mediating role of BI. The target population comprises employees from the 108 public shareholding companies listed on the Amman Stock Exchange, totaling an estimated 1,080 senior managers involved in strategic planning. A random sample of 285 employees was surveyed to achieve a 95% confidence level with a 5% margin of error. Data were collected using a structured questionnaire with multi-item scales adapted from prior studies. Structural equation modeling (SEM) was employed to test the conceptual framework and hypothesized relationships, utilizing the two-step SEM approach with AMOS software. The results reveal significant positive relationships among MIS, BI, and SP. Specifically, MIS exhibits a statistically significant positive effect on SP, supporting previous research indicating MIS provides comprehensive data for informed planning. Furthermore, MIS significantly influences BI capabilities, underscoring the importance of robust MIS infrastructure for advanced BI analytics. BI, in turn, positively impacts SP, aligning with literature suggesting BI tools enhance planning agility and effectiveness through data-driven insights. Bootstrapping analysis demonstrates that BI partially mediates the relationship between MIS and SP. While BI acted as a significant mediating variable, MIS had a significant direct impact on SP, implying that though MIS has a direct impact on SP, it has an indirect impact, through BI, as well. Further analysis revealed that the constructs are interconnected, and that the mediation of BI is a necessary part of the process in Jordanian Public Listed Companies. As such, acknowledging the relevance of MIS, BI, and SP and the mediating role of BI, organizations can adapt their decision-making to achieve sustained competitive advantage within the dynamic business environment in Jordan

A Novel Human-Vehicle Interaction Assistive Device for Arab Drivers Using Speech Recognition

About one-quarter of all car collisions in the United States are caused by distracted driving, and this ratio is expected to rise. As vehicles are equipped with more elaborate and complex technology, human-vehicle interaction via dashboard displays and controls will become more complex and distracting. Human-vehicle interaction via voice-based technology offers a less distracting alternative. In this study we aim to develop a voice-based car assistant, with a focus on Arabic language speech recognition. We prepare a new 4000-word domain-specific lexicon to comprehensively support driver-vehicle interactions, and we create corresponding text and speech corpora. Then we extract acoustic feature vectors and use various acoustic models to support speech recognition. The language model is created using an n-gram model. Then acoustic and language models, and the lexicon are combined to generate a decoding graph. The text corpus consists of 6110 elements, including words, phrases, and sentences. The speech corpus has more than 60000 recordings (almost 50 hours). For the decoding of noise-free audio, a Deep Neural Network + Hidden Markov Model provided 94.832% accuracy, a Subspace Gaussian Mixture Model + Hidden Markov Model provided 94.2% accuracy, and the best Gaussian Mixture Model + Hidden Markov Model provided 94.13% accuracy. For the decoding of noisy audio, a Deep Neural Network + Hidden Markov Model provided 93.316% accuracy, a Subspace Gaussian Mixture Model + Hidden Markov Model provided 92.62% accuracy, and the best Gaussian Mixture Model + Hidden Markov Model provided 91.82% accuracy. A usability study was conducted on the system with 10 participants. Almost all of the results of that study showed usability ratings of greater than 4.0 out of 5.0. These usability ratings indicate that the proposed system was seen by the participants as important, and useful for reducing driver distraction