In the contemporary business landscape, software has evolved into a strategic asset crucial for organizations seeking sustainable competitive advantage. The imperative of ensuring software quality becomes evident as l...
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In the contemporary business landscape, software has evolved into a strategic asset crucial for organizations seeking sustainable competitive advantage. The imperative of ensuring software quality becomes evident as low-quality software systems pose formidable challenges to organizational performance. This study delves into the profound impact of three key dimensions of information system quality on organizational performance—information quality (IQ), quality of service (QoS), and software quality (SQ). Anchored in the DeLone and McLean information system (IS) success model, a quantitative questionnaire was administered to 360 industry experts and academics. Rigorous data analysis, employing exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and structural equation modeling (SEM), revealed significant positive effects of all three quality dimensions on organizational performance. Among these dimensions, software quality emerged as the most influential, showcasing substantial total effects, closely followed by information and service qualities. The study underscores the tangible value derived from strategic investments in enhancing software, information, and service quality. Elevating these facets manifests as a catalyst for improved organizational performance, empowering decision-makers with accurate and timely information while enhancing user satisfaction with the system. This research contributes significantly to the IS success literature by empirically validating the synergistic relationship between information quality, service quality, software quality, and organizational outcomes. The systematic analysis offered in this study goes beyond theoretical validation, providing actionable insights for managers. The findings guide the prioritization of quality initiatives and resource allocation, enabling organizations to maximize competitive advantage. As a future research direction, investigating moderator influences and exploring alternate qualit
Cab booking services help people order taxis. Existing cab booking services use client server-based architecture. The paper gives a study of the architecture and workings of the Uber cab booking website (Dissanayake, ...
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To ensure the high availability of modern online systems, effective maintenance is of critical importance. Today's software maintenance techniques for online systems heavily rely on metrics, which are time series ...
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ISBN:
(纸本)9783031664557;9783031664564
To ensure the high availability of modern online systems, effective maintenance is of critical importance. Today's software maintenance techniques for online systems heavily rely on metrics, which are time series data that can describe the real-time state of a system from various perspectives. Typically, software engineers generate dashboards with metrics to aid software maintenance. Though several attempts have been devoted to metric analysis for automatic software maintenance, the primary step, i.e., dashboard generation, remains manual to a large extent. In this paper, we develop a metric recommendation service, which can automate the dashboard generation practice and greatly ease the burden in maintaining an online system. Specifically, we analyze the needs of two essential steps of online system maintenance, i.e., anomaly detection and fault diagnosis, and design metric recommendation mechanisms for them respectively. Graph learning techniques are employed in the automation of metric recommendation. Our experiments demonstrate that the proposed approach can achieve an F1-score of 0.912 in selecting metrics for anomaly detection, and an accuracy of 0.859 in retrieving metrics for faults diagnosis, which significantly outperforms the compared baselines.
The demand for personalized manufacturing service recommendations is expanding with the popularity and application of industrial Internet platforms. However, the recommendation system has drawbacks in data privacy and...
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The demand for personalized manufacturing service recommendations is expanding with the popularity and application of industrial Internet platforms. However, the recommendation system has drawbacks in data privacy and security when exchanging parameters of clients. Therefore, this paper proposes a hybrid graph neural network-based federated learning method for personalized manufacturing service composition recommendation (FLGRC). First, a hybrid differential privacy algorithm based on federated learning is designed to solve the data island problem and achieve collaborative training. Second, an improved method of data mining is established to discover the collaborative relationships between different enterprises. Third, the graph neural network algorithm is employed to predict missing QoS (Quality of service) data, and the lists of recommendations are generated in accordance with fast non-dominated sorting and Top-N sorting rules. Finally, a real industry Internet platform case is adopted in this paper. The experiments analyze the accuracy of the prediction results. Moreover, the results obtained from the proposed algorithm are compared with those collected from other recommendation algorithms to verify the recommendation effect of the model.
Enhancing the operational and management capabilities of power service enterprises is crucial to people's livelihood. This paper conducts a study on the performance evaluation of power service enterprises by emplo...
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We consider a two-buffer polling queueing system with a changing service rate. One of the buffers has an infinite capacity, while another is finite. Changes in service rates occur during service at random moments. Int...
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Natural language processing (NLP) is becoming more and more widely used in the field of user services. This paper discusses the innovative application of NLP algorithm based on deep learning in improving user service ...
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The World Health Organisation (WHO) has recommended the use of open-source software solutions instead of proprietary software where possible, especially for resource-constrained countries. A widely adopted open-source...
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The present investigation proposes a Convolutional Neural Network (CNN)-based method for human-computer interaction (HCI) speaker emotion identification. Optimising the user experience through clearer and sympathetic ...
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Organizations today are investing heavily in effective data processing. A factor which has a significant effect on the effectiveness of data processing is the performance of the database system where the data is store...
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