...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Technological forecasting and social change >Big data analytics capability for improved performance of higher education institutions in the Era of IR 4.0: A multi-analytical SEM & ANN perspective.
【24h】

Big data analytics capability for improved performance of higher education institutions in the Era of IR 4.0: A multi-analytical SEM & ANN perspective.

机译：IR 4.0时代高等教育机构改善绩效的大数据分析能力：多分析SEM与ANN视角。

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Despite the growing interest towards big data within higher education institutions (HEI), research on big data analytics capability within the HEI context is somewhat limited. This study's main objective is to have a better understanding of the utilisation of big data analytics capability for data-driven decision-making to achieve better performance from Malaysian HEIs. Despite the growing interest towards big data within higher education institutions (HEI), research on big data analytics capability within the HEI context is rather limited. This study's main objective is to have a better understanding of the utilisation of big data analytics capability for data-driven decision-making to achieve better performance from Malaysian HEIs. This study validates an integrative model by combining information processing theory and resource-based view theory. Unlike extant literature, this study proposed methodology involving dual-stage analysis involving of Partial Least Squares Structural Equation Modelling and evolving Artificial Intelligence named deep learning (Artificial Neural Network) were performed. The application of deep ANN architecture can predict 83% of accuracy for the proposed model. Besides, the outcome of data-driven decision making from the relationship between big data analytic capability and datadriven decision making towards the performance of HEIs has significant findings. Results revealed that datadriven decision making could positively play an essential role in the relationship between big data analytic capability and performance of HEIs. Theoretically, the newly integrated theoretical model that incorporates information processing theory and resource-based view provides useful guidelines to HEI's about the crucial capabilities and resources that must be put into place to reap the benefits associated with big data implementations in the wake of Industry Revolution 4.0.
机译：尽管对高等教育机构（HEI）内的大数据兴趣越来越令人兴趣，但HEI上下文中的大数据分析能力的研究有所限制。本研究的主要目标是更好地了解利用大数据分析能力，以实现数据驱动的决策，以实现马来西亚赫斯的更好表现。尽管对高等教育机构（HEI）内的大数据兴趣越来越令人兴趣，但赫西上下文中的大数据分析能力的研究相当有限。本研究的主要目标是更好地了解利用大数据分析能力，以实现数据驱动的决策，以实现马来西亚赫斯的更好表现。本研究通过结合信息处理理论和基于资源的视图理论来验证一体化模型。与现存文献不同，该研究提出了涉及涉及部分最小二乘结构方程模型和不断发展的人工智能的双阶段分析的方法，命名为深度学习（人工神经网络）。深度ANN架构的应用可以预测所提出的模型的准确性的83％。此外，来自大数据分析能力与数据分析决策之间的关系的数据驱动决策的结果具有重要发现。结果表明，DataDriven决策可以积极地在大数据分析能力与Heis性能之间的关系中发挥重要作用。从理论上讲，包含信息处理理论和资源视图的新集成的理论模型为赫西提供了有用的赫西关于必须建立在行业革命之后与大数据实现相关的优势的重要功能和资源的有用指导方针4.0 。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号