用户对遗留系统集成的接受程度

Reuben L. Mathule, B. Kalema
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引用次数: 2

摘要

集成和处理驻留在不同遗留系统中的数据通常仍然是一个费力的过程,需要手动步骤或复杂的编程,而在筒仓中使用遗留系统时,共享信息、安全和管理控制是一场噩梦。这种数据碎片化导致系统处理效率低下,报告同步性差,决策延迟,阻碍对业务需求的快速响应。这需要将遗留系统集成到单个存储库系统中,以支持数据的中央处理并改进决策制定。然而,这种集成总是受到用户的抵制。本文的目的是确定影响用户接受遗留系统集成的因素。这项研究的数据是通过使用南非政府部门的封闭式问卷收集的。对收集到的数据进行了定量分析,结果表明文化、信任、社会影响、信息质量和系统质量是接受遗留系统集成的核心先决条件。本文考察了对用户接受遗留系统集成有积极或消极影响的因素,以及系统开发人员在对遗留系统进行现代化时可能考虑的那些因素。在处理技术接受问题时,组织内部的管理可以利用这项研究的结果。本文进一步为需要一种方法来集成分布在遗留系统中的标准数据集的管理人员、开发人员和研究人员可以考虑的一个相对有影响的因素提供了一个模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
User acceptance of legacy systems integration
Integrating and processing data that reside in different legacy systems remains an often laborious process, requiring either manual steps or complicated programming yet legacy systems when used in silos make the sharing of information, security and management controls a nightmare. Such fragmentation of data leads to challenges of system processing inefficiencies, poor synchronization of reports, delayed decision making and hinders quick response to business demands. This calls for the integration of legacy systems into a single repository system to enable a central processing of data and improve decision making. However, such integration is always resisted by user. The objective of this paper was to identify factors influencing user acceptance of Legacy system integration. Data for the study was collected by using a close-ended questionnaire from a government department in South Africa. The collected data was analysed quantitatively and results indicated that culture, trust, social influence, information quality, and system quality are core antecedents of the acceptance of Legacy systems integration. This paper examines the factors that contribute positively or negatively to the user acceptance of legacy systems integration as well as those factors that could be considered by system developers when modernizing legacy systems. Management within organizations could leverage the findings of this study when dealing with issues of technology acceptance. This paper further provides a model for a relatively influential factors that could be considered by managers, developers and researchers that need a way to integrate standard data sets that are distributed across legacy systems.
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