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Model of Acceptance with Peer Support: A Social Network Perspective to Understand Employees' System Use

机译:同伴支持下的接受模型:从社交网络的角度了解员工系统的使用

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Prior research has extensively studied individual adoption and use of information systems, primarily using beliefs as predictors of behavioral intention to use a system that in turn predicts system use. We propose a model of acceptance with peer support (MAPS) that integrates prior individual-level research with social networks constructs. We argue that anrnindividual's embeddedness in the social network of the organizational unit implementing a new information system can enhance our understanding of technology use. An individual 's coworkers can be important sources of help in overcoming knowledge barriers constraining use of a complex system, and such interactions with others can determine an employee's ability to influence eventual system configuration and features. We incorporate network density (reflecting "get-help" ties for an employee) and network centrality (reflecting "give-help " ties for an employee), drawn from prior social network research, as key predictors of system use. Further, we conceptualize valued network density and valued network centrality, both of which take into account ties to those with relevant system-related information, knowledge, and resources, and employ them as additional predictors. We suggest that these constructs together are coping and influencing pathways by which they have an effect on system use. We conducted a 3-month long study of 87 employees in one business unit in an organization. The results confirmed our theory that social network constructs can significantly enhance our understanding of system use over and above predictors from prior individual-level adoption research.
机译:先前的研究已经广泛研究了个人对信息系统的采用和使用,主要是使用信念作为使用系统的行为意图的预测指标,从而反过来预测系统的使用情况。我们提出了一个带有同伴支持(MAPS)的接受模型,该模型将先前的个人水平研究与社交网络结构进行了整合。我们认为,匿名人士在实施新信息系统的组织单位的社交网络中的嵌入可以增强我们对技术使用的理解。个人的同事可能是克服限制复杂系统使用的知识障碍的重要帮助来源,与他人的这种互动可以确定员工影响最终系统配置和功能的能力。我们将网络密度(反映员工的“获得帮助”关系)和网络集中度(反映员工的“提供帮助”关系)作为先前系统使用的主要预测指标,而这些社交关系是根据先前的社交网络研究得出的。此外,我们将有价值的网络密度和有价值的网络中心性概念化,这两者都考虑到了与具有相关系统相关信息,知识和资源的人的联系,并将它们用作附加的预测因子。我们建议这些构建体共同应对并影响它们对系统使用产生影响的途径。我们对组织中一个业务部门的87名员工进行了为期3个月的研究。结果证实了我们的理论,即社交网络构造可以大大增强我们对系统使用的理解,超越了先前个人水平采用研究的预测指标。

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