MODEL OF ACCEPTANCE WITH PEER SUPPORT: A SOCIAL NETWORK PERSPECTIVE TO UNDERSTAND EMPLOYEES' SYSTEM USE

• 成果类型: Article
• 署名作者: Sykes, Tracy Ann; Venkatesh, Viswanath; Gosain, Sanjay
• 署名单位: University of Arkansas System; University of Arkansas Fayetteville
• 刊物名称: MIS QUARTERLY
• ISSN/ISSBN: 0276-7783
• 发表日期: 2009
• 页码: 371-393
• 关键词: information-technology; behavioral intention; structural-analysis; gender-differences; decision-making; REASONED ACTION; centrality; usage; work; performance

摘要

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 an individual'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.