银行IT员工满意度

Gabriel Jipa
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引用次数: 0

摘要

员工IT用户满意度对于公司非常重要,特别是在金融服务等受监管的行业中,特别是对于直接与客户交互的员工。本文分析了各因素对某银行IT用户满意度评分的影响。在零售银行业务中,面向客户的员工需要为简单或复杂的交易提供服务,并提供财务建议。我们发现,IT用户体验受到组织基于用户反馈的改变意愿的信任的积极影响,以及作为帮助台提供的支持,但受到多个应用程序性能,稳定性问题或基础设施性能的负面影响,作为使用IT作为工作支持工具的预期价值的一部分。使用分割、决策树或多项线性回归等技术进行定性探索和定量研究。数据分析是对3000人中的608份调查回复进行的。在采访期间收集的文字记录数据在Python中使用自然语言处理技术进行处理,与人类驱动的分类并行,作为内容分析阶段的一部分提供额外的潜在见解(基于tfidf向量得分的关键词聚类,提取最相关的聚类词)。我们发现文本数据非常强大,特别是在使用可视化时,但通常由于语料库规模有限和面试中选择过程的偏见,可能从帮助台系统和电子邮件通信中收集更多数据是有用的。通过技术接受模型的镜头来讨论理论和实践意义,以解释影响整体IT景观感知的主要因素的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
IT Employee Satisfaction in a Bank
Employee IT user satisfaction is important for companies, especially in regulated industries as financial services, especially for workers interacting directly with the clients. This paper analyses the impact of various factors against rating given for IT User Satisfaction in a bank. In retail banking, customer-facing employees need to provide service for simple or complex transaction, as well as financial advice. We found that IT user experience is influenced positively by the trust in organization willingness to change based on the user’s feedback as well as the support provided as helpdesk but negatively impacted by multiple application performance, stability issues or infrastructure performance, as part of the expected value of using IT as a job support tool. Qualitative-exploratory and quantitative research was performed using techniques as segmentation, decision tree or multinomial linear regression. Data analysis was performed on 608 survey responses out of a population of 3000 individuals. Transcript data collected during interviews was processed using natural language processing technics in Python in parallel to human driven classification to provide additional potential insight as part of content analysis phase (clustering of keywords based on tfidf vectors scores, extraction of most relevant words for clusters). We found that textual data are very powerful especially when using visualization but in general due limited corpus size and bias from selection process in interviews would be useful to collect more data, maybe from helpdesk system and email communication for IT support. Theoretical and practical implications are discussed through the lens of the Technology Acceptance Model to explain the impact of the main factors influencing the perception of the overall IT landscape.
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