Leveraging Mobile Interaction Technologies for Real-Time Decision Making in Enterprise Management Systems
With the widespread adoption of mobile internet and smart devices, enterprises face rapidly changing market environments and complex decision-making requirements. How to utilize mobile interaction technologies to enhance real-time decision-making capabilities in enterprise management systems has become a critical issue in modern business management. Through real-time data transmission and interaction via mobile devices, businesses can more flexibly acquire information, monitor statuses, and optimize decision-making processes, thereby improving the timeliness and accuracy of decisions. Existing research primarily focuses on traditional decision support systems (DSS) and data mining technologies, but these methods fall short when addressing the dynamic and real-time decision-making needs, especially in complex enterprise management scenarios. One major challenge remains the effective integration of multiple data sources. Therefore, leveraging real-time interactive data from mobile technologies to improve decision support in enterprise management systems becomes a pressing research topic. This paper proposes a real-time decision-making framework for enterprise management systems based on mobile interaction technologies, focusing on the problem description of real-time decision-making in enterprise management and a decision-making mining framework based on mobile interaction relationships and mixed user discourse (including decision belief propagation based on mobile interaction relationships and the UCBert model based on user content analysis). Through this framework, the paper aims to enhance decision-making accuracy and timeliness, enabling enterprises to make more informed and efficient decisions in a dynamically changing market.
Authors
- Fan Yang (ORCID: https://orcid.org/0000-0002-2840-490X)
Institutions
- Shijiazhuang University (CN)
Publication Details
- Journal
- International Journal of Interactive Mobile Technologies (iJIM)
- Published
- 2025-01-27
- DOI
- https://doi.org/10.3991/ijim.v19i02.53743
- Citations
- 1
- Primary Topic
- Big Data and Business Intelligence
- Type
- article
- Field-Weighted Citation Impact
- 2.28