Modeling and Estimation of knowledge-based Trust in Organizational Knowledge Flow
سال انتشار: 1394
نوع سند: مقاله کنفرانسی
زبان: انگلیسی
مشاهده: 724
فایل این مقاله در 13 صفحه با فرمت PDF قابل دریافت می باشد
- صدور گواهی نمایه سازی
- من نویسنده این مقاله هستم
استخراج به نرم افزارهای پژوهشی:
شناسه ملی سند علمی:
IKMC07_272
تاریخ نمایه سازی: 9 مرداد 1395
چکیده مقاله:
Modeling and estimation of trust helps organizations to implement secure knowledge management using trust-based access control of knowledge assets in knowledge sharing. Trust and knowledge flow have a bidirectional cause and effect relationship. Increase or decrease in the value of trust between subjects that are involved in a knowledge flow, results in high or low performance of flow respectively. On the other hand, a high or low performed knowledge flow can increase or decrease the value of trust between subjects involved. In this paper, a method and an algorithm for modeling and estimating the value of trust is proposed. When a knowledge flow is done, each knowledge worker’s feedback toward other knowledge worker is gathered and a credit corresponding to gathered feedbacks is assigned to inter-individuals’ trust which may have an increasing or decreasing effect on the previous value. Then the effect on trust value between the individuals propagates to their respective groups and organizations. Such propagation allows to estimate the trust value not only between individuals but also between other subjects such as organizational groups, non-organizational groups, and organizations. The proposed model is evaluated by two simulated scenarios.
کلیدواژه ها:
نویسندگان
Mohammad Ghaem Tajgardoon
Ph.D Candidate in Computer Engineering, Sharif University of Technology, Iran
Mohammad Taghi Manzuri Shalmani
Professor in Computer Engineering, Sharif University of Technology, Iran
Jafar Habibi
Professor in Computer Engineering, Sharif University of Technology, Iran
مراجع و منابع این مقاله:
لیست زیر مراجع و منابع استفاده شده در این مقاله را نمایش می دهد. این مراجع به صورت کاملا ماشینی و بر اساس هوش مصنوعی استخراج شده اند و لذا ممکن است دارای اشکالاتی باشند که به مرور زمان دقت استخراج این محتوا افزایش می یابد. مراجعی که مقالات مربوط به آنها در سیویلیکا نمایه شده و پیدا شده اند، به خود مقاله لینک شده اند :