Success Models Using Knowledge Seekers' and Experts' Responses for Knowledge and E-Learning Portals: Knowledge Systems and E-Learning Portal Evaluation

Success Models Using Knowledge Seekers' and Experts' Responses for Knowledge and E-Learning Portals: Knowledge Systems and E-Learning Portal Evaluation

Venkata Subramanian Dayanandan (Hindustan University, India) and Angelina Geetha (B. S. Abdur Rahman University, India)
Copyright: © 2016 |Pages: 15
DOI: 10.4018/978-1-4666-9932-8.ch004
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The purpose of this chapter is to present a comprehensive summary on the frameworks and metrics which are useful for evaluating the knowledge systems and portals. In addition, this paper further explores the relationship of knowledge seekers and expert's response in addition to other feedback. The primary goal of this chapter is to provide a literature survey and description of service based technology framework which will be helpful for evaluating the effectiveness of the E-learning portals and/or KM Systems. This proposed work also finds the correlation between the evaluation results of knowledge providers and seekers. The secondary part of this chapter provides a comparative summary on four success models and factors for evaluating knowledge systems and E-learning portals.
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Literature Survey

Knowledge quality can be defined as fit for the purpose of fitness for use (Alavi and Leidner, 2011). There are many benefits with a well-designed Knowledge Systems and portals, which saves time and cost. E-learning systems also assists in leveraging the knowledge within the company for improving efficiency in resolving issues in handling products, projects and processes. According to the current trend in industries, it is believed that Knowledge systems are the supporting power of any organization. The product companies can benefit from KM and KMS, by increasing the orders, customer satisfaction, knowledge of products and consistency with the product delivery and support. In addition to this, a well-defined KMS can reduce the cost in producing the products, services and solutions, by ensuring timely deliveries. By implementing KMS, organizations can also expect Return on Investments (RoI) through indirect savings and benefits for the technical support groups. The support calls or requests can be reduced with reduced turnaround time with consistent and accurate solutions. The training time and cost of the skilled resources can be saved. The availability of the knowledge assets would increase the morale among staff, and the efficient utilization of engineers, thereby reducing escalation calls to the second and third level support team. Considering the floating job market, there is always a possibility of the support staff moving from one job to another. When skilled staffs leave a company, they also carry their skills and experience, especially their field knowledge with them. This can be avoided by ensuring that all the required knowledge of the skilled employees is maintained in the KMS. Output metrics measure the characteristics at the project or task level, such as the effectiveness of the lesson learned for future operations. Considering the complexity and multidimensional nature of KMS, it is more appropriate to construct the multidimensional model and metric database for evaluating any KMS.

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