Why People Keep Using Knowledge Management Systems: A Causal Analysis of Continuance Behavior

This study examines the factors that cause a person to become a continuous user of a knowledge management system by examining continuance behavior. Continuance behavior is the decision to continue using a product after initial use. The data for this study were obtained using an online survey. The results were analyzed using partial least squares structural equation modeling. Six main hypotheses were developed which resulted in the evaluation of fourteen hypotheses. The results show that the technological features of a knowledge management system positively influence a user’s evaluation with limited influence from the system’s community features. The results produced a 58% coefficient of determination for knowledge management systems continuance intention and 37% for knowledge management systems continuance behavior. This investigation serves as a foundation for further research on the continuance usage of knowledge management systems. It addresses the needs of practitioners by examining which conditions they can manage to increase the purposeful use of their organizations’ knowledge management systems. The study also addresses the needs of academia by expanding the literature on continuance behavior of knowledge management systems.

A New Adaptive Corporate Learning Model

The article discusses issues related to the main trends in corporate learning and the creation of a model of adaptive learning organizations. As one of the main conditions for their activities, the idea of forming a knowledge management system in adaptive learning organizations, widespread use of corporate knowledge portals, and empowering communities of practice is put forward and substantiated. In this regard, the article focuses on the peculiarities of their construction and functioning. A list of the main problems that arise in the knowledge management system is given, and the ways of their solution are determined. Particularly interesting is the question of modern new functions of the knowledge management system, an overview of the current existing experience of solving this using e-learning and vr-technologies is given.

Using Artificial Neural Networks (ANNs) to Improve Agricultural Knowledge Management System (KMS)

Agriculture is an important sector of the Indian economy. In the present paper an attempt has been made to theoretically explore the development of an agricultural knowledge management system (KMS) in respect of various micro irrigation techniques for agriculture, as well as relevant crop-/region-specific agricultural practices in different regions of the country, as the same has been observed to be very much necessary for the overall benefits of wider cross section of farmers, agricultural scientists, economists, and other stakeholders in the domain. It is further observed that artificial neural networks (ANNs), which are a part of soft computing techniques, can be used as a KMS tool for effective management of various sub sectors of agriculture. In this context, it has been shown that use of ANNs as a KMS tool can improve the effectiveness of applications of the above mentioned agricultural KMS by accurately forecasting the year-wise estimated yield of food grains of India with the help of past data of various relevant parameters.

PERSPECTIVES ON KNOWLEDGE MANAGEMENT APPLICATION IN BPPT LIBRARY AS AN ORGANIZATIONAL KNOWLEDGE COLLECTION PLATFORM

This study evaluates the practice of a knowledge management system through the knowledge management application used by the Agency for the Assessment and Application of Technology (BPPT). The purpose of this study is to understand and get an overview from BPPT employees related to BPPT knowledge management (KM) application in its function as an organizational knowledge collection platform. This study is hopefully beneficial to see the effectiveness of the features of KM application in supporting the work activities of BPPT employees. The method used in this research is quantitative, using a questionnaire technique based on a survey to gather the data. The questionnaire was distributed to employees who are the administrator of the KM BPPT application from each BPPT working unit. The collected data were processed and analyzed using the AIDA model. This study indicates that BPPT employees have used the BPPT KM application, and some of the features also have been used, such as chats, tasks, drives, and events. Moreover, based on the results obtained, it is known that BPPT employees trust the app and consider the BPPT KM application reliable to store and manage organizational knowledge properly.

Knowledge sharing and discovery across heterogeneous research infrastructures

Research infrastructures play an increasingly essential role in scientific research. They provide rich data sources for scientists, such as services and software packages, via catalog and virtual research environments. However, such research infrastructures are typically domain-specific and often not connected. Accordingly, researchers and practitioners face fundamental challenges introduced by fragmented knowledge from heterogeneous, autonomous sources with complicated and uncertain relations in particular research domains. Additionally, the exponential growth rate of knowledge in a specific domain surpasses human experts’ ability to formalize and capture tacit and explicit knowledge efficiently. Thus, a knowledge management system is required to discover knowledge effectively, automate the knowledge acquisition based on artificial intelligence approaches, integrate the captured knowledge, and deliver consistent knowledge to agents, research communities, and end-users. In this study, we present the development process of a knowledge management system for ENVironmental Research Infrastructures, which are crucial pillars for environmental scientists in their quest for understanding and interpreting the complex Earth System. Furthermore, we report the challenges we have faced and discuss the lessons learned during the development process.

Designing Knowledge Management System with Big Data for Data and Information Center Ministry of Health Indonesia

Sharing and capture knowledge is one of the important indicators in the ministry of health. The role of staff’s knowledge is a benchmark in determining service performance and quality, in this case the Knowledge Management System (KMS) is a solution in supporting the process of knowledge capture and knowledge sharing. However, the growth of innovation in knowledge management is also required in practice. The implementation of big data into the Knowledge Management System plays a role in extracting, transforming, loading, which can help the information system development division and this information technology and infrastructure management subdivision to collect, filter and combine data from various sources. The results of the KMS design are in the form of a site-based application that has a wiki module, discussion forums and document management to share medical experiences and there are search features needed by staff. Keywords—Knowledge, Knowledge Management, KMS, Big Data