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Analyzing technical and social dimension in knowledge sharing intention behavior in E-learning system
Tesavrita C.a,b, Yusuf I.M.a, Suryadi K.b
a Department of Industrial Engineering, UNPAR, Bandung, Indonesia
b Bandung Institute of Technology, Bandung, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© IEOM Society International.Nowadays, information technology has been widely used to support an organization’s knowledge management systems. UNPAR is one of many University that also has implemented this system since 2008, in hope to maximize its knowledge management system. But after more than 6 years, this system still encounter some obstacles, particularly in terms of low participation of the user to share their knowledge. The purpose of this research was to explore and analyze user’s knowledge sharing intention in E-learning system and then to give some recommendation that can improve the systems. This research will be based on the knowledge sharing model (KSM) that will analyze the relationship between knowledge sharing intention with selected factors from technical and social dimension. Technical dimensions will evaluate the system from the users’ perceived ease of use and perceived usefulness in accordance with the theory of technology acceptance model (TAM). The social dimension will discuss the variables from the social capital theory: network ties, knowledge self-efficacy, trust, and identification. Data will be collected by giving questionnaires to the lecturer of UNPAR as respondents. From a total of 101 samples obtained, the data then will be analyzed using Structural Equation Modeling (SEM) with maximum likelihood method. Based on the results of data processing and analysis, some improvement recommendation can be suggested based on exogenous variables which are proved affecting the knowledge sharing intention: perceived ease of use (PEOU), knowledge self-efficacy (KSE), network ties (NET), and identification (IDENT) in order to improve knowledge sharing intention through e-learning UNPAR. © IEOM Society International.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Academic organization,E-learning,KMS,KSI[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]