Analysis of Fishermen's Basic Skills Training With A Computer-Based SECI Knowledge Management Approach

D. Darmawan, M. Tahar, Lalan Erlani
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Abstract

This study aims to explore an integrated framework that integrates SECI knowledge management, basic fishing techniques, diversity of ecological knowledge, innovation abilities, fisherman's performance. The research design uses descriptive quantitative research, and the research methodology uses survey research with 150 respondents with purposive sampling in Kupang district, West Nusa Tenggara, which forms the target population. Statistical techniques use confirmatory factor analysis and structural equation modeling (SEM) using SmartPLS4 to test hypotheses. The test revealed a significant increase in the overall score of the trainees and skills. The specific score for the X2 table value was 149.334 compared to the calculated X2 value of 944.501, so the form of this model can be said to be FIT because X2 count X2 table. Furthermore, the value of Adjusted R Square gives a more substantial picture than R Square in assessing the ability of an exogenous construct to explain endogenous constructs, namely BFT_0.788, DEK_0.815, FP_0.636 and IA_0.725. Furthermore, the path coefficient values in this study are FP_ - IA_0.852, IA_ - BFT_0.324, IA_ - DEK_0.525, SKM_ - BFT_ 0.605, SKM_ - DEK_0.423 and SKM_- FP_0.798. From these tests, only BFT and DEK were seen to be less than 0.5. The results show that process knowledge management affects several observational variables, namely organizational knowledge, information management affects the nautical ability, process knowledge management involves Fishing basic techniques, Diversity of ecological expertise, maritime learning through mediators, Information management, and organizational knowledge. Therefore, the application of computer-based SECI knowledge management in this study is said to be significant.
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基于计算机SECI知识管理方法的渔民基本技能培训分析
本研究旨在探索一个整合SECI知识管理、基本捕鱼技术、生态知识多样性、创新能力和渔民绩效的综合框架。研究设计采用描述性定量研究,研究方法采用调查研究,在构成目标人群的西努沙登加拉Kupang区对150名受访者进行有目的的抽样。统计技术使用验证性因素分析和结构方程建模(SEM),使用SmartPLS4来检验假设。测试显示,受训人员的总体得分和技能有了显著提高。X2表值的具体得分为149.334,而计算的X2值为944.501,因此该模型的形式可以说是FIT,因为X2计数X2表。此外,在评估外源构建体(即BFT_0.788、DEK_0.815、FP_0.636和IA_0.725)解释内源构建体的能力方面,调整后的R平方的值比R平方给出了更真实的画面。此外,本研究中的路径系数值分别为FP_-IA_0.852、IA_-BFT_0.324、IA_-DEK_0.525、SKM_-BFT_00.605、SKM_-DEK_0.423和SKM_-FP_0798。从这些测试中,只有BFT和DEK小于0.5。结果表明,过程知识管理影响几个观察变量,即组织知识、信息管理影响航海能力、过程知识管理涉及渔业基本技术、生态专业知识的多样性、通过中介进行的海事学习、信息管理和组织知识。因此,基于计算机的SECI知识管理在本研究中的应用具有重要意义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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