Knowledge Integration model in enhancing performance of small and medium enterprises through social media - Ismarau Tajuddin, Nur Ilyana (2019)
Knowledge Integration (KI) has been identified as a key enabler of knowledge management. KI can integrate scattered knowledge, seek for specialist domains, products or services, increase the pool of services available to users, remove cooperation barriers, and enable the network effect that matters at the application level. Organization’s capability to integrate various sources of knowledge across geographical and organizational boundaries constitutes a key competence for obtaining and sustaining competitive advantages. Because a large part of the required knowledge in innovation process is tacit, technological cooperation among organizations is deemed important. Also, as we are moving into a more digital world and people are more engaged through social media, it is seen that the knowledge flow among organizations can be as well facilitated via social media (SM). SM can be considered as a tool to integrate knowledge effectively. In previous research, there are lack of studies that explore on how SM may facilitate KI. Therefore, in this study, a KI model is developed with the adaption of SM focusing in enhancing the performances in SMEs. A survey was conducted using questionnaire for data collection among SMEs organization in Malaysia. The data was analyzed using statistical tools (SPSS) and Partial Least Square- Structural Equation Modeling (PLS-SEM), as a method to test the hypotheses and evaluate the model. A prototype namely E-Knowledge Integration System (KIS) was developed to perform the functionality of KI through SM in enhancing the SMEs performances. The results of this study provide empirical support for the research model. The research model focuses on the measurement of 10 factors which are social network, IT capability, credibility, specialization, coordination, technology turbulence, market turbulence, KI, SM and service quality. The findings show that credibility (β=0.158, P<0.5), social network (β=0.206,P<0.5), IT capability (β=0.181, P<0.5) and technology turbulence (β=0.23, P<0.5) have significant effect on KI over SM. SM (β=0.611, P<0.5) has significant effect on KI. KI has significant effect on service quality (β=0.585, P<0.5). KI (β=0.275, P<0.5) and service quality (β=0.56, P<0.5) have significant effect on organization’s performance. In contrast, coordination (β=0.096, P>0.5), market turbulence (β=0.175, P>0.5) and specialization (β=0.014, P>0.5) have no significant effect on KI over social media. EKIS prototype has been developed as a method for model validation. The findings for this study can be used as a guideline for SMEs in integrating knowledge through social media.