Enhancing customer satisfaction through open innovation communities: A comparison of knowledge management approaches - Lee, Chien Sing *; Yew, Lee Yin (The University of Hong Kong, 2022)
Organizational learning integrates core specialized tacit resources and knowledge to facilitate development of strategic interdisciplinary knowledge development, integration, management and innovation. To promote open innovation within a gig economy, we address three problems: first, to identify which knowledge management view may contribute more in deriving, creating and increasing value and customer satisfaction; second, to educate users to learn, improve, and transfer the value to his/her designs via the user innovation community’s (UIC) feedback; third, from the reviews and findings, to identify implications/factors that we should pay more attention to when synergizing strategies and technology amidst co-evolving markets. Scoping our research to individual and additive/incremental Resource-based view (RBVKM), Knowledge-based view (KBV-KM), and Mixed-based view (MBV-KM) knowledge management approaches, the UIC are framed (positioned) as novice product designers-customers learning via HerAll, a Malaysian B2C niche card design e-commerce website. Hypothetically, RBV-KM may evidence more participation; KBV-KM more meaningful knowledge-sharing, moderated by the leader’s design and leadership skills; MBV-KM better design outcomes, knowledge sharing and the highest designer-customer satisfaction. Findings indicate 75.85% overall average customer satisfaction for RBV-KM, 71.40% KBV-KM and 81.35% MBV-KM. These correspond with the Diamond model and Customer Relationship Models. With perceived value in the midst of interconnected, co-evolving business models as motivator, customer satisfaction is influenced most by familiarity with the learning environment and tasks, followed by the type and quality of leadership, feedback/comments from the UIC, which influence the development of community and identity, ability, and cultural fit. Findings on the type and timing of rewards and (intelligent) guidance concur with prior literature.