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Social media influencers (SMIs) have become vital components of interactive marketing to promote beauty-endorsed products. However, there are three major research gaps in the literature on influencer marketing. This research aimed to fill these gaps by integrating the stimulus-organism-response (SOR) model with theories of source credibility and parasocial interaction (PSI).

A qualitative method was used to gain a deep understanding of the Korean beauty field in YouTube influencer marketing.

The data showed that YouTube influencer marketing is an ongoing PSI process that conforms to the extended SOR model. This model was based on four concepts: stimulus, which was the SMIs’ source credibility; organism, which was the followers’ perceptions of homophily/relevance to the SMIs in PSIs; response, which was the desire to imitate and engage in impulse buying behavior, and management, which was the parasocial trust friendship and/or relationship.

This study expands the new concept into the existing SOR model and releases an insight into ongoing PSIs in YouTube influencer marketing.

Study on knowledge-based systems for scientific publications is growing very broadly. However, most of these studies do not explicitly discuss the knowledge management (KM) component as knowledge management system (KMS) implementation. This background causes academic institutions to face challenges in developing KMS to support scholarly publication cycle (SPC). Therefore, this study aims to develop a new KMS conceptual model, Identify critical components and provide research gap opportunities for future KM studies on SPC.

This study used a systematic literature review (SLR) method with the procedure from Kitchenham et al. Then, the SLR results are compiled into a conceptual model design based on a framework on KM foundations and KM solutions. Finally, the model design was validated through interviews with related field experts.

The KMS for SPC focuses on the discovery, sharing and application of knowledge. The majority of KMS use recommendation systems technology with content-based filtering and collaborative filtering personalization approaches. The characteristics data used in KMS for SPC are structured and unstructured. Metadata and article abstracts are considered sufficiently representative of the entire article content to be used as a search tool and can provide recommendations. The KMS model for SPC has layers of KM infrastructure, processes, systems, strategies, outputs and outcomes.

This study has limitations in discussing tacit knowledge. In contrast, tacit knowledge for SPC is essential for scientific publication performance. The tacit knowledge includes experience in searching, writing, submitting, publishing and disseminating scientific publications. Tacit knowledge plays a vital role in the development of knowledge sharing system (KSS) and KCS. Therefore, KSS and KCS for SPC are still very challenging to be researched in the future. KMS opportunities that might be developed further are lessons learned databases and interactive forums that capture tacit knowledge about SPC. Future work potential could identify other types of KMS in academia and focus more on SPC.

This study proposes a novel comprehensive KMS model to support scientific publication performance. This model has a critical path as a KMS implementation solution for SPC. This model proposes and recommends appropriate components for SPC requirements (KM processes, technology, methods/techniques and data). This study also proposes novel research gaps as KMS research opportunities for SPC in the future.