A Social recommendation framework for Chinese long-stayers in Chiang Mai

Abstract

Due to the phenomenal growth of the Intemet, e-commerce and Social Networks (SNs), research on Social Recommendation Systems (SRSs) has become a hot topic to address information overload and respond to diverse individual demands by exploiting users' social networks. However, many of the existing social recommendation methods lack an in-depth analysis of long-stay users from cross-cultural backgrounds, causing insufficient social recommendation retrieving and matching because the social attributes and required information recommendations of these users differ from those of common users suitable for general information system recommendations such as short-stay tourists, local organisations or residents. Therefore, this paper aims to develop a social network-based recommendation framework to address the research gap by taking Chinese long-stayers in Chiang Mai as a case study. Based on the processes of Knowledge Management (KM), this paper adopts a mixed methods research design, combining qualitative and quantitative approaches in the form of online questionnaire surveys and semi-structured in-depth interviews, to investigate the research problems and analyse the collected data from the perspective of Social Network Analysis (SNA). An initial qualitative pilot study on 32 sample respondents validates the significance of the research problems and a further quantitative questionnaire survey of 229 respondents highlights the positive impact of individual demographic characteristics, social networks and latent factors on social recommendation performance for Chinese long-stayers in Chiang Mai. The subsequent semi-structured Focus Group (FG) interviews on ten key social actors qualifies the questionnaire survey results and facilitates the design of a social network-based recommendation framework, which aims to provide Chinese long- stayers or any long-stay users that need it with filtering and efficient social recommendations in a cross-cultural context. According to the evaluation of four experts using After Action Review (AAR), the experimental results demonstrated that the proposed framework is effective in providing highly accurate social recommendations that enable Chinese users to develop strategies and make choices during their long-term residence in Chiang Mai. The preliminary work will contribute to the current understanding of the impact of social networks in theory and practice on the social recommendation performance for researchers and practitioners who need social recommendations or seek innovative business and management strategies concerning Chinese long-stayers in a cross-cultural context. However, further studies are required to identify other factors that could strengthen the effectiveness and usefulness of these needs-oriented social recommendations for long-tem residents in a cross-cultural environment in practice.