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    Facilitating knowledge management through filtered big data: SME competitiveness in an agri-food sector


    O'Connor, Christina and Kelly, Stephen (2017) Facilitating knowledge management through filtered big data: SME competitiveness in an agri-food sector. Journal of Knowledge Management, 21 (1). pp. 156-179. ISSN 1367-3270

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    Abstract

    Purpose – This paper aims to critique a facilitated knowledge management (KM) process that utilises filtered big data and, specifically, the process effectiveness in overcoming barriers to small and medium-sized enterprises’ (SMEs’) use of big data, the processes enablement of SME engagement with and use of big data and the process effect on SME competitiveness within an agri-food sector. Design/methodology/approach – From 300 participant firms, SME owner-managers representing seven longitudinal case studies were contacted by the facilitator at least once-monthly over six months. Findings – Results indicate that explicit and tacit knowledge can be enhanced when SMEs have access to a facilitated programme that analyses, packages and explains big data consumer analytics captured by a large pillar firm in a food network. Additionally, big data and knowledge are mutually exclusive unless effective KM processes are implemented. Several barriers to knowledge acquisition and application stem from SME resource limitations, strategic orientation and asymmetrical power relationships within a network. Research limitations/implications – By using Dunnhumby data, this study captured the impact of only one form of big data, consumer analytics. However, this is a significant data set for SME agri-food businesses. Additionally, although the SMEs were based in only one UK region, Northern Ireland, there is wide scope for future research across multiple UK regions with the same Dunnhumby data set. Originality/value – The study demonstrates the potential relevance of big data to SMEs’ activities and developments, explicitly identifying that realising this potential requires the data to be filtered and presented as market-relevant information that engages SMEs, recognises relationship dynamics and supports learning through feedback and two-way dialogue. This is the first study that empirically analyses filtered big data and SME competitiveness. The examination of relationship dynamics also overcomes existing literature limitations where SMEs’ constraints are seen as the prime factor restricting knowledge transfer.

    Item Type: Article
    Keywords: Case studies; Knowledge management; SMEs; Big data; Agri-food;
    Academic Unit: Faculty of Social Sciences > School of Business
    Item ID: 11229
    Identification Number: https://doi.org/10.1108/JKM-08-2016-0357
    Depositing User: Christina O'Connor
    Date Deposited: 14 Oct 2019 13:33
    Journal or Publication Title: Journal of Knowledge Management
    Publisher: Emerald
    Refereed: Yes
    URI:

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