Facilitating knowledge management through filtered big data: SME competitiveness in an agri-food sector
Journal of Knowledge Management
Published online on March 07, 2017
Abstract
Journal of Knowledge Management, Volume 21, Issue 1, Page 156-179, February 2017.
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.
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.