Purpose With the advent of new technologies, the integration of blockchain-enabled food supply chain (FSC) implementations is on the rise. Considering the multilateral, comprehensive and complex nature of the whole blockchain-enabled FSC implementation process, understanding knowledge management (KM) practices will add value. Prior literature shows that only a few studies have focused on this topic. As such, this study aims to identify and establish key KM enablers for blockchain-enabled FSC implementations. Design/methodology/approach This study conducted a qualitative research approach. By conducting 22 in-depth interviews with experienced blockchain implementation experts in FSC contexts, this study provides interesting insights for academics and practitioners. Findings The results of the analysis highlighted eight critical KM enablers that directly influence blockchain-enabled FSC implementations. They include external enablers (i.e. regulation and market competition) as well as internal enablers (i.e. people – organizational learning, strategy and leadership, culture, information technology, organizational infrastructure, processes and activities). Originality/value This study is one of the few studies that identify KM enablers for blockchain-enabled FSC implementations. Considering the novelty of decentralized blockchain implementations in FSC and its importance in transforming silo-based knowledge exchange to a decentralized operational structure, this study attempts to identify factors that increase the efficiency of blockchain implementations in FSC contexts.

In recent years, big data analytics has become widely applied in cybersecurity, leading to the novel approach of big data cybersecurity analytics. While some organizations have been adopting this new approach in tackling cybercrime, there are limited guidelines to which companies can refer. Therefore, the renowned maturity model concept, which offers a systematic approach for an organization to measure and improve its maturity level, is applied in this study. On the basis of a comprehensive literature review, this chapter proposes a maturity framework for big data cybersecurity analytics. This synthesized comprehensive maturity framework comprises seven dimensions across five stage levels—namely organization, human, infrastructure, data management, analytics application, governance, and security dimensions. Knowing which dimensions need to be improved and which pathway to follow ensures the successful implementation of big data cybersecurity analytics within organizations.

In recent years, big data analytics has become widely applied in cybersecurity, leading to the novel approach of big data cybersecurity analytics. While some organizations have been adopting this new approach in tackling cybercrime, there are limited guidelines to which companies can refer. Therefore, the renowned maturity model concept, which offers a systematic approach for an organization to measure and improve its maturity level, is applied in this study. On the basis of a comprehensive literature review, this chapter proposes a maturity framework for big data cybersecurity analytics. This synthesized comprehensive maturity framework comprises seven dimensions across five stage levels—namely organization, human, infrastructure, data management, analytics application, governance, and security dimensions. Knowing which dimensions need to be improved and which pathway to follow ensures the successful implementation of big data cybersecurity analytics within organizations.