A conceptual framework for information-leakage-resilience

Abstract

In today's dynamic and competitive business environment, it is critical for firms to share information selectively and develop organizational resilience. However, only a few existing studies examine the relationships between information management and supply chain resilience. Aiming to bridge the gaps between both domains, we propose a model encompassing information security culture, information leakage, information sharing effectiveness and supply chain resilience and to derive insights from their inter-relationships in this research. A cross-sectional survey of the multi-national corporations and small and medium enterprises among their senior managers in the United Kingdom was carried out for data collection. The data set was analysed using a structural equation modelling approach. The results obtained validate the proposed model. The findings ascertain that information security culture and information leakage are negatively correlated, which influence the supply chain resilience. Specifically, information security and information leakage affect the effectiveness of information sharing, which in turn positively and negatively influence the supply chain resilience, respectively. This study posits that information security culture is instrumental to mitigate information leakage and foster effective information sharing to strengthen supply chain resilience.

Artificial intelligence and big data analytics for supply chain resilience: a systematic literature review

“…To achieve this, scholars indicate that what is needed is superior information processing capabilities (Belhadi, Kamble, Fosso Wamba, et al, 2021), particularly because knowledge and information exchange among supply chain partners is considered conducive to risk reduction (Brandon-Jones et al, 2014 ). Indeed, information sharing can minimise ambiguities along the supply chain and increase visibility and performance (Wong et al, 2021 ), especially at times when these are put to the test. Wong et al, ( 2021 ) indicate that having access to accurate information before, during and after disruptions is of paramount importance, and that this can be achieved via real-time information exchange amongst supply chain partners.…”

“…Indeed, information sharing can minimise ambiguities along the supply chain and increase visibility and performance (Wong et al, 2021 ), especially at times when these are put to the test. Wong et al, ( 2021 ) indicate that having access to accurate information before, during and after disruptions is of paramount importance, and that this can be achieved via real-time information exchange amongst supply chain partners. Such information exchange can support timely decision making and enhance the efficiency of the supply chain system (Li & Lin, 2006 ).…”

Total interpretive structural modelling (TISM) and MICMAC approach in analysing knowledge risks in ICT-supported collaborative project

“…In the process of communication, there is a tendency of revealing material knowledge to the outsider unknowingly, which in effect could result in knowledge loss. Hence, communication has been recognised as a tool (risk) to instigate knowledge loss (Christina et al, 2016) 2.9 Outmoded technologies One of the key facilitators of knowledge loss is the use of outmoded technologies (Wong et al, 2021). Most often technologies are updated when experts identify flaws in the technology architecture, which could likely expose businesses to risks.…”

“…One of the key facilitators of knowledge loss is the use of outmoded technologies (Wong et al , 2021). Most often technologies are updated when experts identify flaws in the technology architecture, which could likely expose businesses to risks.…”

Examining the impact of deep learning technology capability on manufacturing firms: moderating roles of technology turbulence and top management support

“…Studies have demonstrated how deep learning ability of a manufacturing firm could help the firm for its quality control, predictive maintenance along with appropriate detection of anomaly (Chen et al, 2021 ; Liu et al, 2020 ). Studies are found to have documented that for effective predictive performance, deep learning technology could supersede the effectiveness of machine-learning applications in this context (Guan et al, 2019 ; Wong et al, 2021 ). Studies have also documented how deep learning ability with the help of depth-wise neural network could detect COVID-19 infection and its appropriate diagnosis (Qayyum et al, 2021 ).…”