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Table 7 Construct and indicator items with the supporting research

From: Big data analytics: a link between knowledge management capabilities and superior cyber protection

  Construct and indicator items Supporting research
  KM acquisition process  
ACQ1 Has processes for acquiring knowledge about our customers. Gold, Malhotra & Segars, 2001
ACQ2 Has processes for acquiring knowledge about our suppliers. Gold, Malhotra & Segars, 2002
ACQ3 Has processes for exchanging knowledge with our business partners. Gold, Malhotra & Segars, 2004
ACQ4 Has processes for acquiring knowledge about competitors within our industry Gold, Malhotra & Segars, 2006
  KM conversion  
COV1 Has processes for converting knowledge into the design of new products and services Gold, Malhotra & Segars, 2011
COV2 Has processes for converting competitive intelligence into plans of action Gold, Malhotra & Segars, 2012
COV3 Has processes for transferring organizational knowledge to individuals. Gold, Malhotra & Segars, 2014
COV4 Has processes for absorbing knowledge from business partners into the organization Gold, Malhotra & Segars, 2016
COV5 Has processes for distributing knowledge throughout the organization. Gold, Malhotra & Segars, 2017
  KM application  
APP1 Has processes for using knowledge in development of new products/service Gold, Malhotra & Segars, 2022
APP2 Has processes for using knowledge to solve new problems. Gold, Malhotra & Segars, 2023
APP3 Uses knowledge to adjust strategic direction. Gold, Malhotra & Segars, 2025
APP4 Is able to locate and apply knowledge to changing competitive condition Gold, Malhotra & Segars, 2026
APP5 Makes knowledge accessible to those who need it. Gold, Malhotra & Segars, 2027
APP6 Quickly applies knowledge to critical competitive needs. Gold, Malhotra & Segars, 2029
  Pre-cyber incidence agility  
PRC1 We identify and detect cyber anomalies faster than previous  
PRC2 The speed of identifying cyber threats has improved  
PRC3 We identify vulnerabilities faster  
PRC4 We are able to respond faster to changing threat landscape  
  Post-cyber incidence Agility  
POC1 Our speed of responding to potential threat has increased  
POC2 Our incidence recovery rate has improved  
POC3 The time to recover from an incidence has gone down  
  Improved cyber incidence detection and resolution  
CID1 We have reduced the amount of time to collect and analyze data that are relevant to investigations  
CID2 We have reduced amount of time it takes to resolve cyber incidences  
CID3 We have reduced risk of cybertheft by reducing the number of false positives  
CID4 Our threat detection rate has gone up  
  Reduced business impacts  
RBI1 Cyber Incidences has less impacts on our productivity  
RBI2 Financial loss from cyber incidence has reduced considerably  
RBI3 Risk to theft of intellectual property by cyber criminals have dropped  
RBI4 Overall the business impacts of breaches have reduced significantly  
  Big Data Analytics Adoption measurements  
BDA1 We have access to very large, unstructured, or fast-moving data for analysis Davenport (2014)
BDA2 We integrate data from multiple internal sources into a data warehouse or mart for easy access Davenport (2014)
BDA3 We have explored or adopted parallel computing approaches (e.g. Hadoop) to big data processing Davenport (2014)
BDA4 We have explored or adopted different data visualization tools Davenport (2014)
BDA5 We have explored or adopted new forms of databases such as Not Only SQL (NoSQL) for storing data Gordon (2007)
BDA6 We have explored or adopted open source software for big data analytics Davenport (2014)