Application of Algorithmic Cognitive Decision Trust Modeling for Cyber Security Within Organisations

Abstract

Cybercrime continues to cause increasing threat to business processes, eroding stakeholders' trust in internet technologies. In this research paper, we explore how six dominant algorithmic trust positions facilitate cognitive processing, which, in turn, can influence an organisation's productivity and align its values and support structures for combating cybercrimes. This conceptual paper uses a cognitive perspective described as a Throughput Model. This modeling perspective captures several dominant algorithmic trust positions for organisations, providing a new and powerful approach which seeks to enhance our understanding of the cognitive representation of decision-making processes. These trust positions are (1) rational-based trust, (2) rule-based trust, (3) category-based trust, (4) third-party based trust, (5) role-based trust, and (6) knowledge-based trust. Finally, we provide conclusions and implications for future research.

Research on Sports Training Decision Support System Based on Improved Association Rules Algorithm

“…Many experts have proposed methods of applying technology in the field of scientific training centers, mainly for personalized information recommendation services in foreign university training halls, and these methods have achieved good results [8]. In recent years, foreign data mining technology has become more and more mature, and its application in the training hall has also been well studied [9].…”

Advertising Benefits from Ethical Artificial Intelligence Algorithmic Purchase Decision Pathways

“…According to the six dominant purchase decision pathways in Table 1, AI ethical considerations can support marketers in choosing the appropriate messages for potential consumers (Rodgers et al, 2020). That is, each customer has…”

Artificial intelligence-driven music biometrics influencing customers’ retail buying behavior

“…The term "artificial intelligence" describes computing systems that exhibit some form of human intelligence. It covers many interlinked technologies, including data mining, machine learning, speech recognition, image recognition, and sentiment analysis (Ferraris, Mazzoleni, Devalle & Couturier, 2019;National Science & Technology Council, 2016;Olson & Levy, 2018;Rodgers, 2020;Rodgers, Attah-Boakye, & Adams, 2020;Rialti, Zollo, Ferraris, & Alon, 2019).…”