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Empirical examination of ai-powered decision support systems: ensuring trust and transparency in information and knowledge security

Żywiołek Justyna

Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska

2024

z. 197

679--695

Purpose: The aim of this publication is to present the applications of the use of decision support systems using artificial intelligence to ensure trust in information and knowledge. Design/methodology/approach: Literature analysis based on the Scopus database, taken into account from 2020 and 2024. Using this analysis, research criteria were developed and 11,315 surveys were conducted among owners and managers of small or medium-sized companies. The survey was conducted using the CAWI method, and respondents rated as many as 5 segments on a Likert scale. Findings: The manuscript presents an empirical analysis of AI-powered decision support systems (DSS) and their role in ensuring trust and transparency in information and knowledge security. By examining 11,315 surveys from small and medium-sized business owners and managers, the study investigates how these AI systems influence trust in information security practices. The research fills a gap in the literature by providing a comprehensive empirical analysis of AI-based DSS, highlighting their technological intricacies and social implications, and proposing solutions to enhance trustworthiness and decision-making processes in AI applications. Research limitations/implications: The study has several research limitations. Firstly, it was conducted exclusively in Poland, focusing on small and medium-sized enterprises, which may limit the generalizability of the findings to other geographical regions or larger organizations. Additionally, the sampling technique employed was non-random and based on the researcher's subjective judgment, potentially introducing bias and affecting the representativeness of the sample. The survey method used, Computer-Assisted Web Interviewing (CAWI), and the reliance on self-reported data may lead to response bias, impacting the accuracy of the results. In terms of research implications, the study offers practical solutions to enhance trustworthiness and decision-making processes in AI applications, which can be beneficial for organizations looking to implement AI-driven decision support systems. It also highlights various deficiencies in existing studies, suggesting future research directions, such as investigating the long-term effects of AI on organizational frameworks, exploring ethical implications, and developing new theoretical models. Originality/value: The manuscript fills the gap in the analysis of AI-based decision support systems for the area of trust in the security of information and knowledge resources.