People’s expectations and experiences of big data collection in the Saudi context

Abstract

Big data and machine learning technologies facilitate various business intelligence activities for businesses. However, personal data collection can generate adverse effects on consumers. Big data collection can compromise people’s sense of autonomy, harming digital privacy, transparency and trust. This research investigates personal data collection, control, awareness, and privacy regulation on people’s autonomy in Saudi. This study used a hybrid analytical model that incorporates symmetrical and asymmetrical analysis via fuzzy set qualitative comparative analysis (fsQCA) to analyze consumer sense of autonomy regarding big data collection. The symmetrical shows that ‘Control’ had the most significant influence on people’s autonomy, followed by ‘Big data collection’ and ‘Awareness’. The fsQCA shows 84% of the variation, explaining the people’s autonomy.

Determinants of continuous intention to use e-government services: an extension of technology continuance theory

“…Finally, the researchers in this study used net effect modelling, also known as a symmetrical method, to investigate the phenomena under investigation, which, as is often the case in studies of human behaviour, fails to reveal its intricacies. Therefore, researchers have recommended an asymmetrical strategy (Pappas and Woodside, 2021;Al-Emran et al, 2023;Abbasi et al, 2022aAbbasi et al, , 2022bBinsawad et al, 2022) to uncover the full complexity of human behaviour.…”

Effects of Big Data Analytics in Learning Management Systems for Improving Learners’ Academic Success

“…In other words, it is important to transfer from focusing solely on the average student to individualized attention based on students' interaction data in the digital environment (Saeed;Al Aghbari;Alsharidah, 2020;Aguilar, 2018). Technology of big data analysis may maximize the benefits of learning management systems (LMSs) and, in the same time, enhance the learning experience by making timely data-driven decisions (Binsawad;Abbasi;Sohaib, 2022;Cantabella et al, 2019). Consequently, the learner's behavior in LMS will generate vast data, which can aid academic decision-making process and impact the learners' academic motivation and learning styles (Aguilar, 2018).…”