Impact of modality on cognitive load and data-driven decisions as perceived by novice users

With rapid technological advancement, firms are required to make data-driven decisions to gain a competitive advantage. To transform data into knowledge and information the interaction of human cognitive functions is required. This has led to decision-support systems which reduce cognitive errors and use data in the decision-making process. The cognitive load theory discusses the modality effect and indicates that learning through visualization alongside spoken text is more effective than learning from visualizations with written text. There are limited studies that discuss the modality effect on cognitive load and the adoption of data-driven decisions by novice data users, particularly in the context of Oman. Thus, the main aim of this study is to examine the modality effect on cognitive load and data-driven decisions as perceived by novice users. The psychometric instrument was applied to examine the cognitive load associated with data and decision characteristics, and the dual-coding theory was used to select two modality conditions (data plus visual-written text and data plus visual-spoken text) and effect. The results showed that 73.1% of the participants perceived that the modality effect becomes more significant when the data information is presented with auditory learning mode. The regression results indicated that modality has a statistical significance on cognitive load. This implies that a higher data cognitive load has an adverse effect on the quality of decision-making. The findings of this study suggest that to support high-load human-data interaction, human cognition principles relating to modality must be incorporated into the data-driven decision-making process to enable novice users to learn and perform like experts.