Allocation of attention to metadata and retrieval functions: Implications for perceived value and open data discovery and reuse

Abstract

Metadata and retrieval functions play a vital role in aiding researchers in the discovery and reuse of open data. However, the diversity of metadata elements and retrieval functions poses a challenge to data searchers’ limited attentional resources. This study aims to examine the allocation of attention to metadata elements and retrieval functions and its implications for perceived value and intentions to discover and reuse open data by drawing upon the attentional drift-diffusion model, flow theory, and perceived value literature. An experiment with 48 participants was conducted to explore the proposed relationships. Multiple linear regression analysis was performed to analyze the data. The results suggest that researchers’ attention to high-value functions amplifies the perceived value and motivates data discovery intention. Attention to high-value metadata elements motivates data discovery and reuse intention. In contrast, attention to low-value metadata elements hampers the perceived value and inhibits data discovery and reuse intention. These findings put forward a new lens for exploring the attention mechanisms underlying perceived value, data discovery and reuse intention and highlight the important role of the value of metadata and retrieval functions in attention mechanisms. Additionally, this paper identifies the positive effect of perceived ease of use on users’ intentions to find, evaluate, and access open data. Perceived usefulness positively affects users’ intentions to evaluate open data. However, in contrast to perceived intentions to reuse open data assessed by self-reported measures, perceived value is not a salient motivator of open data reuse intention measured by behavioral indicators. These findings reveal the distinct effects of perceived value on perceived intention and intentional action in data reuse. With these insights, this study develops practical strategies to optimize the design of metadata and retrieval functions in data retrieval systems.