Beyond technical performance: Preliminary findings on algorithm aversion in digital library environments

Abstract

Introduction. This study examines algorithm aversion in digital library contexts, where users increasingly encounter algorithm-enabled services. Prior research in healthcare and finance shows that users often resist algorithmic systems even when they outperform human alternatives, yet little is known about how this phenomenon manifests in the digital libraries context. Method. As part of an ongoing project, we conducted semi-structured interviews with 18 users from two Chinese universities. Participants were selected through theoretical sampling. Interviews were analysed using constructivist grounded theory. Analysis. Open and axial coding of the first 18 interviews produced 42 initial concepts organised into 16 categories under five dimensions: algorithmic, platform, individual, task, and cognitive factors. Results. Preliminary findings suggest that algorithm aversion in digital libraries is multidimensional. Users reported concerns about inaccurate or irrelevant results, cumbersome interfaces, lack of familiarity, and limited trust in recommendations. These patterns suggest that aversion is shaped by both technical shortcomings and user-level factors, leading to frustration, avoidance, and reliance on manual methods. Conclusion(s). These early insights extend algorithm aversion research into a complex service environment, offering evidence that resistance arises from the interplay of technical, individual, and contextual conditions. The ongoing study will expand the dataset and employ DEMATEL to identify causal relationships among factors.

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