Employees’ trust in information systems is crucial for their successful adoption. This study examines potential trust antecedents, including personal factors, system factors (reliability, credibility, usability, design aesthetics), and contextual and service structures (support, participation, abilities of involved individuals). We conducted a longitudinal field study with four measurement points in four companies introducing new information systems (N = 157). Results revealed that trust in information systems was positively correlated with employees’ reported performance and cognitive resources, and negatively with strain. Multigroup latent growth curve analyses identified three distinct trust trajectories: positive (linear increase), stable, and negative (linear decrease). Employees in companies with positive trust trajectories perceived system properties and contextual and service structures more favourably than those with stable or negative trust trajectories. However, consistently across time points, only system properties were positively associated with trust. Follow-up interviews highlighted organisational strategies to foster trust, including selection, customisation, and continuous improvement of information systems. Emphasising the pivotal role of trust in the adoption of these systems, our research advances the understanding of trust dynamics over extended periods. Practically, this study provides strategies for organisations to support employees in developing trust in information systems, moving beyond mere reliance on the systems’ inherent properties.

For users to adopt information systems, they must develop trust in such systems. Even though trust theories consistently define trust as dynamic, the development of trust over time has received little empirical attention. The present study examined the development of trust in a newly introduced information system and its association with antecedents related to the individual (e.g. disposition to trust), the information system (e.g. reliability), and the context (e.g. support) at different time points. We further assessed users’ reliance, performance, and well-being as outcomes of trust. Employees (N = 313) of a German public university assessed a newly introduced invoice processing system on four occasions (before system launch, after initial use, five months after launch, ten months after launch). Results from latent growth curve modelling show a non-linear increase of trust in the information system over time with changing predictors: Person factors were stronger predictors of trust in early phases, whereas system characteristics were stronger predictors later in the process. Moreover, users’ trust in the information system correlated positively with reliance, performance, and well-being. Our results highlight the central role of trust for the successful adoption of information systems at work, and offer specific suggestions for their building and maintenance.

The aesthetic appeal of a website has strong effects on users’ reactions, appraisals, and even behaviors. However, evaluating website aesthetics through user ratings is resource intensive, and extant models to predict website aesthetics are limited in performance and ability. We contribute a novel and more precise approach to predict website aesthetics that considers rating distributions. Moreover, we use this approach as a baseline model to illustrate how future research might be conducted using predictions instead of participants. Our approach is based on a deep convolutional neural network model and uses innovations in the field of image aesthetic prediction. It was trained with the dataset from Reinecke and Gajos [2014] and was validated using two independent large datasets. The final model reached an unprecedented cross-validated correlation between the ground truth and predicted rating of LCC = 0.752. We then used the model to successfully replicate prior findings and conduct original research as an illustration for AI-based research.