ChatGPT for information search: investigating the influence of adaptive behaviour and search experience on individuals behavioural intentions

This study aims to unravel individuals’ technology and task adaptation behaviours, together with search experience constructs such as perceived diagnosticity and serendipity, in shaping behavioural intentions to use ChatGPT for information search. It specifically examines the duality of technology and task adaptation through exploitative and exploratory dimensions, respectively. Building on the extended Adaptive Structuration Theory for Individuals (ASTI), this study integrates social influence and search experience theories into a unified explanatory model. A structural equation modelling (SEM) approach was used to evaluate the proposed relationships. Data were collected via the Netquest panel using an online survey administered to 400 active ChatGPT users in Spain. The survey used validated measurement scales to capture social influence, individual characteristics, technology, task adaptation behaviours, perceived diagnosticity, and serendipity. The research model and hypotheses were assessed using partial least squares structural equation modelling (PLS-SEM). The results reveal a dual structuration of behavioural intentions, where exploitative technology and task adaptation behaviours are primarily driven by perceived diagnosticity, whereas exploratory technology and task adaptation are explained by perceived serendipity. In addition, social influence significantly affects both exploitative and exploratory technology-adaptation behaviour.