Exploring librarians’ learning motivation through generative AI chatbots: a self-determination theory approach

Purpose This study aims to assess the influence of generative artificial intelligence (GenAI) chatbot usage on autonomy (AT), competency (CMP) and relatedness (RLT), along with the effect of AT, CMP and RLT on library and information science (LIS) professionals’ learning motivation to use GenAI chatbots. Design/methodology/approach It opted quantitative research design based on a nonprobability survey of 300 working librarians in Punjab province. A Likert scale-based questionnaire was developed based on self-determination theory (SDT), and data were collected online with a 68% response rate. Findings Findings revealed that LIS professionals are autonomous, and they feel RLT in using GenAI chatbots. Library professionals’ usage of GenAI chatbots is not significantly related to CMP. Respondents feel a sense of AT, CMP and RLT in learning motivation using GenAI chatbots. Data showed that AT, CMP and RLT have a positive relationship with motivation (MTV). The effect of AT and RLT is also found to be positive on CMP. Practical implications This study has many theoretical and practical implications. Theoretically, the SDT model can be combined with other theories (cognitive load theory, goal setting theory and expectancy theory) to create a new framework to assess the effect of different AI tools on the learning motivation of diverse user groups. Practically, these findings can help the administration understand librarians’ learning motivation and support informed decision-making. Originality/value It will help identify tech-savvy librarians and organizations to incorporate chatbots into routine library tasks to become more familiar with their use.