Investigating librarians’ intention to use artificial intelligence for effective library service delivery: A partial least square-structural equation modeling-based approach

Abstract

Due to the information exploration coupled with the increased number of library users across the globe, servicing the users efficiently and in a simple manner became difficult, leading librarians to think of a better alternative. Artificial intelligence is one of the best options to mitigate issues of inefficient service delivery but has not been used enough in most Nigerian academic libraries and specifically North-eastern Nigerian academic libraries. This research, therefore, aims at examining the factors that can influence the librarians’ intention to use artificial intelligence in their libraries for better and more efficient service delivery to library patrons using the theoretical lenses of the Theory of Plan Behaviour (TPB). A quantitative method using a cross-sectional approach was adopted and a questionnaire was used as an instrument for data collection. Three federal university libraries from North-eastern Nigeria were covered and 242 professionals and para-professionals librarians composed the population of the research. G*Power application was used to estimate the minimum sample size of the research amounting to 119 samples thus, a proportionate stratified random sampling technique was used in obtaining the sampling. Statistical Package for Social Science (SPSS) version 20 and Partial Least Square – Structural Equation Modelling (PLS-SEM) were used to analyze the data. The findings revealed that TPB’s theoretical variables were positively significant factors that influenced the librarians’ intention to use Artificial Intelligence in their respective libraries. Equally, the finding further revealed that the librarians indicated a high intention to use artificial intelligence in their libraries. The use of more advanced theory, the inclusion of more samples and considering a specific artificial intelligence tool were recommended for future research.