Impact of Gender and Nationality on Acceptance of a Digital Library: An Empirical Validation of Nationality Based UTAUT Using SEM

Abstract

Electronic Library Systems (ELS) has become an inevitable part of educational institutions. Though millions of dollars are spent building and developing these systems, research findings indicates that millions of potential users may still be ignoring them. Consequently, different Technology Acceptance Models have been applied towards understanding the effect of various factors on end users acceptance of Information Systems (IS). Gender has been found to be an important factor and, as such, has attracted a lot of attention from the research community. Our research, however, recognized the existence of a different cohort group of users of ELS with different usage behavior not just based on gender but also based on nationality and, therefore, developed a Nationality Based UTAUT (NUTAUT) adapted from UTAUT to account for the effect of gender and nationality on acceptance, simultaneously. Nationality was introduced based on the assumption that the UTAUT independent constructs will impact on acceptance and usage differently when moderated by gender and nationality simultaneously. The result obtained from the analysis of data collected from 116 student participants provides support for NUTAUT by showing that the various UTAUT constructs exert varying degree of effects when moderated by gender and nationality combined. It not only confirms the NUTAUT robustness in predicting acceptance of both Male and Female Students (91 % and 85 % respectively) but also determines the importance of each independent construct to each group, based on their nationality and gender. Social Influence was found to be significant for both international males and females while effort expectancy is a more significant factor for

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 50 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.