An adoption of acceptance model for the multi-purpose system in university library

Abstract

Since previous study offered a more general context of the IT acceptance model, here we place specific emphasis on the library context. Our study examines whether and why the Technology Acceptance Model (T.A.M.) can be used in a multipurpose system (M.P.S.) in a university. The M.P.S. is a system for users to borrow, return and renew books on loan. The variables in this study were modified, such as Perceived Usefulness (P.U.) and Perceived Ease of Use (P.E.O.U.) as independent variables and acceptance of IT as a dependent variable. The sampling technique used was proportionate stratified random, using 98 students of the Faculty of Teacher Training and Education who have implemented the M.P.S. system. Data processing techniques used multiple linear regression analysis with the SPSS data processing tool. The results showed that usefulness and ease of use have significantly positive effect on the M.P.S. acceptance model. The research focused on a university context in single province in Indonesia. Further research may extend the study with a focus on profit or non-profit organisations and different geographical areas.

Technology Acceptance of E-Banking Services in an Unnatural Environment

“…The effect of PU is found to be four times that of PEOU because the relative advantage of the technology is far more than what can be obtained presently in an unnatural environment as long queue continues to surface at the banks. These results corroborate the earlier studies of Isaac et al (2017;2018), Gbongli et al (2019), Ding et al (2019) and Islam, (2011). Similarly, actual system use of the electronic banking system has a significant effect on the extent of satisfaction of the staff of the Federal Medical Centre, Owo.…”

“…The logistic results also disclosed PU is having an odder ratio than the PEOU. In Indonesia, Ding et al (2019) focused on single university-based research and examined the role played by PU and PEOU on the acceptance of IT. The study based on stratified random sampling to select 98 students and multiple linear regression as a statistical technique.…”

Knowledge Data Discovery (Frequent Pattern Growth): The Association Rules for Evergreen Activities on Computer Monitoring

“…Methods here include dimension reduction, such as feature selection, and extraction, and attribute transformation [14]. (5) Choosing the appropriate data mining task. Prediction is often referred to as supervised data mining while descriptive includes the unsupervised and visualization [17].…”

“…( 9) Using the discovered knowledge. The knowledge becomes active in the sense that we may make changes to the system and measure the effects [5,18].…”

“…Data mining is not stand-alone technology. It is an important step in the KDD mainly relating to the extraction and calculation of the patterns of the data being sliced [5,6].…”