Mobile Learning for Just-In-Time Knowledge Acquisition at the Science Museum Group

The Science Museum Group (SMG) Service Desk team in the United Kingdom (UK) faces the challenges of Service Level Agreement (SLA) breaches. Furthermore, the museum sector suffers significant reductions in funding made by a major sponsor in the UK. Thus, ICT Service desk staff are required to manage incidents and other demands with minimal resources. To address this problem, this paper recommends serving just-in-time knowledge in the form of knowledge articles that are also responsive to mobile devices to service users. This offering could reduce ICT support calls, increase productivity for both service desk staffs and the service user. Moreover, it presents an opportunity to develop functional technical knowledge among non-ICT SMG staff. The use of knowledge articles log files and ICT incident report log files were used to find out which staff are more likely to read knowledge articles or report ICT incidents for the purpose of targeting those staff with the just-in-time knowledge articles.
As with any technological change, challenges are pervasive in technological adoption. This study uses the unified theory of acceptance and use of technology (UTAUT) model to explain the determinants of mLearning adoption at SMG. The current study makes an original contribution to theory and practice by broadening the body of knowledge pertaining to understanding the factors contributing to mLearning adoption and its potential use for just-in-time knowledge acquisition for staff in a UK Museum context.
The results from this study indicate that the UTAUT constructs Performance expectancy, Effort expectancy, Social influence and Facilitating conditions are all significant determinants of behavioural intention to use mLearning. Surprisingly, the newly proposed construct, Self-determined learning was not a significant determinant of behaviour intentions. Further examination found age and gender moderate the relationship between the UTAUT constructs. These findings present several beneficial implications for mLearning research and practice at SMG and in a wider context. For example, to inform a broader set of technical adoption research and strategy.