Unified Theory of Acceptance and Use of Technology and the VET sector Sarah-Jane Saravani Shar-e-Fest, Hamilton, 11 October, 2013

Investigation Competencies required of vocational education and training (VET) sector library staff in Australia and New Zealand to deliver services to mobile technologies

Specific Objectives Investigate library staff confidence in using mobile technologies Determine the skills and knowledge required by library staff to develop library services to mobile technologies Examine professional development opportunities available to library staff Determine preferred method of library staff engaging in professional development Examine the usefulness of applying a research model of technology acceptance to predict library staff mobile technologies usage

Technology acceptance models Rogers, the Innovation Diffusion Theory (IDT) Theory of Reasoned Action (TRA, Fishbein & Ajzen) 1986, Theory of Planned Behavior (TPB, Ajzen & Madden) Technology Acceptance Model (TAM, Davis) Model of PC Utilization (MPCU, Thompson, Higgins, & Howell)

Motivational Model (MM, Davis, Bagozzi, & Warshaw) Combined Theory of Planned Behavior/Technology Acceptance Model (C-TPB-TAM, Taylor & Todd) 1986/ Social Cognitive Theory (SCT, Bandura, 1986; Compeau & Higgins, 1995)

UTAUT Adapted from “User Acceptance of Information Technology: Toward a Unified View,” by V. Venkatesh, M. G. Morris, G. B. Davis and F. D. Davis, 2003, MIS Quarterly, 27(3), p. 447.

UTAUT - modified

Determinant constructs UTAUT determinantDefinition Performance Expectancy (PE) Degree to which an individual believes that using the system will help him/her to attain gains in job performance Effort Expectancy (EE)Degree of ease associated with use of the system Social Influence (SI) Degree to which an individual perceives that important others believe he/she should use the new system Facilitating Conditions (FC)Degree to which an individual believes that an organisational and technical infrastructure exists to support the system

Moderator constructs Service length Service experience (position) Voluntariness of use Technology competence

Hypotheses H1. The influence of performance expectancy on behavioural intention will be moderated by service length, service experience and technology competence, such that the effect will be stronger for shorter service length, for service experience that excludes the position of Library Manager, and for greater technology competence.

H2. The influence of effort expectancy on behavioural intention will be moderated by service length, service experience and technology competence, such that the effect will be stronger for greater service length, for service experience that excludes the position of Systems Librarian, and for lesser technology competence.

H3. The influence of social influence on behavioural intention will be moderated by service length, service experience, voluntariness of use and technology competence, such that the effect will be stronger for shorter service length, for service experience that excludes the position of Library Manager, particularly in mandatory situations and for lesser technology competence.

H4. The influence of facilitating conditions on use behaviour will be moderated by service length and technology competence, such that the effect will be stronger for greater service length and greater technology competence.

H5. Behavioural Intention (independent variable) will have an influence on mobile technology usage (dependent variable) H6. Use Behaviour (independent variable) will have a direct influence on Service Delivery to mobile technologies (dependent variable).

Informing Use Behaviour: Impact of Adoption of New Technologies upon Workforce Attitude - Perceived Benefits for Patrons Performance expectancy (3 responses) H2: Staff like to see improvements in technology, from a professional point of view - it means they are improving services to their customers. N3: Others are focussed on customer service and can help students. It is an advantage to them that they do not feel stupid and can help someone. They are feeling empowered and can make a difference – they don't have to wait for ITS to help. They can show the students instead themselves. Effort expectancy (1 responses) F3: Other areas have implemented it when they realised it was something that could be done - interloans and... students. Social influence (3 responses) E3: The main feedback from students is positive, this makes the staff feel good about what they are doing. They are very positive about the changes. H3: I feel more fulfilled being able to assist the distance students. We feel that we are not stagnant, we are moving ahead, learning. Facilitating conditions (3 responses) E2: It is not a lack of adoption of new technologies as such. We haven’t adopted hardware for students – we have gone down a virtual library route.... Online learning is promoted. A lot of our courses are either blended or online N1: It has given staff a more creative outlet, we need to keep relevant in the educational area otherwise we become a dinosaur.

Informing Use Behaviour: Impact of Adoption of New Technologies upon Workforce Attitude - Perceived Benefits for Patrons by Position PositionCodeNumber & Percentage Library ManagerD1, K1, N13 (30) Systems LibrarianA2, E2, H23 (30) Qualified LibrarianE3, F3, H3, N34 (40)

Informing Use Behaviour: Impact of Adoption of New Technologies upon Workforce Attitude - Perceived Benefits for Patrons by Construct Mapping DC MC Service Length Service Experience (Position) Voluntariness of Use Technology Competence Performance Expectancy 1 shorter 1 medium 1 greater 2 Systems 1 Lbn n/a 2 competent 1 comp/advanced Effort Expectancy 1 greater1 Lbnn/a1 average Social Influence 2 medium 1 greater 1 LM 2 Lbn 3 voluntary 1 beginner 1 average 1 competent Facilitating Conditions 1 shorter 1 medium 1 greater n/a 1 competent 1 comp/advanced 1 advanced

Hypotheses results H1. Result: Effect spread evenly across service length (Partially Supported), for service experience excluding Library Manager (Supported) and for greater technology competence (Supported). H2. Result: Effect stronger for greater service length (Supported), for service experience that includes Librarian (Supported) and for lesser technology competence (Supported). H3. Result: Effect is stronger for medium to greater service length (Unsupported), for service experience that excludes Systems Librarian (Unsupported), for voluntary situations (Unsupported) and is spread evenly across lesser and greater technology competence (Partially Supported). H4. Result: Effect is equal across service length (Partially Supported), and stronger for greater technology competence (Supported).

Post-mortem The model proved useful in testing the majority of the coded data, problems of reduced reliability occurred when participants were asked to assess external variables, such as perceived student response. The four hypotheses accompanying the model generated full and highly-detailed results. However, in many cases the findings that emerged did not support the hypotheses. This is illustrative of the complexity of the factors influencing technology acceptance and associated outcomes and the difficulties of any single model fully addressing such complexities.