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Unified Theory of Acceptance and Use of Technology and the VET sector

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

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Unified Theory of Acceptance and Use of Technology and the VET sector

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

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

  3. 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

  4. Technology acceptance models • 1963 - Rogers, the Innovation Diffusion Theory (IDT) • 1975 - Theory of Reasoned Action (TRA, Fishbein & Ajzen) • 1986, 1991 - Theory of Planned Behavior (TPB, Ajzen & Madden) • 1989 - Technology Acceptance Model (TAM, Davis) • 1991 - Model of PC Utilization (MPCU, Thompson, Higgins, & Howell)

  5. 1992 - Motivational Model (MM, Davis, Bagozzi, & Warshaw) • 1995 - Combined Theory of Planned Behavior/Technology Acceptance Model (C-TPB-TAM, Taylor & Todd) • 1986/1995 - Social Cognitive Theory (SCT, Bandura, 1986; Compeau & Higgins, 1995)

  6. 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.

  7. UTAUT - modified

  8. Determinant constructs

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

  10. 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.

  11. 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.

  12. 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.

  13. 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.

  14. 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).

  15. Informing Use Behaviour: Impact of Adoption of New Technologies upon Workforce Attitude - Perceived Benefits for Patrons

  16. Informing Use Behaviour: Impact of Adoption of New Technologies upon Workforce Attitude - Perceived Benefits for Patrons by Position

  17. Informing Use Behaviour: Impact of Adoption of New Technologies upon Workforce Attitude - Perceived Benefits for Patrons by Construct Mapping

  18. 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).

  19. 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.

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