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
• 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)
• 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)
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 determinant
Definition
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
Position
Code
Number & Percentage
Library Manager
D1, K1, N1
3 (30)
Systems Librarian
A2, E2, H2
3 (30)
Qualified Librarian
E3, F3, H3, N3
4 (40)
Informing Use Behaviour: Impact of Adoption of New
Technologies upon Workforce Attitude - Perceived
Benefits for Patrons by Construct Mapping
MC
DC
Service
Service
Voluntariness
Technology
Length
Experience
of Use
Competence
n/a
2 competent
(Position)
Performance
1 shorter
2 Systems
Expectancy
1 medium
1 Lbn
1 comp/advanced
1 greater
Effort
1 greater
1 Lbn
n/a
1 average
Social
2 medium
1 LM
3 voluntary
1 beginner
Influence
1 greater
2 Lbn
Expectancy
1 average
1 competent
Facilitating
1 shorter
n/a
n/a
1 competent
Conditions
1 medium
1 comp/advanced
1 greater
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.
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