user acceptance of information technology
DESCRIPTION
User Acceptance of Information TechnologyTRANSCRIPT
1
USER ACCEPTANCE OF INFORMATION
TECHNOLOGY: TOWARD A UNIFIED
VIEW
B004020003 張家瑄
B004020007 林漪寒
B004020013 羅珮綺
B004020019 周紹文
B004020036 游騰方
B004020047 簡志樺
OUTLINE
2
a. Abstract
b. Empirical Comparison of the Eight Models
c. Formulation of the Unified Theory of
Acceptance and Use of Technology
d. Empirical Validation of UTAUT
ABSTRACT
ABSTRACT
(1) Review user acceptance literature and
discuss eight prominent model1. the Theory of Reasoned Action (TRA)
2. the Technology Acceptance Model (TAM)
3. the Motivational Model (MM)
4. the Theory of Planned Behavior (TPB)
5. a model Combining the Technology Acceptance Model and the Theory of Planned Behavior (C-TAM-TPB)
6. the Model of PC Utilization (MPCU)
7. the Innovation Diffusion Theory (IDT)
8. the Social Cognitive Theory (SCT) 4
ABSTRACT
(2) Empirically compare the eight model and their extensions
1. 17 ~ 53% of the variance in user intention
2. Within-subjects, longitudinal validation and comparison
3. A baseline assessment of the relative explanatory
5
ABSTRACT
(3) Formulate a unified model that integrates elements across the eight models
four core determinants of intention and usage, and four moderators of key relationship
6
(4) Empirically validate the unified model
1. UTAUT outperforms each of the eight original
models
2. UTAUT is cross-validated using data from two new
organization
ABSTRACT
7
a. Since the 1980s, 50 percent investment in organizations used for IT.
b. Technologies to improve productivity, they must be accepted and used by employees in organizations.
c. Research in this area roots in IS, psychology, and sociology.
d. Researchers are confronted with a choice among a multitude of models.
INTRODUCTION
8
a. IS research has long studied how and
why individuals adopt new information
technologies.
b. There have been several streams of
research
One stream of research focuses on individual acceptance of technology about intention or usage.
Other stream have focused on implementation success at the organizational level and task-technology fit.
DESCRIPTION OF MODELS AND CONSTRUCT
9
Figure 1 presents the basic conceptual
explaining individual acceptance of
information technology that forms the basis of
this research.
Figure 1. Basic Concept Underlying User Acceptance Models
DESCRIPTION OF MODELS AND CONSTRUCT
10
Model Core
Constructs
Definitions
Theory of
Reasoned
Action (TRA)
Attitude
Toward
Behavior
individual’s positive or negative feelings
about performing the target behavior
Subjective
Norm
the person’s perception that most
people who are important to him think
he should or should not perform the
behavior in question
TABLE 1. THEORY OF REASONED ACTION (TRA)
11
Model Core
Constructs
Definitions
Technology
Acceptance
Model (TAM)
Perceived
Usefulness
the degree to which a person believes
that using a particular system would
enhance job performance
Perceived
Ease of Use
the degree to which a person believes
that using a particular system would be
free of effort
Subjective
Norm
Adapted from TRA/TPB. Included in
TAM2 only
TABLE 1. TECHNOLOGY ACCEPTANCE MODEL (TAM)
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TABLE 1. TECHNOLOGY ACCEPTANCE MODEL (TAM)
13
Social Influence Processes
Cognitive Instrumental Processes
TABLE 1. TECHNOLOGY ACCEPTANCE MODEL 2(TAM2)
14
Model Core
Constructs
Definitions
Motivational
Model (MM)
Extrinsic
Motivation
The perception that users want to
perform an activity “because it is
perceived to be instrumental in
achieving valued outcomes that are
distinct from the activity itself, such as
improved job performance, pay, or
promotions”
Intrinsic
Motivation
The perception that users want to
perform an activity “for no apparent
reinforcement other than the process of
performing the activity per se”
TABLE 1. MOTIVATIONAL MODEL (MM)
15
Model Core
Constructs
Definitions
Theory of
Planned
Behavior (TPB)
Attitude
Toward
Behavior
Adapted from TRA
Subjective
Norm
Adapted from TRA
Perceived
Behavioral
Control
• the perceived ease or difficulty of
performing the behavior
• In context of IS research, perceptions
of internal and external constraints on
behavior
TABLE 1. THEORY OF PLANNED BEHAVIOR (TPB)
16
TABLE 1. THEORY OF PLANNED BEHAVIOR (TPB)
17
Model Core
Constructs
Definitions
Combined
TAM and TPB
(C-TAM-TPB)
Attitude
Toward
Behavior
Adapted from TRA/TPB
Subjective
Norm
Adapted from TRA/TPB
Perceived
Behavioral
Control
Adapted from TRA/TPB
Perceived
Usefulness
Adapted from TAM
TABLE 1. C-TAM-TPB
18
TABLE 1. C-TAM-TPB
19
Model Core
Constructs
Definitions
Model of PC
Utilization
(MPCU)
Job-fit
the extent to which an individual
believes that using [a technology] can
enhance the performance of his or her
job
Complexity
the degree to which an innovation is
perceived as relatively difficult to
understand and use
Long-term
Consequences
Outcomes that have a pay-off in the
future
TABLE 1.MODEL OF PC UTILIZATION (MPCU)
20
21
Model Core
Constructs
Definitions
Model of PC
Utilization
(MPCU)
Affect Towards
Use
feelings of joy, elation, or pleasure, or
depression, disgust, displeasure, or
hate associated by an individual with a
particular act
Social Factors
the individual’s internalization of the
reference group’s subjective culture
specific interpersonal agreements that
the individual has made with others, in
specific social situations
Facilitating
Conditions
Objective factors in the environment that
observers agree make an act easy to
accomplish
TABLE 1.MODEL OF PC UTILIZATION (MPCU)
Model Core
Constructs
Definitions
Innovation
Diffusion
Theory (IDT)
Relative
Advantage
the degree to which an innovation is
perceived as being better than its
precursor
Ease of Use the degree to which an innovation is
perceived as being difficult to use
Image The degree to which use of an
innovation is perceived to enhance
one’s image or status in one’s social
system
TABLE 1. INNOVATION DIFFUSION THEORY (IDT)
22
Model Core
Constructs
Definitions
Innovation
Diffusion
Theory (IDT)
Visibility The degree to which one can see
others using the system in the
organization
Compatibility the degree to which an innovation is
perceived as being consistent with the
existing values, needs, and past
experiences of potential adopters
Results
Demonstrabilit
y
the tangibility of the results of using the
innovation, including their observability
and communicability
Voluntariness
of Use
the degree to which use of the
innovation is perceived as being
voluntary, or of free will
TABLE 1. INNOVATION DIFFUSION THEORY (IDT)
23
創新的
採納
創新的可試用性
創新的自願性
創新的相對優勢 個人形象的提升
創新的可見度
與既有知識或工作
的相容性
創新本身的複雜性
創新結果的可呈
現性
INNOVATION DIFFUSION THEORY STRUCTURE
24
BEHAVIOR
ENVIRONMENTAL
PERSONAL
SOCIAL COGNITIVE THEORY STRUCTURE
25
TABLE 1. SOCIAL COGNITIVE THEORY (SCT)
26
Model Core
Constructs
Definitions
Social
Cognitive
Theory (SCT)
Outcome
Expectations—
Performance
The performance-related consequences
of the behavior. Specifically, job-related
outcomes
Outcome
Expectations—
Personal
The personal consequences of the
behavior. Specifically, individual esteem
and sense of accomplishment
Self-efficacy
Judgment of one’s ability to use a
technology to accomplish a particular
job or task.
AffectAn individual’s liking for a particular
behavior
AnxietyEvoking anxious or emotional reactions
when it comes to performing a behavior
KEY MODERATING VARIABLES
1. Experience
2. Voluntariness
3. Gender
4. Age
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Model Experience Voluntariness Gender Age
TRAMore experience Attitude ↑
Subjective norm ↓
Less voluntary Subjective norm ↑
N/A N/A
TAM
(and
TAM2)
More experience Ease of use ↓
Within TAM2:
Mandatory and
limited experience Subjective norm ↑
Men Perceived usefulness ↑
Women Perceived ease of use ↑
Women in the early
stages of experience Subjective norm ↑
N/A
MM N/A N/A N/A N/A
TABLE 2. ROLE OF MODERATORS IN EXISTING MODELS
28
Model ExperienceVoluntarines
s Gender Age
TPBMore experience Subjective norm ↓
Less
voluntary Subjective
norm↑
MenAttitude ↑
Women in the early
stages of
experience Subjective norm ↑
Perceived behavioral
control ↑
Younger
workers Attitude ↑
Older workers Perceived
behavioral
control↑
Older womenSubjective norm ↑
Combine
d
TAM-
TPB
More experiencePerceived
usefulness↑
Attitude toward
behavior ↑
Perceived
behavioral control ↑
Subjective norm ↓
N/A N/A N/A
TABLE 2. ROLE OF MODERATORS IN EXISTING MODELS
29
Model Experience VoluntarinessGende
r Age
MPCU
Less experienceComplexity ↑
Affect toward use ↑
Social factors ↑
Facilitating conditions ↑
More experienceLong-term consequences ↑
N/A N/A N/A
IDT
For adoption (no/low experience)Relative advantage, Ease of use,
Trialability, Results demonstrability
and Visibility
For usage (greater experience)Relative advantage and image
Voluntariness was not
tested as a moderator,
but was shown to have
a direct effect on
Intention
N/A N/A
SCT N/A N/A N/A N/A
TABLE 2. ROLE OF MODERATORS IN EXISTING MODELS
30
TABLE 3. REVIEW OF PRIOR MODEL COMPARISONS
Model
Compariso
n
Studies
Theories/
Models
Compare
d
Context of
Study
(Incl.Technolog
y)
Participant
s
Newness of
Technology
Studied
Number of
Points of
Measureme
nt
Cross-
Sectional or
Longitudinal
Analysis
Findings
Davis et al.
(1989)
TRA, TAM • Within-subjects
• intention and
use of a word
processor
107
students
new to the
technology
214 weeks
apart
Cross-
sectional
The variance
in intention
and use TRA:32% ,26%
TAM:47% ,
51%
Mathieson
(1991)
TAM, TPB • Between-
subjects
• intention to use
a spreadsheet
and calculator
262
students
Some
familiarity
with the
technology
1 Cross-
sectional
The variance
in intention
TAM:70%
TPB:62%
Taylor and
Todd
(1995b)
TAM,
TPB/DTP
B
• Within-subjects
• intention to use
a computing
resource center
786
students
Many
students
were
already
familiar with
the center
For a three-
month
period
Cross-
sectional
The variance
in intention
TAM:52%
TPB:57%
DTPB:60%
Plouffe et al.
(2001)
TAM, IDT • Within-subjects
• intention to use
in the context of
a market trial of
an electronic
payment system
using smart card
176
merchants
Survey
administere
d
after 10
months
of use
1 Cross-
sectional
The variance
in intention
TAM:33%
IDT:45%
31
TABLE 3. REVIEW OF PRIOR MODEL COMPARISONS
Model
Compariso
n
Studies
Theories/
Models
Compare
d
Context of
Study
(Incl.Technolog
y)
Participant
s
Newness of
Technology
Studied
Number of
Points of
Measureme
nt
Cross-
Sectional or
Longitudinal
Analysis
Findings
Davis et al.
(1989)
TRA, TAM • Within-subjects
• intention and
use of a word
processor
107
students
new to the
technology
214 weeks
apart
Cross-
sectional
The variance
in intention
and use TRA:32% ,26%
TAM:47% ,
51%
Mathieson
(1991)
TAM, TPB • Between-
subjects
• intention to use
a spreadsheet
and calculator
262
students
Some
familiarity
with the
technology
1 Cross-
sectional
The variance
in intention
TAM:70%
TPB:62%
Taylor and
Todd
(1995b)
TAM,
TPB/DTP
B
• Within-subjects
• intention to use
a computing
resource center
786
students
Many
students
were
already
familiar with
the center
For a three-
month
period
Cross-
sectional
The variance
in intention
TAM:52%
TPB:57%
DTPB:60%
Plouffe et al.
(2001)
TAM, IDT • Within-subjects
• intention to use
in the context of
a market trial of
an electronic
payment system
using smart card
176
merchants
Survey
administere
d
after 10
months
of use
1 Cross-
sectional
The variance
in intention
TAM:33%
IDT:45%
32
Five limitations of these prior model tests and comparisons:
1. Technology studied- Prior : simple, individual-oriented IT
- UTAUT : complex, organizational IT, managerial concern
2. Participants- Prior : most are students
- UTAUT : employees in organizations
3. Timing of measurement- Prior : after the participants’ acceptance or rejection decision
- UTAUT : from the initial introduction to stages of greater experience
PRIOR MODEL TESTS AND MODEL COMPARISONS
33
4. Nature of measurement- Prior : cross-sectional and/or between-subjects
comparisons
- UTAUT : various stages of experience with a new technology and compares all models on all participants
5. Voluntary vs. mandatory contexts- Prior : voluntary usage contexts
- UTAUT : both voluntary and mandatory contexts
PRIOR MODEL TESTS AND MODEL COMPARISONS
34
EMPIRICAL COMPARISON OF
THE EIGHT MODELS
• Settings and
Participants
• Measurement
• Results
35
We sampled for heterogeneity across :
1. technologies
2. organizations
3. industries
4. business functions
And nature of use :
voluntary vs. mandatory
SETTINGS AND PARTICIPANTS
36
SETTINGS AND PARTICIPANTS
37
Measuring constructs from all eight models was administered at three different points in time:
- T1:post-training
- T2:one month after implementation
- T3:three month after implementation
SETTINGS AND PARTICIPANTS
38
A questionnaire was created with items
validated in prior research.
Behavioral intention to use the system was
measured using a three-item scale.
Seven point scales were used for all of the
aforementioned constructs’ measurement.
Actual usage behavior was measured as
duration of use via system logs.
MEASUREMENT
39
RESULTS - USING PARTIAL LEAST SQUARES (PLS)
40
Key findings :
1. Variance in intention explained ranging from 17 percent to 42 percent.
2. Constructs related to social influence were more significant in the Mandatory settings .
3. Some determinants going from significant to nonsignificant with increasing experience.
RESULTS - USING PARTIAL LEAST SQUARES (PLS)
41
The data were pooled across studies and time periods.1. Voluntariness
2. Gender
3. Age
4. Experience
Pooling the data across the three points of measurement
Time1+Time2+Time3 = 215x3= 645(N)
RESULTS - USING PARTIAL LEAST SQUARES (PLS)
42
There is an increase in the variance explained in the case of TAM2
RESULTS - USING PARTIAL LEAST SQUARES (PLS)
43
1. With the exception of MM and SCT, the predictive validity of the models increased after including the moderating variables.
2. The extensions to the various models mostly enhance the predictive validity of the various models beyond the original specifications.
RESULTS - USING PARTIAL LEAST SQUARES (PLS)
44
1. There was at least one construct that was significant in all time periods.
2. Several other constructs were initially significant, but then became nonsignificantover time.
3. The voluntary vs. mandatory context did have an influence on the significance of constructs related to social influence
4. Unified theory of acceptance and use of technology(UTAUT)
RESULTS - USING PARTIAL LEAST SQUARES (PLS)
45
FORMULATION OF THE UNIFIED
THEORY OF ACCEPTANCE AND
USE OF TECHNOLOGY
• UTAUT
Direct Determinants
• Performance expectancy
• Effort expectancy
Indirect Determinants
• Self-efficacy
• Anxiety
• Attitude toward using
Key moderators
• Gender
• Age
• Voluntariness
• experience47
THE UTAUT RESEARCH MODEL
UTAUT RESEARCH MODEL
48
PERFORMANCE EXPECTANCY
• Definition
The degree to which an individual believes that using the
system will help him or her to attain gains in job
performance.
49
Construct Source Model
Perceived UsefulnessTAM/TAM2/C-TAM-
TPB
Extrinsic Motivation MM
Job-fit MPCU
Relative Advantage IDT
Outcome Expectations SCT
50
TABLE 9. FIVE CONSTRUCTS OF PERFORMANCE EXPECTANCY
PERFORMANCE EXPECTANCY
It has two moderating variables with
gender and age.
• Gender:
It has a more significant effect on men.
• Age:
Stronger for Younger workers.
51
PERFORMANCE EXPECTANCY
H1 :
The influence of performance expectancy on behavioral intention
will be
Moderated by 1. Gender
2. Age
Such that the effect will be stronger for
1. men
2. particularly younger men
52
EFFORT EXPECTANCY
• DefinitionThe degree of ease of associated with the use ofsystem
53
Construct Source Model
Perceived ease of use TAM/TAM2
Complexity MPCU
Ease of use IDT
TABLE 10. THREE OF EFFORT EXPECTANCY
54
EFFORT EXPECTANCY
It has three moderating variables withgender, age and experience.
• Gender:
It has a more significant effect on women.
• Age:
It is significant by older worker.
• Experience:
Person has few experience with system.
55
EFFORT EXPECTANCY
H2 :
The influence of effort expectancy on behavioral intention will
be
Moderated by 1. Gender
2. Age
3. Experience
Such that the effect will be stronger for
1. women
2. particularly older workers
3. particularly at the early stages of experience
56
SOCIAL INFLUENCE
• Definition : The degree to which an individual perceives that important others believe he or she should use the new system.
Construct Source Model
Subjective NormTRA, TAM2, TPB/DTPB
and C-TAM-TPB
Social Factors MPCU
Image IDT
57
58
SOCIAL INFLUENCE
59
T1 T2 T3
In Voluntary
SettingsNonsignificant Nonsignificant Nonsignificant
In Mandatory
SettingsSignificant Significant Nonsignificant
Experience and Voluntariness of use are moderating
variables.
SOCIAL INFLUENCE
• Gender : Women tend to be more salient when forming an intension to use technology, with the effect declining with experience.
• Age :Older workers are more likely to place increased salience
on social influences, with the effect declining with
experience.
Gender and Age are moderating
variables 61
SOCIAL INFLUENCE
H3 :
The influence of social influence on behavioral intention will
be
Moderated by 1. Gender
2. Age
3. Voluntariness
4. Experience
Such that the effect will be stronger for
1. women
2. particularly older women
3. particularly in mandatory settings in the early stages of experience
62
FACILITATING CONDITIONS
• Definition : The degree to which an individual believes that an organizational and technical infrastructure exists to support use of the system.
Construct Source Model
Perceived behavioral
controlTPB/DTPB, C-TAM-TPB
Facilitating conditions MPCU
Compatibility IDT
63
64
FACILITATING CONDITIONS
T1 T2 T3
In Voluntary
SettingsSignificant
Nonsignifica
nt
Nonsignifica
nt
In
Mandatory
Settings
SignificantNonsignifica
nt
Nonsignifica
nt
65
Perceived behavioral control
FACILITATING CONDITIONS
When both performance expectancy constructs and effort expectancy constructs are present facilitating conditions becomes non-significant in predicting intention.
H4a:Facilitating conditions will not have a significant influence on
behavioral intention.
66
FACILITATING CONDITIONS
• Experience : The effect will be stronger with increasing experience.
• Age :Older workers attach more importance to receiving help and assistance on the job.
Experience and Age are moderating
variables
67
FACILITATING CONDITIONS
H4b :The influence of facilitating conditions on usage will be
Moderated by 1. Age
2. Experience
Such that the effect will be stronger for 1. Older workers
2. particularly with increasing experience
68
CONSTRUCTS THEORIZED NOT TO BE DIRECT DETERMINANTS OF INTENTION1. Self-efficacy
2. Anxiety
3. Attitude toward using technology
69
SELF-EFFICACY AND ANXIETY
1. Self-efficacy and anxiety have been modeled as
indirect determinants of intention fully mediated
by perceived ease of use
2. We expect self-efficacy and anxiety to behave
similarly, that is , to be distinct from effort
expectancy and to have no direct effect on
intention above and beyond effort expectancy
70
SELF-EFFICACY AND ANXIETY
H5a:Computer self-efficacy will not have a significant influence on
behavioral intention.
H5b:Computer anxiety will not have a significant influence on
behavioral intention.
71
ATTITUDE TOWARD USING TECHNOLOGY
• Definition : An individual’s overall affective reaction to using a system.
Construct Source Model
Attitude toward behaviorTRA,TPB/DTPB, C-
TAM-TPB
Intrinsic motivation MM
Affect toward use MPCU
Affect SCT
72
73
ATTITUDE TOWARD USING TECHNOLOGY
T1 T2 T3
TRA,
TPB/DTPB,
MM
Significant Significant Significant
TAM-TPB,
MPCU, SCT
Nonsignifica
nt
Nonsignifica
nt
Nonsignifica
nt
74
ATTITUDE TOWARD USING TECHNOLOGY
We expect strong relationships in UTAUT between performance expectancy and intention, and between effort expectancy and intention
We believe that attitude toward using technology will not have a direct or interactive influence on intention.
H5c:Attitude toward using technology will not have a significant influence on behavioral intention.
75
BEHAVIORAL INTENTION
Consistent with the underlying theory for all of the intention models discussed in this paper, we expect that behavioral intention will have a significant positive influence on technology usage.
H6:Behavioral intention will have a significant influence on usage.
76
EMPIRICAL VALIDATION OF
UTAUT
• Preliminary Test of
UTAUT
• Cross-Validation of
UTAUT
77
EMPIRICAL VALIDATION OF UTAUT
1. UTAUT was then tested using the original data
and found to outperform the eight individual
models (adjusted R2 of 69%).
2. UTAUT was then confirmed with data from two
new organizations with similar results (adjusted
R2 of 70%)
79
PERFORMANCE EXPECTANCY - FIVE CONSTRUCTS
U1-6
JF1-6
81
RA1-5
OE1-7
82
83
84
85
86
PRELIMINARY TEST OF UTAUT
87
Performance expectancy The effect was moderated by gender and age such that it was more salient to younger worker, particularly men
SUPPORTING H1
88
H1 Effect stronger for men and younger worker
PRELIMINARY TEST OF UTAUT
89
Effort expectancy The effect was moderated by gender and age, and effect decreasing with experience
SUPPORTING H2
90
H2
Effect stronger for women, older
worker,and those with limited experience
PRELIMINARY TEST OF UTAUT
91
Social influence Its role being more important in the context of mandatory use, and more so among older women, more significant in the early stages of individual experience with the technology
SUPPORTING H3
92
H3
Effect stronger for women, older worker,
under conditions of mandatory use, and
with limited experience
PRELIMINARY TEST OF UTAUT
93
Facilitating condition In predicting usage behavior, facilitating conditions were significant, with the latter’s effect being moderated by age (more important to order worker), and with increasing experience
SUPPORTING H4B
94
H4b
Effect stronger for older worker with increasing
experience
PRELIMINARY TEST OF UTAUT
95
Behavioral intention In predicting usage behavior, the effect of behavior intention were significant
SUPPORTING H6
96
H6
Direct effect
CROSS-VALIDATION OF UTAUT
97
98
CROSS-VALIDATION OF UTAUT
99
CROSS-VALIDATION OF UTAUT
100
101
CONTRIBUTION
102
1. UTAUT was able to account for 70 percent of variance
2. Integrate the main 32 effects and 4 moderator into 4 main effects and 4 moderators
103
CONCLUSION
104
1. UTAUT provides a refined view of how the determinants of intention and behavior evolve over time
2. Social influence construct has been controversial
3. Focus on integrating UTAUT with research that has identified causal antecedents of the constructs used within the model
4. Identify and test additional boundary conditions of the model
Q&A