User Acceptance of Information Technology

1. 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

2. 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

3. ABSTRACT

4. ABSTRACT
(1) Review user acceptance literature and
discuss eight prominent model
1. 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

5. 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

6. 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

7. ABSTRACT
(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
7

8. INTRODUCTION
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.
8

9. DESCRIPTION OF MODELS
AND CONSTRUCT
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.
9

10. DESCRIPTION OF MODELS
AND CONSTRUCT
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
10

11. TABLE 1. THEORY OF
REASONED ACTION (TRA)
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
11

12. TABLE 1. TECHNOLOGY
ACCEPTANCE MODEL (TAM)
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
12

13. TABLE 1. TECHNOLOGY
ACCEPTANCE MODEL (TAM)
13

14. TABLE 1. TECHNOLOGY
ACCEPTANCE MODEL
2(TAM2)
Social Influence Processes
Cognitive Instrumental Processes
14

15. TABLE 1. MOTIVATIONAL
MODEL (MM)
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”
15

16. TABLE 1. THEORY OF
PLANNED BEHAVIOR (TPB)
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
16

17. TABLE 1. THEORY OF
PLANNED BEHAVIOR (TPB)
17

18. TABLE 1. C-TAM-TPB
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
18

19. TABLE 1. C-TAM-TPB
19

20. TABLE 1.MODEL OF PC
UTILIZATION (MPCU)
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
20

21. 21
TABLE 1.MODEL OF PC
UTILIZATION (MPCU)
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

22. TABLE 1. INNOVATION
DIFFUSION THEORY (IDT)
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
22

23. TABLE 1. INNOVATION
DIFFUSION THEORY (IDT)
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
23

24. INNOVATION DIFFUSION
THEORY STRUCTURE
創新的相對優勢個人形象的提升
創新的
採納
創新的自願性
創新的可試用性
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
24

25. SOCIAL COGNITIVE
THEORY STRUCTURE
BEHAVIOR
ENVIRONMENTA
L
PERSONAL
25

26. 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.
Affect
An individual’s liking for a particular
behavior
Anxiety
Evoking anxious or emotional reactions
when it comes to performing a behavior

27. KEY MODERATING
VARIABLES
1. Experience
2. Voluntariness
3. Gender
4. Age
27

28. TABLE 2. ROLE OF
MODERATORS IN EXISTING
MODELS
Model Experience Voluntariness Gender Age
TRA
More 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
28

29. TABLE 2. ROLE OF
MODERATORS IN EXISTING
MODELS
Model Experience
Voluntarines
s Gender Age
TPB
More experience
Subjective norm ↓
Less
voluntary
Subjective
norm↑
Men
Attitude ↑
Women in the early
stages of
experience
Subjective norm ↑
Perceived behavioral
control ↑
Younger
workers
Attitude ↑
Older workers
Perceived
behavioral
control↑
Older women
Subjective norm ↑
Combine
d
TAM-TPB
More experience
Perceived
usefulness↑
Attitude toward
behavior ↑
Perceived
behavioral control ↑
Subjective norm ↓
N/A N/A N/A
29

30. TABLE 2. ROLE OF
MODERATORS IN EXISTING
MODELS
Model Experience Voluntariness
Gende
r Age
MPCU
Less experience
Complexity ↑
Affect toward use ↑
Social factors ↑
Facilitating conditions ↑
More experience
Long-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
30

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
214 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

32. 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
214 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

33. PRIOR MODEL TESTS AND
MODEL COMPARISONS
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
33

34. PRIOR MODEL TESTS AND
MODEL COMPARISONS
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
34

35. EMPIRICAL
COMPARISON OF
THE EIGHT
MODELS
• Settings and
Participants
• Measurement
• Results
35

36. SETTINGS AND
PARTICIPANTS
We sampled for heterogeneity across :
1. technologies
2. organizations
3. industries
4. business functions
And nature of use :
voluntary vs. mandatory
36

37. SETTINGS AND
PARTICIPANTS
37

38. SETTINGS AND
PARTICIPANTS
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
38

39. MEASUREMENT
 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.
39

40. RESULTS - USING PARTIAL
LEAST SQUARES (PLS)
40

41. RESULTS - USING PARTIAL
LEAST SQUARES (PLS)
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.
41

42. RESULTS - USING PARTIAL
LEAST SQUARES (PLS)
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)
42

43. RESULTS - USING PARTIAL
LEAST SQUARES (PLS)
There is an increase
in the variance
explained in the
case of TAM2
43

44. RESULTS - USING PARTIAL
LEAST SQUARES (PLS)
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.
44

45. RESULTS - USING PARTIAL
LEAST SQUARES (PLS)
1. There was at least one construct that was
significant in all time periods.
2. Several other constructs were initially
significant, but then became nonsignificant
over 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)
45

46. FORMULATION OF
THE UNIFIED
THEORY OF
ACCEPTANCE AND
USE OF
TECHNOLOGY
• UTAUT

47. Direct
Determinants
• Performance
expectancy
• Effort
expectancy
Indirect
Determinants
• Self-efficacy
• Anxiety
• Attitude toward
using
Key
moderators
• Gender
• Age
• Voluntariness
• experience
47
THE UTAUT RESEARCH
MODEL

48. UTAUT RESEARCH MODEL
48

49. 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 Usefulness
TAM/TAM2/C-TAM-TPB
Extrinsic Motivation MM
Job-fit MPCU
Relative Advantage IDT
Outcome Expectations SCT

50. 50
TABLE 9. FIVE
CONSTRUCTS OF
PERFORMANCE
EXPECTANCY

51. 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

52. 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

53. EFFORT EXPECTANCY
• Definition
The degree of ease of associated with the use of
system
53
Construct Source Model
Perceived ease of use TAM/TAM2
Complexity MPCU
Ease of use IDT

54. TABLE 10. THREE OF
EFFORT EXPECTANCY
54

55. EFFORT EXPECTANCY
It has three moderating variables with
gender, 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

56. 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

57. 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 Norm
TRA, TAM2, TPB/DTPB
and C-TAM-TPB
Social Factors MPCU
Image IDT
57

58. 58

59. SOCIAL INFLUENCE
T1 T2 T3
59
In Voluntary
Settings
Nonsignificant Nonsignificant Nonsignificant
In Mandatory
Settings
Significant Significant Nonsignificant
 Experience and Voluntariness of use are moderating
variables.

60. 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

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

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
control
TPB/DTPB, C-TAM-TPB
Facilitating conditions MPCU
Compatibility IDT
63

63. 64

64. FACILITATING CONDITIONS
T1 T2 T3
In Voluntary
Settings
Significant
Nonsignifica
nt
Nonsignifica
nt
In
Mandatory
Settings
Significant
Nonsignifica
nt
Nonsignifica
nt
65
Perceived behavioral control

65. 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

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

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

68. CONSTRUCTS THEORIZED
NOT TO BE DIRECT
DETERMINANTS OF
INTENTION
1. Self-efficacy
2. Anxiety
3. Attitude toward using technology
69

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

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

71. ATTITUDE TOWARD USING
TECHNOLOGY
• Definition :
An individual’s overall affective reaction to using a system.
Construct Source Model
Attitude toward behavior
TRA,TPB/DTPB, C-TAM-
TPB
Intrinsic motivation MM
Affect toward use MPCU
Affect SCT
72

72. 73

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

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

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

76. EMPIRICAL
VALIDATION OF
UTAUT
• Preliminary Test of
UTAUT
• Cross-Validation of
UTAUT
77

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%)

78. 79

80. PERFORMANCE
EXPECTANCY
- FIVE CONSTRUCTS
U1-6
JF1-6
81

81. RA1-5
OE1-7
82

82. 83

83. 84

84. 85

85. 86

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

87. SUPPORTING H1
88
H1 Effect stronger for men
and younger worker

88. PRELIMINARY TEST OF
UTAUT
89
Effort expectancy
 The effect was moderated by gender and age,
and effect decreasing with experience

89. SUPPORTING H2
90
H2
Effect stronger for
women, older
worker,and those with
limited experience

90. 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

91. SUPPORTING H3
92
H3
Effect stronger for
women, older worker,
under conditions of
mandatory use, and
with limited experience

92. PRELIMINARY TEST OF
UTAUT
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
93

93. SUPPORTING H4B
94
H4b
Effect stronger for older
worker with increasing
experience

94. PRELIMINARY TEST OF
UTAUT
95
Behavioral intention
 In predicting usage behavior, the effect of behavior
intention were significant

95. SUPPORTING H6
96
Direct effect
H6

96. CROSS-VALIDATION OF
UTAUT
97

97. 98

98. CROSS-VALIDATION OF
UTAUT
99

99. CROSS-VALIDATION OF
UTAUT
100

100. 101

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

102. 103

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

104. Q&A