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ANALYZING CUSTOMERS’ ACCEPTANCE TOWARDS TASK
MANAGEMENT APPLICATION USING AFFECTIVE TECHNOLOGY
ACCEPTANCE MODEL (ATAM)
Iwan Setiawan*, Heri Soebana, Maskur
Budi Luhur University, Indonesia
iwansetiawan@gmail.com
PAPER INFO ABSTRACT
Received:
October 2021
Revised:
December 2021
Approved:
December 2021
Background: State the reason why the research needs to be conducted.
Aim: This study aims to explain and analyze the influence of user perceptions
of PT Collega Inti Pratama employees on attitudes and use of Task
Management applications.
Method: This study uses quantitative analysis and data sampling using the
saturated sample method. Samples taken from the respondents’ data are users
of the Task Management application. Data collection techniques using a
questionnaire. In data analysis, researchers used multiple linear regression
analysis techniques and path analysis in proving the mediating variable.
Findings: The results show that simultaneously Positive Affect and Negative
Affect have an effect on Perceived Ease of Use. Perceived Ease of Use,
Positive Affect and Negative Affect influence together on Perceived
Usefulness. Perceived Usefulness and Perceived Ease of Use jointly affect
Attitude Toward Using. Positive Affect, Negative Affect and Attitude
Toward Using together influence Behavioral Intention.
KEYWORDS
Information system, task management, Technology Acceptance Model (TAM),
Affective Technology Acceptance Model (ATAM)
INTRODUCTION
PT. Collega Inti Pratama (Collega) is an Information Technology (IT) company
established since February 3, 2001, has been more engaged and focused on developing banking
technology and has become the Market Leader for Core Banking System at Regional
Development Bank (BPD) throughout Indonesia. As an IT company that develops applications
and systems for banking, Collega implements a standard application development procedure
known as the System Development Life Cycle (SDLC). The orientation of the system that
Collega developed not only considers aspects of technology and product development but also
time to serve, time to deliver and time to market to be more effective and efficient for partners.
SDLC is a series of activity stages that provide a model for the development and
management of cycles of applications or software. The SDLC stage consists of initiation and
planning stages, definition of needs, design, programming, trials, implementation, post-
implementation, maintenance, and disposal review.
With the development of the times, especially in the IT industry caused by the development
of competition, customer demands and job efficiency, requires changes in terms of job
management so as to improve service products and the survival of the company. So Collega
developed the Task Management application, so that the SDLC process can be controlled and
run well and every document / data in each stage of SDLC can be recorded in the system.
Task Management applications are developed with ease in the flow arrangement of the
work process. Each unit or department can determine the type of work that is in their respective
Analyzing Customers’ Acceptance towards Task Management Application Using Affective Technology
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222 Interdisciplinary Social Studies, Theft(No), Nov 2021
units. Users of this application will get a notification via email if there is a job assigned by the
supervisor concerned. Employers can also monitor job progress and conduct job reviews.
Looking at the usability and functionality of the Task Management application, it will
greatly help increase effectiveness and efficiency in the work as well as improve the quality of
operational services. However, the reality is that there are still some units and users who have
not maximized the functionality and usability of the application. To address this, there needs
to be a measurement of the level of acceptance and understanding in using the Task
Management (TM) application, by measuring the behavior of its users.
In information systems there are several theories that can be used to study and measure
user behavior in receiving information systems. And one of the most commonly used theories
is TAM (Technology Acceptance Model) (Hartono, 2008: 14),
Davis defines TAM (Technology Acceptance Model) as a model designed to predict the
acceptance of information technology to be used by users. So by using the TAM model, it can
be estimated the factors that affect the acceptance of a technology by users. Individual
acceptance of information technology can be determined by 2 main constructs owned by TAM
in the form of perceived ease of use and perceived usefulness (Hartono, 2008: 111-112).
Users will feel satisfied using the Task Management (TM) application if they believe that
the service is easy to use and can increase productivity which will then be followed by the
attitude shown by the user. The individual perception and attitude of the user can determine the
user's decision in choosing to use the Task Management (TM) application, which will then be
able to shape the user's behavior in using the Task Management (TM) application in carrying
out its operations or work. Behavior in information technology system theory is also referred
to as the real usage application of Task Management (TM). Actual usage can be defined as a
person's behavior in doing an activity / work as desired (Hartono, 2008: 117)
Hypothesis
1)
Positive Affect on Behavioral Intention Task Management (TM)
2)
Negative Affect on Behavioral Intention Task Management (TM)
3)
Pengaruh Positive Affect terhadap Perceived Usefulness Task Management (TM)
4)
Pengaruh Positive Affect terhadap Perceived Ease Of Use Task Management
(TM)
5)
Pengaruh Negative Affect terhadap Perceived Usefulness Task Management
(TM)
6)
Pengaruh Negative Affect terhadap Perceived Ease Of Use Task
Management (TM)
7)
Pengaruh Perceived Ease Of Use terhadap Perceived Usefulness Task
Management (TM)
8)
Pengaruh Perceived Ease Of Use terhadap Attitude Toward Using Task
Management (TM)
9)
Pengaruh Perceived Usefulness terhadap Attitude Toward Using Task
Management (TM)
10)
Pengaruh Attitude Toward Using terhadap Behavioral Intention Task
Management (TM)
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223 Interdisciplinary Social Studies, Theft(No), Nov 2021
Impact of PA and NA on PU, PEOU and BI
Based on previous research conducted by Hoong, Thi and Lin (2017) that there is a
significant impact of PA on BI, PU and PEOU. There was also a significant impact of
NA on BI and PEOU but no impact of NA on PU was found.
H1: Positive Affect positively affects Behavioral Intention Task Management
H2: Negative Affect positively affects Behavioral Intention Task Management
H3: Positive Affect positively affects Perceived Usefulness Task Management
H4 : Positive Affect berpengaruh positif terhadap Perceived Ease Of Use Task Management
H5: Negative Affect positively affects Perceived Usefulness Task Management
H6 : Negative Affect berpengaruh positif terhadap Perceived Ease Of Use Task
Management
Perceived Ease of Use, Perceived Usefulness, Attitude toward Using, and Behavioral
Intention in Technology Acceptance Model
Based on previous literature and research conducted by Hoong, Thi and Lin 2017 that there
is a consistent influence relationship between perceived ease of use, perceived usefulness,
attitude toward using and behavioral intention. An individual's actual use of technology is
determined by behavioral purposes, which are determined by perceived usability and perceived
ease of use. The perceived value of usability is that an individual's level of trust in the use of
technology will improve the performance of his or her work, and the perception of ease of use
is the extent of an individual's belief that utilizing technology will be easy.
H7 : Perceived Ease of Use berpengaruh positif terhadap Perceived Usefulness Using Task
Management
H8 : Perceived Ease of Use berpengaruh positif terhadap Attitude Toward Using Task
Management
H9: Perceived Usefulness positively affects Attitude Toward Using Task Management
H10: Attitude Toward Using positively affects Behavioral Intention Task Management
METHOD
This study uses a type of quantitative research. Quantitative research is a type of research
used to prove values by measuring relationships between variables, so that data can be obtained
in the form of numbers so that they can be analyzed in a statistical order (Noor, 2011: 38). This
research method is used to find out about the Affective Technology Acceptance Model (ATAM)
Analysis of the use of Task Management applications across employees of PT Collega Inti Pratama.
Population
Population is the entire subject of the study conducted (Arikunto, 2006: 131). This
population can be used to mention the entire member of a region / place that is used as the target
of research conducted (Noor, 2011: 147). The population in this study is the users of task
management application which numbered 150 of all employees of PT Collega Inti Pratama.
Sample
Analyzing Customers’ Acceptance towards Task Management Application Using Affective Technology
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224 Interdisciplinary Social Studies, Theft(No), Nov 2021
The sample is a portion of the elected members of the population (Suhartanto, 2014: 230).
The sample in this study was a user of the Task Management application from all employees of PT
Collega Inti Pratama.
The samples in this study were taken by a non probability samplingmethod. The technique
used in this method is purposive sampling. Purposive sampling or conditional sample is the
selection of samples based on certain criteria.
According to Notoatmodjo, 2003 initiated by Setyarini (2007: 41) to find out the sample size
of representatives obtained based on a simple formula is as follows:
Where:
N: Bpopulation
n: The size of the sample
d: the desired level of trust/accuracy of 10%.
With the formula can be calculated the sample size of the population of 150
by taking the level of trust (d ) = 10%, as follows:
N
n =
Nd2 + 1
150
n = (150) (0.10)
2
+ 1
150
= 2.5
= 60
Sampling Techniques
Thisresearch uses purposive sampling techniques. Purposive sampling is a technique used in
sampling that is based on criteria. The criteria of users (users) who were sampled in this study
are:1) Users of task management applications all employees of PT. Collega Inti Pratama;and 2) Have
used the Task Management application at least 1 time.
Data Collection Techniques
Questionnaire is a list of questions or statements that have previously been formulated by
researchers who are then answered by respondents (Sekaran, 2006). This questionnaire is
disseminated by sharing some of a set of questions/ written statements for respondents to
answer (Sugiyono,2008). The questionnaire in this study is intended for users of task
management application at PT. Collega Core Pratama and have used it at least 1 time. Then
theinstrument used to measure the variable of this study is the Likert scale of 4 points.
Respondents' answers are a choice of five alternatives, namely:
S: AgreeCS: Simply Agree
N
n =
Nd2 + 1
Analyzing Customers’ Acceptance towards Task Management Application Using Affective Technology
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225 Interdisciplinary Social Studies, Theft(No), Nov 2021
KS: Disagree: Disagree
Each answer has a value:
S: 4CS : 3
KS: 2TS : 1
Validity Test
According to Sekaran (2006: 248) in (Sarjono & Julianita, 2013: 35) defining
validity is as evidence of instruments, techniques and processes used in measuring a
concept so as to actually measure the intended concept. This validity test aims to find
out the validity of a question contained in the questionnaire.
An indicator can be said to be valid can be seen with the following conditions
(Arikunto, 2006: 178):
Result r
calculate
> r
table
= valid Result r
calculate
< r
table
= invalid
Reliability Test
According to Sekaran (2006: 40) in (Arikunto, 2006: 35) defining reliability is a
measurement that shows that the extent to which the measurement is done without bias (error-
free). This reliability test aims to measure the consistency of a person's answers to question
items contained in the questionnaire.
This test will only be done for valid items, where the valid items are obtained through
validity testing. To measure reliability using alpha cronbach statistical tests. According to
Nunnally (1967) in Ghozali (2005: 140) states that variables can be said to be reliable if they
give a value of ɑ > 0.60.
Data Analysis
Classical assumption tests include normality, heteroskedasticity, multicollinearity and
autocorrelation tests. The results of the processing are asfollows.
Normality Test
The normality test is a comparison between the data owned with normal distributed data
that has the same mean and standard deviation as the data owned (Sarjono & Julianita, 2013:
53). Normality test is done to find out whether or not the distribution of data. Normality testing
becomes an important thing because it becomes one of the requirements of parametric testing,
which must be distributed normally.
Heteroskedasticity Test
According to Wijaya (2009: 124) in Sarjono & Julianita (2013: 66) defining
heteroskedasticity is a state that indicates that variable variance is not the same (constant)
between observations with other observations. To detect the or absence of heteroskedasticity,
there are several statistical tests that can be used including: glejser test, park test, White test and
scatterplot test.
The occurrence of heteroskedasticity can be characterized by points that form certain
patterns that are regular such as wavy, widening, then narrowing. However, if heteroskedasticity
Analyzing Customers’ Acceptance towards Task Management Application Using Affective Technology
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226 Interdisciplinary Social Studies, Theft(No), Nov 2021
does not occur, it can be characterized by the spread of points above and below the number 0
on the Y axis without forming a specific pattern (Ghozali, 2005: 162).
Multicollinearity Test
The multikolinearity test aims to find out if regression models found correlations between
free variables. A good model will not have a correlation between free variables. If free
variables occur correlations then the variables are not orthogonal where free variables whose
correlation values between fellow free variables are equal to zero (Ghozali, 2005: 150).
The multikolinearity test can be detected using tolerance and Variance Inflation Factor
(VIF) values. Both values can show which independent variables are described by other
independent variables. The value usually used to indicate the presence of multicollinearity is
VIF ≥ 10 or equal to the value of Tolerance ≤ 0.10 (Ghozali, 2005: 95).
Autocorrelation Test
The Autocorrelation test aims to test whether in a linear regression model there is a
correlation between a confounding error between a series of observations in the current period
and a disruptor error in the previous period. Autocorrelation testing can be done with several
tests including the Durbin-Watson test, the Lagrange Multiplier test of Q statistics, and the Run
Test (Sarjono & Julianita, 2013: 80).
Multiple Linear Regression Analysis
Regression analysis is an analysis used to measure the influence of independent variables
with dependent variables (Sarjono & Julianita, 2013: 91). In this study, the bound variables
were Perceived Ease of Use, Perceived Usefulness, Attitude Toward Using and Behavioral
Intention. As for the free variables are Positive Affect and Negative Affect. In this study, the
data analysis using Multiple Regression Analysis with the help of SPSS. The general equations
of linear regression are as follows (Devi and Suartana, 2014):
Y
1
= a + b
1
X
1
+ b
2
X
2
+ e
Where:
Y
1
: Perceived Ease of UseX
1
= Positive AffectX
2
= Negative Affect
a = Constant b = Regression Coefficient e = Error / Bully Error Rate
Y
4
= a + b
1
X
1
+ b
2
X
2
+ b
3
X
3
+ e
Where:
Y
4
: Behavioral IntentionX
1
: Positive AffectX
2
: Negative Affect
X
3
: Attitude Toward Using a: Konstanb : Regression Coefficient
E: Error / Bully Error Rate
Model Accuracy Test
F Test (Simultaneous Test)
The F test is intended to determine the extent to which independent variables used are able
to explain dependent variables simultaneously. In determining Ftabel, the significance level
Analyzing Customers’ Acceptance towards Task Management Application Using Affective Technology
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227 Interdisciplinary Social Studies, Theft(No), Nov 2021
used is 5% with the degree of freedom df = (n - k), where it is explained that n is the number of
samples and k is the number of independent variables. The steps used in the f test test are as
follows Djarwanto & Subagyo (1993: 269):
1) Determination of hypothesis formulation
H0: insignificant regression coefficient H1: significant simultaneous regression
coefficient
2) Determine the level of significant ɑ = 5%, with a significant level value of 95% with a
degree of freedom (k - 1, n - k).
3) Determine testing criteria
H0 is accepted when F
calculates
≤ F
table
H0 is rejected if F
calculates
≥ F
table
4) Conclusions are made bycomparing the results obtained, then H0 is accepted or
rejected.
Determination (R2)
The coefficient of determination is one of the statistical values that can be used to measure
how far the model's ability to explain the variation of dependent variables, the value of R2 lies
between 0% to 100%. If the R2 obtained is close to 100%, then it can be said that the stronger
the model describes the variation of the free variable against the bound variable. Conversely, if
it is close to 0 then the weaker the variation of the variable freely describes the bound variable
(Ghozali, 2005: 83).
Hypothesis Test
Test t
According to Ghozali (2005: 105) this partial test (t test) is used to determine the effect of
each independent variable on a dependent variable. The t test is a test performed to see if an
independent variable can individually have a significant effect on dependent variables by
assuming that other variables are constant.
The decision is based on the comparison of the thitung values of each regression coefficient
with a ttabel. In determining the value of the ttabel, the significance value used is 0.05 (5%)
with the degree of freedom df = (n - k). Using the following testingcriteria.
1) Determining hypothesis zero and alternative hypotheses
H0: b1 = 0, meaning that there is no influence between variable x and variable y
individually.
Ha: b1 ≠ 0, meaning that there is an influence between variable x on variable y
individually.
2) Menentukan level of signifikan ɑ = 0,05; Df = (ɑ / 2; n – k 1).
3) Testing criteria
H0 is accepted if - t
calculate ≤
t
table
≤ t
calculate
Ha rejected if – t
calculate
≤ t
table
≤ t
calculate
or t
count
≥ t
table
4) Conclusion dibuat dengan compare the results obtained, then H0 accepted or rejected.
Mediation Effect Test with Path Analysis
Baron and Kenny (1986) in Latan (2013: 109) states that the mediation effect indicates the
relationship between free variables and bound variables through connecting variables. This
Analyzing Customers’ Acceptance towards Task Management Application Using Affective Technology
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228 Interdisciplinary Social Studies, Theft(No), Nov 2021
means that the effect of free variables on bound variables can be directly or can be through
mediation variables. This effect test aims to find outthe direct effect (direct effect)and indirect
effect (indirecteffect). To be able to see its effects can use the path analysis method. Path analysis
according to Sugiyono (2011:297) is an extension of linear regression analysis to test complex
models using multiple regression equations.
For directeffectisobtained from the results of SPSS processing on standardized coefficient.
For indirect influence is obtained from the number of multiplication standardized coefficients
of one of the variables in the first equation and the second equation. Then for the conclusion can be
seen if the indirect influence is greater than the direct influence, then the mediation variable
used, has been able to mediate.
RESULTS AND DISCUSSION
Multiple Linear Regression Testing Analysis
Analysis of Multiple Linear Regression Testing Equation-1
Multiple linear regression aims to test positive affect variables, negative affect with
perceived ease of use variables either partially or simultaneously. The results can be
seen in the following tabel.
Table 1. Equation 1
Positive Affect dan Negative Affect terhadap Perceived Ease of Use
Coefficients
a
Model
Unstandardized Coefficients
t
Itself.
B
Std. Error
1
(Constant)
1.542
.417
3.701
.000
AND THE
.735
.102
7.204
.000
ON
-.149
.089
-1.689
.094
a. Dependent Variable: PEOU
Source: Primary data processed, 2021
Based on Table 4.32 above known regression equations are known:
Y = α + B
1
X
1
+ B
2
X
2
+ €
Y = 1.542 + 0.578x
1
- 0.136x
2
+ €
t
count
= (7,204)(-1,689)
F
count
= 28,759
𝑅
2
= 0.367
Information:
Y: Perceived Ease of Use
α: Constant value, i.e. value Y if X
1,
X
2
β: A directional value as a determinant of a forecast (prediction) that indicates the value of the
increase (+) or decrease (-) of variable Y.
𝑥
1
: Positive Affect
𝑥
2
: Negative Affect
Other variables that affect
The interpretation of the regression equation is as follows:
Analyzing Customers’ Acceptance towards Task Management Application Using Affective Technology
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229 Interdisciplinary Social Studies, Theft(No), Nov 2021
1)
The constant value is 1.542 Meaning that if the Positive affect and negative affect
value is 0 then the Perceived Ease of Use value is 1,542
2)
Positive affect variable regression coefficient value of 0.578 Means that if other
independent variables remain and Positive affect increases by 1 unit, then
Perceived Ease of Use increases by 0.578 x 1 = 0.578 units increase or vice
versa if other independent variables are fixed value and Positive affect decreases
by 1 unit then Perceived Ease of Use decreased by 0.578 x 1 = 0.0578 units
decreased.
3)
Negative affect variable regression coefficient value of -0.136. This means that if
other independent variables remain and negative affect increases by 1 unit, then
Perceived Ease of Use decreases by -0.136 x 1 = -0.136 units increase or vice
versa if other independent variables are fixed value and Negative affect
decreases by 1 unit then Perceived Ease of Use decreases by -0.136 x (-1) =
0.136 units increase.
4)
Positive affect value Sig 0.000, because Sig 0.000 < 0.05 can be concluded
Positive affect affect Perceived Ease of Use.
5)
Negative affect value Sig 0.094, because Sig 0.094 > 0.05 it can be concluded
negative affect does not affect positively perceived ease of use.
Analysis of Multiple Linear Regression Testing Equation-2
Multiple linear regression aims to test perceived ease of use, positive affect and negative
affect variables against perceived usefulness both partially and simultaneously. The results can
be seen at the following table.
Table 2. Equation 2
Perceived Ease of Use, Positive Affect dan Negative Affect terhadap Perceived
Usefulness
Coefficients
a
Model
Unstandardized Coefficients
Standardized
Coefficients
t
Itself.
B
Std. Error
Be
ta
1
(Constant)
.863
.292
2.957
.004
AND THE
.023
.083
.021
.273
.785
ON
.108
.059
.117
1.824
.071
PEOU
.654
.066
.783
9.909
.000
a. Dependent Variable: PU
Source: Primary data processed, 2021
Based on Table 4.33 above known regression equations are: Y: α
+ B
1
X
1
+ B
2
X
2
+ B
3
X
3
+ €
Y: 0.863 + 0.783X
1
+ 0.021X
2
+ 0.117X
3
+ €
t
count:
(9,909) (1,824) (0.273)
F
count:
51.637
Sig: (0.001) (0.000) (0.002)
𝑅
2
: 0.613
Information:
Y: Perceived Usefulness
Analyzing Customers’ Acceptance towards Task Management Application Using Affective Technology
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230 Interdisciplinary Social Studies, Theft(No), Nov 2021
α: Constant value, i.e. value Y if X
1,
X
2,
X
3
β: A directional value as a determinant of a forecast (prediction) that indicates the value
of the increase (+) or decrease (-) of variable Y.
𝑥
1
: Perceived Ease of Use
𝑥
2
: Positive affect
𝑥
3
: Negative affect
Other variables that affect Y
The interpretation of the regression equation is as follows.
1.
The constant value is 0.863 Meaning that if the Positive Affect, Negative
Affect and Perceived Ease of Use value is 0 then the Perceived Usefulness
value is 0.863
2.
Positive affect variable regression coefficient value of 0.021. This means that
if other independent variables remain and Positive affect increases by 1
unit, perceived usefulness increases by 1 unit.
0.021 x 1 = 0.021 units increase or vice versa if other independent variables
are fixed value and Positive affect decreases by 1 unit then Perceived
Usefulness decreases by 0.021 x 1 = 0.021 units of decline.
3.
Negative affect variable regression coefficient value of 0.117. This means
that if other independent variables remain and Negative affect increases
by 1 unit, perceived usefulness increases by 0.117 x 1 = 0.117 units
increase or vice versa if other independent variables are fixed value and
Negative affect decreases by 1 unit then Perceived Usefulness decreases
by 0.117 x 1 = 0.117 units decrease.
4.
The regression coefficient value of the Perceived Ease of Use variable is
0.783. This means that if other independent variables remain and
Perceived Ease of Use increases by 1 unit, then Perceived Usefulness
increases by 0.783 x 1 = 0.783 units increases or vice versa if other
independent variables are fixed in value and Perceived Ease of Use
decreases by 1 unit then Perceived Usefulness decreases by 0.783 x 1 =
0.783 units decreases.
5.
Perceived Ease of Use nilai Sig 0.000, karena Sig 0.000 < 0.05 maka dapat
disimpulkan Perceived Ease of Use mempengaruhi Perceived Usefulness.
6.
Positive affect value Sig 0.785, because Sig 0.785 > 0.05 can be concluded
Positive affect does not affect Perceived Usefulness.
7.
Negative affect value Sig 0.071, because Sig 0.071 > 0.05 can be
concluded Negative affect does not affect Perceived Usefulness.
Analysis of Multiple Linear Regression Testing Equation-3
Multiple linear regression aims to test perceived usefulness and perceived ease of use
variables against attitude toward using both partially and simultaneously. The results can
be seen in the following tabel.
Table 3. Equation 3
Perceived Usefulness dan Perceived Ease of Use terhadap Attitude Toward
Using
Analyzing Customers’ Acceptance towards Task Management Application Using Affective Technology
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231 Interdisciplinary Social Studies, Theft(No), Nov 2021
Coefficients
a
Model
Unstandardized Coefficients
Standardized
Coefficients
t
Itself.
B
Std. Error
Be
ta
1
(Constant)
.212
.270
.784
.435
PEOU
.403
.099
.392
4.056
.000
PU
.554
.119
.450
4.658
.000
a. Dependent Variable: ATU
Source: Primary data processed, 2021
Based on the above tabel known regression equations are:
Y: α + B1X1 + B2X2 + €
Y: 0.212 + 0.450X1 + 0.392X2 + €
t
calculated
: (4,658) (4,056) F count : 84,013
𝑅2: 0.629
Information:
Y: Attitude Toward Using
α: Constant value, i.e. value Y if X1, X2
β: A directional value as a determinant of a forecast (prediction) that indicates the value of the
increase (+) or decrease (-) of variable Y.
𝑥
1
: Perceived Usefulness
𝑥
2
: Perceived Ease of Use
€: Other variables that affect Y
The interpretation of the regression equation is as follows:
1.
The constant value is 0.212 Meaning that if perceived usefulness and
perceived ease of use are 0 then the attitude toward using value is 0.212.
2.
The regression coefficient value of the Perceived Usefulness variable is 0.450.
This means that if other independent variables remain and Perceived
Usefulness increases by 1 unit, then Attitude Toward Using increases by 0.450
x 1 = 0.450 units increases or vice versa if other independent variables are fixed
in value and Perceived Usefulness decreases by 1 unit then Attitude Toward
Using decreases by 0,450 x 1 = 0,450 units decreases.
3.
Perceived Ease of Use variable regression coefficient value of 0.392 Means that if
other independent variables remain and Perceived Ease of Use increases by 1
unit, then Attitude Toward Using increases by 0.392 x 1 = 0.392 units increases
or vice versa if other independent variables are fixed value and Perceived Ease
of Use decreases by 1 unit then Attitude Toward Using decreased by 0.392 x 1
= 0.392 units of decline.
4.
Perceived Usefulness value sig 0.000, because Sig 0,000 < 0.05 can be
concluded Perceived Usefulness affects Attitude Toward Using.
5.
Perceived Ease of Use nilai Sig 0.000, karena Sig 0.000 < 0.05 maka dapat
disimpulkan Perceived Ease of Use mempengaruhi Attitude Toward Using.
Analysis of Multiple Linear Regression Testing Equation-4
Analyzing Customers’ Acceptance towards Task Management Application Using Affective Technology
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232 Interdisciplinary Social Studies, Theft(No), Nov 2021
Multiple linear regression aims to test the variables Positive affect, Negative affect and
Attitude Toward Using against Behavioral Intention both partially and simultaneously. The
results can be seen in the following table.
Table 4.35: Equation 4
Positive Affect, Negative Affect dan Attitude Toward Using terhadap
Behavioral Intention
Coefficients
a
Model
Unstandardized Coefficients
Standardized
Coefficients
t
Itself.
B
Std. Error
Be
ta
1
(Constant)
.020
.260
.075
.940
ATU
.845
.057
.827
14.742
.000
AND THE
.139
.075
.104
1.856
.067
ON
.042
.053
.037
.794
.429
a. Dependent Variable: BI
Source: Primary data processed, 2021
Based on the table above, regression equations are known, namely:
Y: α + B
1
X
1
+ B
2
X
2
+ B
3
X
3
+ €
Y: 0.020 + 0.104X
1
+ 0.037X
2
+ 0.827X
3
+ €
t
count:
(1,856) (0.794) (14,742)
F
count
: 124,643
Sig: (0.067) (0.429) (0.000)
𝑅
2
: 0.792
Information:
Y: Behavioral Intention
α: Constant value, i.e. value Y if X
1,
X
2,
X
3
β: A directional value as a determinant of a forecast (prediction) that indicates the value of
the increase (+) or decrease (-) of variable Y.
𝑥
1
: Positive affect
𝑥
2
: Negative affect
𝑥
3
: Attitude Toward Using
Other variables that affect Y
The interpretation of the regression equation is as follows:
1.
The constant value is 0.020. This means that if the Positive Affect,
Negative Affect and Attitude Toward Using value is 0 then the value of
Behavioral Intention is 0.212.
2.
Positive Affect variable regression coefficient value of 0.104. This means that
if other independent variables remain and Positive Affect increases by 1
Analyzing Customers’ Acceptance towards Task Management Application Using Affective Technology
Acceptance Model (ATAM)
233 Interdisciplinary Social Studies, Theft(No), Nov 2021
unit, then Behavioral Intention increases by 0.104 x 1 = 0.104 units
increase or vice versa if other independent variables are fixed in value and
Positive Affect decreases by 1 unit then Behavioral Intention decreases by
0.104 x 1 = 0.104 units decrease.
3.
Negative Affect variable regression coefficient value of 0.037. This means
that if other independent variables remain and Negative Affect increases
by 1 unit, then Behavioral Intention increases by 0.037 x 1 = 0.037 units
increase or vice versa if other independent variables are fixed in value and
Negative Affect decreases by 1 unit then Behavioral Intention decreases by
0.037 x 1 = 0.037 units of decline
4.
The regression coefficient value of the Attitude Toward Using variable is
0.827. This means that if other independent variables remain and Attitude
Toward Using increases by 1 unit, then Behavioral Intention increases by
0.827 x 1 = 0.827 units increase or vice versa if other independent
variables are fixed value and Attitude Toward Using decreases by 1 unit
then Behavioral Intention decreases by 0.827 x 1 = 0.827 units decreases.
5.
Positive affect value Sig 0.067, because Sig 0.067 > 0.05 then it can be
concluded positive affect does not affect behavioral intention.
6.
Negative affect value Sig 0.471, because Sig 0.429 > 0.05 can be
concluded Negative affect does not affect behavioral intention.
7.
Attitude Toward Using a Sig value of 0.000, because Sig 0.000 < 0.05 can
be concluded attitude toward using affects behavioral intention.
CONCLUSION
Based on the results of analysis of research data on the influence of Positive Affect and
Negative Affect on users of PT Collega Inti Pratama in using task management (TM)
applications, some conclusions have been produced as follows.
1) Positive Affect had a significant effect on perceived ease of use, with a contribution of
34.15%. In other words, the more supporting features provided by task management
applications make it easier to operate the application and facilitate thework.
2) Perceived Ease of Use had a significant effect on Perceived Usefulness, with a
contribution of 60.60%. The easier it is to use the Task Management application, the
more benefits for users.
3) Perceived Usefulness had a significant effect on Attitude Toward Using, with a
contribution of 33.89%. The more benefits obtained by users will further encourage
interest in using the application.
4) Perceived Ease of Use had a significant effect on Attitude Toward Using, with a
contribution of 29.01%. The easier it is to use the application, the more it encourages
interest in using the application.
5) Attitude Toward Using had a significant effect on Behavioral Intention, with a
contribution of 73.27%. In other words, the greater the interest in using the application
will further encourage user behavior to use the application.
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