Analysis of Digital Legal Acceptance based on the Technology Acceptance Model 3 (TAM3)

This study aims to analyze the acceptance of the PeduliLindungi application from a public perspective using the Technology Acceptance Model (TAM) concept. The government requires the PeduliLindung application to track community activities through the Decree of the Minister of Communication and Informatics No. 171 of 2020. The Indonesian government hopes that this application records the mobility of infected patients and becomes a solution to reduce the transmission of COVID-19. However, PeduliLindungi is a new application that developed after the pandemic. Thus, the user acceptance of this app is questionable. This is a survey analysis from 102 respondents using Structural Equation Model-PLS. The results found that perceived usefulness, system failure, and social influences affect the user intent of the PeduliLindungi app while perceived ease of use is not. This app was applicable to record individual movement as virus prevention in the future. This is a contribution to theory and practice in economics and business. In theory, this research provides a new and more comprehensive view of fundamental human behaviour idea. In practice, this research is able to measure the effectiveness of government regulations in responding to an unexpected global crisis


Introduction
This study purposes to examine users' intention of PeduliLindungi Application as a technology that helps improve the safety and comfort of mobility.This study uses the TAM 3 framework.The Covid-19 phenomenon has become a pandemic because tracing the mobility history of infected patients is difficult.The Indonesian government responded through the Analysis of Digital Legal Acceptance based on the Technology Acceptance Model 3 (TAM3)  Siti Nur Azizah, Hermin Endratno, Harjono [213] Decree of the Minister of Communication and Informatics No. 171 of 2020 to track community mobility through the PeduliLindung application.All public areas must install this application and require visitors to scan at the entrance and exit.PeduliLindung is an application that has just been introduced to the public, and immediately requires them to become users.In fact, each individual has various considerations before deciding to accept a new system.Technology Acceptance Model (TAM) is able to analyze the level of acceptance of a system for its users.
Research analysis of new systems is important before the system is implemented.A system needs to ensure the feasibility and responsiveness of its users.If the system is feasible and well-accepted, then the system can be implemented or required to be used.This study examines behavioral intentions with TAM framework to analyze user intention of PeduliLindungi application.Factors that influence intention in the framework of TAM are positive, such as: perceived ease of use and perceived usefulness.A person's behavior in the framework of TAM is influenced by individual considerations, such as perceived ease of use and perceived usefulness.
From 102 data using Structural Equation Model-PLS, the results found that perceived usefulness, system failure, and social influences affect the user intent of the PeduliLindungi app while perceived ease of use is not.This research provides several contributions, both theoretical and practical.First, it provides a new, more complete framework for the technology acceptance model.Second, expand the information systems research literature in the discussion of technology acceptance models.Third, the PeduliLindungi application can be accepted and used by the community at large.Fourth, increase public knowledge and understanding of the simple concept of information system technology.Fifth, giving consideration to application developers to see what factors influence so that applications can be accepted by the wider community.

Technology Acceptance Model -TAM
TAM is one of the most influential frameworks in information systems research1 .The TAM framework tries to explain what factors influence a user intention to accept new technology.This framework has 2 main factors that influence intention, namely: perceived usefulness and perceived ease of use.This framework is one of the most influential frameworks because of its parsimony 2 .Many researchers from various fields use the technology acceptance model,  2007) 6 .The proposal that was responded t So was the need to add other antecedent factors to the TAM framework.Venkatesh and Bala (2008)  7 adding a theoretical construct as an antecedent of the perceived ease of use variable.There are six proposed antecedents, namely: computer self-efficacy, perception of external control, computer anxiety, computer playfulness, perceived enjoyment., and objective usability.

PeduliLindungi App
The PeduliLindung application has three main functions.The first is screening, so that users who often enter public areas or want to travel long distances using trains, planes, ships and so on, are actually selected using the system.So it can be ascertained that the person concerned has been vaccinated, and has not been exposed to Covid or has not been in close contact with COVID-19 patients.In addition, this feature can also limit people who enter the public area automatically according to the level of restriction.

Analysis of Digital Legal Acceptance based on the Technology Acceptance Model 3 (TAM3)
Siti Nur Azizah, Hermin Endratno, Harjono [215] This application we can also check the health status.The green color means the user has been vaccinated twice and is not currently infected.Yellow color means that the user has been vaccinated once and is not infected.Then, the red color means that the user's vaccination data cannot be found (not yet vaccinated) but is not infected, and the black color means that the user is infected or has been in contact with a Covid-19 positive patient for less than 14 days.The goal is that users do not harm others by tracking backwards, so that the spread of the virus can be limited.Testing the user intention the PeduliLindungi application can be explained in the following research framework:

Population and Sample
The population of this research is the flight passengers in Indonesia.The sampling frame of this research is participants who are accustomed to using mobile applications.It was chosen because this study deals with the use of new mobile applications.The sample of this research is airplane passengers who are in 34 airports in Indonesia.The survey method was used in this study to collect data.The survey technique of this research is a self-administered survey.The sample method used is convenience sampling.The research data was obtained 102 data from direct answers by research respondents through questionnaires distributed online via a google form link.

Questionnaire
The questionnaire is organized into 2 parts.The first part contains construct statements that will be analyzed using a 5-point Likert scale, score 1 for strongly disagree, to score 5 for strongly agree.All respondents should filling out total 45 statement items.According to8 scale measurement for perceived ease of use (four items), perceived external control (four items), selfbelief (four items), playability (four items), perceived usefulness (four items), description (three items), visibility of results (four items).items), and behavioral intentions (three items).Six items from9 , Five items from10 , and four items from 11 used to measure system failure, insecurity, and social influence.All respondents have 15 minutes to answer all the statements.The second part contains demographic data of respondents, such as: gender, age, educational background, regional origin, and experience using mobile applications.

Data Collection
This research use Structural Equation Modeling (SEM) to analyze the data because of several reasons.First, this research is developing theory in an exploratory context.Second, this research is also a development of the existing structural model.Third, SEM-PLS is also able to minimize the errors contained in the construct and maximize the R2 value of the endogenous variables.
Model testing using SEM-PLS has 2 stages (Two-Step Structural Equation Modeling).The first stage is the measurement model (outer model).The measurement model explains the relationship between latent variables and their measuring indicators.The second stage is the structural model (inner model).The structural model explains the relationship between the latent variables that are formulated.This study uses the SMART PLS 3.0 analysis tool.This analytical tool was chosen for 3 reasons.First, SMART PLS used to correlation between latent variables and indicator variables.Second, SMART PLS suitable to test formative and reflective variables.Third, SMART PLS has a characteristic for testing small samples.

Measurement Model Results
The data should passed validity and reliability measurement12 which are consist of item reliability, convergent validity of measurements, and discriminant validity13 .Hair (2017)  14 states that the individual item reliability assessment uses several provisions, namely: 1) if the loadings value is greater than 0.7 then the item is retained; 2) if the loadings value is between 0.3 to 0.7 then the item is considered (if item deletion increases the AVE value above 0.5) to be maintained; and 3) if the loadings value is less than 0.3 then the item is deleted.The outer loadings value of all items can be assessed as good even though there are some items that do not meet the criteria.
According to the test, all variables have above 0.5.This indicates that each item explain its construct.Assessment of reliability is determined on the condition that composite reliability is greater than 0.7 and Cronbach's Alpha is greater than 0.615 .Of all the existing constructs, the selfefficacy construct unqualified value because 3 out of 4 measurement items cannot meet the standard loading factor.Therefore, the researcher decided to delete the three items to obtain the expected level of construct reliability.

Structural Model Test Results
The structural model was tested by path analysis method with SMART PLS 3.0.The hypothesis test supports seven paths of analysis and the other four are not supported.Hypotheses 1 and 2 explain the impact of the antecedents of perceived usefulness of images on perceived usefulness.The image construct showed insignificant results (0.267) while Result Demonstrability had a significant effect on perceived usefulness (0.018).Thus, hypothesis 1 is not supported and hypothesis 2 is supported with the ability to explain the construct of perceived usefulness of 30.3%.

H8
Perceived Ease of Use -> Perceived Usefulness 0,208 0,118 Not Supported Hypotheses 3, 4, and 5 explain the impact of three internal factors, there are: Perception of External Control, Playfulness, and Self-Efficacy on the construct of Perceived Ease of Use.Perceived ease of use was significantly influenced by perceived external control (0.000), playfulness (0.001), and self-efficacy (0.012).Thus, all three hypotheses are supported and 65.7% of their effect on perceived ease of use is explained by these constructs.
Hypotheses 6 and 7 examine the influence of positive factors, namely Perceived Usefulness and Perceived Ease of Use on Behavioral Intentions.The perceived usefulness construct had a significant effect 0.000, while the perceived ease-of-use showed insignificant results (0.179).Therefore, hypothesis 6 is supported and hypothesis 7 is not supported.Hypothesis 8 examines the mediating effect of the perceived ease of use construct on perceived usefulness.The findings show an insignificant value (0.118) so that hypothesis 8 is not supported.Figure 4 shows the level of the coefficient of determination of the endogenous variables.The R-Square value of the Behavioral Intention construct is 0.444 which means that the constructs of perceived usefulness, perceived ease of use, insecurity, system failure, and social influence can explain 44.4% of behavioral intention constructs while the remaining 55.6% by other constructs outside the model.R-Square value of the Perceived Ease of Use construct is 0.657 and perceived usefulness construct is 0.303.This study shows unique finding that is inconsistent with previous studies 17 .The results show that perceived ease of use does not have a significant positive effect on behavioral intentions.However, this study supports research 18 .Kasilingam (2020) 19 examines consumer interest in using chatbots on mobile devices for shopping.He did some test schemes.In testing the perceived ease of use of the user intention, the results show the hypothesis is not supported.Kasilingam (2020)  20 also tested the perceived ease of use of the user intention mediated by the construct of Attitude and the hypothesis was supported.Thus, there is a strong possibility that the inconsistent findings are due to the need for a perceived ease of use constduaruct mediated by an attitude construct to influence behavioral intentions.Further research is needed to confirm this.

IV. Conclusion
This study aims to examine the user intention the PeduliLindungi application with TAM3 theory.The findings prove that Result Demonstrability affects perceived usefulness positively while Image and ease of use are not.Then, Perception of External Control, Playfulness and Self-Efficacy effects Perceived Ease of Use positively.However, Perceived Ease of Use does not effects Behavioral Intention positively.

Figure 1
Path Analysis Test Results and R-Square LevelBlindfolding TestBlindfolding is a test to calculate the value of Stone-Geisser's Q2.The value of Q2 represents the evaluation criteria for the predictive relevance of the PLS path model 16 .The following are the results of the blindfolding test.

Table 1
Inner Model Test Results -Path Coefficient

Table 2
Blindfolding Test Results

Table 2
shows the blindfolding test result which all Q2 values of each endogenous variable have a value greater than 0. Therefore, it can be concluded that the path model of the endogenous variables has predictive relevance.