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Analysis of factors influencing millennial’s technology acceptance of chatbot in the banking industry in Indonesia
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The purpose of this research is to analyze factors that influence millennial’s technology acceptance of chatbot in the banking industry in Indonesia.

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  1. International Journal of Management (IJM)
    Volume 10, Issue 3, May-June 2019, pp. 107-118, Article ID: IJM_10_03_011
    Available online at http://www.iaeme.com/ijm/issues.asp?JType=IJM&VType=10&IType=3
    Journal Impact Factor (2019): 9.6780 (Calculated by GISI) www.jifactor.com
    ISSN Print: 0976-6502 and ISSN Online: 0976-6510
    © IAEME Publication

    ANALYSIS OF FACTORS INFLUENCING
    MILLENNIAL’S TECHNOLOGY ACCEPTANCE
    OF CHATBOT IN THE BANKING INDUSTRY IN
    INDONESIA
    Richad Richad, Vivensius Vivensius, Sfenrianto Sfenrianto and Emil R. Kaburuan
    Department of Information Systems Management
    BINUS Graduate Program – Master of Information Systems Management
    Bina Nusantara University, Jakarta, Indonesia, 11480.

    ABSTRACT
    The purpose of this research is to analyze factors that influence millennial’s
    technology acceptance of chatbot in the banking industry in Indonesia. In this
    quantitative research, innovativeness is the exogenous variable, while the endogenous
    variables are perceived usefulness, perceived ease of use, and attitude towards using
    and behavioral intention. This research used primary data gathered from distributed
    questionnaires, directly from the millennial people in Indonesia. Using simple random
    sampling technique to total sample of 400 people out of the total population of 90
    million people. Statistical analysis in this research is conducted using Partial Least
    Square Structural Equation Model (PLS–SEM). The result shows that innovativeness,
    perceived usefulness, perceived ease of use and attitude towards using the chatbot
    affected behavioral intention.
    Keywords: Chatbot, Banking Industry, Millennial, Technology Acceptance Model
    (TAM), Partial Least Square–Structural Equation Model (PLS–SEM).
    Cite this Article: Richad Richad, Vivensius Vivensius, Sfenrianto Sfenrianto and Emil
    R. Kaburuan, Analysis of Factors Influencing Millennial’s Technology Acceptance of
    Chatbot in the Banking Industry in Indonesia, International Journal of Management,
    10 (3), 2019, pp. 107 – 118,
    http://www.iaeme.com/IJM/issues.asp?JType=IJM&VType=10&IType=3

    1. INTRODUCTION
    The technological developments have touched every aspect of human life from business,
    education, health, to financial services for the community. One form of technological
    development in the field of financial services is the emergence of chatbot applications that are
    part of artificial intelligence and are available for various platforms such as LINE, Facebook
    Messenger and Google Assistant. The presence of several chatbots in the banking industry in

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  2. Richad Richad, Vivensius Vivensius, Sfenrianto Sfenrianto and Emil R. Kaburuan

    Indonesia such as Vira, Sabrina and Mita is also due to the existence of several supporting
    technologies such as the development of the internet and smartphones in Indonesia.
    Among on data from wearesocial.com specifically in Indonesia itself as of January 2018
    internet usage has touched the figure of 50% or half of the total population of 265.4 million.
    This shows that most people in Indonesia have used internet technology, where several
    activities carried out by the people in Indonesia are to access social media, online shopping,
    and browsing. Likewise, with data obtained from statista.com regarding the development of
    smartphone use in Indonesia which has reached 26.26% of the total population in Indonesia in
    2018.
    There are many organizations, including banks, use chatbot applications that are available
    in various social media platforms, such as LINE and Facebook Messenger. With the chatbot,
    banks can provide customer services for 24 hours per day in a week, can be accessed anywhere,
    and can also provide efficiency in customer service activities. Chatbot can provide a quick
    response to questions from customers, to provide a good customer experience.
    Millennials with a population of 90 million, or around 34.45% of Indonesia’s total
    population constitute a large market share for the banking industry. Based on the Indonesia
    Millennial Report 2019 report conducted by the IDN Research Institute, the results of the
    research show that 94.4% of millennials in Indonesia have been connected to the internet. The
    internet is a major need for millennials. And most or about 98.2% of activities carried out on
    the internet are accessed via smartphones. With the chatbot, millennial customers are now able
    to find out various kinds of banking products and services quickly, such as promotion
    information, exchange rates, the nearest ATM location, and can also register for credit cards
    and mortgages. In addition, millennial customers can also check balances, check accounts,
    credit card information, and other administrative services.
    The purpose of this study was to determine the factors of acceptance of chatbot technology
    in the banking industry in Indonesia, especially for the millennial generation. Therefore, an
    appropriate model is needed to be able to know the factors of acceptance of the technology, so
    we used the Technology Acceptance Model (TAM), modified by Davis in 1989.

    2. LITERATURE REVIEW
    2.1. Chatbot
    Chatbot (also known as a talkbot, chatterbox, Bot, Instant Messaging-bot or Artificial
    Conversational Entity) is a computer program that mimics human conversations in its natural
    format including text or spoken language using artificial intelligence techniques such as Natural
    Language Processing (NLP), image and video processing, and audio analysis [4].
    Chatbot enables its users to communicate with it to form an intelligent communication [9,
    11] along with providing results or completed tasks as the user instructed it to [12]. The
    implementation of Chatbot has existed for years, but in various forms—it gained popularity
    ever since the release of Apple’s Siri and Alexa for Amazon [11].
    The advancement of Chatbot technology development, mainly in programming language,
    drives the performance of Chatbot known nowadays [19]. While it is used to performed tasks
    and answers questions, Chatbot are now capable of doing the business itself [16]. Even so,
    Chatbot are still facing issues regarding securities and trusts [8]. It is still vulnerable to web
    attacks and needs serious attention in development of its security systems. Regardless of it,
    Chatbot are still proven to be in the leading position of business enabler, due to having various
    utilities and little to none compatibility issues, it is the future of business assistance tool. [15,
    25, 27].

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  3. Analysis of Factors Influencing Millennial’s Technology Acceptance of Chatbot in the Banking
    Industry in Indonesia

    2.2. Millennials
    Individuals who are born in between year 1980 and 2000 are called Millennials, due to having
    been born close to the next millennium (year 2000) and were raised in modern age where
    technology have been further advanced. The millennials are easily identified by having high
    acceptance rate on new technologies, and even greater acceptance on capturing new values of
    non-traditional families and customs [24].

    2.3. Banking Industry
    As an intermediary for financial transactions, a bank is doing its business processes primarily
    on offering savings and lending money to potential borrowers in order to develop economy
    within organizations. Advancement of technology enables a bank to penetrate market on larger,
    wider scale by further enhancing its presence and it is obligated to provide fast, secure and
    ubiquitous services (as in financial services) to customers, in order to create profit, with the
    implementation of various business objectives and strategies [1].
    Compilations of bank are known as banking industry; this is to segregate the purposes of
    the banking itself—from financial support, savings, and insurance, banking industry are moving
    toward to provide beneficial supports for people [7, 21, 23].

    2.4. Technology Acceptance Model (TAM)
    TAM helps researchers determine which factors dominates the acceptance rate within a system,
    or subsystems [17]. It was developed by researchers to achieve its main purpose—determining
    the acceptance rate of a technology by individuals or organizations and its usage; thus called
    behavioral intention, which is determined from two subsets; the perceived usefulness and
    perceived ease of use. Perceived usefulness depends on how a technology enhances one’s daily
    routines by measuring the improvement of the performance of oneself, whilst perceived ease of
    use defined as the effortless attempt in using the technology to do the daily routines [26].

    2.5. Partial Least Square – Structural Equation Model (PLS–SEM)
    PLS-SEM differs from general, factor-based SEM. In fact, PLS-SEM does calculation on
    several values of latent variables in research with specified algorithm. Different from factor-
    based SEM, PLS-SEM explicitly calculates case values for the latent variables as part of the
    algorithm, with unobservable variables defines the estimate for exact linear combinations from
    indicators within it, empirically. It is then resulting composite results for most of the exogenous
    constructs’ indicators variant—which is useful to predict the endogenous ones. It is using the
    composite to determine constructs in its path model, as an estimation of the conceptual variable
    of the research [22].

    2.6. Previous Study
    Based on the empirical study determination that in the last time of the last 10 years,
    accumulatively there are 5 international publications that discuss about the technology
    acceptance in several applications that can be seen on Table 1.

    Table 1 Previous Study on Significant Factors

    [2] [3] [6] [13] [20] Reference

    Perceived Usefulness     
    Perceived Ease of Use     

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  4. Richad Richad, Vivensius Vivensius, Sfenrianto Sfenrianto and Emil R. Kaburuan

    Attitude Towards Using   
    Behavioral Intention    
    Innovativeness 

    3. THEORETICAL ANALYSIS
    3.1. Research Model
    This research aims to examine the millennial’s technology acceptance of chatbot in banking
    industry in Indonesia with TAM, consisting of external variables, perceived usefulness,
    perceived ease of use, attitude towards of using and behavioral intention. External variable that
    will be discussed in this paper is Innovativeness of chatbot. The variable indicators in this paper
    can be seen in Table 2.

    Table 2 Variable Indicators

    Variable Variable Indicator References

    IV1: New innovations in chatbot application [5]

    IV2: Innovation can increase convenience using chatbot
    Innovativeness
    (IV)
    IV3: Innovation can increase customer desires using chatbot

    IV4: In general, customer is ready to accept new ideas

    PU1: Chatbot can improve performance of getting information and
    [10,18] doing transactions.
    PU2: By using chatbot, customer can get information and do
    Perceived transaction faster
    Usefulness (PU)
    PU3: Chatbot can improve productivity of customer transactions

    PU4: Chatbot can improve the quality of getting information and
    doing transactions

    PEOU1: Chatbot is easy to learn [18, 25]

    PEOU2: Customer can use chatbot without help from anyone
    Perceived Ease
    of Use (PEOU) PEOU3: Interaction between customer and chatbot is clear and
    easy to understand

    PEOU4: Customer need much effort to use chatbot

    AT1: Getting information and transaction in chatbot is not a good
    [18] idea
    Attitude
    Towards Using AT2: Customer is willing to use chatbot if it is affordable
    (AT)
    AT3: Customer like the idea of using chatbot to facilitate getting
    information and doing transactions

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  5. Analysis of Factors Influencing Millennial’s Technology Acceptance of Chatbot in the Banking
    Industry in Indonesia

    AT4: Using chatbot can be a good experience

    BI1: Customer choose to use chatbot for getting information and
    [18] doing transaction

    BI2: There is possibility that Customer will use chatbot
    Behavioral
    Intention (BI)
    BI3: Customer will not recommend anyone to use chatbot

    BI4: Customer expect to always be able to use chatbot

    The research model used in this article is a modification to fit the scope of the research,
    which is derived from modified version of TAM developed by Davis, Bogozzi and Warshaw
    in 1989 [14]. This modified version firstly defined in order to explain various behavior of users
    on using a technology, where it is believed that an external variable is influencing the
    acceptance of the users. It is illustrated in Figure 1.

    Figure 1. Research Model a
    a
    Adapted from a research article by Lai, P. C. (2017) [14]

    3.2. Analysis Model
    The process of analysis is carried out on the results of the stages of data collection with
    questionnaire instruments. Data analysis was supported using SmartPLS version 2 software.
    Scenario analysis was carried out using the Structural Equation Model Partial Least Squares
    (SEM-PLS) method. PLS method consists of 2 models, namely measurement (outer model) and
    structural (inner model).

    4. RESULTS AND DISCUSSIONS
    4.1. Results
    Data type used in this research is primary data that is obtained by collecting questionnaire
    directly from millennials in Indonesia with a total population of 90 million. Sampling method
    that is used in this research is simple random sampling and Slovin’s formula to determine
    sample size, which is 400 respondents. Statistical analysis that is used in this research is PLS-
    SEM.

    4.1.1. The Assessment of Measurement Model
    The measurement model used in this research consist of six constructs, Innovativeness (IV) as
    external variable, Perceived Usefulness (PU), Perceived Ease of Use (PEOU), Attitude towards
    Using (AT) and Behavioural Intention (BI). Assessment of the reflective measurement model
    requires to examine the validity and reliability for every latent variable in the model. First, to

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  6. Richad Richad, Vivensius Vivensius, Sfenrianto Sfenrianto and Emil R. Kaburuan

    evaluate the convergent validity of the measurement model, outer loading, composite reliability
    (CR), and average variance extracted (AVE) were assessed.
    In evaluating reliability of a model, calculating the loading score of each indicator
    associated with a latent variable and comparing this calculation with the threshold score is
    needed. In general, outer loading scores that are above 0.70 indicate reliability. The score of
    each outer loading in this research (0.70-0.87) is above the threshold score of 0.70.
    The convergent validity can be examined by the score of Average Variances Extracted
    (AVE), where it needs to exceed 0.5 to indicate adequate convergent validity. All constructs
    used in this research exceeded the threshold score. The internal consistency of all the constructs
    in the model was examined by employing Cronbach’s α and composite reliability (CR). The
    threshold score used is 0.70. The Cronbach’s α score and CR of the model were determined to
    exceed the recommended score of 0.70. Thus, the measurement model has acceptable
    reliability. Table 3 shows outer loading, Cronbach’s α, CR and AVE for all constructs.

    Table 3 Assessment Result Of The Measurement Model

    Outer Composite Cronbach’s
    Construct Items AVE
    Loading Reliability α

    Innovativeness 0.78 0.51 0.71

    IV1 New innovations in chatbot application 0.71

    Innovation can increase convenience
    IV2 0.85
    using chatbot
    Innovation can increase customer desires
    IV3 0.86
    using chatbot
    In general, customer is ready to accept
    IV4 0.75
    new ideas

    Perceived Usefulness 0.77 0.57 0.70
    Chatbot can improve performance of
    PU1 0.70
    getting information and doing
    transactions
    By using chatbot, customer can get
    PU2 0.85
    information and do transaction faster
    Chatbot can improve productivity of
    PU3 0.70
    customer transactions
    Chatbot can improve the quality of
    PU4 0.72
    getting information and doing
    transactions
    Perceived Ease of Use 0.85 0.59 0.76

    PEOU1 Chatbot is easy to learn 0.82

    Customer can use chatbot without help
    PEOU2 0.78
    from anyone
    Interaction between customer and chatbot
    PEOU3 0.79
    is clear and easy to understand
    Customer need much effort to use
    PEOU4 0.76
    chatbot

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  7. Analysis of Factors Influencing Millennial’s Technology Acceptance of Chatbot in the Banking
    Industry in Indonesia

    Attitude Towards Using 0.84 0.58 0.74

    Getting information and transaction in
    AT1 0.78
    chatbot is not good idea
    Customer is willing to use chatbot if
    AT2 0.85
    affordable
    Customer like the idea of using chatbot
    AT3 0.84
    to facilitate getting information and
    doing transactions
    AT4 Using chatbot can be a good experience 0.79

    Behavioral Intention 0.82 0.55 0.72

    Customer choose to use chatbot for
    BI1 0.87
    getting information and doing transaction
    There is possibility that Customer will
    BI2 0.79
    use chatbot
    Customer will not recommend anyone to
    BI3 0.76
    use chatbot
    Customer expect to always be able to use
    BI4 0.79
    chatbot
    One of the most common methods for examining discriminant validity is the Fornell
    Larcker criterion. If a latent variable has more variance than the indicator variables, it is shared
    with other constructs in the same model, then a discriminant validity is established. Table 4
    shows that the cross-loading score of each construct is larger than its corresponding correlation
    coefficients pointing towards adequate discriminant validity.

    Table 4 Discriminant Validity

    Construct AT BI IV PEOU PU

    AT 1 0.489642 0.401301 0.255072 0.262867 0.225331
    AT 2 0.855026 0.658730 0.341741 0.350247 0.429058
    AT 3 0.841843 0.630868 0.435162 0.303814 0.460368
    AT 4 0.794486 0.614683 0.410812 0.227994 0.326014
    BI1 0.718578 0.870790 0.474339 0.468067 0.480192
    BI2 0.619690 0.799495 0.358543 0.288143 0.387301
    BI3 0.431162 0.566912 0.290886 0.300086 0.302178
    BI4 0.461569 0.696639 0.452868 0.316980 0.481617
    IV1 0.448370 0.400372 0.716654 0.301125 0.396634
    IV2 0.310918 0.358667 0.858346 0.399123 0.484138
    IV3 0.316809 0.404435 0.855546 0.396798 0.499713
    IV4 0.348226 0.403352 0.250305 0.216198 0.226139
    PEOU 1 0.276437 0.447141 0.376450 0.823667 0.634624
    PEOU 2 0.271534 0.374796 0.299090 0.782843 0.414240
    PEOU 3 0.383693 0.363932 0.485476 0.787654 0.472885

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  8. Richad Richad, Vivensius Vivensius, Sfenrianto Sfenrianto and Emil R. Kaburuan

    PEOU 4 0.199645 0.221852 0.254675 0.662307 0.447453
    PU 1 0.220688 0.304478 0.417136 0.430329 0.704078
    PU 2 0.402068 0.493636 0.465063 0.539714 0.854016
    PU 3 0.341931 0.301703 0.228531 0.254571 0.400343
    PU 4 0.372061 0.401314 0.462532 0.514806 0.724912

    4.1.2. Evaluation of Structural Model
    The structural model was examined to test the hypotheses that are proposed for conceptual
    modelling in this research. Like the CB-SEM, the PLS-SEM approach utilizing SmartPLS
    software does not provide a conventional assessment of the overall model match. Thus, basic
    measures such as R Square, Original Sample and t-statistics along with the predictive relevance
    (Q2) and effect size (f2) measurements and the bootstrapping process with 400 samples, were
    examined to evaluate the structural model, with the corrected R Square score of all constructs,
    Goodness of fit index was calculated. This criterion is defined by the geometric mean of the
    average communality and the model’s average R square score, reported the following cut-off
    score for assessing the results of the Goodness of fit index analysis: GoF small = 0.1; GoF
    medium = 0.25; GoF large = 0.36.

    Figure 2. Structural Result Of The Proposed Model
    As shown in Table 5, the 0.47 GoF score for the structural model indicates very good global
    model fit. However, GoF does not represent a true global fit measure, the corrected R Square
    score and t-statistics of the structural model are analyzed in Figure 2. Primary criterion for inner
    model assessment is the coefficient of determination (R Square), which represents the amount
    of explained variance of each endogenous latent variable. Innovativeness explains 23% of
    perceived ease of used. Innovativeness and perceived ease of use explain 52% perceived
    usefulness. Perceived usefulness and perceived ease of use explain 24% attitude towards using,
    and attitude towards using explain 59% behavioral intention.

    Table 5 Goodness of Fit Index

    Construct AVE Calculation R Square (R2)

    Innovations (IV) 0.51 –
    Perceived Usefulness (PU) 0.57 0.52
    Perceived Ease of Use (PEOU) 0.59 0.23

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  9. Analysis of Factors Influencing Millennial’s Technology Acceptance of Chatbot in the Banking
    Industry in Indonesia

    Attitude Towards Using (AT) 0.58 0.24
    Behavioral Intention (BI) 0.55 0.59
    Average Score 0.56 0.40
    AVE * R Square 0.22
    GoF = √(AVE*R Square) 0.47
    2 2
    Examining Q , f and multicollinearity in addition to R Square when evaluating the
    reflective inner model is necessary. Researchers can evaluate the effect size of the predictor
    constructs using Cohen’s f2. The effect size is computed along with the increase in R Square,
    which is relative to the proportion of variance that remains unexplained in the endogenous latent
    variable. The following equation is used in the calculation of the f2. f2 scores of 0.35, 0.15 and
    0.02 are considered large, medium, and small, respectively. In this study, behavioral intentions
    are predicted attitudes towards using chatbot. Therefore, relative effect sizes (f2) of the
    predicting (exogenous) constructs were calculated and are shown in Table 6. As can be seen
    from the result of GOF Index in Table 6, attitude towards using had large effects on behavioral
    intentions.
    In addition to f2, the predictive sample reuse technique (Q2) could be used effectively as a
    criterion for predictive relevance. Based on blindfolding procedure, Q2 evaluates the predictive
    validity of a complex model by omitting data for a given block of indicators and then predicts
    the omitted part based on the calculated parameters. Researchers can use the cross-validated
    redundancy as a measure of Q2 since it includes the key element of the path model and the
    structural model to predict eliminated data points. Thus, for this research, Q2 was obtained using
    cross-validated redundancy procedures. If Q2 > 0, then, the model is viewed as having
    predictive relevance. As shown in Table 6, Q2 for perceived usefulness, perceived ease of use,
    attitudes towards using, and behavioral intentions are 0.22, 0.12, 0.12, and 0.31 respectively,
    which means they are all indicating acceptable predictive relevance for this research.

    Table 6 Predictive Relevance (Q2) And Effect Size (f2)
    Construct Predictive Relevance (Q2) Effect Size (f2) – Behavioral Intention
    Innovativeness (IV) – –
    Perceived Usefulness (PU) 0.227322 –
    Perceived Ease of Use (PEOU) 0.123793 –
    Attitude Towards Using (AT) 0.122302 0.770127
    Behavioral Intention (BI) 0.311781 –
    After estimating the structural model, the complete results are summarized in Table 7. When
    examined the relationships, Innovativeness positively and significantly affected perceived
    usefulness (O = 0.46, which is > 0.1 and T-Statistics = 4.67, which is > 1.96). Innovativeness
    positively and significantly affected perceived ease of use (O = 0.37, which is > 0.1 and T-
    Statistics = 5.06, which is > 1.96). Perceived ease of use positively and significantly affected
    perceived usefulness (O = 0.55, which is > 0.1 and T-Statistics = 5.86, which is > 1.96).
    Perceived usefulness positively and significantly affected attitude towards using (O = 0.38,
    which is > 0.1 and T-Statistics = 3.19, which is > 1.96). Perceived ease of use positively and
    significantly affected attitude towards using (O = 0.109, which is > 0.1 and T-Statistics = 4.86,
    which is > 1.96). Attitude towards using significantly affected behavioral intention (O = 0.79,
    which is > 0.1 and T-Statistics = 22.31, which is > 1.96).

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  10. Richad Richad, Vivensius Vivensius, Sfenrianto Sfenrianto and Emil R. Kaburuan

    Table 7 Result Of The Structural Model

    Hypothesis Construct Original Sample (O) T – Statistics Decision

    H1 IV  PU 0.326492 4.681581 Supported
    H2 IV  PEOU 0.373251 5.068954 Supported
    H3 PEOU  PU 0.558378 5.863173 Supported
    H4 PU  AT 0.386073 3.196994 Supported
    H5 PEOU  AT 0.109301 4.860479 Supported
    H6 AT  BI 0.798120 22.318477 Supported

    4.2. Discussions
    The results show that the score of R Square in Table 5 shows the influence of exogenous latent
    variables on endogenous latent variables for the research, which are 0.52 for perceived
    usefulness (PU), 0.23 for perceived ease of use (PEOU), 0.24 for attitude towards using (AT),
    and 0.59 for behavioural intention (BI). Furthermore, from the test results on the score of the
    effect size (f2) in Table 6, there is a significant effect of the attitude towards using (AT) to
    behavioural intention (BI), which is 0.77. In addition to effect size test (f2) score, the predictive
    relevance score (Q2) is also done to show that the model has predictive relevance for certain
    endogenous constructs. As a result, all scores of endogenous variables meet the standard set,
    which is greater than 0.
    Every hypotheses in this research is proven to be in positive results with all O > 0.1 and T-
    Statistics > 1.96; IV significantly and positively affected PEOU and PU (O = 0.37 with T-
    Statistics = 5.06, and O = 0.46 with T-Statistics = 4.67, respectively), PEOU significantly and
    positively affected PU with O = 0.55 and T-Statistics = 5.86 where AT has been significantly
    and positively affected by PEOU and PU (O = 0.109 with T-Statistics = 4.86, and O = 0.38 with
    T-Statistics = 3.19, respectively). Lastly, AT significantly and positively affected BI with O =
    0.79 and T-Statistics = 22.31.

    5. CONCLUSIONS
    The result of this research shows that factors in this research model has positive impacts on
    millennial Indonesians upon accepting Chatbot as a technology for banking industry.
    Innovation as the exogenous variable drives those millennial to accept Chatbot to help them do
    financial transactions with a bank; given from positive impacts from its usefulness and the ease
    of use. This severely impact their attitudes towards chatbot—which is developed by having
    various experiences that they may receive upon using it, and ended up prioritizing the use of it,
    or even as far as recommending it to others.

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  11. Analysis of Factors Influencing Millennial’s Technology Acceptance of Chatbot in the Banking
    Industry in Indonesia

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