A technology acceptance model of satellite-based hydrometeorological hazards early warning system in Indonesia: an-extended technology acceptance model

Prihanto, Igif Gimin et al. Article A technology acceptance model of satellite-based hydrometeorological hazards early warning system in Indonesia: an-extended technology acceptance model Cogent Business & Management Provided in Cooperation with: Taylor & Francis Group Suggested Citation: Prihanto, Igif Gimin et al. (2024) : A technology acceptance model of satellitebased hydrometeorological hazards early warning system in Indonesia: an-extended technology acceptance model, Cogent Business & Management, ISSN 2331-1975, Taylor & Francis, Abingdon, Vol. 11, Iss. 1, pp. 1-16, https://doi.org/10.1080/23311975.2024.2374880 This Version is available at: https://hdl.handle.net/10419/326405 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Cogent Business & Management ISSN: 2331-1975 (Online) Journal homepage: www.tandfonline.com/journals/oabm20 A technology acceptance model of satellite-based hydrometeorological hazards early warning system in Indonesia: an-extended technology acceptance model Igif Gimin Prihanto, Hendy Gunawan, Budhi Riyanto, Wiji Prasetio, Sik Sumaedi, Tri Rakhmawati, Nidya Judhi Astrini & Mahmudi To cite this article: Igif Gimin Prihanto, Hendy Gunawan, Budhi Riyanto, Wiji Prasetio, Sik Sumaedi, Tri Rakhmawati, Nidya Judhi Astrini & Mahmudi (2024) A technology acceptance model of satellite-based hydrometeorological hazards early warning system in Indonesia: anextended technology acceptance model, Cogent Business & Management, 11:1, 2374880, DOI: 10.1080/23311975.2024.2374880 To link to this article: https://doi.org/10.1080/23311975.2024.2374880 © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. Published online: 01 Aug 2024. Submit your article to this journal Article views: 1456 View related articles View Crossmark data Citing articles: 1 View citing articles Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=oabm20 InformatIon & technology management | research artIcle Cogent Business & ManageMent 2024, VoL. 11, no. 1, 2374880 A technology acceptance model of satellite-based hydrometeorological hazards early warning system in Indonesia: an-extended technology acceptance model Igif gimin Prihantoa , hendy gunawana, Budhi riyantob , Wiji Prasetioc , sik sumaedia, tri rakhmawatia, nidya Judhi astrinia and mahmudia aResearch Center for testing technology and standards, national Research and innovation agency (BRin), south tangerang, indonesia; bResearch Center for artificial intelligence and Cyber security, national Research and innovation agency (BRin), south tangerang, indonesia; cResearch Center for Remote sensing, national Research and innovation agency (BRin), south tangerang, indonesia ABSTRACT Purpose: this study aims to develop and test a technology acceptance model for a satellite-based hydrometeorology early warning system in Indonesia. Design/methodology/approach: the model was developed by extending tam, through the inclusion of three additional variables: trust (t), image (I), and information quality (IQ). thus, the proposed model involves seven variables: Perceived Usefulness (PU), Perceived ease of Use (PeU), attitude to Use (aU), Intention to Use (IU), trust (t), image (I), and Information Quality (IQ). for model testing purposes, data were collected from 100 users through a survey conducted in three Indonesian provinces: central Java, West Kalimantan, and riau. the analysis consists of measurement model analysis, structural model analysis, and model fit test. the analysis was conducted using partial least squares structural equation modeling (Pls-sem). Findings:the model was proven to be fit, valid, and reliable, with all hypotheses being confirmed. In the context of a satellite-based hydrometeorology early warning system, perceived usefulness (PU) positively and significantly influences the attitude to use (aU). furthermore, perceived usefulness (PU) also has a positive and significant impact on the intention to use (IU). additionally, perceived usefulness (PU) is shown to be affected by external factors, namely trust (t) and image (I). Perceived ease of use (PeU) is found to have a positive and significant impact on attitude to use (aU). furthermore, perceived ease of use (PeU) is influenced by external factors specifically image (I) and information quality (IQ). the attitude to use (aU) is found to have a positive and significant effect on the intention to use (IU). Originality/value: an extended technology acceptance model (tam) to test the acceptance of disaster early warning system technology is rare, even though the technology is vital. Research limitations/implications: In terms of generalization, this study is limited to the acceptance of the satellite-based hydrometeorology early warning system in Indonesia. future research should apply the model to other contexts to test its applicability and stability. 1. Introduction climate change has garnered global attention because of its propensity to create hydrometeorological hazards that have vast impacts on human life (mazzoglio et al., 2021; ramadhan et al., 2022). approximately 75% of the world’s population borne the brunt of floods, drought, and extreme winds, causing social and economic losses (the economic & social commission for asia and the Pacific (escaP), 2021). the severity of disasters and their effects will only continue to rise with the rise in the earth’s temperature (ramadhan et al., 2022). © 2024 the author(s). Published by informa uK Limited, trading as taylor & Francis group. CONTACT igif gimin Prihanto [email protected].id Research Center for testing technology and standards, national Research and innovation agency (BRin), Jl. Kawasan Puspiptek, serpong, Muncul, setu, south tangerang 15314, indonesia. https://doi.org/10.1080/23311975.2024.2374880 this is an open access article distributed under the terms of the Creative Commons attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. the terms on which this article has been published allow the posting of the accepted Manuscript in a repository by the author(s) or with their consent. ARTICLE HISTORY received 18 December 2023 revised 31 January 2024 accepted 20 may 2024 KEYWORDS early warning system; hydrometeorology; Indonesia; partial least squares structural equation modeling; satellite; technology acceptance model REVIEWING EDITOR hui shan loh, singapore University of social sciences, singapore SUBJECTS earth sciences; environmental management; statistics & Probability 2 I. g. PrIhanto etal. Indonesia’s 2022 Disaster Information Database shows that hydrometeorological hazards (e.g. floods, tornadoes, drought, and landslides) in the last ten years have contributed to approximately 60% of Indonesia’s total disasters (adi etal., 2021). therefore, preventive actions, including the use of early warning systems, are needed to manage risks (Kordzakhia et al., 2011; susandi et al., 2018). a satellite-based early warning system dedicated to hydrometeorological hazards in Indonesia was developed by the national research and Innovation agency (BrIn) and called saDeWa. the system monitors atmospheric conditions (susandi et al., 2018) and provides much-needed information for disaster preparedness, allowing individuals to react effectively and efficiently (sufri et al., 2020). the system also serves as an input for formulating strategic plans for disaster-risk management (ramadhan et al., 2022). one of the problems identified in the application and implementation of new technology is user acceptance. for a technology to be useful, it must be accepted by potential users. for early warning system technology to be successfully implemented and used, stakeholders are responsible for disaster mitigation (chen et al., 2017; sufri et al., 2020). this has been a problem with saDeWa. the use of this system might put Indonesians at an unnecessary risk. therefore, a study on the acceptance of satellite-based hydrometeorology early warning systems is required. one of the most widely used theories to explain a social phenomenon related to technology acceptance is the technology acceptance model (tam), which is capable of exploring factors affecting an individual to reject, accept, or continue to utilize new technologies (herrenkind et al., 2019; Park et al., 2022). this theory could accurately measure user acceptance of the technology (chen et al., 2017). the use of tam is not limited to the context of disaster management. Brar et al. (2022) used tam to study users, rescue personnel, and perceptions of the Internet of things (Iot) in Punjab, India. meechang et al. (2020) adopted the tam to investigate the factors affecting users’ acceptance of risk management information technology in 38 countries between 2011 and 2018. there has not been any tam-based research probing users’ acceptance of the hydrometeorological hazard early warning system. according to the original tam constructs, intention only involves four factors: perceived usefulness (PU), perceived ease of use (PeU), attitude to use (aU), and intention to use (IU) (singh & srivastava, 2018). although generalizable, the original tam was deemed inadequate to explain users’ acceptance in certain contexts (chen et al., 2017; Kamal et al., 2020), prompting various additions of external factors (chen etal., 2017; rafique etal., 2020). along with the development of early warning system technology, which is relatively new in Indonesia, the system needs to build trust (t) (Dhagarra et al., 2020) and image (I) (Venkatesh, 2015; yuen et al., 2021) as well and to ensure information quality (IQ) (salloum et al., 2019). as in previous research, this study adopted trust (t), image (I), and information quality (IQ) because (a) trust represents a feeling of belief or confidence in the service of the technology (Dhagarra et al., 2020; meechang et al., 2020). trust is essential for elucidating the relationship between technology and user acceptance. a feeling of trust or distrust will shape potential users’ decision to either exploit or avoid a particular technology. (b) Image signifies a social impact that might be acquired if someone opts to use technology. the evaluation of this impact guides potential users’ choices in terms of technology usage, and (c) information quality refers to users’ perception of how accurate, comprehensive, and timely the information provided by the new technology is (salloum et al., 2019). this perception influences users’ determination to use technology. In this case, the information quality of the early warning system influences users’ decisions to use (or not use) the system. although these three external factors might affect users’ acceptance, they are rarely considered in the extended tam for evaluating the acceptance of satellite-based hydrometeorological hazard early warning systems in Indonesia. a study involved these three factors for evaluating the acceptance of satellite-based hydrometeorological hazard early warning systems was Prihanto etal. (2023). they tested the direct influence of t, I, and IQ on IU. the analysis results show that t, I, and IQ are not proven to have a direct effect on IU. Based on rationalization and previous empirical research, all three may affect IU but indirectly. Unfortunately, for the context of evaluating the acceptance of satellite-based hydrometeorological hazard early warning systems, the possibility of this indirect influence has never been tested. to fill the gaps in the literature, this research aims to test the indirect effect of t, I, and IQ on IU. specifically, this research will develop and test a technology acceptance model of satellite-based hydrometeorological hazard early warning systems in Indonesia under the extended tam using the following variables: PU, cogent BUsIness & management 3 PeU, aU, IU, t, I, and IQ. the results of this study will contribute to the development and testing of an extended tam. furthermore, this research aims to answer the questions: • Does trust have a significant effect on perceived usefulness? • Does image have a significant effect on perceived usefulness and perceived ease of use? • Does information quality have a significant effect on perceived ease of use? • Does perceived usefulness have a significant effect on attitude to use and intention to use? • Does perceived ease of use affect attitude to use significantly? • Does attitude to use influence intention to use significantly? 2. Literature review and hypothesis 2.1. Satellite-based disaster early warning system (EWS) technology the satellite-based disaster eWs was developed as a risk management and community protection effort based on communication, monitoring, warning, risk forecasting, and response capacity (chikalamo etal., 2020; mukhtar, 2018). many satellite-based early warning systems have been developed, such as Unified river Basin simulator/UrBs, providing flood warnings and monitoring based on strata, created based on numerical models of weather forecasts and displayed through text (Pagano et al., 2016); erIcha rainfall-based flood warning system and flood-Proofs, utilize rain radar data with a spatial scale of 1 sq. km to predict flood predictions in near real-time using a rainfall-runoff model to improve response time (corral et al., 2019); extreme rainfall Detection system/erDs, provides weather forecasts for the next 4 hours using satellite imagery data with a spatial resolution of 100 sq.km with 30 minutes time updates (mazzoglio et al., 2021); and Drought early Warning systems/DeWs, developed to monitor and estimate drought hazards through a combination of spatial models and drought indices whose information is displayed through the web (Prudhomme et al., 2024). the satellite-based disaster early warning system in Indonesia, saDeWa, was developed as part of hydrometeorological hazard risk management, and it can be accessed through the website https:// sadewa.brin.go.id/. It uses both terrestrial sensors and satellites to gather information. It aims to prepare citizens by providing near real-time information on the entire Indonesian landscape at a five-kilometer square resolution (lasmono et al., 2021; susandi et al., 2018). It was run by Weather research and forecasting (Wrf) with advanced research Wrf (arW) core version 3, the validation of which is carried out on data observations such as the automatic Weather station (aWs) to increase the accuracy values and then delivers the information through websites, emails, and text messages a one-day forecast of hydrometeorological conditions (Purwalaksana, 2015). the Wrf model is a numerical weather prediction model capable of calculating the distribution of water vapor and rainfall variances with a high spatiotemporal resolution through a meteorological observation assimilation system that provides better observation results (gong et al., 2023). 2.2. Technology acceptance model (TAM) the technology acceptance model (tam) is a model adopted from the theory of reasoned action (tra) developed by fred Davis to predict and explain technology acceptance (marangunic & granic, 2015; Zhang et al., 2020). the model has been applied in many research fields to elucidate individuals’ intentions to either accept or reject a particular technology (herrenkind et al., 2019; Park et al., 2022). tam accurately measures acceptance (chen et al., 2017) and provides adequate empirical evidence (Zhong et al., 2021). theoretically, the original tam yielded four constructs: PU, PeU, aU, and IU (yang et al., 2021). PU measures the extent to which an individual believes that the use of a system or particular technology can enhance their performance (Verma et al., 2018; Zhong et al., 2021). on the other hand, PeU was defined as the perceived easiness of a certain technology to be understood, operated, and mastered without substantial effort (scherer et al., 2019; Zhong et al., 2021). an aU is a subjective favorable or unfavorable evaluation of the use of a system or technology (Wu & chen, 2017; Zhong et al., 2021). IU 4 I. g. PrIhanto etal. refers to an individual’s intention to use a system or technology shortly (Verma et al., 2018). Intention goes further than interest and manifests itself in the form of a plan. Previous studies have suggested PU and PeU as tam’s main constructs, determining factors of intention to accept a particular technology (alfadda & mahdi, 2021; Balakrishnan et al., 2021; salloum et al., 2019). PU and PeU shape an individual’s attitude toward the use of technology (herrenkind etal., 2019), which eventually leads to the intention to use technology (malaquias etal., 2018; tu & yang, 2019; Zhong et al., 2021). 2.3. Trust in satellite-based disaster EWS (T) trust (t) is imperative in any social relationship as well as in human and technological interactions. trust represents confidence in someone or something: a judgment that the target of our trust will either act or provide service in accordance with our expectations. In this context, trust refers to users’ confidence that technology will deliver early warning services, as expected by users (Kamal et al., 2020). Previous research has found a significant positive relationship between t and PU (Dhagarra et al., 2020; le, 2021; Wang etal., 2023). this study assumed the same relationship. If the potential user believes that the early warning technology will provide them with valuable services, they will perceive the technology as useful. thus, the first hypothesis is as follows: H1: trust in satellite-based disaster eWs (t) has a positive significant impact on the Perceived Usefulness (PU) 2.4. Image of satellite-based disaster EWS (I) Image (I) acts as an embodiment of social influence. It signifies the use of technology as an image or social status booster. If society collectively thinks that using certain technology is favorable, people will tend to use the technology to be accepted in society and to have their image uplifted by the technology (alhasan etal., 2020; herrenkind et al., 2019; yuen etal., 2021). as proven by previous studies (Karahoca etal., 2017; yuen etal., 2021), the logic dictates that image will positively associate with PU. this research proposes that if the use of a satellite-based disaster eWs is deemed favorable by the community, people will perceive its usefulness as an image booster. to address this proposal, the following hypothesis is offered: H2: Image of satellite-based disaster eWs (I) has a positive significant impact on the Perceived Usefulness (PU) In addition to its effect on PU, a previous study found a positive effect of image on perceived ease of use (PeU) (alhasan et al., 2020). a positive image of something may produce positive evaluation results (sumaedi et al., 2014). human behavior literature has shown that images can affect human evaluation because human evaluation may be biased when there is precedent information in certain contexts, such as in the context of new technology evaluation that is unfamiliar previously (solomon, 2012; sumaedi etal., 2014). since PeU is a form of human evaluation, while satellite-based disaster early warning system technology is a new technology, where image positively affects PeU in this research. the third hypothesis is formulated as follows: H3: Image of satellite-based disaster eWs has a positive significant impact on perceived ease of use (PeU) 2.5. Information quality (IQ) of satellite-based disaster EWS Information quality (IQ) refers to the extent to which an individual believes technology can provide complete, accurate, and timely information (salloum etal., 2019). reasonably, if people believe that a system or technology has good information quality, they would assume it is easy to find information or obtain a service from the system. In this context, if potential users perceive that the early warning system provides them with necessary information, they will think it is easy to use. Previous empirical studies have found a significant positive causal relationship between IQ and PeU (alyoussef, 2023; salloum etal., 2019; Wongvilaisakul & lekcharoen, 2015). this study expected a similar relationship. cogent BUsIness & management 5 H4: Information quality (IQ) of satellite-based disaster eWs has a positive significant impact on perceived ease of use (PeU) 2.6. Perceived usefulness of satellite-based disaster EWS (PU) Perceived Usefulness (PU) represents how individuals deem a system or technology useful in enhancing performance (na et al., 2022; Verma et al., 2018). PU is a subjective assessment that can generate favorable or unfavorable results. When people perceive a system or technology as useful to them, they tend to see it in an affirmative light, creating a positive attitude towards the use of this system. In this context, if potential users realize the value of an early warning system, their attitude toward utilizing the system will likely be favorable. Previous studies have shown a positive and significant relationship between PU and attitudes (georgiou etal., 2023; huang etal., 2023; matubatuba & De meyer-heydenrych, 2022). to accommodate this relationship, the following hypothesis is proposed. H5: Perceived usefulness of satellite-based disaster eWs (PU) has a positive significant impact on attitude to use it (aU) aside from its apparent influence on attitude, previous studies have also found an impact of PU on IU (georgiou et al., 2023; Katebi et al., 2022). reasonably, the realized usefulness of a system attracts its utilization. once stakeholders recognize the value of the early warning system, they will be willing to operate it. the PU is a key determinant of the usage (salloum et al., 2019). thus, the propose following hypothesis: H6: Perceived usefulness of satellite-based disaster eWs (PU) has a positive significant impact on intention to use it 2.7. Perceived ease of use of satellite-based disaster EWS (PEU) Perceived ease of use (PeU) signifies the extent to which an individual believes that the system or technology is easy to use and understand without significant effort (scherer et al., 2019; Zhong et al., 2021). When facing something new, either a technology or a system, people tend to think about whether this new technology will be easy to use or navigate. If they consider that the technology is easy to utilize, they will have a positive attitude towards the use of that technology. ruminating on the early warning system, if stakeholders consider obtaining information from the website easy, they will have an approving attitude. Previous studies have found a significant positive relationship between PeU and attitudes (huang et al., 2023; Islam, 2023; Wang et al., 2023). the following hypothesis supported this relationship: H7: Perceived ease of Use of satellite-based disaster eWs (PeU) has a positive significant impact on attitude to use it 2.8. Attitude to use satellite-based disaster EWS (AU) attitude to use (aU) is a subjective evaluation of whether a certain behavior leads to favorable or unfavorable outcomes (tu & yang, 2019) and a primary determinant of behavioral intention (Vanduhe et al., 2020). People with a positive attitude towards technology are more likely to use it. In the context of early warning systems, if stakeholders perceive that using the early warning system will give them valuable hydrometeorological assessment results that are beneficial for them, they will have a higher intention to use it. researchers have investigated the influence of attitudes on behavioral intention (alam etal., 2021; muhaimin etal., 2019; sukendro etal., 2020). the following hypothesis supports this narration: H8: attitude to use satellite-based disaster eWs (aU) has a positive and significant impact on intention to use it 3. Research design In explaining the acceptance of satellite-based hydrometeorological hazard early warning systems in Indonesia, this paper improved Prihanto et al. (2023) by developing a more comprehensive technology acceptance model that involves direct and indirect antecedent of IU and adding the sample size in 6 I. g. PrIhanto etal. testing the model. this research aims to develop and test a technology acceptance model for a satellite-based hydrometeorological disaster early warning system in Indonesia based on the extended tam which involves the variables: perceived usefulness (PU), perceived ease of use (PeU), attitude to use (aU), intention to use (IU), trust (t), image (I), and information quality (IQ). Based on the literature review, the proposed model is depicted in figure 1. 3.1. Variables and measures this study involved seven main variables: PU, PeU, aU, IU, t, I, and IQ. the indicators for the variables were adapted from previous studies. the indicators were adjusted to accommodate the context of early warning systems. there were 23 indicators and each was measured using a six-point likert scale ranging from ‘extremely disagree’ (1) to ‘extremely agree’ (6). table 1 lists the variables and indicators. 3.2. Sample and data collection according to the code of ethics in Indonesian law number 11 of 2019 concerning the national system of science and technology which stipulates in line with the principles of the 1964 Declaration of Figure 1. the proposed technology acceptance model for a satellite-based hydrometeorological disaster early warning system in indonesia. Table 1. Variables and indicators of the proposed model. Variables Definition indicators adapted from Perceived usefulness (Pu) the extent to which individuals deem satellite-based disaster eWs useful in enhancing their performance improved effectiveness (Pu1) (Verma et al., 2018; Zhong et al., 2021)increased productivity (Pu2) improved performance (Pu3) Positive experiences (Pu4) Perceived ease of use (Peu) the extent to which an individual believes that satellite-based disaster eWs is easy to use and understand without significant effort easy to operate (Peu1) (Vanduhe et al., 2020; Zhong et al., 2021)easy to access (Peu2) easy to navigate (Peu3) easy to understand (Peu4) information Quality (iQ) the extent to which an individual believes that satellite-based disaster eWs can provide complete, accurate, and timely information accurate information (iQ1) (salloum et al., 2019; Zhong et al., 2021)Clear and easy-to-understand information (iQ2) trust (t) Confidence in satellite-based disaster eWs: a judgment that the satellite-based disaster eWs will either act or provide service in accordance with our expectations trustworthy (t1) (Dhagarra et al., 2020; Kamal et al., 2020).Reliable (t2) attitude to use (au) a subjective evaluation of whether satellite-based disaster eWs leads to favorable or unfavorable outcomes satisfied (au1) (sukendro et al., 2020; Zhong et al., 2021)easy to make decision (au2) Feel motivated to use (au3) Feel that the system matches with users’ work demands (au3) image (i) satellite-based disaster eWs as an image or social status booster Locally made (i1) (alhasan et al., 2020; Yuen et al., 2021)Reputable (i2) intention to use (iu) an individual’s intention to use satellite-based disaster eWs Will use in the future (iu1) (Vanduhe et al., 2020; Yang et al., 2021)Will use if there is an opportunity (iu2) Will use in the near future (iu3) cogent BUsIness & management 7 helsinki and cIoms 2002, where ethics approval was issued by the secretariat of the research ethics committee of the national research and Innovation agency. collecting research data was preceded by sending a survey request letter to the structural management of the relevant institution with a statement that the survey would be conducted anonymously and the data would not be used other than for this research. a statement regarding respondent consent was obtained and informed verbally due to increase flexibility and smoothness of the data collection process. on the other hand verbal communication allows for immediate clarification and responses to questions and allays respondent’s concerns. Data was collected using a questionnaire. the questionnaire was divided into two parts. first, it questioned the respondents’ demographic profiles. the second section deals with the seven main variables used in this study. the survey was conducted in three provinces, central Java, West Kalimantan, and riau. the three have been hit by severe hydrometeorological hazards, such as floods, tornadoes, landslides, and forest fires, based on data from the Indonesia Disaster Information Database for 2022 (adi et al., 2021). this study used purposive sampling with two criteria: the respondent must have had previous interaction with the early warning system and he or she lived in the predetermined area. the sampling technique was used to ensure that the respondents were well-equipped to answer the research question (ames etal., 2019). the number of samples was 100, which was double that of the study by Prihanto et al. (2023). the number was adequate for partial least squares structural equation modelling (Pls-sem) (Willaby etal., 2015). the minimum sample size in Pls-sem analysis was ten times the maximum number of arrows included in the endogenous variable (hair et al., 2019). In the Pls-sem model (figure 1), because the number of incoming arrows was eight, the minimum sample size was 80 (10 times the number of incoming arrows). thus, the sample size is 100, meaning it has exceeded the minimum sample size of 80 and is acceptable in this research. 3.3. Data analysis Data analysis was conducted using Pls-sem because it can test a model with complex constructs (hair et al., 2019). the analysis was conducted in three stages. first, a measurement model analysis is conducted. this stage aimed to evaluate the construct validity and reliability of the measurement model. construct validity was based on: (1) convergent validity with a threshold of >0.70 for loading factors and >0.5, for average Variance extracted (aVe) (hair et al., 2019) and (2) divergent validity measured using fornell-larcker or cross loading (hair et al., 2019). the reliability of the measurement was ensured by comparing the values of cronbach’s alpha, rho-a, and composite reliability (cr) with >0.70 threshold (hair et al., 2019). the second stage was a structural model analysis to examine the relationship between the constructs. the significance of the hypotheses was measured using Pvalue tests. Pvalue must be less than 0.05 for a beta to be called significant (hair et al., 2019). r2 was also used to gauge how well the variance of the exogenous variables explained the variance in endogenous variables. the third measure was the predictive relevance (Q2). a Q2 higher than zero indicates that the exogenous variables have adequate predictive relevance (sholihin & ratmono, 2021). the third stage was the model fit test. at this stage, the fit was based on the standardized root mean square residual (srmr) and normed fit Indices (nfI). this test was used to ensure that the model fits the observed phenomenon. the model is considered to be fit if the srmr is less than 0.08 or the nfI is higher than 0.08 (henseler et al., 2016; tan & ooi, 2018). 4. Results 4.1. Respondents’ demographic profile the demographic profile of respondents is displayed in table 2. of the 100 respondents, 66% were male at birth and 34% were female at birth. most had a bachelor’s or master’s degree (97%). almost 55% of the respondents were analysts, researchers, or engineers, and nearly 72% of the respondents worked in the government sector. regarding the eWs utilization period, 64% of respondents have less than one year of utilization period. 14 I. g. 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