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An Evaluation of Community Adoption of the InaRISK BNPB Platform for

Disaster Management: An Application of the Technology Acceptance Model
(TAM)

Article in International Journal of Safety and Security Engineering · September 2023

DOI: 10.18280/ijsse.130409

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 International Journal of Safety and Security Engineering
Vol. 13, No. 4, August, 2023, pp. 673-684
Journal homepage: http://iieta.org/journals/ijsse

An Evaluation of Community Adoption of the InaRISK BNPB Platform for Disaster

Management: An Application of the Technology Acceptance Model (TAM)
Erni Suharini1* , Supriyadi1 , Mohammad Syifauddin1 , Ervando Tommy Al-Hanif2 , Edi Kurniawan2 , Satya
Budi Nugraha2
1
Postgraduate School, Universitas Negeri Semarang, Semarang 50237, Indonesia
2
Geography Department, Faculty of Social Sciences, Universitas Negeri Semarang, Semarang 50229, Indonesia

Corresponding Author Email: erni.suharini@mail.unnes.ac.id

https://doi.org/10.18280/ijsse.130409 ABSTRACT

Received: 2 July 2023 This study explores the community acceptance of the InaRISK BNPB platform, a novel
Revised: 28 July 2023 approach to disaster management that integrates digital technology, Geographic
Accepted: 10 August 2023 Information Systems (GIS), and the Internet of Things (IoT). The Technology Acceptance
Available online: 28 September 2023 Model (TAM) is utilized as a theoretical framework to decipher the acceptance patterns.
Employing a quantitative research design, a survey methodology was adopted involving
47 participants, each over 18 years of age and having prior experience with the InaRISK
Keywords: BNPB platform. Data was collated from both primary and secondary sources. The primary
disaster management, information and data was gathered through questionnaires, while secondary data was obtained via an
communication technology (ICT), InaRISK exhaustive literature review. The study implemented a quantitative descriptive analysis,
BNPB, perceived usefulness, technology alongside simple and multiple regression analyses for data interpretation. Findings
acceptance model (TAM) suggest a significant impact of perceived ease of use on perceived usefulness, thereby
influencing attitudes towards use and behavioral intentions to use the platform. Notably,
attitude towards use was found to directly affect behavioral intention to use the platform.
These findings underscore the salience of usability and intuitiveness in fostering
technology acceptance. Consequently, it is imperative to enrich the features of InaRISK,
making it not only more informative but also user-friendly, to bolster its adoption within
the community. To augment the platform further, promoting transparency and information
sharing across diverse sectors and stakeholders is deemed essential. This collaborative
endeavor can enhance the quality and comprehensiveness of the information available on
the InaRISK platform, thereby transforming it into an integrated disaster information hub.
The potential contribution of this transformation to the advancement of digital IT-based
disaster management is substantial.

1. INTRODUCTION disaster risk reduction and the enhancement of disaster

resilience within Indonesian society and its governmental
Indonesia, as articulated in global reports [1, 2], is situated structures have become paramount [6]. Disasters, as integral
among the countries bearing the highest disaster risks components of human existence, should not be underestimated,
worldwide. The nation is frequently plagued by a diverse array given that their impacts transcend individual and group levels,
of disasters, encompassing seismic phenomena (such as affecting the nation as a whole [7, 8]. Consequently, disaster
earthquakes and tsunamis), volcanic eruptions, and risk reduction emerges as a crucial strategy for preemptive
hydrological events (including floods and landslides). Several mitigation and expedited recovery post-disaster. The reduction
factors contribute to the amplification of disaster incidences in and prevention of disasters can be achieved through the
Indonesia, including its unique geological, geographical, and enhancement of the community's capability to mitigate
climatological positions, as well as socio-economic conditions hazards [9, 10].
and persistent environmental damage [3, 4]. Positioned at the In the context of the 21st century and the ongoing Industry
convergence of the Eurasian Plate, the Indo-Australian Plate, 4.0 revolution, disaster risk reduction and community capacity
and the Pacific Plate, Indonesia forms an integral part of the enhancement necessitate an approach rooted in scientific and
highly volatile Pacific Ring of Fire. This geologically active digital technology paradigms. Science and technology have
location, coupled with the country's tropical climate been universally recognized as vital catalysts for promoting
characterized by high rainfall and frequent climatic anomalies, and implementing disaster risk reduction efforts, tracing back
intensifies the complexity of its disaster risk profile. to the International Decade for Natural Disaster Reduction
Concurrently, Indonesia faces compounded challenges with (IDNDR) in the 1990s, extending through the Hyogo
rapid population growth, muted economic advancement, Framework for Disaster Risk Reduction (HFA) from 2005 to
urbanization trends, and a prevalent tendency towards 2015, and currently under the auspices of the Sendai
economic development, often overshadowing social and Framework for Disaster Risk Reduction (SFDRR) from 2015
environmental considerations [5]. to 2030 [11]. The Sendai Framework, which spans from 2015
Given the heightened disaster risk, the prioritization of to 2030, underscores the need for all stakeholders to prioritize

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four critical areas: (1) Enhancing the understanding of disaster The acceptance and utilization of the InaRISK platform can
risk, (2) strengthening governance mechanisms for effective be evaluated using the Technology Acceptance Model (TAM)
disaster risk management, (3) allocating resources to disaster approach, which is designed to assess information systems and
risk reduction to fortify resilience, and (4) improving disaster elucidate how users accept and utilize a system [24]. As
preparedness for a more efficient response and the pursuit to postulated by Davis [25], the Technology Acceptance Model
"Build Back Better" during recovery, rehabilitation, and (TAM) is primarily influenced by two pivotal factors that
reconstruction phases [12]. Consequently, the role of impact an individual's willingness to adopt a new technology:
technology in disaster risk reduction warrants emphasis, as Perceived Ease of Use and Perceived Usefulness. Accordingly,
advancements in technological innovation necessitate the this study aims to analyze the acceptance of the BNPB
adaptation and evolution of disaster management systems [13]. InaRISK platform in the community utilizing the Technology
In the current epoch of the Fourth Industrial Revolution Acceptance Model (TAM). The study scrutinizes how
(Industry 4.0), opportunities abound for devising both individuals' attitudes towards using the InaRISK platform, as
structural and non-structural disaster management strategies. well as their intention to adopt it, are impacted by their
The industrial revolution, marked by digitalization and perceived usefulness and ease of use. Additionally, the study
information technology advancements, facilitates swift, investigates how attitudes towards usage influence the
accessible, practical, efficient, and widespread information intention to utilize the InaRISK platform. Through this TAM
transfer, thereby possessing significant potential in aiding analysis, the study anticipates providing evaluative insights for
disaster risk reduction endeavors [14]. The strides made in stakeholders to augment the effectiveness of the InaRISK
digital information technology over the past decade have been platform, thereby enhancing its utility for diverse societal
instrumental for various disaster practitioners, including the segments.
realms of big data, the Internet of Things (IoT) [15, 16],
Artificial Intelligence (AI), remote sensing, geospatial data
[17], cloud computing, and social media communication [18, 2. LITERATURE REVIEW
19].
Indonesia, classified among the nations grappling with the 2.1 The role of digital technology in disaster risk reduction
most substantial disaster risks worldwide, has undeniably
adopted digital technology as a strategic tool for risk Aligned with the Sendai Framework for Disaster Reduction
mitigation. In the Indonesian context, the incorporation of 2015-2030, disaster risk reduction endeavors must underscore
technological advancements into disaster management the pivotal role of science and technology. This technological
predominantly initiates with the deployment of a Geographic arsenal encompasses remote sensing technology, Geographic
Information System (GIS), serving as an essential mapping Information Systems (GIS), Global Positioning System (GPS),
tool utilized by the National Disaster Management Agency satellite navigation systems, communication satellites,
(BNPB). In a step towards innovation, BNPB has developed a amateur radio, radio and television broadcasting, email, online
platform named InaRISK, accessible through a website data management, disaster information systems, and robotics.
(inarisk.bnpb.go.id) and a smartphone application. The These technologies find widespread application in establishing
InaRISK platform integrates digital technology, Geographic Early Warning Systems, processing systems, and disaster
Information Systems (GIS), and the Internet of Things (IoT) analysis. Moreover, they are employed for tasks like
in its operations, providing real-time access to various disaster constructing databases, integrating and analyzing information,
data and information, including disaster risk, disaster events, creating disaster maps and conducting scenario simulations,
and other pertinent information [20]. assessing hazards and monitoring them, predicting disaster
The InaRISK platform harbors significant potential to trends, evaluating vulnerability, facilitating emergency
fortify disaster preparedness, particularly public knowledge response decision-making, crafting disaster response plans,
pertaining to disaster risk and early warning systems. preparing logistics, and supporting Search and Rescue (SAR)
Nevertheless, initial surveys suggest a lack of public teams [26].
understanding regarding the platform's functionality, with its Technology has a huge role in disaster risk reduction efforts,
community utilization remaining minimal. Many individuals both at the preparedness stage, disaster mitigation stage,
report unfamiliarity with the InaRISK platform and an absence response stage, and recovery stage [26]. According to the
of socialization regarding the platform. Predominantly, United Nations [27], here are potential technologies associated
disaster-related information within the community is procured with each stage of disaster management:
from social media or direct communication from relevant 1. Mitigation - Focused on reducing disaster impact.
disaster stakeholders. Examples include building codes, zoning regulations,
The availability of a robust disaster information platform is vulnerability analysis, and community education.
indispensable, as individuals require information about 2. Preparedness - Involves planning for disaster
disaster risks to respond effectively [21]. Consequently, an response. Examples encompass preparedness plans,
analysis of the acceptance and utilization of the InaRISK emergency response exercises, and early warning
platform within the community is crucial. The analysis aims to systems.
uncover community perspectives and assessments of the 3. Response - Concentrated on minimizing disaster-
InaRISK platform, thereby offering a reference for related dangers. Examples include Search and Rescue
stakeholders to undertake development and enhancement (Robotic) operations, critical area mapping, and
measures in terms of both platform quality and its community information management.
adoption frequency. This is particularly important given that 4. Recovery - Aimed at restoring the community to
disaster information platforms are infrequently used compared normal conditions. Examples comprise trauma
to social media [22] and are often relegated to times of crisis healing, mapping, and planning for the reconstruction
[23]. of various public facilities and settlements.

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 Source: Asian Development Bank [28]

Figure 1. The stages of digital technology in disaster management

The utilization of digital technology in disaster management potent tool for analytical endeavors, given that each phase
can be categorized into three primary stages, each reflecting within the disaster management cycle is intricately connected
an increasing level of sophistication. The stages of digital in terms of geography and spatial relationships. GIS plays a
technology adoption in disaster management are categorized significant role in spatial analysis to make it more accessible
as follows: and adaptable, making it well-suited for disaster risk analysis
1. First Stage: This stage includes the utilization of and spatial planning [32].
databases, remote sensing, internet, satellite images and In the disaster prevention and preparedness phase, one of
photos, mobile phones, GIS. the fundamental roles of GIS is to assess and map disaster-
2. Second Stage: In this stage, there is an adoption of social prone areas. GIS can also contribute to the assessment and
media, smartphones, and cloud computing. mapping of disaster risk levels in various regions [33]. For
3. Third Stage: The third stage represents cutting-edge example, a study by Assilzadeh et al. [34] used GIS to generate
technologies such as advanced technologies such as landslide hazard maps, maps of landslide-affected areas,
Artificial Intelligence (AI), machine learning, deep landslide disaster risk maps, and emergency response maps for
learning, the Internet of Things (IoT) coupled with landslides. Another study by Oluwasegun [35] analyzed the
intelligent systems, distributed ledger technology role of GIS in mapping flood-prone areas. Additionally, in the
including blockchain, harnessing Big Data for predictive prevention phase, GIS can serve multiple purposes, including
analysis, immersive experiences through Virtual and the planning of evacuation routes, designing evacuation zones
Mixed Reality, Robotics, and Unmanned Vehicles [28]. or locations, disseminating public information and involving
These advanced technologies are at the forefront of the community, as well as creating scenario models to address
disaster management. hypothetical situations [36-40].
These stages of digital technology adoption in disaster GIS can also be valuable in the aspect of disaster data
management are visually represented in Figure 1. management. GIS can serve as a data platform that allows
integration with other alternative data systems. For example,
2.2 The significance of geographic information systems GIS can display data about disaster phenomena such as
(GIS) in disaster risk reduction landslides, floods, earthquakes, including their locations,
frequencies, and intensities. Furthermore, GIS can showcase
In the initial stage of employing digital technology in data about the surrounding conditions of the location during a
disaster management, GIS and remote sensing stand out as disaster, such as its topography, geology, geophysics, soil
pivotal technologies. Consequently, disaster management composition, hydrology, land use, vegetation, and more.
must confront challenges associated with data collection, data Moreover, GIS can present data about variables that could be
management, interpretation, integration, and communication. affected or impacted during a disaster, such as infrastructure,
Hence, advancements in information technology, including settlements, demographics, and socio-economic knowledge
remote sensing, communication satellites, and Geographic [41]. A study by Cao et al. [42] also states that GIS integrated
Information Systems (GIS), hold the potential to enhance the with building information modeling (BIM) is highly beneficial
effectiveness of disaster management [29, 30]. in various aspects of disaster management in urban areas.
Geographic Information Systems (GIS) can facilitate During the response phase, GIS proves invaluable in
geographical exploration and inform spatial decision-making. various critical processes such as rescue operations,
GIS has the capability to narrate, process, and analyze spatial evacuation planning, medical services, food distribution, and
data sets from various sources and across different time spans. shelter management. For example, GIS can be harnessed to
GIS can also perform modeling, simulation, and visualization establish networks and monitor its extension through web and
of geospatial information, making it a foundational decision- mobile applications, which plays a pivotal role in supporting
making tool for stakeholders [31]. Hence, GIS emerges as a crisis managers to expedite their response efforts. Furthermore,

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GIS aids in the integration of data and information, addressing results revealed that the InaRISK application had been
potential hurdles in the swift, accurate, and efficient downloaded and used by only a few individuals, mainly those
distribution of aid [43]. Subsequently, in the recovery phase, residing in disaster-prone areas. Existing research highlights
GIS assumes a crucial role in restoring the crisis-affected areas the necessity for analyzing user acceptance of the InaRISK
to normal or even improved conditions. For instance, GIS is platform with one approach utilizing the Technology
employed in mapping, data organization, spatial and 3D Acceptance Model (TAM).
analysis, as well as geostatistical assessments to facilitate
recovery efforts, including site selection for permanent 2.3 Technology acceptance model (TAM)
housing and the restoration of essential services and
infrastructure [44-46]. In addition, during the recovery phase, The Technology Acceptance Model (TAM) is a framework
GIS assists in identifying areas suitable for reconstruction designed to illustrate users' intentions to accept and embrace
projects and recalibrating vulnerability models to enhance new technology [53]. TAM is widely regarded as one of the
predictions of future disaster impacts [40]. It also aids in most robust, effective, and influential models for elucidating
assessing disaster damages, conducting recovery analyses, user behavior in adopting systems or technology. Its primary
designing disaster databases, and supporting risk education aim is to forecast the acceptance of a system or technology and
initiatives [37]. to pinpoint necessary adjustments for the system to gain user
The pivotal role of GIS has spurred the development of acceptance [24]. According to TAM, perceived usefulness and
numerous applications with location-based functionalities and perceived ease of use stand as the primary determinants in the
map-centric disaster applications, serving as a platform for adoption of technology or systems within an organization.
disseminating information, enhancing preparedness, and Consequently, these two determinants serve as the
facilitating emergency response efforts [47]. The significant fundamental underpinnings for usage behavior and have
potential of GIS for the emergence of map and location-based implications for actual technology or system usage [54]. It's
disaster applications serves as one of the foundations for the worth noting that the TAM model is versatile and adaptable,
development of the InaRISK Platform in Indonesia. InaRisk applicable across various fields of development, and can be
was developed and launched by the National Disaster augmented with additional variables to examine the factors
Management Agency (BNPB). Inarisk is a disaster influencing the adoption of new technology [55].
information platform that integrates digital technology, Perceived usefulness represents an individual's level of
Geographic Information Systems (GIS), and the Internet of belief that a system will enhance their work performance. A
Things (IoT). InaRisk is capable of delivering real-time data system deemed useful is one that users perceive as having a
and up-to-the-minute information on risk and disaster events, positive impact on their performance. On the other hand,
ensuring its constant updates. Some of the information Perceived Ease of Use indicates a person's confidence in
available on the InaRisk platform includes information on whether using a system will be straightforward and not entail
types of disasters in each area, disaster maps, infographics on significant effort. Users will more accept a system or
disaster vulnerability and disaster risk, disaster threats and application that is easy to use. So, according to the Technology
hazards, government and population capacities, and Acceptance Model, a system or application, or technology will
population and other regional data that are closely related to be more accepted by users if it has benefits that can be felt by
disasters. InaRisk is able to provide disaster information up to users and are easy to use [25].
district and city levels [20].
The InaRisk platform has much potential in helping to
strengthen public disaster preparedness, especially aspects of 3. METHODS
risk knowledge and early warning systems. The community
can learn all information in InaRisk to increase their 3.1 Research design
knowledge of disaster risks around them. InaRISK can also
function as an early warning system because it provides The objective of this research is to assess the acceptance of
various preventive methods and steps to save themselves when the InaRISK BNPB platform within the community, utilizing
a disaster occurs in an area [20]. the Technology Acceptance Model (TAM). This study utilizes
Nevertheless, despite its potential, the utilization of a quantitative approach, employing a survey method and
InaRISK still needs to be further improved, as its current usage adopting the TAM framework as introduced by Davis [25],
remains limited, and there needs to be more extensive research which encompasses two determinant variables and two
analyzing its effectiveness. Several studies have focused on dependent variables. In this study, the two determinant
the effectiveness of InaRISK's usage as an educational tool in variables under analysis are perceived usefulness and ease of
schools, such as the study by Febrianto et al. [48] and Khusna use. These variables were examined for their impact on users'
et al. [49]. Subsequent research by Syaiful et al. [50] analyzed attitudes toward using the InaRisk platform and their
the application of InaRISK in educating about COVID-19 behavioral intention to use it. The study's design is illustrated
disaster mitigation. Furthermore, Diliawan's study [51] in Figure 2 below.
employed InaRISK to determine recommended locations for
warning signs along the Cimandiri Fault lane. Another 3.2 Research hypothesis
research conducted by Afisa et al. [52] examined the
utilization of the InaRISK personal application for advancing The research hypotheses include the following:
disaster mitigation efforts in Indonesia and the associated 1. H1: The Perceived Ease of Use variable will have an
challenges. The study's findings indicated that the information impact on the Perceived Usefulness variable.
aspect of InaRISK obtained a score of 42%, indicating a good 2. H2: The Perceived Usefulness variable will influence
rating, followed by the communication aspect with a score of the Attitude to Use variable.
31% and the coordination aspect with a score of only 26%. The 3. H3: The Perceived Ease of Use variable will

676
 influence the Attitude to Use variable. articles. Additionally, secondary data is collected through
4. H4: The Perceived Usefulness variable will impact online sources, including the internet and other online
the Behavior Intention to Use variable. platforms that offer data access and retrieval capabilities. The
5. H5: The Perceived Ease of Use variable will affect secondary data in this study is utilized to bolster the research
the Behavior Intention to Use variable. foundation. Through secondary data, this research becomes
6. H6: The Attitude to Use variable will impact the more focused as it is designed to further develop previous
Behavior Intention to Use variable. studies. Additionally, secondary data is also used to elaborate
on research outcomes, allowing for a more comprehensive
3.3 Participants synthesis to address the issues within the study.

This study involved 47 participants who used the InaRisk 3.5 Data analysis technique
BNPB platform with criteria over 18 years of age. Participants
were selected randomly so that every resident who met the age The study employed various data analysis methods,
criteria could be a participant. Initially, a survey was comprising quantitative descriptive analysis techniques,
conducted involving 153 individuals to analyze the utilization simple linear regression analysis techniques, and multiple
of the InaRISK Platform. Based on the survey, out of the 153 regression analysis techniques. Descriptive analysis was
respondents, 106 individuals had yet to be aware of or used the utilized to assess the respondent's profile and the frequency
InaRISK platform. Meanwhile, the remaining 47 individuals distribution of each research variable. With descriptive
were already familiar with and using the InaRISK platform analysis, we can determine how respondents answered each
and subsequently became participants in the study for the variable, including the minimum and maximum values
TAM analysis. Therefore, the researchers only included 47 obtained by respondents, and the average responses of the
respondents due to the limited number of individuals who were respondents. The results of the descriptive analysis also show
aware of and using the InaRISK platform. Participants will be the categories or levels of acceptance that respondents
analyzed based on age, location of residence, occupation, provided for the InaRISK platform in general.
disaster experience, education level, gender, and membership Linear regression was utilized to examine the influence of
status in disaster management organizations or communities. the determinant variable on the dependent variable within the
TAM model. The linear regression analysis encompasses both
3.4 Sources, techniques, and data collection tools simple and multiple regression analyses and the coefficients of
Beta and determination. Simple regression analysis was
This study draws its data from both primary and secondary applied to assess the impact of the perceived ease of use
sources. Primary data pertains to information obtained directly variable on the usefulness variable and to evaluate the
from the field, specifically concerning the reception of the influence of the attitude toward the use variable on the
BNPB InaRISK platform within the community. Primary data behavioral intention to use variable. In contrast, multiple
collection was executed through a questionnaire approach, regression analysis was employed to investigate the combined
with the data collection instrument being the questionnaire effects of perceived ease of use and perceived usefulness
itself. The questionnaire used for TAM analysis comprises a variables on the attitude toward the use variable and to analyze
total of 25 items, distributed as follows: 8 items for the the concurrent impact of these two variables on the behavioral
perceived usefulness variable, 5 items for the perceived ease intention to use variable. Furthermore, the analysis
of use variable, 6 items for the attitude toward use variable, encompasses a coefficient of determination assessment to
and 6 items for the behavioral intention to use variable. The gauge the strength of influence, as well as beta coefficient
questionnaire was structured using a Likert Scale, utilizing a analysis to scrutinize the significant contribution of
5-point scoring scale that ranges from 1 to 5. A more positive independent variables to each hypothesis's dependent variable.
response from the respondents corresponds to a higher score Additionally, the data analysis encompasses preliminary tests
for each questionnaire item. Before implementation in the or classical assumption tests to determine the suitability of the
study, the questionnaire underwent a validity analysis, data for regression analysis. The classical assumption tests
including Pearson's Product Moment Correlation analysis, to conducted in this study encompass the normality test using the
ensure its reliability and accuracy. Kolmogorov-Smirnov technique, the multicollinearity test
Secondary data is acquired through the retrieval of written involving the examination of Tolerance and VIF values, and
documents, central and local government policies and the heteroscedasticity test conducted using Spearman's Rho.
regulations, as well as library resources from previous journal

Figure 2. Design of the study

677
4. RESULT ease of use variable results in a 36.7% increase in the value of
the perceived usefulness variable. A detailed summary of the
4.1 Descriptive analysis outcomes of the simple linear regression analysis concerning
the impact of perceived ease of use on the perceived usefulness
A set of questionnaires was distributed to 47 respondents variable can be found in Table 2 below.
who had experience using the BNPB InaRisk Platform. The
gathered data was subsequently subjected to analysis using the 4.3 Hypothesis 2 and Hypothesis 3: The effect of perceived
SPSS program. The study examined four variables to validate usefulness and perceived ease of use on attitude to use
the research hypotheses, which encompassed the following
variables: X1 (Perceived Usefulness), X2 (Perceived Ease of The multiple linear regression analysis demonstrated that
Use), Y1 (Attitude to Use), and Y2 (Behavior Intention to Use). both the perceived usefulness variable and the perceived ease
In summary, an overview of the research variables is presented of use variable, when considered together, had a significant
in Table 1 below. impact on the attitude to use variable (F=15.415, P<0.05). The
The results of the descriptive analysis show that the variable combined influence of both the perceived usefulness variable
with the highest average is the perceived usefulness variable and the perceived ease of use variable on the attitude to use
(X1), with a score of 83.72%. In the second place, there is the variable accounts for 41.2%, while the remaining impact is
behavior intention to use variable (Y2), with a score of 81.50%. attributed to other variables. You can access comprehensive
In third place is the attitude to use variable (Y1), with a score results of the multiple regression analysis concerning the
of 78.73%. On the other hand, the variable with the lowest effects of the perceived usefulness and perceived ease of use
mean value is perceived ease of use, which scored 75.16%. variables on the attitude to use variable in Table 3 below.
Moreover, the data analysis results indicate that all four The perceived usefulness variable significantly and
variables have an average score falling within the high positively impacts the attitude to use variable (t=4.163,
category. P<0.05). The extent of the increase in the attitude to use
variable can be gauged by the Beta coefficient associated with
4.2 Hypothesis 1: The effect of perceived ease of use on perceived usefulness. The analysis results indicate that the
perceived usefulness Beta coefficient for the perceived usefulness variable is 46.4%.
Given the positive Beta coefficient results for the perceived
A straightforward analysis using linear regression was usefulness variable, it's important to note that a one-unit
conducted to explore how the perceived ease of use variable increase in the perceived usefulness variable will result in a
affects the perceived usefulness variable. The findings of this 46.4% increase in the value of the attitude to use variable.
analysis reveal that the perceived ease of use variable has a Furthermore, the perceived ease of use variable also exerts
positive and statistically significant impact on the perceived a significant influence on the attitude to use variable, as
usefulness variable (t=2.172, P<0.05). The perceived ease of evident from the values t=2.212 and P<0.05 (0.032). The
use variable explains 9.5% (R Square) of the variation perceived ease of use variable demonstrates a positive and
observed in the perceived usefulness variable. Although this statistically significant effect on the attitude to use variable,
contribution might appear relatively small, it emphasizes the characterized by a Beta coefficient of 29.4%. The Beta
significance of the perceived ease of use variable in coefficient associated with perceived ease of use signifies the
influencing the perceived usefulness variable. Furthermore, extent of the increase in the attitude to use variable. According
the substantial enhancement of the perceived usefulness to the analysis results, a one-unit increase in the perceived ease
variable is reflected in the Beta coefficient associated with the of use variable will result in a 29.4% increase in the value of
perceived ease of use variable. Notably, a positive Beta the attitude to use variable.
coefficient indicates that a one-unit increase in the perceived

Table 1. The result of descriptive analysis of variables

Variable N Minimum Maximum Mean Std. Deviation Category

X1 47 62.5% 97.5% 83.72% 3.20 High
X2 47 52% 100% 75.16% 2.69 High
Y1 47 60% 100% 78.73% 2.94 High
Y2 47 63.3% 100% 81.50% 3.27 High
Valid N (listwise) 47
Source: Data analysis result (2022)

Table 2. The effect of perceived ease of use on the perceived usefulness variable

Hypothesis Coefficients t Statistics P-value Result R Square

Perceived Ease of Use → Perceived Usefulness 36.7% 2.172 0.035 Significant 9.5%
Source: Data analysis result (2022)

Table 3. The effect of perceived usefulness and perceived ease of use on attitude to use variable

Hypothesis Coefficients Standard Error t Statistics P-Value R Square

Perceived Usefulness → Attitude to Use 46.4% 0.111 4.163 0.000 0.412
Perceived Ease of Use → Attitude to Use 29.4% 0.133 2.212 0.032 0.412
Source: Data analysis result (2022)

678
4.4 Hypothesis 4 and 5: The effect of perceived usefulness value of 0.000 (<0.05). The Beta coefficient associated with
and perceived ease of use on behavior intention to use the perceived ease of use variable is positive, measuring at
75.8%. In practical terms, a one-unit increase in the perceived
A multiple linear regression analysis was employed to ease of use variable will lead to a substantial 75.8% increase
assess both the collective and individual impacts of the in the behavioral intention to use variable.
perceived usefulness and perceived ease of use variables on
the behavior intention to use variable. The results of this 4.5 Hypothesis 6: The influence of attitude to use on
multiple linear regression analysis revealed that both the behavior intention to use
perceived usefulness variable and the perceived ease of use
variable had a simultaneous effect on the behavioral intention The results of the simple linear regression analysis clearly
to use variable (F=43.676, P<0.05). Collectively, the demonstrate the profound and statistically significant impact
perceived usefulness variable and the perceived ease of use of the attitude to use variable on the behavioral intention to use
variable accounted for 66.5% of the variance in the behavior variable. The analysis revealed a t-value of 7.189 and a P-
intention to use variable, while the remaining 33.5% was value of 0.000 (<0.05), indicating a robust and statistically
attributed to other factors. Further details regarding the significant influence of the attitude to use variable on the
outcomes of the multiple regression analysis on the effects of behavioral intention to use variable. In fact, the attitude to use
the perceived usefulness and perceived ease of use variables variable explains 53.5% of the variance in the behavioral
on the behavior intention to use variables can be found in intention to use variable, with the remaining 46.5% attributed
Table 4 below. to other factors. Furthermore, the Beta coefficient associated
The analysis of the perceived usefulness variable's impact with the attitude to use variable is notably high, measuring at
on the behavioral intention to use variable yielded a t-value of 81.3%. In practical terms, this means that a one-unit increase
4.014 and a P-value of 0.000 (<0.05). These findings strongly in the attitude to use variable leads to an impressive 81.3%
suggest that the perceived usefulness variable significantly increase in the behavioral intention to use variable. Detailed
influences the behavioral intention to use variable. findings regarding the impact of the attitude to use variable on
Furthermore, the Beta coefficient associated with the the behavioral intention to use variable can be found in Table
perceived usefulness variable stands at 37.6%. In practical 5 below.
terms, if the perceived usefulness variable increases by one The findings of this study reveal that all independent
unit, the behavioral intention to use variable will experience a variables exert a positive and substantial influence on the
37.6% increase. dependent variable. Out of the six hypotheses put forth, each
Additionally, the results of the multiple linear regression one has been corroborated by the analysis. A comprehensive
analysis demonstrate that the perceived ease of use variable overview of the outcomes for all six hypotheses within the
also significantly affects the behavioral intention to use TAM model employed in this research is depicted in Figure 3.
variable. This is evident through the t-value of 6.793 and a P-

Table 4. The effect of perceived usefulness and perceived ease of use on behavior intention to use variable

Hypothesis Coefficients Standard Error t Statistics P-Value R Square

Perceived Usefulness → Behavior Intention to Use 37.6% 0.094 4.014 0.000 0.665
Perceived Ease of Use → Behavior Intention to Use 75.8% 0.112 6.793 0.000 0.665
Source: Data analysis result (2022)

Table 5. The influence of attitude to use on behavior intention to use variable

Variable Coefficients t Statistics P-Value Result R Square

Attitude to Use → Behavior Intention to Use 81.3% 7.189 0.000 Significant 53.5%
Source: Data analysis result (2022)

Source: Data analysis result (2022)

Figure 3. Results of hypotheses analysis in the TAM model

679
5. DISCUSSION AND CONCLUSION because in the entire disaster management process from start
to finish, information technology plays a major role in
The outcomes of this investigation highlight several achieving successful disaster risk reduction whenever a
significant relationships within the TAM model. Firstly, it has disaster occurs. The use of digital information technology is
been established that perceived ease of use indeed affects effective in the preparedness, response, recovery, and
perceived usefulness. Furthermore, both perceived ease of use mitigation phases [29]. Hence, it would be more advantageous
and perceived usefulness were observed to exert a substantial if the disaster information platform were designed to facilitate
influence on the attitude toward use and the behavioral comprehensive disaster management, encompassing
intention to use. Moreover, it was evident that the attitude mitigation, preparedness, response, and recovery stages.
toward use had a substantial impact on the behavioral intention However, what has happened so far is that often the
to use. These findings underscore the importance of enhancing development of disaster information platforms is separate and
the quality of the InaRisk platform to improve its usability, independent at each stage of disaster management. This
effectiveness, and user-friendliness, ultimately leading to condition causes disaster information to become less
increased acceptance and intention to use among its users. integrated and less effective. It is necessary to better integrate
These findings align with research conducted by Brar et al. information system so that it will be more effective. The
[56], who investigated the usage of an IoT-Based Indoor combination and integration of various systems will
Disaster Management Software Tool among disaster rescue effectively present disaster issues in the field. As an
workers. Brar et al. [56] similarly found that perceived ease of illustration, data regarding disaster risk preparedness can be
use influenced perceived usefulness, which, in turn, affected seamlessly integrated with real-time information gathered
the attitude toward use. The attitude toward use was identified during the response phase. This integration not only enhances
as a significant factor influencing the intention to use the resilience but also elevates public awareness. By
system. Additionally, perceived ease of use was found to have amalgamating disaster risk information with real-time data
a direct impact on the intention to use. Another study by during the response phase, local governments can issue timely,
Aloudat et al. [57] also produced relevant findings, indicating specific warnings that prove invaluable to residents residing in
that both perceived ease of use and perceived usefulness high-risk regions [64].
played roles in shaping the attitude toward use, and this In addition, in order to be useful in disasters, disaster
attitude had an impact on the intention to use the system. information technology must be used routinely and can be
The results of other studies that produced similar findings compatible with other systems. If the system cannot be
include a study from Mailizar et al. [58] regarding the E- compatible with the other stakeholder terms, it will not be used
Learning platform. Then, the study of An et al. [59] regarding by stakeholders in a disaster [63]. Therefore, the information
the use of Telehealth innovations during the COVID-19 system must also be designed to enable the sharing of
Pandemic. Another study by Andy et al. [60] researched TAM information among different stakeholders to improve the
on the Digital Village application. Furthermore, there is also a quality of coordination and collaboration. It is also necessary
study from Alifiardi [61] which examines the GoJek platform to standardize the data shared in the system by each
or startup. From this study, it can be generalized that the stakeholder so that information sharing can run well and
complete and useful features of the application and its ease of effectively [64].
use affect the use of the application in the broader community. Sharing information between various stakeholders will be
The results of the study by Sari and Kenegae [62] also state so effective because it will enable forming an integrated
that perceived usefulness has a substantial and significant disaster management information system expected to
effect on behavior intention to use for users of the Magelang coordinate various government agencies from every level and
District Disaster Information System (SIKK Magelang). field. This is important because disaster management is an
These results strengthen that the primary determinant that interdisciplinary and interagency task and responsibility
affects technology adoption in a person is the aspect of involving many government agencies. However, even though
usefulness. When technology is able to facilitate and benefit a each department or agency has a complete database, the data
person, then dependence on technology will increase, and usually has incompatible units, formats, precision, and
increase the possibility of long-term use of the technology. accuracy, making integrating data into disaster management
The important aspect of perceived usefulness requires information systems challenging. Therefore, there is a need for
stakeholders to emphasize the importance of the usefulness standardization of data among government agencies
aspect in disaster applications. responsible for disaster management.
The addition of features on the InaRisk platform needs to be Disaster is also a spatial phenomenon. Thus, it has become
done to increase the completeness of the information users can necessary for disaster management and disaster information
access and learn. Achieving this objective can be facilitated by systems to integrate geospatial technology or technology
creating avenues for community engagement, particularly for based on Geographic Information Systems (GIS). GIS
individuals residing in high-risk regions, enabling them to provides a spatial database that is very useful in disaster
contribute to application development and offer insights into management. Leveraging GIS enables the identification and
the essential information to be incorporated into the mitigation of disaster risks, more effective disaster planning
application [62]. Bjerge et al. [63] also stated that adding and preparation, improved disaster response capabilities, and
features to the disaster information system will increase the faster disaster recovery processes. With GIS, it will be easy to
use of the information platform. Modifications to the technical make decisions in disaster management [65, 66]. Based on that
architecture, functionalities, and graphical user interface potential, GIS must be the starting point of the disaster
enhancements contribute to refining user engagement and information system that is created, or in the sense that every
optimizing system performance. disaster information system must include GIS and become the
The improvement of quality, features, and utility of the system's core.
disaster information platform becomes highly important Therefore, as the InaRisk platform discussed in this study is

680
a GIS-based disaster information platform, InaRisk has the simplifying the process for users to analyze and comprehend.
potential to be a starting point for developing an integrated Moreover, the level of detail of the information displayed by
disaster information system. Many research results suggest the InaRisk must be increased to have a more tangible impact on
role and potential of GIS in disaster risk reduction efforts [17] the community with a smaller scope than the district/city scale.
[67-72]. As a GIS-based disaster information system, InaRisk To acquire more comprehensive insights, fostering
must continuously improve its quality and features. InaRISk openness and facilitating information sharing among sectors
should be more useful, and more people use it. InaRisk is and stakeholders is of paramount importance, for example,
expected to be able to collaborate with data from other between the Meteorology, Climatology, and Geophysics
departments responsible for disaster management so that the Agency (BMKG), relevant Ministries, Indonesian Red Cross
information displayed is more detailed and in real-time. With (PMI), Indonesian Geological Agency, and others, so it will
a database integrated with other institutions, InaRisk can assist improve the quality and detail of the information presented on
in the decision-making process in the disaster management the InaRisk platform. Coordination and cooperation with
process at every stage, from mitigation, preparedness, and various institutions and related stakeholders are also crucial to
response, to recovery. increase the usefulness and detail of InaRisk's information and
services or features. Collaborating with experts and
practitioners in the information and communication
6. IMPLICATIONS technology field must also be carried out so that the
development of InaRisk can be directed to be better and more
6.1 Theoretical implications useful and help the community.
It is imperative to formulate a comprehensive IT strategy
The theoretical implication of this research is that further that encompasses the management of IT resources.
research can be directed at developing and innovating an Government policies are needed to strengthen the realization
integrated disaster information platform that can facilitate of IT-based disaster management both at the government level
disaster management spanning from the pre-disaster phase, and at the general public. Various staff of institutions and
through the duration of the disaster, and into the post-disaster actors, as well as the community who are responsible and play
period, GIS plays a pivotal role in disaster management. For a role in disaster management, also need to receive education
example, integrating information about disaster risks with and training in the use of information and communication
evacuation routes, evacuation points, nearest rescue facilities, technology in disaster management. To realize this, access to
disaster-affected areas, number of disaster victims, technology and information for the public must also continue
distribution of shelter locations, and so on. Indeed, in improving.
Indonesia, several disaster information platforms are used at Advocating for the increased use of information and
the national and local levels. Further research can further communication technology in disaster management within the
analyze the existing platforms to get a more detailed picture community is crucial. It is essential to elevate the adoption of
and develop better platform innovations that various disaster information and communication technology in disaster
management actors can use. Further research can also develop management to bolster community preparedness. The primary
a more integrated disaster information platform at the local target for advancing information and communication
level, as platform development at the local level often technology is the community; thus, individuals should be
accommodates the need for more detailed, comprehensive, and adequately informed and educated about the capabilities,
up-to-date databases. attributes, and applications of InaRisk technology. Vulnerable
Future research endeavors should encompass a wider communities should also be prioritized to obtain clear and
spectrum of participants, extending beyond the general public detailed information about disasters to improve their
to encompass various stakeholders engaged in disaster preparedness.
management. This includes members of disaster preparedness Using the InaRISK platform can be promoted through
groups, Regional Disaster Management Agency (BPBD) educational institutions in schools to ensure that the public
personnel, and the Indonesian Red Cross (PMI), the Police and becomes familiar with it from an early age. Mass promotion
the Military, as well as other security agencies, Search and can also be conducted within community-based disaster
Rescue teams, humanitarian agencies and NGOs working in management organizations in each region, such as Disaster
disasters, nurses and doctors, and all government agencies Preparedness Groups (KSB) and Disaster-Resilient Villages
responsible for disaster issues. All stakeholders responsible for (KATANA), as well as other community organizations at the
disaster problems must understand and use technology. Then, local level. Furthermore, promoting the use of the InaRISK
the technology needed and used by each actor involved is platform can also be done through village and sub-district
usually also different. This study also only uses a quantitative governments, thus reaching the smallest community levels.
approach. Future research is expected to combine quantitative
and qualitative methods to obtain more detailed data.
ACKNOWLEDGMENT
6.2 Practical implications
We express our sincere gratitude to Universitas Negeri
The feature development in InaRisk is critical to do by the Semarang for supporting this research endeavor. We also
National Disaster Management Agency (BNPB). More extend our appreciation to the experts who provided valuable
complete features will assist users in accessing more detailed and constructive suggestions to enhance the quality of this
and useful disaster information. The resolution of the research.
displayed image data must also be increased with the aim of

681
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