Task-technology Fit Research Articles

Do the technological anxiety, privacy and physical risks matter in retail customers'‏ ‏adoption of AR‏ ‏apps? An extended UTAUT2 approach

PurposeDue to augmented reality (AR) technology improvements, the retail industry has embraced smart retailing as its primary business model. Therefore, organizations must comprehend the intricacies of AR adoption to persuade clients to adopt this revolutionary technology effectively. Thus, the current study proposes and evaluates a comprehensive model that includes unified theory of acceptance and use of technology (UTAUT2), privacy concerns, physical risks and technological anxiety to predict customers’ intention to use AR apps in the retail industry in the Egyptian context.Design/methodology/approachThe current study examines 398 responses from Egyptian shoppers using partial least squares structural equation modeling (PLS-SEM). Snowball sampling was employed in the existing study. The participants were selected using a “self-selection” strategy, which is an excellent method for research investigations in which the participants freely participate.FindingsConsumers’ intentions to use AR apps in retail settings are positively impacted by task-technology fit, performance expectation, effort expectancy, social influence, facilitating conditions and hedonic motivation. Conversely, privacy and physical risks negatively affect customers’ intention to use AR apps in retail. Furthermore, technological anxiety serves as a moderator factor in these connections.Originality/valueTo the best of our knowledge, the current study is considered the first to test the role of UTAUT2, privacy and physical risks on users' behavioral intentions toward adopting AR apps in retail. It also examines technological anxiety as a moderator in the retail setting.

Robot anthropomorphism at luxury hotels: a dual-congruity mechanism with luxury and task under anxiety

ABSTRACT Although service robots have been extensively studied recently, few empirical investigations have focused on the luxury setting. Targeting the setting of luxury hotels, in this study, expectation-confirmation theory and dual-congruity theory are combined to examine the impact of robot anthropomorphism on consumers’ expectations of service robots. The findings obtained from a sample of 556 respondents indicated that robot anthropomorphism increased consumers’ perceived social capability, performance expectancy, and perceived importance. Furthermore, luxury-technology fit and task-technology fit mediated the effects of social capability, performance expectancy, and perceived importance on the intention to use robotic services. Furthermore, the moderating role of anxiety toward robots in strengthening the effect of luxury-technology fit on the intention to use robotic services was revealed. The findings contribute to an enhanced understanding of consumer psychology in the context of advanced service technologies, offering valuable insights for the strategic implementation of service robots at luxury hotels.

Impacting Elements of Metaverse Platforms’ Intentional Use in Cultural Education: Empirical Data Drawn from UTAUT, TTF, and Flow Theory

This study aims to address the need for design guidelines in developing a cultural-heritage-based metaverse educational system. Using the UTAUT, the TTF model, and Flow Theory, a theoretical framework is constructed. Through qualitative research based on the GT, three user perception factors—presence, interactivity, and narrativity—are introduced as external variables to explore the relationship between these factors and users’ willingness to adopt the cultural heritage metaverse system. The study examines this relationship from the dual perspectives of user perception and technology acceptance. A scale was designed to test the theoretical model empirically, and 298 valid responses were collected through a structured process involving GT coding, pre-testing, and formal surveys. The findings indicate that interactivity, narrativity, and presence significantly enhance the flow experience, while factors such as performance expectancy, effort expectancy, social influence, facilitating conditions, technology–task fit, and flow positively influence users’ intention to adopt the system. Among these, technology–task fit emerged as the most influential factor. This integrated approach reduces subjectivity and bias in criteria determination, enhancing the objectivity and precision of cultural heritage metaverse system assessments and making the system more responsive to user needs.

Investigating factors influencing AI customer service adoption: an integrated model of stimulus–organism–response (SOR) and task-technology fit (TTF) theory

PurposeArtificial intelligence (AI) customer service has grown rapidly in recent years due to the emergence of COVID-19 and the growth of the e-commerce industry. Therefore, this study employs the integration of the stimuli–organism–response (SOR) and the task-technology fit (TTF) frameworks to understand the factors that affect individuals’ intentions towards AI customer service adoption in Malaysia.Design/methodology/approachThe study utilised a survey-based research approach to investigate the factors that affect individuals’ intentions towards AI customer service adoption in Malaysia. The data were collected by conducting an online survey targeting individuals aged 18 or above who had prior customer service interaction experience with human service agents but had not yet adopted AI customer service. A sample of 339 respondents was used to evaluate the hypotheses, adopting partial least squares structural equation modelling as a symmetric analytic technique.FindingsThe PLS-SEM analysis revealed that social influence and anthropomorphism have a positive direct relationship with emotional trust. Furthermore, communicative competence, technology characteristics and perceived intelligence were positively correlated with TTF. Moreover, emotional trust significantly impacts AI customer service adoption. In addition, AI readiness positively moderates the association between task technology fit and AI customer service adoption.Practical implicationsThe study provides insights to individuals, organisations, the government and educational institutions to improve the features of AI customer service and its development in Malaysia.Originality/valueThe originality of this study is found in its adoption of the SOR theory and TTF to understand the factors affecting AI customer service adoption. Additionally, it incorporates moderating variables during the analysis, adding depth to the findings. This approach introduces a new perspective on the factors that impact the adoption of AI customer service and offers valuable insights for practitioners seeking to formulate effective strategies to promote its adoption.

Exploring the Application of ISSM and TTF Theories: A Literature Review

The Information System Success Model (ISSM) and Task-Technology Fit (TTF) theories provide valuable frameworks for assessing how effectively technology supports tasks within a system. These models are widely recognized and have been applied separately in numerous studies. However, there has been limited effort in the existing literature to integrate ISSM and TTF into a cohesive framework. This study aims to (1) review the relevant literature applying ISSM and TTF in higher education settings, (2) determine which model is more frequently utilized, and (3) evaluate studies that have integrated both models, with a particular focus on digital libraries. The results indicate that most research has centered on theory development or practical real-world applications, especially in public universities. The integration of ISSM and TTF in digital library contexts remains relatively rare despite their potential to complement one another. ISSM assesses system success through dimensions like service quality, user satisfaction, and net benefits, while TTF focuses on how well the technology fits the tasks it is intended to support, making it highly relevant for evaluating digital services. This study contributes to a deeper understanding of how ISSM and TTF are applied, offering valuable insights for future research. It highlights the untapped potential of combining these models to enhance technology adoption, user satisfaction, and overall system performance in digital libraries and other educational settings. By identifying research gaps, this paper lays the groundwork for further exploration and refinement of these theoretical frameworks to improve technology implementation in education.

Students’ and faculty continuous intention to use gamification in higher education: A structural analysis

Over the past decade, the integration of technology, particularly gamification, has initiated a substantial transformation within the field of education. However, educators frequently confront the challenge of identifying suitable competitive game-based learning platforms amidst the growing emphasis on cultivating creativity within the classroom and effectively integrating technology into pedagogical practices. The current study examines students and faculty continuous intention to use gamification in higher education. The data was collected through an online survey with a sample size of 763 Pakistani respondents from various universities around Pakistan. The structural equation modeling was used to analyze the data and to investigate how continuous intention to use gamification is influenced by, extended TAM model with inclusion of variables such as task technology fit, social influence, social recognition and hedonic motivation. The results have shown that task technology has no significant influence on perceived usefulness (PU) where as it has a significant influence on perceived ease of use (PEOU). Social influence (SI) indicates no significant influence on perceived ease of use. Social recognition (SR) indicates positive influence on perceived usefulness, perceived ease of use, and continuous intention. The dimensional analysis indicated that perceived ease of use has insignificant influence on perceived usefulness. Both PEOU and PU exhibit positive influence on attitude. Hedonic motivation (HM) and attitude were observed to have a positive influence on continuous intention (CI). Moreover, gamification is found to efficiently and effectively achieve meaningful goals by tapping intrinsic motivation of the users through engaging them in playful experiences.

Exploring willingness to use adverse drug reaction reporting systems: a multicentre qualitative study in China based on the technology acceptance model and task-technology fit integration approach

BackgroundAdverse drug reaction (ADR) reporting systems are critical for monitoring and managing drug safety. However, various factors influence the willingness to use these systems. This study aimed to investigate the...

The implications of blockchain for logistics operations and sustainability

Empirical studies based on detailed, theory-based analyses are essential for a deep understanding of technology adoption. This study provides an overview of blockchain applications in logistics management, employing a comprehensive theoretical framework. Blockchain is considered a critical digital infrastructure for logistics operations due to its distinctive characteristics, including decentralization, transparency, immutability, real-time information sharing, reliability, and end-to-end visibility. These characteristics address many contemporary logistics challenges. The study introduces a research model that integrates the fit-viability model (FVM) and task technology fit theory (TTF), demonstrating blockchain's suitability for enhancing logistics operational functions and sustainability performance. To validate the model, data were collected from logistics managers of 576 companies and analyzed using partial least squares (PLS) regression. This research offers valuable insights for managers, policymakers, and decision-makers on practical challenges and potential solutions in logistics through the application of blockchain. Furthermore, the study demonstrates that the implementation of blockchain can improve the alignment, resilience, transparency, integration, and sustainability of logistics tasks.

Cloud Computing Performance Evaluation Model for Open and Distance Learners in Nigeria

The increasing need for fast, secure and dynamic processing of information has necessitated the wide scale adoption of cloud computing. The educational sector especially the open and distance learning institutions are not left out of the need for distributed technology. This study presented a model that provided insight into the pre and post usage adoption variables associated with cloud computing adoption and performance in open and distance learning (ODL) setting. Partial Least Square (PLS) was adapted to test seven hypotheses on the causal relationship between the variables. Five out of the seven hypotheses were supported. The pre-usage and satisfaction oriented constructs showed more significant contributions in determining performance impact than the task technology fit oriented constructs. The study also provided insights on user preferences for decision making by educational institutions, service providers, business owners and government. Keywords: Cloud Computing, TUSPEM, Performance, Utilization, Cost, Ease Of Use

Mediating effect of ambiguity tolerance in automated writing evaluation research model

In light of the advancement of generative artificial intelligence (GenAI) technology-empowered automated writing evaluation (AWE) system represents a revolutionary paradigm at the forefront of mobile assisted English learning (MAEL). Numerous empirical studies have been carried out to test the efficacy of automated writing corrective feedback in writing assistance. However, it has seldom been investigated from the cognitive stance of AWE technology enhanced embodied learning. Embodied cognitive linguistics intensifies that learning experience is enhanced in the process of conceptualizing the empirical world. Therefore, the current study explores technology characteristics and individual characteristics incorporating the psychological construct of ambiguity tolerance. Emanating from psychology, ambiguity tolerance describes people’s preponderance to manage uncertainties and unpredictable challenges in the learning process. This study utilized the partial least square structural equation modeling (PLS-SEM) method to analyze 679 valid questionnaire responses via SPSS 29 and SmartPLS 4. The results elucidated that the AWE research model incorporating the technology acceptance model (TAM model), task technology fit model (TTF model), and individual characteristics could predict the user’s adoption of AWE software in the completion of writing tasks. Moreover, ambiguity tolerance functions as an effective mediator in the GenAI technology-empowered automated writing evaluation research model. This study provides technical implications for AWE developers to design AWE software suitable for individual characteristics. Future research could combine qualitative research methods with multivariant statistical approaches to meticulously investigate the interaction of literacy cultivation and emotional intelligence in the utilization of AWE technology for academic purposes in the GenAI era.

Student Perceptions of Generative Artificial Intelligence: Investigating Utilization, Benefits, and Challenges in Higher Education

This research explores the use of Generative Artificial Intelligence (GenAI) tools among higher education students in Saudi Arabia, aiming to understand their current perceptions of these technologies. This study utilizes the Technology Acceptance Model (TAM) and the theory of Task-Technology Fit (TTF) to examine students’ utilization, perceived benefits, and challenges associated with these tools. A cross-sectional survey was conducted, yielding 859 responses. The findings indicate that 78.7% of students frequently use GenAI tools, while 21.3% do not, often due to a lack of knowledge or interest. ChatGPT emerged as the most widely used GenAI tool, utilized by 86.2% of respondents, followed by other tools like Gemini, Socratic, and CoPilot. Students primarily use these tools for defining or clarifying concepts, translation, generating ideas in writing, and summarizing academic literature. They cite benefits such as ease of access, time-saving, and instant feedback. However, they express concerns about the challenges, including subscription fees, unreliable information, plagiarism, reduced human-to-human interaction, and impacts on learning autonomy. This study underscores the need for increased awareness, ethical guidelines, and robust academic integrity measures to ensure the responsible use of GenAI tools in educational settings. These findings highlight the need for a balanced utilization of GenAI tools in higher education that maximizes benefits while addressing potential challenges and guides the development of policies, curricula, and support systems.

Lower than expected but still willing to use: User acceptance toward current intelligent conversational agents

Intelligent conversational agents (ICAs) are revolutionizing how humans interact with information systems. Designed to provide human-like service, ICAs are generally evaluated by users in comparison to their human counterparts, often resulting in less-than-expected user experiences. Our research investigates user acceptance of ICAs in this suboptimal condition of commercial customer service. Drawing from the dual perspectives of expectancy confirmation theory and task technology fit theory, we theorize and test an integrated research model on the collective impact of user expectancy confirmation regarding ICA capabilities and their assessment of service-ICA fit on user acceptance. Results from a field survey of 350 users of five ICAs deployed by major Canadian telecom service providers reveal the significant influence of both user expectancy confirmation with ICA capabilities and their assessment of ICA fit-to-service, with the latter playing a more prominent role in shaping user acceptance. Even though ICA performance may not always meet user expectations, users are still willing to engage with ICA services when they perceive the ICA as a fitting solution for their specific service complexity and availability requirements.

Establishing an Evaluation Indicator System for User Satisfaction With Hypertension Management Apps: Combining User-Generated Content and Analytic Hierarchy Process.

Hypertension management apps (HMAs) can be effective in controlling blood pressure, but their actual impact is often suboptimal. Establishing a user satisfaction evaluation indicator system for HMAs can assist app developers in enhancing app design and functionality, while also helping users identify apps that best meet their needs. This approach aims to improve the overall effectiveness of app usage. This study aims to systematically collect data on HMAs and their user reviews in the United States and China. It analyzes app usage patterns and functional characteristics, identifies factors influencing user satisfaction from existing research, and develops a satisfaction evaluation indicator system to provide more accurate recommendations for improving user satisfaction. We conducted a descriptive statistical analysis to assess the development status of HMAs in both countries and applied the task-technology fit model to evaluate whether the app functionalities align with business needs. We separately summarized the factors influencing user satisfaction in both countries from previous research, utilized the analytic hierarchy process to develop an evaluation indicator system for HMA user satisfaction, and calculated satisfaction levels. Based on these findings, we propose improvements to enhance app functionality and user satisfaction. In terms of current development status, there were fewer HMAs and user reviews in China compared with the United States. Regarding app functional availability, fewer than 5% (4/91) of the apps achieved a demand fulfillment rate exceeding 80% (8/10). Overall, user satisfaction in both countries was low. In the United States, user satisfaction was lowest for advertising distribution, data synchronization, and reliability. By contrast, Chinese apps need improvements in cost efficiency and compatibility.

Factors Influencing the Intention to Use Human Resource Information Systems Among Employees of SMEs in Iraq

Introduction:In light of technological development and digital transformation, today's Small and Medium-Sized Enterprises (SMEs) rely heavily on their ability to use technology to succeed. Employees' acceptance or rejection of modern technology and the factors affecting it are crucial topics for SMEs. Methods:This study investigates the moderating roles of Technology Readiness (TR), Experience (EX), Trust, and Voluntariness of Use (VU) on the relationship between Effort Expectancy (EE), Performance Expectancy (PE), Social Influence (SI), Task-Technology Fit (TTF), Facilitating Condition (FC), and the Intention to Use (ITO) Human Resources Information Systems (HRIS) among employees of SMEs in Iraq. Data from 304 employees of Iraqi SMEs will be collected. Statistical analysis will be performed using SPSS and Partial Least Squares (PLS). Results:This research provides insight into the reasons behind employees' resistance to adopting HRIS, supporting the organization's policy of developing employee skills and training them in information technology systems. Discussion:Additionally, evaluating the acceptance of information technology systems can develop the framework for technical services in companies, including human resource units. Furthermore, defining the model's architecture will update stakeholder knowledge and enhance human resource management services in Iraq

Cloud PLM adoption: a multiple perspectives approach

Cloud PLM system, it is predicted, will be used in all phases of a product’s lifecycle, enabling corporations to share all information between providers, partners, and customers. For business workers, cloud PLM system is a new application model which will universally influence operations and habits. This study references the concept of TTF (Task Technology Fit) and UTAUT (Unified Theory of Acceptance and Use of Technology) and adopts quantitative research methods to investigate the behaviour intention of corporate employees using the cloud PLM system. This study help such corporations to improve the intention of their business endeavours when using the cloud PLM system. This study is an empirical research based on questionnaire data drawn from electronics manufacturing industry employees. Through these empirical results, two key findings were reached in ‘Research-Model-1’. Firstly, PLM Task characteristics and cloud computing technology characteristics displayed a positive correlation to TTF. Secondly, TTF, performance expectancy, and social influence are the main factors which affect the behavioural intention of employees. The conclusion of this ‘Research-Model-2’ shows that if ‘PLM use experience’ is used as the moderating variable item, the facilitating conditions and the TTF are important factors that affect the use behaviour intention.

Artificial intelligence powering education: ChatGPT's impact on students' academic performance through the lens of technology-to-performance chain theory

PurposeIn the context of rapid technological progress, this study investigates the factors that improve the academic performance of Saudi Arabian university students when they use ChatGPT. Using the technology-to-performance chain theory as a framework, this study identifies the variables that may affect the students' academic performance, thereby contributing to the discourse on the use of technology in education.Design/methodology/approachA survey is conducted on 257 respondents, and an online questionnaire is used to collect the data. Analysis of Moment Structures (AMOS) is employed to analyse the structural model to determine the direct connections between the different elements.FindingsFindings reveal that task characteristics, technology characteristics and individual characteristics can significantly impact task-technology fit. Furthermore, task-technology fit can influence the utilisation of ChatGPT and students' academic performance. In addition, utilisation can significantly impact students' academic performance. Students are likely to utilise ChatGPT efficiently and demonstrate improved academic performance when they believe that the technology is a good fit for their tasks.Research limitations/implicationsThis study’s shortcoming is its exclusive focus on a single public university in Saudi Arabia, which limits its generalisability. Comparative studies among multiple universities in Saudi Arabia and in other Gulf nations should be conducted to enhance the generalisability of the results. In addition, diversifying the participants by including students from various universities and exploring the moderating variables would deepen our understanding of the utilisation of ChatGPT by students.Practical implicationsThe practical implications of this study include the existence of a positive relationship between task characteristics and task-technology fit, which can guide organisations in aligning ChatGPT with specific activities for enhanced efficacy and workflow integration. In addition, understanding the association between technology characteristics and task-technology fit can help in selecting suitable technologies that will encourage user adoption and improve academic outcomes. Furthermore, the recognition of the impact of individual characteristics on task-technology fit and their utilisation can inform tailored support and training programmes to enhance user acceptance and utilisation of ChatGPT, particularly in educational settings such as those in Saudi Arabia, which will ultimately improve students’ academic performance.Originality/valueThis study’s focus on ChatGPT and how it affects the academic performance of Saudi Arabian university students distinguishes it from previous studies. This study provides insightful information on technology adoption in educational settings and contributes to our understanding of the factors that can impact academic performance through ChatGPT adoption by utilising technology-to-performance chain theory. Moreover, this study’s examination of task characteristics, technology characteristics and individual characteristics can significantly enrich discussions on optimal technology integration for educational objectives. This contribution is relevant in dynamic contexts, such as the rapidly evolving technological environment of Saudi Arabia.

Digital transformation in financial industry: antecedents of fintech adoption, financial literacy and quality of life

Purpose This paper aims to investigate the variables that could contribute to facilitating or hindering FinTech adoption in Jordan and how that will affect human well-being (quality of life [QoL]). Design/methodology/approach A conceptual framework was formulated through the integration of “the unified theory of acceptance and use of technology” (UTAUT), “task-technology fit” (TTF) model and two additional factors, namely, “financial literacy” (FL) and “quality of life” (QoL). A cross-sectional online survey was used to obtain data from 378 FinTech users employing a quantitative method. AMOS 26.0 was utilized to analyse the data based on “structural equation modelling” (SEM). Findings The analysis of the structural path found that UTAUT constructs including “performance expectancy (PE), effort expectancy (EE), facilitating conditions (FC), social influence (SI)”, and TTF were significant determinants of FinTech adoption. Only technology characteristics (TECH) was a significant predictor of TTF. Also, the analysis of empirical data revealed a significant mediating impact of FinTech adoption on the association between FL and QoL, underlining the important role of digital FL in digitalizing societies. Likewise, FL affected the QoL directly. Practical implications This research will be beneficial for “FinTech service providers” (FSPs) and policymakers to offer thorough insights regarding the current relatively low acceptance rates of FinTech, contributing to strategies’ formulation that could promote FinTech usage by Jordanian customers, where FinTech is still considered an innovative technology. In addition, FL needs to integrate digital literacy to utilize state-of-the-art technologies for more effective financial management. This is with being able to make decisions facilitating the management of life outcomes which could result in better QoL. Originality/value To the best of the author’s knowledge, this research is the first research paper that integrates the UTAUT and TTF models and also adds two additional constructs, namely, FL and QoL, to investigate the FinTech in the Jordanian setting. This study could contribute to the literature on IT adoption by considering FinTech usage and incorporation into individuals’ life in Jordan.

Anthropomorphic last-mile robots and consumer intention: An empirical test under a theoretical framework

Despite booming online retail in cities, there is almost no research on the complex relationship between anthropomorphic delivery robot targets and consumer behavior. This study employs an in-depth investigation into the relationship between last-mile anthropomorphic delivery robots and consumer usage intentions by integrating current literature and applying the theories of service quality hierarchy and task-technology fit. The study gathered data from 663 Chinese customers and employed structural equation modeling to analyze the findings. The study results suggest that the anthropomorphic qualities of delivery robots, such as their intelligence, autonomy, ability to learn, and social behavior, positively influence consumers’ willingness to use these robots for delivery tasks. In addition, the quality of human-robot interaction and task-technology fit are critical to increasing consumer willingness to use these systems. This study enriches the discussion of consumer-robot dynamics in urban retail and provides strategic guidance for optimizing smart delivery solutions in the logistics and robotics industries.

Sanctions and opportunities: Factors affecting China's high-tech SMEs adoption of artificial intelligence computing leasing business

Due to sanctions, more Chinese high-tech SMEs are turning to rent AI computing power through cloud service providers. Therefore, it is necessary to give a variety of suggestions for China's high-tech SMEs to better develop AI applications through computing power leasing. Because traditional theories are difficult to explain this new technology adoption behavior, this research combines and extends TTF and UTAUT2 theories to take an empirical research. A total of 387 questionnaires were received, of which incomplete questionnaires and invalid questionnaires were issued, leaving 281 valid questionnaires. The results indicate that SME innovativeness, perceived risk, performance expectancy, price value and task technology fit are all significantly related to usage, whereas task technology fit moderates the other relationships significantly. Results give a variety of suggestions for China's high-tech SMEs to better develop AI applications through computing power leasing in the context of sanctions. This study not only suggests ways to increase the competitiveness of SMEs by optimizing leasing services but also give directions in investors' investment decisions. The findings are also applicable to the large-scale application of China's domestic AI chips in computing power leasing scenarios in the future.

A Geospatial Drug Abuse Risk Assessment and Monitoring Dashboard Tailored for School Students: Development Study With Requirement Analysis and Acceptance Evaluation.

The enormous consequences of drugs include suicides, traffic accidents, and violence, affecting the individual, family, society, and country. Therefore, it is necessary to constantly identify and monitor the drug abuse rate among school-going youth. A geospatial dashboard is vital for the monitoring of drug abuse and related crime incidence in a decision support system. This paper mainly focuses on developing MyAsriGeo, a geospatial drug abuse risk assessment and monitoring dashboard tailored for school students. It introduces innovative functionality, seamlessly orchestrating the assessment of drug abuse usage patterns and risks using multivariate student data. A geospatial drug abuse dashboard for monitoring and analysis was designed and developed in this study based on agile methodology and prototyping. Using focus group and interviews, we first examined and gathered the requirements, feedback, and user approval of the MyAsriGeo dashboard. Experts and stakeholders such as the National Anti-Drugs Agency, police, the Federal Department of Town and Country Planning, school instructors, students, and researchers were among those who responded. A total of 20 specialists were involved in the requirement analysis and acceptance evaluation of the pilot and final version of the dashboard. The evaluation sought to identify various user acceptance aspects, such as ease of use and usefulness, for both the pilot and final versions, and 2 additional factors based on the Post-Study System Usability Questionnaire and Task-Technology Fit models were enlisted to assess the interface quality and dashboard sufficiency for the final version. The MyAsriGeo geospatial dashboard was designed to meet the needs of all user types, as identified through a requirement gathering process. It includes several key functions, such as a geospatial map that shows the locations of high-risk areas for drug abuse, data on drug abuse among students, tools for assessing the risk of drug abuse in different areas, demographic information, and a self-problem test. It also includes the Alcohol, Smoking, and Substance Involvement Screening Test and its risk assessment to help users understand and interpret the results of student risk. The initial prototype and final version of the dashboard were evaluated by 20 experts, which revealed a significant improvement in the ease of use (P=.047) and usefulness (P=.02) factors and showed a high acceptance mean scores for ease of use (4.2), usefulness (4.46), interface quality (4.29), and sufficiency (4.13). The MyAsriGeo geospatial dashboard is useful for monitoring and analyzing drug abuse among school-going youth in Malaysia. It was developed based on the needs of various stakeholders and includes a range of functions. The dashboard was evaluated by a group of experts. Overall, the MyAsriGeo geospatial dashboard is a valuable resource for helping stakeholders understand and respond to the issue of drug abuse among youth.