Design Science Research Methodology Research Articles

Design of a Reminder and Recall System in a Contact Tracing Application to Support Coronavirus Booster Vaccination.

The rate of coronavirus disease 2019 (COVID-19) booster vaccination in Indonesia remains relatively low, representing 15.33% of the overall vaccination target as of April 2022. The implementation of a reminder and recall system has been shown to be effective in increasing vaccination rates. In prior research, reminders and recalls were sent through traditional media, such as mail, and had not yet been integrated into modern media, such as smartphone applications and (in particular) contact tracing applications. Therefore, the present study was conducted to design a reminder and recall system for the PeduliLindungi contact tracing application. We used the design science research (DSR) methodology with three iterations. The first iteration produced a low-fidelity prototype (or wireframe), and the next yielded a high-fidelity (clickable) prototype. The final prototype included three main features: a reminder and recall mechanism, online registration for COVID-19 booster vaccination, and educational articles. The evaluation consisted of interviews in the first iteration, interviews and the System Usability Scale (SUS) questionnaire in the second, and the Post-Study System Usability Questionnaire (PSSUQ) in the third. The SUS value obtained in the second iteration was 71.6, indicating good (acceptable) results, while in the third iteration, the system usefulness, information quality, interface quality, and overall PSSUQ values were 2.456, 2.473, 2.230, and 2.397, respectively, indicating good quality of the resulting design. This research contributes to two areas: implementation of a reminder and recall system in the PeduliLindungi contact tracing application and enhancement of contact tracing applications using DSR methodology.

Developing a Digital Business Incubator Model to Foster Entrepreneurship, Business Growth, and Academia–Industry Connections

The roles and positive impacts of business incubators, including virtual ones, in promoting entrepreneurship and innovation in multiple industries, and their consequent contributions to fostering sustainable economic growth and social development, have been highly advocated in the extant literature. Nonetheless, several authors highlight the urgent need to further carry out research concerning these structures’ operation models by involving end users, since knowledge related to this phenomenon remains scant. This study sets out to convey the third phase of a broader research and development project following a design science research methodology. Ultimately, this contributes to the field by proposing a new virtual business incubator model with immediate practical applicability. This model was cocreated and validated with 23 incubator managers and startup founders from 15 business incubation centers during focus group sessions and organized into 8 dimensions concluded as fundamental in future digital incubation programs. Moreover, this study offers knowledgeable guidelines to support academics, practitioners, organization managers, or other professionals interested in building and running virtual business incubators. The main strengths and weaknesses of the proposed model are also highlighted, and several recommendations are provided to surpass the expected challenging stages of its implementation.

Investigating Student and Teacher Perceptions in e-Learning with Learning Analytics and Ontologies

This work is an approach that brings together Learning Analytics and Ontologies for a data classification that promotes improvements and behavioral changes for students and teachers on e-Learning platforms. Combining training courses, dashboards, user's evaluations, and based on Design Science Research (DSR) methodology, artifacts were created. One of the most important artifacts of our work is the Sapes tool that aims to improve students’ perceptions of their learning path and to promote a better teacher overview to follow their students' progress. The results showed high approval by the participating students and teachers, who perceived the Sapes tool as a good facilitator of the teaching-learning process, with possibilities for self-monitoring, dynamization of the learning sequence and better interactivity with colleagues, highlighted as absent in standard e-Learning courses. In addition, the application changed the behavior of users towards the content provided by the teacher, with students performing self-management and self-regulation that were not commonly performed previously.

Keeping Your Maturity Assessment Alive

Maturity models are valuable management tools for assessing and managing capabilities and therefore creating a basis for their identification, prioritization, and further development. Numerous maturity assessment methods have been developed to support organizations in applying maturity models. However, these methods are mostly used for unique assessments and only provide a snapshot of the current state of capabilities and their maturity. Certainly, this does not reflect the continuous change of capabilities within dynamic organizational environments. Moreover, the systematic selection of suitable maturity models and the identification of the actions that should be targeted following the maturity assessment require more attention. To fill these research gaps, this study proposes the generally applicable Continuous Maturity Assessment Method (CMAM) that enables comprehensive and continuous maturity assessments. The CMAM comprises five steps that extend and advance existing principles of maturity assessment and can be implemented as an organizational routine. The rigorous development of the CMAM followed basic principles of the design science research methodology, including an evaluation of six organizations in different industry sectors and an extensive industrial case study.

Building information modelling for airport facility management: the case of a US airport

Facility management (FM) at airports involves the use of systems such as computerised maintenance-management systems (CMMSs). Currently, the integration of building information modelling (BIM) with CMMSs is yet to reach maturity, resulting in the inability to reuse BIM data produced prior to the operation and maintenance phase. The lack of accurate equipment data and its untimely handover contribute to failed CMMS programmes. The studied US airport had a low CMMS utilisation, resulting from a lack of equipment data from recent and old construction projects, and it also lacked an efficient process for acquiring, storing and exchanging asset data from construction into its CMMS. To address this, the airport sought solutions to reuse BIM data in the CMMS. Utilising a design science research methodology, this research proposed and tested a solution technology innovation comprising a BIM–FM integration framework and a structured workflow based on BIM and Construction Operations Building Information Exchange (Cobie) for FM data delivery into a CMMS. The proposed solution was tested on a real-world project at the airport, showing efficacy in providing the required equipment data in a timely and accurate manner as compared with the existing practice. The study provides practical evidence of application of BIM and Cobie in FM data delivery from a handover perspective.

BIM-driven computational design for robotic manufacturing in off-site construction: an integrated Design-to-Manufacturing (DtM) approach

Technological interoperability is a driver for seamless data and information exchange between project team members in the Architecture, Engineering, and Construction (AEC) industry. It is defined as the ability of different systems to exchange information with minimum loss. Therefore, interoperability lack is often a barrier in modern construction applications, such as robotics. Construction and robotics, seen from their respective areas, are highly divergent in context, organization, procedures, and technologies. However, both paradigms use computation, which gives computational systems the potential to enable construction robotics. This research is based on the Design Science Research (DSR) methodology and aims to develop a framework for operationalizing industrial robots in construction. To this end, it uses Computational Design (CD) driven by Building Information Modeling (BIM) for Robotic Manufacturing (RM) within Off-Site Construction (OSC) systems. This technological alignment allowed the development of an integrated Design-to-Manufacturing (DtM) framework, validated by 16 evaluators.

Co-Design, Development, and Evaluation of a Health Monitoring Tool Using Smartwatch Data: A Proof-of-Concept Study

Computational analysis and integration of smartwatch data with Electronic Medical Records (EMR) present potential uses in preventing, diagnosing, and managing chronic diseases. One of the key requirements for the successful clinical application of smartwatch data is understanding healthcare professional (HCP) perspectives on whether these devices can play a role in preventive care. Gaining insights from the vast amount of smartwatch data is a challenge for HCPs, thus tools are needed to support HCPs when integrating personalized health monitoring devices with EMR. This study aimed to develop and evaluate an application prototype, co-designed with HCPs and employing design science research methodology and diffusion of innovation frameworks to identify the potential for clinical integration. A machine learning algorithm was developed to detect possible health anomalies in smartwatch data, and these were presented visually to HCPs in a web-based platform. HCPs completed a usability questionnaire to evaluate the prototype, and over 60% of HCPs scored positively on usability. This preliminary study tested the proposed research to solve the practical challenges of HCP in interpreting smartwatch data before fully integrating smartwatches into the EMR. The findings provide design directions for future applications that use smartwatch data to improve clinical decision-making and reduce HCP workloads.

A Proposed Hybridized Model for Image-Based Virtual Reality in Technology-Driven Self-Therapy

Stress is one of the issues in mental health among the societies. Self-therapy has been an alternative to provide relaxation and to reduce stress that includes the use of guided imagery therapy (GIT). It could also take advantages of technologies that support the sense of presence. In this technology-driven era, Spatial Presence Model (SPM) has been applied in existing studies to develop virtual reality (VR) tools, while Guided Imagery Therapy (GIT) has been used as a treatment tool for potential psychological problems. Currently, no study has utilized both SPM and GIT in supporting the practice of self-therapy to reduce stress. Hence, this study aims to propose a hybridized model for Image-based VR (IBVR) that incorporates SPM and GIT for the purpose of technology-driven self-therapy. The Design science research methodology (DSRM) is used as the basis for conducting the study. The proposed model is expected to benefit the application designers as a reference in developing IBVR tools for self-therapy.

Software Approach to BMI and Obesity Determination using Facial Extraction Techniques

The focus on COVID-19 pandemic has led doctors to reduce consultation and restrict hospital visitations by patients, a decision that has resulted in the increased cases of obesity coupled with restrictions on movement and exercises. Obesity places the health of people in danger, since it is associated with poor mental health, reduce quality of life, increases the risk of diabetes, heart disease, stroke and introduces certain types of cancer. The aim of this study therefore is to design and develop a face-to-BMI mobile application that enable people to learn about their Body Mass Index (BMI), get their obesity status, as well as receive doctor recommendations (or counsel) from the comfort of their homes using their mobile devices. The design science research methodology was used in the design and development of the mobile application for obesity assessment. A mobile phone is required to take the face photo of the users after which the algorithm will perform face detection, looking for all possible facial features such as the width-to-upper facial height ratio (WHR), cheek-to-jaw width (WJWR), perimeter of area ratio (PAR), lower face to face height ratio (FW/FH), mean of eyebrow height (MEH) using facial measurements such as the iris, mouth corners, eyebrows, and nostril. The algorithm then performs Active Shape Model (ASM) fitting. The mobile app was tested with five (5) participants and the results have shown significant improvement in obesity detection and ease of use

Development of an Augmented Reality based Serious Game for the Training of Children with Dyslexia and Dysgraphia

Therapists have used paper-based exercises to treat learning disorders for many years. Dyslexia and Dysgraphia are one of the reading and writing disorders that are common nowadays. Different serious games and interventions are developed for the improvement of dyslexic and dysgraphic children. But these games cannot engage these children for a long time. Moreover, there is a need for an assessment criterion for the performance of dyslexic and dysgraphic children. Augmented Reality is an emerging technology that is used in the educational and medical fields. Augmented Reality in learning apps increases children's performance and makes them attentive. Research shows that it has a positive impact on dyslexic children. In this case study, an augmented reality-based serious game called ARLexic Game is developed to train children with Dyslexia and Dysgraphia. Design Science Research Methodology is used to design and develop the application. An experiment is conducted with specialists in special education and children with dyslexia and Dysgraphia aged 7 to 14 years old. The outcome shows that the ARLexic Game engagement level increases with time. Children learn quickly without being bored and enjoying it.

Development of an evaluation framework for health information communication technology in contemporary pharmacy practice

BackgroundHealth information communication technology (ICT) has rapidly evolved in contemporary pharmacy practice worldwide. The Australian healthcare system is experiencing a paradigm shift to real-time interconnectivity for practitioners and consumers and interoperable digital health. With these developments come a need to evaluate use of technologies specifically in pharmacy practice to optimize their clinical functionality. There are no published frameworks for evaluating ICT needs or implementation in pharmacy practice. ObjectiveThis paper proposes a theoretical framework for evaluating health ICT in pharmacy. MethodsDevelopment of the evaluation framework was informed by a systematic scoping review and health informatics literature. Specifically, the framework drew upon critical appraisal and concept mapping of the TAM, ISS and HOT-fit validated models, with respect to health ICT in contemporary pharmacy practice. ResultsThe proposed model was named the Technology Evaluation Key (TEK). The TEK comprises of 10 domains; healthcare system, organization, practitioner, user interface, ICT, use, operational outcomes, system outcomes, clinical outcomes and timely access to care. ConclusionsThis is the first published proposed evaluation framework developed for health ICT specifically in contemporary pharmacy practice. TEK represents a pragmatic way to ensure the development, refinement and implementation of new and existing technologies in contemporary pharmacy practice to keep pace with the clinical and professional requirements of community pharmacists. Operational, clinical and system outcomes should be evaluated as coexisting factors that may impact implementation. Validation research utilizing Design Science Research Methodology will enhance utility for end users and ensure the relevance and application of the TEK to contemporary pharmacy practice.

Social Media Manipulation Deep Learning based Disinformation Detection

The rapid grow and use of different social platforms enhanced communication between different entities and their audiences plus the transformation through digitalization of existing, e.g., ideas and businesses, or the creation of new ones fully existing or depending on this digital environment. Nevertheless, next to these promising aspects, social media is a vulnerable digital environment where a diverse plethora of cyber incidents are planned and executed engaging a diverse range of targets. Among these, social media manipulation through threats like disinformation and misinformation produce a broad span of effects that cross digital borders into the human realm by influencing and altering human believes, behaviour, and attitudes towards specific ideas, institutions, or people. To tackle these issues, existing academic, social platforms, dedicated organizations, and institutions efforts exist for building specific advanced and intelligent solutions for detecting and preventing them. Regardless, these efforts embed defender’s perspective and are focused locally, at target level, without being designed to fit a broader agenda of producing and/or strengthening social media security awareness. On this behalf, this research proposes a deep learning-based disinformation detection solution for facilitating and/or enhancing social media security awareness in respect to offender’s perspective. To achieve this objective, a Data Science approach is taken based on the Design Science Research methodology, and the results obtained are discussed with a keen on further field developments regarding intelligent, transparent, and responsible solutions countering social manipulation through realistic participation and contribution of different stakeholders from different disciplines.

Social Media Manipulation Awareness through Deep Learning based Disinformation Generation

As a digital environment introduced for establishing and enhancing human communication through different social networks and channels, social media continued to develop and spread at an incredible rate making it difficult to find or imagine a concept, technology, or business that does not have or plan to have its social media representation and space. Concurrently, social media became a playground and even a battlefield where different ideas carrying out diverse validity degrees are spread for reaching their target audiences generated by clear and trustable well-known, uncertain, or even evil aimed entities. In the stride carried out for preventing, containing, and limiting the effects of social manipulation of the last two types of entities, proper/effective security awareness is critical and mandatory in the first place. On this behalf, several strategies, policies, methods, and technologies were proposed by research and practitioner communities, but such initiatives take mostly a defender perspective, and this is not enough in cyberspace where the offender is in advantage in attack. Therefore, this research aims to produce social media manipulation security awareness taking the offender stance by generating and analysing disinformation tweets using deep learning. To reach this goal, a Design Science Research methodology is followed in a Data Science approach, and the results obtained are analysed and positioned in the ongoing discourses showing the effectiveness of such approach and its role in building future social media manipulation detection solutions. This research also intends to contribute to the design of further transparent and responsible modelling and gaming solutions for building/enhancing social manipulation awareness and the definition of realistic cyber/information operations scenarios dedicated/engaging large multi-domain (non)expert audiences.

Public Health Framework for Smart Cities within the Comprehensive Approach to Sustainability in Europe: Case Study of Diabetes

European cities should address the climate change challenges, improving quality of life and reducing costs. They need potential smart and digital approaches. Public health (PH) has recognized climate change as a major challenge. The development of urban policies should be guided by evidence-based PH practices. The environmental health determinants and the climate crisis now represent a clear PH threat. The core of the Smart City is sustainability, and its basic condition is active PH. The inclusion of public health into the pillars of the Smart City concept to contribute toward mitigating PH crises, such as the COVID-19 pandemic, is a framework for action. Design Science Research Methodology (DSRM) is used to elicit a Smart Public Health City (SPHEC) framework. A set of PH and smart city experts participated in the DSRM process, using diabetes as a case study. The European Green Deal served as a blueprint for this transformational change toward a healthier and more sustainable city. The SPHEC framework was defined by elucidating clearly the several dimensions of the PH functions within a digital city, via the identification of a set of digital PH services that are required to support the SPHEC framework. This allows for an assessment of the actual benefits that are obtained with the digital health services, and provides evidence for guiding decision-making. The role of digital PH services emerges from the analysis of the SPHEC framework, through the development of proper digital health services within the smart city, strengthening capacity and resilience in future climate emergencies, and motivating policy makers to take this challenge more seriously.

A Decentralized Marketplace for Patient-Generated Health Data: Design Science Approach.

Wearable devices have limited ability to store and process such data. Currently, individual users or data aggregators are unable to monetize or contribute such data to wider analytics use cases. When combined with clinical health data, such data can improve the predictive power of data-driven analytics and can proffer many benefits to improve the quality of care. We propose and provide a marketplace mechanism to make these data available while benefiting data providers. We aimed to propose the concept of a decentralized marketplace for patient-generated health data that can improve provenance, data accuracy, security, and privacy. Using a proof-of-concept prototype with an interplanetary file system (IPFS) and Ethereum smart contracts, we aimed to demonstrate decentralized marketplace functionality with the blockchain. We also aimed to illustrate and demonstrate the benefits of such a marketplace. We used a design science research methodology to define and prototype our decentralized marketplace and used the Ethereum blockchain, solidity smart-contract programming language, the web3.js library, and node.js with the MetaMask application to prototype our system. We designed and implemented a prototype of a decentralized health care marketplace catering to health data. We used an IPFS to store data, provide an encryption scheme for the data, and provide smart contracts to communicate with users on the Ethereum blockchain. We met the design goals we set out to accomplish in this study. A decentralized marketplace for trading patient-generated health data can be created using smart-contract technology and IPFS-based data storage. Such a marketplace can improve quality, availability, and provenance and satisfy data privacy, access, auditability, and security needs for such data when compared with centralized systems.

Optimization of purchasing business process in Moroccan public universities based on COBIT and artificial intelligence techniques

PurposeThe main goal of this research work is the optimization of the purchasing business process in the Moroccan public sector in terms of transparency and budgetary optimization. The authors have worked on the public university as an implementation field.Design/methodology/approachThe design of the research work followed the design science research (DSR) methodology for information systems. DSR is a research paradigm wherein a designer answers questions relevant to human problems through the creation of innovative artifacts, thereby contributing new knowledge to the body of scientific evidence. The authors have adopted a techno-functional approach. The technical part consists of the development of an intelligent recommendation system that supports the choice of optimal information technology (IT) equipment for decision-makers. This intelligent recommendation system relies on a set of functional and business concepts, namely the Moroccan normative laws and Control Objectives for Information and Related Technology's (COBIT) guidelines in information system governance.FindingsThe modeling of business processes in public universities is established using business process model and notation (BPMN) in accordance with official regulations. The set of BPMN models constitute a powerful repository not only for business process execution but also for further optimization. Governance generally aims to reduce budgetary wastes, and the authors' recommendation system demonstrates a technical and methodological approach enabling this feature. Implementation of artificial intelligence techniques can bring great value in terms of transparency and fluidity in purchasing business process execution.Research limitations/implicationsBusiness limitations: First, the proposed system was modeled to handle one type products, which are computer-related equipment. Hence, the authors intend to extend the model to other types of products in future works. Conversely, the system proposes optimal purchasing order and assumes that decision makers will rely on this optimal purchasing order to choose between offers. In fact, as a perspective, the authors plan to work on a complete automation of the workflow to also include vendor selection and offer validation. Technical limitations: Natural language processing (NLP) is a widely used sentiment analysis (SA) technique that enabled the authors to validate the proposed system. Even working on samples of datasets, the authors noticed NLP dependency on huge computing power. The authors intend to experiment with learning and knowledge-based SA and assess the' computing power consumption and accuracy of the analysis compared to NLP. Another technical limitation is related to the web scraping technique; in fact, the users' reviews are crucial for the authors' system. To guarantee timeliness and reliable reviews, the system has to look automatically in websites, which confront the authors with the limitations of the web scraping like the permanent changing of website structure and scraping restrictions.Practical implicationsThe modeling of business processes in public universities is established using BPMN in accordance with official regulations. The set of BPMN models constitute a powerful repository not only for business process execution but also for further optimization. Governance generally aims to reduce budgetary wastes, and the authors' recommendation system demonstrates a technical and methodological approach enabling this feature.Originality/valueThe adopted techno-functional approach enabled the authors to bring information system governance from a highly abstract level to a practical implementation where the theoretical best practices and guidelines are transformed to a tangible application.

Design, Development, and Evaluation of an Automated Solution for Electronic Information Exchange Between Acute and Long-term Postacute Care Facilities: Design Science Research.

Information exchange is essential for transitioning high-quality care between care settings. Inadequate or delayed information exchange can result in medication errors, missed test results, considerable delays in care, and even readmissions. Unfortunately, long-term and postacute care facilities often lag behind other health care facilities in adopting health information technologies, increasing difficulty in facilitating care transitions through electronic information exchange. The research gap is most evident when considering the implications of the inability to electronically transfer patients' health records between these facilities. This study aimed to design and evaluate an open standards-based interoperability solution that facilitates seamless bidirectional information exchange between acute care and long-term and postacute care facilities using 2 vendor electronic health record (EHR) systems. Using the design science research methodology, we designed an interoperability solution that improves the bidirectional information exchange between acute care and long-term care (LTC) facilities using different EHR systems. Different approaches were applied in the study with a focus on the relevance cycle, including eliciting detailed requirements from stakeholders in the health system who understand the complex data formats, constraints, and workflows associated with transferring patient records between 2 different EHR systems. We performed literature reviews and sought experts in the health care industry from different organizations with a focus on the rigor cycle to identify the components relevant to the interoperability solution. The design cycle focused on iterating between the core activities of implementing and evaluating the proposed artifact. The artifact was evaluated at a health care organization with a combined footprint of acute and postacute care operations using 2 different EHR systems. The resulting interoperability solution offered integrations with source systems and was proven to facilitate bidirectional information exchange for patients transferring between an acute care facility using an Epic EHR system and an LTC facility using a PointClickCare EHR system. This solution serves as a proof of concept for bidirectional data exchange between Epic and PointClickCare for medications, yet the solution is designed to expand to additional data elements such as allergies, problem lists, and diagnoses. Historically, the interoperability topic has centered on hospital-to-hospital data exchange, making it more challenging to evaluate the efficacy of data exchange between other care settings. In acute and LTC settings, there are differences in patients' needs and delivery of care workflows that are distinctly unique. In addition, the health care system's components that offer long-term and acute care in the United States have evolved independently and separately. This study demonstrates that the interoperability solution improves the information exchange between acute and LTC facilities by simplifying data transfer, eliminating manual processes, and reducing data discrepancies using a design science research methodology.

A design concept of big data analytics model for managers in hospitality industries.

The hospitality and tourism sector has long played a significant role in Australia's economy, especially in regional areas. Due to the onslaught of COVID-19, numerous businesses have experienced lockdowns, restrictions, and closures due to the fact that people's activity in restaurants, shopping centers, and recreational destinations was restricted, and many other places went into hibernation. After about 2years since the outbreak, businesses in this sector are gradually starting to reopen and revitalize themselves, but in order to have better decision support about the future of this sector, thus being able to plan, businesses are suffering from an effective analytics solution due to the lack of broken data trends. Starting from fresh day-to-day real-time big data, the study aims to develop a new data analytics model, adopting the design science research methodology, which can provide invaluable options and techniques to make prediction easier from immediate past datasets. This study introduces an innovative design artifact as a big data solution for hospitality managers to utilize analytics for predictive strategic decision-making in post-COVID situation. The artifact can also be generalized for other sectors with tailoring aspects which are subject to further studies. The proposed artifact is then compared with other design artifacts related to big data solutions where it outperforms them in terms of comprehensiveness. The proposed artifact also shows promises for primarily available UGC in managers' decision support aids.

A method for selecting processes for automation with AHP and TOPSIS

Organizations are more frequently turning towards robotic process automation (RPA) as a solution for employees to focus on higher complexity and more valuable tasks while delegating routine, monotonous and rule-based tasks to their digital colleagues. These software robots can handle various rule-based, digital, repetitive tasks. However, currently available process identification methods must be qualified to select suitable automation processes accurately. Wrong process selection and failed attempts are often the origin of process automation's bad reputation within organizations and often result in the avoidance of this technology. As a result, in this research, a method for selecting processes for automation combining two multi-criteria decision-making techniques, 'Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), will be proposed, demonstrated, and evaluated. This study follows the Design Science Research Methodology (DSRM) and applies the proposed method for selecting processes for automation to a real-life scenario. The result will be a method to support the proper selection of business processes for automation, increasing the success of implementing RPA tools in an organization.

A spatial data integration and visualization approach for occupational health and safety risks management: Application to Algerian electricity and gas company

AbstractIn the safety 4.0 era, occupational health and safety (OHS) related data has become one of the most valuable assets for organizations. Risk Management in the OHS field is a data‐driven process. However, geospatial data has not been fully considered and few works have used visualization for decision‐making purposes. Our research was conducted using a design science research methodology. It is a methodology for creating and evaluating IT artifacts to solve identified organizational problems. We used Wang's safety intelligence framework and the recommendations of the ISO 45001 model from extant literature to inform the Design. While creating our artifact, we chose to make an inventory of occupational risk prevention practices in Algeria. A qualitative study using interviews with experts in the field consolidated the requirements for such a system. The key findings from the analysis of collected data state that the development of safety data‐driven decision‐making requires top management support and involvement, which is in line with previous studies. A second concern is the availability of spatial data. The designed artifact is an approach called spatial data integration and visualization approach for occupational health and safety management systems (SDIVA‐OHSMS). SDIVA‐OHSMS provides a practical approach for organizations in charge of OHS services to implement OHS data‐driven decision‐making solutions highlighting spatial and semantic dimensions. The evaluation was performed through a representative case study within the Algerian electricity and gas company (Sonelgaz), employing expert opinion and software implementation. As a result, experts concluded that such systems could provide a more comprehensive and accurate view of occupational health issues and guide further prevention policies.