Design Science Research Research Articles

Construction of the Invoicing Process through Process Mining and Business Intelligence in the Colombian Pharmaceutical Sector

The invoicing process is critical to the financial management of organizations. However, modeling this process presents challenges such as data updating, information availability, and aligning planned activities with the actual execution of the process. One difficulty is that designing the invoicing process requires extensive knowledge of the activities involved, and process representations based on organizational repositories are not necessarily aligned with the actual invoicing processes in the organization. Process Mining is complemented by the use of dashboards, which are inherent to business intelligence and allow for visual tracking of process behavior. This paper explores how the combination of process mining and business intelligence can enable a new level of process modeling that addresses specific issues in constructing processes that are aligned with real-world activities. To accomplish this, we first propose the Design Science Research (DSR) methodology, which outlines how a researcher or practitioner should approach the task of modeling a specific process using Process Mining augmented with dashboard resources. The research strategy was to identify the most appropriate methodology to construct the actual billing process, which led to the identification of the DSR methodology. This methodology, with its 12-step plan, allowed the construction of an artifact representing the actual invoicing process. Ultimately, the objective of constructing a real invoicing process in the Colombian pharmaceutical sector is achieved through the development of an artifact, complemented by business intelligence dashboards that ensure the alignment of the execution of activities within the process.

Designing A Blockchain-Empowered Telehealth Artifact for Decentralized Identity Management and Trustworthy Communication: Interdisciplinary Approach.

Telehealth played a critical role during the COVID-19 pandemic and continues to function as an essential component of health care. Existing platforms cannot ensure privacy and prevent cyberattacks. The main objectives of this study are to understand existing cybersecurity issues in identity management and trustworthy communication processes in telehealth platforms and to design a software architecture integrated with blockchain to improve security and trustworthiness with acceptable performance. We improved personal information security in existing telehealth platforms by adopting an innovative interdisciplinary approach combining design science, social science, and computer science in the health care domain, with prototype implementation. We used the design science research methodology to implement our overall design. We innovated over existing telehealth platforms with blockchain integration that improves health care delivery services in terms of security, privacy, and efficiency. We adopted a user-centric design approach and started with user requirement collection, followed by system functionality development. Overall system implementation facilitates user requirements, thus promoting user behavior for the adoption of the telehealth platform with decentralized identity management and an access control mechanism. Our investigation identified key challenges to identity management and trustworthy communication processes in telehealth platforms used in the current health care domain. By adopting distributed ledger technology, we proposed a decentralized telehealth platform to support identity management and a trustworthy communication process. Our design and prototype implementation using a smart contract-driven telehealth platform to provide decentralized identity management and trustworthy communication with token-based access control addressed several security challenges. This was substantiated by testing with 10,000 simulated transactions across 5 peers in the Rahasak blockchain network. The proposed design provides resistance to common attacks while maintaining a linear time overhead, demonstrating improved security and efficiency in telehealth services. We evaluated the performance in terms of transaction throughput, smart contract execution time, and block generation time. To create a block with 10,000 transactions, it takes 8 seconds on average, which is an acceptable overhead for blockchain-based applications. We identified technical limitations in current telehealth platforms. We presented several design innovations using blockchain to prototype a system. We also presented the implementation details of a unique distributed architecture for a trustworthy communication system. We illustrated how this design can overcome privacy, security, and scalability limitations. Moreover, we illustrated how improving these factors sets the stage for improving and standardizing the application and for the wide adoption of blockchain-enabled telehealth platforms.

From Fragmentation to Open Access: Building an Open Data Portal for Health Data Dissemination.

Open data is defined as data that can be used and redistributed by anyone with minimal or no restrictions. A design science research methodology was applied to the development of an open data portal for theMinistry of Health Sri Lanka (MoH) to share national datasets. Following requirement gathering and literature review, the open data portal was developed using open-source software and implemented at the MoH Sri Lanka. Fifty datasets obtained from the MoH were categorised and published in the open data portal. However, several barriers cast doubt on the long-term feasibility of the open data portal project.

Navigating Business Model Redesign: The Compass Method for Identifying Changes to the Operating Model

In today’s dynamic business environment, organizations constantly change their business models to respond to emerging digital technologies and shifting customer expectations. It is a fundamental challenge to translate these changes into the organization’s operating model. When organizations redesign their business models, significant adjustments to the operating model and its underlying business processes are necessary to ensure the effective delivery of the value proposition to customers. Existing research falls short in detailing how changes to the business model at the tactical level impact the operating model at the operational level. To address this gap, this paper introduces the Compass Method. This method provides guidance for decision-makers at the tactical and operational levels in identifying necessary changes to their operating model using a set of operating model design cards. The method has been developed following the design science research methodology and is grounded in extant knowledge from both business model research and process management research. Three rounds of design and evaluation of the method were completed in multiple settings. The study contributes to the understanding of the relationship between business models, operating models, and business processes, paving the way for the development of complementary methods and tools to further investigate this relationship.

A conversational agent for enhanced Self-Management after cardiothoracic surgery

BackgroundEnhanced self-management is crucial for long-term survival following cardiothoracic surgery. ObjectivesThis study aimed to develop a conversational agent to enhance patient self-management after cardiothoracic surgery. MethodologyThe solution was designed and implemented following the Design Science Research Methodology. A pilot study was conducted at the hospital to assess the feasibility, usability, and perceived effectiveness of the solution. Feedback was gathered to inform further interactions. Additionally, a focus group with clinicians was conducted to evaluate the acceptability of the solution, integrating insights from the pilot study. ResultsThe conversational agent, implemented using a rule-based model, was successfully tested with patients in the cardiothoracic surgery unit (n = 4). Patients received one month of text messages reinforcing clinical team recommendations on a healthy diet and regular physical activity. The system received a high usability score, and two patients suggested adding a feature to answer user prompts for future improvements. The focus group feedback indicated that while the solution met the initial requirements, further testing with a larger patient cohort is necessary to establish personalized profiles. Moreover, clinicians recommended that future iterations prioritize enhanced personalization and interoperability with other hospital platforms. Additionally, while the use of artificial generative intelligence was seen as relevant for content personalization, clinicians expressed concerns regarding content safety, highlighting the necessity for rigorous testing. ConclusionsThis study marks a significant step towards enhancing post-cardiothoracic surgery care through conversational agents. The integration of a diversity of stakeholder knowledge enriches the solution, grants ownership and ensures its sustainability. Future research should focus on automating message generation and delivery based on patient data and environmental factors. While the integration of artificial generative intelligence holds promise for enhancing patient interaction, ensuring the safety of its content is essential.

Immigrants, asylum-seekers, and refugees: Navigating conceptual challenges through multidimensional migration space-time

Migration is a pervasive social, political, and economic experience. Despite its ubiquity, the complexities of migration present challenges for scholars. Ongoing debates revolve around defining central categories that describe and understand human movement. Concepts such as migrant, asylum-seeker, and refugee are fraught with tensions and overlapping characteristics. Disaggregating these categories is complex due to the messy and varied nature of individuals’ lived experiences. This complexity prompts scholars to critically examine how these categories are constructed and applied. Considering this, I argue that reconceptualizing migration categories as interconnected positions within a temporally and geographically contingent multidimensional migration space-time allows scholars to better examine the complexities of movement. Moreover, the intricate nature of migration necessitates the construction and reconstruction of adaptable concepts to better understand highly contextual experiences. Therefore, researchers must design research projects that can capture the nuances, tensions, and contradictions inherent in migration experiences, even when these nuances do not align with narrow, instrumentalized definitions used in legal regimes and public policy. While more flexible concepts offer valuable opportunities for deeper insights, there are also significant risks to consider.

Blockchain-Based Fraud Detection System for Healthcare Insurance Claims

Healthcare fraud is a huge concern that affects not only the financial viability of insurance companies but also the well-being of patients who may receive compromised care due to fraudulent acts. Addressing this issue demands novel solutions that can detect and prevent fraudulent conduct in healthcare insurance claims. The project intends to establish an automated fraud detection system using blockchain technology, which has advantages such as security, transparency, and data immutability. By leveraging blockchain's decentralized ledger, the system creates a tamper-proof platform for processing healthcare insurance claims, preventing fraudulent alterations and enhancing trust in the integrity of the claims process. Ethereum's blockchain platform and smart contracts play a critical role in ensuring the secure recording of transactions while preventing retroactive alterations. Moreover, an on-chain database is employed to manage relevant claim data, thereby safeguarding its integrity and ensuring accessibility. The decentralized nature of blockchain technology brings additional advantages by eliminating the need for intermediaries, thereby reducing administrative costs and streamlining the claim processing workflow. The adoption of methodologies such as Personal Extreme Programming (PXP) and Design Science Research Methodology (DSRM) fortifies the project's framework. PXP facilitates continuous improvement through incremental and iterative development, while DSRM ensures a structured approach to problem-solving, yielding reliable results. Through rigorous testing and validation, the automated fraud detection system enhances the efficiency and accuracy of fraud identification in healthcare insurance claims. By combining blockchain technology with methodological frameworks, this project offers a promising solution to combat healthcare fraud, safeguarding insurance systems' integrity and ensuring quality care for patients. Future iterations will focus on expanding the system's capabilities and refining its algorithms to counter the evolving fraudulent tactics prevalent in the healthcare industry.

Holding ourselves to account: The precarity dividend and the ethics of researching academic precarity

This article uses critical reflexivity as a method to document and analyse the ethical dilemmas that emerge when researching academic precarity across the permanent/precarious divide. With our project on long-term academic precarity as a case study, and as people who experienced long-term academic precarity, we take as the starting point other researchers’ silences on their positionality and about who does the work in the production of research on academic precarity. Although our small, unfunded project was driven by feminist ethics and transformative feminist praxis, there were some ethical issues we did not foresee, nor could we resolve. We focus on three main ethical dilemmas that arose as moments of discomfort, triggering extensive reflection and discussion: (1) authenticity and subjectivity, (2) disclosure of employment status and (3) complicity in and benefit from the precarisation of academic work, or what we term the ‘precarity dividend’. The article seeks to push the boundaries around how researchers hold themselves to account in the process of knowledge production. We suggest that precarity and especially the precarity dividend must become an inherent ethical consideration in all social scientific research design. It is a call for social researchers to make explicit – in writing, in ethics reviews and in presentations of their work – the labour process and labour conditions of all those involved.

HumanEnerg Hotspot: Conceptual Design of an Agile Toolkit for Human Energy Reinforcement in Industry 5.0

This paper presents the conceptual design of the HumanEnerg Hotspot, an agile toolkit aimed at addressing the human energy crisis in the context of Industry 5.0. The toolkit has been developed using a blend of Design Science Research (DSR) and Human-Centered Design (HCD) methodologies, enabling a comprehensive human-centered problem identification and solution-seeking approach. The toolkit includes a variety of strategies, techniques, frameworks, and resource recommendations for industry use and has been designed to be easily adaptable for use in diverse industry settings. The toolkit is intended to support the European Union’s goal for industry to influence society through a human-centric approach to Industry 5.0 by prioritizing human energy reinforcement and creating a more resilient and productive workforce. The toolkit provides a valuable resource for employees and managers alike and offers a promising solution for addressing the human energy crisis in the era of Industry 5.0.

An MCDM-GIS framework for assessing flooding resilience of urban metro systems

Extreme rainstorms pose significant challenges to the operation of urban metro systems. This study proposes a data-driven framework for evaluating the flood resilience of metro systems, considering the spatio-temporal differences in rainfall patterns. Furthermore, it offers recommendations to bolster resilience against flooding, ensuring the safety and continuity of metro systems in the face of increasing climatic challenges. The proposed framework adheres to the tenets of design science research, a methodological approach that synergizes theoretical and empirical aspects. This framework was examined by assessing flood resilience at 254 metro stations in Wuhan. The study reveals insights into the spatial distribution of flood resilience among metro stations, with those located in suburban areas being riskier than their counterparts in city centers. Additionally, the implementation of emergency flood response strategies and the execution of pre-disaster drills play a pivotal role in reducing the potential losses caused by flooding.

A framework for modeling and simulating blockchain-based supply chain traceability systems

Traceability improves supply chain operations transparency, benefiting the overall performance and the end customer perception. Blockchain technology is a promising alternative to create robust traceability systems, as it promotes trust and supports information sharing based on data immutability. Despite the hype surrounding blockchain technology and the vast literature in the field, the effectiveness and efficiency of blockchain-based supply chain traceability systems remain uncertain across multiple industry sectors, due to the lack of empirical evidence. Considering its characteristics, simulation is a valuable tool to evaluate the performance of supply chain management processes and technology alternatives. However, a structured guide to support the building process of simulation models regarding blockchain in the supply chain is still missing, making it hard for modelers to create realistic simulation models. Thus, this work proposes a novel methodological framework with guidelines to develop HS models regarding supply chain traceability capable of integrating the main blockchain concepts for managerial purposes. Scenarios with different granularity levels may be designed through the combination of both discrete-event and agent-based simulation paradigms. The proposed approach integrates concepts and terminologies from foundational references, including the Supply Chain Operations Reference (SCOR) model and the Global Traceability Standard (GTS). In addition, it is anchored on the ADACOR holonic architecture for the agentification process. Following the Design Science Research methodology, a proof-of-concept application is presented, based on a real-scale industrial case of a lobster supply chain from Canada. Results obtained reflect the framework's versatility to assist the development of a hybrid simulation model, as traceability is explored through the perspectives of time to react, item separation, temporal screening, and blockchain participation rate.

Digital Maturity Assessment Model (DMAM): assimilation of Design Science Research (DSR) and Capability Maturity Model Integration (CMMI)

PurposeThis study introduces the Digital Maturity Assessment Model (DMAM), a model tailored to assess the digital maturity of SMEs, tracing its development from addressing business challenges to establishing a comparative analysis framework grounded in Resource Dependence Theory (RDT).Design/methodology/approachDMAM is based on positivist philosophy and objectivist epistemology, supported by Design Science Research (DSR) and Capability Maturity Model Integration (CMMI). The methodology involves iterative development, from problem identification to creating a practical solution for assessing SMEs' digital maturity and guiding digitalization efforts.FindingsDMAM offers a clear and specific methodology, distinguishing itself by addressing the unique needs of SMEs, particularly resource-dependent ones. The model’s development fills critical gaps in existing literature and provides a practical artifact for SMEs' digitalization.Originality/valueDMAM is original in its focus on the specific needs of resource-dependent SMEs, offering actionable recommendations and addressing shortcomings in existing models. It serves as a foundational framework for SMEs' digital transformation, making a significant contribution to the digital maturity assessment literature.

Empowering software startups with agile methods and practices: A design science research

Empowering software startups with agile methods and practices: A design science research

How Patient-Generated Data Enhance Patient-Provider Communication in Chronic Care: Field Study in Design Science Research.

Modern approaches such as patient-centered care ask health care providers (eg, nurses, physicians, and dietitians) to activate and include patients to participate in their health care. Mobile health (mHealth) is integral in this endeavor to be more patient centric. However, structural and regulatory barriers have hindered its adoption. Existing mHealth apps often fail to activate and engage patients sufficiently. Moreover, such systems seldom integrate well with health care providers' workflow. This study investigated how patient-provider communication behaviors change when introducing patient-generated data into patient-provider communication. We adopted the design science approach to design PatientHub, an integrated digital health system that engages patients and providers in patient-centered care for weight management. PatientHub was developed in 4 iterations and was evaluated in a 3-week field study with 27 patients and 6 physicians. We analyzed 54 video recordings of PatientHub-supported consultations and interviews with patients and physicians. PatientHub introduces patient-generated data into patient-provider communication. We observed 3 emerging behaviors when introducing patient-generated data into consultations. We named these behaviors emotion labeling, expectation decelerating, and decision ping-pong. Our findings show how these behaviors enhance patient-provider communication and facilitate patient-centered care. Introducing patient-generated data leads to behaviors that make consultations more personal, actionable, trustworthy, and equal. The results of this study indicate that patient-generated data facilitate patient-centered care by activating and engaging patients and providers. We propose 3 design principles for patient-centered communication. Patient-centered communication informs the design of future mHealth systems and offers insights into the inner workings of mHealth-supported patient-provider communication in chronic care.

Performance measurement of legacy equipment through its connection to the cloud

Goal: Objective focuses on development and application of an architecture for measuring the performance of legacy equipment through its connection to the cloud. Methodology: The research was approached by the Design Science Research (DSR) method, which focuses on the development and evaluation of the application of the artifact (legacy equipment performance measurement system) through its technological base. Results: Both in the development and in the evaluation of the application of the system architecture in a real manufacturing system, the results were satisfactory through the accuracy obtained of 86% in the measurement carried out. Regarding equipment performance measurement, the measured index achieved an OEE of 27% efficiency, which is considered low compared to the average of companies with world-class manufacturing. It is concluded that the results made it possible to verify the reliability of the information generated by the architecture of the application system and to measure the performance of the equipment through its applicability in a real manufacturing scenario with a focus on manufacturing management. Search limitations: The development of this research is limited to the application architecture for measuring performance for bread production line. Practical Implications: The cloud architecture contextualizes the use of IIoT technologies (sensors, devices, among others) through cloud application architecture and how this has been transforming the industry and helping in the management of manufacturing operations. Originality / Value: This research underscores the efficacy of a customized integration approach for leveraging existing technologies. While cloud-based Industrial Internet of Things (IIoT) solutions are readily available, this work transcends the limitations of off-the-shelf options by tailoring the architecture to the specific requirements of a real-world production line. This focus on customization enables the architecture to be adapted to a broad spectrum of legacy equipment and production lines across diverse industries, thereby unlocking the full potential of IIoT technologies. Consequently, this research demonstrates the replicability of the proposed methodology for various manufacturing scenarios.

TOWARDS AN INFORMATIONAL UPCYLING: LEVERAGING COMPUTATION TO (RE)DESIGN BIOMATERIALS’ LIFE CYCLES

As the imperative to mitigate climate-altering emissions and curb natural resources exploitation intensifies, the fields of architecture and industrial design are compelled to explore alternative material practices. This imperative requires a transition from the traditional cradle-to-grave model to a cradle-to-cradle design paradigm, emphasizing upcycling, the process of repurposing waste into higher-value products. However, the definition of “value” in this context remains vague. In this paper, we posit value as an informational shift, denoting the “ecological intelligence” embedded within recycled materials compared to their prior life cycles. Amidst the concurrent ecological and digital transitions, the concept of “Informational Upcycling” (IU) emerges, bridging Circular Economy principles with the creative potential offered by digital technologies and Artificial Intelligence (AI) in architectural and technological design. IU leverages computational methods to address the complexity, non-linearity, and unpredictability inherent in reclaimed materials, which pose challenges to their reuse and upcycling, thereby questioning established design methodologies and tools.This conceptual framework is informed by a case study analysis of international critical research practices and is articulated through a comprehensive digital workflow, pointing out the enabling technologies for IU, with a specific focus on wood waste. Finally, the paper illustrates the application of this theoretical framework through the FoRWARD (Furniture Waste for Circular Design) research project, funded under MICS – NextGenEU, PE11, Spoke 4. Led by the Department of Architecture at the University of Naples “Federico II”, this initiative aims to test this approach within the Italian furniture industry, showcasing practical implementations of IU principles.

Researcher-Practitioner Collaboration in Action Design Research

Action Design Research (ADR) is a well-known research method within Design Science Research (DSR). An essential characteristic of the ADR method is the need for researcher-practitioner collaboration (RPC). While there is abundant research on RPC regarding information systems projects in general concerning explanatory and normative knowledge, there is very limited prescriptive knowledge on how to execute RPC in ADR projects. Successful collaboration in ADR projects is imperative since the development of socio-technical IT artefacts requires frequent interaction in organisational contexts. However, RPC can be hard to manage due to competing interests. Therefore, the purpose of this paper is to present prescriptive knowledge for how to manage RPC. We have analysed a collaborative ADR project consisting of several researchers and practitioners. Based on a grounded theory approach, we have developed theoretical models based on challenges identified in an ADR project. The models provide prescriptive knowledge regarding: shape the IT artefact based on organisational intervention, exploit the mutual dependency between developing design principles and IT artefacts, and contextualise and generalise learning. Each model involves logical relationships between: conditions for the challenges, actions taken to address the challenges and consequences of the actions taken. The guidelines were deducted from the models and consist of recommendations that could be considered in future ADR projects.

A conceptual framework for the government big data ecosystem (‘datagov.eco’)

The public sector, private firms, and civil society constantly create data of high volume, velocity, and veracity from diverse sources. This kind of data is known as big data. As in other industries, public administrations consider big data as the “new oil" and employ data-centric policies to transform data into knowledge, stimulate good governance, innovative digital services, transparency, and citizens' engagement in public policy. More and more public organizations understand the value created by exploiting internal and external data sources, delivering new capabilities, and fostering collaboration inside and outside of public administrations. Despite the broad interest in this ecosystem, we still lack a detailed and systematic view of it. In this paper, we attempt to describe the emerging Government Big Data Ecosystem as a socio-technical network of people, organizations, processes, technology, infrastructure, standards & policies, procedures, and resources. This ecosystem supports data functions such as data collection, integration, analysis, storage, sharing, use, protection, and archiving. Through these functions, value is created by promoting evidence-based policymaking, modern public services delivery, data-driven administration and open government, and boosting the data economy. Through a Design Science Research methodology, we propose a conceptual framework, which we call ‘datagov.eco’. We believe our ‘datagov.eco’ framework will provide insights and support to different stakeholders’ profiles, including administrators, consultants, data engineers, and data scientists.

Meta-organizing and responsibilization: Government strategies for community engagement and high-quality development in public–private megaprojects

Community engagement is essential for creating social value and fostering high-quality development in megaprojects. However, organizing such engagement within public-private partnerships is particularly complex in weak institutional contexts, such as those found in sub-Saharan Africa. Existing research often depoliticizes community engagement, overlooking the critical role of government. Additionally, empirical studies on this phenomenon in sub-Saharan Africa remain scarce. In this study, we adopt a governmentality lens and a design science research method to identify meta-organizing and responsibilization as governmental strategies for organizing community engagement in weak institutional contexts, such as those in sub-Saharan Africa. Through an embedded case study of a megaproject in Nigeria, we develop two sets of design principles that form a transformational community engagement framework. Our findings have implications for community engagement practices in megaprojects within weak institutional contexts and contribute to mainstream external stakeholder engagement research.

Human-centered AI development in practice—insights from a multidisciplinary approach

Industry 5.0 will focus on human-centered digitalization and put people at the center of manufacturing. To achieve this, it is necessary to consider employees throughout the transformation process. For this purpose, a practical approach to human-centered AI integration based on the Design Science Research method is presented in this article. The main requirements of the approach were evaluated in interviews with ten pilot companies, especially SMEs. The practical procedure was then examined in more detail for three application examples. The results show that there is still a lack of a uniform approach regarding the involvement of employees. Information on AI projects often reach those affected only during implementation. Even though the interdisciplinary approach is understood as a feasible approach for holistic AI development and implementation, there is still a lack of practical guidelines for adopting this multi-perspective approach. This work provides an initial starting point for a practice-oriented approach to the introduction of human-centered AI.Practical Relevance: As a result of exponential digitalization and the associated in-depth use of partially to fully automated technologies such as AI, collaboration between humans and machines is becoming increasingly important. It is essential for companies to work according to human-centered approaches to consider motivation and satisfaction as well as aspects of data protection and personal protection for employees. The necessity for companies to adopt a human-centric and sustainable approach to digitalization will intensify in coming years. The work addresses the needs for operationalization of research findings for companies. Thus, the proposed guidelines can be understood as an approach to practical knowledge transfer from science to industry.