Information Systems Frontiers最新文献

Information Technology Service Management (ITSM) constitutes a suite of specialized organizational capabilities aimed at optimizing the value derived from IT services. The practice of ITSM encompasses a range of complementary frameworks. However, the practitioner community lacks a comprehensive, holistic understanding of the knowledge embedded within these frameworks.

Addressing this gap, we developed Service-Symphony, an instance of a Learning Digital Commons (LEDICO) designed to empower the ITSM community with a holistic knowledge-learning experience, with design principles based on epistemic logic, cognitive constructivist learning theory and cognitive schema theory. Leveraging the Design Science Research (DSR) paradigm, we systematically constructed and assessed Service-Symphony. Evaluation tools included surveys and web analytics. Survey responses revealed a consensus among participants, with the majority expressing alignment with the four design principles underpinning Service-Symphony. Web analytics data further indicated significant engagement, with 148,796 users accessing Service-Symphony between April 2019 and September 2022.

This paper contributes both to theory and practice. Theoretical contributions include the establishment of a conceptual model and a multi-grounded design theory based on cognitive constructivist learning, cognitive schema and epistemic logic. The practical contributions include e the deployment of a public domain ITSM digital commons tailored to the specific needs of the ITSM community, which is also used as supplementary learning resource for ITSM students.

Abstract

This study addresses the paradoxical tensions that arise during additive manufacturing (AM) implementation for circular economy goals in the healthcare sector. Using the lens of paradox theory, this study identifies four competing priorities that stakeholders may encounter while adopting AM. Focus group discussions among 12 industry experts from the healthcare supply chain were conducted to verify the paradoxes. Semi-structured interviews were then conducted with 10 industry experts to derive the solutions to manage these tensions from an Industry 5.0 perspective to achieve the full benefits of AM. This study expands paradox theory into the AM literature and provides a novel ‘both/and’ perspective (i.e. a pluralistic rather than a dualistic perspective) to look at emerging tensions encountered while implementing AM in the healthcare sector. This perspective will help decision-makers realise that these tensions can be managed over time to turn them into creative, rather than destructive, forces.

Gamified information systems have become widespread in organizations along with unintended ethical consequences. In parallel, recent advances in artificial intelligence and their promise for gamification raise new ethical concerns in the workplace. Building on a generational review of research progress in the field, we draw from work design, affordances, and value-sensitive design literatures to develop a theoretical framework and a related ethical design approach to encourage more meaningful work with organizational gamification. Our frameworks specify how game elements and their associated affordances can help transition organizational gamification from an add-on to an integrated part of instrumental work systems. We discuss how tying an ethical reflection into a historically informed view of progress in the field overcomes limitations in previous generations of gamification research and helps to resolve long-standing as well as emerging ethical concerns.

Detecting and accurately locating kidney stones, which are common urological conditions, can be challenging when using imaging examinations. Therefore, the primary objective of this research is to develop an ensemble model that integrates segmentation and registration techniques. This model aims to visualize the inner structure of the kidney and accurately identify any underlying kidney stones. To achieve this, three separate datasets, namely non-contrast computed tomography (CT) scans, corticomedullary CT scans, and CT excretory scans, are annotated to enhance the three-dimensional (3D) reconstruction of the kidney’s complex anatomy. Initially, the research focuses on utilizing segmentation models to identify and annotate specific classes within the annotated datasets. Subsequently, a registration algorithm is employed to align and combine the segmented results, resulting in a comprehensive 3D representation of the kidney’s anatomical structure. Three cutting-edge segmentation algorithms are employed and evaluated during the segmentation phase, with the most accurate segments being selected for the subsequent registration process. Ultimately, the registration process successfully aligns the kidneys across all three phases and combines the segmented labels, producing a detailed 3D visualization of the complete kidney structure. For kidney segmentation, Swin UNETR exhibited the highest Dice score of 95.21%; for stone segmentation, ResU-Net achieved the highest Dice score of 87.69%. Regarding Artery, Cortex, and Medulla segmentation, ResU-Net and 3D U-Net show comparable performance with similar Dice scores. Considering the Collecting System and Parenchyma, ResU-Net and 3D U-Net demonstrate similar performance in Dice scores. In conclusion, the proposed ensemble model shows potential in accurately visualizing the internal structure of the kidney and precisely localizing kidney stones. This advancement improves the diagnosis process and preoperative planning in percutaneous nephrolithotomy.

Abstract

Feature selection (FS) is one of the major tasks in data cleansing step in machine learning. However, multi-objective FS is more challenging because it tries to optimize two conflicting objectives, namely minimizing the feature set and classification error. In this way, evolutionary algorithms are promising solutions aimed to obtain more reliable Pareto fronts. However, unfortunately they suffer from consuming much time due to exploration in a large search space. Another issue encountered in multi-objective FS approaches is related to the correlation between features. This challenge arises because choosing such features reduces the performance of the classification. To address these challenges, we introduce a multi-objective FS approach that makes several significant contributions. First, the proposed method deals with the correlation between features through a novel probability structure. Secondly, it relies on the Pareto Archived Evolution Strategy (PAES) method, which offers many advantages, including simplicity and its ability to explore the solution space at an acceptable speed. We enhance the PAES structure in a manner that promotes the intelligent generation of offsprings. Consequently, our proposed approach benefits from the introduced probability structure to generate more promising offspring. Lastly, it incorporates a novel strategy to guide the algorithm to find the optimal subset throughout the evolutionary process. The obtained results on real-world datasets reveal a substantial enhancement in the quality of the final solutions.

Abstract

The prevalence of dementia is increasing due to a longer life expectancy and an ageing population. Cognitive impairment affects patients’ daily lives and places a financial and healthcare burden on them and their families. Reminiscence therapy (RT) is a non-pharmacological approach that has been found to enhance the quality of life for older adults with or without dementia. There is an urgent need for more functional and effective treatments, such as virtual reality (VR), which are becoming increasingly popular. VR in RT is a relatively new field, and this paper proposes a conceptual model for developing proof-of-concept virtual reality-based reminiscence therapy (VR-RT) for people with dementia, based on the 4Is: immersion, interaction, imagination, and impression. This model is intended to offer an alternative therapeutic experience for older adults with dementia. This paper demonstrates VR-RT in accordance with the results from the proposed 4Is model, to depict different periods of the olden days of scenes, such as restoring the historical objects and appearance of the citizens. The proposed VR-RT is then evaluated with the occupational therapists (OTs) through pilot testing. The findings reveal that VR-RT based on the proposed model may elicit a positive attitude and motivation in older adults with dementia. The proposed 4Is model serves to offer essential features for VR development, followed by a survey to identify suitable reminiscence materials and themes tailored to older adults with dementia. A pilot test with OTs revealed that VR-RT has the potential to improve the conditions of people with dementia. The research contributes valuable insights and lays the groundwork for future development of VR-RT for people with dementia.

In response to the dynamic and ever-evolving landscape of network attacks and cybersecurity, this study aims to enhance network security by identifying critical nodes and optimizing resource allocation within budget constraints. We introduce a novel approach leveraging node centrality scores from four widely-recognized centrality measures. Our unique contribution lies in converting these centrality metrics into actionable insights for identifying network attack probabilities, providing an unconventional yet effective method to bolster network robustness. Additionally, we propose a closed-form expression correlating network robustness with node-centric features, including importance scores and attack probabilities. At the core of our approach lies the development of a nonlinear optimization model that integrates predictive insights into node attack likelihood. Through this framework, we successfully determine an optimal resource allocation strategy, minimizing cyberattack risks on critical nodes while maximizing network robustness. Numerical results validate our approach, offering further insights into network dynamics and improved resilience against emerging cybersecurity threats.

The present study addresses two critical controversies surrounding the emerging Industry 5.0 agenda. Firstly, it seeks to elucidate the driving forces behind the accelerated momentum of the Industry 5.0 agenda amidst the ongoing digital industrial transformation. Secondly, it explores how the agenda’s sustainability values can be effectively realised. The study conducted a comprehensive content-centric literature synthesis and identified how Industry 4.0 shortcomings adversely impacted sustainability values. Furthermore, the study implements a novel approach that determines how and in what order the sustainability functions of Industry 4.0 should be leveraged to promote the sustainability objectives of Industry 5.0. Results reveal that Industry 4.0 has benefited economic and environmental sustainability values most at the organisational and supply chain levels. Nonetheless, most micro and meso-social sustainability values have been adversely impacted by Industry 4.0. Similarly, Industry 4.0 has been worryingly detrimental to macro sustainability values like social or economic growth equality. These contradictory implications of Industry 4.0 have pulled the Industry 5.0 agenda. However, the results identified nine sustainability functions of Industry 4.0 that, when leveraged appropriately and in the correct order, can offer important implications for realising the economic and socio-environmental goals of Industry 5.0. For example, under extreme unpredictability of business world uncertainties, the business should first leverage the automation and integration capabilities of Industry 4.0 to gain the necessary cost-saving, resource efficiency, risk management capability, and business antifragility that allow them to introduce sustainable innovation into their business model without jeopardising their survival. Various scenarios for empowering Industry 5.0 sustainability values identified in the present study offer important implications for knowledge and practice.

Current software development practices are transforming the governance and management of software projects with the objective of aligning software products/services with business needs, ensuring business continuity, optimizing resource allocation, and fostering strong stakeholder relationships. The innovative BizDevOps approach has emerged as a response to these challenges, since it extends DevOps by incorporating an additional cycle and involving non-IT stakeholders with a focus on business-IT alignment. The application of IT Governance practices is crucial as regards ensuring the success of complex BizDevOps projects, and this paper, therefore, presents a systematic mapping study that explores the approaches for BizDevOps and encourages DevOps proposals that will seamlessly integrate with the business lifecycle. It examines the support provided by IT Governance practices and investigates the potential roles of Enterprise Architecture. The study analyzed 86 primary studies and 11 secondary studies, revealing a lack of empirical validations and a prevalence of recommendation-oriented papers without concrete solution proposals. These findings highlight the need for further research with which to validate BizDevOps practices and provide actionable insights.

Public administrators receive conflicting signals on the transformative benefits of Artificial Intelligence (AI) and the counternarratives of AI’s ethical impacts on society and democracy. Against this backdrop, this paper explores the factors that affect the sensemaking of AI benefits in Canadian public administration. A mixed-method research design using PLS-SEM (n = 272) and interviews (n = 38) tests and explains the effect of institutional and consultant pressures on the perceived benefits of AI use. The quantitative study shows only service coercive pressures have a significant effect on perceived benefits of AI use and consultant pressures are significant in generating all institutional pressures. The qualitative study explains the results and highlights the underlying mechanisms. The key conclusion is that in the earlier stages of AI adoption, demand pull is the main driver rather than technology push. A processual sensemaking model is developed extending the theory on institutions and sensemaking. And several managerial implications are discussed.