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Rationalizing Effects of Mobile Applications: A Systematic Review of Literature
No full textPart 3: General TrackInternational audienceThis paper adapts a Systematic Literature Review (SLR) process to investigate the influence of selected mobile applications on service delivery improvement and then categorizes their developmental contributions. Using conceptual categories from a framework for ICT-based development initiatives, we categorize descriptive perceptions for consequences of app implementations from literature. The two categories adopted are improved government services and enhanced internal economic activity; used in providing denoted contextual literature that relates selected apps to developmental contributions. Our literature findings provide an interpretive understanding of the significance of apps chosen towards service delivery and growth in particular sectors. The success of some apps manifested in development of new apps such as MomConnect, Mose and NurseConnect in South Africa. From the literature, we generated and tabulated themes or concepts related to the developmental contributions of the apps. However, the study was limited by inadequate theoretical literature associated with the service delivery influence of mobile apps on development - Mobile for Development (M4D). Future studies aim to develop an M4D framework for the analysis of mobile app developmental contributions
Organisational Support for ICT4D Practitioner Performance: A New Pathway for Enhancing ICT4D Project Outcomes
No full textPart 4: Philosophical, Theoretical and Methodological Approaches to Researching ICT4DInternational audienceOrganisational literature shows that outcomes such as project success are significantly influenced by how organisational practices influence actions and behaviours of organisation actors, but the mechanisms through which this may happen in ICT4D are unknown. This article describes a two-phase research study to understand how the organisational and individual levels of ICT4D context may interact and influence ICT4D project outcomes. The first phase involved a multiple case studies research design and the second action research, conducted in a total of five ICT4D implementing organisations in East Africa. Data was collected using semi-structured interviews with a total of 33 participants; 23 in the first phase and 10 in the second. The concept of Organisational Support enabled understanding of how organisational practices influence ICT4D practitioner performance, identifying eight key findings. Namely Mechanisms of Organisational Support for ICT4D Practitioner Performance, these are: (1) Reinforcing Mission-Congruence; (2) Skills Hybridisation; (3) Supervisory Mentorship; (4) Socioemotional Support; (5) Support for Personal Aspirations; (6) Balancing Monetary with Non-monetary Rewards; (7) Involvement in Reward Decisions; and (8) Autonomy Support. Given existing record of poor ICT4D project outcomes and insufficient research on the context of ICT4D implementing organisations, these findings contribute new and practical pathways for enhancing ICT4D project outcomes
Bridging the Digital Divide: Securing Information and Computer Systems in an Unequal World
No full textPart 2: Information and Computer SecurityInternational audienceThe study examines security challenges modeled by the digital divide, mainly focusing on how disparities in technological access and economic conditions impact cybersecurity readiness. Key questions addressed include: Does inequality contribute to security challenges in the digital world? What factors should guide the deployment of cybersecurity measures in the Global South? We hypothesize that the cybersecurity landscape significantly varies between developed and developing regions, influenced by distinct technological and socioeconomic factors. Utilizing the Global Cybersecurity Index (GCI) as a metric, our analysis reveals how advancements in Artificial Intelligence (AI) and disparities in income and digital infrastructure critically impact the efficacy of cybersecurity measures. The findings indicate that income inequality and inadequate digital infrastructure disproportionately increase security vulnerabilities, particularly in the Global South. The study accentuates the necessity for adaptive security strategies that are responsive to the varied needs of different regions to mitigate these vulnerabilities effectively. The results advocate for policy interventions that prioritize accessibility, enhance digital literacy, and foster international cooperation to achieve digital equity. Future research directions include exploring inclusive and culturally sensitive security solutions that bridge the digital divide more comprehensively, thereby enhancing global cybersecurity resilience
Product Lifecycle Management. Leveraging Digital Twins, Circular Economy, and Knowledge Management for Sustainable Innovation: 20th IFIP WG 5.1 International Conference, PLM 2023, Montreal, QC, Canada, July 9–12, 2023, Revised Selected Papers, Part I
No full textInternational audienceBook Front Matter of AICT 70
Intelligent Information Processing XII: 13th IFIP TC 12 International Conference, IIP 2024, Shenzhen, China, May 3–6, 2024, Proceedings, Part I
No full textInternational audienceBook Front Matter of AICT 70
FedPV-FS: A Feature Selection Method for Federated Learning in Insurance Precision Marketing
No full textPart 5: Business Intelligence and Risk ControlInternational audienceInsurance companies always use federated learning to integrate external data sources for data analysis and improve the conversion rate of insurance precision marketing. However, due to imbalanced data distribution and the presence of null data, the joint modeling often suffers from low robustness and is prone to falling into the dilemma of under-fitting. Therefore, the feature selection for federated learning needs to be incorporated before the joint modeling to improve the accuracy of predictions. In this paper, we propose the FedPV-FS method, which includes two-party feature selection based on public verifiable covert (PVC), and multi-party federated feature selection based on verifiable secret sharing (VSS). Moreover, we iteratively optimize federated feature selection using data selection, transformation, and integration. Experiments show that our method can achieve high-quality feature selection for increasing the optimization objective to 88.4%, promote the continuous increase of insurance premiums, and has good applications in insurance precision marketing scenarios
A Stock Price Trend Prediction Method Based on Market Sentiment Factors and Multi-layer Stacking Ensemble Learning with Dual-CNN-LSTM Models and Nested Heterogeneous Learners
No full textPart 5: Business Intelligence and Risk ControlInternational audienceInvestor sentiment, as a factor influencing stock price volatility, has received increasing research attention in recent years. This study proposes a more comprehensive representation of sentiment by incorporating social attributes when constructing investor factors. Notably, a novel market sentiment factor, γ, is introduced in this paper, which combines investor sentiment, stock data, and policy influences to enhance prediction accuracy beyond individual models. A multi-level nested ensemble model based on stacking is constructed in this study, which integrates the sentiment-stock Dual-CNN-LSTM model with learners to improve the accuracy of stock price volatility prediction. The experimental results demonstrate that: (1) The proposed market sentiment factor γ shows improved predictive accuracy compared to using investor sentiment factors alone, with an average increase of 5.55%; (2) The Dual-CNN-LSTM model outperforms the CNN-LSTM model using stock data alone in terms of volatility prediction accuracy, with an improvement of 9.81%. (3) The proposed multi-level nested ensemble algorithm, which adopts stacking nested Learner, achieves an accuracy of 88.24% in stock trend prediction. Overall, this research constructs a better sentiment indicator factor γ and provides a new approach for predicting stock price volatility through the integrated nested model, highlighting the effectiveness of hybrid architectures in addressing financial forecasting challenges
Structure Optimization for Wide-Channel Plate Heat Exchanger Based on Interval Constraints
No full textPart 3: Neural and Evolutionary ComputingInternational audienceWide-channel plate heat exchanger is a widely-used high performance heat exchanger, and its structure has a significant effect on heat exchange effect. However, the density and flow rate of the heat transfer medium is uncertain, and we only can obtain their possible ranges. Based on this, interval number is introduced to describe uncertainty factor, and then formulate the interval constraint of wide-channel plate heat exchanger. The triangular fuzzy number is employed to define the degree of constraint violation. Due to the difficulty of modeling heat exchange efficiency, its surrogate model is trained by neural network. To solve this issue, multi-objective particle swarm optimization algorithm is developed to find the optimal structural variable of heat exchanger under uncertain conditions. The experimental results indicate that the proposed algorithm obtains the structure variable of heat exchanger with the most preferable heat effect and lowest cost quickly
Dynamic Capabilities in the Public Sector to Deal with GovTech
No full textPart 6: Emerging TopicsInternational audienceGovTech is a socio-technical solution based on government technologies promoted by ecosystems that involve public sector organizations with startups and small businesses. Government technologies represent a new frontier for digital transformation in terms of capabilities to examine, co-create, experiment, and implement solutions in the public sector. Govtech also requires changes in how public organizations operate, manage projects, and report to new intermediaries that comprise stakeholder ecosystems. It is an exploratory study on mobilizing capabilities for GovTech solutions and ecosystems. We analyze critical indicators based on the GovTech Enablers Index to indicate which capabilities can enhance GovTech. The study informs how public sector organizations can mobilize capabilities to harness and promote GovTech through a roadmap of requirements and use cases. It also contributes to the discussion on GovTech through organizational and cooperative aspects and mutual agreements between parties and private sectors that comprise the Govtech ecosystem
Theory Development in Digital Government Research: Status and Ways Forward
No full textPart 1: FoundationsInternational audienceResearchers within the digital government domain have debated if the domain is under-theorized. With a surge in publications over the past decade, examining its theoretical progress is timely. This reflection paper assessed theory development in digital government research based on a literature review of 41 articles in leading digital government journals. Our findings revealed a preponderance of theoretical extensions based on theories in other domains as well as “unfinished” digital government theories, indicating a need for further development and refinement of the domain’s theories. We further posit that theory testing is a necessary step towards strengthening the theoretical underpinnings of digital government research, and calls for more empirical work to validate, challenge, and refine existing theories, fostering the establishment of a solid theoretical foundation for the domain. In addition, we make suggestions for further research