Collaborative Framework Research Articles

The Strategies to Improve Primary Health Care Services: A Scoping Review

Context: Evaluation of health policies and identification of their challenges are vital for improving and implementing reforms in the healthcare system. The present study was conducted to identify interventions aimed at improving primary healthcare (PHC) services in Iran. Evidence Acquisition: This research utilizes a scoping review to examine reform interventions in PHC services across 10 selected countries: Qatar, Oman, Turkey, Georgia, Armenia, United Arab Emirates, Saudi Arabia, Bahrain, Kazakhstan, and Kuwait. The study covers areas such as the PHC delivery system, human resource management, financial mechanisms, and the framework of community participation and intersectoral collaboration from 2010 to 2022. Results: The main reform strategies for PHC systems in the reviewed countries included the establishment of family medical centers with nurse support as a comprehensive strategy for service provision in public health centers; providing comprehensive and quality healthcare service packages including maternal and child health, infectious disease immunization, chronic disease monitoring, and dental care services; health education; access to essential medications; improvement of electronic health services; implementation of health promotion and continuous prevention programs; capacity enhancement; and a greater focus on health screening programs and grading of healthcare centers. Conclusions: The major findings from the reviewed countries indicate that healthcare policymakers focus on providing preventive care services, reducing maternal and child mortality, and increasing life expectancy. Programs such as referral systems, service grading, and the adoption of electronic health services are part of their reform agenda.

Scaling social impact. A framework for collaboration between social enterprises and for-profit enterprises

Purpose The purpose of this study is to contribute to a better understanding of innovative forms of collaboration between different types of enterprises – aimed at scaling social impact – and address the challenges and complexities inherent to these specific types of partnerships. The particular focus is on strategic collaboration between work-integration social enterprises (WISEs) and mainstream, or for-profit enterprises (FPEs) with the shared objective to create more and better employment opportunities for disadvantaged individuals in the labour market. Design/methodology/approach This study used a qualitative research design. The total sample consisted of 16 small- and medium-sized enterprises (both WISEs and FPEs), which were selected for their proven, business-to-business revenue model and their explicit ambition to create more inclusive jobs for disadvantaged individuals. Data collection and analysis took place between 2021 and 2023 and consisted of: semi-structured interviews with representatives of the participating enterprises to get a better understanding of the way in which current partnerships operate; and co-creative research methods to facilitate change processes – within and outside these partnerships – aimed at creating more social impact. Findings Most collaborations between WISEs and FPEs start purely transactional, with the exchange of products or services, but once they become more familiarised with each other, the realisation of (joint) social impact becomes more significant. The ambition to further coordinate and integrate operations is prominent, but the partnership process is not without challenges and requires time, commitment and trust. So far, only few collaborations can be considered truly transformational. Originality/value This study contributes to the discussion on strategic alliances and cross-sector collaborations by providing a conceptual framework and a practical instrument to shape strategic collaboration between social enterprises and FPEs that aim to create more social impact.

The role of collaborative governance in translating national cancer programs into network-based practices: A longitudinal case study in Canada.

Networks (multiple organizations or actors coordinating their activities towards a common goal) have been promoted in the cancer programs of a number of countries. But there is little empirical evidence on whether and how they overcome the siloed functioning endemic in specialized domains. This study examines how collaborative governance takes shape to support integrated network-based practices within a prescribed national cancer program. A longitudinal qualitative single-case study was conducted of the Quebec cancer network in Canada. Data were collected in 2018-2020 through semi-structured interviews with stakeholders (n = 37) involved in regional and/or national cancer network structures and a review of documents (n = 45) generated at national and regional level. Abductive thematic analysis during and post-field work was based on Emerson's collaborative governance framework. It aimed to identify how collaborative governance mechanisms (principled engagement, shared motivation and capacity for joint action) were activated in the network, and their contribution to translating a national cancer program into network-based practices at the point of care. Principled engagement was driven through interdisciplinary committees at national and regional level, communities of practice and trajectory-development efforts. These mandated structures supported knowledge exchange and contributed to the recognition of interdependencies, distribution of leadership and development of mutual understanding and trust. Shared motivation benefitted from a vision of patient-centred care but was hindered by top-down communication vehicles that did not allow regional priorities to filter upwards to central level. Between care providers in different settings, trust and candidacy were identified as mechanisms important to shared motivation, though network actions did not sufficiently support trust across care settings, or even between members of the same profession. Candidacy issues hindered family physician participation in cancer network structures that mirrored ongoing difficulties to including them in cancer care practice. Institutional arrangements were important drivers of capacity for joint action in the network. Common indicators were recognized as important to generating efforts towards common goals; however, questions around their validity reduced their contribution to capacities for joint action. Despite favorable starting conditions from the national cancer program and its central leadership promoting collaborative governance, tensions that emerge through the pursuit of network integration limit the transition to a more collaborative practice. Taking the time to work out these tensions as integration proceeds in waves appears essential to arrive at a governance model that is appropriate and acceptable for all network members.

Integrating public health & primary care: a framework for seamless collaboration.

Integration between public health and primary care is rising on the health policy agenda but the terms and concepts involved can be confusing. This article reviews the relevant literature and presents a new framework to help policymakers think through what they are aiming to achieve and why. We unpack different degrees and types of integration and show how they fit together. We argue that the merger of public health and primary care into a single entity with one aim, budget, and one multidisciplinary team isn't necessarily the desired end-point for most health systems, but that seamless collaboration will likely improve patient and health system outcomes, save resources, and improve population outcomes. We recommend that efforts to foster better collaboration should take an activity-based approach, promoting alignment of teams, training, budgets, values and culture around specific tasks, and in proportion to need.

Collaborative control framework at isolated signalized intersections under the mixed connected automated vehicles environment

AbstractThis study proposes a collaborative control framework under the mixed traffic environment of connected and automated vehicles and connected human‐driven vehicles, which can simultaneously optimize the signal timing, lane settings, and vehicle trajectories at isolated intersections. Initially, considering the dynamics of traffic demand and incompatible signals, we analyze the vehicle delay of each lane. Based on the delay analysis, the spatiotemporal resource collaborative optimization model of lane setting and signal timing is established to minimize the average delay. Subsequently, in the buffer zone, a graph‐theoretic‐based sorting and platooning model provides a clear and concise representation of the transformation process from the initial state to the target state of vehicles, enabling the platoon formation. Additionally, trajectory optimization is integrated into the collaborative control framework by the optimal control model and car‐following model in the passing zone. Simulation experiments and sensitivity analyses demonstrate the effectiveness of the proposed framework in reducing average vehicle delay, improving fuel consumption, and coping with changing traffic demand at intersections.

Real-time fault detection for IIoT facilities using GA-Att-LSTM based on edge-cloud collaboration

With the rapid development of Industrial Internet of Things (IIoT) technology, various IIoT devices are generating large amounts of industrial sensor data that are spatiotemporally correlated and heterogeneous from multi-source and multi-domain. This poses a challenge to current detection algorithms. Therefore, this paper proposes an improved long short-term memory (LSTM) neural network model based on the genetic algorithm, attention mechanism and edge-cloud collaboration (GA-Att-LSTM) framework is proposed to detect anomalies of IIoT facilities. Firstly, an edge-cloud collaboration framework is established to real-time process a large amount of sensor data at the edge node in real time, which reduces the time of uploading sensor data to the cloud platform. Secondly, to overcome the problem of insufficient attention to important features in the input sequence in traditional LSTM algorithms, we introduce an attention mechanism to adaptively adjust the weights of important features in the model. Meanwhile, a genetic algorithm optimized hyperparameters of the LSTM neural network is proposed to transform anomaly detection into a classification problem and effectively extract the correlation of time-series data, which improves the recognition rate of fault detection. Finally, the proposed method has been evaluated on a publicly available fault database. The results indicate an accuracy of 99.6%, an F1-score of 84.2%, a precision of 89.8%, and a recall of 77.6%, all of which exceed the performance of five traditional machine learning methods.

Applying circular business models in the Chilean construction sector: A system dynamics perspective

AbstractThe construction sector remains a significant contributor to global climate change, accounting for one‐third of global greenhouse gas emissions as well as a substantial portion of solid waste. In response to this pressing crisis, the adoption of circular business models (CBMs) emerges as a way to avoid negative environmental impacts. This study sought to model the interconnected factors influencing future CBM implementation in the Chilean construction industry using a system dynamics modeling approach. By engaging 20 sector experts in a system modeling workshop, it was possible to model and analyze these interconnected factors as a CBM System. The structural analysis of the resulting CBM system model revealed the triumvirate importance of information dissemination, certification of projects and products, and professional training and education. Recommendations for policy and practice that successfully leverage these mechanisms center on a three‐pronged approach of combining effective stakeholder collaboration, technological innovation, and thoughtful product certification frameworks. Overall, a systems thinking and modeling approach, such as the one applied here, is essential for identifying the complex interactions between enablers and barriers that promote or inhibit sustainable transformation in the construction industry in Chile and beyond.

Collaborative governance in action: driving ecological sustainability in the Yangtze River basin

Implementing a collaborative governance framework across administrative boundaries is crucial for preserving the Basin’s ecological integrity and mitigating institutional fragmentation. Based on 19 cases of inter-provincial collaborative governance of ecological environment in the Yangtze River Basin of China, this study uses the fuzzy set qualitative comparative analysis method to explore the influencing factors and complex causal mechanism of the effect of inter-provincial collaborative governance. The results show that technology empowers relationship driving, institutions reinforce interactive driving, and internal and external interaction driving modes promote collaborative governance. Perceived factors are essential in motivating provinces to participate in collaborative governance. External factors play a hygienic role in collaborative governance, and internal factors play a motivated role. The roles of the two types of factors are separate but complementary. In view of this, government should attention to regional environmental concerns, ensuring consistent alignment of internal and external factors and fostering synergies to improve governance effectiveness.

The Convergence of Artificial Intelligence and Human Marketing: A Framework for Enhanced Customer Insights and Personalization

This article examines the transformative integration of artificial intelligence and human expertise in modern marketing analytics, focusing on customer insight generation and personalization strategies. We present a comprehensive framework for human-AI collaboration in marketing operations through a mixed-methods approach combining quantitative analysis of customer data from 127 retail organizations and qualitative interviews with 34 marketing professionals. The findings reveal that organizations implementing AI-enhanced customer analytics in conjunction with human-driven creative strategy achieved a 47% improvement in customer engagement metrics and a 31% increase in conversion rates compared to traditional approaches. The article demonstrates that while AI excels at real-time pattern recognition and predictive modeling of customer behavior, human marketers provide crucial emotional intelligence and contextual interpretation that significantly enhance the effectiveness of personalization efforts. The article introduces the Dual Intelligence Marketing Framework (DIMF), which outlines optimal integration points between AI capabilities and human expertise throughout the customer journey. The framework addresses critical challenges in implementation, including technical infrastructure requirements, talent adaptation, and ethical considerations. This article contributes to both theoretical understanding and practical application of AI in marketing, offering actionable insights for organizations seeking to leverage the complementary strengths of artificial and human intelligence in their marketing operations.

Exploring the collaborative governance of Olympic stadium renovations: a multiple case study

ABSTRACT Research question Drawing on a collaborative governance framework, this study examined the collaborative governance of Olympic stadium renovation projects in two Olympic host communities: Los Angeles and Atlanta. Research methods Data included archival materials and 13 semi-structured interviews with relevant stakeholders involved in the stadium renovations in LA and Atlanta. Results and Findings This research offers a comprehensive picture of the collaborative governance systems of the post-Games venue renovation projects including multiple elements and components, such as general system contexts, drivers, dynamics, actions, and outcomes. Implications We provide theoretical and practical implications that extend our understanding of post-Games venue projects through effective collaborative governance systems.

Place-based regional planning: shaping governance, planning policy and practices in a South African region, 1994–2022

ABSTRACT The governance of regions is activated through actors and institutions representing diverse interests. Using place-based planning, this paper reviews local planning in South Africa drawing upon a case study of the iLembe District. We argue that region building is constructed through economic and political forces, currently blocked by poor relationships between the state, civil society, business, and inter-governmental complexities. The study uses empirical evidence on the regional conditions of the iLembe District and associated municipalities, reviews of planning documents and legislation, and interviews with key stakeholders. The authors argue that the local planning framework is ineffectual, lacking strategic direction, and primarily servicing provincial and nationally derived compliance requirements, rather than meeting strategic developmental objectives. Most recently, the global pandemic emphasized interlinked vulnerabilities and weak linkages associated with globalisation and sustainable development, particularly with global supply and value chains, health systems, settlement patterns, energy production and food security. We argue for a new form of collaborative regional planning and governance framework which could be laid through a “learning region” approach as identified in the place-based literature. An example is unlocking green energy production as a value addition to the established but crisis-laden sugar industry in the case study.

Tata Kelola (Collaborative Governance) dalam Pengelolaan Sampah Organik di Pusat Daur Ulang Jambangan Kota Surabaya

This study aims to analyze collaborative governance in organic waste management at the Recycling Center (PDU) Jambangan, Surabaya City. The research employs a descriptive qualitative method by collecting data through in-depth interviews, direct observations in the field, and relevant documentation. The findings indicate that the collaboration process between the government, the community, and Regional Owned Enterprises (BUMD), specifically PDAM Kota Surabaya in their role as Corporate Social Responsibility (CSR) executors, has been effectively implemented despite facing several challenges. Using the collaborative governance framework proposed by Ansell and Gash, this study finds that the success of collaboration heavily relies on facilitative leadership that can build trust and commitment among stakeholders. This collaborative process includes regular face-to-face dialogues, trust-building through transparency and openness, strong commitment to the process, continuous enhancement of shared understanding, and interim results achieved as motivation to continue the cooperation. This study concludes that collaborative governance at PDU Jambangan has made a significant contribution in reducing the volume of organic waste and enhancing public awareness of the importance of effective waste management. This success is achieved through joint efforts between the government, the community, and PDAM Kota Surabaya in creating a sustainable and environmentally friendly waste management system

CSS-Net: A Collaborative Framework for Synthesis and Segmentation of Missing Contrast-enhanced Image with Error-Prediction Consistency

Abstract Automated tumor segmentation plays a critical role in facilitating the diagnosis and assessment of disease progression. Within the realm of tumor segmentation, Contrast-Enhanced (CE) scans are an effective imaging tool that allows for more intuitive observation of tumor characteristics and generally provide better segmentation results compared with Non-Contrast-Enhanced (NCE) scans alone. However, CE images are not available in most cases due to the time-consuming and costly need for contrast and repeat scans. To solve this issue, this paper proposes a Collaborative framework for the Synthesis and Segmentation of missing CE images in medical imaging with error-prediction consistency (CSS-Net). CSS-Net simultaneously addresses synthesis and segmentation tasks, generating both the synthesized CE images and coarse segmentation results. Subsequently, a multi-layer adaptive feature fusion strategy is utilized to effectively leverage the correlation between these tasks, resulting in refined segmentation results. Additionally, the proposed method incorporates a multi-layer feature fusion block, which adaptively selects features pertinent to segmentation. Furthermore, error-prediction consistency is also introduced between coarse and refined segmentation for regularization, leading to high-performance segmentation results. What’s more, we constructed a multimodal esophageal tumor segmentation dataset with 902 patients and validated it on this dataset and two publicly available multimodal brain tumor datasets. The results indicate that our method achieved Dice scores of 89.04% in esophageal tumor segmentation, 77.01% in whole glioma segmentation, and 91.14% in Vestibular Schwannoma segmentation. This performance surpasses that of segmentation using only available modalities and other image synthesis-based segmentation methods, demonstrating the superior robustness of CSS-Net.

Collaborative supply chain network design under demand uncertainty: A robust optimization approach

This paper studies a collaborative robust supply chain network design (CRSCND) problem aimed at maximizing economic and social benefits by enabling enterprises to jointly address demand uncertainties. Through strategies including joint inventory replenishment, shared distribution centers (DCs), and pooled transportation resources, the CRSCND problem seeks to optimize plant and DC locations and the allocation of DCs to customers under a collaborative framework. To address this, we develop two robust optimization models incorporating a budget uncertainty set, each model representing a distinct risk-pooling policy. These models are then reformulated into solvable linear programming structures. Results from numerical experiments confirm the cost-reduction benefits of collaboration and robust optimization. Sensitivity analysis reveals that factors like violated probability and high demand volatility minimally impact cost savings enabled by collaboration and robustness. Moreover, each robust model shows distinct suitability depending on specific scenario parameters. Finally, we test three cost-saving allocation mechanisms, finding that only the Shapley value method yields best allocations in cases involving overlapping demand.

Enhancing Carbon Trading Mechanisms through Innovative Collaboration: Case Studies from Developing Nations

Against a backdrop of global climate change mitigation efforts, carbon trading has emerged as a critical mechanism, yet developing countries often lack the necessary infrastructure and collaborative frameworks. The research investigates how intermediaries facilitate stakeholders and employ innovative practices to foster effective carbon trading markets. Methodologically, it employs a qualitative approach, conducting in-depth case studies of four intermediaries through 32 semi-structured interviews and archival document. Findings underscore the pivotal role of digital platforms in enabling real-time trading, rigorous standardization processes to ensure market credibility, and multi-stakeholder engagement strategies that promote inclusive participation. This research contributes to the theoretical understanding of carbon trading by illustrating how digital platforms and technological integration enhance resource acquisition and allocation. It emphasizes the significance of standardization and verification processes in building trust among stakeholders, crucial for effective supply chain collaboration. Practically, the study highlights the benefits of these approaches in enhancing market efficiency and transparency. It also underscores the importance of multi-stakeholder engagement strategies and strategic alliances for creating resilient and inclusive carbon trading markets, offering actionable insights for stakeholders and policymakers alike.

A personalized federated cloud-edge collaboration framework via cross-client knowledge distillation

A personalized federated cloud-edge collaboration framework via cross-client knowledge distillation

Artificial Intelligence And Big Data For Enhancing Public Health Surveillance And Disease Prevention: A Systematic Review

This systematic review explores the transformative role of Artificial Intelligence (AI) and Big Data analytics in enhancing public health outcomes, focusing on key areas such as disease surveillance, resource allocation, and personalized preventive healthcare. In the wake of increasing healthcare challenges, the integration of AI technologies and Big Data offers unprecedented opportunities for improving health monitoring, early disease detection, and strategic resource management. Adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a comprehensive review was conducted across multiple databases, resulting in an initial pool of 450 articles. After applying rigorous inclusion and exclusion criteria, a total of 90 high-quality studies were systematically analyzed. The findings demonstrate that AI models, particularly those leveraging machine learning, significantly enhance the early detection of outbreaks and optimize healthcare resource allocation, especially during health crises like the COVID-19 pandemic. Additionally, the use of predictive analytics in personalized preventive healthcare has shown promise in reducing the burden of chronic diseases by identifying at-risk populations and tailoring interventions based on individual risk profiles. However, challenges related to data quality, standardization, and ethical concerns continue to hinder the widespread adoption of these technologies. The review emphasizes the need for interdisciplinary collaboration and robust data governance frameworks to fully realize the potential of AI and Big Data in public health. This study not only highlights current advancements but also identifies gaps in research, offering insights into future directions for integrating AI-driven solutions to strengthen public health systems globally.

Increasing the collaboration of data science stakeholders with a knowledge management system

PurposeThis paper presents the design and implementation of collaborative data science framework (CoDS), a knowledge management system for consolidating data science activities in an enterprise.Design/methodology/approachThe development of the CoDS framework is grounded on the design science research methodology for information systems research. In our case study, we first designed the initial framework for CoDS based on a systematic literature review. Then, we collected the expert opinions of eight data scientists to validate the need for generic content for such a knowledge management system. In the second iteration, a portfolio prototype is developed by the same data scientists as a part of our technical action research. Finally, a survey is conducted with 57 data analyst candidates in the last iteration.FindingsUsing the CoDS portfolio strengthened the communication among data scientists and stakeholders to improve development and scaling activities. It eased the reuse or modification of existing analytical solutions in other company processes.Practical implicationsThe CoDS presents a platform on which business details, data-related knowledge, modeling procedures and deployment steps are shared for (1) mediating and scaling ongoing projects, (2) enriching knowledge transfer among stakeholders, (3) facilitating ideation of new products and (4) supporting the onboarding of new employees and developers.Originality/valueThis study proposes a novel structure and a roadmap for creating a data science knowledge management system for the collaboration of all stakeholders in an enterprise.

Advanced Technologies as a Solution for Transportation Challenges in Developing Megacities: The Positive Case of China

Rapid urbanization in developing megacities like Beijing has led to pressing transportation challenges, including severe traffic congestion, increased emissions, and poor road safety. This paper examines how advanced software systems, new technologies, and innovative engineering solutions adopted in Beijing can serve as models for sustainable transportation improvements in other emerging megacities. Intelligent transportation systems, advanced traffic control platforms, autonomous vehicle networks, smart mobility apps, big data analytics, and cloud computing have positively impacted congestion, emissions, and safety in Beijing. Developing cities must adopt and localize such software-driven tech innovations within collaborative frameworks to make measurable progress on intricate transportation issues.

Enhancing food security through the use of cluster cooperative societies in fish farming enterprises in South-East Nigeria

Food security remains a critical issue in Nigeria, particularly in the South-East, where agricultural productivity faces challenges due to limited resources and infrastructure. This study explores the role of cluster cooperative societies in enhancing food security through fish farming enterprises. By pooling resources and creating collaborative frameworks, cooperative societies in the region have the potential to increase fish production, improve market access, and bolster livelihoods. This paper presents the findings from a study conducted among fish farmers in the South-East, evaluating the effectiveness of cooperative societies in addressing food insecurity. The research utilizes a mixed-methods approach, including surveys and interviews, with data analyzed through descriptive and inferential statistics. Results indicate that cooperative societies contribute significantly to fish farming success, with notable improvements in productivity and market linkages. The paper concludes with policy recommendations to support the development of cooperative societies in fish farming to further improve food security in the region. Keywords: Food Security, Fish Farming, Cluster Cooperative Societies, Market Access.