Transparent Decision-making Process Research Articles

Where to plan shared streets: Development and application of a multicriteria spatial decision support tool

In response to the growing recognition of the vital role played by streets as public spaces in enhancing the vibrancy of urban life, various concepts aiming at creating greener and more inclusive streets have gained popularity in recent years, especially in North America. Shared streets are one example of such concepts that have attracted the attention of citizens and of urban and transportation planning professionals alike. This was the case in the city of Sherbrooke (Quebec, Canada) where, in response to numerous citizens’ requests, a need was identified to develop decision aid tools to help evaluate and rank street segments based on their potential to become shared streets. To achieve this, an action-research project was initiated in which we conducted a socio-technical process based on MACBETH, a multicriteria evaluation method. The project led to the development of a spatial decision support tool, operationally used today by the city professionals. This tool ensures a more informed and transparent decision-making process and supports shared streets planning policy. The methods developed are generalizable and can be adapted to other cities facing similar planning problems.

Application of artificial intelligence in career development of women in STEM field: Counseling, network construction and recruitment

With the rapid development of science and technology, the application of artificial intelligence (AI) in STEM (science, technology, engineering and mathematics) is increasingly extensive, which provides a new idea for solving the challenges of women's career development in STEM. This paper deeply discusses the application of AI in counseling, network construction and recruitment of women's STEM career development, aiming at promoting gender equality and women's career growth. In career counseling, AI technology provides customized career advice and development path for women through deep learning and big data analysis. According to personal education background, skill level, hobbies and career goals, the system intelligently recommends suitable career directions, and updates industry trends and job market data in real time to help women make more informed career choices. The AI tutoring system also enhances women's professional self-confidence through functions such as simulated interviews and career planning exercises. In terms of network construction, AI helps women to expand their professional networks more effectively through intelligent recommendation system. Based on user behavior data and preferences, AI accurately recommends industry seminars, technical exchange activities and online communities to promote women to establish contacts with like-minded professionals. AI also assists women in managing and maintaining network relationships, ensures the continuity of network interaction through intelligent reminding functions, and identifies key nodes and influential people to enhance network quality. In the recruitment process, AI technology reduces gender bias and improves the fairness and transparency of recruitment through automatic resume screening, standardized interview process and competency-based evaluation model. AI system can analyze a large amount of data, accurately match the skills and job requirements of female candidates, and improve the employment rate of women in STEM field. At the same time, through transparent algorithm decision-making process and fairness check, the potential gender bias problem in AI algorithm is overcome. The research in this paper provides theoretical support and practical guidance for using AI technology to promote women's career development in STEM field, and helps to build a more equal and inclusive scientific and technological working environment.

Brain tumor classification utilizing pixel distribution and spatial dependencies higher-order statistical measurements through explainable ML models

Brain tumors are among the most fatal and devastating diseases, and they often result in a significant reduction in life expectancy. The devising of treatment plans that can extend the lives of affected individuals hinges on an accurate diagnosis of these tumors. Identifying and analyzing large volumes of magnetic resonance imaging (MRI) data manually proves to be both challenging and time-consuming. As a result, there exists a pressing need for a reliable machine-learning approach to accurately diagnose brain tumors, and numerous methods have already been proposed over the last decade. In this paper, a novel, comprehensive approach is proposed for identifying and classifying a given MR brain image as abnormal. Three common brain diseases, namely glioma, meningioma, and pituitary tumor, are chosen as abnormal brains, and the Figshare MRI brain image dataset was collected from the Kaggle and IEEE websites. The proposed method is initiated by employing 1st-order statistics, 2nd-order statistics, and higher-order transformed (DWT) feature extraction to extract features from images. Then missing data is addressed and handled using KNNImputer, followed by the application of the ExtratreesClassifier and PCA feature selection methods to identify the most relevant features and reduce the dimensions of these features. Subsequently, the reduced features are submitted to seven machine learning models, namely RF, GB, CB, SVM, LGBM, DT, and LR. The strategy of k-fold cross-validation is utilized to enhance the performance of those models. Finally, the models are evaluated using XAI approaches, which ensure transparent decision-making processes and provide insights into the model’s predictions. Remarkably, our approach achieves the highest accuracy, precision, recall, F1 score, MCC, Kappa, AUC-ROC, and R2, as well as the lowest loss, among the seven models evaluated, proving its effectiveness and applicability in multiple analytic applications relying on publicly available datasets.

20 years review of the multi actor multi criteria analysis (MAMCA) framework: a proposition of a systematic guideline

AbstractMulti-criteria Decision Making (MCDM) involves evaluating alternatives based on a comprehensive set of conflicting criteria, often requiring the involvement of varied decision makers. This has led to the emergence of stakeholder-based multi-criteria group decision-making (MCGDM) frameworks. However, traditional MCGDM frequently overlooks the interactions and trade-offs among different actors and criteria. The Multi-actor Multi-criteria Analysis (MAMCA), developed in 2000, provides a transparent decision-making process explicitly considering these interrelationships. It allows diverse stakeholder groups to represent their priorities, thereby enhancing their understanding of their own and others’ positions. Over the past two decades, MAMCA has seen a significant rise in popularity and has been widely applied in diverse contexts, proving valuable as both a decision-making and stakeholder engagement tool. However, our analysis of publications on cases in which MAMCA was applied over the years shows that considerable variation exists in the overall process approach, contingent upon the specific goals and context. To address these variations, this paper proposes a modularized MAMCA structure, complemented by systematic application guidelines, to aid future users in navigating the process steps and identifying the most suitable methods for each step. Additionally, future research directions are suggested for potential enhancements to the MAMCA framework by integrating varied methodologies.

Strengthening Healthcare Data Security with Ai-Powered Threat Detection

As the healthcare industry undergoes rapid digital transformation, the need for robust cybersecurity measures has never been more critical. AI-driven solutions, such as Machine Learning (ML) and anomaly detection, are proving to be pivotal in securing healthcare data. These technologies enable healthcare organizations to identify cyber threats proactively, automate incident response, and enhance data security. Furthermore, AI's ability to provide continuous network monitoring, predictive analytics, and real-time anomaly detection offers healthcare providers the tools needed to mitigate risks before they escalate. This research highlights the key applications of AI in healthcare data security, including its effectiveness in vulnerability management and regulatory compliance. It also delves into the ethical and operational challenges of integrating AI-driven threat detection systems into healthcare settings, including concerns related to bias in AI models, regulatory hurdles, and the complexity of AI system integration into existing healthcare infrastructures. In a 2023 study, Accenture reported that AI-based cybersecurity systems reduced detection and response time by up to 60%, illustrating how AI accelerates response to potential data breaches​. Similarly, HIMSS noted that the risk of data breaches in healthcare could be halved by AI technologies that continuously monitor and analyze data​. These findings emphasize AI's role in minimizing the delays and errors typically associated with human-driven responses​. AI techniques such as supervised, unsupervised, and semi-supervised learning play a crucial role in anomaly detection. These models identify irregular patterns in healthcare systems, flagging potential threats even when subtle​. For example, deep learning approaches, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), can uncover outliers in healthcare data, enhancing system security​. The ability of AI to continuously learn from previous attacks ensures that systems evolve with emerging threats, further solidifying its place in healthcare cybersecurity​. Additionally, AI aids in predictive analytics, which can forecast future cyber threats based on historical data, allowing healthcare providers to address vulnerabilities proactively​. By leveraging such capabilities, AI not only safeguards sensitive patient data but also enables healthcare institutions to stay compliant with stringent regulatory standards like HIPAA and GDPR​. However, AI implementation in healthcare security comes with its own challenges. Integrating AI into legacy systems requires substantial infrastructure upgrades, and the complexity of dynamic encryption can strain system resources​. Furthermore, the ethical concerns surrounding AI, such as the transparency of decision-making processes and potential bias in AI algorithms, must be carefully managed to ensure equitable and effective security solutions​

Hybrid lane change strategy of autonomous vehicles based on SOAR cognitive architecture and deep reinforcement learning

Research on lane change strategies for autonomous vehicles holds paramount importance in optimizing traffic flow efficiency, enhancing driving safety, and adapting to complex traffic environments. While numerous rule-based or machine-learning approaches have been explored to tackle the challenge of lane change on highways, they frequently exhibit limited performance owing to the complexity of driving environments. This study proposes a novel lane change strategy for autonomous vehicles, which utilizes a hybrid framework integrating the SOAR cognitive architecture and deep reinforcement learning (DRL) to address the lane change challenge on highways. First, we introduce a rule extraction algorithm, the RuleCOSI+, which is based on tree ensemble algorithms, designed to extract concise lane change rules from large-scale human driving data. These straightforward rules, together with traffic regulations and safety rules, constitute the long-term memory of the SOAR cognitive architecture, enabling transparent decision-making processes. Next, by analyzing the clipping mechanism of the proximal policy optimization (PPO) algorithm, we propose an Adaptive Clipping PPO (ACPPO) algorithm which is based on the importance of samples. This algorithm adopts different clipping strategies for SOAR samples and ACPPO samples during the training process, enabling the algorithm to more effectively utilize samples with different levels of importance. Then, we propose a hybrid decision-making algorithm: SOAR-ACPPO, which combines the SOAR cognitive architecture with the ACPPO algorithm. This algorithm leverages SOAR’s prior knowledge to effectively and safely guide agent learning. Finally, by selecting appropriate intervention probability and weaning strategy, the system avoids inappropriate knowledge intervention and ensures adequate environment exploration. Simulation experiments conducted using the CARLA simulator illustrate that the proposed strategy not only improves model learning efficiency but also enhances driving efficiency and safety. Additionally, it demonstrates a certain degree of human-like characteristics and interpretability.

Analysis of payment methods in merger and acquisition deals within the United Kingdom

Purpose This study aims to examine the effects of payment methods used in mergers and acquisitions (M&A) conducted by UK companies spanning the period from 2007 to 2019. Design/methodology/approach The study used the estimated expected returns method to identify abnormal returns during the deal announcement period, applying event study analysis with both univariate and multivariate regression models to detect cumulative abnormal returns around the announcement timeframe. Findings The results show a short-term positive return increase for acquiring firms, controlling for deal-specific characteristics like target firm location and payment methods. The authors observed a preference for cash financing across domestic and cross-border transactions. Multivariate analysis revealed insignificance between payment methods and deal characteristics like cross-border acquisitions and diversification. Research limitations/implications The study’s focus on publicly traded firms in the UK and the absence of a comparative analysis across different regions and markets limits the sample size and may impact the generalizability of findings. Practical implications The study proposes three practical implications. Firstly, firms should tailor payment methods to each transaction, aligning with strategic goals to optimize value and mitigate risks. Secondly, decision-makers must prioritize comprehensive due diligence and strategic alignment throughout M&A processes to enhance success and maximize synergies. Finally, analysing broader strategic contexts and regulatory landscapes when structuring transactions enables goal attainment, such as market expansion or value creation. Social implications The study’s findings can promote transparency and accountability among corporate decision-makers in M&A transactions. Stakeholders can advocate for transparent decision-making processes, enhancing trust in corporate governance. Originality/value This study provides valuable insights into the impact of payment methods on shareholder value in M&A transactions involving UK companies, informing strategic decision-making and contributing to the understanding of corporate finance dynamics.

Weld seam object detection system based on the fusion of 2D images and 3D point clouds using interpretable neural networks

This study introduces a novel approach that addresses the limitations of existing methods by integrating 2D image processing with 3D point cloud analysis, enhanced by interpretable neural networks. Unlike traditional methods that rely on either 2D or 3D data alone, our approach leverages the complementary strengths of both data types to improve detection accuracy in environments adversely affected by welding spatter and smoke. Our system employs an improved Faster R-CNN model with a ResNet50 backbone for 2D image analysis, coupled with an innovative orthogonal plane intersection line extraction algorithm for 3D point cloud processing. By incorporating explainable components such as visualizable feature maps and a transparent region proposal network, we address the “black box” issue common in deep learning models.This architecture enables a more transparent decision-making process, providing technicians with necessary insights to understand and trust the system’s outputs. The Faster-RCNN structure is designed to break down the object detection process into distinct, understandable steps, from initial feature extraction to final bounding box refinement. This fusion of 2D-3D data analysis and interpretability not only improves detection performance but also sets a new standard for transparency and reliability in automated welding systems, facilitating wider adoption in industrial applications.

Community Engagement and Social Equity in Urban Development Projects

The critical role of community engagement and social equity in urban development projects. As cities expand and evolve, ensuring that development processes are inclusive and equitable becomes increasingly important. various urban development initiatives to assess how effectively they incorporate community participation and address social equity concerns. By analyzing case studies from diverse urban contexts, the research identifies best practices for fostering meaningful community involvement and achieving equitable outcomes. The findings reveal that projects which prioritize community engagement and social equity not only enhance the social fabric of neighbourhoods but also contribute to more sustainable and resilient urban environments. Key strategies include transparent decision-making processes, inclusive planning practices, and the incorporation of community feedback into project design and implementation. The study concludes with recommendations for policymakers and urban planners on how to integrate community engagement and social equity into future urban development projects, emphasizing the need for ongoing dialogue and collaboration with local communities to ensure that urban growth benefits all residents.

Pharmacological and ethical comparisons of lung cancer medicine accessibility in Australia and New Zealand

Gaps in funded cancer medicines between New Zealand and Australia can have significant implications for patients and their families. Pharmac, the New Zealand pharmaceutical funding agency, has been criticised for...

Assessing the Relationship between Political Financing and Policy Bias in B40 Welfare Policies during COVID-19

This conceptual paper delves into the intricate relationship between political financing and policy bias in the implementation of welfare policies for the B40 (Below 40 is the 40% of the total population that earns the lowest tier of household income compared to the first group of T20-Top 20% and second category M40-Middle 40%) cluster in Kuala Lumpur Malaysia amidst the COVID-19 pandemic. The B40 cluster comprises the most economically vulnerable segment of the population residing in public housing (the heavily subsidized government-owned residential areas for underprivileged citizens with certain household income categories). This study aims to unravel how political financing influences the formulation and execution of welfare policies, potentially leading to bias in resource allocation and support provision. Through an exhaustive review of existing scholarly literature and analysis of pertinent case studies, this paper embarks on examining the degree to which political financing impacts the decision-making process concerning welfare policies. The analysis focuses on identifying potential biases in policy formulation that may favor the interests of political entities providing financial backing to policymakers. Furthermore, the study also explores the implications of such biases on the equitable distribution of resources to the B40 cluster during the global COVID-19 crisis. The conceptual framework employed in this paper draws from theories of political economy, corruption, and public policy. By dissecting the extant body of literature, the paper aims to offer a nuanced comprehension of the dynamics between political financing and policy bias in the realm of welfare policies for the B40 cluster. The eventual findings are poised to enrich the theoretical understanding of the influence exerted by political financing on the formulation and execution of welfare policies, particularly during periods of global crisis. By identifying policy biases stemming from political financing, this study aspires to catalyze critical discussions on the imperative of transparent and accountable decision-making processes. Such deliberations are vital to ensuring the fair and effective implementation of welfare policies for the B40 cluster in Kuala Lumpur amidst the COVID-19 pandemic. This paper thus serves as a clarion call for enhanced governance mechanisms and institutional reforms to uphold the principles of fairness, equity, and social justice in welfare policy formulation and implementation. This study contributes to the existing literature by shedding light on the nexus between political financing and policy bias in the context of welfare policies for vulnerable populations. It underscores the importance of addressing systemic flaws in governance and decision-making processes to mitigate the adverse effects of political influence on welfare policy outcomes. Moreover, the findings of this study have practical implications for policymakers, advocacy groups, civil society organizations and other stakeholders involved in shaping welfare policy agendas and advocating for reform initiatives. In conclusion, this paper calls for a concerted effort to promote transparency, accountability, and integrity in the formulation and implementation of welfare policies. By fostering a culture of ethical governance and democratic participation, policymakers can ensure that welfare programs serve the interests of the most vulnerable members of society and contribute to equitable and sustainable development outcomes.

Artificial intelligence and nonoperating room anesthesia.

The integration of artificial intelligence (AI) in nonoperating room anesthesia (NORA) represents a timely and significant advancement. As the demand for NORA services expands, the application of AI is poised to improve patient selection, perioperative care, and anesthesia delivery. This review examines AI's growing impact on NORA and how it can optimize our clinical practice in the near future. AI has already improved various aspects of anesthesia, including preoperative assessment, intraoperative management, and postoperative care. Studies highlight AI's role in patient risk stratification, real-time decision support, and predictive modeling for patient outcomes. Notably, AI applications can be used to target patients at risk of complications, alert clinicians to the upcoming occurrence of an intraoperative adverse event such as hypotension or hypoxemia, or predict their tolerance of anesthesia after the procedure. Despite these advances, challenges persist, including ethical considerations, algorithmic bias, data security, and the need for transparent decision-making processes within AI systems. The findings underscore the substantial benefits of AI in NORA, which include improved safety, efficiency, and personalized care. AI's predictive capabilities in assessing hypoxemia risk and other perioperative events, have demonstrated potential to exceed human prognostic accuracy. The implications of these findings advocate for a careful yet progressive adoption of AI in clinical practice, encouraging the development of robust ethical guidelines, continual professional training, and comprehensive data management strategies. Furthermore, AI's role in anesthesia underscores the need for multidisciplinary research to address the limitations and fully leverage AI's capabilities for patient-centered anesthesia care.

Urgensi Penerapan Prinsip Checks and Balances dalam Pengaturan Kepala dan Wakil Kepala Otorita IKN

This study explores the urgency of implementing the principle of checks and balances in the regulation of the Head and Deputy Head of the Nusantara Capital City Authority (IKN) according to Law Number 3 of 2022. The research addresses two main issues: (1) What are the roles and authorities of the Head and Deputy Head of the Nusantara Capital City Authority, and (2 What is the urgency of applying the principle of checks and balances to the Head and Deputy Head of the IKN Authority? Using a normative juridical approach, this study finds that the Head of the Authority holds the primary authority in directing and managing the development of IKN, while the Deputy Head plays a significant supporting role in performing administrative and operational functions. The study underscores the importance of checks and balances in preventing abuse of power and enhancing transparency and accountability in decision-making. To achieve an ideal checks and balances framework, the study recommends a clear division of authority, robust oversight mechanisms, regular evaluations, transparent decision-making processes, and the involvement of external oversight bodies. The effective implementation of the checks and balances principle is expected to maintain the integrity and accountability in the governance of the Nusantara Capital City Authority.

Development of Rules and Algorithms for an Intelligent and Integrated Older Care Model.

To break through the current bottleneck in home-based older care globally, we developed an intelligent and integrated older care model (SMART model) to facilitate integrated care for home-dwelling older people. As a knowledge-based clinical decision support system, the SMART model relies on rules and algorithms to ensure its transparent and well-supported decision-making process with clear rationales. Therefore, we conducted a mixed study combining qualitative research, literature review of the latest literature and guidelines, and expert consultation. Following the intervention mapping framework and nursing process, we determined 138 care problems along with their diagnostic criteria and care goals. Building upon this, we curated 450 evidence-informed methods, each accompanied by at least one implementation approach. Two sets of IF-THEN rules and algorithms including diagnostic rules and method trigger rules were employed to trigger appropriate care problems and customized methods and implementation approaches.

Enhancing sustainable development in the energy sector through strategic commercial negotiations

Sustainable development in the energy sector necessitates a delicate balance between economic growth, environmental stewardship, and social well-being. Strategic commercial negotiations play a pivotal role in achieving this balance, facilitating agreements that align with sustainable development goals (SDGs). This paper explores the intersection of commercial negotiations and sustainable energy practices, emphasizing how strategic negotiations can drive progress towards sustainability. In the contemporary energy landscape, the shift towards renewable energy sources and the implementation of green technologies have become imperative. Effective commercial negotiations are essential in securing investments, fostering partnerships, and establishing frameworks that support the adoption and integration of sustainable practices. Key negotiation strategies include the incorporation of environmental, social, and governance (ESG) criteria, long-term planning, and the adoption of flexible yet robust contract structures. ESG considerations ensure that negotiations address not only financial outcomes but also the broader impact on society and the environment. By prioritizing ESG factors, negotiators can promote responsible business practices that mitigate environmental risks and enhance social benefits. Additionally, long-term planning in negotiations enables stakeholders to align their objectives with global sustainability targets, such as the Paris Agreement and the United Nations SDGs, ensuring that energy projects contribute positively to climate action and sustainable development. The adoption of flexible contract structures is also critical. These structures should accommodate technological advancements and evolving regulatory frameworks, allowing for adaptive management of energy projects. Such adaptability is crucial in a sector characterized by rapid innovation and changing market dynamics. Furthermore, collaborative approaches in negotiations, involving multiple stakeholders including governments, private sector entities, and civil society, can foster inclusive and transparent decision-making processes, enhancing the legitimacy and acceptance of negotiated agreements. In conclusion, strategic commercial negotiations are instrumental in advancing sustainable development in the energy sector. By integrating ESG criteria, long-term planning, and flexible contract structures, negotiators can secure agreements that not only drive economic growth but also promote environmental sustainability and social equity. This approach ensures that the energy sector can contribute effectively to global sustainability goals, creating a resilient and sustainable future. Keywords: Energy Sector, Enhancing, Sustainable Development, Strategic, Commercial Negotiations.

Examining the Integration of Responsible AI Principles in Social Media Marketing for Digital Health: A Theoretical Analysis

This study explores the integration of responsible artificial intelligence (AI) principles into social media marketing strategies within the digital health sector. With the proliferation of digital health platforms and the growing use of AI technologies in marketing, ensuring ethical practices is paramount. By examining the application of responsible AI principles in this context, this research aims to address concerns related to privacy, fairness, transparency, and accountability. Through a comprehensive analysis of current practices and emerging trends, this study highlights the importance of balancing marketing objectives with ethical considerations to promote trust and engagement among users. Key aspects include leveraging AI algorithms for personalized content delivery while safeguarding user data, implementing transparent AI-driven decision-making processes, and establishing mechanisms for accountability and user empowerment. This investigation offers valuable insights for marketers, policymakers, and stakeholders in navigating the evolving landscape of digital health marketing while upholding ethical standards and fostering positive user experiences. Key Words: Responsible AI, Social Media Marketing, Digital Health, Ethical Practices, Transparency

Effect of Corporate Governance on Financial Performance in China

Purpose: The aim of the study was to assess the effect of corporate governance on financial performance in China. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: The study indicated a positive correlation between strong corporate governance practices and improved financial outcomes for companies. These practices typically include transparent decision-making processes, effective board oversight, and alignment of management incentives with long-term shareholder value. Study suggests that companies with robust corporate governance structures tend to experience higher profitability, better risk management, increased investor confidence, and enhanced access to capital. Additionally, strong corporate governance is associated with lower instances of financial misconduct and improved overall corporate reputation, which can further contribute to sustainable financial performance over time. Implications to Theory, Practice and Policy: Agency theory, stewardship theory and resource dependency theory may be used to anchor future studies on assessing the effect of corporate governance on financial performance in China. Implement best practices in corporate governance that align with empirical findings and theoretical insights to optimize financial performance. Advocate for regulatory frameworks and policies that incentivize effective corporate governance practices and align them with desired financial performance outcomes.

Multi-attentional causal intervention networks for medical image diagnosis

Medical image diagnosis has developed rapidly under the impetus of the deep network. Previous works mainly focus on improving the diagnostic accuracy of models, i.e., first use a backbone network to extract image global features and then feed it into the classifier for diagnosis. However, these methods do not fully explore the transparent and reasonable decision-making process of the final classification results, which is crucial for medical diagnosis. In this paper, we propose a framework called Causal Intervention-based Multi-head Attention network (CaIMA) to enhance the explainability of medical diagnosis from a causal inference perspective, by exploring the inherent causal relationship between multi-region attention and diagnosis results. Specifically, it consists of three key components: (1) The multi-region attention module enables the network to focus on the distinct discriminative lesion regions that hold causal relationships with the predicted outcome. (2) The attention-driven data augmentation module provides accurate localization of discriminative regions and enhances model explainability. (3) The causal intervention module aims to explore the intrinsic causal relationship between the attention map and the predicted outcome, encouraging the network to learn more useful attention maps for medical image diagnosis. Besides, to address the learning difficulty of this network, we further introduce a non-overlapping multiple attentional guidance loss that encourages the learned multiple attention maps to focus on specific lesion regions without overlapping. We compare the proposed CaIMA with state-of-the-art methods on multimedia medical datasets, including three public medical image datasets (Kvasir, ISIC2018, COVID-19) and one private dataset (CLC), and the experimental results substantiate the effectiveness of CaIMA in terms of diagnosis accuracy and explainability.

Navigating Crisis: Strategic Adaptations in Family Businesses Amid Economic Uncertainty

This research delves into the strategic adaptations made by family businesses to navigate crises amidst economic uncertainty. Family businesses represent a significant portion of the global economy and face unique challenges during periods of economic instability. Through a comprehensive review of academic literature, case studies, and expert insights, this study identifies key strategies employed by family businesses to withstand turbulent economic conditions. The findings highlight the critical role of clear communication and transparent decision-making processes in ensuring business resilience during crises. Family businesses that prioritize open dialogue among stakeholders are better equipped to respond effectively to challenges and maintain continuity. Moreover, flexibility within business models emerges as essential for adapting to rapidly changing market dynamics. Family businesses often leverage their inherent strengths, such as strong relationships with customers and communities, to navigate crises successfully. Prudent financial planning and risk management are identified as fundamental for safeguarding the financial stability of family businesses amidst economic uncertainty. Effective management of resources and proactive risk mitigation strategies enable businesses to weather downturns and position themselves for recovery. Additionally, the study emphasizes the importance of maintaining a long-term perspective and fostering family unity during crises. Family businesses that prioritize cohesion and collaboration among family members demonstrate greater resilience and are more likely to emerge stronger from adversity. By synthesizing these insights, this research provides valuable guidance for family business owners, managers, and policymakers facing economic uncertainty.

Overcome Medical Algorithm Aversion: Conditional Joint Effect of Direct and Indirect Information

The just-ended COVID-19 pandemic and the looming global aging remind us that we need to be prepared for the shortage of doctors, which might become an urgent medical crisis in the future. Medical AI could relieve this urgency and sometimes perform better than human doctors; however, people are reluctant to trust medical AI because of algorithm aversion. Although several factors that can minimize algorithm aversion have been identified, they are not effective enough to promote medical AI as people’s first choice. Therefore, inspired by the direct and indirect information model of trust and media equation hypothesis, this research explored a new method to minimize aversion to medical AI by highlighting its social attributes. In 3 between-subject studies, a medical AI system’s direct information (i.e., transparency and quantitation of the decision-making process (DMP)) and indirect information (i.e., social proof) were manipulated. Study 1 (N = 193) and 2 (N = 429) showed that transparency of DMP and social proof increased trust in AI, but did not affect trust in human doctors. Social proof jointly affected trust in AI with non-quantitative DMP but not quantitative DMP. Study 3 (N = 184) further revealed the joint effect of the transparent non-quantitative DMP and near-perfect social proof, which could minimize algorithm aversion. These results extended the direct-indirect information model in interpersonal trust, revealed conditional media equation in human-AI trust, and offered practical implications for medical AI interface design.