Ethical Considerations Research Articles

A new framework for ethical artificial intelligence: keeping HRD in the loop

ABSTRACT As a result of the rapid development of technologies, artificial intelligence (AI) is changing human experiences and workplaces in ways that we are not prepared for. Recognized as a double-edged tool, AI has brought both positive and negative outcomes to organisations and employees. With its increasing popularity in the workplace and our daily practice, how to use AI intelligently and responsibly has become a top concern raised by both academics and practitioners. In this article, we discuss the desired and unintended consequences of AI as documented in current literature, examine the ethical concerns, and identify current solutions. We propose ‘keeping HRD in the loop’ as a means to addressing ethical challenges associated with AI and offer a new conceptual framework as a tool for reference. Integrating general AI principles and ethical guidelines developed by the Academy of HRD, we map out an ethical boundary consisting of nine human-centred principles and suggest a sample of six HRD interventions that can facilitate responsible AI practices and productive human-machine interfaces. By bringing HRD perspectives into the domain of AI practice, we hope to bring ethical considerations to the forefront of AI decision-making and stimulate more interest in interdisciplinary research and academic-industry collaboration.

Artificial Intelligence in Enhancing the Kosovo Health Information System

The contemporary healthcare landscape faces unprecedented challenges, ranging from data fragmentation within health information systems to the need for timely and accurate diagnostics. This research explores the transformative potential of Artificial Intelligence (AI) in enhancing the Kosovo Health Information System (HIS). By leveraging the capabilities of AI, we aim to address existing limitations in data interoperability, predictive analytics, and personalized healthcare. The study incorporates a comprehensive literature review, methodological data collection, and analysis of the current state of the Kosovo HIS. Drawing inspiration from successful global implementations, we delve into the possibilities of AI applications in diagnosis, treatment personalization, and population health management. The research also examines ongoing initiatives and collaborations aimed at integrating AI into the Kosovo HIS. Through a critical assessment of technical challenges and ethical considerations, the paper provides insights into the opportunities and hurdles associated with the implementation of AI in healthcare. Ultimately, this research contributes to the discourse on the future prospects of healthcare in Kosovo, highlighting the potential long-term impacts of AI integration and offering recommendations for advancing the country's health information infrastructure. Received: 5 February 2024 / Accepted: 23 April 2024 / Published: 5 May 2024

Environmental data in epidemic forecasting: Insights from predictive analytics

Epidemic forecasting plays a critical role in public health preparedness and response, enabling proactive measures to mitigate the impact of infectious diseases. Environmental data, encompassing factors such as temperature, humidity, air quality, and geographical features, holds valuable insights for predicting and identifying areas prone to epidemics. This paper explores the integration of predictive analytics with environmental data to enhance epidemic forecasting capabilities. By leveraging predictive analytics techniques, researchers and public health officials can analyze environmental data to identify regions at higher risk of experiencing epidemic outbreaks. Through statistical modeling, machine learning algorithms, and computational simulations, predictive analytics utilize environmental indicators to forecast the likelihood and spread of diseases. For example, areas with high temperatures and humidity may be conducive to mosquito-borne diseases, while regions with poor air quality may experience increased rates of respiratory infections. Case studies highlight the application of predictive analytics in various contexts, including forecasting mosquito-borne diseases in tropical regions and tracking respiratory infections in urban areas with poor air quality. Early warning systems, informed by environmental data, provide timely alerts to potential epidemic threats, enabling proactive interventions and resource allocation. While the integration of environmental data into epidemic forecasting offers significant benefits, challenges remain, including data quality, availability, and ethical considerations. Continued research and collaboration are essential to address these challenges and further enhance the effectiveness of predictive analytics in identifying and mitigating epidemic risks. In conclusion, this paper underscores the importance of leveraging environmental data and predictive analytics for epidemic forecasting, emphasizing their potential to improve public health outcomes and enhance preparedness efforts in the face of emerging infectious diseases and climate change. Keywords: Environmental Data, Epidemic Forecasting, Predictive Analytics.

The Philosophical Implications of Neuroscience Research on Free Will and Moral Responsibility

Purpose: This study sought to analyze the philosophical implications of neuroscience research on free will and moral responsibility. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. Findings: The findings reveal that there exists a contextual and methodological gap relating to the philosophical implications of neuroscience research on free will and moral responsibility. Preliminary empirical review recognized that the compatibility between neuroscientific determinism and philosophical concepts of free will. It highlighted the need for interdisciplinary collaboration to address the ethical considerations and societal implications of using neuroscientific evidence in legal and ethical contexts. The study emphasized the importance of public engagement to foster informed discourse on the ethical and philosophical implications of neuroscience research. Overall, the findings underscored the complexity of navigating the intersection of neuroscience and philosophy, calling for continued interdisciplinary dialogue to address the multifaceted implications of neuroscience research on free will and moral responsibility. Unique Contribution to Theory, Practice and Policy: Compatibilism, Libertarianism and Determinism may be used to anchor future studies on philosophical implications of neuroscience research on free will and moral responsibility. The study provided valuable insights and recommendations across theory, practice, and policy. It contributed to theoretical advancements by synthesizing neuroscientific findings with philosophical theories, fostering interdisciplinary collaboration. In practice, the study emphasized ethical considerations in research conduct and interpretation, aiming to enhance the reliability and validity of neuroscientific evidence. Policy recommendations focused on developing evidence-based policies governing the use of neuroscientific evidence and promoting public engagement and education. Additionally, the study identified areas for further research, such as longitudinal and cross-cultural studies, to advance understanding in the field. Overall, the study highlighted the importance of ethical considerations and responsible innovation in the development and application of neuroscience research. Keywords: Philosophical Implications, Neuroscience Research, Free Will, Moral Responsibility, Ethics, Interdisciplinary Collaboration, Neuroscientific Findings, Public Engagement, Education, Ethical Considerations, Responsible Innovation

Integrating Modern Neuroscience with Traditional Philosophical Theories

Purpose: The general objective of the study was to examine how changes or advancements in modern neuroscience may impact or influence traditional philosophical theories. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. Findings: The findings reveal that there exists a contextual and methodological gap relating to modern neuroscience with traditional philosophical theories. Preliminary empirical review revealed that interdisciplinary collaboration between these fields holds significant potential. Through a comprehensive review of existing literature, it was found that while challenges existed, there were also considerable opportunities for integration. The study emphasized the importance of theoretical integration and methodological innovation in advancing understanding at the intersection of neuroscience and philosophy. Practical implications were highlighted for stakeholders, including researchers, educators, policymakers, and the public, with the conclusion suggesting that embracing interdisciplinary perspectives could lead to deeper insights into the nature of the human mind and its philosophical implications. Unique Contribution to Theory, Practice and Policy: The Dual-Aspect Monism, Embodied Cognition and Enactivism theory may be used to anchor future studies on modern neuroscience with traditional philosophical theories. The study provided recommendations aimed at advancing theory, practice, and policy. Interdisciplinary dialogue and collaboration between neuroscientists and philosophers were fostered to enrich theoretical discussions. Insights from the integration of neuroscience and philosophy were incorporated into educational curricula and training programs, promoting critical thinking skills. Ethical considerations were integrated into neuroscience research through the development of guidelines and regulations. Interdisciplinary research teams were formed to design innovative methodologies, bridging terminological and conceptual differences. Furthermore, international collaboration was encouraged to leverage diverse perspectives and resources. Keywords: Integrating, Modern Neuroscience, Traditional Philosophical Theories, Interdisciplinary, Dialogue, Collaboration, Ethical Considerations, Education, Terminological Differences, International Collaboration, Critical Thinking

Investigator Participation in Clinical Research: An Examination of Barriers and Facilitators in the Indian Clinical Trials Settings

Background: This study aims to improve clinical research by identifying the barriers and facilitators for investigators conducting trials. We explore investigator demographics, challenges faced, and factors promoting engagement. Additionally, we examine how investigators’ background influences their experiences with both barriers and facilitators, ultimately aiming to foster a patient-centric clinical trial ecosystem. Methods: A quantitative, exploratory study investigated barriers and facilitators in clinical trials among 132 investigators in India. A self-designed questionnaire captured experiences with challenges, strategies, and facilitators. Pilot testing ensured tool validity and reliability. Ethical considerations were addressed, and data analysis will identify key factors impacting investigator engagement. This study aims to improve the clinical research ecosystem in India. Results: The study aims to identify the facilitators and barriers experienced by the research investigators. An online questionnaire survey was used in this cross-sectional investigation. The study's population consists of 132 respondents, with 68 (51.5%) males and 64 (48.5%) females. Clinical Research Coordinators (CRCs) (41.7%), Research Associates (RA’s) (27.3%), Supporting staff (19.7%), Co-Investigators (Co-I) (6.8%), and Principal Investigators (PIs) (4.5%) which were the majority of respondents. The most common barriers found among the principal investigators regarding the limited resources (p=0.014) towards the clinical trials. Inadequate training (p=0.461), Complex regulations (p=0.625), and Communication barriers (p=0.853) did not seem like barriers to the clinical research staff. Whereas some of the staff faced barriers (p=0.118) with the complex regulations and Inadequate training on the other hand most of the respondents considered the same statements as facilitators (p=0.963) with their daily working activities. Conclusion: The Barriers and Facilitators experienced by the clinical investigators, It was found that there is no difference in viewpoint. As per the results, Research associates agreed more with the facilitators and the principal investigators faced barriers.

Sentiment-driven Movie Recommendation System: A Machine Learning Approach

Pioneering method for enhancing movie recommendation systems through the integration of sentiment analysis of micro-blogging data. Leveraging natural language processing (NLP) techniques, the proposed system extracts sentiment from tweets and other micro-blogging sources pertinent to movies systematic approach encompassing the collection, preprocessing, and analysis of movie review data tailored for sentiment analysis tasks. Key aspects covered include the acquisition of publicly available datasets, methodologies for web scraping, preprocessing techniques, strategies for sentiment labeling, methods for data augmentation, procedures for data splitting, optimal data storage formats, and ethical considerations inherent in data collection and utilization. By offering a comprehensive guide, furnish both researchers and practitioners with the necessary tools to proficiently manage movie review data while navigating the ethical intricacies associated with its acquisition and application.

Challenges and directions for digital twin implementation in otorhinolaryngology.

Digital twin technology heralds a transformative era in Otorhinolaryngology (ORL), merging the physical and digital worlds to offer dynamic, virtual models of physical entities or processes. These models, capable of simulating, predicting, and optimizing real-world counterparts, are evolving from static replicas to intelligent, adaptive systems. Fueled by advancements in communication, sensor technology, big data analytics, Internet of Things (IoT), and simulation technologies, artificial intelligence (AI), digital twins in ORL promise personalized treatment planning, virtual experimentation, and therapeutic intervention optimization. Despite their potential, the integration of digital twins in ORL faces challenges including data privacy and security, data integration and interoperability, computational demands, model validation and accuracy, ethical and regulatory considerations, patient engagement, and cost and accessibility issues. Overcoming these challenges requires robust data protection measures, seamless data integration, substantial computational resources, rigorous validation studies, ethical transparency, patient education, and making the technology accessible and affordable. Looking ahead, the future of digital twins in ORL is bright, with advancements in AI and machine learning, omics data integration, real-time monitoring, virtual clinical trials, patient empowerment, seamless healthcare integration, longitudinal data analysis, and collaborative research. These developments promise to refine diagnostic and treatment strategies, enhance patient care, and facilitate more efficient and tailored ORL research, ultimately leading to more effective and personalized ORL management.

Microengineered neuronal networks: Enhancing brain-machine interfaces

The Brain-Machine Interface (BMI), a crucial conduit between the human brain and computers, holds transformative potential for various applications in neuroscience. This manuscript explores the role of micro-engineered neuronal networks (MNNs) in advancing BMI technologies and their therapeutic applications. As the interdisciplinary collaboration intensifies, the need for innovative and user-friendly BMI technologies becomes paramount. A comprehensive literature review sourced from reputable databases (PubMed Central, Medline, EBSCOhost, Google Scholar) aided in the foundation of the manuscript, emphasizing the pivotal role of MNNs. This study aims to synthesize and analyse the diverse facets of MNNs in the context of BMI technologies, contributing insights into neural processes, technological advancements, therapeutic potentials, and ethical considerations surrounding BMIs. MNNs, exemplified by dual-mode neural microelectrodes, offer a controlled platform for understanding complex neural processes. Through case studies, we showcase the pivotal role of MNNs in BMI innovation, addressing challenges, and paving the way for therapeutic applications. The integration of MNNs with BMI technologies marks a revolutionary stride in neuroscience, refining brain-computer interactions and offering therapeutic avenues for neurological disorders. Challenges, ethical considerations, and future trends in BMI research necessitate a balanced approach, leveraging interdisciplinary collaboration to ensure responsible and ethical advancements. Embracing the potential of MNNs is paramount for the betterment of individuals with neurological conditions and the broader community.

Moral Agency and Responsibility in AI Systems

Purpose: The general objective of this study was to explore moral agency and responsibility in AI systems. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. Findings: The findings reveal that there exists a contextual and methodological gap relating to moral agency and responsibility in AI systems. Preliminary empirical review revealed that AI systems possess a form of moral agency, albeit different from human agents, and promoting transparency and accountability was deemed crucial in ensuring ethical decision-making. Interdisciplinary collaboration and stakeholder engagement were emphasized for addressing ethical challenges. Ultimately, the study highlighted the importance of upholding ethical principles to ensure that AI systems contribute positively to society. Unique Contribution to Theory, Practice and Policy: Utilitarianism, Kantianism and Aristotelian Virtue Ethics may be used to anchor future studies on the moral agency and responsibility in AI systems. The study provided a nuanced analysis of moral agency in AI systems, offering practical recommendations for developers, policymakers, and stakeholders. The study emphasized the importance of integrating ethical considerations into AI development and deployment, advocating for transparency, accountability, and regulatory frameworks to address ethical challenges. Its insights informed interdisciplinary collaboration and ethical reflection, shaping the discourse on responsible AI innovation and governance. Keywords: Moral Agency, Responsibility, AI Systems, Ethics, Decision-Making, Framework, Analysis, Regulation, Governance, Transparency, Accountability, Interdisciplinary, Innovation, Deployment, Stakeholders

The Power of Artificial Intelligence in Digital Marketing

In the dynamic landscape of digital marketing, businesses are constantly challenged to engage their target audience effectively amidst a plethora of information and choices. Artificial Intelligence (AI) emerges as a pivotal solution in addressing these challenges, revolutionizing traditional marketing approaches through its diverse applications and capabilities. This paper explores the multifaceted role of AI in digital marketing, drawing upon insights from existing literature and practical examples. AI empowers marketers with enhanced customer insights, predictive analytics, and automated campaign optimization, enabling them to tailor marketing strategies to individual preferences and behaviors. Moreover, AI-driven tools such as chatbots and virtual assistants streamline customer interactions, while also offering cost efficiencies and improved return on investment (ROI). Despite the undeniable benefits, the integration of AI in digital marketing raises concerns regarding data privacy, algorithmic bias, and ethical considerations. Nevertheless, by navigating these challenges responsibly and adopting ethical frameworks, businesses can leverage AI to unlock new opportunities for growth, differentiation, and sustainable success in the digital era

The use of artificial intelligence in marketing strategies: Automation, personalization and forecasting

The integration of Artificial Intelligence (AI) in marketing strategies is pivotal in the era of digital transformation, especially in automation, personalization, and forecasting. This research investigates the evolutionary role of AI in transitioning from traditional marketing frameworks to data-driven methodologies, thereby enhancing marketing efficiency and customer engagement. The increasing reliance on AI for strategic decision-making in marketing underscores the significance of this study. Employing a systematic literature review and thematic analysis, this research synthesizes data from an array of studies to thoroughly understand the impact of AI on marketing. The findings reveal that AI significantly streamlines marketing operations, fosters highly personalized marketing strategies, and enhances the accuracy of forecasting market trends and consumer behavior. However, this study also sheds light on the ethical and privacy concerns associated with the use of AI in marketing. Results point towards a significant transformation in marketing practices propelled by AI, marked by improvements in operational efficiency and customer interaction. Nevertheless, the study advocates the importance of addressing ethical considerations and privacy issues, emphasizing responsible AI deployment. The study offers a comprehensive perspective on the integration of AI in marketing and suggests insights into prospective trends. It recommends a balanced approach to leveraging AI’s capabilities while upholding ethical standards. The research’s practical implications aim to guide marketers and researchers towards responsible and effective AI adoption in marketing strategies, paving the way for a future where technology enhances marketing endevaours without compromising ethical integrity.

Ethical Implications of Environmental Policies and Practices

Purpose: The aim of this study was to investigate the ethical implications of environmental policies and practices. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. Findings: The findings reveal that there exists a contextual and methodological gap relating to the ethical implications of environmental policies and practices. Preliminary empirical review revealed that environmental decision-making is complex, involving trade-offs between competing values. It emphasized the importance of incorporating principles of justice and equity into governance processes, recognizing the disproportionate impact of environmental degradation on marginalized communities. The study identified gaps and shortcomings in current policies, highlighting the need for enhanced accountability and inclusivity. It advocated for a holistic and interdisciplinary approach to environmental governance, involving collaboration across diverse stakeholders to develop more effective and morally defensible strategies for addressing environmental challenges. Unique Contribution to Theory, Practice and Policy: The Environmental Justice theory, Ethical Decision- Making theory and Sustainability theory may be used to anchor future studies on the ethical implications of environmental policies and practices. The study made several recommendations, contributing to theory, practice, and policy. It recommended integrating ethical frameworks into policy development, strengthening community engagement, promoting environmental justice and equity, enhancing transparency and accountability, implementing ethical education and training, and fostering international cooperation. These recommendations aimed to ensure that environmental policies reflected ethical considerations, empowered communities, addressed systemic inequalities, promoted democratic governance, enhanced ethical awareness, and facilitated collaborative solutions to global environmental challenges. Keywords: Ethical Implications, Environmental Policies, Sustainability, Environmental Justice, Equity, Community Engagement, Transparency, Accountability, Policy Development, Participatory Governance, Democratic Deliberation, Environmental Inequalities, Ethical Frameworks, Education And Training, International Cooperation, Solidarity, Stakeholders, Decision-Making Processes, Global Environmental Challenges

A comprehensive exploration of artificial intelligence in orthopaedics within lower-middle-income countries: a narrative review.

Integrating Artificial Intelligence (AI) in orthopaedic within lower-middle-income countries (LMICs) promises landmark improvement in patient care. Delving into specific use cases-fracture detection, spine imaging, bone tumour classification, and joint surgery optimisation-the review illuminates the areas where AI can significantly enhance orthopaedic practices. AI could play a pivotal role in improving diagnoses, enabling early detection, and ultimately enhancing patient outcomes- crucial in regions with constrained healthcare services. Challenges to the integration of AI include financial constraints, shortage of skilled professionals, data limitations, and cultural and ethical considerations. Emphasising AI's collaborative role, it can act as a complementary tool working in tandem with physicians, aiming to address gaps in healthcare access and education. We need continued research and a conscientious approach, envisioning AI as a catalyst for equitable, efficient, and accessible orthopaedic healthcare for patients in LMICs.

The influence of artificial intelligence in higher education based on four thematic axes: a bibliometric study

The article presents a structured bibliometric study examining the impact of artificial intelligence (AI) in higher education across four thematic axes: AI in higher education, AI in education review, AI in the teaching-learning process, and AI tools applied to higher education. Research productivity and impact indicators are analyzed using data from major databases like Scopus, Web of Science, and ScienceDirect. Results reveal a significant increase in AI-related research output, particularly in machine learning, data mining, and learning analytics. The study highlights China and the United States as leading contributors to AI research in higher education. The findings highlight AI's evolving role in transforming higher education and the need for multidisciplinary research approaches to address emerging challenges and opportunities. However, limitations include the reliance on quantitative measures, the narrow temporal scope, and the limited focus on high-production countries. Future research should incorporate qualitative methods to explore practical applications and social impacts more comprehensively, consider a broader range of geographic contexts, and discuss ethical considerations around integrating AI into higher education.

Crisis communication strategies: A qualitative study about a natural medicine controversy in Colombia

Crisis communication strategies: A qualitative study about a natural medicine controversy in Colombia

Interventions to support children after a parental acquired brain injury: a scoping review

ABSTRACT Objective This scoping review aimed to identify manualised programs and practice suggestions to support children’s health literacy, behaviors and emotions after a parental acquired brain injury. Methods A systematic search of five scientific databases (PsychINFO, MEDLINE, ProQuest, Scopus, Cochrane) and gray literature occurred. Inclusion criteria included: studies and gray literature published 1989 to 2023, in English, child populations with relationship to parental acquired brain injury, identifying manualised programs or practice suggestions via content analysis approach. Ethical considerations No data were collected from human participants. All included studies, where relevant, demonstrated consent and/or ethical processes. Results Sixteen relevant studies and three gray literature resources (n = 19) were identified, including two studies that detailed manualised programs, and fifteen studies and two resource packs that included practice suggestions. Five common domains within practice suggestions were identified: systemic commitment (n = 17); family-centered approaches (n = 16); child-centered practices (n = 15); structured programs (n = 9); and peer support (n = 8). Conclusions More rigorous evaluation is required to test the potential benefits of manualised programs and practice suggestions. A systemic commitment at clinical and organizational levels to provide child and family-centered practices, structured programs, and access to peer support, early and throughout adult-health care settings, may help to meet the support needs of children.

Understanding Media Consumption Habits of Gen Z in Metaverse and Virtual Reality Project

Generation Z, born amidst the digital revolution, navigates a media landscape vastly different from previous generations. Their unique habits, characterized by mobile-first content, community-driven engagement, and a preference for short-form, interactive experiences, present both challenges and opportunities for emerging technologies like the Metaverse and Virtual Reality (VR). This research embarks on an exploratory journey, delving into the nexus between Gen Z's media consumption patterns and their potential adoption of these immersive realities. Through a mixed-methods approach, the research will analyze how Gen Z's digital media preferences, ranging from short-form video platforms to online communities, influence their perception and potential engagement with the Metaverse and VR. In-depth interviews and focus groups will uncover their desired features and functionalities within these virtual environments, ensuring alignment with their existing media consumption habits. Additionally, content analysis of online forums and social media discussions will provide valuable insights into their concerns and expectations surrounding these new frontiers. By understanding the interplay between Gen Z's media consumption and their potential engagement with the Metaverse and VR, this research aims to achieve several key objectives. Firstly, it seeks to identify the potential influence of their media habits on their willingness to adopt these technologies. Secondly, it aims to explore their desired functionalities and the types of content they envision within these virtual spaces. Furthermore, the research strives to investigate the role of online communities and influencers in shaping their attitudes and decisions regarding the Metaverse and VR. Finally, it seeks to address the potential risks and ethical considerations associated with Gen Z's participation in these immersive realities, aiming to pave the way for a more inclusive and responsible digital future.

Asteroid mining vs the carbon bubble: ethical considerations for space resource extraction

PurposeThe purpose of this paper is to consider the ethical and environmental implications of allowing space resource extraction to disrupt existing fuel economies, including how companies can be held accountable for ensuring the responsible use of their space assets. It will also briefly consider how such assets should be taxed, and the cost/benefit analyses required to justify the considerable expense of supporting this emerging space industry.Design/methodology/approachThis paper adopts theoretical bioethics methodologies to explore issues of normative ethics and the formulation of moral rules to govern individual, collective and institutional behaviour. Specifically, it considers social justice and social contract theory, consequentialist and deontological accounts of ethical evaluation. It also draws on sociological and organisational literature to discuss Dowling and Pfeffer’s (1975) and Suchman’s (1995) theories of pragmatic, cognitive and moral legitimacy as they may be applied to off-world mining regulations and the handling of space assets.FindingsThe findings of this conceptual paper indicate there is both a growing appetite for tighter resource extraction regulations to address climate change and wealth concentration globally, and an opportunity to establish and legitimise new ethical norms for commercial activity in space that can avoid some of the challenges currently facing fossil fuel divestment movements on Earth.Originality/valueBy adopting methodologies from theoretical bioethics, sociology and business studies, including applying a legitimacy lens to the issue of off-world mining, this paper synthesises existing knowledges from these fields and brings them to the new context of the future space resource economy.

USER PERCEPTION TOWARDS PREDICTIVE ANALYTICS IN CREDIT RISK MANAGEMENT

This comprehensive review explores the dynamic landscape of predictive analytics in credit risk management within the banking sector. Anchored in a qualitative research design, the study synthesizes existing literature and real-world case studies to provide a multifaceted understanding of predictive analytics' role in modern banking. The review identifies key trends, highlighting the integration of predictive analytics across diverse banking operations, the transition to advanced machine learning algorithms, the democratization of predictive analytics tools, and the growing emphasis on ethical and regulatory compliance. It underscores the effectiveness of predictive analytics, showcasing its ability to enhance risk assessment precision, decision-making agility, and overall banking performance. Comparative analyses reveal the varying performance of predictive models across contexts, emphasizing the importance of tailored model selection. However, challenges such as data quality, model interpretability, talent scarcity, ethical considerations, and implementation costs pose significant hurdles. Looking forward, predictive analytics promises to be an indispensable tool for mitigating credit risk in the banking sector, offering refined risk assessments, smarter decisions, and enhanced resilience. The insights from this review provide valuable guidance for banking professionals, regulators, and researchers navigating the evolving landscape of predictive analytics in banking.