Search Results (510)

As a developing and evolving phenomenon, Islamic finance is continuously questioned regarding its performance and efficiency, especially in the context of higher ethical objectives, also termed as maqasid al Shariah, to achieve falah by practicing ihsan. A vast group of researchers has measured the unsatisfactory performance of Islamic financial institutions against the maqasid al Shariah, reflecting their convergence with capitalist systems. This raises a question of whether the Islamic finance industry interprets the concept of maqasid al Shariah the same way as academia and whether they assign maqasid al Shariah the same high level of relevance and importance. This study explores how the practitioners of the Islamic banking industry in Pakistan understands and implement maqasid al Shariah in practice. Adopting a qualitative, multiple-case approach, it draws on 20 in-depth narrative interviews with Islamic bankers and Shariah scholars. The findings of the research suggest ten different perspectives of practitioners, which they hold regarding maqasid al Shariah. They are (1) public welfare (maslahah), (2) business motives alongside banks do not consider maqasid al Shariah as their responsibility, (3) wrong interpretation and wrong evaluation of Islamic institutions on maqasid, (4) new industry and over expectation from the industry, (5) justice/equity (‘adl/ihsan), (6) bankers consider auto inclusion of maqasid al Shariah in every transaction, (7) prevention from prohibitions and provisioning of halal options, (8) Shariah compliance, (9) more focus on protection of wealth (10) maqasid are not divine and are man-made interpretations. These findings contribute to developing more effective performance measurement frameworks for the industry in the future and can compel both regulators and practitioners to consider comprehensive objectives of Shariah in product development rather than focusing merely on compliance. Full article

The rapid diffusion of generative artificial intelligence (GenAI) is reshaping organisational systems and digital transformation strategies, yet it remains unclear which organisational conditions are associated with long-term competitive performance in business-to-business (B2B) contexts. This study adopts a systems-informed perspective and examines how ethical governance, environmental dynamism, exploratory and exploitative innovation, and GenAI adoption are associated with long-term competitive performance in B2B firms. Using survey data from 104 Portuguese B2B managers and Partial Least Squares Structural Equation Modelling (PLS-SEM), the findings show that ethical governance is the strongest organisational correlate of long-term competitive performance, underscoring the central role of governance structures in responsible GenAI use. GenAI adoption is positively associated with performance, but its role is complementary rather than dominant. Exploratory innovation does not show a significant direct association with performance; instead, its association with performance operates through GenAI adoption in the estimated model, suggesting that experimentation becomes more performance-relevant when translated into digitally enabled routines. In contrast, exploitative innovation is directly associated with performance through incremental efficiency mechanisms. These findings challenge technology-deterministic assumptions and suggest that long-term competitive performance in B2B firms is more closely associated with the organisational alignment of governance structures, innovation capabilities, and GenAI adoption than with technology adoption alone. Full article

This study aims to systematically review how artificial intelligence (AI) is being adopted by small and medium-sized enterprises (SMEs) in Latin America to improve resilience and support sustainable practices in uncertain business environments. Based on the PRISMA protocol, fourteen peer-reviewed studies published between 2020 and 2025 were analyzed. The results identify significant barriers such as lack of adoption, including insufficient technological infrastructure, limited specialized talent, and budgetary constraints. Despite these obstacles, AI is increasingly used to automate processes and enhance predictive decision-making. While most applications remain concentrated in logistics, manufacturing, and telecommunications, their potential to improve efficiency and competitiveness is evident. The review also identifies the absence of robust regulatory frameworks as a critical limitation, particularly regarding ethical issues such as data privacy and algorithmic transparency. Although these aspects are not extensively covered in the reviewed literature, they represent important gaps for future research. In conclusion, the responsible adoption of AI in SMEs can contribute to business performance and sustainability goals, provided that appropriate public policies and sectoral strategies are implemented. Full article

This study examines female microenterprise entrepreneurship in the city of Juliaca, Peru, as a response to structural conditions of poverty, informality, and limited inclusion in public policies. The research aims to understand and interpret the dynamics of women-led entrepreneurship and its relationship with sustainable local socioeconomic development. A qualitative methodological approach based on an interpretive phenomenological design was adopted. Data was collected through in-depth interviews, direct observation, and document analysis with sixteen microentrepreneurs selected through purposive and snowball sampling. The findings reveal that intrinsic motivations (resilience, leadership, and self-fulfillment) and extrinsic motivations (economic independence, access to financing, and education) are key factors in the entrepreneurial process. In addition, entrepreneurial social capital, expressed through family, community, and institutional networks, plays a strategic role in the sustainability of businesses. The results also show that women entrepreneurs actively and significantly contribute to sustainable local socioeconomic development by strengthening local development ecosystems, generating employment, and promoting socially, fiscally, and ethically responsible practices. Despite their role as agents of change and transformation, women entrepreneurs continue to face structural barriers, highlighting the need for public policies with territorial and gender-sensitive approaches to strengthen their impact and sustainability. Full article

This study explores the intricate behavioral consumer psychology dynamics of how certain elements—color, price, gender differences, and the concept of the frequency illusion—affect emotions, brand awareness, and consumer decision-making in a digital environment. Going beyond conventional analyses, this study also explores the intersection of sustainable business practices, elucidating the potential for ethical, environmentally conscious, and business-sustainable decision-making. Utilizing a quantitative method and survey data from 207 respondents, this research contributes to a more profound level of understanding of consumer decision-making in the Lebanese retail sector, offering strategic insights for organizations seeking to enhance brand recognition, while aligning with responsible and sustainable practices in today’s dynamic and competitive environment. The study found that psychological cues—color, price, gender differences, and frequency illusion—significantly influence emotions, brand awareness, and consumer decision-making in retail. Future research should examine the tensions in consumer decision-making, where brand awareness and emotional cues can simultaneously facilitate and bias choices, with effects contingent on exposure, demographic characteristics, digital fluency, and cultural context. Full article

Artificial Intelligence (AI) has transitioned from a specialized research area to a ubiquitous socio-technical infrastructure influencing sectors from healthcare and law to manufacturing and defense. In tandem with its transformative promise, AI has created an exponentially expanding ethics literature questioning, fairness, transparency, accountability, and justice. This review synthesizes publications and key policy developments between 2019 and 2025, bringing sectoral discourses together with cross-cutting frameworks. Grounded in a systematic scoping review methodology, we frame the field along four meta-dimensions: trust and transparency, bias and fairness, governance & regulation, and justice, while we investigate their expression across diverse sectors. Special attention is dedicated to healthcare (patient trust and algorithmic bias), education (integrity and authorship), media (misinformation), law (accountability), and the industrial sector (data integrity, intellectual property protection, and environmental safety). We ground abstract principles in concrete case studies to illustrate real-world harms and mitigation strategies. Furthermore, we incorporate pluralistic ethics (e.g., Ubuntu, Islamic perspectives), environmental ethics, and emerging challenges posed by Generative AI and neuro-AI interfaces. To bridge theory and practice, we propose an operational governance framework for organizations. We contend that success involves transitioning from principles toward ethics-by-design, pluralistic governance, sustainability, and adaptive oversight. This review is intended for scholars, practitioners, and policymakers who need a comprehensive and actionable framework for navigating the complex landscape of AI ethics. Full article

Sustainability has become a strategic priority in the hospitality sector, particularly in luxury hotels where environmental responsibility must be balanced with high service quality. This study explores hotel managers’ perceptions and experiences of implementing environmentally friendly practices in five-star hotels in Gauteng, South Africa. A qualitative research approach, guided by a constructivist paradigm, was employed using semi-structured interviews with seventeen middle-level managers from major departments in the hotels. Data were manually and software-coded, and thematic analysis produced nine interrelated themes: Adoption Culture, Collaboration Networks, Consumption Tracking, Guest Revenue Drivers, Operational Shifts, Operational Prioritisation, Staff Enablement, Structural Constraints, and Valued Pragmatism. The findings indicate that managers generally perceive sustainability as both an ethical responsibility and a business imperative, particularly in relation to brand reputation, guest expectations, and cost efficiency. However, implementation is constrained by infrastructural instability, high initial investment costs, limited supplier availability, and occasional resistance from staff and guests. The study highlights the importance of embedding sustainability within governance systems, staff practices, and organisational culture to support long-term adoption. This research offers context-specific insights into sustainability implementation in South African luxury hotels and provides practical value for hotel managers, policymakers, and sustainability stakeholders operating in resource-constrained environments. Full article

This study examines brand trust as a governance mechanism within the Mongolian cashmere value chain and explores its impact on profit distribution and business relationships. Using a qualitative methodology involving key stakeholder interviews, document analysis and case studies, the study shows that brand trust acts as a powerful form of soft power. It institutionalises values such as ethical sourcing and sustainability, which simultaneously strengthen consumer loyalty and reconfigure power dynamics upstream. Transparency and traceability are the tools that enforce compliance with brand standards. These findings extend global value chain theory by incorporating intangible factors such as trust and reputation into models of value creation and distribution. Consequently, policies aimed at enhancing brand trust are presented as a viable strategy to promote sustainable and equitable outcomes in similar resource-based sectors. Full article

This study examines how ethical leadership promotes employees’ corporate social responsibility (CSR) engagement in small- and medium-sized enterprises (SMEs) through a sequential mechanism of Trust in leadership and organizational commitment, and whether this process differs across national contexts. Survey data were collected from SME employees in Pakistan (n = 102), India (n = 70), and Taiwan (n = 96). Using a multi-group partial least squares structural equation modeling (PLS-SEM) approach with bootstrapping, we test direct, indirect, sequential mediation, and moderation effects linked to national culture (collectivism) and institutional support. Results show that ethical leadership has a positive direct effect on CSR engagement and an additional indirect effect via Trust in leadership and organizational commitment. The strength of these relationships varies across the three countries, underscoring the importance of cultural and institutional conditions when translating leadership ethics into CSR-oriented behaviors. The findings extend CSR micro foundations in SMEs and offer actionable guidance for leadership development and policy support in emerging and advanced economies. Full article

This study conducts a bibliometric and content analysis of ‘artificial intelligence-enabled auditing’ over three decades. The use of artificial intelligence (AI) tools in auditing has evolved and is now an imperative practice in the auditing space. Using bibliometric methods via Bibliometrix R-package (Biblioshiny) and VOSviewer, this research mainly examines the scholarly discussion on AI-enabled auditing, using the Scopus database. The main themes identified are: Theme 1: AI in auditing: readiness, representation, and implementation; Theme 2: data-driven audit ecosystems and digital technologies; and Theme 3: audit quality, professional skepticism, and ethical governance. On the descriptive end, publication trends, prominent authors, articles, and sources are identified. The findings highlight a significant increase in AI-enabled auditing studies since 2018, coinciding with growing global awareness on the importance of AI across all spheres of business. The outcome of this research contributes to a wide array of stakeholders, including businesses, audit firms, shareholders, and policymakers; it should give insights to business organizations on the capabilities of AI-assisted auditing, while policymakers should have access to verifiable, auditable and regulatory-compliant systems for the implementation of their regulations. Investors may further enhance their knowledge in terms of how AI-assisted auditing increases the quality of their investment decisions and, at the same time, the risks involved. Finally, auditing firms should further invest in improving the application of technology in the auditing environment and ensure quality, evidence-based audit outcomes, and reporting. Full article

This study investigates managers’ perceptions of digitalization and artificial intelligence (AI) adoption within the framework of Industry 4.0, emphasizing the relationship between technological modernization, organizational culture, and sustainability. Drawing on empirical data collected in 2025 from 150 Romanian companies ’managers by applying a structured questionnaire, followed by a multivariate analytical approach supported by the Benjamini–Hochberg correction, the research identifies critical managerial perceptions that influence the success of digital transformation. The findings show that managers recognize digitalization as a strategic opportunity for process optimization and competitiveness. At the same time, they perceive it as a structural challenge driven by legacy systems, financial constraints, and limited digital competencies. Similarly, managers view AI as a valuable tool for data analysis and market forecasting, while also expressing concerns related to ethical, technical, and cybersecurity risks. The study further reveals managerial ambivalence toward Industry 4.0. Although automation and IoT are considered inevitable for maintaining competitiveness, their implementation remains constrained by logistical and cultural barriers. By integrating technological, organizational, and human dimensions, this research contributes to the literature on sustainable digital transformation. It provides an in-depth understanding of how managerial perceptions mediate the balance between innovation, responsibility, and long-term resilience. Finally, the results offer actionable insights for policymakers and business leaders seeking to align digitalization and AI initiatives with sustainable development objectives. Full article

In recent years, Latin America has experienced a growing incorporation of Artificial Intelligence (AI) into business and organizational environments, driven by digital transformation, data availability, and competitive pressures. Across multiple sectors, AI-based tools are increasingly used to support complex decision-making processes, raising both opportunities and challenges related to efficiency, ethics, and organizational readiness. Within this context, this systematic review examines the scientific evidence on the implementation of AI in business decision-making in Latin America. Following PRISMA 2020 guidelines, a systematic search was conducted in the Scopus database for articles published between 2021 and 2025. The search strategy combined Boolean operators related to AI and decision-making. Inclusion criteria comprised original, open-access research articles conducted in Latin American countries and published in Spanish or Portuguese. After screening for temporality, geographic focus, language, document type, accessibility, duplication, and relevance, 27 studies were selected from an initial pool of 276,302 records. The studies originated mainly from Peru, Colombia, Chile, and Ecuador. The findings show that AI is applied across sectors such as industry, agriculture, finance, education, and public services, primarily to enhance predictive capacity, automate processes, and support data-driven decisions. While AI adoption improves efficiency, cost reduction, and strategic innovation, its effectiveness depends on staff training, ethical governance, and strategic alignment. Persistent challenges include resistance to change, data quality limitations, and concerns regarding transparency and algorithmic bias. Overall, AI emerges as a transformative but context-dependent tool for business decision-making in Latin America. Full article

Managerial decision-making is a core component of business management and plays a particularly critical role in Sustainable Business Models (SBMs), where it supports long-term competitiveness, adaptability, and positive environmental and social impact. SBMs are inherently complex, dynamic, and data-intensive, requiring advanced analytical capabilities to continuously monitor and optimize sustainability performance across Environmental, Social, and Governance (ESG) dimensions. Artificial Intelligence (AI) introduces new technological opportunities that fundamentally transform managerial decision-making by enabling advanced modeling, simulation, and the analysis of incomplete and heterogeneous data. The purpose of this research is to systematically analyze and synthesize existing AI-supported decision-making approaches used in sustainable business models, with a focus on how these methods transform traditional managerial decision-making frameworks through the integration of Environmental, Social, and Governance (ESG) criteria, and to assess the key benefits, limitations, and implementation conditions of AI-supported decision systems for achieving long-term organizational sustainability. Using a systematic literature review and comparative synthesis of recent theoretical and empirical studies, the research maps key AI-based decision-making approaches applied in sustainable business models and compares their managerial relevance across ESG dimensions. The results provide a structured overview of how different AI techniques contribute to sustainability monitoring, resource optimization, and risk assessment, while also outlining critical organizational, governance, and ethical constraints affecting their practical deployment. Full article

The scientific study of affect has been historically characterized by a profound lack of terminological consensus, leading to a state of conceptual fragmentation that persists in psychology, neuroscience and many other fields. This ambiguity is not merely an academic concern; it has significant consequences for the development of artificial intelligence (AI) systems designed to recognize and respond to human “emotions”. In fact, it has an influence on the entire field of affective computing. The problem is obvious. Without a distinct definition of “emotion” it is difficult to train an algorithm to recognize it. The Walla Emotion Model, also known as the ESCAPE (Emotions Convey Affective Processing Effects) model, provides a potentially helpful and neurobiologically grounded framework to resolve this impasse and to improve any discourse about it, for businesses and even lawmakers aiming at healthy societies. By establishing clear, non-overlapping definitions for affective processing, feelings, and emotions, this model offers a path toward more precise research and more ethically sound affective computing including AI-driven “emotion” recognition. It introduces a concept that allows for the detection of incongruences between internal states and external signals with a very clear terminology supporting understandable communication. This is critical for identifying feigned or socially masked inner affective states, a challenge that traditional “face-reading” AI models frequently fail to address. Even tone of voice and body postures as well as gestures can be and are often voluntarily modified. Through the separation of subcortical affective processing (evaluation of valence; neural activity) from subjective experience (feeling) and external communication (emotion), the Walla model provides a helpful framework for AI-designs meant to have the capacity to infer an internal affective state from collected signals in the wild bypassing verbal self-report. This paper is purely theoretical; it does not provide any algorithm models or other distinct suggestions to train a software package. Its main purpose is the introduction of a new emotion model, particularly a new terminology that is considered helpful in order to proceed with this endeavor. It is considered important to first enable the clearest-possible form of communication about anything related to the term emotion across all disciplines dealing with it. Only then can progress be made. Full article

Background/Objectives: Machiavellianism has long been associated with unethical tendencies and behaviors. High-Mach people have been stereotyped to choose business-related professions, contrary to low-Mach individuals choosing the helping professions. There has been a clear shift in scholarly focus, such as Machiavellian leadership and Machiavellian personality traits in healthcare. The objective of this narrative literature review was to provide a structured synthesis of empirical evidence on Machiavellianism within healthcare settings, focusing on its prevalence, manifestations, and organizational implications, while identifying conceptual and research gaps in the field. Methods: Literature research was conducted for articles published in PubMed, Scopus, and Google Scholar from 2014 until 2025. Articles written in English, examining Machiavellian traits in healthcare workers and students, were included in this review. Results: The search strategy produced 347 items, of which 11 original studies were included. Machiavellianism was described as a personality trait featuring emotional coldness and manipulativeness for the achievement of one’s own ends; individuals exhibiting those traits may prioritize personal gain over collective welfare. Machiavellian tendencies manifesting in organizational culture often lead to a toxic work environment where manipulation might become normalized. Machiavellians show high commitment to their careers, but low commitment to their current organizations, supervisors, and teams. Conclusions: Machiavellianism emerges as a relevant but underexplored personality trait in healthcare, associated with unethical behaviors, reduced organizational commitment, and toxic work environments. Addressing its impact requires ethical leadership development, supportive organizational environments, and early identification during professional training to safeguard workforce well-being and patient safety. Full article