Modern businesses increasingly recognise the need for ethical conduct and sustainability as essential components of long-term organisational success. This study examines how the integration of Islamic principles—specifically justice (‘adl), honesty (ṣidq), trustworthiness (amānah), social responsibility (mas’uliyyah), and fair dealings (iḥsān)—can enhance ethical conduct and promote sustainable growth within contemporary business environments. Using a mixed-methods approach combining surveys, interviews, and case studies, the research investigates the extent to which these Islamic values shape organisational behaviour, influence decision-making, and contribute to stakeholder well-being. The findings reveal that businesses integrating Islamic ethical principles demonstrate higher levels of transparency, employee satisfaction, and community engagement, which collectively strengthen organisational sustainability. However, challenges such as limited awareness, inadequate institutional support, and inconsistencies in implementation remain significant barriers. The study concludes that Islamic principles offer a comprehensive ethical framework capable of reinforcing responsible business conduct and fostering long-term growth when effectively integrated into modern business practices. The research provides practical recommendations for business leaders and policymakers seeking to promote ethical, inclusive, and sustainable economic development.
Tetiana Zavalii, Ramazan Eren, Serhii Lehenchuk
et al.
Business leadership driven by sustainability certifications has become a critical approach for signaling environmental responsibility and building customer trust in the hospitality industry. However, their effectiveness in enhancing guest satisfaction remains poorly understood, particularly regarding whether the quantity of certification or specific credential types drives positive outcomes. This study examines the relationship between sustainability certifications, customer trust, and satisfaction among certified hotels and resorts in Türkiye's Mediterranean region, a destination experiencing significant environmental pressures and intense tourism competition. The research analyzes 95 eco-certified properties across 15 districts during June 2025, selected through purposive sampling to ensure representation of diverse certification types and hotel characteristics. Customer satisfaction data were aggregated from three major digital platforms (Booking.com, Google Reviews, and TripAdvisor) providing a comprehensive multi-platform assessment of guest perceptions. The study employs ordinary least squares regression analysis to test hypotheses derived from signaling theory, examining both cumulative certification effects and individual credential impacts while controlling organizational and locational factors. Results reveal that the quantity of certifications shows no significant association with customer satisfaction, challenging the assumption that cumulative signalling effects are present. However, internationally recognized certifications (Green Key, Travelife for Accommodation, and LEED) demonstrate statistically significant positive associations with guest ratings, while regional mandatory programs show no measurable impact. Review volume emerges as the strongest predictor of satisfaction scores, indicating that operational excellence and guest engagement outweigh formal credentials. These findings provide actionable insights for business leadership in hotel managers developing certification strategies, policymakers promoting sustainable tourism, and certification bodies seeking to enhance program effectiveness through transparency-focused business leadership and trust-building mechanisms in competitive hospitality markets.
Kung-Hsiang Huang, Akshara Prabhakar, Onkar Thorat
et al.
While AI agents hold transformative potential in business, effective performance benchmarking is hindered by the scarcity of public, realistic business data on widely used platforms. Existing benchmarks often lack fidelity in their environments, data, and agent-user interactions, with limited coverage of diverse business scenarios and industries. To address these gaps, we introduce CRMArena-Pro, a novel benchmark for holistic, realistic assessment of LLM agents in diverse professional settings. CRMArena-Pro expands on CRMArena with nineteen expert-validated tasks across sales, service, and 'configure, price, and quote' processes, for both Business-to-Business and Business-to-Customer scenarios. It distinctively incorporates multi-turn interactions guided by diverse personas and robust confidentiality awareness assessments. Experiments reveal leading LLM agents achieve only around 58% single-turn success on CRMArena-Pro, with performance dropping significantly to approximately 35% in multi-turn settings. While Workflow Execution proves more tractable for top agents (over 83% single-turn success), other evaluated business skills present greater challenges. Furthermore, agents exhibit near-zero inherent confidentiality awareness; though targeted prompting can improve this, it often compromises task performance. These findings highlight a substantial gap between current LLM capabilities and enterprise demands, underscoring the need for advancements in multi-turn reasoning, confidentiality adherence, and versatile skill acquisition.
Does AI conform to humans, or will we conform to AI? An ethical evaluation of AI-intensive companies will allow investors to knowledgeably participate in the decision. The evaluation is built from nine performance indicators that can be analyzed and scored to reflect a technology's human-centering. The result is objective investment guidance, as well as investors empowered to act in accordance with their own values. Incorporating ethics into financial decisions is a strategy that will be recognized by participants in environmental, social, and governance investing, however, this paper argues that conventional ESG frameworks are inadequate to companies that function with AI at their core. Fully accounting for contemporary big data, predictive analytics, and machine learning requires specialized metrics customized from established AI ethics principles. With these metrics established, the larger goal is a model for humanist investing in AI-intensive companies that is intellectually robust, manageable for analysts, useful for portfolio managers, and credible for investors.
With the deepening of digital transformation, business process optimisation has become the key to improve the competitiveness of enterprises. This study constructs a business process optimisation model integrating artificial intelligence and big data to achieve intelligent management of the whole life cycle of processes. The model adopts a three-layer architecture incorporating data processing, AI algorithms, and business logic to enable real-time process monitoring and optimization. Through distributed computing and deep learning techniques, the system can handle complex business scenarios while maintaining high performance and reliability. Experimental validation across multiple enterprise scenarios shows that the model shortens process processing time by 42%, improves resource utilisation by 28%, and reduces operating costs by 35%. The system maintained 99.9% availability under high concurrent loads. The research results have important theoretical and practical value for promoting the digital transformation of enterprises, and provide new ideas for improving the operational efficiency of enterprises.
Tassilo Klein, Clemens Biehl, Margarida Costa
et al.
Foundation models, particularly those that incorporate Transformer architectures, have demonstrated exceptional performance in domains such as natural language processing and image processing. Adapting these models to structured data, like tables, however, introduces significant challenges. These difficulties are even more pronounced when addressing multi-table data linked via foreign key, which is prevalent in the enterprise realm and crucial for empowering business use cases. Despite its substantial impact, research focusing on such linked business tables within enterprise settings remains a significantly important yet underexplored domain. To address this, we introduce a curated dataset sourced from an Enterprise Resource Planning (ERP) system, featuring extensive linked tables. This dataset is specifically designed to support research endeavors in table representation learning. By providing access to authentic enterprise data, our goal is to potentially enhance the effectiveness and applicability of models for real-world business contexts.
James Weichert, Daniel Dunlap, Mohammed Farghally
et al.
As artificial intelligence (AI) further embeds itself into many settings across personal and professional contexts, increasing attention must be paid not only to AI ethics, but also to the governance and regulation of AI technologies through AI policy. However, the prevailing post-secondary computing curriculum is currently ill-equipped to prepare future AI practitioners to confront increasing demands to implement abstract ethical principles and normative policy preferences into the design and development of AI systems. We believe that familiarity with the 'AI policy landscape' and the ability to translate ethical principles to practices will in the future constitute an important responsibility for even the most technically-focused AI engineers. Toward preparing current computer science (CS) students for these new expectations, we developed an AI Policy Module to introduce discussions of AI policy into the CS curriculum. Building on a successful pilot in fall 2024, in this innovative practice full paper we present an updated and expanded version of the module, including a technical assignment on "AI regulation". We present the findings from our pilot of the AI Policy Module 2.0, evaluating student attitudes towards AI ethics and policy through pre- and post-module surveys. Following the module, students reported increased concern about the ethical impacts of AI technologies while also expressing greater confidence in their abilities to engage in discussions about AI regulation. Finally, we highlight the AI Regulation Assignment as an effective and engaging tool for exploring the limits of AI alignment and emphasizing the role of 'policy' in addressing ethical challenges.
Objective: This review explores the trustworthiness of multimodal artificial intelligence (AI) systems, specifically focusing on vision-language tasks. It addresses critical challenges related to fairness, transparency, and ethical implications in these systems, providing a comparative analysis of key tasks such as Visual Question Answering (VQA), image captioning, and visual dialogue. Background: Multimodal models, particularly vision-language models, enhance artificial intelligence (AI) capabilities by integrating visual and textual data, mimicking human learning processes. Despite significant advancements, the trustworthiness of these models remains a crucial concern, particularly as AI systems increasingly confront issues regarding fairness, transparency, and ethics. Methods: This review examines research conducted from 2017 to 2024 focusing on forenamed core vision-language tasks. It employs a comparative approach to analyze these tasks through the lens of trustworthiness, underlining fairness, explainability, and ethics. This study synthesizes findings from recent literature to identify trends, challenges, and state-of-the-art solutions. Results: Several key findings were highlighted. Transparency: Explainability of vision language tasks is important for user trust. Techniques, such as attention maps and gradient-based methods, have successfully addressed this issue. Fairness: Bias mitigation in VQA and visual dialogue systems is essential for ensuring unbiased outcomes across diverse demographic groups. Ethical Implications: Addressing biases in multilingual models and ensuring ethical data handling is critical for the responsible deployment of vision-language systems. Conclusion: This study underscores the importance of integrating fairness, transparency, and ethical considerations in developing vision-language models within a unified framework.
We design a novel, nonlinear single-source-of-error model for analysis of multiple business cycles. The model's specification is intended to capture key empirical characteristics of business cycle data by allowing for simultaneous cycles of different types and lengths, as well as time-variable amplitude and phase shift. The model is shown to feature relevant theoretical properties, including stationarity and pseudo-cyclical autocovariance function, and enables a decomposition of overall cyclic fluctuations into separate frequency-specific components. We develop a Bayesian framework for estimation and inference in the model, along with an MCMC procedure for posterior sampling, combining the Gibbs sampler and the Metropolis-Hastings algorithm, suitably adapted to address encountered numerical issues. Empirical results obtained from the model applied to the Polish GDP growth rates imply co-existence of two types of economic fluctuations: the investment and inventory cycles, and support the stochastic variability of the amplitude and phase shift, also capturing some business cycle asymmetries. Finally, the Bayesian framework enables a fully probabilistic inference on the business cycle clocks and dating, which seems the most relevant approach in view of economic uncertainties.
Laura Minkova, Jessica López Espejel, Taki Eddine Toufik Djaidja
et al.
As businesses increasingly rely on automation to streamline operations, the limitations of Robotic Process Automation (RPA) have become apparent, particularly its dependence on expert knowledge and inability to handle complex decision-making tasks. Recent advancements in Artificial Intelligence (AI), particularly Generative AI (GenAI) and Large Language Models (LLMs), have paved the way for Intelligent Automation (IA), which integrates cognitive capabilities to overcome the shortcomings of RPA. This paper introduces Text2Workflow, a novel method that automatically generates workflows from natural language user requests. Unlike traditional automation approaches, Text2Workflow offers a generalized solution for automating any business process, translating user inputs into a sequence of executable steps represented in JavaScript Object Notation (JSON) format. Leveraging the decision-making and instruction-following capabilities of LLMs, this method provides a scalable, adaptable framework that enables users to visualize and execute workflows with minimal manual intervention. This research outlines the Text2Workflow methodology and its broader implications for automating complex business processes.
En el Perú, la descentralización de los servicios de atención en salud mental y la priorización de sus recursos en el nivel primario representan acciones principales para el desarrollo de la salud mental en el país. Sin embargo, las barreras socioculturales y económicas que aún persisten en la población dificultan su acceso a dichos servicios, situación que se ha agudizado durante el contexto de pandemia por COVID-19. El presente artículo busca analizar las experiencias de atención primaria en salud mental en acompañantes de un centro de consejería de Lima Metropolitana durante los años 2020 y 2021. Mediante un análisis narrativo, los resultados mostraron que la pandemia ha exacerbado vulnerabilidades psicológicas, sociales y materiales tanto para acompañantes como usuarios, rescatándose la necesidad de sostener procesos de capacitación y supervisión grupal constantes que garanticen el autocuidado y cuidado de los equipos, así como la calidad de la atención brindada.
Medical philosophy. Medical ethics, Business ethics
Pivithuru Thejan Amarasinghe, Su Nguyen, Yuan Sun
et al.
Business optimisation has been used extensively to determine optimal solutions for challenging business operations. Problem formulation is an important part of business optimisation as it influences both the validity of solutions and the efficiency of the optimisation process. While different optimisation modelling languages have been developed, problem formulation is still not a trivial task and usually requires optimisation expertise and problem-domain knowledge. Recently, Large Language Models (LLMs) have demonstrated outstanding performance across different language-related tasks. Since problem formulation can be viewed as a translation task, there is a potential to leverage LLMs to automate problem formulation. However, developing an LLM for problem formulation is challenging, due to limited training data, and the complexity of real-world optimisation problems. Several prompt engineering methods have been proposed in the literature to automate problem formulation with LLMs. While the initial results are encouraging, the accuracy of formulations generated by these methods can still be significantly improved. In this paper, we present an LLM-based framework for automating problem formulation in business optimization. Our approach introduces a method for fine-tuning cost-efficient LLMs specifically tailored to specialized business optimization challenges. The experiment results demonstrate that our framework can generate accurate formulations for conventional and real-world business optimisation problems in production scheduling. Extensive analyses show the effectiveness and the convergence of the proposed fine-tuning method. The proposed method also shows very competitive performance when compared with the state-of-the-art prompt engineering methods in the literature when tested on general linear programming problems.
This article appears as chapter 21 of Prince (2023, Understanding Deep Learning); a complete draft of the textbook is available here: http://udlbook.com. This chapter considers potential harms arising from the design and use of AI systems. These include algorithmic bias, lack of explainability, data privacy violations, militarization, fraud, and environmental concerns. The aim is not to provide advice on being more ethical. Instead, the goal is to express ideas and start conversations in key areas that have received attention in philosophy, political science, and the broader social sciences.
This paper delves into the realm of ChatGPT, an AI-powered chatbot that utilizes topic modeling and reinforcement learning to generate natural responses. Although ChatGPT holds immense promise across various industries, such as customer service, education, mental health treatment, personal productivity, and content creation, it is essential to address its security, privacy, and ethical implications. By exploring the upgrade path from GPT-1 to GPT-4, discussing the model's features, limitations, and potential applications, this study aims to shed light on the potential risks of integrating ChatGPT into our daily lives. Focusing on security, privacy, and ethics issues, we highlight the challenges these concerns pose for widespread adoption. Finally, we analyze the open problems in these areas, calling for concerted efforts to ensure the development of secure and ethically sound large language models.
Nearly half of all sub-Saharan African countries lack operational Diabetes Mellitus policies. This represents an opportunity to build reliable evidence to underpin such policies when they are eventually developed. Representing the interests of those with the experience of living with the condition in national diabetes policies is important, particularly the interests regarding medicine access, a key pillar in diabetes management. One way to achieve this representation is to publish patient perceptions. Patient perspectives are especially valuable in the context of diabetes in Sub-Saharan Africa, where much of the empirical work has focused on clinical and epidemiological questions. We therefore captured the challenges and suggestions around medicine access articulated by a population of diabetes patients and their caregivers. This was a qualitative interpretivist study based on data from focus group discussions with adult diabetes patients and their caregivers. Eight FGDs of 4-13 participants each whose duration averaged 13.35 minutes were conducted. Participants were recruited from diabetes outpatient clinics at two health facilities in Harare. One site was Parirenyatwa Hospital, the largest public referral and teaching hospital in Zimbabwe. The other was a private for-profit facility. Ethics approval was granted by the Joint Research Ethics Committee for University of Zimbabwe College of Health Sciences and the Parirenyatwa Group of Hospitals (Ref: JREC 295/18). Diabetes patients and their caregivers are interested in affordable access to medicines of acceptable form and quality with minimum effort. Yet, they often find themselves privileging one dimension of access over another e.g. prioritising affordability over acceptability. Based on participants' articulations, a sound diabetes policy should: 1. provide for financial and consumer protections, 2. regulate healthcare business practices and medicine prices, 3. provide for a responsive health workforce attentive to patient problems, 4. accord the same importance to diabetes that is accorded to communicable diseases, 5. decentralize diabetes management to lower levels of care, 6. limit wastage, corruption, bad macro-financial governance and a lack of transparency about how funding for health is used, and 7. provide support to strengthen patients' and caregivers' psychosocial networks. A diabetes policy acceptable to patients is one infused with principles of good governance, fairness, inclusiveness and humanity; characterised by: financial protection and price regulation, consumer protection, equity in the attention accorded to different diseases, decentralized service delivery, inclusion of patient voice in political decision-making, a responsive compassionate health workforce, psychosocial support for patients and their caregivers and allocative efficiency and transparency in public expenditure.
This study is devoted to the analysis of the concept of subsidiarity, which allowed the formulation of the subsidiarity principle, acting in various spheres of collective human activity. Systematization of the literary sources and approaches to the management of business activities proved that the ethical principle of subsidiarity aims to develop the well-being of collective human life, but it also has its limitations. On the one hand, numerous positive examples of mutual support, cooperation, help, mentoring, and employee development are known in business activity, as well as in management theory and practice. On the other hand, there have always been conflicts, rivalry, and competition at certain times. Besides that, not all businessmen and managers have always behaved following generally accepted principles. The main purpose of this article is to analyze the features of the application of the principle of subsidiarity in management and business in general and in specific examples. The methodological tools of the research are methods of critical analysis of literature and praxeological analysis of human actions. The article presents the results of the analysis of the function and features of the application of the concept of subsidiarity, including functional attributes specific to the principle of subsidiarity and the attributes that contradict it. The examples for the use of the concept of subsidiarity in management and business offered in this paper are not exhaustive. The Motivator-Hygiene theory and job enrichment, workers participation in organization or participation in decision making, corporate social responsibility and microfinance, initiated by M. Yunus as microcredit, Grameen Bank and Grameen Movement, were used as tools for the illustration of attributes characteristic of the principle of subsidiarity. The analysis of functional attributes opposed to the principle of subsidiarity or simulating it was carried out on the example of the theory of bureaucracy dysfunctions, discrimination, corruption, mobbing/bullying, paternalism, Taylorism, or the Scientific Management. The article presents the results of the analysis, which proved that the application of the subsidiarity principle is a specific case of the Aristotelian principle of the golden mean, the pursuit of balance, harmony, equilibrium, i.e., individual, and collective human development and practice of virtues, distributive justice. In this case, however, there is no external criterion that could be used to establish this balance or equilibrium. The research empirically confirms and theoretically proves the existence of an intersubjective and historical evaluation (criterion) subject to manipulation. This situation creates a practical problem for the effectiveness of this principle because people with insufficient knowledge, weak in spirit (weak character), or bad intentions can use this principle for their purposes, explaining their behaviors by ignorance, good intentions, etc.
Yara Rizk, Praveen Venkateswaran, Vatche Isahagian
et al.
The inception of large language models has helped advance state-of-the-art performance on numerous natural language tasks. This has also opened the door for the development of foundation models for other domains and data modalities such as images, code, and music. In this paper, we argue that business process data representations have unique characteristics that warrant the development of a new class of foundation models to handle tasks like process mining, optimization, and decision making. These models should also tackle the unique challenges of applying AI to business processes which include data scarcity, multi-modal representations, domain specific terminology, and privacy concerns.