SBS Journal of Applied Business Research
SBS Journal of Applied Business Research (SBS JABR), published by SBS Swiss Business School in Zurich, Switzerland, is a peer-reviewed, multidisciplinary journal dedicated to advancing applied business research. Our mission is to bridge the gap between academic theory and practical application, fostering collaboration between scholars and practitioners worldwide. Readership SBS JABR serves a diverse audience, including academics, researchers, and students—particularly those in Doctor of Business Administration (DBA) programs—executives, policymakers, administrators in both private and public sectors, strategists, management consultants, and others interested in advanced management practices. Peer Review Process To maintain the highest standards of academic rigor and integrity, all manuscripts submitted to SBS JABR undergo a stringent external and independent peer review process. This includes a double-blind review system, selection of external reviewers with specialized knowledge, strict conflict-of-interest disclosures, and a commitment to transparency and confidentiality throughout the review process. The final decision on manuscript acceptance is made by the editorial board, ensuring that published articles meet scholarly and ethical requirements. ​ Open Access Policy SBS JABR operates under a Platinum/Diamond Open Access model, providing immediate and unrestricted access to all published content. There are no article processing charges (APCs) or submission fees for authors, and all articles are freely accessible to readers upon publication. The journal is licensed under the Creative Commons Attribution 4.0 International License (CC-BY 4.0), allowing for free distribution and adaptation of the work, provided proper credit is given. Publication Volume and Sustainability Publication volume and journal sustainability represent essential indicators of a journal’s vitality, scholarly relevance, and long-term viability within its disciplinary community. While there is no universally fixed benchmark for the number of articles a journal must publish annually, established indexing bodies emphasize consistency, regularity, and demonstrable engagement from the research community as key evaluation criteria. A stable and sufficiently broad publication output signals that the journal attracts a continuous flow of quality submissions, maintains an active editorial and peer-review process, and is capable of supporting meaningful longitudinal assessment of its scholarly impact. To this end, the SBS Journal of Applied Business Research aims to strengthen its sustainability by maintaining regular publication schedules, expanding international outreach, and fostering thematic diversity aligned with its scope, while ensuring that any increase in publication volume is underpinned by rigorous peer review and adherence to internationally recognized publishing standards, as emphasized in the scholarly publishing literature.
SBS Swiss Business School, Flughafenstrasse 3 8302 Kloten-Zurich, Switzerland
Publication Agreement This Publication Agreement ("Agreement") is made between the author(s) ("Author") and the SBS Journal of Applied Business Research ("Journal"). By submitting a manuscript for publication, the Author agrees to the following terms: 1. Grant of License The Author retains full copyright ownership of the submitted and published work. The Author grants the Journal a non-exclusive license to publish, distribute, and archive the article in any format or medium, including but not limited to online and print versions. 2. Open Access and Licensing All articles published in the Journal are fully open access under the Creative Commons Attribution 4.0 International License (CC BY 4.0). This means that: • The work can be freely used, shared, and adapted by anyone, provided that proper attribution is given to the original Author. • The full license terms can be found at https://creativecommons.org/licenses/by/4.0/. • No additional restrictions are placed on the use of published articles. 3. Author Responsibilities and Warranties The Author guarantees that: • The submitted work is original and has not been published or submitted for publication elsewhere. • The work does not infringe on any third-party rights, including copyright, trademark, or proprietary rights. • All necessary permissions for copyrighted materials used in the manuscript (e.g., figures, tables) have been obtained. • The manuscript complies with ethical research standards and does not contain any form of plagiarism or falsified data. 4. Editorial and Publication Process • The Journal reserves the right to perform editorial revisions for clarity, formatting, and consistency while maintaining the integrity of the Author’s work. • The publication of an article is subject to peer review, and acceptance is not guaranteed upon submission. 5. Retraction and Corrections • The Journal follows COPE (Committee on Publication Ethics) guidelines and reserves the right to retract, correct, or withdraw an article in cases of misconduct, errors, or ethical concerns. 6. Governing Law This Agreement is governed by the laws of Switzerland. Any disputes arising under this Agreement shall be resolved in the courts of Zurich, Switzerland. 7. Agreement Acceptance By submitting a manuscript, the Author acknowledges and agrees to the terms outlined in this Agreement. Editor-In-Chief Prof. Dr. Milos Petkovic
Towards Integrated Stock Market Forecasting: A Literature Review of Fundamental, Technical, and Sentiment-Based Approaches with Machine Learning
Abstract Stock market forecasting remains one of the most challenging and extensively studied problems in financial research, driven by its significant implications for investment decision-making and portfolio management. Traditional financial theories, such as the Efficient Market Hypothesis and the random walk model, suggest that stock prices are inherently unpredictable. However, a growing body of empirical evidence indicates that forecasting may be feasible when multiple sources of information are jointly considered. This study provides a comprehensive literature review of three primary analytical approaches, fundamental, technical, and sentiment analysis, and examines how their integration through machine learning techniques enhances predictive performance. Fundamental analysis focuses on intrinsic value by evaluating financial statements, macroeconomic indicators, and firm-specific metrics such as profitability, leverage, and valuation ratios. While effective for long-term valuation, its limitations include difficulty in capturing nonlinear relationships and adapting to rapidly changing market conditions. Technical analysis, in contrast, relies on historical price and volume patterns to identify trends and trading signals, proving particularly useful for short-term forecasting. However, it often neglects broader economic and firm-level information. Sentiment analysis introduces a behavioral dimension by extracting investor mood and expectations from unstructured data sources such as news and social media, offering valuable insights into short-term market dynamics, though it faces challenges related to data quality and interpretation. Recent advancements in machine learning and artificial intelligence have significantly transformed stock market forecasting by enabling the integration of these heterogeneous data sources into unified predictive frameworks. Models such as Random Forest, Support Vector Machines, Artificial Neural Networks, and deep learning architectures, including Convolutional Neural Networks and Long Short-Term Memory networks, demonstrate superior ability to capture nonlinear dependencies and complex interactions. In particular, Hybrid Neural Networks (HNNs), which combine multiple modeling techniques, have emerged as a highly effective approach, consistently outperforming standalone models and traditional statistical methods. The literature reviewed in this paper highlights that integrated, multimodal forecasting models leveraging fundamental, technical, and sentiment inputs achieve higher accuracy and robustness compared to single-method approaches. These findings suggest that the future of stock market forecasting lies in hybrid machine learning frameworks capable of processing both structured and unstructured data while adapting to dynamic market conditions. Despite these advancements, challenges related to model interpretability, data quality, and market volatility remain, indicating important directions for future research.
Stock Market Forecasting; Machine Learning; Hybrid Neural Networks; Fundamental Analysis; Technical Analysis; Sentiment Analysis; Financial Prediction; Deep Learning; Multimodal Data Integration; Predictive Modeling
Finance Economics Computer Science Artificial Intelligence Data Science
Copyright (c) 2026 Veljko Drakulic (Author) https://creativecommons.org/licenses/by/4.0
From Deplorability to ROI Evaluability: Expert Perceptions of AI-Enabled Capabilities in Digital Marketing, Armenia
Artificial intelligence (AI) is widely used in digital marketing and e-commerce, but its strategic value is often harder to prove than its operational usefulness. This tension is especially visible in small and emerging markets, where tool adoption may outpace measurement and governance capacity (Gama & Magistretti, 2023; Islam et al., 2024). The study adopts a capability-based and governance-oriented perspective on AI value in digital marketing management (Abrardi et al., 2021; Mariani et al., 2021). It examines how Armenian marketing experts evaluate four AI domains—predictive analytics, sentiment analysis, personalization, and programmatic advertising—and tests how these evaluations relate to perceived ROI-oriented impact, adoption outlook, and governance perceptions (Mariani et al., 2021; Haleem et al., 2022; Dumitriu & Popescu, 2020). A cross-sectional expert survey (N = 29) was used because objective performance data are limited and fragmented in this small-market context. The survey collected 5-point Likert ratings and one open-ended response on adoption barriers. Differences in perceived tool effectiveness were assessed using the Friedman test with Durbin–Conover post hoc comparisons. Hypothesized relationships were examined using Spearman correlations, supplemented by exploratory regression models. Personalization and programmatic advertising were rated highest in perceived day-to-day effectiveness, but only predictive analytics showed a clear positive association with perceived ROI-oriented impact. Perceived ROI-oriented impact was positively associated with adoption outlook. Governance perceptions were asymmetric: privacy concerns were negatively associated with adoption outlook, whereas bias/fairness concerns were positively associated with oversight/accountability expectations. Open-ended responses identified weak measurement discipline, capability gaps, and poor strategic integration as the main barriers to realizing AI value. These findings imply that AI value realization in digital marketing depends not only on tool deplorability but also on measurement discipline, integration routines, and governance capacity. The study is limited by its small, non-probability, expert sample, perception-based measures, and cross-sectional design, so the findings should be interpreted as exploratory and context-bound. The study contributes to theory by distinguishing deplorability from ROI evaluability, and to practice by showing that AI value realization in small and emerging markets depends on measurement discipline, integration, and governance (Avram et al., 2020; Pop, 2020; Akter et al., 2023; Schmauder et al., 2023).
personalization; predictive analytics; sentiment analysis; programmatic advertising
Artificial Intelligence in Customer Behavior
Copyright (c) 2026 Nane Davtyan (Author) https://creativecommons.org/licenses/by/4.0
Key Success Factors of Total Laboratory Automation and Their Impact on the Turnaround Time of Patient Results in Qatar
The Qatar healthcare laboratories have considerable challenges with regard to effective realization of laboratory automation systems. The positivist philosophy and the quantitative, cross sectional research design used in the study examines the empirical associations of these variables. The research design is the investigation of the key success factors (independent variables) total laboratory automation (mediation variable) and the effect on the turnaround time of patient results (dependent variable) in Qatar. Laboratories, which depend on large automation systems, are highly complex and there are high chances of failures in the system that can considerably discontinue the laboratory activities. The purpose of the study is to identify and analyze the impact of the main success factors on the laboratory performance and turnaround time related to providing patient results. A questionnaire was created to do this, and it was sent to the professionals of the laboratories using email and LinkedIn through Qualtrics. A 7-point Likert scale was used to obtain more than 400 answers in an attempt to obtain valid and reliable data to do further analysis. The focus on EFA in order to recognize underlying factors. The structural model was affirmed by using Confirmatory Factor Analysis (CFA). Structural Research hypotheses and mediation effects were tested by Equation Modelling (SEM). Using Indirect effect, SEM, is the path relationships that are not direct subsidiary to P1 × P2 where P1 represents the path coefficient of the path from<|human|>Indirect effect, SEM, is the relationship paths that were not direct as subsidiaries of P1 × P2 where P1 is the path coefficient of the path between. The path coefficient of MV to DV, IV to MV and P2, were tested whether they were statistically significant. using the formula C ′ = P1 × P2 + P3. The findings revealed that there are substantial indirect effects (p < 0.05). of six of the six IVs which means that it was the IVs which affected the DV using the MV therefore endorsing partial mediation in five cases on main and case study sample.
Copyright (c) 2026 Aarathy Radhakrishnan (Author) https://creativecommons.org/licenses/by/4.0
Influencer Marketing and eWOM Credibility in Airline Product Purchase Intentions in Hong Kong
Many airlines have been using influencers to market their products. However, in the competitive airline industry, the factors associating with the effectiveness of influencer marketing remain a question. This dissertation aims to investigate how influencer expertise and argument quality are associated with eWOM credibility, as well as how brand image and customer price sensitivity moderate the relationship between eWOM credibility, brand credibility, and purchase intention for airline products in Hong Kong. An online quantitative survey and focus group interviews were used to investigate the issue. The quantitative survey involved 258 respondents who were airline passengers originating from the Greater China region. The results suggest that influencer expertise (.697, p < .001) was more important than argument quality (.202, p < .001) in associating with eWOM credibility, and brand credibility partially mediates the relationship between eWOM credibility and purchase intention. It also suggests that airline brand image amplified the relationship between eWOM credibility and brand credibility. At the same time, purchase intention and consumer price sensitivity weakened the relationship between brand credibility and purchase intention. Lastly, a one-way ANOVA analysis suggests that customers of low-cost airlines are more price sensitive. The focus group interviews consisted of two groups, each with 10 members. The interviews revealed that influencer expertise, experience, authenticity, objectivity, the interaction between influencers and their subscribers, and their track record are key factors in establishing trust in influencers. Theoretically, the result confirmed that consumers rely more on expertise for more complicated products. It also suggests that eWOM credibility can be associated with brand credibility before associating with purchase intention. Practically, the results suggest that airlines should prioritize influencer expertise and focus on both branding and pricing, depending on customer segmentation.
Airlines, influencer expertise, eWOM credibility, brand credibility, consumer price sensitivity
Copyright (c) 2026 WING YIN LEUNG (Author) https://creativecommons.org/licenses/by/4.0
Problem-based Learning with Management Consulting Methods: a Pedagogy for Critical Thinking in Higher Education
This research aims to address the critical gaps in critical thinking skills highlighted by the World Economic Forum and the National Association for Colleges and Employers, which reveal a disparity between employer expectations and graduate preparedness. By developing and evaluating Problem-Based Learning with Management Consulting Methods, this study seeks to enhance critical thinking education through combining management consulting with engaging problem-based learning in a culturally responsive pedagogy tailored for a diverse cohort. The research assesses the effectiveness of this approach using the California Critical Thinking Skills Test and explores variables such as gender and private/public high school education. This study employs a mixed-method design, combining longitudinal quantitative research (n = 177) from 18 nationalities with qualitative analysis of anonymous student questionnaires collected by the university's central research unit from 2023 to 2024. Quantitative outcomes are benchmarked against the meta-analysis by Huber and Kuncel (2016). Results indicate that a single semester of treatment is as effective as four years of traditional education to improve critical thinking skills, while multiple treatments with less than a one-year gap demonstrate even greater improvements.
Copyright (c) 2026 Betty Lin (Author) https://creativecommons.org/licenses/by/4.0
INVESTIGATING THE FACTORS THAT INFLUENCE JOB SATISFACTION OF NURSES IN ABU DHABI, UAE: THE MODERATING EFFECT OF COVID-19 IMPACT
Purpose of the study: This study investigates the relationship between the six factors of the Index of Work Satisfaction (IWS)—Interaction, Task Requirements, Pay, Autonomy, Organizational Policies, and Professional Status—and nurses’ job satisfaction in Abu Dhabi, UAE. Furthermore, it examines the moderating role of the COVID-19 pandemic on these relationships. Methodology: A mixed-methods approach was employed, combining a quantitative survey (N=363) using the IWS scale and a qualitative case study involving in-depth interviews with nurses. Quantitative data were analyzed using descriptive statistics, correlation, ANOVA, and multiple linear regressions, including moderation analysis. Research Findings: All six IWS factors were found to have a significant positive relationship with nurses’ job satisfaction. Specifically, Autonomy (β=.213, p < 0.001) and Pay (β = 0.202, p < 0.001) were the strongest predictors. The COVID-19 pandemic was found to significantly moderate the relationship between Interaction and job satisfaction (β = 0.049, p < .001), suggesting that the pandemic context altered the influence of workplace social dynamics. Implications: The findings provide critical insights for healthcare administrators in the UAE, emphasizing the need for targeted interventions to enhance nurse autonomy, ensure equitable pay, and foster positive workplace interactions, especially during public health crises. The study validates the IWS as a robust tool for assessing job satisfaction in the Emirati context. Limitations: The study is geographically limited to Abu Dhabi, and the cross-sectional design limits causal inferences. Future research should consider longitudinal designs and expand the scope to other Emirates. Significance of the Study: This research is one of the first to empirically test the IWS model in the UAE nursing sector while simultaneously quantifying the moderating effect of a major global health crisis (COVID-19). It contributes to both job satisfaction theory and practical human resource management in healthcare.
Copyright (c) 2026 Heba mohamed Attia Elatr (Author) https://creativecommons.org/licenses/by/4.0
Analyzing Financial and Non-Financial Factors Affecting the Performance of Employees in Public Organizations in the UAE
Employee performance (EP) refers to the behavior of employees within an organization (Bert & van, 2023). It refers to the activities carried out by employees, including both input and output activities, that contribute to achieving organizational objectives. The main objectives of the current study are: i) to examine the impact of the financial incentives namely, salary, end of the term benefits, other financial incentives on the EP in public sector in United Arab Emirates (UAE); ii) to analyze the impact of the non-financial incentives namely, work environment, recognition, leadership support, and flexibility on the EP in public sectors in UAE. The results confirm the significance of the reward system in the UAE public sector considering that both types of incentives (financial and non-financial) positively affect the employees' performance. All forms of financial incentives, including salary, end-of-term compensation, and other financial rewards, clearly affect performance positively. Thus, a suggestion can be made that public organizations in the UAE should discontinue their policies focusing solely on salary and instead implement integrated reward systems that include recognition, developmental, and relational incentives alongside financial ones. The results of this study, however, suggest that HR in the UAE public sector should encourage supportive leadership, recognition system(s), a favorable work environment, and alternative work arrangements in addition to flexible pay to improve performance, retention, and service quality.
Financial Factors, Non-Financial Factors, Employee Performance
Copyright (c) 2026 Abdulla Ibrahim Mohammad Bin Humaidan (Author) https://creativecommons.org/licenses/by/4.0
CHALLENGES AND OPPORTUNITIES FOR SME GROWTH AND SCALABILITY IN BAHRAIN: AN ANALYSIS OF THE 2024 BUSINESS ENVIRONMENT
This research examines the ongoing challenges that impede the growth and scalability of small and medium-sized enterprises (SMEs) in Bahrain, despite various government initiatives aimed at bolstering the sector. The central issue is the high failure rate of SMEs in achieving sustainable success, which has necessitated an exploration of factors such as strategic leadership, human resource practices, and innovation capabilities. The study underscores that deficiencies in strategic leadership and human resource development are pivotal factors contributing to business stagnation and failure. It advocates for a national framework to enhance SME capabilities through dedicated investment in leadership and employee retention strategies. Addressing these human capital gaps is crucial for strengthening Bahrain's SME sector and advancing its economic diversification objectives.
Small and medium-sized enterprises (SMEs), People Management Skills, Leadership, Employee Retention, Employee Recruitment
Copyright (c) 2026 Zuhair Baba (Author) https://creativecommons.org/licenses/by/4.0
Inside Hyundai Motor Group: Changing the Mind of the Company
Every day, we are bombarded with advertising messages that waste our precious time. The result of the research shows that a normal consumer receives about 3000 commercial messages daily. This is due to the intense competition for organizations. Today, with Artificial Intelligence, the competition has become tougher and smarter. Artificial Intelligence is not enough without the human cleverness and intelligence of managers. "Why should a customer buy our organization's product or service among all these options?" The answer is simple. Managers must approve of advertisements that skip over the mind’s limbic system, the emotionally reactive part of our brain, because it is rarely your best response. The Neurochemistry behind this is fascinating. When you are triggered by an advertisement that has resonance, your body releases cortisol and adrenaline stress hormones that prepare you for fight or flight. These chemicals actually reduce blood flow to your prefrontal cortex, limiting your access to your full intellectual capacity. The key point is for managers to tap into the potential customer’s emotional intelligence and personal power. Much of what we share in this article is based on our management consulting experience with Hyundai Motor Group in Sydney and Melbourne.
Copyright (c) 2026 Mostafa Sayyadi (Author) https://creativecommons.org/licenses/by/4.0
The Impact of Empathetic Leadership on Recruitment and Retention in Hong Kong's Pink-Collar SMEs
This research investigates the labor shortage and employee retention challenges faced by small and medium-sized enterprises (SMEs) in Hong Kong’s pink-collar sector and outlines the pathway for remediation. The research framework focuses on the causal chains linking Empathetic Leadership to Employee Recruitment and Retention through the mediating influence of Intrinsic and Extrinsic Motivation, Job Satisfaction, and the moderating effect of Hong Kong Cultural Identity on the relationships between Empathetic Leadership and Intrinsic / Extrinsic Motivation. A mixed methods approach was adopted for triangulation, combining quantitative and qualitative analyses through questionnaires and interviews with employers and employees. Statistical analyses, including ANOVA and PLS-SEM, validated the relationships among the constructs. Qualitative findings from the interviews were coded and decoded for themes and patterns to obtain and triangulate findings. The practical implications of this research are to address the current literature gap and develop a holistic framework by providing insights into ongoing labor challenges and formulating possible solutions for retaining and recruiting pink-collar employees.
Copyright (c) 2026 Royan Lam (Author) https://creativecommons.org/licenses/by/4.0
Is Market Development an Effective Strategy for Enhancing Performance? A Study of SME Suppliers in the Zambian Mining Sector
This study investigates whether market development enhances performance among Small and Medium Enterprises (SMEs) mining suppliers in Zambia, a country heavily reliant on copper mining. Therefore, research examines the vulnerability of SMEs whose survival depends on mining contracts and explores market development as a resilient strategy to reduce suppliers' dependency on a single market. The research employed a qualitative research design. Data were collected from 30 SME suppliers in Zambia’s Copperbelt and North-Western regions and analyzed using NVivo 11 to identify patterns linking market development strategies to SME performance outcomes. The results reveal that expanding into new market segments and geographic areas enhances performance through diversifying revenue streams, reducing risk and improving competitiveness. However, some barriers constrain the effectiveness of market development among the suppliers. Barriers include overreliance on slow-paying mining clients, financial constraints, technological challenges, and fierce competition in new markets where established firms already enjoy brand recognition and loyalty. The study offers practical insights for policymakers and practitioners on market development as a resilience strategy that can improve firm performance in resource-dependent economies. The study’s regional and sector-specific focus limits the generalizability of its findings to other regions and industry sectors; however, it provides a foundation for comparative analysis. Future research should therefore broaden its scope to include suppliers from diverse areas and sectors. The study clarifies the inconsistent findings on market development and SME performance by providing empirical evidence. It also extends Ansoff Growth Matrix and Resource Dependence Theory to a resource-dependent, under-explored Sub-Saharan context. Therefore, it offers practical insights on market development as a resilience strategy that can improve firm performance.
Market Development, Performance, SMEs, Mining Sector, Zambia
Copyright (c) 2025 Beenzu Siachinji, Dr Yvonne Kabeya-Saini, Professor Bruce Mwiya, Dr Stella Zulu-Chisanga (Author) https://creativecommons.org/licenses/by/4.0
Business Family Values, Professional Practices: Agile Leadership for Sustainable Arab Enterprise Growth
The current study examines how an agile leadership approach can modernize family-owned businesses in GCC economies, with organizational innovation and digital transformation as intervening variables, drawing on dynamic capability theory. A quantitative research design was adopted, and data were collected through a structured survey of managers and employees working in family businesses. The proposed model was tested using structural equation modeling (SEM) to examine the direct and indirect relationships between agile leadership, dynamic capabilities, and business modernization and organizational innovation. All hypothesized relationships were significant (p < .05) in the theoretical framework. Agile leadership had a positive and significant effect on the mediating constructs (organizational innovation and digital transformation) and a less significant but significant effect on business modernization, indicating partial mediation. The study contributes to the agile leadership literature by providing empirical evidence in the underexplored context of emerging-economy family businesses and by conceptualizing business modernization as a higher-order capability-driven outcome rather than a short-term organizational performance metric. The present study has certain limitations. The cross-sectional nature of the study allowed us to draw inferences about the causal mechanisms underlying the issues, and a longitudinal design should be considered in future research to observe how agile leadership practices evolve over time. Although the sample was rather heterogeneous, it was a convenience sample, which can lead to a lack of generalizability. Further studies should be conducted using probability sampling or sector analysis. In addition, self-reported measures can be biased, and future studies could include objective performance measures, such as profitability, the number of innovations, or indices of digital adoption. This study offers practical insights for family business owners, leadership practitioners, and policymakers in emerging economies. This highlights that investing in agile leadership behavior strengthens dynamic capabilities, enabling family firms to modernize business processes and digital infrastructure and remain competitive in evolving markets.
Agile Leadership, Family Business, GCC Economies, Digital Transformation
Copyright (c) 2026 Wissam Dey (Author) https://creativecommons.org/licenses/by/4.0
Development of Authentic Leadership and Cultural Intelligence Sustainability in Australian SMEs: A Mixed Methods Study
This study investigates the relationship between authentic leadership and the development and sustainability of cultural intelligence in Australian small and medium-sized enterprises (SMEs). Using a mixed methods approach, the research addresses four key questions: (1) How do the four components of authentic leadership contribute to cultural intelligence development? (2) What mechanisms sustain cultural intelligence capabilities? (3) How does the relationship between authentic leadership and cultural intelligence influence competitive advantage? (4) To what extent can cultural intelligence be sustained independently of the founder/CEO? The findings reveal that authentic leadership, particularly through self-awareness and relational transparency, creates foundational conditions for the development of cultural intelligence in Australian SMEs. Self-awareness enhances metacognitive cultural intelligence through cultural self-reflection; relational transparency fosters motivational cultural intelligence through psychological safety; balanced processing supports cognitive cultural intelligence through diverse perspective-taking; and internalized moral perspective enhances behavioral cultural intelligence through values-based adaptation. Sustainable cultural intelligence is supported by institutionalized practices, distributed cultural leadership, and adaptive learning systems. The combination of authentic leadership and cultural intelligence enhances competitive advantage by improving market understanding, strengthening international relationships, facilitating effective knowledge integration, and promoting organizational resilience. These findings contribute to both theory and practice, extending our understanding of how authentic leadership shapes cultural capabilities and providing practical insights for Australian SME leaders seeking to develop and leverage cultural intelligence for competitive advantage in an increasingly globalized business environment.
Copyright (c) 2025 Vincent English , Brian Kenny (Author) https://creativecommons.org/licenses/by/4.0
Ethical Dilemmas in Artificial Intelligence Implementations: How to Overcome It?
This research explores the ethical dilemmas of implementing artificial intelligence (AI) in businesses and discusses the ethical principles required to address them. This chapter explores the role of ethical considerations in the responsible deployment of AI. It utilizes qualitative research methods, including a systematic literature review on AI and ethics and expert interviews. The literature analysis highlights several key ethical dilemmas associated with AI, including privacy violations, biased decision-making, responsible AI usage, intellectual property rights, and challenges in monitoring AI applications, among others, that arise from AI implementation and its effects on businesses. Although there are currently no guidelines for the ethical implementation of AI, the analysis suggests the potential to create distinct guidelines that can incorporate ethical principles into AI applications, assisting organizations in addressing the identified dilemmas. The dynamic nature of AI technology and socio-economic development necessitates longitudinal research. Future studies should examine these broader societal implications to grasp AI’s influence on business and society. AI applications in business processes, product design, services, and governance raise ethical dilemmas. The authors offer unique insights by exploring the complex relationship between the ethical challenges of AI implementation and the imperative for business innovation and sustainability. Understanding and applying these insights may help organizations achieve their goals by providing more trustworthy and socially acceptable AI-driven products and improving AI-led business processes. This study offers valuable insights for academics, industry leaders, and policymakers seeking to promote responsible and sustainable AI deployment by adhering to ethical principles in its application.
Book of Chapters 2026: Artificial Intelligence and Economic Disruptions: Business Transformation, Leadership, and Future Organizational Models
AI ethical dilemmas, AI accountability, Ethics principles, AI ethics, Sustainable AI;
Copyright (c) 2025 Gatis Polis, Tatjana Volkova (Author) https://creativecommons.org/licenses/by/4.0
Preserving the Human Touch in the Age of AI: Pursuing Sustainability in the Fashion Industry
The fashion industry is often cited as the second most polluting industry globally. Achieving environmental, social, and economic sustainability requires substantial investment in technological innovations and revised practices to transform traditional manufacturing and distribution processes. Artificial Intelligence (AI) presents a promising solution to enhance sustainability initiatives through various mechanisms: product innovation to minimize material waste, process optimization to optimize resource utilization, inventory management systems to prevent overproduction, and data-driven consumer engagement to promote conscious consumption. However, the implementation of AI technologies also faces significant challenges and skepticism, including potential risks to established brand values, complexities in maintaining meaningful employee engagement, and the delicate balance of preserving consumer trust in an increasingly automated environment. This chapter explores the current state of AI adoption in the fashion industry and its short- and long-term implications. Through a case study involving interviews with key executives in AI, sustainability, retail strategy, global guest innovation, localization, circularity innovation, global production, and creative direction at a leading athleisure brand, this research investigates the multifaceted opportunities, challenges, and potential risks associated with AI implementation across various organizational functions. The findings offer strategic insights and practical recommendations to guide future AI development and deployment. By providing a nuanced understanding of AI’s role in driving sustainability, this study contributes to both academic discourse and industry practice, shedding light on how the fashion sector can leverage AI to achieve its sustainability goals.
artificial intelligence (AI) in fashion, fashion industry sustainability, retail technology, sustainable business practices, fashion industry;
Copyright (c) 2025 Joey Chan (Author) https://creativecommons.org/licenses/by/4.0
Leading in the Age of Artificial Intelligence: Empowering Gen Z in a data driven world Enabling the Next Generation to Build Sustainable Success in Business and Beyond
Artificial intelligence (AI) has foreseen mind-boggling developments along with digital transformation. New business patterns emerge, establishing a ground for leadership, both an opportunity and a challenge in this age of dramatic insecurity caused by technology. To leverage their digital fluency and commitment to honor integrity, they are also incorporating the ideas of sustainability that have been engraved by living in a boundless virtual world where things are ever-changing as rapidly as electricity goes off and on. To what extent can Generation Z navigate these changes? What kind of impact will their accomplishment have on organizational success, including how much of that influence extends to different levels of respect and authority one has? Indeed, on the one hand, was the degrading resolve by many young Generation Z shift toward transparent working processes, along with an unassuming manner, the absence of red carpeting to enter an executive’s office or double-doored conference room in the array rather than lines and so forth seems to change. However, its potential and outlook far surpass previous generations that reprioritize the working environment as a cooperative community. Numerous industries are struggling long-term because they are slow to embrace new leadership models such as those of Generation Z. This eventually leads to conflict between traditional management ways and what young people look forward to as a way of life. Ethical AI governance, digital literacy, and sustainability have emerged as critical areas where Gen Z leaders can impact. However, the current leadership development systems leave them without essential skills or opportunities to pursue these fields. In light of the increasing influence that Generation Z leadership is playing in shaping future work trends, this chapter looks into the problems and opportunities for a new generation capable of ethical AI adoption and sustainable business practices. Using qualitative research and case studies based on real events, the study delivers insight into how organizations might strengthen Generation Z leaders through mentorship programs, training in digital ethics, and inclusive leadership strategies. The study found that organizations pursuing Gen Z’s leadership vision are more likely to achieve integrative success in the digital economy vision. Investing in the next generation of leaders and aligning its strategies with their values can promote innovation, increase employee engagement, and build a more sustainable and ethical corporate culture. This work is an image created for this comfortable society, furnishing our knowledge store with valuable insights into changes needed by leaders that the rising AI era demands. Thus, it offers practical suggestions to organizations hoping for some survival modes, and then dust settles on an age past.
Generation Z, leadership, ethical AI, digital transformation, corporate sustainability.
Copyright (c) 2025 Noora Abdulla Rashid (Author) https://creativecommons.org/licenses/by/4.0
Human-AI Collaboration for Inclusive Growth and Sustainable Development
AI is a transformative technology that, when paired with human intelligence, can serve as the foundation of inclusive economic prosperity and sustainable development, rather than just an automation tool. As such, human-AI collaboration is essential for designing responsible AI systems, maximizing resource efficiency, and guaranteeing access to AI-powered opportunities. AI-human synergy will improve innovation, better decision-making and a more sustainable and inclusive economy. We explore the role of AI in achieving the United Nations Sustainable Development Goals (SDGs), using case studies from smart cities, precision agriculture, green supply chains, AI-enabled financial inclusion and workforce augmentation. The conversation emphasizes the need for regulation, outlines ethical dilemmas and discusses the environmental consequences of AI systems. It also examines how AI could be deployed responsibly to meet global challenges, lessen inequalities and augment not replace humans workers. The chapter closes with policy recommendations and a road map by which AI can inform a transition to sustainable business and economic models, as well as to ensure that benefits of AI are equitably shared across industries and communities.
artificial intelligence, human resource, sustainability;
Copyright (c) 2025 Nada Ahmed (Author) https://creativecommons.org/licenses/by/4.0
AI Agents and Sustainable Development Goals: Bridging the Gap Through Innovation
Autonomous AI agents represent an innovative advancement in artificial intelligence, transitioning from AI copilots and assistants reliant on human input to independent operators. The reasoning capabilities of large language models enable agents to develop strategies, plan and then execute tasks autonomously to achieve high-level goals. When finding problems, they can solve them automatically by utilizing resources such as the Internet, numerous data sources and specialized applications, such as delegating tasks to other specialized AI agents. They independently research problems and adapt without human oversight, presenting transformative potential for Sustainable Development Goals (SDGs) through scalable solutions to global challenges. The autonomous application of AI potentially improves many areas such as water management, agriculture, and biodiversity conservation, thereby optimizing decision-making processes for greater efficiency. Agentic AI possesses the capability to analyze extensive datasets, systematically identify gaps in sustainability efforts, and model potential intervention outcomes. This analytical capacity directly aligns with the goals of environmental conservation, resource efficiency, and equitable access to essential services. However, challenges, including environmental costs, energy consumption, and algorithmic biases, threaten fairness and inclusivity, which are important for realizing AI agents’ potential for achieving the goals. Establishing robust ethical frameworks is essential for guiding the deployment of AI agents, ensuring transparency and accountability. By embedding principles of fairness and inclusivity, autonomous AI agents can facilitate responsible advancements toward the Sustainable Development Goals.
AI Agents, Sustainability, SDG, Ethical, multi-agent, agentic AI, opportunities
Copyright (c) 2025 Imran Chughtai (Author) https://creativecommons.org/licenses/by/4.0
AI Development Probe into Decarbonization
The rise of artificial intelligence (AI) brings with it a broad range of challenges and opportunities for organizations. As we are aware that AI is a collection of tools, an intelligent toolbox. This article will explain why this toolbox is so valuable in helping humanity overcome key bottlenecks that currently hinder sustainability progress. Each passing year brings new record -high temperatures, larger catastrophic wildfires, and more frequent devasting floods. In response, international agreements have set ambitious global sustainability targets. the United Nations (UN) urges that net zero commitments require credible action for a living climate. Net zero refers to reducing carbon emissions to a chump change of residual emissions. Although there are numerous applications of AI, there is no leading-edge review of how AI applications can reduce net-zero carbon emissions (NZCEs) for sustainable building projects. By mid-century (2050), carbon dioxide emissions must reach net zero and by 2030, the loss of biodiversity must be suspended and reversed. The encouraging news is that significant progress is being made toward these goals. More than 9,000 companies across more than 140 countries have joined the Race to Zero, a coalition that pledges to take immediate action to halve global emissions by 2030. But change is simply not happening fast enough. The scale and speed of changes that are needed to meet global sustainability goals is daunting. Consider a few examples, such as Global renewable power generation must triple in less than a decade – International Energy Agency (IEA) and methane emissions from fossil fuel operations must be reduced by 75%. Over the next couple of decades, food production must increase by 50%, and we will need to develop 1,000 times more durable carbon removal capacity than exists today. A total of 124 published articles were retrieved and used to conduct science mapping analyses and qualitative discussions, including mainstream research topics, gaps, and future research directions. AI-powered learning platforms can help by providing:
artificial intelligence; net-zero carbon emissions; science mapping approach; AI-powered learning platforms;
Copyright (c) 2025 Shoba Krishnan (Author) https://creativecommons.org/licenses/by/4.0
AI Artificial Intelligence, Sustainability and Strategic Leadership
Artificial Intelligence (AI) has grown drastically in recent years. Organization leadership teams are focusing on analyzing the data through Artificial Intelligence (AI) and deriving it to the constructive decisions. This chapter shall focus on the leadership strategies which are used to develop the business operations by simplifying the model of operations with the support of AI, minimizing the timelines, operation cost and enhancing the speed and accuracy of the results, also aligning the sustainable development goals, reducing carbon emission and footprint, which can be done by adapting the approaches like use of solar and wind (renewable) energy, water and waste management, sustainable agricultural developments, use of preserved biodiversity, respectively depending upon suitability in contributing to the different industries and sectors. This chapter will also focus on the use of generative AI, the positive and challenging impact of the same, also how it can be environmentally friendly, by using renewable energy and moderating emissions. Sustainable Business Practices is important but along with this, ethicality and transparency of data usage, code of conduct, proper documentations, managing and analyzing risk along with the responsible behavior is also significant. AI experts, policy makers, Government guidelines and business leaders need to align, plan and design the strategy which is supporting the concept of AI, sustainable development of the company and leadership teams leading the company’s defined social and economic goals. The chapter will also shed light on scope and suggestions for future positive outcomes.
Artificial Intelligence, Strategic leadership, Ethicality, Transparency, Operations efficiency, Cost saving, Policy makers, Government guidelines, AI experts, Environment Sustainability, Social and Economic impacts, Renewable energy, Carbon emission;
Copyright (c) 2025 Neha Ahuja (Author) https://creativecommons.org/licenses/by/4.0
Artificial intelligence (AI) is crucial for productivity and efficiency. Employees’ deteriorating commitment and resilience add to organizations’ concerns on sustainability. An innovative culture facilitates organizations to benefit from AI, whilst leadership influences innovation, employees’ commitment and resilience. The current study aims to examine the mediating effects of innovative culture on the positive relationships of transformational leadership with employees’ affective organizational commitment and resilience and identify practical recommendations for employers, leaders and HR practitioners to drive innovation and sustainability. Data were collected from a random sample of 430 employees in Hong Kong via an online survey with validated scales. Structured equation modelling through SmartPLS (Version 4.1.0.9) was used to examine the linear relations and the mediating effects. Ten semi-structured interviews were conducted to validate how transformational leaders drive AI development and adoption, innovation and workforce sustainability in their organization. Transformational leadership was positively related to innovative culture (p=0.000), employees’ affective organizational commitment (p=0.000) and resilience (p=0.000), whilst innovative culture was positively related to employees’ affective organizational commitment (p=0.000) and resilience (p=0.010). Hence, innovative culture partially mediated the positive relationships between transformational leadership and employees’ affective organizational commitment and resilience. The interviews showed that transformational leaders are role models who create psychological safety for their employees to take risk with accountability to enable experimentation, expose employees to stimulations for innovation and growth, establish trust and transparent communication with everyone to strengthen their affective organizational commitment and resilience to adversity. Leaders should demonstrate transformational leadership to drive employees’ affective commitment and resilience, which are crucial for their organization’s sustainability. Transformational leadership also helps leaders create an innovative culture to facilitate the development and implementation of AI. HR practitioners should implement leadership development programs focused on transformational leadership.
leadership, innovation, commitment, resilience, artificial intelligence or AI, sustainability;
Copyright (c) 2025 Anthony Wong (Author) https://creativecommons.org/licenses/by/4.0
The Sustainability Paradox of Artificial Intelligence: How AI Both Saves and Challenges Resource Management Efforts
The net gain of AI and sustainable development remains a critical area of inquiry, as Artificial Intelligence (AI) offers opportunities and challenges in advancing sustainability. AI offers benefits like optimized energy use and improved resource efficiency, but its rapid adoption also results in high energy consumption, increased e-waste, and resource depletion. This contradiction is referred to as the Sustainability Paradox and calls for a structured evaluation of AI’s impact. The Sustainable AI Impact Assessment Framework (SAIAF) serves as a tool to measure AI’s role in sustainability while accounting for its unintended consequences. It assesses AI across three dimensions: environmental (carbon footprint, energy use), social (labor market changes, ethical issues), and economic (cost efficiency, long-term resilience). Case studies in precision agriculture and smart energy grids demonstrate how SAIAF aids policymakers and industries in minimizing negative impacts while enhancing the sustainability gains of AI. However, fragmented global policies complicate the effective implementation of AI for sustainability, leading to inconsistent regulations and misaligned objectives. This paper highlights the importance of cohesive AI governance and shared sustainability standards. By incorporating SAIAF into policies and industry practices, AI can shift from being resource-heavy to becoming a strategic sustainability ally. The study suggests further research on the sustainability of AI lifecycles, adaptive policies, and innovations in energy-efficient AI systems for a more balanced and responsible future.
Sustainable AI, AI and Environmental Sustainability, Energy-Efficient AI and Green Technology, Sustainable AI Impact Assessment, AI Policy and Global Sustainability;
Copyright (c) 2025 Victor Frimpong (Author) https://creativecommons.org/licenses/by/4.0
Artificial Intelligence and Sustainability: Innovations in Business and managerial Practices
Artificial Intelligence (AI) will, more than ever, play a critical role in every aspect of organizational progression and influence decisions across the board. Human, talent, and overall workforce management is no exception to this influence and impact; AI’s influence will be through organizational leadership via guiding decision-making, team management, and innovation processes. As its potential is explored, it becomes clear that leaders must adapt to leverage innovation effectively and address the new ethical and cultural issues they raise. Organizations, leadership, cultures, and pillars of organizational structures and systems must do this while remaining ethical, mindful, and aware of not affecting creativity (INSEAD, 2024). The leadership of any organization must lead with AI while keeping people, mindfulness, ethics, and values, as well as creativity and the human touch at the heart of everything that they do and each AI strategy (AON, 2024). AI has the potential to unleash creativity, foster human connections, imagine new ways of learning, enable the automation of existing tasks, and promote new adaptive tasks that require human ingenuity and empathy. That is quite a list, which raises equal challenges and opportunities (INSEAD, 2024). What is clear is that leaders will remain indispensable in helping their teams and firms negotiate this brave new world. To do so successfully, they must adopt a dual mindset, while helping to maintain and create moments of deep, thoughtful human interactions. Four challenges may arise from AI’s influence and leverage: 1) HR’s operational complexities, 2) data’s readiness, accuracy, and availability, 3) legalities that may arise and conform to compliant approaches, and 4) Manpower’s reactions and behavior against and towards algorithm-based decisions (Jobylon, 2024).
Diversity, Inclusion, Creativity, Human and Machines, Artificial Intelligence, Human Resources, and Human Touch;
Copyright (c) 2025 Kelly Salame (Author) https://creativecommons.org/licenses/by/4.0
The Potential of AI in HRM: Boosting Employee Wellbeing and Engagement in Today’s Hybrid Work Environments
The Human Resource Management (HRM) is in need to effectively design workplace dynamics where employees feel they are well equipped to perform their daily tasks, whilst protecting their mental and physical wellbeing. This need is accentuated when employees are full or part-time remote. A sense of being connected with the organization, an organization which provides for useful resources to succeed at work may incentivize a desire to reciprocate the employer through higher engagement and better performance. The deployment of AI-powered HRM solutions has the potential to provide for a personalized employee experience that each remote worker can tailor to their daily needs. While working from home (WFH) offers benefits like increased autonomy and work-life balance, it also presents challenges such as work-life spillover and social isolation. This article posits that AI-assisted HRM solutions can significantly improve teleworkers’ engagement through increased wellbeing at work. Through the exploitation of AI, HRM practitioners can design and create a more supportive and personalized remote work environment. This article reviews AI in HRM, highlighting uses like personalized support, predictive analytics, automated tasks, enhanced learning, and real-time feedback. It suggests that combining AI with human efforts can improve employee satisfaction and organizational performance by creating a more inclusive, supportive, and engaging work environment. AI tools such as chatbots, virtual assistants, and predictive analytics can provide real-time support, personalized wellbeing programs, and data-driven insights. These can be brought to the front of the organization’s environment, fostering a more engaged and satisfied workforce. The article reviews AI in HRM, highlighting uses like personalized support, predictive analytics, automated tasks, enhanced learning, and real-time feedback. It suggests that combining AI with human efforts can improve employee satisfaction and organizational performance by creating a more inclusive, supportive, and engaging work environment. Key applications of AI in HRM include automated resume screening, performance evaluation, and employee engagement. Research on AI’s impact on employee engagement is limited, but existing studies highlight AI’s dual role in enhancing efficiency and reducing bias, while also raising concerns about job security, fairness, and privacy. The findings derived from this study suggest AI-assisted HRM solutions may offer significant opportunities to enhance employee engagement and wellbeing, especially for remote or hybrid workers. The overall success of such implementations will depend on surpassing AIs major threats, that is risk of bias, privacy concerns, potential inconsistent feedback, or an over reliance on technology, versus human-to-human interactions. AI-assisted HRM solutions thus must be carefully managed to maximize their benefits.
Key words: Employee engagement; employee wellbeing; AI-based HRM tools; AI-based employee support; HR analytics.
Copyright (c) 2025 Maribel Labrado (Author) https://creativecommons.org/licenses/by/4.0
Harnessing Artificial Intelligence for Sustainable Finance: Innovations, Challenges, and Opportunities
This Artificial Intelligence (AI) is increasingly transforming the landscape of sustainable finance, offering innovative solutions for Environmental, Social, and Governance (ESG) investments, financial risk assessment, and responsible decision-making. This chapter presents a qualitative analysis of 56 recent scientific articles from the EBSCO host database, examining the intersection of AI and sustainable finance through an automated coding approach using Iramuteq. The analysis identifies five thematic categories: Innovation, AI Models, Learning, Environment, and Relationship, forming the IRAML Model (Innovation-Relationship-AI Model-Learning). The results highlight that Innovation is the primary driver of AI adoption, particularly in digital finance and ESG applications. The Relationship category acts as a critical bridge, linking AI models and learning to financial sustainability outcomes. However, the study also reveals that AI models and financial learning remain largely independent, suggesting a gap in integrating AI-driven financial decision-making with broader sustainability goals. This chapter proposes strategic recommendations to enhance AI’s transparency, foster human-AI collaboration, and strengthen regulatory frameworks in sustainable finance. The findings contribute to ongoing discussions on AI-driven financial innovation, emphasizing the need for relationship-based frameworks to align AI applications with sustainable investment strategies.
Artificial Intelligence, Sustainable Finance, ESG Investments, AI Models, Financial Innovation, Machine Learning, Responsible Investing, Relationship-Based Finance, AI Governance, IRAML Model
Copyright (c) 2025 Milos Petkovic (Author) https://creativecommons.org/licenses/by/4.0
Managing Cyber Risks in the Face of AI- and ML-driven Adversarial Attacks
This paper presents a critical analysis of current cyber risk management practices in light of new and evolving Artificial Intelligence (AI) and Machine Learning driven adversarial attacks. Many enterprises are constantly grappling with cybersecurity risks and increased threats from phishing, ransomware and many other forms of cyber attacks, often resulting in substantial financial losses when the risks are not adequately addressed. With the advent of Artificial Intelligence (AI) and Machine Learning (ML), such cyber attacks and incidents will become more prevalent and potentially more devastating to businesses large and small. With AI and ML tools at their disposal, cybercriminals can dramatically reduce technical barriers for launching cyberattacks. They can easily develop more sophisticated social engineering tactics and ‘deep fakes’ that are not easily identifiable as such, thereby increasing the risks of unauthorized data disclosure. Drawing on literature review analysis, this research explores current and emerging AI- and ML-driven cyber threats faced by enterprises, effectiveness of current cyber mitigation measures and future management practices that can be leveraged to improve the security posture of enterprises. The study evaluates both technical and non-technical cyber risk management and mitigation measures and frameworks. The findings from this study help inform enterprise cyber risk managers and practitioners about the enormity of AI- and ML-driven cyber risks and presents emerging best practices to adequately mitigate those risks. The study contributes to the growing research on how threat actors are leveraging and AI and ML to expand cyber threats and how enterprises and organizations should respond to these ever evolving cyber risks.
SBS International Research Conference 2024
cyber risk management, AI-driven, ML-driven, adversarial attacks, cyber risk frameworks
Copyright (c) 2025 Godwill Chimamiwa (Author) https://creativecommons.org/licenses/by/4.0
AI in Consumer Behavior Analysis and Digital Marketing: A Strategic Approach
The rapid advancement of Artificial Intelligence (AI) has revolutionized consumer behavior analysis and digital marketing strategies by enabling personalized and efficient data-driven approaches. AI-driven tools like predictive analytics, natural language processing (NLP), machine learning, and programmatic advertising allow marketers to process vast amounts of real-time consumer data, facilitating optimized campaign performance and precise targeting. This paper explores the integration of AI in marketing, highlighting its role in enhancing predictive analytics, sentiment analysis, and real-time segmentation. Compared to traditional methods, AI-driven insights significantly improve engagement, accuracy, and return on investment (ROI). AI also plays a vital role in marketing automation, allowing dynamic adjustments in campaigns, ad placements, and content creation, improving efficiency and reducing costs. However, AI's reliance on consumer data raises concerns regarding data privacy and algorithmic bias, especially in targeting. This paper stresses the importance of ensuring transparency, fairness, and regular audits in AI systems to maintain consumer trust and promote ethical AI use. Future research directions are discussed, focusing on enhancing transparency and algorithmic accountability while navigating the ethical challenges of AI in marketing.
Artificial Intelligence (AI), Consumer behavior analysis, Digital marketing, Predictive analytics, Natural language processing (NLP)
Copyright (c) 2025 Nane Davtyan (Author) https://creativecommons.org/licenses/by/4.0
Artificial Intelligence in Non-Clinical Functions: A Strategic Framework for Healthcare Organizations
Artificial Intelligence (AI) has demonstrated significant potential in healthcare, particularly in clinical applications such as diagnostics and personalized treatment. However, the application of AI in non-clinical areas, such as operational efficiency, data governance, and data monetization, remains underexplored. This paper addresses this gap by proposing an AI-driven framework for healthcare organizations, synthesizing existing literature on AI applications and data management. Using a qualitative approach, this study identifies six key areas where AI can enhance non-clinical operations: data governance and quality management, technological infrastructure and scalability, leadership and workforce development, operational efficiency, data monetization, and ethical considerations. The framework provides a strategic framework for healthcare organizations to adopt AI technologies effectively while ensuring compliance with local and international regulations. This paper contributes to the growing body of research by offering practical solutions for leveraging AI to improve healthcare administration and create new revenue streams through data valorization.
Copyright (c) 2025 Aziz Alzeqri (Author) https://creativecommons.org/licenses/by/4.0
Leadership in the Age of Artificial Intelligence
Artificial intelligence is becoming an integral part of company strategy. AI's capabilities are always evolving, with the focus shifting from what it can accomplish to what it cannot. Furthermore, substantially transforming in every aspect, the senior management leads from how they raise and coach, to the way they inspire teams, to the way they use AI and Human power together to achieve their goal for the firm, to the manner they drive transformation and challenge. AI promises, concerns, and excitement over the wayward role it will play are running High because of its limitless breadth. One thing is clear: ensuring that your organization is prepared. For the paradigm transition, AI is no longer an option. The challenge now is: are leaders ready to go up and deliver the vision, and seize the opportunities? To respond, the UAE's leading smelting manufacturing businesses polled. Over 450 CLevel executives across the country, by analyzing their perspectives on their acceptability and Adaptability within themselves and the organization of the next AI generation. The company, even to further refine the findings, I conducted approximately 12 to 15 interviews with worldwide leaders. The poll and interviews clearly indicated that leaders in general are very Strong see AI as an opportunity, rather than a danger, but they also see the route to the relationship between development and success remains ambiguous. On the one hand, the material opportunity identified by leaders, both for their role and for their organization, was the increased efficiency that the effective use of AI could bring, more effective decision-making, improved risk management, and the creation of innovative products and services. On the other hand, executives were watchful to the potential threat associated with AI. ‘Manpower redundancy’ and ‘data protection’ appeared as the two main concerns for leaders, both as individual roles and their organizations. The implications of these technologies on the workforce and the nature of work have caused Extensive debate and analysis. By 2030, roughly 375 million workers will constitute approximately 14% of the worldwide workforce, and they may need to migrate into new occupations or acquire new skills as Automation and artificial intelligence have influenced the development of new skills. This shift will provide challenges and executives who muxt adjust to a changing workforce and develop new ways for managing people in a technologically driven workplace. A cross-functional team of researchers at MIT Sloan demonstrated an outstanding gain of up to 40% in highly Using generative AI to improve skilled workers' performance (* The term "Generative AI" relates to Deep-learning AI models can generate high-quality writing, graphics, and other content depending on the data on which they were taught has the potential to add trillions to the global economy. This monumental shift will revolutionize the way people work, study, and interact. It has the ability to shake up entire industries and even society as a whole. According to a Boston Consulting Group report, 61% of executives in the Middle East anticipate these technologies will enhance productivity by over 10% by 2024. Despite regional training surpassing global norms, issues remain in fully equipping the personnel and overcoming Future AI regulations. This article offers insight into how leaders anticipate AI's influence. organizations, employment, and their positions in the Middle East, and how ready they feel to lead through this Revolutionary disruption. We will investigate the factors and determinants of leadership and organization. Leaders should be prepared to navigate the inevitable technological revolution.
Harnessing Artificial Intelligence for Smarter Decisions: Shapping the Future of Contemporary Management for Modern Busines
This study examines the principal factors influencing organizational management utilizing Artificial Intelligence (AI) in the modern era. The primary emphasis is on the issues and developments impacting contemporary organizations worldwide after the emergence of AI. Initially, the critical elements that influence internal and external management were explored while assessing the ramifications of these factors on management. Then, the impact of numerous factors on organizational management strategies was thoroughly studied alongside adequate contemporary AI models that conceptualized these tactics and led to a competitive advantage stage. Although AI has tremendous advantages for contemporary business and management, it also has disadvantages. The human-feeling process is a fundamental practical sense that AI is limited. Recent studies demonstrated that the AI era lacks human-like creativity and empathy, a proven fact of human brains’ vitality in making intelligent decisions. Therefore, organizations’ members can be complemented by AI for better, more intelligent decision-making that will elevate the related businesses. Conversely, AI can result in ethical concerns about bias and privacy. This issue will prevent modern organizations from considering corrective actions since their decisions might not lead to the anticipated business outcomes, including but not limited to the set Key Performance Indicators (KPIs). Another side-effect of AI is the inadequate data for making the required decision without contemplating empathy. Thus, the AI shall be tackled from 360 degrees to ensure that the AI-driven decision-making system will optimize human interference while minimizing the probable impacts of the related risks, biases, and hallucination. The paper employs genuine case studies and empirical research findings to critically and analytically examine the management concerns presented by applying AI-driven decision-making practice. By harnessing AI for smarter decisions, a practical case study about the Electrical Submersible Pump (ESP) and its related technologies to extract crude oil will be demonstrated using the components and elements of the Contemporary Management Module in the AI age for a smarter-driven decision-making process. This methodology will boost modern organizations’ performances while fostering the employees’ recitals, yielding a successful business journey and evident productivity.
Artificial Intelligence (AI), Decision-Making, Contemporary Management, Globalization, Digitization, Technologies, Societal Changes, Skills, Strategy and Innovation.
Copyright (c) 2025 Hisham Al-Shuwaikhat (Author) https://creativecommons.org/licenses/by/4.0