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Administrative Sciences
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Artificial Intelligence in Business: Redefining Competencies, Finance, and Entrepreneurship
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Artificial Intelligence in Business: Redefining Competencies, Finance, and Entrepreneurship

A special issue of Administrative Sciences (ISSN 2076-3387).

Deadline for manuscript submissions: closed (28 February 2026) | Viewed by 12353

Special Issue Editors

Prof. Dr. Nada Mallah Boustani

E-Mail Website
Guest Editor
Faculty of Business and Administration, Université Saint-Joseph de Beyrouth, Beirut, Lebanon
Interests: innovation; finance; entrepreneurship; sustainability
Special Issues, Collections and Topics in MDPI journals
Dr. Elisabetta Magnaghi

E-Mail Website
Guest Editor
KEDGE Business School, 33405 Talence, France
Interests: disciplines; financial economics; accounting scholarship; business administration

Special Issue Information

Dear Colleagues,

The global rise of Artificial Intelligence (AI)—particularly with the emergence of generative tools like ChatGPT and Bard—has initiated a profound transformation not only in managerial practices but also in the broader domains of business strategy, financial systems, and entrepreneurship. Across industries, AI is accelerating a shift from reactive to predictive management, from static business models to dynamic entrepreneurial ecosystems, and from traditional finance to AI-enhanced forecasting and risk modeling (McKinsey, 2025; Chui et al., 2023).

AI's integration into organizational processes enables decision-makers to convert data into strategic insights, automate both routine and complex cognitive tasks, personalize customer and employee experiences, and manage uncertainty with unprecedented agility. In financial services, AI is being used for algorithmic trading, fraud detection, and credit risk assessment. In entrepreneurship, AI enables lean startups to scale quickly through automation, customer analytics, and agile experimentation (Mei et al., 2024)

This Special Issue invites contributions that examine how AI adoption is reshaping:

  • Managerial competencies: New demands for techno-managerial literacy, data interpretation skills, and AI-augmented leadership.
  • Entrepreneurial capabilities: The evolution of digital entrepreneurship and how founders leverage AI to identify market opportunities, reduce operational costs, and innovate at scale.
  • Financial strategies: The transformation of financial planning, investment behavior, and compliance in an era of AI-assisted analytics and regulation.
  • Workforce development: Reskilling needs, digital inclusion, and human-AI collaboration challenges.
  • Organizational design: From centralized hierarchies to data-driven, agile, and responsive structures.

We aim to explore both the drivers of AI integration such as national digital strategies, corporate innovation initiatives, and open-source ecosystems and the barriers, including digital divides, ethical concerns, employee resistance, and regulatory gaps (Stahl & Eke, 2024).

Comparative and interdisciplinary contributions are especially encouraged. The issue seeks to connect research from management sciences, finance, entrepreneurship, information systems, human resources, organizational behavior, and ethics. Frameworks such as TAM (Davis, 1989), UTAUT (Venkatesh et al., 2003) are welcome for studies addressing AI acceptability and behavioral intention, particularly in cross-cultural or cross-sectoral contexts. 

The Special Issue also aims to surface practical insights and innovative practices that support sustainable, inclusive, and human-centered AI transformation—especially for SMEs and startups in both developed and developing economies (Bobillier Chaumon, 2013; McKinsey, 2025).

Topics of Interest (include but are not limited to):

  • Emerging competencies in the era of AI, automation, and digital finance
  • Organizational readiness and technological literacy for AI adoption
  • Entrepreneurial strategies and business model innovation with AI
  • AI in financial decision-making, forecasting, and risk management
  • Human-AI collaboration in complex and high-stakes business tasks
  • AI’s impact on SMEs and entrepreneurial ecosystems in developed and developing countries
  • Ethical, legal, and regulatory implications of AI in business and finance
  • Resistance to AI-driven change and strategies for inclusive transformation
  • AI in performance management, employee monitoring, and leadership development
  • Industry-specific challenges and case studies (banking, tech startups, healthcare, education)
  • Adaptive learning, reskilling, and financial literacy in AI-augmented environments

Methodological Diversity:

We welcome diverse methodological approaches:

  • Empirical research (qualitative, quantitative, or mixed-method)
  • Conceptual or theoretical frameworks
  • Systematic reviews
  • Case studies and comparative studies

  • Experimental or AI-driven analytics

Manuscript Submission Starts on the 1rst of july 2025 and the Deadline is 31rst March 2026

All manuscripts will undergo rigorous double blind peer review in accordance with Administrative Sciences editorial guidelines. Submissions should be made through the MDPI online submission system, specifying the Special Issue title.

References

Chui, M., Hazan, E., Roberts, R., Singla, A., Smaje, K., & Sukharevsky, A. (2023). The economic potential of generative AI: The next productivity frontier. McKinsey Digital. https://www.mckinsey.com

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008

Gursoy, D., Chi, O. H., Lu, L., & Nunkoo, R. (2019). Consumers’ acceptance of artificially intelligent (AI) device use in service delivery. International Journal of Information Management, 49, 157–169. https://doi.org/10.1016/j.ijinfomgt.2019.03.008

McKinsey & Company. (2025). The state of AI: How organizations are rewiring to capture value. https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai

Mei, H., Bodog, S. A., & Badulescu, D. (2024). Artificial intelligence adoption in sustainable banking services: The critical role of technological literacy. Sustainability, 16(20), 8934. https://doi.org/10.3390/su16208934

Stahl, B. C., & Eke, D. (2024). The ethics of ChatGPT – Exploring the ethical issues of an emerging technology. International Journal of Information Management, 74, 102700. https://doi.org/10.1016/j.ijinfomgt.2023.102700

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540 

Prof. Dr. Nada Mallah Boustani
Dr. Elisabetta Magnaghi
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a double-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Administrative Sciences is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • AI
  • automation
  • digital finance

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (9 papers)

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Research

21 pages, 638 KB  
Article
Artificial Intelligence and New Quality Productive Forces: Evidence from Vietnam’s Banking Sector
by Anh Phuong Hoang and Vinh Thi Vu
Adm. Sci. 2026, 16(4), 182; https://doi.org/10.3390/admsci16040182 - 9 Apr 2026
Viewed by 575
Abstract
This study examines how artificial intelligence (AI) contributes to the formation of new quality productive forces (NQPF) at the employee level. While prior research has largely treated AI as an external technological driver, this study investigates whether AI becomes embedded within employees’ capabilities [...] Read more.
This study examines how artificial intelligence (AI) contributes to the formation of new quality productive forces (NQPF) at the employee level. While prior research has largely treated AI as an external technological driver, this study investigates whether AI becomes embedded within employees’ capabilities through confidence and skill transformation. Using survey data from 303 employees in Vietnamese commercial banks, the study applies exploratory factor analysis and regression models to analyze the relationships among AI confidence, skill transformation, work experience, and NQPF. The results show that AI confidence has a significant positive effect on NQPF, and this relationship is strengthened by skill transformation. However, work experience weakens this effect, suggesting uneven adaptation across employee groups. These findings indicate that the impact of AI on productive transformation depends not only on technological deployment but also on workforce capability development. The study contributes to the literature by providing micro-level evidence on how AI may be internalized within labor processes in emerging economies. Full article
(This article belongs to the Special Issue Artificial Intelligence in Business: Redefining Competencies, Finance, and Entrepreneurship)
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29 pages, 960 KB  
Article
How Generative Artificial Intelligence Creates Value: A Function and Readiness Perspective in Small and Medium-Sized Enterprises
by Leandro Bitetti, Carmine Garzia and Emanuele Carpanzano
Adm. Sci. 2026, 16(4), 176; https://doi.org/10.3390/admsci16040176 - 3 Apr 2026
Viewed by 1042
Abstract
Generative artificial intelligence (GenAI) is increasingly portrayed as a transformative technology capable of simultaneously enhancing operational efficiency and enabling strategic growth. Yet small and medium-sized enterprises (SMEs) experience heterogeneous outcomes, suggesting that GenAI does not generate value uniformly across firms. This study develops [...] Read more.
Generative artificial intelligence (GenAI) is increasingly portrayed as a transformative technology capable of simultaneously enhancing operational efficiency and enabling strategic growth. Yet small and medium-sized enterprises (SMEs) experience heterogeneous outcomes, suggesting that GenAI does not generate value uniformly across firms. This study develops and empirically informs a contingency framework explaining how distinct GenAI functions relate to differentiated strategic objectives and how technological, organizational, and environmental (TOE) readiness conditions shape this relationship. Using a three-round Delphi study with an interdisciplinary expert panel, including GenAI consultants, corporate managers, legal experts, academic researchers, and public-sector policymakers, we identify six core GenAI functional domains associated with efficiency-oriented and growth-oriented strategies. The findings suggest that operational automation and data intelligence are more strongly associated with efficiency objectives, whereas market intelligence, market testing, linguistic expansion, and idea generation are more closely related to growth objectives, although none is exclusively linked to a single strategic goal. Importantly, TOE readiness is found to play a key role in shaping the extent to which function-specific GenAI deployment translates into realized strategic value, with organizational readiness appearing more prominent than technological or environmental conditions. By shifting the focus from adoption to function-specific strategic alignment and readiness configurations, this study advances understanding of GenAI-enabled strategic value realization and heterogeneous transformation pathways in SMEs. Full article
(This article belongs to the Special Issue Artificial Intelligence in Business: Redefining Competencies, Finance, and Entrepreneurship)
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41 pages, 4823 KB  
Article
AI-Driven Bankruptcy Prediction in Manufacturing SMEs: Comparing Machine Learning Techniques with Logistic Regression
by Stanislav Letkovský, Sylvia Jenčová, Petra Vašaničová, Marta Miškufová and Michal Erben
Adm. Sci. 2026, 16(3), 148; https://doi.org/10.3390/admsci16030148 - 18 Mar 2026
Viewed by 1014
Abstract
Bankruptcy prediction is currently a widely researched topic, as it typically results from a chain of negative events. Logistic Regression (LR) is one of the standard prediction tools; however, with advances in technology, machine learning (ML) methods are gaining prominence and demonstrating improvements [...] Read more.
Bankruptcy prediction is currently a widely researched topic, as it typically results from a chain of negative events. Logistic Regression (LR) is one of the standard prediction tools; however, with advances in technology, machine learning (ML) methods are gaining prominence and demonstrating improvements in performance and accuracy. It remains inconclusive whether ML methods significantly outperform traditional approaches such as LR in bankruptcy prediction. In this study, we identified the most commonly applied basic ML techniques—namely, Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), and Decision Trees (DTs)—which are frequently used in the literature for classification tasks. These methods were selected for empirical comparison with LR to evaluate their relative predictive performance and potential advantages in bankruptcy forecasting. In the EU, small and medium-sized enterprises (SMEs) constitute more than 99% of the economy; however, only a few survive beyond five years. This study examines bankruptcy prediction in the specific context of the Slovak Republic, using a sample of 2754 SME manufacturing enterprises from 2020 to 2021 and 3158 from 2022 to 2023. All models show good predictive performance; however, the small statistical difference between the results does not conclusively demonstrate the superiority of ML methods over LR. Full article
(This article belongs to the Special Issue Artificial Intelligence in Business: Redefining Competencies, Finance, and Entrepreneurship)
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19 pages, 870 KB  
Article
Explainable AI Interviews and Organizational Attractiveness: The Roles of Perceived Organizational Support and Innovativeness
by Qianfu Zhou, Chia-Huei Wu, Huizhen Long and Xin Zhang
Adm. Sci. 2026, 16(3), 144; https://doi.org/10.3390/admsci16030144 - 16 Mar 2026
Viewed by 953
Abstract
As artificial intelligence (AI) systems are increasingly adopted in recruitment practices, applicants’ responses to AI-mediated interviews have become an important issue for organizations. Understanding how applicants interpret these systems is relevant for organizational attractiveness and employer branding. Drawing on social exchange theory and [...] Read more.
As artificial intelligence (AI) systems are increasingly adopted in recruitment practices, applicants’ responses to AI-mediated interviews have become an important issue for organizations. Understanding how applicants interpret these systems is relevant for organizational attractiveness and employer branding. Drawing on social exchange theory and signaling theory, this study examines the role of AI interview explainability in shaping applicants’ evaluations of organizations. It proposes that explainability influences organizational attractiveness through two parallel mechanisms: perceived organizational support and perceived innovativeness. Survey data were collected from 196 job applicants with experience in AI-based interviews. The results show that higher perceived explainability of AI interviews is associated with stronger perceptions of organizational support and organizational innovativeness. Both perceptions are positively related to organizational attractiveness. These findings support a dual-mediation model and suggest that explainable AI interview systems communicate both supportive intentions and technological capability to applicants. By focusing on applicants’ perceptions, this study contributes to the growing literature on AI use in human resource management. It highlights the importance of explainable system design in shaping early applicant reactions. The findings also provide practical implications for organizations seeking to implement AI-based recruitment tools that are transparent, credible, and attractive to potential applicants. Full article
(This article belongs to the Special Issue Artificial Intelligence in Business: Redefining Competencies, Finance, and Entrepreneurship)
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20 pages, 491 KB  
Article
Governing Artificial Intelligence for Sustainable Territorial Development in Fragile Contexts: Insights from North Lebanon
by Chadi Khneyzer, Zaher Boustany and Jean Dagher
Adm. Sci. 2026, 16(3), 130; https://doi.org/10.3390/admsci16030130 - 6 Mar 2026
Viewed by 742
Abstract
Sustainable territorial development seeks to balance economic growth, social well-being, and environmental preservation across spatial contexts. In fragile and resource-constrained regions, achieving this balance remains particularly challenging. With the growing diffusion of artificial intelligence (AI), digital tools are increasingly presented as potential enablers [...] Read more.
Sustainable territorial development seeks to balance economic growth, social well-being, and environmental preservation across spatial contexts. In fragile and resource-constrained regions, achieving this balance remains particularly challenging. With the growing diffusion of artificial intelligence (AI), digital tools are increasingly presented as potential enablers of sustainability-driven territorial strategies. This study explores the role of AI in supporting sustainable territorial development across rural and urban areas of North Lebanon, a region characterized by infrastructural deficits, governance constraints, and socio-economic vulnerability. Adopting a qualitative research design, the study draws on semi-structured interviews with five key stakeholders from the public sector, civil society, business, and sustainability expertise, complemented by an illustrative case study of the proposed AI-enabled redevelopment of Klayaat (René Mouawad) Airport. The findings reveal that while stakeholders recognize AI’s potential to enhance resource optimization, smart agriculture, urban mobility, and disaster preparedness, its effective adoption remains constrained by limited digital infrastructure, insufficient policy frameworks, funding shortages, and gaps in digital literacy. Interpreted through the lenses of the Triple Bottom Line and Diffusion of Innovation theories, the results show that AI-driven sustainability outcomes in fragile territorial contexts are highly conditional on institutional readiness, governance capacity, and contextual alignment. The study contributes to the literature by providing context-specific insights into AI-enabled sustainable development in a developing and crisis-affected region, highlighting the need to complement technological innovation with policy reform, capacity building, and inclusive territorial governance. Full article
(This article belongs to the Special Issue Artificial Intelligence in Business: Redefining Competencies, Finance, and Entrepreneurship)
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17 pages, 605 KB  
Article
Influence of Business Intelligence on Organizational Performance: The Moderating Role of Employee BI Experiences
by Hamzeh Ahmad Mustafa Alawamleh, Rukana Ghalib Alshweesh, Nisrein Jamal Sanad Abu-darwish, Ala’mahmoud hussein Aljundi and Ali Atallah Salah
Adm. Sci. 2026, 16(2), 100; https://doi.org/10.3390/admsci16020100 - 13 Feb 2026
Viewed by 1112
Abstract
The use of business intelligence (BI) is becoming more common in many fields to help managers make better decisions. There are not many empirical studies on BI. The objective of this research is to analyze the influence of business intelligence capabilities, business intelligence [...] Read more.
The use of business intelligence (BI) is becoming more common in many fields to help managers make better decisions. There are not many empirical studies on BI. The objective of this research is to analyze the influence of business intelligence capabilities, business intelligence infrastructure, and collaboration capability on organizational performance, in addition to investigating the moderating impact of employee BI experiences. The study distributed 500 questionnaires to individuals from various divisions of management, involving managers of IT, supervisors, and directors in banking institutions in Jordan. A total of 212 individuals responded to the questionnaire that was sent out, and 197 of those responses were considered valid for the purpose of statistical analysis. We used structural equation modeling (SEM) to investigate the proposed relationships. The results indicated that business intelligence capabilities, business intelligence infrastructure, and collaboration capability significantly impacted organizational performance (p < 0.05), thereby supporting all relevant research hypotheses. There is a strong link between employee BI experiences and how well a bank performs. The research found a substantial moderating influence of employee BI experiences on the correlation among business intelligence capabilities, business intelligence infrastructure, and bank performance. However, no substantial moderating effect was determined between the collaboration capabilities and bank performance in Jordan. The results of this study offer pragmatic insights for the top management of commercial banks as well as for other banking industries and stakeholders. Full article
(This article belongs to the Special Issue Artificial Intelligence in Business: Redefining Competencies, Finance, and Entrepreneurship)
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19 pages, 932 KB  
Article
Harnessing AI to Unlock Logistics and Port Efficiency in the Sultanate of Oman
by Abebe Ejigu Alemu, Amer H. Alhabsi, Faiza Kiran, Khalid Salim Said Al Kalbani, Hoorya Yaqoob AlRashdi and Shuhd Ali Nasser Al-Rasbi
Adm. Sci. 2026, 16(1), 54; https://doi.org/10.3390/admsci16010054 - 21 Jan 2026
Viewed by 1595
Abstract
The global maritime and logistics sectors are undergoing rapid digital transformation driven by emerging technologies such as automation, the Internet of Things (IoT), and blockchain. Artificial Intelligence (AI), with its ability to analyze complex datasets, predict operational patterns, and optimize resource allocation, offers [...] Read more.
The global maritime and logistics sectors are undergoing rapid digital transformation driven by emerging technologies such as automation, the Internet of Things (IoT), and blockchain. Artificial Intelligence (AI), with its ability to analyze complex datasets, predict operational patterns, and optimize resource allocation, offers a transformative potential beyond the capabilities of conventional technologies. However, mixed results are shown in its implementation. This study examines the current state of AI applications to unlock higher levels of efficiency and competitiveness in logistics firms. A mixed-methods approach was employed, combining surveys from logistics companies with in-depth interviews from key stakeholders in ports and logistics firms to triangulate insights and enhance the validity of the findings. Our results reveal that while technologies such as automation and digital tracking are increasingly utilized to improve operational transparency and cargo management, AI applications remain limited and largely experimental. Where implemented, AI contributes to strategic decision-making, predictive maintenance, customer service enhancement, and cargo flow optimization. Nonetheless, financial conditions, data integration challenges, and a shortage of AI-skilled professionals continue to impede its wider adoption. To overcome these challenges, this study recommends targeted investments in AI infrastructure, the establishment of collaborative frameworks between public authorities, financial institutions, and technology-driven Higher Education Institutions (HEIs), and the development of human capital capable of sustaining AI-enabled transformation. By strategically leveraging AI, Oman can position its ports and logistics sector as a regional leader in efficiency, innovation, and sustainable growth. Full article
(This article belongs to the Special Issue Artificial Intelligence in Business: Redefining Competencies, Finance, and Entrepreneurship)
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18 pages, 295 KB  
Article
Mind the AI Gap: Asymmetrical Age Differences in Entrepreneurs’ Perceptions of Artificial Intelligence
by Katja Crnogaj, Pina Slaček and Maja Rožman
Adm. Sci. 2026, 16(1), 8; https://doi.org/10.3390/admsci16010008 - 24 Dec 2025
Cited by 1 | Viewed by 1525
Abstract
As artificial intelligence (AI) becomes embedded in entrepreneurial practice, an unresolved question is whether age shapes founders’ perceptions of its opportunities and risks. Drawing on diffusion-of-innovations and technology adoption theories, this study examines whether age cohorts differ in their perceived benefits of AI, [...] Read more.
As artificial intelligence (AI) becomes embedded in entrepreneurial practice, an unresolved question is whether age shapes founders’ perceptions of its opportunities and risks. Drawing on diffusion-of-innovations and technology adoption theories, this study examines whether age cohorts differ in their perceived benefits of AI, perceived risks, and short-term expectations regarding AI’s business impact. Using data from the 2024 Global Entrepreneurship Monitor (GEM) survey for Slovenia, we analyze ordinal indicators across all three domains. Bivariate comparisons using Mann–Whitney U tests with effect sizes are complemented by multivariate ordinal logistic regression models controlling for sector, education, and gender. The analysis reveals an asymmetrical age gap in AI perceptions. Younger entrepreneurs report higher perceived benefits and more positive impact expectations, while AI-related risk perceptions do not vary by age. Multivariate analyses show that age effects on perceived benefits are context-dependent, whereas age remains a robust predictor of future-oriented impact expectations. The study offers a theoretically grounded and methodologically transparent analysis integrating technology adoption frameworks with entrepreneurial psychology. Practically, it underscores the need for differentiated AI-readiness initiatives that address age-related differences in strategic orientation and preparedness. Future research could further explore the roles of capabilities, industry context, and entrepreneurial experience. Full article
(This article belongs to the Special Issue Artificial Intelligence in Business: Redefining Competencies, Finance, and Entrepreneurship)
23 pages, 1721 KB  
Article
A Complex Leadership Perspective on Generative AI Adoption in SMEs: The Interplay of TAM, TMT, and RBV
by Montserrat Peñarroya-Farell, Maryam Vaziri, Sasha Katalina Soto Rivera and Francesc Miralles
Adm. Sci. 2025, 15(12), 494; https://doi.org/10.3390/admsci15120494 - 16 Dec 2025
Cited by 1 | Viewed by 2734
Abstract
Although Generative Artificial Intelligence (GenAI) is one of the strategic choices for digital transformation in small and medium-sized enterprises (SMEs), its adoption remains constrained by leadership decision-making that must balance strategic aspirations with resource limitations and organizational inertia. Organizational leadership must face the [...] Read more.
Although Generative Artificial Intelligence (GenAI) is one of the strategic choices for digital transformation in small and medium-sized enterprises (SMEs), its adoption remains constrained by leadership decision-making that must balance strategic aspirations with resource limitations and organizational inertia. Organizational leadership must face the dynamic and complex characteristics of digital transformation in the knowledge era. Drawing on Complexity Theory and integrating the Technology Acceptance Model (TAM), Temporal Motivation Theory (TMT), and the Resource-Based View (RBV), this study proposes a conceptual framework reflecting distinct strategic leadership orientations. Following a qualitative approach based on semi-structured interviews with SME leaders and an Interpretative Phenomenological Analysis (IPA) this conceptual framework contributes by reframing GenAI adoption as a complex, nonlinear process rather than a straightforward diffusion model, that includes four strategic profiles (Strategic Adopters, Aspiring Adopters, Opportunistic Adopters, and Operational Stabilizers) that affect a dynamic relationship between three key adoption dimensions: intention, motivation, and resource allocation. SME leaders can benefit from a delimitation of their strategic and operational goals while overcoming adoption barriers. Full article
(This article belongs to the Special Issue Artificial Intelligence in Business: Redefining Competencies, Finance, and Entrepreneurship)
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