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Artificial Intelligence in Socio-Technical Systems: Human, Organizational, and Ethical Dimensions...
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Artificial Intelligence in Socio-Technical Systems: Human, Organizational, and Ethical Dimensions of Intelligent Transformation

A Special Issue of Systems (ISSN 2079-8954) belonging to the section "Systems Practice in Social Science".

Deadline for manuscript submissions: 25 February 2027 | Viewed by 10755

Editor

Prof. Dr. Maja Meško

E-Mail Website
Guest Editor
Department of Human Resources Systems, University of Maribor, Maribor, Slovenia
Interests: management; Industry 4.0; leadership; decision making; socio-technical systems; psychology in management; occupational health and management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) is reshaping organizational systems, decision-making processes and the nature of human work. Beyond its technical capacities, AI acts as a catalyst for systemic change—transforming how people, technologies and institutions interact. This Special Issue invites contributions that examine AI as part of socio-technical systems in which human resource management, leadership and ethical governance play critical roles. We welcome theoretical, empirical and modeling studies that adopt a systems perspective to explore interdependencies between human and machine intelligence, organizational adaptation and the sustainability of AI-enabled transformation. Potential topics include AI-driven decision support in HRM, feedback mechanisms between human performance and algorithmic predictions, systems approaches to ethical and transparent AI use and frameworks for responsible organizational design. The goal is to deepen our understanding of AI as a complex adaptive system, emphasizing the co-evolution of technology and human agency in shaping resilient, equitable and intelligent organizations.

Prof. Dr. Maja Meško
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 single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Systems 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 2400 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

  • artificial intelligence
  • systems thinking
  • socio-technical systems
  • human–AI collaboration
  • HRM systems
  • organizational transformation
  • ethical AI
  • system dynamics
  • intelligent organizations
  • responsible innovation

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Published Papers (7 papers)

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Research

28 pages, 1658 KB  
Article
When AI Joins the Diagnosis: How Doctor–AI Collaboration Shapes Perceived Doctor Responsibility Under Perceived Diagnostic Errors
by Ruxia Cheng, Rui Sun and Wenlong Tang
Systems 2026, 14(9), 1081; https://doi.org/10.3390/systems14091081 - 2 Sep 2026
Viewed by 218
Abstract
Artificial intelligence (AI), especially generative AI, is becoming deeply embedded in healthcare, making doctor–AI collaborative diagnosis a common mode of medical decision-making. This development raises a critical question about responsibility attribution: When people perceive that a diagnostic error has occurred, how does AI [...] Read more.
Artificial intelligence (AI), especially generative AI, is becoming deeply embedded in healthcare, making doctor–AI collaborative diagnosis a common mode of medical decision-making. This development raises a critical question about responsibility attribution: When people perceive that a diagnostic error has occurred, how does AI involvement shape patients’ and observers’ judgments of the doctor’s responsibility? Drawing on responsibility attribution theory, we examine this question across five studies—one event-related potential (ERP) experiment and four scenario experiments. We find that when a diagnostic error is perceived, doctor–AI collaborative diagnosis (vs. doctor-only diagnosis) reduces perceived doctor responsibility by increasing perceived shared agency. This responsibility-reducing effect is weaker when the doctor rejects correct AI advice than when the doctor accepts incorrect AI advice. Theoretically, our findings show that responsibility attribution in human–AI collaboration involves two stages: agent identification and responsibility allocation. This account extends responsibility attribution theory to human–AI collaboration and identifies perceived shared agency as a key psychological mechanism underlying responsibility judgments in these settings. Practically, the findings can inform technology deployment, responsibility communication, and governance mechanisms in hospitals, AI firms, and regulatory agencies. Full article
(This article belongs to the Special Issue Artificial Intelligence in Socio-Technical Systems: Human, Organizational, and Ethical Dimensions of Intelligent Transformation)
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33 pages, 4819 KB  
Article
Evolution and Ecological Activation Mechanisms of Chinese Electric Vehicles’ International Image: A Complex Adaptive Systems Perspective
by Yueqin Wu and Zhipeng Yu
Systems 2026, 14(7), 880; https://doi.org/10.3390/systems14070880 - 22 Jul 2026
Viewed by 517
Abstract
Amid the accelerated global transition toward sustainable electromobility, Chinese Electric Vehicles (EVs) have forged a complex, evolving communication ecosystem across overseas social media platforms. Conceptualizing global digital discourse as a complex adaptive system (CAS), this study integrates CAS theory with Competitive Framing theory [...] Read more.
Amid the accelerated global transition toward sustainable electromobility, Chinese Electric Vehicles (EVs) have forged a complex, evolving communication ecosystem across overseas social media platforms. Conceptualizing global digital discourse as a complex adaptive system (CAS), this study integrates CAS theory with Competitive Framing theory to systematically elucidate the thematic configurations, framework dynamics, and ecological activation mechanisms underlying the international image of Chinese EVs. By integrating unsupervised BERTopic modeling, Large Language Model (LLM) semantic mapping, the Entropy Weight Method (EWM), and Social Network Analysis (SNA), this inquiry operationalizes a comprehensive computational communication framework to mine large-scale behavioral and textual data from YouTube. The empirical findings unveil that: (1) international audience perceptions have broken through the traditional “low-cost manufacturing” stereotype, spontaneously giving rise to a multidimensional, composite cognitive schema centered on smart ecosystems and design experiences; (2) driven by the interplay of rational and irrational user feedback loops, the ecological activation efficiencies across diverse discursive dimensions exhibit pronounced nonlinear variances, characterized by a “strong activation of intelligent ecosystems versus a long-tail stagnation of cost-effectiveness salience”; and (3) positive technological frameworks and negative geopolitical or regulatory risks engage in fierce, adversarial contestation and structural hybridization within a highly volatile network topology, culminating in a unique “dual-core” configuration. Theoretically, this study enriches the scholarly understanding of country-of-origin and corporate brand images through a complex systems lens; methodologically and practically, it offers a high-fidelity, actionable quantitative paradigm for global brand empowerment and targeted cross-border public opinion governance. Full article
(This article belongs to the Special Issue Artificial Intelligence in Socio-Technical Systems: Human, Organizational, and Ethical Dimensions of Intelligent Transformation)
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52 pages, 1187 KB  
Article
Beyond AI Narratives: AI Washing and Organizational Resilience
by Yufei Xia, Jikang Sun, Jiarun Liu, Kun Fang, Huiyi Shi and Na Li
Systems 2026, 14(7), 853; https://doi.org/10.3390/systems14070853 - 17 Jul 2026
Viewed by 809
Abstract
Artificial intelligence (AI) is widely viewed as a technological foundation for organizational resilience. Yet firms may strategically exaggerate their AI-related narratives without corresponding substantive investment. This study examines whether such AI washing is associated with lower organizational resilience. We conceptualize AI washing as [...] Read more.
Artificial intelligence (AI) is widely viewed as a technological foundation for organizational resilience. Yet firms may strategically exaggerate their AI-related narratives without corresponding substantive investment. This study examines whether such AI washing is associated with lower organizational resilience. We conceptualize AI washing as a narrative–investment misalignment within organizational systems, in which symbolic AI claims move ahead of substantive AI investment and capability formation. Based on Chinese A-share listed firms during 2010–2024, we develop a firm-level AI washing index by comparing firms’ within-industry ranking in AI disclosure with their within-industry ranking in actual AI investment. AI disclosure is identified from annual reports using a large language model, while actual AI investment is measured through AI-related software and hardware investments. Using double-debiased machine learning, we estimate a significantly negative association between AI washing and organizational resilience. Economically, a one-standard-deviation increase in AI washing is associated with a decline in organizational resilience equivalent to approximately 3.276% of the average annual change in organizational resilience. This estimated pattern remains stable when we employ alternative variable constructions, replace the machine learning algorithms, adjust the cross-fitting folds, use propensity score matching, and further apply a deep instrumental variable strategy. Mechanism tests based on organizational legitimacy provide evidence consistent with legitimacy-related transmission channels, suggesting that AI washing is associated with lower resilience through weakened pragmatic, moral, and cognitive legitimacy under the maintained mediation assumptions. Further analysis reveals an asymmetric pattern: firms whose AI narratives exceed actual investment experience lower resilience, whereas firms whose actual investment exceeds external narratives exhibit higher resilience. The negative estimated association is particularly evident in high-tech industries, enterprises with established bank-firm ties, and enterprises with higher educational heterogeneity in their top management teams. This study advances research on AI disclosure and organizational resilience by showing that symbolic AI narratives can signal system-level fragility when technological claims are misaligned with substantive capability formation. Full article
(This article belongs to the Special Issue Artificial Intelligence in Socio-Technical Systems: Human, Organizational, and Ethical Dimensions of Intelligent Transformation)
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23 pages, 1425 KB  
Article
AI Usage and Employee Performance: The Dual Roles of AI Self-Efficacy and AI-Enabled HRM
by Yannan Li and Xiaoxiao Geng
Systems 2026, 14(7), 786; https://doi.org/10.3390/systems14070786 - 6 Jul 2026
Viewed by 1246
Abstract
In the context of accelerating artificial intelligence (AI) development, this study explores how AI Usage contributes to employee job performance and innovation performance by activating cognitive and HR system–level mechanisms. Adopting an integrative individual–organizational perspective, this study examines the mediating roles of AI [...] Read more.
In the context of accelerating artificial intelligence (AI) development, this study explores how AI Usage contributes to employee job performance and innovation performance by activating cognitive and HR system–level mechanisms. Adopting an integrative individual–organizational perspective, this study examines the mediating roles of AI self-efficacy and digital human resource management (HRM) practices in translating AI adoption into employee performance outcomes. Survey data were collected from firms located in major Chinese cities (Beijing, Shenzhen, Xi’an, and Zhengzhou), resulting in 750 valid responses for analysis. The results indicate that AI self-efficacy and digital HRM practices function as significant positive mediators, facilitating the conversion of AI adoption into enhanced work performance and innovation outcomes. Theoretically, this study advances knowledge management studies by highlighting the complementary roles of individual cognitive beliefs and HR systems in enabling AI-driven learning and capability development. Practically, the findings suggest that organizations should embed AI technologies in HR systems that foster learning, knowledge utilization, and continuous innovation. Full article
(This article belongs to the Special Issue Artificial Intelligence in Socio-Technical Systems: Human, Organizational, and Ethical Dimensions of Intelligent Transformation)
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23 pages, 1183 KB  
Article
Modeling AI-Assisted Plagiarism in Academic Social Environments Using Qualitative Plausibility Assessment Supports of the Simulation by Large Language Models
by Ihsan Ibrahim, Anak Agung Putri Ratna, Prima Dewi Purnamasari and Naoki Fukuta
Systems 2026, 14(6), 721; https://doi.org/10.3390/systems14060721 - 22 Jun 2026
Viewed by 616
Abstract
This study investigates how AI-assisted plagiarism changes dishonest academic behavior in a socially interactive learning environment under different educational conditions. To this end, this study develops a scenario-based simulation to examine how AI-assisted plagiarism influences dishonest academic behavior in socially interactive learning environments. [...] Read more.
This study investigates how AI-assisted plagiarism changes dishonest academic behavior in a socially interactive learning environment under different educational conditions. To this end, this study develops a scenario-based simulation to examine how AI-assisted plagiarism influences dishonest academic behavior in socially interactive learning environments. The model represents students as autonomous agents embedded in local peer networks who adapt their weekly behavior under academic pressure, institutional intervention, and available cheating options. Two behavioral scenarios are considered: a conventional plagiarism environment, in which agents choose between honest submission and direct copying, and an AI-augmented environment, in which AI-assisted plagiarism is introduced as an additional dishonest strategy. Intervention is modeled through environmental and institutional conditions, specifically detection probability and sanction severity, rather than through direct internal reward manipulation. Q-learning is used as a simplified adaptive mechanism for repeated agent choice. Experimental results show that the possibility of producing and assessing a simulation to see the availability of AI-assisted plagiarism substantially changes the behavioral composition of misconduct by increasing total dishonest behavior and shifting a large share of it toward the AI-assisted category. In the simulation, active intervention reduces dishonest behavior overall but does not eliminate AI-assisted plagiarism as the dominant dishonest strategy in the AI-augmented environment. These observations in the simulation suggest that academic misconduct in the AI era should be understood not only as a problem of deterrence but also as a problem of behavioral adaptation under changing technological and institutional conditions. To support the realism assessment of the simulation design, the study also conducts a structured qualitative plausibility review using multiple large language models under a shared prompt. Across these reviews, the model is judged to be acceptable as a first-stage stylized baseline, while important limitations are identified in agent heterogeneity, social influence depth, and the use of Q-learning as a simplified adaptive heuristic to reproduce the behaviors of actors in there. Full article
(This article belongs to the Special Issue Artificial Intelligence in Socio-Technical Systems: Human, Organizational, and Ethical Dimensions of Intelligent Transformation)
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56 pages, 1948 KB  
Article
Human-Centered Governance of Algorithmic Management in 3PL Warehousing: A DMFF-BN-PCRO Decision Framework
by Filiz Mizrak and Gonca Reyhan Akkartal
Systems 2026, 14(6), 679; https://doi.org/10.3390/systems14060679 - 12 Jun 2026
Viewed by 748
Abstract
Artificial intelligence is reshaping warehouse work through algorithmic task allocation, scanner-based monitoring, KPI feedback, dynamic scheduling, and real-time performance control. Although these systems can improve coordination and operational visibility, they also create governance risks related to fairness, transparency, autonomy, privacy, workload pressure, trust, [...] Read more.
Artificial intelligence is reshaping warehouse work through algorithmic task allocation, scanner-based monitoring, KPI feedback, dynamic scheduling, and real-time performance control. Although these systems can improve coordination and operational visibility, they also create governance risks related to fairness, transparency, autonomy, privacy, workload pressure, trust, and employee resistance. This study develops a human-centered decision framework for prioritizing algorithmic management governance packages in third-party logistics (3PL) warehousing. The main contribution is to translate employee-level governance concerns into a scenario-sensitive decision model that helps managers select appropriate governance packages under different operational pressures. The study uses survey data from 380 warehouse employees to examine key psychological and behavioral mechanisms, including procedural fairness, transparency, system/information quality, autonomy, privacy concern, workload, trust, acceptance, and resistance/disengagement. These survey-supported constructs are then converted into six governance criteria: procedural fairness, transparency and contestability clarity, system and information quality, autonomy support, privacy boundary governance, and workload protection. A seven-expert panel evaluates five governance packages under three scenarios: peak season surge, labor shortage/high turnover, and audit pressure/compliance scrutiny. Methodologically, the framework combines Dynamic Multi-Facet Fuzzy Sets to capture membership, non-membership, hesitancy, engagement, and resistance; Bayesian Network weighting to reflect dependencies among governance criteria; and PCA-based ranking optimization to generate scenario-specific and robust rankings. Comparative validation with SAW and TOPSIS is also used to assess ranking consistency. The findings show that effective algorithmic management governance is not a fixed compliance solution. Transparency, workload protection, autonomy support, privacy boundary governance, and procedural fairness become more or less important depending on the operational scenario. A2, which combines transparency, workload protection, and autonomy support, emerges as the strongest robust package. A1 performs best under labor shortage/high turnover, while A3 performs best under audit pressure/compliance scrutiny. These results suggest that 3PL warehouses should adopt adaptive governance routines that combine explainability, contestability, workload safeguards, privacy boundaries, and employee voice mechanisms. The study contributes to the literature on AI in socio-technical systems by showing how human, organizational, and ethical concerns can be embedded into an interpretable decision framework for responsible algorithmic management in logistics work environments. Full article
(This article belongs to the Special Issue Artificial Intelligence in Socio-Technical Systems: Human, Organizational, and Ethical Dimensions of Intelligent Transformation)
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29 pages, 651 KB  
Article
Public Perceptions of Generative AI in Creative Industries: A Reddit-Based Text Mining Study
by Mitja Bervar, Mirjana Pejić Bach and Tine Bertoncel
Systems 2026, 14(1), 116; https://doi.org/10.3390/systems14010116 - 22 Jan 2026
Cited by 4 | Viewed by 4663
Abstract
The integration of generative AI into creative industries is reshaping how content is produced, evaluated, and distributed. While recent advancements offer new opportunities for automation and innovation, they also raise questions about authorship, authenticity, and professional identity. This study examines public discourse on [...] Read more.
The integration of generative AI into creative industries is reshaping how content is produced, evaluated, and distributed. While recent advancements offer new opportunities for automation and innovation, they also raise questions about authorship, authenticity, and professional identity. This study examines public discourse on generative AI in creative domains through a text-mining analysis of nearly 4000 Reddit posts and comments. Drawing on six relevant subreddits from 2022 to 2025, the research investigates the structure of user engagement, interaction dynamics, and language patterns. It identifies dominant terms and phrases related to AI creativity, explores thematic clusters, and compares discussion styles across key tools such as Midjourney, ChatGPT, Stable Diffusion, and DALL·E. Additionally, it provides a sentiment overview based on automated classification and narrative interpretation. The findings show that Reddit users engage with generative AI not only as a set of technical tools but as a source of cultural, ethical, and creative negotiation. This study contributes to a deeper understanding of how digital transformation in creative industries is shaped by public perception, platform discourse, and evolving community norms. Full article
(This article belongs to the Special Issue Artificial Intelligence in Socio-Technical Systems: Human, Organizational, and Ethical Dimensions of Intelligent Transformation)
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