HCEA: A Multi-Agent Framework for Sustainable Human-Centered Entrepreneurship Based on a Large Language Model

You’re listening to “HCEA: A Multi-Agent Framework for Sustainable Human-Centered Entrepreneurship Based on a Large Language Model,” by Y. Gao, Y. Piao, and D. Xuan. Published in 2026.

Abstract.

Human-centered entrepreneurship considers employee well-being and uses the Sustainable Development Goals as its fundamental pillars. However, existing research predominantly focuses on institutional interventions and fails to provide integrated intelligent solutions for tackling human–machine collaboration issues in the context of digital transformation. Large language models (LLMs) offer potential for affective computing and personalized support, but face critical gaps in ethical governance, privacy protection, and real-time risk intervention in sensitive entrepreneurial contexts.

Our proposed Human-Centered Entrepreneurial Intelligent Agent (HCEA) framework achieves the unified optimization of task utility, empathetic expression, and ethical security by integrating a large language model core fine-tuned via a multi-objective hybrid loss function and a cluster of task- specialized intelligent agents. HCEA integrates retrieval-enhanced generation to ensure suggestion accuracy, a hierarchical data governance system for sensitivity-based privacy protection, and an independent risk detection module for real-time intervention and referral. We build the framework by constructing a hybrid entrepreneurial dataset, design the multi-agent architecture of decision support, emotion understanding and ethical risk tracking, and empirically evaluate both comparisons and ablation experiments.

The results demonstrate that HCEA outperforms five baseline models across six key metrics, including entrepreneurship guidance relevance, emotion recognition, and high-risk recall. This study contributes to the intersection of digital transformation and sustainable entrepreneurship by providing a technically feasible, ethically grounded intelligent framework that empowers enterprises to reconcile efficiency with human-centric values, advancing SDG 8 (decent work and economic growth) and SDG 9 (industry, innovation, and infrastructure).

1. Introduction.

Current entrepreneurial practices are at the intersection of digital transformation and the evolution of social values, transitioning from a sole focus on cost efficiency to-wards an integrated model that encompasses economic, humanistic, and sustainable goals aligned with the United Nations’ Sustainable Development Goals (SDGs). Traditional entrepreneurial theory, rooted in the capital logic of the industrial economy era, prioritizes profit maximization and treats employees as instrumental production factors. However,

this approach is incompatible with the demands of digital economies, knowledge-intensive industries, and global sustainability imperatives. This disconnect manifests as two critical challenges. First, knowledge workers increasingly seek meaningful work, mental health support, and career development, making employee well-being a determinant of talent retention and innovation. Second, regulatory frameworks (e.g., ESG, CSR, algorithmic responsibility) and social expectations require enterprises to balance economic performance with social value, ethical accountability, and SDG alignment—notably SDG 8 (decent work and economic growth) and SDG 9 (industry, innovation, and infrastructure).

Against this backdrop, human-centered entrepreneurship has attracted increasing attention in recent years. This perspective emphasizes ethical stewardship and employee welfare as fundamental principles for achieving sustainable entrepreneurial development, highlighting that entrepreneurial success should not only be measured by economic effi-ciency but also by the well-being and development of individuals within organizations. It draws on Maslow’s needs, Freeman’s stakeholder theory, and Elkington’s triple bottom line to prioritize people, planet, and profit equally. Nevertheless, an “implementation gap” persists between current human-based entrepreneurship research and practical application. Most existing studies focus on institutional intervention, such as flexible work, welfare plans, and organization culture building.

While these studies demonstrate positive relationships between people-centered management practices and organizational performance, they suffer from methodological limitations. Specifically, empirical research relies heavily on qualitative examples or cross-sectional surveys, which lack the ability to establish causal relationships. More importantly, these studies treat technology as an external tool rather than an intelligent system embedded within the entrepreneurial ecosys-tem, failing to adequately address the demands of human–machine cooperation in the digital era.

In recent years, LLMs have offered new opportunities to bridge the gap between humanistic entrepreneurship theory and practice. LLMs such as the GPT series and LLaMA exhibit strong capabilities in natural language understanding and generation. They have evolved from early automatic content generation to handling complex scenarios, including affective computing, personalized tutoring, and interactive dialog. For instance, Park et al. developed generative agents that exhibit believable human behaviors in a simulated envi-ronment; they engage in social interactions, form daily plans, and develop emergent social dynamics, demonstrating the potential of LLMs to simulate complex human interactions essential for applications in emotional support and collaborative decision-making.

They also showed that it can recognize users’ emotional state, offer personalized support, and have potential applications in mental health studies and career development. Multi-agent systems have further extended the application boundaries of LLMs. Through task decompo-sition and collaboration, multi-agent systems can handle high-complexity decision-clinics with multi-dimensional goals. However, three key challenges must be addressed when directly applying LLMs to the highly sensitive context of human-centered entrepreneur-ship. First, most existing studies focus on efficiency and accuracy, while overlooking LLMs’ capacity to address complex ethical issues and enhance employee well-being. Second, privacy protection and ethical governance remain inadequately addressed in applications involving sensitive data (e.g., employee mental health records and career development information).

Although existing technical solutions, such as differential privacy and feder-ated learning only attempt to protect individual privacy, they fail to adequately address organizational-level ethical governance and stakeholder conflicts. Third, the unique nature of well-being-related dialogs necessitates the real-time identification of high-risk cases and professional referrals. Current LLM systems lack reliable mechanisms for handling such edge cases, which may give rise to significant ethical risks. Despite recent advances, existing conversational agents, however, still do not capture long-range contextual depen-dencies and social cues in human interactions, which limits their ability to build sincere and empathetic employee support dialogs.

Moreover, responsible AI and digital entrepreneurship are being widely considered. Many studies have considered AI-enabled entrepreneurial decision support and intelligent advice systems, where they can better process information and optimize decision-making in entrepreneurial processes. While much of the work has focused on technological efficiency or performance, few studies have reported that human-centered values such as employee health, emotion awareness, and ethical responsibility can be embedded in AI-assisted entrepreneurial systems. Building on this discussion, we present HCEA—a multi-agent combination of entrepreneurial decision and emotion recognition and ethical risk detection in a unified framework.

By incorporating human-centric considerations into AI-based entrepreneurial systems, we contribute to the development of responsible AI as well as digital entrepreneurship and provide a new insight into intelligent human-driven management in digital transformation. Through a multi-objective hybrid loss function to fine-tune an LLM, task utility, empathetic expression, and ethical safety, we leverage a joint optimization strategy to ensure the model maintains human values while completing a task. We design a task-specialized agent cluster consisting of career development consultants, holistic people’s partners, and self-efficacy assistants, and use retrieval-enabled generation technology to further improve the accuracy and interpretation of our suggestions.

We con-duct hierarchical data management and independent ethical checking, provide protection policies to the data for different sensitivities, and deploy a high-risk detector for real-time intervention and referral. We adopt a hybrid evaluation framework that integrates an entrepreneurial case database, a mental health dialog database, and agent evaluation bench-marks to systematically evaluate the performance of HCEA in career guidance, emotional support, and task management. Our experimental results indicate that HCEA outperforms the baseline models in terms of key indicators. The main contributions of this study are:

• An HCEA multi-agent model combined with an LLM core with multi-objective loss function fine-tuning and task-specialized agent clusters to jointly optimize task utility, empathy expression, and ethics.

• We combined data governance and an ethics audit module to propose specific protec-tion strategies for data of different sensitivities. We combined a high-risk detection module for real-time intervention and referral, thereby addressing privacy protection and ethics governance issues in sensitive settings.

• We conducted an evaluation on a mixed dataset, including a startup case scenario database, a mental health dialog database, and an intelligent agent evaluation bench-mark. The five base models are compared using six evaluation metrics.

2. Related Work.

2.1. Human-Centered Entrepreneurship Theory.

The theoretical roots of human-centered entrepreneurship can be traced to several influential perspectives in management and social sciences. Maslow’s hierarchy of needs theory suggests that individuals pursue not only economic security but also psychological well-being, autonomy, and self-actualization. In entrepreneurial organizations, this perspective highlights the importance of addressing employees’ psychological needs and personal development rather than focusing solely on productivity or performance out-comes. Freeman’s stakeholder theory further emphasizes that firms should create value not only for shareholders but also for a broader range of stakeholders, including employ-ees, communities, and society. This perspective provides an important conceptual basis for incorporating employee welfare and social responsibility into entrepreneurial decision-making.

In addition, Elkington’s triple bottom line framework proposes that sustainable development should balance economic, social, and environmental value cre-ation. Together, these theoretical perspectives provide the intellectual foundation for human-centered entrepreneurship, which evaluates entrepreneurial success not only by financial performance but also by its contributions to human well-being and sustainable development. Previous research about human-centric entrepreneurship focuses on institu-tional or policy strategies, aiming to improve organizational well-being through human resource practices such as flexible work arrangements, welfare programs, and organi-zational culture development. RN Baptiste et al. reported the benefits of flexible working for employee wellness and demonstrated a positive correlation between human-driven management practices and organizational performance.

However, such research exhibits significant methodological limitations. First, empirical studies rely on qualitative case analyses or cross-sectional surveys, lacking longitudinal tracking and causal inference, which hinders the accurate assessment of long-term impacts. Second, most studies regard technology as an external tool rather than an intelligent system embedded within the entrepreneurial ecosystem. This oversight results in insufficient interdisciplinary integration with ethical governance frameworks. Foss et al. emphasized dynamic capabilities in entrepreneurship but did not address data-driven decision support. Al-though some researchers proposed social entrepreneurship ethics, they neglected the conflict between efficiency improvement and data leakage.

These contradictions present the challenge of human-centered entrepreneurship theory: an over-reliance on institutional intervention and an insufficient understanding of the complexity of human and machine interactions. Therefore, current research urgently needs to bridge the gap between traditional human-centered entrepreneurship theory and computer science and explore a new paradigm that integrates high-efficiency technology with high-level ethical goals.

2.2. Application of Large Language Models.

LLMs are developed through large-scale pretraining on diverse corpora and have demonstrated strong generalization capabilities across tasks. They have become a vital tool for driving affective computing and personalized interventions, while also raising challenges related to reliability, bias, and interpretability. LLMs were initially adopted for efficiency tasks, including automated content generation and information extraction. However, due to technology, LLMs are being increasingly used for more challenging and context-sensitive tasks such as affective computer and conversational AI. In recent years, LLM has been used for personalized tutoring, emotional care, interaction with clients, and intelligent agent workflows. LLMs have shown the capability to handle complex human–computer interactions with employees in terms of emotional care, mental health assistance, and job satisfaction.

Recently, there has been research on “humanizing” LLMs. Kabir et al. showed that “reasons” in LLMs when tuning, instead of just labels, could make the model reasoning similar to human reasoning, which is in large agreement with the design of optimizing model behavior using the empathy loss function in HCEA. Additionally, Zendel et al.’s work shows that advanced LLMs like GPT-4 could achieve similar consistency in terms of human experts on specific annotation tasks like cognitive complexity classification. This provides empirical support for using LLMs to automate some complex judgment tasks in human-centered entrepreneurship, such as assessing the psychological load of task requirements.

Nevertheless, research on the systematic integration of LLMs into humanistic en-trepreneurship and organizational behavior remains relatively limited. Although existing research has made some progress in affective computing and conversational intervention, such as AI-assisted cognitive behavioral therapy (CBT) systems, which have demonstrated effectiveness in improving users’ mental well-being, few studies have systematically explored how to embed LLMs into corporate management practices to enhance employee well-being. In particular, the pathways to achieving ethical intelligence and Sustainable Development Goals at the organizational level remain under-explored.

Despite the growing body of research on AI ethics, existing work has yet to systematically address the unique challenges posed by large language models in sensitive domains such as ethical governance, employee well-being, personalization, and privacy protection. It is yet to be explored how LLM can be integrated into the humanistic entrepreneurship community to consider privacy and ethics issues.

2.3. Privacy Protection and Ethical Governance.

Privacy protection, ethical governance, and decision making are the main constraints on applications of LLMs to sensitive areas, e.g., employee well-being, mental health, and emotional support. While emerging technical tools such as differential privacy and federated learning focus on minimization and access control, their applicability to entrepreneurial contexts remains limited. First, these methods are primarily aimed at protecting individual privacy and do not fully consider ethical governance at the organi-zational level, such as stakeholder conflicts. Second, the special nature of well-being dialogs requires the real-time identification and professional referral of high-risk scenar-ios, yet ensuring reliability and trustworthiness remains a key challenge in deploying LLMs in high-stakes domains.

Floridi et al. emphasized the human interest nature of AI, but technical research often makes ethics less ethical than compliance checks, ignoring cultural and social fac-tors. Zuboff et al. mentioned in their criticisms of surveillance capitalism that data colonization can erode the trust of employees and that the human-based concept for entrepreneurship violates the human nature of the innovation. To address these challenges, previous research has proposed memory-enhanced neural architectures that incorporate explicit memory modules into language models, enabling the storage and re-trieval of long-sequence contextual information. These mechanisms can improve semantic consistency and support stability in long-term interactions.

This research provides technical insights for developing digital partners that can track employees’ mental state and career development in a long-term and coherent manner. Meanwhile, in terms of inter-pretability, several studies have proposed convergence methodologies that leverage inverse problem solving, cognitive modeling, and other methods to make the decision-making process of LLMs more purposeful and sustainable for human decision-makers. This provides an essential theoretical foundation for implementing evidence retrospection and ethical auditing modules within the HCEA framework.

3. Methods.

We propose a HCEA framework that enhances ethical intelligence and employee well-being through LLMs and multi-agent systems (MASSs). Our HCEA systems employ affective computing, task optimization, and ethical management, allowing enterprises to improve employee wellness, career quality, and efficiency through agent interactions and feedback.

3.1. Overall Framework.

HCEA depends on a large LTM Core with dual objective functions that focus on ethics and empathy. Its outer model is composed of three agents each doing a task. The agents share information and collaborate, improving the performance of the overall system.

Modules interact and couple functions with standardized APIs. Hierarchical data manage-ment and independent ethical auditing support all data collection, storage, and processing to respect privacy and ethics. Figure 1 shows the overall architecture diagram of the HCEA framework.

3.1.1. Core Structure: Fine-Tuned LLM Core.

As the core component of HCEA, the LLM is fine-tuned for task-specific requirements to optimize its performance in affective computing, knowledge retrieval, and text gen-eration. The LLM not only generates task-relevant decision recommendations but also provides a unified semantic processing interface for all intelligent agents, thereby ensuring semantic consistency in cross-agent interactions.

3.1.2. Peripheral Structure: Task-Oriented Intelligent Agent Cluster.

Using LLM core calls and scenario-based prompt engineering, the LLM generalizes well to entrepreneurial and well-being problems with the following three agents:

• Career Development Consultant: He provides employees with long-term personalized career path planning, skills matching, and suggestions based on their strengths to make them more flexible and satisfied with their job.

• Holistic Well-being Partner: He improves employee mental health using affective computing, stress detection, and intervention via CBT or mindfulness for stimulating conversations. This agent should integrate a well-being risk-detection module.

• Autonomous Efficiency Assistant: By splitting tasks into well-defined steps, low-value tasks, and automating tasks, workers focus on more creative and meaningful work, resulting in increased work autonomy and creativity, rather than efficiency.

3.1.3. Underlying Support: Data Governance and Ethical Auditing.

This paper contends that ethics should not be a luxury, but rather a prerequisite in every stage of the data flow. To handle sensitive data, it adopts data governance and ethical grading directly from the ground up and translates ethical principles into concrete technologies in order to maximize data precision and transparency in ethical audits. The tiered data governance is defined as three levels: A, B, and C. Figure 2 shows the hierarchical data governance flowchart.

• Category-A data (high privacy/well-being dimension): Encompasses extremely sensi-tive information such as mental health conversations. For this type of data, this study employs end-to-end encryption and differential privacy as dual protections. Model training uses only anonymized data to ensure that the original data remains within the domain. From a legal and ethical perspective, Category-A data is absolutely prohibited from use in any form of performance evaluation or personnel decision-making, and its use requires a special audit by an independent ethics committee.

• Level-B data (sensitive/developmental data): Personal life, work experience, etc. Entity anonymizations, such as replacing names or departments with random strings. The anonymized data can be used for organizational-level statistics analysis, but it must be done via a federated learning framework, as the original data will not be stored locally.

• Level-C data (efficiency/publicly available dimensions): Task workflows, project logs, and other operational records. Such data can be utilized for productivity and effi-ciency evaluation. However, assessment algorithms must adopt a multi-dimensional fusion model, and Level-C data shall not be used independently as the basis for evaluating individual well-being. This constraint prevents the reduction in indi-viduals to mere efficiency metrics and avoids objectification or dehumanization in organizational evaluation.

Level-A data is encrypted end-to-end, anonymized, and can only be used for model evaluation in training, but not parameter updating; the only content from Level A contains only local feature statistics (e.g., sentiment distribution) to fit the model. Level-B data undergoes entity desensitization and field well-being and can be used for LoRA low-rank fine-tuning. Level-C data can be directly used for task optimization. Furthermore, an independent ethical mechanism is established to audit the use of sensitive data, and a transparent ethical review process is implemented. The auditing system automatically scans call logs every 24 h. Imperfect calls (cross-level access) are always blocked and written to an immutable chained log such that all data collection and processing satisfy ethical demands.

3.2. Search Enhancement and Evidence Retrospection.

To enhance the accuracy of generated content and avoid illusion, the HCEA integrates Retrieval Enhanced Generation (RAG). RAG combines retrieval from external knowledge sources (e.g., psychology, management, and business ethics) and the generative capabilities of LLM, which are evidence-based for propositional suggestions. The retrieval strategy is updated based on dialog context and agent type, e.g., the Holistic Well-being Partner Agent is focused on retrieving mental health intervention strategies and the Career Development Consultant Agent is tasked with retrieving business cases and career development strate-gies.

Unlike traditional generative models, for each key output yi of the LLM, the system simultaneously generates an evidence-backing set σ = {(k1, c1), (k2, c2)......}, where ki denotes the index of the retrieved knowledge entry and ci represents the confidence score assigned by the LLM to support its output for that entry. This significantly enhances the transparency and auditability of the system.

3.3. LLM Fine-Tuning and Objective Function.

To embed the concept of human-centered entrepreneurship into model behavior, HCEA introduces a multi-objective hybrid loss function for supervised fine-tuning and reinforcement learning in the LLM:

L = λu Lutility + λe Lempathy + λr Lreg

Task utility loss (Lutility): Measures the accuracy and effectiveness of the model in completing a specific task (such as path planning, task decomposition). It is calculated using standard negative log-likelihood based on a high-quality expert-annotated dataset:

Lutility = −i∑ log P(yi |xi, θ) i

Empathy loss (Lempathy): A key ethical driver. This loss is learned under supervision based on manually labeled dialog empathy levels and relationship quality metrics. Mean squared error (MSE) or cross-entropy loss is used to minimize the gap between the model output and human expert empathy assessments.

N Lempathy = 1 2  ∑ Empathy Model,j − Empathy Human,j N j=1

Regularization terms (Lreg): Used to prevent the model from overfitting sensitive data, reduce potential attack surfaces, and ensure the model’s robustness. These include weight decay (L2 Regularization) and adversarial training models to enhance the model’s safety margin.

Weighting coefficient (λu, λe, λr): These coefficients determine the model’s theoretical importance of the three loss terms. They are determined during the model development phase through a grid search on the ethical validation set and Bayesian optimization to achieve the optimal balance between utility maximization and ethical compliance.

The HCEA approaches use multi-objective optimization with three objectives (task util-ity, empathy, and safety) representing different aspects of human-centered entrepreneurial support. Task utility measures how good the generated advice is when dealing with entrepreneurial decision problems. Empathy measures how well a system adapts to emo-tional signals and reacts to stressful behaviors in a psychologically sensitive environment. Safety measures whether a produced output avoids harmful or ethically dangerous recom-mendations. In practical business applications, the objectives may interact in other ways. For example, recommendations which maximize decision efficiency may ignore emotional stress experienced by team members and highly empathic answers may reduce decision efficiency. Therefore, the model introduces weighted coefficients to balance these objectives while training.

During training, the weight coefficients are adjusted through Bayesian optimization on an ethical validation set containing both decision-making and emotional interaction scenarios.

Recent studies suggest that aligning AI systems with human values is inherently a multi-objective problem. Ethical constraints, task performance, and social values often represent competing objectives that cannot be reduced to a single scalar utility function. Therefore, multi-objective optimization has been widely proposed as a practical approach for balancing efficiency with ethical considerations in human-centered AI systems. More recent studies on aligning large language models with human preferences demonstrate that model alignment is inherently a multi-objective optimization process. For example, Ouyang et al. show that reinforcement learning from human feedback enables language models to si-multaneously optimize multiple alignment dimensions, such as helpfulness, harmlessness, and truthfulness, which often involve trade-offs rather than a single unified objective.

These findings provide strong empirical evidence that balancing multiple objectives can sig-nificantly improve overall system performance compared to single-objective optimization. Therefore, adopting a multi-objective loss function in HCEA is both theoretically grounded and practically justified.

3.4. Risk Detection and Referral Mechanism.

An independent, non-LLM-driven risk detection module is integrated into the output pipeline of the autonomous efficacy assistant agent to enable timely intervention when the dialog context is sensitive.

• Risk Control Module: This is a lightweight, clinically tuned classifier designed explic-itly for the real-time assessment of high-risk disclosures in user-input text. It outputs a risk probability Prisk.

• Intervention Triggering and Referral Process: If Prisk exceeds the preset safety threshold τ, the system will immediately interrupt, automatically respond and trigger an offline, professional human intervention process. The referral process itself is not operated by any human or performance evaluation system in the company and is without risk to user privacy and integrity. Its process ensures the principle of minimal disclosure, disclosing only absolutely necessary information to the clinical referral team.

The risk detection module is implemented as a lightweight transformer-based text classifier independent from the LLM generation pipeline. The classifier is trained on an-notated psychological risk datasets derived from CLPsych and supplementary mental health dialog corpora. The model architecture consists of a Sentence-BERT encoder fol-lowed by a fully connected classification layer that outputs the probability of high-risk disclosure. Training labels include categories such as self-harm risk, severe emotional distress, and crisis-level expressions. The safety threshold τ is determined through 5-fold cross-validation on the validation set, balancing recall and false-positive rates to prioritize early detection of high-risk scenarios.

3.5. Experimental Pipeline.

To clarify how HCEA is implemented and evaluated, we present a complete experi-mental pipeline ranging from data preprocessing to agent-level response generation. The pipeline comprises five stages. First, heterogeneous data such as entrepreneurial case cases, mental health dialogs and agent evaluation standards are collected and unified using a predefined preprocessing procedure: text cleaning, token normalization, bilingual align-ment, and annotation check. Second, the clean data are routed through the hierarchical data management system where information is classified into three types (A, B, and C) and each class follows different privacy protection and training policies to meet ethical and privacy constraints.

Third, processed data is used to fine-tune the LLM core using the proposed multi-objective hybrid loss function, optimize task utility, empathetic in-teraction, and ethical safety. Fourth, user input is processed by the multi-agent system during inference. Each agent invokes the basic LLM using prompt templates and retrieves external knowledge using the RAG module to produce evidence-specific responses. Finally, all submitted responses pass through an independent risk detection module and receive users. If high-risk signals are detected, the system interrupts the automatic interaction and activates a human referral process. This pipeline ensures that HCEA maintains consistency between ethical control, multi-agent collaboration, and evidence-based decision support.

4. Experiments.

4.1. Dataset.

We construct a hybrid evaluation dataset using multi-source public data, ensuring the accuracy of non-intrusion and reproducibility. The data sources are three main phases: an entrepreneurial case database, a mental health dialog database, and an agent evalua-tion benchmark. Specifically, the entrepreneurial case database utilizes Harvard Business Review cases, a Chinese entrepreneurial policy text database (12,000 policy clauses and interview transcripts), and a Trello project management database (multi-dimensional test material for planning and task execution). The mental health dialog database uses the CLPsych 2022 dataset (50,000 emotion-risk-labeled dialog records) converted to Chinese and adds the Warra Well-being Scale and PERMA model as theoretical guides. The agent evaluation baseline relies on the few commercial standards such as AgentBench for veri-fying basic features.

All data underwent unified preprocessing, where text cleaning was completed by loading the domain dictionary using Python’s (version 3.8) jieba word seg-mentation and filtering with the Harbin Institute of Technology’s Python list. Missing annotations were completed by domain experts (Kappa ≥ 0.85). Finally, the dataset was divided into a stratified sample of a 7:2:1 ratio to ensure consistency in sample distribution and the robustness of the evaluation.

In order to train heterogeneous datasets, hybrid datasets are mapped using a task-aligned mapping strategy. For example, entrepreneurial case datasets are mapped to the task utility objective, in which the model learns the strategy and organization. Mental health dialog datasets are mapped to the empathy objective, which allows the model to recognize emotion signals and produce the response. Agent benchmark datasets are used to evaluate structural reasoning ability and task decomposition capabilities. During fine-tuning, samples from the three datasets are mixed in mini-batches using sampling ratios so that no data source dominates and the model learns to balance models of entrepreneurial reasoning, emotion understanding and task execution.

Overall, the three datasets serve complementary roles in training and evaluation rather than being directly merged, forming a task-aligned framework that supports balanced learning across utility, empathy, and reasoning objectives.

Since the dataset used in this study includes both Chinese and English corpora, and tasks such as emotion recognition and ethical judgment are known to be culturally dependent, cross-cultural alignment is needed in model training. In order to deal with the problem, we trained cross-lingual semantic alignment during preprocessing. We used a unified text encoding to map multilingual dialog to the same semantic representation space and filtered the samples with low semantic consistency to avoid the unintentional overlap. During training, we did not treat the Chinese and the English data as equally distributed sources, but used task-based weighting. For example, in emotion recognition tasks and empathy generation tasks, dialog data with explicit emotions are sampled more frequently. Conversely, business decision tasks have entrepreneurial scenario data.

This task-aware sampling scheme preserves cross-cultural generalization but can eliminate biases generated by heterogeneous data sources.

4.2. Comparison with Baseline.

This study selected five baseline models for comparative analysis. Specifically, PRISM, an interpretable bias-aware representation framework based on cross-encoder architectures, was proposed by Sun. GAIN, a gated adaptive feature interaction net-work designed to capture complex feature dependencies, was developed by Liu. AIET, originally introduced by Terunuma et al. in the context of immune enhancement ther-apy, is adopted in this study as the inspiration for adaptive and individualized system modeling. In addition, LLM-based human–agent collaboration and interaction systems (LLM-HAS) are incorporated as a representative paradigm grounded in hybrid intelligence theory, as discussed by Dellermann, emphasizing human-in-the-loop decision-making and collaborative intelligence.

Finally, AgentVerse, a multi-agent framework that facilitates collaborative interactions among large language model-based agents, was proposed by Chen. These models collectively span a range of capabilities, from low-level semantic matching to high-level multi-agent decision-making. While these models were not designed for human entrepreneurship or employee welfare problems, they are well suited to multi-agent coordination, context and human–AI interaction and can serve as comparison points for complex socio-technical systems.

PRISM excels at extracting interpretable latent biases through controversial topic min-ing and a political-aware cross-encoder. The authors validated its proficiency in capturing implicit stance signals in textual data, a capability critical for analyzing employee feedback to identify unmet psychological needs or ethical risks in humanistic management. This aligns with the task’s requirement for a nuanced understanding of human attitudes. GAIN employs a gated adaptive mechanism to prioritize high-value feature interactions, with empirical validation demonstrating its efficiency in processing high-dimensional heteroge-neous data and superior performance compared to state-of-the-art models in click-through rate prediction tasks.

This core capability directly translates to the integration of multi-source data (e.g., task workflows, employee emotional feedback) for employee well-being assessment and entrepreneurial decision support, thereby justifying its suitability as a comparative benchmark. AIET, rooted in personalized cancer immunotherapy, dynam-ically optimizes strategies based on individual status. Its validated efficacy in targeted intervention aligns with the individualized well-being support needed in human-centered entrepreneurship, demonstrating the applicability of tailored frameworks to humanistic management. LLM-HAS enhances reliability through human–agent synergy, matching HCEA’s need to integrate entrepreneurial expertise and employee feedback.

Agent Verse efficiently decomposes complex tasks via dynamic multi-agent collaboration, directly supporting the evaluation of HCEA’s collaborative capabilities in decision support and well-being management.

In summary, these models’ proven strengths in heterogeneous data integration, im-plicit signal recognition, personalized adaptation, and multi-agent collaboration are inher-ently compatible with the target tasks, fully justifying their use as comparative benchmarks.

4.3. Evaluation Indicators.

To comprehensively evaluate the effectiveness of the HCEA framework, this study employs a combined approach of expert evaluation and LLM-based automated evaluation (LLM-as-a-judge). Recognized evaluation criteria from the fields of human-centered en-trepreneurship and large language models were selected. These indicators measure the core capabilities of the HCEA multi-agent framework, including personalized career guidance, emotional well-being, and improved work efficiency. Specific evaluation indicators include:

• PPL: Measures the modeling ability of a language model. The lower the PPL value, the better the model performs in generating task-relevant and context-consistent suggestions.

N ∑N 1 i=1 −lnP(xi) PPL = e

• BLEU-4: Evaluates the consistency between the generated content and the reference text, using the BLEU algorithm with n = 4.

  4 ∑ BLEU = BP × exp n=1 ωn logpn

• Well-being Entrepreneurship Guidance: We invited entrepreneurship management experts to assess the practicality of career development recommendations using a 5-point Likert scale, with weighted mean scores calculated.

• Emotion Recognition F1-score: This is a core indicator for the welfare companionship agent, using macro F1 to calculate the accuracy of multi-category emotion recognition.

F1 = 2 × Precision × Recall Precision + Recall

• Task Structuring Accuracy: A typical task efficiency indicator for automated in-telligent agents is the percentage of tasks that extract time nodes, priorities, and dependencies correctly.

• High-risk Recall Rate: Measures the effectiveness of the high-risk detection module and calculates the proportion of high-risk scenarios correctly identified by the model.

4.4. Parameter Configuration.

The equipment used in this study was NVIDIA A100 GPUs, manufactured by NVIDIA Corporation, Santa Clara, CA, USA. The experiment is executed on a Linux server. The nodes used were Inspur NF5488A5 4 NVIDIA A100 GPUs (40GB VRAM/card), 2 Intel Xeon Platinum 8369B CPUs (32 cores/CPU), 512GB DDR4 memory, and 2TB NVMe SSDs to support large model training and inference. The system parameters used for the LLM core are Llama 3-70B with AdamW as optimizer using PEFT-LoRA (rank 16, learning rate 0.00005, batch size 8, 8 training times). The multi-objective loss weights are set to λu = 0.4, λe = 0.3 and λu = 0.3. In the RAG-E mechanism, the BM25 parameters are k1 = 1.2 and b = 0.75. Sentence-BERT uses all-MiniLM-L6-v2 to retrieve the top-5 entries. The high-risk threshold is 0.7 (determined through 5-fold cross-validation).

When the threshold is triggered, an external ethics review agent is called for secondary judgment. The experiment employs stratified validation and 5-fold cross-validation, and all parameters are optimized through a grid search on the validation set.

5. Results.

5.1. Performance Comparison.

The experimental results demonstrate that the proposed HCEA system consistently outperforms state-of-the-art baselines for several metrics, and may provide additional benefit by combining task utility, empathy expression, and ethical risk prediction to support AI-assisted entrepreneurship. In terms of response quality, it achieves a PPL of 0.684 and BLEU-4 of 0.894, which represent improvements of 2.56% and 3.71%, respectively, from the best baselines. A lower PPL indicates that the generated responses are more stable and context-oriented, and a higher BLEU-4 means that they are more semantic in the sense of reference answers. The latter suggests that the system is able to generate responses that are both linguistically clear and semantically meaningful and can be interpreted as a reliable source for future entrepreneurial guidance.

Beyond this improvement in response quality with the model, the model can provide better advice for entrepreneurial decision support tasks. The entrepreneurship guidance relevance score is 0.846, which outperforms LLM-HAS by 3.68% and the task structure accuracy is 0.888, which is 2.42% higher than Agent Verse. These values suggest that the model is capable of producing more logically structured and contextually appropriate suggestions for startup situations. For instance, in practical environments, these values may translate into more informed guidelines for entrepreneurs on operational choices or when they respond to uncertain market conditions. In other words, the system could collect information to structured decision paths to avoid informational gaps that could arise in complex entrepreneurial decisions.

The proposed model also provides significant benefits in emotional understanding and risk detection. It achieves an emotion recognition F1-score of 0.826, a 6.17% improvement over AIET and a higher recall rate of high-risk cases increased to 0.947, which is higher than the baseline by 11.15%. These results show that empathetic modeling and ethical risk monitoring could be better employed in the framework to detect emotional stress or ethical dilemmas within business communication. In practical entrepreneurial environments, such capabilities could detect situations when employees or founders are emotional pressure or ethical stress and recommend intervention. Table 1 presents a performance comparison of various models across multidimensional metrics.

To provide a more concrete illustration, consider a growing startup that expands quickly. As workload increases, there may be early signs of stress in internal commu-nication, such as reduced participation, negative sentiment, and delayed task answers. A system that is high risk may spot early signs of burnout potential, and point them to the founder or manager, while providing timely intervention (e.g., reducing team work-load, seeking professional help, avoiding crisis). Similarly, an increasing importance of entrepreneurship guidance may help identify strategic blind spots. In particular, HCEA may detect patterns of over-expansion risk (e.g., a shift from hiring pace to operation capacity), and prompt decision makers to re-examine growth plans.

Besides expansion risk, HCEA may also uncover other latent issues such as a shift from team responsibilities to own competencies or communication delays within functional units. Larger groups of startups can be identified by highlighting these less visible constraints, enabling entrepreneurs to remedy structural and managerial shortcomings that may otherwise be ignored, so model performance scores can be translated into actionable insights that directly support sustainable entrepreneurial decisions.

To verify the impact of weight coefficients in the multi-objective loss function on model performance, three sets of comparative experiments were designed: the original scheme (λu = 0.4, λe = 0.3, λr = 0.3), the utility-first scheme (λu = 0.6, λe = 0.2, λr = 0.2), the empathy-first scheme (λu = 0.2, λe = 0.6, λr = 0.2), and the ethics–safety-first scheme (λu = 0.2, λe = 0.2, λr = 0.6). The results are shown in Figure 3. The original scheme has the best overall performance, with an entrepreneurship guidance relevance score of 0.846, an emotion recognition F1-score of 0.826, and a high-risk recall rate of 0.947. While the utility-prioritized scheme achieves a slight improvement in task structuring accuracy (0.895), it leads to significant declines in the emotion recognition F1-score (0.782) and a high-risk recall rate (0.889).

The empathy-prioritized scheme attains the highest emotion recognition F1-score (0.841) but results in a reduced task structuring accuracy (0.832). In contrast, the ethics–safety-prioritized scheme yields the optimal high-risk recall rate (0.963), yet exhibits a noticeable drop in entrepreneurship guidance relevance (0.798). Weight-efficient experiments also highlight the importance of balancing efficiency-oriented and human-centered goals. The higher the weight assigned to task utility, the better the structural reasoning performance but the less empathy and risk-detection performance can be learned. This indicates that if the task efficiency is only optimized, the broad human-oriented goals of entrepreneurial support systems can be improved by exploiting multi-objective optimization.

These results, together with others, are in line with recent research on AI-based social interaction and decision support. Previous work proposed that large language model-based agent architectures can model complex social behaviors and interactions in dynamics. Other studies showed that emotional understanding mechanisms can improve the per-ceived helpfulness and responsiveness of conversational AI systems. Compared to these techniques, HCEA extends existing work by jointly optimizing entrepreneurial guidance, emotional understanding, and ethics in a unified architecture. Meanwhile, retrieval-augmented generation is comparable with the previous research, showing that grounding language models with external sources can improve the factual confidence and contextual relevance of generated responses.

These results provide empirical support for the proposed human-centered multi-agent framework.

The three correlation matrix heatmaps in Figures 4–6 systematically reveal the intrin-sic connections among HCEA’s model performance, hierarchical data governance, and employee well-being dimensions. This analysis provides quantitative support for the rationality of the framework’s human-centered design. Correlation values range from −1 to 1: values closer to 1 indicate a stronger positive correlation, values closer to −1 a stronger negative correlation, and 0 denotes no correlation.

Figure 4 illustrates the collaborative optimization characteristics among the model’s technical indicators. Specifically, PPL and BLEU-4 show a significant negative correla-tion with a coefficient of −0.75, indicating that the improvement in the coherence of the model-generated content (a lower PPL indicates a better performance) directly drives the optimization of the consistency between the generated content and the reference text (a higher BLEU-4 indicates a better performance). The correlation coefficients between entrepreneurship guidance relevance and RAG evidence retrieval accuracy, as well as between emotion recognition F1 and high-risk recall rate, reach 0.91 and 0.88, respectively, verifying the synergistic enhancement mechanism of the core technical modules.

Figure 5 confirms the scientific nature of the hierarchical governance strategy: the internal correlation coefficient of Class-A high-privacy data (dialog text, risk labels) is 0.92, and that of Class-B sensitive data (planning data, skill results) is 0.89. In contrast, the correlation coefficients between data of the same level and cross-level data are only 0.10–0.41. This indicates that hierarchical governance can effectively achieve the isolated protection of high-privacy data while meeting the data collaboration needs in business scenarios (the correlation coefficients between cross-level interaction frequency and Class-B data range from 0.65 to 0.68).

Figure 6 shows that the technical optimization of HCEA can be directly transformed into the improvement of humanistic value: the correlation coefficients between mental health and stress relief, career satisfaction and career adaptability, as well as work autonomy and creativity level are 0.93, 0.87, and 0.82, respectively. Combined with the correlation logic between technical indicators and well-being dimensions in Figure 4 (e.g., the correlation coefficient between emotion recognition F1 and mental health is 0.93), it is confirmed that core modules such as the “Holographic Well-being Partner”, “Career Development Advi-sor”, and “Autonomous Productivity Assistant” can accurately empower the improvement of employee well-being.

5.2. Ablation Experiment.

To verify the effectiveness of each core module of HCEA, ablation experiments were designed: HCEA-full (complete model), HCEA-w/o RAG (removal of search enhancement module), HCEA-w/o RiskDet (removal of risk detection module), and HCEA-w/o Eth-icAudit (removal of ethics audit module). The results are shown in Table 2. After the removal of the search optimization module, entrepreneurship guidance fell from 0.846 to 0.765 (9.57% drop) and well-being-4 dropped from 0.894 to 0.821 (8.17% drop). After the removal of the risk detection module, well-being risk recall declined from 0.947 to 0.723 (23.65% drop), and other indicators continued to be stable. After removing the ethics audit module, the quantitative indicators did not change significantly.

However, an anonymous review of 200 randomly selected generated content items by three domain experts revealed that 23.7% of the content had ethical controversies (such as privacy risks and conflicts of interest). At the same time, this proportion was only 4.2% in the complete model. The inter-expert consensus coefficient (Kappa) is 0.82, indicating that the evaluation results have high reliability.

6. Discussion.

In this section, we discuss the theoretical implications, practical relevance, deployment feasibility, and limitations of the proposed HCEA framework.

6.1. Human-Centered Entrepreneurship.

Human-centered entrepreneurship deals with the integration of economic performance with human well-being and developing people in entrepreneurial organizations. While earlier studies tackled this goal through institutions or leadership practices, in HCEA, the system demonstrates how computational systems can support this model. Combining entrepreneurial reasoning agents with emotion recognition and risk detectors, the net-work measures both the feasibility of entrepreneurial strategies and their psychological and ethical consequences for investors. In practice, it can not only predict the optimal business performance as part of the economy or market opportunities, but also it can detect emotional signals in discussions, detect high-risk situations due to stress or ethics conflicts, and re-implement its recommendations accordingly.

This kind of decision support mechanism, therefore, provides a platform to entrepreneurs that takes into account the efficiencies, good behavior, and ethical demands of business practitioners. In this sense, the framework takes into consideration the notion of human-centered entrepreneurship in an AI-assisted advisory environment instead of acting as a standard principle. For an example to illustrate the difference between efficiency and human-centered models, consider an efficient entrepreneur in a tight time-stamped startup environment. An efficiency-based AI may recommend tasks and working time to maximize short-term results, but this may only increase employee stress and can potentially lead to burnout.

On the other hand, HCEA would detect early stress signals from employees and recommend improvements, such as reducing workload, increasing time-restamped times, or providing external help. Instead of trying to get immediate performance gains, this AI focuses on the long-term health of the team by balancing productivity and employee well-being. This example shows how human-based AI shifts decision logic from short-time optimization to sustainable organizational health.

6.2. Intelligent Human-Centered Management.

In theory, the HCEA model could contribute to a discussion on intelligent human-centered management. Traditional mechanisms to protect employees’ well-being in en-trepreneurial environments rely on institutional measures such as organizational policies, ethical policies or leadership standards. These processes are still important; however, they often happen retrospectively and are heavily subject to human attention. HCEA suggests that the smart system can play a complementary role in monitoring and mediating en-trepreneurial decisions. By embedding empathy evaluation, ethical risk detection and multi-objective optimization in the decision system, the system can continuously assess performance as well as human impact.

Rather than institutional management, the dig-ital systems can serve as an adaptive governance system that can guide entrepreneurs in balancing efficiency versus human-centric considerations in real time. This suggests a wider theoretical model in which AI systems may act as supporting agents in human-based management systems.

6.3. Practical Implications.

Apart from its theoretical result, HCEA can also be useful for entrepreneurs. Startup investors often make difficult decisions during uncertain, time-critical periods with limited managerial experience. AI-based advisors such as HCEA can serve as complementary decision systems for entrepreneurial advisors to detect potential gaps in strategies. For example, by linking entrepreneurial reasoning with emotion recognition and ethical risk monitoring, an advisor can highlight cases in which decisions can easily generate stress, ethical issues, or communication failures, which can drive the entrepreneur to reconsider organizing or communication in the early stages of problems. In this sense, it may be useful especially for startups, digital entrepreneurs, and organizational coaches where founders seek guidance at critical decision stages.

6.4. Deployment Feasibility.

We performed the tests on a high-performance computing device with multiple GPUs, which may raise concerns about the viability of deployment for small and medium startups. On the ground, there may be a number of ways to reduce computation costs. Model compression, parameter pruning, knowledge distillation, and low-rank adaptation can save inference costs while keeping most of the model performance. Cloud-based AI services provide scalable deployment options where organizations can view large-scale language models by API without requiring local high-end hardware. In such environments, the HCEA can be used as a modular advisor service integrated into entrepreneurial support systems. In the future, we might explore lightweight implementations of the system and assess their cost effectiveness in real entrepreneurial environments.

6.5. Limitations.

Despite the promising findings, several limitations should be acknowledged. First, large language models may generate positive answers even when accuracy is unknown. When used in advisory applications, users can be reluctant to make new suggestions with-out some careful analysis. Therefore HCEA should be regarded as a decision support tool rather than a human judgment tool. Second, the long-term organizational impact of AI partners in teams of companies is little understood. Interacting with AI advisor agents may influence organizational culture, decisions, and even the psychological reliance on automated advice. For example, mental health disclosure practices may differ in collectivist and individualist cultures. In collectivist cultures, employees may be less likely to explicitly report psychological distress due to a group balance or social stigma.

In individualist cultures, however, their self-disclosure might be expected and accepted. Consequently, the same model results could be interpreted or accepted in cultural environments. Sim-ilarly, organizational structure could influence how AI recommendations are perceived and implemented. In highly hierarchical organizations, employees might more rely on authority-driven decisions than on AI suggestions without guidance by a managerial leader. However, in flat organizations, employees could be more willing to directly engage with AI-generated guidance. These differences suggest that adaptive mechanisms that reflect ethical recommendations from cultural norms and organizational environments should be adapted. Longitudinal studies will be needed to understand how AI-assisted entrepreneurial environments change as time goes by and how they may be designed to preserve human autonomy.

7. Conclusions.

The HCEA framework proposed in this study aims to resolve the inherent contradic-tion between “efficiency first” and “human-centered care” in digital entrepreneurship. An important advantage of this model lies in its multi-objective loss function. This design safeguards a baseline level of task efficiency while simultaneously incorporating ethical and empathetic considerations; by building intelligent agent groups (e.g., career design consultants, holistic well-being partners) with human-centered employees, it decomposes employees’ requirements into concrete and practical service functions; and through a hier-archical data governance mechanism, privacy protection and ethical constraints are set as insurmountable rigid constraints in the data flow process.

We see from the experimental results that HCEA obtains a higher performance than previous baselines, showing intu-itively that complex human-centered values can be achieved using more complex technical decomposition and implementation. From a theoretical perspective, this achievement also enables humanistic entrepreneurship research to transcend the limitations of traditional “static institutional design” and expand into a dynamic and intelligent perspective that em-powers technology. It also reveals a vital possibility: artificial intelligence is no longer just a tool to improve efficiency but can also become an empowerment platform for organizations to convey humanistic care and help managers strengthen empathy and strategic judgment.

In practical applications, the HCEA framework offers startups concrete and feasible reference points. Its multi-objective collaborative optimization solves the common industry concern of making efficiency less high-end than human factors; its unified privacy manage-ment problem directly aligns with data security rules in terms of saving compliance costs and protecting sensitive employee information (e.g., mental health and career planning); and its proactive risk prevention mechanism (such as automatically triggering human intervention in high-risk scenarios) avoids ethical risks that may arise from autonomous decision-making by intelligent systems.

These designs collectively constitute a complete engineering blueprint that startups can directly apply to build employee support tools or optimize organizational management processes, eliminating the need to construct related systems from scratch and significantly reducing the barriers to technology implementation and trial-and-error costs.

This study also has certain limitations. First, the long-term effect of HCEA, particularly its long-lasting impact on organizational culture, needs to be further tested in long-duration case studies in real businesses. Second, ethical norms vary among various cultures and workers’ humanistic care varies; therefore, the system may need more flexibility and cross-cultural adaptations in the future. Building on these limitations, there are some directions for future work. First, future work may perform large-scale field experiments within real companies to see if the sustained impact of human-centered AI systems for employee well-being, culture, and decision quality remains persistent.

Second, because ethical standards and human-based management practices differ between cultures and institutions, future research may also explore how human-driven entrepreneurial agents adapt to other ethical expectations and social situations. Third, with the rapid development of large language models and intelligent agent technologies with sophisticated reasoning capabilities, long-term memory and explainable AI approaches, human-centric AI systems will be more transparent, adaptive, and trustworthy. These directions could improve HCEA from an experimental prototype to a more powerful and widespread intelligent management framework.

data curation, D.X. and Y.P.; writing—original draft preparation, Y.G.; writing—review and editing, D.X. All authors have read and agreed to the published version of the manuscript.

Funding: This research was funded by the 2025 Science and Technology Research Project of the Department of Education of Jilin Province (NO. JJKH20250410KJ) and the 2026 Social Science Research Program of the Department of Education of Jilin Province (NO. JJKH20260791SK).

Institutional Review Board Statement: Not applicable.

Informed Consent Statement: Not applicable.

Data Availability Statement: The data underlying this article will be shared on reasonable request to the corresponding author.

Conflicts of Interest: The authors declare no conflicts of interest.

1. Abraham, H.M.

A theory of human motivation. Psychol. Rev. 1943, 50, 370. [CrossRef]

Edward, R.F. Strategic Management: A Stakeholder Approach; Cambridge University Press: Cambridge, UK, 2010; pp. 1–2. 2.

John, E. Towards the sustainable corporation: Win-win-win business strategies for sustainable development. Calif. Manag. Rev. 3. 1994, 36, 90–100.

4. Park, J.S.; O’Brien, J.; Cai, C.J.; Morris, M.R.; Liang, P.; Bernstein, M.S.

Generative agents: Interactive simulacra of human behavior. In Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology, San Francisco, CA, USA, 29 October–1 November 2023; pp. 1–22.

Shum, H.Y.; He, X.D.; Li, D. From Eliza to XiaoIce: Challenges and opportunities with social chatbots. Front. Inf. Technol. Electron. 5. Eng. 2018, 19, 10–26. [CrossRef] 6. Dwivedi, Y.K.; Kshetri, N.; Hughes, L.; Slade, E.L.; Jeyaraj, A.; Kar, A.K.; Baabdullah, A.M.; Koohang, A.; Raghavan, V.; Ahuja, M.; et al. Opinion Paper: “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. Int. J. Inf. Manag. 2023, 71, 102642. [CrossRef]

7. Huang, M.-H.; Rust, R.T.

Artificial intelligence in service. J. Serv. Res. 2018, 21, 155–172. [CrossRef]

8. Sophie, B.; Ruth, V.A.

Stakeholder governance for responsible innovation: A theory of value creation, appropriation, and distribution. J. Manag. Stud. 2022, 59, 29–60.

9. Nicole, R.B.

Tightening the link between employee well-being at work and performance: A new dimension for HRM. Manag. Decis. 2008, 46, 284–309.

Nicolai, J.F.; Peter, G.K. Organizing Entrepreneurial Judgment: A New Approach to the Firm; Cambridge University Press: Cambridge, 10. UK, 2012; p. 299, ISBN 9780521697262.

11. Dean, A.S.; Marc, G.

The lean startup framework: Closing the academic–practitioner divide. Entrep. Theory Pract. 2020, 45, 967–998.

12. Frederik, V.B.; Per, D.; Jan, R.

Digital technologies as external enablers of new venture creation in the IT hardware sector. Entrep. Theory Pract. 2018, 42, 47–69.

Martin, O.; David, B.A. Artificial intelligence and big data in entrepreneurship: A new era has begun. Small Bus. Econ. 2020, 13. 55, 529–539.

Author Contributions: Methodology, Y.G. and Y.P.; validation, Y.G. and D.X.; formal analysis, Y.G.;

14. Kasneci, E.; Sessler, K.; Küchemann, S.; Bannert, M.; Dementieva, D.; Fischer, F.; Gasser, U.; Groh, G.; Günnemann, S.; Hüllermeier, E.; et al. ChatGPT for good? On opportunities and challenges of large language models for education. Learn. Individ. Differ. 2023, 103, 102274. [CrossRef] 15. Brown, T.; Mann, B.; Ryder, N.; Subbiah, M.; Kaplan, J.D.; Dhariwal, P.; Neelakantan, A.; Shyam, P.; Sastry, G.; Askell, A.; et al. Language models are few-shot learners. Adv. Neural Inf. Process. Syst. 2020, 33, 1877–1901.

16. Min, B.; Ross, H.; Sulem, E.; Veyseh, A.P.; Nguyen, T.H.; Sainz, O.; Agirre, E.; Heintz, I.; Roth, D. Recent advances in natural language processing via large pre-trained language models: A survey. ACM Comput. Surv. 2023, 56, 30. [CrossRef]

17. Alexander, T.N.; Yin, Y.; Sulayman, S.; Stefan, D.; Matthias, J.

An LLM-driven chatbot in higher education for databases and information systems. IEEE Trans. Educ. 2024, 68, 103–116. [CrossRef]

18. Robin, S.; Xia, M.; Amos, A.; Tom, M.

Ruffle & riley: Insights from designing and evaluating a large language model-based conversational tutoring system. In Proceedings of the International Conference on Artificial Intelligence in Education, Xiamen, China, 22–24 November 2024; pp. 75–90.

19. Poria, S.; Majumder, N.; Mihalcea, R.; Hovy, E.

Emotion recognition in conversation: Research challenges, datasets, and recent advances. IEEE Access 2019, 7, 100943–100953. [CrossRef]

20. Li, A.; Xu, L.; Ling, C.; Zhang, J.; Wang, P.

Emoverse: Enhancing multimodal large language models for affective computing via multitask learning. Neurocomputing 2025, 650, 130810. [CrossRef] 21. Hong, S.; Zhuge, M.; Chen, J.; Zheng, X.; Cheng, Y.; Wang, J.; Zhang, C.; Wang, Z.; Yau, S.K.; Lin, Z.; et al. MetaGPT: Meta programming for a multi-agent collaborative framework. In Proceedings of the Twelfth International Conference on Learning Representations, Vienna, Austria, 7–11 May 2024.

22. Yuan, A.J.; Edlin, G.C.; Bernice, P.; Song, H.J.; Sagar, S.

Improving workplace well-being in modern organizations: A review of large language model-based mental health chatbots. ACM Trans. Manag. Inf. Syst. 2025, 16, 3. [CrossRef] 23. Vedant, D.S.; Zhong, Q.Y.; Jash, R.P.; Jeon, Y.; Zimmermann, R.; Czerwinski, M.P.; Suh, J.; Mishra, V.; Saha, K.; Hernandez, J. Ai on my shoulder: Supporting emotional labor in front-office roles with an LLM-based empathetic coworker. In Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems, Yokohama, Japan, 26 April–1 May 2025; pp. 1–29.

24. Muhammad, R.K.; Rafeed, M.S.; Ihsanul, H.A.; Ahad, J.I.; Rahman, F.; Amin, M.R.; Mohammed, N.; Rahman, S. Beyond labels: Aligning large language models with human-like reasoning. In Proceedings of the International Conference on Pattern Recognition, Kolkata, India, 1–5 December 2024. [CrossRef]

25. Oleg, Z.; Culpepper, J.S.; Falk, S.; Paul, T.

Enhancing human annotation: Leveraging large language models and efficient batch processing. In Proceedings of the 2024 Conference on Human Information Interaction and Retrieval, Sheffield, UK, 10–14 March 2024; pp. 340–345.

26. Fitzpatrick, K.K.; Darcy, A.; Vierhile, M.

Delivering cognitive behavior therapy to young adults with symptoms of depression and anxiety using a fully automated conversational agent (Woebot): A randomized controlled trial. JMIR Ment. Health 2017, 4, e7785. [CrossRef]

27. Sebastian, R.; Sebastian, K.

Artificial intelligence and management: The automation–augmentation paradox. Acad. Manag. Rev. 2021, 46, 192–210. [CrossRef]

Jiao, J.J.; Afroogh, S.; Xu, Y.; Phillips, C. Navigating LLM ethics: Advancements, challenges, and future directions. AI Ethics 2025, 28. 5, 5795–5819. [CrossRef]

Shneiderman, B. Human-Centered AI; Oxford University Press: Oxford, UK, 2022. 29.

30. Cynthia, D.; Frank, M.; Kobbi, N.; Adam, S.

Calibrating noise to sensitivity in private data analysis. In Proceedings of the Theory of Cryptography Conference, New York, NY, USA, 4–7 March 2006; pp. 265–284.

Brent, D.M.; Patrick, A.; Mariarosaria, T.; Sandra, W.; Luciano, F. The ethics of algorithms: Mapping the debate. Big Data Soc. 2016, 31. 3, 2053951716679679. [CrossRef] 32. Peter, K.; McMahan, H.B

Advances and open problems in federated learning. Found. Trends Mach. Learn. 2021, 14, 1–210.

Kirsten, E.M. Designing ethical algorithms. MIS Q. Exec. 2019, 18, 129–142. [CrossRef] 33.

34. Torous, J.; Bucci, S.; Bell, I.H.; Kessing, L.V.; Faurholt-Jepsen, M.; Whelan, P.; Carvalho, A.F.; Keshavan, M.; Linardon, J.; Firth, J. The growing field of digital psychiatry: Current evidence and the future of apps, social media, chatbots, and virtual reality. World Psychiatry 2021, 20, 318–335. [CrossRef] 35. Arrieta, A.B.; Díaz-Rodríguez, N.; Del Ser, J.; Bennetot, A.; Tabik, S.; Barbado, A.; Garcia, S.; Gil-Lopez, S.; Molina, D.; Benjamins, R.; et al. Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Inf. Fusion 2020, 58, 82–115. [CrossRef] 36. Floridi, L.; Cowls, J.; Beltrametti, M.; Chatila, R.; Chazerand, P.; Dignum, V.; Luetge, C.; Madelin, R.; Pagallo, U.; Rossi, F.; et al. AI4People—An ethical framework for a good AI society: Opportunities, risks, principles, and recommendations. Minds Mach.

2018, 28, 689–707. [CrossRef] 37. Jakob, M.; Jessica, M.; Mariarosaria, T.; Luciano, F

Ethics-based auditing of automated decision-making systems: Nature, scope, and limitations. Sci. Eng. Ethics 2021, 27, 44.

38. Shoshana, Z

The age of surveillance capitalism: The fight for a human future at the new frontier of power. Surveill. Soc. 2019, 17, 240–245.

Katherine, C.K.; Melissa, A.V.; Angèle, C. Algorithms at work: The new contested terrain of control. Acad. Manag. Ann. 2020, 39. 14, 366–410. [CrossRef] 40. Weston, J.E.; Szlam, A.D.; Fergus, R.D.; Sukhbaatar, S

End-to-End Memory Networks. U.S. Patent Application Publication No. US20170200077A1, 13 July 2017.

41. Aleksandr, R.; Alberto, G.; Massimiliano, P.; Luca, S.; Meng, G

Accelerating human–computer interaction through convergent conditions for LLM explanation. Front. Artif. Intell. 2024, 7, 1406773. [CrossRef] 42. Vamplew, P.; Dazeley, R.; Foale, C.; Firmin, S.; Mummery, J

Human-aligned artificial intelligence is a multiobjective problem. Ethics Inf. Technol. 2018, 20, 27–40. [CrossRef] 43. Ouyang, L.; Wu, J.; Jiang, X.; Almeida, D.; Wainwright, C.; Mishkin, P.; Zhang, C.; Agarwal, S.; Slama, K.; Ray, A.; et al. Training language models to follow instructions with human feedback. Adv. Neural Inf. Process. Syst. 2022, 35, 27730–27744.

44. Sun, Y.; Huang, Q.; Tung, A.K.H.; Yu, J

PRISM: A Framework for Producing Interpretable Political Bias Embeddings with Political-Aware Cross-Encoder. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics, Vienna, Austria, 27 July–1 August 2025; pp. 27719–27733.

45. Liu, Y.X.; Ma, L.L.; Wang, M.Y

GAIN: A gated adaptive feature interaction network for click-through rate prediction. Sensors 2022, 22, 7280. [CrossRef] 46. Terunuma, H.; Deng, X.; Nishino, N.; Watanabe, K

NK cell-based autologous immune enhancement therapy (AIET) for cancer. J. Stem Cells Regen. Med. 2013, 9, 9. [CrossRef]

Dellermann, D.; Ebel, P.; Söllner, M.; Leimeister, J.M. Hybrid intelligence. Bus. Inf. Syst. Eng. 2019, 61, 637–643. [CrossRef] 47.

48. Chen, W.; Su, Y.; Zuo, J.; Yang, C.; Yuan, C.; Chan, C.-M.; Yu, H.; Lu, Y.; Hung, Y.-H.; Qian, C.; et al. Agentverse: Facilitating multi-agent collaboration and exploring emergent behaviors. In Proceedings of the Twelfth International Conference on Learning Representations, Vienna, Austria, 7–11 May 2024.

49. Rashkin, H.; Smith, E.M.; Li, M.; Boureau, Y.-L

Towards empathetic open-domain conversation models: A new benchmark and dataset. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, Florence, Italy, 28 July–2 August 2019; pp. 5370–5381.

50. Lewis, P.; Perez, E.; Piktus, A.; Petroni, F.; Karpukhin, V.; Goyal, N.; Küttler, H.; Lewis, M.; Yih, W.; Rocktäschel, T.; et al. Retrieval-augmented generation for knowledge-intensive NLP tasks. Adv. Neural Inf. Process. Syst. 2020, 33, 9459–9474.

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.